From 37fdf6727760518730eca9a62d8820b098f38ca6 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 07:23:35 +0000 Subject: [PATCH 01/78] data visualization notebook added --- .../calib_validation/data_visualize.ipynb | 11974 ++++++++++++++++ 1 file changed, 11974 insertions(+) create mode 100644 app/services/calib_validation/data_visualize.ipynb diff --git a/app/services/calib_validation/data_visualize.ipynb b/app/services/calib_validation/data_visualize.ipynb new file mode 100644 index 00000000..f951f385 --- /dev/null +++ b/app/services/calib_validation/data_visualize.ipynb @@ -0,0 +1,11974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Necessary libraries\n", + "import math\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_yscreen_heightscreen_width
0506.971497282.207611406.131836278.6587831001008641536
1518.564636280.534271412.582733279.6885381001008641536
2524.403320282.937195417.401550282.7178651001008641536
3530.841187287.072388422.359680283.8919071001008641536
4534.370300287.437531426.682861285.8136601001008641536
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y \\\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100 \n", + "1 518.564636 280.534271 412.582733 279.688538 100 100 \n", + "2 524.403320 282.937195 417.401550 282.717865 100 100 \n", + "3 530.841187 287.072388 422.359680 283.891907 100 100 \n", + "4 534.370300 287.437531 426.682861 285.813660 100 100 \n", + "\n", + " screen_height screen_width \n", + "0 864 1536 \n", + "1 864 1536 \n", + "2 864 1536 \n", + "3 864 1536 \n", + "4 864 1536 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Prefix to identify the dataset\n", + "prefix = \"e2e_test3\"\n", + "dataset_train_path = f\"./csv/data/{prefix}_fixed_train_data.csv\"\n", + "\n", + "# Load the dataset\n", + "raw_dataset = pd.read_csv(dataset_train_path)\n", + "\n", + "# Display the dataset for 5 rows\n", + "display(raw_dataset.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gaze Path Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "\n", + "# Plotting left iris path\n", + "plt.plot(\n", + " raw_dataset[\"left_iris_x\"],\n", + " raw_dataset[\"left_iris_y\"],\n", + " color=\"#1f77b4\",\n", + " label=\"Left Iris Path\",\n", + " alpha=0.6,\n", + " linewidth=2,\n", + ")\n", + "plt.scatter(\n", + " raw_dataset[\"left_iris_x\"].iloc[0],\n", + " raw_dataset[\"left_iris_y\"].iloc[0],\n", + " color=\"#1f77b4\",\n", + " edgecolors=\"k\",\n", + " marker=\"o\",\n", + " s=100,\n", + " label=\"Left Iris Start\",\n", + ")\n", + "plt.scatter(\n", + " raw_dataset[\"left_iris_x\"].iloc[-1],\n", + " raw_dataset[\"left_iris_y\"].iloc[-1],\n", + " color=\"#1f77b4\",\n", + " edgecolors=\"k\",\n", + " marker=\"X\",\n", + " s=100,\n", + " label=\"Left Iris End\",\n", + ")\n", + "\n", + "# Plotting right iris path\n", + "plt.plot(\n", + " raw_dataset[\"right_iris_x\"],\n", + " raw_dataset[\"right_iris_y\"],\n", + " color=\"#ff7f0e\",\n", + " label=\"Right Iris Path\",\n", + " alpha=0.6,\n", + " linewidth=2,\n", + ")\n", + "plt.scatter(\n", + " raw_dataset[\"right_iris_x\"].iloc[0],\n", + " raw_dataset[\"right_iris_y\"].iloc[0],\n", + " color=\"#ff7f0e\",\n", + " edgecolors=\"k\",\n", + " marker=\"o\",\n", + " s=100,\n", + " label=\"Right Iris Start\",\n", + ")\n", + "plt.scatter(\n", + " raw_dataset[\"right_iris_x\"].iloc[-1],\n", + " raw_dataset[\"right_iris_y\"].iloc[-1],\n", + " color=\"#ff7f0e\",\n", + " edgecolors=\"k\",\n", + " marker=\"X\",\n", + " s=100,\n", + " label=\"Right Iris End\",\n", + ")\n", + "\n", + "# Adding title and labels\n", + "plt.title(\"Gaze Path Visualization\")\n", + "plt.xlabel(\"X Coordinate\")\n", + "plt.ylabel(\"Y Coordinate\")\n", + "plt.legend()\n", + "\n", + "# Adding grid\n", + "plt.grid(True)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Heatmap Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create separate heatmaps for left and right iris\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Left iris heatmap\n", + "plt.subplot(1, 2, 1)\n", + "sns.kdeplot(\n", + " x=raw_dataset[\"left_iris_x\"],\n", + " y=raw_dataset[\"left_iris_y\"],\n", + " cmap=\"Blues\",\n", + " fill=True,\n", + " thresh=0,\n", + " levels=100,\n", + ")\n", + "\n", + "# Adding title and labels\n", + "plt.title(\"Left Iris Heatmap\")\n", + "plt.xlabel(\"X Coordinate\")\n", + "plt.ylabel(\"Y Coordinate\")\n", + "\n", + "# Right iris heatmap\n", + "plt.subplot(1, 2, 2)\n", + "sns.kdeplot(\n", + " x=raw_dataset[\"right_iris_x\"],\n", + " y=raw_dataset[\"right_iris_y\"],\n", + " cmap=\"Oranges\",\n", + " fill=True,\n", + " thresh=0,\n", + " levels=100,\n", + ")\n", + "\n", + "# Adding title and labels\n", + "plt.title(\"Right Iris Heatmap\")\n", + "plt.xlabel(\"X Coordinate\")\n", + "plt.ylabel(\"Y Coordinate\")\n", + "\n", + "# Show plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Eye Movement Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Left Eye Movement Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot settings\n", + "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n", + "plt.rcParams[\"figure.autolayout\"] = True\n", + "\n", + "# x and y values\n", + "x = raw_dataset[\"left_iris_x\"]\n", + "y = raw_dataset[\"left_iris_y\"]\n", + "\n", + "# Grid settings\n", + "grid_size = 1\n", + "h = 10\n", + "\n", + "# Get the min and max values of x and y\n", + "x_min, x_max = min(x), max(x)\n", + "y_min, y_max = min(y), max(y)\n", + "\n", + "# Create a grid\n", + "x_grid = np.arange(x_min - h, x_max + h, grid_size)\n", + "y_grid = np.arange(y_min - h, y_max + h, grid_size)\n", + "x_mesh, y_mesh = np.meshgrid(x_grid, y_grid)\n", + "\n", + "# Calculate the center of each grid cell\n", + "xc = x_mesh + (grid_size / 2)\n", + "yc = y_mesh + (grid_size / 2)\n", + "\n", + "\n", + "def kde_quartic(d, h):\n", + " \"\"\"\n", + " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", + "\n", + " Parameters:\n", + " d (float): The distance.\n", + " h (float): The bandwidth.\n", + "\n", + " Returns:\n", + " float: The quartic KDE value.\n", + " \"\"\"\n", + " dn = d / h\n", + " P = (15 / 16) * (1 - dn**2) ** 2\n", + "\n", + " return P\n", + "\n", + "\n", + "# Intensity list\n", + "intensity_list = []\n", + "\n", + "for j in range(len(xc)):\n", + " # List to store the intensity values for each row\n", + " intensity_row = []\n", + "\n", + " for k in range(len(xc[0])):\n", + " # List to store the KDE values\n", + " kde_value_list = []\n", + "\n", + " # Iterate over all the points\n", + " for i in range(len(x)):\n", + " # Calculate distance\n", + " d = math.sqrt((xc[j][k] - x[i]) ** 2 + (yc[j][k] - y[i]) ** 2)\n", + "\n", + " if d <= h:\n", + " p = kde_quartic(d, h)\n", + " else:\n", + " p = 0\n", + "\n", + " # Append the KDE value to the list\n", + " kde_value_list.append(p)\n", + "\n", + " # Calculate the total intensity and append it to the row\n", + " p_total = sum(kde_value_list)\n", + " intensity_row.append(p_total)\n", + "\n", + " # Append the row to the intensity list\n", + " intensity_list.append(intensity_row)\n", + "\n", + "\n", + "# Convert the intensity list to a numpy array\n", + "intensity = np.array(intensity_list)\n", + "\n", + "# Plot the heatmap\n", + "plt.title(\"Left Iris Sacades and Heatmap\")\n", + "plt.pcolormesh(x_mesh, y_mesh, intensity)\n", + "plt.plot(x, y, \"r\", linestyle=\"-\")\n", + "plt.colorbar()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Right Eye Movement Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot settings\n", + "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n", + "plt.rcParams[\"figure.autolayout\"] = True\n", + "\n", + "# x and y values\n", + "x = raw_dataset[\"right_iris_x\"]\n", + "y = raw_dataset[\"right_iris_y\"]\n", + "\n", + "# Grid settings\n", + "grid_size = 1\n", + "h = 10\n", + "\n", + "# Get the min and max values of x and y\n", + "x_min, x_max = min(x), max(x)\n", + "y_min, y_max = min(y), max(y)\n", + "\n", + "# Create a grid\n", + "x_grid = np.arange(x_min - h, x_max + h, grid_size)\n", + "y_grid = np.arange(y_min - h, y_max + h, grid_size)\n", + "x_mesh, y_mesh = np.meshgrid(x_grid, y_grid)\n", + "\n", + "# Calculate the center of each grid cell\n", + "xc = x_mesh + (grid_size / 2)\n", + "yc = y_mesh + (grid_size / 2)\n", + "\n", + "\n", + "def kde_quartic(d, h):\n", + " \"\"\"\n", + " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", + "\n", + " Parameters:\n", + " d (float): The distance.\n", + " h (float): The bandwidth.\n", + "\n", + " Returns:\n", + " float: The quartic KDE value.\n", + " \"\"\"\n", + " dn = d / h\n", + " P = (15 / 16) * (1 - dn**2) ** 2\n", + "\n", + " return P\n", + "\n", + "\n", + "# Intensity list\n", + "intensity_list = []\n", + "\n", + "for j in range(len(xc)):\n", + " # List to store the intensity values for each row\n", + " intensity_row = []\n", + "\n", + " for k in range(len(xc[0])):\n", + " # List to store the KDE values\n", + " kde_value_list = []\n", + "\n", + " # Iterate over all the points\n", + " for i in range(len(x)):\n", + " # Calculate distance\n", + " d = math.sqrt((xc[j][k] - x[i]) ** 2 + (yc[j][k] - y[i]) ** 2)\n", + "\n", + " if d <= h:\n", + " p = kde_quartic(d, h)\n", + " else:\n", + " p = 0\n", + "\n", + " # Append the KDE value to the list\n", + " kde_value_list.append(p)\n", + "\n", + " # Calculate the total intensity and append it to the row\n", + " p_total = sum(kde_value_list)\n", + " intensity_row.append(p_total)\n", + "\n", + " # Append the row to the intensity list\n", + " intensity_list.append(intensity_row)\n", + "\n", + "\n", + "# Convert the intensity list to a numpy array\n", + "intensity = np.array(intensity_list)\n", + "\n", + "# Plot the heatmap\n", + "plt.title(\"Right Iris Sacades and Heatmap\")\n", + "plt.pcolormesh(x_mesh, y_mesh, intensity)\n", + "plt.plot(x, y, \"r\", linestyle=\"-\")\n", + "plt.colorbar()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scatter Plot Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Left Eye Scatter Plot Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "blue", + "opacity": 0.7, + "size": 5 + }, + "mode": "markers", + "type": "scatter", + "x": [ + 506.9714965820313, + 518.5646362304688, + 524.4033203125, + 530.8411865234375, + 534.3703002929688, + 535.4397583007812, + 538.3284912109375, + 538.0055541992188, + 538.7401733398438, + 538.3395385742188, + 538.4655151367188, + 540.5523681640625, + 539.9481201171875, + 541.1350708007812, + 540.5191650390625, + 540.1651000976562, + 540.8129272460938, + 540.279052734375, + 541.3689575195312, + 539.712890625, + 540.1910400390625, + 539.932861328125, + 541.1317138671875, + 540.2360229492188, + 540.2733764648438, + 539.6839599609375, + 539.857177734375, + 539.76513671875, + 540.7872924804688, + 541.02587890625, + 541.4639892578125, + 541.7560424804688, + 542.5582275390625, + 542.26904296875, + 542.1211547851562, + 542.1524047851562, + 541.4745483398438, + 542.6224975585938, + 542.355224609375, + 543.4385375976562, + 543.4129028320312, + 543.640380859375, + 544.1436767578125, + 544.71044921875, + 544.9417114257812, + 544.8046264648438, + 544.907470703125, + 544.4990844726562, + 545.1011962890625, + 544.0618286132812, + 545.6531982421875, + 545.2763061523438, + 544.7899169921875, + 544.9967651367188, + 544.7830200195312, + 544.9871826171875, + 545.9523315429688, + 544.5263061523438, + 545.6052856445312, + 547.1279907226562, + 546.5455932617188, + 545.49658203125, + 545.4258422851562, + 546.29052734375, + 545.1207275390625, + 545.0530395507812, + 544.8782348632812, + 545.7645874023438, + 544.9891357421875, + 545.5903930664062, + 544.2805786132812, + 544.8512573242188, + 545.3428955078125, + 545.0262451171875, + 545.3805541992188, + 545.14990234375, + 545.1900024414062, + 544.8680419921875, + 545.3555908203125, + 544.9789428710938, + 544.938232421875, + 545.3932495117188, + 546.3397216796875, + 544.82275390625, + 545.5991821289062, + 547.0126953125, + 547.4599609375, + 546.29248046875, + 546.36279296875, + 546.7230224609375, + 556.6884765625, + 556.7714233398438, + 558.01806640625, + 558.6127319335938, + 558.4219970703125, + 559.459228515625, + 557.6658935546875, + 557.7333374023438, + 558.7186889648438, + 559.3433227539062, + 559.3925170898438, + 557.5004272460938, + 556.7421875, + 557.2520141601562, + 558.5250244140625, + 558.9024047851562, + 559.8341674804688, + 560.17822265625, + 559.509033203125, + 560.9150390625, + 560.495849609375, + 561.2053833007812, + 560.7686767578125, + 561.5689697265625, + 561.7825927734375, + 561.5682983398438, + 562.0491333007812, + 560.752197265625, + 561.6281127929688, + 560.6880493164062, + 560.9950561523438, + 560.450439453125, + 561.3142700195312, + 560.588134765625, + 560.8603515625, + 560.75634765625, + 560.5144653320312, + 561.158935546875, + 561.055908203125, + 560.0401611328125, + 560.5009765625, + 560.7841186523438, + 559.9788208007812, + 560.2263793945312, + 560.5133056640625, + 559.9036254882812, + 559.499755859375, + 559.7028198242188, + 559.5144653320312, + 560.4923095703125, + 560.7322387695312, + 560.2394409179688, + 561.4515380859375, + 560.7643432617188, + 561.7992553710938, + 559.970947265625, + 560.3557739257812, + 560.290771484375, + 560.0529174804688, + 559.5285034179688, + 560.670166015625, + 560.2899169921875, + 560.081298828125, + 560.2073364257812, + 559.8053588867188, + 560.414794921875, + 558.9434814453125, + 560.27490234375, + 559.3911743164062, + 559.0752563476562, + 559.697265625, + 560.1272583007812, + 559.2277221679688, + 559.3869018554688, + 559.9826049804688, + 559.4216918945312, + 559.6560668945312, + 558.8399047851562, + 559.0958862304688, + 559.0140991210938, + 559.040771484375, + 558.0556640625, + 558.7276611328125, + 559.2193603515625, + 557.57373046875, + 559.2841186523438, + 558.77783203125, + 558.8466186523438, + 558.0785522460938, + 557.7658081054688, + 563.245361328125, + 564.2551879882812, + 562.8328857421875, + 563.7398071289062, + 563.3372802734375, + 563.2427368164062, + 564.5738525390625, + 563.7952880859375, + 563.6834106445312, + 563.590087890625, + 563.843994140625, + 564.1339111328125, + 563.8959350585938, + 563.1615600585938, + 563.9400634765625, + 564.6978759765625, + 564.9093627929688, + 564.5678100585938, + 564.2595825195312, + 564.0352783203125, + 563.34326171875, + 563.3900756835938, + 563.1842041015625, + 564.3395385742188, + 564.5511474609375, + 564.1090087890625, + 564.3811645507812, + 563.2430419921875, + 563.9806518554688, + 563.2244873046875, + 563.4888916015625, + 563.2081298828125, + 563.9127807617188, + 564.5554809570312, + 563.4066772460938, + 563.7935791015625, + 563.3814086914062, + 564.099609375, + 564.6023559570312, + 563.572021484375, + 564.1981811523438, + 564.698974609375, + 563.6094360351562, + 564.7529296875, + 564.6073608398438, + 564.0097045898438, + 564.8242797851562, + 564.1727905273438, + 563.5795288085938, + 564.2640380859375, + 563.4013671875, + 564.9345092773438, + 564.2282104492188, + 564.46875, + 563.7816772460938, + 564.1197509765625, + 564.59033203125, + 564.2525024414062, + 564.015869140625, + 563.8367919921875, + 564.6503295898438, + 563.52587890625, + 564.1806030273438, + 563.266845703125, + 562.9224853515625, + 564.1807250976562, + 563.2727661132812, + 563.299560546875, + 563.62353515625, + 564.00732421875, + 562.4202880859375, + 563.630615234375, + 563.1637573242188, + 563.57763671875, + 563.6665649414062, + 562.9893188476562, + 562.9876708984375, + 564.6296997070312, + 563.970947265625, + 563.1293334960938, + 563.7843017578125, + 562.9188842773438, + 562.9011840820312, + 563.2520751953125, + 563.2647094726562, + 562.3236694335938, + 563.6578369140625, + 562.6688232421875, + 561.7875366210938, + 561.8511352539062, + 386.8733215332031, + 389.1112060546875, + 388.4430236816406, + 386.2240905761719, + 386.522705078125, + 387.0731201171875, + 388.2835388183594, + 389.2976379394531, + 391.3636169433594, + 392.0768737792969, + 391.8057556152344, + 390.96044921875, + 391.4176025390625, + 391.9043273925781, + 391.1206359863281, + 390.6957702636719, + 389.2049865722656, + 386.5533447265625, + 385.7132873535156, + 384.8652648925781, + 385.8428039550781, + 385.4671020507813, + 384.34283447265625, + 384.0664367675781, + 383.3593444824219, + 384.3056335449219, + 384.5469055175781, + 384.6551818847656, + 385.57855224609375, + 385.7691345214844, + 384.7511291503906, + 385.135498046875, + 383.86334228515625, + 383.4415588378906, + 383.454345703125, + 384.17919921875, + 384.719482421875, + 383.4304809570313, + 383.1623229980469, + 383.5268249511719, + 383.262939453125, + 383.3402099609375, + 384.17425537109375, + 382.6044006347656, + 382.4395446777344, + 382.8009948730469, + 381.7204895019531, + 381.7982177734375, + 382.9085693359375, + 383.0549621582031, + 382.3975524902344, + 383.0088195800781, + 382.0330810546875, + 381.8932189941406, + 381.27056884765625, + 382.7771911621094, + 382.6194152832031, + 382.0088806152344, + 381.561279296875, + 381.9228820800781, + 382.0288696289063, + 381.9415588378906, + 382.1340942382813, + 382.461181640625, + 381.5500183105469, + 381.1759338378906, + 382.1652526855469, + 382.0784912109375, + 382.4464416503906, + 382.7380981445313, + 382.2915649414063, + 381.9553527832031, + 381.3691101074219, + 381.4131774902344, + 382.156005859375, + 381.64459228515625, + 381.9104614257813, + 381.26947021484375, + 380.5329895019531, + 381.65283203125, + 382.0446166992188, + 384.8185729980469, + 385.3858337402344, + 385.2966003417969, + 385.534423828125, + 383.7272033691406, + 383.4385681152344, + 383.5013732910156, + 383.727294921875, + 384.6271667480469, + 392.2987060546875, + 390.9298400878906, + 390.5198059082031, + 391.0887451171875, + 391.9738464355469, + 392.5810546875, + 391.8455810546875, + 391.3519287109375, + 391.9194030761719, + 391.18896484375, + 391.2001647949219, + 392.0416870117188, + 392.1141662597656, + 391.0634765625, + 391.2837524414063, + 391.0296020507813, + 390.9624938964844, + 391.3521118164063, + 391.9414367675781, + 391.28131103515625, + 390.0772705078125, + 390.766845703125, + 390.6370239257813, + 391.571044921875, + 392.6316833496094, + 393.62005615234375, + 392.7270202636719, + 392.1440124511719, + 391.4623718261719, + 391.2567443847656, + 391.2491455078125, + 392.0492248535156, + 392.97406005859375, + 392.5518798828125, + 391.5374145507813, + 391.9832763671875, + 391.3318481445313, + 391.7645263671875, + 392.3851013183594, + 392.6951599121094, + 392.6311645507813, + 391.3670349121094, + 392.1858215332031, + 391.7538146972656, + 391.7377014160156, + 393.0763244628906, + 392.08837890625, + 391.4899597167969, + 390.5305786132813, + 391.50341796875, + 391.1092224121094, + 391.588134765625, + 392.3507385253906, + 392.1233520507813, + 391.7057800292969, + 391.2481994628906, + 391.5884704589844, + 390.5691223144531, + 391.0623474121094, + 390.9736633300781, + 390.6160888671875, + 390.4902038574219, + 389.9366455078125, + 390.9982604980469, + 391.6872253417969, + 391.7391357421875, + 391.5924072265625, + 391.41802978515625, + 391.6833801269531, + 391.37445068359375, + 392.07415771484375, + 392.7206726074219, + 393.2634887695313, + 392.4910278320313, + 392.0633544921875, + 393.7765502929688, + 393.4232482910156, + 394.2220764160156, + 393.6095275878906, + 394.15667724609375, + 393.4875183105469, + 392.8297119140625, + 394.16998291015625, + 394.0672302246094, + 392.9748840332031, + 393.5560302734375, + 393.2106323242188, + 393.33441162109375, + 393.4196472167969, + 393.71478271484375, + 206.52713012695312, + 164.5104522705078, + 160.8257293701172, + 159.94509887695312, + 158.15142822265625, + 156.966064453125, + 154.28981018066406, + 155.70187377929688, + 157.3920135498047, + 158.03475952148438, + 158.3424835205078, + 158.5967254638672, + 159.40176391601562, + 159.62611389160156, + 160.3441925048828, + 161.95750427246094, + 162.78770446777344, + 163.9203338623047, + 164.05679321289062, + 163.61239624023438, + 162.3110809326172, + 160.68348693847656, + 161.2389678955078, + 161.8617706298828, + 161.7224578857422, + 161.5873565673828, + 161.697998046875, + 160.6887664794922, + 160.90513610839844, + 161.5122528076172, + 163.12106323242188, + 163.2836151123047, + 163.1234130859375, + 162.56678771972656, + 163.26580810546875, + 162.26510620117188, + 162.7144012451172, + 162.9204864501953, + 163.18833923339844, + 161.77597045898438, + 162.94151306152344, + 162.46495056152344, + 161.21502685546875, + 162.0072784423828, + 162.68634033203125, + 162.5944366455078, + 163.5371856689453, + 164.09872436523438, + 165.6779022216797, + 165.8667449951172, + 166.32266235351562, + 164.79193115234375, + 163.90377807617188, + 163.7904052734375, + 164.2099151611328, + 164.77955627441406, + 165.05450439453125, + 164.2294464111328, + 164.08570861816406, + 163.34518432617188, + 163.45913696289062, + 164.08705139160156, + 165.77426147460938, + 165.16416931152344, + 165.16429138183594, + 164.826416015625, + 164.78598022460938, + 164.13497924804688, + 164.4682159423828, + 164.04443359375, + 163.5697784423828, + 163.5968475341797, + 163.9339141845703, + 162.9560089111328, + 163.58253479003906, + 164.11155700683594, + 164.7197723388672, + 165.00100708007812, + 165.133544921875, + 166.4665069580078, + 167.7435760498047, + 167.59291076660156, + 166.82740783691406, + 165.736572265625, + 166.41957092285156, + 167.6148681640625, + 166.76416015625, + 166.6020050048828, + 167.75057983398438, + 169.78160095214844, + 155.68035888671875, + 155.09588623046875, + 153.63722229003906, + 152.8428497314453, + 152.65121459960938, + 151.72764587402344, + 150.72190856933594, + 151.5420684814453, + 152.0251922607422, + 151.2655792236328, + 150.939697265625, + 152.90640258789062, + 153.12242126464844, + 153.10452270507812, + 152.26072692871094, + 151.64308166503906, + 152.9226531982422, + 152.4237060546875, + 152.7340545654297, + 153.490966796875, + 153.57879638671875, + 152.3104705810547, + 152.79908752441406, + 152.33689880371094, + 151.8495330810547, + 152.9599609375, + 153.7440185546875, + 154.30519104003906, + 154.04161071777344, + 153.7908477783203, + 153.554931640625, + 153.46279907226562, + 153.51593017578125, + 155.07705688476562, + 154.7864990234375, + 154.69993591308594, + 153.99600219726562, + 154.087158203125, + 154.01202392578125, + 153.976806640625, + 154.44520568847656, + 155.6165313720703, + 155.00680541992188, + 155.48812866210938, + 155.64285278320312, + 156.74017333984375, + 157.74325561523438, + 159.00485229492188, + 159.50628662109375, + 160.2045440673828, + 160.77537536621094, + 160.6865692138672, + 160.68832397460938, + 160.29129028320312, + 160.11904907226562, + 159.3603057861328, + 159.54530334472656, + 160.18441772460938, + 159.17247009277344, + 159.68408203125, + 159.12098693847656, + 159.0812225341797, + 159.98953247070312, + 160.09048461914062, + 160.097900390625, + 160.0994873046875, + 159.95619201660156, + 160.38719177246094, + 160.11700439453125, + 160.1515350341797, + 159.65382385253906, + 158.9752960205078, + 158.7391357421875, + 158.18887329101562, + 158.01068115234375, + 158.18417358398438, + 157.61814880371094, + 157.346923828125, + 157.910888671875, + 158.77239990234375, + 157.95574951171875, + 158.6696319580078, + 159.56243896484375, + 159.6716766357422, + 160.45989990234375, + 161.8671112060547, + 162.16030883789062, + 162.5677490234375, + 161.8557586669922, + 162.04176330566406, + 157.50010681152344, + 159.3820037841797, + 159.08851623535156, + 157.26992797851562, + 158.04066467285156, + 159.15382385253906, + 159.97293090820312, + 159.39479064941406, + 159.95103454589844, + 160.0814666748047, + 160.249755859375, + 160.0652313232422, + 160.229736328125, + 160.0924835205078, + 160.53305053710938, + 159.8092041015625, + 159.6959991455078, + 160.0650177001953, + 161.3235626220703, + 159.66697692871094, + 159.78353881835938, + 160.67752075195312, + 160.68569946289062, + 160.8162078857422, + 161.235595703125, + 161.46214294433594, + 161.1281280517578, + 161.1355743408203, + 161.2970428466797, + 161.5305633544922, + 161.4741973876953, + 162.0054473876953, + 161.41726684570312, + 161.10675048828125, + 160.4036407470703, + 160.09158325195312, + 160.57183837890625, + 160.57093811035156, + 160.77517700195312, + 159.99217224121094, + 161.51437377929688, + 160.39105224609375, + 161.01345825195312, + 160.3650665283203, + 161.45236206054688, + 160.9958953857422, + 162.43373107910156, + 161.40997314453125, + 161.59603881835938, + 161.0695343017578, + 162.07237243652344, + 160.92994689941406, + 160.24266052246094, + 161.5585174560547, + 161.36654663085938, + 161.27212524414062, + 161.7323455810547, + 163.031494140625, + 163.6909637451172, + 163.76133728027344, + 163.29086303710938, + 163.44139099121094, + 163.69017028808594, + 162.35855102539062, + 162.63076782226562, + 163.24417114257812, + 161.7393341064453, + 162.6964111328125, + 162.0379638671875, + 162.69371032714844, + 161.49351501464844, + 162.68215942382812, + 162.55369567871094, + 164.70501708984375, + 165.9799346923828, + 165.53823852539062, + 165.68682861328125, + 164.0941925048828, + 164.26763916015625, + 164.7672576904297, + 164.5294189453125, + 164.66827392578125, + 163.53201293945312, + 163.96859741210938, + 164.1597137451172, + 165.41360473632812, + 166.34632873535156, + 166.5220947265625, + 165.86631774902344, + 166.4485626220703 + ], + "y": [ + 282.2076110839844, + 280.5342712402344, + 282.93719482421875, + 287.0723876953125, + 287.4375305175781, + 287.5365905761719, + 288.4130859375, + 288.6388244628906, + 289.3452453613281, + 289.7811279296875, + 290.22711181640625, + 290.9339599609375, + 290.5479431152344, + 291.7066650390625, + 291.5911865234375, + 292.04962158203125, + 292.3465270996094, + 292.2814025878906, + 292.49078369140625, + 292.7622375488281, + 292.71893310546875, + 292.9272155761719, + 294.1483459472656, + 293.9292907714844, + 294.00311279296875, + 294.1448669433594, + 294.8172607421875, + 294.82232666015625, + 295.5307006835937, + 295.06085205078125, + 295.3816223144531, + 295.5812683105469, + 295.0591735839844, + 295.3460388183594, + 295.4446105957031, + 295.5614929199219, + 295.44500732421875, + 296.1403503417969, + 296.61981201171875, + 296.5223083496094, + 296.8663635253906, + 297.1263732910156, + 297.5341186523437, + 297.5152893066406, + 297.8847045898437, + 298.1744689941406, + 297.80499267578125, + 297.81671142578125, + 297.5389099121094, + 297.54278564453125, + 296.96588134765625, + 297.4913635253906, + 297.3685913085937, + 297.3280029296875, + 297.8356628417969, + 298.40679931640625, + 298.2266540527344, + 298.30535888671875, + 298.4940185546875, + 297.4447937011719, + 297.5924072265625, + 298.1912841796875, + 298.5841979980469, + 298.8329772949219, + 299.5053405761719, + 299.30767822265625, + 298.82208251953125, + 298.2673645019531, + 298.5625915527344, + 297.8983764648437, + 298.0111389160156, + 298.27618408203125, + 298.119140625, + 298.1170959472656, + 298.8129577636719, + 298.2842712402344, + 298.8199462890625, + 298.62860107421875, + 298.57208251953125, + 298.71978759765625, + 298.2862243652344, + 298.48504638671875, + 298.6028137207031, + 299.3833923339844, + 299.3365478515625, + 299.5429077148437, + 300.181396484375, + 300.7275695800781, + 301.2965393066406, + 301.1711730957031, + 319.7274169921875, + 320.1485290527344, + 320.3963623046875, + 320.8817443847656, + 321.3941955566406, + 321.82830810546875, + 322.6673278808594, + 322.4001159667969, + 321.92041015625, + 323.2173767089844, + 323.269287109375, + 322.7093811035156, + 322.864501953125, + 323.1563415527344, + 324.3063659667969, + 324.7597351074219, + 326.0099792480469, + 326.513916015625, + 327.3671264648437, + 326.8199157714844, + 328.6741638183594, + 327.9971618652344, + 328.7611694335937, + 328.4200439453125, + 329.1309509277344, + 328.8747863769531, + 329.45001220703125, + 329.01861572265625, + 328.81640625, + 327.8397521972656, + 327.8627319335937, + 327.34246826171875, + 327.9814147949219, + 328.0308837890625, + 328.7354736328125, + 328.449951171875, + 328.2172241210937, + 329.1683654785156, + 329.76885986328125, + 329.6261291503906, + 329.7510070800781, + 328.900634765625, + 329.2919921875, + 328.506103515625, + 329.0175170898437, + 328.4369506835937, + 328.9645690917969, + 328.07196044921875, + 328.972412109375, + 329.53497314453125, + 329.8018493652344, + 330.4311828613281, + 330.0008239746094, + 330.6437072753906, + 330.0613708496094, + 329.9943542480469, + 329.2314147949219, + 328.51080322265625, + 328.8717346191406, + 328.10089111328125, + 328.6083984375, + 328.0151062011719, + 328.2041931152344, + 327.8868713378906, + 328.57452392578125, + 328.1100158691406, + 328.1842346191406, + 329.1793212890625, + 329.5846862792969, + 329.0926208496094, + 329.8020324707031, + 330.3591613769531, + 329.634521484375, + 329.447509765625, + 329.1663818359375, + 329.30828857421875, + 330.3620300292969, + 329.5806579589844, + 330.1665954589844, + 329.85565185546875, + 330.66650390625, + 330.1508483886719, + 330.78765869140625, + 331.9918212890625, + 331.98748779296875, + 332.9851989746094, + 332.0752258300781, + 332.2947692871094, + 332.4147644042969, + 331.9770202636719, + 350.9775390625, + 351.7432556152344, + 351.60009765625, + 352.0672302246094, + 352.6164855957031, + 351.2296447753906, + 352.37567138671875, + 352.318603515625, + 352.034912109375, + 353.1311340332031, + 352.57135009765625, + 353.1632995605469, + 352.6967163085937, + 352.944580078125, + 352.559814453125, + 352.54443359375, + 352.7675476074219, + 352.70684814453125, + 352.4716796875, + 352.41619873046875, + 352.0777893066406, + 352.45806884765625, + 352.06646728515625, + 351.4954833984375, + 351.3597412109375, + 351.322509765625, + 352.1612243652344, + 351.8356628417969, + 351.980712890625, + 352.2920837402344, + 351.956787109375, + 352.0262756347656, + 351.4659423828125, + 351.7904663085937, + 351.5843200683594, + 351.2974548339844, + 351.4638366699219, + 351.4024963378906, + 351.047607421875, + 351.1163635253906, + 350.716552734375, + 350.6739196777344, + 350.69598388671875, + 350.9443054199219, + 350.9685363769531, + 351.3089599609375, + 351.580322265625, + 351.7095642089844, + 351.2278747558594, + 351.76519775390625, + 351.51593017578125, + 351.7939147949219, + 351.9923095703125, + 351.6347045898437, + 350.9519958496094, + 351.61376953125, + 351.7122802734375, + 351.5579833984375, + 351.8509826660156, + 352.43438720703125, + 352.5101013183594, + 352.1939392089844, + 352.24267578125, + 352.0521240234375, + 352.5223083496094, + 351.5856018066406, + 351.8611145019531, + 351.68310546875, + 351.5499267578125, + 351.0977478027344, + 351.5973205566406, + 350.90679931640625, + 350.94683837890625, + 351.92559814453125, + 352.22479248046875, + 351.9316711425781, + 352.1537170410156, + 351.9091491699219, + 352.0411071777344, + 351.6563720703125, + 352.3119201660156, + 352.12725830078125, + 351.9329528808594, + 351.8695373535156, + 350.7582702636719, + 350.2596130371094, + 350.2446594238281, + 350.65252685546875, + 350.68408203125, + 351.0732727050781, + 292.3349609375, + 296.24432373046875, + 298.6556091308594, + 299.4758605957031, + 300.6333312988281, + 301.9535522460937, + 301.3894958496094, + 302.7728576660156, + 302.8666076660156, + 303.5379638671875, + 304.5206298828125, + 304.407958984375, + 304.51385498046875, + 305.230224609375, + 305.2932434082031, + 304.9880065917969, + 304.92340087890625, + 303.5197448730469, + 302.63934326171875, + 303.0179748535156, + 302.7064514160156, + 304.7342834472656, + 304.6186218261719, + 304.10211181640625, + 304.1875915527344, + 304.1259460449219, + 303.5084533691406, + 305.0895690917969, + 304.783203125, + 303.73876953125, + 303.4207458496094, + 303.3563232421875, + 303.4652099609375, + 301.84423828125, + 302.1918640136719, + 303.00244140625, + 303.4349670410156, + 304.18072509765625, + 304.5085754394531, + 304.9608764648437, + 304.0409240722656, + 305.06500244140625, + 304.9576110839844, + 305.3370361328125, + 306.0732421875, + 304.8749694824219, + 304.0507507324219, + 304.475830078125, + 304.6947631835937, + 303.4704284667969, + 304.4704895019531, + 303.5428771972656, + 304.781982421875, + 304.0555114746094, + 304.49481201171875, + 304.2380676269531, + 305.00897216796875, + 305.53338623046875, + 305.1978759765625, + 304.98590087890625, + 304.6657409667969, + 304.9930419921875, + 305.5421142578125, + 305.1078186035156, + 304.5313415527344, + 304.3753662109375, + 303.03179931640625, + 304.1632080078125, + 304.3042907714844, + 304.41473388671875, + 304.8538818359375, + 305.47491455078125, + 305.4259338378906, + 305.0736389160156, + 305.13995361328125, + 305.175537109375, + 305.39361572265625, + 305.4103088378906, + 305.52813720703125, + 305.8585510253906, + 304.9250793457031, + 304.41070556640625, + 305.33453369140625, + 306.24383544921875, + 307.0990905761719, + 306.621826171875, + 306.93536376953125, + 306.5973815917969, + 306.42413330078125, + 307.0582580566406, + 350.8975830078125, + 352.3148498535156, + 352.3035888671875, + 352.8794860839844, + 353.10162353515625, + 352.9873046875, + 353.63848876953125, + 353.9334716796875, + 354.6578674316406, + 355.0750427246094, + 353.8217468261719, + 354.6893920898437, + 354.994873046875, + 355.25079345703125, + 355.0981750488281, + 355.4383544921875, + 355.9784851074219, + 354.9351806640625, + 355.3053894042969, + 355.5157470703125, + 355.1551208496094, + 355.447265625, + 355.2961730957031, + 355.8099365234375, + 355.92828369140625, + 357.7392272949219, + 358.41424560546875, + 359.2053527832031, + 358.14471435546875, + 358.8660583496094, + 359.3887634277344, + 359.673095703125, + 359.3397521972656, + 359.1601867675781, + 358.8623046875, + 358.3875427246094, + 358.9278259277344, + 358.6763916015625, + 358.5888366699219, + 358.2840576171875, + 357.4374389648437, + 356.85498046875, + 357.75018310546875, + 358.7865600585937, + 359.68707275390625, + 358.9818725585937, + 358.9618225097656, + 359.5857849121094, + 359.5823059082031, + 359.7642517089844, + 359.7159118652344, + 359.38885498046875, + 359.05816650390625, + 358.8500061035156, + 359.00457763671875, + 357.85888671875, + 357.5047912597656, + 358.6526184082031, + 357.02703857421875, + 357.2925109863281, + 358.2082214355469, + 358.3887939453125, + 358.5728454589844, + 358.5884094238281, + 359.3389892578125, + 358.44207763671875, + 358.58026123046875, + 358.8177795410156, + 358.7875061035156, + 359.2876281738281, + 359.4195861816406, + 358.8738098144531, + 359.01422119140625, + 359.4451599121094, + 359.5342102050781, + 360.4178771972656, + 359.9522399902344, + 360.3809509277344, + 360.2109375, + 361.2432556152344, + 361.1715393066406, + 361.6592102050781, + 362.0090942382813, + 361.7130432128906, + 361.9664306640625, + 362.40283203125, + 362.6456604003906, + 363.7590942382813, + 362.39251708984375, + 361.2790832519531, + 282.8052673339844, + 279.04608154296875, + 282.4010314941406, + 286.5472106933594, + 287.6993713378906, + 290.406982421875, + 290.8794860839844, + 290.19757080078125, + 291.0823059082031, + 291.2209167480469, + 292.6325988769531, + 291.61700439453125, + 292.16632080078125, + 292.5622863769531, + 292.4338684082031, + 292.2705993652344, + 293.248291015625, + 292.8450012207031, + 292.98272705078125, + 292.75714111328125, + 294.0396423339844, + 293.994384765625, + 294.278076171875, + 294.2740783691406, + 294.7635192871094, + 294.9712829589844, + 295.33648681640625, + 294.9649963378906, + 294.8724975585937, + 294.3507385253906, + 293.3451232910156, + 294.27984619140625, + 294.1650390625, + 294.7659912109375, + 293.84869384765625, + 294.0479736328125, + 293.2484741210937, + 294.4142150878906, + 295.04510498046875, + 295.46710205078125, + 295.8202209472656, + 295.21063232421875, + 294.40069580078125, + 295.12994384765625, + 294.8354797363281, + 295.0543518066406, + 294.6632385253906, + 294.9977722167969, + 294.648681640625, + 294.4620361328125, + 294.154296875, + 295.5210266113281, + 295.8841857910156, + 295.41485595703125, + 296.2046203613281, + 295.6978759765625, + 294.0210266113281, + 295.6363525390625, + 296.2043762207031, + 297.2204284667969, + 295.9385070800781, + 295.921630859375, + 296.44891357421875, + 295.9752502441406, + 296.81817626953125, + 296.9077453613281, + 296.87310791015625, + 296.3519287109375, + 296.68902587890625, + 296.1576232910156, + 296.9183044433594, + 297.1040344238281, + 297.8329162597656, + 297.06231689453125, + 296.9719543457031, + 296.657958984375, + 296.74774169921875, + 297.07476806640625, + 297.3730163574219, + 296.8524475097656, + 297.0059509277344, + 297.1058654785156, + 297.9693908691406, + 297.88165283203125, + 297.6462097167969, + 298.9822692871094, + 298.156494140625, + 298.7214050292969, + 299.01531982421875, + 298.5400390625, + 328.64178466796875, + 329.9387512207031, + 331.3897399902344, + 333.08154296875, + 333.2943420410156, + 332.7924499511719, + 332.92315673828125, + 332.815673828125, + 333.61181640625, + 333.5367431640625, + 333.6248474121094, + 333.2030029296875, + 332.8619079589844, + 333.013916015625, + 332.4386901855469, + 333.16680908203125, + 332.7801208496094, + 331.8240966796875, + 332.1011962890625, + 331.939208984375, + 332.37261962890625, + 332.9047546386719, + 333.19439697265625, + 332.86968994140625, + 333.1973876953125, + 333.3371887207031, + 333.83575439453125, + 333.1569519042969, + 333.3797607421875, + 333.9742431640625, + 333.7681884765625, + 333.0032958984375, + 333.155517578125, + 331.91455078125, + 332.46685791015625, + 331.79510498046875, + 333.2541809082031, + 333.2222595214844, + 332.966552734375, + 333.3083190917969, + 333.8191223144531, + 333.2041015625, + 333.4326171875, + 333.544921875, + 332.38287353515625, + 332.2857666015625, + 331.4556884765625, + 330.2707214355469, + 330.4195556640625, + 329.48785400390625, + 329.5577087402344, + 330.2663879394531, + 331.84771728515625, + 334.0539245605469, + 334.1283569335937, + 334.0681457519531, + 335.3121337890625, + 334.9388732910156, + 334.34344482421875, + 334.5809631347656, + 333.64898681640625, + 334.1307678222656, + 334.7008361816406, + 333.2722473144531, + 333.0137023925781, + 331.9921264648437, + 331.6800537109375, + 332.4163513183594, + 333.3384704589844, + 333.3754272460937, + 333.4186096191406, + 333.58648681640625, + 334.43231201171875, + 333.68096923828125, + 334.2570495605469, + 334.1923217773437, + 334.4856872558594, + 335.22772216796875, + 335.40460205078125, + 334.2890625, + 334.9296569824219, + 334.9083251953125, + 334.18994140625, + 334.6949157714844, + 334.1930236816406, + 334.9540405273437, + 335.1471862792969, + 335.65679931640625, + 335.99609375, + 335.7073669433594, + 367.628173828125, + 368.2905578613281, + 370.3262634277344, + 370.60302734375, + 370.2281188964844, + 370.8107604980469, + 369.5078430175781, + 368.945068359375, + 367.7029418945313, + 367.1958618164063, + 366.49810791015625, + 367.2691650390625, + 365.921875, + 366.8121643066406, + 365.2463989257813, + 365.9017333984375, + 365.5550231933594, + 365.9224548339844, + 366.9606018066406, + 366.4556274414063, + 366.3463134765625, + 366.6302795410156, + 366.6630859375, + 365.9984436035156, + 366.2511901855469, + 365.93499755859375, + 365.9416809082031, + 365.8621520996094, + 365.5891418457031, + 365.06243896484375, + 365.1203918457031, + 364.2437744140625, + 364.4126586914063, + 363.8743286132813, + 364.836181640625, + 363.8137512207031, + 363.75042724609375, + 364.3686828613281, + 364.4854736328125, + 364.2892150878906, + 362.9789733886719, + 363.73345947265625, + 363.5594787597656, + 363.84613037109375, + 362.3800048828125, + 362.3746337890625, + 362.0014343261719, + 362.4912414550781, + 362.0827026367188, + 362.0335388183594, + 362.3829040527344, + 362.2856750488281, + 362.233642578125, + 362.625244140625, + 363.3910827636719, + 362.9295349121094, + 362.8480529785156, + 362.0069274902344, + 361.6875305175781, + 361.629638671875, + 361.3429870605469, + 361.6567687988281, + 361.94281005859375, + 362.4664916992188, + 363.2919006347656, + 362.9890747070313, + 363.1798400878906, + 362.4972534179688, + 362.9107666015625, + 363.2173156738281, + 363.8522338867188, + 362.6447448730469, + 362.1781005859375, + 361.7898864746094, + 361.0286254882813, + 361.7830505371094, + 361.5848083496094, + 361.5703430175781, + 363.220703125, + 363.9089050292969, + 363.8181762695313, + 364.6590881347656, + 363.9401245117188, + 363.6384887695313, + 363.1525268554688, + 362.4663391113281, + 362.360595703125, + 362.8992919921875, + 362.9682922363281, + 362.1788024902344 + ] + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Left Iris Scatter Plot" + }, + "xaxis": { + "title": { + "text": "left_iris_x" + } + }, + "yaxis": { + "title": { + "text": "left_iris_y" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a scatter plot\n", + "fig = go.Figure(\n", + " data=go.Scatter(\n", + " x=raw_dataset[\"left_iris_x\"],\n", + " y=raw_dataset[\"left_iris_y\"],\n", + " mode=\"markers\",\n", + " marker=dict(color=\"blue\", size=5, opacity=0.7),\n", + " )\n", + ")\n", + "\n", + "# Set the layout\n", + "fig.update_layout(\n", + " title=\"Left Iris Scatter Plot\",\n", + " xaxis=dict(title=\"left_iris_x\"),\n", + " yaxis=dict(title=\"left_iris_y\"),\n", + ")\n", + "\n", + "# Show the plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Right Eye Scatter Plot Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "orange", + "opacity": 0.7, + "size": 5 + }, + "mode": "markers", + "type": "scatter", + "x": [ + 406.1318359375, + 412.5827331542969, + 417.4015502929688, + 422.3596801757813, + 426.682861328125, + 427.4379577636719, + 429.4768371582031, + 429.4253845214844, + 429.2258605957031, + 429.0636291503906, + 429.1285705566406, + 430.4913940429688, + 430.351806640625, + 430.6640625, + 431.08050537109375, + 430.9166870117188, + 431.0350341796875, + 431.4494018554688, + 431.22357177734375, + 430.7623291015625, + 430.09912109375, + 429.2122497558594, + 430.30023193359375, + 430.0816955566406, + 430.0627746582031, + 429.6393127441406, + 430.2427368164063, + 429.7746887207031, + 430.1240234375, + 430.8858337402344, + 431.1208190917969, + 431.5374755859375, + 432.2371520996094, + 432.4436645507813, + 432.4572143554688, + 432.1759948730469, + 432.1097412109375, + 432.213623046875, + 432.3042297363281, + 432.6994323730469, + 432.92578125, + 433.2131958007813, + 433.8449401855469, + 433.5662536621094, + 434.1123962402344, + 433.8692932128906, + 433.99658203125, + 433.8401184082031, + 433.9212951660156, + 433.8475341796875, + 433.9205627441406, + 434.3721618652344, + 434.3191833496094, + 433.919677734375, + 433.7450256347656, + 433.8492736816406, + 434.0353393554688, + 434.5631408691406, + 434.8211975097656, + 435.2414855957031, + 435.2912902832031, + 434.5606994628906, + 434.2222900390625, + 434.2960205078125, + 434.348876953125, + 434.505126953125, + 433.8631896972656, + 435.11724853515625, + 434.4216613769531, + 434.1930847167969, + 434.3545837402344, + 434.4975891113281, + 434.7269287109375, + 434.5565490722656, + 434.1631774902344, + 434.3444213867188, + 434.8882751464844, + 434.7594299316406, + 434.4430541992188, + 434.56201171875, + 433.77630615234375, + 433.9733276367188, + 434.1980895996094, + 434.6843872070313, + 434.430908203125, + 435.2957153320313, + 435.5030517578125, + 435.2530517578125, + 435.46282958984375, + 435.980712890625, + 444.0892333984375, + 444.41705322265625, + 445.90130615234375, + 445.6699523925781, + 445.9026184082031, + 445.4825439453125, + 445.4889526367188, + 445.2583923339844, + 445.8470458984375, + 446.3668823242188, + 446.1805419921875, + 445.3009338378906, + 444.0154113769531, + 445.0160217285156, + 445.6287231445313, + 447.1579895019531, + 448.6547546386719, + 448.3166809082031, + 448.0406188964844, + 448.871337890625, + 448.9178466796875, + 449.3208312988281, + 449.3345031738281, + 449.1978759765625, + 449.385498046875, + 449.2699279785156, + 449.3627624511719, + 448.64202880859375, + 448.8035583496094, + 448.9733581542969, + 449.1926574707031, + 449.4210510253906, + 449.0714721679688, + 449.0001525878906, + 448.8619079589844, + 448.7075805664063, + 448.78033447265625, + 448.6696472167969, + 448.7021789550781, + 448.06292724609375, + 448.0857849121094, + 448.6421203613281, + 448.248291015625, + 447.09814453125, + 447.9884033203125, + 446.88275146484375, + 447.4859924316406, + 447.8331909179688, + 447.5699768066406, + 448.0003356933594, + 448.262939453125, + 448.558837890625, + 448.6809692382813, + 448.0343627929688, + 448.7224731445313, + 447.85302734375, + 447.796630859375, + 448.6554870605469, + 448.9084167480469, + 447.4429626464844, + 448.2933349609375, + 447.2593383789063, + 447.9982299804688, + 448.2411193847656, + 447.9476318359375, + 447.754150390625, + 447.3958435058594, + 447.7849426269531, + 447.0710144042969, + 446.9820251464844, + 446.9034118652344, + 446.2467651367188, + 447.0499572753906, + 446.1643981933594, + 446.6454772949219, + 446.5910339355469, + 446.7907104492188, + 446.53802490234375, + 446.6819763183594, + 445.7714538574219, + 445.640380859375, + 445.4433288574219, + 445.8753967285156, + 446.0535888671875, + 445.0797119140625, + 445.494384765625, + 445.2247924804688, + 445.4984130859375, + 445.0411376953125, + 444.9069519042969, + 449.2264404296875, + 451.1160583496094, + 449.7301025390625, + 449.5608825683594, + 449.7897338867188, + 449.5671997070313, + 450.2485046386719, + 450.7918395996094, + 450.8941650390625, + 450.4359130859375, + 450.6560363769531, + 450.1979370117188, + 450.59967041015625, + 450.1383056640625, + 450.3994140625, + 450.7129211425781, + 450.2789306640625, + 450.849609375, + 449.5040893554688, + 449.1178894042969, + 449.7105407714844, + 449.7755432128906, + 449.8971557617188, + 449.9110107421875, + 450.76861572265625, + 450.42193603515625, + 449.9213562011719, + 449.9787902832031, + 449.40765380859375, + 449.6307373046875, + 449.6105041503906, + 449.9841918945313, + 449.85687255859375, + 449.8398742675781, + 449.6881713867188, + 450.4194030761719, + 450.198974609375, + 450.9894714355469, + 450.3315734863281, + 450.7117309570313, + 451.1371154785156, + 451.57366943359375, + 451.2061462402344, + 451.2710266113281, + 451.636474609375, + 451.0251159667969, + 450.2214965820313, + 451.6646118164063, + 451.18988037109375, + 451.0827941894531, + 450.9614562988281, + 451.3120422363281, + 450.5003662109375, + 450.3892211914063, + 451.0728149414063, + 451.4847106933594, + 450.99713134765625, + 450.6154479980469, + 450.5690002441406, + 450.8346862792969, + 450.6088562011719, + 449.61334228515625, + 450.4630126953125, + 450.3612060546875, + 450.0874633789063, + 449.9324645996094, + 449.68463134765625, + 451.1805114746094, + 449.9660949707031, + 449.5711669921875, + 449.7459716796875, + 450.4726867675781, + 450.7731628417969, + 449.4170227050781, + 450.9215698242188, + 449.6248474121094, + 450.8277282714844, + 449.6275024414063, + 449.7189636230469, + 449.4582824707031, + 449.31591796875, + 449.3548889160156, + 450.3228454589844, + 448.5444641113281, + 449.0289611816406, + 448.8732604980469, + 449.6137390136719, + 448.7755737304688, + 449.05712890625, + 448.6424865722656, + 267.6096496582031, + 264.635009765625, + 263.9613647460937, + 261.4496459960937, + 260.7673034667969, + 260.8083801269531, + 261.67059326171875, + 263.4019470214844, + 264.6551818847656, + 265.4794616699219, + 265.5356750488281, + 264.7018737792969, + 264.9388427734375, + 264.8728332519531, + 264.8164978027344, + 264.2341613769531, + 262.6849365234375, + 260.7075500488281, + 258.8023376464844, + 258.3414306640625, + 259.0959777832031, + 258.0563049316406, + 257.4774780273437, + 256.3160095214844, + 256.1571655273437, + 256.41485595703125, + 256.9517517089844, + 257.2745056152344, + 259.113525390625, + 259.81036376953125, + 258.9732971191406, + 258.9285583496094, + 258.0595397949219, + 257.32049560546875, + 257.3846740722656, + 257.9485168457031, + 258.9082946777344, + 257.76519775390625, + 256.8997802734375, + 256.74029541015625, + 256.0796813964844, + 256.3431701660156, + 257.3079528808594, + 256.40509033203125, + 255.68588256835935, + 255.86460876464844, + 255.44415283203125, + 255.77525329589844, + 256.1136169433594, + 257.114990234375, + 256.2130126953125, + 256.2140197753906, + 256.166748046875, + 255.4566650390625, + 255.47206115722656, + 256.2168884277344, + 256.1624450683594, + 255.51617431640625, + 254.944580078125, + 255.2301177978516, + 255.2362060546875, + 255.3248748779297, + 255.9229431152344, + 255.03370666503903, + 255.1517639160156, + 255.0474548339844, + 255.0279998779297, + 255.2436981201172, + 256.251953125, + 256.0742492675781, + 255.4818115234375, + 255.09901428222656, + 254.2883758544922, + 254.92572021484372, + 254.961181640625, + 255.54705810546875, + 255.08973693847656, + 254.4845733642578, + 254.5088806152344, + 254.77322387695312, + 256.3195495605469, + 258.0282897949219, + 258.4302673339844, + 258.339111328125, + 258.1498107910156, + 256.94256591796875, + 256.9183959960937, + 257.08819580078125, + 257.431884765625, + 257.5097961425781, + 260.4217834472656, + 260.8016357421875, + 259.3453369140625, + 259.6009826660156, + 260.3341979980469, + 260.9388732910156, + 260.978271484375, + 259.93072509765625, + 260.05718994140625, + 259.88116455078125, + 259.41162109375, + 259.9818115234375, + 260.2087097167969, + 259.0680847167969, + 259.5036926269531, + 258.55157470703125, + 258.95733642578125, + 259.0809936523437, + 259.9766540527344, + 258.52056884765625, + 258.3110656738281, + 259.0587158203125, + 258.45953369140625, + 259.5116271972656, + 260.4410095214844, + 261.42333984375, + 260.5399780273437, + 259.6140441894531, + 259.3943176269531, + 258.9061584472656, + 259.356689453125, + 259.4344787597656, + 260.4978942871094, + 260.5018615722656, + 259.1136474609375, + 258.92510986328125, + 258.4620361328125, + 258.9327697753906, + 259.7791442871094, + 260.5358581542969, + 259.9435119628906, + 259.34503173828125, + 259.8689270019531, + 259.4232482910156, + 259.2808837890625, + 260.3185729980469, + 259.9832763671875, + 259.0973815917969, + 257.9602355957031, + 258.8209533691406, + 258.85302734375, + 259.535888671875, + 260.1162414550781, + 260.3266296386719, + 259.5599365234375, + 259.6501159667969, + 259.5000305175781, + 259.2994384765625, + 259.08673095703125, + 259.5550231933594, + 258.643310546875, + 258.2224426269531, + 257.2529602050781, + 258.3711853027344, + 258.757080078125, + 258.9466247558594, + 259.22125244140625, + 259.2728576660156, + 258.5894775390625, + 258.7743530273437, + 258.9397277832031, + 260.0585327148437, + 259.97833251953125, + 259.32330322265625, + 259.3568725585937, + 259.7400817871094, + 260.1372375488281, + 261.0435791015625, + 260.7040710449219, + 260.8114013671875, + 260.31256103515625, + 259.8341064453125, + 260.3416442871094, + 260.6206359863281, + 259.56500244140625, + 260.0645751953125, + 259.8295288085937, + 260.5435791015625, + 260.20489501953125, + 260.3587951660156, + 115.00743865966795, + 51.50483703613281, + 47.19908142089844, + 45.54025650024414, + 43.83247375488281, + 42.40047454833984, + 40.2406005859375, + 42.07778930664063, + 42.50164413452149, + 42.00001525878906, + 42.91915512084961, + 43.213897705078125, + 43.50798416137695, + 44.31502151489258, + 43.78851318359375, + 44.429935455322266, + 46.468894958496094, + 46.24443817138672, + 46.46269607543945, + 44.8902702331543, + 46.47064971923828, + 45.94384765625, + 43.76075744628906, + 44.77442169189453, + 45.79913330078125, + 43.82310104370117, + 46.28888702392578, + 44.76016998291016, + 46.02631759643555, + 43.62628936767578, + 44.95112991333008, + 45.45626449584961, + 45.49966430664063, + 45.979736328125, + 46.07496643066406, + 45.617374420166016, + 45.787330627441406, + 46.35049819946289, + 44.63794708251953, + 44.41941452026367, + 45.85277557373047, + 43.75278091430664, + 45.59061813354492, + 44.54585647583008, + 44.60078811645508, + 43.90929412841797, + 46.83700942993164, + 44.84295272827149, + 48.001407623291016, + 45.41876602172852, + 46.58343505859375, + 45.120059967041016, + 44.71129989624024, + 45.21213912963867, + 46.4685173034668, + 45.42568206787109, + 46.381324768066406, + 44.63027191162109, + 44.40367126464844, + 44.0250358581543, + 45.18380737304688, + 45.1017951965332, + 45.666290283203125, + 47.257572174072266, + 45.262542724609375, + 45.0211067199707, + 44.41584014892578, + 45.1391487121582, + 45.761844635009766, + 44.73664855957031, + 46.01229095458984, + 45.23928451538086, + 45.23316955566406, + 45.00680541992188, + 45.22780990600586, + 44.243934631347656, + 44.73038101196289, + 46.284278869628906, + 45.15336608886719, + 46.8207893371582, + 47.61547088623047, + 46.922733306884766, + 46.44148635864258, + 46.8725814819336, + 45.653167724609375, + 45.65256118774414, + 46.2941780090332, + 46.48394775390625, + 47.314674377441406, + 47.6170539855957, + 35.90129089355469, + 35.95975112915039, + 35.540706634521484, + 33.65265655517578, + 34.373661041259766, + 33.2919921875, + 32.97258377075195, + 33.626277923583984, + 32.92985916137695, + 33.34880828857422, + 32.873291015625, + 33.55250930786133, + 34.168922424316406, + 33.951419830322266, + 33.08818435668945, + 33.909515380859375, + 34.62171173095703, + 33.606048583984375, + 34.90583038330078, + 33.61172103881836, + 34.786842346191406, + 34.79957580566406, + 34.042911529541016, + 33.48689651489258, + 33.10163497924805, + 34.68852615356445, + 34.65605163574219, + 34.55024337768555, + 34.42736053466797, + 34.320213317871094, + 33.690940856933594, + 34.484745025634766, + 34.25856399536133, + 34.81216049194336, + 35.287208557128906, + 35.278953552246094, + 34.95246124267578, + 35.20370864868164, + 34.5460205078125, + 34.88454055786133, + 34.21610641479492, + 34.57807540893555, + 34.42404556274414, + 35.10045623779297, + 34.808326721191406, + 35.482147216796875, + 36.45640182495117, + 36.83344268798828, + 38.20791244506836, + 38.63803863525391, + 39.81988143920898, + 39.00847244262695, + 39.05519485473633, + 38.413841247558594, + 38.783233642578125, + 37.48418045043945, + 38.21521759033203, + 37.67402267456055, + 37.0682373046875, + 36.39672088623047, + 36.66973114013672, + 36.61091995239258, + 38.586402893066406, + 37.30756378173828, + 38.81692123413086, + 38.95041275024414, + 38.48569869995117, + 38.97962188720703, + 38.36358642578125, + 38.72758102416992, + 37.67115020751953, + 38.01158905029297, + 37.47981643676758, + 37.74861145019531, + 36.30741882324219, + 36.53149032592773, + 38.01092147827149, + 36.2879524230957, + 36.25053024291992, + 37.63771057128906, + 37.06993865966797, + 37.7302360534668, + 38.80353927612305, + 38.89839172363281, + 39.13541030883789, + 39.74293899536133, + 39.65838241577149, + 38.84358978271485, + 38.68531036376953, + 40.232154846191406, + 36.50090408325195, + 34.36490631103516, + 33.52898025512695, + 33.06907272338867, + 33.362327575683594, + 34.24589920043945, + 34.9149055480957, + 34.91694259643555, + 34.331809997558594, + 35.293392181396484, + 35.3702507019043, + 36.18892288208008, + 36.06906509399414, + 36.95792007446289, + 35.95009231567383, + 36.25725936889648, + 36.549293518066406, + 36.13301849365234, + 36.1459846496582, + 35.74244689941406, + 36.17335891723633, + 36.59944534301758, + 36.27548599243164, + 36.20467758178711, + 36.19569396972656, + 37.04568862915039, + 36.8834114074707, + 36.70774459838867, + 35.86980056762695, + 37.080322265625, + 36.99509048461914, + 36.91960525512695, + 36.86191940307617, + 36.8362922668457, + 36.27985763549805, + 36.2735710144043, + 35.88302993774414, + 36.66389465332031, + 36.36712646484375, + 36.9935302734375, + 36.41986846923828, + 37.21148300170898, + 36.97147750854492, + 37.714637756347656, + 38.129676818847656, + 38.093170166015625, + 38.260284423828125, + 38.320594787597656, + 38.030181884765625, + 37.723270416259766, + 37.542606353759766, + 37.4668197631836, + 37.52609634399414, + 36.77667999267578, + 37.51485443115234, + 37.638973236083984, + 38.32381057739258, + 37.9429931640625, + 37.92679977416992, + 38.502197265625, + 37.54156494140625, + 38.247169494628906, + 37.81153106689453, + 37.70357131958008, + 37.86711883544922, + 38.29137802124024, + 37.94681167602539, + 37.495208740234375, + 37.75660705566406, + 38.093074798583984, + 38.216556549072266, + 39.04142761230469, + 39.82356262207031, + 39.75300216674805, + 39.92704391479492, + 39.15785217285156, + 39.38557434082031, + 38.49618148803711, + 39.26626205444336, + 38.8640251159668, + 38.48637390136719, + 39.06260299682617, + 39.033203125, + 39.381568908691406, + 39.10884094238281, + 39.80299377441406, + 40.03264617919922, + 39.36104583740234, + 39.99951553344727, + 40.40206146240234 + ], + "y": [ + 278.6587829589844, + 279.68853759765625, + 282.7178649902344, + 283.89190673828125, + 285.81365966796875, + 286.3018798828125, + 287.5920104980469, + 288.2318420410156, + 288.6129150390625, + 289.5033264160156, + 289.94873046875, + 290.6874084472656, + 291.2796936035156, + 291.0555725097656, + 291.7254943847656, + 291.98583984375, + 291.6319885253906, + 291.76806640625, + 292.4360046386719, + 292.7138671875, + 292.8938293457031, + 293.4069519042969, + 293.4486999511719, + 293.9561767578125, + 294.07550048828125, + 294.3245849609375, + 294.8427734375, + 295.6548156738281, + 295.77783203125, + 295.8098449707031, + 295.5585327148437, + 295.5684814453125, + 294.8382873535156, + 294.7930603027344, + 295.16436767578125, + 295.2525939941406, + 295.3656311035156, + 295.9725952148437, + 296.1670227050781, + 296.5223388671875, + 297.2243957519531, + 297.2395324707031, + 296.90643310546875, + 297.58966064453125, + 297.7384948730469, + 297.9042053222656, + 297.80975341796875, + 297.86944580078125, + 297.5381774902344, + 297.0322570800781, + 296.8061828613281, + 296.7678527832031, + 296.5919189453125, + 297.0115661621094, + 297.4121398925781, + 298.4136047363281, + 298.01171875, + 297.9859313964844, + 298.060791015625, + 298.0162048339844, + 298.3176574707031, + 298.590576171875, + 298.64617919921875, + 298.9942626953125, + 299.2660522460937, + 299.3917541503906, + 298.4346923828125, + 297.7711181640625, + 297.58746337890625, + 297.47039794921875, + 297.716552734375, + 297.8401184082031, + 297.8969421386719, + 298.11749267578125, + 298.1728210449219, + 297.8927001953125, + 298.1684265136719, + 298.7659912109375, + 298.8337707519531, + 298.9140930175781, + 298.6416320800781, + 298.4979553222656, + 298.9032592773437, + 299.4254455566406, + 299.2529602050781, + 299.87158203125, + 300.4452514648437, + 300.62213134765625, + 300.96002197265625, + 301.022705078125, + 319.5253601074219, + 320.4919128417969, + 320.50567626953125, + 320.48193359375, + 321.093017578125, + 321.0408630371094, + 322.30010986328125, + 322.0480041503906, + 321.8678283691406, + 322.2386169433594, + 322.0103454589844, + 321.4308776855469, + 321.4642944335937, + 322.18243408203125, + 322.6790466308594, + 323.9134826660156, + 324.16485595703125, + 325.55908203125, + 325.787353515625, + 325.3703308105469, + 326.6930236816406, + 326.8828125, + 326.71734619140625, + 326.5733642578125, + 327.0733947753906, + 327.2944641113281, + 327.1143493652344, + 327.20562744140625, + 326.7191162109375, + 326.83123779296875, + 325.9166259765625, + 325.7221984863281, + 325.59442138671875, + 325.6973571777344, + 326.8763122558594, + 327.4244689941406, + 327.3643798828125, + 328.2643127441406, + 328.31707763671875, + 328.4647827148437, + 328.44207763671875, + 328.5230712890625, + 327.95947265625, + 327.63629150390625, + 327.3911437988281, + 327.33837890625, + 326.98199462890625, + 327.8212585449219, + 327.69903564453125, + 327.9450988769531, + 328.1687316894531, + 329.6231994628906, + 328.9214782714844, + 329.9704895019531, + 328.6716613769531, + 328.47003173828125, + 329.00494384765625, + 329.10760498046875, + 328.4041442871094, + 327.4507141113281, + 326.9197692871094, + 326.76373291015625, + 326.6081848144531, + 326.3944091796875, + 326.5724182128906, + 326.7880554199219, + 327.6852722167969, + 327.9430847167969, + 327.7869873046875, + 328.4037780761719, + 328.6939697265625, + 328.6634521484375, + 328.9014892578125, + 328.7609558105469, + 328.8634338378906, + 329.2910461425781, + 330.0882263183594, + 329.9927673339844, + 329.704833984375, + 329.6427917480469, + 329.9592590332031, + 330.540771484375, + 330.5394897460937, + 330.9948425292969, + 331.4999389648437, + 331.7212829589844, + 332.3335266113281, + 331.8922119140625, + 331.9176940917969, + 332.3169860839844, + 350.939208984375, + 353.0888977050781, + 352.606201171875, + 353.556640625, + 352.9193420410156, + 352.5098571777344, + 352.8683776855469, + 353.0800476074219, + 353.516845703125, + 352.6256408691406, + 353.6251525878906, + 352.9017639160156, + 353.7345886230469, + 353.2970275878906, + 353.599853515625, + 353.9906311035156, + 352.9421081542969, + 353.5935363769531, + 353.3539123535156, + 352.4195861816406, + 352.654052734375, + 352.2733154296875, + 352.5403137207031, + 352.0572814941406, + 352.2721252441406, + 353.123291015625, + 352.0639038085937, + 352.6796875, + 352.1979675292969, + 353.97149658203125, + 352.9234924316406, + 352.6513671875, + 351.8056335449219, + 351.85968017578125, + 352.3974304199219, + 352.2980041503906, + 351.04217529296875, + 351.1416931152344, + 350.89385986328125, + 350.3295288085937, + 350.96923828125, + 350.96112060546875, + 350.4766845703125, + 351.76446533203125, + 352.2030029296875, + 352.4102478027344, + 351.5936279296875, + 351.549072265625, + 352.5652770996094, + 352.70465087890625, + 352.2307434082031, + 351.8585510253906, + 352.2607116699219, + 352.2417297363281, + 351.6682434082031, + 352.4817199707031, + 352.2401428222656, + 351.8228454589844, + 351.46307373046875, + 353.2096252441406, + 352.3231811523437, + 353.0716552734375, + 351.7247314453125, + 351.3336486816406, + 352.8054504394531, + 352.7503662109375, + 352.5203552246094, + 352.90869140625, + 352.447998046875, + 351.9095153808594, + 351.2599792480469, + 351.7578430175781, + 352.2601013183594, + 352.7870178222656, + 353.28466796875, + 352.9905700683594, + 353.4673156738281, + 352.4403076171875, + 352.4091491699219, + 352.970947265625, + 352.89263916015625, + 352.2474975585937, + 352.39825439453125, + 350.60125732421875, + 351.09698486328125, + 350.6036682128906, + 351.4814453125, + 351.0400695800781, + 349.7654724121094, + 351.10711669921875, + 294.4920349121094, + 297.326904296875, + 299.714599609375, + 301.1353454589844, + 301.8423767089844, + 302.6703186035156, + 303.5560913085937, + 304.38531494140625, + 304.05419921875, + 304.2489929199219, + 305.4444580078125, + 305.84002685546875, + 305.80096435546875, + 306.1268615722656, + 307.1083679199219, + 306.5302124023437, + 306.1732788085937, + 306.0162048339844, + 305.67144775390625, + 304.8768310546875, + 305.1669616699219, + 306.9148864746094, + 307.0751342773437, + 307.5413513183594, + 307.1670227050781, + 306.3147888183594, + 307.1473083496094, + 308.1194152832031, + 307.73468017578125, + 307.36016845703125, + 306.0953063964844, + 306.1095275878906, + 305.6893615722656, + 305.4668884277344, + 305.2475891113281, + 306.1022644042969, + 306.648681640625, + 307.1986389160156, + 307.5337829589844, + 307.1620483398437, + 307.2368469238281, + 307.4396667480469, + 307.7168884277344, + 308.0772399902344, + 308.3499450683594, + 308.0889892578125, + 307.9378356933594, + 307.504638671875, + 306.4655456542969, + 306.5191955566406, + 306.5966491699219, + 306.9457702636719, + 307.5461120605469, + 307.4769897460937, + 307.204833984375, + 307.2298889160156, + 307.7707824707031, + 307.49725341796875, + 307.63104248046875, + 308.037353515625, + 307.5618896484375, + 307.5862731933594, + 307.9329833984375, + 307.0826416015625, + 307.3892822265625, + 307.8087463378906, + 307.4072570800781, + 307.0108947753906, + 307.4296569824219, + 308.001220703125, + 307.8639221191406, + 308.46502685546875, + 307.9075622558594, + 308.2838439941406, + 308.6385192871094, + 307.9942932128906, + 308.15380859375, + 308.5730895996094, + 308.2528076171875, + 308.2806396484375, + 307.99951171875, + 307.46209716796875, + 307.713134765625, + 308.11895751953125, + 308.3320617675781, + 308.76239013671875, + 308.71392822265625, + 309.0941772460937, + 308.5256042480469, + 308.76806640625, + 352.0924987792969, + 353.2456359863281, + 353.2266845703125, + 353.2334899902344, + 353.4187927246094, + 353.0394897460937, + 354.1445617675781, + 354.9068298339844, + 355.1820983886719, + 355.41607666015625, + 355.6459655761719, + 355.5621032714844, + 355.69891357421875, + 356.3218078613281, + 355.81036376953125, + 356.7961120605469, + 356.45166015625, + 356.5221252441406, + 355.67742919921875, + 356.352294921875, + 356.13238525390625, + 356.0939025878906, + 356.52203369140625, + 356.1376647949219, + 356.849365234375, + 357.2838134765625, + 358.9883728027344, + 359.2791748046875, + 359.7857360839844, + 359.5943603515625, + 359.6523742675781, + 359.6972045898437, + 359.3805847167969, + 359.7417297363281, + 359.9708251953125, + 359.6861267089844, + 359.4214172363281, + 359.9985656738281, + 359.587890625, + 358.8582458496094, + 358.1878356933594, + 358.5143127441406, + 358.191162109375, + 359.13360595703125, + 359.6601257324219, + 359.0382385253906, + 360.00543212890625, + 360.20220947265625, + 359.7325134277344, + 360.508544921875, + 360.46234130859375, + 360.3439636230469, + 359.77789306640625, + 358.8164367675781, + 358.9874267578125, + 358.4429626464844, + 358.6956787109375, + 358.4779357910156, + 358.4689636230469, + 358.05078125, + 357.93035888671875, + 358.45025634765625, + 359.0059509277344, + 359.4864501953125, + 359.1791687011719, + 359.2250061035156, + 358.6745300292969, + 359.3856811523437, + 359.47589111328125, + 359.7814025878906, + 359.8115234375, + 359.9916687011719, + 359.7430419921875, + 360.6611633300781, + 360.2921752929688, + 360.8587646484375, + 361.5252075195313, + 360.894775390625, + 361.2752380371094, + 361.0769348144531, + 362.08111572265625, + 362.0903930664063, + 361.572021484375, + 362.14544677734375, + 363.1383056640625, + 363.4327087402344, + 363.44964599609375, + 363.1249694824219, + 363.1203308105469, + 362.1066284179688, + 298.627197265625, + 288.0232849121094, + 296.6806335449219, + 299.1449890136719, + 300.3717346191406, + 301.794921875, + 302.8209533691406, + 303.5932006835937, + 301.73553466796875, + 303.79217529296875, + 303.12164306640625, + 303.6776733398437, + 305.1678466796875, + 303.6844787597656, + 303.1785888671875, + 303.9682312011719, + 306.1701965332031, + 304.42059326171875, + 304.44696044921875, + 304.9167785644531, + 304.147216796875, + 302.0964050292969, + 305.8660583496094, + 305.5189819335937, + 305.7559814453125, + 306.3141174316406, + 306.5978088378906, + 307.6017150878906, + 309.1418151855469, + 306.1195373535156, + 306.6746520996094, + 305.9591064453125, + 307.5207824707031, + 305.7107238769531, + 307.32989501953125, + 305.837890625, + 307.4850769042969, + 306.3861083984375, + 308.34881591796875, + 309.10699462890625, + 309.1021728515625, + 307.49822998046875, + 308.05352783203125, + 307.431884765625, + 307.647216796875, + 307.537841796875, + 307.8241882324219, + 307.52618408203125, + 307.5517883300781, + 306.6041259765625, + 307.245361328125, + 307.8837890625, + 308.469482421875, + 308.5936584472656, + 309.5561828613281, + 308.7938537597656, + 308.5149841308594, + 308.3275756835937, + 307.7242736816406, + 307.6890563964844, + 308.7651062011719, + 307.9826354980469, + 308.48272705078125, + 308.5583801269531, + 308.6339416503906, + 309.50762939453125, + 308.5377502441406, + 309.348876953125, + 309.6953125, + 309.049072265625, + 310.1925354003906, + 309.77935791015625, + 310.7265014648437, + 310.42138671875, + 309.849853515625, + 309.2418518066406, + 309.5338439941406, + 310.0146789550781, + 310.15203857421875, + 309.73468017578125, + 310.444580078125, + 309.863525390625, + 309.8780517578125, + 309.90594482421875, + 310.4373779296875, + 309.4589538574219, + 310.2410583496094, + 310.38848876953125, + 311.2431945800781, + 310.06280517578125, + 340.015869140625, + 340.078369140625, + 341.0238952636719, + 342.3067626953125, + 344.1671142578125, + 343.94903564453125, + 343.4977111816406, + 342.8972473144531, + 343.5950012207031, + 343.986328125, + 343.232177734375, + 343.5883483886719, + 343.2100524902344, + 342.64874267578125, + 342.75714111328125, + 341.78692626953125, + 343.7675476074219, + 342.85418701171875, + 342.5851135253906, + 342.7261657714844, + 342.3158874511719, + 342.6714172363281, + 343.2811584472656, + 342.8993835449219, + 343.91925048828125, + 343.83502197265625, + 343.8655700683594, + 341.9017333984375, + 341.8788146972656, + 344.34375, + 344.2005310058594, + 341.7691345214844, + 343.2500915527344, + 342.6436462402344, + 342.7397155761719, + 343.0450744628906, + 343.457763671875, + 342.78973388671875, + 343.63262939453125, + 343.1576538085937, + 344.3313293457031, + 343.45672607421875, + 342.716064453125, + 343.0313720703125, + 342.3461303710937, + 341.0517883300781, + 341.6321105957031, + 340.63970947265625, + 340.054931640625, + 339.06817626953125, + 338.679931640625, + 339.4184265136719, + 340.4853210449219, + 342.84393310546875, + 343.7859191894531, + 344.6409606933594, + 344.7259521484375, + 344.5099792480469, + 345.7684936523437, + 344.08319091796875, + 345.15167236328125, + 343.0729675292969, + 344.088134765625, + 343.65008544921875, + 343.41796875, + 341.8653869628906, + 342.66552734375, + 341.97784423828125, + 341.6732177734375, + 344.0194091796875, + 343.357421875, + 343.4201354980469, + 344.08013916015625, + 343.8168640136719, + 344.4685974121094, + 344.09002685546875, + 343.8713073730469, + 344.4443664550781, + 344.5775451660156, + 343.6693420410156, + 344.2877502441406, + 342.4261474609375, + 343.81085205078125, + 344.21929931640625, + 343.7770690917969, + 344.4793701171875, + 345.5018920898437, + 344.7492980957031, + 344.8187866210937, + 344.99090576171875, + 372.1086730957031, + 374.6158447265625, + 376.6040954589844, + 376.7322082519531, + 377.2171325683594, + 376.187255859375, + 376.0777587890625, + 375.8951416015625, + 375.1945190429688, + 373.7886352539063, + 373.9190979003906, + 373.67529296875, + 372.86627197265625, + 371.9799499511719, + 372.75146484375, + 372.1177673339844, + 372.9661254882813, + 372.4846801757813, + 373.6862182617188, + 373.8683776855469, + 373.2644348144531, + 373.3934631347656, + 374.08929443359375, + 372.4411315917969, + 373.1167907714844, + 372.7849426269531, + 373.0943298339844, + 372.4122924804688, + 372.1253051757813, + 372.00726318359375, + 371.1231689453125, + 371.1307678222656, + 370.2953796386719, + 370.9921875, + 371.39398193359375, + 370.7185363769531, + 371.17388916015625, + 370.734619140625, + 371.5354614257813, + 369.9682312011719, + 370.833740234375, + 369.952880859375, + 370.8332824707031, + 370.0201110839844, + 369.5139770507813, + 368.73773193359375, + 368.6415710449219, + 368.8769836425781, + 368.4453125, + 369.727294921875, + 369.1140747070313, + 369.8925476074219, + 369.9811401367188, + 369.229248046875, + 370.6836242675781, + 370.8212890625, + 370.4302978515625, + 369.1702270507813, + 368.3958435058594, + 368.74407958984375, + 368.3941040039063, + 367.86614990234375, + 367.992919921875, + 368.6070251464844, + 368.3948059082031, + 369.7008666992188, + 369.7635498046875, + 370.0177307128906, + 368.5213012695313, + 369.356201171875, + 369.4495544433594, + 369.8826599121094, + 369.3760986328125, + 368.6969604492188, + 368.7887268066406, + 368.2723693847656, + 368.4844055175781, + 369.4713439941406, + 369.412353515625, + 370.15179443359375, + 370.674560546875, + 370.302978515625, + 370.0084838867188, + 370.4146423339844, + 369.7380065917969, + 370.2562561035156, + 369.8150939941406, + 369.991943359375, + 369.3482055664063, + 369.583251953125 + ] + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Right Iris Scatter Plot" + }, + "xaxis": { + "title": { + "text": "right_iris_x" + } + }, + "yaxis": { + "title": { + "text": "right_iris_y" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a scatter plot\n", + "fig = go.Figure(\n", + " data=go.Scatter(\n", + " x=raw_dataset[\"right_iris_x\"],\n", + " y=raw_dataset[\"right_iris_y\"],\n", + " mode=\"markers\",\n", + " marker=dict(color=\"orange\", size=5, opacity=0.7),\n", + " )\n", + ")\n", + "\n", + "# Set the layout\n", + "fig.update_layout(\n", + " title=\"Right Iris Scatter Plot\",\n", + " xaxis=dict(title=\"right_iris_x\"),\n", + " yaxis=dict(title=\"right_iris_y\"),\n", + ")\n", + "\n", + "# Show the plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Line Plot Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "variable=left_iris_x
index=%{x}
value=%{y}", + "legendgroup": "left_iris_x", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "left_iris_x", + "showlegend": true, + "type": "scattergl", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139, + 140, + 141, + 142, + 143, + 144, + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155, + 156, + 157, + 158, + 159, + 160, + 161, + 162, + 163, + 164, + 165, + 166, + 167, + 168, + 169, + 170, + 171, + 172, + 173, + 174, + 175, + 176, + 177, + 178, + 179, + 180, + 181, + 182, + 183, + 184, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210, + 211, + 212, + 213, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 222, + 223, + 224, + 225, + 226, + 227, + 228, + 229, + 230, + 231, + 232, + 233, + 234, + 235, + 236, + 237, + 238, + 239, + 240, + 241, + 242, + 243, + 244, + 245, + 246, + 247, + 248, + 249, + 250, + 251, + 252, + 253, + 254, + 255, + 256, + 257, + 258, + 259, + 260, + 261, + 262, + 263, + 264, + 265, + 266, + 267, + 268, + 269, + 270, + 271, + 272, + 273, + 274, + 275, + 276, + 277, + 278, + 279, + 280, + 281, + 282, + 283, + 284, + 285, + 286, + 287, + 288, + 289, + 290, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 299, + 300, + 301, + 302, + 303, + 304, + 305, + 306, + 307, + 308, + 309, + 310, + 311, + 312, + 313, + 314, + 315, + 316, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 324, + 325, + 326, + 327, + 328, + 329, + 330, + 331, + 332, + 333, + 334, + 335, + 336, + 337, + 338, + 339, + 340, + 341, + 342, + 343, + 344, + 345, + 346, + 347, + 348, + 349, + 350, + 351, + 352, + 353, + 354, + 355, + 356, + 357, + 358, + 359, + 360, + 361, + 362, + 363, + 364, + 365, + 366, + 367, + 368, + 369, + 370, + 371, + 372, + 373, + 374, + 375, + 376, + 377, + 378, + 379, + 380, + 381, + 382, + 383, + 384, + 385, + 386, + 387, + 388, + 389, + 390, + 391, + 392, + 393, + 394, + 395, + 396, + 397, + 398, + 399, + 400, + 401, + 402, + 403, + 404, + 405, + 406, + 407, + 408, + 409, + 410, + 411, + 412, + 413, + 414, + 415, + 416, + 417, + 418, + 419, + 420, + 421, + 422, + 423, + 424, + 425, + 426, + 427, + 428, + 429, + 430, + 431, + 432, + 433, + 434, + 435, + 436, + 437, + 438, + 439, + 440, + 441, + 442, + 443, + 444, + 445, + 446, + 447, + 448, + 449, + 450, + 451, + 452, + 453, + 454, + 455, + 456, + 457, + 458, + 459, + 460, + 461, + 462, + 463, + 464, + 465, + 466, + 467, + 468, + 469, + 470, + 471, + 472, + 473, + 474, + 475, + 476, + 477, + 478, + 479, + 480, + 481, + 482, + 483, + 484, + 485, + 486, + 487, + 488, + 489, + 490, + 491, + 492, + 493, + 494, + 495, + 496, + 497, + 498, + 499, + 500, + 501, + 502, + 503, + 504, + 505, + 506, + 507, + 508, + 509, + 510, + 511, + 512, + 513, + 514, + 515, + 516, + 517, + 518, + 519, + 520, + 521, + 522, + 523, + 524, + 525, + 526, + 527, + 528, + 529, + 530, + 531, + 532, + 533, + 534, + 535, + 536, + 537, + 538, + 539, + 540, + 541, + 542, + 543, + 544, + 545, + 546, + 547, + 548, + 549, + 550, + 551, + 552, + 553, + 554, + 555, + 556, + 557, + 558, + 559, + 560, + 561, + 562, + 563, + 564, + 565, + 566, + 567, + 568, + 569, + 570, + 571, + 572, + 573, + 574, + 575, + 576, + 577, + 578, + 579, + 580, + 581, + 582, + 583, + 584, + 585, + 586, + 587, + 588, + 589, + 590, + 591, + 592, + 593, + 594, + 595, + 596, + 597, + 598, + 599, + 600, + 601, + 602, + 603, + 604, + 605, + 606, + 607, + 608, + 609, + 610, + 611, + 612, + 613, + 614, + 615, + 616, + 617, + 618, + 619, + 620, + 621, + 622, + 623, + 624, + 625, + 626, + 627, + 628, + 629, + 630, + 631, + 632, + 633, + 634, + 635, + 636, + 637, + 638, + 639, + 640, + 641, + 642, + 643, + 644, + 645, + 646, + 647, + 648, + 649, + 650, + 651, + 652, + 653, + 654, + 655, + 656, + 657, + 658, + 659, + 660, + 661, + 662, + 663, + 664, + 665, + 666, + 667, + 668, + 669, + 670, + 671, + 672, + 673, + 674, + 675, + 676, + 677, + 678, + 679, + 680, + 681, + 682, + 683, + 684, + 685, + 686, + 687, + 688, + 689, + 690, + 691, + 692, + 693, + 694, + 695, + 696, + 697, + 698, + 699, + 700, + 701, + 702, + 703, + 704, + 705, + 706, + 707, + 708, + 709, + 710, + 711, + 712, + 713, + 714, + 715, + 716, + 717, + 718, + 719 + ], + "xaxis": "x", + "y": [ + 506.9714965820313, + 518.5646362304688, + 524.4033203125, + 530.8411865234375, + 534.3703002929688, + 535.4397583007812, + 538.3284912109375, + 538.0055541992188, + 538.7401733398438, + 538.3395385742188, + 538.4655151367188, + 540.5523681640625, + 539.9481201171875, + 541.1350708007812, + 540.5191650390625, + 540.1651000976562, + 540.8129272460938, + 540.279052734375, + 541.3689575195312, + 539.712890625, + 540.1910400390625, + 539.932861328125, + 541.1317138671875, + 540.2360229492188, + 540.2733764648438, + 539.6839599609375, + 539.857177734375, + 539.76513671875, + 540.7872924804688, + 541.02587890625, + 541.4639892578125, + 541.7560424804688, + 542.5582275390625, + 542.26904296875, + 542.1211547851562, + 542.1524047851562, + 541.4745483398438, + 542.6224975585938, + 542.355224609375, + 543.4385375976562, + 543.4129028320312, + 543.640380859375, + 544.1436767578125, + 544.71044921875, + 544.9417114257812, + 544.8046264648438, + 544.907470703125, + 544.4990844726562, + 545.1011962890625, + 544.0618286132812, + 545.6531982421875, + 545.2763061523438, + 544.7899169921875, + 544.9967651367188, + 544.7830200195312, + 544.9871826171875, + 545.9523315429688, + 544.5263061523438, + 545.6052856445312, + 547.1279907226562, + 546.5455932617188, + 545.49658203125, + 545.4258422851562, + 546.29052734375, + 545.1207275390625, + 545.0530395507812, + 544.8782348632812, + 545.7645874023438, + 544.9891357421875, + 545.5903930664062, + 544.2805786132812, + 544.8512573242188, + 545.3428955078125, + 545.0262451171875, + 545.3805541992188, + 545.14990234375, + 545.1900024414062, + 544.8680419921875, + 545.3555908203125, + 544.9789428710938, + 544.938232421875, + 545.3932495117188, + 546.3397216796875, + 544.82275390625, + 545.5991821289062, + 547.0126953125, + 547.4599609375, + 546.29248046875, + 546.36279296875, + 546.7230224609375, + 556.6884765625, + 556.7714233398438, + 558.01806640625, + 558.6127319335938, + 558.4219970703125, + 559.459228515625, + 557.6658935546875, + 557.7333374023438, + 558.7186889648438, + 559.3433227539062, + 559.3925170898438, + 557.5004272460938, + 556.7421875, + 557.2520141601562, + 558.5250244140625, + 558.9024047851562, + 559.8341674804688, + 560.17822265625, + 559.509033203125, + 560.9150390625, + 560.495849609375, + 561.2053833007812, + 560.7686767578125, + 561.5689697265625, + 561.7825927734375, + 561.5682983398438, + 562.0491333007812, + 560.752197265625, + 561.6281127929688, + 560.6880493164062, + 560.9950561523438, + 560.450439453125, + 561.3142700195312, + 560.588134765625, + 560.8603515625, + 560.75634765625, + 560.5144653320312, + 561.158935546875, + 561.055908203125, + 560.0401611328125, + 560.5009765625, + 560.7841186523438, + 559.9788208007812, + 560.2263793945312, + 560.5133056640625, + 559.9036254882812, + 559.499755859375, + 559.7028198242188, + 559.5144653320312, + 560.4923095703125, + 560.7322387695312, + 560.2394409179688, + 561.4515380859375, + 560.7643432617188, + 561.7992553710938, + 559.970947265625, + 560.3557739257812, + 560.290771484375, + 560.0529174804688, + 559.5285034179688, + 560.670166015625, + 560.2899169921875, + 560.081298828125, + 560.2073364257812, + 559.8053588867188, + 560.414794921875, + 558.9434814453125, + 560.27490234375, + 559.3911743164062, + 559.0752563476562, + 559.697265625, + 560.1272583007812, + 559.2277221679688, + 559.3869018554688, + 559.9826049804688, + 559.4216918945312, + 559.6560668945312, + 558.8399047851562, + 559.0958862304688, + 559.0140991210938, + 559.040771484375, + 558.0556640625, + 558.7276611328125, + 559.2193603515625, + 557.57373046875, + 559.2841186523438, + 558.77783203125, + 558.8466186523438, + 558.0785522460938, + 557.7658081054688, + 563.245361328125, + 564.2551879882812, + 562.8328857421875, + 563.7398071289062, + 563.3372802734375, + 563.2427368164062, + 564.5738525390625, + 563.7952880859375, + 563.6834106445312, + 563.590087890625, + 563.843994140625, + 564.1339111328125, + 563.8959350585938, + 563.1615600585938, + 563.9400634765625, + 564.6978759765625, + 564.9093627929688, + 564.5678100585938, + 564.2595825195312, + 564.0352783203125, + 563.34326171875, + 563.3900756835938, + 563.1842041015625, + 564.3395385742188, + 564.5511474609375, + 564.1090087890625, + 564.3811645507812, + 563.2430419921875, + 563.9806518554688, + 563.2244873046875, + 563.4888916015625, + 563.2081298828125, + 563.9127807617188, + 564.5554809570312, + 563.4066772460938, + 563.7935791015625, + 563.3814086914062, + 564.099609375, + 564.6023559570312, + 563.572021484375, + 564.1981811523438, + 564.698974609375, + 563.6094360351562, + 564.7529296875, + 564.6073608398438, + 564.0097045898438, + 564.8242797851562, + 564.1727905273438, + 563.5795288085938, + 564.2640380859375, + 563.4013671875, + 564.9345092773438, + 564.2282104492188, + 564.46875, + 563.7816772460938, + 564.1197509765625, + 564.59033203125, + 564.2525024414062, + 564.015869140625, + 563.8367919921875, + 564.6503295898438, + 563.52587890625, + 564.1806030273438, + 563.266845703125, + 562.9224853515625, + 564.1807250976562, + 563.2727661132812, + 563.299560546875, + 563.62353515625, + 564.00732421875, + 562.4202880859375, + 563.630615234375, + 563.1637573242188, + 563.57763671875, + 563.6665649414062, + 562.9893188476562, + 562.9876708984375, + 564.6296997070312, + 563.970947265625, + 563.1293334960938, + 563.7843017578125, + 562.9188842773438, + 562.9011840820312, + 563.2520751953125, + 563.2647094726562, + 562.3236694335938, + 563.6578369140625, + 562.6688232421875, + 561.7875366210938, + 561.8511352539062, + 386.8733215332031, + 389.1112060546875, + 388.4430236816406, + 386.2240905761719, + 386.522705078125, + 387.0731201171875, + 388.2835388183594, + 389.2976379394531, + 391.3636169433594, + 392.0768737792969, + 391.8057556152344, + 390.96044921875, + 391.4176025390625, + 391.9043273925781, + 391.1206359863281, + 390.6957702636719, + 389.2049865722656, + 386.5533447265625, + 385.7132873535156, + 384.8652648925781, + 385.8428039550781, + 385.4671020507813, + 384.34283447265625, + 384.0664367675781, + 383.3593444824219, + 384.3056335449219, + 384.5469055175781, + 384.6551818847656, + 385.57855224609375, + 385.7691345214844, + 384.7511291503906, + 385.135498046875, + 383.86334228515625, + 383.4415588378906, + 383.454345703125, + 384.17919921875, + 384.719482421875, + 383.4304809570313, + 383.1623229980469, + 383.5268249511719, + 383.262939453125, + 383.3402099609375, + 384.17425537109375, + 382.6044006347656, + 382.4395446777344, + 382.8009948730469, + 381.7204895019531, + 381.7982177734375, + 382.9085693359375, + 383.0549621582031, + 382.3975524902344, + 383.0088195800781, + 382.0330810546875, + 381.8932189941406, + 381.27056884765625, + 382.7771911621094, + 382.6194152832031, + 382.0088806152344, + 381.561279296875, + 381.9228820800781, + 382.0288696289063, + 381.9415588378906, + 382.1340942382813, + 382.461181640625, + 381.5500183105469, + 381.1759338378906, + 382.1652526855469, + 382.0784912109375, + 382.4464416503906, + 382.7380981445313, + 382.2915649414063, + 381.9553527832031, + 381.3691101074219, + 381.4131774902344, + 382.156005859375, + 381.64459228515625, + 381.9104614257813, + 381.26947021484375, + 380.5329895019531, + 381.65283203125, + 382.0446166992188, + 384.8185729980469, + 385.3858337402344, + 385.2966003417969, + 385.534423828125, + 383.7272033691406, + 383.4385681152344, + 383.5013732910156, + 383.727294921875, + 384.6271667480469, + 392.2987060546875, + 390.9298400878906, + 390.5198059082031, + 391.0887451171875, + 391.9738464355469, + 392.5810546875, + 391.8455810546875, + 391.3519287109375, + 391.9194030761719, + 391.18896484375, + 391.2001647949219, + 392.0416870117188, + 392.1141662597656, + 391.0634765625, + 391.2837524414063, + 391.0296020507813, + 390.9624938964844, + 391.3521118164063, + 391.9414367675781, + 391.28131103515625, + 390.0772705078125, + 390.766845703125, + 390.6370239257813, + 391.571044921875, + 392.6316833496094, + 393.62005615234375, + 392.7270202636719, + 392.1440124511719, + 391.4623718261719, + 391.2567443847656, + 391.2491455078125, + 392.0492248535156, + 392.97406005859375, + 392.5518798828125, + 391.5374145507813, + 391.9832763671875, + 391.3318481445313, + 391.7645263671875, + 392.3851013183594, + 392.6951599121094, + 392.6311645507813, + 391.3670349121094, + 392.1858215332031, + 391.7538146972656, + 391.7377014160156, + 393.0763244628906, + 392.08837890625, + 391.4899597167969, + 390.5305786132813, + 391.50341796875, + 391.1092224121094, + 391.588134765625, + 392.3507385253906, + 392.1233520507813, + 391.7057800292969, + 391.2481994628906, + 391.5884704589844, + 390.5691223144531, + 391.0623474121094, + 390.9736633300781, + 390.6160888671875, + 390.4902038574219, + 389.9366455078125, + 390.9982604980469, + 391.6872253417969, + 391.7391357421875, + 391.5924072265625, + 391.41802978515625, + 391.6833801269531, + 391.37445068359375, + 392.07415771484375, + 392.7206726074219, + 393.2634887695313, + 392.4910278320313, + 392.0633544921875, + 393.7765502929688, + 393.4232482910156, + 394.2220764160156, + 393.6095275878906, + 394.15667724609375, + 393.4875183105469, + 392.8297119140625, + 394.16998291015625, + 394.0672302246094, + 392.9748840332031, + 393.5560302734375, + 393.2106323242188, + 393.33441162109375, + 393.4196472167969, + 393.71478271484375, + 206.52713012695312, + 164.5104522705078, + 160.8257293701172, + 159.94509887695312, + 158.15142822265625, + 156.966064453125, + 154.28981018066406, + 155.70187377929688, + 157.3920135498047, + 158.03475952148438, + 158.3424835205078, + 158.5967254638672, + 159.40176391601562, + 159.62611389160156, + 160.3441925048828, + 161.95750427246094, + 162.78770446777344, + 163.9203338623047, + 164.05679321289062, + 163.61239624023438, + 162.3110809326172, + 160.68348693847656, + 161.2389678955078, + 161.8617706298828, + 161.7224578857422, + 161.5873565673828, + 161.697998046875, + 160.6887664794922, + 160.90513610839844, + 161.5122528076172, + 163.12106323242188, + 163.2836151123047, + 163.1234130859375, + 162.56678771972656, + 163.26580810546875, + 162.26510620117188, + 162.7144012451172, + 162.9204864501953, + 163.18833923339844, + 161.77597045898438, + 162.94151306152344, + 162.46495056152344, + 161.21502685546875, + 162.0072784423828, + 162.68634033203125, + 162.5944366455078, + 163.5371856689453, + 164.09872436523438, + 165.6779022216797, + 165.8667449951172, + 166.32266235351562, + 164.79193115234375, + 163.90377807617188, + 163.7904052734375, + 164.2099151611328, + 164.77955627441406, + 165.05450439453125, + 164.2294464111328, + 164.08570861816406, + 163.34518432617188, + 163.45913696289062, + 164.08705139160156, + 165.77426147460938, + 165.16416931152344, + 165.16429138183594, + 164.826416015625, + 164.78598022460938, + 164.13497924804688, + 164.4682159423828, + 164.04443359375, + 163.5697784423828, + 163.5968475341797, + 163.9339141845703, + 162.9560089111328, + 163.58253479003906, + 164.11155700683594, + 164.7197723388672, + 165.00100708007812, + 165.133544921875, + 166.4665069580078, + 167.7435760498047, + 167.59291076660156, + 166.82740783691406, + 165.736572265625, + 166.41957092285156, + 167.6148681640625, + 166.76416015625, + 166.6020050048828, + 167.75057983398438, + 169.78160095214844, + 155.68035888671875, + 155.09588623046875, + 153.63722229003906, + 152.8428497314453, + 152.65121459960938, + 151.72764587402344, + 150.72190856933594, + 151.5420684814453, + 152.0251922607422, + 151.2655792236328, + 150.939697265625, + 152.90640258789062, + 153.12242126464844, + 153.10452270507812, + 152.26072692871094, + 151.64308166503906, + 152.9226531982422, + 152.4237060546875, + 152.7340545654297, + 153.490966796875, + 153.57879638671875, + 152.3104705810547, + 152.79908752441406, + 152.33689880371094, + 151.8495330810547, + 152.9599609375, + 153.7440185546875, + 154.30519104003906, + 154.04161071777344, + 153.7908477783203, + 153.554931640625, + 153.46279907226562, + 153.51593017578125, + 155.07705688476562, + 154.7864990234375, + 154.69993591308594, + 153.99600219726562, + 154.087158203125, + 154.01202392578125, + 153.976806640625, + 154.44520568847656, + 155.6165313720703, + 155.00680541992188, + 155.48812866210938, + 155.64285278320312, + 156.74017333984375, + 157.74325561523438, + 159.00485229492188, + 159.50628662109375, + 160.2045440673828, + 160.77537536621094, + 160.6865692138672, + 160.68832397460938, + 160.29129028320312, + 160.11904907226562, + 159.3603057861328, + 159.54530334472656, + 160.18441772460938, + 159.17247009277344, + 159.68408203125, + 159.12098693847656, + 159.0812225341797, + 159.98953247070312, + 160.09048461914062, + 160.097900390625, + 160.0994873046875, + 159.95619201660156, + 160.38719177246094, + 160.11700439453125, + 160.1515350341797, + 159.65382385253906, + 158.9752960205078, + 158.7391357421875, + 158.18887329101562, + 158.01068115234375, + 158.18417358398438, + 157.61814880371094, + 157.346923828125, + 157.910888671875, + 158.77239990234375, + 157.95574951171875, + 158.6696319580078, + 159.56243896484375, + 159.6716766357422, + 160.45989990234375, + 161.8671112060547, + 162.16030883789062, + 162.5677490234375, + 161.8557586669922, + 162.04176330566406, + 157.50010681152344, + 159.3820037841797, + 159.08851623535156, + 157.26992797851562, + 158.04066467285156, + 159.15382385253906, + 159.97293090820312, + 159.39479064941406, + 159.95103454589844, + 160.0814666748047, + 160.249755859375, + 160.0652313232422, + 160.229736328125, + 160.0924835205078, + 160.53305053710938, + 159.8092041015625, + 159.6959991455078, + 160.0650177001953, + 161.3235626220703, + 159.66697692871094, + 159.78353881835938, + 160.67752075195312, + 160.68569946289062, + 160.8162078857422, + 161.235595703125, + 161.46214294433594, + 161.1281280517578, + 161.1355743408203, + 161.2970428466797, + 161.5305633544922, + 161.4741973876953, + 162.0054473876953, + 161.41726684570312, + 161.10675048828125, + 160.4036407470703, + 160.09158325195312, + 160.57183837890625, + 160.57093811035156, + 160.77517700195312, + 159.99217224121094, + 161.51437377929688, + 160.39105224609375, + 161.01345825195312, + 160.3650665283203, + 161.45236206054688, + 160.9958953857422, + 162.43373107910156, + 161.40997314453125, + 161.59603881835938, + 161.0695343017578, + 162.07237243652344, + 160.92994689941406, + 160.24266052246094, + 161.5585174560547, + 161.36654663085938, + 161.27212524414062, + 161.7323455810547, + 163.031494140625, + 163.6909637451172, + 163.76133728027344, + 163.29086303710938, + 163.44139099121094, + 163.69017028808594, + 162.35855102539062, + 162.63076782226562, + 163.24417114257812, + 161.7393341064453, + 162.6964111328125, + 162.0379638671875, + 162.69371032714844, + 161.49351501464844, + 162.68215942382812, + 162.55369567871094, + 164.70501708984375, + 165.9799346923828, + 165.53823852539062, + 165.68682861328125, + 164.0941925048828, + 164.26763916015625, + 164.7672576904297, + 164.5294189453125, + 164.66827392578125, + 163.53201293945312, + 163.96859741210938, + 164.1597137451172, + 165.41360473632812, + 166.34632873535156, + 166.5220947265625, + 165.86631774902344, + 166.4485626220703 + ], + "yaxis": "y" + }, + { + "hovertemplate": "variable=left_iris_y
index=%{x}
value=%{y}", + "legendgroup": "left_iris_y", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "left_iris_y", + "showlegend": true, + "type": "scattergl", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139, + 140, + 141, + 142, + 143, + 144, + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155, + 156, + 157, + 158, + 159, + 160, + 161, + 162, + 163, + 164, + 165, + 166, + 167, + 168, + 169, + 170, + 171, + 172, + 173, + 174, + 175, + 176, + 177, + 178, + 179, + 180, + 181, + 182, + 183, + 184, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210, + 211, + 212, + 213, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 222, + 223, + 224, + 225, + 226, + 227, + 228, + 229, + 230, + 231, + 232, + 233, + 234, + 235, + 236, + 237, + 238, + 239, + 240, + 241, + 242, + 243, + 244, + 245, + 246, + 247, + 248, + 249, + 250, + 251, + 252, + 253, + 254, + 255, + 256, + 257, + 258, + 259, + 260, + 261, + 262, + 263, + 264, + 265, + 266, + 267, + 268, + 269, + 270, + 271, + 272, + 273, + 274, + 275, + 276, + 277, + 278, + 279, + 280, + 281, + 282, + 283, + 284, + 285, + 286, + 287, + 288, + 289, + 290, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 299, + 300, + 301, + 302, + 303, + 304, + 305, + 306, + 307, + 308, + 309, + 310, + 311, + 312, + 313, + 314, + 315, + 316, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 324, + 325, + 326, + 327, + 328, + 329, + 330, + 331, + 332, + 333, + 334, + 335, + 336, + 337, + 338, + 339, + 340, + 341, + 342, + 343, + 344, + 345, + 346, + 347, + 348, + 349, + 350, + 351, + 352, + 353, + 354, + 355, + 356, + 357, + 358, + 359, + 360, + 361, + 362, + 363, + 364, + 365, + 366, + 367, + 368, + 369, + 370, + 371, + 372, + 373, + 374, + 375, + 376, + 377, + 378, + 379, + 380, + 381, + 382, + 383, + 384, + 385, + 386, + 387, + 388, + 389, + 390, + 391, + 392, + 393, + 394, + 395, + 396, + 397, + 398, + 399, + 400, + 401, + 402, + 403, + 404, + 405, + 406, + 407, + 408, + 409, + 410, + 411, + 412, + 413, + 414, + 415, + 416, + 417, + 418, + 419, + 420, + 421, + 422, + 423, + 424, + 425, + 426, + 427, + 428, + 429, + 430, + 431, + 432, + 433, + 434, + 435, + 436, + 437, + 438, + 439, + 440, + 441, + 442, + 443, + 444, + 445, + 446, + 447, + 448, + 449, + 450, + 451, + 452, + 453, + 454, + 455, + 456, + 457, + 458, + 459, + 460, + 461, + 462, + 463, + 464, + 465, + 466, + 467, + 468, + 469, + 470, + 471, + 472, + 473, + 474, + 475, + 476, + 477, + 478, + 479, + 480, + 481, + 482, + 483, + 484, + 485, + 486, + 487, + 488, + 489, + 490, + 491, + 492, + 493, + 494, + 495, + 496, + 497, + 498, + 499, + 500, + 501, + 502, + 503, + 504, + 505, + 506, + 507, + 508, + 509, + 510, + 511, + 512, + 513, + 514, + 515, + 516, + 517, + 518, + 519, + 520, + 521, + 522, + 523, + 524, + 525, + 526, + 527, + 528, + 529, + 530, + 531, + 532, + 533, + 534, + 535, + 536, + 537, + 538, + 539, + 540, + 541, + 542, + 543, + 544, + 545, + 546, + 547, + 548, + 549, + 550, + 551, + 552, + 553, + 554, + 555, + 556, + 557, + 558, + 559, + 560, + 561, + 562, + 563, + 564, + 565, + 566, + 567, + 568, + 569, + 570, + 571, + 572, + 573, + 574, + 575, + 576, + 577, + 578, + 579, + 580, + 581, + 582, + 583, + 584, + 585, + 586, + 587, + 588, + 589, + 590, + 591, + 592, + 593, + 594, + 595, + 596, + 597, + 598, + 599, + 600, + 601, + 602, + 603, + 604, + 605, + 606, + 607, + 608, + 609, + 610, + 611, + 612, + 613, + 614, + 615, + 616, + 617, + 618, + 619, + 620, + 621, + 622, + 623, + 624, + 625, + 626, + 627, + 628, + 629, + 630, + 631, + 632, + 633, + 634, + 635, + 636, + 637, + 638, + 639, + 640, + 641, + 642, + 643, + 644, + 645, + 646, + 647, + 648, + 649, + 650, + 651, + 652, + 653, + 654, + 655, + 656, + 657, + 658, + 659, + 660, + 661, + 662, + 663, + 664, + 665, + 666, + 667, + 668, + 669, + 670, + 671, + 672, + 673, + 674, + 675, + 676, + 677, + 678, + 679, + 680, + 681, + 682, + 683, + 684, + 685, + 686, + 687, + 688, + 689, + 690, + 691, + 692, + 693, + 694, + 695, + 696, + 697, + 698, + 699, + 700, + 701, + 702, + 703, + 704, + 705, + 706, + 707, + 708, + 709, + 710, + 711, + 712, + 713, + 714, + 715, + 716, + 717, + 718, + 719 + ], + "xaxis": "x", + "y": [ + 282.2076110839844, + 280.5342712402344, + 282.93719482421875, + 287.0723876953125, + 287.4375305175781, + 287.5365905761719, + 288.4130859375, + 288.6388244628906, + 289.3452453613281, + 289.7811279296875, + 290.22711181640625, + 290.9339599609375, + 290.5479431152344, + 291.7066650390625, + 291.5911865234375, + 292.04962158203125, + 292.3465270996094, + 292.2814025878906, + 292.49078369140625, + 292.7622375488281, + 292.71893310546875, + 292.9272155761719, + 294.1483459472656, + 293.9292907714844, + 294.00311279296875, + 294.1448669433594, + 294.8172607421875, + 294.82232666015625, + 295.5307006835937, + 295.06085205078125, + 295.3816223144531, + 295.5812683105469, + 295.0591735839844, + 295.3460388183594, + 295.4446105957031, + 295.5614929199219, + 295.44500732421875, + 296.1403503417969, + 296.61981201171875, + 296.5223083496094, + 296.8663635253906, + 297.1263732910156, + 297.5341186523437, + 297.5152893066406, + 297.8847045898437, + 298.1744689941406, + 297.80499267578125, + 297.81671142578125, + 297.5389099121094, + 297.54278564453125, + 296.96588134765625, + 297.4913635253906, + 297.3685913085937, + 297.3280029296875, + 297.8356628417969, + 298.40679931640625, + 298.2266540527344, + 298.30535888671875, + 298.4940185546875, + 297.4447937011719, + 297.5924072265625, + 298.1912841796875, + 298.5841979980469, + 298.8329772949219, + 299.5053405761719, + 299.30767822265625, + 298.82208251953125, + 298.2673645019531, + 298.5625915527344, + 297.8983764648437, + 298.0111389160156, + 298.27618408203125, + 298.119140625, + 298.1170959472656, + 298.8129577636719, + 298.2842712402344, + 298.8199462890625, + 298.62860107421875, + 298.57208251953125, + 298.71978759765625, + 298.2862243652344, + 298.48504638671875, + 298.6028137207031, + 299.3833923339844, + 299.3365478515625, + 299.5429077148437, + 300.181396484375, + 300.7275695800781, + 301.2965393066406, + 301.1711730957031, + 319.7274169921875, + 320.1485290527344, + 320.3963623046875, + 320.8817443847656, + 321.3941955566406, + 321.82830810546875, + 322.6673278808594, + 322.4001159667969, + 321.92041015625, + 323.2173767089844, + 323.269287109375, + 322.7093811035156, + 322.864501953125, + 323.1563415527344, + 324.3063659667969, + 324.7597351074219, + 326.0099792480469, + 326.513916015625, + 327.3671264648437, + 326.8199157714844, + 328.6741638183594, + 327.9971618652344, + 328.7611694335937, + 328.4200439453125, + 329.1309509277344, + 328.8747863769531, + 329.45001220703125, + 329.01861572265625, + 328.81640625, + 327.8397521972656, + 327.8627319335937, + 327.34246826171875, + 327.9814147949219, + 328.0308837890625, + 328.7354736328125, + 328.449951171875, + 328.2172241210937, + 329.1683654785156, + 329.76885986328125, + 329.6261291503906, + 329.7510070800781, + 328.900634765625, + 329.2919921875, + 328.506103515625, + 329.0175170898437, + 328.4369506835937, + 328.9645690917969, + 328.07196044921875, + 328.972412109375, + 329.53497314453125, + 329.8018493652344, + 330.4311828613281, + 330.0008239746094, + 330.6437072753906, + 330.0613708496094, + 329.9943542480469, + 329.2314147949219, + 328.51080322265625, + 328.8717346191406, + 328.10089111328125, + 328.6083984375, + 328.0151062011719, + 328.2041931152344, + 327.8868713378906, + 328.57452392578125, + 328.1100158691406, + 328.1842346191406, + 329.1793212890625, + 329.5846862792969, + 329.0926208496094, + 329.8020324707031, + 330.3591613769531, + 329.634521484375, + 329.447509765625, + 329.1663818359375, + 329.30828857421875, + 330.3620300292969, + 329.5806579589844, + 330.1665954589844, + 329.85565185546875, + 330.66650390625, + 330.1508483886719, + 330.78765869140625, + 331.9918212890625, + 331.98748779296875, + 332.9851989746094, + 332.0752258300781, + 332.2947692871094, + 332.4147644042969, + 331.9770202636719, + 350.9775390625, + 351.7432556152344, + 351.60009765625, + 352.0672302246094, + 352.6164855957031, + 351.2296447753906, + 352.37567138671875, + 352.318603515625, + 352.034912109375, + 353.1311340332031, + 352.57135009765625, + 353.1632995605469, + 352.6967163085937, + 352.944580078125, + 352.559814453125, + 352.54443359375, + 352.7675476074219, + 352.70684814453125, + 352.4716796875, + 352.41619873046875, + 352.0777893066406, + 352.45806884765625, + 352.06646728515625, + 351.4954833984375, + 351.3597412109375, + 351.322509765625, + 352.1612243652344, + 351.8356628417969, + 351.980712890625, + 352.2920837402344, + 351.956787109375, + 352.0262756347656, + 351.4659423828125, + 351.7904663085937, + 351.5843200683594, + 351.2974548339844, + 351.4638366699219, + 351.4024963378906, + 351.047607421875, + 351.1163635253906, + 350.716552734375, + 350.6739196777344, + 350.69598388671875, + 350.9443054199219, + 350.9685363769531, + 351.3089599609375, + 351.580322265625, + 351.7095642089844, + 351.2278747558594, + 351.76519775390625, + 351.51593017578125, + 351.7939147949219, + 351.9923095703125, + 351.6347045898437, + 350.9519958496094, + 351.61376953125, + 351.7122802734375, + 351.5579833984375, + 351.8509826660156, + 352.43438720703125, + 352.5101013183594, + 352.1939392089844, + 352.24267578125, + 352.0521240234375, + 352.5223083496094, + 351.5856018066406, + 351.8611145019531, + 351.68310546875, + 351.5499267578125, + 351.0977478027344, + 351.5973205566406, + 350.90679931640625, + 350.94683837890625, + 351.92559814453125, + 352.22479248046875, + 351.9316711425781, + 352.1537170410156, + 351.9091491699219, + 352.0411071777344, + 351.6563720703125, + 352.3119201660156, + 352.12725830078125, + 351.9329528808594, + 351.8695373535156, + 350.7582702636719, + 350.2596130371094, + 350.2446594238281, + 350.65252685546875, + 350.68408203125, + 351.0732727050781, + 292.3349609375, + 296.24432373046875, + 298.6556091308594, + 299.4758605957031, + 300.6333312988281, + 301.9535522460937, + 301.3894958496094, + 302.7728576660156, + 302.8666076660156, + 303.5379638671875, + 304.5206298828125, + 304.407958984375, + 304.51385498046875, + 305.230224609375, + 305.2932434082031, + 304.9880065917969, + 304.92340087890625, + 303.5197448730469, + 302.63934326171875, + 303.0179748535156, + 302.7064514160156, + 304.7342834472656, + 304.6186218261719, + 304.10211181640625, + 304.1875915527344, + 304.1259460449219, + 303.5084533691406, + 305.0895690917969, + 304.783203125, + 303.73876953125, + 303.4207458496094, + 303.3563232421875, + 303.4652099609375, + 301.84423828125, + 302.1918640136719, + 303.00244140625, + 303.4349670410156, + 304.18072509765625, + 304.5085754394531, + 304.9608764648437, + 304.0409240722656, + 305.06500244140625, + 304.9576110839844, + 305.3370361328125, + 306.0732421875, + 304.8749694824219, + 304.0507507324219, + 304.475830078125, + 304.6947631835937, + 303.4704284667969, + 304.4704895019531, + 303.5428771972656, + 304.781982421875, + 304.0555114746094, + 304.49481201171875, + 304.2380676269531, + 305.00897216796875, + 305.53338623046875, + 305.1978759765625, + 304.98590087890625, + 304.6657409667969, + 304.9930419921875, + 305.5421142578125, + 305.1078186035156, + 304.5313415527344, + 304.3753662109375, + 303.03179931640625, + 304.1632080078125, + 304.3042907714844, + 304.41473388671875, + 304.8538818359375, + 305.47491455078125, + 305.4259338378906, + 305.0736389160156, + 305.13995361328125, + 305.175537109375, + 305.39361572265625, + 305.4103088378906, + 305.52813720703125, + 305.8585510253906, + 304.9250793457031, + 304.41070556640625, + 305.33453369140625, + 306.24383544921875, + 307.0990905761719, + 306.621826171875, + 306.93536376953125, + 306.5973815917969, + 306.42413330078125, + 307.0582580566406, + 350.8975830078125, + 352.3148498535156, + 352.3035888671875, + 352.8794860839844, + 353.10162353515625, + 352.9873046875, + 353.63848876953125, + 353.9334716796875, + 354.6578674316406, + 355.0750427246094, + 353.8217468261719, + 354.6893920898437, + 354.994873046875, + 355.25079345703125, + 355.0981750488281, + 355.4383544921875, + 355.9784851074219, + 354.9351806640625, + 355.3053894042969, + 355.5157470703125, + 355.1551208496094, + 355.447265625, + 355.2961730957031, + 355.8099365234375, + 355.92828369140625, + 357.7392272949219, + 358.41424560546875, + 359.2053527832031, + 358.14471435546875, + 358.8660583496094, + 359.3887634277344, + 359.673095703125, + 359.3397521972656, + 359.1601867675781, + 358.8623046875, + 358.3875427246094, + 358.9278259277344, + 358.6763916015625, + 358.5888366699219, + 358.2840576171875, + 357.4374389648437, + 356.85498046875, + 357.75018310546875, + 358.7865600585937, + 359.68707275390625, + 358.9818725585937, + 358.9618225097656, + 359.5857849121094, + 359.5823059082031, + 359.7642517089844, + 359.7159118652344, + 359.38885498046875, + 359.05816650390625, + 358.8500061035156, + 359.00457763671875, + 357.85888671875, + 357.5047912597656, + 358.6526184082031, + 357.02703857421875, + 357.2925109863281, + 358.2082214355469, + 358.3887939453125, + 358.5728454589844, + 358.5884094238281, + 359.3389892578125, + 358.44207763671875, + 358.58026123046875, + 358.8177795410156, + 358.7875061035156, + 359.2876281738281, + 359.4195861816406, + 358.8738098144531, + 359.01422119140625, + 359.4451599121094, + 359.5342102050781, + 360.4178771972656, + 359.9522399902344, + 360.3809509277344, + 360.2109375, + 361.2432556152344, + 361.1715393066406, + 361.6592102050781, + 362.0090942382813, + 361.7130432128906, + 361.9664306640625, + 362.40283203125, + 362.6456604003906, + 363.7590942382813, + 362.39251708984375, + 361.2790832519531, + 282.8052673339844, + 279.04608154296875, + 282.4010314941406, + 286.5472106933594, + 287.6993713378906, + 290.406982421875, + 290.8794860839844, + 290.19757080078125, + 291.0823059082031, + 291.2209167480469, + 292.6325988769531, + 291.61700439453125, + 292.16632080078125, + 292.5622863769531, + 292.4338684082031, + 292.2705993652344, + 293.248291015625, + 292.8450012207031, + 292.98272705078125, + 292.75714111328125, + 294.0396423339844, + 293.994384765625, + 294.278076171875, + 294.2740783691406, + 294.7635192871094, + 294.9712829589844, + 295.33648681640625, + 294.9649963378906, + 294.8724975585937, + 294.3507385253906, + 293.3451232910156, + 294.27984619140625, + 294.1650390625, + 294.7659912109375, + 293.84869384765625, + 294.0479736328125, + 293.2484741210937, + 294.4142150878906, + 295.04510498046875, + 295.46710205078125, + 295.8202209472656, + 295.21063232421875, + 294.40069580078125, + 295.12994384765625, + 294.8354797363281, + 295.0543518066406, + 294.6632385253906, + 294.9977722167969, + 294.648681640625, + 294.4620361328125, + 294.154296875, + 295.5210266113281, + 295.8841857910156, + 295.41485595703125, + 296.2046203613281, + 295.6978759765625, + 294.0210266113281, + 295.6363525390625, + 296.2043762207031, + 297.2204284667969, + 295.9385070800781, + 295.921630859375, + 296.44891357421875, + 295.9752502441406, + 296.81817626953125, + 296.9077453613281, + 296.87310791015625, + 296.3519287109375, + 296.68902587890625, + 296.1576232910156, + 296.9183044433594, + 297.1040344238281, + 297.8329162597656, + 297.06231689453125, + 296.9719543457031, + 296.657958984375, + 296.74774169921875, + 297.07476806640625, + 297.3730163574219, + 296.8524475097656, + 297.0059509277344, + 297.1058654785156, + 297.9693908691406, + 297.88165283203125, + 297.6462097167969, + 298.9822692871094, + 298.156494140625, + 298.7214050292969, + 299.01531982421875, + 298.5400390625, + 328.64178466796875, + 329.9387512207031, + 331.3897399902344, + 333.08154296875, + 333.2943420410156, + 332.7924499511719, + 332.92315673828125, + 332.815673828125, + 333.61181640625, + 333.5367431640625, + 333.6248474121094, + 333.2030029296875, + 332.8619079589844, + 333.013916015625, + 332.4386901855469, + 333.16680908203125, + 332.7801208496094, + 331.8240966796875, + 332.1011962890625, + 331.939208984375, + 332.37261962890625, + 332.9047546386719, + 333.19439697265625, + 332.86968994140625, + 333.1973876953125, + 333.3371887207031, + 333.83575439453125, + 333.1569519042969, + 333.3797607421875, + 333.9742431640625, + 333.7681884765625, + 333.0032958984375, + 333.155517578125, + 331.91455078125, + 332.46685791015625, + 331.79510498046875, + 333.2541809082031, + 333.2222595214844, + 332.966552734375, + 333.3083190917969, + 333.8191223144531, + 333.2041015625, + 333.4326171875, + 333.544921875, + 332.38287353515625, + 332.2857666015625, + 331.4556884765625, + 330.2707214355469, + 330.4195556640625, + 329.48785400390625, + 329.5577087402344, + 330.2663879394531, + 331.84771728515625, + 334.0539245605469, + 334.1283569335937, + 334.0681457519531, + 335.3121337890625, + 334.9388732910156, + 334.34344482421875, + 334.5809631347656, + 333.64898681640625, + 334.1307678222656, + 334.7008361816406, + 333.2722473144531, + 333.0137023925781, + 331.9921264648437, + 331.6800537109375, + 332.4163513183594, + 333.3384704589844, + 333.3754272460937, + 333.4186096191406, + 333.58648681640625, + 334.43231201171875, + 333.68096923828125, + 334.2570495605469, + 334.1923217773437, + 334.4856872558594, + 335.22772216796875, + 335.40460205078125, + 334.2890625, + 334.9296569824219, + 334.9083251953125, + 334.18994140625, + 334.6949157714844, + 334.1930236816406, + 334.9540405273437, + 335.1471862792969, + 335.65679931640625, + 335.99609375, + 335.7073669433594, + 367.628173828125, + 368.2905578613281, + 370.3262634277344, + 370.60302734375, + 370.2281188964844, + 370.8107604980469, + 369.5078430175781, + 368.945068359375, + 367.7029418945313, + 367.1958618164063, + 366.49810791015625, + 367.2691650390625, + 365.921875, + 366.8121643066406, + 365.2463989257813, + 365.9017333984375, + 365.5550231933594, + 365.9224548339844, + 366.9606018066406, + 366.4556274414063, + 366.3463134765625, + 366.6302795410156, + 366.6630859375, + 365.9984436035156, + 366.2511901855469, + 365.93499755859375, + 365.9416809082031, + 365.8621520996094, + 365.5891418457031, + 365.06243896484375, + 365.1203918457031, + 364.2437744140625, + 364.4126586914063, + 363.8743286132813, + 364.836181640625, + 363.8137512207031, + 363.75042724609375, + 364.3686828613281, + 364.4854736328125, + 364.2892150878906, + 362.9789733886719, + 363.73345947265625, + 363.5594787597656, + 363.84613037109375, + 362.3800048828125, + 362.3746337890625, + 362.0014343261719, + 362.4912414550781, + 362.0827026367188, + 362.0335388183594, + 362.3829040527344, + 362.2856750488281, + 362.233642578125, + 362.625244140625, + 363.3910827636719, + 362.9295349121094, + 362.8480529785156, + 362.0069274902344, + 361.6875305175781, + 361.629638671875, + 361.3429870605469, + 361.6567687988281, + 361.94281005859375, + 362.4664916992188, + 363.2919006347656, + 362.9890747070313, + 363.1798400878906, + 362.4972534179688, + 362.9107666015625, + 363.2173156738281, + 363.8522338867188, + 362.6447448730469, + 362.1781005859375, + 361.7898864746094, + 361.0286254882813, + 361.7830505371094, + 361.5848083496094, + 361.5703430175781, + 363.220703125, + 363.9089050292969, + 363.8181762695313, + 364.6590881347656, + 363.9401245117188, + 363.6384887695313, + 363.1525268554688, + 362.4663391113281, + 362.360595703125, + 362.8992919921875, + 362.9682922363281, + 362.1788024902344 + ], + "yaxis": "y" + }, + { + "hovertemplate": "variable=right_iris_x
index=%{x}
value=%{y}", + "legendgroup": "right_iris_x", + "line": { + "color": "#00cc96", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "right_iris_x", + "showlegend": true, + "type": "scattergl", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139, + 140, + 141, + 142, + 143, + 144, + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155, + 156, + 157, + 158, + 159, + 160, + 161, + 162, + 163, + 164, + 165, + 166, + 167, + 168, + 169, + 170, + 171, + 172, + 173, + 174, + 175, + 176, + 177, + 178, + 179, + 180, + 181, + 182, + 183, + 184, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210, + 211, + 212, + 213, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 222, + 223, + 224, + 225, + 226, + 227, + 228, + 229, + 230, + 231, + 232, + 233, + 234, + 235, + 236, + 237, + 238, + 239, + 240, + 241, + 242, + 243, + 244, + 245, + 246, + 247, + 248, + 249, + 250, + 251, + 252, + 253, + 254, + 255, + 256, + 257, + 258, + 259, + 260, + 261, + 262, + 263, + 264, + 265, + 266, + 267, + 268, + 269, + 270, + 271, + 272, + 273, + 274, + 275, + 276, + 277, + 278, + 279, + 280, + 281, + 282, + 283, + 284, + 285, + 286, + 287, + 288, + 289, + 290, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 299, + 300, + 301, + 302, + 303, + 304, + 305, + 306, + 307, + 308, + 309, + 310, + 311, + 312, + 313, + 314, + 315, + 316, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 324, + 325, + 326, + 327, + 328, + 329, + 330, + 331, + 332, + 333, + 334, + 335, + 336, + 337, + 338, + 339, + 340, + 341, + 342, + 343, + 344, + 345, + 346, + 347, + 348, + 349, + 350, + 351, + 352, + 353, + 354, + 355, + 356, + 357, + 358, + 359, + 360, + 361, + 362, + 363, + 364, + 365, + 366, + 367, + 368, + 369, + 370, + 371, + 372, + 373, + 374, + 375, + 376, + 377, + 378, + 379, + 380, + 381, + 382, + 383, + 384, + 385, + 386, + 387, + 388, + 389, + 390, + 391, + 392, + 393, + 394, + 395, + 396, + 397, + 398, + 399, + 400, + 401, + 402, + 403, + 404, + 405, + 406, + 407, + 408, + 409, + 410, + 411, + 412, + 413, + 414, + 415, + 416, + 417, + 418, + 419, + 420, + 421, + 422, + 423, + 424, + 425, + 426, + 427, + 428, + 429, + 430, + 431, + 432, + 433, + 434, + 435, + 436, + 437, + 438, + 439, + 440, + 441, + 442, + 443, + 444, + 445, + 446, + 447, + 448, + 449, + 450, + 451, + 452, + 453, + 454, + 455, + 456, + 457, + 458, + 459, + 460, + 461, + 462, + 463, + 464, + 465, + 466, + 467, + 468, + 469, + 470, + 471, + 472, + 473, + 474, + 475, + 476, + 477, + 478, + 479, + 480, + 481, + 482, + 483, + 484, + 485, + 486, + 487, + 488, + 489, + 490, + 491, + 492, + 493, + 494, + 495, + 496, + 497, + 498, + 499, + 500, + 501, + 502, + 503, + 504, + 505, + 506, + 507, + 508, + 509, + 510, + 511, + 512, + 513, + 514, + 515, + 516, + 517, + 518, + 519, + 520, + 521, + 522, + 523, + 524, + 525, + 526, + 527, + 528, + 529, + 530, + 531, + 532, + 533, + 534, + 535, + 536, + 537, + 538, + 539, + 540, + 541, + 542, + 543, + 544, + 545, + 546, + 547, + 548, + 549, + 550, + 551, + 552, + 553, + 554, + 555, + 556, + 557, + 558, + 559, + 560, + 561, + 562, + 563, + 564, + 565, + 566, + 567, + 568, + 569, + 570, + 571, + 572, + 573, + 574, + 575, + 576, + 577, + 578, + 579, + 580, + 581, + 582, + 583, + 584, + 585, + 586, + 587, + 588, + 589, + 590, + 591, + 592, + 593, + 594, + 595, + 596, + 597, + 598, + 599, + 600, + 601, + 602, + 603, + 604, + 605, + 606, + 607, + 608, + 609, + 610, + 611, + 612, + 613, + 614, + 615, + 616, + 617, + 618, + 619, + 620, + 621, + 622, + 623, + 624, + 625, + 626, + 627, + 628, + 629, + 630, + 631, + 632, + 633, + 634, + 635, + 636, + 637, + 638, + 639, + 640, + 641, + 642, + 643, + 644, + 645, + 646, + 647, + 648, + 649, + 650, + 651, + 652, + 653, + 654, + 655, + 656, + 657, + 658, + 659, + 660, + 661, + 662, + 663, + 664, + 665, + 666, + 667, + 668, + 669, + 670, + 671, + 672, + 673, + 674, + 675, + 676, + 677, + 678, + 679, + 680, + 681, + 682, + 683, + 684, + 685, + 686, + 687, + 688, + 689, + 690, + 691, + 692, + 693, + 694, + 695, + 696, + 697, + 698, + 699, + 700, + 701, + 702, + 703, + 704, + 705, + 706, + 707, + 708, + 709, + 710, + 711, + 712, + 713, + 714, + 715, + 716, + 717, + 718, + 719 + ], + "xaxis": "x", + "y": [ + 406.1318359375, + 412.5827331542969, + 417.4015502929688, + 422.3596801757813, + 426.682861328125, + 427.4379577636719, + 429.4768371582031, + 429.4253845214844, + 429.2258605957031, + 429.0636291503906, + 429.1285705566406, + 430.4913940429688, + 430.351806640625, + 430.6640625, + 431.08050537109375, + 430.9166870117188, + 431.0350341796875, + 431.4494018554688, + 431.22357177734375, + 430.7623291015625, + 430.09912109375, + 429.2122497558594, + 430.30023193359375, + 430.0816955566406, + 430.0627746582031, + 429.6393127441406, + 430.2427368164063, + 429.7746887207031, + 430.1240234375, + 430.8858337402344, + 431.1208190917969, + 431.5374755859375, + 432.2371520996094, + 432.4436645507813, + 432.4572143554688, + 432.1759948730469, + 432.1097412109375, + 432.213623046875, + 432.3042297363281, + 432.6994323730469, + 432.92578125, + 433.2131958007813, + 433.8449401855469, + 433.5662536621094, + 434.1123962402344, + 433.8692932128906, + 433.99658203125, + 433.8401184082031, + 433.9212951660156, + 433.8475341796875, + 433.9205627441406, + 434.3721618652344, + 434.3191833496094, + 433.919677734375, + 433.7450256347656, + 433.8492736816406, + 434.0353393554688, + 434.5631408691406, + 434.8211975097656, + 435.2414855957031, + 435.2912902832031, + 434.5606994628906, + 434.2222900390625, + 434.2960205078125, + 434.348876953125, + 434.505126953125, + 433.8631896972656, + 435.11724853515625, + 434.4216613769531, + 434.1930847167969, + 434.3545837402344, + 434.4975891113281, + 434.7269287109375, + 434.5565490722656, + 434.1631774902344, + 434.3444213867188, + 434.8882751464844, + 434.7594299316406, + 434.4430541992188, + 434.56201171875, + 433.77630615234375, + 433.9733276367188, + 434.1980895996094, + 434.6843872070313, + 434.430908203125, + 435.2957153320313, + 435.5030517578125, + 435.2530517578125, + 435.46282958984375, + 435.980712890625, + 444.0892333984375, + 444.41705322265625, + 445.90130615234375, + 445.6699523925781, + 445.9026184082031, + 445.4825439453125, + 445.4889526367188, + 445.2583923339844, + 445.8470458984375, + 446.3668823242188, + 446.1805419921875, + 445.3009338378906, + 444.0154113769531, + 445.0160217285156, + 445.6287231445313, + 447.1579895019531, + 448.6547546386719, + 448.3166809082031, + 448.0406188964844, + 448.871337890625, + 448.9178466796875, + 449.3208312988281, + 449.3345031738281, + 449.1978759765625, + 449.385498046875, + 449.2699279785156, + 449.3627624511719, + 448.64202880859375, + 448.8035583496094, + 448.9733581542969, + 449.1926574707031, + 449.4210510253906, + 449.0714721679688, + 449.0001525878906, + 448.8619079589844, + 448.7075805664063, + 448.78033447265625, + 448.6696472167969, + 448.7021789550781, + 448.06292724609375, + 448.0857849121094, + 448.6421203613281, + 448.248291015625, + 447.09814453125, + 447.9884033203125, + 446.88275146484375, + 447.4859924316406, + 447.8331909179688, + 447.5699768066406, + 448.0003356933594, + 448.262939453125, + 448.558837890625, + 448.6809692382813, + 448.0343627929688, + 448.7224731445313, + 447.85302734375, + 447.796630859375, + 448.6554870605469, + 448.9084167480469, + 447.4429626464844, + 448.2933349609375, + 447.2593383789063, + 447.9982299804688, + 448.2411193847656, + 447.9476318359375, + 447.754150390625, + 447.3958435058594, + 447.7849426269531, + 447.0710144042969, + 446.9820251464844, + 446.9034118652344, + 446.2467651367188, + 447.0499572753906, + 446.1643981933594, + 446.6454772949219, + 446.5910339355469, + 446.7907104492188, + 446.53802490234375, + 446.6819763183594, + 445.7714538574219, + 445.640380859375, + 445.4433288574219, + 445.8753967285156, + 446.0535888671875, + 445.0797119140625, + 445.494384765625, + 445.2247924804688, + 445.4984130859375, + 445.0411376953125, + 444.9069519042969, + 449.2264404296875, + 451.1160583496094, + 449.7301025390625, + 449.5608825683594, + 449.7897338867188, + 449.5671997070313, + 450.2485046386719, + 450.7918395996094, + 450.8941650390625, + 450.4359130859375, + 450.6560363769531, + 450.1979370117188, + 450.59967041015625, + 450.1383056640625, + 450.3994140625, + 450.7129211425781, + 450.2789306640625, + 450.849609375, + 449.5040893554688, + 449.1178894042969, + 449.7105407714844, + 449.7755432128906, + 449.8971557617188, + 449.9110107421875, + 450.76861572265625, + 450.42193603515625, + 449.9213562011719, + 449.9787902832031, + 449.40765380859375, + 449.6307373046875, + 449.6105041503906, + 449.9841918945313, + 449.85687255859375, + 449.8398742675781, + 449.6881713867188, + 450.4194030761719, + 450.198974609375, + 450.9894714355469, + 450.3315734863281, + 450.7117309570313, + 451.1371154785156, + 451.57366943359375, + 451.2061462402344, + 451.2710266113281, + 451.636474609375, + 451.0251159667969, + 450.2214965820313, + 451.6646118164063, + 451.18988037109375, + 451.0827941894531, + 450.9614562988281, + 451.3120422363281, + 450.5003662109375, + 450.3892211914063, + 451.0728149414063, + 451.4847106933594, + 450.99713134765625, + 450.6154479980469, + 450.5690002441406, + 450.8346862792969, + 450.6088562011719, + 449.61334228515625, + 450.4630126953125, + 450.3612060546875, + 450.0874633789063, + 449.9324645996094, + 449.68463134765625, + 451.1805114746094, + 449.9660949707031, + 449.5711669921875, + 449.7459716796875, + 450.4726867675781, + 450.7731628417969, + 449.4170227050781, + 450.9215698242188, + 449.6248474121094, + 450.8277282714844, + 449.6275024414063, + 449.7189636230469, + 449.4582824707031, + 449.31591796875, + 449.3548889160156, + 450.3228454589844, + 448.5444641113281, + 449.0289611816406, + 448.8732604980469, + 449.6137390136719, + 448.7755737304688, + 449.05712890625, + 448.6424865722656, + 267.6096496582031, + 264.635009765625, + 263.9613647460937, + 261.4496459960937, + 260.7673034667969, + 260.8083801269531, + 261.67059326171875, + 263.4019470214844, + 264.6551818847656, + 265.4794616699219, + 265.5356750488281, + 264.7018737792969, + 264.9388427734375, + 264.8728332519531, + 264.8164978027344, + 264.2341613769531, + 262.6849365234375, + 260.7075500488281, + 258.8023376464844, + 258.3414306640625, + 259.0959777832031, + 258.0563049316406, + 257.4774780273437, + 256.3160095214844, + 256.1571655273437, + 256.41485595703125, + 256.9517517089844, + 257.2745056152344, + 259.113525390625, + 259.81036376953125, + 258.9732971191406, + 258.9285583496094, + 258.0595397949219, + 257.32049560546875, + 257.3846740722656, + 257.9485168457031, + 258.9082946777344, + 257.76519775390625, + 256.8997802734375, + 256.74029541015625, + 256.0796813964844, + 256.3431701660156, + 257.3079528808594, + 256.40509033203125, + 255.68588256835935, + 255.86460876464844, + 255.44415283203125, + 255.77525329589844, + 256.1136169433594, + 257.114990234375, + 256.2130126953125, + 256.2140197753906, + 256.166748046875, + 255.4566650390625, + 255.47206115722656, + 256.2168884277344, + 256.1624450683594, + 255.51617431640625, + 254.944580078125, + 255.2301177978516, + 255.2362060546875, + 255.3248748779297, + 255.9229431152344, + 255.03370666503903, + 255.1517639160156, + 255.0474548339844, + 255.0279998779297, + 255.2436981201172, + 256.251953125, + 256.0742492675781, + 255.4818115234375, + 255.09901428222656, + 254.2883758544922, + 254.92572021484372, + 254.961181640625, + 255.54705810546875, + 255.08973693847656, + 254.4845733642578, + 254.5088806152344, + 254.77322387695312, + 256.3195495605469, + 258.0282897949219, + 258.4302673339844, + 258.339111328125, + 258.1498107910156, + 256.94256591796875, + 256.9183959960937, + 257.08819580078125, + 257.431884765625, + 257.5097961425781, + 260.4217834472656, + 260.8016357421875, + 259.3453369140625, + 259.6009826660156, + 260.3341979980469, + 260.9388732910156, + 260.978271484375, + 259.93072509765625, + 260.05718994140625, + 259.88116455078125, + 259.41162109375, + 259.9818115234375, + 260.2087097167969, + 259.0680847167969, + 259.5036926269531, + 258.55157470703125, + 258.95733642578125, + 259.0809936523437, + 259.9766540527344, + 258.52056884765625, + 258.3110656738281, + 259.0587158203125, + 258.45953369140625, + 259.5116271972656, + 260.4410095214844, + 261.42333984375, + 260.5399780273437, + 259.6140441894531, + 259.3943176269531, + 258.9061584472656, + 259.356689453125, + 259.4344787597656, + 260.4978942871094, + 260.5018615722656, + 259.1136474609375, + 258.92510986328125, + 258.4620361328125, + 258.9327697753906, + 259.7791442871094, + 260.5358581542969, + 259.9435119628906, + 259.34503173828125, + 259.8689270019531, + 259.4232482910156, + 259.2808837890625, + 260.3185729980469, + 259.9832763671875, + 259.0973815917969, + 257.9602355957031, + 258.8209533691406, + 258.85302734375, + 259.535888671875, + 260.1162414550781, + 260.3266296386719, + 259.5599365234375, + 259.6501159667969, + 259.5000305175781, + 259.2994384765625, + 259.08673095703125, + 259.5550231933594, + 258.643310546875, + 258.2224426269531, + 257.2529602050781, + 258.3711853027344, + 258.757080078125, + 258.9466247558594, + 259.22125244140625, + 259.2728576660156, + 258.5894775390625, + 258.7743530273437, + 258.9397277832031, + 260.0585327148437, + 259.97833251953125, + 259.32330322265625, + 259.3568725585937, + 259.7400817871094, + 260.1372375488281, + 261.0435791015625, + 260.7040710449219, + 260.8114013671875, + 260.31256103515625, + 259.8341064453125, + 260.3416442871094, + 260.6206359863281, + 259.56500244140625, + 260.0645751953125, + 259.8295288085937, + 260.5435791015625, + 260.20489501953125, + 260.3587951660156, + 115.00743865966795, + 51.50483703613281, + 47.19908142089844, + 45.54025650024414, + 43.83247375488281, + 42.40047454833984, + 40.2406005859375, + 42.07778930664063, + 42.50164413452149, + 42.00001525878906, + 42.91915512084961, + 43.213897705078125, + 43.50798416137695, + 44.31502151489258, + 43.78851318359375, + 44.429935455322266, + 46.468894958496094, + 46.24443817138672, + 46.46269607543945, + 44.8902702331543, + 46.47064971923828, + 45.94384765625, + 43.76075744628906, + 44.77442169189453, + 45.79913330078125, + 43.82310104370117, + 46.28888702392578, + 44.76016998291016, + 46.02631759643555, + 43.62628936767578, + 44.95112991333008, + 45.45626449584961, + 45.49966430664063, + 45.979736328125, + 46.07496643066406, + 45.617374420166016, + 45.787330627441406, + 46.35049819946289, + 44.63794708251953, + 44.41941452026367, + 45.85277557373047, + 43.75278091430664, + 45.59061813354492, + 44.54585647583008, + 44.60078811645508, + 43.90929412841797, + 46.83700942993164, + 44.84295272827149, + 48.001407623291016, + 45.41876602172852, + 46.58343505859375, + 45.120059967041016, + 44.71129989624024, + 45.21213912963867, + 46.4685173034668, + 45.42568206787109, + 46.381324768066406, + 44.63027191162109, + 44.40367126464844, + 44.0250358581543, + 45.18380737304688, + 45.1017951965332, + 45.666290283203125, + 47.257572174072266, + 45.262542724609375, + 45.0211067199707, + 44.41584014892578, + 45.1391487121582, + 45.761844635009766, + 44.73664855957031, + 46.01229095458984, + 45.23928451538086, + 45.23316955566406, + 45.00680541992188, + 45.22780990600586, + 44.243934631347656, + 44.73038101196289, + 46.284278869628906, + 45.15336608886719, + 46.8207893371582, + 47.61547088623047, + 46.922733306884766, + 46.44148635864258, + 46.8725814819336, + 45.653167724609375, + 45.65256118774414, + 46.2941780090332, + 46.48394775390625, + 47.314674377441406, + 47.6170539855957, + 35.90129089355469, + 35.95975112915039, + 35.540706634521484, + 33.65265655517578, + 34.373661041259766, + 33.2919921875, + 32.97258377075195, + 33.626277923583984, + 32.92985916137695, + 33.34880828857422, + 32.873291015625, + 33.55250930786133, + 34.168922424316406, + 33.951419830322266, + 33.08818435668945, + 33.909515380859375, + 34.62171173095703, + 33.606048583984375, + 34.90583038330078, + 33.61172103881836, + 34.786842346191406, + 34.79957580566406, + 34.042911529541016, + 33.48689651489258, + 33.10163497924805, + 34.68852615356445, + 34.65605163574219, + 34.55024337768555, + 34.42736053466797, + 34.320213317871094, + 33.690940856933594, + 34.484745025634766, + 34.25856399536133, + 34.81216049194336, + 35.287208557128906, + 35.278953552246094, + 34.95246124267578, + 35.20370864868164, + 34.5460205078125, + 34.88454055786133, + 34.21610641479492, + 34.57807540893555, + 34.42404556274414, + 35.10045623779297, + 34.808326721191406, + 35.482147216796875, + 36.45640182495117, + 36.83344268798828, + 38.20791244506836, + 38.63803863525391, + 39.81988143920898, + 39.00847244262695, + 39.05519485473633, + 38.413841247558594, + 38.783233642578125, + 37.48418045043945, + 38.21521759033203, + 37.67402267456055, + 37.0682373046875, + 36.39672088623047, + 36.66973114013672, + 36.61091995239258, + 38.586402893066406, + 37.30756378173828, + 38.81692123413086, + 38.95041275024414, + 38.48569869995117, + 38.97962188720703, + 38.36358642578125, + 38.72758102416992, + 37.67115020751953, + 38.01158905029297, + 37.47981643676758, + 37.74861145019531, + 36.30741882324219, + 36.53149032592773, + 38.01092147827149, + 36.2879524230957, + 36.25053024291992, + 37.63771057128906, + 37.06993865966797, + 37.7302360534668, + 38.80353927612305, + 38.89839172363281, + 39.13541030883789, + 39.74293899536133, + 39.65838241577149, + 38.84358978271485, + 38.68531036376953, + 40.232154846191406, + 36.50090408325195, + 34.36490631103516, + 33.52898025512695, + 33.06907272338867, + 33.362327575683594, + 34.24589920043945, + 34.9149055480957, + 34.91694259643555, + 34.331809997558594, + 35.293392181396484, + 35.3702507019043, + 36.18892288208008, + 36.06906509399414, + 36.95792007446289, + 35.95009231567383, + 36.25725936889648, + 36.549293518066406, + 36.13301849365234, + 36.1459846496582, + 35.74244689941406, + 36.17335891723633, + 36.59944534301758, + 36.27548599243164, + 36.20467758178711, + 36.19569396972656, + 37.04568862915039, + 36.8834114074707, + 36.70774459838867, + 35.86980056762695, + 37.080322265625, + 36.99509048461914, + 36.91960525512695, + 36.86191940307617, + 36.8362922668457, + 36.27985763549805, + 36.2735710144043, + 35.88302993774414, + 36.66389465332031, + 36.36712646484375, + 36.9935302734375, + 36.41986846923828, + 37.21148300170898, + 36.97147750854492, + 37.714637756347656, + 38.129676818847656, + 38.093170166015625, + 38.260284423828125, + 38.320594787597656, + 38.030181884765625, + 37.723270416259766, + 37.542606353759766, + 37.4668197631836, + 37.52609634399414, + 36.77667999267578, + 37.51485443115234, + 37.638973236083984, + 38.32381057739258, + 37.9429931640625, + 37.92679977416992, + 38.502197265625, + 37.54156494140625, + 38.247169494628906, + 37.81153106689453, + 37.70357131958008, + 37.86711883544922, + 38.29137802124024, + 37.94681167602539, + 37.495208740234375, + 37.75660705566406, + 38.093074798583984, + 38.216556549072266, + 39.04142761230469, + 39.82356262207031, + 39.75300216674805, + 39.92704391479492, + 39.15785217285156, + 39.38557434082031, + 38.49618148803711, + 39.26626205444336, + 38.8640251159668, + 38.48637390136719, + 39.06260299682617, + 39.033203125, + 39.381568908691406, + 39.10884094238281, + 39.80299377441406, + 40.03264617919922, + 39.36104583740234, + 39.99951553344727, + 40.40206146240234 + ], + "yaxis": "y" + }, + { + "hovertemplate": "variable=right_iris_y
index=%{x}
value=%{y}", + "legendgroup": "right_iris_y", + "line": { + "color": "#ab63fa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "right_iris_y", + "showlegend": true, + "type": "scattergl", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139, + 140, + 141, + 142, + 143, + 144, + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155, + 156, + 157, + 158, + 159, + 160, + 161, + 162, + 163, + 164, + 165, + 166, + 167, + 168, + 169, + 170, + 171, + 172, + 173, + 174, + 175, + 176, + 177, + 178, + 179, + 180, + 181, + 182, + 183, + 184, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210, + 211, + 212, + 213, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 222, + 223, + 224, + 225, + 226, + 227, + 228, + 229, + 230, + 231, + 232, + 233, + 234, + 235, + 236, + 237, + 238, + 239, + 240, + 241, + 242, + 243, + 244, + 245, + 246, + 247, + 248, + 249, + 250, + 251, + 252, + 253, + 254, + 255, + 256, + 257, + 258, + 259, + 260, + 261, + 262, + 263, + 264, + 265, + 266, + 267, + 268, + 269, + 270, + 271, + 272, + 273, + 274, + 275, + 276, + 277, + 278, + 279, + 280, + 281, + 282, + 283, + 284, + 285, + 286, + 287, + 288, + 289, + 290, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 299, + 300, + 301, + 302, + 303, + 304, + 305, + 306, + 307, + 308, + 309, + 310, + 311, + 312, + 313, + 314, + 315, + 316, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 324, + 325, + 326, + 327, + 328, + 329, + 330, + 331, + 332, + 333, + 334, + 335, + 336, + 337, + 338, + 339, + 340, + 341, + 342, + 343, + 344, + 345, + 346, + 347, + 348, + 349, + 350, + 351, + 352, + 353, + 354, + 355, + 356, + 357, + 358, + 359, + 360, + 361, + 362, + 363, + 364, + 365, + 366, + 367, + 368, + 369, + 370, + 371, + 372, + 373, + 374, + 375, + 376, + 377, + 378, + 379, + 380, + 381, + 382, + 383, + 384, + 385, + 386, + 387, + 388, + 389, + 390, + 391, + 392, + 393, + 394, + 395, + 396, + 397, + 398, + 399, + 400, + 401, + 402, + 403, + 404, + 405, + 406, + 407, + 408, + 409, + 410, + 411, + 412, + 413, + 414, + 415, + 416, + 417, + 418, + 419, + 420, + 421, + 422, + 423, + 424, + 425, + 426, + 427, + 428, + 429, + 430, + 431, + 432, + 433, + 434, + 435, + 436, + 437, + 438, + 439, + 440, + 441, + 442, + 443, + 444, + 445, + 446, + 447, + 448, + 449, + 450, + 451, + 452, + 453, + 454, + 455, + 456, + 457, + 458, + 459, + 460, + 461, + 462, + 463, + 464, + 465, + 466, + 467, + 468, + 469, + 470, + 471, + 472, + 473, + 474, + 475, + 476, + 477, + 478, + 479, + 480, + 481, + 482, + 483, + 484, + 485, + 486, + 487, + 488, + 489, + 490, + 491, + 492, + 493, + 494, + 495, + 496, + 497, + 498, + 499, + 500, + 501, + 502, + 503, + 504, + 505, + 506, + 507, + 508, + 509, + 510, + 511, + 512, + 513, + 514, + 515, + 516, + 517, + 518, + 519, + 520, + 521, + 522, + 523, + 524, + 525, + 526, + 527, + 528, + 529, + 530, + 531, + 532, + 533, + 534, + 535, + 536, + 537, + 538, + 539, + 540, + 541, + 542, + 543, + 544, + 545, + 546, + 547, + 548, + 549, + 550, + 551, + 552, + 553, + 554, + 555, + 556, + 557, + 558, + 559, + 560, + 561, + 562, + 563, + 564, + 565, + 566, + 567, + 568, + 569, + 570, + 571, + 572, + 573, + 574, + 575, + 576, + 577, + 578, + 579, + 580, + 581, + 582, + 583, + 584, + 585, + 586, + 587, + 588, + 589, + 590, + 591, + 592, + 593, + 594, + 595, + 596, + 597, + 598, + 599, + 600, + 601, + 602, + 603, + 604, + 605, + 606, + 607, + 608, + 609, + 610, + 611, + 612, + 613, + 614, + 615, + 616, + 617, + 618, + 619, + 620, + 621, + 622, + 623, + 624, + 625, + 626, + 627, + 628, + 629, + 630, + 631, + 632, + 633, + 634, + 635, + 636, + 637, + 638, + 639, + 640, + 641, + 642, + 643, + 644, + 645, + 646, + 647, + 648, + 649, + 650, + 651, + 652, + 653, + 654, + 655, + 656, + 657, + 658, + 659, + 660, + 661, + 662, + 663, + 664, + 665, + 666, + 667, + 668, + 669, + 670, + 671, + 672, + 673, + 674, + 675, + 676, + 677, + 678, + 679, + 680, + 681, + 682, + 683, + 684, + 685, + 686, + 687, + 688, + 689, + 690, + 691, + 692, + 693, + 694, + 695, + 696, + 697, + 698, + 699, + 700, + 701, + 702, + 703, + 704, + 705, + 706, + 707, + 708, + 709, + 710, + 711, + 712, + 713, + 714, + 715, + 716, + 717, + 718, + 719 + ], + "xaxis": "x", + "y": [ + 278.6587829589844, + 279.68853759765625, + 282.7178649902344, + 283.89190673828125, + 285.81365966796875, + 286.3018798828125, + 287.5920104980469, + 288.2318420410156, + 288.6129150390625, + 289.5033264160156, + 289.94873046875, + 290.6874084472656, + 291.2796936035156, + 291.0555725097656, + 291.7254943847656, + 291.98583984375, + 291.6319885253906, + 291.76806640625, + 292.4360046386719, + 292.7138671875, + 292.8938293457031, + 293.4069519042969, + 293.4486999511719, + 293.9561767578125, + 294.07550048828125, + 294.3245849609375, + 294.8427734375, + 295.6548156738281, + 295.77783203125, + 295.8098449707031, + 295.5585327148437, + 295.5684814453125, + 294.8382873535156, + 294.7930603027344, + 295.16436767578125, + 295.2525939941406, + 295.3656311035156, + 295.9725952148437, + 296.1670227050781, + 296.5223388671875, + 297.2243957519531, + 297.2395324707031, + 296.90643310546875, + 297.58966064453125, + 297.7384948730469, + 297.9042053222656, + 297.80975341796875, + 297.86944580078125, + 297.5381774902344, + 297.0322570800781, + 296.8061828613281, + 296.7678527832031, + 296.5919189453125, + 297.0115661621094, + 297.4121398925781, + 298.4136047363281, + 298.01171875, + 297.9859313964844, + 298.060791015625, + 298.0162048339844, + 298.3176574707031, + 298.590576171875, + 298.64617919921875, + 298.9942626953125, + 299.2660522460937, + 299.3917541503906, + 298.4346923828125, + 297.7711181640625, + 297.58746337890625, + 297.47039794921875, + 297.716552734375, + 297.8401184082031, + 297.8969421386719, + 298.11749267578125, + 298.1728210449219, + 297.8927001953125, + 298.1684265136719, + 298.7659912109375, + 298.8337707519531, + 298.9140930175781, + 298.6416320800781, + 298.4979553222656, + 298.9032592773437, + 299.4254455566406, + 299.2529602050781, + 299.87158203125, + 300.4452514648437, + 300.62213134765625, + 300.96002197265625, + 301.022705078125, + 319.5253601074219, + 320.4919128417969, + 320.50567626953125, + 320.48193359375, + 321.093017578125, + 321.0408630371094, + 322.30010986328125, + 322.0480041503906, + 321.8678283691406, + 322.2386169433594, + 322.0103454589844, + 321.4308776855469, + 321.4642944335937, + 322.18243408203125, + 322.6790466308594, + 323.9134826660156, + 324.16485595703125, + 325.55908203125, + 325.787353515625, + 325.3703308105469, + 326.6930236816406, + 326.8828125, + 326.71734619140625, + 326.5733642578125, + 327.0733947753906, + 327.2944641113281, + 327.1143493652344, + 327.20562744140625, + 326.7191162109375, + 326.83123779296875, + 325.9166259765625, + 325.7221984863281, + 325.59442138671875, + 325.6973571777344, + 326.8763122558594, + 327.4244689941406, + 327.3643798828125, + 328.2643127441406, + 328.31707763671875, + 328.4647827148437, + 328.44207763671875, + 328.5230712890625, + 327.95947265625, + 327.63629150390625, + 327.3911437988281, + 327.33837890625, + 326.98199462890625, + 327.8212585449219, + 327.69903564453125, + 327.9450988769531, + 328.1687316894531, + 329.6231994628906, + 328.9214782714844, + 329.9704895019531, + 328.6716613769531, + 328.47003173828125, + 329.00494384765625, + 329.10760498046875, + 328.4041442871094, + 327.4507141113281, + 326.9197692871094, + 326.76373291015625, + 326.6081848144531, + 326.3944091796875, + 326.5724182128906, + 326.7880554199219, + 327.6852722167969, + 327.9430847167969, + 327.7869873046875, + 328.4037780761719, + 328.6939697265625, + 328.6634521484375, + 328.9014892578125, + 328.7609558105469, + 328.8634338378906, + 329.2910461425781, + 330.0882263183594, + 329.9927673339844, + 329.704833984375, + 329.6427917480469, + 329.9592590332031, + 330.540771484375, + 330.5394897460937, + 330.9948425292969, + 331.4999389648437, + 331.7212829589844, + 332.3335266113281, + 331.8922119140625, + 331.9176940917969, + 332.3169860839844, + 350.939208984375, + 353.0888977050781, + 352.606201171875, + 353.556640625, + 352.9193420410156, + 352.5098571777344, + 352.8683776855469, + 353.0800476074219, + 353.516845703125, + 352.6256408691406, + 353.6251525878906, + 352.9017639160156, + 353.7345886230469, + 353.2970275878906, + 353.599853515625, + 353.9906311035156, + 352.9421081542969, + 353.5935363769531, + 353.3539123535156, + 352.4195861816406, + 352.654052734375, + 352.2733154296875, + 352.5403137207031, + 352.0572814941406, + 352.2721252441406, + 353.123291015625, + 352.0639038085937, + 352.6796875, + 352.1979675292969, + 353.97149658203125, + 352.9234924316406, + 352.6513671875, + 351.8056335449219, + 351.85968017578125, + 352.3974304199219, + 352.2980041503906, + 351.04217529296875, + 351.1416931152344, + 350.89385986328125, + 350.3295288085937, + 350.96923828125, + 350.96112060546875, + 350.4766845703125, + 351.76446533203125, + 352.2030029296875, + 352.4102478027344, + 351.5936279296875, + 351.549072265625, + 352.5652770996094, + 352.70465087890625, + 352.2307434082031, + 351.8585510253906, + 352.2607116699219, + 352.2417297363281, + 351.6682434082031, + 352.4817199707031, + 352.2401428222656, + 351.8228454589844, + 351.46307373046875, + 353.2096252441406, + 352.3231811523437, + 353.0716552734375, + 351.7247314453125, + 351.3336486816406, + 352.8054504394531, + 352.7503662109375, + 352.5203552246094, + 352.90869140625, + 352.447998046875, + 351.9095153808594, + 351.2599792480469, + 351.7578430175781, + 352.2601013183594, + 352.7870178222656, + 353.28466796875, + 352.9905700683594, + 353.4673156738281, + 352.4403076171875, + 352.4091491699219, + 352.970947265625, + 352.89263916015625, + 352.2474975585937, + 352.39825439453125, + 350.60125732421875, + 351.09698486328125, + 350.6036682128906, + 351.4814453125, + 351.0400695800781, + 349.7654724121094, + 351.10711669921875, + 294.4920349121094, + 297.326904296875, + 299.714599609375, + 301.1353454589844, + 301.8423767089844, + 302.6703186035156, + 303.5560913085937, + 304.38531494140625, + 304.05419921875, + 304.2489929199219, + 305.4444580078125, + 305.84002685546875, + 305.80096435546875, + 306.1268615722656, + 307.1083679199219, + 306.5302124023437, + 306.1732788085937, + 306.0162048339844, + 305.67144775390625, + 304.8768310546875, + 305.1669616699219, + 306.9148864746094, + 307.0751342773437, + 307.5413513183594, + 307.1670227050781, + 306.3147888183594, + 307.1473083496094, + 308.1194152832031, + 307.73468017578125, + 307.36016845703125, + 306.0953063964844, + 306.1095275878906, + 305.6893615722656, + 305.4668884277344, + 305.2475891113281, + 306.1022644042969, + 306.648681640625, + 307.1986389160156, + 307.5337829589844, + 307.1620483398437, + 307.2368469238281, + 307.4396667480469, + 307.7168884277344, + 308.0772399902344, + 308.3499450683594, + 308.0889892578125, + 307.9378356933594, + 307.504638671875, + 306.4655456542969, + 306.5191955566406, + 306.5966491699219, + 306.9457702636719, + 307.5461120605469, + 307.4769897460937, + 307.204833984375, + 307.2298889160156, + 307.7707824707031, + 307.49725341796875, + 307.63104248046875, + 308.037353515625, + 307.5618896484375, + 307.5862731933594, + 307.9329833984375, + 307.0826416015625, + 307.3892822265625, + 307.8087463378906, + 307.4072570800781, + 307.0108947753906, + 307.4296569824219, + 308.001220703125, + 307.8639221191406, + 308.46502685546875, + 307.9075622558594, + 308.2838439941406, + 308.6385192871094, + 307.9942932128906, + 308.15380859375, + 308.5730895996094, + 308.2528076171875, + 308.2806396484375, + 307.99951171875, + 307.46209716796875, + 307.713134765625, + 308.11895751953125, + 308.3320617675781, + 308.76239013671875, + 308.71392822265625, + 309.0941772460937, + 308.5256042480469, + 308.76806640625, + 352.0924987792969, + 353.2456359863281, + 353.2266845703125, + 353.2334899902344, + 353.4187927246094, + 353.0394897460937, + 354.1445617675781, + 354.9068298339844, + 355.1820983886719, + 355.41607666015625, + 355.6459655761719, + 355.5621032714844, + 355.69891357421875, + 356.3218078613281, + 355.81036376953125, + 356.7961120605469, + 356.45166015625, + 356.5221252441406, + 355.67742919921875, + 356.352294921875, + 356.13238525390625, + 356.0939025878906, + 356.52203369140625, + 356.1376647949219, + 356.849365234375, + 357.2838134765625, + 358.9883728027344, + 359.2791748046875, + 359.7857360839844, + 359.5943603515625, + 359.6523742675781, + 359.6972045898437, + 359.3805847167969, + 359.7417297363281, + 359.9708251953125, + 359.6861267089844, + 359.4214172363281, + 359.9985656738281, + 359.587890625, + 358.8582458496094, + 358.1878356933594, + 358.5143127441406, + 358.191162109375, + 359.13360595703125, + 359.6601257324219, + 359.0382385253906, + 360.00543212890625, + 360.20220947265625, + 359.7325134277344, + 360.508544921875, + 360.46234130859375, + 360.3439636230469, + 359.77789306640625, + 358.8164367675781, + 358.9874267578125, + 358.4429626464844, + 358.6956787109375, + 358.4779357910156, + 358.4689636230469, + 358.05078125, + 357.93035888671875, + 358.45025634765625, + 359.0059509277344, + 359.4864501953125, + 359.1791687011719, + 359.2250061035156, + 358.6745300292969, + 359.3856811523437, + 359.47589111328125, + 359.7814025878906, + 359.8115234375, + 359.9916687011719, + 359.7430419921875, + 360.6611633300781, + 360.2921752929688, + 360.8587646484375, + 361.5252075195313, + 360.894775390625, + 361.2752380371094, + 361.0769348144531, + 362.08111572265625, + 362.0903930664063, + 361.572021484375, + 362.14544677734375, + 363.1383056640625, + 363.4327087402344, + 363.44964599609375, + 363.1249694824219, + 363.1203308105469, + 362.1066284179688, + 298.627197265625, + 288.0232849121094, + 296.6806335449219, + 299.1449890136719, + 300.3717346191406, + 301.794921875, + 302.8209533691406, + 303.5932006835937, + 301.73553466796875, + 303.79217529296875, + 303.12164306640625, + 303.6776733398437, + 305.1678466796875, + 303.6844787597656, + 303.1785888671875, + 303.9682312011719, + 306.1701965332031, + 304.42059326171875, + 304.44696044921875, + 304.9167785644531, + 304.147216796875, + 302.0964050292969, + 305.8660583496094, + 305.5189819335937, + 305.7559814453125, + 306.3141174316406, + 306.5978088378906, + 307.6017150878906, + 309.1418151855469, + 306.1195373535156, + 306.6746520996094, + 305.9591064453125, + 307.5207824707031, + 305.7107238769531, + 307.32989501953125, + 305.837890625, + 307.4850769042969, + 306.3861083984375, + 308.34881591796875, + 309.10699462890625, + 309.1021728515625, + 307.49822998046875, + 308.05352783203125, + 307.431884765625, + 307.647216796875, + 307.537841796875, + 307.8241882324219, + 307.52618408203125, + 307.5517883300781, + 306.6041259765625, + 307.245361328125, + 307.8837890625, + 308.469482421875, + 308.5936584472656, + 309.5561828613281, + 308.7938537597656, + 308.5149841308594, + 308.3275756835937, + 307.7242736816406, + 307.6890563964844, + 308.7651062011719, + 307.9826354980469, + 308.48272705078125, + 308.5583801269531, + 308.6339416503906, + 309.50762939453125, + 308.5377502441406, + 309.348876953125, + 309.6953125, + 309.049072265625, + 310.1925354003906, + 309.77935791015625, + 310.7265014648437, + 310.42138671875, + 309.849853515625, + 309.2418518066406, + 309.5338439941406, + 310.0146789550781, + 310.15203857421875, + 309.73468017578125, + 310.444580078125, + 309.863525390625, + 309.8780517578125, + 309.90594482421875, + 310.4373779296875, + 309.4589538574219, + 310.2410583496094, + 310.38848876953125, + 311.2431945800781, + 310.06280517578125, + 340.015869140625, + 340.078369140625, + 341.0238952636719, + 342.3067626953125, + 344.1671142578125, + 343.94903564453125, + 343.4977111816406, + 342.8972473144531, + 343.5950012207031, + 343.986328125, + 343.232177734375, + 343.5883483886719, + 343.2100524902344, + 342.64874267578125, + 342.75714111328125, + 341.78692626953125, + 343.7675476074219, + 342.85418701171875, + 342.5851135253906, + 342.7261657714844, + 342.3158874511719, + 342.6714172363281, + 343.2811584472656, + 342.8993835449219, + 343.91925048828125, + 343.83502197265625, + 343.8655700683594, + 341.9017333984375, + 341.8788146972656, + 344.34375, + 344.2005310058594, + 341.7691345214844, + 343.2500915527344, + 342.6436462402344, + 342.7397155761719, + 343.0450744628906, + 343.457763671875, + 342.78973388671875, + 343.63262939453125, + 343.1576538085937, + 344.3313293457031, + 343.45672607421875, + 342.716064453125, + 343.0313720703125, + 342.3461303710937, + 341.0517883300781, + 341.6321105957031, + 340.63970947265625, + 340.054931640625, + 339.06817626953125, + 338.679931640625, + 339.4184265136719, + 340.4853210449219, + 342.84393310546875, + 343.7859191894531, + 344.6409606933594, + 344.7259521484375, + 344.5099792480469, + 345.7684936523437, + 344.08319091796875, + 345.15167236328125, + 343.0729675292969, + 344.088134765625, + 343.65008544921875, + 343.41796875, + 341.8653869628906, + 342.66552734375, + 341.97784423828125, + 341.6732177734375, + 344.0194091796875, + 343.357421875, + 343.4201354980469, + 344.08013916015625, + 343.8168640136719, + 344.4685974121094, + 344.09002685546875, + 343.8713073730469, + 344.4443664550781, + 344.5775451660156, + 343.6693420410156, + 344.2877502441406, + 342.4261474609375, + 343.81085205078125, + 344.21929931640625, + 343.7770690917969, + 344.4793701171875, + 345.5018920898437, + 344.7492980957031, + 344.8187866210937, + 344.99090576171875, + 372.1086730957031, + 374.6158447265625, + 376.6040954589844, + 376.7322082519531, + 377.2171325683594, + 376.187255859375, + 376.0777587890625, + 375.8951416015625, + 375.1945190429688, + 373.7886352539063, + 373.9190979003906, + 373.67529296875, + 372.86627197265625, + 371.9799499511719, + 372.75146484375, + 372.1177673339844, + 372.9661254882813, + 372.4846801757813, + 373.6862182617188, + 373.8683776855469, + 373.2644348144531, + 373.3934631347656, + 374.08929443359375, + 372.4411315917969, + 373.1167907714844, + 372.7849426269531, + 373.0943298339844, + 372.4122924804688, + 372.1253051757813, + 372.00726318359375, + 371.1231689453125, + 371.1307678222656, + 370.2953796386719, + 370.9921875, + 371.39398193359375, + 370.7185363769531, + 371.17388916015625, + 370.734619140625, + 371.5354614257813, + 369.9682312011719, + 370.833740234375, + 369.952880859375, + 370.8332824707031, + 370.0201110839844, + 369.5139770507813, + 368.73773193359375, + 368.6415710449219, + 368.8769836425781, + 368.4453125, + 369.727294921875, + 369.1140747070313, + 369.8925476074219, + 369.9811401367188, + 369.229248046875, + 370.6836242675781, + 370.8212890625, + 370.4302978515625, + 369.1702270507813, + 368.3958435058594, + 368.74407958984375, + 368.3941040039063, + 367.86614990234375, + 367.992919921875, + 368.6070251464844, + 368.3948059082031, + 369.7008666992188, + 369.7635498046875, + 370.0177307128906, + 368.5213012695313, + 369.356201171875, + 369.4495544433594, + 369.8826599121094, + 369.3760986328125, + 368.6969604492188, + 368.7887268066406, + 368.2723693847656, + 368.4844055175781, + 369.4713439941406, + 369.412353515625, + 370.15179443359375, + 370.674560546875, + 370.302978515625, + 370.0084838867188, + 370.4146423339844, + 369.7380065917969, + 370.2562561035156, + 369.8150939941406, + 369.991943359375, + 369.3482055664063, + 369.583251953125 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "title": { + "text": "variable" + }, + "tracegroupgap": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Left and Right Iris Position" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "index" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "value" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotly line plot for the left and right iris position\n", + "px.line(raw_dataset, y=[\"left_iris_x\", \"left_iris_y\", \"right_iris_x\", \"right_iris_y\"], title=\"Left and Right Iris Position\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eye", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3bcc3a106cbc79dd63beed56c572914627ddb366 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 07:27:32 +0000 Subject: [PATCH 02/78] old data visualization file removed and reproduced under calib_validation --- app/services/data_visualize.ipynb | 8896 ----------------------------- 1 file changed, 8896 deletions(-) delete mode 100644 app/services/data_visualize.ipynb diff --git a/app/services/data_visualize.ipynb b/app/services/data_visualize.ipynb deleted file mode 100644 index 3cf5b9fb..00000000 --- a/app/services/data_visualize.ipynb +++ /dev/null @@ -1,8896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import sklearn as sk\n", - "from sklearn import linear_model\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sn" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
left_iris_xleft_iris_yright_iris_xright_iris_y
0405.850555231.186356354.501923228.196136
1407.114532231.539841355.974915228.950607
2407.976379233.015640356.671295229.347351
3408.378906232.014603356.412415229.194199
4408.041260232.819061356.285004229.593658
\n", - "
" - ], - "text/plain": [ - " left_iris_x left_iris_y right_iris_x right_iris_y\n", - "0 405.850555 231.186356 354.501923 228.196136\n", - "1 407.114532 231.539841 355.974915 228.950607\n", - "2 407.976379 233.015640 356.671295 229.347351\n", - "3 408.378906 232.014603 356.412415 229.194199\n", - "4 408.041260 232.819061 356.285004 229.593658" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dataset_train_path = '/home/nata-brain/Documents/tcc/web-eye-tracker/public/training/1685126241.2630084natanael/train_data.csv'\n", - "dataset_session_path = '/home/nata-brain/Documents/tcc/web-eye-tracker/public/sessions/1685126241.2630084natanael/session_data.csv'\n", - "\n", - "raw_dataset = pd.read_csv(dataset_train_path)\n", - "session_dataset = pd.read_csv(dataset_session_path)\n", - "dataset = raw_dataset\n", - "dataset_s = session_dataset.drop(['timestamp'], axis = 1)\n", - "\n", - "display(dataset_s.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(raw_dataset.timestamp, raw_dataset.left_iris_x)\n", - "plt.xlabel('Timestamp')\n", - "plt.ylabel('Left Iris X')\n", - "plt.title('Left Iris X')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-5-26 15:34:5\n" - ] - } - ], - "source": [ - "print(raw_dataset.timestamp[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15\n", - "34\n", - "5\n" - ] - } - ], - "source": [ - "from datetime import datetime\n", - "obj = datetime.strptime(raw_dataset.timestamp[0], \"%Y-%m-%d %H:%M:%S\")\n", - "print(obj.hour)\n", - "print(obj.minute)\n", - "print(obj.second)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import math\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n", - "plt.rcParams[\"figure.autolayout\"] = True\n", - "\n", - "x = raw_dataset.left_iris_x\n", - "y = raw_dataset.left_iris_y\n", - "datetime = raw_dataset.timestamp\n", - "grid_size = 1\n", - "h = 10\n", - "x_min = min(x)\n", - "x_max = max(x)\n", - "y_min = min(y)\n", - "y_max = max(y)\n", - "\n", - "x_grid = np.arange(x_min-h, x_max+h, grid_size)\n", - "y_grid = np.arange(y_min-h,y_max+h,grid_size)\n", - "x_mesh,y_mesh = np.meshgrid(x_grid,y_grid)\n", - "\n", - "\n", - "xc = x_mesh+(grid_size/2)\n", - "yc = y_mesh+(grid_size/2)\n", - "\n", - "def kde_quartic(d,h):\n", - " dn=d/h\n", - " P=(15/16)*(1-dn**2)**2\n", - " \n", - " return P\n", - " \n", - "intensity_list=[]\n", - "\n", - "for j in range(len(xc)):\n", - " \n", - " intensity_row=[]\n", - " \n", - " for k in range(len(xc[0])):\n", - " kde_value_list=[]\n", - " for i in range(len(x)):\n", - " #CALCULATE DISTANCE\n", - " d=math.sqrt((xc[j][k]-x[i])**2+(yc[j][k]-y[i])**2) \n", - " if d<=h:\n", - " p=kde_quartic(d,h)\n", - " else:\n", - " p=0\n", - " kde_value_list.append(p)\n", - " \n", - " #SUM ALL INTENSITY VALUE\n", - " p_total=sum(kde_value_list)\n", - " intensity_row.append(p_total)\n", - " \n", - " intensity_list.append(intensity_row)\n", - " \n", - "intensity = np.array(intensity_list)\n", - "\n", - "plt.title(\"Left Iris Sacades and Heatmap\")\n", - "plt.pcolormesh(x_mesh,y_mesh,intensity)\n", - "plt.plot(x,y,'r', linestyle = '-')\n", - "plt.colorbar()\n", - "\n", - "\"\"\" i = 0\n", - "\n", - "for xy in zip(x, y):\n", - " i = i+1\n", - " plt.annotate(f'{i}', xy)\n", - " \"\"\"\n", - "plt.show() \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]])" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(intensity)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "variable=left_iris_x
index=%{x}
value=%{y}", - "legendgroup": "left_iris_x", - "line": { - "color": "#636efa", - "dash": "solid" - }, - "marker": { - "symbol": "circle" - }, - "mode": "lines", - "name": "left_iris_x", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195 - ], - "xaxis": "x", - "y": [ - 336.72576904296875, - 349.4696350097656, - 355.3423767089844, - 342.3531799316406, - 337.32257080078125, - 341.7294921875, - 339.3572998046875, - 341.78399658203125, - 340.70281982421875, - 340.1320495605469, - 342.2511291503906, - 340.634521484375, - 343.62762451171875, - 341.7653503417969, - 342.6405334472656, - 340.8123474121094, - 340.5118103027344, - 341.645263671875, - 342.5506286621094, - 343.2059326171875, - 344.5043029785156, - 345.2703857421875, - 345.5244140625, - 345.8507080078125, - 346.5741577148437, - 347.33660888671875, - 348.1502380371094, - 349.404052734375, - 350.0457458496094, - 351.20391845703125, - 351.7628173828125, - 354.1990661621094, - 355.8505249023437, - 355.623291015625, - 355.7632751464844, - 356.7904357910156, - 357.8786315917969, - 357.9951477050781, - 356.2341613769531, - 353.81207275390625, - 347.57568359375, - 349.6695251464844, - 348.45220947265625, - 348.6149597167969, - 347.84771728515625, - 347.4283142089844, - 347.2074890136719, - 346.6120910644531, - 346.3581848144531, - 354.82098388671875, - 347.8380126953125, - 345.27838134765625, - 346.0719299316406, - 345.5565795898437, - 344.7796325683594, - 343.962646484375, - 343.2431335449219, - 344.5191650390625, - 344.92218017578125, - 346.1254577636719, - 347.04327392578125, - 345.885009765625, - 346.4061889648437, - 345.49761962890625, - 347.5816345214844, - 348.3536376953125, - 349.3419494628906, - 352.2123718261719, - 357.7505187988281, - 359.87615966796875, - 360.18377685546875, - 357.6771545410156, - 351.9995422363281, - 345.2372741699219, - 344.9791564941406, - 343.74298095703125, - 349.7840270996094, - 348.6497497558594, - 333.18231201171875, - 331.13531494140625, - 340.68768310546875, - 368.7179260253906, - 400.7218627929688, - 376.80853271484375, - 366.1715393066406, - 376.2972717285156, - 381.69970703125, - 378.2664489746094, - 379.3263244628906, - 371.8378601074219, - 367.6902465820313, - 370.9522705078125, - 365.8194274902344, - 364.93695068359375, - 363.488037109375, - 362.1673889160156, - 362.3691711425781, - 362.75140380859375, - 363.6519165039063, - 365.3569030761719, - 372.3004455566406, - 393.6053161621094, - 406.147705078125, - 405.5985412597656, - 408.23388671875, - 408.6369934082031, - 418.603759765625, - 417.7785339355469, - 413.0149230957031, - 391.2703247070313, - 399.5924377441406, - 400.5579223632813, - 399.10833740234375, - 400.159423828125, - 401.5528869628906, - 401.9990234375, - 403.449951171875, - 403.1153259277344, - 398.2532348632813, - 391.94378662109375, - 390.2604064941406, - 390.1572265625, - 390.1495056152344, - 385.9006042480469, - 382.0497741699219, - 381.0337829589844, - 380.7168273925781, - 379.1099853515625, - 377.7236328125, - 377.2712707519531, - 376.8092651367188, - 376.9216613769531, - 376.1701354980469, - 375.7994689941406, - 375.64404296875, - 375.95904541015625, - 376.59417724609375, - 376.3937377929688, - 377.2090759277344, - 378.25341796875, - 379.0548400878906, - 381.0333251953125, - 383.188232421875, - 383.6612243652344, - 383.4259643554688, - 384.8764953613281, - 385.2247619628906, - 385.4185180664063, - 386.1218872070313, - 387.3418273925781, - 387.8011169433594, - 388.4589538574219, - 389.6048278808594, - 389.7867431640625, - 390.6592102050781, - 391.4608154296875, - 392.6442565917969, - 393.9162292480469, - 392.1313781738281, - 391.5135803222656, - 388.56585693359375, - 379.78662109375, - 378.4542846679688, - 378.010009765625, - 376.738525390625, - 377.7132568359375, - 378.5142517089844, - 378.1591491699219, - 378.3596496582031, - 378.3343200683594, - 377.3188781738281, - 377.4431762695313, - 377.9720153808594, - 377.42193603515625, - 376.8049621582031, - 376.1614379882813, - 377.7637023925781, - 377.5328369140625, - 379.664794921875, - 382.0987243652344, - 385.0165100097656, - 385.4192199707031, - 386.6877746582031, - 389.4333801269531, - 390.250244140625, - 391.012939453125, - 391.4132080078125, - 392.1314697265625, - 391.8728332519531, - 393.0383911132813, - 394.0306396484375, - 394.6489868164063, - 394.5662841796875, - 395.3863525390625, - 396.5464477539063, - 395.83636474609375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "variable=left_iris_y
index=%{x}
value=%{y}", - "legendgroup": "left_iris_y", - "line": { - "color": "#EF553B", - "dash": "solid" - }, - "marker": { - "symbol": "circle" - }, - "mode": "lines", - "name": "left_iris_y", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195 - ], - "xaxis": "x", - "y": [ - 244.8616943359375, - 243.63853454589844, - 243.80886840820312, - 241.62982177734372, - 243.933837890625, - 246.378173828125, - 246.77609252929688, - 245.98866271972656, - 245.5472717285156, - 245.06521606445312, - 244.38247680664065, - 245.67222595214844, - 244.3156280517578, - 244.70736694335935, - 244.66603088378903, - 245.02735900878903, - 244.79583740234372, - 245.2458038330078, - 246.30270385742188, - 246.0033416748047, - 246.60989379882807, - 246.6776428222656, - 246.34930419921875, - 246.64073181152344, - 246.93862915039065, - 246.21771240234372, - 246.04684448242188, - 246.30133056640625, - 246.96380615234372, - 246.84280395507807, - 246.2956085205078, - 245.7473907470703, - 246.55078125, - 247.0106964111328, - 246.2460784912109, - 246.56578063964844, - 247.31396484375, - 248.2170867919922, - 249.1038055419922, - 249.2506866455078, - 247.75485229492188, - 247.4834899902344, - 246.7423095703125, - 246.6168212890625, - 246.8317413330078, - 245.8498687744141, - 246.6627960205078, - 246.64926147460935, - 245.4321136474609, - 248.9513702392578, - 248.3270568847656, - 247.46897888183597, - 247.33395385742188, - 247.56895446777344, - 247.18521118164065, - 247.86383056640625, - 247.76112365722656, - 248.0429534912109, - 248.0757598876953, - 248.3733062744141, - 247.81759643554688, - 248.3751220703125, - 248.06634521484372, - 247.87374877929688, - 247.98309326171875, - 247.8445587158203, - 248.2787170410156, - 247.52017211914065, - 248.1148223876953, - 249.04249572753903, - 249.27012634277344, - 250.4065704345703, - 250.6879425048828, - 251.8431854248047, - 252.0347442626953, - 252.831787109375, - 256.96856689453125, - 255.73480224609372, - 253.6480865478516, - 254.45379638671875, - 245.52281188964844, - 226.99546813964844, - 229.91009521484372, - 232.16661071777344, - 233.86187744140625, - 244.1218719482422, - 246.6307830810547, - 250.83184814453125, - 255.3619842529297, - 255.61007690429688, - 247.8414764404297, - 247.06460571289065, - 246.8446350097656, - 247.7544708251953, - 247.82522583007807, - 249.13626098632807, - 249.36094665527344, - 249.6959686279297, - 249.3852996826172, - 249.68206787109372, - 249.50393676757807, - 253.43702697753903, - 249.1090240478516, - 250.42922973632807, - 249.82247924804688, - 249.2805328369141, - 253.47239685058597, - 252.93223571777344, - 256.1706237792969, - 255.36512756347656, - 256.6875, - 254.2693023681641, - 252.6764678955078, - 253.69747924804688, - 253.9561004638672, - 253.6152801513672, - 255.4622802734375, - 256.0425720214844, - 253.6457061767578, - 251.1595764160156, - 250.5823211669922, - 250.2887725830078, - 251.01600646972656, - 251.7333526611328, - 252.45529174804688, - 252.54931640625, - 252.7377777099609, - 252.4236297607422, - 252.21304321289065, - 252.5850830078125, - 252.95704650878903, - 252.9291076660156, - 253.1386566162109, - 252.6885986328125, - 252.8970184326172, - 253.5596466064453, - 253.10922241210935, - 253.7964630126953, - 254.4389190673828, - 253.53370666503903, - 254.13427734375, - 254.63168334960935, - 253.8018493652344, - 254.67556762695312, - 254.71080017089844, - 254.78485107421875, - 254.4068603515625, - 253.7210998535156, - 253.4900054931641, - 252.99365234375, - 253.51788330078125, - 253.2886199951172, - 253.6822204589844, - 253.2578125, - 253.61126708984372, - 254.01629638671875, - 254.12918090820312, - 254.4144744873047, - 253.84820556640625, - 253.75369262695312, - 251.3873748779297, - 246.7122039794922, - 246.7001953125, - 245.8634185791016, - 247.14324951171875, - 246.8687438964844, - 246.63270568847656, - 247.3026428222656, - 247.30198669433597, - 247.05203247070312, - 247.18344116210935, - 247.68463134765625, - 246.5991058349609, - 247.8953857421875, - 247.19384765625, - 248.03732299804688, - 249.16400146484372, - 249.12364196777344, - 249.60595703125, - 249.7975616455078, - 247.65377807617188, - 248.1067657470703, - 248.29275512695312, - 248.19517517089844, - 248.10821533203125, - 248.2213134765625, - 248.6955718994141, - 249.05523681640625, - 248.9473114013672, - 249.25132751464844, - 249.1324920654297, - 249.29379272460935, - 249.3704833984375, - 249.65093994140625, - 250.29249572753903, - 250.2917633056641 - ], - "yaxis": "y" - }, - { - "hovertemplate": "variable=right_iris_x
index=%{x}
value=%{y}", - "legendgroup": "right_iris_x", - "line": { - "color": "#00cc96", - "dash": "solid" - }, - "marker": { - "symbol": "circle" - }, - "mode": "lines", - "name": "right_iris_x", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195 - ], - "xaxis": "x", - "y": [ - 281.00994873046875, - 294.0434265136719, - 299.24346923828125, - 287.34783935546875, - 281.9974975585937, - 287.3016357421875, - 284.5080261230469, - 287.01910400390625, - 286.43896484375, - 285.9729919433594, - 288.2286071777344, - 286.6668701171875, - 289.268798828125, - 287.7573547363281, - 289.001220703125, - 287.2309265136719, - 287.0069274902344, - 287.9508056640625, - 288.6215515136719, - 289.1201477050781, - 290.66436767578125, - 290.7962951660156, - 291.1791381835937, - 291.44219970703125, - 292.4138488769531, - 292.6872863769531, - 293.9319763183594, - 295.14373779296875, - 295.9242248535156, - 296.7142028808594, - 297.700927734375, - 300.7943725585937, - 301.730712890625, - 302.21685791015625, - 302.02740478515625, - 302.9414978027344, - 303.95684814453125, - 304.620361328125, - 302.7709350585937, - 300.2864990234375, - 293.4208374023437, - 295.509765625, - 294.1509704589844, - 294.1507263183594, - 293.3088684082031, - 293.0907897949219, - 293.01318359375, - 292.1659545898437, - 291.6329040527344, - 300.34100341796875, - 293.2793273925781, - 290.8286437988281, - 291.5323791503906, - 291.42010498046875, - 290.013671875, - 289.5024719238281, - 288.9352722167969, - 289.7358703613281, - 290.2877502441406, - 291.52099609375, - 291.718505859375, - 291.40411376953125, - 291.86883544921875, - 291.17840576171875, - 293.0785522460937, - 293.6483764648437, - 294.9472961425781, - 298.1772766113281, - 304.2350158691406, - 306.6288146972656, - 306.5779724121094, - 304.0621337890625, - 299.0968933105469, - 292.69683837890625, - 292.6676330566406, - 290.8785095214844, - 296.58355712890625, - 291.8524169921875, - 273.1587829589844, - 271.9227905273437, - 280.8437194824219, - 307.2192687988281, - 334.73907470703125, - 311.71160888671875, - 300.94024658203125, - 311.15020751953125, - 317.1183166503906, - 313.1645202636719, - 315.0546264648437, - 308.5799560546875, - 306.00848388671875, - 310.1253662109375, - 304.467529296875, - 303.48980712890625, - 302.38787841796875, - 301.0506896972656, - 301.50732421875, - 301.3418884277344, - 302.6826171875, - 303.6941223144531, - 310.65240478515625, - 331.8554992675781, - 344.3677978515625, - 344.69219970703125, - 347.45074462890625, - 347.68963623046875, - 358.03717041015625, - 357.8898620605469, - 351.9286499023437, - 329.39208984375, - 339.1072692871094, - 340.5765075683594, - 339.4915771484375, - 339.97772216796875, - 342.156005859375, - 341.9480895996094, - 343.9365234375, - 343.1395568847656, - 338.2764587402344, - 331.2217102050781, - 328.899169921875, - 329.08624267578125, - 328.8934936523437, - 323.9149169921875, - 320.3712158203125, - 319.7218933105469, - 319.30438232421875, - 317.86553955078125, - 316.2848815917969, - 315.5992126464844, - 315.25146484375, - 315.83251953125, - 314.67578125, - 314.5634460449219, - 313.7829895019531, - 314.7174377441406, - 315.1033020019531, - 314.9140930175781, - 315.6985168457031, - 317.1331481933594, - 317.7732238769531, - 319.7561950683594, - 320.7448425292969, - 322.3811950683594, - 322.08209228515625, - 323.43829345703125, - 323.4086303710937, - 324.1528015136719, - 324.7528381347656, - 325.1247863769531, - 326.1542358398437, - 327.5195007324219, - 328.5780639648437, - 328.6953125, - 329.5796813964844, - 330.8584899902344, - 331.9091186523437, - 332.784912109375, - 331.3593139648437, - 330.5898742675781, - 326.99884033203125, - 318.6114807128906, - 317.4617614746094, - 316.4630126953125, - 316.0626525878906, - 317.00421142578125, - 317.4332580566406, - 317.047607421875, - 317.37261962890625, - 317.1649475097656, - 315.9587097167969, - 316.0513916015625, - 317.2482604980469, - 316.65557861328125, - 316.0101928710937, - 315.4787292480469, - 316.7816162109375, - 316.8691101074219, - 319.14892578125, - 321.4789123535156, - 324.5621032714844, - 324.9008178710937, - 325.9631652832031, - 328.84381103515625, - 329.1578369140625, - 329.7847595214844, - 330.14459228515625, - 331.2735900878906, - 330.90338134765625, - 331.638671875, - 332.2633972167969, - 333.3970031738281, - 333.0212097167969, - 333.85333251953125, - 334.9815368652344, - 334.9507751464844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "variable=right_iris_y
index=%{x}
value=%{y}", - "legendgroup": "right_iris_y", - "line": { - "color": "#ab63fa", - "dash": "solid" - }, - "marker": { - "symbol": "circle" - }, - "mode": "lines", - "name": "right_iris_y", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195 - ], - "xaxis": "x", - "y": [ - 243.6756591796875, - 242.52574157714844, - 242.9994659423828, - 242.88409423828125, - 243.8262939453125, - 244.4114990234375, - 245.94400024414065, - 245.0969696044922, - 244.9880523681641, - 244.50726318359372, - 243.71612548828125, - 244.5425262451172, - 243.995849609375, - 243.77491760253903, - 244.2878875732422, - 243.71829223632807, - 243.88961791992188, - 244.1165008544922, - 244.68116760253903, - 244.23550415039065, - 244.80848693847656, - 244.50547790527344, - 243.70127868652344, - 243.8648376464844, - 244.0737152099609, - 244.01077270507807, - 243.81759643554688, - 244.1376190185547, - 243.8858337402344, - 243.6802215576172, - 243.7144012451172, - 243.23861694335935, - 244.3197326660156, - 244.13604736328125, - 244.38723754882807, - 244.8167266845703, - 245.29144287109372, - 245.47512817382807, - 246.37332153320312, - 247.4286651611328, - 247.267822265625, - 246.2525787353516, - 246.26124572753903, - 246.62078857421875, - 246.32765197753903, - 245.5774383544922, - 245.2097625732422, - 245.31710815429688, - 244.83482360839844, - 246.53993225097656, - 248.44403076171875, - 247.3284149169922, - 246.39877319335935, - 246.6956024169922, - 246.5362548828125, - 246.6126708984375, - 247.52294921875, - 247.57032775878903, - 247.1043701171875, - 247.03228759765625, - 246.8974609375, - 246.94461059570312, - 246.67645263671875, - 247.0118408203125, - 246.40005493164065, - 245.9633026123047, - 246.2359161376953, - 247.01841735839844, - 246.2452087402344, - 246.13986206054688, - 247.06222534179688, - 248.10379028320312, - 247.9071044921875, - 250.6181640625, - 250.79754638671875, - 250.95745849609372, - 255.61546325683597, - 257.6585388183594, - 256.74249267578125, - 258.2909240722656, - 248.2049865722656, - 225.341796875, - 226.3760223388672, - 234.70835876464844, - 236.32516479492188, - 241.3519744873047, - 240.7587890625, - 245.7991485595703, - 249.2885589599609, - 250.3720703125, - 243.69168090820312, - 242.10037231445312, - 243.97268676757807, - 244.08328247070312, - 245.3052215576172, - 245.73651123046875, - 246.4352264404297, - 246.65057373046875, - 246.72412109375, - 246.9327087402344, - 246.2825927734375, - 246.14797973632807, - 240.93585205078125, - 242.43728637695312, - 242.00765991210935, - 241.83038330078125, - 244.4667510986328, - 242.5886688232422, - 247.23284912109372, - 249.80381774902344, - 250.3027801513672, - 247.7738342285156, - 247.5417022705078, - 248.66259765625, - 248.41238403320312, - 248.5725250244141, - 250.29464721679688, - 250.0631561279297, - 249.2835693359375, - 248.6039123535156, - 248.47337341308597, - 248.30581665039065, - 248.90283203125, - 249.39181518554688, - 249.7982940673828, - 249.75875854492188, - 249.13185119628903, - 248.89663696289065, - 249.1757659912109, - 249.2108612060547, - 250.04991149902344, - 249.47750854492188, - 249.1193389892578, - 249.3509521484375, - 249.9076690673828, - 249.23167419433597, - 249.4301300048828, - 250.13616943359372, - 249.6038360595703, - 249.71438598632807, - 250.9133758544922, - 250.52706909179688, - 250.24073791503903, - 250.760986328125, - 250.25624084472656, - 249.8977508544922, - 250.06553649902344, - 249.51417541503903, - 249.55210876464844, - 248.98362731933597, - 249.4673767089844, - 249.10543823242188, - 249.0207977294922, - 249.41775512695312, - 248.8712158203125, - 248.94029235839844, - 249.6480712890625, - 249.8184814453125, - 249.4232482910156, - 249.5642852783203, - 247.9935302734375, - 245.8523712158203, - 245.7636260986328, - 245.53836059570312, - 246.0818023681641, - 246.12594604492188, - 245.0069732666016, - 245.7371826171875, - 245.54049682617188, - 245.18246459960935, - 245.17286682128903, - 245.66542053222656, - 244.7180633544922, - 245.5977935791016, - 245.01661682128903, - 246.12229919433597, - 246.23863220214844, - 246.26177978515625, - 246.25881958007807, - 245.76760864257807, - 243.7947235107422, - 244.5395050048828, - 244.10427856445312, - 244.00247192382807, - 243.5891876220703, - 244.32330322265625, - 244.5563507080078, - 244.79721069335935, - 245.0463409423828, - 244.944091796875, - 244.94761657714844, - 245.0943298339844, - 244.8842315673828, - 245.3980255126953, - 245.26231384277344, - 245.44793701171875 - ], - "yaxis": "y" - } - ], - "layout": { - "legend": { - "title": { - "text": "variable" - }, - "tracegroupgap": 0 - }, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Left and Right Iris Position" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "index" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "value" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import plotly.express as px\n", - "\n", - "px.line(raw_dataset, y=[\"left_iris_x\", \"left_iris_y\", \"right_iris_x\", \"right_iris_y\"], title=\"Left and Right Iris Position\")\n", - "#px.line(raw_dataset, y=\"left_iris_y\", title=\"Left Iris Position in Y\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "336.72576904296875", - "marker": { - "color": [ - 336.72576904296875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "336.72576904296875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:5" - ], - "xaxis": "x", - "y": [ - 336.72576904296875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "349.4696350097656", - "marker": { - "color": [ - 349.4696350097656 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "349.4696350097656", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:6" - ], - "xaxis": "x", - "y": [ - 349.4696350097656 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "355.3423767089844", - "marker": { - "color": [ - 355.3423767089844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "355.3423767089844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:7" - ], - "xaxis": "x", - "y": [ - 355.3423767089844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "342.3531799316406", - "marker": { - "color": [ - 342.3531799316406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "342.3531799316406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:7" - ], - "xaxis": "x", - "y": [ - 342.3531799316406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "337.32257080078125", - "marker": { - "color": [ - 337.32257080078125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "337.32257080078125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:8" - ], - "xaxis": "x", - "y": [ - 337.32257080078125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "341.7294921875", - "marker": { - "color": [ - 341.7294921875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "341.7294921875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:8" - ], - "xaxis": "x", - "y": [ - 341.7294921875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "339.3572998046875", - "marker": { - "color": [ - 339.3572998046875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "339.3572998046875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:9" - ], - "xaxis": "x", - "y": [ - 339.3572998046875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "341.78399658203125", - "marker": { - "color": [ - 341.78399658203125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "341.78399658203125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:9" - ], - "xaxis": "x", - "y": [ - 341.78399658203125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "340.70281982421875", - "marker": { - "color": [ - 340.70281982421875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "340.70281982421875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:10" - ], - "xaxis": "x", - "y": [ - 340.70281982421875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "340.1320495605469", - "marker": { - "color": [ - 340.1320495605469 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "340.1320495605469", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:10" - ], - "xaxis": "x", - "y": [ - 340.1320495605469 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "342.2511291503906", - "marker": { - "color": [ - 342.2511291503906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "342.2511291503906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:11" - ], - "xaxis": "x", - "y": [ - 342.2511291503906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "340.634521484375", - "marker": { - "color": [ - 340.634521484375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "340.634521484375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:11" - ], - "xaxis": "x", - "y": [ - 340.634521484375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "343.62762451171875", - "marker": { - "color": [ - 343.62762451171875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "343.62762451171875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:12" - ], - "xaxis": "x", - "y": [ - 343.62762451171875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "341.7653503417969", - "marker": { - "color": [ - 341.7653503417969 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "341.7653503417969", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:12" - ], - "xaxis": "x", - "y": [ - 341.7653503417969 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "342.6405334472656", - "marker": { - "color": [ - 342.6405334472656 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "342.6405334472656", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:13" - ], - "xaxis": "x", - "y": [ - 342.6405334472656 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "340.8123474121094", - "marker": { - "color": [ - 340.8123474121094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "340.8123474121094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:13" - ], - "xaxis": "x", - "y": [ - 340.8123474121094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "340.5118103027344", - "marker": { - "color": [ - 340.5118103027344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "340.5118103027344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:14" - ], - "xaxis": "x", - "y": [ - 340.5118103027344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "341.645263671875", - "marker": { - "color": [ - 341.645263671875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "341.645263671875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:14" - ], - "xaxis": "x", - "y": [ - 341.645263671875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "342.5506286621094", - "marker": { - "color": [ - 342.5506286621094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "342.5506286621094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:15" - ], - "xaxis": "x", - "y": [ - 342.5506286621094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "343.2059326171875", - "marker": { - "color": [ - 343.2059326171875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "343.2059326171875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:15" - ], - "xaxis": "x", - "y": [ - 343.2059326171875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "344.5043029785156", - "marker": { - "color": [ - 344.5043029785156 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "344.5043029785156", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:16" - ], - "xaxis": "x", - "y": [ - 344.5043029785156 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.2703857421875", - "marker": { - "color": [ - 345.2703857421875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "345.2703857421875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:16" - ], - "xaxis": "x", - "y": [ - 345.2703857421875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.5244140625", - "marker": { - "color": [ - 345.5244140625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "345.5244140625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:17" - ], - "xaxis": "x", - "y": [ - 345.5244140625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.8507080078125", - "marker": { - "color": [ - 345.8507080078125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "345.8507080078125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:18" - ], - "xaxis": "x", - "y": [ - 345.8507080078125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "346.5741577148437", - "marker": { - "color": [ - 346.5741577148437 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "346.5741577148437", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:18" - ], - "xaxis": "x", - "y": [ - 346.5741577148437 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.33660888671875", - "marker": { - "color": [ - 347.33660888671875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "347.33660888671875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:19" - ], - "xaxis": "x", - "y": [ - 347.33660888671875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "348.1502380371094", - "marker": { - "color": [ - 348.1502380371094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "348.1502380371094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:19" - ], - "xaxis": "x", - "y": [ - 348.1502380371094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "349.404052734375", - "marker": { - "color": [ - 349.404052734375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "349.404052734375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:20" - ], - "xaxis": "x", - "y": [ - 349.404052734375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "350.0457458496094", - "marker": { - "color": [ - 350.0457458496094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "350.0457458496094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:20" - ], - "xaxis": "x", - "y": [ - 350.0457458496094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "351.20391845703125", - "marker": { - "color": [ - 351.20391845703125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "351.20391845703125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:21" - ], - "xaxis": "x", - "y": [ - 351.20391845703125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "351.7628173828125", - "marker": { - "color": [ - 351.7628173828125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "351.7628173828125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:21" - ], - "xaxis": "x", - "y": [ - 351.7628173828125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "354.1990661621094", - "marker": { - "color": [ - 354.1990661621094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "354.1990661621094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:22" - ], - "xaxis": "x", - "y": [ - 354.1990661621094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "355.8505249023437", - "marker": { - "color": [ - 355.8505249023437 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "355.8505249023437", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:22" - ], - "xaxis": "x", - "y": [ - 355.8505249023437 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "355.623291015625", - "marker": { - "color": [ - 355.623291015625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "355.623291015625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:23" - ], - "xaxis": "x", - "y": [ - 355.623291015625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "355.7632751464844", - "marker": { - "color": [ - 355.7632751464844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "355.7632751464844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:23" - ], - "xaxis": "x", - "y": [ - 355.7632751464844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "356.7904357910156", - "marker": { - "color": [ - 356.7904357910156 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "356.7904357910156", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:24" - ], - "xaxis": "x", - "y": [ - 356.7904357910156 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "357.8786315917969", - "marker": { - "color": [ - 357.8786315917969 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "357.8786315917969", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:24" - ], - "xaxis": "x", - "y": [ - 357.8786315917969 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "357.9951477050781", - "marker": { - "color": [ - 357.9951477050781 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "357.9951477050781", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:25" - ], - "xaxis": "x", - "y": [ - 357.9951477050781 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "356.2341613769531", - "marker": { - "color": [ - 356.2341613769531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "356.2341613769531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:25" - ], - "xaxis": "x", - "y": [ - 356.2341613769531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "353.81207275390625", - "marker": { - "color": [ - 353.81207275390625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "353.81207275390625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:26" - ], - "xaxis": "x", - "y": [ - 353.81207275390625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.57568359375", - "marker": { - "color": [ - 347.57568359375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "347.57568359375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:26" - ], - "xaxis": "x", - "y": [ - 347.57568359375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "349.6695251464844", - "marker": { - "color": [ - 349.6695251464844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "349.6695251464844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:27" - ], - "xaxis": "x", - "y": [ - 349.6695251464844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "348.45220947265625", - "marker": { - "color": [ - 348.45220947265625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "348.45220947265625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:27" - ], - "xaxis": "x", - "y": [ - 348.45220947265625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "348.6149597167969", - "marker": { - "color": [ - 348.6149597167969 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "348.6149597167969", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:28" - ], - "xaxis": "x", - "y": [ - 348.6149597167969 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.84771728515625", - "marker": { - "color": [ - 347.84771728515625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "347.84771728515625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:28" - ], - "xaxis": "x", - "y": [ - 347.84771728515625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.4283142089844", - "marker": { - "color": [ - 347.4283142089844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "347.4283142089844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:29" - ], - "xaxis": "x", - "y": [ - 347.4283142089844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.2074890136719", - "marker": { - "color": [ - 347.2074890136719 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "347.2074890136719", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:29" - ], - "xaxis": "x", - "y": [ - 347.2074890136719 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "346.6120910644531", - "marker": { - "color": [ - 346.6120910644531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "346.6120910644531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:30" - ], - "xaxis": "x", - "y": [ - 346.6120910644531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "346.3581848144531", - "marker": { - "color": [ - 346.3581848144531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "346.3581848144531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:31" - ], - "xaxis": "x", - "y": [ - 346.3581848144531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "354.82098388671875", - "marker": { - "color": [ - 354.82098388671875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "354.82098388671875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:31" - ], - "xaxis": "x", - "y": [ - 354.82098388671875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.8380126953125", - "marker": { - "color": [ - 347.8380126953125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "347.8380126953125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:32" - ], - "xaxis": "x", - "y": [ - 347.8380126953125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.27838134765625", - "marker": { - "color": [ - 345.27838134765625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "345.27838134765625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:32" - ], - "xaxis": "x", - "y": [ - 345.27838134765625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "346.0719299316406", - "marker": { - "color": [ - 346.0719299316406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "346.0719299316406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:33" - ], - "xaxis": "x", - "y": [ - 346.0719299316406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.5565795898437", - "marker": { - "color": [ - 345.5565795898437 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "345.5565795898437", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:33" - ], - "xaxis": "x", - "y": [ - 345.5565795898437 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "344.7796325683594", - "marker": { - "color": [ - 344.7796325683594 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "344.7796325683594", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:34" - ], - "xaxis": "x", - "y": [ - 344.7796325683594 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "343.962646484375", - "marker": { - "color": [ - 343.962646484375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "343.962646484375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:34" - ], - "xaxis": "x", - "y": [ - 343.962646484375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "343.2431335449219", - "marker": { - "color": [ - 343.2431335449219 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "343.2431335449219", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:35" - ], - "xaxis": "x", - "y": [ - 343.2431335449219 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "344.5191650390625", - "marker": { - "color": [ - 344.5191650390625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "344.5191650390625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:35" - ], - "xaxis": "x", - "y": [ - 344.5191650390625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "344.92218017578125", - "marker": { - "color": [ - 344.92218017578125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "344.92218017578125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:36" - ], - "xaxis": "x", - "y": [ - 344.92218017578125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "346.1254577636719", - "marker": { - "color": [ - 346.1254577636719 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "346.1254577636719", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:36" - ], - "xaxis": "x", - "y": [ - 346.1254577636719 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.04327392578125", - "marker": { - "color": [ - 347.04327392578125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "347.04327392578125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:37" - ], - "xaxis": "x", - "y": [ - 347.04327392578125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.885009765625", - "marker": { - "color": [ - 345.885009765625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "345.885009765625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:38" - ], - "xaxis": "x", - "y": [ - 345.885009765625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "346.4061889648437", - "marker": { - "color": [ - 346.4061889648437 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "346.4061889648437", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:38" - ], - "xaxis": "x", - "y": [ - 346.4061889648437 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.49761962890625", - "marker": { - "color": [ - 345.49761962890625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "345.49761962890625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:39" - ], - "xaxis": "x", - "y": [ - 345.49761962890625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "347.5816345214844", - "marker": { - "color": [ - 347.5816345214844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "347.5816345214844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:39" - ], - "xaxis": "x", - "y": [ - 347.5816345214844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "348.3536376953125", - "marker": { - "color": [ - 348.3536376953125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "348.3536376953125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:40" - ], - "xaxis": "x", - "y": [ - 348.3536376953125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "349.3419494628906", - "marker": { - "color": [ - 349.3419494628906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "349.3419494628906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:40" - ], - "xaxis": "x", - "y": [ - 349.3419494628906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "352.2123718261719", - "marker": { - "color": [ - 352.2123718261719 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "352.2123718261719", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:41" - ], - "xaxis": "x", - "y": [ - 352.2123718261719 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "357.7505187988281", - "marker": { - "color": [ - 357.7505187988281 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "357.7505187988281", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:41" - ], - "xaxis": "x", - "y": [ - 357.7505187988281 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "359.87615966796875", - "marker": { - "color": [ - 359.87615966796875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "359.87615966796875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:42" - ], - "xaxis": "x", - "y": [ - 359.87615966796875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "360.18377685546875", - "marker": { - "color": [ - 360.18377685546875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "360.18377685546875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:42" - ], - "xaxis": "x", - "y": [ - 360.18377685546875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "357.6771545410156", - "marker": { - "color": [ - 357.6771545410156 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "357.6771545410156", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:43" - ], - "xaxis": "x", - "y": [ - 357.6771545410156 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "351.9995422363281", - "marker": { - "color": [ - 351.9995422363281 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "351.9995422363281", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:43" - ], - "xaxis": "x", - "y": [ - 351.9995422363281 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "345.2372741699219", - "marker": { - "color": [ - 345.2372741699219 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "345.2372741699219", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:44" - ], - "xaxis": "x", - "y": [ - 345.2372741699219 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "344.9791564941406", - "marker": { - "color": [ - 344.9791564941406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "344.9791564941406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:44" - ], - "xaxis": "x", - "y": [ - 344.9791564941406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "343.74298095703125", - "marker": { - "color": [ - 343.74298095703125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "343.74298095703125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:45" - ], - "xaxis": "x", - "y": [ - 343.74298095703125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "349.7840270996094", - "marker": { - "color": [ - 349.7840270996094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "349.7840270996094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:45" - ], - "xaxis": "x", - "y": [ - 349.7840270996094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "348.6497497558594", - "marker": { - "color": [ - 348.6497497558594 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "348.6497497558594", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:46" - ], - "xaxis": "x", - "y": [ - 348.6497497558594 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "333.18231201171875", - "marker": { - "color": [ - 333.18231201171875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "333.18231201171875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:46" - ], - "xaxis": "x", - "y": [ - 333.18231201171875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "331.13531494140625", - "marker": { - "color": [ - 331.13531494140625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "331.13531494140625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:47" - ], - "xaxis": "x", - "y": [ - 331.13531494140625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "340.68768310546875", - "marker": { - "color": [ - 340.68768310546875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "340.68768310546875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:47" - ], - "xaxis": "x", - "y": [ - 340.68768310546875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "368.7179260253906", - "marker": { - "color": [ - 368.7179260253906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "368.7179260253906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:48" - ], - "xaxis": "x", - "y": [ - 368.7179260253906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "400.7218627929688", - "marker": { - "color": [ - 400.7218627929688 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "400.7218627929688", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:48" - ], - "xaxis": "x", - "y": [ - 400.7218627929688 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.80853271484375", - "marker": { - "color": [ - 376.80853271484375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "376.80853271484375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:49" - ], - "xaxis": "x", - "y": [ - 376.80853271484375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "366.1715393066406", - "marker": { - "color": [ - 366.1715393066406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "366.1715393066406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:49" - ], - "xaxis": "x", - "y": [ - 366.1715393066406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.2972717285156", - "marker": { - "color": [ - 376.2972717285156 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "376.2972717285156", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:50" - ], - "xaxis": "x", - "y": [ - 376.2972717285156 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "381.69970703125", - "marker": { - "color": [ - 381.69970703125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "381.69970703125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:51" - ], - "xaxis": "x", - "y": [ - 381.69970703125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.2664489746094", - "marker": { - "color": [ - 378.2664489746094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "378.2664489746094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:51" - ], - "xaxis": "x", - "y": [ - 378.2664489746094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "379.3263244628906", - "marker": { - "color": [ - 379.3263244628906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "379.3263244628906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:52" - ], - "xaxis": "x", - "y": [ - 379.3263244628906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "371.8378601074219", - "marker": { - "color": [ - 371.8378601074219 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "371.8378601074219", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:52" - ], - "xaxis": "x", - "y": [ - 371.8378601074219 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "367.6902465820313", - "marker": { - "color": [ - 367.6902465820313 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "367.6902465820313", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:53" - ], - "xaxis": "x", - "y": [ - 367.6902465820313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "370.9522705078125", - "marker": { - "color": [ - 370.9522705078125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "370.9522705078125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:53" - ], - "xaxis": "x", - "y": [ - 370.9522705078125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "365.8194274902344", - "marker": { - "color": [ - 365.8194274902344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "365.8194274902344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:54" - ], - "xaxis": "x", - "y": [ - 365.8194274902344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "364.93695068359375", - "marker": { - "color": [ - 364.93695068359375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "364.93695068359375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:54" - ], - "xaxis": "x", - "y": [ - 364.93695068359375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "363.488037109375", - "marker": { - "color": [ - 363.488037109375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "363.488037109375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:55" - ], - "xaxis": "x", - "y": [ - 363.488037109375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "362.1673889160156", - "marker": { - "color": [ - 362.1673889160156 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "362.1673889160156", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:55" - ], - "xaxis": "x", - "y": [ - 362.1673889160156 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "362.3691711425781", - "marker": { - "color": [ - 362.3691711425781 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "362.3691711425781", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:56" - ], - "xaxis": "x", - "y": [ - 362.3691711425781 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "362.75140380859375", - "marker": { - "color": [ - 362.75140380859375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "362.75140380859375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:56" - ], - "xaxis": "x", - "y": [ - 362.75140380859375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "363.6519165039063", - "marker": { - "color": [ - 363.6519165039063 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "363.6519165039063", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:57" - ], - "xaxis": "x", - "y": [ - 363.6519165039063 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "365.3569030761719", - "marker": { - "color": [ - 365.3569030761719 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "365.3569030761719", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:57" - ], - "xaxis": "x", - "y": [ - 365.3569030761719 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "372.3004455566406", - "marker": { - "color": [ - 372.3004455566406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "372.3004455566406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:58" - ], - "xaxis": "x", - "y": [ - 372.3004455566406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "393.6053161621094", - "marker": { - "color": [ - 393.6053161621094 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "393.6053161621094", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:58" - ], - "xaxis": "x", - "y": [ - 393.6053161621094 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "406.147705078125", - "marker": { - "color": [ - 406.147705078125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "406.147705078125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:59" - ], - "xaxis": "x", - "y": [ - 406.147705078125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "405.5985412597656", - "marker": { - "color": [ - 405.5985412597656 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "405.5985412597656", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:34:59" - ], - "xaxis": "x", - "y": [ - 405.5985412597656 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "408.23388671875", - "marker": { - "color": [ - 408.23388671875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "408.23388671875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:0" - ], - "xaxis": "x", - "y": [ - 408.23388671875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "408.6369934082031", - "marker": { - "color": [ - 408.6369934082031 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "408.6369934082031", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:0" - ], - "xaxis": "x", - "y": [ - 408.6369934082031 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "418.603759765625", - "marker": { - "color": [ - 418.603759765625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "418.603759765625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:1" - ], - "xaxis": "x", - "y": [ - 418.603759765625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "417.7785339355469", - "marker": { - "color": [ - 417.7785339355469 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "417.7785339355469", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:1" - ], - "xaxis": "x", - "y": [ - 417.7785339355469 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "413.0149230957031", - "marker": { - "color": [ - 413.0149230957031 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "413.0149230957031", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:2" - ], - "xaxis": "x", - "y": [ - 413.0149230957031 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.2703247070313", - "marker": { - "color": [ - 391.2703247070313 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "391.2703247070313", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:2" - ], - "xaxis": "x", - "y": [ - 391.2703247070313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "399.5924377441406", - "marker": { - "color": [ - 399.5924377441406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "399.5924377441406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:3" - ], - "xaxis": "x", - "y": [ - 399.5924377441406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "400.5579223632813", - "marker": { - "color": [ - 400.5579223632813 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "400.5579223632813", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:3" - ], - "xaxis": "x", - "y": [ - 400.5579223632813 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "399.10833740234375", - "marker": { - "color": [ - 399.10833740234375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "399.10833740234375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:4" - ], - "xaxis": "x", - "y": [ - 399.10833740234375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "400.159423828125", - "marker": { - "color": [ - 400.159423828125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "400.159423828125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:5" - ], - "xaxis": "x", - "y": [ - 400.159423828125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "401.5528869628906", - "marker": { - "color": [ - 401.5528869628906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "401.5528869628906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:5" - ], - "xaxis": "x", - "y": [ - 401.5528869628906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "401.9990234375", - "marker": { - "color": [ - 401.9990234375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "401.9990234375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:6" - ], - "xaxis": "x", - "y": [ - 401.9990234375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "403.449951171875", - "marker": { - "color": [ - 403.449951171875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "403.449951171875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:6" - ], - "xaxis": "x", - "y": [ - 403.449951171875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "403.1153259277344", - "marker": { - "color": [ - 403.1153259277344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "403.1153259277344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:7" - ], - "xaxis": "x", - "y": [ - 403.1153259277344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "398.2532348632813", - "marker": { - "color": [ - 398.2532348632813 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "398.2532348632813", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:7" - ], - "xaxis": "x", - "y": [ - 398.2532348632813 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.94378662109375", - "marker": { - "color": [ - 391.94378662109375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "391.94378662109375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:8" - ], - "xaxis": "x", - "y": [ - 391.94378662109375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "390.2604064941406", - "marker": { - "color": [ - 390.2604064941406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "390.2604064941406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:8" - ], - "xaxis": "x", - "y": [ - 390.2604064941406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "390.1572265625", - "marker": { - "color": [ - 390.1572265625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "390.1572265625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:9" - ], - "xaxis": "x", - "y": [ - 390.1572265625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "390.1495056152344", - "marker": { - "color": [ - 390.1495056152344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "390.1495056152344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:9" - ], - "xaxis": "x", - "y": [ - 390.1495056152344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "385.9006042480469", - "marker": { - "color": [ - 385.9006042480469 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "385.9006042480469", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:10" - ], - "xaxis": "x", - "y": [ - 385.9006042480469 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "382.0497741699219", - "marker": { - "color": [ - 382.0497741699219 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "382.0497741699219", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:10" - ], - "xaxis": "x", - "y": [ - 382.0497741699219 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "381.0337829589844", - "marker": { - "color": [ - 381.0337829589844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "381.0337829589844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:11" - ], - "xaxis": "x", - "y": [ - 381.0337829589844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "380.7168273925781", - "marker": { - "color": [ - 380.7168273925781 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "380.7168273925781", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:11" - ], - "xaxis": "x", - "y": [ - 380.7168273925781 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "379.1099853515625", - "marker": { - "color": [ - 379.1099853515625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "379.1099853515625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:12" - ], - "xaxis": "x", - "y": [ - 379.1099853515625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.7236328125", - "marker": { - "color": [ - 377.7236328125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "377.7236328125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:12" - ], - "xaxis": "x", - "y": [ - 377.7236328125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.2712707519531", - "marker": { - "color": [ - 377.2712707519531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "377.2712707519531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:13" - ], - "xaxis": "x", - "y": [ - 377.2712707519531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.8092651367188", - "marker": { - "color": [ - 376.8092651367188 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "376.8092651367188", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:13" - ], - "xaxis": "x", - "y": [ - 376.8092651367188 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.9216613769531", - "marker": { - "color": [ - 376.9216613769531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "376.9216613769531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:14" - ], - "xaxis": "x", - "y": [ - 376.9216613769531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.1701354980469", - "marker": { - "color": [ - 376.1701354980469 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "376.1701354980469", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:14" - ], - "xaxis": "x", - "y": [ - 376.1701354980469 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "375.7994689941406", - "marker": { - "color": [ - 375.7994689941406 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "375.7994689941406", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:15" - ], - "xaxis": "x", - "y": [ - 375.7994689941406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "375.64404296875", - "marker": { - "color": [ - 375.64404296875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "375.64404296875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:15" - ], - "xaxis": "x", - "y": [ - 375.64404296875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "375.95904541015625", - "marker": { - "color": [ - 375.95904541015625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "375.95904541015625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:16" - ], - "xaxis": "x", - "y": [ - 375.95904541015625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.59417724609375", - "marker": { - "color": [ - 376.59417724609375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "376.59417724609375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:16" - ], - "xaxis": "x", - "y": [ - 376.59417724609375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.3937377929688", - "marker": { - "color": [ - 376.3937377929688 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "376.3937377929688", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:17" - ], - "xaxis": "x", - "y": [ - 376.3937377929688 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.2090759277344", - "marker": { - "color": [ - 377.2090759277344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "377.2090759277344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:18" - ], - "xaxis": "x", - "y": [ - 377.2090759277344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.25341796875", - "marker": { - "color": [ - 378.25341796875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "378.25341796875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:18" - ], - "xaxis": "x", - "y": [ - 378.25341796875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "379.0548400878906", - "marker": { - "color": [ - 379.0548400878906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "379.0548400878906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:19" - ], - "xaxis": "x", - "y": [ - 379.0548400878906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "381.0333251953125", - "marker": { - "color": [ - 381.0333251953125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "381.0333251953125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:19" - ], - "xaxis": "x", - "y": [ - 381.0333251953125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "383.188232421875", - "marker": { - "color": [ - 383.188232421875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "383.188232421875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:20" - ], - "xaxis": "x", - "y": [ - 383.188232421875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "383.6612243652344", - "marker": { - "color": [ - 383.6612243652344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "383.6612243652344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:20" - ], - "xaxis": "x", - "y": [ - 383.6612243652344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "383.4259643554688", - "marker": { - "color": [ - 383.4259643554688 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "383.4259643554688", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:21" - ], - "xaxis": "x", - "y": [ - 383.4259643554688 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "384.8764953613281", - "marker": { - "color": [ - 384.8764953613281 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "384.8764953613281", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:21" - ], - "xaxis": "x", - "y": [ - 384.8764953613281 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "385.2247619628906", - "marker": { - "color": [ - 385.2247619628906 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "385.2247619628906", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:22" - ], - "xaxis": "x", - "y": [ - 385.2247619628906 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "385.4185180664063", - "marker": { - "color": [ - 385.4185180664063 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "385.4185180664063", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:22" - ], - "xaxis": "x", - "y": [ - 385.4185180664063 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "386.1218872070313", - "marker": { - "color": [ - 386.1218872070313 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "386.1218872070313", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:23" - ], - "xaxis": "x", - "y": [ - 386.1218872070313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "387.3418273925781", - "marker": { - "color": [ - 387.3418273925781 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "387.3418273925781", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:23" - ], - "xaxis": "x", - "y": [ - 387.3418273925781 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "387.8011169433594", - "marker": { - "color": [ - 387.8011169433594 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "387.8011169433594", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:24" - ], - "xaxis": "x", - "y": [ - 387.8011169433594 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "388.4589538574219", - "marker": { - "color": [ - 388.4589538574219 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "388.4589538574219", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:24" - ], - "xaxis": "x", - "y": [ - 388.4589538574219 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "389.6048278808594", - "marker": { - "color": [ - 389.6048278808594 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "389.6048278808594", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:25" - ], - "xaxis": "x", - "y": [ - 389.6048278808594 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "389.7867431640625", - "marker": { - "color": [ - 389.7867431640625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "389.7867431640625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:25" - ], - "xaxis": "x", - "y": [ - 389.7867431640625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "390.6592102050781", - "marker": { - "color": [ - 390.6592102050781 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "390.6592102050781", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:26" - ], - "xaxis": "x", - "y": [ - 390.6592102050781 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.4608154296875", - "marker": { - "color": [ - 391.4608154296875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "391.4608154296875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:26" - ], - "xaxis": "x", - "y": [ - 391.4608154296875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "392.6442565917969", - "marker": { - "color": [ - 392.6442565917969 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "392.6442565917969", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:27" - ], - "xaxis": "x", - "y": [ - 392.6442565917969 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "393.9162292480469", - "marker": { - "color": [ - 393.9162292480469 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "393.9162292480469", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:27" - ], - "xaxis": "x", - "y": [ - 393.9162292480469 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "392.1313781738281", - "marker": { - "color": [ - 392.1313781738281 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "392.1313781738281", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:28" - ], - "xaxis": "x", - "y": [ - 392.1313781738281 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.5135803222656", - "marker": { - "color": [ - 391.5135803222656 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "391.5135803222656", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:28" - ], - "xaxis": "x", - "y": [ - 391.5135803222656 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "388.56585693359375", - "marker": { - "color": [ - 388.56585693359375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "388.56585693359375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:29" - ], - "xaxis": "x", - "y": [ - 388.56585693359375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "379.78662109375", - "marker": { - "color": [ - 379.78662109375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "379.78662109375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:29" - ], - "xaxis": "x", - "y": [ - 379.78662109375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.4542846679688", - "marker": { - "color": [ - 378.4542846679688 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "378.4542846679688", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:30" - ], - "xaxis": "x", - "y": [ - 378.4542846679688 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.010009765625", - "marker": { - "color": [ - 378.010009765625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "378.010009765625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:31" - ], - "xaxis": "x", - "y": [ - 378.010009765625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.738525390625", - "marker": { - "color": [ - 376.738525390625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "376.738525390625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:31" - ], - "xaxis": "x", - "y": [ - 376.738525390625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.7132568359375", - "marker": { - "color": [ - 377.7132568359375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "377.7132568359375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:32" - ], - "xaxis": "x", - "y": [ - 377.7132568359375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.5142517089844", - "marker": { - "color": [ - 378.5142517089844 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "378.5142517089844", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:32" - ], - "xaxis": "x", - "y": [ - 378.5142517089844 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.1591491699219", - "marker": { - "color": [ - 378.1591491699219 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "378.1591491699219", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:33" - ], - "xaxis": "x", - "y": [ - 378.1591491699219 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.3596496582031", - "marker": { - "color": [ - 378.3596496582031 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "378.3596496582031", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:33" - ], - "xaxis": "x", - "y": [ - 378.3596496582031 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "378.3343200683594", - "marker": { - "color": [ - 378.3343200683594 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "378.3343200683594", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:34" - ], - "xaxis": "x", - "y": [ - 378.3343200683594 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.3188781738281", - "marker": { - "color": [ - 377.3188781738281 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "377.3188781738281", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:34" - ], - "xaxis": "x", - "y": [ - 377.3188781738281 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.4431762695313", - "marker": { - "color": [ - 377.4431762695313 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "377.4431762695313", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:35" - ], - "xaxis": "x", - "y": [ - 377.4431762695313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.9720153808594", - "marker": { - "color": [ - 377.9720153808594 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "377.9720153808594", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:35" - ], - "xaxis": "x", - "y": [ - 377.9720153808594 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.42193603515625", - "marker": { - "color": [ - 377.42193603515625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "377.42193603515625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:36" - ], - "xaxis": "x", - "y": [ - 377.42193603515625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.8049621582031", - "marker": { - "color": [ - 376.8049621582031 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "376.8049621582031", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:36" - ], - "xaxis": "x", - "y": [ - 376.8049621582031 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "376.1614379882813", - "marker": { - "color": [ - 376.1614379882813 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "376.1614379882813", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:37" - ], - "xaxis": "x", - "y": [ - 376.1614379882813 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.7637023925781", - "marker": { - "color": [ - 377.7637023925781 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "377.7637023925781", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:37" - ], - "xaxis": "x", - "y": [ - 377.7637023925781 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "377.5328369140625", - "marker": { - "color": [ - 377.5328369140625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "377.5328369140625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:38" - ], - "xaxis": "x", - "y": [ - 377.5328369140625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "379.664794921875", - "marker": { - "color": [ - 379.664794921875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "379.664794921875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:38" - ], - "xaxis": "x", - "y": [ - 379.664794921875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "382.0987243652344", - "marker": { - "color": [ - 382.0987243652344 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "382.0987243652344", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:39" - ], - "xaxis": "x", - "y": [ - 382.0987243652344 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "385.0165100097656", - "marker": { - "color": [ - 385.0165100097656 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "385.0165100097656", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:39" - ], - "xaxis": "x", - "y": [ - 385.0165100097656 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "385.4192199707031", - "marker": { - "color": [ - 385.4192199707031 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "385.4192199707031", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:40" - ], - "xaxis": "x", - "y": [ - 385.4192199707031 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "386.6877746582031", - "marker": { - "color": [ - 386.6877746582031 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "386.6877746582031", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:40" - ], - "xaxis": "x", - "y": [ - 386.6877746582031 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "389.4333801269531", - "marker": { - "color": [ - 389.4333801269531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "389.4333801269531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:41" - ], - "xaxis": "x", - "y": [ - 389.4333801269531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "390.250244140625", - "marker": { - "color": [ - 390.250244140625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "390.250244140625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:41" - ], - "xaxis": "x", - "y": [ - 390.250244140625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.012939453125", - "marker": { - "color": [ - 391.012939453125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "391.012939453125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:42" - ], - "xaxis": "x", - "y": [ - 391.012939453125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.4132080078125", - "marker": { - "color": [ - 391.4132080078125 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "391.4132080078125", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:42" - ], - "xaxis": "x", - "y": [ - 391.4132080078125 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "392.1314697265625", - "marker": { - "color": [ - 392.1314697265625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "392.1314697265625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:43" - ], - "xaxis": "x", - "y": [ - 392.1314697265625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "391.8728332519531", - "marker": { - "color": [ - 391.8728332519531 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "391.8728332519531", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:44" - ], - "xaxis": "x", - "y": [ - 391.8728332519531 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "393.0383911132813", - "marker": { - "color": [ - 393.0383911132813 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "393.0383911132813", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:44" - ], - "xaxis": "x", - "y": [ - 393.0383911132813 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "394.0306396484375", - "marker": { - "color": [ - 394.0306396484375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "394.0306396484375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:45" - ], - "xaxis": "x", - "y": [ - 394.0306396484375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "394.6489868164063", - "marker": { - "color": [ - 394.6489868164063 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "diamond" - }, - "mode": "markers", - "name": "394.6489868164063", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:45" - ], - "xaxis": "x", - "y": [ - 394.6489868164063 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "394.5662841796875", - "marker": { - "color": [ - 394.5662841796875 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "square" - }, - "mode": "markers", - "name": "394.5662841796875", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:46" - ], - "xaxis": "x", - "y": [ - 394.5662841796875 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "395.3863525390625", - "marker": { - "color": [ - 395.3863525390625 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "x" - }, - "mode": "markers", - "name": "395.3863525390625", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:46" - ], - "xaxis": "x", - "y": [ - 395.3863525390625 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "396.5464477539063", - "marker": { - "color": [ - 396.5464477539063 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "cross" - }, - "mode": "markers", - "name": "396.5464477539063", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:47" - ], - "xaxis": "x", - "y": [ - 396.5464477539063 - ], - "yaxis": "y" - }, - { - "hovertemplate": "left_iris_x=%{marker.color}
timestamp=%{x}", - "legendgroup": "395.83636474609375", - "marker": { - "color": [ - 395.83636474609375 - ], - "coloraxis": "coloraxis", - "size": 10, - "symbol": "circle" - }, - "mode": "markers", - "name": "395.83636474609375", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - "2023-5-26 15:35:47" - ], - "xaxis": "x", - "y": [ - 395.83636474609375 - ], - "yaxis": "y" - } - ], - "layout": { - "coloraxis": { - "colorbar": { - "title": { - "text": "left_iris_x" - } - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "legend": { - "title": { - "text": "left_iris_x" - }, - "tracegroupgap": 0 - }, - "margin": { - "t": 60 - }, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "timestamp" - } - }, - "yaxis": { - "anchor": "x", - "categoryarray": [ - 395.83636474609375, - 396.5464477539063, - 395.3863525390625, - 394.5662841796875, - 394.6489868164063, - 394.0306396484375, - 393.0383911132813, - 391.8728332519531, - 392.1314697265625, - 391.4132080078125, - 391.012939453125, - 390.250244140625, - 389.4333801269531, - 386.6877746582031, - 385.4192199707031, - 385.0165100097656, - 382.0987243652344, - 379.664794921875, - 377.5328369140625, - 377.7637023925781, - 376.1614379882813, - 376.8049621582031, - 377.42193603515625, - 377.9720153808594, - 377.4431762695313, - 377.3188781738281, - 378.3343200683594, - 378.3596496582031, - 378.1591491699219, - 378.5142517089844, - 377.7132568359375, - 376.738525390625, - 378.010009765625, - 378.4542846679688, - 379.78662109375, - 388.56585693359375, - 391.5135803222656, - 392.1313781738281, - 393.9162292480469, - 392.6442565917969, - 391.4608154296875, - 390.6592102050781, - 389.7867431640625, - 389.6048278808594, - 388.4589538574219, - 387.8011169433594, - 387.3418273925781, - 386.1218872070313, - 385.4185180664063, - 385.2247619628906, - 384.8764953613281, - 383.4259643554688, - 383.6612243652344, - 383.188232421875, - 381.0333251953125, - 379.0548400878906, - 378.25341796875, - 377.2090759277344, - 376.3937377929688, - 376.59417724609375, - 375.95904541015625, - 375.64404296875, - 375.7994689941406, - 376.1701354980469, - 376.9216613769531, - 376.8092651367188, - 377.2712707519531, - 377.7236328125, - 379.1099853515625, - 380.7168273925781, - 381.0337829589844, - 382.0497741699219, - 385.9006042480469, - 390.1495056152344, - 390.1572265625, - 390.2604064941406, - 391.94378662109375, - 398.2532348632813, - 403.1153259277344, - 403.449951171875, - 401.9990234375, - 401.5528869628906, - 400.159423828125, - 399.10833740234375, - 400.5579223632813, - 399.5924377441406, - 391.2703247070313, - 413.0149230957031, - 417.7785339355469, - 418.603759765625, - 408.6369934082031, - 408.23388671875, - 405.5985412597656, - 406.147705078125, - 393.6053161621094, - 372.3004455566406, - 365.3569030761719, - 363.6519165039063, - 362.75140380859375, - 362.3691711425781, - 362.1673889160156, - 363.488037109375, - 364.93695068359375, - 365.8194274902344, - 370.9522705078125, - 367.6902465820313, - 371.8378601074219, - 379.3263244628906, - 378.2664489746094, - 381.69970703125, - 376.2972717285156, - 366.1715393066406, - 376.80853271484375, - 400.7218627929688, - 368.7179260253906, - 340.68768310546875, - 331.13531494140625, - 333.18231201171875, - 348.6497497558594, - 349.7840270996094, - 343.74298095703125, - 344.9791564941406, - 345.2372741699219, - 351.9995422363281, - 357.6771545410156, - 360.18377685546875, - 359.87615966796875, - 357.7505187988281, - 352.2123718261719, - 349.3419494628906, - 348.3536376953125, - 347.5816345214844, - 345.49761962890625, - 346.4061889648437, - 345.885009765625, - 347.04327392578125, - 346.1254577636719, - 344.92218017578125, - 344.5191650390625, - 343.2431335449219, - 343.962646484375, - 344.7796325683594, - 345.5565795898437, - 346.0719299316406, - 345.27838134765625, - 347.8380126953125, - 354.82098388671875, - 346.3581848144531, - 346.6120910644531, - 347.2074890136719, - 347.4283142089844, - 347.84771728515625, - 348.6149597167969, - 348.45220947265625, - 349.6695251464844, - 347.57568359375, - 353.81207275390625, - 356.2341613769531, - 357.9951477050781, - 357.8786315917969, - 356.7904357910156, - 355.7632751464844, - 355.623291015625, - 355.8505249023437, - 354.1990661621094, - 351.7628173828125, - 351.20391845703125, - 350.0457458496094, - 349.404052734375, - 348.1502380371094, - 347.33660888671875, - 346.5741577148437, - 345.8507080078125, - 345.5244140625, - 345.2703857421875, - 344.5043029785156, - 343.2059326171875, - 342.5506286621094, - 341.645263671875, - 340.5118103027344, - 340.8123474121094, - 342.6405334472656, - 341.7653503417969, - 343.62762451171875, - 340.634521484375, - 342.2511291503906, - 340.1320495605469, - 340.70281982421875, - 341.78399658203125, - 339.3572998046875, - 341.7294921875, - 337.32257080078125, - 342.3531799316406, - 355.3423767089844, - 349.4696350097656, - 336.72576904296875 - ], - "categoryorder": "array", - "domain": [ - 0, - 1 - ], - "title": { - "text": "left_iris_x" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = raw_dataset.left_iris_x\n", - "y = raw_dataset.left_iris_y\n", - "datetime = raw_dataset.timestamp\n", - "\n", - "fig = px.scatter(raw_dataset, y = 'left_iris_x', x = 'timestamp', color = 'left_iris_x', symbol = 'left_iris_x')\n", - "fig.update_traces(marker_size = 10)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eye", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From fb98e560c81c91d211dbaf29850669ff85405653 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 07:29:49 +0000 Subject: [PATCH 03/78] separate test folder made for all model testing --- app/services/calib_validation/{ => test}/test.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename app/services/calib_validation/{ => test}/test.ipynb (100%) diff --git a/app/services/calib_validation/test.ipynb b/app/services/calib_validation/test/test.ipynb similarity index 100% rename from app/services/calib_validation/test.ipynb rename to app/services/calib_validation/test/test.ipynb From 7430e3706cbc37ca4430a528c1ecedb464fbe5ca Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 09:58:04 +0000 Subject: [PATCH 04/78] linear regression model test added --- .../test/test_linear_regression.ipynb | 1645 +++++++++++++++++ 1 file changed, 1645 insertions(+) create mode 100644 app/services/calib_validation/test/test_linear_regression.ipynb diff --git a/app/services/calib_validation/test/test_linear_regression.ipynb b/app/services/calib_validation/test/test_linear_regression.ipynb new file mode 100644 index 00000000..4abaeeae --- /dev/null +++ b/app/services/calib_validation/test/test_linear_regression.ipynb @@ -0,0 +1,1645 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9959810132981148" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.LinearRegression())\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 776.84988302, 766.35789676, 117.2848686 , 115.50592737,\n", + " 1424.77684183, 108.25862684, 128.10790659, 86.84217241,\n", + " 771.21171463, 769.54492763, 1419.35044712, 766.56716404,\n", + " 116.6373205 , 95.97878074, 770.01953515, 778.06950784,\n", + " 123.79194499, 1431.61264912, 102.05938908, 113.98134806,\n", + " 767.43613154, 768.05863298, 105.03301574, 1434.36655184,\n", + " 1423.24269732, 776.27609255, 85.49452003, 104.22152689,\n", + " 78.37854189, 102.45742368, 769.79093824, 768.95376077,\n", + " 778.67065945, 771.49331258, 1458.48477777, 1438.65857832,\n", + " 1417.30960204, 1458.07833397, 1453.52102482, 1427.27233654,\n", + " 764.79030801, 101.74487353, 105.83488731, 1428.11503314,\n", + " 769.09583142, 108.46765051, 780.70440993, 1439.40091835,\n", + " 1437.34785382, 101.44510265, 1423.57620991, 1436.31833361,\n", + " 764.34857499, 1437.60058851, 84.67320261, 768.63114329,\n", + " 94.67819987, 95.26177462, 116.82661235, 777.37745449,\n", + " 1416.75690733, 109.03899678, 113.37271241, 766.39094081,\n", + " 88.25150848, 99.7371789 , 1434.75628674, 94.32416988,\n", + " 1420.09796088, 1441.0906355 , 1436.79638599, 773.03868478,\n", + " 121.62022678, 771.88325951, 70.32179945, 1417.06161512,\n", + " 1453.58097803, 778.94828933, 1425.66535428, 100.30733607,\n", + " 1417.96177268, 765.05121587, 93.69368105, 762.75345052,\n", + " 113.34429605, 1423.8122625 , 1463.96334073, 95.6763439 ,\n", + " 1436.30107537, 1434.55410566, 84.5443611 , 118.28183569,\n", + " 100.53639461, 773.52742369, 100.54931879, 772.38214121,\n", + " 103.67551972, 114.58052647, 1463.37873218, 82.63650322,\n", + " 770.42437836, 1421.15337448, 774.68314307, 769.50130212,\n", + " 113.54931712, 771.52715652, 90.04395205, 82.27768659,\n", + " 80.03169183, 1435.65405713, 93.17685007, 1028.56758718,\n", + " 1440.29734398, 105.03879269, 1421.52574209, 776.20530631,\n", + " 770.16071447, 115.22259864, 1456.96063609, 116.37821974,\n", + " 120.62110516, 768.11195493, 1440.63856026, 113.31576245,\n", + " 775.53664099, 100.23842548, 769.38950807, 1437.07423936,\n", + " 1441.19806239, 1431.7060965 , 1438.82779386, 771.45955747,\n", + " 88.96126645, 1459.56848776, 776.88494348, 1423.29277038,\n", + " 86.61192985, 101.11787086, 93.99768403, 106.55472906,\n", + " 100.19262039, 118.6801186 , 80.39883193, 1422.40902372])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9808758718535199" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.LinearRegression())\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([130.67618428, 117.5607336 , 107.64806238, 584.10104876,\n", + " 55.48643684, 113.14102363, 108.49650633, 404.95340585,\n", + " 132.55016326, 667.02214947, 611.70390354, 690.50192114,\n", + " 92.22424288, 332.58719292, 664.97347873, 137.28743261,\n", + " 117.99065863, 371.73722617, 568.89552983, 591.18554066,\n", + " 691.8401563 , 597.58313353, 89.6950944 , 621.34855563,\n", + " 134.22337742, 122.14733867, 578.6888548 , 577.46371526,\n", + " 392.83401485, 379.09910319, 667.56019394, 597.30479521,\n", + " 110.41650384, 606.8558844 , 348.47247265, 693.57491895,\n", + " 617.0004819 , 363.41444089, 355.42803537, 102.55648214,\n", + " 121.65105882, 382.45880103, 106.43207541, 640.11942491,\n", + " 630.94865931, 107.39613896, 138.73143523, 367.40451345,\n", + " 662.98518814, 107.15950336, 128.50114477, 629.59654705,\n", + " 672.94321597, 327.01845273, 374.52104798, 138.77544514,\n", + " 583.84771733, 570.20970451, 115.35886091, 131.45443637,\n", + " 114.92711021, 47.08176042, 105.30371623, 658.78603596,\n", + " 596.73556972, 314.55371257, 658.5730693 , 383.40577976,\n", + " 124.4839212 , 369.91654367, 639.85468446, 671.20753501,\n", + " 107.47286873, 607.49724124, 375.72601818, 118.54980658,\n", + " 356.40178551, 130.14758969, 628.95547283, 574.43833604,\n", + " 115.95355693, 118.46752567, 583.85828901, 118.71937342,\n", + " 85.04883616, 118.0893951 , 360.30582139, 591.30404795,\n", + " 90.4582307 , 652.54446573, 571.35273131, 106.92731916,\n", + " 569.81629748, 125.70386047, 405.32432714, 130.84063192,\n", + " 118.40971388, -7.37897177, 358.28447649, 373.6467893 ,\n", + " 135.55683954, 616.9083156 , 136.67486606, 126.96925571,\n", + " 590.48009253, 147.90468012, 569.29626374, 575.58165279,\n", + " 594.57148601, 656.45369337, 404.8608222 , 99.67074706,\n", + " 365.17280466, 288.37941458, 106.30823469, 143.67069988,\n", + " 598.11958715, 92.70750315, 355.10399397, 81.37434843,\n", + " 108.200512 , 645.48212159, 361.36243311, 104.62720816,\n", + " 125.66877497, 107.42996631, 630.39860067, 659.98968061,\n", + " 330.35253838, 95.39665539, 638.45728553, 664.85653005,\n", + " 576.89306479, 354.16209494, 124.55834674, 634.14666556,\n", + " 403.99766701, 39.80623563, 569.55562756, 375.78576739,\n", + " 372.57409374, 598.83710192, 394.73113528, 105.89888195])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGxCAYAAABIjE2TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABcW0lEQVR4nO3deXxU1f0//tfsWzKTPZNAdhAIu4DJsGqhomLrgjsKAoLS4Eel9eMHa+vyacWvn/7qVgURBW1Fq7ZQi6IiSNjCDsomZIMEkkmALJNt1nt+f4RMmQQkyySZZF7PxyOtuedk5twrZF7ee855y4QQAkREREQBRN7dAyAiIiJqjgGFiIiIAg4DChEREQUcBhQiIiIKOAwoREREFHAYUIiIiCjgMKAQERFRwGFAISIiooCj7O4BtIckSSgpKUFoaChkMll3D4eIiIhaQQiBmpoaxMfHQy7/6XskPTKglJSUICEhobuHQURERO1QXFyMvn37/mSfHhlQQkNDATSeoNFo7ObREBERUWvYbDYkJCR4P8d/So8MKE2PdYxGIwMKERFRD9Oa6RmcJEtEREQBhwGFiIiIAg4DChEREQUcBhQiIiIKOAwoREREFHAYUIiIiCjgtCmgJCcnQyaTtfjKysoCANjtdmRlZSEyMhIhISGYPn06ysrKfF6jqKgI06ZNg16vR0xMDJ588km43W7/nRERERH1eG0KKHv27EFpaan3a8OGDQCAO++8EwDwxBNP4N///jc+/fRTZGdno6SkBLfffrv35z0eD6ZNmwan04kdO3bg/fffx6pVq/D73//ej6dEREREPZ1MCCHa+8OPP/441q1bh9zcXNhsNkRHR2P16tW44447AAA//vgjBg0ahJycHGRmZmL9+vW4+eabUVJSgtjYWADAsmXL8NRTT+Hs2bNQq9Wtel+bzQaTyYTq6mpu1EZERNRDtOXzu91zUJxOJ/72t79hzpw5kMlk2LdvH1wuF6ZMmeLtM3DgQCQmJiInJwcAkJOTg6FDh3rDCQBMnToVNpsNR44cae9QiIiIyE8kSeDQ6WpknziLQ6erIUntvo/RIe3e6n7t2rWoqqrCgw8+CACwWq1Qq9UICwvz6RcbGwur1ertc3E4aWpvarsch8MBh8Ph/d5ms7V32ERERHQZO/LO4c3v8nCk1AaXW4JKKcfgOCOyruuHsf2iunQs7b6D8u677+LGG29EfHy8P8dzSUuWLIHJZPJ+sZIxERGRf+3IO4es1fuxI/88qupdqHN6UFXvwo78843H88516XjaFVBOnTqFb7/9Fg899JD3mNlshtPpRFVVlU/fsrIymM1mb5/mq3qavm/qcymLFy9GdXW196u4uLg9wyYiIqJLkCSB3649hMp6FwQA2UVfAkBlvQu/XXuoSx/3tCugrFy5EjExMZg2bZr32KhRo6BSqbBx40bvsePHj6OoqAgWiwUAYLFYcOjQIZSXl3v7bNiwAUajEenp6Zd9P41G461czArGRERE/vVDcRVOnq8H0BhKLk4oTXWHT56vxw/FVV02pjbPQZEkCStXrsSsWbOgVP7nx00mE+bOnYtFixYhIiICRqMRjz76KCwWCzIzMwEA119/PdLT0/HAAw/g5ZdfhtVqxTPPPIOsrCxoNBr/nRURERG12vojVnjX9MqaNV64jSJEY78RSeFdMqY2B5Rvv/0WRUVFmDNnTou2V155BXK5HNOnT4fD4cDUqVPx1ltvedsVCgXWrVuHBQsWwGKxwGAwYNasWXjhhRc6dhZERETUbnXO1m2Y2tp+/tChfVC6C/dBISIi8p9/7DuNX3/6vfd72UV3US5OCf/fncMxfVTfdr9Pl+yDQkRERL3DLcPjYdAovN8L8Z+vJgaNArcM7/yVu00YUIiIiIKcUinH45P7Q958/skFchnw+OT+UCq7Lja0e6M2IiIi6j3mTUwDALyxKRc1do93uXGoVoFHf9bf295VeAeFiIiIAACD400YEm+CQauERimHQavEkHgTBsebunwsDChERESEHXnn8MQnB7HnVCUaHB64PBIaHB7sOVWJJz452DN2kiUiIqLeQ5IElqw/hrM1Drg8Ah4hIAnAIwRcHoGzNQ4sWX8s8HeSJSIiot7j0Jlq/Fhag8vlD0kAP5bW4NCZ6i4bEwMKERFRkDtwqhKuK9wdcUkCB05VdtGIGFCIiIiC3pmqBr/28wcGFCIioiBXXtO64NHafv7AgEJERBTkymucfu3nDwwoREREQc6obd2+ra3t5w8MKEREREEuXK/2az9/YEAhIiIKcrLL1OBpbz9/YEAhIiIKcg0uya/9/IEBhYiIKMhFhbTu0U1r+/kDAwoREVGQ6xum92s/f2BAISIiCnIjk8KhuML8EoWssV9XYUAhIiIKcoPjjNCpFT/ZR6dWYHCcsYtGxIBCREQU9I5ZayC/whIduUyGY9aaLhoRAwoREVHQO1/rQK3D/ZN9ah1unK91dNGIGFCIiIiC3tlaB65QzBiSaOzXVRhQiIiIgtzxstY9umltP39gQCEiIgpydofHr/38gQGFiIgoyEUZNX7t5w8MKEREREEuPSbUr/38gQGFiIgoyH2Xd9av/fyBAYWIiCjIWasb/NrPHxhQiIiIgly4vnVzS1rbzx8YUIiIiILcPWP6tqrffdckdPJI/kPZZe9EREREAUUIAVuDGxX1riv2lQFQKX+6Xo8/MaAQEREFoVqHG5V1Trg8EipbE1BkQEW9swtG1ogBhYiIKIjYXR6cr3PC4WrcdM3h8uDrw9Yr/pwkgIpaBhQiIiLyI6dbQkWdE/XOxqKAHkngm6NlWLX9ZKtr7NQ0XPlOi78woBAREfVi7guPcGrsjeFCCIGdBRV4Z2sBTp6vb9NrCVlnjPDSGFCIiIh6IUkSqG5wobrBBUk0lio+VmrD21sK8MPpap++CpkMHnGFcsYAjFpVp4z1UhhQiIiIehmb3YWqOhfckgQAKK6ox7vbCrEl95xPPxmAyYNiMKyvCX/ekHvF1zXquy42tHkflDNnzuD+++9HZGQkdDodhg4dir1793rbhRD4/e9/j7i4OOh0OkyZMgW5ub4nXVFRgRkzZsBoNCIsLAxz585FbW1tx8+GiIgoiNU73SiuqMe5GgfcUuOck1e/zcXsVXtahJPRSeF4+4FRePqmQaisbd3ckmNnajpj2JfUpihUWVmJcePG4brrrsP69esRHR2N3NxchIeHe/u8/PLLeP311/H+++8jJSUFv/vd7zB16lQcPXoUWq0WADBjxgyUlpZiw4YNcLlcmD17NubPn4/Vq1f79+yIiIiCgN3lQUWdE/YLK3PqnW58suc0PtlXDLtL8ul7VWwI5k9IxdVJ//nsVipbN7lEFqhzUP7f//t/SEhIwMqVK73HUlJSvP8shMCrr76KZ555BrfccgsA4IMPPkBsbCzWrl2Le+65B8eOHcNXX32FPXv2YPTo0QCAN954AzfddBP+9Kc/IT4+3h/nRURE1Ou5PBIq65yodbi936/7oRR/zTmFqmYrbuJMWjw0PgWTBkRDfiFpqJVyRBjUSI8zQQbgp2ahyAD0Ddd3zolcQpse8Xz++ecYPXo07rzzTsTExGDkyJF45513vO2FhYWwWq2YMmWK95jJZEJGRgZycnIAADk5OQgLC/OGEwCYMmUK5HI5du3a1dHzISIi6vU8ksD5WgdOVzag1uGGJAS++7Ecs1ftwRub8nzCiUmnwsLr+mHV7DG4bmAM5DIZVAo5okM16Buuh16txLQh5p8MJ0BjeJk2xNyp53WxNt1BKSgowNKlS7Fo0SI8/fTT2LNnD/7rv/4LarUas2bNgtXauNFLbGysz8/FxsZ626xWK2JiYnwHoVQiIiLC26c5h8MBh+M/a7RtNltbhk1ERNQrCNG4Mqeq/j8rc/YXVWL5lgKcKPOdy6lVynHn6L64a3QCDJrGj3uFXIYwvRpGrRKyi57X/HC6qlXv/8PpKoxOjfTPyVxBmwKKJEkYPXo0XnzxRQDAyJEjcfjwYSxbtgyzZs3qlAECwJIlS/D888932usTEREFuhq7C5UXrczJL6/FO1sLsPtkpU8/uQyYNiwOMzOTEBmiuXBMhjC9CkatCnJ5y4kkf9mc16ox/GVzHlYFYkCJi4tDenq6z7FBgwbhH//4BwDAbG689VNWVoa4uDhvn7KyMowYMcLbp7y83Oc13G43KioqvD/f3OLFi7Fo0SLv9zabDQkJXVdRkYiIqLs0OD04X+eA090YTKw2O1ZuP4lvj5a1eCwzsX8U5oxPQWJE41wRmUwGo1aJML0aiksEkyZnquytGktr+/lDmwLKuHHjcPz4cZ9jJ06cQFJSEoDGCbNmsxkbN270BhKbzYZdu3ZhwYIFAACLxYKqqirs27cPo0aNAgBs2rQJkiQhIyPjku+r0Wig0WjadGJEREQ9mcPtQWWdy7s1fXWDC6t3FWHtwTNweXyjydA+Jjw8MRXp8UbvsVCtCuF6FZSKK083jTNqkFt+5e0+4oxd91ncpoDyxBNPYOzYsXjxxRdx1113Yffu3Vi+fDmWL18OoDGpPf744/jDH/6A/v37e5cZx8fH49ZbbwXQeMflhhtuwLx587Bs2TK4XC4sXLgQ99xzD1fwEBFR0Gu+Nb3D5cE/9p/BR3uKUOfw+PRNitRj/oRUZKZGeOeUGDRKhOvVUCtbvw4mLkzr137+0KaAMmbMGKxZswaLFy/GCy+8gJSUFLz66quYMWOGt89///d/o66uDvPnz0dVVRXGjx+Pr776yrsHCgB8+OGHWLhwISZPngy5XI7p06fj9ddf999ZERER9TCSJFB1YWt6IQQ8ksDXR6xYteMkzjWrIhwdosGD45JxfXqs99GNTq1AuF4NrUrR5vdufkemo/38QSZEKzbfDzA2mw0mkwnV1dUwGo1X/gEiIqIAJYSAze5GVb0THklACIEd+eexYlshTjUr5mfQKDDjmkTcNrIPNBeCiEalQIReDZ267cGkyZ83HMfrG688Ufa/JvfDop8PaPf7tOXzm7V4iIiIukmdw42KOidcnsYJsEdKqrF8SyEOnfEt5qdSyHDriD6YkZEIo0514VjjJmtNS4g7pLX3KrrwngYDChERURdrvjV9UUU9VmwtxLa8lsX8fp4eiwfHJcNsbJwqoZTLEWZQ+bWycI9fxUNERETt53RLqKx3ou7C1vTnax14P+cUvjxUCqnZzYlrksMxb0Iq0mJCAFzYZE2nhlHnu8maPySE6/zazx8YUIiIiDqZRxKorHeixu6GEAJ1Djc+3lOMf+w7Dbvbt5jfgNhQzJuYgqsTG4v5yWUymHQqmHSX3mTNH6JbuXy4tf38gQGFiIiokzTfmt7lkfD59yX4284iVDcr5hcf1ljMb+JVjcX8ZDIZQrWNS4Z/apM1f9iWe+7KnS70m5GR3KljacKAQkRE1AlsdheqLmxN31TM773tJ1Fa7TuPI0ynwgOWJNw8LA6qC5uqhVwIJqpWbLLmD9X1rit3akM/f2BAISIi8qN6Z+PKnKat6fedaizm13ynVq1KjrtGJeCuMX2hVzd+HOvVSoQbVNAo279kuD0i9a2bcNvafv7AgEJEROQHDnfjypwGZ+PKnNyyGizfWoh9p1oW87t5WDxmWpIQYVADALQqBSIM7dtkzR9OVzX4tZ8/MKAQERF1gNsjoaLeiVp748qc0uoGvLftJDb+WN6i76SrojFnXDISLhTzUysb9zJpuoPSXWqcnit3akM/f2BAISIiaofmW9NX17vwt12n8Pn3JS22hB/e14T5E1MxKK5x91SVQo5wgxoh/thkzQ/6humQV17Xqn5dJTCuDBERUQ/RfGt6u8uDf+w/jY93F6Ou2R2GlCgD5k1IQUZKYzE/pVwOk14Fo9b/e5l0RNbP+mHziSuv5Mn6Wb8uGE0jBhQiIqJWunhreo8ksP6wFe/vOInzdb7F/GJCNZg9LhlTBjUW85PLZAjTN+5lEkjBpMmoxAiE61Wo/IlVOuF6FUYlRnTZmBhQiIiIruDirem9xfy2FuJUhW8xv1CtEvdek4jbR/aBWimH7KJN1jp7L5OOkMtlePO+qzHn/T2wu6QW7VqVHG/ed3WnbRR3KQwoREREl+HySKisc6L2wtb0h89U4+0tBThSYvPpp1LIMP3qvrj3mgSEahvvkoRolAjXq6Dsor1MOmpsvyi8N2sM3th4HAdP2+D2CCgVMozoa8SjkwdgbL+oLh0PAwoREVEzHkmgqt4J24Wt6U+dr8OKrYXYnn/ep58MwNTBZjw4NgkxF4r5hWiUCNOroVb2jGBysbH9opCZGokjJTZU1DsRoVdjcLyxS++cNGFAISIiukAIAVuDG1UNjRNgz9Y48H7OSXx12NqimF9magTmTUhFSpQBAKBTKxCu7769TPxFLpdhaF9Tdw+DAYWIiAgAah1uVF6YAFvrcOPj3UX4x/4zcDQr5jfQHIqHJ6ZieEIYAECjUiBCr4ZO3bODSaBhQCEioqBmd3lwvs4Jh8sDp1vCv74vwYc7T8F2YeO1Jn3DdZg7PgUT+0dBJpMF3F4mvQ2vKhERBSWXR0JFnRN1DjckIbDxWDne216IMpvDp1+4XoWZlmRMG2qGUiGHUi5HmEEFo7br6tIEIwYUIiIKKhdPgJUkCXtPVeKdLYXIO+tbzE+nUuDuMX1x56gE6NQKKOQyhOnUMOoCa5O13ooBhYiIgkLTBNjKeickIXCirAbLtxRgf1GVTz+FXIabh8XhgczGYn7yi/Yy6Y7VLMGKAYWIiHq9iyfAllQ14N1thfju+NkW/a69Khpzx6egT7gOMpkMRm3jkuFA3mStt2JAISKiXuviCbBV9U78dWcR/v19CdzN1gyPSAjD/IkpGGhuLOYXolUiQq/uMZus9UYMKERE1Os43Y0TYOudbjS4PPhs72n8fW8x6psV80uNNmD+hFSMSQ6HTCaDQaNEeA/dZK23YUAhIqJewyMJVNQ5UWN3wSMJfHmoFO/nnELFJYr5zRmfgskDY6CQy3rNJmu9CQMKERH1eJIkUN3gQnWDCx5Jwta8c1ixtRCnKxt8+hm1SszISMQtIxqL+XGTtcDFgEJERD2aze5CVZ0LbknCD6ersHxLAY6W1vj0USvlmH51H9w7JhEhWiU3WesB+G+GiIh6pHqnGxV1TjjdEgrPNRbzyynwLeYnlzUV80tGdKiGm6z1IAwoRETUozjcHlTUOdHg9OBsjQOrdpzE10daFvMbmxaJueNTkBJl4CZrPRADChER9Qhuj4SKeidq7W7U2t1YvbsI/zxwBs5mxfzS40Ixf2IqhvUN4yZrPRgDChERBTRJEqi6MAHW4fJg7cEz+HBXEWqaFfNLCNfhoQmpGN8vEnK5nJus9XAMKEREFJCEELDZ3aiqb5xnsvFYGd7bfhLlNb7F/CIMasyyJOGmoXFQyGUI1aoQrldxk7UejgGFiIgCTp2jaQKsB7tPVuCdrYUoOFvn00evVuDu0Qm4Y3Rf6FQKhGga75hwk7XegQGFiIgCht3VOAHW7vLgR6sNy7cU4mBxlU8fpVyGXw6Px/2ZiQjTq6FXKxFuUEGj5F4mvQkDChERdTunW0JlvRN1DjfOVDYW89t8omUxv+sGRGPO+BT0CdNBq1IgwsDdX3srBhQiIuo2TVvT1zrcqKhz4IOcU1j3Qyk8zdYMX50YhvkTU3FVbCjUSjkiDI13Tqj34r9dIiLqckI0bk1fVe9CncONT/cV4+97TqPB5VvMr190COZNTMHopHColQru/hpE2jST6LnnnoNMJvP5GjhwoLfdbrcjKysLkZGRCAkJwfTp01FWVubzGkVFRZg2bRr0ej1iYmLw5JNPwu12N38rIiLqpWrsLhRXNKDcZseaA2dw/7u7sGrHKZ9wYjZqsfjGgVj2wNWwpEYh2qhF33Adw0kQafO/6cGDB+Pbb7/9zwso//MSTzzxBL744gt8+umnMJlMWLhwIW6//XZs374dAODxeDBt2jSYzWbs2LEDpaWlmDlzJlQqFV588UU/nA4REQWqpq3pHS4PtuSew7vbLl3M7/7MJPxyeDx0agV3fw1ibQ4oSqUSZrO5xfHq6mq8++67WL16NX72s58BAFauXIlBgwZh586dyMzMxDfffIOjR4/i22+/RWxsLEaMGIH//d//xVNPPYXnnnsOarW642dEREQBxe7yoLK+cWv674ursHxrAY41K+anuVDM755rEmHUqmDUqRDG3V+DWpsXi+fm5iI+Ph6pqamYMWMGioqKAAD79u2Dy+XClClTvH0HDhyIxMRE5OTkAABycnIwdOhQxMbGevtMnToVNpsNR44c6ei5EBFRAHF5JJTb7CipasCRM9V4es0hPPHJ9z7hRC4Dpg2NwwdzrsG8iWmID9MhIUKPCIOa4STItekOSkZGBlatWoUBAwagtLQUzz//PCZMmIDDhw/DarVCrVYjLCzM52diY2NhtVoBAFar1SecNLU3tV2Ow+GAw/GfnQNtNltbhk1ERF3IIwlU1Tths7tRVt2AlTtO4psjZWhWyw/j+kXiofEpSIo0IESrRLheDRV3f6UL2hRQbrzxRu8/Dxs2DBkZGUhKSsInn3wCnU7n98E1WbJkCZ5//vlOe30iIuq4i1fmVDc4sXpXYzE/l8c3mgyON+LhiakY0sfETdbosjo0HTosLAxXXXUV8vLy8POf/xxOpxNVVVU+d1HKysq8c1bMZjN2797t8xpNq3wuNa+lyeLFi7Fo0SLv9zabDQkJCR0ZOhER+VGN3YXKOhfqHC6sOViC1buKUOvwXaGZGKHHvAkpGJsWCZ1ayU3W6Cd1KKDU1tYiPz8fDzzwAEaNGgWVSoWNGzdi+vTpAIDjx4+jqKgIFosFAGCxWPDHP/4R5eXliImJAQBs2LABRqMR6enpl30fjUYDjUbTkaESEVEnaHB6cL7OgQanBxuOlmHVjpbF/CINajw4Nhk3DDFDp1ZwkzVqlTb9CfnNb36DX/ziF0hKSkJJSQmeffZZKBQK3HvvvTCZTJg7dy4WLVqEiIgIGI1GPProo7BYLMjMzAQAXH/99UhPT8cDDzyAl19+GVarFc888wyysrIYQIiIehCnW0JFnRN1Dhd2FTYW8ys851vMz6BW4J5rEjD96r4I1aoQplchVKvqphFTT9OmgHL69Gnce++9OH/+PKKjozF+/Hjs3LkT0dHRAIBXXnkFcrkc06dPh8PhwNSpU/HWW295f16hUGDdunVYsGABLBYLDAYDZs2ahRdeeMG/Z0VERJ2iaWv6GrsLx0ptWL6lAN+frvbpo5TLcMuIeNyfkYSIEDXC9GoYtdzLhNpGJoRoPrE64NlsNphMJlRXV8NoNHb3cIiIer2LJ8CeOl+Hd7cXYsuJcy36TRkUg9njktEnTA+TTgUT9zKhi7Tl85sPAYmI6Cc1TYAtr7FfKOZXgma1/DA6KRzzJqTgKrMRRq0SYXo1FAwm1AEMKEREdElNE2Cr6p34ZM9pfLKvGHaX5NOnX0wIHp6YilFJ4QjVqhCuV0HJvUzIDxhQiIjIh8PtQUWdE7YGF9b9UIq/5pxCVYPLp0+cSYs541Jw3cDoC8FEDbWSwYT8hwGFiIgANG5NX1nvRE2DC9knzmLFtkKUVNl9+ph0KjyQmYibh8XDpG8MJtzLhDoDAwoRUZC7eGv6/acqsHxrIY5bfYv5aZVy3DG6L+4enYBwgxqRBg10agYT6jwMKEREQUoIAVuDG1UNTpwoq8E7Wwuxu7DCp09TMb+ZliSYTTqEG9QI0fCjgzof/5QREQWhGnvjkuHTlfVYuf0kNhxtWcxvfL8oPDQ+BanRITDpVdzLhLoUAwoRURBpWplzrsaBD3cVYe3BlsX8hvYxYv7EVAztE4YwfeNeJgwm1NUYUIiIgoDD7UFlnQuVdQ7888AZrN5dhDqHx6dPUmRTMb8ohOnVMOlU3MuEug0DChFRL+b2SKisd6Gq3olvjlixcsdJnKt1+vSJClFj9thkTB1ihkmn5l4mFBAYUIiIeiFJEqhqaAwmO/LP4Z2thTh1vt6nj0GjwH3XJOL2kX0QGaJBuEENFYMJBQgGFCKiXsZmd6GyzokfTldh+ZZCHDrjW8xPpZDh1hF9cF9GIuJMOoQbVNAouWSYAgsDChFRL1HncKOizon8s7V4d1shtub6FvOTAZiSHovZ45KRFGlAhF7NvUwoYDGgEBH1cHZX49b0Zyrr8UHOKXxxqLRFMb8xyeGYPyEVA+OMiDCoYeBeJhTg+CeUiKiHcnkkVNY5UWaz4+97i/HZ3tOwu32L+V0VG4L5E1NxTXIkwg0qhGpV3TRaorZhQCEi6mGatqY/X+fE5wfP4K87i1B9iWJ+D41Pwc8GxSBCr4FRx03WqGdhQCEi6iGatqavqHfg26PleG97IUqrfYv5helUuD8zCbeMiEdUiAYmnQpy7mVCPRADChFRD1DrcKOyzoldBeexfGsBTpTV+rRrVXLcNSoBd41JgNmkRbhezU3WqEdjQCEiCmANTg8q6p04cqYay7cUYO+pSp92uQy4eVg8ZlqSkBipR7iee5lQ78CAQkQUgJxuCRV1ThScrcV72wux8Vh5i2J+E6+KwtxxKRhgNnIvE+p1GFCIiAJI09b0pyvr8eGuU/jXwZIWxfyG9TXh4YmpGJkYjgiDGloVgwn1PgwoREQBQJIEqhtcKLPZ8dm+0/hodxHqnL7F/FKiDJg3IQUT+kchMkQDvZq/wqn34p9uIqJuZrO7cK7GgS9+KMWqnJM436yYX3SIBrPHJeOmoXGIDFFzLxMKCgwoRETdpN7pxrkaB7JPnMWKrYU4VeFbzC9Eo8R9GYm44+o+iDXpYNRyLxMKHgwoRERdzOFu3Jp+T2EFlm8pwOESm0+7SiHD7SP7YEZG48oco5Z7mVDwYUAhIuoibo/kXTK8Ymshtuef92mXAbh+cCxmj0tB/5gQhHEvEwpiDChERJ1MkgSqGlzIP1uLVdtPYv3hlsX8MlIiMG9CCoYnhCNcr4KSe5lQkGNAISLqJEII2OxunK6sx+pdRfhs32k4mhXzG2AOxcMTUzGuXxTC9WqolQwmRAADChFRp6hzuGGttuMf+0/jbztPwWZ3+7T3CdNh7vgU3DAkFhEGDfcyIWqGAYWIyI/sLg/O1Trw5aFSvLftJKw232J+4XoVZlqScNvIPogxarmXCdFl8G8GEZEfON0SKuud2Hy8HO9sKUTe2ZbF/O4enYD7MhLRJ1yPEA1//RL9FP4NISLqgKat6fedqsA7Wwqwr6jKp10hl+HmYXGYPTYFKdEG7mVC1EoMKERE7dC0MufHUhtWbCvEph/LW/S59qpoPDQhBUP6mGDSqRhMiNqAAYWIqA2EELA1uHHyfC3ezzmFzw+WwN1szfCIBBMenpiGa1IiuJcJUTsxoBBRwHA6PVi2pQCnKuqQFGHAIxNToVYHzuqWGrsLpVV2fLynCB/vKUZ9s2J+qVEGzJuYgp8NjEGkQcO9TIg6gAGFiALCb//5Az7eUwzPRTcjXtt4AveMScAfbx/WfQMD0OD0oLzWjn8dOIP3d5zC+TrfYn4xoRrMGZeMXwzvg6hQNTTKwAlVRD0VAwoRdbvf/vMHfLi7uMVxj4D3eHeEFKdbwvlaB745WoYVWwtQXNng0x6qVWJGRiLuGpOAeJOOe5kQ+VGH7j++9NJLkMlkePzxx73H7HY7srKyEBkZiZCQEEyfPh1lZWU+P1dUVIRp06ZBr9cjJiYGTz75JNxuN4go+DidHny0p2U4udhHe4rhbPY4pTN5JIGzNQ6sP1SKue/vxbOfH/EJJ2qlHPeMScDf52fi0Z/1R2pUCMMJkZ+1+w7Knj178Pbbb2PYMN//qnniiSfwxRdf4NNPP4XJZMLChQtx++23Y/v27QAAj8eDadOmwWw2Y8eOHSgtLcXMmTOhUqnw4osvduxsiKjHWZqd36IuTXOSaOz32M+v6tSxCCFQVe/C98VVWL61ADuaFfOTy4Dr082YOz4FA+JCEarhkmGiztKugFJbW4sZM2bgnXfewR/+8Afv8erqarz77rtYvXo1fvaznwEAVq5ciUGDBmHnzp3IzMzEN998g6NHj+Lbb79FbGwsRowYgf/93//FU089heeeew5qtdo/Z0ZEPcL3Z6r82q+9bHYXTlhr8N62Qnx1xNoiNFlSIzFvQgpGJobDpFNBzpU5RJ2qXY94srKyMG3aNEyZMsXn+L59++ByuXyODxw4EImJicjJyQEA5OTkYOjQoYiNjfX2mTp1Kmw2G44cOdKe4RBRDxama91/lLS2X1vVO904VlqNJV8ew30rduHLw77hJD0uFK/ePQJv3DcSkwbEINygZjgh6gJtvoPy8ccfY//+/dizZ0+LNqvVCrVajbCwMJ/jsbGxsFqt3j4Xh5Om9qa2S3E4HHA4HN7vbTZbW4dNRAHq/oxE/PPAmVb18yeH24PS6gb8fXcxPtxV1KKYX99wHR6akIIbB5sREaKBikuGibpUmwJKcXExHnvsMWzYsAFarbazxtTCkiVL8Pzzz3fZ+xFR1xnWNwxKuazFZmcXU8plGNY3zC/v5/ZIOFvrwL8OluC9bYUor3H4tIfrVZg1NhnTr+6D6FAtJ78SdZM2/SfBvn37UF5ejquvvhpKpRJKpRLZ2dl4/fXXoVQqERsbC6fTiaqqKp+fKysrg9lsBgCYzeYWq3qavm/q09zixYtRXV3t/Sou/ukZ/0TUcxwpteFK80xlssZ+HSFJAudrHfjH/tO4f8UuvLT+R59wolMpMHtsMv7+cCbmT0xFQoSB4YSoG7XpDsrkyZNx6NAhn2OzZ8/GwIED8dRTTyEhIQEqlQobN27E9OnTAQDHjx9HUVERLBYLAMBiseCPf/wjysvLERMTAwDYsGEDjEYj0tPTL/m+Go0GGo2mzSdHRIHvYFEVPJKAUt64WufiGylyWeOXRxI4WFSF4QlhbX79pq3pdxaew9LNBThYXOXTrpTL8Ivh8Zg9Nhkp0QaEalUdOyEi8os2BZTQ0FAMGTLE55jBYEBkZKT3+Ny5c7Fo0SJERETAaDTi0UcfhcViQWZmJgDg+uuvR3p6Oh544AG8/PLLsFqteOaZZ5CVlcUQQhSEhPfuiQzApR7zNB4X7ZiXWutw4/DpaizLzsfmE2dbtF83IBrzJqRicB8TqwwTBRi/7yT7yiuvQC6XY/r06XA4HJg6dSreeustb7tCocC6deuwYMECWCwWGAwGzJo1Cy+88IK/h0JEPcDIhDAoZDK4LjEHpfGOioBKLsPINtw9aXB6kH+2Fiu2FuDfP5TC0+y1RyaG4eGJqchIieSSYaIAJRNCXGGLpMBjs9lgMplQXV0No9HY3cMhog5wuyUMef5r2F3SZftoVXIcfnYqlMqfnjbncHtwprIBH+ScxN/3nEaDy3f32bRoA+ZPTMPkgdEIN2hYZZioi7Xl85u1eIioWx0ptbW4w9GcRxI4Umq77BwUt0dCeY0dn+w9jQ9yTqGiWTG/WKMGc8al4JaR8Yg0cMkwUU/AgEJE3epgUdVPLjEGAPdlJslKkkBFnQPrDlmxYmsBTjcr5mfUKjEjMwn3jklErEnDKsNEPQgDChF1KyEErvSgWYjGfhf/jK3BjewT5VianY9jpTU+/dVKOe64ug9mjk1GciSXCxP1RAwoRNStdJrWhYemfrUON/adrMDS7HzsLKjw6SOXATcMMeOh8akYYA6FQcNfcUQ9Ff/2ElG3OlFWc+VOAI6WVGN/USWWZxfgm6Mti/mNS4vEwxPTMCIpDEbuZULU4zGgEFG3OnKmulX9vjxkxUd7TsPp9l3tMzjeiIcnpmLiVdEw6VTcy4Sol2BAIaJuVef0XLkTgLO1vitzEiP0mDchBTcOMXPJMFEvxIBCRN0qRN22CayRBjVmjU3GnaP7IjpEAyWXDBP1SgwoRNStYoytK3GhkMvw4NgkzMxMRlyYDuorbNpGRD0bAwoRdauokNYFlF8OjcWTUwdyyTBRkOB/ghBRt7G7PLDa7K3qGxmqZTghCiK8g0JEXc7lkZBXVoOl2QX48lBZdw+HiAIQAwoRdRmPJHC6sh7vbSvE3/cW/2SBwOausBs+EfUyDChE1OkkSeBcrQMf7irCBzknUVnvavNrnK9puHInIuo1GFCIqFNVNzix9kAJlm8pwJmqlsX8tCo5ymucl/np/yg8z4BCFEwYUIioU9Q53Nh4rAxvbc7Hj1bf7ew1SjnuGNUXc8en4DefHmxVQFFxWTFRUGFAISK/srs82FNYgb98l4ddhS2L+d00NA4PT0zDVeYQaJQKjO8fjf1FV97ufly/qM4aMhEFIAYUIvILl0fCsRIb3tych2+OlKH5nNZx/SKRdV0/XJ0Y7rNceNJV0Xh9Y94VX3/SVdF+HjERBTIGFCLqEI8kcPJ8HZZnF+CfB07D5fGNJkPijfjVdf1w7YBo6NUtf+XUOjzQKuSwey6/okerlKPW0bqaPUTUOzCgEFG7CCFQZrNj5faT+HBXEWodbp/2pAg9Hp6UhpuHmWHUqS/7OhF6NSJC1Kh1uGCztwwhRq0CIRoVIvSXfw0i6n0YUIiozarqnfh4dzHe3VaIs7UOn7bIEDXmjEvB3WP6ItKggUz201WGB8cbkRYTgmOlNRgUq8X5OjecHglqhRyRBiXO1rmRFhOCwfHGzjwlIgowDChE1Gp1Dhe+OFSKpZsLUHiuzqfNoFbgvoxEzBqbjDiTDgr5TweTJnK5DAsmpeHpNYdwts4FnUoBrVoOIYCzdS6EaJRYMCkN8la+HhH1DgwoRHRFDrcHW0+cxeub8vDDad8VNyqFDLeMiMf8iWlIiTJApWj7cuCx/aIwIyMRf/kuDxW1TggAMgChOiVmZCRiLFfwEAUdBhQiuiyXR8LB4iq8sTEXW3LP+bTJAEweFINfXdsPg/sYoVG2v5DfjrxzWLGtEHUONyBrfG0AqHN4sGJbIQbHmxhSiIIMAwoRteCRBPLKa/Dmd/lY90NJizo4Y5LD8atr05CZGgWdumMVhiVJYMn6YyivcUA0ex9JCJTXOLBk/TH8K2s8H/MQBREGFCLyEkKgpMqOt7fk45M9xbC7fZf+XhUbggWT0jAlPRahWpVf3vPQmWr8WFrjDScXRxABQAjgx9IaHDpTjeEJYX55TyIKfAwoRAQAqKxzYtWOk3h/x0lUNfgW84szaTF/YipuHdEHYXrVFVfmtMX+okq4LtyikXn/p5FMNIYUlySwv6iSAYUoiDCgEAW5Orsbn+0vxttbClBSZfdpM+lUmGlJwv2ZSYgO0XTKIxZr9UXv2fzlZUDTlrQ+/Yio12NAIQpSDrcH3xyx4o1NeThRVuvTplXKcefovpgzPhUJ4Too27Eyp7XijFq/9iOi3oEBhSjIuD0Sdhaex2vf5mLPyUqfNrkMmDYsDo9MTEP/2FCou6CC8MikcKjkMrgkASFazkEBAJVchpFJ4Z0+FiIKHAwoREFCkgSOltrw2sZcfHu0ZTG/if2jsODaNIxsVsyvsw3tY8LAuFAcPmNrnBTbrF0GYGBcKIb2MXXZmIio+zGgEPVyQggUVdTjze/ysObAmRbF/Ib1NeFX16ZhQv9oGDRd/ytBLpfhl8PjcaTE1mKZMQDIZMAvh8dziTFRkGFAIerFztU68M6WAvxt1ynUNasGnBypxyOT0nDjUDNMP1HMr7NJksCW3HMI1SrhcktweiRIovFxk1ohh0opx5bcc5g7PpUhhSiIMKAQ9UK1dhf+tqsIK7YW4Fyt06ctOkSDOeOScdeYBEQY1H5dMtweR0psyC+vRUyoFhqVHHanBLckQSmXQ6uWw+6SkF9eiyMlNgzty8c8RMGCAYWoF7G73Pj8+1K8+V0eTp2v92kL0ShxX0YiZlqS2lTMr7NV1Dvh8gioFXLIILuwM+1/5sBoFHJUSwIV9c7LvwgR9ToMKES9gMsjIftEOV77NheHzth82lQKGW4b2QfzJqQiuZ3F/DpThF4NlUIGp0eCVt5ycq7DI0EllyFC332PoYio6zGgEPVgHkngQFElXv02F9vyWhbzu35wLB6ZlIb0+I4V8+tMg+ONSIsJwbHSGpiNcp9HTkIIVNW7MCguFIPjjd04SiLqam36T6mlS5di2LBhMBqNMBqNsFgsWL9+vbfdbrcjKysLkZGRCAkJwfTp01FWVubzGkVFRZg2bRr0ej1iYmLw5JNPwu12++dsiIKEEAK5ZTV4/OMDuOvtnBbhJCMlAitnj8Grd4/EyMTwgA0nQOMqngWT0hCiUcBqc6DB5YEkCTS4PLDaHAjRKLBgUhonyBIFmTbdQenbty9eeukl9O/fH0IIvP/++7jllltw4MABDB48GE888QS++OILfPrppzCZTFi4cCFuv/12bN++HQDg8Xgwbdo0mM1m7NixA6WlpZg5cyZUKhVefPHFTjlBot6mtLoBSzfn4+97iuFoVsxvgDkUWdem4bqBMX4r5tcVxvaLwou3DcXS7Hzkl9eiWhJQyWUYFBeKBZPSMLZfVHcPkYi6mEyIS+080HoRERH4v//7P9xxxx2Ijo7G6tWrcccddwAAfvzxRwwaNAg5OTnIzMzE+vXrcfPNN6OkpASxsbEAgGXLluGpp57C2bNnoVa37hmzzWaDyWRCdXU1jEbe9qXgYGtw4d1thVi5vRA2u+9dxz5hOsybmIJbh/eByc/F/LqSJAkcKbGhot6JCL0ag+ONvHNC1Iu05fO73XNQPB4PPv30U9TV1cFisWDfvn1wuVyYMmWKt8/AgQORmJjoDSg5OTkYOnSoN5wAwNSpU7FgwQIcOXIEI0eOvOR7ORwOOBwOnxMkChZ2pwd/31uMZdn5KG1WMC9cr8JMSzLuy0jstGJ+XUkul3EpMREBaEdAOXToECwWC+x2O0JCQrBmzRqkp6fj4MGDUKvVCAsL8+kfGxsLq9UKALBarT7hpKm9qe1ylixZgueff76tQyXq0dweCV8dtuLVjbnIK29WzE8lx12jEzBnXAr6dnIxPyKi7tDmgDJgwAAcPHgQ1dXV+OyzzzBr1ixkZ2d3xti8Fi9ejEWLFnm/t9lsSEhI6NT3JOoukiSws+A8/rzhBPae8i3mp5DLcPPQOMyflIr+MV1TzK8r8REPETVpc0BRq9Xo168fAGDUqFHYs2cPXnvtNdx9991wOp2oqqryuYtSVlYGs9kMADCbzdi9e7fP6zWt8mnqcykajQYajaatQyXqUYRoLOb3yoYT+PZYeYv2SVdFY8G1aRiRENalxfy6yo68c95Jsi6PgEohQ1pMCCfJEgWpDv/nlyRJcDgcGDVqFFQqFTZu3OhtO378OIqKimCxWAAAFosFhw4dQnn5f375btiwAUajEenp6R0dClGPVVxRj//+7Afc+ub2FuFkeF8T3n5gFN6ccTUyUyN7bTh5es0hHCu1waBRIiZUA4NGiWOlNXh6zSHsaLaMmoh6vzbdQVm8eDFuvPFGJCYmoqamBqtXr8bmzZvx9ddfw2QyYe7cuVi0aBEiIiJgNBrx6KOPwmKxIDMzEwBw/fXXIz09HQ888ABefvllWK1WPPPMM8jKyuIdEgpKlXVOLM3Ox4c7T6HO6VvMLzXKgIcnpWLqYDNMup67MudKJElgaXY+ah1umI1a73lq5QqYjXJYbQ4szc5HZmokH/cQBZE2BZTy8nLMnDkTpaWlMJlMGDZsGL7++mv8/Oc/BwC88sorkMvlmD59OhwOB6ZOnYq33nrL+/MKhQLr1q3DggULYLFYYDAYMGvWLLzwwgv+PSuiAFfvcOODnafwzpYCnK/zrTETE6rB3PEpuP3qvog0qHv9h3JTscBwfcvChTKZDGF6FYsFEgWhDu+D0h24Dwr1VC63B2sOluAvm/JQVOFbzC9Uq8SMjEQ8YElCbKg2aFbmZJ84i9988j1iQi+9TFqSBMprHfjTncMx6arobhghEflLl+yDQkSt55EENh8vx583nMCRkpbF/KZf3RcPjU9BUgAW8+tsLBZIRJfCgELUiYRoLOb3/204ge15533amor5Lbg2DQPNxl45+bU1WCyQiC6FAYWok+SW1+C1b3Px5aFSSM0epGamRuBX16ZhdHIE9Org/mvYVCzw6TWHYLU5EKZXQaOQw+GRUFXvYrFAoiAV3L8ZiTqBtboBf/kuD5/uPd2imN9Acyh+1QOL+XU2FgskouYYUIj8xNbgwoqtBVi142SLYn59w3V4aELPL+bXmcb2i0JmaiR3kiUiAAwoRB1md3rw8d4iLN2cjzKbw6ctXK/CrLHJuGdMAqJDtVDww/YnsVggETVhQCFqJ5fbgy8PW/H6xlzkn63zadOpFLh7TF88OC4ZfcP0QbNkmIjIXxhQiNrIIwnk5J/DnzecwP6iKp82hVyGXwyLw/yJaUiLMUCjDM6VOUREHcWAQtRKQggcPlONV77NxaYfWxbzu/aqaDxybSqG9w2HTs1gQkTUEQwoRK1QeK4Wf9mUh38dLIG72ZrhkYlh+NWkNFj6RSFEw79SRET+wN+mRD+h3GbH21sKsHpXERpcvsX80qINeGRSGn4+KJYrc4iI/IwBhegSauwufJBzCiu2FqCy3uXT1lTMb/qovojQ9/5ifkRE3YEBhegiDpcHaw6cwV++y8PpygaftlCtEvdnJOK+zETEGXVcmUNE1IkYUIgAuD0SNv1Yjle/PYGjpTU+bWqlHNOv7oM541KQGKnnyhwioi7AgEJBzSMJ7D9ViVe+PYEd+b7F/OQy4IbBZsybmIqBZiNX5hARdSEGFApKQgicKKvBaxtz8dVha4tifmPTIvHIpDRcnRTOlTlERN2Av3kp6JyurMdb3+Xjs/2n4WxWzC89LhSPXJuGSVfFwKhVcmUOEVE3YUChoFFR68C72wrxwc5TqGlWzC8hXId5E1MxbWgcwrkyh4io2zGgUK9X53Dj491FeHtLAcprfIv5RRjUmGVJwl1jEhAdouHKHCKiAMGAQr2Ww+XBF4dK8Zfv8lDQrJifXq3A3WMSMDMzGX3CdVArGUyIiAIJAwr1Om6PhG155/Dqt7k4WFzl06aUy/DL4fGYMyEFaVEhXJlDRBSgGFCo1/BIAodOV+HVjbnYfPxsi/afDYzBwxNTMbiPiStziIgCHH9LU48nhEDhuTq8sSkPn39fAk+zNcNXJ4bhkUlpyEiN5MocIqIeggGFejRrdQOWbynAR7uLWxTz6xcdgvmTUjFlUCzCdCquzCEi6kEYUKhHqqp34oOcU1i5vbBFMT+zUYs545Nxy4g+iDSouTKHiKgHYkChHqXB6cY/9p/Bsuz8FsX8jFol7s9Mwr3XJCLWqOXKHCKiHowBhXoEh9uDjcfK8frGXPxo9S3mp7lQzG/W2BQkReqhVXFlDhFRT8eAQgHN5ZGw52QFXt+Yi50FFT5tchlw45A4PDQ+BVeZQ2Hgyhwiol6Dv9EpIHkkgWOlNvxlUx6+PmJFs1p+GJcWifmTUjEiIZwrc4iIeiEGFAookiRQXFGPpdn5+Mf+03B5fKPJ4HgjHpmUhvH9omDiyhwiol6LAYUCghAC5TUOrNxeiL/tLEKtw7eYX2KEHvMmpuKGwbGIMGigYDAhIurVGFCo21U3uPDx7iK8u62wRTG/yBA1HrQk4/ZRfRAdwpU5RETBggGFuk2dw4UvDpVi6eYCFJ7zLeZnUCtwzzUJuC8jCX3CdFyZQ0QUZBhQqMvZXR5syzuHNzbm4vvT1T5tSrkMt4yIx4Njk5EaHcKVOUREQYq//anLON0Svi+uwhvf5WLLiXMt2qcMisFD41OQ3seEUA1X5hARBTMGFOp0bo+E3PJaLN2cj3U/lKBZLT+MTgrHwxNTMTo5gitziIgIAAMKdSJJEjhT1YB3thbgk73FsLskn/Z+MSF4eGIqrh0QgwiDmitziIjIq01LIpYsWYIxY8YgNDQUMTExuPXWW3H8+HGfPna7HVlZWYiMjERISAimT5+OsrIynz5FRUWYNm0a9Ho9YmJi8OSTT8Lt9l1WSj2XEALnahx4fWMufvmXbfgg55RPOIkzafHbmwbhr3OvwS0j+iA6lMuGiYjIV5vuoGRnZyMrKwtjxoyB2+3G008/jeuvvx5Hjx6FwWAAADzxxBP44osv8Omnn8JkMmHhwoW4/fbbsX37dgCAx+PBtGnTYDabsWPHDpSWlmLmzJlQqVR48cUX/X+G1KVsDU78c38Jlm/NR0mV3afNpFPhgcxETB+VgDiTlitziIjosmRCiOa7iLfa2bNnERMTg+zsbEycOBHV1dWIjo7G6tWrcccddwAAfvzxRwwaNAg5OTnIzMzE+vXrcfPNN6OkpASxsbEAgGXLluGpp57C2bNnoVarr/i+NpsNJpMJ1dXVMBqN7R0++VG9041vj5bhze/ycbzMt5ifVinHHaP74v6MJCRE6Lkyh4goSLXl87tDnxTV1Y1LRCMiIgAA+/btg8vlwpQpU7x9Bg4ciMTERG9AycnJwdChQ73hBACmTp2KBQsW4MiRIxg5cmSL93E4HHA4/rOBl81m68iwyY/sLg92F1bgL5tysftkpU+bXAZMGxqH2eOSkRYTypo5RETUau0OKJIk4fHHH8e4ceMwZMgQAIDVaoVarUZYWJhP39jYWFitVm+fi8NJU3tT26UsWbIEzz//fHuHSp3A6ZZwtLQab32Xjw1Hy1oU85vQPwoPjU/B0L5hCOPKHCIiaqN2B5SsrCwcPnwY27Zt8+d4Lmnx4sVYtGiR93ubzYaEhIROf19qye2RUHiuDsu3FGDtwTMtivkN7WPE/ImpyEyNQrheBaWCW9MTEVHbtSugLFy4EOvWrcOWLVvQt29f73Gz2Qyn04mqqiqfuyhlZWUwm83ePrt37/Z5vaZVPk19mtNoNNBoNO0ZKvmJJAlYbXas3F6I1buLUOfw+LQnReoxb0IKJg+MRUSIGholJ8ASEVH7tSmgCCHw6KOPYs2aNdi8eTNSUlJ82keNGgWVSoWNGzdi+vTpAIDjx4+jqKgIFosFAGCxWPDHP/4R5eXliImJAQBs2LABRqMR6enp/jgn8iMhBCrrXPh4TxHe216Ic7VOn/aoEDVmj03GL0b0QXSIBjo1gwkREXVcmwJKVlYWVq9ejX/9618IDQ31zhkxmUzQ6XQwmUyYO3cuFi1ahIiICBiNRjz66KOwWCzIzMwEAFx//fVIT0/HAw88gJdffhlWqxXPPPMMsrKyeJckwNganPjykBVLs/Nx6ny9T5tBo8B91yTi7tEJMIfpEMKVOURE5EdtWmZ8uRUYK1euxIMPPgigcaO2X//61/joo4/gcDgwdepUvPXWWz6Pb06dOoUFCxZg8+bNMBgMmDVrFl566SUola37kOMy485V53Bja+5ZvLU5Hz80K+anUshw64g+uD8zCcmRBhh1XJlDRESt05bP7w7tg9JdGFA6h93lwYGiSry1OR9bc32L+ckATEmPxZxxKbgqNgRhem5NT0REbdNl+6BQ7+Bwe5BbVou3s/PxxaHSFsX8rkkOx7wJqRiWEIYIgxoqrswhIqJOxoASxFweCcUV9XhveyE+23sadrdvMb8BsaGYNzEFlrQoRBrU3JqeiIi6DANKEPJIAuU1dny4qwh/zTmF6gaXT3t8mBYPjU/B5EGxiAzRcAIsERF1OX7yBBFJEqisd2LtgTNYsa0QpdW+xfzCdCrMtCThlyPiER2i5QRYIiLqNgwoQUAIAZvdjU3HyrA0Ox8nymp92rUqOe4alYC7r0mA2ajlBFgiIup2DCi9XK3Djd0F57E0Ox97LlXMb1gcZlmSkRihRzgnwBIRUYBgQOmlGpweHCmpxrLsfGw8Vt6imN/Eq6Iwd1wKrjKHIsLAremJiCiwMKD0Mg63ByfP1uOdbfn418GSFsX8hvU14eGJqRh+YcmwXs0/AkREFHj46dRLuDwSSqsa8EHOKXy0uwh1Tt9ifilRBsybkIJxaVGICFEjVKvqppESERFdGQNKD+eRBM7W2vHZ3tNYueMkzjcr5hcdosHsccm4YYgZkQYNV+YQEVGPwIDSQ0mSQFW9E18etuKdLQU4VeFbzC9Eo8R9GYm4fWQfRIdquDKHiIh6FAaUHkYIAVtDYzG/Zdn5OFxi82lXKWS4fWQf3JeRiDiTjitziIioR2JA6UFq7C4cKKrC29n52J5/3qdNBuD6wbGYPTYZiZEGRHBreiIi6sEYUC4iSQJHSmyoqHciQq/G4Hgj5AHwWKTO4cYJaw1WbCvE+sMti/llpERg3oQUDDAbERnClTlERNTz8ZPsgh1557A0Ox/55bVweQRUChnSYkKwYFIaxvaL6pYx2V0enDpfjw9yTuKzfafhaF7MzxyKhyemYnRSBMIMKhi5MoeIiHoJBhQ0hpOn1xxCjd0FvVoJlUIOSQgcLbHh6TWH8OJtQ7s0pDjcHpRVO/DxniL8becp2Oxun/Y+YTrMHZ+C6wZEI9yghkmn4socIiLqVYI+oEiSwNLsfFTUOeGRBGx2O4QAZDJArZDD5Wlsz0yN7PTHPS6PhPO1Dnz+fQne23YSVptvMb9wfWMxv5uHxSPcoEY4V+YQEVEvFfQB5UiJDUdLqtHg8kASgFIug0wGCAB2twS5TMLRkmocKbFhaF9Tp4zBc6HK8KYfy7E8uwB5Z1sW87t7dALuGp2AqFANwvVqqJVcmUNERL1X0AeUc3UO2OxuSEJAKZfBIwkINK6KUcoBt9RYCfhcncPv7y1JAlUNLuw9WYG3s/Oxr6jKp10hl+HmYXF4IDMJcWE6RHJlDhERBYmgDyhVdS5IkoAkAEez5TFuqTGoQCZQVefy23s27WVytLQa72wtxKYfy1v0ufaqaMwZn4yUqBCEG9QI0QT9vyoiIgoiQf+pF65XQQAtqv02aWoL1/tnhUyN3YXCs3VYlXMSnx8sgbtZKBqRYMK8CakYHG9CuF7NremJiCgoBX1AMWqVLfYVaU4Sjf06os7hxpnKBny8pwgf7ylGfbNifqlRBsybmIKMlEiYdCpuTU9EREEt6ANK9vGWj1cu1+/q5Ig2v77d5UFZjR3/OnAG7+84hfN1vsX8YkI1mDMuGZMHxcKkU3FreiIiIjCgYHvh+St3utDviTa8rsPtQUWtE98cLcOKrQUormzwaQ/VKjEjIxG3jugDk16FcD0nwBIRETUJ+oDicElX7tSGfi6PhMo6J3Lyz+PtLQU4WupbzE+tlDcW87smEREhakQYuDU9ERFRc0H/yRima90luFK/pr1MDp2uxvItBcgp8L0zI5cBUweb8eDY5AtVhlUI5db0RERElxT0AaWitnX7m1yuX9NeJnnlNVi5/SS+PmJtMenWkhqJhyakIC06BGF6FbemJyIiuoKgDyi1Ds+VO12iX9NeJsWVdfhwVxH+sf8MnM2K+Q2KC8X8iakYkRAOo1bJlTlEREStFPQBRdnKFTMX96uxu1Bms+Mf+07jw11FLYr59Q3X4aHxKZjQPwqhWq7MISIiaqugDyg6detWzujUCtQ73Thb48D6w1a8t60Q5TW+j33C9SrMGpuMm4aYEarjyhwiIqL2CvqAIsQVdmm7wOFy4/ODJXhnawHyz9b5tOlUCtwzJgF3jOoLk17FlTlEREQdFPSfopX1zit3AnCywo7/+echn2NKuQy/GB6P+zMTER2i5cocIiIiPwn6gNJ8y/nLcXl877RcNyAac8anICFcz5U5REREfhb0AQWibaFiZGIY5k9IxcA4I1fmEBERdZKgDyhGjQJVzVbhXIpSDvzxtqEYnRTOlTlERESdLOgDSlK0AUXVV96sbVgfIyb0j0aEgStziIiIOlvQBxS9onWreMK1csSH6Tp5NERERAQAbX5GsWXLFvziF79AfHw8ZDIZ1q5d69MuhMDvf/97xMXFQafTYcqUKcjNzfXpU1FRgRkzZsBoNCIsLAxz585FbW1th06kvbYWVLeqX85J25U7ERERkV+0OaDU1dVh+PDhePPNNy/Z/vLLL+P111/HsmXLsGvXLhgMBkydOhV2u93bZ8aMGThy5Ag2bNiAdevWYcuWLZg/f377z6IDHO5WVjNuZT8iIiLquDY/4rnxxhtx4403XrJNCIFXX30VzzzzDG655RYAwAcffIDY2FisXbsW99xzD44dO4avvvoKe/bswejRowEAb7zxBm666Sb86U9/Qnx8fAdOp+3UShkaXFd+zKNWcqUOERFRV/HrMpTCwkJYrVZMmTLFe8xkMiEjIwM5OTkAgJycHISFhXnDCQBMmTIFcrkcu3btuuTrOhwO2Gw2ny9/ua5/lF/7ERERUcf5NaBYrVYAQGxsrM/x2NhYb5vVakVMTIxPu1KpREREhLdPc0uWLIHJZPJ+JSQk+G3Mo1NbFzxa24+IiIg6rkds5LF48WJUV1d7v4qLi/322iMTw/3aj4iIiDrOrwHFbDYDAMrKynyOl5WVedvMZjPKy8t92t1uNyoqKrx9mtNoNDAajT5f/iKk1i0zbm0/IiIi6ji/BpSUlBSYzWZs3LjRe8xms2HXrl2wWCwAAIvFgqqqKuzbt8/bZ9OmTZAkCRkZGf4cTqusP3zpx0rt7UdEREQd1+ZVPLW1tcjLy/N+X1hYiIMHDyIiIgKJiYl4/PHH8Yc//AH9+/dHSkoKfve73yE+Ph633norAGDQoEG44YYbMG/ePCxbtgwulwsLFy7EPffc0+UreADgSGnrJty2th8RERF1XJsDyt69e3Hdddd5v1+0aBEAYNasWVi1ahX++7//G3V1dZg/fz6qqqowfvx4fPXVV9Bqtd6f+fDDD7Fw4UJMnjwZcrkc06dPx+uvv+6H02m7UE3rLkFr+xEREVHHyYQQPW5yhc1mg8lkQnV1dYfno7yy4The25h3xX6PTe6HJ34+oEPvRUREFMza8vndI1bxdKahfUx+7UdEREQdF/QBpcbuwZX2iJVd6EdERERdI+gDSrheBaVCdtmQIgOgVMgQrld15bCIiIiCWtDP/IwM0cCoVaG6wQUB+ASVpu+NWhUiQzTdM0AiIqIgFPR3UAbHG5Eeb4RBo4BWKYfsQkKRyQCtUg6DRoH0eCMGx/tvczgiIiL6aUEfUORyGRZMSkO4Xg2dWgGzUYv4MC3MRi10agXC9WosmJQGuZzVjImIiLpK0AcUABjbLwov3jYUg+KM8EgCDS4JHklgUJwRL942FGP7sVAgERFRVwr6OShNxvaLQmZqJI6U2FBR70SEXo3B8UbeOSEiIuoGDCgXkctlGNqX+50QERF1Nz7iISIiooDDOygXkSTBRzxEREQBgAHlgh1557A0Ox/55bVweQRUChnSYkKwYFIaJ8kSERF1MT7iQWM4eXrNIRwrtcGgUSImVAODRoljpTV4es0h7Mg7191DJCIiCipBH1AkSWBpdj5qHW6YjVpoVQrI5TJoVQqYjRrUOjxYmp0PSepxRZ+JiIh6rKAPKEdKbMgvr0W4Xg0AaHB6UGN3ocHZWBwwTK9CfnktjpTYunOYREREQSXo56BU1Dvh8gg43RJKqxvgcEsQonGre41SjkiDBi5JoKLe2d1DJSIiChpBH1Ai9GpIQkJJtROSAJRyGWSyxkKBDS4JJdUNMGqViLhwh4WIiIg6X9A/4hlkDoVHAG6PgELWOM+kabqJQibg9gh4RGM/IiIi6hpBH1COWWugkDXeNXF6AIdbgtMjXfj/xkc9CpkMx6w13T1UIiKioBH0AaWi3gm3R0Auu/SGbHKZDG7OQSEiIupSQT8HJUynQoPLAyEENEoZAJl3kizQ+IinwelBmE7VzSMlIiIKHkEfUICmMALIZDLIIAMufC8u/O9lbq4QERFRJwn6RzxVDa7GzdlkMrg9ApIQEGj8/6ZHP1qVAlUNru4eKhERUdAI+jsoEXo1DGoFQjRKVDe44HB7IKTGuypalQImnQpCCC4zJiIi6kJBH1AGxxuRFhOCY6U1SIrUweEScEsSlHI5NCoZymxODIoLxeB4Y3cPlYiIKGgE/SMeuVyGBZPSEKJRoMzmBGSAQa0EZECZzYkQjQILJqVBLudEFCIioq4S9AEFAMb2i8KLtw3FoLhQ1DvcKK91oN7hxqC4ULx421CM7RfV3UMkIiIKKkH/iKfJ2H5RyEyNxJESGyrqnYjQqzE43sg7J0RERN2AAeUicrkMQ/uaunsYREREQY+PeIiIiCjgMKAQERFRwGFAISIiooDDgEJEREQBhwGFiIiIAg4DChEREQUcBhQiIiIKOAwoREREFHC6NaC8+eabSE5OhlarRUZGBnbv3t2dwyEiIqIA0W0B5e9//zsWLVqEZ599Fvv378fw4cMxdepUlJeXd9eQiIiIKEB0W0D585//jHnz5mH27NlIT0/HsmXLoNfr8d5773XXkIiIiChAdEstHqfTiX379mHx4sXeY3K5HFOmTEFOTk6L/g6HAw6Hw/u9zWbrlHFJkmCxQCIiogDQLQHl3Llz8Hg8iI2N9TkeGxuLH3/8sUX/JUuW4Pnnn+/UMe3IO4el2fnIL6+FyyOgUsiQFhOCBZPSMLZfVKe+NxEREfnqEat4Fi9ejOrqau9XcXGxX19/R945PL3mEI6V2mDQKBETqoFBo8Sx0ho8veYQduSd8+v7ERER0U/rljsoUVFRUCgUKCsr8zleVlYGs9ncor9Go4FGo+mUsUiSwNLsfNQ63DAbtZDJGh/paOUKmI1yWG0OLM3OR2ZqJB/3EBERdZFuuYOiVqsxatQobNy40XtMkiRs3LgRFoulS8dypMSG/PJahOvV3nDSRCaTIUyvQn55LY6UdM68FyIiImqpW+6gAMCiRYswa9YsjB49Gtdccw1effVV1NXVYfbs2V06jop6J1weAbXi0llNo5CjWhKoqHd26biIiIiCWbcFlLvvvhtnz57F73//e1itVowYMQJfffVVi4mznS1Cr4ZKIYPTI0ErV7Rod3gkqOQyROjVXTouIiKiYCYTQojuHkRb2Ww2mEwmVFdXw2g0dui1JElg1srdOFZaA7NR4/OYRwgBq82BQXGheH/2NZyDQkRE1AFt+fzuEat4OpNcLsOCSWkI0ShgtTnQ4PJAkgQaXB5YbQ6EaBRYMCmN4YSIiKgLBX1AAYCx/aLw4m1DMSguFPUON8prHah3uDEoLhQv3jaU+6AQERF1sW6bgxJoxvaLQmZqJHeSJSIiCgAMKBeRy2UY2tfU3cMgIiIKenzEQ0RERAGHAYWIiIgCDgMKERERBRwGFCIiIgo4DChEREQUcBhQiIiIKOAwoBAREVHAYUAhIiKigMOAQkRERAGnR+4k21SA2WazdfNIiIiIqLWaPrebPsd/So8MKDU1NQCAhISEbh4JERERtVVNTQ1Mpp8uLSMTrYkxAUaSJJSUlCA0NBQymX+L+dlsNiQkJKC4uBhGo9Gvrx2seE39j9fU/3hN/Y/X1P96+jUVQqCmpgbx8fGQy396lkmPvIMil8vRt2/fTn0Po9HYI//lBzJeU//jNfU/XlP/4zX1v558Ta9056QJJ8kSERFRwGFAISIiooDDgNKMRqPBs88+C41G091D6TV4Tf2P19T/eE39j9fU/4LpmvbISbJERETUu/EOChEREQUcBhQiIiIKOAwoREREFHCCIqBs2bIFv/jFLxAfHw+ZTIa1a9f6tAsh8Pvf/x5xcXHQ6XSYMmUKcnNzffpUVFRgxowZMBqNCAsLw9y5c1FbW9uFZxFYlixZgjFjxiA0NBQxMTG49dZbcfz4cZ8+drsdWVlZiIyMREhICKZPn46ysjKfPkVFRZg2bRr0ej1iYmLw5JNPwu12d+WpBIylS5di2LBh3v0NLBYL1q9f723n9eyYl156CTKZDI8//rj3GK9p2z333HOQyWQ+XwMHDvS285q2z5kzZ3D//fcjMjISOp0OQ4cOxd69e73tQfk5JYLAl19+KX7729+Kf/7znwKAWLNmjU/7Sy+9JEwmk1i7dq34/vvvxS9/+UuRkpIiGhoavH1uuOEGMXz4cLFz506xdetW0a9fP3Hvvfd28ZkEjqlTp4qVK1eKw4cPi4MHD4qbbrpJJCYmitraWm+fRx55RCQkJIiNGzeKvXv3iszMTDF27Fhvu9vtFkOGDBFTpkwRBw4cEF9++aWIiooSixcv7o5T6naff/65+OKLL8SJEyfE8ePHxdNPPy1UKpU4fPiwEILXsyN2794tkpOTxbBhw8Rjjz3mPc5r2nbPPvusGDx4sCgtLfV+nT171tvOa9p2FRUVIikpSTz44INi165doqCgQHz99dciLy/P2ycYP6eCIqBcrHlAkSRJmM1m8X//93/eY1VVVUKj0YiPPvpICCHE0aNHBQCxZ88eb5/169cLmUwmzpw502VjD2Tl5eUCgMjOzhZCNF5DlUolPv30U2+fY8eOCQAiJydHCNEYHOVyubBard4+S5cuFUajUTgcjq49gQAVHh4uVqxYwevZATU1NaJ///5iw4YNYtKkSd6AwmvaPs8++6wYPnz4Jdt4TdvnqaeeEuPHj79se7B+TgXFI56fUlhYCKvViilTpniPmUwmZGRkICcnBwCQk5ODsLAwjB492ttnypQpkMvl2LVrV5ePORBVV1cDACIiIgAA+/btg8vl8rmuAwcORGJios91HTp0KGJjY719pk6dCpvNhiNHjnTh6AOPx+PBxx9/jLq6OlgsFl7PDsjKysK0adN8rh3AP6MdkZubi/j4eKSmpmLGjBkoKioCwGvaXp9//jlGjx6NO++8EzExMRg5ciTeeecdb3uwfk4FfUCxWq0A4POXpen7pjar1YqYmBifdqVSiYiICG+fYCZJEh5//HGMGzcOQ4YMAdB4zdRqNcLCwnz6Nr+ul7ruTW3B6NChQwgJCYFGo8EjjzyCNWvWID09ndeznT7++GPs378fS5YsadHGa9o+GRkZWLVqFb766issXboUhYWFmDBhAmpqanhN26mgoABLly5F//798fXXX2PBggX4r//6L7z//vsAgvdzqkcWC6TAkpWVhcOHD2Pbtm3dPZQeb8CAATh48CCqq6vx2WefYdasWcjOzu7uYfVIxcXFeOyxx7BhwwZotdruHk6vceONN3r/ediwYcjIyEBSUhI++eQT6HS6bhxZzyVJEkaPHo0XX3wRADBy5EgcPnwYy5Ytw6xZs7p5dN0n6O+gmM1mAGgxy7ysrMzbZjabUV5e7tPudrtRUVHh7ROsFi5ciHXr1uG7777zqTBtNpvhdDpRVVXl07/5db3UdW9qC0ZqtRr9+vXDqFGjsGTJEgwfPhyvvfYar2c77Nu3D+Xl5bj66quhVCqhVCqRnZ2N119/HUqlErGxsbymfhAWFoarrroKeXl5/HPaTnFxcUhPT/c5NmjQIO+js2D9nAr6gJKSkgKz2YyNGzd6j9lsNuzatQsWiwUAYLFYUFVVhX379nn7bNq0CZIkISMjo8vHHAiEEFi4cCHWrFmDTZs2ISUlxad91KhRUKlUPtf1+PHjKCoq8rmuhw4d8vlLtWHDBhiNxhZ/WYOVJElwOBy8nu0wefJkHDp0CAcPHvR+jR49GjNmzPD+M69px9XW1iI/Px9xcXH8c9pO48aNa7FNw4kTJ5CUlAQgiD+nunuWbleoqakRBw4cEAcOHBAAxJ///Gdx4MABcerUKSFE4/KtsLAw8a9//Uv88MMP4pZbbrnk8q2RI0eKXbt2iW3bton+/fv36OVbHbVgwQJhMpnE5s2bfZYb1tfXe/s88sgjIjExUWzatEns3btXWCwWYbFYvO1Nyw2vv/56cfDgQfHVV1+J6OjooF1u+D//8z8iOztbFBYWih9++EH8z//8j5DJZOKbb74RQvB6+sPFq3iE4DVtj1//+tdi8+bNorCwUGzfvl1MmTJFREVFifLyciEEr2l77N69WyiVSvHHP/5R5Obmig8//FDo9Xrxt7/9zdsnGD+ngiKgfPfddwJAi69Zs2YJIRqXcP3ud78TsbGxQqPRiMmTJ4vjx4/7vMb58+fFvffeK0JCQoTRaBSzZ88WNTU13XA2geFS1xOAWLlypbdPQ0OD+NWvfiXCw8OFXq8Xt912mygtLfV5nZMnT4obb7xR6HQ6ERUVJX79618Ll8vVxWcTGObMmSOSkpKEWq0W0dHRYvLkyd5wIgSvpz80Dyi8pm139913i7i4OKFWq0WfPn3E3Xff7bNfB69p+/z73/8WQ4YMERqNRgwcOFAsX77cpz0YP6dYzZiIiIgCTtDPQSEiIqLAw4BCREREAYcBhYiIiAIOAwoREREFHAYUIiIiCjgMKERERBRwGFCIiIgo4DCgEBERUcBhQCGiLrdq1SqEhYV19zCIKIBxJ1ki6nINDQ2oqalBTExMq3/m2muvxYgRI/Dqq6923sCIKGAou3sARBR8dDoddDpddw+DiAIYH/EQUZtde+21WLhwIRYuXAiTyYSoqCj87ne/Q9MN2crKSsycORPh4eHQ6/W48cYbkZub6/355o94nnvuOYwYMQJ//etfkZycDJPJhHvuuQc1NTUAgAcffBDZ2dl47bXXIJPJIJPJcPLkycuOTwiBfv364U9/+pPP8YMHD0ImkyEvL89/F4OIOgUDChG1y/vvvw+lUondu3fjtddew5///GesWLECQGOg2Lt3Lz7//HPk5ORACIGbbroJLpfrsq+Xn5+PtWvXYt26dVi3bh2ys7Px0ksvAQBee+01WCwWzJs3D6WlpSgtLUVCQsJlX0smk2HOnDlYuXKlz/GVK1di4sSJ6Nevnx+uABF1JgYUImqXhIQEvPLKKxgwYABmzJiBRx99FK+88gpyc3Px+eefY8WKFZgwYQKGDx+ODz/8EGfOnMHatWsv+3qSJGHVqlUYMmQIJkyYgAceeAAbN24EAJhMJqjVauj1epjNZpjNZigUip8c34MPPojjx49j9+7dAACXy4XVq1djzpw5frsGRNR5GFCIqF0yMzMhk8m831ssFuTm5uLo0aNQKpXIyMjwtkVGRmLAgAE4duzYZV8vOTkZoaGh3u/j4uJQXl7e7vHFx8dj2rRpeO+99wAA//73v+FwOHDnnXe2+zWJqOswoBBRQFCpVD7fy2QySJLUodd86KGH8PHHH6OhoQErV67E3XffDb1e36HXJKKuwYBCRO2ya9cun+937tyJ/v37Iz09HW6326f9/PnzOH78ONLT09v9fmq1Gh6Pp00/c9NNN8FgMGDp0qX46quv+HiHqAdhQCGidikqKsKiRYtw/PhxfPTRR3jjjTfw2GOPoX///rjlllswb948bNu2Dd9//z3uv/9+9OnTB7fccku73y85ORm7du3CyZMnce7cuVbdXVEoFHjwwQexePFi9O/fHxaLpd3vT0RdiwGFiNpl5syZaGhowDXXXIOsrCw89thjmD9/PoDG1TKjRo3CzTffDIvFAiEEvvzyyxaPcdriN7/5DRQKBdLT0xEdHY2ioqJW/dzcuXPhdDoxe/bsdr83EXU97iRLRG3Wk3Z13bp1KyZPnozi4mLExsZ293CIqJW4kywR9UoOhwNnz57Fc889hzvvvJPhhKiH4SMeIuqRHnnkEYSEhFzy65FHHsFHH32EpKQkVFVV4eWXX+7u4RJRG/ERDxH1SOXl5bDZbJdsMxqNbSpESESBhwGFiIiIAg4f8RAREVHAYUAhIiKigMOAQkRERAGHAYWIiIgCDgMKERERBRwGFCIiIgo4DChEREQUcBhQiIiIKOD8//QNkwu0i+bXAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768776.849883100130.676184(768, 100)
290768766.357897100117.560734(768, 100)
54100117.284869100107.648062(100, 100)
198100115.505927630584.101049(100, 630)
45314361424.77684210055.486437(1436, 100)
..................
164100106.554729365375.785767(100, 365)
165100100.192620365372.574094(100, 365)
199100118.680119630598.837102(100, 630)
13210080.398832365394.731135(100, 365)
50114361422.409024100105.898882(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 776.849883 100 130.676184 (768, 100)\n", + "290 768 766.357897 100 117.560734 (768, 100)\n", + "54 100 117.284869 100 107.648062 (100, 100)\n", + "198 100 115.505927 630 584.101049 (100, 630)\n", + "453 1436 1424.776842 100 55.486437 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.554729 365 375.785767 (100, 365)\n", + "165 100 100.192620 365 372.574094 (100, 365)\n", + "199 100 118.680119 630 598.837102 (100, 630)\n", + "132 100 80.398832 365 394.731135 (100, 365)\n", + "501 1436 1422.409024 100 105.898882 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768776.849883100130.676184(768, 100)
290768766.357897100117.560734(768, 100)
54100117.284869100107.648062(100, 100)
198100115.505927630584.101049(100, 630)
45314361424.77684210055.486437(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 776.849883 100 130.676184 (768, 100)\n", + "290 768 766.357897 100 117.560734 (768, 100)\n", + "54 100 117.284869 100 107.648062 (100, 100)\n", + "198 100 115.505927 630 584.101049 (100, 630)\n", + "453 1436 1424.776842 100 55.486437 (1436, 100)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.211019\n", + "(100, 365) 0.418368\n", + "(100, 630) 0.685219\n", + "(768, 100) 0.903707\n", + "(768, 630) 1.260640\n", + "(1436, 100) 1.203166\n", + "(1436, 365) 1.518377\n", + "(1436, 630) 1.799504\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_19584\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_19584\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 5 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACV90lEQVR4nOzdeXxU1f3/8dedLBMIZEECAQyYCBHCJoQKEVxYY2tpraltv19xA0QgYMPSgtaFRYhWsFQLCAhKf7h8q6itRAmQsEgFWWKosoMJQUkwikkIkG1mfn8MmRKSQCaZySTk/Xw85hHn3nPu/dzEB5N37rnnGDabzYaIiIiIiIjUmMnTBYiIiIiIiDQ2ClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiItIo3XDDDTz88MOeLkNERJooBSkREWlwjh8/zmOPPUZERAR+fn4EBAQwcOBA/vrXv3LhwgW3n//8+fPMmjWLLVu2uP1cIiLSOHl7ugAREZFLJSUlcd9992E2m3nwwQfp0aMHJSUlbN++nT/84Q/s37+f5cuXu7WG8+fPM3v2bADuvPNOt55LREQaJwUpERFpMDIyMvjd735Hp06dSE1NpV27do598fHxHDt2jKSkJA9WWDfnzp3D39/f02WIiIgLaGifiIg0GH/+858pLCxk5cqVFUJUuc6dO/P73/++yr6zZs3CMIxK29944w0MwyAzM9Oxbc+ePcTGxtK6dWuaNWtGeHg4o0ePBiAzM5OQkBAAZs+ejWEYGIbBrFmzHP0PHTrEr3/9a1q1aoWfnx/9+vXjX//6V5Xn3bp1KxMnTqRNmzZcf/31AJw9e5aEhARuuOEGzGYzbdq0Yfjw4aSlpTn1/RIREc/RHSkREWkwPvroIyIiIrj11lvddo7vvvuOESNGEBISwsyZMwkKCiIzM5P3338fgJCQEJYuXcqECRP41a9+xb333gtAr169ANi/fz8DBw6kQ4cOzJw5E39/f/7xj39wzz33sHbtWn71q19VON/EiRMJCQnhmWee4dy5cwCMHz+e9957j0mTJhEVFcUPP/zA9u3bOXjwIH379nXbtYuIiOsoSImISINQUFDAt99+yy9/+Uu3nuezzz7jxx9/ZMOGDfTr18+x/bnnngPA39+fX//610yYMIFevXoxatSoCv1///vf07FjR3bv3o3ZbAbsYWnQoEHMmDGjUpBq1aoVKSkpeHl5ObYlJSXx6KOPsnDhQse2P/7xjy6/VhERcR8N7RMRkQahoKAAgJYtW7r1PEFBQQCsW7eO0tJSp/qeOXOG1NRUfvOb33D27Fm+//57vv/+e3744QdiY2M5evQo3377bYU+jz76aIUQVV7D559/zqlTp+p0LSIi4jkKUiIi0iAEBAQA9ueH3OmOO+4gLi6O2bNn07p1a375y1/y+uuvU1xcfNW+x44dw2az8fTTTxMSElLh9eyzzwL2oYOXCg8Pr3ScP//5z3z11VeEhYVxyy23MGvWLL7++mvXXKCIiNQLDe0TEZEGISAggPbt2/PVV1/Vqn9VE00AWCyWSu3ee+89du7cyUcffURycjKjR49m4cKF7Ny5kxYtWlR7DqvVCsD06dOJjY2tsk3nzp0rvG/WrFmlNr/5zW+47bbb+OCDD9iwYQMvvvgiL7zwAu+//z4//elPr3idIiLSMOiOlIiINBg///nPOX78ODt27HC6b3BwMAB5eXkVtp84caLK9gMGDGDevHns2bOHN998k/379/POO+8A1YeyiIgIAHx8fBg2bFiVr5oOTWzXrh0TJ07kww8/JCMjg+uuu4558+bVqK+IiHiegpSIiDQYf/zjH/H392fs2LGcPn260v7jx4/z17/+tcq+N954IwDbtm1zbDt37hyrV6+u0O7HH3/EZrNV2HbzzTcDOIb3NW/eHKgcytq0acOdd97JsmXLyM7OrlRDbm7uFa7OzmKxkJ+fX+m47du3r9HwQhERaRg0tE9ERBqMG2+8kbfeeovf/va3dOvWjQcffJAePXpQUlLCZ599xrvvvsvDDz9cZd8RI0bQsWNHxowZwx/+8Ae8vLxYtWoVISEhZGVlOdqtXr2aJUuW8Ktf/Yobb7yRs2fPsmLFCgICAvjZz34G2IfjRUVF8X//939ERkbSqlUrevToQY8ePVi8eDGDBg2iZ8+ePProo0RERHD69Gl27NjBN998w759+654jWfPnuX666/n17/+Nb1796ZFixZs2rSJ3bt3V5jFT0REGjYFKRERaVB+8Ytf8J///IcXX3yRf/7znyxduhSz2UyvXr1YuHAhjz76aJX9fHx8+OCDD5g4cSJPP/00oaGhJCQkEBwczCOPPOJod8cdd7Br1y7eeecdTp8+TWBgILfccgtvvvlmhYkhXnvtNSZPnsyUKVMoKSnh2WefpUePHkRFRbFnzx5mz57NG2+8wQ8//ECbNm3o06cPzzzzzFWvr3nz5kycOJENGzbw/vvvY7Va6dy5M0uWLGHChAl1/waKiEi9MGyXj28QERERERGRK9IzUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJ2kdKcBqtXLq1ClatmyJYRieLkdERERERDzEZrNx9uxZ2rdvj8lU/X0nBSng1KlThIWFeboMERERERFpIE6ePMn1119f7X4FKaBly5aA/ZsVEBDg4WpERERERMRTCgoKCAsLc2SE6ihIgWM4X0BAgIKUiIiIiIhc9ZEfTTYhIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJ3k0SN1www0YhlHpFR8fD0BRURHx8fFcd911tGjRgri4OE6fPl3hGFlZWdx99900b96cNm3a8Ic//IGysjJPXI6IiIiIiDQR3p48+e7du7FYLI73X331FcOHD+e+++4DYMqUKSQlJfHuu+8SGBjIpEmTuPfee/n3v/8NgMVi4e677yY0NJTPPvuM7OxsHnzwQXx8fJg/f75HrklEREREpCk6l3uOL1Z+QUZqBkX5RfgF+hE+NJw+o/vgH+Lv6fJczrDZbDZPF1EuISGBdevWcfToUQoKCggJCeGtt97i17/+NQCHDh2iW7du7NixgwEDBvDJJ5/w85//nFOnTtG2bVsAXn31VWbMmEFubi6+vr41Om9BQQGBgYHk5+cTEBDgtusTEZGG57z1PPtL9vNN6TcU24oxG2bCfMKI8o2iuam5p8sTEWnwyorKWJ+wni9WfoHNasNm/W+8MEwGhsmg79i+xC6Kxdvs0fs4NVLTbNBgnpEqKSlhzZo1jB49GsMw2Lt3L6WlpQwbNszRpmvXrnTs2JEdO3YAsGPHDnr27OkIUQCxsbEUFBSwf//+as9VXFxMQUFBhZeIiDQtZbYyUs+l8lr+a3x24TOyyrI4bTlNVlkW/77wb17Lf43Uc6mU2TRcXESkOmVFZayJXUPaijSsZdYKIQrAZrVhLbOyd/le1sSuoaz42vk3tcEEqQ8//JC8vDwefvhhAHJycvD19SUoKKhCu7Zt25KTk+Noc2mIKt9fvq86iYmJBAYGOl5hYWGuuxAREWnwymxlvH/2fb4s+RIbVQ/MsGHjy5Iv+eDsBwpTIiLVSJ6STNb2rEoB6nI2q42sT7NITkiup8rcr8EEqZUrV/LTn/6U9u3bu/1cTzzxBPn5+Y7XyZMn3X5OERFpOLac30K2JbtGbU9ZTrHl/Bb3FiQi0gidyz1H2mtpVw1R5WxWG2mvpXH++/Nurqx+NIggdeLECTZt2sTYsWMd20JDQykpKSEvL69C29OnTxMaGupoc/ksfuXvy9tUxWw2ExAQUOElIiJNQ/kzUc7YX7KfC9YLbqpIRKRx+mLVFzUOUeVsVhtpK9PcVFH9ahBB6vXXX6dNmzbcfffdjm3R0dH4+PiQkpLi2Hb48GGysrKIiYkBICYmhi+//JLvvvvO0Wbjxo0EBAQQFRVVfxcgIiKNRnpxer32ExG5VmWkZNQqSGWkZripovrl8WkzrFYrr7/+Og899BDe3v8tJzAwkDFjxjB16lRatWpFQEAAkydPJiYmhgEDBgAwYsQIoqKieOCBB/jzn/9MTk4OTz31FPHx8ZjNZk9dkoiINGBHSo7Uqt/hksPENItxcTUiIo1XUX5RrfoV5xW7uBLP8HiQ2rRpE1lZWYwePbrSvr/85S+YTCbi4uIoLi4mNjaWJUuWOPZ7eXmxbt06JkyYQExMDP7+/jz00EPMmTOnPi9BREQakdoO0dPQPhGRivwC/WrVzxx0bdzw8HiQGjFiBNUtZeXn58fixYtZvHhxtf07derExx9/7K7yRERERESkCuFDw50e3meYDMKHhLuxqvrTIJ6REhERqS+1XWRXi/OKiFTUZ3QfDJPhVB/Dy6DvmL5uqqh+KUiJiEiTEukbWa/9RESuVf4h/vQd27fGYcow2UNU89bXxh+mFKRERKRJ6W3uXat+N5tvdm0hIiLXgNhFsXQc1PGqYcowGXS8rSOxi2LrqTL3U5ASEZEmpbmpOd28uznV5ybvm2hmauamikREGi9vszejkkcRPS4ak7epUqAyTAYmbxPR46IZlTwKb7PHp2hwmWvnSkRERGqgzFZGni3PqT5Hy47ie86X25vfjrehj04RkUt5+3lz99K7uXPOnXyx6gsyUjMozivGHGQmfEg4fUb3wT/E39Nlupxhq27KvCakoKCAwMBA8vPzCQgI8HQ5IiLiRqnnUvmq5CtsOPfxZ2DQ3rs997S4R2FKROQaVtNsoKF9IiLSZJy3nmd/yX6nQxSADRvfln3LtvPb3FCZiIg0NgpSIiLSZBwoOYAVa52Osb9kvxbnFRERPSMlIiJNx8nSk3U+hhUr+0v208+vnwsqEhG5NpzLPccXK+3PRxXlF+EX6Ef40Gv3+ShQkBIRkSak2FbskuOcLD2pICUiApQVlbE+YT1frPwCm9WGzfrfodMZKRlsfmozfcf2JXZR7DU1Yx8oSImISBNiNswuOY6rApmISGNWVlTGmtg1ZG3PqhCgypUHq73L95J7MPeam/5cz0iJiEiTEeYT5pLjuCqQiYg0ZslTkjnx6YkqQ9SlbFYbJ7adIDkhuZ4qqx8KUiIi0mRE+UZhcsFHn6sCmYhIY3Uu9xx7lu+hxpOg2mDvir2c//68W+uqTwpSIiLSZDQ3Nae7b/c6H8cVxxARacz2LNuDs5Og2iw20lamuacgD1CQEhGRJuX25rfXqb+BQTNTMxdVIyLSOO1evLtW/Y6tP+biSjxHQUpERJqUEltJnfr74uuiSkREGqdzuec4l3OuVn1/PPaji6vxHAUpERFpUg6UHKhT/1DvUBdVIiLSOH3+8ue17lt6odSFlXiWgpSIiDQpdV2U945md7ioEhGRxmn/O/tr3denmY8LK/Gsa2cidxERkRqoag2o7459x47VO8hKz6KooAi/AD863tyRmIdiaNO5jaNdK1Mrgr2D67NcEZEGp/B0Ya37Bne+dv4NVZASEZEm5dI1oL796ls+fOpDjm47isnLhNXy3ymoMnZmsHnxZrrc3oV7nruH63tcz29a/sYTJYuINCiWEkut+3a+q7MLK/EsBSkREWlSwnzCyCrL4sjWI6z43xWUlZQBVAhRl74/9u9jLIpdxMJ3F2K+WwvxiogYXgYA3/M9e9nLKU5RTDFmzLSnPdFE05rWVfbtO6ZvfZbqVgpSIiLSpET5RvHe7vfsIaqoDJvtyqtJ2iw2yorLmHHfDG7fcTu9e/eup0pFRBqmM/5n+OD8B2SQgYGB7ZJVebPIYgc7CCecWGIJ5b8T9Hg396Z56+aeKNktNNmEiIg0Kc1Nzdnw9AbKSq4eosrZrDaKS4qZNm2am6sTEWnYUlJSeOWHV8gkE6BCiLr0fSaZvMZrfM3Xjn3t+7Wvtzrrg4KUiIg0KUeOHOGLrV9gs9QsRJWzWqykpKRw9OhRN1UmItKw7d21l5/F/owya1mlAHU5GzYsWHiLt8ghB4AuP+tSH2XWGwUpERFpUpYvX46Xl1et+pq8TCxbtszFFYmINHxlRWU8EPsApZbSq4aocuVhKplkAKLui3JnifVOQUpERJqUPXv2YLHUbsYpq8XK3r17XVyRiEjDt+qRVRzMO1jjEFXOho0MMviBH3h90OuUFZe5qcL6pyAlIiJNSn5+fp365+XluaYQEZFGIv9kPm+88wYGRq36GxjsYQ+F2YWsG7/OxdV5joKUiIg0KYGBgXXqHxQU5JpCREQagbKiMt644w1Occrpu1HlbNjIJhuA//z9P5z//rwrS/QYBSkREWlS+vXrV6dnpKKjo11ckYhIw5U8JZm8jDyKKa7TcYooAuyzoKatTHNFaR6nICUiIk3KuHHj6vSM1GOPPebiikREGqZzuedIe80eeszUbUFyP/wc/52RmlGnYzUUClIiItKkREZGMmTIEExezn0EmrxMDBs2jC5drq3pe0VEqvPFqi+wWe3D+drTvk7PSLWjneN9cV7d7m41FApSIiLS5MxbMA8vXy8MU81+KTBMBl6+Xjz35+fcXJmISMORkZLhCFLRRNfpGal+9HO8NwfV7e5WQ6EgJSIiTY6tq41H33oUb7M3hteVw5ThZeBt9ubRtx7F2tVaTxWKiHheUX6R479b05pwwp2+K2VgEEEE13Gd/b3JIHxIuEvr9BQFKRERaXKOlBwh8o5IEpIT6DLIPlTv8qF+5e+7DOpCQnICkXdEcqTkSL3XKiLiKX6BfhXexxKLF141DlMGBl54MYIR/93mZdB3TF+X1ukp3p4uQEREpL6dt9qn3u3QowMTP5hI7vFcPnvjM07uO8mF/As0C2xGWO8wbn34VkJuDKnUT0SkKQgfGs7XG792vA8llP/lf3mLt7BgueJQv/IQ9b/8L6GEOrb3HdOX5q2bu7Xu+qIgJSIiTV7IjSH8cu4vPV2GiEiD0md0H1KeSOHSvBRBBGMZSzLJZJCBgVEhUJW/DyecEYyoEKJ8W/oSuyi2Pi/BrRSkRESkyWlmakaJtaRW/UREmgr/EH9ahLagMLuwwvZQQnmIh/iBH9jDHrLJpogi/PCjHe3oRz/HM1GX6tC/A97mayd+XDtXIiIiUkORvpHsLtrtdL+bfG9yQzUiIg1PWVEZ6xPWVwpRl7qO64ilZneYDJNBxLAIV5XXIGiyCRERaXJuNt9cr/1ERBqTsqIy1sSuYe/yvS497rUyyUQ5BSkREWlympua0923u1N9uvt219A+EWkSkqckk7U9i1ouG1WlFu1bXDOTTJRTkBIRkSbpzuZ30s6rXY3atvdqz53N73RvQSIiDcC53HOkvZbmWIjXVVqGtnTp8RoCBSkREWmSvA1v7m15Lz19e15xTZS2Xm25y/8uvA09Viwi174vVn3h8hAF4NfK7+qNGhkFKRERabK8DW+G+A/hkYBHCDGFVNnmtOU0bxS8Qeq5VMpsZfVcoYhI/cpIyXB5kDJMBuFDwl16zIZAQUpERJq0ImsR75x9h1xrbrVtrFj5quQrPiz8UGFKRK5pRflFLj+m4WVccxNNgIKUiIg0YWW2Mv5fwf/jvO38VdvasPFt2bdsO7+tHioTEfEMv0DXDsEzTPYQda1NNAEKUiIi0oSlnkutUYi61P6S/VywXnBTRSIinhU+1HVD8AyTQcfbOhK7qGZrTTU2ClJNRG4uPP88jBgB/fvbv77wgn27iEhTdN56nkOlh5zuZ8XK/pL9bqhIRMTz+ozuwxXm36kRw2Rg8jYRPS6aUcmj8DZfm5P1XJtXJQ5FRZCQACtXgtVqf5VLSYGnnoKxY2HRIjCbPVWliEj9O1ByAFstF0k5WXqSfn79XFyRiIjn+Yf407JDS85+c9bpviYfEzcMvoHwIeH0Gd0H/xB/N1TYcChINSK5ufZAlJoK+fkQGAhDh8Lo0RBSxWRTRUUQGwvbt1cMUOXKg9Xy5XDwICQnK0yJSNNxsvRkrfsW24pdWImISMPSb3w/Nj+12el+dzx7B7f/6XY3VNQweXxo37fffsuoUaO47rrraNasGT179mTPnj2O/TabjWeeeYZ27drRrFkzhg0bxtGjRysc48yZM9x///0EBAQQFBTEmDFjKCwsrO9Lcbny4XjDhkGbNvbXk0/Cxo2wa5f965NPQvv2MGECFF/2uT5lSvUh6lJWK3z6qf3OlYhIU1GX55zMhv7qJCLXruhx0Rhezo3vM7wN+j3WtO7UezRI/fjjjwwcOBAfHx8++eQTDhw4wMKFCwkODna0+fOf/8zLL7/Mq6++yueff46/vz+xsbEUFf13asb777+f/fv3s3HjRtatW8e2bdsYN26cJy7JJYqKYPx4e0D605/sQ/DKn2WyXTYKxWqFsjL7XaXY2P+GqdxceO21q4eoS4/z2mvw/feuuw4RkYbsR+uPte4b5hPmwkpERBoW/xB/oh+NxjDVLEwZJoPosdHX5Mx8V2LYbJf/al5/Zs6cyb///W8+/fTTKvfbbDbat2/PtGnTmD59OgD5+fm0bduWN954g9/97nccPHiQqKgodu/eTb9+9hS8fv16fvazn/HNN9/Qvn37q9ZRUFBAYGAg+fn5BAQEuO4Ca+Fqw/Gupn9/+OgjWLXKfrfKmWOYTDB/PsyY4fx5RUQakx/KfmDN2TW17j8ucBzNTM1cWJGISMNSVlzGmhFryNqedcUFestn5ruWJpWoaTbw6B2pf/3rX/Tr14/77ruPNm3a0KdPH1asWOHYn5GRQU5ODsOGDXNsCwwMpH///uzYsQOAHTt2EBQU5AhRAMOGDcNkMvH5559Xed7i4mIKCgoqvBqKmg7Hq87nn9vvZL38svPHsFrtz1+JiFzrPr1Q9R/waiLEFKIQJSLXPG+zN6OSRxE9LhqTt6nS3ammMjPflXj0ir/++muWLl3K1KlTefLJJ9m9ezePP/44vr6+PPTQQ+Tk5ADQtm3bCv3atm3r2JeTk0ObNm0q7Pf29qZVq1aONpdLTExk9uzZbriiunF2OF51ysrg1Kna9c3Lq9u5RUQag9Nlp2vd18/k2sUqRUQaKm8/b+5eejd3zrmTL1Z9QUZqBsV5xZiDzE1mZr4r8WiQslqt9OvXj/nz5wPQp08fvvrqK1599VUeeught533iSeeYOrUqY73BQUFhIV5frz7qlV1D1F1FRTk2fOLiNSHMspq3bfEVuLCSkREGj7/EH8GzRjEoBmDPF1Kg+LRoX3t2rUjKiqqwrZu3bqRlZUFQGhoKACnT1f8y+Hp06cd+0JDQ/nuu+8q7C8rK+PMmTOONpczm80EBARUeDUEKSmeDVImEwwZ4rnzi4jUF+86/B1RM/aJiAh4OEgNHDiQw4cPV9h25MgROnXqBEB4eDihoaGkpKQ49hcUFPD5558TExMDQExMDHl5eezdu9fRJjU1FavVSv/+/evhKlwnP9/ZHkeA6cCdQJ+LX6df3O48Ly8YM6ZWXUVEGpW23m2v3qgamrFPRETAw0P7pkyZwq233sr8+fP5zW9+w65du1i+fDnLly8HwDAMEhISeO655+jSpQvh4eE8/fTTtG/fnnvuuQew38G66667ePTRR3n11VcpLS1l0qRJ/O53v6vRjH0NSWBgTVvuA6YCqYAXYLlk33ZgITD04tfeNT7/mDHQunWNm4uINFq3NbuNE2dPON3PhInuvt3dUJGIiDQ2Hr0j9ZOf/IQPPviAt99+mx49ejB37lwWLVrE/fff72jzxz/+kcmTJzNu3Dh+8pOfUFhYyPr16/Hz++/Dvm+++SZdu3Zl6NCh/OxnP2PQoEGOMNaYDB1qH153ZSlADLD14nvLZfvL32+52C6Fmlq0qMZNRUQateu8r6OVqZXT/br7dteMfSIiAnh4HamGoqGsI5Wba5+6vKzaZ6D3YQ9HRUBNfmwmwAzsoCZ3pnJzdUdKRJqOYmsxK/NXUkppjdq382rHvS3vxdtoelP8iog0JY1iHSmpKCQExo690l2pqUAJNQtRANaL7afVqPXKlTU8rIjINcBsMjM6cDTBRvBV20b5RClEiYhIBbojRcO5IwVQXAwjRlS1KO8R4KY6HPkI0OWKLUaMgOTkOpxCRKSR+qHsB7ac30KOJYcyyjAwMGOmm283+jXrR3NTc0+XKCIi9aSm2UB/WmtgzGb45z8hKgqysy/ds5zKE0vUlBewDFhwxVZajFdEmqrrvK8jLiDO02WIiEgjoqF9DdATT8BlS2cBe6hdiOJiv71XbaXFeEVEREREakZBqoHJzYXXXqtqYV6nF5m6TN4V92oxXhERERGRmlOQamBWraoqRAHUeJGpagRdca8W4xURERERqTkFqQYmJaW6INUP+7NOteEFRFe712TSYrwiIiIiIs5QkGpg8qsdwTeOuj0j9ViVe0wmuO02LcYrIiIiIuIMBakGJrDaEXyRwBCcvyvlBQzj8qnPTSbw9oZx4+xTnpvNzlYqIiIiItJ0KUg1MEOHXmnvS4AvNf+xmS62rzjt+Z13wvz5cOoULF2qECUiIiIi4iwtyEvDWpA3NxfatoXqfyopwEighCsP9fPCHqI+Av6bzq6/Hk6edE2tIiIiIiLXmppmA92RamBCQqBDhyu1GArsAO68+P7yoX7l7wdfbFfxFldoaJ1LFBERERFp8hSkGqDx46/WojewCTgCJGAPVTdf/JpwcfvGi+0qatXKNTWKiIiIiDRl3p4uQCobNw6efRYsV52krwuXP/90JYahRXdFRERERFxBd6QaoJAQePRR+8x6rqRFd0VEREREXENBqoFatAgGDXJtmBo7VovuioiIiIi4goJUA2U229d3GjfOfieprrToroiIiIiI6yhINWB+fvZ1nrKzYe5c+9TlzjIM+zDBjRu1XpSIiIiIiKtoHSka1jpSV5ObC6tWwb/+BV99BRcu2Ld7edmHARqG/b/btoXf/hYef9z+zJWIiIiIiFxdTbOBghSNK0iJiIiIiIj7aEFeERERERERN1GQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkb0+efNasWcyePbvCtptuuolDhw4BUFRUxLRp03jnnXcoLi4mNjaWJUuW0LZtW0f7rKwsJkyYwObNm2nRogUPPfQQiYmJeHt79NJERK5JFouF0tJST5chdeDr64vJpL+jiojUlcfTRvfu3dm0aZPj/aUBaMqUKSQlJfHuu+8SGBjIpEmTuPfee/n3v/8N2D/Q7777bkJDQ/nss8/Izs7mwQcfxMfHh/nz59f7tYiIXKtsNhs5OTnk5eV5uhSpI5PJRHh4OL6+vp4uRUSkUfN4kPL29iY0NLTS9vz8fFauXMlbb73FkCFDAHj99dfp1q0bO3fuZMCAAWzYsIEDBw6wadMm2rZty80338zcuXOZMWMGs2bN0oeEiIiLlIeoNm3a0Lx5cwzD8HRJUgtWq5VTp06RnZ1Nx44d9XMUEakDjwepo0eP0r59e/z8/IiJiSExMZGOHTuyd+9eSktLGTZsmKNt165d6dixIzt27GDAgAHs2LGDnj17VhjqFxsby4QJE9i/fz99+vSp8pzFxcUUFxc73hcUFLjvAkVEGjmLxeIIUdddd52ny5E6CgkJ4dSpU5SVleHj4+PpckREGi2PDpLu378/b7zxBuvXr2fp0qVkZGRw2223cfbsWXJycvD19SUoKKhCn7Zt25KTkwPY/0J6aYgq31++rzqJiYkEBgY6XmFhYa69MBGRa0j5M1HNmzf3cCXiCuWjNSwWi4crERFp3Dx6R+qnP/2p47979epF//796dSpE//4xz9o1qyZ2877xBNPMHXqVMf7goIChSkRkavQMLBrg36OIiKu0aCm7QkKCiIyMpJjx44RGhpKSUlJpQebT58+7XimKjQ0lNOnT1faX76vOmazmYCAgAovERERERGRmmpQQaqwsJDjx4/Trl07oqOj8fHxISUlxbH/8OHDZGVlERMTA0BMTAxffvkl3333naPNxo0bCQgIICoqqt7rFxGRquXmwvPPw4gR0L+//esLL9i3i4iINEYeDVLTp09n69atZGZm8tlnn/GrX/0KLy8v/ud//ofAwEDGjBnD1KlT2bx5M3v37uWRRx4hJiaGAQMGADBixAiioqJ44IEH2LdvH8nJyTz11FPEx8djNps9eWkiIgIUFcH48dC+PfzpT7BxI+zaZf/65JP27RMmwCXz/9Q7wzD48MMPPVeAiIg0Sh4NUt988w3/8z//w0033cRvfvMbrrvuOnbu3ElISAgAf/nLX/j5z39OXFwct99+O6Ghobz//vuO/l5eXqxbtw4vLy9iYmIYNWoUDz74IHPmzPHUJYmIyEVFRRAbCytWQFkZWK0V91ut9u3Ll9vbuSNM5eTkMHnyZCIiIjCbzYSFhTFy5MgKox1cacuWLRiG4db1tubNm8ett95K8+bNK03IJCIi9cejk0288847V9zv5+fH4sWLWbx4cbVtOnXqxMcff+zq0kREpI6mTIHt2ysHqMtZrfDpp5CQAEuXuu78mZmZDBw4kKCgIF588UV69uxJaWkpycnJxMfHc+jQIdedzMVsNhsWi6XCIvXlSkpKuO+++4iJiWHlypUeqE5ERKCBPSMlIiLXhtxceO21q4eoclarvf3337uuhokTJ2IYBrt27SIuLo7IyEi6d+/O1KlT2blzZ5V9qrqjlJ6ejmEYZGZmAnDixAlGjhxJcHAw/v7+dO/enY8//pjMzEwGDx4MQHBwMIZh8PDDD1+8PiuJiYmEh4fTrFkzevfuzXvvvVfpvJ988gnR0dGYzWa2b99eZY2zZ89mypQp9OzZs+7fJBERqTWPL8grIiLXnlWrah6iylmtsHIlzJhR9/OfOXOG9evXM2/ePPz9/Svtr8uQuPj4eEpKSti2bRv+/v4cOHCAFi1aEBYWxtq1a4mLi+Pw4cMEBAQ4lvJITExkzZo1vPrqq3Tp0oVt27YxatQoQkJCuOOOOxzHnjlzJgsWLCAiIoLg4OBa1ygiIu6nICUiIi6XklK7IJWa6pogdezYMWw2G127dq37wS6TlZVFXFyc445QRESEY1+rVq0AaNOmjSOsFRcXM3/+fDZt2uSYdTYiIoLt27ezbNmyCkFqzpw5DB8+3OU1i4iI6ylIiYiIy+Xn166fq+ZosNlsrjlQFR5//HEmTJjAhg0bGDZsGHFxcfTq1ava9seOHeP8+fOVAlJJSQl9+vSpsK1fv35uqVlERFxPQUpERFwuMLB2/Vw1CV2XLl0wDMPpCSVMJvujw5cGsdLS0gptxo4dS2xsLElJSWzYsIHExEQWLlzI5MmTqzxmYWEhAElJSXTo0KHCvsuX6qhqGKKIiDRMmmxCRERcbuhQMDn5CWMywZAhrjl/q1atiI2NZfHixZw7d67S/uqmJy9ffiM7O9uxLT09vVK7sLAwxo8fz/vvv8+0adNYsWIFAL6+vgBYLBZH26ioKMxmM1lZWXTu3LnCKywsrLaXKCIiHqYgJSIiLjd6tPNByssLxoxxXQ2LFy/GYrFwyy23sHbtWo4ePcrBgwd5+eWXHc8qXa483MyaNYujR4+SlJTEwoULK7RJSEggOTmZjIwM0tLS2Lx5M926dQPsS3IYhsG6devIzc2lsLCQli1bMn36dKZMmcLq1as5fvw4aWlpvPLKK6xevdrp68rKyiI9PZ2srCwsFgvp6emkp6c77nyJiEj9UJASERGXCwmBsWNrHqZMJnuIat3adTVERESQlpbG4MGDmTZtGj169GD48OGkpKSwtJoFq3x8fHj77bc5dOgQvXr14oUXXuC5556r0MZisRAfH0+3bt246667iIyMZMmSJQB06NCB2bNnM3PmTNq2bcukSZMAmDt3Lk8//TSJiYmOfklJSYSHhzt9Xc888wx9+vTh2WefpbCwkD59+tCnTx/27Nnj9LFERKT2DJs7n8htJAoKCggMDCQ/P5+AgABPlyMi0qAUFRWRkZFBeHg4fn5+Ne5XXAwjRlx9UV6TCW67DZKT4bJHhsQNavvzFBFpKmqaDXRHSkRE3MJstoejcePA27vy3SmTyb593DiFKBERaXw0a5+IiLiNnx8sXQpz5tgX6U1NtU9xHhRkn1hi9Gj7MEAREZHGRkFKRETcLiTEvtCuKxbbFRERaQg0tE9ERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIi7pebC88/b1+ht39/+9cXXrBv9zDDMPjwww89XYaIiDQyClIiIuI+RUUwfjy0bw9/+hNs3Ai7dtm/PvmkffuECVBc7JbT5+TkMHnyZCIiIjCbzYSFhTFy5EhSUlLccr4tW7ZgGAZ5eXluOX5mZiZjxowhPDycZs2aceONN/Lss89SUlLilvOJiEj1tI6UiIi4R1ERxMbC9u1gtVbeb7XaX8uXw8GDkJwMZrPLTp+ZmcnAgQMJCgrixRdfpGfPnpSWlpKcnEx8fDyHDh1y2blczWazYbFY8Pau+DF96NAhrFYry5Yto3Pnznz11Vc8+uijnDt3jgULFnioWhGRpkl3pERExD2mTKk+RF3KaoVPP4WEBJeefuLEiRiGwa5du4iLiyMyMpLu3bszdepUdu7cWWWfqu4opaenYxgGmZmZAJw4cYKRI0cSHByMv78/3bt35+OPPyYzM5PBgwcDEBwcjGEYPPzwwxcv0UpiYqLjTlLv3r157733Kp33k08+ITo6GrPZzPbt2yvVd9ddd/H6668zYsQIIiIi+MUvfsH06dN5//33XfNNExGRGtMdKRERcb3cXHjttauHqHJWq7393LnQunWdT3/mzBnWr1/PvHnz8Pf3r7Q/KCio1seOj4+npKSEbdu24e/vz4EDB2jRogVhYWGsXbuWuLg4Dh8+TEBAAM2aNQMgMTGRNWvW8Oqrr9KlSxe2bdvGqFGjCAkJ4Y477nAce+bMmSxYsICIiAiCg4NrVE9+fj6tWrWq9fWIiEjtKEiJiIjrrVpV8xBVzmqFlSthxow6n/7YsWPYbDa6du1a52NdLisri7i4OHr27AlARESEY195oGnTpo0jrBUXFzN//nw2bdpETEyMo8/27dtZtmxZhSA1Z84chg8fXuNajh07xiuvvKJhfSIiHqAgJSIirpeSUrsglZrqkiBls9nqfIzqPP7440yYMIENGzYwbNgw4uLi6NWrV7Xtjx07xvnz5ysFpJKSEvr06VNhW79+/Wpcx7fffstdd93Ffffdx6OPPurcRYiISJ0pSImIiOvl59eun4tmu+vSpQuGYTg9oYTJZH90+NIgVlpaWqHN2LFjiY2NJSkpiQ0bNpCYmMjChQuZPHlylccsLCwEICkpiQ4dOlTYZ75sco2qhiFW5dSpUwwePJhbb72V5cuX16iPiIi4liabEBER1wsMrF2/Ojy7dKlWrVoRGxvL4sWLOXfuXKX91U1PHhISAkB2drZjW3p6eqV2YWFhjB8/nvfff59p06axYsUKAHx9fQGwWCyOtlFRUZjNZrKysujcuXOFV1hYmNPX9u2333LnnXcSHR3N66+/7gh/IiJSv/Svr4iIuN7QoeDsL/gmEwwZ4rISFi9ejMVi4ZZbbmHt2rUcPXqUgwcP8vLLLzueVbpcebiZNWsWR48eJSkpiYULF1Zok5CQQHJyMhkZGaSlpbF582a6desGQKdOnTAMg3Xr1pGbm0thYSEtW7Zk+vTpTJkyhdWrV3P8+HHS0tJ45ZVXWL16tVPXVB6iOnbsyIIFC8jNzSUnJ4ecnJzafZNERKTWFKRERMT1Ro92Pkh5ecGYMS4rISIigrS0NAYPHsy0adPo0aMHw4cPJyUlhaVLl1bZx8fHh7fffptDhw7Rq1cvXnjhBZ577rkKbSwWC/Hx8XTr1o277rqLyMhIlixZAkCHDh2YPXs2M2fOpG3btkyaNAmAuXPn8vTTT5OYmOjol5SURHh4uFPXtHHjRo4dO0ZKSgrXX3897dq1c7xERKR+GTZ3PpHbSBQUFBAYGEh+fj4BAQGeLkdEpEEpKioiIyOD8PBw/Pz8at5xwgT7Yrs1mXTCZIJx46CagCOuU+ufp4hIE1HTbKA7UiIi4h6LFsGgQVe/M2UywW232duLiIg0EgpSIiLiHmYzJCfb7zR5e1cOVCaTffu4cfZ2l81gJyIi0pBp+nMREXEfPz/7cL05c+yL9Kam2qc4DwqyTywxejRcnClPRESkMVGQEhER9wsJsS+064LFdkVERBoCDe0TERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKRERERERESdp1j4REXG73HO5rPxiJakZqeQX5RPoF8jQ8KGM7jOaEH/PTn9uGAYffPAB99xzj0frEBGRxkV3pERExG2KyooYv2487V9qz59S/8TGrzey69QuNn69kSdTn6T9S+2ZkDSB4rJit5w/JyeHyZMnExERgdlsJiwsjJEjR5KSkuKW823ZsgXDMMjLy3PL8QF+8Ytf0LFjR/z8/GjXrh0PPPAAp06dctv5RESkagpSIiLiFkVlRcSuiWVF2grKrGVYbdYK+602K2XWMpbvXU7smliXh6nMzEyio6NJTU3lxRdf5Msvv2T9+vUMHjyY+Ph4l57L1Ww2G2VlZVXuGzx4MP/4xz84fPgwa9eu5fjx4/z617+u5wpFRERBSkRE3GJK8hS2Z22vFKAuZ7VZ+TTrUxKSE1x6/okTJ2IYBrt27SIuLo7IyEi6d+/O1KlT2blzZ5V9qrqjlJ6ejmEYZGZmAnDixAlGjhxJcHAw/v7+dO/enY8//pjMzEwGDx4MQHBwMIZh8PDDD9uv0WolMTGR8PBwmjVrRu/evXnvvfcqnfeTTz4hOjoas9nM9u3bq6xxypQpDBgwgE6dOnHrrbcyc+ZMdu7cSWlpad2/aSIiUmN6RupaUFIKOd9D3lkos4C3FwS1hNDW4Ovj6epEpAnKPZfLa2mvXTVElbParLyW9hpzB8+ldfPWdT7/mTNnWL9+PfPmzcPf37/S/qCgoFofOz4+npKSErZt24a/vz8HDhygRYsWhIWFsXbtWuLi4jh8+DABAQE0a9YMgMTERNasWcOrr75Kly5d2LZtG6NGjSIkJIQ77rjDceyZM2eyYMECIiIiCA4OrtF1vvnmm9x66634+OjfexGR+qQg1VicuwDHT8LZc2C1ATYwmcBmA0sVv6j8WACZ30JoCHQOs7cVEaknq75YVeMQVc5qs7IybSUzBs2o8/mPHTuGzWaja9eudT7W5bKysoiLi6Nnz54AREREOPa1atUKgDZt2jjCWnFxMfPnz2fTpk3ExMQ4+mzfvp1ly5ZVCFJz5sxh+PDhV61hxowZ/O1vf+P8+fMMGDCAdevWueryRESkhvTbdUNXVga7voQ9++3hqMwCVqs9TJVZqg5R5WxAdi7854i9j4hIPUnJSKlVkErNSHXJ+W02m0uOU5XHH3+c5557joEDB/Lss8/yn//854rtjx07xvnz5xk+fDgtWrRwvP7+979z/PjxCm379etXoxr+8Ic/8MUXX7Bhwwa8vLx48MEH3XrNIiJSme5INWRlZbDzP1cOSzWRXwjHTkJkJ9fUJSJyFflF+bXql1ec55Lzd+nSBcMwOHTokFP9TBfv3l8aSi5/9mjs2LHExsaSlJTEhg0bSExMZOHChUyePLnKYxYWFgKQlJREhw4dKuwzm80V3lc1DLEqrVu3pnXr1kRGRtKtWzfCwsLYuXOn446XiIi4n+5INWRph+oeosrl5IIeRBaRehLoF1irfkHmIJecv1WrVsTGxrJ48WLOnTtXaX9105OHhNjXtMrOznZsS09Pr9QuLCyM8ePH8/777zNt2jRWrFgBgK+vLwAWi8XRNioqCrPZTFZWFp07d67wCgsLq+0lOlgvjjgoLnbPFPIiIlI1BamG6twFuFDkuuPZgOzvXXc8EZErGBo+FJPh3EeMyTAxJHyIy2pYvHgxFouFW265hbVr13L06FEOHjzIyy+/XO2dm/JwM2vWLI4ePUpSUhILFy6s0CYhIYHk5GQyMjJIS0tj8+bNdOvWDYBOnTphGAbr1q0jNzeXwsJCWrZsyfTp05kyZQqrV6/m+PHjpKWl8corr7B69Wqnrunzzz/nb3/7G+np6Zw4cYLU1FT+53/+hxtvvFF3o0RE6pmCVEN1/KTrj5l31vXHFBGpwug+o50OUl6GF2P6jnFZDREREaSlpTF48GCmTZtGjx49GD58OCkpKSxdurTKPj4+Prz99tscOnSIXr168cILL/Dcc89VaGOxWIiPj6dbt27cddddREZGsmTJEgA6dOjA7NmzmTlzJm3btmXSpEkAzJ07l6effprExERHv6SkJMLDw526pubNm/P+++8zdOhQbrrpJsaMGUOvXr3YunVrpWGCIiLiXoatgTyd+vzzz/PEE0/w+9//nkWLFgFQVFTEtGnTeOeddyguLiY2NpYlS5bQtm1bR7+srCwmTJjA5s2badGiBQ899BCJiYl4e9f88a+CggICAwPJz88nICDA1ZdWO//+wj6ZhCu19Ie+3Vx7TBG55hUVFZGRkUF4eDh+fn417jchaQLL9y6v0aQTJsPEuOhxLL276oAjrlPbn6eISFNR02zQIO5I7d69m2XLltGrV68K26dMmcJHH33Eu+++y9atWzl16hT33nuvY7/FYuHuu++mpKSEzz77jNWrV/PGG2/wzDPP1PcluJbV6voQBfb1pURE6smi2EUM6jjoqnemTIaJ2zrexqLYRfVTmIiIiAt4PEgVFhZy//33s2LFigqLD+bn57Ny5UpeeuklhgwZQnR0NK+//jqfffaZY0X6DRs2cODAAdasWcPNN9/MT3/6U+bOncvixYspKSnx1CXVTflMfe4Q1NI9xxURqYLZ20zyqGTGRY/D2+RdKVCZDBPeJm/GRY8jeVQyZm8NTRMRkcbD40EqPj6eu+++m2HDhlXYvnfvXkpLSyts79q1Kx07dmTHjh0A7Nixg549e1YY6hcbG0tBQQH79++v9pzFxcUUFBRUeDUIVivs+gpKy1x/bMOAdq1df1wRkSvw8/Zj6d1LOTX1FPOHzGdExAhu6XALIyJGMH/IfE5NPcXSu5cqRImISKPj0XWk3nnnHdLS0ti9e3elfTk5Ofj6+jpWhi/Xtm1bcnJyHG0uDVHl+8v3VScxMZHZs2fXsXo3OHzCPSEKILQ1+Pi459giIlcR4h/CjEEzmDFohqdLERERcQmP3ZE6efIkv//973nzzTfr/WHXJ554gvz8fMfr5Ek3zJDnrJJS+O4H9xw7sAV0rvtaJSIiIiIiYuexILV3716+++47+vbti7e3N97e3mzdupWXX34Zb29v2rZtS0lJSaVFE0+fPk1oaCgAoaGhnD59utL+8n3VMZvNBAQEVHh5XI4b1ngygHYh0CsSTB4fxSkiIiIics3w2G/XQ4cO5csvvyQ9Pd3x6tevH/fff7/jv318fEhJSXH0OXz4MFlZWY5FB2NiYvjyyy/57rvvHG02btxIQEAAUVFR9X5NdeLKNZ5MBjQzQ1go3NBeIUpERERExMU89oxUy5Yt6dGjR4Vt/v7+XHfddY7tY8aMYerUqbRq1YqAgAAmT55MTEwMAwYMAGDEiBFERUXxwAMP8Oc//5mcnByeeuop4uPjG9/ChK6c7txqgwvFkJUDJ3MgNMQ+tE+BSkRERETEJTw62cTV/OUvf8FkMhEXF1dhQd5yXl5erFu3jgkTJhATE4O/vz8PPfQQc+bM8WDVteSuNZ5sQHYunL+gIX4iIiIiIi5i2Gw2m6eL8LSarl7sVlnZkPGte8/RLgQiO7n3HCJyzSkqKiIjI4Pw8PA6TA6UC6wEUoF8IBAYCowGQlxTaC0ZhsEHH3zAPffc49E66otrfp4iIteummYD3Z5oKK4LqnHTIydPMH3JIu78/WP0GXs/d/7+MaYvWcSRkyeu3DEnF0pL61aniIhTioDxQHvgT8BGYNfFr09e3D4BKHbL2XNycpg8eTIRERGYzWbCwsIYOXJkhedvXWnLli0YhlFpoiR3KC4u5uabb8YwDNLT091+PhERqahBD+1rEqxWOJZVo1n79h07wtQli0hN242XyQuL9b/PVW3/ch8L//EmQ/v+hIUTE+jdObLyAWzAqVzo1N6FFyAiUp0iIBbYDlir2G+9+FoOHASSAdc935qZmcnAgQMJCgrixRdfpGfPnpSWlpKcnEx8fDyHDh1y2blczWazYbFY8Pau/mP6j3/8I+3bt2ffvn31WJmIiJTTHSlPslrhP0cg+3t7yLmClL27iIkfzdb0vQAVQtSl77ek7yUmfjQpe3dVfaCTOfbzioi43RSqD1GXsgKfAgkuPfvEiRMxDINdu3YRFxdHZGQk3bt3Z+rUqezcubPKPlXdUUpPT8cwDDIzMwE4ceIEI0eOJDg4GH9/f7p3787HH39MZmYmgwcPBiA4OBjDMHj44YftV2i1kpiYSHh4OM2aNaN379689957lc77ySefEB0djdlsZvv27dVe2yeffMKGDRtYsGBB3b5JIiJSa7oj5UnHTkJ+4VWb7Tt2hJFPTqWopISrPdJmsVopLilh5JNT2bF4VeU7Uxar/bx6VkpE3CoXeI2rh6hy1ovt5wKt63z2M2fOsH79eubNm4e/v3+l/UFBQbU+dnx8PCUlJWzbtg1/f38OHDhAixYtCAsLY+3atcTFxXH48GECAgJo1qwZAImJiaxZs4ZXX32VLl26sG3bNkaNGkVISAh33HGH49gzZ85kwYIFREREEBwcXOX5T58+zaOPPsqHH35I8+bNa30dIiJ1lpsLK1dCairk50NgIAwdCqNHQ4hnn3+tDwpSnlJSan9mqQamLllESWnpVUNUOavNRklpKdOWLGLTS0sqN8jJhfD24OPjTMUiIk5YRc1DVDkr9gkpZtT57MeOHcNms9G1a9c6H+tyWVlZxMXF0bNnTwAiIiIc+1q1agVAmzZtHGGtuLiY+fPns2nTJsc6iBEREWzfvp1ly5ZVCFJz5sxh+PDh1Z7bZrPx8MMPM378ePr16+e4SyYiUq+KiiAhwR6irNaKo502bYInn4T27SE0FIKDr9lwpaF9npJz9eF8YJ9YIjVtNxYnh+NZrFZS0nZz9Jusyjtt2IcTioi4TQq1C1KpLjm7Oyekffzxx3nuuecYOHAgzz77LP/5z3+u2P7YsWOcP3+e4cOH06JFC8fr73//O8ePH6/Qtl+/flc81iuvvMLZs2d54okn6nwdIiK1UlQEsbGwYgWUlVV+ZMRms2/75hvYswc2bvxvsJowAYrdM7mQJyhIeUre2Ro1W/7RB3iZarfGlJfJxLJ/vV+n84uI1E5+LfvlueTsXbp0wTAMpyeUMF1ca+/SIFZ62WynY8eO5euvv+aBBx7gyy+/pF+/frzyyivVHrOw0D6EOykpifT0dMfrwIEDFZ6TAqochnip1NRUduzYgdlsxtvbm86dOwP2APbQQw/V/EJFRGpryhTYvt25Z+6tVnvoWr7cHsKukTClIOUpZZartwH2HD5YaWKJmrJYrew9Us0vETU8v4hI7QTWsl+QS87eqlUrYmNjWbx4MefOnau0v7rpyUMuDjvJzs52bKtqavGwsDDGjx/P+++/z7Rp01ixYgUAvr6+AFgs//03NioqCrPZTFZWFp07d67wCgsLc+q6Xn75Zfbt2+cIYx9//DEA//d//8e8efOcOpaIiNNyc+G112o/cZnVCp9+ah8WeA3QM1Ke4l2zu0z5564+GcWV5BVWc+ephucXEamdoTg/vM8EDHFZBYsXL2bgwIHccsstzJkzh169elFWVsbGjRtZunQpBw8erNSnPNzMmjWLefPmceTIERYuXFihTUJCAj/96U+JjIzkxx9/ZPPmzXTr1g2ATp06YRgG69at42c/+xnNmjWjZcuWTJ8+nSlTpmC1Whk0aBD5+fn8+9//JiAgwKk7SR07dqzwvkWLFgDceOONXH/99c5+i0REnLNqVd1nf7Za7XemDh6ECxca9QQVuiPlKUEta9Qs0L9F3U7Toprz1PD8IiK1MxrnP2K8gDEuqyAiIoK0tDQGDx7MtGnT6NGjB8OHDyclJYWlS5dW2cfHx4e3336bQ4cO0atXL1544QWee+65Cm0sFgvx8fF069aNu+66i8jISJYssU/s06FDB2bPns3MmTNp27YtkyZNAmDu3Lk8/fTTJCYmOvolJSURHh7ususVEXG7lBTXLKNjtcLWrbBrl/0ZqpkzoU0bCAuDefPsd74aAcPmzidyG4mCggICAwPJz88nICCgfk5aUgo79111wonpSxax6L13ajW8z8tkIuHX/8OCiQkVdxgGxPTSrH0iUiNFRUVkZGQQHh6On5+fEz0nYF9styYfuiZgHFB1wBHXqf3PU0SavP797eHH3by9YexYWLQIzK5bqL2mapoNdEfKU3x9IPTqty/HjfxVnZ6ReuwX91beEdpaIUpE6sEiYBBX/6gxAbddbC8iIg1WoPPPvx4BpgN3An0ufp1+cXu1GsnEFApSntQ5DAKvPHQvMqwTQ/r+BC+Tcz8qL5OJYdE/ocv1FcfTE9jCfl4REbczA8nY7zR5U/kjx3Rx+7iL7er/r44iIuKEoUOhhr+T7sP+tOxN2P9MthVIv/h10cXtwy62q5LVCtu2NeiJKRSkPMlkgl6R0C4EjOqbvTQxAV8fH0zGFRpdeljDwNfHhwUTEiruaBdiP5+ToUxEpPb8sA/XOwXMB0YAt1z8Ov/i9qUoRImINAKjR9fo98gUIAZ7aAK4fGxV+fstF9ulVHcgm82+XtX3DXP9U/1G7WkmE0R2ggG9IbwDBAdAS3/7ZBAtmgPQu3MkH81/CbOv71XvTHmZTJh9fflo/kv07hxp3+jnC/172s+jECUiHhECzMB+5+nzi19nXNwuIiKNQkiI/dmlK/w+uQ8YCRRROUBdzgIUX2xf7Z0piwVWrnS+1nqg36obCl8f6NjOfseobzfofRNER0FMb+jYjqEDb2PHq29wZx/7qvdeXhWnLy9ftHdwn2h2LF7F0Ohb7He52oXAT3qAn/7aKyIiIiJ1tGgRDBpUbZiaCpRw1fnUHKwX20+7UqP1650osP5oHamGztfHfqcqvAO9+/dk08O/4+jRoyxbtoy9e/eSl5dHUGAg0ZFdeWzkvXTpEGZfIyqopX1SCV9NKiEiIiIiLmI2Q3IyTJliH3Z3yQLkR4DUWhzSgn1431GgS1UNjh2rTaVupyDVCHXp0oUFCxZ4ugwRERERaYr8/GDpUpgzBzp3hoICwL7ghRdXH9JXFS9gGVDlb7gXLtS2UrfS0D4REREREXFeSAj88Y+Ot3uoXYjiYr+91e1s1qyWR3UvBSkREREREamdcePsC+gC+XU8VF51Ozp3ruOR3UNBSkRE3O689Ty7i3bzwdkPeKfgHT44+wF7ivZw3nre06VhGAYffvihp8sQEWmcLpnJz/nleisKqm7HXXfV8cjuoSAlIiJuU2YrI/VcKivzV/LZhc/IKsvitOU0WWVZ/PvCv1mZv5LUc6mU2crccv6cnBwmT55MREQEZrOZsLAwRo4cSUpKtauW1MmWLVswDIO8vDy3HB/ghhtuwDCMCq/nn3/ebecTEbmqizP59cP+rFNteAHRVe3w8YExY2pbmVtpsgkREXGLMlsZHxR+QHZZNrZqJsK1YuWrkq84Yz3DPS3uwdtw3cdSZmYmAwcOJCgoiBdffJGePXtSWlpKcnIy8fHxHDp0yGXncjWbzYbFYsHbu+rvx5w5c3j00Ucd71u2bFlfpYmIVHZxJr9xjzzCwnfeqdUhLMBjVe0YMwZat65LdW7j9B2pCxcusH37dg4cOFBpX1FREX//+99dUpiIiDRu285vu2KIKmfDxrdl37Lt/DaXnn/ixIkYhsGuXbuIi4sjMjKS7t27M3XqVHbu3Flln6ruKKWnp2MYBpmZmQCcOHGCkSNHEhwcjL+/P927d+fjjz8mMzOTwYMHAxAcHIxhGDz88MMAWK1WEhMTCQ8Pp1mzZvTu3Zv33nuv0nk/+eQToqOjMZvNbN++vdpra9myJaGhoY6Xv79/3b5ZIiJ15edH5NtvM+S22/AyDKe6egHDqGLq88BA+92uBsqpIHXkyBG6devG7bffTs+ePbnjjjvIzs527M/Pz+eRRx5xeZEiItK4nLeeZ3/J/quGqEvtL9nPBatrprg9c+YM69evJz4+vsqQERQUVOtjx8fHU1xczLZt2/jyyy954YUXaNGiBWFhYaxduxaAw4cPk52dzV//+lcAEhMT+fvf/86rr77K/v37mTJlCqNGjWLr1q0Vjj1z5kyef/55Dh48SK9evaqt4fnnn+e6666jT58+vPjii5SVuWdopIiIs1565RV8/fwwVbNg7+VMgC/VTHvepYv9blcD5dQYihkzZtCjRw/27NlDXl4eCQkJDBw4kC1bttCxY0d31SgiIo3MgZIDWLE61ceKlf0l++nn16/O5z927Bg2m42uXbvW+ViXy8rKIi4ujp49ewIQERHh2NeqVSsA2rRp4whrxcXFzJ8/n02bNhETE+Pos337dpYtW8Ydd9zh6D9nzhyGDx9+xfM//vjj9O3bl1atWvHZZ5/xxBNPkJ2dzUsvveTKyxQRqZXevXvz0UcfMXLkSEpKSrBYqp8Q3Qt7iPoI6F1Vg4v/pjZUTgWpzz77jE2bNtG6dWtat27NRx99xMSJE7ntttvYvHmzhhbUt5JSyPke8s5CmQW8vSCoJYS2Bl8fT1cnIk3YydKTte7niiBls9X8TpizHn/8cSZMmMCGDRsYNmwYcXFxV7x7dOzYMc6fP18pIJWUlNCnT58K2/r1u/q1T5061fHfvXr1wtfXl8cee4zExETMDfgvtyLSdAwdOpQdO3Ywbdo0UlJS8DKZsFj/+8e18kV7B2O/E1VliDKZYMiQeqm3tpwKUhcuXKjw4KthGCxdupRJkyZxxx138NZbb7m8QKmC1QrHsuwh6vLfFX4sgMxvITQEOofZ/ycUEalnxbbieu13uS5dumAYhtMTSpQPRbk0iJWWllZoM3bsWGJjY0lKSmLDhg0kJiaycOFCJk+eXOUxCwsLAUhKSqJDhw4V9l0efGrzB8n+/ftTVlZGZmYmN910k9P9RURcKjcXVq6kd2oqm86e5eitt7LMamXvzp3kYZ/iPBr7xBKVnom6lJdXg52tr5xTQapr167s2bOHbt26Vdj+t7/9DYBf/OIXrqtMqma1wn+OQH5h9W1sQHYunL8AvSIVpkSk3pmN2t0ZqW2/y7Vq1YrY2FgWL17M448/Ximg5OXlVfmcVEhICADZ2dkEBwcD9skmLhcWFsb48eMZP348TzzxBCtWrGDy5Mn4+voCVBjKEhUVhdlsJisrq8IwPldJT0/HZDLRpk0blx9bRKTGioogIQFWrrT/vnrxDlQXYIHJBIYBNR0tYDI16Nn6yjn1G/avfvUr3n777Sr3/e1vf+N//ud/3DqcQoBjJ68coi6VX2hvLyJSz8J8wuq1X1UWL16MxWLhlltuYe3atRw9epSDBw/y8ssvO55Vulznzp0JCwtj1qxZHD16lKSkJBYuXFihTUJCAsnJyWRkZJCWlsbmzZsdf2Ds1KkThmGwbt06cnNzKSwspGXLlkyfPp0pU6awevVqjh8/TlpaGq+88gqrV6926pp27NjBokWL2LdvH19//TVvvvmmY+KK8uAnIlLviorsw/CWL4eyMkeIcrBanQtRt93WoGfrK+dUkPrtb3/LunXrqt2/ZMkSrJd/48R1SkohJ9e5Pjm5cNmwFBERd4vyjcLk5AobJkx09+3ushoiIiJIS0tj8ODBTJs2jR49ejB8+HBSUlJYunRplX18fHx4++23OXToEL169eKFF17gueeeq9DGYrEQHx9Pt27duOuuu4iMjGTJkiUAdOjQgdmzZzNz5kzatm3LpEmTAJg7dy5PP/00iYmJjn5JSUmEh4c7dU1ms5l33nmHO+64g+7duzNv3jymTJnC8uXLa/EdEhFxgaIiiI6GHTtqHpYMo/KIKZMJvL1h3DhITm7Qs/WVM2xO3ELy8vIiOzvbMXzgt7/9LS+//DJt27Z1W4H1oaCggMDAQPLz8wkICPB0OdXLyoaMb53vF94BOrZzfT0i0iQUFRWRkZFBeHg4fn5+Ne6Xei6Vr0q+qvEU6D19ezLEv2E/WHwtqO3PU0SkkqIiGDwYqlmbr1re3vDEE/D555CXB0FB9jtao0fDxSHWnlTTbODUM1KXZ66PP/6YxMTE2lUozss7W/t+ClIiUs9ub347P1h/qNGivB28O3B789vrqTIREXGJKVPsYchZViv4+9vvPDVimoWgMSmrfh5+t/QTEakDb8ObX7X4FT18e1Q7zM+EiZ6+PbmnxT14G079bU9ERDwpNxdee63mw/kuZbVCaqrra6pnTn1qGYaBYRiVtkk98faq334iInXkbXgzxH8IA5oN4EDJAU6WnqTYVozZMBPmE0aUbxTNTc09XaaIiDhr1arKk0o4Iy/PZaV4itND+x5++GHHuhdFRUWMHz++0rSy77//vusqlP8KamlfJ6o2/UREPKi5qTn9/Pq5ZLFdERFpAFJS6hakqliCorFxKkg99NBDFd6PGjXKpcXIVYS2ti+268wdVMOAdg17Dn4RERERaWTy82vf12SyTy7RyDkVpF5//XV31SE14esDoSH2xXZrKrQ1+Pi4ryYRERERaXoCA2vf18vLvuBuI6fJJhqbzmEQ2KLm7W22ut12FRERERG53NChldeCqgnDsIeo1o1/xJSCVGNjMkGvSPudpprI+R7+c0RhSkRERERcZ/To2gWpmBhYtMjl5XiCglRjZDLZ03xN5RfCsZPuq0dEREREmpaQEBg71rkwFRVln/b84sR1jZ0W7WiMSkohx4nnpMDePry9npcSEY84l3uOL1Z+QUZqBkX5RfgF+hE+NJw+o/vgH+J/9QO4kWEYfPDBB9xzzz0erUNEpNFZtAgOHIDt2688+skwYMAA2Lz5mglRoDtSjVPO987N3Af29tnfu6MaEZFqlRWVsW78Ol5q/xKpf0rl641fc2rXKb7e+DWpT6byUvuXSJqQRFlxmVvOn5OTw+TJk4mIiMBsNhMWFsbIkSNJSUlxy/m2bNmCYRjkuXl9lKSkJPr370+zZs0IDg5WCBQRzzCbITkZxo0Db+/Kd6dMJvv2xx675kIU6I5U45R3tvb9OrZzbS0iItUoKypjTewasrZnYbNW/uuPzWrDZrWxd/lecg/mMip5FN5m130sZWZmMnDgQIKCgnjxxRfp2bMnpaWlJCcnEx8fz6FDh1x2Llez2WxYLBa8vSt/P9auXcujjz7K/PnzGTJkCGVlZXz11VceqFJEBPDzg6VLYc4c+yK9qan2xXaDguxTnI8ebR8GeA3SHanGqMxSv/1ERGoheUpytSHqUjarjaxPs0hOSHbp+SdOnIhhGOzatYu4uDgiIyPp3r07U6dOZefOnVX2qeqOUnp6OoZhkJmZCcCJEycYOXIkwcHB+Pv70717dz7++GMyMzMZPHgwAMHBwRiGwcMPPwyA1WolMTGR8PBwmjVrRu/evXnvvfcqnfeTTz4hOjoas9nM9u3bK9VXVlbG73//e1588UXGjx9PZGQkUVFR/OY3v3HNN01EpLZCQmDGDPsdqs8/t3+dMeOaDVGgO1KNk7dX/fYTEXHSudxzpL2WdtUQVc5mtZH2WhqD5w6meevmdT7/mTNnWL9+PfPmzcPfv/IzWEFBQbU+dnx8PCUlJWzbtg1/f38OHDhAixYtCAsLY+3atcTFxXH48GECAgJo1qwZAImJiaxZs4ZXX32VLl26sG3bNkaNGkVISAh33HGH49gzZ85kwYIFREREEBwcXOncaWlpfPvtt5hMJvr06UNOTg4333wzL774Ij169Kj1NYmIiPMUpBqjoJbwY0Ht+omI1IMvVn1R4xBVzma1kbYyjUEzBtX5/MeOHcNms9G1a9c6H+tyWVlZxMXF0bNnTwAiIiIc+1q1agVAmzZtHGGtuLiY+fPns2nTJmJiYhx9tm/fzrJlyyoEqTlz5jB8+PBqz/31118DMGvWLF566SVuuOEGFi5cyJ133smRI0cc5xcREffT0L7GKLQ1ODH7OWCfLaVd41/4TEQah4yUjFoFqYzUDJec32Zzdkaemnv88cd57rnnGDhwIM8++yz/+c9/rtj+2LFjnD9/nuHDh9OiRQvH6+9//zvHjx+v0LZfv35XPJb14qxYf/rTn4iLiyM6OprXX38dwzB4991363ZhIiLiFAWpxsjXB0KdHG8a2lpTn4tIvSnKL6pVv+K8Ypecv0uXLhiG4fSEEqaLM05dGsRKS0srtBk7dixff/01DzzwAF9++SX9+vXjlVdeqfaYhYWFgH2mvfT0dMfrwIEDFZ6TAqochnipdu3sEwZFRUU5tpnNZiIiIsjKyqrBFYqIiKt4NEgtXbqUXr16ERAQQEBAADExMXzyySeO/UVFRcTHx3PdddfRokUL4uLiOH36dIVjZGVlcffdd9O8eXPatGnDH/7wB8rK3DONboPSOQwCW9SsbWALe3sRkXriF+hXq37mINdMjduqVStiY2NZvHgx586dq7S/uunJQy4+FJ2dne3Ylp6eXqldWFgY48eP5/3332fatGmsWLECAF9fXwAslv9O7hMVFYXZbCYrK4vOnTtXeIWFOfdvc/lEFIcPH3ZsKy0tJTMzk06dOjl1LBERqRuPBqnrr7+e559/nr1797Jnzx6GDBnCL3/5S/bv3w/AlClT+Oijj3j33XfZunUrp06d4t5773X0t1gs3H333ZSUlPDZZ5+xevVq3njjDZ555hlPXVL9MZmgVyS0C6l+mJ+BfX+vSOdWnRYRqaPwoeEYJufGIBsmg/Ah4S6rYfHixVgsFm655RbWrl3L0aNHOXjwIC+//LLjWaXLlYebWbNmcfToUZKSkli4cGGFNgkJCSQnJ5ORkUFaWhqbN2+mW7duAHTq1AnDMFi3bh25ubkUFhbSsmVLpk+fzpQpU1i9ejXHjx8nLS2NV155hdWrVzt1TQEBAYwfP55nn32WDRs2cPjwYSZMmADAfffdV4vvkoiI1JZHJ5sYOXJkhffz5s1j6dKl7Ny5k+uvv56VK1fy1ltvMWTIEABef/11unXrxs6dOxkwYAAbNmzgwIEDbNq0ibZt23LzzTczd+5cZsyYwaxZsxx/GbxmmUwQ2QluaG9fpDfvrH2Kc28v+8QSoa3twwBFROpZn9F92PzUZqeekzK8DPqO6euyGiIiIkhLS2PevHlMmzaN7OxsQkJCiI6OZunSpVX28fHx4e2332bChAn06tWLn/zkJzz33HMVQorFYiE+Pp5vvvmGgIAA7rrrLv7yl78A0KFDB2bPns3MmTN55JFHePDBB3njjTeYO3cuISEhJCYm8vXXXxMUFETfvn158sknnb6uF198EW9vbx544AEuXLhA//79SU1NrXKWPxERcR/D5s4ncp1gsVh49913eeihh/jiiy/Iyclh6NCh/PjjjxWmqe3UqRMJCQlMmTKFZ555hn/9618Vhl1kZGQ4Pjz79OlT5bmKi4spLv7vOPyCggLCwsLIz88nICDAXZcoItIoFRUVkZGRQXh4OH5+NR+ylzQhib3L99YoTBkmg+hx0dy99O66lCo1UNufp4hIU1FQUEBgYOBVs4HHpz//8ssviYmJoaioiBYtWvDBBx8QFRVFeno6vr6+ldb6aNu2LTk5OQDk5OTQtm3bSvvL91UnMTGR2bNnu/ZCGoKSUt2ZEpEGI3ZRLLkHcq+6KK9hMuh4W0diF8XWY3UiIiJ14/EHZ2666SbS09P5/PPPmTBhAg899BAHDhxw6zmfeOIJ8vPzHa+TJ0+69XxuZ7XCkUzYuQ8yvrWvMXX2nP1rxrf27UdO2NuJiNQTb7M3o5JHET0uGpO3qdIzU4bJwORtInpcNKOSR+Ft9vjf9kRERGrM459avr6+dO7cGbDPRrR7927++te/8tvf/paSkhLy8vIq3JU6ffo0oaGhAISGhrJr164Kxyuf1a+8TVXMZjNms2tmhvI4qxX+cwTyC6tvYwOyc+H8BU08ISL1ytvPm7uX3s2dc+7ki1VfkJGaQXFeMeYgM+FDwukzug/+IVee8ltERKQh8niQupzVaqW4uJjo6Gh8fHxISUkhLi4OgMOHD5OVleWYbSkmJoZ58+bx3Xff0aZNGwA2btxIQEBAhTU2rmnHTl45RF0qv9DePlJT5IpI/fIP8WfQjEEMmjHI06WIiIi4hEeD1BNPPMFPf/pTOnbsyNmzZ3nrrbfYsmULycnJBAYGMmbMGKZOnUqrVq0ICAhg8uTJxMTEMGDAAABGjBhBVFQUDzzwAH/+85/JycnhqaeeIj4+/tq543QlJaWQk+tcn5xcCG+vxXlFREREROrAo0Hqu+++48EHHyQ7O5vAwEB69epFcnIyw4cPB+Avf/kLJpOJuLg4iouLiY2NZcmSJY7+Xl5erFu3jgkTJhATE4O/vz8PPfQQc+bM8dQl1a+c7+3D9pxhA7K/h47t3FGRiIiIiEiT0GCmP/ekmk5x2OD854h9QglnBQfYn5USEakBTZd9bdHPU0TkymqaDTTrQGNWZqnffiIiIiIiAihINW7eXvXbT0REREREAAWpxi2oZf32ExGpraJc2P88pI6A5P72rwdesG/3MMMw+PDDDz1dhoiINDIKUo1ZaGswrt6sAsOAdq3dUo6ISCWWItg1Hj5oD/v+BDkb4Ydd9q/pT9q375oAlmK3nD4nJ4fJkycTERGB2WwmLCyMkSNHkpKS4pbzbdmyBcMwyMvLc+vxq3rt3r3bLecUEZGqNbh1pMQJvj4QGmJfbLemQltr6nMRqR+WIkiNhdztgLWKBlawWeHYcig4CIOTwct1S1dkZmYycOBAgoKCePHFF+nZsyelpaUkJycTHx/PoUOHXHYuV7PZbFgsFry9K35M33rrrWRnZ1fY9vTTT5OSkkK/fv3qs0QRkSZPd6Qau85hENiiZm0DW9jbi4jUh71TrhCiLmWF7z6FvQkuPf3EiRMxDINdu3YRFxdHZGQk3bt3Z+rUqezcubPKPlXdUUpPT8cwDDIzMwE4ceIEI0eOJDg4GH9/f7p3787HH39MZmYmgwcPBiA4OBjDMHj44YftV2i1kpiYSHh4OM2aNaN379689957lc77ySefEB0djdlsZvv27ZXq8/X1JTQ01PG67rrr+Oc//8kjjzyCYTg7REFEROpCd6QaO5PJPpX5sZP2xXarmszewH7nqnOYvb2IiLsV5cLx17h6iCpntbfvNRf86j78+MyZM6xfv5558+bh7+9faX9QUFCtjx0fH09JSQnbtm3D39+fAwcO0KJFC8LCwli7di1xcXEcPnyYgIAAmjVrBkBiYiJr1qzh1VdfpUuXLmzbto1Ro0YREhLCHXfc4Tj2zJkzWbBgAREREQQHB1+1ln/961/88MMPPPLII7W+HhERqR0FqWuByQSRneCG9vZFevPO2qc49/ayTywR2to+DFBEpL58vco+bM8ZNit8vRKiZtT59MeOHcNms9G1a9c6H+tyWVlZxMXF0bNnTwAiIiIc+1q1agVAmzZtHGGtuLiY+fPns2nTJmJiYhx9tm/fzrJlyyoEqTlz5jgWpa+JlStXEhsby/XXX1/XyxIREScpSF1LfH2gYzv7S0TEk3JSqPndqHJWyEl1SZBy51rzjz/+OBMmTGDDhg0MGzaMuLg4evXqVW37Y8eOcf78+UoBqaSkhD59+lTY5sxzTt988w3Jycn84x//cO4CRETEJRSkRETE9Urza9kvzyWn79KlC4ZhOD2hhOni8OdLg1hpaWmFNmPHjiU2NpakpCQ2bNhAYmIiCxcuZPLkyVUes7CwEICkpCQ6dOhQYZ/ZXHFyjaqGIVbn9ddf57rrruMXv/hFjfuIiIjr6IEZERFxPZ/AWvYLcsnpW7VqRWxsLIsXL+bcuXOV9lc3PXlISAhAhZnx0tPTK7ULCwtj/PjxvP/++0ybNo0VK1YA9skgACwWi6NtVFQUZrOZrKwsOnfuXOEVFla7CYBsNhuvv/46Dz74ID6aiVVExCMUpERExPVCh+L8R4wJQoe4rITFixdjsVi45ZZbWLt2LUePHuXgwYO8/PLLjmeVLlcebmbNmsXRo0dJSkpi4cKFFdokJCSQnJxMRkYGaWlpbN68mW7dugHQqVMnDMNg3bp15ObmUlhYSMuWLZk+fTpTpkxh9erVHD9+nLS0NF555RVWr15dq2tLTU0lIyODsWPH1qq/iIjUnYKUiIi4XsRoMJz8iDG8IGKM60qIiCAtLY3Bgwczbdo0evTowfDhw0lJSWHp0qVV9vHx8eHtt9/m0KFD9OrVixdeeIHnnnuuQhuLxUJ8fDzdunXjrrvuIjIykiVLlgDQoUMHZs+ezcyZM2nbti2TJk0CYO7cuTz99NMkJiY6+iUlJREeHl6ra1u5ciW33nqrWybTEBGRmjFs7nwit5EoKCggMDCQ/Px8AgICPF2OiEiDUlRUREZGBuHh4fj5+dW8464J9sV2azTphAk6j4Nbqg444jq1/nmKiDQRNc0GuiMlIiLuEb0IQgZx9Y8aE7S5zd5eRESkkVCQEhER9/Ayw5Bk+50mw5vKHzkm+/bO42Bwsr29iIhII6Hpz0VExH28/OzD9XrNsS/Sm5Nqn+LcJ8g+sUTEaPAL8XSVIiIiTlOQEhER9/MLsS+064LFdkVERBoCDe0TERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiPuVlEJWNvznCKQdtH/NyrZv9zDDMPjwww89XYaIiDQyClIiIuI+ViscyYSd+yDjW/ixAM6es3/N+Na+/cgJezs3yMnJYfLkyURERGA2mwkLC2PkyJGkpKS45XxbtmzBMAzy8vLccnyAI0eO8Mtf/pLWrVsTEBDAoEGD2Lx5s9vOJyIiVVOQEhER97Ba7Xeesr8HWzVtbEB2rr2di8NUZmYm0dHRpKam8uKLL/Lll1+yfv16Bg8eTHx8vEvP5Wo2m42ysrIq9/385z+nrKyM1NRU9u7dS+/evfn5z39OTk5OPVcpItK0KUiJiIh7HDsJ+YU1a5tfaG/vQhMnTsQwDHbt2kVcXByRkZF0796dqVOnsnPnzir7VHVHKT09HcMwyMzMBODEiROMHDmS4OBg/P396d69Ox9//DGZmZkMHjwYgODgYAzD4OGHHwbAarWSmJhIeHg4zZo1o3fv3rz33nuVzvvJJ58QHR2N2Wxm+/btler7/vvvOXr0KDNnzqRXr1506dKF559/nvPnz/PVV1+55hsnIiI14u3pAkRE5BpUUgo5uc71ycmF8Pbg41Pn0585c4b169czb948/P39K+0PCgqq9bHj4+MpKSlh27Zt+Pv7c+DAAVq0aEFYWBhr164lLi6Ow4cPExAQQLNmzQBITExkzZo1vPrqq3Tp0oVt27YxatQoQkJCuOOOOxzHnjlzJgsWLCAiIoLg4OBK577uuuu46aab+Pvf/07fvn0xm80sW7aMNm3aEB0dXetrEhER5ylIiYiI6+VcYThfdWzYhwF2bFfn0x87dgybzUbXrl3rfKzLZWVlERcXR8+ePQGIiIhw7GvVqhUAbdq0cYS14uJi5s+fz6ZNm4iJiXH02b59O8uWLasQpObMmcPw4cOrPbdhGGzatIl77rmHli1bYjKZaNOmDevXr68yeImIiPsoSImIiOvlna19PxcEKZvN2RRXc48//jgTJkxgw4YNDBs2jLi4OHr16lVt+2PHjnH+/PlKAamkpIQ+ffpU2NavX78rnttmsxEfH0+bNm349NNPadasGa+99hojR45k9+7dtGtX9++diIjUjJ6REhER1yuz1G+/y3Tp0gXDMDh06JBT/Uwm+8fipUGstLTiFO1jx47l66+/5oEHHuDLL7+kX79+vPLKK9Ues7DQ/pxYUlIS6enpjteBAwcqPCcFVDkM8VKpqamsW7eOd955h4EDB9K3b1+WLFlCs2bNWL16tVPXKiIidaMgJSIiruftVb/9LtOqVStiY2NZvHgx586dq7S/uunJQ0JCAMjOznZsS09Pr9QuLCyM8ePH8/777zNt2jRWrFgBgK+vLwAWy38DYVRUFGazmaysLDp37lzhFRYW5tR1nT9/Hvhv4CtnMpmwumkKeRERqZqClIiIuF5Qy/rtV4XFixdjsVi45ZZbWLt2LUePHuXgwYO8/PLLjmeVLlcebmbNmsXRo0dJSkpi4cKFFdokJCSQnJxMRkYGaWlpbN68mW7dugHQqVMnDMNg3bp15ObmUlhYSMuWLZk+fTpTpkxh9erVHD9+nLS0NF555RWn7yLFxMQQHBzMQw89xL59+zhy5Ah/+MMfyMjI4O67767dN0pERGpFQUpERFwvtDUYTvYxDGjX2mUlREREkJaWxuDBg5k2bRo9evRg+PDhpKSksHTp0ir7+Pj48Pbbb3Po0CF69erFCy+8wHPPPVehjcViIT4+nm7dunHXXXcRGRnJkiVLAOjQoQOzZ89m5syZtG3blkmTJgEwd+5cnn76aRITEx39kpKSCA8Pd+qaWrduzfr16yksLGTIkCH069eP7du3889//pPevXvX4rskIiK1Zdjc+URuI1FQUEBgYCD5+fkEBAR4uhwRkQalqKiIjIwMwsPD8fPzq3nHIyfsi+3WVLsQiOzkfIHilFr/PEVEmoiaZgPdkRIREffoHAaBLWrWNrCFvb2IiEgjoSAlIiLuYTJBr0j7nabqhvkZ2Pf3irS3FxERaSS0jpSIiLiPyWQfrndDe/sivXln7VOce3vZJ5YIbQ2+Pp6uUkRExGkKUiIi4n6+PvaFdl2w2K6IiEhDoHEUIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykySZERMTtjhw5wvLly9mzZw/5+fkEBgbSr18/xo0bR2RkpKfLExERcZruSImIiNvs27ePoUOHctNNN7Fo0SK2bt1Keno6W7duZdGiRdx0000MGzaMffv2ebrUBmPLli0YhkFeXl61bd544w2CgoLqrSYREalMQUpERNwiJSWFmJgYtm7dCoDFYqmwv/z9li1biImJISUlxS115OTkMHnyZCIiIjCbzYSFhTFy5EiXnu/OO+8kISHBJce69dZbyc7OJjAw0CXHExER99DQPhERcbl9+/YxcuRIioqKsNlsV2xrsVgoLi5m5MiR7Nixg969e7usjszMTAYOHEhQUBAvvvgiPXv2pLS0lOTkZOLj4zl06JDLznU1NpsNi8WCt/eVP3p9fX0JDQ2tp6pERKS2dEdKRERcburUqZSUlFw1RJWzWq2UlJQwbdo0l9YxceJEDMNg165dxMXFERkZSffu3Zk6dSo7d+4EIC8vj7FjxxISEkJAQABDhgypMNRw1qxZ3Hzzzfy///f/uOGGGwgMDOR3v/sdZ8+eBeDhhx9m69at/PWvf8UwDAzDIDMz0zFE75NPPiE6Ohqz2cz27dspLi7m8ccfp02bNvj5+TFo0CB2797tOF9VQ/veeOMNOnbsSPPmzfnVr37FDz/8UOE69+3bx+DBg2nZsiUBAQFER0ezZ88el34vRUSkIgUpERFxqSNHjpCamlppKN/VWCwWUlJSOHr0qEvqOHPmDOvXryc+Ph5/f/9K+8ufMbrvvvv47rvv+OSTT9i7dy99+/Zl6NChnDlzxtH2+PHjfPjhh6xbt45169axdetWnn/+eQD++te/EhMTw6OPPkp2djbZ2dmEhYU5+s6cOZPnn3+egwcP0qtXL/74xz+ydu1aVq9eTVpaGp07dyY2NrbC+S71+eefM2bMGCZNmkR6ejqDBw/mueeeq9Dm/vvv5/rrr2f37t3s3buXmTNn4uPjU9dvoYiIXIFHg1RiYiI/+clPaNmyJW3atOGee+7h8OHDFdoUFRURHx/PddddR4sWLYiLi+P06dMV2mRlZXH33XfTvHlz2rRpwx/+8AfKysrq81JEROSi5cuX4+XlVau+Xl5eLFu2zCV1HDt2DJvNRteuXatts337dnbt2sW7775Lv3796NKlCwsWLCAoKIj33nvP0c5qtfLGG2/Qo0cPbrvtNh544AHHM1aBgYH4+vrSvHlzQkNDCQ0NrXD9c+bMYfjw4dx4442YzWaWLl3Kiy++yE9/+lOioqJYsWIFzZo1Y+XKlVXW+Ne//pW77rqLP/7xj0RGRvL4448TGxtboU1WVhbDhg2ja9eudOnShfvuu8+lQyRFRKQyjwaprVu3Eh8fz86dO9m4cSOlpaWMGDGCc+fOOdpMmTKFjz76iHfffZetW7dy6tQp7r33Xsd+i8XC3XffTUlJCZ999hmrV6/mjTfe4JlnnvHEJYmINHl79uxx+m5UOYvFwt69e11SR02GFe7bt4/CwkLHH+vKXxkZGRw/ftzR7oYbbqBly5aO9+3ateO7776rUR39+vVz/Pfx48cpLS1l4MCBjm0+Pj7ccsstHDx4sMr+Bw8epH///hW2xcTEVHg/depUxo4dy7Bhw3j++ecr1C4iIu7h0ckm1q9fX+H9G2+8QZs2bdi7dy+33347+fn5rFy5krfeeoshQ4YA8Prrr9OtWzd27tzJgAED2LBhAwcOHGDTpk20bduWm2++mblz5zJjxgxmzZqFr69vpfMWFxdTXFzseF9QUODeCxURaULy8/Pr1P9K0347o0uXLhiGccUJJQoLC2nXrh1btmyptO/S6cUvHyZnGAZWq7VGdVQ1rNDVZs2axf/+7/+SlJTEJ598wrPPPss777zDr371K7efW0SkqWpQz0iVf/i2atUKgL1791JaWsqwYcMcbbp27UrHjh3ZsWMHADt27KBnz560bdvW0SY2NpaCggL2799f5XkSExMJDAx0vC4dyy4iInVT12m7XbU+UqtWrYiNjWXx4sUVRjqUy8vLo2/fvuTk5ODt7U3nzp0rvFq3bl3jc/n6+tboLtyNN96Ir68v//73vx3bSktL2b17N1FRUVX26datG59//nmFbeUTZVwqMjKSKVOmsGHDBu69915ef/31GtcvIiLOazBBymq1kpCQwMCBA+nRowdgX/vD19e30odq27ZtycnJcbS5NESV7y/fV5UnnniC/Px8x+vkyZMuvhoRkaarX79+dXpGKjo62mW1LF68GIvFwi233MLatWs5evQoBw8e5OWXXyYmJoZhw4YRExPDPffcw4YNG8jMzOSzzz7jT3/6k1Oz3t1www18/vnnZGZm8v3331d7t8rf358JEybwhz/8gfXr13PgwAEeffRRzp8/z5gxY6rs8/jjj7N+/XoWLFjA0aNH+dvf/lZhRMeFCxeYNGkSW7Zs4cSJE/z73/9m9+7ddOvWzblvloiIOKXBBKn4+Hi++uor3nnnHbefy2w2ExAQUOElIiKuMW7cuDo9I/XYY4+5rJaIiAjS0tIYPHgw06ZNo0ePHgwfPpyUlBSWLl2KYRh8/PHH3H777TzyyCNERkbyu9/9jhMnTlT6I92VTJ8+HS8vL6KioggJCSErK6vats8//zxxcXE88MAD9O3bl2PHjpGcnExwcHCV7QcMGMCKFSv461//Su/evdmwYQNPPfWUY7+Xlxc//PADDz74IJGRkfzmN7/hpz/9KbNnz675N0pERJxm2Gq6yIcbTZo0iX/+859s27aN8PBwx/bU1FSGDh3Kjz/+WOGuVKdOnUhISGDKlCk888wz/Otf/yI9Pd2xPyMjw/Hh2adPn6uev6CggMDAQPLz8xWqREQuU1RUREZGBuHh4fj5+dWoz9ChQ9m6datTgcrLy4vBgwezcePG2pYqNVCbn6eISFNS02zg0TtSNpuNSZMm8cEHH5CamlohRAFER0fj4+PjmGIW4PDhw2RlZTlmLIqJieHLL7+sMHvSxo0bCQgIqHa8uYiIuNdLL72Er68vJlPNPmZMJhO+vr4sWLDAzZWJiIi4hkeDVHx8PGvWrOGtt96iZcuW5OTkkJOTw4ULFwD7A8tjxoxh6tSpbN68mb179/LII48QExPDgAEDABgxYgRRUVE88MAD7Nu3j+TkZJ566ini4+Mxm82evDwRkSard+/efPTRR5jN5qs+L+Xl5YXZbOajjz7S2kciItJoeDRILV26lPz8fO68807atWvneP3f//2fo81f/vIXfv7znxMXF8ftt99OaGgo77//vmO/l5cX69atw8vLi5iYGEaNGsWDDz7InDlzPHFJIiJy0dChQ9mxYwd33nknQKVAVf5+8ODB7Nixg6FDh9Z3iSIiIrXWIJ6R8jQ9IyUiUj1XPFNz9OhRli1bxt69e8nLyyMoKIjo6Ggee+wxunTp4uKK5Ur0jJSIyJXVNBt4dEFeERFpPOryd7cuXbro+acGQn8/FRFxjQYz/bmIiDRMPj4+AJw/f97DlYgrlJSUAJWHWoqIiHN0R0pERK7Iy8uLoKAgx+yozZs3xzAMD1cltWG1WsnNzaV58+Z4e+tXABGRutC/oiIiclWhoaEAFZaakMbJZDLRsWNHhWERkTpSkBIRkasyDIN27drRpk0bSktLPV2O1IEz63uJiEj1FKRERKTGvLy89GyNiIgImmxCRERERETEaQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZK3pwsQERFpCHLP5bLyi5WkZqSSX5RPoF8gQ8OHMrrPaEL8QzxdnoiINDCGzWazeboITysoKCAwMJD8/HwCAgI8XY6IiNSjorIiEtYnsPKLlVhtVqw2q2OfyTBhMkyM7TuWRbGLMHubPVipiIjUh5pmAw3tExGRJquorIjYNbGsSFtBmbWsQogCsNqslFnLeHXPq3Rb3I1v8r/xUKUiItLQKEiJiEiTNSV5CtuztlcKUFXJyMug46KOTEiaQHFZcT1UJyIiDZmClIiINEm553J5Le21GoWocjZsLN+7nNg1sQpTIiJNnIKUiIg0Sau+WOVUiCpntVn59MSnJCQnuL4oERFpNBSkRESkSUrJSKlVkAKwYmXZnmV8W/Cti6sSEZHGQkFKRESapB8v/Fin/jZs3Pb6bRriJyLSRClIiYhIk5RzLqfOx8jIy9AQPxFpOopyYf/zkDoCkvvbvx54wb69CdI6UmgdKRGRpib3XC6hC0NrPbTvUt4mb7KnZdO6eWsXVCYi0gBZimBvAhxfCTYrcOm/nSYwTHDjWIheBF6Nf709rSMlIiJSjVVfrMJVf0e02qysTFvpkmOJiDQ4liJIjYVjK8BWRsUQhf29rQyOLYfNsWBpOsOdFaRERKTJSclIwYYTQep7IBl4HXj14tdk+3arzUpqRqo7yhQR8by9UyB3O5UD1OWs8N2n9jtXTYS3pwsQERGpb/lF+TVrmIM9MGUABlTIXlnADiAcTj1wysUViog0AEW5cPw1rh6iylnt7XvNBb9rf7iz7kiJiEiTE+gXePVGXwOvAZkX319+A6v8fSYceP4AKSkpLqpORKSB+HoV2CzO9bFZ4OumMdxZQUpERJqcoeFDr9wgB3gLKKNygLqcDWylNkaOHMm+fftcU6CISEOQk8LV/xG8nA1ymsZwZwUpERFpckb3GX3lBsmAE3+EtdlslJSUMG3atDrVJSLSoBR9V7t+xU1jOnQFKRERaXKKyoqq3/k99meinPwjrMViISUlhaNHj9alNBGRhsFSBAWHa9e3tNC1tTRQClLXlFzgeWAE0P/i1xcubhcREbCHqDveuKP6BnuxTyxRC15eXixbtqx2nUVEGpK9U8B6hT86XUkt/w1tbDRr3zWhCEgAVmKfVeXSmVVSgKeAscAiwIw9WK0EUoF8IBAYCowGQuqpZhERz5j8yWQy8jKqb3AK5x8JuMhisbB3797adRYRaSiKcu3rRtWWd0vX1dKAKUg1ekVALFDd/P7lwWoZ8A72H/n3VJ7Ht6rAJSJybTmZf5LX0l67cqM6riWZl5dXtwOIiHjasWU49aDo5czX/tTnoKF914ApVB+iLmUD8rCHqPL3l7Jin55qOfZg1nRWpRaRpuO+d++7eqM6/h0pKCiobgcQEfG0o3UZomxA6BCXldKQKUg1arnYFzmp6SJpNWEFPsU+VFBE5NqRey6XXd/uunrD9tTpGano6OjadRYRaQiKcuHCN3U7RsQY19TSwGloX6O2CteGqHJW7AFtLtA0bs2KyLVv1RersNXk4adoYEftzmGxWHjsscdq11lEpCH4elW1u45kw/JU2PM15F+AwGbQLwLGDYHIdvVYYwOhINWopeCeIMXF464EZrjp+CIi9SslI6VmDVsD4UAmTk064eXlxeDBg+nSpYvzxYmINBTf/LPSpn0nYOqbkLofvExgueTXz+1HYOHHMLQ7LLwfency4OuVEHXt/w6poX2NWr4bj20FNrnx+CIi9Su/yIl/M2MBL2o8xM9kMuHr68uCBQtqU5qISMORv7/C25SvIGYWbD1of2+57G/45e+3HLS3S/nKCjmpbi+zIVCQatRauPn4n6NJJ0TkWhHoF1jzxqHA/1KjMOXl5YXZbOajjz6id+/edahQRKQBKDvv+M99J2DkQigqrRygLmexQnGpvf2+w6fcXGTDoCDVaBUBx918jrNo0gkRuVYMDR+K4cwsEhHYV4S44eL7y7tefD/w9oHs2LGDoUOH1rlGERGPM/77j93UN6GkDGw1HOZstdnbT3tNQUoatCnAiXo4z2v8d8p0EZHGa3Sf0XiZvJzrFAo8BEwGBmAPVaEXvw4A43GDn835me5Eici1w8s+4ulItv2ZqKvdibqcxQopaWc4evSoG4prWBSkGqXyac/rQ/mkEyIijVuIfwhj+46tXefrsD839TAw/uLXWLC1spGa0TSeBRCRJqJ1f8A+O59XLZOCl5cXy5bVZS2qxkFBqlFaRZ1Wm3aKFdAvCSJybVgUu4hBHQe59Jh5xXkuPZ54Vu65XJ7f/jwj/t8I+q/oz4j/N4IXtr9A7rlcT5cmUj/6vgTYpzh39m5UOYvFwt69e1xYVMOk6c8bpRScmpO3zvLq8VwiIu5j9jaz8YGNxCfFsyq9+rVSnBFkDnLJccSzisqKSFifwMovVmK1WbHa/vsbZEpGCk9tfoqxfceyKHYRZm+zBysVcbPAbhAQRf6FA3U6TN6pr1xUUMPl0TtS27ZtY+TIkbRv3x7DMPjwww8r7LfZbDzzzDO0a9eOZs2aMWzYsErjLc+cOcP9999PQEAAQUFBjBkzhsLCwnq8Ck+o2RS+R47A9Olw553Qp4/96/Tp9u3OCXK2g4hIg+Xn7cfKX64kKyGLvu361ulYJsPEkPAhLqpMPKWorIjYNbGsSFtBmbWsQogCsNqslFnLWL53ObFrYiku04y2co0bsYPAZnU7RJD3D1B0bT9n79Egde7cOXr37s3ixYur3P/nP/+Zl19+mVdffZXPP/8cf39/YmNjKSoqcrS5//772b9/Pxs3bmTdunVs27aNcePG1dcleMiVp/Ddtw+GDoWbboJFi2DrVkhPt39dtMi+fdgwe7urMwH6JUFErj1hgWHsHbeX76Z/x/NDn+fOTnc6N6sf4GV4MabvGDdVKPVlSvIUtmdtrxSgLme1Wfk061MSkhPqpzART/ENoN+N3rV/RsoE0TcAx67t56QMm62mExq6l2EYfPDBB9xzzz2A/W5U+/btmTZtGtOnTwcgPz+ftm3b8sYbb/C73/2OgwcPEhUVxe7du+nXrx8A69ev52c/+xnffPMN7du3r9G5CwoKCAwMJD8/n4CAALdcn2u9ADxBVcP7UlJg5EgoKQHLFR6j8vICX1/46CN76KqeD3AKaF2nikVEGoMJSRNYvnf5VX+hBvvdqHHR41h699J6qEzcJfdcLu1fak+ZtazGfbxN3mRPy6Z1c302yrXryMtB3PR7JxYyv7z/QugSFgz3ZoNX4xoOW9Ns0GAnm8jIyCAnJ4dhw4Y5tgUGBtK/f3927NgBwI4dOwgKCnKEKIBhw4ZhMpn4/PPPqz12cXExBQUFFV6Ny2jsq0RWtG+fPUQVFV05RIF9f3GxvX31d6ZMwBgUokSkqSifjMJkXPnj0WSYuK3jbSyKXVQ/hYnbrPpiVY2C86WsNisr0zSjrVzbInvGMKS78zP3eZlgWHfoEgqU/gh7E9xRXoPQYINUTk4OAG3btq2wvW3bto59OTk5tGnTpsJ+b29vWrVq5WhTlcTERAIDAx2vsLAwF1fvbiHYV4msOARl6lT7nagaL5pmtbefNq2qvSbgNmBRXQoVEWlUzN5mkkclMy56HN4m70qBymSY8DZ5My56HMmjkjXpwDUgJSOlVkFK097LNa/vS7x0P/h6g6mGo55Nhr39gvsv2XhsGZz/1i0lelqDDVLu9MQTT5Cfn+94nTx50tMl1cIiYKDj3ZEjkJp69TtRl7NY7MMBK87h4QWMA5IB/ZIgIk2Ln7cfS+9eyqmpp5g/ZD4jIkZwS4dbGBExgvlD5nNq6imW3r1UIeoakV9Uu6FLmvZernmB3ejdM4qPpoHZ5+p3prxM9nYfTYPenS7dY4ONt4Hl2pukpcFOfx4aGgrA6dOnadeunWP76dOnufnmmx1tvvvuuwr9ysrKOHPmjKN/VcxmM2ZzY/8ANAMbgYnA6yxfbn/uydkgBfZ+y5bBggVtgcnYQ1SIK4sVEWl0QvxDmDFoBjMGzfB0KeJGgX5XnsCpOpr2XpqEETsYeqEjO2blM+1NSNlvD0yXri9V/n5wN/udqIoh6qJzGfYhfrdcW8+UNtg7UuHh4YSGhpKSkuLYVlBQwOeff05MTAwAMTEx5OXlsXfvXkeb1NRUrFYr/fv3r/ea658f9kBlsGdP7UIU2PvZv4V9gD+hECUiIk3F0PChV30m7nKa9l6aDN8A+GUmvXtGselJ+wQSCXfBnd3g5k72rwl32bdvfLKaEFXu+GvX3HToHr0jVVhYyLFjxxzvMzIySE9Pp1WrVnTs2JGEhASee+45unTpQnh4OE8//TTt27d3zOzXrVs37rrrLh599FFeffVVSktLmTRpEr/73e9qPGNf45YLvAbYyK/9pCoA5OUB7K5zRSIiIo3J6D6jeWrzU049J6Vp76VJ8Q2Cn++HM/voktyPBffXfIbLCmxW+HolRF07d/k9ekdqz5499OnThz59+gAwdepU+vTpwzPPPAPAH//4RyZPnsy4ceP4yU9+QmFhIevXr8fPz89xjDfffJOuXbsydOhQfvaznzFo0CCWL1/ukeupf6sA+z/8gbUbmeAQFARQdJVWIiIi15YQ/xDG9h1b47tSJsPEmL5jNPW5ND2tekPPZ+twACvkXFuTtDSYdaQ8qfGtI1VuBPbnpGD6dPtiu7V9RiohARYsuA64tm65ioiIXE1xWTEj1oy46qK85dPea8ZGabKKcuGD9mCr5V2p626B2OqXKGooGv06UlIT/x3PN25c3Z6ReuwxgJ+4pCoREZHGRNPei9SQXwjcOLb2/X2CXFZKQ6A7Ulwbd6QAhg6FrVudC1ReXjB4MGzcCHAIuMnFNYqIiDQeuedyWfXFKlIzUskrziPIHMSQ8CGM7jOaEH9NxiSCpRjWdbPPxOcUE9w8v1E8I1XTbKAgRWMOUi/w/9u7++CoyruN49duApsETAIEEgMJBGGkCKY0VBqFtkiGSBltbce2TEojYHmgOPLi0IhWndFBsJ2x2BkbFcV2platM4iFsTIxoQLzpLwEAkYpQqFAhRAszQtoILD388fJ7pMNgeSEfT37/czs4J5z787v7K9J9uo5576lR+W7T2rfPqmwULpwwVpstztut+TxSNXVUn6+ZE1ewTXfAAAAuIbzJ6R3h0uyESNcfaR7T0pJ0f9dk0v74sJcdWxhfr60caMVjhISrv3KhARr3MaNvhAlSa+GqlAAAAA4Rb8cadT/SHL18AVu6aZ5MRGi7CBIxbTBkh5QxzZOm2adYfr2t63nnQOV7/nUqda4adM67nXWTCoAAAAIkYI10uAp6j5OuKUhU6zxDhPRdaQQDGskfSJpu3yX+OXnSx98IB06JL30krXYbmOjNcV5QYE1scTo0V29V2N4SgYAAEBsS/BId26WapZai+0ar3zfRS1uyeW2JqcoWGONdxjukVIs3yPl0yppqazFeTv/j9iO6ZI2B6soAAAAxIPWM9KRddY6UW2N1ux8WXdKI+daM/3FGCabsCH2g5TPGVmL9L4i6bDN17olPSMp+mdSAQAAAEKFySbi0mBZQeh/Zf+qzQRJ84JeEQAAAOBEBClHunISimtzywpRzppJBQAAAAgVgpRjrZE0WT2aSUVT2scDAAAA6AmClGN5ZE0cMV/WZX6dW+1u3z6/fZzzZlIBAAAAQoXpzx0tSVK5pKdkTUJRJWuK83RJd8pa0Df2ZlIBAAAAIo0gFRd8k1AwIx8AAACuQ+sZ6Z+vSqerpLYmqU+alDUtZqc6vx4EKQAAAADXdrlVqllihajOi+/WV0r7funoxXe7QpACAAAAcHWXW6WqYunMdgUEKD+vFa4Ovyw1H5Cmbo6LMMVkEwAAAACurmbpNUJUR16pYZt15ioOEKQAAAAAdK31jPTPV9R9iPLxWuNbPw9lVVGBIOVIZyStljRd0qT2f59t3w4AAAD00JF17fdE2WC80pFXQ1NPFCFIOUqrpAWSsiU9JqlC0s72fx9t375Q0oVIFQgAAIBYUl+pnp+N8vFK9VWhqCaqMNmEY7RKKpZ0jZsA5ZX0sqQDYhFeAAAAdKutqZevawxqGdGIM1KOsVRXD1EdeSVtk7Qk1AUBAAAg1vVJ6+Xr0oNaRjQiSDnCAVlnmmzcBKhXJDn/JkAAAABch6xpsh8Z3FLWnaGoJqoQpGKa756ocerVtaty/k2AAAAAuA4j50oum5HBlSCNnBeaeqIIQSpm+e6JWiv7IUrtr3H+TYAAAAC4DkmDpZseUM9jg1u6aZ6UlBHKqqICQSpm9fSeqGtpDE4pAAAAcK6CNdLgyeo+OrilIVOs8XGAIBWTzsi6x+l6QpQkpV9/KQAAAHC2BI9052Zp1HzJlagrI4Tb2j5qvjR1szU+DjD9eUxap+sPUW5Jzr8JEAAAAEGQkCTdVi7d+pS1SG99lTXFeZ90a2KJkXOtywDjCEEqJvVmYbTOEiQ5/yZAAAAABFHSYGlsmfWIc1zaF5N6uTCan1tWiHL+TYAAAABAKBCkYlIvF0aTZLV8iqQ1wSkFAAAAiEMEqZjUm4XR1P6a+ZI2S4qPmwABAACAUCBIxaS56tUK0/pYUrkIUQAAAMD1IUjFpMGSbC6MpvmSxoSsIgAAACCeEKRi1hpJPVwYjXuiAAAAgKAiSMUsj6x7nebLmsW+i4XRlCjuiQIAAACCj3WkYlqSrHuenpK1SG+VpEZJ6bIW250r6zJAAAAAAMFEkHKEwZLK2h8AAAAAQo1L+wAAAADAJoIUAAAAANhEkAIAAAAAmwhSAAAAAGATQQoAAAAAbCJIAQAAAIBNBCkAAAAAsIkgBQAAAAA2EaQAAAAAwCaCFAAAAADYRJACAAAAAJscE6ReeOEFjRgxQklJSZo0aZJ27twZ6ZIAAAAAOJQjgtRbb72lZcuW6cknn9SePXuUn5+v4uJiNTQ0RLo0AAAAAA7kiCD13HPP6Wc/+5nmzJmjsWPH6sUXX1RKSorWrVsX6dKiwBlJqyVNlzSp/d9n27cDAAAA6I3ESBdwvS5evKiamhqtWLHCv83tdquoqEjV1dVdvubChQu6cOGC/3lzc3PI6wy/VklLJL0qydv+8KmU9EtJD0haI8kT5toAAACA2BbzZ6Q+//xzXb58WZmZmQHbMzMzVV9f3+VrVq1apbS0NP8jJycnHKWGUaukYklrJV1SYIhS+/NLkl5uH3dBAAAAAHou5oNUb6xYsUJNTU3+x4kTJyJdUpAtlbRdVwaozryStsk6cwUAAACgp2L+0r6MjAwlJCTo9OnTAdtPnz6trKysLl/j8Xjk8Tj1crYzkl5R9yHKx9s+/mlJGaEqCgAAAHCUmD8j1bdvXxUUFKiystK/zev1qrKyUoWFhRGsLFLWqechyscr614qAAAAAD0R80FKkpYtW6a1a9fqD3/4gw4cOKCFCxfq/PnzmjNnTqRLi4BK9S5IVYWgFgAAAMCZYv7SPkn60Y9+pDNnzuiJJ55QfX29vvrVr+r999+/YgKK+NDUy9c1BrMIAAAAwNFcxhgT6SIirbm5WWlpaWpqalJqamqky7lO0yVV9PJ1m4NcCwAAABBbepoNHHFpHzqaJvttdUu6MwS1AAAAAM5EkHKcubLf1gRJ80JQCwAAAOBMBCnHGSzpAfW8tW5ZIYqpzwEAAICeIkg50hpJk9V9e92SprSPBwAAANBTBClH8siaOGK+rIkZO7fZ3b59fvs4py5ODAAAAISGI6Y/R1eSJJVLekrWIr1VsqY4T5c1scRcWZcBAgAAALCLIOV4gyWVtT8AAAAABAOX9gEAAACATQQpAAAAALCJIAUAAAAANhGkAAAAAMAmghQAAAAA2ESQAgAAAACbCFIAAAAAYBNBCgAAAABsIkgBAAAAgE0EKQAAAACwiSAFAAAAADYlRrqAaGCMkSQ1NzdHuBIAAAAAkeTLBL6McDUEKUktLS2SpJycnAhXAgAAACAatLS0KC0t7ar7Xaa7qBUHvF6vTp48qRtuuEEulyvS5cS95uZm5eTk6MSJE0pNTY10OWhHX6IPPYk+9CT60JPoQ0+iDz0JZIxRS0uLsrOz5XZf/U4ozkhJcrvdGjZsWKTLQCepqan8MEch+hJ96En0oSfRh55EH3oSfejJ/7vWmSgfJpsAAAAAAJsIUgAAAABgE0EKUcfj8ejJJ5+Ux+OJdCnogL5EH3oSfehJ9KEn0YeeRB960jtMNgEAAAAANnFGCgAAAABsIkgBAAAAgE0EKQAAAACwiSAFAAAAADYRpBAWq1at0te//nXdcMMNGjJkiL73ve/p4MGDAWNaW1u1aNEiDRo0SP3799cPfvADnT59OmDM8ePHNXPmTKWkpGjIkCFavny5Ll26FM5DcazVq1fL5XJpyZIl/m30JPw+++wz/eQnP9GgQYOUnJys8ePHa/fu3f79xhg98cQTuvHGG5WcnKyioiIdOnQo4D3Onj2rkpISpaamKj09XfPmzdO5c+fCfSiOcfnyZT3++OPKy8tTcnKybrrpJj399NPqOFcTfQmtrVu36u6771Z2drZcLpc2bNgQsD9Yn//+/fs1ZcoUJSUlKScnR7/61a9CfWgx61o9aWtrU1lZmcaPH69+/fopOztbP/3pT3Xy5MmA96AnwdXdz0lHCxYskMvl0po1awK20xObDBAGxcXF5rXXXjN1dXWmtrbWfOc73zG5ubnm3Llz/jELFiwwOTk5prKy0uzevdt84xvfMLfffrt//6VLl8y4ceNMUVGR2bt3r3nvvfdMRkaGWbFiRSQOyVF27txpRowYYW699VazePFi/3Z6El5nz541w4cPN/fff7/ZsWOHOXLkiNm8ebM5fPiwf8zq1atNWlqa2bBhg9m3b5+55557TF5envnyyy/9Y+666y6Tn59v/v73v5tt27aZUaNGmVmzZkXikBxh5cqVZtCgQWbTpk3m6NGj5u233zb9+/c3zz//vH8MfQmt9957zzz22GNm/fr1RpJ55513AvYH4/NvamoymZmZpqSkxNTV1Zk33njDJCcnm5deeilchxlTrtWTxsZGU1RUZN566y3zj3/8w1RXV5vbbrvNFBQUBLwHPQmu7n5OfNavX2/y8/NNdna2+c1vfhOwj57YQ5BCRDQ0NBhJ5sMPPzTGWL90+/TpY95++23/mAMHDhhJprq62hhj/YJwu92mvr7eP6a8vNykpqaaCxcuhPcAHKSlpcWMHj3aVFRUmG9961v+IEVPwq+srMxMnjz5qvu9Xq/Jysoyv/71r/3bGhsbjcfjMW+88YYxxphPPvnESDK7du3yj/nrX/9qXC6X+eyzz0JXvIPNnDnTzJ07N2Db97//fVNSUmKMoS/h1vkLYrA+/9/97ndmwIABAb+7ysrKzM033xziI4p91/rS7rNz504jyRw7dswYQ09C7Wo9+fe//22GDh1q6urqzPDhwwOCFD2xj0v7EBFNTU2SpIEDB0qSampq1NbWpqKiIv+YMWPGKDc3V9XV1ZKk6upqjR8/XpmZmf4xxcXFam5u1scffxzG6p1l0aJFmjlzZsBnL9GTSPjLX/6iiRMn6r777tOQIUM0YcIErV271r//6NGjqq+vD+hJWlqaJk2aFNCT9PR0TZw40T+mqKhIbrdbO3bsCN/BOMjtt9+uyspKffrpp5Kkffv2afv27ZoxY4Yk+hJpwfr8q6ur9c1vflN9+/b1jykuLtbBgwf13//+N0xH41xNTU1yuVxKT0+XRE8iwev1avbs2Vq+fLluueWWK/bTE/sSI10A4o/X69WSJUt0xx13aNy4cZKk+vp69e3b1/8L1iczM1P19fX+MR2/sPv2+/bBvjfffFN79uzRrl27rthHT8LvyJEjKi8v17Jly/Too49q165deuihh9S3b1+Vlpb6P9OuPvOOPRkyZEjA/sTERA0cOJCe9NIjjzyi5uZmjRkzRgkJCbp8+bJWrlypkpISSaIvERasz7++vl55eXlXvIdv34ABA0JSfzxobW1VWVmZZs2apdTUVEn0JBKeffZZJSYm6qGHHupyPz2xjyCFsFu0aJHq6uq0ffv2SJcS106cOKHFixeroqJCSUlJkS4Hsv5PhokTJ+qZZ56RJE2YMEF1dXV68cUXVVpaGuHq4tef//xnvf766/rTn/6kW265RbW1tVqyZImys7PpC9CNtrY2/fCHP5QxRuXl5ZEuJ27V1NTo+eef1549e+RyuSJdjmNwaR/C6sEHH9SmTZu0ZcsWDRs2zL89KytLFy9eVGNjY8D406dPKysryz+m84xxvue+Mei5mpoaNTQ06Gtf+5oSExOVmJioDz/8UL/97W+VmJiozMxMehJmN954o8aOHRuw7Stf+YqOHz8u6f8/064+8449aWhoCNh/6dIlnT17lp700vLly/XII4/oxz/+scaPH6/Zs2dr6dKlWrVqlST6EmnB+vz5fRZ8vhB17NgxVVRU+M9GSfQk3LZt26aGhgbl5ub6/+YfO3ZMDz/8sEaMGCGJnvQGQQphYYzRgw8+qHfeeUdVVVVXnBYuKChQnz59VFlZ6d928OBBHT9+XIWFhZKkwsJCffTRRwE/5L5fzJ2/fKJ706ZN00cffaTa2lr/Y+LEiSopKfH/Nz0JrzvuuOOKZQE+/fRTDR8+XJKUl5enrKysgJ40Nzdrx44dAT1pbGxUTU2Nf0xVVZW8Xq8mTZoUhqNwni+++EJud+Cfy4SEBHm9Xkn0JdKC9fkXFhZq69atamtr84+pqKjQzTffHHeXKwWDL0QdOnRIH3zwgQYNGhSwn56E1+zZs7V///6Av/nZ2dlavny5Nm/eLIme9EqkZ7tAfFi4cKFJS0szf/vb38ypU6f8jy+++MI/ZsGCBSY3N9dUVVWZ3bt3m8LCQlNYWOjf75tqe/r06aa2tta8//77ZvDgwUy1HUQdZ+0zhp6E286dO01iYqJZuXKlOXTokHn99ddNSkqK+eMf/+gfs3r1apOenm7effdds3//fvPd7363y2meJ0yYYHbs2GG2b99uRo8ezTTb16G0tNQMHTrUP/35+vXrTUZGhvnFL37hH0NfQqulpcXs3bvX7N2710gyzz33nNm7d69/BrhgfP6NjY0mMzPTzJ4929TV1Zk333zTpKSkxO20zt25Vk8uXrxo7rnnHjNs2DBTW1sb8He/42xv9CS4uvs56azzrH3G0BO7CFIIC0ldPl577TX/mC+//NL8/Oc/NwMGDDApKSnm3nvvNadOnQp4n3/9619mxowZJjk52WRkZJiHH37YtLW1hflonKtzkKIn4bdx40Yzbtw44/F4zJgxY8zLL78csN/r9ZrHH3/cZGZmGo/HY6ZNm2YOHjwYMOY///mPmTVrlunfv79JTU01c+bMMS0tLeE8DEdpbm42ixcvNrm5uSYpKcmMHDnSPPbYYwFfCOlLaG3ZsqXLvyGlpaXGmOB9/vv27TOTJ082Ho/HDB061KxevTpchxhzrtWTo0ePXvXv/pYtW/zvQU+Cq7ufk866ClL0xB6XMR2WZgcAAAAAdIt7pAAAAADAJoIUAAAAANhEkAIAAAAAmwhSAAAAAGATQQoAAAAAbCJIAQAAAIBNBCkAAAAAsIkgBQAAAAA2EaQAAAAAwCaCFAAgrtx///1yuVxXPA4fPqytW7fq7rvvVnZ2tlwulzZs2BDpcgEAUYogBQCIO3fddZdOnToV8MjLy9P58+eVn5+vF154IdIlAgCiXGKkCwAAINw8Ho+ysrKu2D5jxgzNmDEjAhUBAGINZ6QAAAAAwCaCFAAg7mzatEn9+/f3P+67775IlwQAiDFc2gcAiDtTp05VeXm5/3m/fv0iWA0AIBYRpAAAcadfv34aNWpUpMsAAMQwLu0DAAAAAJs4IwUAQLtz587p8OHD/udHjx5VbW2tBg4cqNzc3AhWBgCINgQpAADa7d69W1OnTvU/X7ZsmSSptLRUv//97yNUFQAgGrmMMSbSRQAAAABALOEeKQAAAACwiSAFAAAAADYRpAAAAADAJoIUAAAAANhEkAIAAAAAmwhSAAAAAGATQQoAAAAAbCJIAQAAAIBNBCkAAAAAsIkgBQAAAAA2EaQAAAAAwKb/A3zSbzgAKdqaAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and LinearRegression model\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.LinearRegression())\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "prefix = \"e2e_test3\"\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 47b5e9afc71538137648f85c731b8f07463a5a97 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 10:44:58 +0000 Subject: [PATCH 05/78] old test notebook removed --- app/services/calib_validation/test/test.ipynb | 1173 ----------------- 1 file changed, 1173 deletions(-) delete mode 100644 app/services/calib_validation/test/test.ipynb diff --git a/app/services/calib_validation/test/test.ipynb b/app/services/calib_validation/test/test.ipynb deleted file mode 100644 index 9832abb0..00000000 --- a/app/services/calib_validation/test/test.ipynb +++ /dev/null @@ -1,1173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", - "from sklearn.metrics import mean_absolute_error, r2_score\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.cluster import KMeans\n", - "from sklearn import linear_model\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('./csv/data/_fixed_train_data.csv')\n", - "df = df.drop(['screen_height', 'screen_width'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "df_test = pd.read_csv('./csv/data/_fixed_train_data.csv')\n", - "df_test = df_test.drop(['screen_height', 'screen_width'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(15, 6)" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0713.406067382.448242576.005981372.06063850738
1709.982239380.854797574.124084370.91980050738
2706.978210378.248840571.279175369.64007650738
3697.825378373.718597563.162903366.61047450738
4688.999634370.481934555.069763364.33447350738
\n", - "
" - ], - "text/plain": [ - " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", - "0 713.406067 382.448242 576.005981 372.060638 50 738\n", - "1 709.982239 380.854797 574.124084 370.919800 50 738\n", - "2 706.978210 378.248840 571.279175 369.640076 50 738\n", - "3 697.825378 373.718597 563.162903 366.610474 50 738\n", - "4 688.999634 370.481934 555.069763 364.334473 50 738" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count15.00000015.00000015.00000015.00000015.00000015.000000
mean684.265983367.405735548.828121362.533893720.000000394.000000
std15.8918077.64965815.7662444.952132566.253351290.733064
min667.526611361.018280531.639587357.95562750.00000050.000000
25%669.840271362.248199533.896545358.58416750.00000050.000000
50%680.911743362.848175546.262268360.619080720.000000394.000000
75%693.412506372.100266559.116333365.4724731390.000000738.000000
max713.406067382.448242576.005981372.0606381390.000000738.000000
\n", - "
" - ], - "text/plain": [ - " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", - "count 15.000000 15.000000 15.000000 15.000000 15.000000 \n", - "mean 684.265983 367.405735 548.828121 362.533893 720.000000 \n", - "std 15.891807 7.649658 15.766244 4.952132 566.253351 \n", - "min 667.526611 361.018280 531.639587 357.955627 50.000000 \n", - "25% 669.840271 362.248199 533.896545 358.584167 50.000000 \n", - "50% 680.911743 362.848175 546.262268 360.619080 720.000000 \n", - "75% 693.412506 372.100266 559.116333 365.472473 1390.000000 \n", - "max 713.406067 382.448242 576.005981 372.060638 1390.000000 \n", - "\n", - " point_y \n", - "count 15.000000 \n", - "mean 394.000000 \n", - "std 290.733064 \n", - "min 50.000000 \n", - "25% 50.000000 \n", - "50% 394.000000 \n", - "75% 738.000000 \n", - "max 738.000000 " - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(df, x_vars=['left_iris_y', 'right_iris_y', 'left_iris_x',\n", - " 'right_iris_x'], y_vars=['point_x', 'point_y'], kind='reg')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(df.corr(), annot=True, cmap='RdYlGn', linewidths=0.2)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plot eyes point\n", - "plt.scatter(df['left_iris_x'], df['left_iris_y'], color='blue')\n", - "plt.scatter(df['right_iris_x'], df['right_iris_y'], color='red')\n", - "plt.scatter(df['point_x'], df['point_y'], color='green')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "X_train_x = df[['left_iris_x', 'right_iris_x']]\n", - "y_train_x = df['point_x']" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "sc = StandardScaler()\n", - "X_train_x = sc.fit_transform(X_train_x)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.89801278, 1.78430313],\n", - " [1.67500486, 1.6607513 ],\n", - " [1.47934021, 1.47397496],\n", - " [0.88317885, 0.94111883],\n", - " [0.30832202, 0.4097814 ]])" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_x[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 50\n", - "1 50\n", - "2 50\n", - "3 50\n", - "4 50\n", - "Name: point_x, dtype: int64" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_train_x[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "X_test_x = df_test[['left_iris_x', 'right_iris_x']]\n", - "y_test_x = df_test['point_x']" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "sc = StandardScaler()\n", - "X_test_x = sc.fit_transform(X_test_x)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9502720165361552" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_x = make_pipeline(PolynomialFeatures(2), linear_model.LinearRegression())\n", - "model_x.fit(X_train_x, y_train_x)\n", - "y_pred_x = model_x.predict(X_test_x)\n", - "r2_score(y_test_x, y_pred_x)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 80.43413002, -139.78973472, 161.42427965, 203.95317435,\n", - " 83.62711798, 898.34911115, 813.63124394, 602.61975406,\n", - " 566.5263435 , 772.75310198, 1489.21024756, 1481.56072941,\n", - " 1253.710242 , 1246.13234658, 1285.85791253])" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_pred_x" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.regplot(x=y_test_x, y=y_pred_x)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "X_train_y = df[['left_iris_y', 'right_iris_y']]\n", - "y_train_y = df['point_y']" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "sc = StandardScaler()\n", - "X_train_y = sc.fit_transform(X_train_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.03544708, 1.99128746],\n", - " [1.81983325, 1.75282852],\n", - " [1.46721332, 1.48533963],\n", - " [0.85421246, 0.85208996],\n", - " [0.41624969, 0.37635854]])" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_y[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 738\n", - "1 738\n", - "2 738\n", - "3 738\n", - "4 738\n", - "Name: point_y, dtype: int64" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_train_y[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "X_test_y = df_test[['left_iris_y', 'right_iris_y']]\n", - "y_test_y = df_test['point_y']" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [], - "source": [ - "sc = StandardScaler()\n", - "X_test_y = sc.fit_transform(X_test_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9630393377798092" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = make_pipeline(PolynomialFeatures(degree=2), linear_model.LinearRegression())\n", - "model.fit(X_train_y, y_train_y)\n", - "y_pred_y = model.predict(X_test_y)\n", - "r2_score(y_test_y, y_pred_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.regplot(x=y_test_y, y=y_pred_y)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = {'True X': y_test_x, 'Predicted X': y_pred_x,\n", - " 'True Y': y_test_y, 'Predicted Y': y_pred_y}\n", - "\n", - "sns.scatterplot(x='True X', y='True Y', data=data,\n", - " label='True Values', alpha=0.7)\n", - "sns.scatterplot(x='Predicted X', y='Predicted Y', data=data,\n", - " label='Predicted Values', alpha=0.7)\n", - "\n", - "plt.title('True and Predicted Points for X and Y')\n", - "plt.xlabel('X Values')\n", - "plt.ylabel('Y Values')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "df_data = pd.DataFrame(data)\n", - "df_data['True XY'] = list(zip(df_data['True X'], df_data['True Y']))" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
True XPredicted XTrue YPredicted YTrue XY
05080.434130738691.611750(50, 738)
150-139.789735738779.160708(50, 738)
250161.424280738742.013859(50, 738)
350203.953174738776.662459(50, 738)
45083.627118738711.931083(50, 738)
\n", - "
" - ], - "text/plain": [ - " True X Predicted X True Y Predicted Y True XY\n", - "0 50 80.434130 738 691.611750 (50, 738)\n", - "1 50 -139.789735 738 779.160708 (50, 738)\n", - "2 50 161.424280 738 742.013859 (50, 738)\n", - "3 50 203.953174 738 776.662459 (50, 738)\n", - "4 50 83.627118 738 711.931083 (50, 738)" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(15, 5)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "df_data = df_data[(df_data['Predicted X'] >= 0) &\n", - " (df_data['Predicted Y'] >= 0)]\n", - "df_data = df_data[(abs(df_data['Predicted X'] - df_data['True X']) <= 100)\n", - " & (abs(df_data['Predicted Y'] - df_data['True Y']) <= 100)]" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5, 5)" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean True XY\n", - "(50, 738) 0.786588\n", - "(720, 394) 1.153197\n", - "(1390, 50) 1.060215\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# Precision is calculated via the Root Mean Square from the\n", - "# successive data points [in degrees of visual angle θi between\n", - "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", - "# individually and as a mean from the two\n", - "\n", - "# Another option to describe the variation in the data is to\n", - "# measure the standard deviation of the data set, equivalent\n", - "# to the RMS normalized by the mean\n", - "\n", - "def func_x(group): return np.sqrt(\n", - " np.sum(np.square([group['Predicted X'], group['True X']])))\n", - "\n", - "\n", - "def func_y(group): return np.sqrt(\n", - " np.sum(np.square([group['Predicted Y'], group['True Y']])))\n", - "\n", - "\n", - "precision_x = df_data.groupby('True XY').apply(func_x)\n", - "precision_y = df_data.groupby('True XY').apply(func_y)\n", - "\n", - "precision_xy = (precision_x + precision_y) / 2\n", - "precision_xy = precision_xy / np.mean(precision_xy)\n", - "print('mean', precision_xy)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "data = {}\n", - "\n", - "for index, row in df_data.iterrows():\n", - "\n", - " outer_key = str(row['True X'])\n", - " inner_key = str(row['True Y'])\n", - "\n", - " if outer_key not in data:\n", - " data[outer_key] = {}\n", - "\n", - " data[outer_key][inner_key] = {\n", - " 'predicted_x': df_data[(df_data['True X'] == row['True X']) & (df_data['True Y'] == row['True Y'])]['Predicted X'].values.tolist(),\n", - " 'predicted_y': df_data[(df_data['True X'] == row['True X']) & (df_data['True Y'] == row['True Y'])]['Predicted Y'].values.tolist(),\n", - " 'PrecisionSD': precision_xy[(row['True X'], row['True Y'])]\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "data = np.array([y_pred_x, y_pred_y]).T" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(15, 2)" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "model = KMeans(n_clusters=5, n_init='auto', init='k-means++')\n", - "y_kmeans = model.fit_predict(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAABxtElEQVR4nO3de1yUdf7//+cMyKgogygHKSxIXc9msippWwqKZe5W1H7qZ6Z5SkNbj2nbyTyElrWWa5pl6n47bQdrN01JITU3PIeZp9RQKkUpF/AQB5nr98fArCOoDM4wII/77XbdaK7rfV3X63prOk/f1/W+TIZhGAIAAAAAVJjZ2wUAAAAAQE1DkAIAAAAAFxGkAAAAAMBFBCkAAAAAcBFBCgAAAABcRJACAAAAABcRpAAAAADARQQpAAAAAHARQQoAAAAAXESQAgDUSNdff70GDx7s7TIAALUUQQoAUO0cOnRIjzzyiKKiolS3bl0FBASoe/fueuWVV/Tbb795/Pxnz57V1KlTtW7dOo+fCwBQM/l6uwAAAM63cuVK3XfffbJYLHrooYfUrl07FRYWauPGjZo0aZJ2796tRYsWebSGs2fP6rnnnpMk3XbbbR49FwCgZiJIAQCqjYyMDN1///267rrrlJqaqqZNmzq2JSYm6uDBg1q5cqUXK7wyZ86ckb+/v7fLAAC4Abf2AQCqjRdeeEGnT5/W4sWLnUJUqebNm+svf/lLuftOnTpVJpOpzPqlS5fKZDLp8OHDjnXbtm1TfHy8mjRponr16ikyMlJDhgyRJB0+fFjBwcGSpOeee04mk0kmk0lTp0517L9v3z7de++9CgoKUt26dRUdHa1///vf5Z53/fr1evTRRxUSEqJrr71WknTq1CmNHTtW119/vSwWi0JCQtS7d2/t2LHDpf4CAHgPI1IAgGrjs88+U1RUlG6++WaPnePEiRPq06ePgoODNWXKFAUGBurw4cNavny5JCk4OFgLFizQqFGjdPfdd+uee+6RJHXo0EGStHv3bnXv3l3XXHONpkyZIn9/f33wwQe666679PHHH+vuu+92Ot+jjz6q4OBgPfPMMzpz5owkaeTIkfroo480evRotWnTRr/++qs2btyovXv36qabbvLYtQMA3IcgBQCoFvLy8vTzzz/rT3/6k0fP8/XXX+u///2vvvjiC0VHRzvWz5gxQ5Lk7++ve++9V6NGjVKHDh304IMPOu3/l7/8Rc2aNdPWrVtlsVgk2cNSjx49NHny5DJBKigoSCkpKfLx8XGsW7lypYYPH66XXnrJse7xxx93+7UCADyHW/sAANVCXl6eJKlhw4YePU9gYKAkacWKFSoqKnJp35MnTyo1NVV//vOfderUKf3yyy/65Zdf9Ouvvyo+Pl4HDhzQzz//7LTP8OHDnUJUaQ2bN2/W0aNHr+haAADeQ5ACAFQLAQEBkuzPD3nSrbfeqoSEBD333HNq0qSJ/vSnP2nJkiUqKCi47L4HDx6UYRh6+umnFRwc7LQ8++yzkuy3Dp4vMjKyzHFeeOEFfffdd4qIiFCXLl00depU/fDDD+65QABAleDWPgBAtRAQEKDw8HB99913ldq/vIkmJKm4uLhMu48++kibNm3SZ599puTkZA0ZMkQvvfSSNm3apAYNGlz0HDabTZI0ceJExcfHl9umefPmTp/r1atXps2f//xn3XLLLfrkk0/0xRdf6MUXX9Ts2bO1fPly3X777Ze8TgBA9cCIFACg2rjzzjt16NAhpaWlubxvo0aNJEk5OTlO648cOVJu+27dumnmzJnatm2b3nnnHe3evVvvv/++pIuHsqioKElSnTp1FBcXV+5S0VsTmzZtqkcffVSffvqpMjIy1LhxY82cObNC+wIAvI8gBQCoNh5//HH5+/tr2LBhOn78eJnthw4d0iuvvFLuvjfccIMkacOGDY51Z86c0bJly5za/fe//5VhGE7rbrzxRkly3N5Xv359SWVDWUhIiG677Ta9/vrrOnbsWJkasrOzL3F1dsXFxcrNzS1z3PDw8ArdXggAqB64tQ8AUG3ccMMNevfdd/V///d/at26tR566CG1a9dOhYWF+vrrr/Xhhx9q8ODB5e7bp08fNWvWTEOHDtWkSZPk4+Ojt956S8HBwcrMzHS0W7ZsmV577TXdfffduuGGG3Tq1Cm98cYbCggI0B133CHJfjtemzZt9M9//lMtW7ZUUFCQ2rVrp3bt2mn+/Pnq0aOH2rdvr+HDhysqKkrHjx9XWlqafvrpJ+3cufOS13jq1Clde+21uvfee9WxY0c1aNBAa9eu1datW51m8QMAVG8EKQBAtfLHP/5R3377rV588UX961//0oIFC2SxWNShQwe99NJLGj58eLn71alTR5988okeffRRPf300woLC9PYsWPVqFEjPfzww452t956q7Zs2aL3339fx48fl9VqVZcuXfTOO+84TQzx5ptvasyYMRo3bpwKCwv17LPPql27dmrTpo22bdum5557TkuXLtWvv/6qkJAQderUSc8888xlr69+/fp69NFH9cUXX2j58uWy2Wxq3ry5XnvtNY0aNerKOxAAUCVMxoX3NwAAAAAALolnpAAAAADARQQpAAAAAHARQQoAAAAAXESQAgAAAAAXEaQAAAAAwEUEKQAAAABwEe+RkmSz2XT06FE1bNhQJpPJ2+UAAAAA8BLDMHTq1CmFh4fLbL74uBNBStLRo0cVERHh7TIAAAAAVBM//vijrr322otuJ0hJatiwoSR7ZwUEBHi5GgAAAADekpeXp4iICEdGuBiClOS4nS8gIIAgBQAAAOCyj/ww2QQAAAAAuIggBQAAAAAu8mqQKi4u1tNPP63IyEjVq1dPN9xwg6ZPny7DMBxtDMPQM888o6ZNm6pevXqKi4vTgQMHnI5z8uRJDRgwQAEBAQoMDNTQoUN1+vTpqr4cAAAAALWEV5+Rmj17thYsWKBly5apbdu22rZtmx5++GFZrVY99thjkqQXXnhBr776qpYtW6bIyEg9/fTTio+P1549e1S3bl1J0oABA3Ts2DGtWbNGRUVFevjhhzVixAi9++673rw8AAAAXEUMw9C5c+dUXFzs7VJwBXx8fOTr63vFrz0yGecP/1SxO++8U6GhoVq8eLFjXUJCgurVq6e3335bhmEoPDxcEyZM0MSJEyVJubm5Cg0N1dKlS3X//fdr7969atOmjbZu3aro6GhJ0urVq3XHHXfop59+Unh4+GXryMvLk9VqVW5uLpNNAAAAoIzCwkIdO3ZMZ8+e9XYpcIP69euradOm8vPzK7OtotnAqyNSN998sxYtWqTvv/9eLVu21M6dO7Vx40a9/PLLkqSMjAxlZWUpLi7OsY/ValXXrl2Vlpam+++/X2lpaQoMDHSEKEmKi4uT2WzW5s2bdffdd5c5b0FBgQoKChyf8/LyPHiVAAAAqMlsNpsyMjLk4+Oj8PBw+fn5XfFoBrzDMAwVFhYqOztbGRkZatGixSVfunspXg1SU6ZMUV5enlq1aiUfHx8VFxdr5syZGjBggCQpKytLkhQaGuq0X2hoqGNbVlaWQkJCnLb7+voqKCjI0eZCSUlJeu6559x9OQAAALgKFRYWymazKSIiQvXr1/d2ObhC9erVU506dXTkyBEVFhY6HhdylVcnm/jggw/0zjvv6N1339WOHTu0bNkyzZkzR8uWLfPoeZ944gnl5uY6lh9//NGj5wMAAEDNV9mRC1Q/7vi19OqI1KRJkzRlyhTdf//9kqT27dvryJEjSkpK0qBBgxQWFiZJOn78uJo2berY7/jx47rxxhslSWFhYTpx4oTTcc+dO6eTJ0869r+QxWKRxWLxwBUBAAAAqA28GqvPnj1bJg36+PjIZrNJkiIjIxUWFqaUlBTH9ry8PG3evFkxMTGSpJiYGOXk5Gj79u2ONqmpqbLZbOratWsVXAUAAACA2sarI1L9+/fXzJkz1axZM7Vt21bffPONXn75ZQ0ZMkSSZDKZNHbsWM2YMUMtWrRwTH8eHh6uu+66S5LUunVr9e3bV8OHD9fChQtVVFSk0aNH6/7776/QjH3Vx/eSFknaJilXklVStKQRklp6sS4AAAC4RXa2tHixlJoq5eZKVqsUGysNGSIFB3u1NJPJpE8++cTxHRuX59URqXnz5unee+/Vo48+qtatW2vixIl65JFHNH36dEebxx9/XGPGjNGIESP0+9//XqdPn9bq1audHgp755131KpVK8XGxuqOO+5Qjx49tGjRIm9cUiXslBQr6Xey2eZKWi8pXdL6ks+/kxRX0g4AAAA1Tn6+NHKkFB4uPfmktGaNtGWL/edf/2pfP2qUdN6s0u6UlZWlMWPGKCoqShaLRREREerfv7/TXV/utG7dOplMJuXk5Hjk+JI0c+ZM3Xzzzapfv74CAwM9dp5L8ep7pKoL771HKkWG0V82W6F8fC7+YrfiYh+ZzX4ymT6TPXQBl5ctabGkVP1vjDNW0hBJ3v03LwAAapb8/HxlZGQoMjLS9Rne8vOl+Hhp40ap5PGVcpnN0i23SMnJkhuf5T98+LC6d++uwMBATZs2Te3bt1dRUZGSk5O1aNEi7du3T5J7R6TWrVunnj176r///e8VhRzDMFRcXCxf37I30T377LMKDAzUTz/9pMWLF7sc2i71a1rRbMDUI16zU4bRX4aRf8kQJUk+PsUyjAIZRn8xMoXLyZc0UlK4pCclrZG0peTnX0vWj5LkmX/zAgAATsaNu3yIkuzbv/pKGjvWrad/9NFHZTKZtGXLFiUkJKhly5Zq27atxo8fr02bNpW7T3kjSunp6TKZTDp8+LAk6ciRI+rfv78aNWokf39/tW3bVp9//rkOHz6snj17SpIaNWokk8mkwYMHl1yiTUlJSYqMjFS9evXUsWNHffTRR2XOu2rVKnXu3FkWi0UbN24st8bnnntO48aNU/v27a+8kyrJq89I1W7jS0aiKjYgaDbbVFxcKB+fCZLWerY01Fj5kuIlbZRU3h/XtpJlkaS9kpIlMX8lAAAekp0tvfnm5UNUKZvN3n76dKlJkys+/cmTJ7V69WrNnDlT/v7+ZbZfyWhRYmKiCgsLtWHDBvn7+2vPnj1q0KCBIiIi9PHHHyshIUH79+9XQECA6tWrJ8n+Lte3335bCxcuVIsWLbRhwwY9+OCDCg4O1q233uo49pQpUzRnzhxFRUWpUaNGla7R0whSXvG9pFT5+Li2l33kKkXSAUkt3F8WarxxuniIOp9N0leSxkpa4OGaAACotd56q+IhqpTNZp+QYvLkKz79wYMHZRiGWrVqdcXHulBmZqYSEhIcI0JRUVGObUFBQZKkkJAQR1grKCjQ888/r7Vr1zpm346KitLGjRv1+uuvOwWpadOmqXfv3m6v2d24tc8rFslmczFFlbDv97p7y8FVIVvSm7p8iCplK2n/i8cqAgCglktJqVyQSk11y+k9ORXCY489phkzZqh79+569tln9e23316y/cGDB3X27Fn17t1bDRo0cCz/+Mc/dOjQIae20dHRHqvbnQhSXrFNZvOln4u6GPt+2y/bDrXPW6p4iCplk31CCgAA4AG5uZXbz02z3bVo0UImk8kxoURFlb7n9fwgVlRU5NRm2LBh+uGHHzRw4EDt2rVL0dHRmjdv3kWPefr0aUnSypUrlZ6e7lj27Nnj9JyUpHJvQ6yOCFJeUcn/qRxy3FEErjIpqlyQcs+/eQEAgDKs1srt56bpvIOCghQfH6/58+frzJkzZbZfbKa74JJ3Wh07dsyxLj09vUy7iIgIjRw5UsuXL9eECRP0xhtvSJL8/PwkScXF/xs4aNOmjSwWizIzM9W8eXOnJSIiorKX6FUEKa+o5P9UDoHuKAJXmcrG8xx3FgEAAP4nNtY+rbkrzGapVy+3lTB//nwVFxerS5cu+vjjj3XgwAHt3btXr776quNZpQuVhpupU6fqwIEDWrlypV566SWnNmPHjlVycrIyMjK0Y8cOffnll2rdurUk6brrrpPJZNKKFSuUnZ2t06dPq2HDhpo4caLGjRunZcuW6dChQ9qxY4fmzZunZcuWuXxdmZmZSk9PV2ZmpoqLix0jXKUjX1WBIOUV0Vf4jFRn95aDq0Jl43mgO4sAAAD/M2SI60HKx0caOtRtJURFRWnHjh3q2bOnJkyYoHbt2ql3795KSUnRggXlTzlVp04dvffee9q3b586dOig2bNna8aMGU5tiouLlZiYqNatW6tv375q2bKlXnvtNUnSNddco+eee05TpkxRaGioRo8eLUmaPn26nn76aSUlJTn2W7lypSIjI12+rmeeeUadOnXSs88+q9OnT6tTp07q1KmTtm3b5vKxKosX8sobL+T9XtLvrnB/Zu2Ds9myvyfKldv7zJKel3Tl8wIBAHD1uqIX8o4aJS1aVLFJJ8xmacQI6SIBB+7DC3lrrJaSeqm42LVRKXv7OBGiUJ4hcv1/aB9J7vs3LwAAUMbcuVKPHpcfmTKbpVtusbdHjUCQ8pqXZTb7yWar2C+BzWaW2ewnaY5ny0KNFSxpmCr+P7VZ9hB15a/7AwAAF2WxSMnJ9pEmX9+ygcpstq8fMcLezmLxTp1wGS/k9ZqOMpk+k9RfxcWFJS/bLV9xsY/MZr+S9h2rrELUPHMl7dHlX8prlnRLSXsAAOBhdevab9ebNs3+kt7UVPsU54GB9oklhgyRSmbKQ81BkPKqWJlMafLxmSApRTabj9P7pUo/+/j0lH0kihCFS7NISpY0Tv97Oe/5gcpcsgyTPUTxb14AAFSh4GBp8mT7ghqPIOV1HSWtlXRAZvPrsr9sN0dSoMzmzpIeEc9EwRV1JS2QNE32l/SmqvR3lNRL9mep+DcvAACAK0OQqjZaiOef4E7Bss/Gx795AQAAuB+TTQAAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAFSB7Gxp1iypTx+pa1f7z9mz7eu9zWQy6dNPP/V2GTUKQQqoRrIlzZLUR1LXkp+zS9YDAICaKT9fGjlSCg+XnnxSWrNG2rLF/vOvf7WvHzVKKijwzPmzsrI0ZswYRUVFyWKxKCIiQv3791dKSopHzrdu3TqZTCbl5OR45PiHDx/W0KFDFRkZqXr16umGG27Qs88+q8LCQo+c72KYtQ+oBvIljZW0WGXf/ZQi6Snx7icAAGqi/HwpPl7auFGy2cput9nsy6JF0t69UnKyZHHjX/aHDx9W9+7dFRgYqBdffFHt27dXUVGRkpOTlZiYqH379rnvZG5mGIaKi4vl6+scWfbt2yebzabXX39dzZs313fffafhw4frzJkzmjOn6mbBZkQK8LJ8SfGS3pB0Ts4hSiWfz0laVNLOQ/9YBQAAPGDcuIuHqPPZbNJXX0ljx7r3/I8++qhMJpO2bNmihIQEtWzZUm3bttX48eO1adOmcvcpb0QpPT1dJpNJhw8fliQdOXJE/fv3V6NGjeTv76+2bdvq888/1+HDh9WzZ09JUqNGjWQymTR48OCSa7QpKSnJMZLUsWNHffTRR2XOu2rVKnXu3FkWi0UbN24sU1/fvn21ZMkS9enTR1FRUfrjH/+oiRMnavny5e7ptApiRArwsnGSNqpsgLqQTdJXso9cLfBwTQAA4MplZ0tvvnn5EFXKZrO3nz5datLkys9/8uRJrV69WjNnzpS/v3+Z7YGBgZU+dmJiogoLC7Vhwwb5+/trz549atCggSIiIvTxxx8rISFB+/fvV0BAgOrVqydJSkpK0ttvv62FCxeqRYsW2rBhgx588EEFBwfr1ltvdRx7ypQpmjNnjqKiotSoUaMK1ZObm6ugoKBKX09lEKQAL8qW9KYuH6JK2UraT5fkhj9fAQCAB731VsVDVCmbTVq8WJo8+crPf/DgQRmGoVatWl35wS6QmZmphIQEtW/fXpIUFRXl2FYaaEJCQhxhraCgQM8//7zWrl2rmJgYxz4bN27U66+/7hSkpk2bpt69e1e4loMHD2revHlVelufxK19gId9L2mipNskdSr5ObFkvfSWKh6iStlkf5YKAABUbykplQtSqanuOb9hGO45UDkee+wxzZgxQ927d9ezzz6rb7/99pLtDx48qLNnz6p3795q0KCBY/nHP/6hQ4cOObWNjo6ucB0///yz+vbtq/vuu0/Dhw+v1LVUFkEK8IidkmIl/U72KSLWS0ov+Tm3ZH2cMrSzUkHKTX++AgAAD8rNrdx+7prsrkWLFjKZTC5PKGE22yPC+UGsqKjIqc2wYcP0ww8/aODAgdq1a5eio6M1b968ix7z9OnTkqSVK1cqPT3dsezZs8fpOSlJ5d6GWJ6jR4+qZ8+euvnmm7Vo0aIK7eNOBCnA7VIkxcgemiSp+ILtpZ/Xaa5i1EuuTz2aU+naAABAVbFaK7ffFTy65CQoKEjx8fGaP3++zpw5U2b7xaYnDw4OliQdO3bMsS49Pb1Mu4iICI0cOVLLly/XhAkT9MYbb0iS/Pz8JEnFxf/7DtSmTRtZLBZlZmaqefPmTktERITL1/bzzz/rtttuU+fOnbVkyRJH+KtKBCnArXZK6i/7XHwXBqgLFctPBfpM/dVBO106S2DligMAAFUoNlZy9fu92Sz16uW+GubPn6/i4mJ16dJFH3/8sQ4cOKC9e/fq1VdfdTyrdKHScDN16lQdOHBAK1eu1EsvveTUZuzYsUpOTlZGRoZ27NihL7/8Uq1bt5YkXXfddTKZTFqxYoWys7N1+vRpNWzYUBMnTtS4ceO0bNkyHTp0SDt27NC8efO0bNkyl66pNEQ1a9ZMc+bMUXZ2trKyspSVlVW5TqokghTgVuMlFUqq2D3JZtnkp0LN0YQKn8EsyY1/vgIAAA8ZMsT1IOXjIw0d6r4aoqKitGPHDvXs2VMTJkxQu3bt1Lt3b6WkpGjBgvLnAa5Tp47ee+897du3Tx06dNDs2bM1Y8YMpzbFxcVKTExU69at1bdvX7Vs2VKvvfaaJOmaa67Rc889pylTpig0NFSjR4+WJE2fPl1PP/20kpKSHPutXLlSkZGRLl3TmjVrdPDgQaWkpOjaa69V06ZNHUtVMhmefAqthsjLy5PValVubq4CAgK8XQ5qrO9lf/apclroex1Ui8u2qyPpqJi1DwCAqpKfn6+MjAxFRkaqbt26Lu07apT9ZbsVmXTCbJZGjJAukm/gRpf6Na1oNmBECnCbRZJ8KrXnOfnoEb1+2XZmSUNFiAIAoKaYO1fq0ePyI1Nms3TLLfb2qBkIUoDbbNPln4sqn6+K1VnbL9nGLOkW2ef8AwAANYPFIiUn20eafH3LBiqz2b5+xAh7O4vFO3XCdbyQF3CbSs5xWuIG5chX9unNzx/9N5csw2QPUfz5CgBAzVK3rv12vWnT7C/pTU21T3EeGGifWGLIEKlkojzUIAQpwG0qOcdpiWYK1FHZX9KbKvsU54GyTywxRBJ/vgIAULMFB0uTJ9sX1HwEKcBtoiVtVOVu7/OR1FnBkiaXLAAAAKi+eEYKcJsRquwzUvb9HnFjLQAAAPAkRqSAEtmSFst+W12u7DfqxcqV2+payn4j3nq5Fqh8JPWUKjD1OQAAAKoHghRqvXxJY2UPURdO9JAi6Sm5MtHDy5JiJBVccKSLMUvykzSn4gUDAADA6whSqNXyJcXL/mRTebGnNFgtkrRXUrIuF6Y6SvpMUn9Jhbr0yJSP7CHqs5L9AADA1Sz7TLYWf7NYqRmpys3PlbWuVbGRsRrSaYiC/ZlWqqbhGSnUauN08RB1Ppukr2Qfubq8WElpkm4r+XzhS3pLP/csaRdboaMCAICaKf9cvkauGKnwl8P1ZOqTWvPDGm05ukVrflijv6b+VeEvh2vUylEqOFfgtRpNJpM+/fRTr52/JiJIodbKlvSmKnYDnkravSnplwq17ihpraTvZY9ft0m6seTn2JL1a8RIFAAAV7f8c/mKfzteb+x4Q+ds52QznL952AybztnOadH2RYp/O94jYSorK0tjxoxRVFSULBaLIiIi1L9/f6WkpLj9XJK0bt06mUwm5eTkeOT4kvTHP/5RzZo1U926ddW0aVMNHDhQR48e9dj5ykOQQq31lioeokrZZH+WquJayP7805eSvin5OUdMLAEAQO0wLnmcNmZuLBOgLmQzbPoq8yuNTR7r1vMfPnxYnTt3Vmpqql588UXt2rVLq1evVs+ePZWYmOjWc7mbYRg6d+5cudt69uypDz74QPv379fHH3+sQ4cO6d57763S+ghSqLVSVLkgleqBWgAAwNUn+0y23tzx5mVDVCmbYdObO97UL2crdv9LRTz66KMymUzasmWLEhIS1LJlS7Vt21bjx4/Xpk2byt2nvBGl9PR0mUwmHT58WJJ05MgR9e/fX40aNZK/v7/atm2rzz//XIcPH1bPnj0lSY0aNZLJZNLgwYPt12ezKSkpSZGRkapXr546duyojz76qMx5V61apc6dO8tisWjjxo3l1jhu3Dh169ZN1113nW6++WZNmTJFmzZtUlFR0ZV3WgUx2QRqrdxK7pfjziIAAMBV661v3qpwiCplM2xavGOxJveYfMXnP3nypFavXq2ZM2fK39+/zPbAwMBKHzsxMVGFhYXasGGD/P39tWfPHjVo0EARERH6+OOPlZCQoP379ysgIED16tWTJCUlJentt9/WwoUL1aJFC23YsEEPPviggoODdeuttzqOPWXKFM2ZM0dRUVFq1KhRha7znXfe0c0336w6depU+ppcRZBCrWWt5H6B7iwCAABctVIyUioVpFIzUt0SpA4ePCjDMNSqVasrPtaFMjMzlZCQoPbt20uSoqKiHNuCgoIkSSEhIY6wVlBQoOeff15r165VTEyMY5+NGzfq9ddfdwpS06ZNU+/evS9bw+TJk/X3v/9dZ8+eVbdu3bRixQp3XV6FcGsfaq1Yuf4/gFn2V+4CAABcTm5+5e5/ySnIccv5DcNwy3HK89hjj2nGjBnq3r27nn32WX377beXbH/w4EGdPXtWvXv3VoMGDRzLP/7xDx06dMipbXR0dIVqmDRpkr755ht98cUX8vHx0UMPPeTRa76QV4PU9ddfL5PJVGYpffAtPz9fiYmJaty4sRo0aKCEhAQdP37c6RiZmZnq16+f6tevr5CQEE2aNOmiD6UB5xsi1/8H8JE01AO1AACAq4+1buXufwm0BLrl/C1atJDJZNK+fftc2s9stn9DOj+UXPjs0bBhw/TDDz9o4MCB2rVrl6KjozVv3ryLHvP06dOSpJUrVyo9Pd2x7Nmzx+k5KUnl3oZYniZNmqhly5bq3bu33n//fX3++ecXfe7LE7wapLZu3apjx445ljVr1kiS7rvvPkn2h8g+++wzffjhh1q/fr2OHj2qe+65x7F/cXGx+vXrp8LCQn399ddatmyZli5dqmeeecYr14OaJVjSMFX8fwKz7CGqiccqAgAAV5PYyFiZTa593TabzOoV6Z77X4KCghQfH6/58+frzJkzZbZfbHry4GD7y4GPHTvmWJeenl6mXUREhEaOHKnly5drwoQJeuONNyRJfn5+kuzf1Uu1adNGFotFmZmZat68udMSERFR2Ut0sNnst1AWFFTdu7i8GqSCg4MVFhbmWFasWKEbbrhBt956q3Jzc7V48WK9/PLL6tWrlzp37qwlS5bo66+/diTNL774Qnv27NHbb7+tG2+8UbfffrumT5+u+fPnq7Cw0JuXhhogX9I5VWzmPrOkWyTN9WRBAADgqjKk0xCXg5SPyUdDb3Lf/S/z589XcXGxunTpoo8//lgHDhzQ3r179eqrrzqeVbpQabiZOnWqDhw4oJUrV+qll15yajN27FglJycrIyNDO3bs0JdffqnWrVtLkq677jqZTCatWLFC2dnZOn36tBo2bKiJEydq3LhxWrZsmQ4dOqQdO3Zo3rx5WrZsmUvXtHnzZv39739Xenq6jhw5otTUVD3wwAO64YYbLnpNnlBtnpEqLCzU22+/rSFDhshkMmn79u0qKipSXFyco02rVq3UrFkzpaWlSZLS0tLUvn17hYaGOtrEx8crLy9Pu3fvvui5CgoKlJeX57SgdsmXFC/7u6QuxyT7SFSyJIsniwIAAFeVYP9gDbtpWIXDlNlk1tCbhqpJfffd/xIVFaUdO3aoZ8+emjBhgtq1a6fevXsrJSVFCxYsKHefOnXq6L333tO+ffvUoUMHzZ49WzNmzHBqU1xcrMTERLVu3Vp9+/ZVy5Yt9dprr0mSrrnmGj333HOaMmWKQkNDNXr0aEnS9OnT9fTTTyspKcmx38qVKxUZGenSNdWvX1/Lly9XbGysfve732no0KHq0KGD1q9fL4ul6r6tmYyqfCLrEj744AP9f//f/6fMzEyFh4fr3Xff1cMPP1xmeK5Lly7q2bOnZs+erREjRujIkSNKTk52bD979qz8/f31+eef6/bbby/3XFOnTtVzzz1XZn1ubq4CAgLce2GolkZJWqSKjUaZJD0iqfw/agAAwNUuPz9fGRkZioyMVN26dV3at+Bcgfq83eeyL+U1m8y6pdktSn4wWRZf/unW0y71a5qXlyer1XrZbFBtRqQWL16s22+/XeHh4R4/1xNPPKHc3FzH8uOPP3r8nKg+siW9qYq/jNcoae++V+MBAIDawuJrUfKDyRrReYR8zb5lRqfMJrN8zb4a0XkEIaqGqRbvkTpy5IjWrl2r5cuXO9aFhYWpsLBQOTk5Ti8LO378uMLCwhxttmzZ4nSs0ln9StuUx2KxVOmwH6qXt1TxEFXKJmmxpCt/owMAAKht6vrW1YJ+CzTttml665u3lJqRqpyCHAVaAtUrspeGdBqiYP9gb5cJF1WLILVkyRKFhISoX79+jnWdO3dWnTp1lJKSooSEBEnS/v37lZmZ6XiILCYmRjNnztSJEycUEhIiSVqzZo0CAgLUpk2bqr8Q1AgpqlyQShVBCgAAVF6wf7Am95jslpftwvu8HqRsNpuWLFmiQYMGydf3f+VYrVYNHTpU48ePV1BQkAICAjRmzBjFxMSoW7dukqQ+ffqoTZs2GjhwoF544QVlZWXpqaeeUmJiIiNOuKjKvRpPynFnEQAAAKjRvB6k1q5dq8zMTA0ZMqTMtr/97W8ym81KSEhQQUGB4uPjHbOBSJKPj49WrFihUaNGKSYmRv7+/ho0aJCmTZtWlZeAGqZyr8aTAt1ZBAAAAGq0ajNrnzdVdGYOXB1mS/qrXLu9zyzpeXFrHwAAtdGVzNqH6umqmrUPqCpD5PpvfB/Z3yUFAAAASAQp1ELBkoap4r/5zbKHKPe9Gg8AAAA1ndefkQK8Ya6kPZI26tK3+Jkl3VLSHgAA4Epky/46lVTZJ7+ySoqV/W4ZJj+veRiRQq1kkZQsaYTs/5pw4f8I5pL1I0raMQckAACorHxJIyWFS3pS0hpJW0p+/rVk/ShJBd4qUJLJZNKnn37qxQpqHoIUaq26khZIOir7RBJ9JHUp+fl8yfoFIkQBAIDKy5cUL+kNSedU9k4YW8n6RSXtPBGmsrKyNGbMGEVFRclisSgiIkL9+/dXSkqKB84mrVu3TiaTSTk5OR45/vkKCgp04403ymQyKT093ePnOx+39qHWC5Z9Nj5m5AMAAO42Tpd/lEAl27+SNFb2f8h1l8OHD6t79+4KDAzUiy++qPbt26uoqEjJyclKTEzUvn373Hg29zIMQ8XFxU7vmr3Q448/rvDwcO3cubMKK7NjRAoAAADwgGxJb6rir1yxlbT/xY01PProozKZTNqyZYsSEhLUsmVLtW3bVuPHj9emTZvK3ae8EaX09HSZTCYdPnxYknTkyBH1799fjRo1kr+/v9q2bavPP/9chw8fVs+ePSVJjRo1kslk0uDBg+3XZ7MpKSlJkZGRqlevnjp27KiPPvqozHlXrVqlzp07y2KxaOPGjRe9tlWrVumLL77QnDlzrqyTKokRKQAAAMAD3pJr761USfvFcs+dMidPntTq1as1c+ZM+fv7l9keGBhY6WMnJiaqsLBQGzZskL+/v/bs2aMGDRooIiJCH3/8sRISErR//34FBASoXr16kqSkpCS9/fbbWrhwoVq0aKENGzbowQcfVHBwsG699VbHsadMmaI5c+YoKipKjRo1Kvf8x48f1/Dhw/Xpp5+qfv36lb6OK0GQAgAAADwgRZULUqlyT5A6ePCgDMNQq1at3HA0Z5mZmUpISFD79u0lSVFRUY5tQUFBkqSQkBBHWCsoKNDzzz+vtWvXKiYmxrHPxo0b9frrrzsFqWnTpql3794XPbdhGBo8eLBGjhyp6OhoxyhZVSNIAQAAAB6QW8n9ctx0fsMw3HSksh577DGNGjVKX3zxheLi4pSQkKAOHTpctP3Bgwd19uzZMgGpsLBQnTp1cloXHR19yXPPmzdPp06d0hNPPFH5C3ADnpECAAAAPMBayf0C3XT+Fi1ayGQyuTyhhNlsjwjnB7GioiKnNsOGDdMPP/yggQMHateuXYqOjta8efMueszTp09LklauXKn09HTHsmfPHqfnpCSVexvi+VJTU5WWliaLxSJfX181b95ckj2ADRo0qOIXeoUIUgAAAIAHxMr1L9tmSb3cdP6goCDFx8dr/vz5OnPmTJntF5uePDjY/nrgY8eOOdaVN7V4RESERo4cqeXLl2vChAl64403JEl+fn6SpOLiYkfbNm3ayGKxKDMzU82bN3daIiIiXLquV199VTt37nSEsc8//1yS9M9//lMzZ8506VhXglv7AAAAAA8YIukpufaclI+koW6sYf78+erevbu6dOmiadOmqUOHDjp37pzWrFmjBQsWaO/evWX2KQ03U6dO1cyZM/X999/rpZdecmozduxY3X777WrZsqX++9//6ssvv1Tr1q0lSdddd51MJpNWrFihO+64Q/Xq1VPDhg01ceJEjRs3TjabTT169FBubq7+85//KCAgwKWRpGbNmjl9btCggSTphhtu0LXXXutqF1UaI1IAAACABwRLGqaKf+E2yx6imrixhqioKO3YsUM9e/bUhAkT1K5dO/Xu3VspKSlasKD8N1bVqVNH7733nvbt26cOHTpo9uzZmjFjhlOb4uJiJSYmqnXr1urbt69atmyp1157TZJ0zTXX6LnnntOUKVMUGhqq0aNHS5KmT5+up59+WklJSY79Vq5cqcjISDdecdUxGZ58Cq2GyMvLk9VqVW5urgICArxdDgAAAKqR/Px8ZWRkKDIyUnXr1nVp3wJJfXT5l/KaJd0iKVmSpbKFosIu9Wta0WzAiBQAAADgIRbZw9EI2Z+pufDLt7lk/QgRomoanpECAAAAPKiupAWSpsn+kt5U2ac4D5R9Yokhst8GiJqFIAUAAABUgWDZX7Trjpftwvu4tQ8AAAAAXESQAgAAAAAXEaQAAAAAwEUEKQAAAABwEUEKAAAAAFzErH0AAABAVciWtFj2+c9zJVklxYr5z2soRqQAAAAAT8qXNFJSuKQnJa2RtKXk519L1o+SVOCtAiWTyaRPP/3UewXUQAQpAAAAwFPyJcVLekPSOUm2C7bbStYvKmnngTCVlZWlMWPGKCoqShaLRREREerfv79SUlLcfzJJ69atk8lkUk5OjkeOL0nXX3+9TCaT0zJr1iyPna883NoHAAAAeMo4SRtVNkBdyCbpK0ljJS1w3+kPHz6s7t27KzAwUC+++KLat2+voqIiJScnKzExUfv27XPfydzMMAwVFxfL17f8yDJt2jQNHz7c8blhw4ZVVZokRqQAAAAAz8iW9KYuH6JK2Ura/+K+Eh599FGZTCZt2bJFCQkJatmypdq2bavx48dr06ZN5e5T3ohSenq6TCaTDh8+LEk6cuSI+vfvr0aNGsnf319t27bV559/rsOHD6tnz56SpEaNGslkMmnw4MH2y7PZlJSUpMjISNWrV08dO3bURx99VOa8q1atUufOnWWxWLRx48aLXlvDhg0VFhbmWPz9/a+ss1xEkAIAAAA84S1VPESVssk+IYUbnDx5UqtXr1ZiYmK5ISMwMLDSx05MTFRBQYE2bNigXbt2afbs2WrQoIEiIiL08ccfS5L279+vY8eO6ZVXXpEkJSUl6R//+IcWLlyo3bt3a9y4cXrwwQe1fv16p2NPmTJFs2bN0t69e9WhQ4eL1jBr1iw1btxYnTp10osvvqhz585V+noqg1v7AAAAAE9IUeWCVKqkyVd++oMHD8owDLVq1erKD3aBzMxMJSQkqH379pKkqKgox7agoCBJUkhIiCOsFRQU6Pnnn9fatWsVExPj2Gfjxo16/fXXdeuttzr2nzZtmnr37n3J8z/22GO66aabFBQUpK+//lpPPPGEjh07ppdfftmdl3lJBCkAAADAE3IruV+Oe05vGIZ7DlSOxx57TKNGjdIXX3yhuLg4JSQkXHL06ODBgzp79myZgFRYWKhOnTo5rYuOjr7s+cePH+/47w4dOsjPz0+PPPKIkpKSZLFYXLyayuHWPgAAAMATrJXcL9A9p2/RooVMJpPLE0qYzfaIcH4QKyoqcmozbNgw/fDDDxo4cKB27dql6OhozZs376LHPH36tCRp5cqVSk9Pdyx79uxxek5KUqWederatavOnTvneIarKhCkAAAAAE+Ilevfts2Sernn9EFBQYqPj9f8+fN15syZMtsvNj15cLD97cDHjh1zrEtPTy/TLiIiQiNHjtTy5cs1YcIEvfHGG5IkPz8/SVJxcbGjbZs2bWSxWJSZmanmzZs7LREREZW9RKf6zGazQkJCrvhYFUWQAgAAADxhiFz/tu0jaaj7Spg/f76Ki4vVpUsXffzxxzpw4ID27t2rV1991fGs0oVKw83UqVN14MABrVy5Ui+99JJTm7Fjxyo5OVkZGRnasWOHvvzyS7Vu3VqSdN1118lkMmnFihXKzs7W6dOn1bBhQ02cOFHjxo3TsmXLdOjQIe3YsUPz5s3TsmXLXLqmtLQ0zZ07Vzt37tQPP/ygd955xzFxRaNGjSrXUZVAkAIAAAA8IVjSMFX8G7dZ9hDVxH0lREVFaceOHerZs6cmTJigdu3aqXfv3kpJSdGCBeW/sKpOnTp67733tG/fPnXo0EGzZ8/WjBkznNoUFxcrMTFRrVu3Vt++fdWyZUu99tprkqRrrrlGzz33nKZMmaLQ0FCNHj1akjR9+nQ9/fTTSkpKcuy3cuVKRUZGunRNFotF77//vm699Va1bdtWM2fO1Lhx47Ro0aJK9FDlmQxPPoVWQ+Tl5clqtSo3N1cBAQHeLgcAAADVSH5+vjIyMhQZGam6deu6tnOBpD66/Et5zZJukZQsqWrmSqjVLvVrWtFswIgUAAAA4CkW2cPRCNnny77w27e5ZP0IEaJqGKY/BwAAADyprqQFkqbJ/pLeVNmnOA+UfWKJIbLfBogahSAFAAAAVIVg2V+064aX7cL7uLUPAAAAAFxEkAIAAAAAFxGkAAAAAMBFBCkAAAAAcBGTTQAAAABV4ntJiyRtk5QrySopWva5z1t6sS5UBkEKAAAA8KidksbLPu+5j6Ti87ZtlPSSpNiSnx2rvDpUjtdv7fv555/14IMPqnHjxqpXr57at2+vbdu2ObYbhqFnnnlGTZs2Vb169RQXF6cDBw44HePkyZMaMGCAAgICFBgYqKFDh+r06dNVfSkAAADABVIkxUhaX/K5+ILtpZ/XlbRLqZqyqrl169bJZDIpJyfnom2WLl2qwMDAKqvpQl4NUv/973/VvXt31alTR6tWrdKePXv00ksvqVGjRo42L7zwgl599VUtXLhQmzdvlr+/v+Lj45Wfn+9oM2DAAO3evVtr1qzRihUrtGHDBo0YMcIblwQAAACU2Cmpv6R8lQ1QFyqWVFDSfqfbK8nKytKYMWMUFRUli8WiiIgI9e/fXykp7gtut912m8aOHeuWY9188806duyYrFarW47nCV69tW/27NmKiIjQkiVLHOsiIyMd/20YhubOnaunnnpKf/rTnyRJ//jHPxQaGqpPP/1U999/v/bu3avVq1dr69atio6OliTNmzdPd9xxh+bMmaPw8PCqvSgAAABAkv12vkJJRgXb20raT5C01m1VHD58WN27d1dgYKBefPFFtW/fXkVFRUpOTlZiYqL27dvntnNdjmEYKi4ulq/vpWOIn5+fwsLCqqiqyvHqiNS///1vRUdH67777lNISIg6deqkN954w7E9IyNDWVlZiouLc6yzWq3q2rWr0tLSJElpaWkKDAx0hChJiouLk9ls1ubNm8s9b0FBgfLy8pwWAAAAwH2+l/2ZqMuNRF2oWPbb+w5crmGFPfroozKZTNqyZYsSEhLUsmVLtW3bVuPHj9emTZskSTk5ORo2bJiCg4MVEBCgXr16aefO/42MTZ06VTfeeKP+3//7f7r++utltVp1//3369SpU5KkwYMHa/369XrllVdkMplkMpl0+PBhxy16q1atUufOnWWxWLRx40YVFBToscceU0hIiOrWrasePXpo69atjvOVd2vf0qVL1axZM9WvX1933323fv31V6fr3Llzp3r27KmGDRsqICBAnTt3dnpkyN28GqR++OEHLViwQC1atFBycrJGjRqlxx57TMuWLZNkH4KUpNDQUKf9QkNDHduysrIUEhLitN3X11dBQUGONhdKSkqS1Wp1LBEREe6+NAAAANRqi2SfWKIyfCS97pYqTp48qdWrVysxMVH+/v5ltpc+Y3TffffpxIkTWrVqlbZv366bbrpJsbGxOnnypKPtoUOH9Omnn2rFihVasWKF1q9fr1mzZkmSXnnlFcXExGj48OE6duyYjh075vQde8qUKZo1a5b27t2rDh066PHHH9fHH3+sZcuWaceOHWrevLni4+Odzne+zZs3a+jQoRo9erTS09PVs2dPzZgxw6nNgAEDdO2112rr1q3avn27pkyZojp16lxpF16UV2/ts9lsio6O1vPPPy9J6tSpk7777jstXLhQgwYN8th5n3jiCY0fP97xOS8vjzAFAAAAN9om10ejShVL2u6WKg4ePCjDMNSqVauLttm4caO2bNmiEydOyGKxSJLmzJmjTz/9VB999JFj7gGbzaalS5eqYcOGkqSBAwcqJSVFM2fOlNVqlZ+fn+rXr1/uLXnTpk1T7969JUlnzpzRggULtHTpUt1+++2SpDfeeENr1qzR4sWLNWnSpDL7v/LKK+rbt68ef/xxSVLLli319ddfa/Xq1Y42mZmZmjRpkuNaW7Ro4XJ/ucKrI1JNmzZVmzZtnNa1bt1amZmZkuT4RTh+/LhTm+PHjzu2hYWF6cSJE07bz507p5MnT170vkqLxaKAgACnBQAAAHCf3CvcP8cdRcgwLv981s6dO3X69Gk1btxYDRo0cCwZGRk6dOiQo93111/vCFGS/bv8hd/DL+b8x3AOHTqkoqIide/e3bGuTp066tKli/bu3Vvu/nv37lXXrl2d1sXExDh9Hj9+vIYNG6a4uDjNmjXLqXZP8GqQ6t69u/bv3++07vvvv9d1110nyT7xRFhYmNNsInl5edq8ebOj42JiYpSTk6Pt2/+X2lNTU2Wz2cp0NgAAAFA1rnS2uUB3FKEWLVrIZDJdckKJ06dPq2nTpkpPT3da9u/f7zQ6dOFtciaTSTabrUJ1lHdbobtNnTpVu3fvVr9+/ZSamqo2bdrok08+8dj5vBqkxo0bp02bNun555/XwYMH9e6772rRokVKTEyUZP/FGTt2rGbMmKF///vf2rVrlx566CGFh4frrrvukmQfwerbt6+GDx+uLVu26D//+Y9Gjx6t+++/nxn7AAAA4CXRurJnpDq7pYqgoCDFx8dr/vz5OnPmTJntOTk5uummm5SVlSVfX181b97caWnSpEmFz+Xn56fi4svfznjDDTfIz89P//nPfxzrioqKtHXr1jJ3q5Vq3bp1mYnkSifKOF/Lli01btw4ffHFF7rnnnucZgd3N68Gqd///vf65JNP9N5776ldu3aaPn265s6dqwEDBjjaPP744xozZoxGjBih3//+9zp9+rRWr16tunXrOtq88847atWqlWJjY3XHHXeoR48eWrRokTcuCQAAAJA0Qlf2jNQjbqtk/vz5Ki4uVpcuXfTxxx/rwIED2rt3r1599VXFxMQoLi5OMTExuuuuu/TFF1/o8OHD+vrrr/Xkk0+6NOvd9ddfr82bN+vw4cP65ZdfLjpa5e/vr1GjRmnSpElavXq19uzZo+HDh+vs2bMaOnRoufs89thjWr16tebMmaMDBw7o73//u9PzUb/99ptGjx6tdevW6ciRI/rPf/6jrVu3qnXr1q51lgu8OtmEJN1555268847L7rdZDJp2rRpmjZt2kXbBAUF6d133/VEeQAAAEAltJTUS9J6uRaofCT1lOS+iRKioqK0Y8cOzZw5UxMmTNCxY8cUHByszp07a8GCBTKZTPr888/15JNP6uGHH1Z2drbCwsL0hz/8oczs2ZcyceJEDRo0SG3atNFvv/2mjIyMi7adNWuWbDabBg4cqFOnTik6OlrJyclq1KhRue27deumN954Q88++6yeeeYZxcXF6amnntL06dMlST4+Pvr111/10EMP6fjx42rSpInuuecePffcc651lgtMRkWeQLvK5eXlyWq1Kjc3l4knAAAA4CQ/P18ZGRmKjIx0uivq8nZKipFUIPvLdi/HLMkiKU1SR9cLRYVd6te0otnAq7f2AQAAAFevjpI+kz0cXe55KZ+Sdp+JEFUzEKQAAAAAj4mVfYTptpLPFwaq0s89S9rFVk1ZuGJef0YKAAAAuLp1lLRW0gFJr8v+st0c2ac47yz7xBKefXks3I8gBQAAAFSJFpLmeLsIuAm39gEAAAAVUNGXz6L6c8evJSNSAAAAwCX4+fnJbDbr6NGjCg4Olp+fn0wmk7fLQiUYhqHCwkJlZ2fLbDbLz8+v0sciSAEAAACXYDabFRkZqWPHjuno0aPeLgduUL9+fTVr1kxmc+Vv0CNIAQAAAJfh5+enZs2a6dy5cyouduUFu6hufHx85Ovre8WjigQpAAAAoAJMJpPq1KmjOnXqeLsUVANMNgEAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALjIq0Fq6tSpMplMTkurVq0c2/Pz85WYmKjGjRurQYMGSkhI0PHjx52OkZmZqX79+ql+/foKCQnRpEmTdO7cuaq+FAAAAAC1iK+3C2jbtq3Wrl3r+Ozr+7+Sxo0bp5UrV+rDDz+U1WrV6NGjdc899+g///mPJKm4uFj9+vVTWFiYvv76ax07dkwPPfSQ6tSpo+eff77KrwUAAABA7eD1IOXr66uwsLAy63Nzc7V48WK9++676tWrlyRpyZIlat26tTZt2qRu3brpiy++0J49e7R27VqFhobqxhtv1PTp0zV58mRNnTpVfn5+VX05AAAAAGoBrz8jdeDAAYWHhysqKkoDBgxQZmamJGn79u0qKipSXFyco22rVq3UrFkzpaWlSZLS0tLUvn17hYaGOtrEx8crLy9Pu3fvvug5CwoKlJeX57QAAAAAQEV5NUh17dpVS5cu1erVq7VgwQJlZGTolltu0alTp5SVlSU/Pz8FBgY67RMaGqqsrCxJUlZWllOIKt1euu1ikpKSZLVaHUtERIR7LwwAAADAVc2rt/bdfvvtjv/u0KGDunbtquuuu04ffPCB6tWr57HzPvHEExo/frzjc15eHmEKAAAAQIV5/da+8wUGBqply5Y6ePCgwsLCVFhYqJycHKc2x48fdzxTFRYWVmYWv9LP5T13VcpisSggIMBpAQAAAICKqlZB6vTp0zp06JCaNm2qzp07q06dOkpJSXFs379/vzIzMxUTEyNJiomJ0a5du3TixAlHmzVr1iggIEBt2rSp8voBAAAA1A5evbVv4sSJ6t+/v6677jodPXpUzz77rHx8fPTAAw/IarVq6NChGj9+vIKCghQQEKAxY8YoJiZG3bp1kyT16dNHbdq00cCBA/XCCy8oKytLTz31lBITE2WxWLx5aQAAAACuYl4NUj/99JMeeOAB/frrrwoODlaPHj20adMmBQcHS5L+9re/yWw2KyEhQQUFBYqPj9drr73m2N/Hx0crVqzQqFGjFBMTI39/fw0aNEjTpk3z1iUBAAAAqAVMhmEY3i7C2/Ly8mS1WpWbm8vzUgAAAEAtVtFsUK2ekQIAAACAmoAgBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAu8vV2AQAAoIKys6XFi6XUVCk3V7JapdhYacgQKTjY29UBQK1iMgzD8HYR3paXlyer1arc3FwFBAR4uxwAAJzl50tjx9pDlM1mX0qZzfZl2DBp7lzJYvFWlQBwVahoNmBECgCA6iw/X4qPlzZudA5QpUqD1aJF0t69UnIyYQoAqgDPSAEAUJ2NG3fxEHU+m0366iv7yBUAwOMIUgAAVFfZ2dKbb14+RJWy2eztf/nFs3UBAAhSAABUW2+9VfEQVcpmsz9LBQDwKIIUAADVVUpK5YJUaqpn6gEAODDZBADgyjEtt2fk5lZuv5wct5YBACiLIAUAqLxLTcudkiI99RTTcl8Jq7Vy+wUGurUMAEBZ3NoHAKic0mm533hDOneu7C1oNpt9/aJF9nYFBd6psyaLjbW/I8oVZrPUq5dn6gEAOBCkAACVw7TcnjdkiOtBysdHGjrUM/UAABwIUgAA1zEtd9UIDrbfGlnRMGU220NUkyaerQsAQJACAFQC03JXnblzpR49Lh+mzGbpllvs7QEAHkeQAgC4jmm5q47FIiUnSyNGSL6+ZQOV2WxfP2KEvR2TegBAlWDWPgCA65iWu2rVrSstWCBNm2YfDUxNtfdlYKB9YgmmmQeAKudykPrtt9+0fft2BQUFqU2bNk7b8vPz9cEHH+ihhx5yW4EAgGqIabm9IzhYmjzZvgAAvMqlW/u+//57tW7dWn/4wx/Uvn173XrrrTp27Jhje25urh5++GG3FwkAqGaYlhsAUMu59Lfg5MmT1a5dO504cUL79+9Xw4YN1b17d2VmZnqqPgBAdcS03ACAWs6lvwW//vprJSUlqUmTJmrevLk+++wzxcfH65ZbbtEPP/zgqRoBANUN03IDAGo5l4LUb7/9Jl/f/z1WZTKZtGDBAvXv31+33nqrvv/+e7cXCACoppiWGwBQi7kUpFq1aqVt27aVWf/3v/9df/rTn/THP/6x0oXMmjVLJpNJY897831+fr4SExPVuHFjNWjQQAkJCTp+/LjTfpmZmerXr5/q16+vkJAQTZo0SefOnat0HQCACmJabgBALeZSkLr77rv13nvvlbvt73//ux544AEZhuFyEVu3btXrr7+uDh06OK0fN26cPvvsM3344Ydav369jh49qnvuucexvbi4WP369VNhYaG+/vprLVu2TEuXLtUzzzzjcg0AgEoonZb76FHp+eelPn2kLl3sP59/3r5+wQJCFADgqmMyXEg+P/zwg66//nqZXX3A+BJOnz6tm266Sa+99ppmzJihG2+8UXPnzlVubq6Cg4P17rvv6t5775Uk7du3T61bt1ZaWpq6deumVatW6c4779TRo0cVGhoqSVq4cKEmT56s7Oxs+fn5VaiGvLw8Wa1W5ebmKiAgwG3XBgAAAKBmqWg2cCkRtWjRQr/88ovj8//93/+VudXOVYmJierXr5/i4uKc1m/fvl1FRUVO61u1aqVmzZopLS1NkpSWlqb27ds7QpQkxcfHKy8vT7t3777oOQsKCpSXl+e0AAAAAEBFuRSkLhy8+vzzz3XmzJlKn/z999/Xjh07lJSUVGZbVlaW/Pz8FHjByxtDQ0OVlZXlaHN+iCrdXrrtYpKSkmS1Wh1LREREpa8BAAAAQO3jvnv0XPTjjz/qL3/5i9555x3VrVu3Ss/9xBNPKDc317H8+OOPVXp+AAAAADWbS0HKZDLJZDKVWVcZ27dv14kTJ3TTTTfJ19dXvr6+Wr9+vV599VX5+voqNDRUhYWFysnJcdrv+PHjCgsLkySFhYWVubWw9HNpm/JYLBYFBAQ4LQAAAABQUb6Xb/I/hmFo8ODBspTMvpSfn6+RI0fK39/fqd3y5csve6zY2Fjt2rXLad3DDz+sVq1aafLkyYqIiFCdOnWUkpKihIQESdL+/fuVmZmpmJgYSVJMTIxmzpypEydOKCQkRJK0Zs0aBQQEqE2bNq5cGgAAAABUmEtBatCgQU6fH3zwwUqfuGHDhmrXrp3TOn9/fzVu3NixfujQoRo/fryCgoIUEBCgMWPGKCYmRt26dZMk9enTR23atNHAgQP1wgsvKCsrS0899ZQSExMdYQ8AAAAA3M2lILVkyRJP1VGuv/3tbzKbzUpISFBBQYHi4+P12muvObb7+PhoxYoVGjVqlGJiYuTv769BgwZp2rRpVVonAAAAgNrFpfdIXa14jxQAAAAAyUPvkQIAAAAAEKQAAAAAwGUuPSMFAABquOxsafFiKTVVys2VrFYpNlYaMkQKDvZ2dQBQY/CMlHhGCgBQC+TnS2PH2kOUzWZfSpnN9mXYMGnuXImZbwHUYhXNBoxIAQBwtcvPl+LjpY0bnQNUqdJgtWiRtHevlJxMmAKAy+AZKQAAPOp7SRMl3SapU8nPiSXrq8i4cRcPUeez2aSvvrKPXAEALolb+8StfQAAT9gpabykVEk+korP21b6OVbSS5I6eq6M7GwpPFw6d67i+/j6SseOSU2aeK4uAKimmP4cAACvSZEUI2l9yefiC7aXfl5X0i7Fc6W89dblR6IuZLPZn6UCAFwUQQoAALfaKam/pHyVDVAXKpZUUNJ+p2fKSUmpXJBKTfVMPQBwlSBIAQDgVuMlFUqq6J3ztpL2EzxTTm5u5fbLyXFrGQBwtSFIAQDgNt/L/kzU5UaiLlQs++19B9xekazWyu0XGOjWMgDgakOQAgDAbRbJPpFEZfhIet2NtZSIjbW/I8oVZrPUq5f7awGAqwhBCgAAt9km10ejShVL2u7GWkoMGeJ6kPLxkYYOdX8tAHAVIUgBALwvO1uaNUvq00fq2tX+c/Zs+/oapZLPIznkuKMIZ8HB0rBhFQ9TZrM9RDH1OQBcEu+REu+RAgCvyc+3v/x18WL7THHnzy5nNtuXYcOkuXMli8VbVbrgNv1vyvPK7v+lWypxUlBgD6eXeymv2SzdcouUnFxD+hsA3I/3SAEAqrf8fCk+XnrjDfvLYi/8gm+z2dcvWmRvV1DgnTpdEq0re0aqsxtrOY/FYg9HI0bYX7Z74eiU2WxfP2IEIQoAKogRKTEiBQBeMWqUPSRV5B1HZrP9S/6CBZ6v64p8L+l3V7h/CzfVchHZ2faX9Kam2qc4Dwy0TywxZIj9NkAAqOUqmg0IUiJIAUCVy86WwsPtI04V5esrHTtWA57diZX99j5XJp3wkdRT0hqPVAQAqDhu7QMAVF9vvVWxkajz2Wz2Z6mqvZcl+anif8WaS9rP8VhFAAD3I0gBAKpeSkrlglRqqmfqcauOkj6TZNHln5fyKWn3Wcl+AICagiAFAKh6uZWcJjwnx61leE6spDTZZ+GTygaq0s89S9rFVk1ZAAC38fV2AQCAWshqrdx+gYFuLcOzOkpaK+mApNdlf9lujqRA2Wfne0Qen1gCAOAxBCkAQNWLjXX99j6z2T67XI3TQjz/BABXH27tAwBUvSFDyr7L6HJ8fKShQz1TDwAALiJIAQCqXnCwNGxYxcOU2WwPUdV+6nMAQG1BkAIAeMfcuVKPHpcPU2azdMst9vYAAFQTBCkAgHdYLFJysjRihP1luxcGKrPZvn7ECHs7i8U7dQIAUA6TYRiGt4vwtoq+vRgA4CHZ2faX9Kam2qc4Dwy0TywxZIj9NkAAAKpIRbMBQUoEKQAAAAB2Fc0G3NoHAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuMirQWrBggXq0KGDAgICFBAQoJiYGK1atcqxPT8/X4mJiWrcuLEaNGighIQEHT9+3OkYmZmZ6tevn+rXr6+QkBBNmjRJ586dq+pLAQAAAFCLeDVIXXvttZo1a5a2b9+ubdu2qVevXvrTn/6k3bt3S5LGjRunzz77TB9++KHWr1+vo0eP6p577nHsX1xcrH79+qmwsFBff/21li1bpqVLl+qZZ57x1iUBAAAAqAVMhmEY3i7ifEFBQXrxxRd17733Kjg4WO+++67uvfdeSdK+ffvUunVrpaWlqVu3blq1apXuvPNOHT16VKGhoZKkhQsXavLkycrOzpafn1+FzpmXlyer1arc3FwFBAR47NoAAAAAVG8VzQbV5hmp4uJivf/++zpz5oxiYmK0fft2FRUVKS4uztGmVatWatasmdLS0iRJaWlpat++vSNESVJ8fLzy8vIco1rlKSgoUF5entMCAAAAABXl9SC1a9cuNWjQQBaLRSNHjtQnn3yiNm3aKCsrS35+fgoMDHRqHxoaqqysLElSVlaWU4gq3V667WKSkpJktVodS0REhHsvCgAAAMBVzetB6ne/+53S09O1efNmjRo1SoMGDdKePXs8es4nnnhCubm5juXHH3/06PkAAAAAXF18vV2An5+fmjdvLknq3Lmztm7dqldeeUX/93//p8LCQuXk5DiNSh0/flxhYWGSpLCwMG3ZssXpeKWz+pW2KY/FYpHFYnHzlQAAAACoLbw+InUhm82mgoICde7cWXXq1FFKSopj2/79+5WZmamYmBhJUkxMjHbt2qUTJ0442qxZs0YBAQFq06ZNldcOAAAAoHbw6ojUE088odtvv13NmjXTqVOn9O6772rdunVKTk6W1WrV0KFDNX78eAUFBSkgIEBjxoxRTEyMunXrJknq06eP2rRpo4EDB+qFF15QVlaWnnrqKSUmJjLiBAAAAMBjvBqkTpw4oYceekjHjh2T1WpVhw4dlJycrN69e0uS/va3v8lsNishIUEFBQWKj4/Xa6+95tjfx8dHK1as0KhRoxQTEyN/f38NGjRI06ZN89YlAQAAAKgFqt17pLyB90gBAAAAkGrge6QAAAAAoKYgSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAi3y9XQAAAKjlsrOlxYul1FQpN1eyWqXYWGnIECk42NvVAUC5TIZhGN4uwtvy8vJktVqVm5urgIAAb5cDAEDtkJ8vjR1rD1E2m30pZTbbl2HDpLlzJYvFW1UCqGUqmg0YkQIAAFUvP1+Kj5c2bnQOUKVKg9WiRdLevVJyMmEKQLXCM1IAAKDqjRt38RB1PptN+uor+8gVAFQjBCkAAFC1srOlN9+8fIgqZbPZ2//yi2frAgAXEKQAAEDVeuutioeoUjab/VkqAKgmCFIAAKBqpaRULkilpnqmHgCoBIIUAACoWrm5ldsvJ8etZQDAlSBIAQCAqmW1Vm6/wEC3lgEAV4IgBQAAqlZsrP0dUa4wm6VevTxTDwBUAkEKAABUrSFDXA9SPj7S0KGeqQcAKoEgBQAAqlZwsDRsWMXDlNlsD1FNmni2LgBwAUEKAABUvblzpR49Lh+mzGbpllvs7QGgGiFIAQCAqmexSMnJ0ogRkq9v2UBlNtvXjxhhb2exeKdOALgIk2EYhreL8La8vDxZrVbl5uYqICDA2+UAAFC7ZGfbX9Kbmmqf4jww0D6xxJAh9tsAAaAKVTQbEKREkAIAAABgV9FswK19AAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIoIUAAAAALiIIAUAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIu8GqSSkpL0+9//Xg0bNlRISIjuuusu7d+/36lNfn6+EhMT1bhxYzVo0EAJCQk6fvy4U5vMzEz169dP9evXV0hIiCZNmqRz585V5aUAAAAAqEW8GqTWr1+vxMREbdq0SWvWrFFRUZH69OmjM2fOONqMGzdOn332mT788EOtX79eR48e1T333OPYXlxcrH79+qmwsFBff/21li1bpqVLl+qZZ57xxiUBAAAAqAVMhmEY3i6iVHZ2tkJCQrR+/Xr94Q9/UG5uroKDg/Xuu+/q3nvvlSTt27dPrVu3Vlpamrp166ZVq1bpzjvv1NGjRxUaGipJWrhwoSZPnqzs7Gz5+fld9rx5eXmyWq3Kzc1VQECAR68RAAAAQPVV0WxQrZ6Rys3NlSQFBQVJkrZv366ioiLFxcU52rRq1UrNmjVTWlqaJCktLU3t27d3hChJio+PV15ennbv3l3ueQoKCpSXl+e0AAAAAEBFVZsgZbPZNHbsWHXv3l3t2rWTJGVlZcnPz0+BgYFObUNDQ5WVleVoc36IKt1euq08SUlJslqtjiUiIsLNVwMAAADgalZtglRiYqK+++47vf/++x4/1xNPPKHc3FzH8uOPP3r8nAAAAACuHr7eLkCSRo8erRUrVmjDhg269tprHevDwsJUWFionJwcp1Gp48ePKywszNFmy5YtTscrndWvtM2FLBaLLBaLm68CAAAAQG3h1REpwzA0evRoffLJJ0pNTVVkZKTT9s6dO6tOnTpKSUlxrNu/f78yMzMVExMjSYqJidGuXbt04sQJR5s1a9YoICBAbdq0qZoLAQAAAFCreHVEKjExUe+++67+9a9/qWHDho5nmqxWq+rVqyer1aqhQ4dq/PjxCgoKUkBAgMaMGaOYmBh169ZNktSnTx+1adNGAwcO1AsvvKCsrCw99dRTSkxMZNQJAAAAgEd4dfpzk8lU7volS5Zo8ODBkuwv5J0wYYLee+89FRQUKD4+Xq+99prTbXtHjhzRqFGjtG7dOvn7+2vQoEGaNWuWfH0rlhOZ/hwAAACAVPFsUK3eI+UtBCkAAAAAUg19jxQAAAAA1AQEKQAAAABwEUEKAAAAAFxEkAIAAAAAFxGkAAAAAMBFBCkAAAAAcBFBCgAAAABcRJACAAAAABcRpAAAAADARb7eLgAAAABA7ZN9JluLv1ms1IxU5ebnylrXqtjIWA3pNETB/sHeLu+yTIZhGN4uwtvy8vJktVqVm5urgIAAb5cDAAAAXLXyz+Vr7OqxWvzNYtkMm2yGzbHNbDLLbDJr2E3DNDd+riy+liqvr6LZgBEpAAAAAFUi/1y+4t+O18bMjU4BqlRpsFq0fZH2Zu9V8oPJXglTFcEzUgAAAACqxLjkcRcNUeezGTZ9lfmVxiaPrZrCKoEgBQAAAMDjss9k680db142RJWyGTa9ueNN/XL2Fw9XVjkEKQAAAAAe99Y3b1U4RJWyGTYt3rHYQxVdGYIUAAAAAI9LyUipVJBKzUj1UEVXhiAFAAAAwONy83MrtV9OQY57C3ETghQAAAAAj7PWtVZqv0BLoHsLcROCFAAAAACPi42MldnkWvwwm8zqFdnLQxVdGYIUAAAAAI8b0mmIy0HKx+SjoTcN9VBFV4YgBQAAAMDjgv2DNeymYRUOU2aTWUNvGqom9Zt4uLLKIUgBAAAAqBJz4+eqR7Melw1TZpNZtzS7RXPj51ZNYZVAkAIAAABQJSy+FiU/mKwRnUfI1+xbJlCZTWb5mn01ovMIJT+YLIuvxUuVXp7JMAzD20V4W15enqxWq3JzcxUQEODtcgAAAICrXvaZbL31zVtKzUhVTkGOAi2B6hXZS0M6DVGwf7DX6qpoNiBIiSAFAAAAwK6i2YBb+wAAAADARQQpAAAAAHARQQoAAAAAXESQAgAAAAAXEaQAAAAAwEUEKQAAAABwka+3CwAAAABQ23wvaZGkbZJyJVklRUsaIamlF+uqOIIUAAAAgCqyU9J4SamSfCQVn7dto6SXJMWW/OxY5dW5glv7AAAAAFSBFEkxktaXfC6+YHvp53Ul7VKqpqxKIkgBAAAA8LCdkvpLylfZAHWhYkkFJe13eriuyiNIAQAAAPCw8ZIKJRkVbG8raT/BYxVdKYIUAAAAAA/6XvZnoi43EnWhYtlv7zvg9orcgSAFAAAAwIMWyT6xRGX4SHrdjbW4D0EKAAAAgAdtk+ujUaWKJW13Yy3uQ5ACAAAA4EG5V7h/jjuKcDuCFAAAAAAPsl7h/oHuKMLtCFIAAAAAPChaV/aMVGc31uI+BCkAAAAAHjRCV/aM1CNurMV9CFIAAAAAPKilpF5yfVTKR1KcpBZur8gdCFIAAAAAPOxlSX6qePwwl7Sf47GKrhRBCgAAAICHdZT0mSSLLj8y5VPS7rOS/aonrwapDRs2qH///goPD5fJZNKnn37qtN0wDD3zzDNq2rSp6tWrp7i4OB044Pxm45MnT2rAgAEKCAhQYGCghg4dqtOnT1fhVQAAAAC4vFhJaZJuK/l8YaAq/dyzpF1s1ZRVSV4NUmfOnFHHjh01f/78cre/8MILevXVV7Vw4UJt3rxZ/v7+io+PV35+vqPNgAEDtHv3bq1Zs0YrVqzQhg0bNGLEiKq6BAAAAAAV1lHSWknfSxore6i6seTn2JL1a1SdR6JKmQzDMLxdhCSZTCZ98sknuuuuuyTZR6PCw8M1YcIETZw4UZKUm5ur0NBQLV26VPfff7/27t2rNm3aaOvWrYqOjpYkrV69WnfccYd++uknhYeHl3uugoICFRQUOD7n5eUpIiJCubm5CggI8OyFAgAAAKi28vLyZLVaL5sNqu0zUhkZGcrKylJcXJxjndVqVdeuXZWWliZJSktLU2BgoCNESVJcXJzMZrM2b9580WMnJSXJarU6loiICM9dCAAAAICrTrUNUllZWZKk0NBQp/WhoaGObVlZWQoJCXHa7uvrq6CgIEeb8jzxxBPKzc11LD/++KObqwcAAABwNfP1dgHeYLFYZLFYvF0GAAAAgBqq2o5IhYWFSZKOHz/utP748eOObWFhYTpx4oTT9nPnzunkyZOONgAAAADgbtU2SEVGRiosLEwpKSmOdXl5edq8ebNiYmIkSTExMcrJydH27dsdbVJTU2Wz2dS1a9cqrxkAAABA7eDVW/tOnz6tgwcPOj5nZGQoPT1dQUFBatasmcaOHasZM2aoRYsWioyM1NNPP63w8HDHzH6tW7dW3759NXz4cC1cuFBFRUUaPXq07r///ovO2AcAAAAAV8qrQWrbtm3q2bOn4/P48eMlSYMGDdLSpUv1+OOP68yZMxoxYoRycnLUo0cPrV69WnXr1nXs884772j06NGKjY2V2WxWQkKCXn311Sq/FgAAAACXln0mW4u/WazUjFTl5ufKWteq2MhYDek0RMH+wd4uzyXV5j1S3lTRueIBAAAAuC7/XL7Grh6rxd8sls2wyWbYHNvMJrPMJrOG3TRMc+PnyuLr3UnhKpoNauWsfQAAAACqRv65fMW/Ha+NmRudAlSp0mC1aPsi7c3eq+QHk70epiqi2k42AQAAAKDmG5c87qIh6nw2w6avMr/S2OSxVVPYFSJIAQAAAPCI7DPZenPHm5cNUaVshk1v7nhTv5z9xcOVXTmCFAAAAACPeOubtyocokrZDJsW71jsoYrchyAFAAAAwCNSMlIqFaRSM1I9VJH7EKQAAAAAeERufm6l9sspyHFvIR5AkAIAAADgEda61krtF2gJdG8hHkCQAgAAAOARsZGxMptcixxmk1m9Int5qCL3IUgBAAAA8IghnYa4HKR8TD4aetNQD1XkPgQpAAAAXP2yJc2S1EdS15Kfs0vWw2OC/YM17KZhFQ5TZpNZQ28aqib1m3i4sitnMgzD8HYR3paXlyer1arc3FwFBAR4uxwAAAC4S76ksZIWS7KVLKXMJcswSXMlWaq4tlqi4FyB+rzd57Iv5TWbzLql2S1KfjBZFl/v/WJUNBswIgUAAICrT7ak6ZKaSnpd0jk5hyiVfD4naZGkeEkFVVlg7WHxtSj5wWSN6DxCvmbfMqNTZpNZvmZfjeg8wushyhWMSIkRKQAAgKvG+SNQ51zYzyxphKQFHqgJDtlnsvXWN28pNSNVOQU5CrQEqldkLw3pNETB/sHeLk9SxbMBQUoEKQAAgKtCvuwjSxtVdvTpfC2+l0YskqK3SdZcKdcqbYuW3hohbWgpVf/Hc+BBFc0GvlVYEwAAAOA543TpENVhp/TSeCkuVTrnI/kW/29bj43SxJekw7FSk5ckdfR8vajReEYKAAAANV+2pDd18RDVK0VKi5FuW2//fH6IOv9zxDpJMZJSPFElriIEKQAAANR8b+nSI1Gf9Zcs+WUD1IV8imWfdaK/pJ1uLRFXF4IUAAAAar4UXTxIvTRe8iuUfCo6NYBNUqGkCW4pDVcnghQAAABqvtyLrG/xvf2ZqMuNRJVRLHs6O3BldeGqRZACAABAzWe9yPoRi+wTS1SKj+wvoQLKIkgBAACg5otV+d9so7dVYjSqVLGk7ZWvCVc1ghQAAABqviEq/5ut9WL3/FVQVo7UR1JX2X/Oln2GQNR6BCkAAADUfMGShqnst9vci93zV0F7A6U1krbI/vOvksIljZJ9cj/UWgQpAAAAXB3mSuoh52+426Ir/4zUOR9pe2fndTZJ5yQtkhQvwlQtRpACAADA1cEiKVnSCEm+sn/TXTSi8s9I+RZLrz9S/jabpK8kja3coVHzEaQAAABw9agraYGko5KelxTZUtraSyp2cVTqnI+0Jk462OLibWyS3pT0S2WLRU1GkAIAAMDVJ1jSZNlHqH7/suTjpwp/9S02S4V+0sQ5l29rk7S40lWiBiNIAQAA4CrXUdJnst/7d5mRqXM+UoFF6v+Z9G3Hyx/aJin1yitEzUOQAgAAQC0QKylN0m0lny8MVCWfv+wpxaRJqbEVP3TOldaGmsjX2wUAAAAAVaOjpLWSDkh6XfaX7eZICpTUWRr8iLTsEs9EXUygu+pDTUKQAgAAQC3TQlI5zz+1lv1+LZsLhzJL6uWWolDDcGsfAAAAIElD5Pq3Yx9JQz1QC6o9ghQAAAAg2Wf6G6aKf0M2yx6imnisIlRjBCkAAACg1FxJPXT5b8lmSbeUtEetRJACAAAASllkf/fUCNlnE7jw27K5ZP2IknaWKq0O1QiTTQAAAADnqytpgaRpkt6S/T1RObLPztdL9mepgr1UG6oNghQAAABQnmBJk0sW4ALc2gcAAAAALiJIAQAAAICLCFIAAAAA4CKCFAAAAAC4iCAFAAAAAC4iSAEAAACAiwhSAAAAAOAighQAAAAAuIggBQAAAAAuIkgBAAAAgIsIUgAAAADgIl9vF1AdGIYhScrLy/NyJQAAAAC8qTQTlGaEiyFISTp16pQkKSIiwsuVAAAAAKgOTp06JavVetHtJuNyUasWsNlsOnr0qBo2bCiTyVRum7y8PEVEROjHH39UQEBAFVd49aJfPYe+9Qz61XPoW8+gXz2HvvUM+tVz6NuKMQxDp06dUnh4uMzmiz8JxYiUJLPZrGuvvbZCbQMCAviN5wH0q+fQt55Bv3oOfesZ9Kvn0LeeQb96Dn17eZcaiSrFZBMAAAAA4CKCFAAAAAC4iCBVQRaLRc8++6wsFou3S7mq0K+eQ996Bv3qOfStZ9CvnkPfegb96jn0rXsx2QQAAAAAuIgRKQAAAABwEUEKAAAAAFxEkAIAAAAAFxGkAAAAAMBFBKnzHD58WEOHDlVkZKTq1aunG264Qc8++6wKCwud2n377be65ZZbVLduXUVEROiFF14oc6wPP/xQrVq1Ut26ddW+fXt9/vnnVXUZNcr8+fN1/fXXq27duuratau2bNni7ZKqtaSkJP3+979Xw4YNFRISorvuukv79+93apOfn6/ExEQ1btxYDRo0UEJCgo4fP+7UJjMzU/369VP9+vUVEhKiSZMm6dy5c1V5KdXarFmzZDKZNHbsWMc6+rXyfv75Zz344INq3Lix6tWrp/bt22vbtm2O7YZh6JlnnlHTpk1Vr149xcXF6cCBA07HOHnypAYMGKCAgAAFBgZq6NChOn36dFVfSrVRXFysp59+2unvq+nTp+v8+aPo14rZsGGD+vfvr/DwcJlMJn366adO293VjxX57nA1uVS/FhUVafLkyWrfvr38/f0VHh6uhx56SEePHnU6Bv1avsv9nj3fyJEjZTKZNHfuXKf19K2bGHBYtWqVMXjwYCM5Odk4dOiQ8a9//csICQkxJkyY4GiTm5trhIaGGgMGDDC+++4747333jPq1atnvP766442//nPfwwfHx/jhRdeMPbs2WM89dRTRp06dYxdu3Z547Kqrffff9/w8/Mz3nrrLWP37t3G8OHDjcDAQOP48ePeLq3aio+PN5YsWWJ89913Rnp6unHHHXcYzZo1M06fPu1oM3LkSCMiIsJISUkxtm3bZnTr1s24+eabHdvPnTtntGvXzoiLizO++eYb4/PPPzeaNGliPPHEE964pGpny5YtxvXXX2906NDB+Mtf/uJYT79WzsmTJ43rrrvOGDx4sLF582bjhx9+MJKTk42DBw862syaNcuwWq3Gp59+auzcudP44x//aERGRhq//fabo03fvn2Njh07Gps2bTK++uoro3nz5sYDDzzgjUuqFmbOnGk0btzYWLFihZGRkWF8+OGHRoMGDYxXXnnF0YZ+rZjPP//cePLJJ43ly5cbkoxPPvnEabs7+rEi3x2uNpfq15ycHCMuLs745z//aezbt89IS0szunTpYnTu3NnpGPRr+S73e7bU8uXLjY4dOxrh4eHG3/72N6dt9K17EKQu44UXXjAiIyMdn1977TWjUaNGRkFBgWPd5MmTjd/97neOz3/+85+Nfv36OR2na9euxiOPPOL5gmuQLl26GImJiY7PxcXFRnh4uJGUlOTFqmqWEydOGJKM9evXG4Zh/8upTp06xocffuhos3fvXkOSkZaWZhiG/Q9gs9lsZGVlOdosWLDACAgIcPp9XRudOnXKaNGihbFmzRrj1ltvdQQp+rXyJk+ebPTo0eOi2202mxEWFma8+OKLjnU5OTmGxWIx3nvvPcMwDGPPnj2GJGPr1q2ONqtWrTJMJpPx888/e674aqxfv37GkCFDnNbdc889xoABAwzDoF8r68Ivpe7qx4p8d7iaXerLfqktW7YYkowjR44YhkG/VtTF+vann34yrrnmGuO7774zrrvuOqcgRd+6D7f2XUZubq6CgoIcn9PS0vSHP/xBfn5+jnXx8fHav3+//vvf/zraxMXFOR0nPj5eaWlpVVN0DVBYWKjt27c79ZPZbFZcXBz95ILc3FxJcvwe3b59u4qKipz6tVWrVmrWrJmjX9PS0tS+fXuFhoY62sTHxysvL0+7d++uwuqrn8TERPXr16/M/7/0a+X9+9//VnR0tO677z6FhISoU6dOeuONNxzbMzIylJWV5dS3VqtVXbt2derbwMBARUdHO9rExcXJbDZr8+bNVXcx1cjNN9+slJQUff/995KknTt3auPGjbr99tsl0a/u4q5+rMh3h9ouNzdXJpNJgYGBkujXK2Gz2TRw4EBNmjRJbdu2LbOdvnUfgtQlHDx4UPPmzdMjjzziWJeVleX0RUmS43NWVtYl25Ruh/TLL7+ouLiYfroCNptNY8eOVffu3dWuXTtJ9t97fn5+jr+ISp3frxX5PVwbvf/++9qxY4eSkpLKbKNfK++HH37QggUL1KJFCyUnJ2vUqFF67LHHtGzZMkn/65tL/VmQlZWlkJAQp+2+vr4KCgqqtX07ZcoU3X///WrVqpXq1KmjTp06aezYsRowYIAk+tVd3NWP/Plwafn5+Zo8ebIeeOABBQQESKJfr8Ts2bPl6+urxx57rNzt9K37+Hq7gKowZcoUzZ49+5Jt9u7dq1atWjk+//zzz+rbt6/uu+8+DR8+3NMlAi5LTEzUd999p40bN3q7lBrvxx9/1F/+8hetWbNGdevW9XY5VxWbzabo6Gg9//zzkqROnTrpu+++08KFCzVo0CAvV1dzffDBB3rnnXf07rvvqm3btkpPT9fYsWMVHh5Ov6JGKSoq0p///GcZhqEFCxZ4u5wab/v27XrllVe0Y8cOmUwmb5dz1asVI1ITJkzQ3r17L7lERUU52h89elQ9e/bUzTffrEWLFjkdKywsrMxMXaWfw8LCLtmmdDukJk2ayMfHh36qpNGjR2vFihX68ssvde211zrWh4WFqbCwUDk5OU7tz+/Xivwerm22b9+uEydO6KabbpKvr698fX21fv16vfrqq/L19VVoaCj9WklNmzZVmzZtnNa1bt1amZmZkv7XN5f6syAsLEwnTpxw2n7u3DmdPHmy1vbtpEmTHKNS7du318CBAzVu3DjHiCr96h7u6kf+fChfaYg6cuSI1qxZ4xiNkujXyvrqq6904sQJNWvWzPH32ZEjRzRhwgRdf/31kuhbd6oVQSo4OFitWrW65FJ6D+jPP/+s2267TZ07d9aSJUtkNjt3UUxMjDZs2KCioiLHujVr1uh3v/udGjVq5GiTkpLitN+aNWsUExPj4SutOfz8/NS5c2enfrLZbEpJSaGfLsEwDI0ePVqffPKJUlNTFRkZ6bS9c+fOqlOnjlO/7t+/X5mZmY5+jYmJ0a5du5z+EC39C+zCL7y1RWxsrHbt2qX09HTHEh0drQEDBjj+m36tnO7du5eZov/777/XddddJ0mKjIxUWFiYU9/m5eVp8+bNTn2bk5Oj7du3O9qkpqbKZrOpa9euVXAV1c/Zs2fL/P3k4+Mjm80miX51F3f1Y0W+O9Q2pSHqwIEDWrt2rRo3buy0nX6tnIEDB+rbb791+vssPDxckyZNUnJysiT61q28PdtFdfLTTz8ZzZs3N2JjY42ffvrJOHbsmGMplZOTY4SGhhoDBw40vvvuO+P999836tevX2b6c19fX2POnDnG3r17jWeffZbpz8vx/vvvGxaLxVi6dKmxZ88eY8SIEUZgYKDTrGdwNmrUKMNqtRrr1q1z+v159uxZR5uRI0cazZo1M1JTU41t27YZMTExRkxMjGN76TTdffr0MdLT043Vq1cbwcHBtX6a7gudP2ufYdCvlbVlyxbD19fXmDlzpnHgwAHjnXfeMerXr2+8/fbbjjazZs0yAgMDjX/961/Gt99+a/zpT38qd3rpTp06GZs3bzY2btxotGjRotZN032+QYMGGddcc41j+vPly5cbTZo0MR5//HFHG/q1Yk6dOmV88803xjfffGNIMl5++WXjm2++ccwe545+rMh3h6vNpfq1sLDQ+OMf/2hce+21Rnp6utPfZ+fPEke/lu9yv2cvdOGsfYZB37oLQeo8S5YsMSSVu5xv586dRo8ePQyLxWJcc801xqxZs8oc64MPPjBatmxp+Pn5GW3btjVWrlxZVZdRo8ybN89o1qyZ4efnZ3Tp0sXYtGmTt0uq1i72+3PJkiWONr/99pvx6KOPGo0aNTLq169v3H333U7/GGAYhnH48GHj9ttvN+rVq2c0adLEmDBhglFUVFTFV1O9XRik6NfK++yzz4x27doZFovFaNWqlbFo0SKn7TabzXj66aeN0NBQw2KxGLGxscb+/fud2vz666/GAw88YDRo0MAICAgwHn74YePUqVNVeRnVSl5envGXv/zFaNasmVG3bl0jKirKePLJJ52+hNKvFfPll1+W++fqoEGDDMNwXz9W5LvD1eRS/ZqRkXHRv8++/PJLxzHo1/Jd7vfshcoLUvSte5gM47zXoAMAAAAALqtWPCMFAAAAAO5EkAIAAAAAFxGkAAAAAMBFBCkAAAAAcBFBCgAAAABcRJACAAAAABcRpAAAAADARQQpAAAAAHARQQoAAAAAXESQAgDUKoMHD5bJZCqzHDx4UBs2bFD//v0VHh4uk8mkTz/91NvlAgCqKYIUAKDW6du3r44dO+a0REZG6syZM+rYsaPmz5/v7RIBANWcr7cLAACgqlksFoWFhZVZf/vtt+v222/3QkUAgJqGESkAAAAAcBFBCgBQ66xYsUINGjRwLPfdd5+3SwIA1DDc2gcAqHV69uypBQsWOD77+/t7sRoAQE1EkAIA1Dr+/v5q3ry5t8sAANRg3NoHAAAAAC5iRAoAgBKnT5/WwYMHHZ8zMjKUnp6uoKAgNWvWzIuVAQCqG4IUAAAltm3bpp49ezo+jx8/XpI0aNAgLV261EtVAQCqI5NhGIa3iwAAAACAmoRnpAAAAADARQQpAAAAAHARQQoAAAAAXESQAgAAAAAXEaQAAAAAwEUEKQAAAABwEUEKAAAAAFxEkAIAAAAAFxGkAAAAAMBFBCkAAAAAcBFBCgAAAABc9P8DqtS79YdtVREAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 6))\n", - "\n", - "plt.scatter(data[y_kmeans == 0, 0], data[y_kmeans == 0, 1],\n", - " s=90, c='red', label='Cluster 1')\n", - "plt.scatter(data[y_kmeans == 1, 0], data[y_kmeans == 1, 1],\n", - " s=90, c='blue', label='Cluster 2')\n", - "plt.scatter(data[y_kmeans == 2, 0], data[y_kmeans == 2, 1],\n", - " s=90, c='green', label='Cluster 3')\n", - "plt.scatter(data[y_kmeans == 3, 0], data[y_kmeans == 3, 1],\n", - " s=90, c='cyan', label='Cluster 4')\n", - "plt.scatter(data[y_kmeans == 4, 0], data[y_kmeans == 4, 1],\n", - " s=90, c='magenta', label='Cluster 5')\n", - "plt.scatter(model.cluster_centers_[:, 0], model.cluster_centers_[\n", - " :, 1], s=120, c='yellow', label='Centroids')\n", - "\n", - "plt.title('Clusters')\n", - "\n", - "plt.xlabel('F1')\n", - "plt.ylabel('F2')\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### -------- teste --------" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", - "\n", - " y_test_x = np.array(y_test_x)\n", - " y_test_y = np.array(y_test_y)\n", - "\n", - " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", - "\n", - " error_range = 0.05\n", - "\n", - " data = {'True X': y_test_x, 'Predicted X': y_pred_x,\n", - " 'True Y': y_test_y, 'Predicted Y': y_pred_y}\n", - "\n", - " sns.scatterplot(x='True X', y='True Y', data=data,\n", - " label='True Values', alpha=0.7, ax=ax, color='red')\n", - " sns.scatterplot(x='Predicted X', y='Predicted Y', data=data,\n", - " label='Predicted Values', alpha=0.7, ax=ax, color='green')\n", - "\n", - " circle_radius = error_range * (max(y_test_x) - min(y_test_x)\n", - " + max(y_test_y) - min(y_test_y)) / 2\n", - "\n", - " for true_x, true_y in true_points:\n", - "\n", - " x_within_range = [y_pred_x[j] for j in range(len(y_test_x)) if abs(\n", - " y_test_x[j] - true_x) <= error_range]\n", - " y_within_range = [y_pred_y[j] for j in range(len(y_test_y)) if abs(\n", - " y_test_y[j] - true_y) <= error_range]\n", - "\n", - " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", - "\n", - " combined_predictions = x_within_range + y_within_range\n", - " combined_true = [true_x] * len(x_within_range) + \\\n", - " [true_y] * len(y_within_range)\n", - " #true_values = [true_x] * len(x_within_range) + \\\n", - " # [true_y] * len(y_within_range)\n", - "\n", - " r2_combined = r2_score(combined_true, combined_predictions)\n", - " mae_combined = mean_absolute_error(\n", - " combined_true, combined_predictions)\n", - "\n", - " circle = plt.Circle((true_x, true_y), circle_radius,\n", - " color='yellow', fill=False)\n", - " ax.add_patch(circle)\n", - "\n", - " ax.text(true_x + 0.1, true_y + 0.1, f'R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}',\n", - " fontsize=8, color='blue')\n", - "\n", - " title = title if title else 'True and Predicted Points for X and Y'\n", - " ax.set_title(title)\n", - " ax.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "def analysis(df, ax=None, title=None):\n", - "\n", - " # x\n", - " X_x = df[['left_iris_x', 'right_iris_x']]\n", - " X_y = df['point_x']\n", - "\n", - " sc = StandardScaler()\n", - " X_x = sc.fit_transform(X_x)\n", - "\n", - " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", - " X_x, X_y, test_size=0.2, random_state=42)\n", - "\n", - " model = linear_model.LinearRegression()\n", - " model.fit(X_train_x, y_train_x)\n", - " y_pred_x = model.predict(X_test_x)\n", - " r2_score(y_test_x, y_pred_x)\n", - "\n", - " # y\n", - " X_y = df[['left_iris_y', 'right_iris_y']]\n", - " y_y = df['point_y']\n", - "\n", - " sc = StandardScaler()\n", - " X_y = sc.fit_transform(X_y)\n", - "\n", - " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", - " X_y, y_y, test_size=0.2, random_state=42)\n", - "\n", - " model = linear_model.LinearRegression()\n", - " model.fit(X_train_y, y_train_y)\n", - " y_pred_y = model.predict(X_test_y)\n", - " r2_score(y_test_y, y_pred_y)\n", - "\n", - " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.read_csv('./csv/data/_fixed_train_data.csv')\n", - "df = df.drop(['screen_height', 'screen_width'], axis=1)\n", - "\n", - "df_list = [df]\n", - "\n", - "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", - "# num_cols = min(2, len(df_list))\n", - "\n", - "num_rows = len(df_list)\n", - "num_cols = 1\n", - "\n", - "fig_height = 5 * num_rows\n", - "fig, axes = plt.subplots(\n", - " num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", - "\n", - "for i, df in enumerate(df_list):\n", - "\n", - " # row_idx = i // num_cols\n", - " # col_idx = i % num_cols\n", - "\n", - " row_idx = i\n", - " col_idx = 0\n", - "\n", - " ax = axes[row_idx, col_idx]\n", - " analysis(df, ax)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 90ae9bb15c0b2b1b412339a6ea86d43946e2fc9f Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 10:49:35 +0000 Subject: [PATCH 06/78] ridge regression model added under test folder --- .../test/test_ridge_regression.ipynb | 1644 +++++++++++++++++ 1 file changed, 1644 insertions(+) create mode 100644 app/services/calib_validation/test/test_ridge_regression.ipynb diff --git a/app/services/calib_validation/test/test_ridge_regression.ipynb b/app/services/calib_validation/test/test_ridge_regression.ipynb new file mode 100644 index 00000000..6bebd81e --- /dev/null +++ b/app/services/calib_validation/test/test_ridge_regression.ipynb @@ -0,0 +1,1644 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9979769240845753" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 777.76596343, 765.93107474, 139.51506252, 79.85553907,\n", + " 1435.9187586 , 136.31337857, 137.81778199, 84.74883019,\n", + " 771.16565515, 761.25093397, 1422.86582297, 757.55217937,\n", + " 143.46126635, 94.8359387 , 761.71406876, 779.68341603,\n", + " 133.08892546, 1430.76514394, 79.36568085, 78.51353434,\n", + " 758.65301652, 759.72126028, 144.04748863, 1432.96210265,\n", + " 1433.36219166, 778.1454125 , 73.03695827, 78.89486796,\n", + " 79.28272673, 88.31244713, 761.88829374, 760.74771531,\n", + " 778.99478172, 763.44526412, 1454.68074484, 1437.96034167,\n", + " 1421.10028784, 1453.85535651, 1449.37316876, 1430.95057164,\n", + " 767.08225864, 89.09532064, 138.7310988 , 1428.31077236,\n", + " 760.64349738, 135.47759084, 781.46449776, 1436.98611109,\n", + " 1435.91910262, 135.11171635, 1425.94049561, 1435.00945126,\n", + " 754.85405749, 1435.47559541, 81.65486637, 767.96214294,\n", + " 77.0232892 , 75.40309346, 136.8966017 , 778.68529821,\n", + " 1418.41616071, 156.79742146, 136.7280092 , 757.41056886,\n", + " 77.62588701, 95.15984845, 1434.57496892, 85.45779156,\n", + " 1423.00245968, 1438.51089444, 1435.7098284 , 765.29784366,\n", + " 138.8031703 , 763.96190836, 80.01534544, 1418.61202535,\n", + " 1449.11752258, 779.16810915, 1426.72599729, 77.3610738 ,\n", + " 1421.01230512, 767.27094091, 74.64541356, 764.17943387,\n", + " 147.76582922, 1426.30525503, 1460.53110895, 72.04457356,\n", + " 1447.56775744, 1433.84328243, 72.41845612, 139.15467798,\n", + " 78.64038715, 775.72314514, 95.87248068, 775.87362999,\n", + " 134.69782567, 201.68322957, 1458.60726216, 80.69318553,\n", + " 768.84148701, 1423.46414986, 774.78995579, 769.4122411 ,\n", + " 80.12046076, 772.79931109, 72.51457116, 70.74858465,\n", + " 69.9602185 , 1434.74477172, 81.42685508, 1283.14382773,\n", + " 1438.0661663 , 99.81999104, 1422.85814554, 777.90515883,\n", + " 761.87461015, 133.57493463, 1453.29678004, 149.44843414,\n", + " 137.93434844, 759.36586875, 1438.36081974, 137.9815362 ,\n", + " 775.23944647, 136.61239062, 761.00201832, 1435.9505198 ,\n", + " 1438.61119866, 1437.79057397, 1436.52185808, 763.51320291,\n", + " 72.38312798, 1455.60427611, 779.73666057, 1425.91868457,\n", + " 86.13315623, 155.23337983, 72.11800429, 90.2128626 ,\n", + " 90.12997158, 81.47034246, 82.96396599, 1423.82324744])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.975215340154245" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([158.49127326, 141.26857472, 97.89535932, 587.3734379 ,\n", + " 52.78091935, 110.94785664, 107.18820455, 384.86167257,\n", + " 156.39670628, 661.94548856, 616.23326541, 687.87868774,\n", + " 88.01393969, 315.74035441, 661.02961346, 160.99561532,\n", + " 118.40587012, 381.58437496, 575.84460606, 587.33603499,\n", + " 689.67455541, 600.964088 , 77.11527916, 620.22975998,\n", + " 101.60956704, 152.44652655, 583.48035074, 581.63141439,\n", + " 366.11974227, 368.32494321, 658.60441632, 595.8028418 ,\n", + " 145.15004897, 612.38725101, 362.96162946, 692.41322869,\n", + " 617.26819892, 361.43907616, 363.17135408, 100.4229667 ,\n", + " 147.05356509, 368.51972904, 95.29963364, 646.58911465,\n", + " 634.29776429, 100.62596101, 162.82927273, 373.28091413,\n", + " 663.55180176, 105.47364039, 116.71915099, 629.94968735,\n", + " 669.59229204, 340.13195617, 370.14815542, 162.06602779,\n", + " 585.12422266, 574.91767937, 110.90476667, 159.54249788,\n", + " 116.66149274, 36.49506069, 105.23466404, 656.59420344,\n", + " 588.70002589, 310.50945798, 658.90697846, 366.25498888,\n", + " 112.39723538, 377.04654301, 640.76590022, 664.99914892,\n", + " 104.07252262, 612.16401994, 370.22297965, 117.92851311,\n", + " 356.37358833, 160.88820954, 633.76013569, 578.21624462,\n", + " 116.24409974, 147.51724111, 588.27832845, 150.10388531,\n", + " 80.34034449, 103.22096842, 357.74192332, 587.4075227 ,\n", + " 74.72453674, 658.22135681, 573.8376494 , 103.41977838,\n", + " 578.97420998, 154.23780673, 399.4052781 , 159.07374448,\n", + " 105.62754957, -20.54593704, 360.91337042, 354.55423303,\n", + " 157.18784275, 614.59689853, 159.91407764, 155.03515328,\n", + " 588.01788314, 172.18559623, 576.43204141, 579.08642483,\n", + " 588.39356233, 658.92534923, 375.77616123, 43.98384932,\n", + " 371.69378243, 286.11435102, 109.71631596, 163.51831257,\n", + " 605.42587442, 98.16628144, 359.38098951, 79.1778599 ,\n", + " 98.40461332, 646.88754024, 367.30753979, 98.845929 ,\n", + " 153.2533156 , 99.59663648, 636.40535051, 656.22089335,\n", + " 338.89832737, 84.73735442, 644.57147041, 655.03020157,\n", + " 575.22527036, 353.31089549, 154.93096799, 636.85596459,\n", + " 376.77455207, 20.02842692, 572.92783024, 372.32434162,\n", + " 372.52208455, 594.10421085, 377.4539325 , 102.77211001])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768777.765963100158.491273(768, 100)
290768765.931075100141.268575(768, 100)
54100139.51506310097.895359(100, 100)
19810079.855539630587.373438(100, 630)
45314361435.91875910052.780919(1436, 100)
..................
16410090.212863365372.324342(100, 365)
16510090.129972365372.522085(100, 365)
19910081.470342630594.104211(100, 630)
13210082.963966365377.453932(100, 365)
50114361423.823247100102.772110(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 777.765963 100 158.491273 (768, 100)\n", + "290 768 765.931075 100 141.268575 (768, 100)\n", + "54 100 139.515063 100 97.895359 (100, 100)\n", + "198 100 79.855539 630 587.373438 (100, 630)\n", + "453 1436 1435.918759 100 52.780919 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 90.212863 365 372.324342 (100, 365)\n", + "165 100 90.129972 365 372.522085 (100, 365)\n", + "199 100 81.470342 630 594.104211 (100, 630)\n", + "132 100 82.963966 365 377.453932 (100, 365)\n", + "501 1436 1423.823247 100 102.772110 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768777.765963100158.491273(768, 100)
290768765.931075100141.268575(768, 100)
54100139.51506310097.895359(100, 100)
19810079.855539630587.373438(100, 630)
45314361435.91875910052.780919(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 777.765963 100 158.491273 (768, 100)\n", + "290 768 765.931075 100 141.268575 (768, 100)\n", + "54 100 139.515063 100 97.895359 (100, 100)\n", + "198 100 79.855539 630 587.373438 (100, 630)\n", + "453 1436 1435.918759 100 52.780919 (1436, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.223782\n", + "(100, 365) 0.409683\n", + "(100, 630) 0.675336\n", + "(768, 100) 0.918995\n", + "(768, 630) 1.255215\n", + "(1436, 100) 1.200635\n", + "(1436, 365) 1.517800\n", + "(1436, 630) 1.798556\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_16012\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_16012\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 5 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and LinearRegression model\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cea7499d0fe06c999d8188e51f8927b462ac242e Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 11:06:36 +0000 Subject: [PATCH 07/78] lasso model added to the test folder --- .../test/test_lasso_regression.ipynb | 1660 +++++++++++++++++ 1 file changed, 1660 insertions(+) create mode 100644 app/services/calib_validation/test/test_lasso_regression.ipynb diff --git a/app/services/calib_validation/test/test_lasso_regression.ipynb b/app/services/calib_validation/test/test_lasso_regression.ipynb new file mode 100644 index 00000000..ff22d3f4 --- /dev/null +++ b/app/services/calib_validation/test/test_lasso_regression.ipynb @@ -0,0 +1,1660 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.709e+04, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.997926029023532" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 779.53548512, 767.65128778, 141.06513298, 77.01639266,\n", + " 1440.84187245, 138.13955149, 138.61843694, 84.01368723,\n", + " 772.84576825, 759.37004708, 1419.66624991, 754.7205936 ,\n", + " 145.33504885, 94.32021145, 758.97450656, 781.60056997,\n", + " 133.79149125, 1429.19288405, 77.32249494, 75.66615436,\n", + " 755.71541317, 758.15433583, 146.60786618, 1431.55470864,\n", + " 1438.01726104, 780.17519357, 71.50603895, 76.68365066,\n", + " 78.62966711, 86.92492189, 760.44500236, 759.14339993,\n", + " 780.54768565, 761.17652033, 1455.83161065, 1435.22890343,\n", + " 1417.94050814, 1454.67720008, 1449.66638775, 1433.83832519,\n", + " 769.48034763, 87.80955518, 140.86637035, 1426.2519311 ,\n", + " 758.62134163, 137.23159818, 783.07495035, 1436.10934139,\n", + " 1433.94903324, 137.22932953, 1428.08129937, 1433.09088829,\n", + " 752.02383735, 1435.23628267, 80.81542419, 769.55456333,\n", + " 75.24761274, 73.46956065, 138.28156063, 780.54247729,\n", + " 1419.55834794, 160.04661205, 138.29679286, 755.38711375,\n", + " 76.27701016, 94.44848413, 1431.95274479, 84.3386143 ,\n", + " 1425.20953527, 1438.43597818, 1433.5089705 , 762.98067048,\n", + " 140.05327196, 761.90621431, 79.87205141, 1419.68932481,\n", + " 1448.95179373, 780.68260147, 1424.31564478, 75.27145086,\n", + " 1423.16712439, 769.6475289 , 72.7486099 , 766.42938126,\n", + " 150.13464568, 1428.52338252, 1462.27356639, 69.8305093 ,\n", + " 1452.7318761 , 1431.64407264, 70.89701803, 140.62169806,\n", + " 76.63441998, 777.90140937, 95.16666063, 778.37663034,\n", + " 136.66245437, 207.75727489, 1459.56114768, 79.90000519,\n", + " 770.12657697, 1420.7429688 , 776.40745345, 771.13031632,\n", + " 77.42517745, 774.80420506, 70.67476922, 69.2350124 ,\n", + " 68.51899766, 1432.5569573 , 80.07278995, 1306.92228095,\n", + " 1438.40157995, 99.14502912, 1424.22721452, 779.89440109,\n", + " 759.73615962, 134.80843878, 1454.40545917, 151.76965271,\n", + " 139.17811174, 756.97324325, 1438.69833108, 139.644694 ,\n", + " 776.71884135, 138.90383564, 758.99709351, 1433.74130767,\n", + " 1438.55615983, 1441.60818817, 1435.5578321 , 761.59581226,\n", + " 70.59787519, 1456.74625903, 781.99079523, 1422.81957102,\n", + " 85.51403457, 158.79750824, 70.01102199, 88.72286703,\n", + " 89.01457004, 78.55595008, 82.46716868, 1425.33013517])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9751658030754563" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([159.1211271 , 141.94938788, 98.515362 , 588.21013054,\n", + " 51.55679851, 111.60571909, 107.85485267, 385.47576096,\n", + " 157.05760426, 662.94522909, 615.68001594, 688.8817096 ,\n", + " 88.68237837, 316.30791577, 662.01789845, 161.65166259,\n", + " 119.07157792, 380.3590356 , 576.62506046, 588.22862233,\n", + " 690.67273665, 601.8105376 , 77.73456889, 619.40331152,\n", + " 99.2149826 , 153.05666125, 584.29574088, 582.45312047,\n", + " 366.67861034, 368.95985113, 659.6262177 , 596.69159413,\n", + " 145.68075764, 613.20833002, 361.90807243, 692.00682849,\n", + " 616.49488208, 359.68877824, 361.81760976, 98.96519276,\n", + " 147.7179753 , 369.14355266, 95.91191517, 646.34693986,\n", + " 635.16772751, 101.2643276 , 163.48108409, 371.86711141,\n", + " 663.05951516, 106.13974757, 114.88246277, 629.24962415,\n", + " 670.58470178, 338.97290928, 370.80067136, 162.72286117,\n", + " 585.97888587, 575.72784962, 111.54886435, 160.16814491,\n", + " 115.26390645, 37.18269747, 105.91038825, 657.55921759,\n", + " 589.61113439, 311.12564659, 658.37350247, 366.86332161,\n", + " 110.56963093, 375.69326027, 640.15628013, 666.01229372,\n", + " 104.72956135, 612.9996496 , 370.87344354, 116.44741004,\n", + " 354.69175869, 161.47810368, 633.32766021, 579.0396673 ,\n", + " 114.79984331, 148.1506869 , 589.10193481, 150.70155273,\n", + " 81.01412797, 101.34903724, 355.96341652, 588.300444 ,\n", + " 72.97719636, 658.00699863, 574.67030821, 104.0767804 ,\n", + " 579.71642866, 154.86656445, 400.07989969, 159.69857252,\n", + " 106.21862691, -19.77807257, 359.34422179, 355.14236908,\n", + " 157.85683241, 613.68365383, 160.57366388, 155.66751492,\n", + " 588.90358645, 172.825431 , 577.20987903, 579.91355663,\n", + " 589.29797665, 658.51290015, 376.33190983, 41.35098361,\n", + " 370.30408814, 286.72603896, 108.40216867, 164.18540008,\n", + " 606.21054528, 98.87855228, 357.87623177, 79.86977714,\n", + " 99.02383694, 647.79684868, 365.88544878, 99.4926623 ,\n", + " 153.89279316, 100.228666 , 637.22269888, 655.45994762,\n", + " 337.5393344 , 83.08763032, 644.29138763, 656.05080215,\n", + " 576.09061647, 351.59247919, 155.53603578, 636.32146483,\n", + " 377.34236877, 20.65934893, 573.75105472, 372.97994193,\n", + " 373.18096445, 595.01023661, 378.07281337, 101.27179696])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.535485100159.121127(768, 100)
290768767.651288100141.949388(768, 100)
54100141.06513310098.515362(100, 100)
19810077.016393630588.210131(100, 630)
45314361440.84187210051.556799(1436, 100)
..................
16410088.722867365372.979942(100, 365)
16510089.014570365373.180964(100, 365)
19910078.555950630595.010237(100, 630)
13210082.467169365378.072813(100, 365)
50114361425.330135100101.271797(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.535485 100 159.121127 (768, 100)\n", + "290 768 767.651288 100 141.949388 (768, 100)\n", + "54 100 141.065133 100 98.515362 (100, 100)\n", + "198 100 77.016393 630 588.210131 (100, 630)\n", + "453 1436 1440.841872 100 51.556799 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 88.722867 365 372.979942 (100, 365)\n", + "165 100 89.014570 365 373.180964 (100, 365)\n", + "199 100 78.555950 630 595.010237 (100, 630)\n", + "132 100 82.467169 365 378.072813 (100, 365)\n", + "501 1436 1425.330135 100 101.271797 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.535485100159.121127(768, 100)
290768767.651288100141.949388(768, 100)
54100141.06513310098.515362(100, 100)
19810077.016393630588.210131(100, 630)
45314361440.84187210051.556799(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.535485 100 159.121127 (768, 100)\n", + "290 768 767.651288 100 141.949388 (768, 100)\n", + "54 100 141.065133 100 98.515362 (100, 100)\n", + "198 100 77.016393 630 588.210131 (100, 630)\n", + "453 1436 1440.841872 100 51.556799 (1436, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.225192\n", + "(100, 365) 0.409578\n", + "(100, 630) 0.674855\n", + "(768, 100) 0.920292\n", + "(768, 630) 1.254603\n", + "(1436, 100) 1.201001\n", + "(1436, 365) 1.517256\n", + "(1436, 630) 1.797223\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_3288\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_3288\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 5 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and LinearRegression model\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.709e+04, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From bbb1e9dca23c773c0615c3a760b81da7c6e63d67 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 11:23:34 +0000 Subject: [PATCH 08/78] ridge CV model added to the test folder --- .../test/test_ridgeCV_regression.ipynb | 1650 +++++++++++++++++ 1 file changed, 1650 insertions(+) create mode 100644 app/services/calib_validation/test/test_ridgeCV_regression.ipynb diff --git a/app/services/calib_validation/test/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/test_ridgeCV_regression.ipynb new file mode 100644 index 00000000..b3a0b030 --- /dev/null +++ b/app/services/calib_validation/test/test_ridgeCV_regression.ipynb @@ -0,0 +1,1650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9962372996893408" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 775.9488375 , 765.44646524, 117.46992782, 114.98772163,\n", + " 1425.60842262, 108.58871546, 128.04372944, 86.82552302,\n", + " 770.32361198, 770.14196838, 1419.39765124, 767.69939222,\n", + " 116.90236849, 95.9273306 , 771.11439115, 777.12888377,\n", + " 123.7435898 , 1431.44527262, 101.7226186 , 113.47143036,\n", + " 768.6386489 , 768.48131803, 105.57636972, 1434.19347734,\n", + " 1423.9421898 , 775.30025567, 85.35391255, 103.8479276 ,\n", + " 78.44714396, 102.21159725, 770.15743904, 769.40008838,\n", + " 777.8388113 , 772.31712021, 1458.55673432, 1438.61886872,\n", + " 1417.35182784, 1458.09686899, 1453.44883061, 1427.43953014,\n", + " 763.69556751, 101.51991418, 106.26253837, 1427.98805487,\n", + " 769.76844177, 108.7787584 , 779.85981566, 1439.22131008,\n", + " 1437.22179818, 101.90989851, 1423.55090668, 1436.1863096 ,\n", + " 765.46761981, 1437.41734021, 84.65561441, 767.76280724,\n", + " 94.43140854, 94.98800185, 116.97901041, 776.45230831,\n", + " 1416.55127338, 109.72866561, 113.59668594, 767.05101545,\n", + " 88.11886533, 99.62142055, 1434.70126831, 94.18205055,\n", + " 1420.05801842, 1440.92837538, 1436.69220078, 773.89818439,\n", + " 121.70204396, 772.58809921, 70.5603242 , 1416.85192743,\n", + " 1453.47477909, 778.12898405, 1425.58029512, 99.97644867,\n", + " 1417.89655382, 763.96221723, 93.43644841, 761.6909926 ,\n", + " 113.75955612, 1423.80307031, 1464.19734681, 95.3609168 ,\n", + " 1437.40319904, 1434.4477574 , 84.41258735, 118.43962962,\n", + " 100.21523859, 772.50600096, 100.42814048, 771.2771715 ,\n", + " 104.08148186, 116.11428828, 1463.45615148, 82.64389501,\n", + " 769.65594585, 1421.11500469, 773.82201847, 768.5980017 ,\n", + " 113.05823801, 770.54771329, 89.82168267, 82.1647507 ,\n", + " 79.94817147, 1435.54744746, 93.00558832, 1043.31729817,\n", + " 1440.15455279, 104.88790171, 1421.37161817, 775.24045023,\n", + " 770.90365892, 115.35590471, 1457.01402779, 116.75333443,\n", + " 120.71010652, 768.99022685, 1440.49773319, 113.56181959,\n", + " 774.7198465 , 100.7586774 , 770.0539881 , 1436.97118999,\n", + " 1441.03717148, 1432.19446103, 1438.64806284, 772.0856761 ,\n", + " 88.75513547, 1459.6473705 , 775.85076962, 1423.31696178,\n", + " 86.61817211, 101.97754574, 93.70863022, 106.26391156,\n", + " 100.00618951, 118.13553751, 80.47978862, 1422.27736965])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9808695687527501" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a linear regression model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([130.77711835, 117.6474648 , 107.61582613, 584.12047811,\n", + " 55.4754501 , 113.13585135, 108.49449888, 404.88830082,\n", + " 132.63725089, 667.01492296, 611.7120068 , 690.50314228,\n", + " 92.21187016, 332.53209382, 664.96999878, 137.37399939,\n", + " 117.9948033 , 371.75966214, 568.92695921, 591.18110309,\n", + " 691.84288468, 597.60307529, 89.65270506, 621.33411176,\n", + " 134.09821776, 122.2569714 , 578.71326319, 577.48604329,\n", + " 392.74517085, 379.0663491 , 667.53992792, 597.30841557,\n", + " 110.5414626 , 606.88294508, 348.51284105, 693.56428018,\n", + " 616.99144213, 363.39147946, 355.44231523, 102.54336181,\n", + " 121.74368488, 382.41495312, 106.39489094, 640.13741406,\n", + " 630.96879756, 107.37458863, 138.81935247, 367.41160256,\n", + " 662.97982435, 107.15614713, 128.45120122, 629.58847124,\n", + " 672.94182187, 327.05347783, 374.51071442, 138.86054073,\n", + " 583.86047615, 570.23374655, 115.34560497, 131.55630825,\n", + " 114.92702326, 47.04664706, 105.30614147, 658.78828468,\n", + " 596.7169542 , 314.54358875, 658.5664843 , 383.35060681,\n", + " 124.43320486, 369.92846573, 639.8494254 , 671.19659187,\n", + " 107.46338054, 607.52149607, 375.71173988, 118.54093037,\n", + " 356.38629449, 130.25862991, 628.96569658, 574.45933298,\n", + " 115.94816887, 118.57288776, 583.88152178, 118.83279143,\n", + " 85.03469256, 118.02922192, 360.28064026, 591.29945273,\n", + " 90.3975367 , 652.55993607, 571.36936802, 106.91744765,\n", + " 569.85491228, 125.80734431, 405.30912129, 130.94301332,\n", + " 118.36663949, -7.42292206, 358.27912422, 373.58454239,\n", + " 135.63614369, 616.88886719, 136.75979723, 127.07110164,\n", + " 590.48035059, 147.99316798, 569.328308 , 575.60175341,\n", + " 594.55918 , 656.45585252, 404.76384929, 99.46837477,\n", + " 365.18237482, 288.37521017, 106.31482456, 143.74369738,\n", + " 598.15233122, 92.72970694, 355.10496449, 81.36921695,\n", + " 108.16812159, 645.4960504 , 361.36978947, 104.60919856,\n", + " 125.76896423, 107.40460786, 630.42725435, 659.96640556,\n", + " 330.3699873 , 95.35357566, 638.47363814, 664.83325858,\n", + " 576.89577907, 354.14348354, 124.66819906, 634.14849024,\n", + " 403.9072803 , 39.73804703, 569.57521083, 375.77865049,\n", + " 372.57885505, 598.82980489, 394.67575436, 105.88192018])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768775.948837100130.777118(768, 100)
290768765.446465100117.647465(768, 100)
54100117.469928100107.615826(100, 100)
198100114.987722630584.120478(100, 630)
45314361425.60842310055.475450(1436, 100)
..................
164100106.263912365375.778650(100, 365)
165100100.006190365372.578855(100, 365)
199100118.135538630598.829805(100, 630)
13210080.479789365394.675754(100, 365)
50114361422.277370100105.881920(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 775.948837 100 130.777118 (768, 100)\n", + "290 768 765.446465 100 117.647465 (768, 100)\n", + "54 100 117.469928 100 107.615826 (100, 100)\n", + "198 100 114.987722 630 584.120478 (100, 630)\n", + "453 1436 1425.608423 100 55.475450 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.263912 365 375.778650 (100, 365)\n", + "165 100 100.006190 365 372.578855 (100, 365)\n", + "199 100 118.135538 630 598.829805 (100, 630)\n", + "132 100 80.479789 365 394.675754 (100, 365)\n", + "501 1436 1422.277370 100 105.881920 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768775.948837100130.777118(768, 100)
290768765.446465100117.647465(768, 100)
54100117.469928100107.615826(100, 100)
198100114.987722630584.120478(100, 630)
45314361425.60842310055.475450(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 775.948837 100 130.777118 (768, 100)\n", + "290 768 765.446465 100 117.647465 (768, 100)\n", + "54 100 117.469928 100 107.615826 (100, 100)\n", + "198 100 114.987722 630 584.120478 (100, 630)\n", + "453 1436 1425.608423 100 55.475450 (1436, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.211173\n", + "(100, 365) 0.418316\n", + "(100, 630) 0.685089\n", + "(768, 100) 0.903296\n", + "(768, 630) 1.261005\n", + "(1436, 100) 1.203244\n", + "(1436, 365) 1.518381\n", + "(1436, 630) 1.799496\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_12628\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_12628\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 5 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and LinearRegression model\n", + " sc = StandardScaler()\n", + " model = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From af861e1f55dfcd5300358ca8dfb309bf84d5889e Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 11:30:28 +0000 Subject: [PATCH 09/78] small comment fix in ridge model notebook --- app/services/calib_validation/test/test_ridge_regression.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/app/services/calib_validation/test/test_ridge_regression.ipynb b/app/services/calib_validation/test/test_ridge_regression.ipynb index 6bebd81e..25c5b24c 100644 --- a/app/services/calib_validation/test/test_ridge_regression.ipynb +++ b/app/services/calib_validation/test/test_ridge_regression.ipynb @@ -1517,7 +1517,8 @@ " None\n", " \"\"\"\n", "\n", - " # Initialize the StandardScaler and LinearRegression model\n", + " # Initialize the StandardScaler and Ridge regression model\n", + " # with polynomial features of degree 2 and alpha = 0.5\n", " sc = StandardScaler()\n", " model = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", "\n", From e4b59f8c12ddadd37a3ec06551b17c96e54c9f71 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 14:23:29 +0000 Subject: [PATCH 10/78] ridge cv model comment fix --- .../calib_validation/test/test_ridgeCV_regression.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/app/services/calib_validation/test/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/test_ridgeCV_regression.ipynb index b3a0b030..18ca5911 100644 --- a/app/services/calib_validation/test/test_ridgeCV_regression.ipynb +++ b/app/services/calib_validation/test/test_ridgeCV_regression.ipynb @@ -514,7 +514,7 @@ } ], "source": [ - "# Create a linear regression model and fit the data\n", + "# Create a RidgeCV model and fit the data\n", "model_x = make_pipeline(\n", " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", ")\n", @@ -705,7 +705,7 @@ } ], "source": [ - "# Create a linear regression model and fit the data\n", + "# Create a RidgeCV model and fit the data\n", "model_y = make_pipeline(\n", " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", ")\n", @@ -1304,7 +1304,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Create a KMeans model with 5 clusters\n", + "# Create a KMeans model with 8 clusters\n", "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", "\n", "# Fit the data to the model\n", @@ -1521,7 +1521,8 @@ " None\n", " \"\"\"\n", "\n", - " # Initialize the StandardScaler and LinearRegression model\n", + " # Initialize the StandardScaler and RidgeCV model\n", + " # with polynomial features of degree 2 and alpha set to logspace(-6, 6, 13)\n", " sc = StandardScaler()\n", " model = make_pipeline(\n", " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", From ffc5e415e11dbcb289edbc137b86b553fc4c498c Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 14:30:54 +0000 Subject: [PATCH 11/78] comment fix in linear regression notebook --- .../calib_validation/test/test_linear_regression.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/app/services/calib_validation/test/test_linear_regression.ipynb b/app/services/calib_validation/test/test_linear_regression.ipynb index 4abaeeae..cf6e48f6 100644 --- a/app/services/calib_validation/test/test_linear_regression.ipynb +++ b/app/services/calib_validation/test/test_linear_regression.ipynb @@ -1300,7 +1300,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Create a KMeans model with 5 clusters\n", + "# Create a KMeans model with 8 clusters\n", "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", "\n", "# Fit the data to the model\n", @@ -1518,6 +1518,7 @@ " \"\"\"\n", "\n", " # Initialize the StandardScaler and LinearRegression model\n", + " # with 2-degree polynomial features\n", " sc = StandardScaler()\n", " model = make_pipeline(PolynomialFeatures(2), linear_model.LinearRegression())\n", "\n", From 8f99d4332441629fb227f36970961e9a59580e11 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 14:40:06 +0000 Subject: [PATCH 12/78] comment fix in lasso notebook --- .../calib_validation/test/test_lasso_regression.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/app/services/calib_validation/test/test_lasso_regression.ipynb b/app/services/calib_validation/test/test_lasso_regression.ipynb index ff22d3f4..f809e373 100644 --- a/app/services/calib_validation/test/test_lasso_regression.ipynb +++ b/app/services/calib_validation/test/test_lasso_regression.ipynb @@ -522,7 +522,7 @@ } ], "source": [ - "# Create a linear regression model and fit the data\n", + "# Create a lasso regression model and fit the data\n", "model_x = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", "model_x.fit(X_train_x, y_train_x)\n", "\n", @@ -711,7 +711,7 @@ } ], "source": [ - "# Create a linear regression model and fit the data\n", + "# Create a lasso regression model and fit the data\n", "model_y = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", "model_y.fit(X_train_y, y_train_y)\n", "\n", @@ -1308,7 +1308,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Create a KMeans model with 5 clusters\n", + "# Create a KMeans model with 8 clusters\n", "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", "\n", "# Fit the data to the model\n", @@ -1525,7 +1525,8 @@ " None\n", " \"\"\"\n", "\n", - " # Initialize the StandardScaler and LinearRegression model\n", + " # Initialize the StandardScaler and Lasso model\n", + " # with polynomial features of degree 2 and alpha=0.1\n", " sc = StandardScaler()\n", " model = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", "\n", From c97857443df4eed7c809c10a02b7ab55d9a305b3 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 14:46:35 +0000 Subject: [PATCH 13/78] comment fix in ridge model notebook --- .../calib_validation/test/test_ridge_regression.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/app/services/calib_validation/test/test_ridge_regression.ipynb b/app/services/calib_validation/test/test_ridge_regression.ipynb index 25c5b24c..46a434b5 100644 --- a/app/services/calib_validation/test/test_ridge_regression.ipynb +++ b/app/services/calib_validation/test/test_ridge_regression.ipynb @@ -514,7 +514,7 @@ } ], "source": [ - "# Create a linear regression model and fit the data\n", + "# Create a Ridge regression model and fit the data\n", "model_x = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", "model_x.fit(X_train_x, y_train_x)\n", "\n", @@ -703,7 +703,7 @@ } ], "source": [ - "# Create a linear regression model and fit the data\n", + "# Create a Ridge regression model and fit the data\n", "model_y = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", "model_y.fit(X_train_y, y_train_y)\n", "\n", @@ -1300,7 +1300,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Create a KMeans model with 5 clusters\n", + "# Create a KMeans model with 8 clusters\n", "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", "\n", "# Fit the data to the model\n", From 19f8acaf8ea50b661e81267bb085aa248d96632b Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 14:56:03 +0000 Subject: [PATCH 14/78] move the data exploration notebook under calib_validation --- app/services/{ => calib_validation}/data_exploration.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename app/services/{ => calib_validation}/data_exploration.ipynb (100%) diff --git a/app/services/data_exploration.ipynb b/app/services/calib_validation/data_exploration.ipynb similarity index 100% rename from app/services/data_exploration.ipynb rename to app/services/calib_validation/data_exploration.ipynb From 2befb1311415b7371361a5ac18741c3bae1c6169 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 19:06:15 +0000 Subject: [PATCH 15/78] separate folders for models added --- .../test/{ => linear_regression}/test_linear_regression.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename app/services/calib_validation/test/{ => linear_regression}/test_linear_regression.ipynb (100%) diff --git a/app/services/calib_validation/test/test_linear_regression.ipynb b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb similarity index 100% rename from app/services/calib_validation/test/test_linear_regression.ipynb rename to app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb From 4d2fba5c50a843940e9d472227e84d4b16dc32bb Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 19:10:29 +0000 Subject: [PATCH 16/78] ridge regression model folder added --- .../test/{ => ridge_regression}/test_ridgeCV_regression.ipynb | 0 .../test/{ => ridge_regression}/test_ridge_regression.ipynb | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename app/services/calib_validation/test/{ => ridge_regression}/test_ridgeCV_regression.ipynb (100%) rename app/services/calib_validation/test/{ => ridge_regression}/test_ridge_regression.ipynb (100%) diff --git a/app/services/calib_validation/test/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb similarity index 100% rename from app/services/calib_validation/test/test_ridgeCV_regression.ipynb rename to app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb diff --git a/app/services/calib_validation/test/test_ridge_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb similarity index 100% rename from app/services/calib_validation/test/test_ridge_regression.ipynb rename to app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb From 0d96874127a80ea63b66ff40f6d33324f01ef322 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 19:52:06 +0000 Subject: [PATCH 17/78] ridge model grid search added --- .../test_ridge_regression_grid_search.ipynb | 1668 +++++++++++++++++ 1 file changed, 1668 insertions(+) create mode 100644 app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb new file mode 100644 index 00000000..6cef6e90 --- /dev/null +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb @@ -0,0 +1,1668 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9979769240845753" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a Ridge regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 777.76596343, 765.93107474, 139.51506252, 79.85553907,\n", + " 1435.9187586 , 136.31337857, 137.81778199, 84.74883019,\n", + " 771.16565515, 761.25093397, 1422.86582297, 757.55217937,\n", + " 143.46126635, 94.8359387 , 761.71406876, 779.68341603,\n", + " 133.08892546, 1430.76514394, 79.36568085, 78.51353434,\n", + " 758.65301652, 759.72126028, 144.04748863, 1432.96210265,\n", + " 1433.36219166, 778.1454125 , 73.03695827, 78.89486796,\n", + " 79.28272673, 88.31244713, 761.88829374, 760.74771531,\n", + " 778.99478172, 763.44526412, 1454.68074484, 1437.96034167,\n", + " 1421.10028784, 1453.85535651, 1449.37316876, 1430.95057164,\n", + " 767.08225864, 89.09532064, 138.7310988 , 1428.31077236,\n", + " 760.64349738, 135.47759084, 781.46449776, 1436.98611109,\n", + " 1435.91910262, 135.11171635, 1425.94049561, 1435.00945126,\n", + " 754.85405749, 1435.47559541, 81.65486637, 767.96214294,\n", + " 77.0232892 , 75.40309346, 136.8966017 , 778.68529821,\n", + " 1418.41616071, 156.79742146, 136.7280092 , 757.41056886,\n", + " 77.62588701, 95.15984845, 1434.57496892, 85.45779156,\n", + " 1423.00245968, 1438.51089444, 1435.7098284 , 765.29784366,\n", + " 138.8031703 , 763.96190836, 80.01534544, 1418.61202535,\n", + " 1449.11752258, 779.16810915, 1426.72599729, 77.3610738 ,\n", + " 1421.01230512, 767.27094091, 74.64541356, 764.17943387,\n", + " 147.76582922, 1426.30525503, 1460.53110895, 72.04457356,\n", + " 1447.56775744, 1433.84328243, 72.41845612, 139.15467798,\n", + " 78.64038715, 775.72314514, 95.87248068, 775.87362999,\n", + " 134.69782567, 201.68322957, 1458.60726216, 80.69318553,\n", + " 768.84148701, 1423.46414986, 774.78995579, 769.4122411 ,\n", + " 80.12046076, 772.79931109, 72.51457116, 70.74858465,\n", + " 69.9602185 , 1434.74477172, 81.42685508, 1283.14382773,\n", + " 1438.0661663 , 99.81999104, 1422.85814554, 777.90515883,\n", + " 761.87461015, 133.57493463, 1453.29678004, 149.44843414,\n", + " 137.93434844, 759.36586875, 1438.36081974, 137.9815362 ,\n", + " 775.23944647, 136.61239062, 761.00201832, 1435.9505198 ,\n", + " 1438.61119866, 1437.79057397, 1436.52185808, 763.51320291,\n", + " 72.38312798, 1455.60427611, 779.73666057, 1425.91868457,\n", + " 86.13315623, 155.23337983, 72.11800429, 90.2128626 ,\n", + " 90.12997158, 81.47034246, 82.96396599, 1423.82324744])" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.975215340154245" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a Ridge regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=.5))\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([158.49127326, 141.26857472, 97.89535932, 587.3734379 ,\n", + " 52.78091935, 110.94785664, 107.18820455, 384.86167257,\n", + " 156.39670628, 661.94548856, 616.23326541, 687.87868774,\n", + " 88.01393969, 315.74035441, 661.02961346, 160.99561532,\n", + " 118.40587012, 381.58437496, 575.84460606, 587.33603499,\n", + " 689.67455541, 600.964088 , 77.11527916, 620.22975998,\n", + " 101.60956704, 152.44652655, 583.48035074, 581.63141439,\n", + " 366.11974227, 368.32494321, 658.60441632, 595.8028418 ,\n", + " 145.15004897, 612.38725101, 362.96162946, 692.41322869,\n", + " 617.26819892, 361.43907616, 363.17135408, 100.4229667 ,\n", + " 147.05356509, 368.51972904, 95.29963364, 646.58911465,\n", + " 634.29776429, 100.62596101, 162.82927273, 373.28091413,\n", + " 663.55180176, 105.47364039, 116.71915099, 629.94968735,\n", + " 669.59229204, 340.13195617, 370.14815542, 162.06602779,\n", + " 585.12422266, 574.91767937, 110.90476667, 159.54249788,\n", + " 116.66149274, 36.49506069, 105.23466404, 656.59420344,\n", + " 588.70002589, 310.50945798, 658.90697846, 366.25498888,\n", + " 112.39723538, 377.04654301, 640.76590022, 664.99914892,\n", + " 104.07252262, 612.16401994, 370.22297965, 117.92851311,\n", + " 356.37358833, 160.88820954, 633.76013569, 578.21624462,\n", + " 116.24409974, 147.51724111, 588.27832845, 150.10388531,\n", + " 80.34034449, 103.22096842, 357.74192332, 587.4075227 ,\n", + " 74.72453674, 658.22135681, 573.8376494 , 103.41977838,\n", + " 578.97420998, 154.23780673, 399.4052781 , 159.07374448,\n", + " 105.62754957, -20.54593704, 360.91337042, 354.55423303,\n", + " 157.18784275, 614.59689853, 159.91407764, 155.03515328,\n", + " 588.01788314, 172.18559623, 576.43204141, 579.08642483,\n", + " 588.39356233, 658.92534923, 375.77616123, 43.98384932,\n", + " 371.69378243, 286.11435102, 109.71631596, 163.51831257,\n", + " 605.42587442, 98.16628144, 359.38098951, 79.1778599 ,\n", + " 98.40461332, 646.88754024, 367.30753979, 98.845929 ,\n", + " 153.2533156 , 99.59663648, 636.40535051, 656.22089335,\n", + " 338.89832737, 84.73735442, 644.57147041, 655.03020157,\n", + " 575.22527036, 353.31089549, 154.93096799, 636.85596459,\n", + " 376.77455207, 20.02842692, 572.92783024, 372.32434162,\n", + " 372.52208455, 594.10421085, 377.4539325 , 102.77211001])" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGxCAYAAABIjE2TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABfvElEQVR4nO3deXjU5b3//+fsySSZyUJWyMaiEGQTkASwtpVKFT0uaNWiIKD9lQOeKud4PHjsYr+t9Nhz1S7nCK0iao/Woz3FulQtRUWBsIiigIAsgQDJJECWyTrr5/dHTOoAShKGLMzrcV25JHO/Z+aejwnz4jOf+32bDMMwEBEREelDzL09AREREZGTKaCIiIhIn6OAIiIiIn2OAoqIiIj0OQooIiIi0ucooIiIiEifo4AiIiIifY4CioiIiPQ51t6eQHeEw2EqKipISkrCZDL19nRERESkEwzDoKGhgZycHMzmLz9H0i8DSkVFBbm5ub09DREREemGw4cPM2jQoC+t6ZcBJSkpCWh7gS6Xq5dnIyIiIp3h9XrJzc3teB//Mv0yoLR/rONyuRRQRERE+pnOXJ6hi2RFRESkz1FAERERkT5HAUVERET6HAUUERER6XMUUERERKTPUUARERGRPqdLAaWgoACTyXTK18KFCwFobW1l4cKFpKWlkZiYyMyZM6mqqop4jPLycmbMmIHT6SQjI4P77ruPYDAYvVckIiIi/V6XAsqWLVuorKzs+Fq9ejUAN910EwD33nsvr7zyCi+++CJr166loqKCG264oeP+oVCIGTNm4Pf72bBhA08//TRPPfUUP/jBD6L4kkRERKS/MxmGYXT3zvfccw+vvvoqe/fuxev1kp6eznPPPceNN94IwO7duxkxYgSlpaUUFxfz+uuvc/XVV1NRUUFmZiYAy5cv5/777+fYsWPY7fZOPa/X68XtdlNfX69GbSIiIv1EV96/u30Nit/v53/+53+YN28eJpOJrVu3EggEmDZtWkfN8OHDycvLo7S0FIDS0lJGjRrVEU4Apk+fjtfrZefOnV/4XD6fD6/XG/ElIiIi0RcOG2w/Us/aT4+x/Ug94XC3z2OclW63un/ppZeoq6vjjjvuAMDj8WC320lOTo6oy8zMxOPxdNR8Ppy0j7ePfZGlS5fy0EMPdXeqIiIi0gkb9h3nsXf2s8fTgD8Uxm4xc2FWEv/41SFMHjqgR+fS7TMoK1as4MorryQnJyea8zmtJUuWUF9f3/F1+PDhc/6cIiIisWTDvuPc+8I2NpWdoKbJR0NLgJomH5vKTnDvC9vYsO94j86nWwHl0KFD/O1vf+POO+/suC0rKwu/309dXV1EbVVVFVlZWR01J6/qaf++veZ0HA5Hx8aA2iBQREQkusJhg6Wv76La6yMQMggZEAZCBgRCBtVeH0tf39WjH/d0K6CsXLmSjIwMZsyY0XHb+PHjsdlsrFmzpuO2PXv2UF5eTklJCQAlJSVs376d6urqjprVq1fjcrkoKirq7msQERGRs7D9aD27PQ20xw8TYDK1/RfAAHZ7Gth+tL7H5tTla1DC4TArV65kzpw5WK1/v7vb7Wb+/PksXryY1NRUXC4Xd999NyUlJRQXFwNwxRVXUFRUxO23384jjzyCx+PhwQcfZOHChTgcjui9KhEREem0D8prCYTa4onJ9LmBz0KK8dmZlA/KaxmTm9wjc+pyQPnb3/5GeXk58+bNO2Xs0UcfxWw2M3PmTHw+H9OnT+exxx7rGLdYLLz66qssWLCAkpISEhISmDNnDj/+8Y/P7lWIiIhIt3nqWqNaFw1n1Qelt6gPioiISPQ8ub6MH7/yScf3nz+L8vmU8INripg3pbDbz9MjfVBERETk/DA+LwWr+e+pxDD+/tXOajYxPi+lx+akgCIiIhLjRg10MyI7CdMXjJuAEdlJjBro7rE5KaCIiIjEOLPZxJIrR5DhcmA1g8UEZlPbf61myHA5WHLlCMzmL4ow52BOPfZMIiIi0mdNHjqAR781lkmFqSTFWYmzWUiKszKpMJVHvzW2xzvJdrvVvYiIiJx/TCYzNosFgzA2ixmTqXfOZegMioiIiLBh33EeWLWdXZX1mE1gt5gwm2BXZT0PrNre463udQZFREQkxoXDBsvW7udYQystgTCf72hvNkFrIMSytfspHpzWY9eh6AyKiIhIjNtZ4eWjw3U0+SPDCUDYgCZ/mI8O17Gzwttjc1JAERERiXHHGlrxtga/tMbbGuRYQ891klVAERERiXEfd3ITwM7WRYMCioiISIyr9nbuzEhn66JBAUVERCTGNbT6o1oXDQooIiIiMe7w8eao1kWDAoqIiEiMq2jwRbUuGhRQREREYlyCvXNt0TpbFw0KKCIiIjFuxpjsqNZFgwKKiIhIjPvHqUOiWhcNCigiIiIx7vVdVVGtiwYFFBERkRj34eHaqNZFgwKKiIhIjIu3WaJaFw0KKCIiIjHuwsykqNZFgwKKiIhIjHMn2KJaFw0KKCIiIjFu55HObQLY2bpoUEARERGJcburGqJaFw0KKCIiIjHPFOW6s6eAIiIiEuOGZyRGtS4aFFBERERi3Ojc5KjWRYMCioiISIxLT4rDFfflGwG64qykJ8X10IwUUERERGLeyBwX+WnOL63JT3MyMsfVQzNSQBEREYl5/mCYupbAl9Y0+oI9NJs2CigiIiIxKhQ2ON7o4+091RytafnS2vITzWw/qj4oIiIico6Ewwa1TX4O1zTjbQmw5hMP4TPcJ2TA1oM1PTI/gC+/IkZERETOG4Zh4G0NUtfsJxQ2OHSiiZXrD/Lu3uOduv/HPXgGRQFFREQkBjS0BqhrDhAIhanytvL0hkP89RMPYaPzj9EaCJ27CZ5EAUVEROQ81uwPUtPkxx8MU9vs59lN5bzyUQWBUBeSyWfibJZzMMPTU0ARERE5D7UGQtQ0+WkNhGj0BXnx/cO8uPUIrYHIq00GpcQzMjuJNz+pPuNjjh7kPlfTPUWXL5I9evQot912G2lpacTHxzNq1Cjef//9jnHDMPjBD35AdnY28fHxTJs2jb1790Y8Rk1NDbNmzcLlcpGcnMz8+fNpbGw8+1cjIiIS43zBEJ76VirqWqhv9vO/Ww5z2xOb+P3G8ohwkp7o4F+uuICVd0zkmrEDO/XY4/JSztW0T9GlMyi1tbVMmTKFr33ta7z++uukp6ezd+9eUlL+PuFHHnmEX//61zz99NMUFhby/e9/n+nTp/PJJ58QF9fWgW7WrFlUVlayevVqAoEAc+fO5Tvf+Q7PPfdcdF+diIhIjAiE2j7CaWwNEgyFeWOnh2dKD3G80R9R54qzMqs4n2vH5GC3mrFZzKQl2LGYTYS+5IIUi9mE2dRzmwV2KaD8x3/8B7m5uaxcubLjtsLCwo4/G4bBL3/5Sx588EGuvfZaAJ555hkyMzN56aWXuOWWW9i1axdvvPEGW7ZsYcKECQD85je/4aqrruI///M/ycnJicbrEhERiQmhsEFdsx9va5BQOMw7e46xcv1BjtZF9jVx2i3cNH4QN44fRILDitVsJjnBRpLDyv7qxjPuU2wCapv8Z6iKni59xPPyyy8zYcIEbrrpJjIyMhg3bhyPP/54x3hZWRkej4dp06Z13OZ2u5k0aRKlpaUAlJaWkpyc3BFOAKZNm4bZbGbTpk2nfV6fz4fX6434EhERiWWG0RZMDtc0U9fsZ+OB43z39x/wk9d2RYQTm8XETeMH8ez8ScyZXIAr3kZagoPc1HhccTZMJhO1zQHCxpdfNBs2DGqbv7zbbDR16QzKgQMHWLZsGYsXL+aBBx5gy5Yt/NM//RN2u505c+bg8XgAyMzMjLhfZmZmx5jH4yEjIyNyElYrqampHTUnW7p0KQ899FBXpioiInJeau9lUt8cIBgOs/1IPU+sO8D2o5H/eDeb4Jsjs5hdkk+GKw6L2YQ73oY7vi2UfJ7LaT3jcuOw0VbXU7r0TOFwmAkTJvDwww8DMG7cOHbs2MHy5cuZM2fOOZkgwJIlS1i8eHHH916vl9zc3HP2fCIiIn1Roy9IbZOfQCjM/upGVqwvY+OBU7u7XnZBOnOnFJCX6sRs+nswMZtP/0FOXVPnzox0ti4auhRQsrOzKSoqirhtxIgR/N///R8AWVlZAFRVVZGdnd1RU1VVxdixYztqqqsjlzIFg0Fqamo67n8yh8OBw+HoylRFRETOGy3+EDXNfnyBEEdrW1i54SBv7T51WfCE/BTuvLSQCzKTMJtMuD4LJpYvCCbtGs6wUWBX66KhSwFlypQp7NmzJ+K2Tz/9lPz8fKDtgtmsrCzWrFnTEUi8Xi+bNm1iwYIFAJSUlFBXV8fWrVsZP348AG+99RbhcJhJkyad7esRERE5b7QGQtQ2+2nxhzje6OP3pYf4yw7PKattirKTuPPSwYzNTcZkMuGKs5LstJ8xmLQ7+SOfs62Lhi4FlHvvvZfJkyfz8MMP861vfYvNmzfzu9/9jt/97ndA28TvuecefvKTnzBs2LCOZcY5OTlcd911QNsZl29+85vcddddLF++nEAgwKJFi7jlllu0gkdERAQ6ur42+YLUtwR4fnM5q7ZV4A9GNlkrHJDAvCkFTB6ShtlsJinOSnK8Daula23OhqU7o1oXDV0KKBMnTmTVqlUsWbKEH//4xxQWFvLLX/6SWbNmddT867/+K01NTXznO9+hrq6OqVOn8sYbb3T0QAF49tlnWbRoEZdffjlms5mZM2fy61//OnqvSkREpB8KhsLUNgdoaA3Q4g/xxw+O8MKWwzT5I/fAyXbHccfkAr4+PAOrxUyiw0qKs+vBpN1P39jd6brXLsg4c2EUmAzjDOuK+iCv14vb7aa+vh6Xy9Xb0xERETkr4bBBXUuA+pYAvkCIVz+u5NlNh05Z1puaYOf24jyuGpWNzWImMc5KitOOrZvBpN3YH/+Vuk4sIU522tj2gyu6/Txdef/WXjwiIiK9pH3JcF1z22Z+f9tVxVMbDlLl9UXUJTqs3DIxl+svHki8zUKio+0aE7v17IJJu3ibmbpO1vUUBRQREZFe0L5k2B8MsW7fCZ5cX8ahE80RNQ6rmZkXD+TmibkkxdlIcFhJdtpwWKO7q/C0C1L5/ZbKTtX1FAUUERGRHvT5JcMflNeyYl0ZuyobImosZhNXj8rmtuI80hIdOO1WUhKiH0zaef2du9qjs3XRoIAiIiLSA3zBEDVNbUuGd3u8rHivjK3ldRE1JuDyERncMbmAnOR44u0WUpx24mznJpi0q6r3nbmoC3XRoIAiIiJyDgVCYWqb/DT6ghw60cST6w/y3t7jp9RNHpLGvCkFDE5PJM7WFkzi7ec2mLQ71ti54NHZumhQQBERETkHgqEwdS0BGlqDVNa18HTpQVZ/UnXKnjdjc93Mn1rIyBw3DpuFFKcNp71n355Pbvx2tnXRoIAiIiISRe1Lhr0tAY43+nh2UzmvfFRB8KQ39wsyE5k/tZAJ+Sk4bBZSE+w9HkzaZSfHc7CmpVN1PUUBRUREJAoMw8DbEqSuxU99S4AX3j/MH7ceoTUQ2f01NyWeuVMKueyCAditFlIS7CQ6evft+IZxAyk9zaaDp6vrKQooIiIiZ8nbGqCuKUCTL8CqbRU8v7kcb2swoiY90cGcyflMH5lFnM1CstNGUpytl2Yc6fpxg/j3l3bgD33xRzh2i4nrxw3qsTkpoIiIiHRTey+TFn+Q13d4eGbjIU40+iNq3PE2vj0pj2vH5OC0W0lOsJHksPboxntnYrWauW/6hTz8l92cLqKYgPumX4g1So3hOjWnHnsmERGR80R7L5MWf5C3dx/jqQ0HOVoXeQ2H027hpvGDuHH8IFzxNpLj7bji+1Yw+by7vjIEgP96ex/eliAGbcHEFW9l0deGdoz3FO3FIyIi0kmtgRC1zX6afUE2ldWwYl0Z+481RdTYLCauGzuQb1+SR0qCHXe8DXe8DbO5bwaTkwWDYV75uJKjdc0MTHZyzejsqJ050V48IiIiUfT5Xibbj9TzxLoDbD/qjagxm+CbI7OYXZJPljse12fBxNJPgkk7q9XM9Rf33MWwXziP3p6AiIhIXxUKG9Q2+2loDbK3qoEV68rYVHbqapfLLkhn7pQC8tMScMW1beTX34JJX6OAIiIicpJw2KC+JUB9S4DymiZWrj/I23uOnVI3sSCF+VMLuTDLRVKcleR4G1ZLz11Iej5TQBEREfmMYRh4W4PUNweorG/h9xsP8Zftlad0fy3KdnHXpYWMzUsh8bMdhm0KJlGlgCIiIsLflwwfb/Txh83lvLStAn8wssla4YAE5k8toGRwGklxNpKdduw9uPQ2liigiIhITGtfMlzX5OePHxzhhS2HafKHImqy3XHcMbmArw/PaFsy7LThsPbMRn6xSgFFRERiki8YoqbJT31zgFc/ruDZTeXUNgcialIT7NxenMdVo7JxxdtIcdqJsymY9AQFFBERiSntS4brWwKs/qSKp0sPUuX1RdQkOqzcMjGX6y8eSIrTTorTTrxdwaQnKaCIiEhMaF8y7G0J8O7eY6xcd5BDNc0RNXFWMzdcPJCbJ+aSlujo1R2GY52OuoiInNc+v2T4/YM1PLGujN2ehogaq9nEjNHZ3F6cT6Yrrk/sMBzrdPRFROS81L5kuK7Zz46j9axYV8YH5XURNSZgWlEmd0zOJy81oU/tMBzrFFBEROS8075k+NOqBp5cX8b6fSdOqZkyJI15UwsZlpGE22nDFdd3N/KLRQooIiJy3mjxhzjR5KP8RDNPlx5k9SdVpzRZG5ubzJ1TCxk1yN2xkZ+CSd+jgCIiIv1e+5LhiroWnt1YzssfVRA8KZlckJnInVMLmViQittpJ7kf7TAcixRQRESk32pfMuzxtvLC+4f549YjtAYiu7/mpTqZN6WAr1yQ3tHLRBv59X0KKCIi0u+0Lxk+3uDjTx8e5fnN5XhbgxE1GUkO5pTkc8XILNzOtmCi/XL6DwUUERHpN9qXDJ9o9PHa9kqe2XiIE43+iBp3vI1Zk/L4hzE5pCTY1Za+n1JAERGRPq99yXBNk4+/fVLNUxsOcrSuJaLGabdw0/hB3DRhEGmJDrWl7+cUUEREpE9r9AWpafSxbt9xVqwrY/+xpohxm8XEdWMH8u1L8shwx5GqtvTnBQUUERHpk5r9QWqa/G3dX98rY0eFN2LcbIJvXpTF7OJ8BqY4SU2wk6Dur+cN/Z8UEZE+pTUQorbZz/Yj9TyxrozNZTWn1Hz1gnTmTilgcHqiur+epxRQRESkT2hfMrzb42Xl+oO8vefYKTWXFKQwb2ohRdlukhNsJDnU/fV8pYAiIiK9qn3J8IFjTTxTepC/bK88pfvryBwXd15ayMV5KSTH23HFK5ic7xRQRESkV4Q+WzJcXtPMc5sO8dK2CvzByCZrgwckMH9qIZOHpJHstONW99eY0aWONT/60Y8wmUwRX8OHD+8Yb21tZeHChaSlpZGYmMjMmTOpqqqKeIzy8nJmzJiB0+kkIyOD++67j2AwePJTiYjIecowDOqa/ezxePnNmr3MenwjL7x/JCKcZLvj+PerhvP4nAlcOSqbvLQEUhLsCicxpMtnUEaOHMnf/va3vz+A9e8Pce+99/Laa6/x4osv4na7WbRoETfccAPr168HIBQKMWPGDLKystiwYQOVlZXMnj0bm83Gww8/HIWXIyIifZVhGDT4glTX+1i17QjPbiynriUQUZOaYOf24nxmjM4mNaFtvxyrur/GpC4HFKvVSlZW1im319fXs2LFCp577jm+/vWvA7By5UpGjBjBxo0bKS4u5q9//SuffPIJf/vb38jMzGTs2LH8v//3/7j//vv50Y9+hN1uP/tXJCIifU6TL0h1Qyt/2e7h6Q0HqW7wRYwnOqzcekku148bSHpSHMlOm9rSx7gu/9/fu3cvOTk5DB48mFmzZlFeXg7A1q1bCQQCTJs2raN2+PDh5OXlUVpaCkBpaSmjRo0iMzOzo2b69Ol4vV527tz5hc/p8/nwer0RXyIi0ve1BkIcrW3mxfcPM+fJLfz8zT0R4STOaubbl+Ty7J2XcNelgxmakUR6kkPhRLp2BmXSpEk89dRTXHjhhVRWVvLQQw9x6aWXsmPHDjweD3a7neTk5Ij7ZGZm4vF4APB4PBHhpH28feyLLF26lIceeqgrUxURkV7kD4apafLz3t5jPPFeGXuqGiLGrWYTV4/O5rbifAalOElJ0H45EqlLAeXKK6/s+PPo0aOZNGkS+fn5vPDCC8THx0d9cu2WLFnC4sWLO773er3k5uaes+cTEZHuCYbC1DT72VJWwxPryviwvC5i3AR8oyiTOZPzGZyeqP1y5Aud1TLj5ORkLrjgAvbt28c3vvEN/H4/dXV1EWdRqqqqOq5ZycrKYvPmzRGP0b7K53TXtbRzOBw4HI6zmaqIiJxDoXDbypyPjtSz4r0DrN9/4pSaKUPTmDelkOHZLu2XI2d0Vh/yNTY2sn//frKzsxk/fjw2m401a9Z0jO/Zs4fy8nJKSkoAKCkpYfv27VRXV3fUrF69GpfLRVFR0dlMRUREekE4bFDb5Gdz2QmW/Gk785/acko4GZvr5r9uHcd/zBxNyZA0BibHK5zIGXXpDMq//Mu/cM0115Cfn09FRQU//OEPsVgs3HrrrbjdbubPn8/ixYtJTU3F5XJx9913U1JSQnFxMQBXXHEFRUVF3H777TzyyCN4PB4efPBBFi5cqDMkIiL9iGEYeFuCHDjeyNMbDvLqx5UET2r/ekFmIndOLaR4cBqpiQ4StZGfdEGXflqOHDnCrbfeyokTJ0hPT2fq1Kls3LiR9PR0AB599FHMZjMzZ87E5/Mxffp0HnvssY77WywWXn31VRYsWEBJSQkJCQnMmTOHH//4x9F9VSIics40tAYoP9HMc5vL+b8PjtAaiOz+mpfqZN6UAr4+PIOUBLs28pNuMRmGYZy5rG/xer243W7q6+txuVy9PR0RkZjQ7A9SUdfCC1sO84cth2lojewCnpHkYM7kAq68KIu0RAeuOO2XI5G68v6t820iIvKlWgMhqr2t/OnDo/y+9BAnmvwR48nxNmYV53Hd2IGkJzlwx9sUTOSsKaCIiMhp+YIhTjT6ee3jClZuOEhFXWvEuNNu4VsTBvGtCblkueNJ1kZ+EkUKKCIiEiEQClPT5ONvn1SzYn0ZB441RYzbLCauGzuQWZPyyE11kuy0Y1EwkShTQBEREaCtl0lts591e4/zxHsH2FERua2I2QTfvCiLOSUFFKYnkOq0ayM/OWcUUEREYlw4bFDfEuD9gzU8/t4BNh+sPaXmaxemc8fkAoZnu0hx2rFbFUzk3FJAERGJUYZh4G0NsuNoHU+8V8bbe46dUnNJQQrzpxYyOjdZbemlRymgiIjEoEZfkD0eL0+uO8jrOyo5qccaI3Nc3HlpIZcUpqktvfQKBRQRkRjS7A9y4FgjT284xEvbjhIIRSaTwekJ3Dm1kKlDB5CW6CBB3V+ll+gnT0QkBrQGQhypbeZ/Nh7ihfeP0OwPRYznJMcxd3IBV4xsa7KmtvTS2/QTKCJyHvMHw3i8LTy/+TDPbSqnriUQMZ6WYOf2knyuHZPDgCSH2tJLn6GAIiJyHgqGwhxr9PGnrUd4uvQQ1Q2+iPGkOCu3TszlxvG5ZLrj1JZe+hwFFBGR80j4s14mr3xUwZPrD1Je0xwxHmc1M3P8IG6ZmEtuqlNt6aXPUkARETkPGIZBXbOfNbur+d27B/i0qjFi3Go2cc2YHG4vzqdgQILa0kufp4AiItKPtfcy2bDvOL999wDbDtdFjJuAbxRlcsfkAi7MSlJbeuk3FFBERPqphtYAH5bX8du1+1m//8Qp41OGpjF/6mBGDXST4rSpLb30KwooIiL9TJMvyM6Kep54r4zVn1RxUo81xuUlc+fUQiYWpJKSYMemYCL9kAKKiEg/0RoI8WlVAyvWlfHax5UET2r/emFmEndeWsjUYQNITbDjsKr7q/RfCigiIn2cLxii/EQzT204yP9tPUJrMBwxnp/qZO7UAqaNyCQtwaG29HJeUEAREemjAqEwlXUt/H7jIZ7fcpiG1mDEeEaSgzmTC7h6dDbpSQ6cdv2VLucP/TSLiPQx7U3W/nfLYZ4pPURNkz9iPDnexqziPG4YN4hMd5za0st5ST/VIiJ9RDhsUNPsZ9UHR3lyfRmV9a0R4wl2C9+akMvNE3PJSYknyaHur3L+UkAREellhmFQ3xzg9Z2VPP5uGQeON0WM261mrhubw23F+eSnJuCKVzCR858CiohIL/K2Bnhn9zF+++5+dlZ4I8bMJrhqVDazS/IZlpGEW91fJYYooIiI9IJGX5BNB07w27X72Xyw9pTxr12YzryphYzMceOOt6n7q8QcBRQRkR7U7A+y7XAdv1t7gHc+PXbK+CWFqdw5tZCL81NIjlf3V4ldCigiIj2gNRBij8fLb989wBs7PJzUY42Lclzc9ZXBTB4ygGSnTd1fJeYpoIiInEP+YJiy44088V4ZL207SiAUmUyGpCcwf2ohlw/PICXBgd2qYCICCigiIudEIBTmSG0zT60/yItbj9DsD0WM5yTHMXdyIVePyVZbepHTUEAREYmiYChMVUMrz24s59lN5dS3BCLG0xLtzC7O54aLB5KeFEecTcFE5HQUUEREoiAUNjjR5OOF94/wzIaDVDf4IsaT4qzcekket0zMJdsdr/1yRM5AAUVE5CwYhkFds58/b6tgxboyDte2RIzH2czMvHgQsyblkZeWoLb0Ip2k3xQRkW4wDIP6lgB/+6Sa3723n0+rGiPGrWYT14zJYU5JAYMzEnDF2XpppiL9kwKKiEgXeVsDrN97nN++e4Bth+sixswm+EZRJnOnFDAiy6229CLdpIAiItJJjb4gWw/W8Nt3D7Bh/4lTxqcOHcD8qYWMyU0mWW3pRc6KAoqIyBk0+4PsOFrP4++V8bdPqjipxxoX5yVz56WDuaQwVd1fRaJEAUVE5Au0BkJ8WtXAivfKeG17JcGT2r9emJXEXVML+coF6aQk2NX9VSSKzuq36Wc/+xkmk4l77rmn47bW1lYWLlxIWloaiYmJzJw5k6qqqoj7lZeXM2PGDJxOJxkZGdx3330Eg8GzmYqInAfCYYPtR+pZ++kxth+pJ3xyP/ge4guG+NTTwI9f2clNy0v580cVEeEkP9XJQ/8wkqfumMg1Y3PIcMUpnIhEWbfPoGzZsoXf/va3jB49OuL2e++9l9dee40XX3wRt9vNokWLuOGGG1i/fj0AoVCIGTNmkJWVxYYNG6isrGT27NnYbDYefvjhs3s1ItJvbdh3nGVr97O/upFAyMBmMTEkI5EFlw1h8tABPTKHQChMZX0Lz2w4xPNbDtPoi/yHU0aSgzsmF3D1mBwykhxqsiZyDpkMw+jyP1EaGxu5+OKLeeyxx/jJT37C2LFj+eUvf0l9fT3p6ek899xz3HjjjQDs3r2bESNGUFpaSnFxMa+//jpXX301FRUVZGZmArB8+XLuv/9+jh07ht1uP+Pze71e3G439fX1uFyurk5fRPqYDfuO88Cq7TT6gqQ47dgtZvyhMLXNARIdFh6+ftQ5DSmhsEF1QyvPbz7M7zceoqbJHzGeHG/jtuI8rh83iCx3HAnqZSLSLV15/+7WOcmFCxcyY8YMpk2bFnH71q1bCQQCEbcPHz6cvLw8SktLASgtLWXUqFEd4QRg+vTpeL1edu7c2Z3piEg/Fg4bLFu7n0ZfkCxXW+t3s9lEnM1ClstBoy/EsrX7z8nHPeGwwYlGH0+uK+OGxzbwqzV7I8JJgt3C3CkF/O//V8z/d9kQhmQkKpyI9JAu/6Y9//zzfPDBB2zZsuWUMY/Hg91uJzk5OeL2zMxMPB5PR83nw0n7ePvY6fh8Pny+v7eN9nq9XZ22iPRROyu87K9uJMVpP6VfiMlkItlpY391IzsrvIwa5I7KcxqGQX1zgL/sqOSJ98o4cLwpYtxuNXP92BxmFeeTn5aAK069TER6WpcCyuHDh/ne977H6tWriYuLO1dzOsXSpUt56KGHeuz5RKTn1DT7CYQM7F9wkanDYqY+bFDT7D/teFd5WwO8s7ua5WsP8Ell5D92zCaYMSqb2SUFDMtMxBWnXiYivaVLAWXr1q1UV1dz8cUXd9wWCoV49913+a//+i/efPNN/H4/dXV1EWdRqqqqyMrKAiArK4vNmzdHPG77Kp/2mpMtWbKExYsXd3zv9XrJzc3tytRFpI9KddqxWUz4Q2HizKdedOoLhbGZTaQ6z3x92pdp8gUp3X+C3767ny0Ha08Z//rwDOZNKaAox60mayJ9QJcCyuWXX8727dsjbps7dy7Dhw/n/vvvJzc3F5vNxpo1a5g5cyYAe/bsoby8nJKSEgBKSkr46U9/SnV1NRkZGQCsXr0al8tFUVHRaZ/X4XDgcDi6/OJEpO8bmeNiSEYiuyobyHSZ8AUMguEwVrMZh81EXXOAEdlJjMzp3gXxLf4QHx6u5bdrD7D202OnjBcPTmX+1MGMy0smxWnHomAi0id0KaAkJSVx0UUXRdyWkJBAWlpax+3z589n8eLFpKam4nK5uPvuuykpKaG4uBiAK664gqKiIm6//XYeeeQRPB4PDz74IAsXLlQIEYlBZrOJBZcN4d4XtvFpVSPhsIFhgMnUNpaWYGfBZUO6fEajNRBit8fL79Ye4I2dHk6+xnbUQBd3Th1MydA0UpxqsibS10T9cvRHH30Us9nMzJkz8fl8TJ8+nccee6xj3GKx8Oqrr7JgwQJKSkpISEhgzpw5/PjHP472VESkH/EHwwRDxt/byBsQDhn4guEuPY4vGKLsWBMr1pXx0rajBEKRyWRIegLzpxbytQszSE2047Cql4lIX9StPii9TX1QRM4f4bDBtf+9jp0VXsIGWEx0nEEJGW0Xro7McfHnhVO/9CxKIBTmSG0zT60/yItbj9DsD0WM5yTHMXdyIVeNziItQU3WRHpDV96/taBfRHrV9qP17PE00v5PpY4THp/91zBgj6eR7UfrGZObfMr9Q2EDj7eFZzeW8+ymcupbAhHjaYl2Zhfnc924gWQkxRFvVzAR6Q8UUESkV314uA5/6Is/xjEAfyjMh4frIgJKOGxwosnHC+8f4ekNB6lu8EXcLynOyq2X5PGt8YPITo5XgzWRfka/sSLSq8JfEk5OV2cYBnXNAf780VFWvFfG4dqWiLo4m5kbxw/i25fkk5saT1KcLepzFpFzTwFFRHpV7UkfyXyRmmY/9S1+Vu+s4rfvHmBvdWPEuM1i4prROdxeks/gAYm44tX9VaQ/U0ARkV61t6qhU3Xr9h5n3b7jbDtcH3G72QTfKMpk7uRCLsxKwq0mayLnBQUUEelVdc2+MxcB247Un3Lb1KEDmD+1kDG5ybjjbWqyJnIeUUARkV7VGuh6p4OL85K589LBXFKYSnK8DauarImcdxRQRKRX1XbyDArAhVlJ3Dm1kMsuTFf3V5HznAKKiPSqppMaqn2RBLuZp+dOJCVB3V9FYoECioj0mlDYwNrJy0aS461kuePP7YREpM9QQBGRHhcOGxxr9PHcpnKONQU7dZ/0RG0mKhJLFFBEpMcYhkFNk58/fXCEJ9cfpLK+tdP3tdt0vYlILFFAEZFzzjAM6lsCvPZxJU+sK6PseFOXH8NsUkARiSUKKCJyTjW0Bnh7dzXL1+7nk8rIpmwWs4lUp41jjf4zPo7DqoAiEksUUETknGjyBSk9cILl7+zn/UO1p4x/fXgG86YUsOK9A7z96fEzPl6cTSt3RGKJAoqIRFVrIMSH5bUsW7ufd08TPIoHpzJ/6mDG5SWT4rTzf1uPdOpxnXb9dSUSS/QbLyJR0RoIsdvj5XdrD/DGTg/hkxrEjhro4s6pgykZmhbRZG30QDertlWc8fFHD3Sfi2mLSB+lgCIiZ8UfDFN2rJHH1x3gz9sqCIQik8mQ9ATmTy3k68MzSE1wYD/pWpKLC1KxmE2ETk40n2Mxm7i4IPWczF9E+iYFFBHplmAozOHaFlauL+PF94/QEojsCDswOZ65Uwq4clQWaQmOL7yGZNRAN0XZSWw/6v3C5yrKTmKUzqCIxBQFFBHpklDYoMrbwu83lvPsxkN4WyMbraUl2plTks+1YweSkRRHvP3LL241m02MHuj+0oAyeqAbs3YqFokpCigi0inhsMGJJh//u+UwT284xLHGyE3+XHFWbr0kj5vGDyI7OZ4ER+f+egkGw7y2wwOACfj8Bz3t37+2w8ND/3ARVi01FokZCigi8qUMw6C22c+ft1WwYl0ZR2pbIsbjbGZuGj+Ib0/KZ1BKPElxti49/isfV9LQEsBmMWExmTAAwwCTqS2ghAyDhpYAr3xcyfUXD4zeCxORPk0BRUROq7376+pPqvjtuwfYV90YMW6zmLhmdA6zS/IpTE/EFWfFZOr6xzBH65oJA1YTmEwmTNCWTD5jxiD0WZ2IxA4FFBE5RUNrgPf2Hmf52v18fKQ+Ysxsgm8UZTJ3ciHDs5NwxdnO6vqQgclOzEDYaHvsk4WNtrwyMNnZ7ecQkf5HAUVEOjT6gmwpq+G37+5n44GaU8YvHTaAeVMKGZObjDvehiUKF65eMzqbh17dSX1zALMpHLHnTtgIEwwZuJ02rhmdfdbPJSL9hwKKiNDiD7G9op7frd3Pml3VnNyRZHxeMndeOpiJhakkx9uwWqJ3sarVambhV4fwH2/swR80sFrCmE1tZ06CIQOL2cTCrw7RBbIiMUYBRSSGtQZC7K1u4PF3y3hte+UpzdKGZyVx59RCvnJBOikJf+/+Gm13fWUIAP/9zn4aWgKEaPtYx+20sfCrQzrGRSR2mAzD+OL2jX2U1+vF7XZTX1+Py+Xq7emI9Dv+YJhDJ5pYsa6MVR8exRcMR4znpzmZP6WQbxRlkppox2HtmY36gsEwr3xcydG6ZgYmO7lmdLbOnIicR7ry/q0zKCIxJBAKU1HXwu9LD/H8lsM0+iKbrGW6HMydXMBVo3PISPri7q/nitlsYmhGIqmJdlKddjVnE4lhCigiMSAYClPd4OP5zeX8fuMhapsDEeMpThuzJuVz/biBZLnjOt1kLZo27DvOsrX72V/dSCBkYLOYGJKRyILLhjB56IAen4+I9C4FFJHzWChscKLRx6oPj/LUhoNU1rdGjCfYLdw8MZdvTcglJyUeVxebrEXLhn3HeWDVdhp9QVKcduwWM/5QmF2VDTywajsPXz9KIUUkxiigiJyHwuG27q+v7/DwxHsHOHgissmZ3WrmhnED+fakPPJTE3DFd6/JWrTmumztfhp9QTKTHPiCBk3+IFazmcwkO1UNfpat3U/x4DR95CMSQxRQRM4j4bCBtzXA27urefy9A3xS2RAxbjGbuGpUFrNLChiSnkhy/Nk1WYuGnRVe9lc34rCaOVTTjC8Y7mh177CaccXb2F/dyM4KL6MGaUdjkVihgCJyHjAMA29LkE1lJ/jduwd4/1DtKTVfH57B3MkFDM92keKMbi+Ts1HT7KfJF6IlECRkgNVswmRq2ySwJRDGH/QRb7dS0+zv7amKSA9SQBHpxwzDoMEX5KPyOh5fd4B3Pz1+Sk3x4FTmTylkdG5y2/UdfWzZbnK8jZZAiFDYwGYxd3zUZAJs5raVRy3+EMnxvXN9jIj0DgUUkX6q0Rdkd6WXFevKeHOnh5N6rDFqoJu7Li1kYmEqKU57jy8Z7oqOy19O/rTJdNK4iMSMLv1TatmyZYwePRqXy4XL5aKkpITXX3+9Y7y1tZWFCxeSlpZGYmIiM2fOpKqqKuIxysvLmTFjBk6nk4yMDO677z6CweDJTyUiX6DZH2T70Tp++Ocd3Pr4Rl7fERlOhqYnsvSGi3hs1jimFWWS7Y7v0+GkriVAnM2C2WQiGDIIGwYGbf8NhgzMJhNxNgt1LYEzP5iInDe6dAZl0KBB/OxnP2PYsGEYhsHTTz/Ntddey4cffsjIkSO59957ee2113jxxRdxu90sWrSIG264gfXr1wMQCoWYMWMGWVlZbNiwgcrKSmbPno3NZuPhhx8+Jy9Q5HzRGghxuKaZ35ce4sWtR2gJhCLGB6XEM3dyAdOKMklLdJDYC71MuiPVaSfBbiHRYaW+JYAvGMIIt501ibNZcMfbMAyDVKe9t6cqIj3orFvdp6am8vOf/5wbb7yR9PR0nnvuOW688UYAdu/ezYgRIygtLaW4uJjXX3+dq6++moqKCjIzMwFYvnw5999/P8eOHcNu79xfQGp1L7HEFwxRWdfK81vKeW5TOd7WyDOOAxLtzC4p4OpR2QxwOUhy9N6S4e4Ihw3mrNzMrsoGMl12fAGDYDiM1WzGYTNR5fUzIjuJp+de0usrjkTk7HTl/bvbV8uFQiGef/55mpqaKCkpYevWrQQCAaZNm9ZRM3z4cPLy8igtLQWgtLSUUaNGdYQTgOnTp+P1etm5c2d3pyJyXmprS9/Mb9ce4MblG1i+9kBEOHHFWfnuZYN5dv4kZpfkU5iegCvO1q/CCbS1t19w2RASHRaqvH4wQYLdCiao8vpJdFhYcNkQhRORGNPlc8Dbt2+npKSE1tZWEhMTWbVqFUVFRWzbtg273U5ycnJEfWZmJh6PBwCPxxMRTtrH28e+iM/nw+fzdXzv9Xq7Om2RfiMYCnOiyc9rH1fy5PoyjtS2RIzH2czcNH4Q35qYx8DkeNzxNiz9/M178tABPHz9qI5W9/VhA5vZxIjsJLW6F4lRXQ4oF154Idu2baO+vp4//vGPzJkzh7Vr156LuXVYunQpDz300Dl9DpHeFgob1Db5WLO7msffK2NfdWPEuM1i4h/G5DBrUj65qc4+1cskGiYPHUDx4DR2VnipafaT6rQzMselMyciMarLAcVutzN06FAAxo8fz5YtW/jVr37FzTffjN/vp66uLuIsSlVVFVlZWQBkZWWxefPmiMdrX+XTXnM6S5YsYfHixR3fe71ecnNzuzp1kT4pHDaobwmwft9xfvfeAT4+Uh8xbjbB9JFZ3F6Sz9D0RFIS7NjOo2AiInI6Z32ZfzgcxufzMX78eGw2G2vWrGHmzJkA7Nmzh/LyckpKSgAoKSnhpz/9KdXV1WRkZACwevVqXC4XRUVFX/gcDocDh8NxtlMV6VPau79uPVTD7947wMYDNafUfGXYAOZOKaAox02y04bD2neXC58t7WYsIp/XpYCyZMkSrrzySvLy8mhoaOC5557jnXfe4c0338TtdjN//nwWL15MamoqLpeLu+++m5KSEoqLiwG44oorKCoq4vbbb+eRRx7B4/Hw4IMPsnDhQgUQiRnt3V93HvHyxLoDvLW7mpOX0o3PT+HOqYWMzUvu803WokG7GYvIyboUUKqrq5k9ezaVlZW43W5Gjx7Nm2++yTe+8Q0AHn30UcxmMzNnzsTn8zF9+nQee+yxjvtbLBZeffVVFixYQElJCQkJCcyZM4cf//jH0X1VIn1UQ2uAfVWNPLmhjL9s9xA6qf3r8Kwk7ry0kJIhA0h12om3n9/BBCJ3M85yxXWsQoozW8hymfF4fdrNWCQGnXUflN6gPijS3zT5ghw83sTvNx5i1YdH8QXDEeP5aU7mTynkqxemk9qPmqxFw/Yj9fx/v3+fBIf1tGeKWgIhmn1Bfnv7BO1mLNLPdeX9O3b+FhTpBS3+EEfqmnl+cznPbzlMky+y+2uWK447JuczfWQ2aUn2ftdkLRpqmv0EQgb2L7jw12ExUx82tJuxSIxRQBE5B3zBEJ76Vl58/wjPbjpEbXPkPjIpThu3FefzD2NySE9y4I7vfw3WoiXVacdmMeEPhYkzn3oGxRcKYzOb1OpeJMYooIhEkT8Y5nijj5e3HeWpDYfweFsjxhMcFm6ZmMvMi3PJcsedF03WztbIHBdDMhLZVdlAlsscEdQMw6CuOcCI7CRG5ujjXJFYooAiEgWBUJiaJh9v7qjiyfVlHDzRHDHusJq5ftxAbpmYy8CU86/J2tlob3X/wKrteLy+tuXUFjO+UJi65oBa3YvEKAUUkbMQDIWpawnw7qfHePy9A+yqbIgYt5hNzBiVzW3FeeSnJbQtobUqmJxMre5F5GQKKCLdEAob1DX72XKwhsffK2ProdqIcRNw+YgM5kwuYEh6IqkJ538vk7OlVvci8nkKKCJdEPqsLf32I3WsWFfGu3uPn1JTMjiNeVMLGJHtIjXBjtOuX7POMptNWkosIoACikinhMMG3tYAezwNrFx/kL9+4uGkHmuMHuTmzqmFjMtLIdlpIynO1juTFRE5DyigiHyJ9v1yyo438szGQ7zyUQWBUGQyGZqRyJ1TCykenEpKggNXXOz1MhERiTYFFJEv4G0NcKSmhT9sPsSLW4/QGojs/jooJZ55n3V/TXHaccfbdL2EiEiUKKCInKTRF8RT18IfPzjCc5vK8bYGI8bTEx3MLsnnylFZpDjtJDvtMd/LREQk2hRQRD7T7A9S3dDKy9sqeab0IMcbI1uru+KszJqUx7VjB5KaYCclwY5NvUxERM4JBRSJea2BEMcbfby5w8PKDQc5UtsSMR5vs3DT+EHcNGEQA5IcpCbYcVi1ZFhE5FxSQJGY5QuGqGn0886n1axYd5B91Y0R4zaLiWvH5vDtS/LIcMVpybCISA/S37YSc/zBMHXNfjYeOMHj75Wx/Wh9xLjZBNNHZjG7JJ+ByU6SE2y4tGRYRKRHKaBIzAiEwtQ2+/movI4V68vYeKDmlJqvXDCAeZMLKUxPwB1vi+ldhkVEepMCipz3Ap9tOrenqoGV68p4a3c1J/VYY0J+CndeWsiFWS5ccVatzBER6WUKKHLeCobC1DYHOHiiiWdKD/KX7R5CJ7V/LcpO4s5LBzMuL4VEh1W7DIuI9BEKKHLead9h+GhtC3/YXM6qD4/iC0Y2WStIczJ/aiGTh6SRFGcjWbsMi4j0KQooct5o32G4yuvj/z44zPNbDtPkC0XUZLniuGNKAZcPzyApzkZKgk1LhkVE+iAFFOn3wp/tMHy80cfLH1XwPxsPUdsciKhJcdq4rTifq0dnkxRnIzXBTpxNwUREpK9SQJF+q32H4ZomP3/d6eGpDYfweFsjahIcFm6dmMf1Fw/EHW9TLxMRkX5Cf1NLv2MYbWdM6pr9vPvpcZ5cX8bBE80RNQ6rmevHDeSWibmkJTpIdtpIUi8TEZF+QwFF+g3DMPC2Bqlr9rPlYA0r1pWxq7IhosZiNnH1qGxuK84j0xWP22nDFWdVLxMRkX5GAUX6hYbWAHXNAbYfrWPFe2VsLa+LGDcBl4/IYM7kAnJTnCQ71WRNRKQ/U0CRPq3ZH6Smyc/eqgaeXH+Q9/YeP6WmZHAa86cWMCQjqaP7q5qsiYj0bwoo0ie1BkLUNPk5eKKJpzccZPUnVZzUY40xg9zMn1rIqEHJarImInKeUUCRPsUfbNsv50htM89uLOeVjysIhCKTydCMRO6cWsjEghQ1WRMROU8poEif0L6Rn6e+lRfeP8wftx6hNRDZ/XVQSjzzphTylQsGkOCwkuJULxMRkfOVAor0qvb9ck40+vjTh0d5fnM53tZgRE16ooPZJfl886Is4u0W9TIREYkB+lteekUobFDb7Ke2yc/rOyp5pvQQxxv9ETWuOCuzJuVx7diBOO1WUhLUy0REJFYooEiPCn3Wlr622c/bu6tZuf4gR+taImribRZumjCIm8YPwhVvIznejitevUxERGKJAor0iPb9cuqa/ZQeOMGT6w6y71hjRI3NYuLasTl8+5I8UhIcuONtJMfbMGvJsIhIzFFAkXOqfb+c+pYA28rreGLdAbYf9UbUmE3wzZFZzC7JJ8MVR2KclVSnXUuGRURimAKKnBOGYeBtCVLX4udTTwMr1pex8UDNKXWXXZDO3CkF5KU6O64zcVi1MkdEJNYpoEhUte+XU98c4NCJJlZuOMhbu6tPqZuQn8KdlxZyQWYSDpuFtAQtGRYRkb/r0jn0pUuXMnHiRJKSksjIyOC6665jz549ETWtra0sXLiQtLQ0EhMTmTlzJlVVVRE15eXlzJgxA6fTSUZGBvfddx/BYOTSUulf2oJJgMM1LezxePn5m7u546ktp4STouwkfvGtMTxy42hG5rjJcMUxMDle4URERCJ06QzK2rVrWbhwIRMnTiQYDPLAAw9wxRVX8Mknn5CQkADAvffey2uvvcaLL76I2+1m0aJF3HDDDaxfvx6AUCjEjBkzyMrKYsOGDVRWVjJ79mxsNhsPP/xw9F+hnHPtG/kdb/Tx/OZyVm2rwB+MbLJWOCCBeVMKmDwkDZvFol2GRUTkS5kMwzDOXHZ6x44dIyMjg7Vr1/KVr3yF+vp60tPTee6557jxxhsB2L17NyNGjKC0tJTi4mJef/11rr76aioqKsjMzARg+fLl3H///Rw7dgy73X7G5/V6vbjdburr63G5XN2dvpylJl/bRn7elgD/98ER/nfLYZr8oYiabHccd0wu4OvDM7BZzB2b+WlljohI7OnK+/dZXYNSX18PQGpqKgBbt24lEAgwbdq0jprhw4eTl5fXEVBKS0sZNWpURzgBmD59OgsWLGDnzp2MGzfubKYkPaDZH6S2OUBDS4BXP67k2U2HqG0ORNSkJti5vTiPq0ZlY7daSIpra02vXYZFRKQzuh1QwuEw99xzD1OmTOGiiy4CwOPxYLfbSU5OjqjNzMzE4/F01Hw+nLSPt4+djs/nw+fzdXzv9XpPWyfnVos/RG2znyZfkL/tquKpDQep8voiahIdVm6ZmMv1Fw8k3mYh8bNgYtOSYRER6YJuB5SFCxeyY8cO1q1bF835nNbSpUt56KGHzvnzyOm1BtqCSbMvyLp9J3hyfRmHTjRH1DisZmZePJCbJ+aSFGfTkmERETkr3QooixYt4tVXX+Xdd99l0KBBHbdnZWXh9/upq6uLOItSVVVFVlZWR83mzZsjHq99lU97zcmWLFnC4sWLO773er3k5uZ2Z+rSBb5giNqmAM3+IB8cquWJdWXs9jRE1FjNJmaMzub24nxSE+xaMiwiIlHRpYBiGAZ33303q1at4p133qGwsDBifPz48dhsNtasWcPMmTMB2LNnD+Xl5ZSUlABQUlLCT3/6U6qrq8nIyABg9erVuFwuioqKTvu8DocDh8PR5Rcn3eMPhjs+ytlV6WXFujI+KK+LqDEBl4/I4I7JBeQkx2OzmElNsJPgUGsdERE5e116N1m4cCHPPfccf/7zn0lKSuq4ZsTtdhMfH4/b7Wb+/PksXryY1NRUXC4Xd999NyUlJRQXFwNwxRVXUFRUxO23384jjzyCx+PhwQcfZOHChQohvSwQagsmja1BDp5o4sl1B1m37/gpdZOHpDFvSgGD0xOxms0kJ9hwaZdhERGJoi4tM/6inhUrV67kjjvuANoatf3zP/8zf/jDH/D5fEyfPp3HHnss4uObQ4cOsWDBAt555x0SEhKYM2cOP/vZz7BaO5eXtMw4uoKhMHUtARpag1TWtfB06UFWf1JF+KSfjLG5buZPLWRkjhuL2aRdhkVEpEu68v59Vn1QeosCSnSEPtthuL4lwIlGH89uKueVjyoInpRMLshMZP7UQibkp2A2m7XLsIiIdEuP9UGR/ql9h+G65gDe1gAvvH+YP249QmsgsvvroJR45k8t5CvDBmA2m0l0WElx2rTLsIiInHMKKDGkfSO/us+WDK/aVsHzm8vxtkbug5Se6GDO5Hymj8zCYjaR4GjrZWK3KpiIiEjPUED5nHDYYGeFl5pmP6lOOyNzXOfNxxjt++W0+IO8vsPDMxsPcaLRH1HjirMyqzifa8fkYLeaibdbSHFqybCIiPQ8BZTPbNh3nGVr97O/upFAyMBmMTEkI5EFlw1h8tABvT29bmvfL8cXDPH27mM8teEgR+taImribRZumjCIm8YPIsFhxW5tWzLstOvHQ0REeofegWgLJw+s2k6jL9j2UYbFjD8UZldlAw+s2s7D14/qdyGlxR+iptlPqz/IprIaVqwrY/+xpogam8XEtWNz+PYleSR/1o4+JcFOonqZiIhIL4v5d6Jw2GDZ2v00+oJkueI6lszGmS1kucx4vD6Wrd1P8eC0fvFxT2sgRE2Tn9ZAiO1H6nli3QG2H43cu8hsgm+OzGJ2ST4Zrri2JcNOO644LRkWEZG+IeYDys4KL/urG0lx2jEwqGsKEAiFsVnMuJ1Wkp029lc3srPCy6hB7t6e7hdq3y+nxR9if3UjK9aXsfFAzSl1l12QztwpBeSlOjGbTLjjbbi1ZFhERPqYmA8oNc1+AiGD+tYAxxt8Ec3JKuphQJIDw2ir64s+v1/O0doWnlxfxtt7jp1SN7EghflTC7kgMwmTyUTSZ7sMWxRMRESkD4r5gJLqtOMLhk5ZagsQNqDa68MVZyXVae+F2X0xXzBEXXOAJl+QYw0+/mfjIV7bXnlK99eibBd3XVrImNxkgLZeJglt15uIiIj0VTEfUC7MSKThNOHk8xpag1yYkdhDM/py/mCYumY/jb4g9S0Bnt9czqptFfiDkU3WBg9IYN7UAkoGp2EymXDaraQk2HBYtWRYRET6vpgPKC99fJQz9fo3Pqv71oS8npjSaX1+I78Wf4g/fnCEF7YcpskfiqjLdscxd0oBX7swA4vZhMNmIS1BvUxERKR/ifmA8qethztd1xsBJRgKU9scoNEXxBcI8erHFTy7qZza5kBEXWqCnduL87lqVBY2ixmbpa2XSYKWDIuISD8U8+9ee481RrUuWj6/w3AwFGb1J1U8XXqQKq8voi7RYeXWS3K5ftxA4mwWrGYzKQk2kuJsPTpfERGRaIr5gBI4aYO8s607W6GwQV2zH29rkHA4zHv7jrNy3UEO1TRH1MVZzdxw8UBunphLUpytrZdJvB1XvHqZiIhI/xfzAcVhs4DvzOHDcY6v4QiHDepaAnhbAoQNgw8O1fLEujJ2exoi6qxmEzNGZ3PbpDzSEh3qZSIiIuelmA8oTrsFCHSyLvrCYQPvZxv5hQ2DXZVeVqwr44Pyuog6E3D5iAzumFxATnK8epmIiMh5LeYDiivODrR2si56DMPA2xKkrsVPKGxQdryJJ9eXsX7fiVNqJw9JY96UAganty11TvwsmKiXiYiInK9iPqBMLEhme4W3U3XRcHIw8dS38nTpQVZ/UnVKk7Uxg9zceWkhI3PaWuyrl4mIiMSKmA8onV3tcrarYgzDwNsapL45QDAcpqbJz7ObynnlowqCJyWTCzITmT+1kAn5KZhM6mUiIiKxJ+YDyonGM19/0pW6kxmGQYOvLZgEQmEafUH+d8th/u+DI7SetDIoNyWeeVML+cqwAZhMJvUyERGRmBXz73ydvfi1OxfJNnx28WsgFKY1EOKlD4/yhy2HT2mtn5HkYE5JPleMzMJiNmE1m0lOsOFSLxMREYlRMR9QhmUlRbUOoNEXpLbJTyAUJhgK8/oOD8+UHuJEU+SOyO54G9+elMe1Y3KwW81YzH9fMqxeJiIiEstiPqAMSHJgNnHKBaqfZza11Z1Jky9IbbMffzBM2DB4e/cxntpwkKN1LRF1TruFm8YP4qYJg3Da2xqrueKsJGvJsIiICKCAwoAEBylOG7XNgdOGFLMJUpw2BiR8cUBp9gepaWoLJoZhsKmshhXryth/rCmizmYxcd3YgXz7kjzczraPbxLjrKQ67Vi1ZFhERKRDzAeUkTkuinLcfHS4nkAwiD9kEDbagondYsJmtVKU42ZkjuuU+7b4Q9Q0+/EF2nYU/vhIHSvWlbH9aOSyZbMJvnlRFrOL88lwxQFaMiwiIvJlYj6gmM0mFlw2hAdWbaeh1USa3YLFZCJkGLT4QyTFWVlw2ZCINvKtgRA1TX5aPwsm+6obWbGujE1lNac8/lcvSGfulAJyU50AxNkspGrJsIiIyJeK+YACMHnoAB6+fhTL1u5nf3UjvnAYm9lEUY6LBZcNYfLQAUBbMKlt9tPibwsmR2qbWbn+IG/vOXbKY15SkMK8qYVckNl2ca2WDIuIiHSe3i0/M3noAC4pSOWVjys5WtfMwGQn14zOxmo14wuGqG0K0OxvWx58rMHH7zce4i/bK0+5bmVkjos7Ly1kzKBkAC0ZFhER6QYFlM9s2Hec36zZw7YjXoIhA6vFxPOby5g9eTAjstuuP6lvCfCHzeW8tK0CfzCyydrg9ATmTymkeHAqJpMJs8lEslNLhkVERLpDAYW2cDLv6S0RnV0DYYNNB+v58Mg2fnh1EXurG3nh/SM0f/bxTrtsdxzzphTwteEZmE0m7TIsIiISBTEfUMJhg4XPfXBK2/l2/qDBgy/t5OQVyKkJdm4vzueqUVkduwonOqykJGiXYRERkbMV8wFla3kNtc1fvs/O58NJUpyVWybmcv24gR0rcbQyR0REJLpiPqD899v7O1VnNsGtl+Rx84RcEuPaDpvd2rYyx2mP+cMoIiISVTH/zlrb6D9zETA41cn8qYVA28qclAQbSVqZIyIick7EfEDJSLJ3rs7twGwykeK044q3amWOiIjIORTzASXe3rnrRpLibOSmOrUyR0REpAd0ebnJu+++yzXXXENOTg4mk4mXXnopYtwwDH7wgx+QnZ1NfHw806ZNY+/evRE1NTU1zJo1C5fLRXJyMvPnz6exsfGsXkh3eVuDnarzBcMKJyIiIj2kywGlqamJMWPG8N///d+nHX/kkUf49a9/zfLly9m0aRMJCQlMnz6d1tbWjppZs2axc+dOVq9ezauvvsq7777Ld77zne6/irPQ7OtcQOlsnYiIiJy9Ln/Ec+WVV3LllVeedswwDH75y1/y4IMPcu211wLwzDPPkJmZyUsvvcQtt9zCrl27eOONN9iyZQsTJkwA4De/+Q1XXXUV//mf/0lOTs5ZvJyu89S3RLVOREREzl5UO4qVlZXh8XiYNm1ax21ut5tJkyZRWloKQGlpKcnJyR3hBGDatGmYzWY2bdoUzel0SoMvdOaiLtSJiIjI2YvqRbIejweAzMzMiNszMzM7xjweDxkZGZGTsFpJTU3tqDmZz+fD5/N1fO/1eqM2Z0snLyvpbJ2IiIicvX7Rk33p0qW43e6Or9zc3Og9eGeDhwKKiIhIj4lqQMnKygKgqqoq4vaqqqqOsaysLKqrqyPGg8EgNTU1HTUnW7JkCfX19R1fhw8fjtqczZ3sZ9LZOhERETl7UQ0ohYWFZGVlsWbNmo7bvF4vmzZtoqSkBICSkhLq6urYunVrR81bb71FOBxm0qRJp31ch8OBy+WK+IqWEdmde6zO1omIiMjZ6/I1KI2Njezbt6/j+7KyMrZt20Zqaip5eXncc889/OQnP2HYsGEUFhby/e9/n5ycHK677joARowYwTe/+U3uuusuli9fTiAQYNGiRdxyyy09voIHYNHlw1i790Sn6kRERKRndDmgvP/++3zta1/r+H7x4sUAzJkzh6eeeop//dd/pampie985zvU1dUxdepU3njjDeLi4jru8+yzz7Jo0SIuv/xyzGYzM2fO5Ne//nUUXk7XjRuUgtkEYeOLa8ymtjoRERHpGSbDML7krblv8nq9uN1u6uvrz/rjno8O13Hjsg0EviSh2Mwm/rhgMmNyk8/quURERGJZV96/+8UqnnPpw8N1BL/s9AkQNAw+PFzXMxMSERERBRTDMDjTKSTDaKsTERGRnhHzASXJ0bnLcDpbJyIiImcv5gOKtyUQ1ToRERE5ezEfUCq9rWcu6kKdiIiInL2YDygiIiLS98R8QMlxx0e1TkRERM5ezAeUi/NTsJm/fJ8dm9nExflq1CYiItJTYj6gjBroZnh20hduVmwChmcnMWqguyenJSIiEtNiPqCYzSaWXDmCDJcDqxkspraDYjGB1QyZLgdLrhyB+QxnWURERCR6Yj6gAEweOoBHvzWW4sEDSE1wkBRvIzXBQfHgAfziW2OZPHRAb09RREQkpqj72GcmDx3AJQWpvPJxJUfrmhmY7OSa0dlYrcpwIiIiPU0B5TMb9h1n2dr97K9uJBAysFlM/OnDIyy4bIjOoIiIiPQwnR6gLZw8sGo7uyq9JDisZCQ5SHBY2VXZwAOrtrNh3/HenqKIiEhMifmAEg4bLFu7n0ZfkCxXHHE2C2aziTibhSyXg0ZfiGVr9xM+w47HIiIiEj0xH1B2VnjZX91IitMOQIs/RENrgBZ/CIBkp4391Y3srPD25jRFRERiSsxfg1LT7CcQMvAHw1TWt+ALhjEMMJnAYTWTluAgEDaoafb39lRFRERiRswHlFSnnbARpqLeT9gAq9mEyQQG0BIIU1HfgivOSupnZ1hERETk3Iv5j3hGZCURMiAYMrCawWwyYTKZMJtMWM1tt4eMtjoRERHpGTEfUHZ5GrCYTFjMJoJhCIbDhMIGwXCYYBgsZhMWk4ldnobenqqIiEjMiPmAUtPsx2wykZbQ9hFOIGTgD4UJhNpW7aQl2DGbTboGRUREpAfFfEBpuwbF4ESTHxNgs5iwW0zYLCZMwIkmP+GwoWtQREREelDMXyTbdg2KQShsYLeaMJv+ntnCRhh/0CBkGLoGRUREpAfF/BmUtmtQwGppuwYlbBgYhkHYMAiG2263mNA1KCIiIj0o5gNK2zUoZnLc8cTbzJ8Fk7aAEm9ru91sNusaFBERkR4U8x/xpDrtbdedWM0UpCXQGggTDIexms3E2cy0BsPYQmFdgyIiItKDYv4MysgcF0MyEqltDgAQb7eQFGcj3m4BoK45wJCMREbmuHpzmiIiIjEl5gOK2WxiwWVDSHRYqKxvpbbZj7clQG2zn8r6VhIdFhZcNgSz2dTbUxUREYkZMR9QACYPHcCsSXmEDIPKuhbKa5uprGshZBjMmpTH5KEDenuKIiIiMSXmr0EB2LDvOM9uKsdqNpGTHN+2F48BLYEQz24qZ2SOWyFFRESkB8X8GZRw2GDZ2v00+oJkueJIdtpxx9tJdtrJcsXR6AuxbO1+wmGjt6cqIiISM2I+oOys8LK/upEUpx2TKfI6E5PJRLLTxv7qRnZWeHtphiIiIrEn5gNKTbOfQMjAbjn9oXBYzATChvqgiIiI9KCYDyjtfVD8ofBpx32hMDazSX1QREREelDMB5TP90ExjMjrTAzDUB8UERGRXhDzAeXzfVA8Xh8tgRDhsEFLIITH61MfFBERkV4Q8wEF2vqgPHz9KEZkJ9HsC1Ld6KPZF2REdhIPXz9KS4xFRER6WK/2Qfnv//5vfv7zn+PxeBgzZgy/+c1vuOSSS3plLpOHDqB4cBo7K7zUNPtJddoZmePSmRMREZFe0GtnUP73f/+XxYsX88Mf/pAPPviAMWPGMH36dKqrq3trSpjNJkYNcnPZBemMGuRWOBEREeklvRZQfvGLX3DXXXcxd+5cioqKWL58OU6nkyeffLK3piQiIiJ9RK8EFL/fz9atW5k2bdrfJ2I2M23aNEpLS0+p9/l8eL3eiC8RERE5f/VKQDl+/DihUIjMzMyI2zMzM/F4PKfUL126FLfb3fGVm5vbU1MVERGRXtAvVvEsWbKE+vr6jq/Dhw/39pRERETkHOqVVTwDBgzAYrFQVVUVcXtVVRVZWVmn1DscDhwOR09NT0RERHpZr5xBsdvtjB8/njVr1nTcFg6HWbNmDSUlJb0xJREREelDeq0PyuLFi5kzZw4TJkzgkksu4Ze//CVNTU3MnTu3t6YkIiIifUSvBZSbb76ZY8eO8YMf/ACPx8PYsWN54403TrlwtieFw4YatYmIiPQBJuPkHfL6Aa/Xi9vtpr6+HpcrOpv4bdh3nGVr97O/upFAyMBmMTEkI5EFlw1Rq3sREZEo6Mr7d79YxXOubdh3nAdWbWdXpZcEh5WMJAcJDiu7Kht4YNV2Nuw73ttTFBERiSkxH1DCYYNla/fT6AuS5YojzmbBbDYRZ7OQ5XLQ6AuxbO1+wuF+d6JJRESk34r5gLKzwsv+6kZSnHZMpsjrTUwmE8lOG/urG9lZoe61IiIiPSXmA0pNs59AyMBuOf2hcFjMBMIGNc3+Hp6ZiIhI7Ir5gJLqtGOzmPCHwqcd94XC2MwmUp32Hp6ZiIhI7Ir5gDIyx8WQjERqmwOcvKDJMAzqmgMMyUhkZE50VguJiIjImcV8QDGbTSy4bAiJDgser4+WQIhw2KAlEMLj9ZHosLDgsiHqhyIiItKDYj6gAEweOoCHrx/FiOwkmn1Bqht9NPuCjMhO4uHrR6kPioiISA/rtU6yfc3koQMoHpymTrIiIiJ9gALK55jNJkYNcvf2NERERGKePuIRERGRPkcBRURERPocBRQRERHpcxRQREREpM9RQBEREZE+RwFFRERE+hwFFBEREelzFFBERESkz1FAERERkT6nX3aSbd912Ov19vJMREREpLPa37fb38e/TL8MKA0NDQDk5ub28kxERESkqxoaGnC7v3xrGZPRmRjTx4TDYSoqKkhKSsJkiu5mfl6vl9zcXA4fPozL5YrqY8cqHdPo0zGNPh3T6NMxjb7+fkwNw6ChoYGcnBzM5i+/yqRfnkExm80MGjTonD6Hy+Xql//z+zId0+jTMY0+HdPo0zGNvv58TM905qSdLpIVERGRPkcBRURERPocBZSTOBwOfvjDH+JwOHp7KucNHdPo0zGNPh3T6NMxjb5YOqb98iJZEREROb/pDIqIiIj0OQooIiIi0ucooIiIiEifExMB5d133+Waa64hJycHk8nESy+9FDFuGAY/+MEPyM7OJj4+nmnTprF3796ImpqaGmbNmoXL5SI5OZn58+fT2NjYg6+ib1m6dCkTJ04kKSmJjIwMrrvuOvbs2RNR09raysKFC0lLSyMxMZGZM2dSVVUVUVNeXs6MGTNwOp1kZGRw3333EQwGe/Kl9BnLli1j9OjRHf0NSkpKeP311zvGdTzPzs9+9jNMJhP33HNPx206pl33ox/9CJPJFPE1fPjwjnEd0+45evQot912G2lpacTHxzNq1Cjef//9jvGYfJ8yYsBf/vIX49///d+NP/3pTwZgrFq1KmL8Zz/7meF2u42XXnrJ+Oijj4x/+Id/MAoLC42WlpaOmm9+85vGmDFjjI0bNxrvvfeeMXToUOPWW2/t4VfSd0yfPt1YuXKlsWPHDmPbtm3GVVddZeTl5RmNjY0dNd/97neN3NxcY82aNcb7779vFBcXG5MnT+4YDwaDxkUXXWRMmzbN+PDDD42//OUvxoABA4wlS5b0xkvqdS+//LLx2muvGZ9++qmxZ88e44EHHjBsNpuxY8cOwzB0PM/G5s2bjYKCAmP06NHG9773vY7bdUy77oc//KExcuRIo7KysuPr2LFjHeM6pl1XU1Nj5OfnG3fccYexadMm48CBA8abb75p7Nu3r6MmFt+nYiKgfN7JASUcDhtZWVnGz3/+847b6urqDIfDYfzhD38wDMMwPvnkEwMwtmzZ0lHz+uuvGyaTyTh69GiPzb0vq66uNgBj7dq1hmG0HUObzWa8+OKLHTW7du0yAKO0tNQwjLbgaDabDY/H01GzbNkyw+VyGT6fr2dfQB+VkpJiPPHEEzqeZ6GhocEYNmyYsXr1auOyyy7rCCg6pt3zwx/+0BgzZsxpx3RMu+f+++83pk6d+oXjsfo+FRMf8XyZsrIyPB4P06ZN67jN7XYzadIkSktLASgtLSU5OZkJEyZ01EybNg2z2cymTZt6fM59UX19PQCpqakAbN26lUAgEHFchw8fTl5eXsRxHTVqFJmZmR0106dPx+v1snPnzh6cfd8TCoV4/vnnaWpqoqSkRMfzLCxcuJAZM2ZEHDvQz+jZ2Lt3Lzk5OQwePJhZs2ZRXl4O6Jh218svv8yECRO46aabyMjIYNy4cTz++OMd47H6PhXzAcXj8QBE/LK0f98+5vF4yMjIiBi3Wq2kpqZ21MSycDjMPffcw5QpU7jooouAtmNmt9tJTk6OqD35uJ7uuLePxaLt27eTmJiIw+Hgu9/9LqtWraKoqEjHs5uef/55PvjgA5YuXXrKmI5p90yaNImnnnqKN954g2XLllFWVsall15KQ0ODjmk3HThwgGXLljFs2DDefPNNFixYwD/90z/x9NNPA7H7PtUvNwuUvmXhwoXs2LGDdevW9fZU+r0LL7yQbdu2UV9fzx//+EfmzJnD2rVre3ta/dLhw4f53ve+x+rVq4mLi+vt6Zw3rrzyyo4/jx49mkmTJpGfn88LL7xAfHx8L86s/wqHw0yYMIGHH34YgHHjxrFjxw6WL1/OnDlzenl2vSfmz6BkZWUBnHKVeVVVVcdYVlYW1dXVEePBYJCampqOmli1aNEiXn31Vd5+++2IHaazsrLw+/3U1dVF1J98XE933NvHYpHdbmfo0KGMHz+epUuXMmbMGH71q1/peHbD1q1bqa6u5uKLL8ZqtWK1Wlm7di2//vWvsVqtZGZm6phGQXJyMhdccAH79u3Tz2k3ZWdnU1RUFHHbiBEjOj46i9X3qZgPKIWFhWRlZbFmzZqO27xeL5s2baKkpASAkpIS6urq2Lp1a0fNW2+9RTgcZtKkST0+577AMAwWLVrEqlWreOuttygsLIwYHz9+PDabLeK47tmzh/Ly8ojjun379ohfqtWrV+NyuU75ZY1V4XAYn8+n49kNl19+Odu3b2fbtm0dXxMmTGDWrFkdf9YxPXuNjY3s37+f7Oxs/Zx205QpU05p0/Dpp5+Sn58PxPD7VG9fpdsTGhoajA8//ND48MMPDcD4xS9+YXz44YfGoUOHDMNoW76VnJxs/PnPfzY+/vhj49prrz3t8q1x48YZmzZtMtatW2cMGzasXy/fOlsLFiww3G638c4770QsN2xubu6o+e53v2vk5eUZb731lvH+++8bJSUlRklJScd4+3LDK664wti2bZvxxhtvGOnp6TG73PDf/u3fjLVr1xplZWXGxx9/bPzbv/2bYTKZjL/+9a+GYeh4RsPnV/EYho5pd/zzP/+z8c477xhlZWXG+vXrjWnTphkDBgwwqqurDcPQMe2OzZs3G1ar1fjpT39q7N2713j22WcNp9Np/M///E9HTSy+T8VEQHn77bcN4JSvOXPmGIbRtoTr+9//vpGZmWk4HA7j8ssvN/bs2RPxGCdOnDBuvfVWIzEx0XC5XMbcuXONhoaGXng1fcPpjidgrFy5sqOmpaXF+Md//EcjJSXFcDqdxvXXX29UVlZGPM7BgweNK6+80oiPjzcGDBhg/PM//7MRCAR6+NX0DfPmzTPy8/MNu91upKenG5dffnlHODEMHc9oODmg6Jh23c0332xkZ2cbdrvdGDhwoHHzzTdH9OvQMe2eV155xbjooosMh8NhDB8+3Pjd734XMR6L71PazVhERET6nJi/BkVERET6HgUUERER6XMUUERERKTPUUARERGRPkcBRURERPocBRQRERHpcxRQREREpM9RQBEREZE+RwFFRHrcU089RXJycm9PQ0T6MHWSFZEe19LSQkNDAxkZGZ2+z1e/+lXGjh3LL3/5y3M3MRHpM6y9PQERiT3x8fHEx8f39jREpA/TRzwi0mVf/epXWbRoEYsWLcLtdjNgwAC+//3v035Ctra2ltmzZ5OSkoLT6eTKK69k7969Hfc/+SOeH/3oR4wdO5bf//73FBQU4Ha7ueWWW2hoaADgjjvuYO3atfzqV7/CZDJhMpk4ePDgF87PMAyGDh3Kf/7nf0bcvm3bNkwmE/v27YvewRCRc0IBRUS65emnn8ZqtbJ582Z+9atf8Ytf/IInnngCaAsU77//Pi+//DKlpaUYhsFVV11FIBD4wsfbv38/L730Eq+++iqvvvoqa9eu5Wc/+xkAv/rVrygpKeGuu+6isrKSyspKcnNzv/CxTCYT8+bNY+XKlRG3r1y5kq985SsMHTo0CkdARM4lBRQR6Zbc3FweffRRLrzwQmbNmsXdd9/No48+yt69e3n55Zd54oknuPTSSxkzZgzPPvssR48e5aWXXvrCxwuHwzz11FNcdNFFXHrppdx+++2sWbMGALfbjd1ux+l0kpWVRVZWFhaL5Uvnd8cdd7Bnzx42b94MQCAQ4LnnnmPevHlROwYicu4ooIhItxQXF2MymTq+LykpYe/evXzyySdYrVYmTZrUMZaWlsaFF17Irl27vvDxCgoKSEpK6vg+Ozub6urqbs8vJyeHGTNm8OSTTwLwyiuv4PP5uOmmm7r9mCLScxRQRKRPsNlsEd+bTCbC4fBZPeadd97J888/T0tLCytXruTmm2/G6XSe1WOKSM9QQBGRbtm0aVPE9xs3bmTYsGEUFRURDAYjxk+cOMGePXsoKirq9vPZ7XZCoVCX7nPVVVeRkJDAsmXLeOONN/Txjkg/ooAiIt1SXl7O4sWL2bNnD3/4wx/4zW9+w/e+9z2GDRvGtddey1133cW6dev46KOPuO222xg4cCDXXnttt5+voKCATZs2cfDgQY4fP96psysWi4U77riDJUuWMGzYMEpKSrr9/CLSsxRQRKRbZs+eTUtLC5dccgkLFy7ke9/7Ht/5zneAttUy48eP5+qrr6akpATDMPjLX/5yysc4XfEv//IvWCwWioqKSE9Pp7y8vFP3mz9/Pn6/n7lz53b7uUWk56mTrIh0WX/q6vree+9x+eWXc/jwYTIzM3t7OiLSSeokKyLnJZ/Px7Fjx/jRj37ETTfdpHAi0s/oIx4R6Ze++93vkpiYeNqv7373u/zhD38gPz+furo6Hnnkkd6eroh0kT7iEZF+qbq6Gq/Xe9oxl8vVpY0IRaTvUUARERGRPkcf8YiIiEifo4AiIiIifY4CioiIiPQ5CigiIiLS5yigiIiISJ+jgCIiIiJ9jgKKiIiI9DkKKCIiItLn/P+zX1yMPGNCogAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768777.765963100158.491273(768, 100)
290768765.931075100141.268575(768, 100)
54100139.51506310097.895359(100, 100)
19810079.855539630587.373438(100, 630)
45314361435.91875910052.780919(1436, 100)
..................
16410090.212863365372.324342(100, 365)
16510090.129972365372.522085(100, 365)
19910081.470342630594.104211(100, 630)
13210082.963966365377.453932(100, 365)
50114361423.823247100102.772110(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 777.765963 100 158.491273 (768, 100)\n", + "290 768 765.931075 100 141.268575 (768, 100)\n", + "54 100 139.515063 100 97.895359 (100, 100)\n", + "198 100 79.855539 630 587.373438 (100, 630)\n", + "453 1436 1435.918759 100 52.780919 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 90.212863 365 372.324342 (100, 365)\n", + "165 100 90.129972 365 372.522085 (100, 365)\n", + "199 100 81.470342 630 594.104211 (100, 630)\n", + "132 100 82.963966 365 377.453932 (100, 365)\n", + "501 1436 1423.823247 100 102.772110 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768777.765963100158.491273(768, 100)
290768765.931075100141.268575(768, 100)
54100139.51506310097.895359(100, 100)
19810079.855539630587.373438(100, 630)
45314361435.91875910052.780919(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 777.765963 100 158.491273 (768, 100)\n", + "290 768 765.931075 100 141.268575 (768, 100)\n", + "54 100 139.515063 100 97.895359 (100, 100)\n", + "198 100 79.855539 630 587.373438 (100, 630)\n", + "453 1436 1435.918759 100 52.780919 (1436, 100)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.223782\n", + "(100, 365) 0.409683\n", + "(100, 630) 0.675336\n", + "(768, 100) 0.918995\n", + "(768, 630) 1.255215\n", + "(1436, 100) 1.200635\n", + "(1436, 365) 1.517800\n", + "(1436, 630) 1.798556\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_22056\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_22056\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing\n" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler\n", + " sc = StandardScaler()\n", + "\n", + " # Define the pipeline with PolynomialFeatures and Ridge regression\n", + " pipeline = make_pipeline(PolynomialFeatures(2), linear_model.Ridge())\n", + "\n", + " # Define the hyperparameter grid for Ridge regression\n", + " param_grid = {\n", + " 'ridge__alpha': [0.1, 0.5, 1.0, 10, 50, 100]\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the pipeline and parameter grid\n", + " grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring=make_scorer(r2_score))\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for X\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(f'Best alpha for X: {grid_search.best_params_[\"ridge__alpha\"]}, R2 score: {r2_score_x}')\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for Y\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(f'Best alpha for Y: {grid_search.best_params_[\"ridge__alpha\"]}, R2 score: {r2_score_y}')\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best alpha for X: 0.1, R2 score: 0.9984578727216439\n", + "Best alpha for Y: 0.1, R2 score: 0.9770010267587734\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 39e4c8fb6e384d321711a511aef2f427119e299c Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 21:37:53 +0000 Subject: [PATCH 18/78] minor change in ridge grid search notebook --- .../ridge_regression/test_ridge_regression_grid_search.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb index 6cef6e90..3223769c 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb @@ -1527,7 +1527,7 @@ "\n", " # Define the hyperparameter grid for Ridge regression\n", " param_grid = {\n", - " 'ridge__alpha': [0.1, 0.5, 1.0, 10, 50, 100]\n", + " 'ridge__alpha': [0.1, 0.5, 1.0, 10.0, 50.0, 100.0]\n", " }\n", "\n", " # Initialize GridSearchCV with the pipeline and parameter grid\n", From e634a426fc72a91a69d76ad344e4039cf7b15f29 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 21:51:50 +0000 Subject: [PATCH 19/78] ridge cv grid search notebook added --- .../test_ridgeCV_regression_grid_search.ipynb | 1678 +++++++++++++++++ 1 file changed, 1678 insertions(+) create mode 100644 app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb new file mode 100644 index 00000000..3cbda5af --- /dev/null +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb @@ -0,0 +1,1678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9962372996893408" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a RidgeCV model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 775.9488375 , 765.44646524, 117.46992782, 114.98772163,\n", + " 1425.60842262, 108.58871546, 128.04372944, 86.82552302,\n", + " 770.32361198, 770.14196838, 1419.39765124, 767.69939222,\n", + " 116.90236849, 95.9273306 , 771.11439115, 777.12888377,\n", + " 123.7435898 , 1431.44527262, 101.7226186 , 113.47143036,\n", + " 768.6386489 , 768.48131803, 105.57636972, 1434.19347734,\n", + " 1423.9421898 , 775.30025567, 85.35391255, 103.8479276 ,\n", + " 78.44714396, 102.21159725, 770.15743904, 769.40008838,\n", + " 777.8388113 , 772.31712021, 1458.55673432, 1438.61886872,\n", + " 1417.35182784, 1458.09686899, 1453.44883061, 1427.43953014,\n", + " 763.69556751, 101.51991418, 106.26253837, 1427.98805487,\n", + " 769.76844177, 108.7787584 , 779.85981566, 1439.22131008,\n", + " 1437.22179818, 101.90989851, 1423.55090668, 1436.1863096 ,\n", + " 765.46761981, 1437.41734021, 84.65561441, 767.76280724,\n", + " 94.43140854, 94.98800185, 116.97901041, 776.45230831,\n", + " 1416.55127338, 109.72866561, 113.59668594, 767.05101545,\n", + " 88.11886533, 99.62142055, 1434.70126831, 94.18205055,\n", + " 1420.05801842, 1440.92837538, 1436.69220078, 773.89818439,\n", + " 121.70204396, 772.58809921, 70.5603242 , 1416.85192743,\n", + " 1453.47477909, 778.12898405, 1425.58029512, 99.97644867,\n", + " 1417.89655382, 763.96221723, 93.43644841, 761.6909926 ,\n", + " 113.75955612, 1423.80307031, 1464.19734681, 95.3609168 ,\n", + " 1437.40319904, 1434.4477574 , 84.41258735, 118.43962962,\n", + " 100.21523859, 772.50600096, 100.42814048, 771.2771715 ,\n", + " 104.08148186, 116.11428828, 1463.45615148, 82.64389501,\n", + " 769.65594585, 1421.11500469, 773.82201847, 768.5980017 ,\n", + " 113.05823801, 770.54771329, 89.82168267, 82.1647507 ,\n", + " 79.94817147, 1435.54744746, 93.00558832, 1043.31729817,\n", + " 1440.15455279, 104.88790171, 1421.37161817, 775.24045023,\n", + " 770.90365892, 115.35590471, 1457.01402779, 116.75333443,\n", + " 120.71010652, 768.99022685, 1440.49773319, 113.56181959,\n", + " 774.7198465 , 100.7586774 , 770.0539881 , 1436.97118999,\n", + " 1441.03717148, 1432.19446103, 1438.64806284, 772.0856761 ,\n", + " 88.75513547, 1459.6473705 , 775.85076962, 1423.31696178,\n", + " 86.61817211, 101.97754574, 93.70863022, 106.26391156,\n", + " 100.00618951, 118.13553751, 80.47978862, 1422.27736965])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9808695687527501" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a RidgeCV model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([130.77711835, 117.6474648 , 107.61582613, 584.12047811,\n", + " 55.4754501 , 113.13585135, 108.49449888, 404.88830082,\n", + " 132.63725089, 667.01492296, 611.7120068 , 690.50314228,\n", + " 92.21187016, 332.53209382, 664.96999878, 137.37399939,\n", + " 117.9948033 , 371.75966214, 568.92695921, 591.18110309,\n", + " 691.84288468, 597.60307529, 89.65270506, 621.33411176,\n", + " 134.09821776, 122.2569714 , 578.71326319, 577.48604329,\n", + " 392.74517085, 379.0663491 , 667.53992792, 597.30841557,\n", + " 110.5414626 , 606.88294508, 348.51284105, 693.56428018,\n", + " 616.99144213, 363.39147946, 355.44231523, 102.54336181,\n", + " 121.74368488, 382.41495312, 106.39489094, 640.13741406,\n", + " 630.96879756, 107.37458863, 138.81935247, 367.41160256,\n", + " 662.97982435, 107.15614713, 128.45120122, 629.58847124,\n", + " 672.94182187, 327.05347783, 374.51071442, 138.86054073,\n", + " 583.86047615, 570.23374655, 115.34560497, 131.55630825,\n", + " 114.92702326, 47.04664706, 105.30614147, 658.78828468,\n", + " 596.7169542 , 314.54358875, 658.5664843 , 383.35060681,\n", + " 124.43320486, 369.92846573, 639.8494254 , 671.19659187,\n", + " 107.46338054, 607.52149607, 375.71173988, 118.54093037,\n", + " 356.38629449, 130.25862991, 628.96569658, 574.45933298,\n", + " 115.94816887, 118.57288776, 583.88152178, 118.83279143,\n", + " 85.03469256, 118.02922192, 360.28064026, 591.29945273,\n", + " 90.3975367 , 652.55993607, 571.36936802, 106.91744765,\n", + " 569.85491228, 125.80734431, 405.30912129, 130.94301332,\n", + " 118.36663949, -7.42292206, 358.27912422, 373.58454239,\n", + " 135.63614369, 616.88886719, 136.75979723, 127.07110164,\n", + " 590.48035059, 147.99316798, 569.328308 , 575.60175341,\n", + " 594.55918 , 656.45585252, 404.76384929, 99.46837477,\n", + " 365.18237482, 288.37521017, 106.31482456, 143.74369738,\n", + " 598.15233122, 92.72970694, 355.10496449, 81.36921695,\n", + " 108.16812159, 645.4960504 , 361.36978947, 104.60919856,\n", + " 125.76896423, 107.40460786, 630.42725435, 659.96640556,\n", + " 330.3699873 , 95.35357566, 638.47363814, 664.83325858,\n", + " 576.89577907, 354.14348354, 124.66819906, 634.14849024,\n", + " 403.9072803 , 39.73804703, 569.57521083, 375.77865049,\n", + " 372.57885505, 598.82980489, 394.67575436, 105.88192018])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768775.948837100130.777118(768, 100)
290768765.446465100117.647465(768, 100)
54100117.469928100107.615826(100, 100)
198100114.987722630584.120478(100, 630)
45314361425.60842310055.475450(1436, 100)
..................
164100106.263912365375.778650(100, 365)
165100100.006190365372.578855(100, 365)
199100118.135538630598.829805(100, 630)
13210080.479789365394.675754(100, 365)
50114361422.277370100105.881920(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 775.948837 100 130.777118 (768, 100)\n", + "290 768 765.446465 100 117.647465 (768, 100)\n", + "54 100 117.469928 100 107.615826 (100, 100)\n", + "198 100 114.987722 630 584.120478 (100, 630)\n", + "453 1436 1425.608423 100 55.475450 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.263912 365 375.778650 (100, 365)\n", + "165 100 100.006190 365 372.578855 (100, 365)\n", + "199 100 118.135538 630 598.829805 (100, 630)\n", + "132 100 80.479789 365 394.675754 (100, 365)\n", + "501 1436 1422.277370 100 105.881920 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768775.948837100130.777118(768, 100)
290768765.446465100117.647465(768, 100)
54100117.469928100107.615826(100, 100)
198100114.987722630584.120478(100, 630)
45314361425.60842310055.475450(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 775.948837 100 130.777118 (768, 100)\n", + "290 768 765.446465 100 117.647465 (768, 100)\n", + "54 100 117.469928 100 107.615826 (100, 100)\n", + "198 100 114.987722 630 584.120478 (100, 630)\n", + "453 1436 1425.608423 100 55.475450 (1436, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.211173\n", + "(100, 365) 0.418316\n", + "(100, 630) 0.685089\n", + "(768, 100) 0.903296\n", + "(768, 630) 1.261005\n", + "(1436, 100) 1.203244\n", + "(1436, 365) 1.518381\n", + "(1436, 630) 1.799496\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_17412\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_17412\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACXFUlEQVR4nOzde1yUZf7/8dc9HAZFAU0QNTRISfGU4lakHRSV2tbdNrbd/X6zk5ppZIvartZ2UEupVVu31nOW7rfTb8tqNylRwUOWpkK45RkFqQSjAyAap5n5/TEyKwLKwAwD8n4+HvOgue/ruu/PDT0c3tzXfV2GzWazISIiIiIiIvVm8nQBIiIiIiIiLY2ClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiItEhXXHEF9913n6fLEBGRVkpBSkREmp2jR4/y4IMPEhERgZ+fHwEBAQwdOpS//e1v/PTTT24//5kzZ5g1axZbtmxx+7lERKRl8vZ0ASIiIudKTk7mzjvvxGw2c88999CvXz/Ky8vZvn07f/zjH9m3bx8rVqxwaw1nzpxh9uzZANx8881uPZeIiLRMClIiItJsZGdn8/vf/54ePXqQlpZGly5dHPsSEhLIysoiOTnZgxU2zunTp/H39/d0GSIi4gIa2iciIs3GX/7yF0pKSli1alW1EFWlZ8+e/OEPf6i176xZszAMo8b21atXYxgGOTk5jm179uwhLi6OTp060aZNG8LDwxk3bhwAOTk5BAcHAzB79mwMw8AwDGbNmuXof/DgQX7zm9/QsWNH/Pz8GDJkCP/+979rPe/WrVt56KGHCAkJ4fLLLwfg1KlTJCYmcsUVV2A2mwkJCWHUqFFkZGQ49f0SERHP0R0pERFpNj744AMiIiK4/vrr3XaOb7/9ltGjRxMcHMzMmTMJCgoiJyeHd999F4Dg4GCWLl3K5MmT+fWvf80dd9wBwIABAwDYt28fQ4cOpVu3bsycORN/f3/++c9/cvvtt7N27Vp+/etfVzvfQw89RHBwME899RSnT58GYNKkSbzzzjs8/PDDREVF8f3337N9+3YOHDjA4MGD3XbtIiLiOgpSIiLSLBQXF/PNN9/wq1/9yq3n+fTTT/nxxx/ZsGEDQ4YMcWx/9tlnAfD39+c3v/kNkydPZsCAAYwdO7Za/z/84Q90796d3bt3YzabAXtYGjZsGDNmzKgRpDp27EhqaipeXl6ObcnJyTzwwAMsXLjQse1Pf/qTy69VRETcR0P7RESkWSguLgagffv2bj1PUFAQAOvWraOiosKpvj/88ANpaWn89re/5dSpU3z33Xd89913fP/998TFxXHkyBG++eaban0eeOCBaiGqqobPPvuMEydONOpaRETEcxSkRESkWQgICADszw+500033UR8fDyzZ8+mU6dO/OpXv+LVV1+lrKzson2zsrKw2Ww8+eSTBAcHV3s9/fTTgH3o4LnCw8NrHOcvf/kLX375JWFhYVxzzTXMmjWLY8eOueYCRUSkSWhon4iINAsBAQF07dqVL7/8skH9a5toAsBisdRo984777Bz504++OADUlJSGDduHAsXLmTnzp20a9euznNYrVYAHn30UeLi4mpt07Nnz2rv27RpU6PNb3/7W2644Qbee+89NmzYwPz583n++ed59913ufXWWy94nSIi0jzojpSIiDQbv/jFLzh69Cg7duxwum+HDh0AKCwsrLb9+PHjtba/7rrrmDt3Lnv27OH1119n3759vPXWW0DdoSwiIgIAHx8fRo4cWeurvkMTu3TpwkMPPcT7779PdnY2l112GXPnzq1XXxER8TwFKRERaTb+9Kc/4e/vz4QJEzh58mSN/UePHuVvf/tbrX2vvPJKALZt2+bYdvr0adasWVOt3Y8//ojNZqu27eqrrwZwDO9r27YtUDOUhYSEcPPNN7N8+XLy8vJq1FBQUHCBq7OzWCwUFRXVOG7Xrl3rNbxQRESaBw3tExGRZuPKK6/kjTfe4He/+x19+vThnnvuoV+/fpSXl/Ppp5/y9ttvc99999Xad/To0XTv3p3x48fzxz/+ES8vL1555RWCg4PJzc11tFuzZg1Llizh17/+NVdeeSWnTp1i5cqVBAQE8POf/xywD8eLiori//2//0dkZCQdO3akX79+9OvXj8WLFzNs2DD69+/PAw88QEREBCdPnmTHjh18/fXX7N2794LXeOrUKS6//HJ+85vfMHDgQNq1a8emTZvYvXt3tVn8RESkeVOQEhGRZuWXv/wl//nPf5g/fz7/+te/WLp0KWazmQEDBrBw4UIeeOCBWvv5+Pjw3nvv8dBDD/Hkk08SGhpKYmIiHTp04P7773e0u+mmm9i1axdvvfUWJ0+eJDAwkGuuuYbXX3+92sQQL7/8MlOmTGHq1KmUl5fz9NNP069fP6KiotizZw+zZ89m9erVfP/994SEhDBo0CCeeuqpi15f27Zteeihh9iwYQPvvvsuVquVnj17smTJEiZPntz4b6CIiDQJw3b++AYRERERERG5ID0jJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxktaRAqxWKydOnKB9+/YYhuHpckRERERExENsNhunTp2ia9eumEx133dSkAJOnDhBWFiYp8sQEREREZFm4quvvuLyyy+vc7+CFNC+fXvA/s0KCAjwcDUiIiIiIuIpxcXFhIWFOTJCXRSkwDGcLyAgQEFKREREREQu+siPJpsQERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkzwapK644goMw6jxSkhIAKC0tJSEhAQuu+wy2rVrR3x8PCdPnqx2jNzcXG677Tbatm1LSEgIf/zjH6msrPTE5YiIiIiISCvh7cmT7969G4vF4nj/5ZdfMmrUKO68804Apk6dSnJyMm+//TaBgYE8/PDD3HHHHXzyyScAWCwWbrvtNkJDQ/n000/Jy8vjnnvuwcfHh3nz5nnkmkREREREWpvTBaf5fNXnZKdlU1pUil+gH+Gx4QwaNwj/YH9Pl+cWhs1ms3m6iCqJiYmsW7eOI0eOUFxcTHBwMG+88Qa/+c1vADh48CB9+vRhx44dXHfddXz00Uf84he/4MSJE3Tu3BmAZcuWMWPGDAoKCvD19a3XeYuLiwkMDKSoqIiAgAC3XZ+IiDRPBQWwahWkpUFREQQGQmwsjBsHwcGerk5EpPmqLK1kfeJ6Pl/1OTarDZv1v9HCMBkYJoPBEwYTtygOb7NH7+HUW32zQbN5Rqq8vJzXXnuNcePGYRgG6enpVFRUMHLkSEeb3r170717d3bs2AHAjh076N+/vyNEAcTFxVFcXMy+ffvqPFdZWRnFxcXVXiIi0vqUlsK990JoKDz2GGzcCLt22b8+/jh07QqTJ0NZmacrFRFpfipLK3kt7jUyVmZgrbRWC1EANqsNa6WV9BXpvBb3GpVll9bjN80mSL3//vsUFhZy3333AZCfn4+vry9BQUHV2nXu3Jn8/HxHm3NDVNX+qn11SUpKIjAw0PEKCwtz3YWIiEiLUFgIERHwj3+A1Vpzv9UKlZWwYgXExSlMiYicL2VqCrnbc2sEqPPZrDZyP84lJTGliSprGs0mSK1atYpbb72Vrl27uv1cjz32GEVFRY7XV1995fZziohI81FaClFRkJd38bZWK3z8MSQmur0sEZEW43TBaTJezrhoiKpis9rIeDmDM9+dcXNlTadZBKnjx4+zadMmJkyY4NgWGhpKeXk5hYWF1dqePHmS0NBQR5vzZ/Grel/VpjZms5mAgIBqLxERaT0mTapfiKpitcLLL8N337mvJhGRluTzVz6vd4iqYrPayFiV4aaKml6zCFKvvvoqISEh3HbbbY5t0dHR+Pj4kJqa6th26NAhcnNziYmJASAmJoYvvviCb7/91tFm48aNBAQEEBUV1XQXICIiLUZBAfzf/znfz2q1T0ghIiKQnZrdoCCVnZbtpoqanseDlNVq5dVXX+Xee+/F2/u/M3kEBgYyfvx4pk2bxubNm0lPT+f+++8nJiaG6667DoDRo0cTFRXF3Xffzd69e0lJSeGJJ54gISEBs9nsqUsSEZFm7JVXan8m6mKsVvusfiIiAqVFpQ3qV1Z46Txw6vE5CDdt2kRubi7jxo2rse+vf/0rJpOJ+Ph4ysrKiIuLY8mSJY79Xl5erFu3jsmTJxMTE4O/vz/33nsvc+bMacpLEBGRFuScgQ5OO2+0uYhIq+UX6NegfuagS+dmh8eD1OjRo6lrKSs/Pz8WL17M4sWL6+zfo0cPPvzwQ3eVJyIil5iioob3PW8iWRGRVis8Ntzp4X2GySB8RLgbq2paHh/aJyIi0pQCAxved8QI19UhItKSDRo3CMNkONXH8DIYPH6wmypqegpSIiLSqsTGguHcZz8AJhOMH+/6ekREWiL/YH8GTxhc7zBlmOwhqm2ntm6urOkoSImISKsybhx4eTnf7557oFMn19cjItJSxS2Ko/uw7hcNU4bJoPsN3YlbFNdElTUNBSkREWlVgoPhvvuc6xMSAsuWuaUcEZEWy9vszdiUsURPjMbkbaoRqAyTgcnbRPTEaMamjMXb7PHpGVzKsNU100MrUlxcTGBgIEVFRVqcV0TkEldaCqNGwfbt9e/j7Q0TJsCiRaDVNUREajpdcJrPX/mc7LRsygrLMAeZCR8RzqBxg/AP9vd0eU6pbzZQkEJBSkSkNZk8GVascH4tKZMJbrgBUlIUpkRELmX1zQYa2iciIq1GQQG8/HLDF+T9+GNITHR5WSIi0gIpSImISKvxyisNC1FVrFZ7EPvuO9fVJCIiLdOl9cSXiIjIBaSmNi5Igb3/qlUwY4ZrahIRuRScLjjN56vsz0iVFpXiF+hHeGzLfEaqvhSkRESk1SgqavwxrFZIS1OQEhEBqCytZH3iej5f9Tk2qw2b9b/TL2SnZrP5ic0MnjCYuEVxl9ysfZfW1YiIiFxAYKBrjlNY6JrjiIi0ZJWllbwW9xq523OrBagqVcEqfUU6BQcKLrkp0PWMlIiItBqxsfbZ9xqrbdvGH0NEpKVLmZrC8Y+P1xqizmWz2ji+7TgpiSlNVFnTUJASEZFWY9w41wQpP7/GH0NEpCU7XXCaPSv2QH0XUrJB+sp0znx3xq11NSUFKRERaTWCg+0L6xpG445TWuqaekREWqo9y/eAk5P32Cw2MlZluKcgD1CQEhGRVmXRIggNbdwxzlw6f1AVEWmQ3Yt3N6hf1vosF1fiOQpSIiLSqhQX2xfmbYygIJeUIiLSIp0uOM3p/NMN6vtj1o8ursZzFKRERKRVaeyivIYBI0a4rh4RkZbm0xc+bXDfip8qXFiJZylIiYhIq9LYRXm9vWH8eNfVIyLS0uxZsqfBfX3a+LiwEs+6dCZyFxERqYfaF+U9DKwA9gBFQCAwBJgIRFZrOX48dOrk3hpFRJqr0wWnKS8ub3D/Dj07uLAaz1KQEhGRVqX6orx7gWlAGuAFWM7Ztx1YCMSe/TqQG26wT1YhItJaff7K543q3/OWni6qxPMUpEREpFWJja0a3pcKjAGq/rJqOa9l1fstQAxXXfUBGzfGYjY3UaEiIs1Qdmq247+/4zvSSecEJyijDDNmutKVaKLpRO237gePH9xUpbqdgpSIiLQq48bBn/+8F3uIKuXiq0lagDKOHx/DwYM7GDhwoNtrFBFprkqLSsknnxRSyCYbAwPbOf+O5pLLDnYQTjhxxBHKf9eb6BLdhbad2nqibLfQZBMiItKqBAdD587TsN+JuliIqmKlrKyc6dOnu7EyEZHm72jlUV7mZXLIAagWos59n0MOL/Myxzjm2Pf7f/2+yepsCgpSIiLSqhw+fJgTJ9KoOZTvwmw2C6mpqRw5csQ9hYmINHPpu9J5IfMFKqmsEaDOZ8OGBQtv8Ab55BM6OJSAbgFNVGnTUJASEZFWZcWKFZhMXg3qaxheLF++3MUViYg0f5WlldwddzeV1sp696kKUymkUPx1MaXFpW6ssOkpSImISKuyZ88erFbn7kZVsdkspKenu7giEZHm75X7X+FA4YGL3ok6nw0b2WTz1bdfsbj3YirL6h/EmjsFKRERaVWKal9Iqt4KCwtdU4iISAtR9FURq99ajYHRoP4GBnvYQ0leCesmrXNxdZ6jICUiIq1KYPWFpJwWFBTkmkJERFqAytJKVt+0mhOccPpuVBUbNvLIA+A///gPZ74748oSPUZBSkREWpUhQ4Y06hmp6OhoF1ckItJ8pUxNoTC7kDLKGnWcUuzPR9msNjJWZbiiNI9TkBIRkVZl4sSJjXpG6sEHH3RxRSIizdPpgtNkvGwPPWYatxq5H36O/85Oy75Ay5ZDQUpERFqVyMhIRowYATh7V8qLESNG0qtXL3eUJSLS7Hz+yufYrPbhfF3p2qhnpLrQxfG+rLBxd7eaCwUpERFpdZ544gXAl/p/DJoAX9q1W+C+okREmpns1GxHkIomulHPSA1hiOO9Oahxd7eaCwUpERFpdXbtGgh8AJi5+J0pr7PtPmDduoF89527qxMRaR5Ki/677lMnOhFOuNN3pQwMIojgMi6zvzcZhI8Id2mdnqIgJSIirU5qKkAssAO4+ezW8wNV1fvhZ9vFYrXCqlVNUaGIiOf5BfpVex9HHF541TtMGRh44cVoRv93m5fB4PGDXVqnpyhIiYhIq/PfpaQGApuAw0Ai9lB19dmviWe3bzzbzi4trWlqFBHxtPDY6neOQgnlf/nfeoWpqhD1v/wvoYQ6tg8eP5i2ndq6pd6m5u3pAkRERJpazaWkegH1e/5J6/GKSGsxaNwgUh9L5dxHoyKIYAITSCGFbLIxMKo9O1X1PpxwRjO6Wojybe9L3KK4prwEt1KQEhGRVic2FjZubFhfrccrIq2Ff7A/7ULbUZJXUm17KKHcy718z/fsYQ955FFKKX740YUuDGGI45moc3W7thve5ksnflw6VyIiIlJP48bB44+D1epcP5MJRoxwT00iIs1JZWkl6xPX1whR57qMy4ijfneYDJNBxMgIV5XXLOgZKRERaXWCg+Huu53v5+UF48e7vh4RkeaksrSS1+JeI31FukuPe6lMMlFFQUpERFql5cuhS5eLt6tiMtlDVKdO7qtJRKQ5SJmaQu72XBq4bFSt2nVtd8lMMlFFQUpERFolsxkOHKhfmDKZ4IYbYNEit5clIuJRpwtOk/FyhmMhXldpH9repcdrDhSkRESk1QoMhGPH4N577WGpNiaTfRhgSoo9fImIXMo+f+Vzl4coAL+Ofhdv1MIoSImISKvm5wfLlsHYsbWHKasVXn8dEhOhrKzJyxMRaVLZqdkuD1KGySB8RPjFG7YwClIiItKqlZbCqFHwf/9X9yx+lZX2Z6pGjVKYEpFLW2lRqcuPaXgZl9xEE6AgJSIirdwjj8D27WC7yB9gbTb4+GOYMqVp6hIR8QS/QNcOwTNM9hB1qU00AQpSIiLSihUUwMsvO9fn5Zfhu+/cU4+IiKeFx7puCJ5hMuh+Q3fiFtVvramWRkGqtSgogOeeg9Gj4dpr7V+ff96+XUSklXrxxYvfiTqfzWbvJyJyKRo0bhAYjTuGYTIweZuInhjN2JSxeJu9XVNcM2PYbM5+hFx6iouLCQwMpKioiICAAE+X41qlpfYnpFetsg/+P/cBAJPJ/powwT6nr6ajEpFWJjISjhxxvl+vXnD4sOvrERFpDl4Ie4FTX59yup/Jx8QVw68gfEQ4g8YNwj/Y3w3VuV99s8GlGQ8vVQUF9kCUlgZFRfZ5e2NjYdw4CA6u2b60FOLi7IP/a3uCuipYrVhhX0xFc/uKSCuTn9+wfidPurYOEZHmZMikIWx+YrPT/W56+iZu/PONbqioefL40L5vvvmGsWPHctlll9GmTRv69+/Pnj17HPttNhtPPfUUXbp0oU2bNowcOZIj5/358IcffuCuu+4iICCAoKAgxo8fT0lJSVNfiutVDccbORJCQuyvxx+HjRth1y7718cfh65dYfLkmlNJTZ1ad4g6l9Vqf4I6MdFtlyIi0twUFsIp5//gKiJyyYueGI3h5dz4PsPbYMiDQ9xUUfPk0SD1448/MnToUHx8fPjoo4/Yv38/CxcupEOHDo42f/nLX3jxxRdZtmwZn332Gf7+/sTFxVFa+t+pGe+66y727dvHxo0bWbduHdu2bWPixImeuCTXKC2FSZPsAenPf4bU1P8+y3T+SEyr1T4v74oV9rtPVWGq6gnqi4Woc4+jJ6hFpBUZOrThfTt3dl0dIiLNjX+wP9EPRGOY6hemDJNB9IToS3Jmvgvx6DNSM2fO5JNPPuHjjz+udb/NZqNr165Mnz6dRx99FICioiI6d+7M6tWr+f3vf8+BAweIiopi9+7dDBliT8Hr16/n5z//OV9//TVdu3a9aB3N6hmpiw3HuxDDgHvugdWr7RNJPP64c8cwmWDePJgxw7nzioi0MAcOQFRUw/s/+STMmeO6ekREmpvKskpeG/0audtzL7hAb9XMfJfSpBL1zQYevSP173//myFDhnDnnXcSEhLCoEGDWLlypWN/dnY2+fn5jBw50rEtMDCQa6+9lh07dgCwY8cOgoKCHCEKYOTIkZhMJj777LNaz1tWVkZxcXG1V7NR3+F4tbHZYM0auPtu+5RSzh7DarU/fyUicombNq1x/R95xDV1iIg0V95mb8amjCV6YjQmb1ONu1OtZWa+C/HoFR87doylS5cybdo0Hn/8cXbv3s0jjzyCr68v9957L/lnnwLufN4Yis6dOzv25efnExISUm2/t7c3HTt2dLQ5X1JSErNnz3bDFTWSs8Px6vLaaw3vW1jYuHOLiLQAu3Y1vG+3btCpk+tqERFprrz9vLlt6W3cPOdmPn/lc7LTsikrLMMcZG7xM/O5gkeDlNVqZciQIcybNw+AQYMG8eWXX7Js2TLuvfdet533scceY9o5f44sLi4mLCzMbeert1deaXyIaqygIM+eX0SkCZw/N48zunRxXR0iIi2Bf7A/w2YMY9iMYZ4upVnx6NC+Ll26EHXeIPU+ffqQm5sLQGhoKAAnz5tn9uTJk459oaGhfPvtt9X2V1ZW8sMPPzjanM9sNhMQEFDt1Sykpno2SJlMMGKE584vItJEGrPSQ8eOrqtDRERaLo8GqaFDh3Lo0KFq2w4fPkyPHj0ACA8PJzQ0lNTUVMf+4uJiPvvsM2JiYgCIiYmhsLCQ9PR0R5u0tDSsVivXXnttE1yFCxUVOdX8MPAocDMw6OzXR89ubxAvLxg/vqG9RURajGuuaXhf/b1JRETAw0Fq6tSp7Ny5k3nz5pGVlcUbb7zBihUrSEhIAMAwDBITE3n22Wf597//zRdffME999xD165duf322wH7HaxbbrmFBx54gF27dvHJJ5/w8MMP8/vf/75eM/Y1K4GB9Wq2F4gFrgIWAVuBzLNfF53dPvJsO6eMH6+B/yLSKrzwQsP6eXvr700iImLn0SD1s5/9jPfee48333yTfv368cwzz7Bo0SLuuusuR5s//elPTJkyhYkTJ/Kzn/2MkpIS1q9fj5+fn6PN66+/Tu/evYmNjeXnP/85w4YNY8WKFZ64pMaJjbUPr7uAVCAGe2gCsJy3v+r9lrPtUqmn9u1h0aL6thYRadH69GnY9OcTJujvTSIiYufRdaSai2azjlRBgX0R3srKWnfvxR6OSoH6/NBMgBnYAQy8WOMbb4StWy/WSkTkklFcDGFh9q/1MXSo/VHWxjxfJSIizV+LWEdKzhMcbP9zZx13paYB5dQvRAFYz7afXp/GbVvXStQiIgEBcPw49O598bbjxilEiYhIdQpSzc2iRTBsWI0wdRhIo+ZQvouxYB/ed+RiDUtLnTyyiEjLFxQEBw7A/v32SSTatrX/8+vlBZddZl+499tvYdUqhSgREamu9S1B3NyZzfCvf9mH+P30k2PzCsAL54MUZ/stBxZcqNGZMw04sojIpaFPH/sdJxERkfrSHanmKDGxWogC2EPDQhRn+6VfrJEW4hURERERqTcFqeamoAD+7/9qbHZuhamaCi+0UwvxioiIiIg4RUGquXnlFbBaa2yu3wpTdQu60E4txCsiIiIi4hQFqeamjkH6Q7A/69QQXkB0XTtNJi3EKyIiIiLiJAWp5qao9kF8E2ncM1IP1rbDZIIbbtBCvCIiIiIiTlKQam4Cax/EFwmMwPm7Ul7ASKDXuRsNA7y9YeJESEnRnL4iIiIiIk5SkGpuYmPr3PUC4Ev9f2ims+1rTHv+xBNw4gQsXaoQJSIiIiLSAApSzc24cfY7RrUYCHwAmLn4nSmvs+0+ONvP4fLLYc4cCA5ufK0iIiIiIq2UglRzExwM3brVuTsW2AHcfPb9+YGq6v3ws+1q3N8KDW10iSIiIiIirZ2CVHM0adIFdw8ENgGHgUTsoerqs18Tz27fyHl3oqp07OiaGkVEREREWjFvTxcgtZg4EZ5+GiwXnqevF7U8/3QhhqGFd0VEREREXEB3pJqj4GB44AH79OSupIV3RURERERcQkGquVq0CIYNc22YmjBBC++KiIiIiLiAglRzZTbb13iaONF+J6mxtPCuiIiIiIjLKEg1Z35+9rWe8vLgmWfsU5c7yzDswwQ3btSaUSIiIiIiLmLYbDabp4vwtOLiYgIDAykqKiIgIMDT5VxYQQG88gr8+9/w5Zfw00/27V5e9mGAhmH/786d4Xe/g0ce0ZpRIiIiIiL1VN9soCBFCwtSIiIiIiLiNvXNBhraJyIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJO8vbkyWfNmsXs2bOrbbvqqqs4ePAgAKWlpUyfPp233nqLsrIy4uLiWLJkCZ07d3a0z83NZfLkyWzevJl27dpx7733kpSUhLe3Ry9NROSSZLFYqKio8HQZ0gi+vr6YTPo7qohIY3k8bfTt25dNmzY53p8bgKZOnUpycjJvv/02gYGBPPzww9xxxx188skngP0D/bbbbiM0NJRPP/2UvLw87rnnHnx8fJg3b16TX4uIyKXKZrORn59PYWGhp0uRRjKZTISHh+Pr6+vpUkREWjSPBylvb29CQ0NrbC8qKmLVqlW88cYbjBgxAoBXX32VPn36sHPnTq677jo2bNjA/v372bRpE507d+bqq6/mmWeeYcaMGcyaNUsfEiIiLlIVokJCQmjbti2GYXi6JGkAq9XKiRMnyMvLo3v37vo5iog0gseD1JEjR+jatSt+fn7ExMSQlJRE9+7dSU9Pp6KigpEjRzra9u7dm+7du7Njxw6uu+46duzYQf/+/asN9YuLi2Py5Mns27ePQYMG1XrOsrIyysrKHO+Li4vdd4EiIi2cxWJxhKjLLrvM0+VIIwUHB3PixAkqKyvx8fHxdDkiIi2WRwdJX3vttaxevZr169ezdOlSsrOzueGGGzh16hT5+fn4+voSFBRUrU/nzp3Jz88H7H8hPTdEVe2v2leXpKQkAgMDHa+wsDDXXpiIyCWk6pmotm3bergScYWq0RoWi8XDlYiItGwevSN16623Ov57wIABXHvttfTo0YN//vOftGnTxm3nfeyxx5g2bZrjfXFxscKUiMhFaBjYpUE/RxER12hW0/YEBQURGRlJVlYWoaGhlJeX13iw+eTJk45nqkJDQzl58mSN/VX76mI2mwkICKj2EhERERERqa9mFaRKSko4evQoXbp0ITo6Gh8fH1JTUx37Dx06RG5uLjExMQDExMTwxRdf8O233zrabNy4kYCAAKKiopq8fhERqV1BATz3HIweDddea//6/PP27SIiIi2RR4PUo48+ytatW8nJyeHTTz/l17/+NV5eXvzP//wPgYGBjB8/nmnTprF582bS09O5//77iYmJ4brrrgNg9OjRREVFcffdd7N3715SUlJ44oknSEhIwGw2e/LSREQEKC2FSZOga1f4859h40bYtcv+9fHH7dsnT4Zz5v9pcoZh8P7773uuABERaZE8GqS+/vpr/ud//oerrrqK3/72t1x22WXs3LmT4OBgAP7617/yi1/8gvj4eG688UZCQ0N59913Hf29vLxYt24dXl5exMTEMHbsWO655x7mzJnjqUsSEZGzSkshLg5WroTKSrBaq++3Wu3bV6ywt3NHmMrPz2fKlClERERgNpsJCwtjzJgx1UY7uNKWLVswDMOt623NnTuX66+/nrZt29aYkElERJqORyebeOutty6438/Pj8WLF7N48eI62/To0YMPP/zQ1aWJiEgjTZ0K27fXDFDns1rh448hMRGWLnXd+XNychg6dChBQUHMnz+f/v37U1FRQUpKCgkJCRw8eNB1J3Mxm82GxWKptkh9lfLycu68805iYmJYtWqVB6oTERFoZs9IiYjIpaGgAF5++eIhqorVam//3Xeuq+Ghhx7CMAx27dpFfHw8kZGR9O3bl2nTprFz585a+9R2RykzMxPDMMjJyQHg+PHjjBkzhg4dOuDv70/fvn358MMPycnJYfjw4QB06NABwzC47777zl6flaSkJMLDw2nTpg0DBw7knXfeqXHejz76iOjoaMxmM9u3b6+1xtmzZzN16lT69+/f+G+SiIg0mMcX5BURkUvPK6/UP0RVsVph1SqYMaPx5//hhx9Yv349c+fOxd/fv8b+xgyJS0hIoLy8nG3btuHv78/+/ftp164dYWFhrF27lvj4eA4dOkRAQIBjKY+kpCRee+01li1bRq9evdi2bRtjx44lODiYm266yXHsmTNnsmDBAiIiIujQoUODaxQREfdTkBIREZdLTW1YkEpLc02QysrKwmaz0bt378Yf7Dy5ubnEx8c77ghFREQ49nXs2BGAkJAQR1grKytj3rx5bNq0yTHrbEREBNu3b2f58uXVgtScOXMYNWqUy2sWERHXU5ASERGXKypqWD9XzdFgs9lcc6BaPPLII0yePJkNGzYwcuRI4uPjGTBgQJ3ts7KyOHPmTI2AVF5ezqBBg6ptGzJkiFtqFhER11OQEhERlwsMbFg/V01C16tXLwzDcHpCCZPJ/ujwuUGsoqKiWpsJEyYQFxdHcnIyGzZsICkpiYULFzJlypRaj1lSUgJAcnIy3bp1q7bv/KU6ahuGKCIizZMmmxAREZeLjQWTk58wJhOMGOGa83fs2JG4uDgWL17M6dOna+yva3ryquU38vLyHNsyMzNrtAsLC2PSpEm8++67TJ8+nZUrVwLg6+sLgMVicbSNiorCbDaTm5tLz549q73CwsIaeokiIuJhClIiIuJy48Y5H6S8vGD8eNfVsHjxYiwWC9dccw1r167lyJEjHDhwgBdffNHxrNL5qsLNrFmzOHLkCMnJySxcuLBam8TERFJSUsjOziYjI4PNmzfTp08fwL4kh2EYrFu3joKCAkpKSmjfvj2PPvooU6dOZc2aNRw9epSMjAxeeukl1qxZ4/R15ebmkpmZSW5uLhaLhczMTDIzMx13vkREpGkoSImIiMsFB8OECfUPUyaTPUR16uS6GiIiIsjIyGD48OFMnz6dfv36MWrUKFJTU1lax4JVPj4+vPnmmxw8eJABAwbw/PPP8+yzz1ZrY7FYSEhIoE+fPtxyyy1ERkayZMkSALp168bs2bOZOXMmnTt35uGHHwbgmWee4cknnyQpKcnRLzk5mfDwcKev66mnnmLQoEE8/fTTlJSUMGjQIAYNGsSePXucPpaIiDScYXPnE7ktRHFxMYGBgRQVFREQEODpckREmpXS0lKys7MJDw/Hz8+v3v3KymD06IsvymsywQ03QEoKnPfIkLhBQ3+eIiKtRX2zge5IiYiIW5jN9nA0cSJ4e9e8O2Uy2bdPnKgQJSIiLY9m7RMREbfx84OlS2HOHPsivWlp9inOg4LsE0uMG2cfBigiItLSKEiJiIjbBQfbF9p1xWK7IiIizYGG9omIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkRETE/QoK4Lnn7Cv0Xnut/evzz9u3e5hhGLz//vueLkNERFoYBSkREXGf0lKYNAm6doU//xk2boRdu+xfH3/cvn3yZCgrc8vp8/PzmTJlChEREZjNZsLCwhgzZgypqaluOd+WLVswDIPCwkK3HD8nJ4fx48cTHh5OmzZtuPLKK3n66acpLy93y/lERKRuWkdKRETco7QU4uJg+3awWmvut1rtrxUr4MABSEkBs9llp8/JyWHo0KEEBQUxf/58+vfvT0VFBSkpKSQkJHDw4EGXncvVbDYbFosFb+/qH9MHDx7EarWyfPlyevbsyZdffskDDzzA6dOnWbBggYeqFRFpnXRHSkRE3GPq1LpD1LmsVvj4Y0hMdOnpH3roIQzDYNeuXcTHxxMZGUnfvn2ZNm0aO3furLVPbXeUMjMzMQyDnJwcAI4fP86YMWPo0KED/v7+9O3blw8//JCcnByGDx8OQIcOHTAMg/vuu+/sJVpJSkpy3EkaOHAg77zzTo3zfvTRR0RHR2M2m9m+fXuN+m655RZeffVVRo8eTUREBL/85S959NFHeffdd13zTRMRkXrTHSkREXG9ggJ4+eWLh6gqVqu9/TPPQKdOjT79Dz/8wPr165k7dy7+/v419gcFBTX42AkJCZSXl7Nt2zb8/f3Zv38/7dq1IywsjLVr1xIfH8+hQ4cICAigTZs2ACQlJfHaa6+xbNkyevXqxbZt2xg7dizBwcHcdNNNjmPPnDmTBQsWEBERQYcOHepVT1FRER07dmzw9YiISMMoSImIiOu98kr9Q1QVqxVWrYIZMxp9+qysLGw2G7179270sc6Xm5tLfHw8/fv3ByAiIsKxryrQhISEOMJaWVkZ8+bNY9OmTcTExDj6bN++neXLl1cLUnPmzGHUqFH1riUrK4uXXnpJw/pERDxAQUpERFwvNbVhQSotzSVBymazNfoYdXnkkUeYPHkyGzZsYOTIkcTHxzNgwIA622dlZXHmzJkaAam8vJxBgwZV2zZkyJB61/HNN99wyy23cOedd/LAAw84dxEiItJoClIiIuJ6RUUN6+ei2e569eqFYRhOTyhhMtkfHT43iFVUVFRrM2HCBOLi4khOTmbDhg0kJSWxcOFCpkyZUusxS0pKAEhOTqZbt27V9pnPm1yjtmGItTlx4gTDhw/n+uuvZ8WKFfXqIyIirqXJJkRExPUCAxvWrxHPLp2rY8eOxMXFsXjxYk6fPl1jf13TkwcHBwOQl5fn2JaZmVmjXVhYGJMmTeLdd99l+vTprFy5EgBfX18ALBaLo21UVBRms5nc3Fx69uxZ7RUWFub0tX3zzTfcfPPNREdH8+qrrzrCn4iINC396ysiIq4XGwvO/oJvMsGIES4rYfHixVgsFq655hrWrl3LkSNHOHDgAC+++KLjWaXzVYWbWbNmceTIEZKTk1m4cGG1NomJiaSkpJCdnU1GRgabN2+mT58+APTo0QPDMFi3bh0FBQWUlJTQvn17Hn30UaZOncqaNWs4evQoGRkZvPTSS6xZs8apa6oKUd27d2fBggUUFBSQn59Pfn5+w75JIiLSYApSIiLieuPGOR+kvLxg/HiXlRAREUFGRgbDhw9n+vTp9OvXj1GjRpGamsrSpUtr7ePj48Obb77JwYMHGTBgAM8//zzPPvtstTYWi4WEhAT69OnDLbfcQmRkJEuWLAGgW7duzJ49m5kzZ9K5c2cefvhhAJ555hmefPJJkpKSHP2Sk5MJDw936po2btxIVlYWqampXH755XTp0sXxEhGRpmXY3PlEbgtRXFxMYGAgRUVFBAQEeLocEZFmpbS0lOzsbMLDw/Hz86t/x8mT7Yvt1mfSCZMJJk6EOgKOuE6Df54iIq1EfbOB7kiJiIh7LFoEw4Zd/M6UyQQ33GBvLyIi0kIoSImIiHuYzZCSYr/T5O1dM1CZTPbtEyfa2503g52IiEhzpunPRUTEffz87MP15syxL9Kblmaf4jwoyD6xxLhxcHamPBERkZZEQUpERNwvONi+0K4LFtsVERFpDjS0T0RERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpFn7RETE7QpOF7Dq81WkZadRVFpEoF8gseGxjBs0jmB/z05/bhgG7733HrfffrtH6xARkZZFd6RERMRtSitLmbRuEl1f6Mqf0/7MxmMb2XViFxuPbeTxtMfp+kJXJidPpqyyzC3nz8/PZ8qUKURERGA2mwkLC2PMmDGkpqa65XxbtmzBMAwKCwvdcnyAX/7yl3Tv3h0/Pz+6dOnC3XffzYkTJ9x2PhERqZ2ClIiIuEVpZSlxr8WxMmMlldZKrDZrtf1Wm5VKayUr0lcQ91qcy8NUTk4O0dHRpKWlMX/+fL744gvWr1/P8OHDSUhIcOm5XM1ms1FZWVnrvuHDh/PPf/6TQ4cOsXbtWo4ePcpvfvObJq5QREQUpERExC2mpkxle+72GgHqfFablY9zPyYxJdGl53/ooYcwDINdu3YRHx9PZGQkffv2Zdq0aezcubPWPrXdUcrMzMQwDHJycgA4fvw4Y8aMoUOHDvj7+9O3b18+/PBDcnJyGD58OAAdOnTAMAzuu+8++zVarSQlJREeHk6bNm0YOHAg77zzTo3zfvTRR0RHR2M2m9m+fXutNU6dOpXrrruOHj16cP311zNz5kx27txJRUVF479pIiJSb3pG6lJQXgH530HhKai0gLcXBLWH0E7g6+Pp6kSkFSo4XcDLGS9fNERVsdqsvJzxMs8Mf4ZObTs1+vw//PAD69evZ+7cufj7+9fYHxQU1OBjJyQkUF5ezrZt2/D392f//v20a9eOsLAw1q5dS3x8PIcOHSIgIIA2bdoAkJSUxGuvvcayZcvo1asX27ZtY+zYsQQHB3PTTTc5jj1z5kwWLFhAREQEHTp0qNd1vv7661x//fX4+OjfexGRpqQg1VKc/gmOfgWnToPVBtjAZAKbDSy1/KLyYzHkfAOhwdAzzN5WRKSJvPL5K/UOUVWsNiurMlYxY9iMRp8/KysLm81G7969G32s8+Xm5hIfH0///v0BiIiIcOzr2LEjACEhIY6wVlZWxrx589i0aRMxMTGOPtu3b2f58uXVgtScOXMYNWrURWuYMWMGf//73zlz5gzXXXcd69atc9XliYhIPem36+aushJ2fQF79tnDUaUFrFZ7mKq01B6iqtiAvAL4z2F7HxGRJpKandqgIJWWneaS89tsNpccpzaPPPIIzz77LEOHDuXpp5/mP//5zwXbZ2VlcebMGUaNGkW7du0cr3/84x8cPXq0WtshQ4bUq4Y//vGPfP7552zYsAEvLy/uuecet16ziIjUpDtSzVllJez8z4XDUn0UlUDWVxDZwzV1iYhcRFFpUYP6FZYVuuT8vXr1wjAMDh486FQ/09m79+eGkvOfPZowYQJxcXEkJyezYcMGkpKSWLhwIVOmTKn1mCUlJQAkJyfTrVu3avvMZnO197UNQ6xNp06d6NSpE5GRkfTp04ewsDB27tzpuOMlIiLupztSzVnGwcaHqCr5BaAHkUWkiQT6BTaoX5A5yCXn79ixI3FxcSxevJjTp0/X2F/X9OTBwfY1rfLy8hzbMjMza7QLCwtj0qRJvPvuu0yfPp2VK1cC4OvrC4DFYnG0jYqKwmw2k5ubS8+ePau9wsLCGnqJDtazIw7KytwzhbyIiNROQaq5Ov0T/FTquuPZgLzvXHc8EZELiA2PxWQ49xFjMkyMCB/hshoWL16MxWLhmmuuYe3atRw5coQDBw7w4osv1nnnpirczJo1iyNHjpCcnMzChQurtUlMTCQlJYXs7GwyMjLYvHkzffr0AaBHjx4YhsG6desoKCigpKSE9u3b8+ijjzJ16lTWrFnD0aNHycjI4KWXXmLNmjVOXdNnn33G3//+dzIzMzl+/DhpaWn8z//8D1deeaXuRomINDEFqebq6FeuP2bhKdcfU0SkFuMGjXM6SHkZXowfPN5lNURERJCRkcHw4cOZPn06/fr1Y9SoUaSmprJ06dJa+/j4+PDmm29y8OBBBgwYwPPPP8+zzz5brY3FYiEhIYE+ffpwyy23EBkZyZIlSwDo1q0bs2fPZubMmXTu3JmHH34YgGeeeYYnn3ySpKQkR7/k5GTCw8Oduqa2bdvy7rvvEhsby1VXXcX48eMZMGAAW7durTFMUERE3MuwNZOnU5977jkee+wx/vCHP7Bo0SIASktLmT59Om+99RZlZWXExcWxZMkSOnfu7OiXm5vL5MmT2bx5M+3atePee+8lKSkJb+/6P/5VXFxMYGAgRUVFBAQEuPrSGuaTz+2TSbhSe38Y3Me1xxSRS15paSnZ2dmEh4fj5+dX736TkyezIn1FvSadMBkmJkZPZOlttQcccZ2G/jxFRFqL+maDZnFHavfu3SxfvpwBAwZU2z516lQ++OAD3n77bbZu3cqJEye44447HPstFgu33XYb5eXlfPrpp6xZs4bVq1fz1FNPNfUluJbV6voQBfb1pUREmsiiuEUM6z7sonemTIaJG7rfwKK4RU1TmIiIiAt4PEiVlJRw1113sXLlymqLDxYVFbFq1SpeeOEFRowYQXR0NK+++iqffvqpY0X6DRs2sH//fl577TWuvvpqbr31Vp555hkWL15MeXm5py6pcapm6nOHoPbuOa6ISC3M3mZSxqYwMXoi3ibvGoHKZJjwNnkzMXoiKWNTMHtraJqIiLQcHg9SCQkJ3HbbbYwcObLa9vT0dCoqKqpt7927N927d2fHjh0A7Nixg/79+1cb6hcXF0dxcTH79u2r85xlZWUUFxdXezULVivs+hIqKl1/bMOALp1cf1wRkQvw8/Zj6W1LOTHtBPNGzGN0xGiu6XYNoyNGM2/EPE5MO8HS25YqRImISIvj0XWk3nrrLTIyMti9e3eNffn5+fj6+jpWhq/SuXNn8vPzHW3ODVFV+6v21SUpKYnZs2c3sno3OHTcPSEKILQT+Pi459giIhcR7B/MjGEzmDFshqdLERERcQmP3ZH66quv+MMf/sDrr7/e5A+7PvbYYxQVFTleX33lhhnynFVeAd9+755jB7aDno1fq0REREREROw8FqTS09P59ttvGTx4MN7e3nh7e7N161ZefPFFvL296dy5M+Xl5TUWTTx58iShoaEAhIaGcvLkyRr7q/bVxWw2ExAQUO3lcfluWOPJALoEw4BIMHl8FKeIiIiIyCXDY79dx8bG8sUXX5CZmel4DRkyhLvuusvx3z4+PqSmpjr6HDp0iNzcXMeigzExMXzxxRd8++23jjYbN24kICCAqKioJr+mRnHlGk8mA9qYISwUruiqECUiIiIi4mIee0aqffv29OvXr9o2f39/LrvsMsf28ePHM23aNDp27EhAQABTpkwhJiaG6667DoDRo0cTFRXF3XffzV/+8hfy8/N54oknSEhIaHkLE7pyunOrDX4qg9x8+CofQoPtQ/sUqEREREREXMKjk01czF//+ldMJhPx8fHVFuSt4uXlxbp165g8eTIxMTH4+/tz7733MmfOHA9W3UDuWuPJBuQVwJmfNMRPRERERMRFDJvNZvN0EZ5W39WL3So3D7K/ce85ugRDZA/3nkNELjmlpaVkZ2cTHh7eiMmBCoBVQBpQBAQCscA4INg1hTaQYRi899573H777R6to6m45ucpInLpqm820O2J5uKyoHo3PfzVcR5dsoib//Aggybcxc1/eJBHlyzi8FfHL9wxvwAqKhpXp4iIU0qBSUBX4M/ARmDX2a+Pn90+GShzy9nz8/OZMmUKERERmM1mwsLCGDNmTLXnb11py5YtGIZRY6IkdygrK+Pqq6/GMAwyMzPdfj4REamuWQ/taxWsVsjKrdesfXuzDjNtySLSMnbjZfLCYv3vc1Xbv9jLwn++Tuzgn7HwoUQG9oyseQAbcKIAenR14QWIiNSlFIgDtgPWWvZbz75WAAeAFMB1z7fm5OQwdOhQgoKCmD9/Pv3796eiooKUlBQSEhI4ePCgy87lajabDYvFgrd33R/Tf/rTn+jatSt79+5twspERKSK7kh5ktUK/zkMed/ZQ84FpKbvIiZhHFsz0wGqhahz32/JTCcmYRyp6btqP9BX+fbzioi43VTqDlHnsgIfA4kuPftDDz2EYRjs2rWL+Ph4IiMj6du3L9OmTWPnzp219qntjlJmZiaGYZCTkwPA8ePHGTNmDB06dMDf35++ffvy4YcfkpOTw/DhwwHo0KEDhmFw33332a/QaiUpKYnw8HDatGnDwIEDeeedd2qc96OPPiI6Ohqz2cz27dvrvLaPPvqIDRs2sGDBgsZ9k0REpMF0R8qTsr6CopKLNtubdZgxj0+jtLyciz3SZrFaKSsvZ8zj09ix+JWad6YsVvt59ayUiLhVAfAyFw9RVaxn2z8DdGr02X/44QfWr1/P3Llz8ff3r7E/KCiowcdOSEigvLycbdu24e/vz/79+2nXrh1hYWGsXbuW+Ph4Dh06REBAAG3atAEgKSmJ1157jWXLltGrVy+2bdvG2LFjCQ4O5qabbnIce+bMmSxYsICIiAg6dOhQ6/lPnjzJAw88wPvvv0/btm0bfB0iIo1VcLqAVZ+vIi07jaLSIgL9AokNj2XcoHEE+3v2+demoCDlKeUV9meW6mHakkWUV1RcNERVsdpslFdUMH3JIja9sKRmg/wCCO8KPj7OVCwi4oRXqH+IqmLFPiHFjEafPSsrC5vNRu/evRt9rPPl5uYSHx9P//79AYiIiHDs69ixIwAhISGOsFZWVsa8efPYtGmTYx3EiIgItm/fzvLly6sFqTlz5jBq1Kg6z22z2bjvvvuYNGkSQ4YMcdwlExFpSqWVpSSuT2TV56uw2qxYbf/9937TsU08nvY4Xdt3JdQ/lA5tOlyy4UpBylPyLz6cD+wTS6Rl7Hb68BarldSM3Rz5Opdel3evvtOGfThh9y5OH1dEpH5SaViQSsMVQcqdE9I+8sgjTJ48mQ0bNjBy5Eji4+MZMGBAne2zsrI4c+ZMjYBUXl7OoEGDqm0bMmTIBc/90ksvcerUKR577LGGX4CISCOUVpYS91oc23O3VwtQVWzYsNlsfF38NV8Xfw1AanYqT2x+ggmDJ7AobhFm7xa23msd9IyUpxSeqlezFR+8h5epYWtMeZlMLP/3u406v4hIwxQ1sF+hS87eq1cvDMNwekIJ09m19s4NYhXnzXY6YcIEjh07xt13380XX3zBkCFDeOmll+o8ZkmJfQh3cnIymZmZjtf+/furPScF1DoM8VxpaWns2LEDs9mMt7c3PXv2BOwB7N57763/hYqINNDUlKl1hqi6WG1WKq2VrEhfQdxrcZRVumem1qamIOUplZaLtwH2HDpQY2KJ+rJYraQfruOXiHqeX0SkYQIb2C/IJWfv2LEjcXFxLF68mNOnT9fYX9f05MHB9mEneXl5jm21TS0eFhbGpEmTePfdd5k+fTorV64EwNfXFwCL5b//xkZFRWE2m8nNzaVnz57VXmFhYU5d14svvsjevXsdYezDDz8E4P/9v//H3LlznTqWiIizCk4X8HLGy06FqHNZbVY+zv2YxJRE1xbmIRra5yne9bvLVHT64pNRXEhhSR13nup5fhGRhonF+eF9JmCEyypYvHgxQ4cO5ZprrmHOnDkMGDCAyspKNm7cyNKlSzlw4ECNPlXhZtasWcydO5fDhw+zcOHCam0SExO59dZbiYyM5Mcff2Tz5s306dMHgB49emAYBuvWrePnP/85bdq0oX379jz66KNMnToVq9XKsGHDKCoq4pNPPiEgIMCpO0ndu1cfqt2uXTsArrzySi6//HJnv0UiIk555fNXGhyiqlhtVlakr+BAwQF+qvipRU9QoTtSnhLUvl7NAv3bNe407eo4Tz3PLyLSMONw/iPGCxjvsgoiIiLIyMhg+PDhTJ8+nX79+jFq1ChSU1NZunRprX18fHx48803OXjwIAMGDOD555/n2WefrdbGYrGQkJBAnz59uOWWW4iMjGTJEvvEPt26dWP27NnMnDmTzp078/DDDwPwzDPP8OSTT5KUlOTol5ycTHh4uMuuV0TE3VKzUxsdpMAeprYe38quE7vYeGwjM1NnErIghLC/hjF321wKTtdvQjZPM2zufCK3hSguLiYwMJCioiICAgKa5qTlFbBz70UnnHh0ySIWvfNWg4b3eZlMJP7mf1jwUGL1HYYBMQM0a5+I1EtpaSnZ2dmEh4fj5+fnRM/J2Bfbrc+HrgmYCNQecMR1Gv7zFJHW7tqV17LrRB1rlbqQt8nboxNT1Dcb6I6Up/j6QOjFb19OHPPrRj0j9eAv76i5I7STQpSINIFFwDAu/lFjAm44215ERJqrQL8GPP/6HZACvAosO/s15ez2OrSUiSkUpDypZxgEXnjoXmRYD0YM/hleJud+VF4mEyOjf1Zz6vPAdvbzioi4nRn7p+VE7I/knv/vmOns9oln210a0+GKiFyqYsNjMRn1/J00H1gD/B3YCRw/u+342fd/P7s/v/buVpuVbce3NeuJKRSkPMlkggGR0CUYjLqbvfBQIr4+PpiMCzQ697CGga+PDwsmJ1bf0SXYfj4nQ5mISMP5YR+udwKYB4wGrjn7dd7Z7UtRiBIRaf7GDRpXvyB1DHgZyDn7/vxHWare55xtd6z2w9iwsTJ9Jd+ducDtKw/Sb9SeZjJBZA+4biCEd4MOAdDe3z4ZRLu2AAzsGckH817A7Ot70TtTXiYTZl9fPpj3AgN7Rto3+vnCtf3t51GIEhGPCMa+0G4K8NnZrzPObhcRkZYg2D+YCYMnXDhM5QNvAJVcdC4AbIDlbPs67kxZbBZWZaxqQLXup9+qmwtfH+jexX7HaHAfGHgVREdBzEDo3oXYoTewY9lqbh5kX/Xey6v69OVVi/YOHxTNjsWvEBt9jf0uV5dg+Fk/8NNfe0VERESkcRbFLWJY92F1h6kU7OGovqrCVErdTdZnrXfigE1H60g1d74+9jtV4d0YeG1/Nt33e44cOcLy5ctJT0+nsLCQoMBAoiN78+CYO+jVLcy+RlRQe/ukEr6aVEJEREREXMPsbSZlbApTU6aybM+y6ju/A7IbcFDb2X7fA5fV3J31Y1YDDup+ClItUK9evViwYIGnyxARERGRVsjP24+lty3lQMEBth7f+t8d6dhHRDVkcSUD2APE1dz1U8VPDarT3TS0T0REREREnHZrz1urD/E7QcNCFGf75dW+q41PmwYe1L0UpERERERExGk1ZvFr7JJPpbVv7tmhZyMP7B4KUiIi4nZnrGfYXbqb9069x1vFb/HeqffYU7qHM9Yzni4NwzB4//33PV2GiEiLU2MWv8bObeZX++Zbet7SyAO7h4KUiIi4TaWtkrTTaawqWsWnP31KbmUuJy0nya3M5ZOfPmFV0SrSTqdRaat0y/nz8/OZMmUKERERmM1mwsLCGDNmDKmpqW4535YtWzAMg8LCQrccH+CKK67AMIxqr+eee85t5xMRuZBqs/h15YJro16QAXSpudnH5MP4weMbUaH7aLIJERFxi0pbJe+VvEdeZR62OgbNW7HyZfmX/GD9gdvb3Y634bqPpZycHIYOHUpQUBDz58+nf//+VFRUkJKSQkJCAgcPHnTZuVzNZrNhsVjw9q79+zFnzhweeOABx/v27ds3VWkiItWcO4vfyu9XYtnhzNzn57ABQ2puHj94PJ3admpUje7i9B2pn376ie3bt7N///4a+0pLS/nHP/7hksJERKRl23Zm2wVDVBUbNr6p/IZtZ7a59PwPPfQQhmGwa9cu4uPjiYyMpG/fvkybNo2dO3fW2qe2O0qZmZkYhkFOTg4Ax48fZ8yYMXTo0AF/f3/69u3Lhx9+SE5ODsOHDwegQ4cOGIbBfffdB4DVaiUpKYnw8HDatGnDwIEDeeedd2qc96OPPiI6Ohqz2cz27dvrvLb27dsTGhrqePn7+zfumyUi0ghVs/jlzcvjysFXOp8wDCCCGlOf+/v4syhukWuKdAOnLvPw4cP06dOHG2+8kf79+3PTTTeRl/ff6TWKioq4//77XV6kiIi0LGesZ9hXvu+iIepc+8r38ZPVNVPc/vDDD6xfv56EhIRaQ0ZQUFCDj52QkEBZWRnbtm3jiy++4Pnnn6ddu3aEhYWxdu1aAA4dOkReXh5/+9vfAEhKSuIf//gHy5YtY9++fUydOpWxY8eydevWaseeOXMmzz33HAcOHGDAgAF11vDcc89x2WWXMWjQIObPn09lpXuGRoqIOCPYP5i1r6yljbkNJlM9Y4YBeAGja+6KuTwGs3djH7xyH6fGUMyYMYN+/fqxZ88eCgsLSUxMZOjQoWzZsoXu3bu7q0YREWlh9pfvx4rVqT5WrOwr38cQv1rGdjgpKysLm81G7969G32s8+Xm5hIfH0///v0BiIiIcOzr2LEjACEhIY6wVlZWxrx589i0aRMxMTGOPtu3b2f58uXcdNNNjv5z5sxh1KhRFzz/I488wuDBg+nYsSOffvopjz32GHl5ebzwwguuvEwRkQYZOHAgH3zwAWPGjKG8vByL5QJD/apC1P8CoefvMhgZMdKNlTaeU0Hq008/ZdOmTXTq1IlOnTrxwQcf8NBDD3HDDTewefNmDS1oauUVkP8dFJ6CSgt4e0FQewjtBL4+nq5ORFqxryq+anA/VwQpm62hC5lc3COPPMLkyZPZsGEDI0eOJD4+/oJ3j7Kysjhz5kyNgFReXs6gQYOqbRsy5OLXPm3aNMd/DxgwAF9fXx588EGSkpIwm5vvX25FpPWIjY1lx44dTJ8+ndTUVLy8vKoHqqpFe8Ox34kKrXkMb5N3s51koopTQeqnn36q9uCrYRgsXbqUhx9+mJtuuok33njD5QVKLaxWyMq1h6jzf1f4sRhyvoHQYOgZBvW9rSoi4kJltoYtJtLQfufr1asXhmE4PaFE1VCUc4NYRUVFtTYTJkwgLi6O5ORkNmzYQFJSEgsXLmTKlCm1HrOkpASA5ORkunXrVm3f+cGnIX+QvPbaa6msrCQnJ4errrrK6f4iIq5UcLqAVZ+vIi07jVO/O8X1cdfjleHFkS+PkP9dvn2K8y7YJ5a4rPZjmAxTs55koopTQap3797s2bOHPn36VNv+97//HYBf/vKXrqtMame1wn8OQ1FJ3W1sQF4BnPkJBkQqTIlIkzMbDbsz0tB+5+vYsSNxcXEsXryYRx55pEZAKSwsrPU5qeDgYADy8vLo0KEDYJ9s4nxhYWFMmjSJSZMm8dhjj7Fy5UqmTJmCr68vQLW/vEZFRWE2m8nNza02jM9VMjMzMZlMhISEuPzYIiL1VVpZSuL6RFZ9vgqrzYrV9t/h3aY+JkxRJrq07UJ+Sf4Fn581GSZu6H5Ds55koopTv2H/+te/5s0336x139///nf+53/+x63DKQTI+urCIepcRSX29iIiTSzMJ6xJ+9Vm8eLFWCwWrrnmGtauXcuRI0c4cOAAL774ouNZpfP17NmTsLAwZs2axZEjR0hOTmbhwoXV2iQmJpKSkkJ2djYZGRls3rzZ8QfGHj16YBgG69ato6CggJKSEtq3b8+jjz7K1KlTWbNmDUePHiUjI4OXXnqJNWvWOHVNO3bsYNGiRezdu5djx47x+uuvOyauqAp+IiJNrbSylBFrRrAifQWV1spqIQrAarNSaa3k5OmThLYLxcvw+u8ivmeZDBPeJm8mRk8kZWxKs55koopTd6R+97vfMWPGjDr3L1myhCVLljS6KKlDeQXkFzjXJ78AwruCj56ZEpGmE+UbxY6fdjg14YQJE319+7qshoiICDIyMpg7dy7Tp08nLy+P4OBgoqOjWbp0aa19fHx8ePPNN5k8eTIDBgzgZz/7Gc8++yx33nmno43FYiEhIYGvv/6agIAAbrnlFv76178C0K1bN2bPns3MmTO5//77ueeee1i9ejXPPPMMwcHBJCUlcezYMYKCghg8eDCPP/64U9dkNpt56623mDVrFmVlZYSHhzN16tRqz02JiDSl0spSopdHs/+7mksjnc9qs3Ly9EnuHnA3fTr1IS07jcKyQoLMQYwIH8G4QeMI9g9ugqpdw7A5cQvJy8uLvLw8x/CB3/3ud7z44ot07tzZbQU2heLiYgIDAykqKiIgIMDT5dQtNw+yv3G+X3g36F7LUtEiIvVQWlpKdnY24eHh+Pn51btf2uk0viz/st5ToPf37c8I/xENLVPqqaE/TxGR85VWljJ8zXB2fl372nx18TZ5kzc9r9k+A1XfbODU0L7zM9eHH37I6dOnG1ahOK/wVNP2ExFphBvb3kgX7y4YGBdt2827Gze2vbEJqhIREVeZmjKVz77+zOl+VpuVVRmr3FBR09IsBC1J5QXm4XdHPxGRRvA2vPl1u1/Tz7cfpjo+bkyY6O/bn9vb3Y634dRocxER8aCC0wW8nPGyUwuvV7HarKRlp7mhqqbl1KeWYRgYhlFjmzQRb6+m7Sci0kjehjcj/EdwXZvr2F++n68qvqLMVobZMBPmE0aUbxRtTW09XaaIiDjplc9fqTGphDMKywpdV4yHOBWkbDYb9913n2Pdi9LSUiZNmlRjWtl3333XdRXKfwW1t68T1ZB+IiIe1NbUliF+Q1yy2K6IiHheanZqo4JUkDnIdcV4iFNB6t577632fuzYsS4tRi4itJN9sV1n7qAaBnRpng/yiYiIiEjLVFRa1OC+JsPEiPCWP7mQU0Hq1VdfdVcdUh++PhAabF9st75CO2nqcxERERFxqUC/wAb39TK8GD94vAur8QxNNtHS9AyDwHb1b2+zgbXht11FRERERM4XGx5bY1Hd+jAwGD94fLOd+twZClItjckEAyLtd5rqI/87+M9hhSkRERERcZlxg8Y1KEjFXB7DorhFri/IAxSkWiKTyf7sU30VlUDWV+6rR0RERERalWD/YCYMnuBUmIoKjiLt3jTM3mY3VtZ0tGhHS1ReAflOPCcF9vbhXfW8lIh4xOmC03y+6nOy07IpLSrFL9CP8NhwBo0bhH+w/8UP4EaGYfDee+9x++23e7QOEZGWZlHcIvYX7Gd77vYLzuBnYHDd5dex+d7Nl0yIAt2Rapnyv3Nu5j6wt8/7zh3ViIjUqbK0knWT1vFC1xdI+3MaxzYe48SuExzbeIy0x9N4oesLJE9OprKs0i3nz8/PZ8qUKURERGA2mwkLC2PMmDGkpqa65XxbtmzBMAwKCwvdcvwqycnJXHvttbRp04YOHTooBIqIR5i9zaSMTWFi9ES8Td417k6ZDBPeJm8eHPLgJReiQHekWqbCUw3v172La2sREalDZWklr8W9Ru72XGzWmn/9sVlt2Kw20lekU3CggLEpY/E2u+5jKScnh6FDhxIUFMT8+fPp378/FRUVpKSkkJCQwMGDB112Llez2WxYLBa8vWt+P9auXcsDDzzAvHnzGDFiBJWVlXz55ZceqFJEBPy8/Vh621Lm3DyHVz5/hbTsNArLCgkyBzEifATjBo0j2D/Y02W6he5ItUSVlqbtJyLSAClTU+oMUeeyWW3kfpxLSmKKS8//0EMPYRgGu3btIj4+nsjISPr27cu0adPYuXNnrX1qu6OUmZmJYRjk5OQAcPz4ccaMGUOHDh3w9/enb9++fPjhh+Tk5DB8+HAAOnTogGEY3HfffQBYrVaSkpIIDw+nTZs2DBw4kHfeeafGeT/66COio6Mxm81s3769Rn2VlZX84Q9/YP78+UyaNInIyEiioqL47W9/65pvmohIAwX7BzNj2AxS7k7hswmfkXJ3CjOGzbhkQxTojlTL5O3VtP1ERJx0uuA0GS9nXDREVbFZbWS8nMHwZ4bTtlPbRp//hx9+YP369cydOxd//5rPYAUFBTX42AkJCZSXl7Nt2zb8/f3Zv38/7dq1IywsjLVr1xIfH8+hQ4cICAigTZs2ACQlJfHaa6+xbNkyevXqxbZt2xg7dizBwcHcdNNNjmPPnDmTBQsWEBERQYcOHWqcOyMjg2+++QaTycSgQYPIz8/n6quvZv78+fTr16/B1yQiIs5TkGqJgtrDj8UN6yci0gQ+f+XzeoeoKjarjYxVGQybMazR58/KysJms9G7d+9GH+t8ubm5xMfH079/fwAiIiIc+zp27AhASEiII6yVlZUxb948Nm3aRExMjKPP9u3bWb58ebUgNWfOHEaNGlXnuY8dOwbArFmzeOGFF7jiiitYuHAhN998M4cPH3acX0RE3E9D+1qi0E7gxOzngH269C4tf+EzEWkZslOzGxSkstOyXXJ+m83ZGXnq75FHHuHZZ59l6NChPP300/znP/+5YPusrCzOnDnDqFGjaNeuneP1j3/8g6NHj1ZrO2TIkAsey3p2TcA///nPxMfHEx0dzauvvophGLz99tuNuzAREXGKglRL5OsDoU6ONw3tpKnPRaTJlBaVNqhfWWGZS87fq1cvDMNwekIJk8n+sXhuEKuoqKjWZsKECRw7doy7776bL774giFDhvDSSy/VecySkhLAPtNeZmam47V///5qz0kBtQ5DPFeXLvYJg6KiohzbzGYzERER5Obm1uMKRUTEVTwapJYuXcqAAQMICAggICCAmJgYPvroI8f+0tJSEhISuOyyy2jXrh3x8fGcPHmy2jFyc3O57bbbaNu2LSEhIfzxj3+kstI90+g2Kz3DILBd/doGtrO3FxFpIn6Bfg3qZw5yzdS4HTt2JC4ujsWLF3P69Oka++uanjw42P5Hqry8PMe2zMzMGu3CwsKYNGkS7777LtOnT2flypUA+Pr6AmCx/Hdyn6ioKMxmM7m5ufTs2bPaKyzMuX+bqyaiOHTokGNbRUUFOTk59OjRw6ljiYhI43g0SF1++eU899xzpKens2fPHkaMGMGvfvUr9u3bB8DUqVP54IMPePvtt9m6dSsnTpzgjjvucPS3WCzcdtttlJeX8+mnn7JmzRpWr17NU0895alLajomEwyIhC7BdQ/zM7DvHxBpby8i0kTCY8MxTM6NQTZMBuEjwl1Ww+LFi7FYLFxzzTWsXbuWI0eOcODAAV588UXHs0rnqwo3s2bN4siRIyQnJ7Nw4cJqbRITE0lJSSE7O5uMjAw2b95Mnz59AOjRoweGYbBu3ToKCgooKSmhffv2PProo0ydOpU1a9Zw9OhRMjIyeOmll1izZo1T1xQQEMCkSZN4+umn2bBhA4cOHWLy5MkA3HnnnQ34LomISEN5dLKJMWPGVHs/d+5cli5dys6dO7n88stZtWoVb7zxBiNGjADg1VdfpU+fPuzcuZPrrruODRs2sH//fjZt2kTnzp25+uqreeaZZ5gxYwazZs1y/GXwkmUyQWQPuKKrfZHewlP2Kc69vewTS4R2sg8DFBFpYoPGDWLzE5udek7K8DIYPH6wy2qIiIggIyODuXPnMn36dPLy8ggODiY6OpqlS5fW2sfHx4c333yTyZMnM2DAAH72s5/x7LPPVgspFouFhIQEvv76awICArjlllv461//CkC3bt2YPXs2M2fO5P777+eee+5h9erVPPPMMwQHB5OUlMSxY8cICgpi8ODBPP74405f1/z58/H29ubuu+/mp59+4tprryUtLa3WWf5ERMR9DJs7n8h1gsVi4e233+bee+/l888/Jz8/n9jYWH788cdq09T26NGDxMREpk6dylNPPcW///3vasMusrOzHR+egwYNqvVcZWVllJX9dxx+cXExYWFhFBUVERAQ4K5LdL/yCgUqEXG50tJSsrOzCQ8Px8+v/kP2kicnk74ivV5hyjAZRE+M5raltzWmVKmHhv48RURai+LiYgIDAy+aDTw+/fkXX3xBTEwMpaWltGvXjvfee4+oqCgyMzPx9fWtsdZH586dyc/PByA/P5/OnTvX2F+1ry5JSUnMnj3btRfiSVYrZOXaQ9T5v6/8WAw539gnp+gZpiF+ItJk4hbFUbC/4KKL8homg+43dCduUVwTViciItI4Hv+t+qqrriIzM5PPPvuMyZMnc++997J//363nvOxxx6jqKjI8frqq6/cej63slrhP4chr5YQVcUG5BXY252dOldExN28zd6MTRlL9MRoTN6mGs9MGSYDk7eJ6InRjE0Zi7fZ43/bExERqTePf2r5+vrSs2dPwD4b0e7du/nb3/7G7373O8rLyyksLKx2V+rkyZOEhoYCEBoayq5du6odr2pWv6o2tTGbzZjNrpkZyuOyvoKikvq1LSqxt4/UzE4i0jS8/by5belt3DznZj5/5XOy07IpKyzDHGQmfEQ4g8YNwj/4wlN+i4iINEceD1Lns1qtlJWVER0djY+PD6mpqcTHxwNw6NAhcnNzHbMtxcTEMHfuXL799ltCQkIA2LhxIwEBAdXW2LhklVdAfoFzffILILyr1pQSkSblH+zPsBnDGDZjmKdLERERcQmPBqnHHnuMW2+9le7du3Pq1CneeOMNtmzZQkpKCoGBgYwfP55p06bRsWNHAgICmDJlCjExMVx33XUAjB49mqioKO6++27+8pe/kJ+fzxNPPEFCQsKlc8fpQmp7JupibNiHAXbv4o6KRERERERaBY8GqW+//ZZ77rmHvLw8AgMDGTBgACkpKYwaNQqAv/71r5hMJuLj4ykrKyMuLo4lS5Y4+nt5ebFu3TomT55MTEwM/v7+3HvvvcyZM8dTl9S0Ck81vJ+ClIiIiIhIgzWb6c89qb5THDY7GQfg1Gnn+7X3h8F9XF+PiFySNF32pUU/TxGRC6tvNvD4rH3SCN5eTdtPREREREQABamWLah90/YTERERERFAQaplC+0ExsWbVWMY0KWTW8oREalTaQHsew7SRkPKtfav+5+3b/cwwzB4//33PV2GiIi0MApSLZmvD4QGO9cntJOmPheRpmMphV2T4L2usPfPkL8Rvt9l/5r5uH37rslgKXPL6fPz85kyZQoRERGYzWbCwsIYM2YMqampbjnfli1bMAyDwsJCtx6/ttfu3bvdck4REalds1tHSpzUMwzO/FS/RXkD29nbi4g0BUsppMVBwXbAWksDK9iskLUCig/A8BTwct3SFTk5OQwdOpSgoCDmz59P//79qaioICUlhYSEBA4ePOiyc7mazWbDYrHg7V39Y/r6668nLy+v2rYnn3yS1NRUhgwZ0pQlioi0eroj1dKZTDAgEroE1z3Mz8C+f0Ckvb2ISFNIn3qBEHUuK3z7MaQnuvT0Dz30EIZhsGvXLuLj44mMjKRv375MmzaNnTt31tqntjtKmZmZGIZBTk4OAMePH2fMmDF06NABf39/+vbty4cffkhOTg7Dhw8HoEOHDhiGwX333We/QquVpKQkwsPDadOmDQMHDuSdd96pcd6PPvqI6OhozGYz27dvr1Gfr68voaGhjtdll13Gv/71L+6//34Mw9mx3iIi0hi6I3UpMJkgsgdc0dW+SG/hKai02GfnC2pvH87nq+F8ItKESgvg6MtcPERVsdrbD3gG/Br/HOcPP/zA+vXrmTt3Lv7+/jX2BwUFNfjYCQkJlJeXs23bNvz9/dm/fz/t2rUjLCyMtWvXEh8fz6FDhwgICKBNmzYAJCUl8dprr7Fs2TJ69erFtm3bGDt2LMHBwdx0002OY8+cOZMFCxYQERFBhw4dLlrLv//9b77//nvuv//+Bl+PiIg0jILUpcTXx77QrhbbFRFPO/aKfdieM2xWOLYKomY0+vRZWVnYbDZ69+7d6GOdLzc3l/j4ePr37w9ARESEY1/Hjh0BCAkJcYS1srIy5s2bx6ZNm4iJiXH02b59O8uXL68WpObMmeNYlL4+Vq1aRVxcHJdffnljL0tERJykICUiIq6Xn0r970ZVsUJ+mkuClDvXmn/kkUeYPHkyGzZsYOTIkcTHxzNgwIA622dlZXHmzJkaAam8vJxBgwZV2+bMc05ff/01KSkp/POf/3TuAkRExCUUpERExPUqihrYr9Alp+/VqxeGYTg9oYTp7HOk5waxioqKam0mTJhAXFwcycnJbNiwgaSkJBYuXMiUKVNqPWZJiX0yoOTkZLp161Ztn9lcfXKN2oYh1uXVV1/lsssu45e//GW9+4iIiOto5gEREXE9n8AG9gtyyek7duxIXFwcixcv5vTp0zX21zU9eXCwfUmJc2fGy8zMrNEuLCyMSZMm8e677zJ9+nRWrlwJ2CeDALBYLI62UVFRmM1mcnNz6dmzZ7VXWFjDZlK12Wy8+uqr3HPPPfhoSQsREY9QkBIREdcLjcX5jxgThI5wWQmLFy/GYrFwzTXXsHbtWo4cOcKBAwd48cUXHc8qna8q3MyaNYsjR46QnJzMwoULq7VJTEwkJSWF7OxsMjIy2Lx5M3369AGgR48eGIbBunXrKCgooKSkhPbt2/Poo48ydepU1qxZw9GjR8nIyOCll15izZo1Dbq2tLQ0srOzmTBhQoP6i4hI4ylIiYiI60WMA8PJjxjDCyLGu66EiAgyMjIYPnw406dPp1+/fowaNYrU1FSWLl1aax8fHx/efPNNDh48yIABA3j++ed59tlnq7WxWCwkJCTQp08fbrnlFiIjI1myZAkA3bp1Y/bs2cycOZPOnTvz8MMPA/DMM8/w5JNPkpSU5OiXnJxMeHh4g65t1apVXH/99W6ZTENEROrHsLnzidwWori4mMDAQIqKiggICPB0OSIizUppaSnZ2dmEh4fj5+dX/467JtsX263XpBMm6DkRrqk94IjrNPjnKSLSStQ3G+iOlIiIuEf0IggexsU/akwQcoO9vYiISAuhICUiIu7hZYYRKfY7TYY3NT9yTPbtPSfC8BR7exERkRZC05+LiIj7ePnZh+sNmGNfpDc/zT7FuU+QfWKJiHHgF+zpKkVERJymICUiIu7nF2xfaNcFi+2KiIg0BxraJyIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERH3K6+A3Dz4z2HIOGD/mptn3+5hhmHw/vvve7oMERFpYRSkRETEfaxWOJwDO/dC9jfwYzGcOm3/mv2Nffvh4/Z2bpCfn8+UKVOIiIjAbDYTFhbGmDFjSE1Ndcv5tmzZgmEYFBYWuuX4AIcPH+ZXv/oVnTp1IiAggGHDhrF582a3nU9ERGqnICUiIu5htdrvPOV9B7Y62tiAvAJ7OxeHqZycHKKjo0lLS2P+/Pl88cUXrF+/nuHDh5OQkODSc7mazWajsrKy1n2/+MUvqKysJC0tjfT0dAYOHMgvfvEL8vPzm7hKEZHWTUFKRETcI+srKCqpX9uiEnt7F3rooYcwDINdu3YRHx9PZGQkffv2Zdq0aezcubPWPrXdUcrMzMQwDHJycgA4fvw4Y8aMoUOHDvj7+9O3b18+/PBDcnJyGD58OAAdOnTAMAzuu+8+AKxWK0lJSYSHh9OmTRsGDhzIO++8U+O8H330EdHR0ZjNZrZv316jvu+++44jR44wc+ZMBgwYQK9evXjuuec4c+YMX375pWu+cSIiUi/eni5AREQuQeUVkF/gXJ/8AgjvCj4+jT79Dz/8wPr165k7dy7+/v419gcFBTX42AkJCZSXl7Nt2zb8/f3Zv38/7dq1IywsjLVr1xIfH8+hQ4cICAigTZs2ACQlJfHaa6+xbNkyevXqxbZt2xg7dizBwcHcdNNNjmPPnDmTBQsWEBERQYcOHWqc+7LLLuOqq67iH//4B4MHD8ZsNrN8+XJCQkKIjo5u8DWJiIjzFKRERMT18i8wnK8uNuzDALt3afTps7KysNls9O7du9HHOl9ubi7x8fH0798fgIiICMe+jh07AhASEuIIa2VlZcybN49NmzYRExPj6LN9+3aWL19eLUjNmTOHUaNG1XluwzDYtGkTt99+O+3bt8dkMhESEsL69etrDV4iIuI+ClIiIuJ6haca3s8FQcpmczbF1d8jjzzC5MmT2bBhAyNHjiQ+Pp4BAwbU2T4rK4szZ87UCEjl5eUMGjSo2rYhQ4Zc8Nw2m42EhARCQkL4+OOPadOmDS+//DJjxoxh9+7ddOnS+O+diIjUj56REhER16u0NG2/8/Tq1QvDMDh48KBT/Uwm+8fiuUGsoqL6FO0TJkzg2LFj3H333XzxxRcMGTKEl156qc5jlpTYnxNLTk4mMzPT8dq/f3+156SAWochnistLY1169bx1ltvMXToUAYPHsySJUto06YNa9ascepaRUSkcRSkRETE9by9mrbfeTp27EhcXByLFy/m9OnTNfbXNT15cHAwAHl5eY5tmZmZNdqFhYUxadIk3n33XaZPn87KlSsB8PX1BcBi+W8gjIqKwmw2k5ubS8+ePau9wsLCnLquM2fOAP8NfFVMJhNWN00hLyIitVOQEhER1wtq37T9arF48WIsFgvXXHMNa9eu5ciRIxw4cIAXX3zR8azS+arCzaxZszhy5AjJycksXLiwWpvExERSUlLIzs4mIyODzZs306dPHwB69OiBYRisW7eOgoICSkpKaN++PY8++ihTp05lzZo1HD16lIyMDF566SWn7yLFxMTQoUMH7r33Xvbu3cvhw4f54x//SHZ2NrfddlvDvlEiItIgClIiIuJ6oZ3AcLKPYUCXTi4rISIigoyMDIYPH8706dPp168fo0aNIjU1laVLl9bax8fHhzfffJODBw8yYMAAnn/+eZ599tlqbSwWCwkJCfTp04dbbrmFyMhIlixZAkC3bt2YPXs2M2fOpHPnzjz88MMAPPPMMzz55JMkJSU5+iUnJxMeHu7UNXXq1In169dTUlLCiBEjGDJkCNu3b+df//oXAwcObMB3SUREGsqwufOJ3BaiuLiYwMBAioqKCAgI8HQ5IiLNSmlpKdnZ2YSHh+Pn51f/joeP2xfbra8uwRDZw/kCxSkN/nmKiLQS9c0GuiMlIiLu0TMMAtvVr21gO3t7ERGRFkJBSkRE3MNkggGR9jtNdQ3zM7DvHxBpby8iItJCaB0pERFxH5PJPlzviq72RXoLT9mnOPf2sk8sEdoJfH08XaWIiIjTFKRERMT9fH3sC+26YLFdERGR5kDjKERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SZNNiIiI2x0+fJgVK1awZ88eioqKCAwMZMiQIUycOJHIyEhPlyciIuI03ZESERG32bt3L7GxsVx11VUsWrSIrVu3kpmZydatW1m0aBFXXXUVI0eOZO/evZ4utdnYsmULhmFQWFhYZ5vVq1cTFBTUZDWJiEhNClIiIuIWqampxMTEsHXrVgAsFku1/VXvt2zZQkxMDKmpqW6pIz8/nylTphAREYHZbCYsLIwxY8a49Hw333wziYmJLjnW9ddfT15eHoGBgS45noiIuIeG9omIiMvt3buXMWPGUFpais1mu2Bbi8VCWVkZY8aMYceOHQwcONBldeTk5DB06FCCgoKYP38+/fv3p6KigpSUFBISEjh48KDLznUxNpsNi8WCt/eFP3p9fX0JDQ1toqpERKShdEdKRERcbtq0aZSXl180RFWxWq2Ul5czffp0l9bx0EMPYRgGu3btIj4+nsjISPr27cu0adPYuXMnAIWFhUyYMIHg4GACAgIYMWJEtaGGs2bN4uqrr+b//u//uOKKKwgMDOT3v/89p06dAuC+++5j69at/O1vf8MwDAzDICcnxzFE76OPPiI6Ohqz2cz27dspKyvjkUceISQkBD8/P4YNG8bu3bsd56ttaN/q1avp3r07bdu25de//jXff/99tevcu3cvw4cPp3379gQEBBAdHc2ePXtc+r0UEZHqFKRERMSlDh8+TFpaWo2hfBdjsVhITU3lyJEjLqnjhx9+YP369SQkJODv719jf9UzRnfeeSfffvstH330Eenp6QwePJjY2Fh++OEHR9ujR4/y/vvvs27dOtatW8fWrVt57rnnAPjb3/5GTEwMDzzwAHl5eeTl5REWFuboO3PmTJ577jkOHDjAgAED+NOf/sTatWtZs2YNGRkZ9OzZk7i4uGrnO9dnn33G+PHjefjhh8nMzGT48OE8++yz1drcddddXH755ezevZv09HRmzpyJj49PY7+FIiJyAR4NUklJSfzsZz+jffv2hISEcPvtt3Po0KFqbUpLS0lISOCyyy6jXbt2xMfHc/LkyWptcnNzue2222jbti0hISH88Y9/pLKysikvRUREzlqxYgVeXl4N6uvl5cXy5ctdUkdWVhY2m43evXvX2Wb79u3s2rWLt99+myFDhtCrVy8WLFhAUFAQ77zzjqOd1Wpl9erV9OvXjxtuuIG7777b8YxVYGAgvr6+tG3bltDQUEJDQ6td/5w5cxg1ahRXXnklZrOZpUuXMn/+fG699VaioqJYuXIlbdq0YdWqVbXW+Le//Y1bbrmFP/3pT0RGRvLII48QFxdXrU1ubi4jR46kd+/e9OrVizvvvNOlQyRFRKQmjwaprVu3kpCQwM6dO9m4cSMVFRWMHj2a06dPO9pMnTqVDz74gLfffputW7dy4sQJ7rjjDsd+i8XCbbfdRnl5OZ9++ilr1qxh9erVPPXUU564JBGRVm/Pnj1O342qYrFYSE9Pd0kd9RlWuHfvXkpKShx/rKt6ZWdnc/ToUUe7K664gvbt2zved+nShW+//bZedQwZMsTx30ePHqWiooKhQ4c6tvn4+HDNNddw4MCBWvsfOHCAa6+9ttq2mJiYau+nTZvGhAkTGDlyJM8991y12kVExD08OtnE+vXrq71fvXo1ISEhpKenc+ONN1JUVMSqVat44403GDFiBACvvvoqffr0YefOnVx33XVs2LCB/fv3s2nTJjp37szVV1/NM888w4wZM5g1axa+vr41zltWVkZZWZnjfXFxsXsvVESkFSkqKmpU/wtN++2MXr16YRjGBSeUKCkpoUuXLmzZsqXGvnOnFz9/mJxhGFit1nrVUduwQlebNWsW//u//0tycjIfffQRTz/9NG+99Ra//vWv3X5uEZHWqlk9I1X14duxY0cA0tPTqaioYOTIkY42vXv3pnv37uzYsQOAHTt20L9/fzp37uxoExcXR3FxMfv27av1PElJSQQGBjpe545lFxGRxmnstN2uWh+pY8eOxMXFsXjx4mojHaoUFhYyePBg8vPz8fb2pmfPntVenTp1qve5fH1963UX7sorr8TX15dPPvnEsa2iooLdu3cTFRVVa58+ffrw2WefVdtWNVHGuSIjI5k6dSobNmzgjjvu4NVXX613/SIi4rxmE6SsViuJiYkMHTqUfv36Afa1P3x9fWt8qHbu3Jn8/HxHm3NDVNX+qn21eeyxxygqKnK8vvrqKxdfjYhI6zVkyJBGPSMVHR3tsloWL16MxWLhmmuuYe3atRw5coQDBw7w4osvEhMTw8iRI4mJieH2229nw4YN5OTk8Omnn/LnP//ZqVnvrrjiCj777DNycnL47rvv6rxb5e/vz+TJk/njH//I+vXr2b9/Pw888ABnzpxh/PjxtfZ55JFHWL9+PQsWLODIkSP8/e9/rzai46effuLhhx9my5YtHD9+nE8++YTdu3fTp08f575ZIiLilGYTpBISEvjyyy9566233H4us9lMQEBAtZeIiLjGxIkTG/WM1IMPPuiyWiIiIsjIyGD48OFMnz6dfv36MWrUKFJTU1m6dCmGYfDhhx9y4403cv/99xMZGcnvf/97jh8/XuOPdBfy6KOP4uXlRVRUFMHBweTm5tbZ9rnnniM+Pp67776bwYMHk5WVRUpKCh06dKi1/XXXXcfKlSv529/+xsCBA9mwYQNPPPGEY7+Xlxfff/8999xzD5GRkfz2t7/l1ltvZfbs2fX/RomIiNMMW30X+XCjhx9+mH/9619s27aN8PBwx/a0tDRiY2P58ccfq92V6tGjB4mJiUydOpWnnnqKf//732RmZjr2Z2dnOz48Bw0adNHzFxcXExgYSFFRkUKViMh5SktLyc7OJjw8HD8/v3r1iY2NZevWrU4FKi8vL4YPH87GjRsbWqrUQ0N+niIirUl9s4FH70jZbDYefvhh3nvvPdLS0qqFKIDo6Gh8fHwcU8wCHDp0iNzcXMeMRTExMXzxxRfVZk/auHEjAQEBdY43FxER93rhhRfw9fXFZKrfx4zJZMLX15cFCxa4uTIRERHX8GiQSkhI4LXXXuONN96gffv25Ofnk5+fz08//QTYH1geP34806ZNY/PmzaSnp3P//fcTExPDddddB8Do0aOJiori7rvvZu/evaSkpPDEE0+QkJCA2Wz25OWJiLRaAwcO5IMPPsBsNl/0eSkvLy/MZjMffPCB1j4SEZEWw6NBaunSpRQVFXHzzTfTpUsXx+v//b//52jz17/+lV/84hfEx8dz4403EhoayrvvvuvY7+Xlxbp16/Dy8iImJoaxY8dyzz33MGfOHE9ckoiInBUbG8uOHTu4+eabAWoEqqr3w4cPZ8eOHcTGxjZ1iSIiIg3WLJ6R8jQ9IyUiUjdXPFNz5MgRli9fTnp6OoWFhQQFBREdHc2DDz5Ir169XFyxXIiekRIRubD6ZgOPLsgrIiItR2P+7tarVy89/9RM6O+nIiKu0WymPxcRkebJx8cHgDNnzni4EnGF8vJyoOZQSxERcY7uSImIyAV5eXkRFBTkmB21bdu2GIbh4aqkIaxWKwUFBbRt2xZvb/0KICLSGPpXVERELio0NBSg2lIT0jKZTCa6d++uMCwi0kgKUiIiclGGYdClSxdCQkKoqKjwdDnSCM6s7yUiInVTkBIRkXrz8vLSszUiIiJosgkRERERERGnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESd6eLkBERKR5KABWAWlAERAIxALjgGAP1iUiIs2RgpSIiLRypUAi9hBlPfuqkgo8AUwAFgHmJq5NRESaKw3tExGRVqwUiANWApVUD1GcfV8JLAeGA2VNWp2IiDRfClIiItKKTQW2UzNAnc8G7AAGozAlIiKgICUiIq1WAfAyFw9R59oPjEBhSkREFKRERKSVegXnQlSVHdifqRIRkdZMQUpERFqpjTQsSNmwPzP1jWvLERGRFkVBSkREWqFSYFcj+tuAG9AQPxGR1kvTn4uISCs0FTjVyGNkYx/it7TR1YiItAilBXB0FZxMg4oi8AmE0FiIGAd+rW+9PcNms9k8XYSnFRcXExgYSFFREQEBAZ4uR0RE3KoA6Ip9WvPG8gbygE4uOJaISDNlKYX0RHuIsp2/3p4JDBNcOQGiF4FXy19vr77ZQHekRESklXFukonDh2HFCtizB4qKIDAQhgyBiRMhMtKKfSHfGe4qVkTEsyylkBYHBXUtFWG1h6usFVB8AIanXBJhqj70jJSIiLQyqdQnSO3dC7GxcNVVsGgRbN0KmZn2r4sW2bePHGll795/ubleEREPSp96gRB1Lit8+7H9zlUroSAlIiKtTNFFW6SmQkyMPTQBWCzV91e937IFYmI+IzU11bUliog0B6UFcNSZ9fas9val37mzqmZDQUpERFqZwAvu3bsXxoyB0tKaAep8FguUlVkZM2YMe/fudWGNIiLNwLFXwHaRfwjPZ7PAsVXuqaeZUZASEZFWJvaCe6dNg/JyqO9UTFYrlJeXM336dBfUJiLSjOSnYl/uwRk2yE9zRzXNjoKUiIi0MuPq3HP4MKSlXfxO1PksFgupqakcOXKkkbWJiDQjpd82rF9ZgWvraKYUpEREpJVpT13Tla9YAV5eDTuql5cXy5cvb3hZIiLNiaUUig81rG9FiWtraaY0/fkl5Iz1DPvK9/F1xdeU2cowG2bCfMKI8o2iramtp8sTEWkGSoE4oPYHoffscf5uVBWLxUJ6enqDKxMRaVbSp4K1tGF9DdeW0lwpSF0CKm2VbDuzjX3l+7CeN6tKbmUuO37aQV/fvtzY9ka8DW8FLhFpxaYA2+rcW3TxCf0uqLCwsHEHEBFpDk5/BVmNuMPu3d51tTRjClItXKWtkvdK3iOvMg9bHQ8DWrHyRfkXHC4/jIFBKTX/ulBb4BIRubR8Bbx8wRaBF57Q76KCgoIadwAREU+zlMKmm3B+kolzmGsfPn2p0TNSLdy2M9suGKLOVUZZrSGqihUrX5Z/yfsl71Npq3RlmSIizcCdF20xZEjjnpGKjo5uWGcRkeYifSqczm7EAQwIHeGycpozBakWrGqIXn1CVH3ZsPFN5TdsO1P30BcRkZanANh10VYTJzbuGakHH3ywYZ1FRJoDxwK8jRQxvvHHaAE0fqsF21++v8YzUa6yr3wfMW1iaGNq45bji4g0rVeozzCVyEgYMQK2bnUuUHl5eTF8+HB69erV8BJFRDzt2CtwgVFJh/NgRRrsOQZFP0FgGxgSARNHQGSXJqyzmVCQasG+qvjKbce2YmVf+T6G+A1x2zlERJpOar1bvvACxMRAWZl9sd2LMZlM+Pr6smDBgkbUJyLSDJz4qNbNe4/DtNchbR94mcByzr+N2w/Dwg8hti8svAsG9jDg2CqImtFERXuOhva1YGW2MrceP7ci163HFxFpOvWfjm/gQPjgAzCbL/68lJeXF2azmQ8++ICBAwc2skYREQ8rOVpjU+qXEDMLth6wv7ec9wemqvdbDtjbpX5phfw0t5bZXChItWC++Lr1+PmV+Zp0QkQuEc5NxxcbCzt2wM0329+fH6iq3g8fPpwdO3YQGxvb+BJFRDyt8ky1t3uPw5iFUFpRM0Cdz2KFsgp7+72HTrixyOZDQaqFqrRVUmgtdOs5KqjQpBMicomIxdkVIgcOhE2b4PBhSEy0h6qrr7Z/TUyEw4e92LjxTd2JEpFLh3f19USnvQ7llWCr57xmVpu9/fSXW0eQ0jNSLdS2M9s4ZTvl9vNo0gkRuTSMA54AnL/L3qsX1P74kw1YBVz6zwGISCvR7ko48zVgn1gibZ/zh7BYITXjB44cOXLJT8CjO1It0BnrGb4s/7JJzlU16YSISMsWDExw8TGtQOt4DkBEWomutzr+c0WafWKJhvDy8mL58uUuKqr5UpBqgfaX73fp2lEX487ZAUVEms4iYJiLj1no4uNJ81cAPAeMBq49+/X5s9tFWriIcVTFgz3HLv5cVF0sFgvp6XtcV1czpSDVAjV1sHH37IAiIk3DDGzEPszPVYJceCxp3kqBSUBX4M/Y/1/adfbr42e3Twb0mSktmF8w9JwI2NeJaozCE00zesqTPBqktm3bxpgxY+jatSuGYfD+++9X22+z2Xjqqafo0qULbdq0YeTIkRw5cqRamx9++IG77rqLgIAAgoKCGD9+PCUlJU14FU2vvsHm26xv+deT/+KlMS8x/6b5vDTmJf715L/4Nutbp85nNswNKVNEpBnyw/5cUy4wuJHHMgEjGl2RtASlQBywEvtzduf/md56dvuKs+0UpqQFi14EnYYR2MjH44O8v4fS71xSUnPl0SB1+vRpBg4cyOLFi2vd/5e//IUXX3yRZcuW8dlnn+Hv709cXBylpaWONnfddRf79u1j48aNrFu3jm3btjFx4sSmugSPuFiw+ebLb1h8+2LmXTOPrcu2cvSTo3zzxTcc/eQoW5dtZd4181h8+2K++fKbep0vzCfMFWWLiDQjYUA68C32YVo34+ysfuAFjHdtWdJMTQW2UzNAnc8KfAwkursgEffxMkPsRoZcaTT8GSkTRF8BZF3az0kZNlt9JzR0L8MweO+997j99tsB+92orl27Mn36dB599FEAioqK6Ny5M6tXr+b3v/89Bw4cICoqit27dzNkyBAA1q9fz89//nO+/vprunbtWq9zFxcXExgYSFFREQEBAW65PlfaU7qHT376pNZ9h7ceZuX/rqSyvBKbpe4freFl4O3rzQNvPEDkTZF1tjNhYkLgBM3aJyKtwGTsdxTq81CACZgILHVrRdIcFGAftufMjI/eQB7QyS0ViTSFwy8GcdUf6r+YeY3+C6FXWAe4I88ezlqQ+maDZvuMVHZ2Nvn5+YwcOdKxLTAwkGuvvZYdO3YAsGPHDoKCghwhCmDkyJGYTCY+++yzOo9dVlZGcXFxtVdLEuUbhVHLX06/+fIbe4gqvXCIArBZbFSWVbLyf1de8M5UX9++ClEi0koswj4ZxcU+Gk3ADWfby6XvFeoXrs9lxT6EVKTliuwfw4i+zs/c52WCkX2hVyhQ8SPsetAt9TUHzTZI5efnA9C5c+dq2zt37uzYl5+fT0hISLX93t7edOzY0dGmNklJSQQGBjpeYWEta+haW1Nb+vn2q7H9/Sfet9+JqudNRpvVRmV5Jf968l+17u/m3Y0b297YqFpFRFoOM5CC/U6TNzU/Ik1nt088265l/YVVGiqVhgUpTY0vLdzgF3jhLvD1BlM9Rz6bDHv7BXedszF7Dey4DyyX3rODzTZIudNjjz1GUVGR4/XVVy1veu8b295IF68ujvffZn3LkW1HLnon6nw2i43DWw9TcPS/07YaGPT37c/t7W7H29CazSLSmvhhH653ApiHfWrra85+nXd2+1IUolqThg5tKnRlESJNL7APA/tH8cF0MPtc/M6Ul8ne7oPpMLDHeTuz/wGb4y65MNVsg1RoaCgAJ0+erLb95MmTjn2hoaF8+231GegqKyv54YcfHG1qYzabCQgIqPZqabwNb+5ofwd9ffsCsGPNDkwNfCLQ5GXi09Wf0oY2xPjFMCFwAiP8RyhEiUgrFgzMwH7n6bOzX2ec3S6tS2AD+wW5sggRzxi9g9hBgeyYBTf3sW86/9fNqvfD+8COWRBbc9AUYINvP4b0RLeV6gnNNkiFh4cTGhpKamqqY1txcTGfffYZMTExAMTExFBYWEh6erqjTVpaGlarlWuvvbbJa25q3oY3I/1H0senD7mZuVgbuGqa1WLlq71fEeIdwjVtrqGtqa2LKxUREWmpYnH+1yVNjS+XCN8A+FUOA/tHselx+wQSibfYQ9XVPexfE2+xb9/4eC13oqqxwtGXL6kp0T16y6GkpISsrCzH++zsbDIzM+nYsSPdu3cnMTGRZ599ll69ehEeHs6TTz5J165dHTP79enTh1tuuYUHHniAZcuWUVFRwcMPP8zvf//7es/Y19KdsZ7hUMUhSotLL974An4q+omTlScv3lBERKRVGQc8gXPPSWlqfLmE+AbBL/bBD3vplTKEBXc5M4PleWxWOLYKoma4rDxP8ugdqT179jBo0CAGDRoEwLRp0xg0aBBPPfUUAH/605+YMmUKEydO5Gc/+xklJSWsX78ePz8/xzFef/11evfuTWxsLD//+c8ZNmwYK1as8Mj1eML+8v1YseIX4HfxxhfQJrANlU5N7SoiItIaBAMTqP+vTCbsIUpTn8slpuNA6P90Iw9ihfxLZyIWj96Ruvnmmy84w5xhGMyZM4c5c+bU2aZjx4688cYb7iivRfiqwj5RRveru5O9M7tBw/tMXibCBobh7dn/HURERJqpRcB+Lr4or6bGl0tczwfhi9lga8Qf3ysKXVaOpzXbZ6Skfsps9tlPYu6NadQzUtffdz2dvTtfvLGIiEiro6nxRQDwC4YrnblDWwufIFdV43EKUi2c2bD/Yx3SM4ReN/bC8KrnRP9nGV4GkTdFEnxlMDe1uckdJYqIiFwCNDW+CADRiyB4GODc75x2Jgi9dCZiUZBq4cJ8/ruY8O3P3o63rzdGPVdNM0wG/7+9ew+Oqr77OP45m2U3WULCPSEhgSAozUVKiUIA4wCZImVUaMe2DGK8PD6DQgVxKFAfdeZxKLSdabVTS9Wn1c4UL3UGaMtYGRqokWnKJRAwwSIUChEJQWkuEEiy7O/5I2bLcktO3Oz1/ZrZGXPO72S+J1+z2Q/nd37H6XLq3ufvlSQlOr7cfVYAAMQ+lsZHnEtwS9O3SDkP2D/WSpBGxc5CLASpKJfrypXjizZm5mfq0TceldPt7PLKlJVgyel26tE3HlVmfqYkqaatptfrBQAAQJRLSJSKXpdyStX9K1MO6aZHpMTYWYiFIBXlPA6P8lx5sr74n/jmO2/W0i1LNWbqGEm66iG9nV+PmTpGS7cs1c133uzf17lwBQAAANCl21+WhtyhriOFQxp6R8e0wBjCMm0xoNhTrM99n+uU95SMjDLzM/X4xsd15p9n9LfX/6ba/bW60HhBSalJyhqXpckPTtaQm66egtC5cAUAAADQpc5pfpVPdjxs1/gUuLKlQ7IcHQtUTHihY3wMscyN1h+PE01NTUpNTVVjY6NSUlLCXU6PeI1X5S3lqmmrkc/WQwP/I9uZrbn95ga5MgAAAMS8i2eko7/peE5Ue0PH6nzp06VRD3es9hdFupsNCFKKjSDVqcXXoi3nt+iE94TtY6ckTVFhYmEvVAUAAABEh+5mA+6RijEeh0cz+870L0DRXQ45lOfK66WqAAAAgNhCkIpBVy5A0R15rjwlOZJ6sSoAAAAgdhCkYlSxp1jDnMO6FaYynZkq9hSHoCoAAAAgNhCkYpTTcmpu8lzlu/KvO83PIYcKXAWakzxHTosFHAEAAIDu4tNzDHNaTk3vO12TkibpYNtB1bbXqtW0ym25ldUnS7muXHkcnnCXCQAAAEQdglQc8Dg8KkwsZEU+AAAAfDkXz0j//LV0epvU3ij1SZXSZ0TlMudfFkEKAAAAwI1duihVLu0IUVc+eLeuTNr/PzH74N3rIUgBAAAAuL5LF6VtM6UzOxQQoPx8HeHqyCtS00fStC1xEaZYbAIAAADA9VU+eYMQdTmfVP9Bx5WrOECQAgAAAHBtF89I//w/dR2iOvk6xl/8rDerighM7YtRLb4W1bTV6JP2T1ipDwAAAD1z9Ddf3BNlg/FJR38t5a7onZoiBEEqxniNV+Ut5appq5Hvin85OOE9oYoLFcpz5anYU8yzowAAAHBjdWXq/tWoTj6pbhtBCtHDa7zaeG6jTnlPychcc4xPPlW3Veus7ywP4gUAAMCNtTf28LiGoJYRibhHKoaUt5TfMER1MjI66T2p8pbyEFUGAACAqNQntYfH9Q9qGZGIIBUjPvd+ruq26i5D1OVq2mp0wXehF6sCAABAVEufIfuRwSGlT++NaiIKQSrKeY1X285v0/rm9bZClNQxza+mraaXKgMAAEDUG/WwZNmMDFaCNOqR3qknghCkoljnPVF2r0Rdrra9NshVAQAAIGYkDpFu+i91PzY4pJsekRIH92ZVEYEgFcW6e0/UjbSa1iBWBAAAgJgz4QVpyFR1HR0c0tA7OsbHAYJUlOp8TtSXCVGS5LbcQaoIAAAAMSnBLU3fIo3+b8ly6uoI4ejYPvq/pWlbOsbHAda+jlIH2w5e9ZyonsjqkxWEagAAABDTEhKl29dJt/5vx0N667Z1LHHep3/HwhKjHu6YBhhHCFJRKhj3NjnkUJ4rLwjVAAAAIC4kDul40G6MP2y3O5jaF6WCcW9TnitPSY6kIFQDAAAAxBeCVJT6svc2ZTozVewpDlI1AAAAQHwhSEWpnt7bZMlSgatAc5LnyGkxsxMAAADoCYJUlMp15cphs32WLN3f735N7zudEAUAAAB8CQSpKOVxeJTnypMlq9vH5LvyNdA5sBerAgAAAOIDQSqKFXuKNcw5rFthinuiAAAAgOAhSEUxp+XU3OS5ynflX3ean0MO7okCAAAAgoxP1lHOaTk1ve90TUqapINtB1XbXqtW0yq35VZWnyzlunLlcXjCXSYAAAAQUwhSMcLj8KgwsVCFiYXhLgUAAACIeUztAwAAAACbCFIAAAAAYBNBCgAAAABsIkgBAAAAgE0EKQAAAACwiSAFAAAAADYRpAAAAADAJoIUAAAAANhEkAIAAAAAmwhSAAAAAGATQQoAAAAAbIqZIPXSSy9p5MiRSkxM1MSJE7Vr165wlwQAAAAgRsVEkHr77be1bNkyPffcc9q7d6/GjRunmTNnqr6+PtylAQAAAIhBljHGhLuIL2vixIm67bbb9Itf/EKS5PP5lJWVpe9973tauXJll8c3NTUpNTVVjY2NSklJ6e1yQ67F16Kathp90v6JWk2r3JZbWX2ylOvKlcfhCXd5AAAAQMTobjZwhrCmXtHW1qbKykqtWrXKv83hcKikpEQVFRXXPKa1tVWtra3+r5uamnq9znDwGq/KW8pV01Yjn3wB+054T6jiQoXyXHkq9hTLaUX9/woAAABAyET91L7PPvtMly5dUlpaWsD2tLQ01dXVXfOYNWvWKDU11f/KysoKRakh5TVebTy3UdVt1VeFqE4++VTdVq1N5zbJa7whrhAAAACIXlEfpHpi1apVamxs9L9qa2vDXVLQlbeU65T3lIxuPHPTyOik96TKW8pDVBkAAAAQ/aJ+PtfgwYOVkJCg06dPB2w/ffq00tPTr3mM2+2W2+0ORXlh0XlPVFch6nI1bTUqSipSkiOpFysDAAAAYkPUX5FyuVyaMGGCysrK/Nt8Pp/KyspUVFQUxsrC52DbwetO57sen3yqaavppYoAAACA2BL1QUqSli1bpldffVW//e1v9dFHH+mxxx7T+fPn9dBDD4W7tLCobe/ZVMWeHgcAAADEm6if2idJ3/nOd3TmzBk9++yzqqur01e/+lW99957Vy1AES9aTWvXg4J4HAAAABBvYiJISdLixYu1ePHicJcREdxWz+7/6ulxAAAAQLyJial9CJTVp2fLuff0OAAAACDeEKRiUK4rVw6brXXIoTxXXi9VBAAAAMQWglQM8jg8ynPlyZLV7WPyXHksfQ4AAAB0E0EqRhV7ijXMOaxbYSrTmaliT3EIqgIAAABiA0EqRjktp+Ymz1W+K/+60/wccqjAVaA5yXPktGJm3REAAACg1/HpOYY5Laem952uSUmTdLDtoGrba9VqWuW23Mrqk6VcV648Dk+4ywQAAACiDkEqDngcHhUmFqowsTDcpQAAAAAxgal9AAAAAGATQQoAAAAAbCJIAQAAAIBNBCkAAAAAsIkgBQAAAAA2EaQAAAAAwCaCFAAAAADYRJACAAAAAJsIUgAAAABgE0EKAAAAAGwiSAEAAACATc5wFxAJjDGSpKampjBXAgAAACCcOjNBZ0a4HoKUpObmZklSVlZWmCsBAAAAEAmam5uVmpp63f2W6SpqxQGfz6dPP/1U/fr1k2VZ4S4n7jU1NSkrK0u1tbVKSUkJdzkQPYlU9CXy0JPIQ08iDz2JPPQkkDFGzc3NysjIkMNx/TuhuCIlyeFwaPjw4eEuA1dISUnhlznC0JPIRF8iDz2JPPQk8tCTyENP/uNGV6I6sdgEAAAAANhEkAIAAAAAmwhSiDhut1vPPfec3G53uEvBF+hJZKIvkYeeRB56EnnoSeShJz3DYhMAAAAAYBNXpAAAAADAJoIUAAAAANhEkAIAAAAAmwhSAAAAAGATQQohsWbNGt12223q16+fhg4dqjlz5ujQoUMBYy5evKhFixZp0KBBSk5O1re+9S2dPn06YMyJEyc0e/ZseTweDR06VMuXL5fX6w3lqcSstWvXyrIsLV261L+NnoTeyZMndf/992vQoEFKSkpSQUGB9uzZ499vjNGzzz6rYcOGKSkpSSUlJTp8+HDA9zh79qzmz5+vlJQU9e/fX4888ojOnTsX6lOJCZcuXdIzzzyjnJwcJSUl6aabbtLzzz+vy9dpoie9r7y8XHfffbcyMjJkWZY2bdoUsD9YPThw4IDuuOMOJSYmKisrSz/+8Y97+9Si1o160t7erhUrVqigoEB9+/ZVRkaGHnjgAX366acB34OeBFdXvyeXW7hwoSzL0gsvvBCwnZ7YZIAQmDlzpnnttddMdXW1qaqqMt/4xjdMdna2OXfunH/MwoULTVZWlikrKzN79uwxkyZNMpMnT/bv93q9Jj8/35SUlJh9+/aZd9991wwePNisWrUqHKcUU3bt2mVGjhxpbr31VrNkyRL/dnoSWmfPnjUjRowwDz74oNm5c6c5evSo2bJlizly5Ih/zNq1a01qaqrZtGmT2b9/v7nnnntMTk6OuXDhgn/MXXfdZcaNG2f+/ve/mw8++MCMHj3azJs3LxynFPVWr15tBg0aZDZv3myOHTtm3nnnHZOcnGxefPFF/xh60vveffdd8/TTT5sNGzYYSWbjxo0B+4PRg8bGRpOWlmbmz59vqqurzZtvvmmSkpLMyy+/HKrTjCo36klDQ4MpKSkxb7/9tvnHP/5hKioqzO23324mTJgQ8D3oSXB19XvSacOGDWbcuHEmIyPD/OxnPwvYR0/sIUghLOrr640k8/777xtjOt50+/TpY9555x3/mI8++shIMhUVFcaYjjcIh8Nh6urq/GPWrVtnUlJSTGtra2hPIIY0NzebMWPGmK1bt5o777zTH6ToSeitWLHCTJ069br7fT6fSU9PNz/5yU/82xoaGozb7TZvvvmmMcaYgwcPGklm9+7d/jF//vOfjWVZ5uTJk71XfIyaPXu2efjhhwO2ffOb3zTz5883xtCTcLjyA2KwevDLX/7SDBgwIOC9a8WKFeaWW27p5TOKfjf60N5p165dRpI5fvy4MYae9Lbr9eSTTz4xmZmZprq62owYMSIgSNET+5jah7BobGyUJA0cOFCSVFlZqfb2dpWUlPjHjB07VtnZ2aqoqJAkVVRUqKCgQGlpaf4xM2fOVFNTk2pqakJYfWxZtGiRZs+eHfCzl+hJOPzxj39UYWGh7rvvPg0dOlTjx4/Xq6++6t9/7Ngx1dXVBfQkNTVVEydODOhJ//79VVhY6B9TUlIih8OhnTt3hu5kYsTkyZNVVlamjz/+WJK0f/9+7dixQ7NmzZJETyJBsHpQUVGh4uJiuVwu/5iZM2fq0KFD+ve//x2is4ldjY2NsixL/fv3l0RPwsHn82nBggVavny58vLyrtpPT+xzhrsAxB+fz6elS5dqypQpys/PlyTV1dXJ5XL532A7paWlqa6uzj/m8g/snfs798G+t956S3v37tXu3buv2kdPQu/o0aNat26dli1bph/84AfavXu3nnjiCblcLpWWlvp/ptf6mV/ek6FDhwbsdzqdGjhwID3pgZUrV6qpqUljx45VQkKCLl26pNWrV2v+/PmSRE8iQLB6UFdXp5ycnKu+R+e+AQMG9Er98eDixYtasWKF5s2bp5SUFEn0JBx+9KMfyel06oknnrjmfnpiH0EKIbdo0SJVV1drx44d4S4lrtXW1mrJkiXaunWrEhMTw10O1PGPDIWFhfrhD38oSRo/fryqq6v1q1/9SqWlpWGuLj79/ve/1/r16/XGG28oLy9PVVVVWrp0qTIyMugJ0A3t7e369re/LWOM1q1bF+5y4lZlZaVefPFF7d27V5ZlhbucmMHUPoTU4sWLtXnzZm3fvl3Dhw/3b09PT1dbW5saGhoCxp8+fVrp6en+MVeuGNf5decYdF9lZaXq6+v1ta99TU6nU06nU++//75+/vOfy+l0Ki0tjZ6E2LBhw5Sbmxuw7Stf+YpOnDgh6T8/02v9zC/vSX19fcB+r9ers2fP0pMeWL58uVauXKnvfve7Kigo0IIFC/Tkk09qzZo1kuhJJAhWD3g/C77OEHX8+HFt3brVfzVKoieh9sEHH6i+vl7Z2dn+v/nHjx/XU089pZEjR0qiJz1BkEJIGGO0ePFibdy4Udu2bbvqsvCECRPUp08flZWV+bcdOnRIJ06cUFFRkSSpqKhIH374YcAveecb85UfPtG1GTNm6MMPP1RVVZX/VVhYqPnz5/v/m56E1pQpU656LMDHH3+sESNGSJJycnKUnp4e0JOmpibt3LkzoCcNDQ2qrKz0j9m2bZt8Pp8mTpwYgrOILS0tLXI4Av9UJiQkyOfzSaInkSBYPSgqKlJ5ebna29v9Y7Zu3apbbrkl7qYrBUNniDp8+LD+8pe/aNCgQQH76UloLViwQAcOHAj4m5+RkaHly5dry5YtkuhJj4R7tQvEh8cee8ykpqaav/71r+bUqVP+V0tLi3/MwoULTXZ2ttm2bZvZs2ePKSoqMkVFRf79nUttf/3rXzdVVVXmvffeM0OGDGGp7SC6fNU+Y+hJqO3atcs4nU6zevVqc/jwYbN+/Xrj8XjM7373O/+YtWvXmv79+5s//OEP5sCBA+bee++95jLP48ePNzt37jQ7duwwY8aMYantHiotLTWZmZn+5c83bNhgBg8ebL7//e/7x9CT3tfc3Gz27dtn9u3bZySZn/70p2bfvn3+FeCC0YOGhgaTlpZmFixYYKqrq81bb71lPB5P3C7r3JUb9aStrc3cc889Zvjw4aaqqirg7/7lq73Rk+Dq6vfkSleu2mcMPbGLIIWQkHTN12uvveYfc+HCBfP444+bAQMGGI/HY+bOnWtOnToV8H3+9a9/mVmzZpmkpCQzePBg89RTT5n29vYQn03sujJI0ZPQ+9Of/mTy8/ON2+02Y8eONa+88krAfp/PZ5555hmTlpZm3G63mTFjhjl06FDAmM8//9zMmzfPJCcnm5SUFPPQQw+Z5ubmUJ5GzGhqajJLliwx2dnZJjEx0YwaNco8/fTTAR8G6Unv2759+zX/hpSWlhpjgteD/fv3m6lTpxq3220yMzPN2rVrQ3WKUedGPTl27Nh1/+5v377d/z3oSXB19XtypWsFKXpij2XMZY9nBwAAAAB0iXukAAAAAMAmghQAAAAA2ESQAgAAAACbCFIAAAAAYBNBCgAAAABsIkgBAAAAgE0EKQAAAACwiSAFAAAAADYRpAAAAADAJoIUACCuPPjgg7Is66rXkSNHVF5errvvvlsZGRmyLEubNm0Kd7kAgAhFkAIAxJ277rpLp06dCnjl5OTo/PnzGjdunF566aVwlwgAiHDOcBcAAECoud1upaenX7V91qxZmjVrVhgqAgBEG65IAQAAAIBNBCkAQNzZvHmzkpOT/a/77rsv3CUBAKIMU/sAAHFn2rRpWrdunf/rvn37hrEaAEA0IkgBAOJO3759NXr06HCXAQCIYkztAwAAAACbuCIFAMAXzp07pyNHjvi/PnbsmKqqqjRw4EBlZ2eHsTIAQKQhSAEA8IU9e/Zo2rRp/q+XLVsmSSotLdXrr78epqoAAJHIMsaYcBcBAAAAANGEe6QAAAAAwCaCFAAAAADYRJACAAAAAJsIUgAAAABgE0EKAAAAAGwiSAEAAACATQQpAAAAALCJIAUAAAAANhGkAAAAAMAmghQAAAAA2ESQAgAAAACb/h9dPqwM9cUbsAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler\n", + " sc = StandardScaler()\n", + "\n", + " # Define the pipeline with PolynomialFeatures and Ridge regression\n", + " pipeline = make_pipeline(PolynomialFeatures(2), linear_model.RidgeCV())\n", + "\n", + " # Define the hyperparameter grid for Ridge regression\n", + " param_grid = {\n", + " \"ridgecv__alphas\": [[0.1, 1.0, 10.0], [0.01, 0.1, 1.0], [0.001, 0.01, 0.1]]\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the pipeline and parameter grid\n", + " grid_search = GridSearchCV(\n", + " pipeline, param_grid, cv=5, scoring=make_scorer(r2_score)\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for X\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\n", + " f'Best alpha for X: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score: {r2_score_x}'\n", + " )\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for Y\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\n", + " f'Best alpha for Y: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score: {r2_score_y}'\n", + " )\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best alpha for X: [0.001, 0.01, 0.1], R2 score: 0.9982537849595848\n", + "Best alpha for Y: [0.001, 0.01, 0.1], R2 score: 0.9808107385120926\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c9c623eec81ae5f27d14767111bfc05beade03f5 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 2 Jul 2024 21:56:56 +0000 Subject: [PATCH 20/78] lasso notebook added to lasso_regression folder --- .../test/{ => lasso_regression}/test_lasso_regression.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename app/services/calib_validation/test/{ => lasso_regression}/test_lasso_regression.ipynb (100%) diff --git a/app/services/calib_validation/test/test_lasso_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb similarity index 100% rename from app/services/calib_validation/test/test_lasso_regression.ipynb rename to app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb From ee10a30e13a78834d9d979ea824b9f7a88b35a04 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 3 Jul 2024 22:49:08 +0000 Subject: [PATCH 21/78] lasso cv model notebook added --- .../test_lassoCV_regression.ipynb | 1889 +++++++++++++++++ 1 file changed, 1889 insertions(+) create mode 100644 app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb new file mode 100644 index 00000000..525bcaf9 --- /dev/null +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb @@ -0,0 +1,1889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61363.50530881889, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54678.73897011955, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50269.74963784958, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47884.76380684636, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46603.934383086365, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62024.307753073794, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56490.87247515278, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52149.69994578107, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49616.16985890787, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48164.10928131883, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62493.216581327004, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56721.080799783784, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52648.89686642215, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50415.68380080664, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49207.59134460457, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65805.63830308433, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57923.24351563832, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53233.31006707088, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50799.131739411896, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49536.218900455904, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59007.70608710826, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53409.3580534048, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49395.31937919355, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47141.11926884786, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45888.828487440434, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.9983086270947197" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a lassoCV model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.LassoCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 778.48127854, 767.04348722, 137.50634772, 83.21529745,\n", + " 1455.41333396, 133.5823542 , 137.14194629, 83.8560983 ,\n", + " 772.06920896, 760.9532296 , 1407.30207245, 757.11533568,\n", + " 141.08267288, 94.14143674, 761.06824159, 780.41602556,\n", + " 132.32070365, 1422.6720953 , 80.94333059, 81.80828345,\n", + " 758.11565608, 759.62563439, 140.42528323, 1425.78515296,\n", + " 1451.38665456, 778.99768147, 73.08086834, 80.81048925,\n", + " 77.84871052, 89.0436865 , 761.72972039, 760.58419566,\n", + " 779.54102982, 762.87790117, 1460.85378801, 1425.81431481,\n", + " 1405.53616185, 1458.52180025, 1451.2255034 , 1441.32293854,\n", + " 768.51129813, 89.6590618 , 135.56755369, 1417.95676987,\n", + " 760.32441535, 132.83150268, 781.95270128, 1432.45558429,\n", + " 1426.7249187 , 131.7525834 , 1432.60778424, 1425.93225084,\n", + " 754.57412144, 1433.42431901, 80.77880402, 768.93550952,\n", + " 77.8649223 , 76.48449354, 135.04244013, 779.41827978,\n", + " 1420.11905903, 152.72002266, 134.51245983, 757.26584889,\n", + " 77.57875017, 94.89624771, 1422.53266418, 85.4307151 ,\n", + " 1429.58896627, 1437.49671432, 1425.5346257 , 764.60814942,\n", + " 137.322858 , 763.44935447, 77.54638924, 1420.07229191,\n", + " 1449.00007175, 779.68589031, 1414.76712742, 78.91169675,\n", + " 1427.14837263, 768.6823163 , 75.59675113, 765.62494876,\n", + " 144.72987143, 1433.33498747, 1470.02389046, 73.5248781 ,\n", + " 1469.74791094, 1423.47916386, 72.40031663, 137.28188093,\n", + " 80.09007763, 776.74423896, 95.64294598, 777.07784823,\n", + " 131.6023471 , 195.65361333, 1464.4668382 , 79.6538671 ,\n", + " 769.62334298, 1409.91355418, 775.52758542, 770.39912279,\n", + " 83.1786281 , 773.822501 , 73.20064316, 70.60121481,\n", + " 69.60121395, 1424.52221713, 81.64112263, 1353.13420125,\n", + " 1438.71046426, 99.77731722, 1425.99237255, 778.74222296,\n", + " 761.44411332, 131.80780938, 1459.10719644, 146.60383825,\n", + " 136.41358109, 758.979804 , 1439.04901805, 135.66377809,\n", + " 775.88408387, 133.02085838, 760.67007518, 1425.76600072,\n", + " 1437.69172132, 1452.92931588, 1431.57611863, 763.07975594,\n", + " 72.94068503, 1461.86270282, 780.66684308, 1411.05302499,\n", + " 85.0859176 , 150.49426696, 73.36648258, 91.2841013 ,\n", + " 90.39703864, 85.0961172 , 81.44547618, 1427.63448175])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7064.336851651082, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200159.05577759317, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249881.27993171554, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249513.53795295014, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 245341.35252676593, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242070.7765863414, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171270.0566077683, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246143.47668155946, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 251129.85143080176, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249045.20598711274, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246886.98673294307, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198408.61779734457, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254758.83851363405, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255745.8701724262, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252078.1847329364, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249080.8396489511, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3302.570484981872, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204417.00538329384, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262996.3810029083, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264448.62337668287, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260965.17792806384, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258077.10223143882, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3388.073061162373, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202273.39377483987, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258802.28148703428, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259630.2984787177, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255782.95718621058, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252602.58914670834, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.398e+05, tolerance: 3.005e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.9768205500574753" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a lassoCV model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.LassoCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([154.35544808, 137.93629756, 100.31473354, 588.58488656,\n", + " 52.48716382, 112.11305014, 108.20706359, 389.7900419 ,\n", + " 153.00838262, 665.20884841, 613.68708933, 690.65803162,\n", + " 89.52328036, 319.81054051, 664.04016349, 157.62424525,\n", + " 119.13254824, 377.05473569, 576.20044299, 590.07344208,\n", + " 692.34965184, 602.18014481, 80.00067828, 618.08358598,\n", + " 104.55656814, 147.84779838, 584.34273622, 582.63050174,\n", + " 372.13594548, 371.50913433, 662.68943017, 598.07634727,\n", + " 139.66930121, 613.11887179, 357.97036905, 691.15891145,\n", + " 614.99438068, 358.11170426, 358.82930581, 99.17479194,\n", + " 143.39800613, 372.27578689, 97.94593031, 644.38350125,\n", + " 635.57577427, 102.55310446, 159.38247766, 369.13177828,\n", + " 661.81289755, 106.55608822, 116.51647578, 627.8099384 ,\n", + " 672.49473217, 335.25386423, 372.17017728, 158.76976875,\n", + " 586.76834361, 575.78198488, 112.44660291, 155.35176762,\n", + " 114.64050937, 39.09126607, 106.0477728 , 659.20021131,\n", + " 592.30000103, 312.28675809, 657.1194534 , 370.57738964,\n", + " 112.30765397, 372.77522648, 638.7185935 , 668.52004661,\n", + " 105.44057683, 613.10132462, 372.45310812, 116.20320007,\n", + " 352.84095965, 156.17551707, 631.42315097, 579.29469596,\n", + " 114.42192949, 143.17553154, 589.23394488, 145.298486 ,\n", + " 81.93675711, 103.67175374, 354.48148916, 590.15495407,\n", + " 75.84257083, 656.21566816, 575.18899487, 104.80583718,\n", + " 578.80663677, 149.97573917, 401.8161623 , 154.85641011,\n", + " 108.54715635, -17.42321016, 357.09639731, 359.17339531,\n", + " 154.20326129, 612.48010958, 156.63103192, 150.86042382,\n", + " 590.46819404, 168.68804039, 576.74520262, 580.22733722,\n", + " 591.61463109, 657.03262926, 382.25521947, 51.69033846,\n", + " 367.47881226, 287.50808557, 107.57417577, 160.84804545,\n", + " 605.72065683, 98.06126501, 355.39243999, 80.36244683,\n", + " 100.831099 , 648.64453807, 363.14842993, 100.60996879,\n", + " 149.17472931, 101.69950378, 637.02677974, 654.65549126,\n", + " 334.47801516, 84.94335673, 642.3426637 , 659.28325502,\n", + " 577.46487406, 349.86963183, 150.30998619, 634.65993495,\n", + " 382.93077531, 24.11219858, 574.08329376, 374.18540157,\n", + " 373.74992035, 597.05038034, 381.84619234, 101.62086784])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768778.481279100154.355448(768, 100)
290768767.043487100137.936298(768, 100)
54100137.506348100100.314734(100, 100)
19810083.215297630588.584887(100, 630)
45314361455.41333410052.487164(1436, 100)
..................
16410091.284101365374.185402(100, 365)
16510090.397039365373.749920(100, 365)
19910085.096117630597.050380(100, 630)
13210081.445476365381.846192(100, 365)
50114361427.634482100101.620868(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 778.481279 100 154.355448 (768, 100)\n", + "290 768 767.043487 100 137.936298 (768, 100)\n", + "54 100 137.506348 100 100.314734 (100, 100)\n", + "198 100 83.215297 630 588.584887 (100, 630)\n", + "453 1436 1455.413334 100 52.487164 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 91.284101 365 374.185402 (100, 365)\n", + "165 100 90.397039 365 373.749920 (100, 365)\n", + "199 100 85.096117 630 597.050380 (100, 630)\n", + "132 100 81.445476 365 381.846192 (100, 365)\n", + "501 1436 1427.634482 100 101.620868 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768778.481279100154.355448(768, 100)
290768767.043487100137.936298(768, 100)
54100137.506348100100.314734(100, 100)
19810083.215297630588.584887(100, 630)
45314361455.41333410052.487164(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 778.481279 100 154.355448 (768, 100)\n", + "290 768 767.043487 100 137.936298 (768, 100)\n", + "54 100 137.506348 100 100.314734 (100, 100)\n", + "198 100 83.215297 630 588.584887 (100, 630)\n", + "453 1436 1455.413334 100 52.487164 (1436, 100)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(143, 5)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.221980\n", + "(100, 365) 0.409091\n", + "(100, 630) 0.673298\n", + "(768, 100) 0.912557\n", + "(768, 630) 1.249868\n", + "(1436, 100) 1.239896\n", + "(1436, 365) 1.509302\n", + "(1436, 630) 1.784008\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_7544\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_7544\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and LassoCV model\n", + " # with polynomial features of degree 2 and alphas set to logspace(-6, 6, 13)\n", + " sc = StandardScaler()\n", + " model = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.LassoCV(alphas=np.logspace(-6, 6, 13))\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61363.50530881889, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54678.73897011955, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50269.74963784958, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47884.76380684636, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46603.934383086365, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62024.307753073794, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56490.87247515278, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52149.69994578107, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49616.16985890787, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48164.10928131883, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62493.216581327004, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56721.080799783784, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52648.89686642215, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50415.68380080664, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49207.59134460457, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65805.63830308433, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57923.24351563832, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53233.31006707088, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50799.131739411896, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49536.218900455904, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59007.70608710826, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53409.3580534048, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49395.31937919355, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47141.11926884786, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45888.828487440434, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7064.336851651082, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200159.05577759317, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249881.27993171554, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249513.53795295014, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 245341.35252676593, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242070.7765863414, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171270.0566077683, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246143.47668155946, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 251129.85143080176, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249045.20598711274, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246886.98673294307, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198408.61779734457, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254758.83851363405, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255745.8701724262, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252078.1847329364, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249080.8396489511, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3302.570484981872, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204417.00538329384, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262996.3810029083, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264448.62337668287, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260965.17792806384, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258077.10223143882, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3388.073061162373, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202273.39377483987, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258802.28148703428, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259630.2984787177, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255782.95718621058, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252602.58914670834, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.398e+05, tolerance: 3.005e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9ff80d1ab59afba350cc34cf9b297efd7d282f87 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 3 Jul 2024 22:58:09 +0000 Subject: [PATCH 22/78] small comment fix in ridge cv notebook --- .../test/ridge_regression/test_ridgeCV_regression.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb index 18ca5911..c9ac3cbe 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb @@ -1522,7 +1522,7 @@ " \"\"\"\n", "\n", " # Initialize the StandardScaler and RidgeCV model\n", - " # with polynomial features of degree 2 and alpha set to logspace(-6, 6, 13)\n", + " # with polynomial features of degree 2 and alphas set to logspace(-6, 6, 13)\n", " sc = StandardScaler()\n", " model = make_pipeline(\n", " PolynomialFeatures(2), linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n", From 1779984bfe7c49cc2f3e00bc132473bf2fc01532 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 4 Jul 2024 06:45:08 +0000 Subject: [PATCH 23/78] scoring metric change in grid search --- .../test_ridge_regression_grid_search.ipynb | 217 ++++++++++++------ 1 file changed, 148 insertions(+), 69 deletions(-) diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb index 3223769c..ee99624f 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 202, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -78,7 +78,7 @@ "(720, 6)" ] }, - "execution_count": 123, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 204, "metadata": {}, "outputs": [ { @@ -181,7 +181,7 @@ "4 534.370300 287.437531 426.682861 285.813660 100 100" ] }, - "execution_count": 124, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -324,7 +324,7 @@ "max 630.000000 " ] }, - "execution_count": 125, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -336,12 +336,12 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 206, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 208, "metadata": {}, "outputs": [ { @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ @@ -427,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 210, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -451,7 +451,7 @@ " [0.98464908, 1.03388465]])" ] }, - "execution_count": 131, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -463,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 212, "metadata": {}, "outputs": [ { @@ -477,7 +477,7 @@ "Name: point_x, dtype: int64" ] }, - "execution_count": 132, + "execution_count": 212, "metadata": {}, "output_type": "execute_result" } @@ -489,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 213, "metadata": {}, "outputs": [], "source": [ @@ -501,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 214, "metadata": {}, "outputs": [ { @@ -510,7 +510,7 @@ "0.9979769240845753" ] }, - "execution_count": 134, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -527,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 215, "metadata": {}, "outputs": [ { @@ -571,7 +571,7 @@ " 90.12997158, 81.47034246, 82.96396599, 1423.82324744])" ] }, - "execution_count": 135, + "execution_count": 215, "metadata": {}, "output_type": "execute_result" } @@ -583,12 +583,12 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 216, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -605,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ @@ -616,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -640,7 +640,7 @@ " [-1.55821997, -1.78924048]])" ] }, - "execution_count": 139, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -652,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 220, "metadata": {}, "outputs": [ { @@ -666,7 +666,7 @@ "Name: point_y, dtype: int64" ] }, - "execution_count": 140, + "execution_count": 220, "metadata": {}, "output_type": "execute_result" } @@ -678,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 221, "metadata": {}, "outputs": [], "source": [ @@ -690,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 222, "metadata": {}, "outputs": [ { @@ -699,7 +699,7 @@ "0.975215340154245" ] }, - "execution_count": 142, + "execution_count": 222, "metadata": {}, "output_type": "execute_result" } @@ -716,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -760,7 +760,7 @@ " 372.52208455, 594.10421085, 377.4539325 , 102.77211001])" ] }, - "execution_count": 143, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } @@ -772,12 +772,12 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 224, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -794,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 225, "metadata": {}, "outputs": [ { @@ -835,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 226, "metadata": {}, "outputs": [], "source": [ @@ -846,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 227, "metadata": {}, "outputs": [ { @@ -988,7 +988,7 @@ "[144 rows x 5 columns]" ] }, - "execution_count": 147, + "execution_count": 227, "metadata": {}, "output_type": "execute_result" } @@ -1000,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 228, "metadata": {}, "outputs": [ { @@ -1085,7 +1085,7 @@ "453 1436 1435.918759 100 52.780919 (1436, 100)" ] }, - "execution_count": 148, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } @@ -1097,7 +1097,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 229, "metadata": {}, "outputs": [ { @@ -1106,7 +1106,7 @@ "(144, 5)" ] }, - "execution_count": 149, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -1118,7 +1118,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 230, "metadata": {}, "outputs": [], "source": [ @@ -1132,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 231, "metadata": {}, "outputs": [ { @@ -1141,7 +1141,7 @@ "(142, 5)" ] }, - "execution_count": 151, + "execution_count": 231, "metadata": {}, "output_type": "execute_result" } @@ -1153,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 232, "metadata": {}, "outputs": [ { @@ -1176,9 +1176,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_22056\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_20172\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", - "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_22056\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_20172\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" ] } @@ -1235,7 +1235,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ @@ -1267,7 +1267,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -1277,7 +1277,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 235, "metadata": {}, "outputs": [ { @@ -1286,7 +1286,7 @@ "(144, 2)" ] }, - "execution_count": 155, + "execution_count": 235, "metadata": {}, "output_type": "execute_result" } @@ -1298,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 236, "metadata": {}, "outputs": [], "source": [ @@ -1311,12 +1311,12 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 237, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACSNklEQVR4nOzde1xUdf7H8dcZLoOigCaIGhqkpHjLYCvSSrxR27pby7a/frt2WTVXJVtvrbbbxUtJF20t18xb6W5t/ba0dpMSFUpj07ywuOb9AmIphrlAaNxm5vfHyCQCysAMw+X9fDzmgXPO93vO56A1vDnf8/0aNpvNhoiIiIiIiNSaydMFiIiIiIiINDUKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIi0iRdc801PPTQQ54uQ0REWigFKRERaXSOHj3Kb3/7WyIiIvDz8yMgIICBAwfy8ssv8/3337v9/OfPn2fWrFl8+umnbj+XiIg0Td6eLkBERORiycnJ3HvvvZjNZh544AH69OlDaWkp6enpPPbYY+zdu5dly5a5tYbz588ze/ZsAAYPHuzWc4mISNOkICUiIo1GVlYW9913H926dSMtLY1OnTo59iUmJnLkyBGSk5M9WGH9nDt3Dn9/f0+XISIiLqChfSIi0mi88MILFBUVsXLlykohqkL37t353e9+V23fWbNmYRhGle2rVq3CMAyys7Md23bu3El8fDwdOnSgVatWhIeHM3r0aACys7MJDg4GYPbs2RiGgWEYzJo1y9H/wIED/OIXv6B9+/b4+fkRExPDP//5z2rPu3nzZiZOnEhISAhXX301AN999x2TJ0/mmmuuwWw2ExISwvDhw8nIyHDq+yUiIp6jO1IiItJofPjhh0RERHDLLbe47RzffPMNI0aMIDg4mJkzZxIUFER2djZr164FIDg4mCVLljBhwgTuuecefv7znwPQr18/APbu3cvAgQPp0qULM2fOxN/fn7///e/cfffdrFmzhnvuuafS+SZOnEhwcDBPPfUU586dA2D8+PG89957PPLII0RFRfHtt9+Snp7O/v37ueGGG9x27SIi4joKUiIi0igUFhby9ddf87Of/cyt5/n888/573//y4YNG4iJiXFsf+aZZwDw9/fnF7/4BRMmTKBfv36MGjWqUv/f/e53dO3alR07dmA2mwF7WBo0aBAzZsyoEqTat29PamoqXl5ejm3Jyck8/PDDLFiwwLHt97//vcuvVURE3EdD+0REpFEoLCwEoG3btm49T1BQEADr1q2jrKzMqb5nz54lLS2NX/7yl3z33XecOXOGM2fO8O233xIfH8/hw4f5+uuvK/V5+OGHK4Woihq++OILTp48Wa9rERERz1GQEhGRRiEgIACwPz/kTrfffjsJCQnMnj2bDh068LOf/Yw33niDkpKSK/Y9cuQINpuNJ598kuDg4Eqvp59+GrAPHbxYeHh4leO88MILfPnll4SFhXHjjTcya9Ysjh075poLFBGRBqGhfSIi0igEBATQuXNnvvzyyzr1r26iCQCLxVKl3Xvvvce2bdv48MMPSUlJYfTo0SxYsIBt27bRpk2bGs9htVoBmD59OvHx8dW26d69e6X3rVq1qtLml7/8Jbfeeivvv/8+GzZs4MUXX+T5559n7dq13HnnnZe9ThERaRx0R0pERBqNn/zkJxw9epStW7c63bddu3YA5OfnV9p+/PjxatvffPPNPPvss+zcuZO33nqLvXv38s477wA1h7KIiAgAfHx8GDZsWLWv2g5N7NSpExMnTuSDDz4gKyuLq666imeffbZWfUVExPMUpEREpNH4/e9/j7+/P2PHjuX06dNV9h89epSXX3652r7XXnstAFu2bHFsO3fuHKtXr67U7r///S82m63Stuuvvx7AMbyvdevWQNVQFhISwuDBg1m6dCmnTp2qUkNeXt5lrs7OYrFQUFBQ5bidO3eu1fBCERFpHDS0T0REGo1rr72Wv/3tb/zP//wPvXr14oEHHqBPnz6Ulpby+eef8+677/LQQw9V23fEiBF07dqVMWPG8Nhjj+Hl5cXrr79OcHAwOTk5jnarV6/m1Vdf5Z577uHaa6/lu+++Y/ny5QQEBPDjH/8YsA/Hi4qK4v/+7/+IjIykffv29OnThz59+rB48WIGDRpE3759efjhh4mIiOD06dNs3bqVr776it27d1/2Gr/77juuvvpqfvGLX9C/f3/atGnDpk2b2LFjR6VZ/EREpHFTkBIRkUblpz/9Kf/5z3948cUX+cc//sGSJUswm83069ePBQsW8PDDD1fbz8fHh/fff5+JEyfy5JNPEhoayuTJk2nXrh2/+c1vHO1uv/12tm/fzjvvvMPp06cJDAzkxhtv5K233qo0McSKFSuYNGkSU6ZMobS0lKeffpo+ffoQFRXFzp07mT17NqtWreLbb78lJCSEAQMG8NRTT13x+lq3bs3EiRPZsGEDa9euxWq10r17d1599VUmTJhQ/2+giIg0CMN26fgGERERERERuSw9IyUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZLWkQKsVisnT56kbdu2GIbh6XJERERERMRDbDYb3333HZ07d8Zkqvm+k4IUcPLkScLCwjxdhoiIiIiINBInTpzg6quvrnG/ghTQtm1bwP7NCggI8HA1IiIiIiLiKYWFhYSFhTkyQk0UpMAxnC8gIEBBSkRERERErvjIjyabEBERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZJHg9Q111yDYRhVXomJiQAUFxeTmJjIVVddRZs2bUhISOD06dOVjpGTk8Ndd91F69atCQkJ4bHHHqO8vNwTlyMiIiIiIi2ER4PUjh07OHXqlOO1ceNGAO69914ApkyZwocffsi7777L5s2bOXnyJD//+c8d/S0WC3fddRelpaV8/vnnrF69mlWrVvHUU0955HpERERERKRlMGw2m83TRVSYPHky69at4/DhwxQWFhIcHMzf/vY3fvGLXwBw4MABevXqxdatW7n55pv5+OOP+clPfsLJkyfp2LEjAK+99hozZswgLy8PX1/fWp23sLCQwMBACgoKCAgIcNv1iYiIiIg0Z+et59lbupevyr6ixFaC2TAT5hNGlG8UrU2tPV1erdQ2G3g3YE2XVVpayptvvsnUqVMxDINdu3ZRVlbGsGHDHG169uxJ165dHUFq69at9O3b1xGiAOLj45kwYQJ79+5lwIAB1Z6rpKSEkpISx/vCwkL3XZiIiDRCecBKIA0oAAKBocBoINiDdYmINE3ltnK2nN/C3tK9WLFW2pdTnsPW77fS27c3t7W+DW+j0USQemk0k0188MEH5Ofn89BDDwGQm5uLr68vQUFBldp17NiR3NxcR5uLQ1TF/op9NUlKSiIwMNDxCgsLc92FiIhII1YMPAiEAo8DG4HtF77+AegMTABKajqAiIhcotxWzvtF7/Nl6ZdVQlQFK1a+LP2SD4o+oNzWPOYzaDRBauXKldx555107tzZ7ed6/PHHKSgocLxOnDjh9nOKiIin5QMRwF+g2g96K1AOLAPiUZgSEamdLee3cKr8FDYu/8SQDRtfl3/NlvNbGqgy92oUQer48eNs2rSJsWPHOraFhoZSWlpKfn5+pbanT58mNDTU0ebSWfwq3le0qY7ZbCYgIKDSS0REmrNiIAo4VYu2VuAzYLI7CxIRaRYqnom6Uoi62N7SvXxv/d6NVTWMRhGk3njjDUJCQrjrrrsc26Kjo/Hx8SE1NdWx7eDBg+Tk5BAbGwtAbGwse/bs4ZtvvnG02bhxIwEBAURFRTXcBYiISCM3ntqFqApWYAVwxj3liIg0E/tK99U4nK8mVqzsLd3rpooajseDlNVq5Y033uDBBx/E2/uHB88CAwMZM2YMU6dO5ZNPPmHXrl385je/ITY2lptvvhmAESNGEBUVxf3338/u3btJSUnhiSeeIDExEbPZ7KlLEhGRRiUP+Gsd+lmxT0ghIiI1OVFWt0dk6tqvMfF4kNq0aRM5OTmMHj26yr4//elP/OQnPyEhIYHbbruN0NBQ1q5d69jv5eXFunXr8PLyIjY2llGjRvHAAw8wZ86chrwEERFp1F6n+meirsSKfVY/ERGpSYmtbs+T1rVfY9Ko1pHyFK0jJSLSnI3APitfXdwIfOHCWkREmpf3v3ufnPIcp/t19e7KPW3vcUNF9VfbbODxO1IiIiLuVVCPvkGuKkJEpFkK86nbMkJ17deYKEiJiEgzF1iPvkNcVoWISHMU5RuFyclIYcJEb9/ebqqo4ShIiYhIMzcUMOrQzwSMcXEtIiLNS2tTa3r79sZw4v+zvX1708rUyo1VNQwFKRERaeZGA1516PcA0MHFtYiIND+3tb6NTt6dahWmunh34bbWtzVAVe6nICUiIs1cMDAW5+5KdQJec085IiLNjLfhzT1t7qGPb58ah/mZMNHXty93t7kbb8O72jZNjWbtQ7P2iYg0fwVATyC3Fm07AQcAfR6IiDjrvPU8+0r3caLsBCW2EsyGmTCfMKJ8o2htau3p8mqlttmgecRBERGRGhUDPwW+qUXbjihEiYjUXWtTa2L8Yojxi/F0KW6noX0iItLMTQHSqd2ivKeB3sDzQJ47ixIRkSZOd6RERKQZywNWULsQVeEr4A/AE9ifrVoImF1emYhIc3Peep69pXv5quyrJjuszxkKUiIi0oy9jnMhqoL1wmsZsB9IQWFKRKR65bZytpzfwt7SvVgv+X9uTnkOW7/fSm/f3tzW+rZmM9EEaGifiIg0a6nULUhVsAKfAZNdUo2ISHNTbivn/aL32VO6p0qIqmDFypelX/JB0QeU28obuEL3UZASEZFmrMAFx7BiHx54xgXHEhFpXtKK0jhZfvKK7WzY+Lr8a7ac39IAVTUMBSkREWnGAl10HCuw0kXHEhFpHs6Wn2V/+X6n+uwt3cv31u/dVFHDUpASEZFmbCiu+aizAmkuOI6ISPPx7nfvOt3HipW9pXvdUE3DU5ASEZFmbDSu+6jLd9FxRESavm/Lv6WY4jr1PVF2wsXVeIaClIiINGPB2Kcwd4UgFx1HRKTp+/T7T+vct8RW4rpCPEhBSkREmrk/AkY9j2EChrigFhGR5iG3PLfOfb2byQpMClIiItLMvYVrgtQYF9QiItI8WLDUuW9zWUuqeVyFiIhIjapfS+rQIVi2DHbuhIICCAyEmBgYNw4iIy9tHQ10aIBaRUSaP4ut7iGsMVGQEhGRZq7yWlK7d8PUqZCWBl5eYLno8zw9HRYsgKFD7V/796/Y07bBqhURaezKbeWYMDnuSn1z5Bu2rt5KTmYOxYXF+AX40fX6rsQ+GEtI95Aq/csoa+iS3UJBSkREmrkf1pJKTYWRI6G01P7ecskvRSvef/opxMbChx/aQxUUNkShIiKNXrmtnPeL3seCha+//JoPnviAw1sOY/IyYbX8cPc/a1sWnyz+hB639eDuZ+6mS58ujn1mw+yJ0l1Oz0iJiEgzZ19Lavdue4gqLq4aoC5lsUBJib397t2gGftEROy2nN/CqfJTHNp8iIXxCznyryMAlULUxe+P/OsIC+MXcmjzIce+MJ+whivYjRSkRESkmRsNGEydar8TZbPVrpfVam8/bRpoxj4REThvPc/e0r189eVXLP/VcsqLy7FZLv8/VZvFRnlJOct/tZyvv/wagN6+vRuiXLdTkBIRkWYumEOHepOWduU7UZeyWOzDAQ8fHuyWykREmpJ9pfuwYuWDJz6gvLQcWy1/M2Wz2igvLecfT/7DzRU2LAUpERFp9pYty8fLq259vbwMli5917UFiYg0QSfKTvDNkW84vOXwFe9EXcpmsXFo8yHyjuaxt3SvmypsWApSIiLS7O3c+V+n70ZVsFhs7Nq1y7UFiYg0QcXWYrau3orJq24RwuRl4vNVn3O89LiLK/MMBSkREWn2Cgqu3OZy8vPzXVKHiEhTds52jpzMnCoTS9SW1WLlxO4T5FvzXVuYhyhIiYhIsxcYWL/FdIOCglxTiIhIE3Xeep7ztvMUFxbX6zjfF3xPua3cRVV5loKUiIg0ezExd9TjGSkvoqOjXVuQiEgTs690HzZs+AX41es4rQJb4W00j6VsFaRERKTZGzducj2ekbLw29/+1rUFiYg0Mdml2QB0vb5rvZ6RCusfRqAp8MqNmwAFKRERafYiIyMZMsTP6btSXl4wbFgrevTo4Z7CRESaiAKr/WHT2Adj6/WM1C0P3cI1vte4sDLPUZASEZEW4aWXvPD1BVMtP/lMJvD1hfnz9VEpIlLxXFNI9xB63NYDw8twqr/hZRB5eyQh14ZoQV4REZGmpH9/Lz78EMxmrnhnysvL3u7DD+39RERauoufa7r7mbvx9vXGMNUuTBkmA29fb34292f09OlJK1Mrd5XZoBSkRESkhQhh6FDYuhUGD7ZvuTRQVbyPi7O3GzoUoGMD1igi0jhd/FxTlz5dePhvD+Nt9r7inSnDy8Db7M3Df3uYLn26EOQV5OZKG46ClIiItBD3AdC/P2zaBIcOweTJ9lB1/fX2r5Mn27dv3Ghvd3E/EZGW7NLnmiJvj2RyymR6DLI/Q3rpBBQV73sM6sHklMlE3h4JwNflX7u/2AZi2Gw2m6eL8LTCwkICAwMpKCggICDA0+WIiIhb5GG/u+TMx54BfAPUbx0qEZGm7rz1PMsLlle7L+9oHp+v+pwTu0/wfcH3tApsRVj/MG556BaCrw2u1LajV0fuC2jcv6CqbTZoHpO4i4iIXFEwMBao/geB6o1FIUpEWrpyWznbvt9W4/7ga4P52dyf1epYZsPsqrI8TkP7RESkBVkEDKpl21svtBcRabnKbeW8X/Q+X5Z+6ZLjhfmEueQ4jYGClIiItCBmYCMwHvDCPnSvOjcAf7vQXkSk5dpyfgunyk9hc2pYdPUMjGYz9TkoSImISIvjB/wJ+yQSNf1gkAF0BcYBJQ1Ul4hI43Leep69pXtdEqIAWhutm83U56AgJSIiLU4xMBx46wrtbNifpxqOwpSItET7Svdhxeqy4/kb/i47VmOgICUiIi3MFCDdifafAZPdU4qISCN2ouyES4/nZ/Jz6fE8TUFKRERakDycm7WvwgrgjItrERFp3Epsrr0b35wmmgAFKRERaVFeByx16GcBVrq4FhGRxs2VU5WbMDWriSZAQUpERFqUDXXsZwPSXFmIiEij58o7SL19ezeriSZAQUpERFqUPfXom++qIkREmoQo3yiMGpeJqL0u3l24rfVtLqiocVGQEhGRFiKP+j3nFOSiOkREmobWptb09OlZ5/4GBn19+3J3m7vxNrxdWFnj0PyuSEREpFpLqXndqCsxgCEurEVEpGkY4j+E7Pxsvud7p/oZGIxqO4r23u3dVJnnefyO1Ndff82oUaO46qqraNWqFX379mXnzp2O/TabjaeeeopOnTrRqlUrhg0bxuHDhysd4+zZs/z6178mICCAoKAgxowZQ1FRUUNfioiINGpL69HXGxjjqkJERJoMb8ObBwIfwAsvp/r18e3TrEMUeDhI/fe//2XgwIH4+Pjw8ccfs2/fPhYsWEC7du0cbV544QVeeeUVXnvtNb744gv8/f2Jj4+nuLjY0ebXv/41e/fuZePGjaxbt44tW7Ywbtw4T1ySy53LO0f6c+n8dcRfWX7Tcv464q+kP5/Oubxzni5NRKQJyQO+qkf/MUAHF9UiItK0+Jn8eDjwYVobrWvVvrk+E3Upw2az1XWcQ73NnDmTf/3rX3z22WfV7rfZbHTu3Jlp06Yxffp0AAoKCujYsSOrVq3ivvvuY//+/URFRbFjxw5iYmIAWL9+PT/+8Y/56quv6Ny58xXrKCwsJDAwkIKCAgICAlx3gfVQXlzO+snr+ffKf2Oz2rBZf/hrMkwGhsnghrE3EL8wHm+zRmiKiFze88DMOvYNAnIB100DLCLSFJXbykk7l8aBsgPYqhkqXTHF+W2tb2vSz0TVNht49I7UP//5T2JiYrj33nsJCQlhwIABLF/+w0KJWVlZ5ObmMmzYMMe2wMBAbrrpJrZu3QrA1q1bCQoKcoQogGHDhmEymfjiiy+qPW9JSQmFhYWVXo1JeXE5b8a/ScbyDKzl1kohCsBmtWEtt7Jr2S7ejH+T8pJyD1UqItJUpNaj73QUokRE7MP8RrQZwdjAsQxsNZCu3l3p6NWRrt5dGdhqIGMCxzDEf0iTDlHO8OhVHjt2jCVLljB16lT+8Ic/sGPHDh599FF8fX158MEHyc3NBaBjx46V+nXs2NGxLzc3l5CQkEr7vb29ad++vaPNpZKSkpg9e7YbrqjuzuWd498r/82R9Uc4ufMkZefKrtjHZrWR81kOKZNTuGvJXQ1QpYhIU1VQj76/cFkVIiLNQWtTa2L8Yojxi7ly42bMo0HKarUSExPDvHnzABgwYABffvklr732Gg8++KDbzvv4448zdepUx/vCwkLCwly34JgzLh7CZy23Ot3fZrWRsSKDuLlxtO5Qu3GrIiItT2A9+n4AzHBRHSIi0lx4dGhfp06diIqKqrStV69e5OTkABAaGgrA6dOnK7U5ffq0Y19oaCjffPNNpf3l5eWcPXvW0eZSZrOZgICASi9PuHQIX13ZrDYyVma4sDIRkeZmaD36prmsChERaT48GqQGDhzIwYMHK207dOgQ3bp1AyA8PJzQ0FBSU38Y215YWMgXX3xBbGwsALGxseTn57Nr1y5Hm7S0NKxWKzfddFMDXEXdpUxJISc9p8ozUGc4QwopvMEbvMZrvMEbpJDCmRoWkrRZbWSlZTVEySIiTdToevTNd1URIiLSjHh0aN+UKVO45ZZbmDdvHr/85S/Zvn07y5YtY9myZQAYhsHkyZN55pln6NGjB+Hh4Tz55JN07tyZu+++G7Dfwbrjjjt4+OGHee211ygrK+ORRx7hvvvuq9WMfZ5yLu8cGSsyKoWoXHJJIYUssjAwKs2GkkMOW9lKOOHEE08ole+2leSXNFjtIiJNTzBwNXWbAj3ItaWIiEiz4NE7Uj/60Y94//33efvtt+nTpw9z585l4cKF/PrXv3a0+f3vf8+kSZMYN24cP/rRjygqKmL9+vX4+fk52rz11lv07NmToUOH8uMf/5hBgwY5wlhj9e/X/10pRB3jGCtYQTbZAFWmlKx4n002K1jBMY5V2m8O0oxSIiKXN74OfUzAEFcXIiIizYBH15FqLDyxjtRfR/yVYxvtYSiXXFawgnJqN425gYEXXoxlrOPO1NDnhjJoxiC31Ssi0vTlAZ2hlv+vtfMBTqLFeEVEWo4msY5US1ZcUOz4cwopWLDUuq8NGxYspJDi2HbDmBtcWp+ISPMTDIyl9h99JmAMClEiIlIdBSkP8Qu0D008wxmyyKp2dejLsWEjiyy+5VtMPiZNfS4iUisLgUFc+ePPBNx6ob2IiEhVClIe0uWmLgDsYhcGRp2OYWCwk50Edq3P+igiIi2JGUgBxmGfb+nSj0HThe3jLrTT86ciIlI9j87aJ3CSk07fjapgw8YpTtHnV31cXJWISHPmBywB5gCvY18nKh/77HxDsE+VHuyh2kREpKlQkPKQr7/4GoAS6jdteTHF9Pt1P1eUJCLSwgQDMy68REREnKOhfR5SMdmEuZ7DRvzw48AHB1xRkoiIiIiI1JKClIdUTDbRmc71ekaqE53ISstyZWkiIiIiInIFClIeEj40HIBoouv1jFQMMZTk1294oIiIiIiIOEdBykMGjB4AQAc6EE6403elDAwiiOAqrsIcpFmlREREREQakoKUh/gH+zv+HE88XnjVOkwZGHjhxQhGABA+JNwtNYqIiIiISPUUpDzI288+aWIoofyKX9UqTFWEqF/xK0IJxeRj4oYxNzREuSIiIiIicoGClAddfcvVjj9HEMFYxnIN1wBUCVQV78MJZyxjiSACgBvG3EDrDq0bpmAREREREQHAsNlsdZvpoBkpLCwkMDCQgoICAgICGuy8efvzeDXq1Srbv+VbdrKTU5yimGL88KMTnYghhqu4ytGu2+3dGJUyCm+zlgMTEREREXGF2mYD/QTuQcG9gunQqwNn9p+ptP0qriKe+Mv2veHhG7hz0Z0KUSIiIiIiHqChfR42ZtsYzAG1n3WvU3QnJp+YzMhlIxWiREREREQ8REHKw/wC/Jh8fDLBUcGXbdc6pDWPHHmEcTvHEXh1YANVJyIiIiIi1dEtjUbAL8iPiXsnkrc/j5SpKZzccZLy4nK8/bzp/KPOxL8UT3CvywctERERERFpOApSjUhwr2BGfTzK02WIiIiIiMgVaGifiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJHg1Ss2bNwjCMSq+ePXs69hcXF5OYmMhVV11FmzZtSEhI4PTp05WOkZOTw1133UXr1q0JCQnhscceo7y8vKEvRUREREREWhBvTxfQu3dvNm3a5Hjv7f1DSVOmTCE5OZl3332XwMBAHnnkEX7+85/zr3/9CwCLxcJdd91FaGgon3/+OadOneKBBx7Ax8eHefPmNfi1iIiIiIhIy+DxIOXt7U1oaGiV7QUFBaxcuZK//e1vDBkyBIA33niDXr16sW3bNm6++WY2bNjAvn372LRpEx07duT6669n7ty5zJgxg1mzZuHr69vQlyMiIiIiIi2Ax4PU4cOH6dy5M35+fsTGxpKUlETXrl3ZtWsXZWVlDBs2zNG2Z8+edO3ala1bt3LzzTezdetW+vbtS8eOHR1t4uPjmTBhAnv37mXAgAHVnrOkpISSkhLH+8LCQvddoIhIM2KxWCgrK/N0GVIPvr6+mEx6RFpEpL48GqRuuukmVq1axXXXXcepU6eYPXs2t956K19++SW5ubn4+voSFBRUqU/Hjh3Jzc0FIDc3t1KIqthfsa8mSUlJzJ4927UXIyLSjNlsNnJzc8nPz/d0KVJPJpOJ8PBwjdoQEaknjwapO++80/Hnfv36cdNNN9GtWzf+/ve/06pVK7ed9/HHH2fq1KmO94WFhYSFhbntfCIiTV1FiAoJCaF169YYhuHpkqQOrFYrJ0+e5NSpU3Tt2lV/jyIi9eDxoX0XCwoKIjIykiNHjjB8+HBKS0vJz8+vdFfq9OnTjmeqQkND2b59e6VjVMzqV91zVxXMZjNms9n1FyAi0gxZLBZHiLrqqqs8XY7UU3BwMCdPnqS8vBwfHx9PlyMi0mQ1qkHSRUVFHD16lE6dOhEdHY2Pjw+pqamO/QcPHiQnJ4fY2FgAYmNj2bNnD998842jzcaNGwkICCAqKqrB6xcRaY4qnolq3bq1hysRV6gY0mexWDxciYhI0+bRO1LTp09n5MiRdOvWjZMnT/L000/j5eXF//7v/xIYGMiYMWOYOnUq7du3JyAggEmTJhEbG8vNN98MwIgRI4iKiuL+++/nhRdeIDc3lyeeeILExETdcRIRcTENA2se9PcoIuIaHg1SX331Ff/7v//Lt99+S3BwMIMGDWLbtm0EBwcD8Kc//QmTyURCQgIlJSXEx8fz6quvOvp7eXmxbt06JkyYQGxsLP7+/jz44IPMmTPHU5ckIiIiIiItgGGz2WyeLsLTCgsLCQwMpKCggICAAE+XIyLSqBQXF5OVlUV4eDh+fn51OkZeHqxcCWlpUFAAgYEwdCiMHg0XfncmDcQVf58iIs1ZbbNBo3pGSkREmpfiYhg/Hjp3hj/+ETZuhO3b7V//8Af79gkT4KKl/RqcYRh88MEHnitARESaJAUpERFxi+JiiI+H5cuhvBys1sr7rVb79mXL7O3cEaZyc3OZNGkSERERmM1mwsLCGDlyZKWJjFzp008/xTAMt6639eyzz3LLLbfQunXrKmstiohIw1GQEhERt5gyBdLTqwaoS1mt8NlnMHmya8+fnZ1NdHQ0aWlpvPjii+zZs4f169cTFxdHYmKia0/mYjabjfLy8mr3lZaWcu+99zJhwoQGrkpERC6mICUiIi6XlwcrVlw5RFWwWu3tz5xxXQ0TJ07EMAy2b99OQkICkZGR9O7dm6lTp7Jt27Zq+1R3RykzMxPDMMjOzgbg+PHjjBw5knbt2uHv70/v3r356KOPyM7OJi4uDoB27dphGAYPPfTQheuzkpSURHh4OK1ataJ///689957Vc778ccfEx0djdlsJj09vdoaZ8+ezZQpU+jbt2/9v0kiIlJnjWpBXhERaR5ef732IaqC1WqfkGLGjPqf/+zZs6xfv55nn30Wf3//KvvrMyQuMTGR0tJStmzZgr+/P/v27aNNmzaEhYWxZs0aEhISOHjwIAEBAbRq1QqApKQk3nzzTV577TV69OjBli1bGDVqFMHBwdx+++2OY8+cOZP58+cTERFBu3bt6lyjiIi4n4KUiIi4XGpq3YJUWpprgtSRI0ew2Wz07Nmz/ge7RE5ODgkJCY47QhEREY597du3ByAkJMQR1kpKSpg3bx6bNm1yLCgfERFBeno6S5curRSk5syZw/Dhw11es4iIuJ6ClIiIuFxBQd36uWqOBneu7PHoo48yYcIENmzYwLBhw0hISKBfv341tj9y5Ajnz5+vEpBKS0sZMGBApW0xMTFuqVlERFxPQUpERFwuMLBu/Vw1CV2PHj0wDIMDBw441c9ksj86fHEQKysrq9Rm7NixxMfHk5yczIYNG0hKSmLBggVMmjSp2mMWFRUBkJycTJcuXSrtM5vNld5XNwxRREQaJ002ISIiLjd0KJic/IQxmWDIENecv3379sTHx7N48WLOnTtXZX9N05MHX1gd+NSpU45tmZmZVdqFhYUxfvx41q5dy7Rp01i+fDkAvr6+AFgsFkfbqKgozGYzOTk5dO/evdIrLCysrpcoIiIepiAlIiIuN3q080HKywvGjHFdDYsXL8ZisXDjjTeyZs0aDh8+zP79+3nllVcczypdqiLczJo1i8OHD5OcnMyCBQsqtZk8eTIpKSlkZWWRkZHBJ598Qq9evQDo1q0bhmGwbt068vLyKCoqom3btkyfPp0pU6awevVqjh49SkZGBosWLWL16tVOX1dOTg6ZmZnk5ORgsVjIzMwkMzPTcedLREQahoKUiIi4XHAwjB1b+zBlMtlDVIcOrqshIiKCjIwM4uLimDZtGn369GH48OGkpqayZMmSavv4+Pjw9ttvc+DAAfr168fzzz/PM888U6mNxWIhMTGRXr16cccddxAZGcmrr74KQJcuXZg9ezYzZ86kY8eOPPLIIwDMnTuXJ598kqSkJEe/5ORkwsPDnb6up556igEDBvD0009TVFTEgAEDGDBgADt37nT6WCIiUneGzZ1P5DYRhYWFBAYGUlBQQEBAgKfLERFpVIqLi8nKyiI8PBw/P79a9yspgREjrrwor8kEt94KKSlwySND4gZ1/fsUEWkpapsNdEdKRETcwmy2h6Nx48Dbu+rdKZPJvn3cOIUoERFpejRrn4iIuI2fHyxZAnPm2BfpTUuzT3EeFGSfWGL0aPswQBERkaZGQUpERNwuONi+0K4rFtsVERFpDDS0T0RERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiLul5cHzz1nX6H3ppvsX59/3r7dwwzD4IMPPvB0GSIi0sQoSImIiPsUF8P48dC5M/zxj7BxI2zfbv/6hz/Yt0+YACUlbjl9bm4ukyZNIiIiArPZTFhYGCNHjiQ1NdUt5/v0008xDIP8/Hy3HD87O5sxY8YQHh5Oq1atuPbaa3n66acpLS11y/lERKRmWkdKRETco7gY4uMhPR2s1qr7rVb7a9ky2L8fUlLAbHbZ6bOzsxk4cCBBQUG8+OKL9O3bl7KyMlJSUkhMTOTAgQMuO5er2Ww2LBYL3t6VP6YPHDiA1Wpl6dKldO/enS+//JKHH36Yc+fOMX/+fA9VKyLSMumOlIiIuMeUKTWHqItZrfDZZzB5sktPP3HiRAzDYPv27SQkJBAZGUnv3r2ZOnUq27Ztq7ZPdXeUMjMzMQyD7OxsAI4fP87IkSNp164d/v7+9O7dm48++ojs7Gzi4uIAaNeuHYZh8NBDD124RCtJSUmOO0n9+/fnvffeq3Lejz/+mOjoaMxmM+np6VXqu+OOO3jjjTcYMWIEERER/PSnP2X69OmsXbvWNd80ERGpNd2REhER18vLgxUrrhyiKlit9vZz50KHDvU+/dmzZ1m/fj3PPvss/v7+VfYHBQXV+diJiYmUlpayZcsW/P392bdvH23atCEsLIw1a9aQkJDAwYMHCQgIoFWrVgAkJSXx5ptv8tprr9GjRw+2bNnCqFGjCA4O5vbbb3cce+bMmcyfP5+IiAjatWtXq3oKCgpo3759na9HRETqRkFKRERc7/XXax+iKlitsHIlzJhR79MfOXIEm81Gz549632sS+Xk5JCQkEDfvn0BiIiIcOyrCDQhISGOsFZSUsK8efPYtGkTsbGxjj7p6eksXbq0UpCaM2cOw4cPr3UtR44cYdGiRRrWJyLiAQpSIiLieqmpdQtSaWkuCVI2m63ex6jJo48+yoQJE9iwYQPDhg0jISGBfv361dj+yJEjnD9/vkpAKi0tZcCAAZW2xcTE1LqOr7/+mjvuuIN7772Xhx9+2LmLEBGRelOQEhER1ysoqFs/F81216NHDwzDcHpCCZPJ/ujwxUGsrKysUpuxY8cSHx9PcnIyGzZsICkpiQULFjBp0qRqj1lUVARAcnIyXbp0qbTPfMnkGtUNQ6zOyZMniYuL45ZbbmHZsmW16iMiIq6lySZERMT1AgPr1q8ezy5drH379sTHx7N48WLOnTtXZX9N05MHBwcDcOrUKce2zMzMKu3CwsIYP348a9euZdq0aSxfvhwAX19fACwWi6NtVFQUZrOZnJwcunfvXukVFhbm9LV9/fXXDB48mOjoaN544w1H+BMRkYal//uKiIjrDR0Kzv6AbzLBkCEuK2Hx4sVYLBZuvPFG1qxZw+HDh9m/fz+vvPKK41mlS1WEm1mzZnH48GGSk5NZsGBBpTaTJ08mJSWFrKwsMjIy+OSTT+jVqxcA3bp1wzAM1q1bR15eHkVFRbRt25bp06czZcoUVq9ezdGjR8nIyGDRokWsXr3aqWuqCFFdu3Zl/vz55OXlkZubS25ubt2+SSIiUmcKUiIi4nqjRzsfpLy8YMwYl5UQERFBRkYGcXFxTJs2jT59+jB8+HBSU1NZsmRJtX18fHx4++23OXDgAP369eP555/nmWeeqdTGYrGQmJhIr169uOOOO4iMjOTVV18FoEuXLsyePZuZM2fSsWNHHnnkEQDmzp3Lk08+SVJSkqNfcnIy4eHhTl3Txo0bOXLkCKmpqVx99dV06tTJ8RIRkYZl2Nz5RG4TUVhYSGBgIAUFBQQEBHi6HBGRRqW4uJisrCzCw8Px8/OrfccJE+yL7dZm0gmTCcaNgxoCjrhOnf8+RURaiNpmA92REhER91i4EAYNuvKdKZMJbr3V3l5ERKSJUJASERH3MJshJcV+p8nbu2qgMpns28eNs7e7ZAY7ERGRxkzTn4uIiPv4+dmH682ZY1+kNy3NPsV5UJB9YonRo+HCTHkiIiJNiYKUiIi4X3CwfaFdFyy2KyIi0hhoaJ+IiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOEmz9omIiNvlnctj5b9XkpaVRkFxAYF+gQwNH8roAaMJ9vfs9OeGYfD+++9z9913e7QOERFpWnRHSkRE3Ka4vJjx68bT+aXO/DHtj2w8tpHtJ7ez8dhG/pD2Bzq/1JkJyRMoKS9xy/lzc3OZNGkSERERmM1mwsLCGDlyJKmpqW4536effophGOTn57vl+AA//elP6dq1K35+fnTq1In777+fkydPuu18IiJSPQUpERFxi+LyYuLfjGd5xnLKreVYbdZK+602K+XWcpbtWkb8m/EuD1PZ2dlER0eTlpbGiy++yJ49e1i/fj1xcXEkJia69FyuZrPZKC8vr3ZfXFwcf//73zl48CBr1qzh6NGj/OIXv2jgCkVEREFKRETcYkrKFNJz0qsEqEtZbVY+y/mMySmTXXr+iRMnYhgG27dvJyEhgcjISHr37s3UqVPZtm1btX2qu6OUmZmJYRhkZ2cDcPz4cUaOHEm7du3w9/end+/efPTRR2RnZxMXFwdAu3btMAyDhx56yH6NVitJSUmEh4fTqlUr+vfvz3vvvVflvB9//DHR0dGYzWbS09OrrXHKlCncfPPNdOvWjVtuuYWZM2eybds2ysrK6v9NExGRWtMzUk1ZXh6sXAlpaVBQAIGBMHQojB4NwZ595kBEWra8c3msyFhxxRBVwWqzsiJjBXPj5tKhdYd6n//s2bOsX7+eZ599Fn9//yr7g4KC6nzsxMRESktL2bJlC/7+/uzbt482bdoQFhbGmjVrSEhI4ODBgwQEBNCqVSsAkpKSePPNN3nttdfo0aMHW7ZsYdSoUQQHB3P77bc7jj1z5kzmz59PREQE7dq1q9V1vvXWW9xyyy34+PjU+ZpERMR5ClJNUXExTJ5sD1FWq/1VITUVnngCxo6FhQvBbPZUlSLSgr3+79drHaIqWG1WVmasZMagGfU+/5EjR7DZbPTs2bPex7pUTk4OCQkJ9O3bF4CIiAjHvvbt2wMQEhLiCGslJSXMmzePTZs2ERsb6+iTnp7O0qVLKwWpOXPmMHz48CvWMGPGDP785z9z/vx5br75ZtatW+eqyxMRkVrS0L6mprgY4uNh+XIoL68cosD+vrwcli2ztytxzwPcIiKXk5qVWqcglZaV5pLz22w2lxynOo8++ijPPPMMAwcO5Omnn+Y///nPZdsfOXKE8+fPM3z4cNq0aeN4/eUvf+Ho0aOV2sbExNSqhscee4x///vfbNiwAS8vLx544AG3XrOIiFTVaILUc889h2EYTJ482bGtuLiYxMRErrrqKtq0aUNCQgKnT5+u1C8nJ4e77rqL1q1bExISwmOPPVbjA7rNwpQpkJ5eNUBdymqFzz6z37kSEWlgBcUFdeqXX5LvkvP36NEDwzA4cOCAU/1MJvvH4sWh5NJnj8aOHcuxY8e4//772bNnDzExMSxatKjGYxYVFQGQnJxMZmam47Vv375Kz0kB1Q5DrE6HDh2IjIxk+PDhvPPOO3z00Uc1PvclIiLu0SiC1I4dO1i6dCn9+vWrtH3KlCl8+OGHvPvuu2zevJmTJ0/y85//3LHfYrFw1113UVpayueff87q1atZtWoVTz31VENfQsPIy4MVK64coipYrfb2Z864ty4RkUsE+gXWqV+QOcgl52/fvj3x8fEsXryYc+fOVdlf0/TkwReeLz116pRjW2ZmZpV2YWFhjB8/nrVr1zJt2jSWL18OgK+vL2D/fKoQFRWF2WwmJyeH7t27V3qFhYXV9RIdrBc+E0o0AkFEpEF5PEgVFRXx61//muXLl1d6sLagoICVK1fy0ksvMWTIEKKjo3njjTf4/PPPHb9127BhA/v27ePNN9/k+uuv584772Tu3LksXryY0tJST12S+7z+eu1DVAWr1f4slYhIAxoaPhST4dxHjMkwMSR8iMtqWLx4MRaLhRtvvJE1a9Zw+PBh9u/fzyuvvOJ4VulSFeFm1qxZHD58mOTkZBYsWFCpzeTJk0lJSSErK4uMjAw++eQTevXqBUC3bt0wDIN169aRl5dHUVERbdu2Zfr06UyZMoXVq1dz9OhRMjIyWLRoEatXr3bqmr744gv+/Oc/k5mZyfHjx0lLS+N///d/ufbaa2u8JhERcQ+PB6nExETuuusuhg0bVmn7rl27KCsrq7S9Z8+edO3ala1btwKwdetW+vbtS8eOHR1t4uPjKSwsZO/evTWes6SkhMLCwkqvJiE1tW5BKs01zxyIiNTW6AGjnQ5SXoYXY24Y47IaIiIiyMjIIC4ujmnTptGnTx+GDx9OamoqS5YsqbaPj48Pb7/9NgcOHKBfv348//zzPPPMM5XaWCwWEhMT6dWrF3fccQeRkZG8+uqrAHTp0oXZs2czc+ZMOnbsyCOPPALA3LlzefLJJ0lKSnL0S05OJjw83Klrat26NWvXrmXo0KFcd911jBkzhn79+rF582bMmlxIRKRBeXTWvnfeeYeMjAx27NhRZV9ubi6+vr5Vpqjt2LEjubm5jjYXh6iK/RX7apKUlMTs2bPrWb0HFNTtmQNqGMIiIuIuwf7BjL1hLMt2LavVpBMmw8SYG8a4ZOrzi3Xq1Ik///nP/PnPf66xzaWTNAwcOLDKBBIXt7nc81AATz75JE8++WSlbYZh8Lvf/Y7f/e531fYZPHhwrSaL6Nu3L2n65ZiISKPgsTtSJ06c4He/+x1vvfUWfn5+DXruxx9/nIKCAsfrxIkTDXr+Ogus2zMH1GO9FBGRuloYv5BBXQdd8c6UyTBxa9dbWRi/sGEKExERcQGPBaldu3bxzTffcMMNN+Dt7Y23tzebN2/mlVdewdvbm44dO1JaWlrlgeDTp08TGhoKQGhoaJVZ/CreV7SpjtlsJiAgoNKrUcvLg+eeg6ws5/uaTDDEdc8ciIjUltnbTMqoFMZFj8Pb5F0lUJkME94mb8ZFjyNlVApmbw1NExGRpsNjQ/uGDh3Knj17Km37zW9+Q8+ePZkxYwZhYWH4+PiQmppKQkICAAcPHiQnJ8fxQG1sbCzPPvss33zzDSEhIQBs3LiRgIAAoqKiGvaC3OFyC+/WlpcXjHHdMwciIs7w8/ZjyV1LmDN4Dq//+3XSstLIL8knyBzEkPAhjB4wmmD/YE+XKSIi4jSPBam2bdvSp0+fStv8/f256qqrHNvHjBnD1KlTad++PQEBAUyaNInY2FhuvvlmAEaMGEFUVBT3338/L7zwArm5uTzxxBMkJiY2/YduKxberc2aUTUxmewhqoNrnzkQEXFWsH8wMwbNYMagGZ4uRUREXCgvz/47/7Q0++P8gYEwdCiMHg3Bzfz3ZB6dbOJK/vSnP2EymUhISKCkpIT4+HjHzEgAXl5erFu3jgkTJhAbG4u/vz8PPvggc+bM8WDVLlLbhXdrYjLBrbfCwoUuLUtERERE5HIDp1JT4YknYOxY+4+iTf3+Rk0MW22mCWrmCgsLCQwMpKCgoHE8L5WXB507Q3m5831NJvuruf/LFZEGU1xcTFZWFuHh4Q0+OZC4nv4+RaS+ajtwquL3+ikpTetH0tpmA4+vIyXVeP11sFic79ejB8ybBydPwpIlTetfrIiIiIg0CbUdOGW1wubNcPvt9vsEzY2CVGO0YQPU5UZheDjMmNH8B6SKiIiIiEfk5cGKFc49ffLFF/bBVhMmQEmJ+2praApSjdElsxnW2tmzrq1DREREROQir79et0f4y8th2TL7kMDmEqYUpBqbvLy63/vMzITCQpeWIyLiGnnAc8AI4KYLX5+/sN2zDMPggw8+8HQZIiJNQmpq3edCs1rhs8/sk1Q0BwpSjc0rr9S9b3k5hIUpTIlII1IMjAc6A38ENgLbL3z9w4XtEwD3/HoyNzeXSZMmERERgdlsJiwsjJEjR5KamuqW83366acYhlFlMXl3KCkp4frrr8cwDDIzM91+PhERsE9xXh9Wq31o4JkzrqnHkxSkGpv/+7/69S8shAsLFouIeFYxEA8sB8qBS3+Fab2wfdmFdq4NU9nZ2URHR5OWlsaLL77Inj17WL9+PXFxcSQmJrr0XK5ms9kov8LMrb///e/p3LlzA1UkImIXGFj/Y1gs9mnTmzoFqcbm9On6H2PfPjh4sP7HERGplylAOlUD1KWswGfAZJeefeLEiRiGwfbt20lISCAyMpLevXszdepUtm3bVm2f6u4oZWZmYhgG2dnZABw/fpyRI0fSrl07/P396d27Nx999BHZ2dnExcUB0K5dOwzD4KGHHrJfodVKUlIS4eHhtGrViv79+/Pee+9VOe/HH39MdHQ0ZrOZ9PT0Gq/t448/ZsOGDcyfP79+3yQREScNHWqf1rw+bDb7Ar5NXaNekLdF+v571xxn8mT4+GPXHEtExGl5wAquHKIqWC+0nwt0qPfZz549y/r163n22Wfx9/evsj8oKKjOx05MTKS0tJQtW7bg7+/Pvn37aNOmDWFhYaxZs4aEhAQOHjxIQEAArVq1AiApKYk333yT1157jR49erBlyxZGjRpFcHAwt99+u+PYM2fOZP78+URERNCuXbtqz3/69GkefvhhPvjgA1q3bl3n6xARqYvRo+2L7db1OakKzWE6dAWpxiQvD8rKatx9CPsAmJ1AARAIxADjgMhLG+/Y4Z4aRURq5XVqH6IqWIGVwIx6n/3IkSPYbDZ69uxZ72NdKicnh4SEBPr27QtARESEY1/79u0BCAkJcYS1kpIS5s2bx6ZNm4i9MPQ6IiKC9PR0li5dWilIzZkzh+HDh9d4bpvNxkMPPcT48eOJiYlx3CUTEWkowcEwdqx9Br7ahanqf4LNzBxHWFgk114Ld95pD2hNbQUfBanG5PXXq928G5gKpAFewMVL9aYDC4ChF772r9jhqjtbIiJ1kkrdglQarghStrqsxVdLjz76KBMmTGDDhg0MGzaMhIQE+vXrV2P7I0eOcP78+SoBqbS0lAEDBlTaFhMTc9lzL1q0iO+++47HH3+87hcgIlJPCxfanyTZsuVyrS7/E6zNtoCvvhrKV18tYPPm/sycCTfdBO+9B1df7b7aXUnPSDUm1QzFSwVigc0X3lsu2V/x/tML7dwzD5WIiLPqOq1TvkvO3qNHDwzD4MCBA071M10Y+H9xECu7ZKTA2LFjOXbsGPfffz979uwhJiaGRYsW1XjMoqIiAJKTk8nMzHS89u3bV+k5KaDaYYgXS0tLY+vWrZjNZry9venevTtgD2APPvhg7S9URKQezGZISblc4KnbT7BffAFdu8K4cU1jrSkFqcbk6NFKb3cDI7HPe3XpP79LWbDPdzXyQj8ujMsXEfGMuk7rFOSSs7dv3574+HgWL17MuXPnquyvaXry4AvjSk6dOuXYVt3U4mFhYYwfP561a9cybdo0li9fDoCvry8AFssP/9eOiorCbDaTk5ND9+7dK73CwsKcuq5XXnmF3bt3O8LYRx99BMD//d//8eyzzzp1LBGR+vDzg0ceqW5PvX6CxWaD5cubxsK9ClKNyfnzld5OBUqB2g5QsV5oPw1AU+KKiEcNxfmPGBMwxGUVLF68GIvFwo033siaNWs4fPgw+/fv55VXXnE8q3SpinAza9YsDh8+THJyMgsWLKjUZvLkyaSkpJCVlUVGRgaffPIJvXr1AqBbt24YhsG6devIy8ujqKiItm3bMn36dKZMmcLq1as5evQoGRkZLFq0iNWrVzt1TV27dqVPnz6OV2Sk/QnZa6+9lqubylgYEWk2Ro+ubmu9foJ12LwZJk2qT3XupyDVmFw0+9Ih7CNKr5TjL2XBfnP0cECA6+oSEXHaaJz/iPECxrisgoiICDIyMoiLi2PatGn06dOH4cOHk5qaypIlS6rt4+Pjw9tvv82BAwfo168fzz//PM8880ylNhaLhcTERHr16sUdd9xBZGQkr776KgBdunRh9uzZzJw5k44dO/LIhV/Xzp07lyeffJKkpCRHv+TkZMLDw112vSIiDS04GDp1unhLvX+CrbR1xQr4+uv6VOhehs2dT+Q2EYWFhQQGBlJQUECAJwPI4MH2+A1MBxbi/D9DsP8oMvnWW5l/+ScARURqpbi4mKysLMLDw/Hz83Oi5wTsMzXVZtIJE/Y5SKsPOOI6df/7FBGp6tln7dOh29X7J1ig8vp4N90ENSz95za1zQa6I9WY3Hmn4487qds/QS7021Xfyf1FROptITCIK3/UmIBbL7QXEZGmZNw48HbMA17vn2CrbP3iCzhzpo6HdDMFqcbkooGmdZ3vqkJ+NQ9Xi4g0LDOQgv1OkzdVP3JMF7aPu9DO3KDViYhI/VWsK2Wf9LTeP8FWu3Xlynoe1k0UpBobwwDqPt9VhYqFIEVEPMsP+3C9k8A8YARw44Wv8y5sX4JClIhI07VwIQwaBC74CbbarWlp9TysmyhINSavv+4IUjHYR4rWhZeXF9HR0S4rS0Sk/oKxL7SbAnxx4euMC9tFRKQpq1hXqn//ev0EC1T/82sNK1Z4nIJUY5KaCheebRpHPUaYWiz89re/dVlZIiIiIiKX4+cHf/97vX6CBar/+bWxDrRSkGpMCn4YVxqJfTUVZzO9l5cXw4YNo0ePHq6sTERERETksiIjIxkyZAheXk7/BAsMA6r+/GoywRDXLTHoUgpSjUlg5XGlLwG+1P4vyWQy4evry/z586/cWERERETEhfLyoE+fl7DZnPoJFvtPvNX//OrlBWNct8SgSylINSZDh1ZMeQJAf+BD7I9gXynXe3l5YTab+fDDD+nfv78bixQRERER+UFxMYwfD507w6JF/bFaa/0T7IV2H2L/ybcyw7CHqA4dXF6ySyhINSajR1cKUgBDga3A4AvvL/3nWPE+Li6OrVu3MnToULeWKCIiIiJSobgY4uNh2TIoLwebDZz4CfZCu+p/fo2Ntc8I2Fh5X7mJNJiKifiXLXNMOgH2fL4JOAwsxb5UWT72CSKj+/fnt+++q2eiRKRRO289z97SvXxV9hUlthLMhpkwnzCifKNobWrt0doMw+D999/n7rvv9mgdIiJN0ZQp8NlnFQHqYpf9CRb7xBI1//waFWWf9tzciFfH0B2pxqZiIn5T1b+aHthHj34C/Ntk4pPbb2f+F18oRIlIo1VuKyftXBorC1by+fefk1Oew2nLaXLKc/jX9/9iZcFK0s6lUW4rd8v5c3NzmTRpEhEREZjNZsLCwhg5ciSpqaluOd+nn36KYRjku3Gu3muuuQbDMCq9nnvuObedT0SkJnl5sGJFdSHqYpV+gr3wdT41hSjDsN+Jysho3CEKdEeq8amYiH/KFPu/TKu10t0pTCb7a+xYe+hq7P/CRKTFKreV837R+5wqP4WN6j9lrVj5svRLzlrPcnebu/E2XPexlJ2dzcCBAwkKCuLFF1+kb9++lJWVkZKSQmJiIgcOHHDZuVzNZrNhsVjw9q7++zFnzhwefvhhx/u2bds2VGkiIg6vvw6Wus52fomm+COu03ekvv/+e9LT09m3b1+VfcXFxfzlL39xSWEtmp8fLFkCJ0/CvHkwYgTceKP967x59u1LljSNf2Ei0mJtOb/lsiGqgg0bX5d/zZbzW1x6/okTJ2IYBtu3bychIYHIyEh69+7N1KlT2bZtW7V9qrujlJmZiWEYZGdnA3D8+HFGjhxJu3bt8Pf3p3fv3nz00UdkZ2cTFxcHQLt27TAMg4ceeggAq9VKUlIS4eHhtGrViv79+/Pee+9VOe/HH39MdHQ0ZrOZ9PT0Gq+tbdu2hIaGOl7+/v71+2aJiNRBauqV7kbV7I47mv6PuE796u/QoUOMGDGCnJwcDMNg0KBBvPPOO3Tq1AmAgoICfvOb3/DAAw+4pdgWJzgYZsywv0REmpCKZ6KuFKIutrd0L7GtYmllalXv8589e5b169fz7LPPVhsyguqxumNiYiKlpaVs2bIFf39/9u3bR5s2bQgLC2PNmjUkJCRw8OBBAgICaNXKfi1JSUm8+eabvPbaa/To0YMtW7YwatQogoODuf322x3HnjlzJvPnzyciIoJ27drVWMNzzz3H3Llz6dq1K7/61a+YMmVKjXevRETc5aIlUJ1mtdoHYTVlTv1fd8aMGfTp04edO3eSn5/P5MmTGThwIJ9++ildu3Z1V40iItLE7CvdhxXrlRtexIqVvaV7ifGLqff5jxw5gs1mo2fPnvU+1qVycnJISEigb9++AERERDj2tW/fHoCQkBBHWCspKWHevHls2rSJ2NhYR5/09HSWLl1aKUjNmTOH4cOHX/b8jz76KDfccAPt27fn888/5/HHH+fUqVO89NJLrrxMEZErumQJVKe48VHSBuNUkPr888/ZtGkTHTp0oEOHDnz44YdMnDiRW2+9lU8++URDC0REBIATZSfq3M8VQcpW17EmtfDoo48yYcIENmzYwLBhw0hISKBfv341tj9y5Ajnz5+vEpBKS0sZMGBApW0xMVe+9qlTpzr+3K9fP3x9ffntb39LUlIS5qYyHkZEmoWhQ2Hjxrr1rcfAgEbDqWekvv/++0pDBwzDYMmSJYwcOZLbb7+dQ4cOubxAERFpekpsJQ3a71I9evTAMAynJ5QwXZgx9eIgVlZWVqnN2LFjOXbsGPfffz979uwhJiaGRYsW1XjMoqIiAJKTk8nMzHS89u3bV+k5KaBOv5C86aabKC8vdzzDJSLSUKpZArVWTCYYMsT19TQ0py69Z8+e7Ny5s8r2P//5z/zsZz/jpz/9qcsKExGRpsts1O3OSF37Xap9+/bEx8ezePFizp07V2V/TdOTBwcHA3Dq1CnHtszMzCrtwsLCGD9+PGvXrmXatGksX74cAF9fXwAsF01jFRUVhdlsJicnh+7du1d6hYWF1fUSK9VnMpkICQmp97FERJwRHAz33+98Py8vGDPG9fU0NKeC1D333MPbb79d7b4///nP/O///q9bh1OIiEjTEOZTt4BQ137VWbx4MRaLhRtvvJE1a9Zw+PBh9u/fzyuvvOJ4VulSFeFm1qxZHD58mOTkZBYsWFCpzeTJk0lJSSErK4uMjAw++eQTevXqBUC3bt0wDIN169aRl5dHUVERbdu2Zfr06UyZMoXVq1dz9OhRMjIyWLRoEatXr3bqmrZu3crChQvZvXs3x44d46233mLKlCmMGjXqspNTiIi4y9KlcGHeuVoxmewhqkMH99XUUJwKUv/zP//DunXratz/6quvYrU693CxiIg0P1G+UZicXGHDhInevr1dVkNERAQZGRnExcUxbdo0+vTpw/Dhw0lNTWXJkiXV9vHx8eHtt9/mwIED9OvXj+eff55nnnmmUhuLxUJiYiK9evXijjvuIDIykldffRWALl26MHv2bGbOnEnHjh155JFHAJg7dy5PPvkkSUlJjn7JycmEh4c7dU1ms5l33nmH22+/nd69e/Pss88yZcoUli1bVofvkIhI/ZnNsH9/7cKUyQS33mpfJ6o5MGxO3ELy8vLi1KlTjuED//M//8Mrr7xCx44d3VZgQygsLCQwMJCCggICAgI8XY6ISKNSXFxMVlYW4eHh+Pn51bpf2rk0viz9stZToPf17csQ/2YwaL6Rq+vfp4jI5RQXw/jx8Ne/2qc2v1hTW2y3ttnAqV8XXpq5Pvroo2rHnksDyMuD556zr2B20032r88/b98uItII3Nb6Njp5d8LAuGLbLt5duK31bQ1QlYiIuIOfH6xaBbm5P/yI2pQX260Nrd7X1BQXw+TJsHKlPe5fHPlTU+GJJ5pO3BeRZs3b8OaeNvew5fwW9pburXZdqYrhfLe1vg1vQx9JIiJNXXAwzJhhfzV3Tn1qGYaBYRhVtkkDKS6G+HhIT696zxR+CFbLltkHq6akKEyJiEd5G94M8R/Cza1uZl/pPk6UnaDEVoLZMBPmE0aUbxStTa09XaaIiIjTnApSNpuNhx56yLHgX3FxMePHj6+y7sXatWtdV6H8YMqUmkPUxaxW+Owz+52rGh6oFhFpSK1NrYnxi3HJYrsiIiKNgVNB6sEHH6z0ftSoUS4tRi4jLw9WrLhyiKpgtcLy5TB3bvOYX1JEREREpBFxKki98cYb7qpDruT112sfoipYLPbJ/f/4R/fUJCIiIiLSQjm3yId4Tmqq80EK4LXXXF+LiIiIiEgLpyDVVBQU1K3f11/DmTOurUVEREREpIVTkGoqAgPr1s9ms0+VLiIiIiIiLqMg1VQMHVr3vmlprqtDRKQOzuWdI/25dP464q8sv2k5fx3xV9KfT+dcnucXdTcMgw8++MDTZYiISBOjINVUjB5d9775+S4rQ0TEGeXF5awbv46XOr9E2h/TOLbxGCe3n+TYxmOk/SGNlzq/RPKEZMpLyt1y/tzcXCZNmkRERARms5mwsDBGjhxJamqqW8736aefYhgG+W7+/25ycjI33XQTrVq1ol27dtx9991uPZ+IiFSlZeSbiuBguPpq+Oor5/sGBbm8HBGRKykvLufN+DfJSc/BZrVV2W+z2rBZbexatou8/XmMShmFt9l1H0vZ2dkMHDiQoKAgXnzxRfr27UtZWRkpKSkkJiZy4MABl53L1Ww2GxaLBW/vqt+PNWvW8PDDDzNv3jyGDBlCeXk5X375pQeqFBFp2Tx6R2rJkiX069ePgIAAAgICiI2N5eOPP3bsLy4uJjExkauuuoo2bdqQkJDA6dOnKx0jJyeHu+66i9atWxMSEsJjjz1Gebl7frPpcePHO9/HZIIhQ1xfi4jIFaRMSakxRF3MZrWR81kOKZNTXHr+iRMnYhgG27dvJyEhgcjISHr37s3UqVPZtm1btX2qu6OUmZmJYRhkZ2cDcPz4cUaOHEm7du3w9/end+/efPTRR2RnZxMXFwdAu3btMAyDhx56CACr1UpSUhLh4eG0atWK/v37895771U578cff0x0dDRms5n09PQq9ZWXl/O73/2OF198kfHjxxMZGUlUVBS//OUvXfNNExGRWvNokLr66qt57rnn2LVrFzt37mTIkCH87Gc/Y+/evQBMmTKFDz/8kHfffZfNmzdz8uRJfv7znzv6WywW7rrrLkpLS/n8889ZvXo1q1at4qmnnvLUJbnXuHFQzW8nL8vLC8aMcU89IiI1OJd3jowVGVcMURVsVhsZKzI4f+a8S85/9uxZ1q9fT2JiIv7+/lX2B9XjTn1iYiIlJSVs2bKFPXv28Pzzz9OmTRvCwsJYs2YNAAcPHuTUqVO8/PLLACQlJfGXv/yF1157jb179zJlyhRGjRrF5s2bKx175syZPPfcc+zfv59+/fpVOXdGRgZff/01JpOJAQMG0KlTJ+68807dkRIR8QCPDu0bOXJkpffPPvssS5YsYdu2bVx99dWsXLmSv/3tbwy5cEfljTfeoFevXmzbto2bb76ZDRs2sG/fPjZt2kTHjh25/vrrmTt3LjNmzGDWrFn4+vp64rLcJzgYxo6FZctqt6aUyWQPUR06uL82EZGL/Pv1f9c6RFWwWW1krMxg0IxB9T7/kSNHsNls9OzZs97HulROTg4JCQn07dsXgIiICMe+9u3bAxASEuIIayUlJcybN49NmzYRGxvr6JOens7SpUu5/fbbHf3nzJnD8OHDazz3sWPHAJg1axYvvfQS11xzDQsWLGDw4MEcOnTIcX4REXG/RjPZhMVi4Z133uHcuXPExsaya9cuysrKGDZsmKNNz5496dq1K1u3bgVg69at9O3bl44dOzraxMfHU1hY6LirVZ2SkhIKCwsrvZqMhQth0CB7SLockwluvdXeXkSkgWWlZtUpSGWlZbnk/Dabc+d2xqOPPsozzzzDwIEDefrpp/nPf/5z2fZHjhzh/PnzDB8+nDZt2jhef/nLXzh69GiltjExMZc9lvXCL9H++Mc/kpCQQHR0NG+88QaGYfDuu+/W78JERMQpHg9Se/bsoU2bNpjNZsaPH8/7779PVFQUubm5+Pr6Vhl+0bFjR3JzcwH7bEwXh6iK/RX7apKUlERgYKDjFRYW5tqLciezGVJSfhjmd2mgMpns28eNs7czmz1Tp4i0aMUFxXXqV5Jf4pLz9+jRA8MwnJ5QwnTh/6kXB7GysrJKbcaOHcuxY8e4//772bNnDzExMSxatKjGYxYVFQH2mfYyMzMdr3379lV6TgqodhjixTp16gRAVFSUY5vZbCYiIoKcnJxaXKGIiLiKx4PUddddR2ZmJl988QUTJkzgwQcfZN++fW495+OPP05BQYHjdeLECbeez+X8/GDJEjh5EubNg8GD7TP6XXUVdO4MAwfCNddAU7rTJiLNil+gX536mYNc88uf9u3bEx8fz+LFizl3rupaVTVNTx4cHAzAqVOnHNsyMzOrtAsLC2P8+PGsXbuWadOmsXz5cgDHkHKLxeJoGxUVhdlsJicnh+7du1d6OfuLvIqJKA4ePOjYVlZWRnZ2Nt26dXPqWCIiUj8eD1K+vr50796d6OhokpKS6N+/Py+//DKhoaGUlpZW+bA7ffo0oaGhAISGhlaZxa/ifUWb6pjNZsdMgRWvJqltW8jKgvR0e6j69lv79OibN8Mf/mAPVRMmQIlrfsMrIlJb4UPDMUyGU30Mk0H4kHCX1bB48WIsFgs33ngja9as4fDhw+zfv59XXnnF8azSpSrCzaxZszh8+DDJycksWLCgUpvJkyeTkpJCVlYWGRkZfPLJJ/Tq1QuAbt26YRgG69atIy8vj6KiItq2bcv06dOZMmUKq1ev5ujRo2RkZLBo0SJWr17t1DUFBAQwfvx4nn76aTZs2MDBgweZMGECAPfee28dvksiIlJXHg9Sl7JarZSUlBAdHY2Pj0+lRRMPHjxITk6O4wMwNjaWPXv28M033zjabNy4kYCAgErDHpql4mKIj4fly6G8vOrkE1arffuyZfZ2ClMi0oAGjB7gfJDyMrhhzA0uqyEiIoKMjAzi4uKYNm0affr0Yfjw4aSmprJkyZJq+/j4+PD2229z4MAB+vXrx/PPP88zzzxTqY3FYiExMZFevXpxxx13EBkZyauvvgpAly5dmD17NjNnzqRjx4488sgjAMydO5cnn3ySpKQkR7/k5GTCw50Pji+++CL33Xcf999/Pz/60Y84fvw4aWlptGvXzuljiYhI3Rk2dz6RewWPP/44d955J127duW7777jb3/7G88//zwpKSkMHz6cCRMm8NFHH7Fq1SoCAgKYNGkSAJ9//jlg/zC7/vrr6dy5My+88AK5ubncf//9jB07lnnz5tW6jsLCQgIDAykoKGg6d6cmTHBu9r5x4+zDAUVEnFRcXExWVhbh4eH4+dV+yF7yhGR2LdtVq0knDJNB9Lho7lpyV31KlVqo69+niEhLUdts4NE7Ut988w0PPPAA1113HUOHDmXHjh2OEAXwpz/9iZ/85CckJCRw2223ERoaytq1ax39vby8WLduHV5eXsTGxjJq1CgeeOAB5syZ46lLahh5ebBiRe1CFNjbrVgBZ864ty4RkYvEL4yn66CuV7wzZZgMut7alfiF8Q1UmYiISP159I5UY9Hk7kg9/7z9GajaBimw35WaNw9mzHBfXSLSLNXnDkZ5cTkpU1Ici/NefHfKMBkYJoMbxt5A/MJ4vM0eXdqwxdAdKRGRy6ttNtCnVlOUmupciAJ7+7Q0BSkRaVDeft7cteQuBs8ZzL9f/zdZaVmU5JdgDjITPiScAaMH4B98+Sm/RUREGiMFqaaooKBu/WqY7ldExN38g/0ZNGMQg2YM8nQpIiIiLtHoZu2TWggMrFu/SxY3FhERERGRulGQaoqGDrU/8+QMkwmGDHFPPSIiIiIiLYyCVFM0erTzQcrLC8aMcU89IiIiIiItjIJUUxQcDGPH1j5MmUz2ENWhg3vrEhERERFpIRSkmqqFC2HQoCuHKZMJbr3V3l5ERERERFxCQaqpMpshJQXGjQNv76qBymSybx83zt7ObPZMnSIiAMV5sPc5SBsBKTfZv+573r7dwwzD4IMPPvB0GSIi0sQoSDVlfn6wZAmcPGlfbHfECLjxRvvXefPs25csUYgSEc+xFMP28fB+Z9j9R8jdCN9ut3/N/IN9+/YJYClxy+lzc3OZNGkSERERmM1mwsLCGDlyJKmpqW4536effophGOS7abmJiuNX99qxY4dbzikiItXTOlLNQXCwfaFdLbYrIo2JpRjS4iEvHahuEXEr2KxwZBkU7oe4FPBy3S9+srOzGThwIEFBQbz44ov07duXsrIyUlJSSExM5MCBAy47l6vZbDYsFgve3pU/pm+55RZOnTpVaduTTz5JamoqMTExDVmiiEiLpztSIiLiHrumXCZEXcwK33wGuya79PQTJ07EMAy2b99OQkICkZGR9O7dm6lTp7Jt27Zq+1R3RykzMxPDMMjOzgbg+PHjjBw5knbt2uHv70/v3r356KOPyM7OJi4uDoB27dphGAYPPfSQ/QqtVpKSkggPD6dVq1b079+f9957r8p5P/74Y6KjozGbzaSnp1epz9fXl9DQUMfrqquu4h//+Ae/+c1vMAzDNd84ERGpFd2REhER1yvOg6MruHKIqmC1t+83F/zqP8Po2bNnWb9+Pc8++yz+/v5V9gfVY4HyxMRESktL2bJlC/7+/uzbt482bdoQFhbGmjVrSEhI4ODBgwQEBNCqVSsAkpKSePPNN3nttdfo0aMHW7ZsYdSoUQQHB3P77bc7jj1z5kzmz59PREQE7dq1u2It//znP/n222/5zW9+U+frERGRulGQEhER1zv2un3YnjNsVji2EqLqP0z5yJEj2Gw2evbsWe9jXSonJ4eEhAT69u0LQEREhGNf+/btAQgJCXGEtZKSEubNm8emTZuIjY119ElPT2fp0qWVgtScOXMYPnx4rWtZuXIl8fHxXH311fW9LBERcZKClIiIuF5uKrW/G1XBCrlpLglSNput3seoyaOPPsqECRPYsGEDw4YNIyEhgX79+tXY/siRI5w/f75KQCotLWXAgAGVtjnznNNXX31FSkoKf//73527ABERcQkFKRERcb2ygjr2y3fJ6Xv06IFhGE5PKGG6sJTExUGsrKysUpuxY8cSHx9PcnIyGzZsICkpiQULFjBp0qRqj1lUVARAcnIyXbp0qbTPfMmsqtUNQ6zJG2+8wVVXXcVPf/rTWvcRERHX0WQTIiLiej6BdewX5JLTt2/fnvj4eBYvXsy5c+eq7K9pevLg4GCASjPjZWZmVmkXFhbG+PHjWbt2LdOmTWP58uWAfTIIAIvF4mgbFRWF2WwmJyeH7t27V3qFhYXV6fpsNhtvvPEGDzzwAD4+PnU6hoiI1I+ClIiIuF7oUJz/iDFB6BCXlbB48WIsFgs33ngja9as4fDhw+zfv59XXnnF8azSpSrCzaxZszh8+DDJycksWLCgUpvJkyeTkpJCVlYWGRkZfPLJJ/Tq1QuAbt26YRgG69atIy8vj6KiItq2bcv06dOZMmUKq1ev5ujRo2RkZLBo0SJWr15dp2tLS0sjKyuLsWPH1qm/iIjUn4KUiIi4XsRoMJz8iDG8IGKM60qIiCAjI4O4uDimTZtGnz59GD58OKmpqSxZsqTaPj4+Prz99tscOHCAfv368fzzz/PMM89UamOxWEhMTKRXr17ccccdREZG8uqrrwLQpUsXZs+ezcyZM+nYsSOPPPIIAHPnzuXJJ58kKSnJ0S85OZnw8PA6XdvKlSu55ZZb3DKZhoiI1I5hc+cTuU1EYWEhgYGBFBQUEBAQ4OlyREQaleLiYrKysggPD8fPz6/2HbdPsC+2W6tJJ0zQfRzcWH3AEdep89+niEgLUdtsoDtSIiLiHtELIXgQV/6oMUHIrfb2IiIiTYSClIiIuIeXGYak2O80Gd5U/cgx2bd3HwdxKfb2IiIiTYSmPxcREffx8rMP1+s3x75Ib26afYpznyD7xBIRo8Ev2NNVioiIOE1BSkRE3M8v2L7QrgsW2xUREWkMNLRPRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIu5XWgY5p+A/hyBjv/1rzin7dg8zDIMPPvjA02WIiEgToyAlIiLuY7XCoWzYthuyvob/FsJ35+xfs762bz903N7ODXJzc5k0aRIRERGYzWbCwsIYOXIkqampbjnfp59+imEY5Ofnu+X4AIcOHeJnP/sZHTp0ICAggEGDBvHJJ5+47XwiIlI9BSkREXEPq9V+5+nUGbDV0MYGnMqzt3NxmMrOziY6Opq0tDRefPFF9uzZw/r164mLiyMxMdGl53I1m81GeXl5tft+8pOfUF5eTlpaGrt27aJ///785Cc/ITc3t4GrFBFp2RSkRETEPY6cgIKi2rUtKLK3d6GJEydiGAbbt28nISGByMhIevfuzdSpU9m2bVu1faq7o5SZmYlhGGRnZwNw/PhxRo4cSbt27fD396d379589NFHZGdnExcXB0C7du0wDIOHHnoIAKvVSlJSEuHh4bRq1Yr+/fvz3nvvVTnvxx9/THR0NGazmfT09Cr1nTlzhsOHDzNz5kz69etHjx49eO655zh//jxffvmla75xIiJSK96eLkBERJqh0jLIzXOuT24ehHcGH596n/7s2bOsX7+eZ599Fn9//yr7g4KC6nzsxMRESktL2bJlC/7+/uzbt482bdoQFhbGmjVrSEhI4ODBgwQEBNCqVSsAkpKSePPNN3nttdfo0aMHW7ZsYdSoUQQHB3P77bc7jj1z5kzmz59PREQE7dq1q3Luq666iuuuu46//OUv3HDDDZjNZpYuXUpISAjR0dF1viYREXGegpSIiLhe7mWG89XEhn0YYNdO9T79kSNHsNls9OzZs97HulROTg4JCQn07dsXgIiICMe+9u3bAxASEuIIayUlJcybN49NmzYRGxvr6JOens7SpUsrBak5c+YwfPjwGs9tGAabNm3i7rvvpm3btphMJkJCQli/fn21wUtERNxHQUpERFwv/7u693NBkLLZnE1xtffoo48yYcIENmzYwLBhw0hISKBfv341tj9y5Ajnz5+vEpBKS0sZMGBApW0xMTGXPbfNZiMxMZGQkBA+++wzWrVqxYoVKxg5ciQ7duygU6f6f+9ERKR29IyUiIi4XrmlYftdokePHhiGwYEDB5zqZzLZPxYvDmJlZZWnaB87dizHjh3j/vvvZ8+ePcTExLBo0aIaj1lUZH9OLDk5mczMTMdr3759lZ6TAqodhnixtLQ01q1bxzvvvMPAgQO54YYbePXVV2nVqhWrV6926lpFRKR+FKRERMT1vL0att8l2rdvT3x8PIsXL+bcuXNV9tc0PXlwcDAAp06dcmzLzMys0i4sLIzx48ezdu1apk2bxvLlywHw9fUFwGL5IRBGRUVhNpvJycmhe/fulV5hYWFOXdf58+eBHwJfBZPJhNVNU8iLiEj1FKRERMT1gto2bL9qLF68GIvFwo033siaNWs4fPgw+/fv55VXXnE8q3SpinAza9YsDh8+THJyMgsWLKjUZvLkyaSkpJCVlUVGRgaffPIJvXr1AqBbt24YhsG6devIy8ujqKiItm3bMn36dKZMmcLq1as5evQoGRkZLFq0yOm7SLGxsbRr144HH3yQ3bt3c+jQIR577DGysrK466676vaNEhGROlGQEhER1wvtAIaTfQwDOnVwWQkRERFkZGQQFxfHtGnT6NOnD8OHDyc1NZUlS5ZU28fHx4e3336bAwcO0K9fP55//nmeeeaZSm0sFguJiYn06tWLO+64g8jISF599VUAunTpwuzZs5k5cyYdO3bkkUceAWDu3Lk8+eSTJCUlOfolJycTHh7u1DV16NCB9evXU1RUxJAhQ4iJiSE9PZ1//OMf9O/fvw7fJRERqSvD5s4ncpuIwsJCAgMDKSgoICAgwNPliIg0KsXFxWRlZREeHo6fn1/tOx46bl9st7Y6BUNkN+cLFKfU+e9TRKSFqG020B0pERFxj+5hENimdm0D29jbi4iINBEKUiIi4h4mE/SLtN9pqmmYn4F9f79Ie3sREZEmQutIiYiI+5hM9uF613S2L9Kb/519inNvL/vEEqEdwNfH01WKiIg4TUFKRETcz9fHvtCuCxbbFRERaQw0jkJERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkzwapJKSkvjRj35E27ZtCQkJ4e677+bgwYOV2hQXF5OYmMhVV11FmzZtSEhI4PTp05Xa5OTkcNddd9G6dWtCQkJ47LHHKC8vb8hLERGRyzh06BDTp09n8ODBDBgwgMGDBzN9+nQOHTrk6dJERETqxKNBavPmzSQmJrJt2zY2btxIWVkZI0aM4Ny5c442U6ZM4cMPP+Tdd99l8+bNnDx5kp///OeO/RaLhbvuuovS0lI+//xzVq9ezapVq3jqqac8cUkiInKR3bt3M3ToUK677joWLlzI5s2byczMZPPmzSxcuJDrrruOYcOGsXv3bk+X2mh8+umnGIZBfn5+jW1WrVpFUFBQg9UkIiJVeTRIrV+/noceeojevXvTv39/Vq1aRU5ODrt27QKgoKCAlStX8tJLLzFkyBCio6N54403+Pzzz9m2bRsAGzZsYN++fbz55ptcf/313HnnncydO5fFixdTWlrqycsTEWnRUlNTiY2NZfPmzYD9F18Xq3j/6aefEhsbS2pqqlvqyM3NZdKkSURERGA2mwkLC2PkyJEuPd/gwYOZPHmyS451yy23cOrUKQIDA11yPBERcY9G9YxUQUEBAO3btwdg165dlJWVMWzYMEebnj170rVrV7Zu3QrA1q1b6du3Lx07dnS0iY+Pp7CwkL1791Z7npKSEgoLCyu9RETEdXbv3s3IkSMpLi6uEqAuZbFYKCkpYeTIkS6/M5WdnU10dDRpaWm8+OKL7Nmzh/Xr1xMXF0diYqJLz3UlNputVsPOfX19CQ0NxTCMBqhKRETqqtEEKavVyuTJkxk4cCB9+vQB7L9F9PX1rTJ8oWPHjuTm5jraXByiKvZX7KtOUlISgYGBjldYWJiLr0ZEpGWbOnUqpaWl2Gy2WrW3Wq2UlpYybdo0l9YxceJEDMNg+/btJCQkEBkZSe/evZk6dapjZEN+fj5jx44lODiYgIAAhgwZUinQzZo1i+uvv56//vWvXHPNNQQGBnLffffx3XffAfDQQw+xefNmXn75ZQzDwDAMsrOzHUP0Pv74Y6KjozGbzaSnp1NSUsKjjz5KSEgIfn5+DBo0iB07djjOV93QvlWrVtG1a1dat27NPffcw7ffflvpOnfv3k1cXBxt27YlICCA6Ohodu7c6dLvpYiIVNZoglRiYiJffvkl77zzjtvP9fjjj1NQUOB4nThxwu3nFBFpKQ4dOkRaWtoV70RdymKxkJqayuHDh11Sx9mzZ1m/fj2JiYn4+/tX2V/xS7p7772Xb775ho8//phdu3Zxww03MHToUM6ePetoe/ToUT744APWrVvHunXr2Lx5M8899xwAL7/8MrGxsTz88MOcOnWKU6dOVfoF3cyZM3nuuefYv38//fr14/e//z1r1qxh9erVZGRk0L17d+Lj4yud72JffPEFY8aM4ZFHHiEzM5O4uDieeeaZSm1+/etfc/XVV7Njxw527drFzJkz8fHxqe+3UERELqNRBKlHHnmEdevW8cknn3D11Vc7toeGhlJaWlrlgdvTp08TGhrqaHPpLH4V7yvaXMpsNhMQEFDpJSIirrFs2TK8vLzq1NfLy4ulS5e6pI4jR45gs9no2bNnjW3S09PZvn077777LjExMfTo0YP58+cTFBTEe++952hntVpZtWoVffr04dZbb+X+++93PGMVGBiIr68vrVu3JjQ0lNDQ0ErXP2fOHIYPH861116L2WxmyZIlvPjii9x5551ERUWxfPlyWrVqxcqVK6ut8eWXX+aOO+7g97//PZGRkTz66KPEx8dXapOTk8OwYcPo2bMnPXr04N5776V///71+faJiMgVeDRI2Ww2HnnkEd5//33S0tIIDw+vtD86OhofH59KDwQfPHiQnJwcYmNjAYiNjWXPnj188803jjYbN24kICCAqKiohrkQERFx2Llzp9N3oypYLBbHhEP1VZthhbt376aoqMixxEbFKysri6NHjzraXXPNNbRt29bxvlOnTpU+dy4nJibG8eejR49SVlbGwIEDHdt8fHy48cYb2b9/f7X99+/fz0033VRpW8VnYIWpU6cyduxYhg0bxnPPPVepdhERcQ9vT548MTGRv/3tb/zjH/+gbdu2jmeaAgMDadWqFYGBgYwZM4apU6fSvn17AgICmDRpErGxsdx8880AjBgxgqioKO6//35eeOEFcnNzeeKJJ0hMTMRsNnvy8kREWqSKiYPq6nLTfjujR48eGIbBgQMHamxTVFREp06d+PTTT6vsu/j53EuHyRmGgdVqrVUd1Q0rdLVZs2bxq1/9iuTkZD7++GOefvpp3nnnHe655x63n1tEpKXy6B2pJUuWUFBQwODBg+nUqZPj9X//93+ONn/605/4yU9+QkJCArfddhuhoaGsXbvWsd/Ly4t169bh5eVFbGwso0aN4oEHHmDOnDmeuCQRkRavvtN2u2p9pPbt2xMfH8/ixYsrrU9YIT8/nxtuuIHc3Fy8vb3p3r17pVeHDh1qfS5fX99a3YW79tpr8fX15V//+pdjW1lZGTt27KhxFEWvXr344osvKm2rmCjjYpGRkUyZMoUNGzbw85//nDfeeKPW9YuIiPM8ekeqNsMu/Pz8WLx4MYsXL66xTbdu3fjoo49cWZqIiNRRTEwM6enpdRre5+XlRXR0tMtqWbx4MQMHDuTGG29kzpw59OvXj/LycjZu3MiSJUvYt28fsbGx3H333bzwwgtERkZy8uRJkpOTueeeeyoNy7uca665hi+++ILs7GzatGnjWMbjUv7+/kyYMIHHHnuM9u3b07VrV1544QXOnz/PmDFjqu3z6KOPMnDgQObPn8/PfvYzUlJSWL9+vWP/999/z2OPPcYvfvELwsPD+eqrr9ixYwcJCQnOf8NERKTWGsVkEyIi0nyMGzeuXs9I/fa3v3VZLREREWRkZBAXF8e0adPo06cPw4cPJzU1lSVLlmAYBh999BG33XYbv/nNb4iMjOS+++7j+PHjVZbWuJzp06fj5eVFVFQUwcHB5OTk1Nj2ueeeIyEhgfvvv58bbriBI0eOkJKSQrt27aptf/PNN7N8+XJefvll+vfvz4YNG3jiiScc+728vPj222954IEHiIyM5Je//CV33nkns2fPrv03SkREnGbYarvIRzNWWFhIYGAgBQUFmsFPROQSxcXFZGVlER4ejp+fX636DB06lM2bNzsVqLy8vIiLi2Pjxo11LVVqoS5/nyIiLUlts4FHh/aJiEjz9NJLLxEbG0tJSUmtJmUwmUz4+voyf/78Bqju8sosZZw5f4bvSr/DYrXgZfKirW9bOrTugI+X1mYSERE7BSkREXG5/v378+GHHzJy5EhKS0sve2fKy8sLX19fPvzwQ4+ufWS1WTlRcIIz589go/JgjcKSQk5+d5IOrTsQFhiGydDIeBGRlk6fBCIi4hZDhw5l69atDB48GKDKIr0V7+Pi4ti6dStDhw5t6BIdrDYrh749RN75vCohqoING3nn8zj87WGsttpNfS4iIs2X7kiJiIjb9O/fn02bNnH48GGWLl3Krl27yM/PJygoiOjoaH7729/So0cPT5fJiYITFJUW1artd6XfcaLgBN2Curm5KhERacwUpEREpFbqMzdRjx49GsXzT9WpeCbKGWfOn6Fz285N8pkpzTElIuIaGtonIiKX5eNjDwvnz5/3cCXuUd0zUVdiw+Z0+GosSktLgapDLUVExDm6IyUiIpfl5eVFUFAQ33zzDQCtW7fGMAwPV+U6+UX5UO58v9P5pwn0DmxSE09YrVby8vJo3bo13t76EUBEpD70f1EREbmi0NBQAEeYak5OfXeKUktpnfrmncyjc9vOTSpMmUwmunbt2qzCsIiIJyhIiYjIFRmGQadOnQgJCaGsrMzT5bhMSXkJ//PG/1BYUljnY4T4h7Dp/k34evu6sDL38fX1xWRqOsFPRKSxUpASEZFa8/LyalbP1kxJnsKXZ7+s+ozUGWAXcBIoAcxAZyAa6FC56fFzx3l006OsunuV+wsWEZFGw7Bp+h4KCwsJDAykoKCAgIAAT5cjIiINIO9cHp1f6ky59aIHpHKBFCALMKBSvqp4Hw7EA6E/7DIZJk5PP02H1pekLBERaXJqmw10b19ERFqk1//9Ohar5YcNx4AVQPaF95f+mrHiffaFdsd+2GW1WVmZsdItdYqISOOkICUiIi1SalbqD0P6coG/YZ+970rjNGyA5UL73B82p2WluaFKERFprBSkRESkRTpz7qJ1oFKwh6PaqghTKT9syi/Jd0ldIiLSNChIiYhIi/Tf4v/a/3AG+zNRzj4xbLvQ71v72yBzkKtKExGRJkBBSkREWpzi8mK++u4r+5td2CeSqAsD2Gn/45DwIS6oTEREmgoFKRERaXGmpEz5Yba+kzh/N6qCDThl/+OYG8a4oDIREWkqFKRERKRFyTuXx4qMFT9sKKnnAYvBx+Sjqc9FRFoYBSkREWlRXv/361ht1h82mOt5QD/w9/Wv50FERKSpUZASEZEWJTUrtXKQ6kz9npHqBG1827igMhERaUoUpEREpEUpKC6ovCGa+j0jFQNdA7rWsyoREWlqFKRERKRFCfQLrLyhAxCO83elDCACuAoCzAEuqU1ERJoOBSkREWlRhoYPrboxHvCi9mHKuNB+hP1tcXmxS2oTEZGmQ0FKRERalNEDRlfdGAr8itqFqYoQ9asL/YDz5eddWKGIiDQFClIiItKiBPsHY/aqZqq+CGAscM2F95cGqor34RfaRfywK8gc5NIaRUSk8fP2dAEiIiIN7dp217LvzL6qO0KBB4FvgZ3YF9stBvyATkAMcFXlLibDxJDwIW6tV0REGh8FKRERaXG6BHSpPkhVuAr7c1O14GV4MeaGMS6pS0REmg4N7RMRkRan2gkn6sBkmBhzwxg6tO7gkuOJiEjToSAlIiItzugBo/E21W9QhskwcWvXW1kYv9A1RYmISJOiICUiIi1OsH8wY28Yi8lw/mPQZJjwNnkzLnocKaNSMHtXM3GFiIg0e3pGSkREWqSF8QvZl7eP9Jx0rDZrje0MDIL8gri2/bW092vPkPAhjB4wmmD/4AasVkREGhsFKRERaZHM3mZSRqUwJWUKKzJWYLVZKwUqk2HCZJgYe8NYFsYv1J0nERGpxLDZbDZPF+FphYWFBAYGUlBQQEBAgKfL8YziPDi6Ek6nQVkB+ARC6FCIGA1++q2riDRveefyeP3fr5OWlUZ+ST5B5iDdeRIRuVhpGeSegfzvoNwC3l4Q1BZCO4Cvj6erc6naZgMFKVp4kLIUw67J9hBlswIXD28xgWGCa8dC9EKobgFLEREREWm+rFY4kmMPUTWlBi8vaNsa2gU0i2BV22ygoX0tmaUY0uIhL53KAaqC1R6ujiyDwv0Ql6IwJSIiItJSWK2w+xAUFl2+ncViv1OV/x1kfw2hwdA9DEzNe1675n11cnm7plwmRF3MCt98Zr9zJSIiIiItw+GcK4eoS9mAU3nwn0P2INaMKUi1VMV5cHQFVw5RFaz29sVn3FmViIiIiDQGFc9E1VVBERw54bp6GiEFqZbq2OsXnolygq0cjix1Tz0iIiIi0nh8/U39j5GbB2Vl9T9OI6Ug1VLlplL7u1EX2T8fLCUuL0dEREREGpFvztb/GDbgVPMdzaQg1VKVFdSxX76elRIRERFp7lx1Jyn/O9ccpxFSkGqpfALr3lfPSomIiIhIbZRbPF2B2yhItVShQwGjbn1t5XBspUvLEREREZFGxFVrQXl7ueY4jZCCVEsVMbp+/b/+yDV1iIiIiEjjE9zONcdp6++a4zRCClItlV8w+ATVvf+3O11WioiIiIg0Ml06uuY4Oafg0PFmuaaUt6cLEA8y6ji0D8B63v6clF8H19UjIiIiIo2Drw+EdnCsJXXoxHGWffg+Ow/up+BcEYH+bYi5rhfjRt5DZFi3yx/rVB6c/x76RYKp+dzHUZBqySzF9et/6BXoN8c1tYiIiIhI49KjK7s/38bUP79EWsYOvExeWKw/TB6Rvmc3C/7+FkNv+BELJk6mf/fImo9VsUBv5BVCVxPi0Ui4ZcsWRo4cSefOnTEMgw8++KDSfpvNxlNPPUWnTp1o1aoVw4YN4/Dhw5XanD17ll//+tcEBAQQFBTEmDFjKCoqasCraMLqe4s1+x3X1CEiIiIijU7qJ58QO/E3bM7cBVApRF38/tPMXcQmjiZ11/bLH7CZLdDr0SB17tw5+vfvz+LFi6vd/8ILL/DKK6/w2muv8cUXX+Dv7098fDzFxT/cSfn1r3/N3r172bhxI+vWrWPLli2MGzeuoS6habPV8x9yyWnX1CEiIiIijcru3bsZOXIkxSUlWK7wy3eL1UpJaSkj/zCV3UcO1dywmS3Qa9hsNpuniwAwDIP333+fu+++G7DfjercuTPTpk1j+vTpABQUFNCxY0dWrVrFfffdx/79+4mKimLHjh3ExMQAsH79en784x/z1Vdf0blz51qdu7CwkMDAQAoKCggICHDL9TU6xXmwNqR+x/BuC78sdE09IiIiItJoDB06lM2bN2Ox1H4dKC+TicHXR7PppVdrbtQuwP6sVCNW22zQaJ/2ysrKIjc3l2HDhjm2BQYGctNNN7F161YAtm7dSlBQkCNEAQwbNgyTycQXX3xR47FLSkooLCys9Gpxjr1+xSaHTsH0t2DwXBjwB/vX6W/ZtwNg0iN2IiIiIs3NoUOHSEtLcypEgf3OVGrGDg5/lVNzo2a0QG+jDVK5ubkAdOxYeerFjh07Ovbl5uYSElL5roq3tzft27d3tKlOUlISgYGBjldYWJiLq28CclNr3LX7OAydB9dNh4XrYfMByDxu/7pwvX37sHmwO6v5/IcgIiIiInbLli3Dy6tuC+l6mUws/efamhs0owV6G22QcqfHH3+cgoICx+vEiROeLqnhlRVUuzn1S4idBZv3299bLhkSW/H+0/0Q+0Qhqak1BzIRERERaXp27tzp9N2oCharlV2HDtTcIKhtHatqfBptkAoNDQXg9OnKExqcPn3asS80NJRvvvmm0v7y8nLOnj3raFMds9lMQEBApVeL4xNYZdPu4zByARSXVQ1Ql7JYoaQMRo4cye7du91UpIiIiIg0tIKC6n/hXlv5Rd9Vv8MwoFPzWYO00Qap8PBwQkNDK93xKCws5IsvviA2NhaA2NhY8vPz2bVrl6NNWloaVquVm266qcFrblJCh3LpX//Ut6C0HGo7/YjVBqUlxUybNs319YmIiIiIRwQGVv2FuzOC2tRw18nPDHUcMtgYeTRIFRUVkZmZSWZmJmCfYCIzM5OcnBwMw2Dy5Mk888wz/POf/2TPnj088MADdO7c2TGzX69evbjjjjt4+OGH2b59O//617945JFHuO+++2o9Y1+LFTEajB/++g+dgrS9V74TdSmL1UZqamqV9b1EREREpGmKiYmp1zNS0ZE9q9/5fTH851D91zJtJDwapHbu3MmAAQMYMGAAAFOnTmXAgAE89dRTAPz+979n0qRJjBs3jh/96EcUFRWxfv16/Pz8HMd466236NmzJ0OHDuXHP/4xgwYNYtmyZR65nibFLxiuHUvFP4FlaeBVx38NXl4GS5cudV1tIiIiIuIx48aNq9czUr/96c9rblBQBEeax/wEjWYdKU9qketIAVhKIG0E5H3G4Lk2Nl/mucArGXz7rXzy6RbX1SYiIiIiHlPXdaTiBkSzccFl1pECMIDY/uDjU78i3aTJryMlDcDLDENSoPtvKfi+fofKz9lqD2YiIiIi0uS99NJL+Pr6YjLVLi6YDANfHx/mT5h85cY24NSZetXXGChItXRefnDjEgK73lKvwwT5lcP28S4qSkREREQ8qX///nz44YeYzeYrPi/lZTJh9vXlw3kv0b97ZO1OcOoMlJa5oFLPUZASAGJujMWrjg9JeZkgOhzI+gsUN/3fLoiIiIiIfXjf1q1bGTx4MECVQFXxPm5ANFsXv87Q6Btrf/DiEti2Gw4db7KTT+gZKVrwM1IXOXToENddd13d+y+AHqHA9c9B1AzXFSYiIiIiHnf48GGWLl3Krl27yM/PJygoiOjoaH57+wh6BLSv38ED20C/SKjlMEJ3q202UJBCQarC0Ju7s3nHUaemQPcyQVwv2PiHCxtCR9ifuxIRERGR5i/nFGR9Xf/jdAqGyG71P44LaLIJcdpLY4Lx9QaTUbv2JgN8vWH+ry/aWJbvjtJEREREpDEK7WCfha++cvOgrGk9M6UgJQ79u1r5cBqYfa68ppSXyd7uw2nQ/+JfHvgEubNEEREREWlMfH0gNLj+x2mCM/kpSMkPfAIZ2ge2zoLBveybLg1UFe/jetnbDe1z8V4ThA5xf50iIiIi0nh0D7M/51Rf+d/V/xgNyNvTBUgjEjoUclPp383Kpj/A4VxYmgq7siD/PAS1ts/O99uhFyaWuJThBRFjGrxsEREREfEgk8k+WcSRE/YhenWdgaG89ov/NgYKUvKDiNGw+wmw2Web6BF6yfNPl2WCa8eAXwe3lSciIiIijZTJZJ8s4prOkHkAvi9x/hjel1+vqrHR0D75gV8wXDsW5/9ZmCDkVohe6IaiRERERKTJ8PWxT0BRF0FtXVuLmylISWXRCyF4ELX+p2F4Q/dxEJcCXmZ3ViYiIiIiTUFdZvIzDOjUtEY2KUhJZV5m+zpQ3cfZQ1JN/0RaXQ39noF7TsKNSxSiRERERMSuLjP5hXYAHx/31OMmekZKqvLys4ejfnPg2OuQm2ZfH8onyD4rX8Ro+zBAEREREZHqdA+D899DQdGV2wa2sbdvYgybzVbXeTWajdquXiwiIiIiIjUoLYPcM/ZpzMst9nVzLBYoOl/9TH4G9jtX3cPsk1U0ErXNBrojJSIiIiIidWe1wpEce4iq6RZNW/8Lwcpqn50vqK19OJ9v0xrOdzEFKRERERERqRurFf5z6MpD+L47Zx/Cd/11jeruU300j6sQEREREZGGd+RE7Z6DAnu7IyfcW08DUpASERERERHnlZZBbp5zfXLzoKzMPfU0MA3tExERERG51KUTJzST53pc6nLPRNXEBpw6A107uaOiBqUgJSIiIiJS4XITJ/y3ELK/bpQzzXlE/nd179cMglQL/9sXEREREbmgYuKEU5e502IDTuXZ21mtDVld41Nuadh+jYyClIiIiIgItOiJE+rE26th+zUyGtondVOcB0dXwuk0KCsAn0AIHQoRo8Ev2NPViYiIiDinrhMnhHcGnxb6zFRQW/twx7r0awYUpMQ5lmLYNdkeomxW4KJb2rmpsPsJuHYsRC8EL7OHihQRERFxUgufOKFOQjvYnxlz5vtmGNCpg9tKakga2ie1ZymGtHg4shxs5VQKUWB/byuHI8vgk3iwlHiiShERERHn1WfihJbK18c+8YYzQjs0mzt4ClJSe7umQF46VQPUpazwzWf2O1ciIiIiTUELnzihzrqHQWCb2rUNbGNv30woSEntFOfB0RVcOURVsNrbF59xZ1UiIiIirtHCJ06oM5MJ+kVCp2AwamhjYN/fL7JZTRmvZ6Skdo69fuGZKCfYrHBsJUTNcE9NIiIiIq7SwidOqBeTCSK7wTWdW9QixgpSUju5qdT+blQFK+SmKUiJiIhI49fCJ05wCV8f+8QbLWTyjeZzb03cq6ygjv3yXVqGiIiIiFu08IkTxHkKUlI7PoF17Bfk0jJERERE3KYFT5wgzlOQktoJHYrz/1xMEDrEHdWIiIiIuF4LnjhBnKdnpKR2IkbbF9t1ZsIJwwsixrivJhERERFXa6ETJ4jzFKSkdvyCIfwhOLailh1McO0Y8NMDmCIiItIEtbCJE8R5uh8ptWMphu8O1L598ECI/v/27j04qvru4/hnNwubBEwChCQGEgjgSLlIKSk0ijpIpgEZ7MXRyqQ0AtbB4gji0GgddKYdBHUGsTM2VKv0j6pYZ5C2DMpgglzmSQMEAqZahIKAyiYozQU1Etjv88eafVhuyeFJ9mw279fMjub8frvzPee7tw9n97eruqwcAAAAwE0EKXRM9cPSyf/p+PyUkVKCv+vqAQAAAFxEkEL7Wk5K//mTHP2O1OE1UsvnXVYSAAAA4CaCFNp3+BVni0xIofmHX+6aegAAAACXEaTQvkC5HJ2NkkLzAxVdUQ0AAADgOoIU2tfaeJXXa+jUMgAAAIBYQZBC+3qlXuX10jq1DAAAACBWEKTQvqypcn5X8UpZt3VFNQAAAIDrCFJo37C5ksfhXcWTIA2b1zX1AAAAAC4jSKF9iQOl4fep43cXrzR8npSY3pVVAQAAAK4hSKFjJqySBk5W+3cZr5Rxc2g+AAAAEKcIUuiYBL902yZpxP2Sx6eL7zre0PYR90tTNoXmAwAAAHHK53YB6EYSEqWJZdINvw39SG+gIrTEea+00MISw+aGPgYIAAAAxLm4OSP1wgsvaOjQoUpMTNSkSZO0c+dOt0uKX4kDpVGloTNURVWh/44qJUQBAACgx4iLIPXGG29o8eLFevLJJ7Vnzx6NGzdORUVFqq+vd7s0AAAAAHEoLoLUypUr9ctf/lJz5szRqFGjtHr1aiUnJ+uVV15xuzQAAAAAcajbB6kzZ86ourpahYWF4W1er1eFhYWqrKy85HW++eYbNTU1RVwAAAAAoKO6fZD6/PPPde7cOWVmZkZsz8zMVCAQuOR1li9frtTU1PAlJycnGqUCAAAAiBPdPkhdjccee0yNjY3hy/Hjx90uCQAAAEA30u2XP09PT1dCQoLq6uoittfV1SkrK+uS1/H7/fL7+Z0jAAAAAFen25+R6t27tyZMmKDy8vLwtmAwqPLychUUFLhYGQAAAIB41e3PSEnS4sWLVVJSovz8fE2cOFGrVq3Sl19+qTlz5rhdGgAAAIA4FBdB6mc/+5lOnjypJ554QoFAQN/97nf1zjvvXLQABbqJlpPSf16W6iqk1kapV6qUNVUaNpcf/QUAAEBM8JiZuV2E25qampSamqrGxkalpKS4XU7Pda5Fql4UClEWlBQ8b9ArebzS8PukCaukBL7jBgAAgM7X0WwQF2ekEAfOtUgVRdLJHYoMUG2CoXB16EWp6UNpyibCFAAAAFzT7RebQJyofvgKIep8Qal+e+jMFQAAAOASghTc13JS+s+f1H6IahMMzW/5vCurAgAAAC6LIAX3HX7l2+9EOWBB6fDLXVMPAAAA0A6CFNwXKFfHz0a1CUqBiq6oBgAAAGgXQQrua228yus1dGoZAAAAQEcRpOC+XqlXeb20Ti0DAAAA6CiCFNyXNVXO74peKeu2rqgGAAAAaBdBCu4bNjf0Y7tOeBKkYfO6ph4AAACgHQQpuC9xoDT8PnX87uiVhs+TEtO7sioAAADgsghSiA0TVkkDJ6v9u6RXyrg5NB8AAABwCUEKsSHBL922SRpxv+Tx6eK7pje0fcT90pRNofkAAACAS3xuFwCEJSRKE8ukG34b+pHeQEVoifNeaaGFJYbNDX0MEAAAAHAZQQqxJ3GgNKo0dAEAAABiEB/tAwAAAACHCFIAAAAA4BBBCgAAAAAcIkgBAAAAgEMEKQAAAABwiCAFAAAAAA4RpAAAAADAIYIUAAAAADhEkAIAAAAAhwhSAAAAAOAQQQoAAAAAHPK5XUAsMDNJUlNTk8uVAAAAAHBTWyZoywiXQ5CS1NzcLEnKyclxuRIAAAAAsaC5uVmpqamXHfdYe1GrBwgGg/rss890zTXXyOPxuF0OFPqXgJycHB0/flwpKSlulwPRk1hET2ITfYk99CT20JPYRF9CzEzNzc3Kzs6W13v5b0JxRkqS1+vV4MGD3S4Dl5CSktKjH8ixiJ7EHnoSm+hL7KEnsYeexCb6oiueiWrDYhMAAAAA4BBBCgAAAAAcIkghJvn9fj355JPy+/1ul4Jv0ZPYQ09iE32JPfQk9tCT2ERfnGGxCQAAAABwiDNSAAAAAOAQQQoAAAAAHCJIAQAAAIBDBCkAAAAAcIgghahZvny5vv/97+uaa65RRkaGfvzjH+vAgQMRc1paWrRgwQINGDBAffv21Z133qm6urqIOceOHdOMGTOUnJysjIwMLVmyRGfPno3mrsStFStWyOPxaNGiReFt9CT6Pv30U/385z/XgAEDlJSUpLFjx2r37t3hcTPTE088oWuvvVZJSUkqLCzUwYMHI27j1KlTKi4uVkpKitLS0jRv3jydPn062rsSN86dO6elS5cqLy9PSUlJGj58uH73u9/p/PWa6EvX2rZtm2bOnKns7Gx5PB6tX78+Yryzjv/+/ft18803KzExUTk5OXrmmWe6ete6rSv1pLW1VaWlpRo7dqz69Omj7Oxs/eIXv9Bnn30WcRv0pPO191g53/z58+XxeLRq1aqI7fSlgwyIkqKiIluzZo3V1tZaTU2N3X777Zabm2unT58Oz5k/f77l5ORYeXm57d69237wgx/YjTfeGB4/e/asjRkzxgoLC23v3r22ceNGS09Pt8cee8yNXYorO3futKFDh9oNN9xgCxcuDG+nJ9F16tQpGzJkiN17771WVVVlhw8ftk2bNtmhQ4fCc1asWGGpqam2fv1627dvn91xxx2Wl5dnX3/9dXjOtGnTbNy4cfbPf/7Ttm/fbiNGjLBZs2a5sUtxYdmyZTZgwADbsGGDHTlyxN58803r27evPf/88+E59KVrbdy40R5//HFbt26dSbK33norYrwzjn9jY6NlZmZacXGx1dbW2uuvv25JSUn2xz/+MVq72a1cqScNDQ1WWFhob7zxhv373/+2yspKmzhxok2YMCHiNuhJ52vvsdJm3bp1Nm7cOMvOzrbnnnsuYoy+dAxBCq6pr683SbZ161YzCz3p9urVy958883wnA8//NAkWWVlpZmFnhy8Xq8FAoHwnLKyMktJSbFvvvkmujsQR5qbm+26666zzZs326233hoOUvQk+kpLS23y5MmXHQ8Gg5aVlWXPPvtseFtDQ4P5/X57/fXXzczsgw8+MEm2a9eu8Jy3337bPB6Pffrpp11XfBybMWOGzZ07N2LbT3/6UysuLjYz+hJtF7457Kzj/4c//MH69esX8dxVWlpq119/fRfvUfd3pTfsbXbu3GmS7OjRo2ZGT6Lhcn355JNPbNCgQVZbW2tDhgyJCFL0peP4aB9c09jYKEnq37+/JKm6ulqtra0qLCwMzxk5cqRyc3NVWVkpSaqsrNTYsWOVmZkZnlNUVKSmpib961//imL18WXBggWaMWNGxLGX6Ikb/v73vys/P1933XWXMjIyNH78eL300kvh8SNHjigQCET0JDU1VZMmTYroSVpamvLz88NzCgsL5fV6VVVVFb2diSM33nijysvL9dFHH0mS9u3bpx07dmj69OmS6IvbOuv4V1ZW6pZbblHv3r3Dc4qKinTgwAH997//jdLexK/GxkZ5PB6lpaVJoiduCQaDmj17tpYsWaLRo0dfNE5fOs7ndgHomYLBoBYtWqSbbrpJY8aMkSQFAgH17t07/ATbJjMzU4FAIDzn/DfsbeNtY3Bu7dq12rNnj3bt2nXRGD2JvsOHD6usrEyLFy/Wb37zG+3atUsPPfSQevfurZKSkvAxvdQxP78nGRkZEeM+n0/9+/enJ1fp0UcfVVNTk0aOHKmEhASdO3dOy5YtU3FxsSTRF5d11vEPBALKy8u76Dbaxvr169cl9fcELS0tKi0t1axZs5SSkiKJnrjl6aefls/n00MPPXTJcfrScQQpuGLBggWqra3Vjh073C6lRzt+/LgWLlyozZs3KzEx0e1yoNA/MuTn5+upp56SJI0fP161tbVavXq1SkpKXK6u5/rrX/+qV199Va+99ppGjx6tmpoaLVq0SNnZ2fQFaEdra6vuvvtumZnKysrcLqdHq66u1vPPP689e/bI4/G4XU63x0f7EHUPPvigNmzYoC1btmjw4MHh7VlZWTpz5owaGhoi5tfV1SkrKys858IV49r+bpuDjquurlZ9fb2+973vyefzyefzaevWrfr9738vn8+nzMxMehJl1157rUaNGhWx7Tvf+Y6OHTsm6f+O6aWO+fk9qa+vjxg/e/asTp06RU+u0pIlS/Too4/qnnvu0dixYzV79mw9/PDDWr58uST64rbOOv48n3W+thB19OhRbd68OXw2SqInbti+fbvq6+uVm5sbft0/evSoHnnkEQ0dOlQSfXGCIIWoMTM9+OCDeuutt1RRUXHRKeEJEyaoV69eKi8vD287cOCAjh07poKCAklSQUGB3n///YgHeNsT84VvPtG+qVOn6v3331dNTU34kp+fr+Li4vD/05Pouummmy76WYCPPvpIQ4YMkSTl5eUpKysroidNTU2qqqqK6ElDQ4Oqq6vDcyoqKhQMBjVp0qQo7EX8+eqrr+T1Rr5kJiQkKBgMSqIvbuus419QUKBt27aptbU1PGfz5s26/vrre8xHlTpTW4g6ePCg3n33XQ0YMCBinJ5E3+zZs7V///6I1/3s7GwtWbJEmzZtkkRfHHF7tQv0HA888IClpqbae++9ZydOnAhfvvrqq/Cc+fPnW25urlVUVNju3butoKDACgoKwuNtS23/8Ic/tJqaGnvnnXds4MCBLLXdic5ftc+MnkTbzp07zefz2bJly+zgwYP26quvWnJysv3lL38Jz1mxYoWlpaXZ3/72N9u/f7/96Ec/uuQyz+PHj7eqqirbsWOHXXfddSyz/f9QUlJigwYNCi9/vm7dOktPT7df//rX4Tn0pWs1Nzfb3r17be/evSbJVq5caXv37g2vANcZx7+hocEyMzNt9uzZVltba2vXrrXk5OQet6RzR12pJ2fOnLE77rjDBg8ebDU1NRGv++ev9EZPOl97j5ULXbhqnxl96SiCFKJG0iUva9asCc/5+uuv7Ve/+pX169fPkpOT7Sc/+YmdOHEi4nY+/vhjmz59uiUlJVl6ero98sgj1traGuW9iV8XBil6En3/+Mc/bMyYMeb3+23kyJH24osvRowHg0FbunSpZWZmmt/vt6lTp9qBAwci5nzxxRc2a9Ys69u3r6WkpNicOXOsubk5mrsRV5qammzhwoWWm5triYmJNmzYMHv88ccj3hDSl661ZcuWS76GlJSUmFnnHf99+/bZ5MmTze/326BBg2zFihXR2sVu50o9OXLkyGVf97ds2RK+DXrS+dp7rFzoUkGKvnSMx+y8n2UHAAAAALSL70gBAAAAgEMEKQAAAABwiCAFAAAAAA4RpAAAAADAIYIUAAAAADhEkAIAAAAAhwhSAAAAAOAQQQoAAAAAHCJIAQAAAIBDBCkAQI9y7733yuPxXHQ5dOiQtm3bppkzZyo7O1sej0fr1693u1wAQIwiSAEAepxp06bpxIkTEZe8vDx9+eWXGjdunF544QW3SwQAxDif2wUAABBtfr9fWVlZF22fPn26pk+f7kJFAIDuhjNSAAAAAOAQQQoA0ONs2LBBffv2DV/uuusut0sCAHQzfLQPANDjTJkyRWVlZeG/+/Tp42I1AIDuiCAFAOhx+vTpoxEjRrhdBgCgG+OjfQAAAADgEGekAAD41unTp3Xo0KHw30eOHFFNTY369++v3NxcFysDAMQaghQAAN/avXu3pkyZEv578eLFkqSSkhL9+c9/dqkqAEAs8piZuV0EAAAAAHQnfEcKAAAAABwiSAEAAACAQwQpAAAAAHCIIAUAAAAADhGkAAAAAMAhghQAAAAAOESQAgAAAACHCFIAAAAA4BBBCgAAAAAcIkgBAAAAgEMEKQAAAABw6H8B0xX13IGUx2EAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -1383,7 +1383,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 238, "metadata": {}, "outputs": [], "source": [ @@ -1499,7 +1499,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 239, "metadata": {}, "outputs": [], "source": [ @@ -1527,11 +1527,38 @@ "\n", " # Define the hyperparameter grid for Ridge regression\n", " param_grid = {\n", - " 'ridge__alpha': [0.1, 0.5, 1.0, 10.0, 50.0, 100.0]\n", + " \"ridge__alpha\": [\n", + " 1e-15,\n", + " 1e-10,\n", + " 1e-8,\n", + " 1e-3,\n", + " 1e-2,\n", + " 1e-1,\n", + " 0.5,\n", + " 1,\n", + " 5,\n", + " 10,\n", + " 20,\n", + " 30,\n", + " 35,\n", + " 40,\n", + " 45,\n", + " 50,\n", + " 55,\n", + " 100,\n", + " ]\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", " }\n", "\n", " # Initialize GridSearchCV with the pipeline and parameter grid\n", - " grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring=make_scorer(r2_score))\n", + " grid_search = GridSearchCV(\n", + " pipeline, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", + " )\n", "\n", " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", " # Left iris X and right iris X\n", @@ -1553,7 +1580,11 @@ " best_model_x = grid_search.best_estimator_\n", " y_pred_x = best_model_x.predict(X_test_x)\n", " r2_score_x = r2_score(y_test_x, y_pred_x)\n", - " print(f'Best alpha for X: {grid_search.best_params_[\"ridge__alpha\"]}, R2 score: {r2_score_x}')\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alpha for X: {grid_search.best_params_[\"ridge__alpha\"]}, R2 score: {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", "\n", " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", " # Left iris Y and right iris Y\n", @@ -1575,7 +1606,11 @@ " best_model_y = grid_search.best_estimator_\n", " y_pred_y = best_model_y.predict(X_test_y)\n", " r2_score_y = r2_score(y_test_y, y_pred_y)\n", - " print(f'Best alpha for Y: {grid_search.best_params_[\"ridge__alpha\"]}, R2 score: {r2_score_y}')\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alpha for Y: {grid_search.best_params_[\"ridge__alpha\"]}, R2 score: {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", "\n", " # Plot the true and predicted points for X and Y\n", " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" @@ -1583,20 +1618,64 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 240, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.17974e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.7762e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.28033e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.37059e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.085e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha for X: 1e-08, R2 score: 0.9959812973287664\n", + "-------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.04822e-18): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.00072e-18): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.03219e-18): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.41968e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.07833e-18): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.13042e-19): result may not be accurate.\n", + " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Best alpha for X: 0.1, R2 score: 0.9984578727216439\n", - "Best alpha for Y: 0.1, R2 score: 0.9770010267587734\n" + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha for Y: 1e-15, R2 score: 0.9808758718535125\n", + "-------------------------------------------------------\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] From 093ed62fb3a1a55fbc356a059f54026a67f2a114 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 4 Jul 2024 07:03:09 +0000 Subject: [PATCH 24/78] ridge cv grid search also updated --- .../test_ridgeCV_regression_grid_search.ipynb | 40 +++++++++++++------ 1 file changed, 27 insertions(+), 13 deletions(-) diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb index 3cbda5af..53fc2086 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb @@ -341,7 +341,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -590,7 +590,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -781,7 +781,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGxCAYAAABIjE2TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABcMUlEQVR4nO3deXxU1f0//tedPTPJzGSfhOwJAmGXLcOqhYqI1gWtWGQXKg1WpR8/frDW7dOCXz/91aVVUFSgVbRqC1VcESVsYRUQAgZIAgkkk0CWmayz3fv7I2RkEpAsk2SSeT0fj7TOnJOZcy8k8+Lec85bkCRJAhEREZEfkXX3AIiIiIiaY0AhIiIiv8OAQkRERH6HAYWIiIj8DgMKERER+R0GFCIiIvI7DChERETkdxhQiIiIyO8ounsA7SGKIoqLixESEgJBELp7OERERNQKkiShuroasbGxkMl++hpJjwwoxcXFiI+P7+5hEBERUTsUFRUhLi7uJ/v0yIASEhICoPEA9Xp9N4+GiIiIWsNmsyE+Pt7zOf5TemRAabqto9frGVCIiIh6mNZMz+AkWSIiIvI7DChERETkdxhQiIiIyO8woBAREZHfYUAhIiIiv8OAQkRERH6nTQElKSkJgiC0+MrMzAQANDQ0IDMzE+Hh4QgODsaMGTNQWlrq9RqFhYWYPn06tFotoqKi8Nhjj8HlcvnuiIiIiKjHa1NA2b9/P0pKSjxfW7ZsAQDcc889AIBHH30Un3zyCT788ENkZWWhuLgYd911l+f73W43pk+fDofDgd27d2P9+vVYt24dnnrqKR8eEhEREfV0giRJUnu/+ZFHHsHmzZtx6tQp2Gw2REZGYsOGDbj77rsBAD/88AMGDBiA7OxsZGRk4PPPP8ett96K4uJiREdHAwBWr16Nxx9/HBcuXIBKpWrV+9psNhgMBlitVm7URkRE1EO05fO73XNQHA4H3nnnHSxYsACCIODgwYNwOp2YMmWKp0///v2RkJCA7OxsAEB2djYGDx7sCScAMHXqVNhsNuTk5LR3KEREROQjoijh6Dkrsk5ewNFzVohiu69jdEi7t7rftGkTqqqqMG/ePACAxWKBSqWC0Wj06hcdHQ2LxeLpc3k4aWpvarsau90Ou93ueWyz2do7bCIiIrqK3acv4tVvTyOnxAanS4RSIcPAGD0yb0zD2LSILh1Lu6+gvPXWW5g2bRpiY2N9OZ4rWrlyJQwGg+eLlYyJiIh8a/fpi8jc8B1255Wjqs6JWocbVXVO7M4rb3z+9MUuHU+7AsrZs2fx9ddf44EHHvA8ZzKZ4HA4UFVV5dW3tLQUJpPJ06f5qp6mx019rmT58uWwWq2er6KiovYMm4iIiK5AFCX8ftNRVNY5IQEQLvuSAFTWOfH7TUe79HZPuwLK2rVrERUVhenTp3ueGzFiBJRKJbZu3ep5Ljc3F4WFhTCbzQAAs9mMo0ePoqyszNNny5Yt0Ov1SE9Pv+r7qdVqT+ViVjAmIiLyre+LqnCmvA5AYyi5PKE01R0+U16H74uqumxMbZ6DIooi1q5di7lz50Kh+PHbDQYDFi5ciGXLliEsLAx6vR4PPfQQzGYzMjIyAAA33XQT0tPTMXv2bLzwwguwWCx48sknkZmZCbVa7bujIiIiolb7PMcCz5peoVnjpcsoktTYb1hiaJeMqc0B5euvv0ZhYSEWLFjQou3FF1+ETCbDjBkzYLfbMXXqVLz22muedrlcjs2bN2PJkiUwm83Q6XSYO3cunnvuuY4dBREREbVbraN1G6a2tp8vdGgflO7CfVCIiIh8518Hz+F3Hx7xPBYuu4pyeUr4/+4Zihkj4tr9Pl2yDwoRERH1DrcPjYVOLfc8lqQfv5ro1HLcPrTzV+42YUAhIiIKcAqFDI9M7gtZ8/knl8gE4JHJfaFQdF1saPdGbURERNR7LJqYCgD46zenUN3g9iw3DtHI8dDP+nrauwqvoBAREREAYGCsAYNiDdBpFFArZNBpFBgUa8DAWEOXj4UBhYiIiLD79EU8+sFh7D9biXq7G063iHq7G/vPVuLRDw73jJ1kiYiIqPcQRQkrPz+BC9V2ON0S3JIEUQLckgSnW8KFajtWfn7C/3eSJSIiot7j6HkrfiipxtXyhygBP5RU4+h5a5eNiQGFiIgowB06WwnnNa6OOEUJh85WdtGIGFCIiIgC3vmqep/28wUGFCIiogBXVt264NHafr7AgEJERBTgyqodPu3nCwwoREREAU6vad2+ra3t5wsMKERERAEuVKvyaT9fYEAhIiIKcMJVavC0t58vMKAQEREFuHqn6NN+vsCAQkREFOAiglt366a1/XyBAYWIiCjAxRm1Pu3nCwwoREREAW54Yijk15hfIhca+3UVBhQiIqIANzBGjyCV/Cf7BKnkGBij76IRMaAQEREFvBOWasiusURHJgg4YanuohExoBAREQW88ho7auyun+xTY3ehvMbeRSNiQCEiIgp4F2rsuEYxY4hSY7+uwoBCREQU4HJLW3frprX9fIEBhYiIKMA12N0+7ecLDChEREQBLkKv9mk/X2BAISIiCnDpUSE+7ecLDChEREQB7tvTF3zazxcYUIiIiAKcxVrv036+wIBCREQU4EK1rZtb0tp+vsCAQkREFODuGdGnVf1+NTq+k0fyI0WXvRMRERH5FZdbREWdA5X1P72LLAAIAJSKn67X40sMKERERAFGFCVU1TthrXdCkiRY653X/B5BACrqHF0wukYMKERERAFCkiTYGlyoqnPAfWlve7vTjS+PWa75vaIEVNQwoBAREZEP1dhdqKx1wOkWAQBuUcJXORas3X0GF1sZPKpbcaXFVxhQiIiIerEGpxvltQ7YnY3b1EuShOz8cqzZUYCz5XVtei1J6IwRXhkDChERUS/kdIuoqHWg1v7jBNicYive2F6Ao+etXn3lggC3dI1yxgD0GqXPx3k1DChERES9iFuUUFnnQHWDC9Kl0FFYUYe3dhZgx6mLXn0FAFPSozEkTo//76tT13xtvbbrYkOb90E5f/487r//foSHhyMoKAiDBw/GgQMHPO2SJOGpp55CTEwMgoKCMGXKFJw65X3QFRUVmDVrFvR6PYxGIxYuXIiampqOHw0REVGAkiQJVXUOFFXUwXZpdU55jR0vbjmJBev2twgno5NC8cbsEVg+rT8qqls3t+TE+erOGPoVtSkKVVZWYty4cbjxxhvx+eefIzIyEqdOnUJoaKinzwsvvIBXXnkF69evR3JyMv7whz9g6tSpOH78ODQaDQBg1qxZKCkpwZYtW+B0OjF//nwsXrwYGzZs8O3RERERBYDqBicqa51wiY0TYGvtLvzzQBE+OnAODS7Rq2+/6BAsmpiM6xN+/OxWKFo3uUTw1zko/+///T/Ex8dj7dq1nueSk5M9/y1JEl566SU8+eSTuP322wEAf//73xEdHY1NmzZh5syZOHHiBL744gvs378fI0eOBAD89a9/xS233II///nPiI2N9cVxERER9Xr1DjfKa+1wXAohTreIT44U4x97ClvsbRJr1OCB8cmYdF0khEtJQ6WQIUynQnqMAQKAn5qFIgCIC9V2zoFcQZtu8Xz88ccYOXIk7rnnHkRFRWH48OFYs2aNp72goAAWiwVTpkzxPGcwGDBmzBhkZ2cDALKzs2E0Gj3hBACmTJkCmUyGvXv3dvR4iIiIej2HS4TF2oASaz0cLhGiJGHriTLMW7sff/s2zyucGIOU+O3P0rB23ijc0C8KgiBAKZchMkSNuFAttCoFpg8y/WQ4ARrDy/RBpk49rsu16QpKfn4+Vq1ahWXLluGJJ57A/v378dvf/hYqlQpz586FxdK40Ut0dLTX90VHR3vaLBYLoqKivAehUCAsLMzTpzm73Q673e55bLPZ2jJsIiKiXuFKE2APnq3EG9vzcarMey6nRinDL0fE45ej4qBVNX7cy2UCjFoV9BqF5yoKAHx/rqpV7//9uSqMTAn3zcFcQ5sCiiiKGDlyJFasWAEAGD58OI4dO4bVq1dj7ty5nTJAAFi5ciWeffbZTnt9IiIif9a0HX1VnRPipWByqrQaa3YU4MDZSq++MgG4dUgs5pgTEaZTXXpOgCFICUOQEjJZy4kkf9t2ulXj+Nu201jnjwElJiYG6enpXs8NGDAA//rXvwAAJlPjpZ/S0lLExMR4+pSWlmLYsGGePmVlZV6v4XK5UFFR4fn+5pYvX45ly5Z5HttsNsTHd11FRSIiou7SfAJsibUea3edwdcnylr0nXhdBBaOS0Z8WONcEUEQoNcoYNSqIL9CMGlyvqqhVWNpbT9faFNAGTduHHJzc72eO3nyJBITEwE0Tpg1mUzYunWrJ5DYbDbs3bsXS5YsAQCYzWZUVVXh4MGDGDFiBADgm2++gSiKGDNmzBXfV61WQ61Wt+nAiIiIerJ6hxsVdT/uAGutc+KdvWfx8ZFiON3eM0aGxhmweGIKBsToPc8FaxQI06qgkF97ummMXt3iFtHV+nWVNgWURx99FGPHjsWKFSvwy1/+Evv27cMbb7yBN954A0BjUnvkkUfwxz/+EX379vUsM46NjcUdd9wBoPGKy80334xFixZh9erVcDqdWLp0KWbOnMkVPEREFPAcrsYdYOscjTvANjjd+Nd35/D+viLUOtxefZMjdFg0IRljksM8c0p0agVCtSqoFK1fBxNj1Pi0ny+0KaCMGjUKGzduxPLly/Hcc88hOTkZL730EmbNmuXp89///d+ora3F4sWLUVVVhfHjx+OLL77w7IECAO+++y6WLl2KyZMnQyaTYcaMGXjllVd8d1REREQ9jFuUUFHrQHWD0/P482MWrN99BuW13sX8IoPVmD8uCT9Pj/bcutEo5QjTqaBRytv83s2vyHS0ny8IktSKzff9jM1mg8FggNVqhV6vv/Y3EBER+anmE2AlScLuvMZifoUV3sX8gtUK/GpMAu4cFgv1pSDStJdJ00qd9vjLlly8svXaE2V/OzkNy37er93v05bPb9biISIi6ibNJ8AeO2/F69vzkVPsvZ2GUi7gruF98KsxCQi5VLBPKZchVKdCsNoHH+WtvVbRhdc0GFCIiIi6WPMdYM+W1+LNHQXYlVfu1U8AcNPAaMwfm4QofeNUCYVMBoNW2WIvk47o8at4iIiIqP2aT4C9WGPH+t1n8fmxEojNLk5kpIThgfHJSIkMBtC4l4lR27iXia+CSZP40CCf9vMFBhQiIqJO1nwH2Bq7C//cX4SPDp6DvVkxv/6mEPx6YgqGxhsBNK6Qbdpk7af2MumIyFYuH25tP19gQCEiIuokzSfAOlwiPj5SjHf2nIWtweXVNy40CA+MT8aEvhEQBAGCICBEo4AxSNmqvUw6Yuepi63uN2tMUqeOpQkDChERUSe4fAKsKEn45ocyvL3zDCw273kcoVol5piTMH2wyRNEgjWNe5koOzmYNLHWOa/dqQ39fIEBhYiIyIeaT4Ddf6YCa7YX4PQF751ag5RyzBwVj7tHxCFI1bhkWKdWwKhVQq1o+14mHRGuVfq0ny8woBAREfmA3eVGRa0D9Zd2ez1ZWo012/NxsLDKq59cJuAXQ2Nxf0YCQrWNxfw6ssmaL5yrqvdpP19gQCEiIuoAl1tERZ0DNZfmlBRX1ePtXWfwzQ8ti/nd2C8SC8Ylo8+l1TC+2GTNF6qbbaHf0X6+wIBCRETUDqIooareCWu9E5IkoarOgXf2FOLjI8VwNVszPCzeiMUTk9Hf1Lh7qk83WfOBOGMQTpfVtqpfV/GPM0NERNSD2BqcqKx1wC1KqHe68dHBc/jn/iLUNbvCkBKhw6KJyRid1FjMTy4TYNSqfLrJmi9k/iwN205eeyVP5s/SumA0jRhQiIiIWqnO4UJ5jQNOtwi3KOGzoyVYn30WFc2K+UWFqLFgfDIm94+CXCZAdtleJrJO2sukI0YkhCFUq0TlT6zSCdUqMSIhrMvGxIBCRER0DQ1ONyrrGifASpKEnafL8eaOfBRVek8aDdEoMGtMAu4Y1gcqhQyCIECvUcCoVXXaJmu+IJMJePVX12PB+v1ocIot2jVKGV791fVdGq4YUIiIiK7C6RZRWetAjb1xAuzRc43F/I6XeBfzUylkuGt4H9w3Ot5TzC9Yo0CYVtXpm6z5yti0CLw9dxT+ujUXh8/Z4HJLUMgFDIvT46HJ/TA2LaJLx8OAQkRE1Ix4aWt626Wt6c+U12LN9gJk53sX85MJwM0DTZg7NgmRIY3bwGtVCoTqun4vE18YmxaBjJRw5BTbUFHnQJhWhYGx+m65LcWAQkREdIkkSbA1uFBV1zgB9kK1Het2n8GXOZYWxfzGpoZj4fhkJEfoAHT/Xia+IpMJGBxn6O5hMKAQEREBQK3dhYraxgmwNQ0ubNhXiH8fOu/ZEbZJeoweiycmY0icEYD/7GXS2/BsEhFRQGtwNu4A2+B0w+ESsenweby7txDVzYr5xYcG4YEJKRifFg5BEKCUy2DUKj1zTsi3GFCIiCggXT4B1i1K2HqiFG/vOoOyartXvzCdCvPGJmLaoBjIZZf2MglSQR/kX3uZ9DYMKEREFFAu3wFWFEXsO1OBNTsKkH/BeydVraqxmN+MEXEIUsr9fi+T3oYBhYiIAkLzCbA/WGx4Y3sBDhdVefVTXFbMz6hVQRAEhGgUCPXzvUx6GwYUIiLq9WrsLlRemgB7vrIeb+0swLaTF1r0m9w/CvPHJSH2Us2Z4EvBRNlD9jLpTRhQiIio17p8AmxlnQN/zz6Lzd+XwN1szfCIBCMWTUzBddEhAACdujGYqBQMJt2FAYWIiHodp1tERa0DtXYX6h1ufHCgCB8cOId6p3cxv7TIYCyamIxRSY01ZoJUcoRqe/5eJr0BAwoREfUa7ks7wFY3uOB0ufHpUQv+nn2mRRE8k16DBeOT8LP+UZAJAtRKOcK0KgSpGEz8BQMKERH1eJIkwVrvRFWdE25RxPZTF/HWzgKca1bMT69R4P6MRPxiaCxUChmU8sZN1nRqfhz6G/6JEBFRj1bd4ERlrRMuUcSRoiq8vj0fP1iqvfqoFTLcPSIO946KR7BawU3WegAGFCIi6pHqHW6U19rhcInIv1CDN3cWYE9+hVcfmQBMGxSDuWMTERGs5iZrPQgDChER9SgOV+ME2DqHC6W2BqzbfQZf5ZSiWS0/jEsNxwMTkpEYruMmaz0QAwoREfUIblFCRa0D1Q1OVDc4sWFvYzE/p9s7mgyM1ePXE1MwqI8BgiBAr1HAyE3WehwGFCIi8muXT4Ctd7iw8XAxNuwtRI3du5hfQpgWD4xPxri0cMhkMgSrFQjVKqHgJms9EgMKERH5raYJsHaXG1uOl2Ld7pbF/MKDVZhnTsLNg0yQywQEqxuvmHCTtZ6NAYWIiPxO0wRYu9ONvQUVeHNHAfIvehfz06nkmDk6HjOuj4NGKYdWpUCoTgm1gnuZ9AYMKERE5DcunwB7osSGNTvycbjI6tVHKRdw+7BYzBqdCINWCY1SjjAdd3/tbRhQiIio27ncIirrGie/FlXU4a1dBdh+8qJXHwHA5AFRWDAuGSaDBipF4yZrWhU/ynoj/qkSEVG3EUUJVfVOWOudKK+xXyrmV4xmtfwwKikUiyakIC0qGEq5DKE6FYK5+2uvxj9dIiLqcpIkwdbgQlVd47LhD/afwwcHi9DgFL369Y0Kxq8npuD6xFAoZDIYtEroNdxkLRC0aYrzM888A0EQvL769+/vaW9oaEBmZibCw8MRHByMGTNmoLS01Os1CgsLMX36dGi1WkRFReGxxx6Dy+Vq/lZERNRL1dpdOFdZD4u1Hh8dPIf739yHv+856xVOYgwa/P6WAVh1//UYmRSGMJ0K8WFBMAQpGU4CRJuvoAwcOBBff/31jy+g+PElHn30UXz66af48MMPYTAYsHTpUtx1113YtWsXAMDtdmP69OkwmUzYvXs3SkpKMGfOHCiVSqxYscIHh0NERP6qwen2TIDNyr2At3YVoLiqwauPIUiJ2RkJuG1oLFQKOTdZC2BtDigKhQImk6nF81arFW+99RY2bNiAn/3sZwCAtWvXYsCAAdizZw8yMjLw1Vdf4fjx4/j6668RHR2NYcOG4X//93/x+OOP45lnnoFKper4ERERkV9xukVU1jpQY3fhUGEl3thegNxS72J+GoUMd4+Mw70j46FTKxCsUSBMq+ImawGszX/yp06dQmxsLFJSUjBr1iwUFhYCAA4ePAin04kpU6Z4+vbv3x8JCQnIzs4GAGRnZ2Pw4MGIjo729Jk6dSpsNhtycnI6eixERORH3KKE8ho7zlXW48i5KvzPv77H7z783iucyATgtiEx+MfC0VgwLhmRIRr0CQ1CVIiG4STAtekKypgxY7Bu3Tr069cPJSUlePbZZzFhwgQcO3YMFosFKpUKRqPR63uio6NhsVgAABaLxSucNLU3tV2N3W6H3f7jzoE2m60twyYioi50+db0xdZ6rN11Bl8fb1nMb3xaBB4Yn4yEcC3USjnCuZcJXaZNAWXatGme/x4yZAjGjBmDxMREfPDBBwgKCvL54JqsXLkSzz77bKe9PhER+UbT1vTltXZs2FuITYdbFvMb3EePxRNTMDDWAKW8cS8THZcMUzMd+hthNBpx3XXX4fTp0/j5z38Oh8OBqqoqr6sopaWlnjkrJpMJ+/bt83qNplU+V5rX0mT58uVYtmyZ57HNZkN8fHxHhk5ERD5U73Cjos4BW50D//ruPN7bX4hau9urT2K4FosmJMOcEg6lXA6jTgm9RtlNIyZ/16GAUlNTg7y8PMyePRsjRoyAUqnE1q1bMWPGDABAbm4uCgsLYTabAQBmsxl/+tOfUFZWhqioKADAli1boNfrkZ6eftX3UavVUKvVHRkqERF1gqat6asbnPgyx4J1u8/gYo3Dq09EsArzxybhpoEmKOUyGLVKLhema2pTQPmv//ov3HbbbUhMTERxcTGefvppyOVy3HfffTAYDFi4cCGWLVuGsLAw6PV6PPTQQzCbzcjIyAAA3HTTTUhPT8fs2bPxwgsvwGKx4Mknn0RmZiYDCBFRD9K0Nb2t3oHdeeV4c2cBzpbXefXRqeW4b1QC7rq+D4JUCi4ZpjZpU0A5d+4c7rvvPpSXlyMyMhLjx4/Hnj17EBkZCQB48cUXIZPJMGPGDNjtdkydOhWvvfaa5/vlcjk2b96MJUuWwGw2Q6fTYe7cuXjuued8e1RERNQpJElCVV3j1vRHz1fhje35OHree+GCUi7gjmF98KsxCTAEKRGsUSBUq4KSq3KoDQRJkppPrPZ7NpsNBoMBVqsVer2+u4dDRBQQbA1OVNU6kX+xBm/uKMDO0y2L+f08PRrzxiXBpNdAp1bAqFVCreDKHGrUls9vTpsmIqKfVOdwoaLWgZKqeqzPPovPjpa0KOY3OjkMi8YnIzUqGEEqOUK1XDJMHcOAQkREV2R3NW5Nf7Hajn8eKMJHB86hweVdzK9fdAgWT0zG8IRQqJVyhGlVCFIxmFDHMaAQEZEXl1tERZ0DlbUOfHKkGP/YUwhrvdOrT6xRgwfGJ2PSdZFQKeTcy4R8jn+biIgIACCKEqrqnaisc+CbE2V4e1cBSqzexfyMQUrMMSdi+pAYaFWNc0xCuJcJdQIGFCKiACdJEmwNLlTVObCvoAJvbM/HqbIarz4apQy/HBmPX46MQ4hGCaNWBb1Gwb1MqNMwoBARBbAauwuVtQ4cL7ZizY4CHDhb6dUulwm4dXAMZpsTERGshiGocZM1GfcyoU7GgEJEFIAanG6U1zpw5mJNYzG/E2Ut+ky8rrGYX3yYjpusUZdjQCEiCiAOl4jKOgeKK+vxzt6z+PhIcYtifkPjDFg8MQUDYvQI0SgRqlVCwU3WqIsxoBARBQC3KDUuGa6x46ODRXh/XxFqHd7F/JIjdFg0IRljksM880xUCgYT6h4MKEREvZgoSrDWO1Fea8dnRy1Yv/sMymu9i/lFhagxf1wSpgyIRrBGgTCdiru/UrdjQCEi6oUkSUK13YXKGgeyTl7AmzsLUFjhXcwvRKPAr0Yn4M7hfRASpOQma+RXGFCIiHqZWnvj1vSHCivx+vZ85BS3LOZ31/DGYn5hOjU3WSO/xL+RRES9RIOzcWv6XIsNb+4owK68cq92AcBNA6Mxf2wSYo1ahOq4yRr5LwYUIqIermllzpmLtViffQZfHLO0KOY3JjkMiyYko290CIxBKuiDuMka+TcGFCKiHsrlFlFZ50SJtR7v7yvEv747D3uzYn79TSFYPDEFwxNCYQhSwshN1qiHYEAhIuphmmrmXKi2Y9Ph83h3z1nYGlxefeJCg7DwUjE//aVgwr1MqCdhQCEi6iGaauZU1NrxVU4p3t5VgFKb3atPqPZSMb/BMTBqVQjVqaBkMKEeiAGFiKgHqLG7UFFjR3Z+Od7Yno+8C7Ve7UFKOe4dFYd7RsQjPFiNUJ2Se5lQj8aAQkTkx5pq5hw9V4XXt+fjUGGVV7tcJuC2IY3F/EyGIO5lQr0GAwoRkR9qWplzqrQab+0swLe5F1r0ubFfJBaMS0ZShI57mVCvw7/NRER+pGllTlFFLf6xpxCfHCmGq9ma4WHxRiyemIzBfYwwarmXCfVODChERH6gqWaOxdqADw4W4Z/7i1DXrJhfSqQOiyekICMlDKE6NfQa7mVCvRcDChFRN7M1OHHRZscn3xdjffZZVFyhmN+C8cm4KT0aoVoVDNzLhAIAAwoRUTepd7hxsaYBW38ow5s7CnCust6rPUSjwP1jEnDH8D6ICFbDqFVBzmBCAYIBhYioizlcIipqHdiTfxFvbM/H8ZJqr3aVQoYZ1/fBfaMSYDJqEKrlXiYUeBhQiIi6SNME2O/PVeHNHQXIzvcu5icTgJsHmjB3bBISw3Xcy4QCGgMKEVEna5oAe6qsGmt3ncGXOS2L+Y1NDcfC8ckYEKNHmE4FjZLBhAIbAwoRUSdp2pq+qKIW7+wpxL8PnYejWTG/9Bg9Fk9MxojEMO5lQnQZ/iQQEXWCGrsLFms9Pjp4Du/uLUR1s2J+8aFBeGBCCm64LhJhwSruZULUDAMKEZEPNTjdKKu247Pvi/H2rjMoq/Yu5hemU2He2ETcMjgGEcFqGIKU3MuE6AoYUIiIfKBxZY4d3+aWYc2OAuQ3K+anVckxc1Q87h4Rj2g9lwwTXQsDChFRB7hFCRW1Dhw4U4HXt+fjcFGVV7tCJuAXw2Jx/5gExIfpEKpVQsElw0TXxIBCRNQOTStzcoqteHNHAbadbFnM72f9o7BgXBL6RocgVKuCSsFgQtRaDChERG1ka3Aiv6wWa3cXYPP3JXA3WzN8fYIRiyemYGi8EaFaLhkmag8GFCLyGw6HG6u35+NsRS0Sw3R4cGIKVCr/+XCvc7hQVF6HDfsK8cGBc6h3ehfzS4sMxqKJyRiXFoEwnQpaFX/FErUXf3qIyC/8/t/f4/39RXBfdjHi5a0nMXNUPP5015DuGxgaV+ZcqG7ARwfP4e/ZZ1FZ5/RqN+k1WDA+CVMHmhAerEYw9zIh6jD+FBFRt/v9v7/Hu/uKWjzvluB5vjtCitMtoqLGjk+PWvDWzgKcr/Iu5qfXKHB/RiLuGh6HSL0aeo2CS4aJfKRDM7aef/55CIKARx55xPNcQ0MDMjMzER4ejuDgYMyYMQOlpaVe31dYWIjp06dDq9UiKioKjz32GFwuF4go8Dgcbry3v2U4udx7+4vgcLh/so8viaKE8ho7Nh8pxpy1+/Hc5uNe4UStkGHWmARsWJSBxRNTkBql434mRD7W7iso+/fvx+uvv44hQ7z/VfPoo4/i008/xYcffgiDwYClS5firrvuwq5duwAAbrcb06dPh8lkwu7du1FSUoI5c+ZAqVRixYoVHTsaIupxVmXltahL05woNfZ7+OfXdepYJEmCrd6Fg4UVeD0rH3sLKrzaZQJw8yAT5o1NRmqkjnuZEHWidgWUmpoazJo1C2vWrMEf//hHz/NWqxVvvfUWNmzYgJ/97GcAgLVr12LAgAHYs2cPMjIy8NVXX+H48eP4+uuvER0djWHDhuF///d/8fjjj+OZZ56BSqXyzZERUY9w5HyVT/u1V63dhRMlNqzZkY+vckrRPDONSwvHA+OTMaiPkXuZEHWBdv2EZWZmYvr06ZgyZYrX8wcPHoTT6fR6vn///khISEB2djYAIDs7G4MHD0Z0dLSnz9SpU2Gz2ZCTk3PF97Pb7bDZbF5fRNQ7GINa94+S1vZrqwanGycsNjz3SQ7uW7MHXzYLJwNj9Xhl5jC8+MthGJcWicgQNcMJURdo8xWU999/H9999x3279/fos1isUClUsFoNHo9Hx0dDYvF4ulzeThpam9qu5KVK1fi2WefbetQiagHuH9MAv596Hyr+vmSwyWixFqPd/cWYsPeQtTYvefBJYRpsWhCMiYPiEKYTs29TIi6WJsCSlFRER5++GFs2bIFGo2ms8bUwvLly7Fs2TLPY5vNhvj4+C57fyLqPEPijFDIBLh+YiKKQiZgSJzRJ+/ncou4WOPAxkPnsPYKxfzCg1WYZ07CbcNiEB0ShCA/2oeFKJC0KaAcPHgQZWVluP766z3Pud1ubN++HX/729/w5ZdfwuFwoKqqyusqSmlpKUwmEwDAZDJh3759Xq/btMqnqU9zarUaarW6LUMloh4ip8SGay1+EYTGfkPjje1+H1GUUFnnwJc5pVizIx8FF72L+elUcswcHY+ZoxIQYwziXiZE3axNP4GTJ0/G0aNHvZ6bP38++vfvj8cffxzx8fFQKpXYunUrZsyYAQDIzc1FYWEhzGYzAMBsNuNPf/oTysrKEBUVBQDYsmUL9Ho90tPTfXFMRNSDHC6sgluUoJA1rta5/EKKTGj8cosSDhdWtSugNK3M2Z13Eauz8nDknNWrXSkXcPuwWMzJSEJSpA4hau5lQuQP2hRQQkJCMGjQIK/ndDodwsPDPc8vXLgQy5YtQ1hYGPR6PR566CGYzWZkZGQAAG666Sakp6dj9uzZeOGFF2CxWPDkk08iMzOTV0mIApDkyQIC0GLtzI/PS+3IDNUNThw9Z8Xq7XnYfvJii/YpA6LwwIRkDDAZoA9iMCHyJz6/hvniiy9CJpNhxowZsNvtmDp1Kl577TVPu1wux+bNm7FkyRKYzWbodDrMnTsXzz33nK+HQkQ9wPB4I+SCAOcV5qA0XlGRoJQJGN6Gqyf1DjdOltrw1s4z2Px9cYt9VkYlhWLxxBSMSAyDMUgJGfcyIfI7giRJ19giyf/YbDYYDAZYrVbo9fruHg4RdYDLJWLQs1+iwSletY9GKcOxp6dCofjp5b12lxvnKuqxfvcZfHCwqMVr9o0Kxq8npWLSdZHcy4SoG7Tl85uzwIioW+WU2OC+xlayblH6yUmyLreI0uoGvL+vCP/IPouqeu9ifjEGDRaOT8atQ2IQHqyGksGEyO8xoBBRtzpcWPWTS4wBwHWVSbKiKKGizoH/HC7GWzvzUVzV4NVuCFJidkYi7h0Vjyi9GmoFlwwT9RQMKETUrSRJwrVuNEtSY7/Lv8dW78I3P5RiVVYeTpbWePXXKGS4Z2QcZpuTkBCm5SZrRD0QAwoRdasgdevCQ1O/GrsL+wsq8Nq209h/ptKrj0wApg+JwYJxybguOgQ67mVC1GPxp5eIutXJ0upW9TtebMX+M+V4PSsfW0+UtViQPKFvBH49MRVD4g3cy4SoF2BAIaJulXPeeu1OAD47asGGfUVwur2jyeA+ejw4KRXj0yK5lwlRL8KAQkTdqtbhblW/CzUOr8eJ4VosnpiCmweaEKpVcS8Tol6GAYWIulVwG4vxRQSrMH9sEmaMiEdEsIp7mRD1UgwoRNStovStK3GhkAmYPy4Js8YkItYYBNU1Nm0jop6NAYWIulVEcOsCyq1DTPjdTf24ZJgoQPCfIETUbeodbpQ021ztaiKC1QwnRAGEV1CIqMvZXW6cKq3B6qw8fJ5T2qrv6XFFw4ioQxhQiKjLuNwiCivr8PbOAnx04BwaXFcvENhceY29E0dGRP6GAYWIOp1blHChugHv7C3EP7LPwtqsmF9r1NtdnTAyIvJXDChE1GkkSUJVnRMbD53Dmh0FKLF6zzcxBimhUchgqb721ZGLNW0PNUTUczGgEFGnqG5w4uvjVynmp5ThlyPjMX9sEp7++FirAkpIEH9dEQUS/sQTkU/VOVzYm1+OV7/Nw4Gz3sX85DIBtw6OwaKJyUiLCoFGKcf1CaHYfqr8mq87LN7YSSMmIn/EgEJEPtHgdON4sRWvbcvD1yfKWrRPvC4CSyalYmi8EVrVj796JlwXiZe2nr7m60+4LtKn4yUi/8aAQkQd4nSLKLhQg9e35+PjI8UtivkNjTNgyQ1pGJcWjhCNssX319jd0MhlaHBffUWPRiFDjb11NXuIqHdgQCGidnGLEkqs9Vi76wze31fYouhfSoQOv56UgqnpJhi0yqtWGQ7TqhAWrEKN3QlbQ8sQotfIEaxWIkyr6pTjICL/xIBCRG0iSRLKa+14f18R1u46g/Ja7yrDUSFqzB+XjLtHxCFcd+0qwwNj9UiNCsaJkmoMiNagvNYFh1uESi5DuE6BC7UupEYFY2CsvjMPi4j8DAMKEbWatd6BzUdK8Pr2fBRW1Hm1hWgUmDUmAfdnJMKk17S6yrBMJmDJpFQ8sfEoLtQ6EaSUQ6OSQZKAC7VOBKsVWDIp9ZpBh4h6FwYUIrqmWrsL23LLsGpbHo4V27zalHIBM66PwwPjk5EQrmtXleGxaRGYNSYBf/v2NCpqHJAACGhcWjxrTALGpkX45kCIqMdgQCGiq2pwuvHd2Uq8+u1p7MrzXgosAJg60IQHb0hBf5O+Q4X8dp++iDd3FqDW7gKExtcGgFq7G2/uLMDAWANDClGAYUAhohYcLhEnS6vx2rbT+OKYBWKzSn0ZKWH4zQ2pGJkU5rVkuD1EUcLKz0+grNoOqdn7iJKEsmo7Vn5+Av/JHM/bPEQBhAGFiDxcbhFFFfVYsyMP//ruPOzNivn1N4VgyaRU/GxA1BWXDLfH0fNW/FBS7Qknl0cQCYAkAT+UVOPoeSuGcrM2ooDBgEJEEMXGKxV/zz6Dd/acha3BuzBfXGgQHhifjNuHxcKoVV11yXB7fFdYCeelSzSC538aCVJjSHGKEr4rrGRAIQogDChEAaypmN+/vjuHN3cUwGLzLuYXqlVi7tgkzBwVj8gQDeSdcIvFcnkBweYvL6AxoTTvR0S9HgMKUYCqbnDiyxwLVm3LQ96FWq+2IKUc946Kw7xxSehj1ELZyiXD7RGj1/i0HxH1DgwoRAGm3uHGrryLeO3b0/iusMqrTS4TcNuQGCyemIrUKB3UivavzGmt4YmhUMoEOEUJktRyDgoAKGUChieGdvpYiMh/MKAQBQi7y42j56x49dvT+Db3Qov2G/tF4sEbUjE0ztihJcNtNbiPAf1jQnDsvK1xUmyzdgFA/5gQDO5j6LIxEVH3Y0Ah6uWcbhH5ZT8W83M1WzM8LN6I39yQinFpEdCpu/5Xgkwm4BdDY5FTbGuxzBgABAH4xdBYLjEmCjAMKES9lFuUUFJVj7d3FeD9/UWoa17ML1KHJZNScdPAaBiCuq8QnyhK2H7qIkI0CjhdIhxuEaIEyARAJZdBqZBh+6mLWDg+hSGFKIAwoBD1Mk3F/DbsLcK63WdQcYVifg+MT8bdI+MQ6uMlw+2RU2xDXlkNokI0UCtlaHCIcIkiFDIZNCoZGpwi8spqkFNsw+A43uYhChQMKES9iLXegY8Pl+D17Xk4V1nv1abXKDArIxGzMxIQrQ/qlCXD7VFR54DTLUEll0GAgCCVHMCPc2DUchmsooSKOsfVX4SIeh0GFKJeoNbuwrc/lOG1badxvKTaq02lkGHG9X3wwIRkJITpOnXJcHuEaVVQygU43CI0spaTc+1uEUqZgDBt992GIqKux4BC1IM1ON04cKYCr36bh+x872J+MgG4eaAJD96Qin6mkC5ZMtweA2P1SI0KxomSapj0Mq9bTk0byQ2ICcHAWH03jpKIulqb/im1atUqDBkyBHq9Hnq9HmazGZ9//rmnvaGhAZmZmQgPD0dwcDBmzJiB0tJSr9coLCzE9OnTodVqERUVhcceewwul6v5WxHRT3C4RHx/rgqP/vMw5ry9r0U4GZsajvULRuMv9w7DkDij34YToHEVz5JJqQhWy2Gx2VHvdEMUJdQ73bDY7AhWy7FkUionyBIFmDZdQYmLi8Pzzz+Pvn37QpIkrF+/HrfffjsOHTqEgQMH4tFHH8Wnn36KDz/8EAaDAUuXLsVdd92FXbt2AQDcbjemT58Ok8mE3bt3o6SkBHPmzIFSqcSKFSs65QCJehOXW0RhRR3e2J6Pfx86D0ezYn7pMXpk3piKG/pFdcuS4fYamxaBFXcOxqqsPOSV1cAqSlDKBAyIaSxOODYtoruHSERdTJCkK+080HphYWH4v//7P9x9992IjIzEhg0bcPfddwMAfvjhBwwYMADZ2dnIyMjA559/jltvvRXFxcWIjo4GAKxevRqPP/44Lly4AJWqdfeYbTYbDAYDrFYr9Hpe9qXer7GYXwPW7z6Ld/aeRXWzYn7xoUH49cRU3DYspluXDHeUKErIKbahos6BMK0KA2P1vHJC1Iu05fO73f/Ecrvd+PDDD1FbWwuz2YyDBw/C6XRiypQpnj79+/dHQkKCJ6BkZ2dj8ODBnnACAFOnTsWSJUuQk5OD4cOHX/G97HY77Ha71wESBQJJklBZ58SHB4rw1s4ClFXbvdrDdSrMG5eEX41OQJiu+5cMd5RMJnApMREBaEdAOXr0KMxmMxoaGhAcHIyNGzciPT0dhw8fhkqlgtFo9OofHR0Ni8UCALBYLF7hpKm9qe1qVq5ciWeffbatQyXq0aobnPjimAWrsvKQ36yYn1Ylx32jE7BgXDJiDBpeZSCiXqfNAaVfv344fPgwrFYrPvroI8ydOxdZWVmdMTaP5cuXY9myZZ7HNpsN8fHxnfqeRN2lqZjf3745jcNFVV5tCpmAXwyLxZJJqUiO0EHhZ0uGO4q3eIioSZsDikqlQlpaGgBgxIgR2L9/P15++WXce++9cDgcqKqq8rqKUlpaCpPJBAAwmUzYt2+f1+s1rfJp6nMlarUaarW6rUMl6lHsLje+L2os5rftZMtifpP7RyHzxjQM6mOAStG7ggkA7D590TNJ1umWoJQLSI0K5iRZogDV4d9yoijCbrdjxIgRUCqV2Lp1q6ctNzcXhYWFMJvNAACz2YyjR4+irKzM02fLli3Q6/VIT0/v6FCIeiSnW8QPFhv++6PvMXPNnhbh5PoEI9bOG4VXZ12P6xNDe204eWLjUZwosUGnViAqRA2dWoETJdV4YuNR7D59sbuHSERdrE1XUJYvX45p06YhISEB1dXV2LBhA7Zt24Yvv/wSBoMBCxcuxLJlyxAWFga9Xo+HHnoIZrMZGRkZAICbbroJ6enpmD17Nl544QVYLBY8+eSTyMzM5BUSCjhuUUJxVR3e2nkG/9xfhHqndzG/tMhgLLkhFdMGm6BV9Zwlw20lihJWZeWhxu6CSa/xTPTVyOQw6WWw2OxYlZWHjJRw3u4hCiBt+q1XVlaGOXPmoKSkBAaDAUOGDMGXX36Jn//85wCAF198ETKZDDNmzIDdbsfUqVPx2muveb5fLpdj8+bNWLJkCcxmM3Q6HebOnYvnnnvOt0dF5MckSUJ5jQPv7DmL9dlnUFnn9Go36TV4YEIy7h4RB2MAbO/eVCzwSoULBUGAUatksUCiANThfVC6A/dBoZ7KVu/ApkPFeH17Ps5XtSzmN9uciDkZSYgMUQfM1YKskxfwXx8cQdRVjlkUJZTV2PHne4Zi0nWR3TBCIvKVLtkHhYhar97hxtcnSvHqt6fxg8W7mJ9aIcPdI+LwwPgUxIcF9bqVOdfCYoFEdCUMKESdyO5yY19BBf72zWnsLajwapMJwLRBMVhyQyquiw7plZNfW4PFAonoShhQiDqB0y3iRIkNr357Gl/llKL5fdRxaeHIvCEN1yeGQqP030J+XaGpWOATG4/CYrPDqFVCLZfB7hZRVedksUCiAMWAQuRDblFCYXktVm/Px7+/Owen2zuaDIz9sZhfb16Z01YsFkhEzfE3JJEPiKKEUlsD1u8+g3f2FqLG7l3MLyFMi19PTMFtQ2Og78HF/DrT2LQIZKSEcydZIgLAgELUIU3F/D44UIi3d55pWcwvWIUF45Iwc1TvKObX2VgskIiaMKAQtVN1gxOfHS3B6qx8FFz0LuanU8kxc3QCFoxPRoyexfyIiNqKAYWojeodbuw4fQGvfnMaR85ZvdqUcgG3D4vF4okpSIkIDrglw0REvsKAQtRKdpcbR4qs+Nu3p7D9pHdtGAHA5AFR+M0NaRjYRw+1IrBX5hARdRQDCtE1uNwiTpZVY/W2fGz+vhhiszXDo5JC8Zsb0mBODQ/4JcNERL7CgEJ0FW5RwvnKOry5swAfHChCg1P0au8bFYzf3JiKn6ebEKzmjxIRkS/xtypRM03F/P6x5wzW7z6LqnrvYn4xBg0WT0jBndf3gSFIyZU5RESdgAGF6DLWOgf+feg81uzIR3FVg1ebIUiJueZEzDYnIiJYzWBCRNSJGFCIANQ5XPj6eCle/TYPuaXexfw0ChnuGRmPhROSER+qhZxLhomIOh0DCgW0Bqcb+wrK8bdvTmPfmUqvNpkATB8SgwcnNRbzU3LJMBFRl2FAoYDkdIs4XmzF377Nw9fHWxbzm9g3AktuSMXwBBbzIyLqDgwoFFDcooSCizV4PSsfmw6fb1HMb3AfAzJvTMWEvpHQcWUOEVG34W9gCgiSJMFia8DbOwuwYV8hau1ur/akcC0enJSKaYNNMLCYHxFRt2NAoV6tsZifA+/vK8Lbuwpwscbh1R4ZrMb8cUm4d1Q8i/kREfkRBhTqtWoanPjkSAlWb8/D2fI6rzadWo5ZoxMxd2wiTIYgrswhIvIzDCjU6zQ43cg6eQF/++YUjp63ebUp5QLuGNYHiyelIClcx5U5RER+igGFeg2HS8Shwkr89ZvT2Hm6ZTG/KenR+M0NqUiPZTE/IiJ/x4BCPZ7LLeJkaTVe25aHz46WtCjmNzo5DL+5IRVjksMRpGIwISLqCRhQqMdyixLOVdbhzR0F+PBAERpc3sX8+kWH4Dc3pGJyejSL+RER9TD8rU09TlMxv/XZZ/D37LOwNivmF2vUYNGEFNw5rA8MWhbzIyLqiRhQqEex1jnwr+8ai/mVWL2L+RmDlJgzNgmzxiQgMlgNGVfmEBH1WAwo1CPUOVz4MseC177Nw6myGq82jVKGX46Mx4JxyYgLDYKCK3OIiHo8BhTyaw1ON7LzyvHqt6dx4Kx3MT+5TMCtg2Pw60kpSIsKgUrBYEJE1FswoJBfcriaivmdxtcnylq0T7wuApk3pGFovJHF/IiIeiEGFPIrTcX8Vmfl4z9XKOY3NM6A39yYhgl9I6BV8a8vEVFvxd/w5BdE8cdifu/tK0Stw7uYX3KEDg9OSsXNg6JZzI+IKAAwoFC3kiQJFXUOvLe3EGt3nUF5rXcxv6iQxmJ+94yIR3gwi/kREQUKBhTqNtUNTnx8pBivZ+WjsMK7mF+IRoFZYxIw25wIk57F/IiIAg0DCnW5eocb3+aW4tVv85BT3LKY313D+2DRhBQkhOu4MoeIKEAxoFCXaXC6caiwEn/75jR25ZV7tQkAbhoYjQcnpWJAjJ4rc4iIAhwDCnU6h0tEbqkNq7bl4YtjlhbF/DJSwvDgpFSMTg7jyhwiIgLAgEKdyOUWUVRRjzU78vCv787D3qyYX39TCJZMSsWNA6Kg1yi7aZREROSP2nSDf+XKlRg1ahRCQkIQFRWFO+64A7m5uV59GhoakJmZifDwcAQHB2PGjBkoLS316lNYWIjp06dDq9UiKioKjz32GFwuV8ePhvyCW5RQUlWPv2w5idtf3YkN+4q8wklcaBCeuS0dGx4Yg9uGxjKcEBFRC226gpKVlYXMzEyMGjUKLpcLTzzxBG666SYcP34cOp0OAPDoo4/i008/xYcffgiDwYClS5firrvuwq5duwAAbrcb06dPh8lkwu7du1FSUoI5c+ZAqVRixYoVvj9C6jKSJKGyzoGPDp7DWzsLUGqze7WHapWYY07CzFHxiAxRs2YOERFdlSBJknTtbld24cIFREVFISsrCxMnToTVakVkZCQ2bNiAu+++GwDwww8/YMCAAcjOzkZGRgY+//xz3HrrrSguLkZ0dDQAYPXq1Xj88cdx4cIFqFTX3oTLZrPBYDDAarVCr9e3d/jkI5IkwdbgxJacUqzKykPehVqv9iClHPeOisMccxLiQrVcmUNEFKDa8vndoTkoVqsVABAWFgYAOHjwIJxOJ6ZMmeLp079/fyQkJHgCSnZ2NgYPHuwJJwAwdepULFmyBDk5ORg+fHiL97Hb7bDbf/zXuM1ma9GHukeN3YU9eeV4ddtpHCqs8mqTywTcNiQGD0xIQVpUMFfmEBFRq7U7oIiiiEceeQTjxo3DoEGDAAAWiwUqlQpGo9Grb3R0NCwWi6fP5eGkqb2p7UpWrlyJZ599tr1DpU7Q4HTjyLkqrN6Wh29zL7Rov7FfJBZPTMHgOCOC1ZyLTUREbdPuT47MzEwcO3YMO3fu9OV4rmj58uVYtmyZ57HNZkN8fHynvy+1ZHe5kX+hFm9sz8cnR4rharZmeFi8EQ9OSoE5JQL6IAW3picionZpV0BZunQpNm/ejO3btyMuLs7zvMlkgsPhQFVVlddVlNLSUphMJk+fffv2eb1e0yqfpj7NqdVqqNXq9gyVfMTpFlFcVY91u8/gn/uLUNesmF9KpA6/npiCKQOiEapVQcat6YmIqAPaFFAkScJDDz2EjRs3Ytu2bUhOTvZqHzFiBJRKJbZu3YoZM2YAAHJzc1FYWAiz2QwAMJvN+NOf/oSysjJERUUBALZs2QK9Xo/09HRfHBP5kFuUcKG6Af88cA7rd59BxRWK+S0Yn4w7h/dBuE7FlTlEROQTbQoomZmZ2LBhA/7zn/8gJCTEM2fEYDAgKCgIBoMBCxcuxLJlyxAWFga9Xo+HHnoIZrMZGRkZAICbbroJ6enpmD17Nl544QVYLBY8+eSTyMzM5FUSPyKKEqrqHNj8fQne2JGPc5X1Xu36S8X8Zo5OQIwhiCtziIjIp9q0zPhq8wnWrl2LefPmAWjcqO13v/sd3nvvPdjtdkydOhWvvfaa1+2bs2fPYsmSJdi2bRt0Oh3mzp2L559/HgpF6/ISlxl3HkmSYKt3IetUGVZvy8PxkmqvdpVChhnX98HcsUlICtdxZQ4REbVaWz6/O7QPSndhQOkctgYnDp2twuqsPGTnexfzkwnAzQNNWDghGf1Neui4MoeIiNqoy/ZBod6h1u7CDxYb3txRgC9zWhbzM6eE49eTUjA8IRR6DVfmEBFR52NACWD1DjfOVNRi/a4z+Peh83A0K+aXHqPHryelYELfSBiDlFyZQ0REXYYBJQDZXW6UWOvx3t4ibNhXiOoG70KN8aFBeGBCCm4eFI1wHWvmEBFR12NACSBOt4iL1Xb85/B5vL3rDMqqvYv5hetUmDs2EXde3wdRIRqoFZwAS0RE3YMBJQC4RQkVtXZsPVGGN7bnI/+idzE/rUqOmaPicd/oBMSFahGkYjAhIqLuxYDSi0mSBGu9E3vyy7E6Kx+Hi6q82hUyAb8YFot55iQkR+oQolF2z0CJiIiaYUDppaobnDh6zoo3tudj28mWxfwm94/CwvHJSI/VwxCk5MocIiLyKwwovUyt3YXTZTV4e1cBNn9fAnezNcMjEoxYPCkVIxNDYdSqIOfKHCIi8kMMKL1Eg9ONc5V1+Ef2WXxw4Bzqnd7F/NKigrF4QjJu6BeFUJ0KSq7MISIiP8aA0sPZXW5csNnx4cEi/D37LCrrnF7tJr0GC8YnYfqQGEQEq7kyh4iIegQGlB7K6RZRUWvHZ0cteGtnwRWL+d2fkYi7R8QhxhDElTlERNSjMKD0MG5RQmWdAztOXsDr2/Pxg8W7mJ9aIcPdI+Jwf0Yi4kKDuDKHiIh6JAaUHkIUG5cMHyqsxOrt+dhXUOHVLhOAaYNisGB8EtIiQ6APYs0cIiLquRhQLiOKEnKKbaiocyBMq8LAWH2315+RJAm2BhdyLTa8tbMAX+WUonn56XFp4Vg8IQWD44ysmUNERL0CA8olu09fxKqsPOSV1cDplqCUC0iNCsaSSakYmxbRLWOqsbtQWF6LdbvPYOOh83C6vaPJwFg9fj0xBRmp4QjTqlgzh4iIeg0GFDSGkyc2HkV1gxNalQJKuQyiJOF4sQ1PbDyKFXcO7tKQUu9oLOb3/v4ibNhbiBq7dzG/hDAtFk1IxuQBUQjnyhwiIuqFAj6giKKEVVl5qKh1wC1KsDU0QJIAQQBUchmc7sb2jJTwTr91Yne5cbHagf8cPo91u69QzC9YhXnmJNw2LAbRIVyZQ0REvVfAB5ScYhuOF1tR73RDlBrr0wgCIAFocImQCSKOF1uRU2zD4DhDp4yhacnw1hNlWLOjAAXNivnpVHLcNzoBvxwZjxijhitziIio1wv4gHKx1g5bgwuiJEEhE+AWJUgABAAKGeASGyepXqy1X+ul2swtSqiqc2BPQQXeyMrDkXNWr3alXMDtw2Jx/5hEJEXoWDOHiIgCRsAHlKpaJ0RRgigB9mZ1a1xiY1CBIKGq1nnF72+PpiXDx85bsWZnPrafvOjVLgCYPKCxmF/f6BCEsmYOEREFmIAPKKFaJSSgxdLdJk1todqO31ZpWjKcV1aNdbvP4tOjLYv5jUoKxaIJKRgab0SoVgWVgitziIgo8AR8QNFrFBCvlk4uEaXGfh1R3eBEcWU93t1biA8OFqHBKXq1940Kxq8npsCcFoFwnQoaJSfAEhFR4Ar4gJKVW9bqftcnhbX59escLpTaGvDv787jH9lnUVXvfasoxqDBwvHJ+Hl6NCKC1dCpA/6PhIiIiAFlV0F5q/s92obXbXC6UV5rxxdHLXhrVwGKqxq82g1BSszOSMQdw2MRGaKBXsOt6YmIiJoEfEBpcLh92s/pFlFZ68D2UxfwxvZ8nCyt8WrXKGS4Z2QcZo5KQKwxCAZuTU9ERNRCwAcUpax1k1Cv1a+pyvChwiqs2Z6HfWcqvdplAjB9SAzmZCQiKSIYoVolt6YnIiK6ioAPKLV2R4f6NS0ZzrVU461dBfj6eMtifhP7RmDB+GQMiNFzZQ4REVErBHxAaV6Ar7X9mpYMny2vxT+yz2LT4ZbF/Ab3MWDxxGRcnxjGlTlERERtEPABRd7KWzyX96uxu2Cpaizm997+QtTaveenJIVrsWhCCib0jUA4V+YQERG1WcB/crZ2HohCLkO9w43S6npsPlKCdbvP4GKN922fiGAV5o9LxrRBJoQHq7kyh4iIqJ0CPqBolK0LEAJE/Ou7IqzZUYCz5XVebTq1HLNGJ2DG9XGI0mu4MoeIiKiDAj6gVNW1bpJs3sV6PLkpx+s5pVzAncP7YNaYRMQag7gyh4iIyEcCPqBYG1q3v4njsgmwAoCfp0dj3rgkpEYGc2UOERGRjwV8QJHEa/e53OjkMCyakIz0WANX5hAREXWSgA8oerUcVQ2ua/ZTygQ8P2MwRieHI1SnQjBX5hAREXWagL8vkRipa1W/IXEhmDLAhLjQIIYTIiKiThbwn7Raees2agvXyGDQKjt5NERERAS04wrK9u3bcdtttyE2NhaCIGDTpk1e7ZIk4amnnkJMTAyCgoIwZcoUnDp1yqtPRUUFZs2aBb1eD6PRiIULF6KmxruoXlfZkW9tVb+dBbZOHgkRERE1aXNAqa2txdChQ/Hqq69esf2FF17AK6+8gtWrV2Pv3r3Q6XSYOnUqGhoaPH1mzZqFnJwcbNmyBZs3b8b27duxePHi9h9FB9hdrZsl29p+RERE1HFtvsUzbdo0TJs27YptkiThpZdewpNPPonbb78dAPD3v/8d0dHR2LRpE2bOnIkTJ07giy++wP79+zFy5EgAwF//+lfccsst+POf/4zY2NgOHE7bqZUy1DmuHT7UyoCfrkNERNRlfPqpW1BQAIvFgilTpnieMxgMGDNmDLKzswEA2dnZMBqNnnACAFOmTIFMJsPevXuv+Lp2ux02m83ry1dmDDP5tB8RERF1nE8DisViAQBER0d7PR8dHe1ps1gsiIqK8mpXKBQICwvz9Glu5cqVMBgMnq/4+HifjXlIQoRP+xEREVHH9Yj7FsuXL4fVavV8FRUV+ey1w3Uqn/YjIiKijvNpQDGZGm+DlJaWej1fWlrqaTOZTCgrK/Nqd7lcqKio8PRpTq1WQ6/Xe335SkVt62rxtLYfERERdZxPA0pycjJMJhO2bt3qec5ms2Hv3r0wm80AALPZjKqqKhw8eNDT55tvvoEoihgzZowvh9MqJyytm8/S2n5ERETUcW1exVNTU4PTp097HhcUFODw4cMICwtDQkICHnnkEfzxj39E3759kZycjD/84Q+IjY3FHXfcAQAYMGAAbr75ZixatAirV6+G0+nE0qVLMXPmzC5fwQMAJ0qqfdqPiIiIOq7NAeXAgQO48cYbPY+XLVsGAJg7dy7WrVuH//7v/0ZtbS0WL16MqqoqjB8/Hl988QU0Go3ne959910sXboUkydPhkwmw4wZM/DKK6/44HDaLqSV29a3th8RERF1nCBJUuv2evcjNpsNBoMBVqu1w/NRXtySi5e3nr5mv4cnp+HRn/fr0HsREREFsrZ8fveIVTydaXAfg0/7ERERUccFfECpbnBDuEYf4VI/IiIi6hoBH1BCtUoo5MJVQ4oAQCEXEMpKxkRERF0m4Gd+hgerodcoYa13QgK8gkrTY71GifBgdfcMkIiIKAAF/BWUgbF6pMfqoVPLoVHIIFxKKIIAaBQy6NRypMfqMTDWd5vDERER0U8L+IAikwlYMikVoVoVglRymPQaxBo1MOk1CFLJEapVYcmkVMhk15qpQkRERL4S8AEFAMamRWDFnYMxIEYPtyih3inCLUoYEKPHijsHY2waCwUSERF1pYCfg9JkbFoEMlLCkVNsQ0WdA2FaFQbG6nnlhIiIqBswoFxGJhMwOI77nRAREXU33uIhIiIiv8MrKJcRRYm3eIiIiPwAA8olu09fxKqsPOSV1cDplqCUC0iNCsaSSamcJEtERNTFeIsHjeHkiY1HcaLEBp1agagQNXRqBU6UVOOJjUex+/TF7h4iERFRQAn4gCKKElZl5aHG7oJJr4FGKYdMJkCjlMOkV6PG7saqrDyIYo8r+kxERNRjBXxAySm2Ia+sBqFaFQCg3uFGdYMT9Y7G4oBGrRJ5ZTXIKbZ15zCJiIgCSsDPQamoc8DpluBwiSix1sPuEiFJjVvdqxUyhOvUcIoSKuoc3T1UIiKigBHwASVMq4IoiSi2OiBKgEImQBAaCwXWO0UUW+uh1ygQdukKCxEREXW+gL/FM8AUArcEuNwS5ELjPJOm6SZyQYLLLcEtNfYjIiKirhHwAeWEpRpyofGqicMN2F0iHG7x0v833uqRCwJOWKq7e6hEREQBI+ADSkWdAy63BJlw5Q3ZZIIAF+egEBERdamAn4NiDFKi3umGJElQKwQAgmeSLNB4i6fe4YYxSNnNIyUiIgocAR9QgKYwAgiCAAECcOmxdOl/r3JxhYiIiDpJwN/iqap3Nm7OJghwuSWIkgQJjf/fdOtHo5Sjqt7Z3UMlIiIKGAF/BSVMq4JOJUewWgFrvRN2lxuS2HhVRaOUwxCkhCRJXGZMRETUhQI+oAyM1SM1KhgnSqqRGB4Eu1OCSxShkMmgVgootTkwICYEA2P13T1UIiKigBHwt3hkMgFLJqUiWC1Hqc0BCIBOpQAEoNTmQLBajiWTUiGTcSIKERFRVwn4gAIAY9MisOLOwRgQE4I6uwtlNXbU2V0YEBOCFXcOxti0iO4eIhERUUAJ+Fs8TcamRSAjJRw5xTZU1DkQplVhYKyeV06IiIi6AQPKZWQyAYPjDN09DCIiooDHWzxERETkdxhQiIiIyO8woBAREZHfYUAhIiIiv8OAQkRERH6HAYWIiIj8DgMKERER+R0GFCIiIvI73RpQXn31VSQlJUGj0WDMmDHYt29fdw6HiIiI/ES3BZR//vOfWLZsGZ5++ml89913GDp0KKZOnYqysrLuGhIRERH5iW4LKH/5y1+waNEizJ8/H+np6Vi9ejW0Wi3efvvt7hoSERER+YluqcXjcDhw8OBBLF++3POcTCbDlClTkJ2d3aK/3W6H3W73PLbZbJ0yLlGUWCyQiIjID3RLQLl48SLcbjeio6O9no+OjsYPP/zQov/KlSvx7LPPduqYdp++iFVZecgrq4HTLUEpF5AaFYwlk1IxNi2iU9+biIiIvPWIVTzLly+H1Wr1fBUVFfn09XefvognNh7FiRIbdGoFokLU0KkVOFFSjSc2HsXu0xd9+n5ERET007rlCkpERATkcjlKS0u9ni8tLYXJZGrRX61WQ61Wd8pYRFHCqqw81NhdMOk1EITGWzoamRwmvQwWmx2rsvKQkRLO2z1ERERdpFuuoKhUKowYMQJbt271PCeKIrZu3Qqz2dylY8kptiGvrAahWpUnnDQRBAFGrRJ5ZTXIKe6ceS9ERETUUrdcQQGAZcuWYe7cuRg5ciRGjx6Nl156CbW1tZg/f36XjqOizgGnW4JKfuWsppbLYBUlVNQ5unRcREREgazbAsq9996LCxcu4KmnnoLFYsGwYcPwxRdftJg429nCtCoo5QIcbhEambxFu90tQikTEKZVdem4iIiIApkgSZLU3YNoK5vNBoPBAKvVCr1e36HXEkUJc9fuw4mSapj0aq/bPJIkwWKzY0BMCNbPH805KERERB3Qls/vHrGKpzPJZAKWTEpFsFoOi82Oeqcboiih3umGxWZHsFqOJZNSGU6IiIi6UMAHFAAYmxaBFXcOxoCYENTZXSirsaPO7sKAmBCsuHMw90EhIiLqYt02B8XfjE2LQEZKOHeSJSIi8gMMKJeRyQQMjjN09zCIiIgCHm/xEBERkd9hQCEiIiK/w4BCREREfocBhYiIiPwOAwoRERH5HQYUIiIi8jsMKEREROR3GFCIiIjI7zCgEBERkd/pkTvJNhVgttls3TwSIiIiaq2mz+2mz/Gf0iMDSnV1NQAgPj6+m0dCREREbVVdXQ2D4adLywhSa2KMnxFFEcXFxQgJCYEg+LaYn81mQ3x8PIqKiqDX63362oGK59T3eE59j+fU93hOfa+nn1NJklBdXY3Y2FjIZD89y6RHXkGRyWSIi4vr1PfQ6/U98g/fn/Gc+h7Pqe/xnPoez6nv9eRzeq0rJ004SZaIiIj8DgMKERER+R0GlGbUajWefvppqNXq7h5Kr8Fz6ns8p77Hc+p7PKe+F0jntEdOkiUiIqLejVdQiIiIyO8woBAREZHfYUAhIiIivxMQAWX79u247bbbEBsbC0EQsGnTJq92SZLw1FNPISYmBkFBQZgyZQpOnTrl1aeiogKzZs2CXq+H0WjEwoULUVNT04VH4V9WrlyJUaNGISQkBFFRUbjjjjuQm5vr1aehoQGZmZkIDw9HcHAwZsyYgdLSUq8+hYWFmD59OrRaLaKiovDYY4/B5XJ15aH4jVWrVmHIkCGe/Q3MZjM+//xzTzvPZ8c8//zzEAQBjzzyiOc5ntO2e+aZZyAIgtdX//79Pe08p+1z/vx53H///QgPD0dQUBAGDx6MAwcOeNoD8nNKCgCfffaZ9Pvf/17697//LQGQNm7c6NX+/PPPSwaDQdq0aZN05MgR6Re/+IWUnJws1dfXe/rcfPPN0tChQ6U9e/ZIO3bskNLS0qT77ruvi4/Ef0ydOlVau3atdOzYMenw4cPSLbfcIiUkJEg1NTWePg8++KAUHx8vbd26VTpw4ICUkZEhjR071tPucrmkQYMGSVOmTJEOHTokffbZZ1JERIS0fPny7jikbvfxxx9Ln376qXTy5EkpNzdXeuKJJySlUikdO3ZMkiSez47Yt2+flJSUJA0ZMkR6+OGHPc/znLbd008/LQ0cOFAqKSnxfF24cMHTznPadhUVFVJiYqI0b948ae/evVJ+fr705ZdfSqdPn/b0CcTPqYAIKJdrHlBEUZRMJpP0f//3f57nqqqqJLVaLb333nuSJEnS8ePHJQDS/v37PX0+//xzSRAE6fz58102dn9WVlYmAZCysrIkSWo8h0qlUvrwww89fU6cOCEBkLKzsyVJagyOMplMslgsnj6rVq2S9Hq9ZLfbu/YA/FRoaKj05ptv8nx2QHV1tdS3b19py5Yt0qRJkzwBhee0fZ5++mlp6NChV2zjOW2fxx9/XBo/fvxV2wP1cyogbvH8lIKCAlgsFkyZMsXznMFgwJgxY5CdnQ0AyM7OhtFoxMiRIz19pkyZAplMhr1793b5mP2R1WoFAISFhQEADh48CKfT6XVe+/fvj4SEBK/zOnjwYERHR3v6TJ06FTabDTk5OV04ev/jdrvx/vvvo7a2FmazmeezAzIzMzF9+nSvcwfw72hHnDp1CrGxsUhJScGsWbNQWFgIgOe0vT7++GOMHDkS99xzD6KiojB8+HCsWbPG0x6on1MBH1AsFgsAeP2wND1uarNYLIiKivJqVygUCAsL8/QJZKIo4pFHHsG4ceMwaNAgAI3nTKVSwWg0evVtfl6vdN6b2gLR0aNHERwcDLVajQcffBAbN25Eeno6z2c7vf/++/juu++wcuXKFm08p+0zZswYrFu3Dl988QVWrVqFgoICTJgwAdXV1Tyn7ZSfn49Vq1ahb9+++PLLL7FkyRL89re/xfr16wEE7udUjywWSP4lMzMTx44dw86dO7t7KD1ev379cPjwYVitVnz00UeYO3cusrKyuntYPVJRUREefvhhbNmyBRqNpruH02tMmzbN899DhgzBmDFjkJiYiA8++ABBQUHdOLKeSxRFjBw5EitWrAAADB8+HMeOHcPq1asxd+7cbh5d9wn4KygmkwkAWswyLy0t9bSZTCaUlZV5tbtcLlRUVHj6BKqlS5di8+bN+Pbbb70qTJtMJjgcDlRVVXn1b35er3Tem9oCkUqlQlpaGkaMGIGVK1di6NChePnll3k+2+HgwYMoKyvD9ddfD4VCAYVCgaysLLzyyitQKBSIjo7mOfUBo9GI6667DqdPn+bf03aKiYlBenq613MDBgzw3DoL1M+pgA8oycnJMJlM2Lp1q+c5m82GvXv3wmw2AwDMZjOqqqpw8OBBT59vvvkGoihizJgxXT5mfyBJEpYuXYqNGzfim2++QXJyslf7iBEjoFQqvc5rbm4uCgsLvc7r0aNHvX6otmzZAr1e3+KHNVCJogi73c7z2Q6TJ0/G0aNHcfjwYc/XyJEjMWvWLM9/85x2XE1NDfLy8hATE8O/p+00bty4Fts0nDx5EomJiQAC+HOqu2fpdoXq6mrp0KFD0qFDhyQA0l/+8hfp0KFD0tmzZyVJaly+ZTQapf/85z/S999/L91+++1XXL41fPhwae/evdLOnTulvn379ujlWx21ZMkSyWAwSNu2bfNablhXV+fp8+CDD0oJCQnSN998Ix04cEAym82S2Wz2tDctN7zpppukw4cPS1988YUUGRkZsMsN/+d//kfKysqSCgoKpO+//176n//5H0kQBOmrr76SJInn0xcuX8UjSTyn7fG73/1O2rZtm1RQUCDt2rVLmjJlihQRESGVlZVJksRz2h779u2TFAqF9Kc//Uk6deqU9O6770parVZ65513PH0C8XMqIALKt99+KwFo8TV37lxJkhqXcP3hD3+QoqOjJbVaLU2ePFnKzc31eo3y8nLpvvvuk4KDgyW9Xi/Nnz9fqq6u7oaj8Q9XOp8ApLVr13r61NfXS7/5zW+k0NBQSavVSnfeeadUUlLi9TpnzpyRpk2bJgUFBUkRERHS7373O8npdHbx0fiHBQsWSImJiZJKpZIiIyOlyZMne8KJJPF8+kLzgMJz2nb33nuvFBMTI6lUKqlPnz7Svffe67VfB89p+3zyySfSoEGDJLVaLfXv31964403vNoD8XOK1YyJiIjI7wT8HBQiIiLyPwwoRERE5HcYUIiIiMjvMKAQERGR32FAISIiIr/DgEJERER+hwGFiIiI/A4DChEREfkdBhQi6nLr1q2D0Wjs7mEQkR/jTrJE1OXq6+tRXV2NqKioVn/PDTfcgGHDhuGll17qvIERkd9QdPcAiCjwBAUFISgoqLuHQUR+jLd4iKjNbrjhBixduhRLly6FwWBAREQE/vCHP6DpgmxlZSXmzJmD0NBQaLVaTJs2DadOnfJ8f/NbPM888wyGDRuGf/zjH0hKSoLBYMDMmTNRXV0NAJg3bx6ysrLw8ssvQxAECIKAM2fOXHV8kiQhLS0Nf/7zn72eP3z4MARBwOnTp313MoioUzCgEFG7rF+/HgqFAvv27cPLL7+Mv/zlL3jzzTcBNAaKAwcO4OOPP0Z2djYkScItt9wCp9N51dfLy8vDpk2bsHnzZmzevBlZWVl4/vnnAQAvv/wyzGYzFi1ahJKSEpSUlCA+Pv6qryUIAhYsWIC1a9d6Pb927VpMnDgRaWlpPjgDRNSZGFCIqF3i4+Px4osvol+/fpg1axYeeughvPjiizh16hQ+/vhjvPnmm5gwYQKGDh2Kd999F+fPn8emTZuu+nqiKGLdunUYNGgQJkyYgNmzZ2Pr1q0AAIPBAJVKBa1WC5PJBJPJBLlc/pPjmzdvHnJzc7Fv3z4AgNPpxIYNG7BgwQKfnQMi6jwMKETULhkZGRAEwfPYbDbj1KlTOH78OBQKBcaMGeNpCw8PR79+/XDixImrvl5SUhJCQkI8j2NiYlBWVtbu8cXGxmL69Ol4++23AQCffPIJ7HY77rnnnna/JhF1HQYUIvILSqXS67EgCBBFsUOv+cADD+D9999HfX091q5di3vvvRdarbZDr0lEXYMBhYjaZe/evV6P9+zZg759+yI9PR0ul8urvby8HLm5uUhPT2/3+6lUKrjd7jZ9zy233AKdTodVq1bhiy++4O0doh6EAYWI2qWwsBDLli1Dbm4u3nvvPfz1r3/Fww8/jL59++L222/HokWLsHPnThw5cgT3338/+vTpg9tvv73d75eUlIS9e/fizJkzuHjxYquursjlcsybNw/Lly9H3759YTab2/3+RNS1GFCIqF3mzJmD+vp6jB49GpmZmXj44YexePFiAI2rZUaMGIFbb70VZrMZkiThs88+a3Ebpy3+67/+C3K5HOnp6YiMjERhYWGrvm/hwoVwOByYP39+u9+biLoed5IlojbrSbu67tixA5MnT0ZRURGio6O7ezhE1ErcSZaIeiW73Y4LFy7gmWeewT333MNwQtTD8BYPEfVIDz74IIKDg6/49eCDD+K9995DYmIiqqqq8MILL3T3cImojXiLh4h6pLKyMthstiu26fX6NhUiJCL/w4BCREREfoe3eIiIiMjvMKAQERGR32FAISIiIr/DgEJERER+hwGFiIiI/A4DChEREfkdBhQiIiLyOwwoRERE5Hf+fyqr1PUXKemeAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -1180,9 +1180,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_17412\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_10292\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", - "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_17412\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_10292\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" ] } @@ -1320,7 +1320,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACYnklEQVR4nOzdeVyVZf7/8dd9WA6KspgoamiQkuKWwlikLYpKjeNMxTTT95ttaqaSDS4zWtPiklKjllOjpmblfNt+U1YzSYoKLlmaKeGU+wJSCUZjQC5s55zfHyfOiIBy4BwOyPv5eJwHnfu+rvv+3NDDw5v7uq/LsNlsNkRERERERKTWTJ4uQEREREREpKlRkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIRkSbpqquu4oEHHvB0GSIi0kwpSImISKNz9OhRHn74YSIiIvDz8yMgIICBAwfy17/+lXPnzrn9/GfPnmXmzJls3rzZ7ecSEZGmydvTBYiIiJwvJSWFu+66C7PZzH333UevXr0oLS1l27Zt/PGPf2Tv3r0sX77crTWcPXuWWbNmAXDLLbe49VwiItI0KUiJiEijkZWVxd13302XLl1IT0+nQ4cOjn2JiYkcOXKElJQUD1ZYP2fOnMHf39/TZYiIiAtoaJ+IiDQaf/nLXzh9+jQrV66sFKIqdO3alT/84Q/V9p05cyaGYVTZ/vrrr2MYBtnZ2Y5tu3btIj4+nrZt29KiRQvCw8MZPXo0ANnZ2YSEhAAwa9YsDMPAMAxmzpzp6H/gwAF++9vf0qZNG/z8/IiJieFf//pXtefdsmULEydOpF27dlx55ZUA/PTTTyQlJXHVVVdhNptp164dw4YNIyMjw6nvl4iIeI7uSImISKPx0UcfERERwQ033OC2c3z//fcMHz6ckJAQZsyYQVBQENnZ2bz//vsAhISEsHTpUiZMmMAdd9zBnXfeCUCfPn0A2Lt3LwMHDqRTp07MmDEDf39//vGPf3D77bezevVq7rjjjkrnmzhxIiEhITz11FOcOXMGgPHjx/Pee+/xyCOPEBUVxX/+8x+2bdvG/v376d+/v9uuXUREXEdBSkREGoWioiK+++47fvOb37j1PJ999hk//vgj69evJyYmxrH9mWeeAcDf35/f/va3TJgwgT59+jBq1KhK/f/whz/QuXNnvvjiC8xmM2APS4MGDWL69OlVglSbNm1IS0vDy8vLsS0lJYWHHnqIhQsXOrb96U9/cvm1ioiI+2hon4iINApFRUUAtG7d2q3nCQoKAmDNmjWUlZU51ffUqVOkp6fzu9/9jp9++okffviBH374gf/85z/Ex8dz+PBhvvvuu0p9HnrooUohqqKGzz//nBMnTtTrWkRExHMUpEREpFEICAgA7M8PudPNN99MQkICs2bNom3btvzmN7/htddeo6Sk5JJ9jxw5gs1m48knnyQkJKTS6+mnnwbsQwfPFx4eXuU4f/nLX/j6668JCwtjwIABzJw5k2PHjrnmAkVEpEFoaJ+IiDQKAQEBdOzYka+//rpO/aubaALAYrFUaffee++xY8cOPvroI1JTUxk9ejQLFy5kx44dtGrVqsZzWK1WAKZNm0Z8fHy1bbp27VrpfYsWLaq0+d3vfseNN97IBx98wPr165k/fz7PPfcc77//PrfddttFr1NERBoH3ZESEZFG41e/+hVHjx5l+/btTvcNDg4GoKCgoNL248ePV9v++uuvZ+7cuezatYs333yTvXv38s477wA1h7KIiAgAfHx8GDp0aLWv2g5N7NChAxMnTuTDDz8kKyuLK664grlz59aqr4iIeJ6ClIiINBp/+tOf8Pf3Z+zYsZw8ebLK/qNHj/LXv/612r5XX301AFu3bnVsO3PmDKtWrarU7scff8Rms1Xadu211wI4hve1bNkSqBrK2rVrxy233MKyZcvIzc2tUkN+fv5Frs7OYrFQWFhY5bgdO3as1fBCERFpHDS0T0REGo2rr76at956i9///vf06NGD++67j169elFaWspnn33Gu+++ywMPPFBt3+HDh9O5c2fGjBnDH//4R7y8vHj11VcJCQkhJyfH0W7VqlUsWbKEO+64g6uvvpqffvqJFStWEBAQwC9/+UvAPhwvKiqK//f//h+RkZG0adOGXr160atXLxYvXsygQYPo3bs3Dz30EBEREZw8eZLt27fz7bffsmfPnote408//cSVV17Jb3/7W/r27UurVq3YuHEjX3zxRaVZ/EREpHFTkBIRkUbl17/+Nf/+97+ZP38+//znP1m6dClms5k+ffqwcOFCHnrooWr7+fj48MEHHzBx4kSefPJJQkNDSUpKIjg4mAcffNDR7uabb2bnzp288847nDx5ksDAQAYMGMCbb75ZaWKIV155hUmTJjF58mRKS0t5+umn6dWrF1FRUezatYtZs2bx+uuv85///Id27drRr18/nnrqqUteX8uWLZk4cSLr16/n/fffx2q10rVrV5YsWcKECRPq/w0UEZEGYdguHN8gIiIiIiIiF6VnpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTtI6UoDVauXEiRO0bt0awzA8XY6IiIiIiHiIzWbjp59+omPHjphMNd93UpACTpw4QVhYmKfLEBERERGRRuKbb77hyiuvrHG/ghTQunVrwP7NCggI8HA1IiIiIiLiKUVFRYSFhTkyQk0UpMAxnC8gIEBBSkRERERELvnIjyabEBERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJM8GqSuuuoqDMOo8kpMTASguLiYxMRErrjiClq1akVCQgInT56sdIycnBxGjBhBy5YtadeuHX/84x8pLy/3xOWIiIiIiEgz4e3Jk3/xxRdYLBbH+6+//pphw4Zx1113ATB58mRSUlJ49913CQwM5JFHHuHOO+/k008/BcBisTBixAhCQ0P57LPPyM3N5b777sPHx4d58+Z55JpERERERJqd4nw4uhJOpkNZIfgEQmgcRIwGvxBPV+cWhs1ms3m6iApJSUmsWbOGw4cPU1RUREhICG+99Ra//e1vAThw4AA9evRg+/btXH/99axdu5Zf/epXnDhxgvbt2wPw8ssvM336dPLz8/H19a3VeYuKiggMDKSwsJCAgAC3XZ+IiDRW+cBKIB0oBAKBOGA0cHn+AiAi4hKWYtidZA9RNitgPW+nCQwTXD0WoheBl9kzNTqpttmg0TwjVVpayhtvvMHo0aMxDIPdu3dTVlbG0KFDHW26d+9O586d2b59OwDbt2+nd+/ejhAFEB8fT1FREXv37q3xXCUlJRQVFVV6iYhIc1QM3A+EAo8BG4CdP399HOgITABKPFWgiEjjZSmG9Hg4sgJs5VQOUdjf28rhyHLYFA+Wy+vf0kYTpD788EMKCgp44IEHAMjLy8PX15egoKBK7dq3b09eXp6jzfkhqmJ/xb6aJCcnExgY6HiFhYW57kJERKSJKAAigL9T9cOfn7eVA8uBeBSmREQusHsy5G+j+n9Dz2eF7z+x37m6jDSaILVy5Upuu+02Onbs6PZzPfbYYxQWFjpe33zzjdvPKSIijUkxEAXk1qKtFfgESHJnQSIiTUtxPhx9hUuHqApWe/viH9xZVYNqFEHq+PHjbNy4kbFjxzq2hYaGUlpaSkFBQaW2J0+eJDQ01NHmwln8Kt5XtKmO2WwmICCg0ktERJqT8dQuRFWwAq8Al88vACIi9XLs1Z+fiXKCzQrHVrqnHg9oFEHqtddeo127dowYMcKxLTo6Gh8fH9LS0hzbDh48SE5ODrGxsQDExsby1Vdf8f333zvabNiwgYCAAKKiohruAkREpAnJB/6vDv2s2CekEBER8tKo/d2oClbIS3dHNR7h0enPAaxWK6+99hr3338/3t7/LScwMJAxY8YwZcoU2rRpQ0BAAJMmTSI2Npbrr78egOHDhxMVFcW9997LX/7yF/Ly8njiiSdITEzEbG4as4KIiEhDexXnP/z5uU86MN215YiINEVlhXXsV+DSMjzJ40Fq48aN5OTkMHr06Cr7XnjhBUwmEwkJCZSUlBAfH8+SJUsc+728vFizZg0TJkwgNjYWf39/7r//fmbPnt2QlyAiIk1K2qWb1KjAVUWIiDRtPoF17Bfk0jI8qVGtI+UpWkdKRKQ5uQ77FOd1MRxIdWEtIiJN1L7nIPNxnLvDb4Jr50FU476z3+TWkRIREWkYdfwrKgBDXFaFiEiTFjHavtiuMwwviBjjnno8QEFKRESamTjAqEM/E3D5/AIgIlIvfiFw9VhqHydMcPUY8GvrzqoalIKUiIg0M6MBrzr0uw+4fH4BEBGpt+hFEDKIS0cKE7S70d7+MqIgJSIizUwI8ICTfdoBL7u+FBGRpszLDENSoes4MLypGi1M9u1dx8HgVHv7y4jHZ+0TERFpWMXAASf7nAKSgEXA5fWLgIhIvXj5wYCl0Ge2fZHevHT7FOc+QRA6xP4slV+Ip6t0C83ah2btExFpXiYAy3F+LSkTcCP2WfsUpkRELleatU9ERKSKfOAV6r4g7yfY70yJiEhzpyAlIiLNyKvULURVsGIPYj+4phwREWmy9IyUiIg0I2nUL0jxc/+VQONeUFJEpEEV58PRlXAyHcoKwScQQuMu62ekFKRERKQZKXTBMaxAOgpSIiKApRh2J9lDlM1KpT9W5aXBnifs601FL9KsfSIiIk1XoIuOU+Ci44iINGGWYkiPh/xtVH+332oPV0eWQ9H+y24KdD0jJSIizUgcrvnoa+mCY4iINHG7J0P+J1x6yLQVvt9qv3N1GVGQEhGRZmQ0rvno83PBMUREmrDifPudJmq7kpINjqyA4stnsh4FKRERaUZCgLGAUc/jFLugFhGRJuzIMpyfvMcCx1a6oxqPUJASEZFmZhEQWs9jnHVBHSIiTdjBxXXrd2Kda+vwIAUpERFpZoqwL8xbH0EuqENEpIkqzoeSvLr1PX3EtbV4kIKUiIg0M/VdlNcAhrioFhGRJujA83XvW37OdXV4mIKUiIg0M/VdlNcbGOOiWkREmqBDS+re17uF6+rwMK0jJSIizUzVRXkPHYLly2HXLigshMBAiImBceMgMvLC1mOAtg1RqIhI41OcD+VFde/fqqvravEwBSkREWlm/rso7549MGUKpKeDlxdYLP9ttW0bLFwIcXH2r337AtyIfbIKEZFm6tir9evf8VbX1NEIKEiJiEgzEwekkZZmZeRIKC21bz0/RJ3/fvNmiI2Fjz66hri4DYC5AWsVEWlk8tIc/3koF5anw65jUHgOAltATASMGwKRHWroH3H5DI02bDZbbVfRumwVFRURGBhIYWEhAQEBni5HRETcKp89ezoQG2uhuBhq8yloMoHZ7Mf27Tvoa781JSLSPKVex56MnUx5E9L3gpcJLOc9dlrxPq4nLLwH+nY5r29wNNy2q8FLdlZts4EmmxARkWYmhClT2lNaWrsQBWC1QmlpCVOnTnVvaSIijVzaV+XEzoQt++3vLRfM3VPxfvN+iJ0JaV+ft/OmfzZAhQ1HQUpERJqVQ4cOkZ5+ospQvkuxWGykpaVx+PBh9xQmItLI7cnYycgnMikuqxqgLmSxQkkZjFwIe44DQf3Bv1OD1NlQFKRERKRZWb58OV5edfv48/IyWLZsmYsrEhFpAizFTBkTT2mZtfZ3821QWg5T3wTOfQul9ZjtrxFSkBIRkWZl165dWC71p9QaWCw2du/e7eKKREQav0MfPEh6ZsEl70RdyGKFtL1w+Pj3sKY7WErcU6AHKEiJiEizUlhYdR0pZxQUFLimEBGRpuLMNyx/7R3qeDMfLxMsSwOKc2HneJeW5kkKUiIi0qwEBgZeutFFBAUFuaYQEZGmwFIMG29m17FLPxdV4yGssDvr5zdZf4fiH1xWnicpSImISLMSExNTr2ekoqOjXVyRiEgjtnsynMmi8Fz9DlNwtuK/rHBsZX2rahQUpEREpFkZN25cvZ6Revjhh11ckYhII1WcD0dfAeyL7dZHUMvz3uSl1+9gjYSClIiINCuRkZEMGTIELy/n+nl5wdChQ+jWrZt7ChMRaWyOvQo2+x+eYiKo1zNS0eHnbSgrqHdpjYGClIiINDvPP/8Evr5gquWnoMkEvr6wYEEr9xYmItKY5KUB9iA1bkj9npF6OO68DT5B9a2sUVCQEhGRZqdv35189BGYzVzyzpSXl73dRx9B375rgMvjIWkRkUsq++8sp5EdYEhP5+9KeZlgaE/oFlqxxQShQ1xWoicpSImISDOURlwcbN8Ot9xi33JhoKp4P3iwvV1cHNj/Mnt5PCQtInJJPpVnOX3+HvD1BpNRu+4mw95+wT3nbTS8IGKM62r0IAUpERFphux/Ze3bFzZuhEOHICnJHqquvdb+NSnJvn3DBnu7/7o8HpIWEbmk0LhKb/t2gY+mgtnn0nemvEz2dh9NtfdzuHoM+LV1fa0eYNhsNpuni/C0oqIiAgMDKSwsJCAgwNPliIiI2w0HNtSx7wDgcxfWIiLSSBXnw/vtgcpxYc9xmPompO21B6bzn52qeD+0p/1OVKUQ5d0aEvLBy9wg5ddVbbOBdwPWJCIi0kjEUfcgFeTCOkREGjG/EPALheLcSpv7doGNj8PhPFiWZl9st+CsfYrz6HD7xBL/fSbqPFdc1+hDlDMUpEREpBkaDTxOxWxUtWcCLo+HpEVELspSDLuTqoSo83ULveD5p4syQYehrqis0dAzUiIi0gyFAPfWoZ8XcHk8JC0iUiNLMaTHw5HlrjumwWUzyUQFBSkREWmmlgEdnGhvwh6iLo+HpEVEarR7MuRv48Jno+rFr+NlM8lEBQUpERFppszAfmoXpkzAjcAidxYkIuJ5xflw9BWcH/p8CS2qe2iqaVOQEhGRZiwQOAbcT80fiSbswwBTsYcvEZHL2LFXwebiEAXg28b1x/QwBSkREWnm/ICXgVFU/7FoBd4EkoCShitLRMQT8tJw+d0oTBB6+U3UoyAlIiLNXDEwDPg/av7loRz7M1XDUJgSkctaWaHrj2l4XXYTTYCClIiINHuPArV5qNoGfAJMcntFIiIe4xPo4gOa4Ooxl91EE6AgJSIizVo+8IqTfV4BfnBDLSIijUBonAsPZoJ2N0L0Ihces/FQkGom8vPh2Wdh+HC47jr71+ees28XEWm+XsT56X1tP/cTEbkMRYzGvuhTfZjA8Iau42BwKnhdnhP1GDabzYUTxDdNRUVFBAYGUlhYSEBAgKfLcaniYkhKgpUrwWq1vyqYTPbX2LGwaBGYL8//x0VELiISOFyHft2AQy6uRUSkkfggDM5963w/wwfaD7ZPLBExGvxCXF9bA6htNvBuwJqknvLz7YEoPR0KCyEwEOLiYPRoCKnm/9PiYoiPh23bKgeoChXBavly2L8fUlMVpkSkucmrY7+TLq1CRKRR6TYe/v2E8/16Pw29/uz6ehopjw/t++677xg1ahRXXHEFLVq0oHfv3uzatcux32az8dRTT9GhQwdatGjB0KFDOXy48l8PT506xT333ENAQABBQUGMGTOG06dPN/SluFzFcLyhQ6FdO/vr8cdhwwbYudP+9fHHoWNHmDABSi6YSGry5JpD1PmsVvjkE/udKxGR5qMA+MnTRYiIND5dxwFeTnbyhq4Pu6OaRsujQerHH39k4MCB+Pj4sHbtWvbt28fChQsJDg52tPnLX/7Ciy++yMsvv8znn3+Ov78/8fHxFBcXO9rcc8897N27lw0bNrBmzRq2bt3KuHHjPHFJLlFcDOPH2wPSn/8MaWn/fZbpwoGYViuUl9vvKsXH/zdM5efDK69cOkSdf5xXXoEf9Py0iDQbA+vRt73LqhARaXT8QqDrQ9Q+Kpig69jLcma+i/HoM1IzZszg008/5ZNPPql2v81mo2PHjkydOpVp06YBUFhYSPv27Xn99de5++672b9/P1FRUXzxxRfExMQAsG7dOn75y1/y7bff0rFjx0vW0ZiekbrUcLyLMQy47z54/XX7RBKPP+7cMUwmmDcPpk937rwiIk3PfiCqHv2fBGa7qBYRkUbIUgLpwyF/GxdfoPfnmfkuo0klapsNPHpH6l//+hcxMTHcddddtGvXjn79+rFixQrH/qysLPLy8hg6dKhjW2BgINdddx3bt28HYPv27QQFBTlCFMDQoUMxmUx8/vnn1Z63pKSEoqKiSq/GorbD8apjs8GqVXDvvfDii84fw2q1P38lInL5m1LP/o+6pAoRkUbLywxDUu3D/AxvqsaG5jEz38V4NEgdO3aMpUuX0q1bN1JTU5kwYQKPPvooq1atAiAvz/4QcPv2lYdQtG/f3rEvLy+Pdu3aVdrv7e1NmzZtHG0ulJycTGBgoOMVFhbm6kurE2eH49XkjTfgxIm69S0oqN+5RUSahp316NsJaF7DV0SkmfLygwFL4Y4TcO08CB0OVwywf712nn37gKXNMkSBh2fts1qtxMTEMG/ePAD69evH119/zcsvv8z999/vtvM+9thjTJny379GFhUVNYow9eqr9Q9R9RUU5Nnzi4g0jJJLN6lRB5dVISLSJPiFQNR0+0scPHpHqkOHDkRFVR6j3qNHD3JycgAIDQ0F4OTJytPMnjx50rEvNDSU77//vtL+8vJyTp065WhzIbPZTEBAQKVXY5CW5tkgZTLBkCGeO7+ISMOpz19P27isChERabo8GqQGDhzIwYMHK207dOgQXbp0ASA8PJzQ0FDS0tIc+4uKivj888+JjY0FIDY2loKCAnbv3u1ok56ejtVq5brrrmuAq3CdwkJnexwCpgG3AP1+/jqNui4S6eUFY8bUqauISBMzoB599RcnERHx8NC+yZMnc8MNNzBv3jx+97vfsXPnTpYvX87y5csBMAyDpKQknnnmGbp160Z4eDhPPvkkHTt25Pbbbwfsd7BuvfVWHnroIV5++WXKysp45JFHuPvuu2s1Y19jEhhY25Z7sD8onY59jn/Lefu2AQuBuJ+/9q31+ceMgbYa9i8izcLzwLo69PMG9BcnERHx8B2pX/ziF3zwwQe8/fbb9OrVizlz5rBo0SLuueceR5s//elPTJo0iXHjxvGLX/yC06dPs27dOvz8/Bxt3nzzTbp3705cXBy//OUvGTRokCOMNSVxcfbhdReXBsQCW35+b7lgf8X7zT+3S6M2WreGRYtq1VRE5DLQg7pNfz4WTTQhIiLg4XWkGovGso5Ufr59Ed7y8ppa7MEejoqB2vzYTNifA9jOpe5M3XQTbNly0SYiIpeZIiDs56+1MRD7H6ea5+xUIiLNRZNYR0oqCwmBsWMvdldqClBK7UIU2BdPKwWmXrJly5a1PKSIyGUjADgOdK9F29EoRImIyPkUpBqZRYtg0KDqwtQh7M9EXTiU71Is2D/8D1+0VXGxk4cVEbksBAH7gX3YJ5Foif2j0Qu4AvsfsL4HVqIQJSIi51OQamTMZvjnP+1fK1uO/YO9LryAZRdtcfZsHQ8tInJZ6IH9j05nsP8Bqhz4AfukPSEerEtERBorBalGKCkJzp27cOsunL8bVcEC7L5oCy3EKyIiIiJSewpSjUx+Pvzf/1W3x+lFpi5QUOMeLcQrIiIiIuIcBalG5tVXwWqtbk+tF5mqQVCNe7QQr4iIiIiIcxSkGpm0Gpd9iqF+z0hFV7vHZNJCvCIiIiIizlKQamQKaxzBN476PSP1cJWtJhPceKMW4hURERERcZaCVCMTWOMIvkjsU/M6e1fKCxgKdHNsMQzw9oZx4yA1tboZAkVERERE5GIUpBqZuLiL7X0e8KX2PzbTz+0XVNr6xBNw4gQsXaoQJSIiIiJSFwpSjczo0fY7RtXrC3yEfVHIS92Z8vq53Uc/97O78kqYPRtCtCyKiIiIiEidKUg1MiEh0KnTxVrEAduBW35+f2Ggqng/+Od2lW9xhYbWu0QRERERkWZPQaoRGj/+Ui36AhuBQ0AS9lB17c9fk37evoHz70RVaNPGNTWKiIiIiDRn3p4uQKoaNw6efhosl5ykrxsXPv90MYahhXdFRERERFxBd6QaoZAQeOgh+/TkrqSFd0VEREREXENBqpFatAgGDXJtmBo7VgvvioiIiIi4goJUI2U229d4GjfOfiepvrTwroiIiIiI6yhINWJ+fva1nnJzYc4c+9TlzjIM+zDBDRu0ZpSIiIiIiKsYNpvN5ukiPK2oqIjAwEAKCwsJCAjwdDkXlZ8Pr74K//oXfP01nDtn3+7lZR8GaBj2/27fHn7/e3j0Ua0ZJSIiIiJSW7XNBgpSNK0gJSIiIiIi7lPbbKChfSIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJG9PnnzmzJnMmjWr0rZrrrmGAwcOAFBcXMzUqVN55513KCkpIT4+niVLltC+fXtH+5ycHCZMmMCmTZto1aoV999/P8nJyXh7e/TSREQuSxaLhbKyMk+XIfXg6+uLyaS/o4qI1JfH00bPnj3ZuHGj4/35AWjy5MmkpKTw7rvvEhgYyCOPPMKdd97Jp59+Ctg/0EeMGEFoaCifffYZubm53Hffffj4+DBv3rwGvxYRkcuVzWYjLy+PgoICT5ci9WQymQgPD8fX19fTpYiINGkeD1Le3t6EhoZW2V5YWMjKlSt56623GDJkCACvvfYaPXr0YMeOHVx//fWsX7+effv2sXHjRtq3b8+1117LnDlzmD59OjNnztSHhIiIi1SEqHbt2tGyZUsMw/B0SVIHVquVEydOkJubS+fOnfVzFBGpB48HqcOHD9OxY0f8/PyIjY0lOTmZzp07s3v3bsrKyhg6dKijbffu3encuTPbt2/n+uuvZ/v27fTu3bvSUL/4+HgmTJjA3r176devX7XnLCkpoaSkxPG+qKjIfRcoItLEWSwWR4i64oorPF2O1FNISAgnTpygvLwcHx8fT5cjItJkeXSQ9HXXXcfrr7/OunXrWLp0KVlZWdx444389NNP5OXl4evrS1BQUKU+7du3Jy8vD7D/hfT8EFWxv2JfTZKTkwkMDHS8wsLCXHthIiKXkYpnolq2bOnhSsQVKkZrWCwWD1ciItK0efSO1G233eb47z59+nDdddfRpUsX/vGPf9CiRQu3nfexxx5jypQpjvdFRUUKUyIil6BhYJcH/RxFRFyjUU3bExQURGRkJEeOHCE0NJTS0tIqDzafPHnS8UxVaGgoJ0+erLK/Yl9NzGYzAQEBlV4iIiIiIiK11aiC1OnTpzl69CgdOnQgOjoaHx8f0tLSHPsPHjxITk4OsbGxAMTGxvLVV1/x/fffO9ps2LCBgIAAoqKiGrx+ERGpXn4+PPssDB8O111n//rcc/btIiIiTZFHg9S0adPYsmUL2dnZfPbZZ9xxxx14eXnxP//zPwQGBjJmzBimTJnCpk2b2L17Nw8++CCxsbFcf/31AAwfPpyoqCjuvfde9uzZQ2pqKk888QSJiYmYzWZPXpqIiADFxTB+PHTsCH/+M2zYADt32r8+/rh9+4QJcN78Pw3OMAw+/PBDzxUgIiJNkkeD1Lfffsv//M//cM011/C73/2OK664gh07dhASEgLACy+8wK9+9SsSEhK46aabCA0N5f3333f09/LyYs2aNXh5eREbG8uoUaO47777mD17tqcuSUREflZcDPHxsGIFlJeD1Vp5v9Vq3758ub2dO8JUXl4ekyZNIiIiArPZTFhYGCNHjqw02sGVNm/ejGEYbl1va+7cudxwww20bNmyyoRMIiLScDw62cQ777xz0f1+fn4sXryYxYsX19imS5cufPzxx64uTURE6mnyZNi2rWqAupDVCp98AklJsHSp686fnZ3NwIEDCQoKYv78+fTu3ZuysjJSU1NJTEzkwIEDrjuZi9lsNiwWS6VF6iuUlpZy1113ERsby8qVKz1QnYiIQCN7RkpERC4P+fnwyiuXDlEVrFZ7+x9+cF0NEydOxDAMdu7cSUJCApGRkfTs2ZMpU6awY8eOavtUd0cpMzMTwzDIzs4G4Pjx44wcOZLg4GD8/f3p2bMnH3/8MdnZ2QwePBiA4OBgDMPggQce+Pn6rCQnJxMeHk6LFi3o27cv7733XpXzrl27lujoaMxmM9u2bau2xlmzZjF58mR69+5d/2+SiIjUmccX5BURkcvPq6/WPkRVsFph5UqYPr3+5z916hTr1q1j7ty5+Pv7V9lfnyFxiYmJlJaWsnXrVvz9/dm3bx+tWrUiLCyM1atXk5CQwMGDBwkICHAs5ZGcnMwbb7zByy+/TLdu3di6dSujRo0iJCSEm2++2XHsGTNmsGDBAiIiIggODq5zjSIi4n4KUiIi4nJpaXULUunprglSR44cwWaz0b179/of7AI5OTkkJCQ47ghFREQ49rVp0waAdu3aOcJaSUkJ8+bNY+PGjY5ZZyMiIti2bRvLli2rFKRmz57NsGHDXF6ziIi4noKUiIi4XGFh3fq5ao4Gm83mmgNV49FHH2XChAmsX7+eoUOHkpCQQJ8+fWpsf+TIEc6ePVslIJWWltKvX79K22JiYtxSs4iIuJ6ClIiIuFxgYN36uWoSum7dumEYhtMTSphM9keHzw9iZWVlldqMHTuW+Ph4UlJSWL9+PcnJySxcuJBJkyZVe8zTp08DkJKSQqdOnSrtu3CpjuqGIYqISOOkySZERMTl4uLA5OQnjMkEQ4a45vxt2rQhPj6exYsXc+bMmSr7a5qevGL5jdzcXMe2zMzMKu3CwsIYP34877//PlOnTmXFihUA+Pr6AmCxWBxto6KiMJvN5OTk0LVr10qvsLCwul6iiIh4mIKUiIi43OjRzgcpLy8YM8Z1NSxevBiLxcKAAQNYvXo1hw8fZv/+/bz44ouOZ5UuVBFuZs6cyeHDh0lJSWHhwoWV2iQlJZGamkpWVhYZGRls2rSJHj16APYlOQzDYM2aNeTn53P69Glat27NtGnTmDx5MqtWreLo0aNkZGTw0ksvsWrVKqevKycnh8zMTHJycrBYLGRmZpKZmem48yUiIg1DQUpERFwuJATGjq19mDKZ7CGqbVvX1RAREUFGRgaDBw9m6tSp9OrVi2HDhpGWlsbSGhas8vHx4e233+bAgQP06dOH5557jmeeeaZSG4vFQmJiIj169ODWW28lMjKSJUuWANCpUydmzZrFjBkzaN++PY888ggAc+bM4cknnyQ5OdnRLyUlhfDwcKev66mnnqJfv348/fTTnD59mn79+tGvXz927drl9LFERKTuDJs7n8htIoqKiggMDKSwsJCAgABPlyMi0qgUFxeTlZVFeHg4fn5+te5XUgLDh196UV6TCW68EVJT4YJHhsQN6vrzFBFpLmqbDXRHSkRE3MJstoejcePA27vq3SmTyb593DiFKBERaXo0a5+IiLiNnx8sXQqzZ9sX6U1Pt09xHhRkn1hi9Gj7MEAREZGmRkFKRETcLiTEvtCuKxbbFRERaQw0tE9ERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIi7pefD88+a1+h97rr7F+fe86+3cMMw+DDDz/0dBkiItLEKEiJiIj7FBfD+PHQsSP8+c+wYQPs3Gn/+vjj9u0TJkBJiVtOn5eXx6RJk4iIiMBsNhMWFsbIkSNJS0tzy/k2b96MYRgUFBS45fjZ2dmMGTOG8PBwWrRowdVXX83TTz9NaWmpW84nIiI10zpSIiLiHsXFEB8P27aB1Vp1v9Vqfy1fDvv3Q2oqmM0uO312djYDBw4kKCiI+fPn07t3b8rKykhNTSUxMZEDBw647FyuZrPZsFgseHtX/pg+cOAAVquVZcuW0bVrV77++mseeughzpw5w4IFCzxUrYhI86Q7UiIi4h6TJ9ccos5ntcInn0BSkktPP3HiRAzDYOfOnSQkJBAZGUnPnj2ZMmUKO3bsqLZPdXeUMjMzMQyD7OxsAI4fP87IkSMJDg7G39+fnj178vHHH5Odnc3gwYMBCA4OxjAMHnjggZ8v0UpycrLjTlLfvn157733qpx37dq1REdHYzab2bZtW5X6br31Vl577TWGDx9OREQEv/71r5k2bRrvv/++a75pIiJSa7ojJSIirpefD6+8cukQVcFqtbefMwfatq336U+dOsW6deuYO3cu/v7+VfYHBQXV+diJiYmUlpaydetW/P392bdvH61atSIsLIzVq1eTkJDAwYMHCQgIoEWLFgAkJyfzxhtv8PLLL9OtWze2bt3KqFGjCAkJ4eabb3Yce8aMGSxYsICIiAiCg4NrVU9hYSFt2rSp8/WIiEjdKEiJiIjrvfpq7UNUBasVVq6E6dPrffojR45gs9no3r17vY91oZycHBISEujduzcAERERjn0VgaZdu3aOsFZSUsK8efPYuHEjsbGxjj7btm1j2bJllYLU7NmzGTZsWK1rOXLkCC+99JKG9YmIeICClIiIuF5aWt2CVHq6S4KUzWar9zFq8uijjzJhwgTWr1/P0KFDSUhIoE+fPjW2P3LkCGfPnq0SkEpLS+nXr1+lbTExMbWu47vvvuPWW2/lrrvu4qGHHnLuIkREpN4UpERExPUKC+vWz0Wz3XXr1g3DMJyeUMJksj86fH4QKysrq9Rm7NixxMfHk5KSwvr160lOTmbhwoVMmjSp2mOePn0agJSUFDp16lRpn/mCyTWqG4ZYnRMnTjB48GBuuOEGli9fXqs+IiLiWppsQkREXC8wsG796vHs0vnatGlDfHw8ixcv5syZM1X21zQ9eUhICAC5ubmObZmZmVXahYWFMX78eN5//32mTp3KihUrAPD19QXAYrE42kZFRWE2m8nJyaFr166VXmFhYU5f23fffcctt9xCdHQ0r732miP8iYhIw9K/viIi4npxceDsL/gmEwwZ4rISFi9ejMViYcCAAaxevZrDhw+zf/9+XnzxRcezSheqCDczZ87k8OHDpKSksHDhwkptkpKSSE1NJSsri4yMDDZt2kSPHj0A6NKlC4ZhsGbNGvLz8zl9+jStW7dm2rRpTJ48mVWrVnH06FEyMjJ46aWXWLVqlVPXVBGiOnfuzIIFC8jPzycvL4+8vLy6fZNERKTOFKRERMT1Ro92Pkh5ecGYMS4rISIigoyMDAYPHszUqVPp1asXw4YNIy0tjaVLl1bbx8fHh7fffpsDBw7Qp08fnnvuOZ555plKbSwWC4mJifTo0YNbb72VyMhIlixZAkCnTp2YNWsWM2bMoH379jzyyCMAzJkzhyeffJLk5GRHv5SUFMLDw526pg0bNnDkyBHS0tK48sor6dChg+MlIiINy7C584ncJqKoqIjAwEAKCwsJCAjwdDkiIo1KcXExWVlZhIeH4+fnV/uOEybYF9utzaQTJhOMGwc1BBxxnTr/PEVEmonaZgPdkRIREfdYtAgGDbr0nSmTCW680d5eRESkiVCQEhER9zCbITXVfqfJ27tqoDKZ7NvHjbO3u2AGOxERkcZM05+LiIj7+PnZh+vNnm1fpDc93T7FeVCQfWKJ0aPh55nyREREmhIFKRERcb+QEPtCuy5YbFdERKQx0NA+ERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSZu0TERG3yz+Tz8ovV5KelU5hcSGBfoHEhccxut9oQvw9O/25YRh88MEH3H777R6tQ0REmhbdkRIREbcpLi9m/JrxdHy+I39O/zMbjm1g54mdbDi2gcfTH6fj8x2ZkDKBkvISt5w/Ly+PSZMmERERgdlsJiwsjJEjR5KWluaW823evBnDMCgoKHDL8QF+/etf07lzZ/z8/OjQoQP33nsvJ06ccNv5RESkegpSIiLiFsXlxcS/Ec+KjBWUW8ux2qyV9lttVsqt5SzfvZz4N+JdHqays7OJjo4mPT2d+fPn89VXX7Fu3ToGDx5MYmKiS8/lajabjfLy8mr3DR48mH/84x8cPHiQ1atXc/ToUX772982cIUiIqIgJSIibjE5dTLbcrZVCVAXstqsfJLzCUmpSS49/8SJEzEMg507d5KQkEBkZCQ9e/ZkypQp7Nixo9o+1d1RyszMxDAMsrOzATh+/DgjR44kODgYf39/evbsyccff0x2djaDBw8GIDg4GMMweOCBB+zXaLWSnJxMeHg4LVq0oG/fvrz33ntVzrt27Vqio6Mxm81s27at2honT57M9ddfT5cuXbjhhhuYMWMGO3bsoKysrP7fNBERqTU9I3U5KC2DvB+g4Ccot4C3FwS1htC24Ovj6epEpBnKP5PPKxmvXDJEVbDarLyS8QpzBs+hbcu29T7/qVOnWLduHXPnzsXf37/K/qCgoDofOzExkdLSUrZu3Yq/vz/79u2jVatWhIWFsXr1ahISEjh48CABAQG0aNECgOTkZN544w1efvllunXrxtatWxk1ahQhISHcfPPNjmPPmDGDBQsWEBERQXBwcK2u88033+SGG27Ax0f/3ouINCQFqabizDk4+g38dAasNsAGJhPYbGCp5heVH4sg+zsIDYGuYfa2IiIN5NUvX611iKpgtVlZmbGS6YOm1/v8R44cwWaz0b1793of60I5OTkkJCTQu3dvACIiIhz72rRpA0C7du0cYa2kpIR58+axceNGYmNjHX22bdvGsmXLKgWp2bNnM2zYsEvWMH36dP72t79x9uxZrr/+etasWeOqyxMRkVrSb9eNXXk57PwKdu21h6NyC1it9jBVbqk+RFWwAbn58O9D9j4iIg0kLSutTkEqPSvdJee32WwuOU51Hn30UZ555hkGDhzI008/zb///e+Ltj9y5Ahnz55l2LBhtGrVyvH6+9//ztGjRyu1jYmJqVUNf/zjH/nyyy9Zv349Xl5e3HfffW69ZhERqUp3pBqz8nLY8e+Lh6XaKDwNR76ByC6uqUtE5BIKiwvr1K+gpMAl5+/WrRuGYXDgwAGn+pl+vnt/fii58NmjsWPHEh8fT0pKCuvXryc5OZmFCxcyadKkao95+vRpAFJSUujUqVOlfWazudL76oYhVqdt27a0bduWyMhIevToQVhYGDt27HDc8RIREffTHanGLONA/UNUhbx80IPIItJAAv0C69QvyBzkkvO3adOG+Ph4Fi9ezJkzZ6rsr2l68pAQ+5pWubm5jm2ZmZlV2oWFhTF+/Hjef/99pk6dyooVKwDw9fUFwGKxONpGRUVhNpvJycmha9eulV5hYWF1vUQH688jDkpK3DOFvIiIVE9BqrE6cw7OFbvueDYg9wfXHU9E5CLiwuMwGc59xJgME0PCh7ishsWLF2OxWBgwYACrV6/m8OHD7N+/nxdffLHGOzcV4WbmzJkcPnyYlJQUFi5cWKlNUlISqampZGVlkZGRwaZNm+jRowcAXbp0wTAM1qxZQ35+PqdPn6Z169ZMmzaNyZMns2rVKo4ePUpGRgYvvfQSq1atcuqaPv/8c/72t7+RmZnJ8ePHSU9P53/+53+4+uqrdTdKRKSBKUg1Vke/cf0xC35y/TFFRKoxut9op4OUl+HFmP5jXFZDREQEGRkZDB48mKlTp9KrVy+GDRtGWloaS5curbaPj48Pb7/9NgcOHKBPnz4899xzPPPMM5XaWCwWEhMT6dGjB7feeiuRkZEsWbIEgE6dOjFr1ixmzJhB+/bteeSRRwCYM2cOTz75JMnJyY5+KSkphIeHO3VNLVu25P333ycuLo5rrrmGMWPG0KdPH7Zs2VJlmKCIiLiXYWskT6c+++yzPPbYY/zhD39g0aJFABQXFzN16lTeeecdSkpKiI+PZ8mSJbRv397RLycnhwkTJrBp0yZatWrF/fffT3JyMt7etX/8q6ioiMDAQAoLCwkICHD1pdXNp1/aJ5Nwpdb+0L+Ha48pIpe94uJisrKyCA8Px8/Pr9b9JqRMYPnu5bWadMJkmBgXPY6lI6oPOOI6df15iog0F7XNBo3ijtQXX3zBsmXL6NOnT6XtkydP5qOPPuLdd99ly5YtnDhxgjvvvNOx32KxMGLECEpLS/nss89YtWoVr7/+Ok899VRDX4JrWa2uD1FgX19KRKSBLIpfxKDOgy55Z8pkmLix840sil/UMIWJiIi4gMeD1OnTp7nnnntYsWJFpcUHCwsLWblyJc8//zxDhgwhOjqa1157jc8++8yxIv369evZt28fb7zxBtdeey233XYbc+bMYfHixZSWlnrqkuqnYqY+dwhq7Z7jiohUw+xtJnVUKuOix+Ft8q4SqEyGCW+TN+Oix5E6KhWzt4amiYhI0+HxIJWYmMiIESMYOnRope27d++mrKys0vbu3bvTuXNntm/fDsD27dvp3bt3paF+8fHxFBUVsXfv3hrPWVJSQlFRUaVXo2C1ws6voazc9cc2DOjQ1vXHFRG5CD9vP5aOWMqJKSeYN2QewyOGM6DTAIZHDGfekHmcmHKCpSOWKkSJiEiT49F1pN555x0yMjL44osvquzLy8vD19fXsTJ8hfbt25OXl+doc36Iqthfsa8mycnJzJo1q57Vu8HB4+4JUQChbcHHxz3HFhG5hBD/EKYPms70QdM9XYqIiIhLeOyO1DfffMMf/vAH3nzzzQZ/2PWxxx6jsLDQ8frmGzfMkOes0jL4/j/uOXZgK+ha/7VKRERERETEzmNBavfu3Xz//ff0798fb29vvL292bJlCy+++CLe3t60b9+e0tLSKosmnjx5ktDQUABCQ0M5efJklf0V+2piNpsJCAio9PK4PDes8WQAHUKgTySYPD6KU0RERETksuGx367j4uL46quvyMzMdLxiYmK45557HP/t4+NDWlqao8/BgwfJyclxLDoYGxvLV199xffff+9os2HDBgICAoiKimrwa6oXV67xZDKghRnCQuGqjgpRIiIiIiIu5rFnpFq3bk2vXr0qbfP39+eKK65wbB8zZgxTpkyhTZs2BAQEMGnSJGJjY7n++usBGD58OFFRUdx777385S9/IS8vjyeeeILExMSmtzChK6c7t9rgXAnk5ME3eRAaYh/ap0AlIiIiIuISHp1s4lJeeOEFTCYTCQkJlRbkreDl5cWaNWuYMGECsbGx+Pv7c//99zN79mwPVl1H7lrjyQbk5sPZcxriJyIiIiLiIobNZrN5ughPq+3qxW6VkwtZ37n3HB1CILKLe88hIped4uJisrKyCA8Pr8fkQPnASiAdKAQCgThgNBDimkLryDAMPvjgA26//XaP1tFQXPPzFBG5fNU2G+j2RGNxRVCtmx765jjTlizilj88TL+x93DLHx5m2pJFHPrm+MU75uVDWVn96hQRcUoxMB7oCPwZ2ADs/Pnr4z9vnwCUuOXseXl5TJo0iYiICMxmM2FhYYwcObLS87eutHnzZgzDqDJRkjuUlJRw7bXXYhgGmZmZbj+fiIhU1qiH9jULViscyanVrH17jhxiypJFpGd8gZfJC4v1v89VbftqDwv/8SZx/X/BwolJ9O0aWfUANuBEPnTp6MILEBGpSTEQD2wDrNXst/78Wg7sB1IB1z3fmp2dzcCBAwkKCmL+/Pn07t2bsrIyUlNTSUxM5MCBAy47l6vZbDYsFgve3jV/TP/pT3+iY8eO7NmzpwErExGRCroj5UlWK/z7EOT+YA85F5G2eyexiaPZkrkboFKIOv/95szdxCaOJm33zuoP9E2e/bwiIm43mZpD1PmswCdAkkvPPnHiRAzDYOfOnSQkJBAZGUnPnj2ZMmUKO3bsqLZPdXeUMjMzMQyD7OxsAI4fP87IkSMJDg7G39+fnj178vHHH5Odnc3gwYMBCA4OxjAMHnjgAfsVWq0kJycTHh5OixYt6Nu3L++9916V865du5bo6GjMZjPbtm2r8drWrl3L+vXrWbBgQf2+SSIiUme6I+VJR76BwtOXbLbnyCFGPj6F4tJSLvVIm8VqpaS0lJGPT2H74ler3pmyWO3n1bNSIuJW+cArXDpEVbD+3H4O0LbeZz916hTr1q1j7ty5+Pv7V9kfFBRU52MnJiZSWlrK1q1b8ff3Z9++fbRq1YqwsDBWr15NQkICBw8eJCAggBYtWgCQnJzMG2+8wcsvv0y3bt3YunUro0aNIiQkhJtvvtlx7BkzZrBgwQIiIiIIDg6u9vwnT57koYce4sMPP6Rly5Z1vg4RkfrKP5PPyi9Xkp6VTmFxIYF+gcSFxzG632hC/D37/GtDUJDylNIy+zNLtTBlySJKy8ouGaIqWG02SsvKmLpkERufX1K1QV4+hHcEHx9nKhYRccKr1D5EVbBin5Bier3PfuTIEWw2G927d6/3sS6Uk5NDQkICvXv3BiAiIsKxr02bNgC0a9fOEdZKSkqYN28eGzdudKyDGBERwbZt21i2bFmlIDV79myGDRtW47ltNhsPPPAA48ePJyYmxnGXTESkIRWXF5O0LomVX67EarNitf333/uNxzbyePrjdGzdkVD/UIJbBF+24UpBylPyLj2cD+wTS6RnfOH04S1WK2kZX3D42xy6Xdm58k4b9uGEnTs4fVwRkdpJo25BKh1XBCl3Tkj76KOPMmHCBNavX8/QoUNJSEigT58+NbY/cuQIZ8+erRKQSktL6devX6VtMTExFz33Sy+9xE8//cRjjz1W9wsQEamH4vJi4t+IZ1vOtkoBqoINGzabjW+LvuXbom8BSMtK44lNTzC2/1gWxS/C7N3E1nutgZ6R8pSCn2rVbPlHH+BlqtsaU14mE8v+9X69zi8iUjeFdexX4JKzd+vWDcMwnJ5QwvTzWnvnB7GyC2Y7HTt2LMeOHePee+/lq6++IiYmhpdeeqnGY54+bR/CnZKSQmZmpuO1b9++Ss9JAdUOQzxfeno627dvx2w24+3tTdeuXQF7ALv//vtrf6EiInU0OXVyjSGqJlablXJrOct3Lyf+jXhKyt0zU2tDU5DylHLLpdsAuw7urzKxRG1ZrFZ2H6rhl4hanl9EpG4C69gvyCVnb9OmDfHx8SxevJgzZ85U2V/T9OQhIfZhJ7m5uY5t1U0tHhYWxvjx43n//feZOnUqK1asAMDX1xcAi+W//8ZGRUVhNpvJycmha9eulV5hYWFOXdeLL77Inj17HGHs448/BuD//b//x9y5c506loiIs/LP5PNKxitOhajzWW1WPsn5hKTUJNcW5iEa2ucp3rW7y1R45tKTUVxMweka7jzV8vwiInUTh/PD+0zAEJdVsHjxYgYOHMiAAQOYPXs2ffr0oby8nA0bNrB06VL2799fpU9FuJk5cyZz587l0KFDLFy4sFKbpKQkbrvtNiIjI/nxxx/ZtGkTPXr0AKBLly4YhsGaNWv45S9/SYsWLWjdujXTpk1j8uTJWK1WBg0aRGFhIZ9++ikBAQFO3Unq3LnyUO1WrVoBcPXVV3PllVc6+y0SEXHKq1++WucQVcFqs7J893L25+/nXNm5Jj1Bhe5IeUpQ61o1C/RvVb/TtKrhPLU8v4hI3YzG+Y8YL2CMyyqIiIggIyODwYMHM3XqVHr16sWwYcNIS0tj6dKl1fbx8fHh7bff5sCBA/Tp04fnnnuOZ555plIbi8VCYmIiPXr04NZbbyUyMpIlS+wT+3Tq1IlZs2YxY8YM2rdvzyOPPALAnDlzePLJJ0lOTnb0S0lJITw83GXXKyLibmlZafUOUmAPU1uOb2HniZ1sOLaBGWkzaLegHWEvhDF361zyz9RuQjZPM2zufCK3iSgqKiIwMJDCwkICAgIa5qSlZbBjzyUnnJi2ZBGL3nunTsP7vEwmkn77PyyYmFR5h2FAbB/N2icitVJcXExWVhbh4eH4+fk50XMC9sV2a/OhawLGAdUHHHGduv88RaS5u27Fdew8UcNapS7kbfL26MQUtc0GuiPlKb4+EHrp25fjRt5Rr2ekHv71nVV3hLZViBKRBrAIGMSlP2pMwI0/txcRkcYq0K+uz786p6lMTKEg5UldwyDw4kP3IsO6MKT/L/AyOfej8jKZGBr9i6pTnwe2sp9XRMTtzEAq9jtN3lT9yDH9vH3cz+0uj+lwRUQuV3HhcZiMhokPVpuVrce3NuqJKRSkPMlkgj6R0CEEjJqbPT8xCV8fH0zGRRqdf1jDwNfHhwUTkirv6BBiP5+ToUxEpO78sA/XOwHMA4YDA37+Ou/n7UtRiBIRafxG9xvdYEEK7GtSrdi9gh/O/tBg53SGfqP2NJMJIrvA9X0hvBMEB0Brf/tkEK1aAtC3ayQfzXses6/vJe9MeZlMmH19+Wje8/TtGmnf6OcL1/W2n0chSkQ8IgT7QrupwOc/f53+83YREWkKQvxDGNt/bIOGKYvNwsqMlQ12Pmfot+rGwtcHOnew3zHq3wP6XgPRURDbFzp3IG7gjWx/+XVu6Wdf9d7Lq/L05RWL9g7uF832xa8SFz3AfperQwj8ohf46a+9IiIiIlI/i+IXMajzoAYNU+uOrGuwczlD60g1dr4+9jtV4Z3oe11vNj5wN4cPH2bZsmXs3r2bgoICggIDiY7szsMj76RbpzD7GlFBre2TSvhqUgkRERERcQ2zt5nUUalMTp3My7tebpBzHvnxSIOcx1kKUk1Qt27dWLBggafLEBEREZFmyM/bj6UjlrI/fz9bjm9x+/nOlZ1z+znqQkP7RERERETEabd1va1Bhvi18Gnh9nPUhYKUiIiIiIg4raFm8esa3NXt56gLBSkREXG7s9azfFH8BR/89AHvFL3DBz99wK7iXZy1nvV0aRiGwYcffujpMkREmpyGmsXv1q63uvX4daUgJSIiblNuKyf9TDorC1fy2bnPyCnP4aTlJDnlOXx67lNWFq4k/Uw65bZyt5w/Ly+PSZMmERERgdlsJiwsjJEjR5KWluaW823evBnDMCgoKHDL8QGuuuoqDMOo9Hr22Wfddj4RkYtx9yx+PiYfxvQf45Zj15cmmxAREbcot5XzwekPyC3PxYat2jZWrHxd+jWnrKe4vdXteBuu+1jKzs5m4MCBBAUFMX/+fHr37k1ZWRmpqakkJiZy4MABl53L1Ww2GxaLBW/v6r8fs2fP5qGHHnK8b926dUOVJiJSyfmz+L2S8QpWmxWrzeqy44/pP4a2Ldu67Hiu5HR0PHfuHNu2bWPfvn1V9hUXF/P3v//dJYWJiEjTtvXs1ouGqAo2bHxX/h1bz2516fknTpyIYRjs3LmThIQEIiMj6dmzJ1OmTGHHjh3V9qnujlJmZiaGYZCdnQ3A8ePHGTlyJMHBwfj7+9OzZ08+/vhjsrOzGTx4MADBwcEYhsEDDzwAgNVqJTk5mfDwcFq0aEHfvn157733qpx37dq1REdHYzab2bZtW43X1rp1a0JDQx0vf3//+n2zRETqoWIWvxNTTjBvyDyGRwxnQKcB+PvU798mfx9/FsUvck2RbuBUkDp06BA9evTgpptuonfv3tx8883k5uY69hcWFvLggw+6vEgREWlazlrPsrd07yVD1Pn2lu7lnNU1U9yeOnWKdevWkZiYWG3ICAoKqvOxExMTKSkpYevWrXz11Vc899xztGrVirCwMFavXg3AwYMHyc3N5a9//SsAycnJ/P3vf+fll19m7969TJ48mVGjRrFlS+Vpg2fMmMGzzz7L/v376dOnT401PPvss1xxxRX069eP+fPnU17unqGRIiLOCPEPYfqg6aTem8rnYz/nxJQTBJoD63y82CtjMXubXVihazk1hmL69On06tWLXbt2UVBQQFJSEgMHDmTz5s107tzZXTWKiEgTs690H1acG9phxcre0r3E+MXU+/xHjhzBZrPRvXv3eh/rQjk5OSQkJNC7d28AIiIiHPvatGkDQLt27RxhraSkhHnz5rFx40ZiY2MdfbZt28ayZcu4+eabHf1nz57NsGHDLnr+Rx99lP79+9OmTRs+++wzHnvsMXJzc3n++eddeZkiIvUW4BdAdlI2kS9Gkn8u36m+BgZDI4a6qTLXcCpIffbZZ2zcuJG2bdvStm1bPvroIyZOnMiNN97Ipk2bNLSgoZWWQd4PUPATlFvA2wuCWkNoW/D18XR1ItKMfVP2TZ37uSJI2Wy1vxPmrEcffZQJEyawfv16hg4dSkJCwkXvHh05coSzZ89WCUilpaX069ev0raYmEtf+5QpUxz/3adPH3x9fXn44YdJTk7GbG68f7kVkeYpyC+IvYl7ab+gvVOjFLxN3o12kokKTgWpc+fOVXrw1TAMli5dyiOPPMLNN9/MW2+95fICpRpWKxzJsYeoC/9//LEIsr+D0BDoGgYmTcwoIg2vxFbSoP0u1K1bNwzDcHpCCdPP/2aeH8TKysoqtRk7dizx8fGkpKSwfv16kpOTWbhwIZMmTar2mKdPnwYgJSWFTp06Vdp3YfCpyx8kr7vuOsrLy8nOzuaaa65xur+IiCvln8ln5ZcrSc9Kp7C4kEC/QOLC47iv733837//r1YTUZgMU6OeZKKCU0Gqe/fu7Nq1ix49elTa/re//Q2AX//6166rTKpntcK/D0Hh6Zrb2IDcfDh7DvpEKkyJSIMzG3W7M1LXfhdq06YN8fHxLF68mEcffbRKQCkoKKj2OamQkBAAcnNzCQ4OBuyTTVwoLCyM8ePHM378eB577DFWrFjBpEmT8PX1BcBisTjaRkVFYTabycnJqTSMz1UyMzMxmUy0a9fO5ccWEamt4vJiktYlsfLLlVVm7kvLSsNkmGjv356803kXvTNlMkzc2PnGRj3JRAWnfsO+4447ePvtt6vd97e//Y3/+Z//cetwCgGOfHPxEHW+wtP29iIiDSzMJ6xB+1Vn8eLFWCwWBgwYwOrVqzl8+DD79+/nxRdfdDyrdKGuXbsSFhbGzJkzOXz4MCkpKSxcuLBSm6SkJFJTU8nKyiIjI4NNmzY5/sDYpUsXDMNgzZo15Ofnc/r0aVq3bs20adOYPHkyq1at4ujRo2RkZPDSSy+xatUqp65p+/btLFq0iD179nDs2DHefPNNx8QVFcFPRKShFZcXM2TVEJbvXk65tbzKXSerzUq5tZyTZ04S2ioUL8OryrpTJsOEt8mbcdHjSB2V2qgnmajg1B2p3//+90yfPr3G/UuWLGHJkiX1LkpqUFoGec49qEdePoR3BB89MyUiDSfKN4rt57Y7NeGECRM9fXu6rIaIiAgyMjKYO3cuU6dOJTc3l5CQEKKjo1m6dGm1fXx8fHj77beZMGECffr04Re/+AXPPPMMd911l6ONxWIhMTGRb7/9loCAAG699VZeeOEFADp16sSsWbOYMWMGDz74IPfddx+vv/46c+bMISQkhOTkZI4dO0ZQUBD9+/fn8ccfd+qazGYz77zzDjNnzqSkpITw8HAmT55c6bkpEZGGVFxeTPSyaPb9UHVppAtZbVZOnjnJvX3upUfbHqRnpVNQUkCQOYgh4UMY3W80If4hDVC1axg2J24heXl5kZub6xg+8Pvf/54XX3yR9u3bu63AhlBUVERgYCCFhYUEBAR4upya5eRC1nfO9wvvBJ07uL4eEWkWiouLycrKIjw8HD8/v1r3Sz+TztelX9f64eLevr0Z4j+krmVKLdX15ykicqHi8mIGrxrMjm+rX5uvJt4mb3Kn5jbaZ6Bqmw2cGtp3Yeb6+OOPOXPmTN0qFOcV/NSw/URE6uGmljfRwbsDBsYl23by7sRNLW9qgKpERMRVJqdO5vNvP3e6n9VmZWXGSjdU1LA0C0FTUm65dBtX9hMRqQdvw5s7Wt1BL99emGr4uDFhordvb25vdTvehlOjzUVExIPyz+TzSsYrTk1pXsFqs5Kele6GqhqWU59ahmFgGEaVbdJAvL0atp+ISD15G94M8R/C9S2uZ1/pPr4p+4YSWwlmw0yYTxhRvlG0NLX0dJkiIuKkV798tVZTmdekoKTAdcV4iFNBymaz8cADDzjWvSguLmb8+PFVppV9//33XVeh/FdQa/s6UXXpJyLiQS1NLYnxi3HJYrsiIuJ5aVlp9QpSQeYg1xXjIU4Fqfvvv7/S+1GjRrm0GLmE0Lb2xXaduYNqGNChcT7IJyIiIiJNU2FxYZ37mgwTQ8Kb/uRCTgWp1157zV11SG34+kBoiH2x3doKbaupz0VERETEpQL9Auvc18vwYkz/MS6sxjM02URT0zUMAlvVvr3NBta633YVEREREblQXHhclUV1a8PAYEz/MY126nNnKEg1NSYT9Im032mqjbwf4N+HFKZERERExGVG9xtdpyAVe2Usi+IXub4gD1CQaopMJvuzT7VVeBqOfOO+ekRERESkWQnxD2Fs/7FOhamokCjS70/H7G12Y2UNR4t2NEWlZZDnxHNSYG8f3lHPS4mIR5zJP8OXK78kKz2L4sJi/AL9CI8Lp9/ofviH+F/6AG5kGAYffPABt99+u0frEBFpahbFL2Jf/j625Wy76Ax+BgbXX3k9m+7fdNmEKNAdqaYp7wfnZu4De/vcH9xRjYhIjcqLy1kzfg3Pd3ye9D+nc2zDMU7sPMGxDcdIfzyd5zs+T8qEFMpLyt1y/ry8PCZNmkRERARms5mwsDBGjhxJWlqaW863efNmDMOgoKDALcevkJKSwnXXXUeLFi0IDg5WCBQRjzB7m0kdlcq46HF4m7yr3J0yGSa8Td48HPPwZReiQHekmqaCn+rer3MH19YiIlKD8uJy3oh/g5xtOdisVf/6Y7PasFlt7F6+m/z9+YxKHYW32XUfS9nZ2QwcOJCgoCDmz59P7969KSsrIzU1lcTERA4cOOCyc7mazWbDYrHg7V31+7F69Woeeugh5s2bx5AhQygvL+frr7/2QJUiIuDn7cfSEUuZfctsXv3yVdKz0ikoKSDIHMSQ8CGM7jeaEP8QT5fpFroj1RSVWxq2n4hIHaROTq0xRJ3PZrWR80kOqUmpLj3/xIkTMQyDnTt3kpCQQGRkJD179mTKlCns2LGj2j7V3VHKzMzEMAyys7MBOH78OCNHjiQ4OBh/f3969uzJxx9/THZ2NoMHDwYgODgYwzB44IEHALBarSQnJxMeHk6LFi3o27cv7733XpXzrl27lujoaMxmM9u2batSX3l5OX/4wx+YP38+48ePJzIykqioKH73u9+55psmIlJHIf4hTB80ndR7U/l87Oek3pvK9EHTL9sQBboj1TR5ezVsPxERJ53JP0PGKxmXDFEVbFYbGa9kMHjOYFq2bVnv8586dYp169Yxd+5c/P2rPoMVFBRU52MnJiZSWlrK1q1b8ff3Z9++fbRq1YqwsDBWr15NQkICBw8eJCAggBYtWgCQnJzMG2+8wcsvv0y3bt3YunUro0aNIiQkhJtvvtlx7BkzZrBgwQIiIiIIDg6ucu6MjAy+++47TCYT/fr1Iy8vj2uvvZb58+fTq1evOl+TiIg4T0GqKQpqDT8W1a2fiEgD+PLVL2sdoirYrDYyVmYwaPqgep//yJEj2Gw2unfvXu9jXSgnJ4eEhAR69+4NQEREhGNfmzZtAGjXrp0jrJWUlDBv3jw2btxIbGyso8+2bdtYtmxZpSA1e/Zshg0bVuO5jx07BsDMmTN5/vnnueqqq1i4cCG33HILhw4dcpxfRETcT0P7mqLQtuDE7OeAfbr0Dk1/4TMRaRqy0rLqFKSy0rNccn6bzdkZeWrv0Ucf5ZlnnmHgwIE8/fTT/Pvf/75o+yNHjnD27FmGDRtGq1atHK+///3vHD16tFLbmJiYix7L+vOagH/+859JSEggOjqa1157DcMwePfdd+t3YSIi4hQFqabI1wdCnRxvGtpWU5+LSIMpLiyuU7+SghKXnL9bt24YhuH0hBImk/1j8fwgVlZWVqnN2LFjOXbsGPfeey9fffUVMTExvPTSSzUe8/Tp04B9pr3MzEzHa9++fZWekwKqHYZ4vg4d7BMGRUVFObaZzWYiIiLIycmpxRWKiIireDRILV26lD59+hAQEEBAQACxsbGsXbvWsb+4uJjExESuuOIKWrVqRUJCAidPnqx0jJycHEaMGEHLli1p164df/zjHykvd880uo1K1zAIbFW7toGt7O1FRBqIX6BfnfqZg1wzNW6bNm2Ij49n8eLFnDlzpsr+mqYnDwmx/5EqNzfXsS0zM7NKu7CwMMaPH8/777/P1KlTWbFiBQC+vr4AWCz/ndwnKioKs9lMTk4OXbt2rfQKC3Pu3+aKiSgOHjzo2FZWVkZ2djZdunRx6lgiIlI/Hg1SV155Jc8++yy7d+9m165dDBkyhN/85jfs3bsXgMmTJ/PRRx/x7rvvsmXLFk6cOMGdd97p6G+xWBgxYgSlpaV89tlnrFq1itdff52nnnrKU5fUcEwm6BMJHUJqHuZnYN/fJ9LeXkSkgYTHhWOYnBuDbJgMwoeEu6yGxYsXY7FYGDBgAKtXr+bw4cPs37+fF1980fGs0oUqws3MmTM5fPgwKSkpLFy4sFKbpKQkUlNTycrKIiMjg02bNtGjRw8AunTpgmEYrFmzhvz8fE6fPk3r1q2ZNm0akydPZtWqVRw9epSMjAxeeuklVq1a5dQ1BQQEMH78eJ5++mnWr1/PwYMHmTBhAgB33XVXHb5LIiJSVx6dbGLkyJGV3s+dO5elS5eyY8cOrrzySlauXMlbb73FkCFDAHjttdfo0aMHO3bs4Prrr2f9+vXs27ePjRs30r59e6699lrmzJnD9OnTmTlzpuMvg5ctkwkiu8BVHe2L9Bb8ZJ/i3NvLPrFEaFv7MEARkQbWb3Q/Nj2xyannpAwvg/5j+rushoiICDIyMpg7dy5Tp04lNzeXkJAQoqOjWbp0abV9fHx8ePvtt5kwYQJ9+vThF7/4Bc8880ylkGKxWEhMTOTbb78lICCAW2+9lRdeeAGATp06MWvWLGbMmMGDDz7Ifffdx+uvv86cOXMICQkhOTmZY8eOERQURP/+/Xn88cedvq758+fj7e3Nvffey7lz57juuutIT0+vdpY/ERFxH8PmzidynWCxWHj33Xe5//77+fLLL8nLyyMuLo4ff/yx0jS1Xbp0ISkpicmTJ/PUU0/xr3/9q9Kwi6ysLMeHZ79+/ao9V0lJCSUl/x2HX1RURFhYGIWFhQQEBLjrEt2vtEyBSkRcrri4mKysLMLDw/Hzq/2QvZQJKexevrtWYcowGUSPi2bE0hH1KVVqoa4/TxGR5qKoqIjAwMBLZgOPT3/+1VdfERsbS3FxMa1ateKDDz4gKiqKzMxMfH19q6z10b59e/Ly8gDIy8ujffv2VfZX7KtJcnIys2bNcu2FeJLVCkdy7CHqwt9XfiyC7O/sk1N0DdMQPxFpMPGL4snfl3/JRXkNk0HnGzsTvyi+AasTERGpH4//Vn3NNdeQmZnJ559/zoQJE7j//vvZt2+fW8/52GOPUVhY6Hh98803bj2fW1mt8O9DkFtNiKpgA3Lz7e1+njpXRMTdvM3ejEodRfS4aEzepirPTBkmA5O3iehx0YxKHYW32eN/2xMREak1j39q+fr60rVrV8A+G9EXX3zBX//6V37/+99TWlpKQUFBpbtSJ0+eJDQ0FIDQ0FB27txZ6XgVs/pVtKmO2WzGbHbNzFAed+QbKDxdu7aFp+3tIzWzk4g0DG8/b0YsHcEts2/hy1e/JCs9i5KCEsxBZsKHhNNvdD/8Qy4+5beIiEhj5PEgdSGr1UpJSQnR0dH4+PiQlpZGQkICAAcPHiQnJ8cx21JsbCxz587l+++/p127dgBs2LCBgICASmtsXLZKyyAv37k+efkQ3lFrSolIg/IP8WfQ9EEMmj7I06WIiIi4hEeD1GOPPcZtt91G586d+emnn3jrrbfYvHkzqampBAYGMmbMGKZMmUKbNm0ICAhg0qRJxMbGcv311wMwfPhwoqKiuPfee/nLX/5CXl4eTzzxBImJiZfPHaeLqe6ZqEuxYR8G2LmDOyoSEREREWkWPBqkvv/+e+677z5yc3MJDAykT58+pKamMmzYMABeeOEFTCYTCQkJlJSUEB8fz5IlSxz9vby8WLNmDRMmTCA2NhZ/f3/uv/9+Zs+e7alLalgFP9W9n4KUiIiIiEidNZrpzz2ptlMcNjoZ++GnM873a+0P/Xu4vh4RuSxpuuzLi36eIiIXV9ts4PFZ+6QevL0atp+IiIiIiAAKUk1bUOuG7SciIiIiIoCCVNMW2haMSzerxDCgQ1u3lCMiUqPifNj7LKQPh9Tr7F/3PWff7mGGYfDhhx96ugwREWliFKSaMl8fCA1xrk9oW019LiINx1IMO8fDBx1hz58hbwP8Z6f9a+bj9u07J4ClxC2nz8vLY9KkSURERGA2mwkLC2PkyJGkpaW55XybN2/GMAwKCgrcevzqXl988YVbzikiItVrdOtIiZO6hsHZc7VblDewlb29iEhDsBRDejzkbwOs1TSwgs0KR5ZD0X4YnAperlu6Ijs7m4EDBxIUFMT8+fPp3bs3ZWVlpKamkpiYyIEDB1x2Llez2WxYLBa8vSt/TN9www3k5uZW2vbkk0+SlpZGTExMQ5YoItLs6Y5UU2cyQZ9I6BBS8zA/A/v+PpH29iIiDWH35IuEqPNZ4ftPYHeSS08/ceJEDMNg586dJCQkEBkZSc+ePZkyZQo7duyotk91d5QyMzMxDIPs7GwAjh8/zsiRIwkODsbf35+ePXvy8ccfk52dzeDBgwEIDg7GMAweeOAB+xVarSQnJxMeHk6LFi3o27cv7733XpXzrl27lujoaMxmM9u2batSn6+vL6GhoY7XFVdcwT//+U8efPBBDMPZsd4iIlIfuiN1OTCZILILXNXRvkhvwU9QbrHPzhfU2j6cz1fD+USkARXnw9FXuHSIqmC1t+8zB/zq/xznqVOnWLduHXPnzsXf37/K/qCgoDofOzExkdLSUrZu3Yq/vz/79u2jVatWhIWFsXr1ahISEjh48CABAQG0aNECgOTkZN544w1efvllunXrxtatWxk1ahQhISHcfPPNjmPPmDGDBQsWEBERQXBw8CVr+de//sV//vMfHnzwwTpfj4iI1I2C1OXE18e+0K4W2xURTzv2qn3YnjNsVji2EqKm1/v0R44cwWaz0b1793of60I5OTkkJCTQu3dvACIiIhz72rRpA0C7du0cYa2kpIR58+axceNGYmNjHX22bdvGsmXLKgWp2bNnOxalr42VK1cSHx/PlVdeWd/LEhERJylIiYiI6+WlUfu7URWskJfukiDlzrXmH330USZMmMD69esZOnQoCQkJ9OnTp8b2R44c4ezZs1UCUmlpKf369au0zZnnnL799ltSU1P5xz/+4dwFiIiISyhIiYiI65UV1rFfgUtO361bNwzDcHpCCdPPz5GeH8TKysoqtRk7dizx8fGkpKSwfv16kpOTWbhwIZMmTar2mKdP2ycDSklJoVOnTpX2mc2VJ9eobhhiTV577TWuuOIKfv3rX9e6j4iIuI5mHhAREdfzCaxjvyCXnL5NmzbEx8ezePFizpw5U2V/TdOTh4TYl5Q4f2a8zMzMKu3CwsIYP34877//PlOnTmXFihWAfTIIAIvF4mgbFRWF2WwmJyeHrl27VnqFhdVtJlWbzcZrr73Gfffdh4+WtBAR8QgFKRERcb3QOJz/iDFB6BCXlbB48WIsFgsDBgxg9erVHD58mP379/Piiy86nlW6UEW4mTlzJocPHyYlJYWFCxdWapOUlERqaipZWVlkZGSwadMmevToAUCXLl0wDIM1a9aQn5/P6dOnad26NdOmTWPy5MmsWrWKo0ePkpGRwUsvvcSqVavqdG3p6elkZWUxduzYOvUXEZH6U5ASERHXixgNhpMfMYYXRIxxXQkREWRkZDB48GCmTp1Kr169GDZsGGlpaSxdurTaPj4+Prz99tscOHCAPn368Nxzz/HMM89UamOxWEhMTKRHjx7ceuutREZGsmTJEgA6derErFmzmDFjBu3bt+eRRx4BYM6cOTz55JMkJyc7+qWkpBAeHl6na1u5ciU33HCDWybTEBGR2jFs7nwit4koKioiMDCQwsJCAgICPF2OiEijUlxcTFZWFuHh4fj5+dW+484J9sV2azXphAm6joMB1QcccZ06/zxFRJqJ2mYD3ZESERH3iF4EIYO49EeNCdrdaG8vIiLSRChIiYiIe3iZYUiq/U6T4U3VjxyTfXvXcTA41d5eRESkidD05yIi4j5efvbhen1m2xfpzUu3T3HuE2SfWCJiNPiFeLpKERERpylIiYiI+/mF2BfadcFiuyIiIo2BhvaJiIiIiIg4SUFKRERERETESRraJyIiIiIi9XLo0CGWL1/Orl27KCwsJDAwkJiYGMaNG0dkZKSny3MLBSkREREREamTPXv2MGXKFNLT0/Hy8sJisTj2bdu2jYULFxIXF8fChQvp27evByt1PQ3tExERERERp6WlpREbG8uWLVsAKoWo899v3ryZ2NhY0tLSGrxGd1KQEhER9ystg5xc+PchyNhv/5qTa9/uYYZh8OGHH3q6DBGRJmXPnj2MHDmS4uLiKgHqQhaLhZKSEkaOHMmePXsaqEL3U5ASERH3sVrhUDbs2ANZ38GPRfDTGfvXrO/s2w8dt7dzg7y8PCZNmkRERARms5mwsDBGjhzptr+Kbt68GcMwKCgocMvxwf4cwm9+8xvatm1LQEAAgwYNYtOmTW47n4hIdaZMmUJpaSk2m61W7a1WK6WlpUydOtXNlTUcBSkREXEPq9V+5yn3B6jpc9YG5Obb27k4TGVnZxMdHU16ejrz58/nq6++Yt26dQwePJjExESXnsvVbDYb5eXl1e771a9+RXl5Oenp6ezevZu+ffvyq1/9iry8vAauUkSaq0OHDpGenn7JO1EXslgspKWlcfjwYTdV1rAUpERExD2OfAOFp2vXtvC0vb0LTZw4EcMw2LlzJwkJCURGRtKzZ0+mTJnCjh07qu1T3R2lzMxMDMMgOzsbgOPHjzNy5EiCg4Px9/enZ8+efPzxx2RnZzN48GAAgoODMQyDBx54ALD/JTY5OZnw8HBatGhB3759ee+996qcd+3atURHR2M2m9m2bVuV+n744QcOHz7MjBkz6NOnD926dePZZ5/l7NmzfP311675xomIXMLy5cvx8vKqU18vLy+WLVvm4oo8Q7P2iYiI65WWQV6+c33y8iG8I/j41Pv0p06dYt26dcydOxd/f/8q+4OCgup87MTEREpLS9m6dSv+/v7s27ePVq1aERYWxurVq0lISODgwYMEBATQokULAJKTk3njjTd4+eWX6datG1u3bmXUqFGEhIRw8803O449Y8YMFixYQEREBMHBwVXOfcUVV3DNNdfw97//nf79+2M2m1m2bBnt2rUjOjq6ztckIuKMXbt2OX03qoLFYmH37t0ursgzFKRERMT18i4ynK8mNuzDADt3qPfpjxw5gs1mo3v37vU+1oVycnJISEigd+/eAERERDj2tWnTBoB27do5wlpJSQnz5s1j48aNxMbGOvps27aNZcuWVQpSs2fPZtiwYTWe2zAMNm7cyO23307r1q0xmUy0a9eOdevWVRu8RETcobCwsF793fkcaUNSkBIREdcr+Knu/VwQpGr78HNdPProo0yYMIH169czdOhQEhIS6NOnT43tjxw5wtmzZ6sEpNLSUvr161dpW0xMzEXPbbPZSExMpF27dnzyySe0aNGCV155hZEjR/LFF1/QoUP9v3ciIpcSGBhYr/71GRXQmOgZKRERcb3yug35qHO/C3Tr1g3DMDhw4IBT/Uwm+8fi+UGsrKzyFO1jx47l2LFj3HvvvXz11VfExMTw0ksv1XjM06ftz4mlpKSQmZnpeO3bt6/Sc1JAtcMQz5eens6aNWt45513GDhwIP3792fJkiW0aNGCVatWOXWtIiJ1FRMTU69npC6XocgKUiIi4nredfuArXO/C7Rp04b4+HgWL17MmTNnquyvaVhJSEgIALm5uY5tmZmZVdqFhYUxfvx43n//faZOncqKFSsA8PX1BSovShkVFYXZbCYnJ4euXbtWeoWFhTl1XWfPngX+G/gqmEwmrG6aQl5E5ELjxo2r1zNSDz/8sIsr8gwFKRERcb2g1g3brxqLFy/GYrEwYMAAVq9ezeHDh9m/fz8vvvii41mlC1WEm5kzZ3L48GFSUlJYuHBhpTZJSUmkpqaSlZVFRkYGmzZtokePHgB06dIFwzBYs2YN+fn5nD59mtatWzNt2jQmT57MqlWrOHr0KBkZGbz00ktO30WKjY0lODiY+++/nz179nDo0CH++Mc/kpWVxYgRI+r2jRIRcVJkZCRDhgxx+q6Ul5cXQ4cOpVu3bm6qrGEpSImIiOuFtgXDyT6GAR3auqyEiIgIMjIyGDx4MFOnTqVXr14MGzaMtLQ0li5dWm0fHx8f3n77bQ4cOECfPn147rnneOaZZyq1sVgsJCYm0qNHD2699VYiIyNZsmQJAJ06dWLWrFnMmDGD9u3b88gjjwAwZ84cnnzySZKTkx39UlJSCA8Pd+qa2rZty7p16zh9+jRDhgwhJiaGbdu28c9//pO+ffvW4bskIlI3zz//PL6+vlXukNfEZDLh6+vLggUL3FxZwzFs7nwit4koKioiMDCQwsJCAgICPF2OiEijUlxcTFZWFuHh4fj5+dW+46Hj9sV2a6tDCER2cb5AcUqdf54iIhdIS0tj5MiRlJaWXnSon5eXF76+vnz00UfExcU1YIV1U9tsoDtSIiLiHl3DILBV7doGtrK3FxGRJiMuLo7t27dzyy23AFQZ6lfxfvDgwWzfvr1JhChnaPpzERFxD5MJ+kTCkW/si+1WN/7BAEJD7CGqlsNDRESk8ejbty8bN27k8OHDLFu2jN27d1NQUEBQUBDR0dE8/PDDl80zURdSkBIREfcxmezD9a7qaF+kt+An+xTn3l72iSVC24Kvj6erFBGReurWrdtl9fxTbShIiYiI+/n62BfadcFiuyIiIo2BxlGIiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZImmxAREbc7dOgQy5cvZ9euXRQWFhIYGEhMTAzjxo0jMjLS0+WJiIg4TXekRETEbfbs2UNcXBzXXHMNixYtYsuWLWRmZrJlyxYWLVrENddcw9ChQ9mzZ4+nS200Nm/ejGEYFBQU1Njm9ddfJygoqMFqEhGRqhSkRETELdLS0oiNjWXLli0AWCyWSvsr3m/evJnY2FjS0tLcUkdeXh6TJk0iIiICs9lMWFgYI0eOdOn5brnlFpKSklxyrBtuuIHc3FwCAwNdcjwREXEPDe0TERGX27NnDyNHjqS4uBibzXbRthaLhZKSEkaOHMn27dvp27evy+rIzs5m4MCBBAUFMX/+fHr37k1ZWRmpqakkJiZy4MABl53rUmw2GxaLBW/vi3/0+vr6Ehoa2kBViYhIXemOlIiIuNyUKVMoLS29ZIiqYLVaKS0tZerUqS6tY+LEiRiGwc6dO0lISCAyMpKePXsyZcoUduzYAUBBQQFjx44lJCSEgIAAhgwZUmmo4cyZM7n22mv5v//7P6666ioCAwO5++67+emnnwB44IEH2LJlC3/9618xDAPDMMjOznYM0Vu7di3R0dGYzWa2bdtGSUkJjz76KO3atcPPz49BgwbxxRdfOM5X3dC+119/nc6dO9OyZUvuuOMO/vOf/1S6zj179jB48GBat25NQEAA0dHR7Nq1y6XfSxERqUxBSkREXOrQoUOkp6dXGcp3KRaLhbS0NA4fPuySOk6dOsW6detITEzE39+/yv6KZ4zuuusuvv/+e9auXcvu3bvp378/cXFxnDp1ytH26NGjfPjhh6xZs4Y1a9awZcsWnn32WQD++te/Ehsby0MPPURubi65ubmEhYU5+s6YMYNnn32W/fv306dPH/70pz+xevVqVq1aRUZGBl27diU+Pr7S+c73+eefM2bMGB555BEyMzMZPHgwzzzzTKU299xzD1deeSVffPEFu3fvZsaMGfj4+NT3WygiIhfh0SCVnJzML37xC1q3bk27du24/fbbOXjwYKU2xcXFJCYmcsUVV9CqVSsSEhI4efJkpTY5OTmMGDGCli1b0q5dO/74xz9SXl7ekJciIiI/W758OV5eXnXq6+XlxbJly1xSx5EjR7DZbHTv3r3GNtu2bWPnzp28++67xMTE0K1bNxYsWEBQUBDvvfeeo53VauX111+nV69e3Hjjjdx7772OZ6wCAwPx9fWlZcuWhIaGEhoaWun6Z8+ezbBhw7j66qsxm80sXbqU+fPnc9tttxEVFcWKFSto0aIFK1eurLbGv/71r9x666386U9/IjIykkcffZT4+PhKbXJychg6dCjdu3enW7du3HXXXS4dIikiIlV5NEht2bKFxMREduzYwYYNGygrK2P48OGcOXPG0Wby5Ml89NFHvPvuu2zZsoUTJ05w5513OvZbLBZGjBhBaWkpn332GatWreL111/nqaee8sQliYg0e7t27XL6blQFi8XC7t27XVJHbYYV7tmzh9OnTzv+WFfxysrK4ujRo452V111Fa1bt3a879ChA99//32t6oiJiXH899GjRykrK2PgwIGObT4+PgwYMID9+/dX23///v1cd911lbbFxsZWej9lyhTGjh3L0KFDefbZZyvVLiIi7uHRySbWrVtX6f3rr79Ou3bt2L17NzfddBOFhYWsXLmSt956iyFDhgDw2muv0aNHD3bs2MH111/P+vXr2bdvHxs3bqR9+/Zce+21zJkzh+nTpzNz5kx8fX2rnLekpISSkhLH+6KiIvdeqIhIM1JYWFiv/heb9tsZ3bp1wzCMi04ocfr0aTp06MDmzZur7Dt/evELh8kZhoHVaq1VHdUNK3S1mTNn8r//+7+kpKSwdu1ann76ad555x3uuOMOt59bRKS5alTPSFV8+LZp0waA3bt3U1ZWxtChQx1tunfvTufOndm+fTsA27dvp3fv3rRv397RJj4+nqKiIvbu3VvteZKTkwkMDHS8zh/LLiIi9VPfabtdtT5SmzZtiI+PZ/HixZVGOlQoKCigf//+5OXl4e3tTdeuXSu92rZtW+tz+fr61uou3NVXX42vry+ffvqpY1tZWRlffPEFUVFR1fbp0aMHn3/+eaVtFRNlnC8yMpLJkyezfv167rzzTl577bVa1y8iIs5rNEHKarWSlJTEwIED6dWrF2Bf+8PX17fKh2r79u3Jy8tztDk/RFXsr9hXnccee4zCwkLH65tvvnHx1YiINF8xMTH1ekYqOjraZbUsXrwYi8XCgAEDWL16NYcPH2b//v28+OKLxMbGMnToUGJjY7n99ttZv3492dnZfPbZZ/z5z392ata7q666is8//5zs7Gx++OGHGu9W+fv7M2HCBP74xz+ybt069u3bx0MPPcTZs2cZM2ZMtX0effRR1q1bx4IFCzh8+DB/+9vfKo3oOHfuHI888gibN2/m+PHjfPrpp3zxxRf06NHDuW+WiIg4pdEEqcTERL7++mveeecdt5/LbDYTEBBQ6SUiIq4xbty4ej0j9fDDD7usloiICDIyMhg8eDBTp06lV69eDBs2jLS0NJYuXYphGHz88cfcdNNNPPjgg0RGRnL33Xdz/PjxKn+ku5hp06bh5eVFVFQUISEh5OTk1Nj22WefJSEhgXvvvZf+/ftz5MgRUlNTCQ4Orrb99ddfz4oVK/jrX/9K3759Wb9+PU888YRjv5eXF//5z3+47777iIyM5He/+x233XYbs2bNqv03SkREnGbYarvIhxs98sgj/POf/2Tr1q2Eh4c7tqenpxMXF8ePP/5Y6a5Uly5dSEpKYvLkyTz11FP861//IjMz07E/KyvL8eHZr1+/S56/qKiIwMBACgsLFapERC5QXFxMVlYW4eHh+Pn51apPXFwcW7ZscSpQeXl5MXjwYDZs2FDXUqUW6vLzFBFpTmqbDTx6R8pms/HII4/wwQcfkJ6eXilEAURHR+Pj4+OYYhbg4MGD5OTkOGYsio2N5auvvqo0e9KGDRsICAiocby5iIi41/PPP4+vry8mU+0+ZkwmE76+vixYsMDNlYmIiLiGR4NUYmIib7zxBm+99RatW7cmLy+PvLw8zp07B9gfWB4zZgxTpkxh06ZN7N69mwcffJDY2Fiuv/56AIYPH05UVBT33nsve/bsITU1lSeeeILExETMZrMnL09EpNnq27cvH330EWaz+ZLPS3l5eWE2m/noo4+09pGIiDQZHg1SS5cupbCwkFtuuYUOHTo4Xv/v//0/R5sXXniBX/3qVyQkJHDTTTcRGhrK+++/79jv5eXFmjVr8PLyIjY2llGjRnHfffcxe/ZsT1ySiIj8LC4uju3bt3PLLbcAVAlUFe8HDx7M9u3biYuLa+gSRURE6qxRPCPlaXpGSkSkZq54pubw4cMsW7aM3bt3U1BQQFBQENHR0Tz88MN069bNxRXLxegZKRGRi6ttNvDogrwiItJ01Ofvbt26ddPzT42E/n4qIuIajWb6cxERaZx8fHwAOHv2rIcrEVcoLS0Fqg61FBER5+iOlIiIXJSXlxdBQUGO2VFbtmyJYRgerkrqwmq1kp+fT8uWLfH21q8AIiL1oX9FRUTkkkJDQwEqLTUhTZPJZKJz584KwyIi9aQgJSIil2QYBh06dKBdu3aUlZV5uhypB2fW9xIRkZopSImISK15eXnp2RoRERE02YSIiIiIiIjTFKREREREREScpCAlIiIiIiLiJAUpERERERERJylIiYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKREREREREScpCAlIiIiIiLiJG9PFyAiItIYnMk/w5crvyQrPYviwmL8Av0Ijwun3+h++If4e7o8ERFpZAybzWbzdBGeVlRURGBgIIWFhQQEBHi6HBERaUDlxeWsS1rHlyu/xGa1YbP+92PRMBkYJoP+Y/sTvygeb7P+/igicrmrbTbQ0D4REWm2yovLeSP+DTJWZGAtt1YKUQA2qw1ruZVdy3axavAqykvKPVSpiIg0NgpSIiLSbKVOTiVnW06VAFWFDb7d/i3L+y9XmBIREUBBSkREmqkz+WfIeCXj0iHqPPn78lk1RHemREREQUpERJqpL1/90qkQVeHb7d+SmpTqhopERKQpUZASEZFm6diGY3UKUthg17JdFH1X5PqiRESkyVCQEhGRZqe8uJzvdn5X9wPY4LUbX9MQPxGRZkzzuIqISLOTOjmV0p9K63WMgqwCUpNSGbF0hIuqEhFp3PLP5LPyy5WkZ6VTWFxIoF8gceFxjO43mhD/EE+X1+C0jhRaR0pEpDk5k3+G5zs+j7XcWu9jmbxNTM2dSsu2LV1QmYhI41RcXkzSuiRWfrkSq82K1fbffz9NhgmTYWJs/7Esil+E2dvswUpdo7bZQHekRESkWbnYJBM/8AO72c0JTlBCCWbMdKQj0UTTlrZV2tusNjJWZjBo+iB3ly0i4hHF5cXEvxHPtpxtlQJUhYpgtXz3cvbn7yd1VOplEaZqQ0FKRESalay0rCpBKo88UkkliywMDGz8d38OOWxnO+GEE088oYQ69tmsNrLSsxSkROSyNTl1co0h6nxWm5VPcj4hKTWJpSOWNlB1nqXJJkREpFkpLiyu9P4Yx3iFV8gmG6BSiDr/fTbZvMIrHONYpf0lBSXuK1ZExIPyz+TzSsYrlwxRFaw2K69kvMIPZ39wc2WNg4KUiIg0K36Bfo7/ziOPt3iLcsqrBKgL2bBhwcJbvEUeeY7t5qDmMYRFRJqfV798FYvV4lQfi9XCyoyVbqqocVGQEhGRZiU8Ltzx36mkYqH2vyRUhKlU/rsgb6frOrm0PhGRxiItK+2Sf2S6kA0b6VnpbqqocVGQEhGRZqXf6H6AfWKJLLLq9EtCFln8h/8AYGC4vEYRkcbg+9Pf16lf/tl8F1fSOClIiYhIs2JubaZF2xbsZnedQ5CBwS52AfDt59+6sjwRkUahuLyYg6cO1qnv6dLTLq6mcdKsfZeRs9az7C3dy7dl31JiK8FsmAnzCSPKN4qWJq1xIiJSXlzOG/FvcO6Hc5zghNN3oyrYsJFLLqDJJkTk8jQ5dTLF5cWXbtiMKUhdBspt5Ww9u5W9pXuxUnlWlZzyHLaf205P357c1PImvA1vBS4RabbWTlrL8a3HASihfgGoGPsvGJpsQkQuN98UfsOyXcvq3L+1b2sXVtN4KUg1ceW2cj44/QG55bk1/mXVipWvSr/iUOkhDAzHh//5qgtcIiKXk8JvCsl4JcPx3kz9ApAffhgmg/Ah4ZduLCLSRBSXF3Pz6zfX+Y49QNuWVRcwvxzpGakmbuvZrRcNUecroaTaEFXBipWvS7/mw9MfUm4rd2WZIiIe9+5d71Z635GO9XpGqgMdMLwM+o/p74ryREQahcmpk8kqyKpzfwODIeFDXFhR46Ug1YRVDNGrz18MLmTDxnfl37H17FaXHVNExNPO5J/hu53fVdoWTXS9npGKIYb+Y/rTsq2GRIvI5aFiAd76GtN/jAuqafwUpJqwfaX7qjwT5Sp7S/dyznrOLccWEWloX776JRdmpra0JZxwp+9KGRhEEMEVxhXEL4p3YZUiIp716pevUm7VqKTaUpBqwr4p+8Ztx7ZiZW/pXrcdX0SkIWWlVT9MJZ54vPCqdZgyMPDCi+EMJ+iqILzNep5URC4fa4+srfcxDMNgZcZKF1TT+ClINWElNvdOuZtTluPW44uINJTiwuqfDw0llP/lf2sVpipC1P/yv4QSSs+7e7qjVBERjzn649F6H8Nqs5Kele6Caho/BakmzBdftx4/rzxPk06IyGXBL9Cvxn0RRDCWsVzFVQBVAlXF+3DCGctYIogA4HRu81hwUkSaj7NlZ11ynIKSApccp7HTmIQmqtxWToG1wK3nKKOMrWe3MsS/ecy8IiKXr/C4cI5tPFblOakKoYRyP/fzH/7DLnaRSy7FFOOHHx3oQAwxXMEVlfp89cZXDJ8/XJNNiMhlo6V3S05xqt7HCTIH1b+YJkBBqonaenYrP9l+cvt59pbuJbZFLC1MLdx+LhERd+k3uh+bntiEtfziE/RcwRXEU7sJJGxWGxkrMxg0fZArShQR8bir21zNtz99W69jmAyTpj+Xxuus9Sxfl37dIOfSpBMicjnwD/Gn/1jXrvdks9rISq/7WivSNB06dIhp06Zxyy230K9fP2655RamTZvGoUOHPF2aSL3d1vU2lxxnVJ9RLjlOY6cg1QTtK93n0rWjLsWdswOKiDSU+EXxdB7U2aXHLClw76Q/0njs2bOHuLg4rrnmGha98AJbtmwhMzOTLVu2sOiFF7jmmmsYOnQoe/bs8XSpInU2ut9oTPWMB1ablXvev4eS8sv/30cFqSaooYONu2cHFBFpCN5mb+7dcC/Xjr7WZcc0B5lddixpvNLS0oiNjWXLpk0AWKyVh4hWvN+cnk5sbCxpaWkNXqOIK4T4hzAuZly9j/NJzickpSbVv6BGzqNBauvWrYwcOZKOHTtiGAYffvhhpf02m42nnnqKDh060KJFC4YOHcrhw4crtTl16hT33HMPAQEBBAUFMWbMGE6fvrxnUqptsPn+yPf888l/8tLIl5h/83xeGvkS/3zyn3x/5Hunzmc29IuCiFwevP28+c3K35CUk0SH/h3qdSzDZBA+JNxFlUljtWfPHkaOHEnxuXNYbBcfDWKx2Sg5d46RI0fqzpQ0WYviFzGoc/2e/bTarLyS8Qo/nP3BRVU1Th4NUmfOnKFv374sXry42v1/+ctfePHFF3n55Zf5/PPP8ff3Jz4+nuLi/64Hcs8997B37142bNjAmjVr2Lp1K+PG1T9JN2aXCjbfff0di29fzLwB89jy8haOfnqU7776jqOfHmXLy1uYN2Aei29fzHdff1er84X5hLmibBGRRiMwLJBxu8cx7ftpxD0bR5dbulDLNXkdDC+D/mNc+9yVND5TpkyhtLi41gPqrUBpcTFTp051Z1kibmP2NrPh3g14m+o3J125tZxlu5a5qKrGybDZLvHnlQZiGAYffPABt99+O2C/G9WxY0emTp3KtGnTACgsLKR9+/a8/vrr3H333ezfv5+oqCi++OILYmJiAFi3bh2//OUv+fbbb+nYsWOtzl1UVERgYCCFhYUEBAS45fpcaVfxLj4992m1+w5tOcSK/11BeWk5NkvNP1rDy8Db15uH3nqIyJsja2xnwsTYwLGatU9ELnspE1LYvXw3NuulPxYNk0H0uGhGLB3RAJWJpxw6dIhrrrmmXv27devmwopEGs4Vf7mCU+fqNxV6sF8wuVNzMXs3rdFNtc0GjfYZqaysLPLy8hg6dKhjW2BgINdddx3bt28HYPv27QQFBTlCFMDQoUMxmUx8/vnnNR67pKSEoqKiSq+mJMo3qsqCkWC/E7Xif1dQXnzxEAVgs9goLylnxf+uuOidqZ6+PRWiRKRZqJiMwjBd/NaUYTLofGNn4hfVbpp0abqWL1+Ol6luvyp5GQbLll3ef42Xy9uAjgPqfYwfi3/k4TUPu6CaxqnRBqm8vDwA2rdvX2l7+/btHfvy8vJo165dpf3e3t60adPG0aY6ycnJBAYGOl5hYU1r6FpLU0t6+faqsv3DJz6034mq5U1Gm9VGeWk5/3zyn9Xu7+TdiZta3lSvWkVEmgpvszejUkcRPS4ak7epSqAyTAYmbxPR46IZlToKb7OWYrzc7dq1q8rEErVlsdnYvXu3iysSaTjPxz/vkuOs2rOKBz584LKcxa9Zfgo89thjTJkyxfG+qKioyYWpm1rexA+WH8i15AL2iSUObz18iV5V2Sw2Dm05RP7RfEKuDgHAwKCXby9uankT3kaz/F9ERJopbz9vRiwdwS2zb+HLV78kKz2LkoISzEFmwoeE0290P/xD/D1dpjSQwsLCevUvKChwTSEiHtAjpAdRIVHsy99X72P9fc/fyS7IJnVUapMb5ncxjfa35NDQUABOnjxJhw7/nVnp5MmTXHvttY42339feQa68vJyTp065ehfHbPZjNnctH+I3oY3d7a+k81nN7O3dC/bV23H5GXCanH+L2cmLxOfvf4Zd8+5m2v9rqWXuRctTS3dULWISNPgH+LPoOmDGDS9fjNXSdMWGBhYr/5BQUGuKUTEQ7aP3k7nRZ0pLKnfHxVs2BxToi8dsdRF1Xleox3aFx4eTmhoaKW1GIqKivj888+JjY0FIDY2loKCgkq3ztPT07FarVx33XUNXnND8za8Geo/lB4+PcjJzKlTiAKwWqx8s+cb2nm3Y0CLAQpRIiIiQExMTL2ekYqOjnZxRSINK8AvgOykbKJCoup9rMtxSnSPBqnTp0+TmZlJZmYmYJ9gIjMzk5ycHAzDICkpiWeeeYZ//etffPXVV9x333107NjRMbNfjx49uPXWW3nooYfYuXMnn376KY888gh33313rWfsa+rOWs9ysOwgxUXFl258EecKz3Gy/KSLqhIREWn6xo0bV69npB5++PJ9yF6ajyC/IPZO3EvmuMx6T4lutVlZmbHSRZV5nkeD1K5du+jXrx/9+vUD7Gs19OvXj6eeegqAP/3pT0yaNIlx48bxi1/8gtOnT7Nu3Tr8/Pwcx3jzzTfp3r07cXFx/PKXv2TQoEEsX77cI9fjCftK92HFil+A36UbX0SLwBaUU+6iqkRERJq+yMhIhgz5/+3dfXBU9b3H8c/ZLLvJBpPwGAgkkCpKCTQCUQhi0JBppFwV2rHKBQTB68VCJeBQoI46c70U2s602KmlPrd3atU6A1gYawYJJTI35TlgguXhmpKIBFCaBwgkWfZ3/8BsWSCQE5J9yvs1szPsOb+z8z35kt395JzzO7mKsezdZCzGspSXl8fU54gqmf0z9fyE52/oNXzGp6KKog6qKPTC5j5SoRRp95G61Lr6dar0Vur9Z9/X1t9ubfc1UhPmTdDDLzys/+zBX88AAGixb98+ZWdnq/HcObXlE9YhyR0Xp5KSEmVmZnZ2eUBQnTp7Sim/SJHX1/4/vt854E5tf7z12xSFg4i/jxTaptFcnEoye1b2DV0jNW72OCU7k68/GACALiQzM1MbNmyQOy7uukemYixL7rg4bdiwgRCFqNQnvo8eH/W4HFb7I0SSO6njCgoxglSEc1sXZx/se0tfDckZIivG3ukHVoylWyfcqj4399GEuAmdUSIAABFt4sSJKikp0T25uZJ0xQQULc/v/XrcxIkTg14jECyr81drfNp4WbL3nVOSHJZDuem5nVBVaBCkIlxqt3/d/2rKf0+R0+W84iaSrbEclpwupx584UFJUqzjxq6zAgAgWmVmZuqjjz7SoUOHVLBoke655x7dfvvtuueee1SwaJEOHTqkTZs2cSQKUc/tdKtwRqEezXzU9rYxVozmjprbCVWFBtdIKbKvkWrwNej12tfl+/rM7UNbD+nVf39V3iavzIXWW2vFXAxR//HH/9CtE26VJN0Vd5eyYrOCUjcAAAAi2+z1s/U/+/5HRtePEw7LoSdGPxER95HiGqkuwuPwKMOV4T+8euuEW1VQWKAh4y/OFOSICWxxy/Mh44eooLDAH6Ikqaq5KkhVAwAAINK9/G8v6+5Bd1/3mimH5dDdaXdrdf7q4BQWJByRUmQfkZIkr/Fq3Zl1Ou49HvAXgVP/d0r/+7v/VdW+Kp2rPae4xDilZqZq3Oxx6nNznyteJzkmWY8kPBLM0gEAABDBznvPa1HhIr225zX5jE8+86/JzxyWQw7LocdHPa7V+avldrpDWGnbtTUbEKQU+UFKuhimihuKVd5U7j/Nz640Z5qm3jS1gysDAABAtDt19pTe2PuGiiqKVNNYoyR3knLTczVn5Bz1ib/yD/jhjCBlQzQEqRYNvgYVni1UpbfS9rZcIwUAAICujmukuiiPw6P8+Hw5bLbWIYcyXBmdVBUAAAAQXQhSUejyCSjaIsOVoThHXCdWBQAAAEQPglSUyvHkqL+zf5vC1ADnAOV4coJQFQAAABAdCFJRymk5NbX7VA13DW/1ND+HHBrhGqEp3afIaTmDXCEAAAAQufj2HMWcllO58bkaGzdWB5oOqKq5So2mUW7LrdRuqRrmGiaPwxPqMgEAAICIQ5DqAjwOj7Jis5iRDwAAADfk1NlTen3v6yqqKFLt+VolxiZqYvrEiJzm/EYx/bmia/pzAAAAoKOd955XwYcFen3v61Fz493WtDUbcEQKAAAAQKvOe88r/w/52la5LSBAtWgJVq/sfkWfnvpUhTMKIz5MtQWTTQAAAABo1aLCRa2GqEv5jE8fV36sgsKC4BQWYgQpAAAAAFd16uwpvbbnteuGqBY+49Nre17Tlw1fdnJlocepfVGqwdeg8qZyfd78OTP1AQAAoF3e2PtGm0NUC5/x6fU9r2vp+KWdVFV4IEhFGa/xqrihWOVN5fIp8D99pbdSJedKlOHKUI4nh3tHAQAA4Jo2V2xuV5AqqigiSCFyeI1X686s03HvcRldfTJGn3wqayrTad9pbsQLAACAa6o9X9uu7Woaazq2kDDENVJRpLih+JohqoWR0THvMRU3FAepMgAAAESixNjEdm2X5E7q2ELCEEEqSnzl/UplTWXXDVGXKm8q1znfuU6sCgAAAJFsYvpEOSx7kcFhOZSbnttJFYUPglSE8xqvis4W6a36t2yFKOniaX7lTeWdVBkAAAAi3ZyRc2wHqRgrRnNHze2kisIHQSqCtVwTZfdI1KWqmqs6uCoAAABEiz7xffT4qMfbHKYclkNzR81Vb0/vTq4s9AhSEayt10RdS6Np7MCKAAAAEG1W56/W+LTx1w1TDsuhu9Pu1ur81cEpLMQIUhGq5T5RNxKiJMltuTuoIgAAAEQjt9OtwhmFemL0E3I6nFcEKoflkNPh1BOjn1DhjEK5nV3j+yVzX0eoA00HrrhPVHukdkvtgGoAAAAQzWKdsVozeY3+657/0ht731BRRZFqGmuU5E5Sbnqu5oycoz7xfUJdZlARpCJUR1zb5JBDGa6MDqgGAAAAXUGf+D5aOn5p1N9sty04tS9CdcS1TRmuDMU54jqgGgAAAKBrIUhFqBu9tmmAc4ByPDkdVA0AAADQtRCkIlR7r22yZGmEa4SmdJ8ip8WZnQAAAEB7EKQi1DDXMDlsts+SpRk3zVBufC4hCgAAALgBBKkI5XF4lOHKkCWrzdsMdw1XT2fPTqwKAAAA6BoIUhEsx5Oj/s7+bQpTXBMFAAAAdByCVARzWk5N7T5Vw13DWz3NzyEH10QBAAAAHYxv1hHOaTmVG5+rsXFjdaDpgKqaq9RoGuW23ErtlqphrmHyODyhLhMAAACIKgSpKOFxeJQVm6Ws2KxQlwIAAABEPU7tAwAAAACbCFIAAAAAYBNBCgAAAABsIkgBAAAAgE0EKQAAAACwiSAFAAAAADYRpAAAAADAJoIUAAAAANhEkAIAAAAAmwhSAAAAAGATQQoAAAAAbIqaIPXSSy9p8ODBio2N1ZgxY7Rjx45QlwQAAAAgSkVFkHr33Xe1ePFiPf/889qzZ48yMzOVn5+vkydPhro0AAAAAFHIMsaYUBdxo8aMGaM77rhDv/71ryVJPp9Pqamp+uEPf6hly5Zdd/u6ujolJiaqtrZWCQkJnV1u0DX4GlTeVK7Pmz9Xo2mU23IrtVuqhrmGyePwhLo8AAAAIGy0NRs4g1hTp2hqatLu3bu1fPly/zKHw6G8vDyVlJRcdZvGxkY1Njb6n9fV1XV6naHgNV4VNxSrvKlcPvkC1lV6K1VyrkQZrgzleHLktCL+vwIAAAAQNBF/at+XX36pCxcuKDk5OWB5cnKyqqurr7rNypUrlZiY6H+kpqYGo9Sg8hqv1p1Zp7KmsitCVAuffCprKtP6M+vlNd4gVwgAAABErogPUu2xfPly1dbW+h9VVVWhLqnDFTcU67j3uIyufeamkdEx7zEVNxQHqTIAAAAg8kX8+Vy9e/dWTEyMTpw4EbD8xIkT6tev31W3cbvdcrvdwSgvJFquibpeiLpUeVO5suOyFeeI68TKAAAAgOgQ8UekXC6XRo8erc2bN/uX+Xw+bd68WdnZ2SGsLHQONB1o9XS+1vjkU3lTeSdVBAAAAESXiA9SkrR48WK9+uqr+v3vf69PP/1UTz75pM6ePavHHnss1KWFRFVz+05VbO92AAAAQFcT8af2SdLDDz+sU6dO6bnnnlN1dbVuv/12ffjhh1dMQNFVNJrG6w/qwO0AAACAriYqgpQkLViwQAsWLAh1GWHBbbXv+q/2bgcAAAB0NVFxah8CpXZr33Tu7d0OAAAA6GoIUlFomGuYHDZb65BDGa6MTqoIAAAAiC4EqSjkcXiU4cqQJavN22S4Mpj6HAAAAGgjglSUyvHkqL+zf5vC1ADnAOV4coJQFQAAABAdCFJRymk5NbX7VA13DW/1ND+HHBrhGqEp3afIaUXNvCMAAABAp+PbcxRzWk7lxudqbNxYHWg6oKrmKjWaRrktt1K7pWqYa5g8Dk+oywQAAAAiDkGqC/A4PMqKzVJWbFaoSwEAAACiAqf2AQAAAIBNBCkAAAAAsIkgBQAAAAA2EaQAAAAAwCaCFAAAAADYRJACAAAAAJsIUgAAAABgE0EKAAAAAGwiSAEAAACATQQpAAAAALCJIAUAAAAANjlDXUA4MMZIkurq6kJcCQAAAIBQaskELRmhNQQpSfX19ZKk1NTUEFcCAAAAIBzU19crMTGx1fWWuV7U6gJ8Pp+++OIL3XTTTbIsK9TldHl1dXVKTU1VVVWVEhISQl0ORE/CFX0JP/Qk/NCT8ENPwg89CWSMUX19vVJSUuRwtH4lFEekJDkcDg0cODDUZeAyCQkJ/DKHGXoSnuhL+KEn4YeehB96En7oyb9c60hUCyabAAAAAACbCFIAAAAAYBNBCmHH7Xbr+eefl9vtDnUp+Bo9CU/0JfzQk/BDT8IPPQk/9KR9mGwCAAAAAGziiBQAAAAA2ESQAgAAAACbCFIAAAAAYBNBCgAAAABsIkghKFauXKk77rhDN910k/r27aspU6bo4MGDAWPOnz+v+fPnq1evXurevbu+973v6cSJEwFjKisrNXnyZHk8HvXt21dLliyR1+sN5q5ErVWrVsmyLBUUFPiX0ZPgO3bsmGbMmKFevXopLi5OI0aM0K5du/zrjTF67rnn1L9/f8XFxSkvL0+HDx8OeI3Tp09r+vTpSkhIUFJSkubOnaszZ84Ee1eiwoULF/Tss88qPT1dcXFxuvnmm/XCCy/o0nma6EnnKy4u1v3336+UlBRZlqX169cHrO+oHuzfv1933323YmNjlZqaqp/97GedvWsR61o9aW5u1tKlSzVixAjFx8crJSVFjz76qL744ouA16AnHet6vyeXmjdvnizL0urVqwOW0xObDBAE+fn55s033zRlZWWmtLTUfOc73zFpaWnmzJkz/jHz5s0zqampZvPmzWbXrl1m7NixZty4cf71Xq/XDB8+3OTl5Zm9e/eaDz74wPTu3dssX748FLsUVXbs2GEGDx5svvWtb5mFCxf6l9OT4Dp9+rQZNGiQmT17ttm+fbv57LPPTGFhoTly5Ih/zKpVq0xiYqJZv3692bdvn3nggQdMenq6OXfunH/MfffdZzIzM83f/vY38/HHH5tbbrnFTJs2LRS7FPFWrFhhevXqZTZu3GgqKirMe++9Z7p3725efPFF/xh60vk++OAD88wzz5i1a9caSWbdunUB6zuiB7W1tSY5OdlMnz7dlJWVmbffftvExcWZl19+OVi7GVGu1ZOamhqTl5dn3n33XfP3v//dlJSUmDvvvNOMHj064DXoSce63u9Ji7Vr15rMzEyTkpJifvnLXwasoyf2EKQQEidPnjSSzNatW40xF990u3XrZt577z3/mE8//dRIMiUlJcaYi28QDofDVFdX+8esWbPGJCQkmMbGxuDuQBSpr683Q4YMMZs2bTITJkzwByl6EnxLly4148ePb3W9z+cz/fr1Mz//+c/9y2pqaozb7TZvv/22McaYAwcOGElm586d/jF/+ctfjGVZ5tixY51XfJSaPHmymTNnTsCy7373u2b69OnGGHoSCpd/QeyoHvzmN78xPXr0CHjvWrp0qbnttts6eY8i37W+tLfYsWOHkWSOHj1qjKEnna21nnz++edmwIABpqyszAwaNCggSNET+zi1DyFRW1srSerZs6ckaffu3WpublZeXp5/zNChQ5WWlqaSkhJJUklJiUaMGKHk5GT/mPz8fNXV1am8vDyI1UeX+fPna/LkyQE/e4mehMKf//xnZWVl6aGHHlLfvn01cuRIvfrqq/71FRUVqq6uDuhJYmKixowZE9CTpKQkZWVl+cfk5eXJ4XBo+/btwduZKDFu3Dht3rxZhw4dkiTt27dP27Zt06RJkyTRk3DQUT0oKSlRTk6OXC6Xf0x+fr4OHjyof/7zn0Ham+hVW1sry7KUlJQkiZ6Egs/n08yZM7VkyRJlZGRcsZ6e2OcMdQHoenw+nwoKCnTXXXdp+PDhkqTq6mq5XC7/G2yL5ORkVVdX+8dc+oW9ZX3LOtj3zjvvaM+ePdq5c+cV6+hJ8H322Wdas2aNFi9erB//+MfauXOnnnrqKblcLs2aNcv/M73az/zSnvTt2zdgvdPpVM+ePelJOyxbtkx1dXUaOnSoYmJidOHCBa1YsULTp0+XJHoSBjqqB9XV1UpPT7/iNVrW9ejRo1Pq7wrOnz+vpUuXatq0aUpISJBET0Lhpz/9qZxOp5566qmrrqcn9hGkEHTz589XWVmZtm3bFupSurSqqiotXLhQmzZtUmxsbKjLgS7+kSErK0s/+clPJEkjR45UWVmZfvvb32rWrFkhrq5r+tOf/qS33npLf/zjH5WRkaHS0lIVFBQoJSWFngBt0NzcrO9///syxmjNmjWhLqfL2r17t1588UXt2bNHlmWFupyowal9CKoFCxZo48aN2rJliwYOHOhf3q9fPzU1NammpiZg/IkTJ9SvXz//mMtnjGt53jIGbbd7926dPHlSo0aNktPplNPp1NatW/WrX/1KTqdTycnJ9CTI+vfvr2HDhgUs++Y3v6nKykpJ//qZXu1nfmlPTp48GbDe6/Xq9OnT9KQdlixZomXLlumRRx7RiBEjNHPmTC1atEgrV66URE/CQUf1gPezjtcSoo4ePapNmzb5j0ZJ9CTYPv74Y508eVJpaWn+z/yjR4/q6aef1uDBgyXRk/YgSCEojDFasGCB1q1bp6KioisOC48ePVrdunXT5s2b/csOHjyoyspKZWdnS5Kys7P1ySefBPySt7wxX/7lE9c3ceJEffLJJyotLfU/srKyNH36dP+/6Ulw3XXXXVfcFuDQoUMaNGiQJCk9PV39+vUL6EldXZ22b98e0JOamhrt3r3bP6aoqEg+n09jxowJwl5El4aGBjkcgR+VMTEx8vl8kuhJOOioHmRnZ6u4uFjNzc3+MZs2bdJtt93W5U5X6ggtIerw4cP66KOP1KtXr4D19CS4Zs6cqf379wd85qekpGjJkiUqLCyURE/aJdSzXaBrePLJJ01iYqL561//ao4fP+5/NDQ0+MfMmzfPpKWlmaKiIrNr1y6TnZ1tsrOz/etbptr+9re/bUpLS82HH35o+vTpw1TbHejSWfuMoSfBtmPHDuN0Os2KFSvM4cOHzVtvvWU8Ho/5wx/+4B+zatUqk5SUZN5//32zf/9+8+CDD151mueRI0ea7du3m23btpkhQ4Yw1XY7zZo1ywwYMMA//fnatWtN7969zY9+9CP/GHrS+err683evXvN3r17jSTzi1/8wuzdu9c/A1xH9KCmpsYkJyebmTNnmrKyMvPOO+8Yj8fTZad1vp5r9aSpqck88MADZuDAgaa0tDTgc//S2d7oSce63u/J5S6ftc8YemIXQQpBIemqjzfffNM/5ty5c+YHP/iB6dGjh/F4PGbq1Knm+PHjAa/zj3/8w0yaNMnExcWZ3r17m6effto0NzcHeW+i1+VBip4E34YNG8zw4cON2+02Q4cONa+88krAep/PZ5599lmTnJxs3G63mThxojl48GDAmK+++spMmzbNdO/e3SQkJJjHHnvM1NfXB3M3okZdXZ1ZuHChSUtLM7GxseYb3/iGeeaZZwK+DNKTzrdly5arfobMmjXLGNNxPdi3b58ZP368cbvdZsCAAWbVqlXB2sWIc62eVFRUtPq5v2XLFv9r0JOOdb3fk8tdLUjRE3ssYy65PTsAAAAA4Lq4RgoAAAAAbCJIAQAAAIBNBCkAAAAAsIkgBQAAAAA2EaQAAAAAwCaCFAAAAADYRJACAAAAAJsIUgAAAABgE0EKAAAAAGwiSAEAupTZs2fLsqwrHkeOHFFxcbHuv/9+paSkyLIsrV+/PtTlAgDCFEEKANDl3HfffTp+/HjAIz09XWfPnlVmZqZeeumlUJcIAAhzzlAXAABAsLndbvXr1++K5ZMmTdKkSZNCUBEAINJwRAoAAAAAbCJIAQC6nI0bN6p79+7+x0MPPRTqkgAAEYZT+wAAXc69996rNWvW+J/Hx8eHsBoAQCQiSAEAupz4+HjdcsstoS4DABDBOLUPAAAAAGziiBQAAF87c+aMjhw54n9eUVGh0tJS9ezZU2lpaSGsDAAQbghSAAB8bdeuXbr33nv9zxcvXixJmjVrln73u9+FqCoAQDiyjDEm1EUAAAAAQCThGikAAAAAsIkgBQAAAAA2EaQAAAAAwCaCFAAAAADYRJACAAAAAJsIUgAAAABgE0EKAAAAAGwiSAEAAACATQQpAAAAALCJIAUAAAAANhGkAAAAAMCm/wdn3/JgAI7gUAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -1526,17 +1526,23 @@ " # Initialize the StandardScaler\n", " sc = StandardScaler()\n", "\n", - " # Define the pipeline with PolynomialFeatures and Ridge regression\n", - " pipeline = make_pipeline(PolynomialFeatures(2), linear_model.RidgeCV())\n", + " # Define the pipeline with PolynomialFeatures and RidgeCV model\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.RidgeCV())\n", "\n", " # Define the hyperparameter grid for Ridge regression\n", " param_grid = {\n", " \"ridgecv__alphas\": [[0.1, 1.0, 10.0], [0.01, 0.1, 1.0], [0.001, 0.01, 0.1]]\n", " }\n", "\n", - " # Initialize GridSearchCV with the pipeline and parameter grid\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", " grid_search = GridSearchCV(\n", - " pipeline, param_grid, cv=5, scoring=make_scorer(r2_score)\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", " )\n", "\n", " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", @@ -1559,9 +1565,11 @@ " best_model_x = grid_search.best_estimator_\n", " y_pred_x = best_model_x.predict(X_test_x)\n", " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", " print(\n", - " f'Best alpha for X: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score: {r2_score_x}'\n", + " f'Best alpha for X: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score : {r2_score_x}'\n", " )\n", + " print(\"-------------------------------------------------------\")\n", "\n", " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", " # Left iris Y and right iris Y\n", @@ -1583,9 +1591,11 @@ " best_model_y = grid_search.best_estimator_\n", " y_pred_y = best_model_y.predict(X_test_y)\n", " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", " print(\n", " f'Best alpha for Y: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score: {r2_score_y}'\n", " )\n", + " print(\"-------------------------------------------------------\")\n", "\n", " # Plot the true and predicted points for X and Y\n", " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" @@ -1600,8 +1610,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best alpha for X: [0.001, 0.01, 0.1], R2 score: 0.9982537849595848\n", - "Best alpha for Y: [0.001, 0.01, 0.1], R2 score: 0.9808107385120926\n" + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha for X: [0.001, 0.01, 0.1], R2 score : 0.9982537849595848\n", + "-------------------------------------------------------\n", + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha for Y: [0.001, 0.01, 0.1], R2 score: 0.9808107385120926\n", + "-------------------------------------------------------\n" ] }, { From e50ee6f58b52311ac405b742ecf0d47e33c24dd6 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Fri, 5 Jul 2024 11:31:55 +0000 Subject: [PATCH 25/78] lasso grid search added --- .../test_lasso_regression_grid_search.ipynb | 1869 +++++++++++++++++ 1 file changed, 1869 insertions(+) create mode 100644 app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb new file mode 100644 index 00000000..55409809 --- /dev/null +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb @@ -0,0 +1,1869 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.709e+04, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.997926029023532" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a lasso regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 779.53548512, 767.65128778, 141.06513298, 77.01639266,\n", + " 1440.84187245, 138.13955149, 138.61843694, 84.01368723,\n", + " 772.84576825, 759.37004708, 1419.66624991, 754.7205936 ,\n", + " 145.33504885, 94.32021145, 758.97450656, 781.60056997,\n", + " 133.79149125, 1429.19288405, 77.32249494, 75.66615436,\n", + " 755.71541317, 758.15433583, 146.60786618, 1431.55470864,\n", + " 1438.01726104, 780.17519357, 71.50603895, 76.68365066,\n", + " 78.62966711, 86.92492189, 760.44500236, 759.14339993,\n", + " 780.54768565, 761.17652033, 1455.83161065, 1435.22890343,\n", + " 1417.94050814, 1454.67720008, 1449.66638775, 1433.83832519,\n", + " 769.48034763, 87.80955518, 140.86637035, 1426.2519311 ,\n", + " 758.62134163, 137.23159818, 783.07495035, 1436.10934139,\n", + " 1433.94903324, 137.22932953, 1428.08129937, 1433.09088829,\n", + " 752.02383735, 1435.23628267, 80.81542419, 769.55456333,\n", + " 75.24761274, 73.46956065, 138.28156063, 780.54247729,\n", + " 1419.55834794, 160.04661205, 138.29679286, 755.38711375,\n", + " 76.27701016, 94.44848413, 1431.95274479, 84.3386143 ,\n", + " 1425.20953527, 1438.43597818, 1433.5089705 , 762.98067048,\n", + " 140.05327196, 761.90621431, 79.87205141, 1419.68932481,\n", + " 1448.95179373, 780.68260147, 1424.31564478, 75.27145086,\n", + " 1423.16712439, 769.6475289 , 72.7486099 , 766.42938126,\n", + " 150.13464568, 1428.52338252, 1462.27356639, 69.8305093 ,\n", + " 1452.7318761 , 1431.64407264, 70.89701803, 140.62169806,\n", + " 76.63441998, 777.90140937, 95.16666063, 778.37663034,\n", + " 136.66245437, 207.75727489, 1459.56114768, 79.90000519,\n", + " 770.12657697, 1420.7429688 , 776.40745345, 771.13031632,\n", + " 77.42517745, 774.80420506, 70.67476922, 69.2350124 ,\n", + " 68.51899766, 1432.5569573 , 80.07278995, 1306.92228095,\n", + " 1438.40157995, 99.14502912, 1424.22721452, 779.89440109,\n", + " 759.73615962, 134.80843878, 1454.40545917, 151.76965271,\n", + " 139.17811174, 756.97324325, 1438.69833108, 139.644694 ,\n", + " 776.71884135, 138.90383564, 758.99709351, 1433.74130767,\n", + " 1438.55615983, 1441.60818817, 1435.5578321 , 761.59581226,\n", + " 70.59787519, 1456.74625903, 781.99079523, 1422.81957102,\n", + " 85.51403457, 158.79750824, 70.01102199, 88.72286703,\n", + " 89.01457004, 78.55595008, 82.46716868, 1425.33013517])" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9751658030754563" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a lasso regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1))\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([159.1211271 , 141.94938788, 98.515362 , 588.21013054,\n", + " 51.55679851, 111.60571909, 107.85485267, 385.47576096,\n", + " 157.05760426, 662.94522909, 615.68001594, 688.8817096 ,\n", + " 88.68237837, 316.30791577, 662.01789845, 161.65166259,\n", + " 119.07157792, 380.3590356 , 576.62506046, 588.22862233,\n", + " 690.67273665, 601.8105376 , 77.73456889, 619.40331152,\n", + " 99.2149826 , 153.05666125, 584.29574088, 582.45312047,\n", + " 366.67861034, 368.95985113, 659.6262177 , 596.69159413,\n", + " 145.68075764, 613.20833002, 361.90807243, 692.00682849,\n", + " 616.49488208, 359.68877824, 361.81760976, 98.96519276,\n", + " 147.7179753 , 369.14355266, 95.91191517, 646.34693986,\n", + " 635.16772751, 101.2643276 , 163.48108409, 371.86711141,\n", + " 663.05951516, 106.13974757, 114.88246277, 629.24962415,\n", + " 670.58470178, 338.97290928, 370.80067136, 162.72286117,\n", + " 585.97888587, 575.72784962, 111.54886435, 160.16814491,\n", + " 115.26390645, 37.18269747, 105.91038825, 657.55921759,\n", + " 589.61113439, 311.12564659, 658.37350247, 366.86332161,\n", + " 110.56963093, 375.69326027, 640.15628013, 666.01229372,\n", + " 104.72956135, 612.9996496 , 370.87344354, 116.44741004,\n", + " 354.69175869, 161.47810368, 633.32766021, 579.0396673 ,\n", + " 114.79984331, 148.1506869 , 589.10193481, 150.70155273,\n", + " 81.01412797, 101.34903724, 355.96341652, 588.300444 ,\n", + " 72.97719636, 658.00699863, 574.67030821, 104.0767804 ,\n", + " 579.71642866, 154.86656445, 400.07989969, 159.69857252,\n", + " 106.21862691, -19.77807257, 359.34422179, 355.14236908,\n", + " 157.85683241, 613.68365383, 160.57366388, 155.66751492,\n", + " 588.90358645, 172.825431 , 577.20987903, 579.91355663,\n", + " 589.29797665, 658.51290015, 376.33190983, 41.35098361,\n", + " 370.30408814, 286.72603896, 108.40216867, 164.18540008,\n", + " 606.21054528, 98.87855228, 357.87623177, 79.86977714,\n", + " 99.02383694, 647.79684868, 365.88544878, 99.4926623 ,\n", + " 153.89279316, 100.228666 , 637.22269888, 655.45994762,\n", + " 337.5393344 , 83.08763032, 644.29138763, 656.05080215,\n", + " 576.09061647, 351.59247919, 155.53603578, 636.32146483,\n", + " 377.34236877, 20.65934893, 573.75105472, 372.97994193,\n", + " 373.18096445, 595.01023661, 378.07281337, 101.27179696])" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGxCAYAAABIjE2TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABf/UlEQVR4nO3deXxU9b038M85s2WWzEz2ScjKIhAMiyAkLLWtVFT0uqBVi7KqT7ngVen1WnzsYm8LPravqu2t2IqAWtSrbaGKokVUBBJWRdmMEBMSSCYBkskkmcx6fs8fQ0YmAckyJJPM5/165XWbOd9kfnME5nPPnN/3KwkhBIiIiIiiiNzXCyAiIiJqjwGFiIiIog4DChEREUUdBhQiIiKKOgwoREREFHUYUIiIiCjqMKAQERFR1GFAISIioqij7usFdIeiKKiurkZ8fDwkSerr5RAREVEnCCHQ1NSEjIwMyPK3XyPplwGluroaWVlZfb0MIiIi6oaqqipkZmZ+a02/DCjx8fEAgi/QbDb38WqIiIioM5xOJ7KyskLv49+mXwaUto91zGYzAwoREVE/05nbM3iTLBEREUUdBhQiIiKKOgwoREREFHUYUIiIiCjqMKAQERFR1GFAISIioqjTpYCSm5sLSZI6fC1evBgA4Ha7sXjxYiQlJcFkMmHWrFmora0N+x2VlZWYOXMmDAYDUlNT8cgjj8Dv90fuFREREVG/16WAsmfPHtTU1IS+Nm/eDAC4/fbbAQAPP/ww3n77bbz55pvYunUrqqurceutt4Z+PhAIYObMmfB6vSguLsZLL72EtWvX4uc//3kEXxIRERH1d5IQQnT3hx966CFs3LgRR48ehdPpREpKCl599VXcdtttAIAvv/wSI0eORElJCQoLC7Fp0ybccMMNqK6uRlpaGgDg+eefx6OPPopTp05Bq9V26nmdTicsFgsaGxvZqI2IiKif6Mr7d7fvQfF6vfjrX/+KBQsWQJIk7Nu3Dz6fD9OnTw/VjBgxAtnZ2SgpKQEAlJSUoKCgIBROAGDGjBlwOp04dOjQBZ/L4/HA6XSGfREREVHk+f0K1n96Ev/z4VGs//Qk/H6lT9bR7Vb3GzZsgMPhwLx58wAAdrsdWq0WVqs1rC4tLQ12uz1Uc244aTveduxCVqxYgSeeeKK7SyUiIqJOeOGTMvzpozI43T4IAUgS8MTbh7D4e0Nw33eG9Opaun0F5cUXX8R1112HjIyMSK7nvJYtW4bGxsbQV1VV1SV/TiIioljywidleHLTl3C0+qAIQABQBOBo9eHJTV/ihU/KenU93Qoox48fxwcffIB777039JjNZoPX64XD4Qirra2thc1mC9W039XT9n1bzfnodLrQYEAOCCQiIoosv1/Bs1uOInD2rlQJwasnbSP9AgJ4dsvRXv24p1sBZc2aNUhNTcXMmTNDj40fPx4ajQZbtmwJPVZaWorKykoUFRUBAIqKinDgwAHU1dWFajZv3gyz2Yz8/PzuvgYiIiLqgX9+Xo1mTwBAMJiEkol09nsAzZ4A/vl5da+tqcsBRVEUrFmzBnPnzoVa/c0tLBaLBQsXLsTSpUvx0UcfYd++fZg/fz6KiopQWFgIALjmmmuQn5+Pe+65B59//jnef/99PP7441i8eDF0Ol3kXhURERF12v5KR0TrIqHLN8l+8MEHqKysxIIFCzoce/rppyHLMmbNmgWPx4MZM2bgueeeCx1XqVTYuHEjFi1ahKKiIhiNRsydOxe/+tWvevYqiIiIqNv0OlVE6yKhR31Q+gr7oBAREUXOZ5UNuPW5YrQFgraPdQBAnHNfyj/+fTLGZSd0+3l6pQ8KERERDQxjMq3ITTaEvhfim682uckGjMm09tqaGFCIiIhinCxL+M3NBUgwaCC1OyYBSDBo8JubCyDL7Y9ewjX12jMRERFR1Jo8NBl/+tEVKBqcAJNOBZ1ahkmnQtHgBPzpR1dg8tDkXl1PtzvJEhER0cAjyyroNWqoZAValQxZ7r0bY8PW0SfPSkRERFGl+NhpPLb+AI7UNCJOI8Oq1yBOI+NIjROPrT+A4mOne3U9vIJCREQU4xRFYOXWMtS3eBFQBBpb3RBCQJIk6NQyfIHg8cLBSb12HwqvoBAREcW4Q9VOHK5uRIs3gBZvAH5FICAAvyLOPubH4epGHKp29tqaGFCIiIhi3OkWDxytfgSU87dGCygCjlY/Trd4em1NDChEREQxrr7Ze8Fw0iagCNQ3e3tpRQwoREREMc/h6lzw6GxdJDCgEBERxbgDJxsjWhcJDChEREQxrrm1c/eWdLYuEhhQiIiIYlxlQ2tE6yKBAYWIiCjGubxKROsigQGFiIgoxiUatRGtiwQGFCIiohg3e2J2ROsigQGFiIgoxt00ZlBE6yKBAYWIiCjGvXPIHtG6SGBAISIiinGfVTVEtC4SGFCIiIhinF6jimhdJDCgEBERxbjhafERrYsEBhQiIqIYZzFqIloXCQwoREREMe7Qic7N2OlsXSQwoBAREcW4L2ubIloXCQwoREREMU+KcF3PMaAQERHFuBGppojWRQIDChERUYwbnWWNaF0kMKAQERHFuJT4OJjj1N9aY45TIyU+rpdWxIBCREQU84anmZBh/fbwkZNkwKgMcy+tCPj2uEREREQDlqIIOFp9aGjxotnj/9baix2PNF5BISIiijFCCDS6fKhqcMHh8qK0tgk1De5v/ZkT9a04cJJ9UIiIiOgSaPb4caKhFWdaPAgoAq2+AF4urkDgIj/nUwQ+O957wwL5EQ8REVEMaPUGcKbFA69fAQD4Agre+aIGr+w8jgaXr1O/o6qh5VIuMQwDChER0QDm8QdQ3+JFqzd4jSSgCHz4ZR3WFlegpvHbP9Zpr+wUAwoRERH1gC+goMHlRbM7eHOrEAIlX5/Bi9srUH46PGjIEqCIi/9OqRc7yTKgEBERDSABRcDh8sLp9kOIYOr4vMqBF7aV43CNM6xWJUu4vsAGnVrC3/ZVX/R3j86yXJI1n0+Xb5I9efIk7r77biQlJUGv16OgoAB79+4NHRdC4Oc//znS09Oh1+sxffp0HD16NOx31NfXY/bs2TCbzbBarVi4cCGam5t7/mqIiIhilBDBYFJV70Jjqw9CCBytbcJP//4FHn7j8w7h5PsjUrF23pV4ePpl+O6w1E49x1XDUi7F0s+rS1dQGhoaMGXKFHzve9/Dpk2bkJKSgqNHjyIhISFU89RTT+EPf/gDXnrpJeTl5eFnP/sZZsyYgcOHDyMuLtgEZvbs2aipqcHmzZvh8/kwf/583H///Xj11Vcj++qIiIhigNPtg6PFB78SvAG2qt6FNTsq8PFXpzrUFg5OxMIpeRhydq6OTqOCRiVf9GMeWQKc7t7rhdKlgPL//t//Q1ZWFtasWRN6LC8vL/S/hRB45pln8Pjjj+Omm24CALz88stIS0vDhg0bcOedd+LIkSN47733sGfPHkyYMAEA8Mc//hHXX389fve73yEjIyMSr4uIiGjAa/H4Ud/ihS8QDCanmjx4ueQ4Nh2s6RA2CgaZce/UwSjIDH5Mo1HJSDRqYdSpsfvrekgIzio+X0ZpO9bZ3T6R0KWPeN566y1MmDABt99+O1JTUzFu3Di88MILoePl5eWw2+2YPn166DGLxYJJkyahpKQEAFBSUgKr1RoKJwAwffp0yLKMXbt2nfd5PR4PnE5n2BcREVGscvsCqHa0otbphi+goLHVh+e3luHuF3fhnQPh4WRwihHLb7kcz9wxFgWZFmhUMlLidchKNMCoC16nsBo1gCSdN5wAZ0OLJAXrekmXrqB8/fXXWLlyJZYuXYrHHnsMe/bswX/8x39Aq9Vi7ty5sNvtAIC0tLSwn0tLSwsds9vtSE0N/6xLrVYjMTExVNPeihUr8MQTT3RlqURERAOO1x/cmdNytu18qzeAv+07gTf2VqHFG95qLcMah/mT8/C9ESmQJQlqWYbVqEG8Tg1JCt+Nk2jQhm6ovRAhBBIN2si+oG/RpYCiKAomTJiA5cuXAwDGjRuHgwcP4vnnn8fcuXMvyQIBYNmyZVi6dGnoe6fTiaysrEv2fERERNHEH1DQ4PKhyR38iMXrV7Dxi2qs21XZ4WOXJKMW9xTl4PrLbVCrZKhlGRaDBua4jsGkjSIELpJPIESwrrd0KaCkp6cjPz8/7LGRI0fi73//OwDAZrMBAGpra5Genh6qqa2txdixY0M1dXV1Yb/D7/ejvr4+9PPt6XQ66HS6riyViIio32sb5te2KyegCHxwpBZriytQ6/SE1Zp0atw1MQu3jBuEOI0KKlmCVa+FWX/hYNLm8xONF/x4p404WzcuO+EilZHRpYAyZcoUlJaWhj321VdfIScnB0DwhlmbzYYtW7aEAonT6cSuXbuwaNEiAEBRUREcDgf27duH8ePHAwA+/PBDKIqCSZMm9fT1EBER9XtCCDhb/XC0ehFQBIQQ2H7sDFbvKMfxM66wWp1axq1XDMKdV2YhPk4DWZJg0Wtg0Wsgy51rrOYLXGwST9fqIqFLAeXhhx/G5MmTsXz5cvzwhz/E7t278Ze//AV/+ctfAACSJOGhhx7Cr3/9awwbNiy0zTgjIwM333wzgOAVl2uvvRb33Xcfnn/+efh8PixZsgR33nknd/AQEVHMa3L74HD5QjtzPqtswKrt5ThS0xRWp5Il3FCQjrsLs5Fk0kGSJJjj1LAatFB1Mpi02f11fafr7ps2pEu/u7u6FFCuvPJKrF+/HsuWLcOvfvUr5OXl4ZlnnsHs2bNDNf/1X/+FlpYW3H///XA4HJg6dSree++9UA8UAFi3bh2WLFmCq6++GrIsY9asWfjDH/4QuVdFRETUz7i8wS3DbcP8Su1NWLW9HPvaTRCWAFw9MhVzJ+dikFUPAIiP0yDBoIFa1eX+qwCAI/bGiNZFgiQudttuFHI6nbBYLGhsbITZbO7r5RAREXWb2xdAg+ubYX6V9S6s3lGOT7463aG2cHAiFk7Nw5CUYJM1ky54xUSr7l4waVO04gPUNHouWpdu0aFk2fSL1l1IV96/OYuHiIioD3j9ChwuL5rPbhmuc7rxcslxvHfI3qHJ2uhMC+6dmofLBwWbrBm0aiQYNdCpVRFZS5pR26mAkmaM0m3GRERE1DNtW4abPcFhfo0uH17dXYkN+0/CFwhPJkNTTFg4LRcTcxMhSRLiNCokGrWI00QmmLQZbjNhf3VTp+p6CwMKERFRL2i/Zdjl9ePNvSfw5r4TcLVrspaZoMf8ybm4aniwyZpOo0KiQQu9NrLBpE19a+dm7HS2LhIYUIiIiC6h9luGvX4Fb30ebLLW2NquyZpJizmFObjubJM1rTo4L8egvbRv16ebL/7xTlfqIoEBhYiI6BI5d8pwQBHYfDjYZK2uKfyNPj5OjbsmZuOWsRmh6cIJRi1Mut55m65r8ka0LhIYUIiIiCLs3C3DQghsO3Yaa7ZX4Hh9eJO1OLWMWeMzcceELJji1NCoZFgNGsTH9d5QPgCI72QQ6mxdJDCgEBERRUj7LcOfHm/AC9vLUWoPvwFVLUu4cUwGZk/KRqJRC40qOC/nfIP8esOI9Hh8WdvcqbrewoBCRETUQ76AgoaWb7YMf2l3YtW2cnxa6QirkwBMz0/DvMk5SLfoOzXIrzfMKczFhv01narrLQwoRERE3RRQBBpcXjS5g1uGj59pweodFdh2tGOTtSlDkrBgah7yko1Qyd/My+nLYNJmbHYCbGYd7M4L3wRrM+swtpcGBQIMKERERF2mKCJ4A6zLB0UI1DrdeKn4OP51uGOTtTGZFtw7LQ+jMizdGuTXG2RZwu9/OBb/55V9aPJ03Eocr1Pj9z8c26trZkAhIiLqJCEEnG4/HC5v6OrJul2VePvz6g5N1oalmnDvtDxMyEmALMvdHuTXWyYPTcaf7xmPP310DF+caIQ3oECrkjE604LF3xuKyUOTe3U9DChERESdcO6U4RbPN03WWn0dm6wtmJKH71yWDFmSejzIrzdNHpqMwsFJOFTtRL3Li0SDFqMyzH1ytYcBhYiI6Fu0ePxocAW3DHv9Cv65/yTW7aqE0x3+UUiySYu5Rbm49nIbVLIUsUF+vU2WJRRkWvp6GQwoRERE5+P2BVDf4oXbF0BAEXj/kB0vFR/HqXbdVM1xavxoUjZuGhNssmbUqWE1RG6QX6xiQCEiIjqHxx9AQ4sPLm9wZ84nR09j9fZyVDW0htXFaWTcPj4Tt0/Igkmnhl6rQoIh8oP8YhUDChEREYJThutdXjSf3TK893gDXtxejq/aNTDTqCTcODoDswuzQ4Ek4RIO8otVDChERBTTAoqAw+WF82wwOVLjxAvbyrG/yhFWJ0vAD/LTMHdyLmzmuF4b5BereFaJiCgmCSHQ2PpNL5Py0y1YvaMcO46d6VA7dWgyFkzNRW6SsdcH+cUqnl0iIoo5TW4fGs5OGbY3urG2uAKbD9eiXY81XJFtxcKpeRiZboZalmE19t28nFjDgEJERDHj3CnD9S3fNFnzt2v/OjwtHvdOy8P4nASoZAlWvRZmPYNJb2JAISKiAe/cLcPNHj/e2FuFv+07AbdPCavLStBjwdQ8fGdYMtQqGRa9Bua46GpLHysYUIiIaMDy+hU4XMEpwx5fABv2V+O13R2brKXG6zC3KAfXjLJBczaYRNu8nFjDgEJERAOOP6CgweVDs8cPf0DBpoN2vFxSgdPN3rA6c5waswtzQk3Won1eTixhQCEiogFDUYI7cxpbgzfAbi09hTXFFTjRrsmaXqPC7RMycfv4TJjiNIiPU8Oq7x/zcmIFAwoREfV7504Z9gcU7KlowKrt5ThW17HJ2r+NycDsSdmwGrQwxamRYNBCw2ASdRhQiIioX2v2+NHQ4oUvoOBQdSNWbSvH5ycaw2pkCZgxyoY5RTlIM8f120F+sYQBhYiI+qVWbwD1Li88vgDKT7fgxe3lKC7r2GRt2rBkLJiSi5wkIwxaNRKMHOTXHzCgEBFRv3LuML+axlasLT6OD87TZG18thULp+VhhM2MOI0KiUYO8utPGFCIiKhfOHeYX32LF3/deRwbv6jp2GTNFo/7pubhipwEzsvpx/hfjIiIotq5w/yaWn34371V+Pu+E3D7w5us5SQasGBqHqYOTYJWreK8nH6O/+WIiCgqnTvMz+X1Y8NnJ/Hanio0nafJ2rzJufhBfhp0ahWsxmD3V+rfGFCIiCjqON0+OFp8cPv8wSZrO4/jTLsma1a9BrMLs3Hj6AzotSpYDVqY4zgvZ6BgQCEioqjh8vpxptkLjz+Aj748hbXFFTjpCG+yZtCq8MMJmbhtfCbi4zSclzNAMaAQEVGfaxvm1+r1Y3dFPV7cVoFjpzo2Wbt57CD8aGI2EoxamPUaWDkvZ8BiQCEioj7j9StocHnR4vHj4MlGvLCtHAdOdmyydm1bkzWLnvNyYgQDChER9bqAItDg8qLJ7cexuia8uL0cO7+u71D3ncuSsWByHnKSjTDp1EgwcF5OrOjSf+Vf/vKXkCQp7GvEiBGh4263G4sXL0ZSUhJMJhNmzZqF2trasN9RWVmJmTNnwmAwIDU1FY888gj8fn/7pyIiogFIUQQaWryoqnfhyxonfvPOYdz/8r4O4WR8TgJWzr4Cv7xxFPIzzBhk1SMlXsdwEkO6fAVl1KhR+OCDD775BepvfsXDDz+Md955B2+++SYsFguWLFmCW2+9FTt27AAABAIBzJw5EzabDcXFxaipqcGcOXOg0WiwfPnyCLwcIiKKRucO86tzuvHXnZXYeKAGgXZN1kamx+PeqXkYl53AtvQxrssBRa1Ww2azdXi8sbERL774Il599VV8//vfBwCsWbMGI0eOxM6dO1FYWIh//etfOHz4MD744AOkpaVh7Nix+O///m88+uij+OUvfwmtVtvzV0RERFGlyR3sZdLQ4sXreyrxj09PdmyylmTAwil5mDI0CQZdcMIw29LHti5fKzt69CgyMjIwePBgzJ49G5WVlQCAffv2wefzYfr06aHaESNGIDs7GyUlJQCAkpISFBQUIC0tLVQzY8YMOJ1OHDp06ILP6fF44HQ6w76IiCi6tXj8ONHgQlW9Cy+XVOBHq3bh1d1VYeHEZo7DT68djlVzJuDq/DRkWA1It+gZTqhrV1AmTZqEtWvXYvjw4aipqcETTzyBadOm4eDBg7Db7dBqtbBarWE/k5aWBrvdDgCw2+1h4aTteNuxC1mxYgWeeOKJriyViIj6SNuU4Ra3D+8etOOVkuM40xLeZC3BoMHsSTm4YXQ6THHBKyZGtqWnc3TpT8N1110X+t+jR4/GpEmTkJOTgzfeeAN6vT7ii2uzbNkyLF26NPS90+lEVlbWJXs+IiLqurYpw80eHz76sg5riitQ7XCH1Ri1KvzwyizcdkVmsI+JQYN4tqWn8+hRXLVarbjssstw7Ngx/OAHP4DX64XD4Qi7ilJbWxu6Z8Vms2H37t1hv6Ntl8/57mtpo9PpoNPperJUIiK6RLx+BQ6XF01uH3aV12PV9nJ8faolrEarlnHz2AzcNTEbSUYdLAYN29LTt+rRfq3m5maUlZUhPT0d48ePh0ajwZYtW0LHS0tLUVlZiaKiIgBAUVERDhw4gLq6ulDN5s2bYTabkZ+f35OlEBFRL/MHFJxq8uCkoxXFZafx4Ov78dj6g2HhRJaAmQXpeGXBRCz+3lAMTjYhK1EPi17DcELfqktXUP7zP/8TN954I3JyclBdXY1f/OIXUKlUuOuuu2CxWLBw4UIsXboUiYmJMJvNeOCBB1BUVITCwkIAwDXXXIP8/Hzcc889eOqpp2C32/H4449j8eLFvEJCRNRPKIqAo9WHxlYfjtYGm6ztKu/YZO17w1Mwb3IucpKMsBo4L4e6pksB5cSJE7jrrrtw5swZpKSkYOrUqdi5cydSUlIAAE8//TRkWcasWbPg8XgwY8YMPPfcc6GfV6lU2LhxIxYtWoSioiIYjUbMnTsXv/rVryL7qoiIKOKEEHC2+uFo9aLyjAtriivw4Zd1Heom5iZg4dQ8DLeZYdYHh/mxLT11lSSEEBcviy5OpxMWiwWNjY0wm819vRwiogGvrZdJTWMrXik5jncP2js0WRuVYca90/IwNisB8Wd35jCY0Lm68v7NPV1ERHRBLq8f9S1enGn24LXdVVj/2Ul42jVZy0s2YuHUXBQNToJZr+W8HIoIBhQiIurA7QugweVFfYsX//j0BF7fU4UWTyCsJt0Sh3mTc/H9Eamw6DWwGrTQqhlMKDIYUIiIKKRty3CDy4uNX9TgrzuPo8HlC6tJMGhwT2EOZo5Oh0Wv5bwcuiQYUIiICP6AggZXcGfOB0dq8VJxBWoa2zVZ06lw55VZuPWKTCQatZyXQ5cUAwoRUQxTFIHGVh8cLi+2HzuN1TsqUH66Y5O1W8cNwp1XZiHFHIdEgxZ6LYMJXVoMKEREMUgIAafbD4fLi08rG/DCJ+U4XBM+iLWtydrdhTkYlKBHolELg5ZvG9Q7+CeNiCjGNHv8aGjx4nB1I17cXo7dFQ0dar4/IhXzJ+ciN9mIBKMWJg7yo17GP3FERDGibcpwWV0T1uyowEelpzrUTMpLxMKpeRiZbuYgP+pTDChERANc25Th42da8MrO43j3QA3a9VjD5WebrF2RnchBfhQVGFCIiAYoX0BBg8uLkw2teH13Jdbvr4a3XZO1wSlG3Ds1D5OHJCHBoINZz2BC0YEBhYhogAkoAg0uL+qcbvxt3wn8754qtHg7NllbMCUXV49MQ4JBC4ueg/woujCgEBENEG1bhk81efDW59VYt6tjk7VEozbUZC3JqIWV83IoSjGgEBH1c21Ths+0ePCvQ3asLT4OuzO8yZpJp8ZdE4NN1pJNOs7LoajHgEJE1I81uX2ob/Zi61ensHpHOSrOuMKO69Qybr0i2GQt3aLnvBzqNxhQiIj6oRZPcMrw7vIzWLW9HEdqmsKOq2QJNxSk4+7CbGQlGjkvh/odBhQion7E7QvgTIsXB044sGpbOfYeD2+yJiHYZG3elFwMSTEh0ch5OdQ/MaAQEfUDbb1MvrQ7sXpHOT756nSHmsLBwSZr+RkWzsuhfo8BhYgoivkCChpavCg/3YKXSirw3kF7hyZrBYMsuG9aHsZlJyDRqIWRbelpAOCfYiKiKNTWy+REvQvrdlViw/6T8AXCk8nQFBMWTsvFlCHJSDBq2ZaeBhQGFCKiKKIoAo5WH+yNrXhjbxXe2HsCrnZN1gZZ9Zg/JRfTR6Yh0aRFvI7dX2ngYUAhIooCbb1M6prc2LD/JNbtrISjNbzJWpJJizmFObhhdDqSTXFsS08DGgMKEVEfa3L7cLrJi00Ha7C2uAJ1TZ6w4/Fxatw1MRuzxg1CijkOVralpxjAgEJE1EdaPH6cafbgo9JTWL29HMfrw5usxallzBqfiTuvzEKGVc+29BRTGFCIiHpZWy+TkrLTWLWtHF/aw5usqWUJN4xOxz1FOchJMsKqZ1t6ij0MKEREvaStl8mnlfV4cVs59lU6wo5LAKbnp2He5BwMS4tHgkELDYMJxSgGFCKiS8wXUNDg8uLQyUas3lGBbUc7NlmbMiQJC6bmoWCQhfNyiMCAQkR0ybT1MjlW14y1Oyrwr8Mdm6yNybTg3ml5uDI3ifNyiM7BgEJEFGGKItDY6sPxMy14ZedxvPV5dYcma8NSTbh3Wh6mDk1GkknHeTlE7TCgEBFFiBACTrcfJxtceH1PFd7cewKtvvAma5kJeiyYkofp+alINulg0PKfYaLz4d8MIqIIaHL7UOt04+/7TmDdrko43f6w48kmLeYW5eKGMelIiY+DifNyiL4V/4YQEfVAi8ePU81ubPy8Bi8VH8ep5vAma+a2JmtXDEKaRQ9zHLu/EnUGAwoRUTe4fQGcafbgX4drsXp7OaoaWsOOx2lk3D4+E3dcmY3MBD3Mcez+StQVDChERF3g8QdQ3+zFJ0dP4cXt5fiqtjnsuEYl4cbRGZhdmIO8ZCMseg27vxJ1AwMKEVEntPUy2VNejxe2lWN/lSPsuCwBP8hPw7zJeRiWZmL3V6IeYkAhIvoWbb1MvjjRiBe3f40dx850qJk6NBkLpubi8kEWdn8lihAGFCKi82jrZfKl3Ym1xRX416FatOuxhnHZVtw7NQ8TchORwO6vRBHVo79NTz75JCRJwkMPPRR6zO12Y/HixUhKSoLJZMKsWbNQW1sb9nOVlZWYOXMmDAYDUlNT8cgjj8Dv94OIYpuiCBw40YitX53CgRONUNq3Xe0FQgg0unz44oQD/73xMO55cTfebxdOhqfF47e3jcZzs6/A1SPTkGaOYzghirBuX0HZs2cP/vznP2P06NFhjz/88MN455138Oabb8JisWDJkiW49dZbsWPHDgBAIBDAzJkzYbPZUFxcjJqaGsyZMwcajQbLly/v2ashon6r+NhprNxahrK6ZvgCAhqVhCGpJiy6aggmD03ulTU43T6cqG/Fa7uP4819J+D2KWHHsxL0WDg1Dz/IT2P3V6JLTBJCdPn/RWlubsYVV1yB5557Dr/+9a8xduxYPPPMM2hsbERKSgpeffVV3HbbbQCAL7/8EiNHjkRJSQkKCwuxadMm3HDDDaiurkZaWhoA4Pnnn8ejjz6KU6dOQavVXvT5nU4nLBYLGhsbYTabu7p8IooyxcdO47H1B9Ds8Qc/KlHJ8AYUNLh8MOlUWH5LwSUNKc0eP+yOVry57wRe292xyVqKSYd5k3Nww9gMpLD7K1G3deX9u1vXJBcvXoyZM2di+vTpYY/v27cPPp8v7PERI0YgOzsbJSUlAICSkhIUFBSEwgkAzJgxA06nE4cOHerOcoioH1MUgZVby9Ds8cNmjkOcRgVZlhCnUcFm1qHZE8DKrWWX5OMel9eP4/UtWL29HHe+sBN//uTrsHBijlNj0XeH4LX7J2HelDzkJBoZToh6SZf/pr3++uv49NNPsWfPng7H7HY7tFotrFZr2ONpaWmw2+2hmnPDSdvxtmPn4/F44PF8053R6XR2ddlEFKUOVTtRVteMBIO2Q4dVSZJgNWhQVteMQ9VOFGRaIvKcbl8AZ1o8eP+gHat3VOBEuyZreo0Kt0/IxI8mZmNQgh7xcZqIPC8RdV6XAkpVVRUefPBBbN68GXFxcZdqTR2sWLECTzzxRK89HxH1nnqXF76AgPYCW3N1KhmNikC9y9vj5/L6FdS3eLD1q1N4YVs5jtV1bLL2b2MyMKcoB7lJJpj1bEtP1Fe6FFD27duHuro6XHHFFaHHAoEAPvnkE/zP//wP3n//fXi9XjgcjrCrKLW1tbDZbAAAm82G3bt3h/3etl0+bTXtLVu2DEuXLg1973Q6kZWV1ZWlE1GUSjRooVFJ8AYU6GQZbq8Cv6JALcuI08rwBBRoZAmJhovfn3YhbU3Wdn19Bqu2lePzE41hx2UJuCbfhnmTczHcFg+Lnm3pifpalwLK1VdfjQMHDoQ9Nn/+fIwYMQKPPvoosrKyoNFosGXLFsyaNQsAUFpaisrKShQVFQEAioqK8Jvf/AZ1dXVITU0FAGzevBlmsxn5+fnnfV6dTgedTtflF0dE0W9UhhlDUk344oQD/oCAN6BACECSAK1KhlolYXSmFaMyun5DfKjJWpUDq7aXo7isY5O1acOSsXBqXqjJGtvSE0WHLgWU+Ph4XH755WGPGY1GJCUlhR5fuHAhli5disTERJjNZjzwwAMoKipCYWEhAOCaa65Bfn4+7rnnHjz11FOw2+14/PHHsXjxYoYQohgkyxK+MywZJWVnEFAE1CoJKhlQBODyBqA6e7wrVzQURcDR6kNpjROriyvwweGOTdbGZ1uxcNo3TdbY/ZUoukT8dvSnn34asixj1qxZ8Hg8mDFjBp577rnQcZVKhY0bN2LRokUoKiqC0WjE3Llz8atf/SrSSyGifkBRBD45ehoGrQoBRcDjV+BXBCRJgkGrgkqW8cnR01g4dfBFQ4oQAs5WP74+3YyXiiuw8Ysa+Nvt/hlui8d9U/MwdVgKEowa6NTsZUIUjbrVB6WvsQ8K0cBx4EQj/s8reyFJQH2zGx4/oCDYA0GnBhJMcYAA/nzPhAvu4hFCoMnjx4n6VqzbdRx/33cCbn94k7WcRAPmT83FD0ayyRpRX+nK+zc39BNRn6p3edHiCaDJ48O5FzsUAK1+wNPoRnyc5oK7eJo9ftQ4WvHm3iq8tqcKTe2arKXG6zB3ci5mjrYhNT6OfUyI+gn+TSWiPmXVazqEk3MpAmhy+2DVh/cicXn9qHO68c/91Xh553GcaQ4PMBa9BncXZuPWcZlINevYy4Son2FAIaI+5VeUC4aTNooI1gHBJmunmz3YdMCOtcUVOOkIb7Jm0KrwwwmZuPPKbGQk6BGvYy8Tov6IAYWI+tSmAzWdqtv4eTUyLHp8VFqHF7eXo+xUS9hxjUrCzWMH4e7CbDZZIxoAGFCIqE8dOtm50RXbjp7G3uMOHDjZscnataOCTdaGpbHJGtFAwYBCRH2qye3rVN2xdldMAOCqy1KwYGoeRmWY2WSNaIBhQCGiPuXy+i9e1M6EnATcOy0PV2QnIMHIJmtEAxEDChH1KZcv0OnakenxuHdqHqYMZZM1ooGOAYWI+pQSUC5eBCBeK+OFOROQZNRBr2UwIRroeF2UiPpMk9sHcyenFGcmGpCZYGA4IYoRvIJCRL2uxRNssrZhfzWqGtyd+plJQ5Iu8aqIKJowoBBRr2n1BnC6xYP3DtRgTXEFqh2dCycAYGKLeqKYwr/xRHTJuX0B1Ld48HHpKazaXo6vz7Nl+GK+qm26BCsjomjFgEJEl4zXr6DB5UVJ2Wms2laOg9XhTdlkCYjXqdHovvhW45p2Le2JaGBjQCGiiPMHFDS4fPissgEvbi/HrvL6DjXfG56CeZNzseLdw50KKEQUWxhQiChiAoqAw+XFkRonXtxejo9KT3WomZibgIVT8zAmKwEJRg0SjDoAF//Ix8RpxEQxhQGFiHpMUQScbh+O1TXjpZIKvHvAjkC7EcX56WbcNy0PkwYnIdGoRZwmuF14VIYFJV93vMLS3qgMyyVZOxFFJwYUIuo2IQScbj8qz7Rg3a5KrP/sJDz+8MZreclGLJyai+8OT0WiUQtDu904NxSkY9X28os+1w0F6RFdOxFFNwYUIuqWJrcPNQ43/ndvJV7fU4UWT3jL+nRLHOZNzsWMUTakxOtg1J3/n5vRWVbkJRtQftp1wefKSzZgdJY1kssnoijHgEJEXdLc1mTts5N4ZedxNLjCpxEnGDS4pzAHN40dhBSzDuaL3DsiyxJ+NDEby9/9EuI8xyUAP5qYDZmTioliCgMKEXWKy+vHqSYP3jtox9riCtQ0hjdZM+pUuPPKLNw+Pgs2Sxwseg0k6eKhQlEE3vq8+ltr3vq8GgunDmZIIYohDChE9K3amqx9+GUdXtxegfLT4TtutGoZt44bhLsmZiMr0QCLXgNVF4LEgZONKLU3Q5YAtSwBkgQhAEkCIAT8ikCpvRkHTjZiDD/mIYoZDChEdF5tTdaKj53GC9vKcbgmvMmaSpZwfYEN9xTmIC/ZhASDBmpV1+ePflblgF9RoFZJkKWzP9+WbyQJKkmBP6DgsyoHAwpRDGFAIaIwvkAwmHx2PNhkbXdFQ4ea749IxfzJubjMFo8EgxZadfcHo0ttN54IfBNMziXa1RFRTGBAISIAwe6vjlYfjtQ4sfoCTdYm5SVi4dQ8FGRakGD4ppdJT4zNtkKjkuEPKJBlAemclCIgEFAENCoZY7OtPX4uIuo/GFCIYlxAEWhsDTZZe7mkAu8eqEG7Hmu4PMOMe6flYUJu4nl7mfREwSALLksz4VC1Ez6/ArVKhiQBQgRDkwBwWZoJBYPYqI0oljCgEMWotu6vx8+4sG7XcWzYXw1vuyZrg1OMWDglD9OGJSPRpIPpAr1MekKWJSy7biQefmM/6lu8CChK6CZZWZaQZNRi2XUjuYOHKMYwoBDFGCEEnK1+VDe68MbeE3hjTxVavB2brC2Ykosf5NuQaNJetJdJT00emoynfzgWz31chlJ7E7wBBVqVjOG2ePz7d4dg8tDkS/r8RBR9GFCIYojT7UNdowcb9p/Eul0dm6wlGrW4pzAHN45JR4opDma9ulO9TCJh8tBkTMxNxNtf1OCkw4VBVgNuHJ0OdQ9uwCWi/osBhSgGNHv8ON3kwXsHa7CmuAK1Tk/YcZNOjTuvzMKtV2TCZomDVa/p9Y9Uio+dxsqtZSira4YvIKBRSfjHZyew6CpeQSGKRQwoRAOYy+vHmWYPPi49hRe3l6PiTPi8mzi1jFuvGIQ7J2Yj3aLvdi+Tnio+dhqPrT+AZo8/uG1ZJcMbUHCkpgmPrT+A5bcUMKQQxRgGFKIBKNj91YuSstNYtb0cR2qawo6rZAk3FKTj7sJsZCcZe9zLpCcURWDl1jI0e/xIi9fB4xdo8fqhlmWkxWtR2+TFyq1lKBycxBtliWIIAwrRAOLxB9DQ4sP+qgas2laOvcfDm6xJAK4emYp5k3MxOMWERGNkepn0xKFqJ8rqmqFTyzhe74LH/80uHp1ahlmvQVldMw5VO1GQya3GRLGCAYVoAPD6FThcXhyucWLNjgps/apjk7XCwcEmayPTzRHvZdIT9S4vWjwBtPr8CIjgPB5JCjaQbfUp8Po90GvVqHd5+3qpRNSLouNfKCLqFn9AQYPLh7K6JrxcchzvHbJ3aLJWMMiC+6blYVx2AqwGDeIv8ZbhrrLqNWj1BUIdY9t2DUkANHKw9X6rNwCrPrrWTUSXFgMKUT8UUAQcLi+q6l1Yt6sSG/afhC8QnkyGppiwcFouigYnIcGg69Utw10VWlb75UntjhNRzOjSXXErV67E6NGjYTabYTabUVRUhE2bNoWOu91uLF68GElJSTCZTJg1axZqa2vDfkdlZSVmzpwJg8GA1NRUPPLII/D7/ZF5NUQDnKIINLR4UWp34o8fHsWPVu3Cm/tOhIWTQVY9Hp85En+eMx4zRqUjO9EIi0ETteHE0epDnEYFWZLgDwgoQkAg+H/9AQFZkhCnUcHR6rv4LyOiAaNLV1AyMzPx5JNPYtiwYRBC4KWXXsJNN92Ezz77DKNGjcLDDz+Md955B2+++SYsFguWLFmCW2+9FTt27AAABAIBzJw5EzabDcXFxaipqcGcOXOg0WiwfPnyS/ICiQaCtu6vdU3uYJO1nZUd3rCTTFrMKczB9QXpsBq0fbZluKsSDVoYtSqYdGo0tvrg8QcglOBVkziNCha9BkIIJBq0fb1UIupFkhCiR0PMExMT8dvf/ha33XYbUlJS8Oqrr+K2224DAHz55ZcYOXIkSkpKUFhYiE2bNuGGG25AdXU10tLSAADPP/88Hn30UZw6dQpabef+AXI6nbBYLGhsbITZbO7J8omintPtw+kmDzYdtOOl4grUNYU3WYuPU+OuK7Nw87hBSDLp+nTLcHcoisDcNbtxpKYJaWYtPD4Bv6JALcvQaSTUOr0YmR6Pl+ZP5DZjon6uK+/f3f5XLBAI4PXXX0dLSwuKioqwb98++Hw+TJ8+PVQzYsQIZGdno6SkBABQUlKCgoKCUDgBgBkzZsDpdOLQoUPdXQrRgNTs8aPyTAv+8ekJzF+zB799vzQsnMSpZcyelI11Cydh/tQ8DE4xIc0c16/CCRAcCLjoqiEw6VSodXoBCTBq1YAE1Dq9MOlUWHTVEIYTohjT5ZtkDxw4gKKiIrjdbphMJqxfvx75+fnYv38/tFotrFZrWH1aWhrsdjsAwG63h4WTtuNtxy7E4/HA4/nmH2an09nVZRP1Gy6vH/UtXuz8+gxWbSvHl/bwJmtqWcINo9Nxd2EObJa4qNoy3F2ThyZj+S0FoVb3jYqARpYwMj2ere6JYlSX/1UbPnw49u/fj8bGRvztb3/D3LlzsXXr1kuxtpAVK1bgiSeeuKTPQdTX2rq/7q9qwIvbyrGv0hF2XALwg/w0zJ2cg+xEIxKMWph0/TuYnGvy0GQUDk7CoWon6l1eJBq0GJVh5pUTohjV5X/dtFothg4dCgAYP3489uzZg2effRZ33HEHvF4vHA5H2FWU2tpa2Gw2AIDNZsPu3bvDfl/bLp+2mvNZtmwZli5dGvre6XQiKyurq0snikpuXwANLi9Ka5qwekc5Pjl6ukPNlCFJWDA1D8NS42E1amCOsl4mRESR1uP/90tRFHg8HowfPx4ajQZbtmzBrFmzAAClpaWorKxEUVERAKCoqAi/+c1vUFdXh9TUVADA5s2bYTabkZ+ff8Hn0Ol00Ol0PV0qUVRp6/5adqoZLxUfx78Od2yyNibTgnun5WF0phVWvTaqe5n01PmmGQ9JNfEjHqIY1aWAsmzZMlx33XXIzs5GU1MTXn31VXz88cd4//33YbFYsHDhQixduhSJiYkwm8144IEHUFRUhMLCQgDANddcg/z8fNxzzz146qmnYLfb8fjjj2Px4sUMIBQzfAEFDS4vTpxtsvbW59UdmqwNSzXh3ml5mJibCItBC6teM6A/6uA0YyJqr0sBpa6uDnPmzEFNTQ0sFgtGjx6N999/Hz/4wQ8AAE8//TRkWcasWbPg8XgwY8YMPPfcc6GfV6lU2LhxIxYtWoSioiIYjUbMnTsXv/rVryL7qoiiUEARaHB5YW904429VXhz7wm0+gJhNZkJeiyYkourhqfCotcgwaCFagAHEyB8mrHNHBe6QhQnq2Azy7A7PZxmTBSDetwHpS+wDwr1JwFFoLHVh1NNbmzYX411O4/D6Q7vnpxs0mJuUS6uvdwWDCZGLTT9oMlaJBw40Yj/88peGHXq805WbvUF4PL48ed7JnCaMVE/15X374GzBYAoyiiKCDZZa/bg/YN2vFRyvEOTNXOcGj+alI2bxmQgwahDglEDnbrjm/RAVu/ywhcQ0F4gkOlUMhoVwWnGRDGGAYUowtra0je4PPio9BRWby9HVUNrWE2cRsbt4zNx+4QsJJl0SDJqz3v1IBYkGrTQqCR4Awri5I7nwBNQoJEltronijEMKEQRIoRAk8ePhmYvdpWfwYvbK1BaG95kTaOScOOYDMyelI3U+GCTNeMA6mXSHaMyzBiSasKRmibYzHLYLiUhBBwuH0amx2NUBj/OJYolsf0vI1GENHv8aGjx4osTDqzaXo7P2jVZk6WzTdaKcpGZYGAvk3O0tbp/bP0B2J0eWA0a6FQyPAEFDpePre6JYhQDClEPtLWl/6q2Cau3V2D7sY5N1qYOTcaCqbkYnGyC1aCBRa8ZsL1Muout7omoPQYUom5o9QZQ7/Li+JkWvFRcgc2Hazs0WRubZcV90/KQn2GBOU4NawxsGe4JtrononMxoBB1QVtb+mpHK9btrMTbX3RssnZZmgn3Ts3D+JwExOs1SDRooY6RLcM9JcsStxITEQAGFKJO8fgDaGjxoa7JjTf3VuHNfSfg9ilhNVkJeiycmodpw5Jh1GlicsswEVGkMKAQfYu2eTlnmj3YsL8ar+2u7NBkLcWkw9zJOZgxygaDTh3TW4aJiCKFAYXoPNrm5TS6fNh00I6XSypwujm8UZhFrwk1WTPq1NwyTEQUQfzXlOgc/oCCBpcPTrcPW0vrsHpHBU60a7Km16hw+4RM3D4+Exa9lluGiYguAQYUIgTn5ThcXjS2+rCnoh6rtpXjaF1zWI1GJeGmsRn40cRsJBp13DJMRHQJMaBQTGsb5Ods9eHgyUas2v419lc1htXIEnBNvg1zJ+fAZtFzyzARUS9gQKGYpJwNJo2tPpSdasbq7eXYUXamQ913hiVj/pRc5CQZYYpTI8EQO1OGiYj6EgMKxRQhvgkmJxpcWFt8HB8crkW7HmsYn23Fwml5GGEzw6BVc8swEVEvY0ChmCCEgNPtR6Mr2MvklZ3H8c4XNfC3a/863BaP+6bm4YqcBOg0KiQatNBrGUyIiHobAwoNaG0Thh0tPjhcXvzv3ir8fd8JuP3hTdZyEg2YPzUX04YmQ6tWIcGohYlbhomI+gz/BaYBq8ntg8PlQ5Pbhw2fncRre6rQ1K7JWmq8DnMn5+Ka/DRo1TKsBi3McWruzCEi6mMMKDTgtHj8aHB54fL4g03Wdh7HmXZN1qx6DWYXZuPG0RmI06hgNQR7mXAwHRFRdGBAoQHD5fWjweVDq9ePj0tPYc2OCpx0hDdZM2hV+OGETNw2PhNGnYZbhomIohQDCvV7bl8A9S1etHr92F1Rjxe3VeDYqY5N1m4eOwg/mpgNi0HDLcNERFGOAYX6LbcvgAaXF63ewNkma+X44kTHJmvXXm7DnMIcpJrjuGWYiKifYEChfsfjD6ChxQeX14+vTzXjxe0VKPn6PE3WLkvGgil5yE40QKdRccowEVE/woBC/YbXr8Dh8qLZ40e1oxVriyuw5UhdhyZrE3IScO+0PFyWFg+NSuaUYSKifoj/alPU8wUUNLi8aHb7cabZg7/urMTGAzUItGuyNjI9HvdOzcO47ASoZZlThomI+jEGFIpa/oACR6sPTW4/nK1evL6nCv/49CQ87ZusJRmwcEoepgxNglolw6rXwqxnLxMiov6MAYWiTkARcLi8cLr9aPX68Y9PT+L1PVVo9oQ3WbOZ4zBvcg6uHpkGtUqGRa+BVc9eJkREAwEDCkWNcycMe/0BvHPAjr/uPI4zLeFN1hIMGsyelIMbRqdDq5YRH6dBgkEDNbcMExENGAwo1OeEEHC2+uFo9cIXUPDRl3VYvaMCNY3usDqjVoU7rszCrCsyodeqYNQFe5lo1QwmREQDDQMK9ZlzJwz7AgHsKq/Hqu3l+PpUS1idVi3jlrEZuHNiNix6DeI0KiRyyzAR0YDGgEJ9om2Qny+g4IsTDqzaVo6D1c6wGlkCri9Ixz2FOUiJ13HLMBFRDOG/9NSrmj1+NLQEP8o5VteMF7eXY1d5fYe67w1PwbzJuchKNHDLMBFRDGJAoV7RNmHY61dwsqEVq3eU46PSUx3qJuYmYMHUYJM1WZJgNWhg0Wu4ZZiIKMYwoNAl1eoNoN7lhccXwOlmD14pOY53D9o7NFnLTzfjvml5GJNlhSRJnDJMRBTjGFDokjh3kJ+z1YfX91Rh/Wcdm6zlJRuxcGouigYnQZIkThkmIiIADCgUYecO8mv1BfCPT0/g9T1VaPEEwurSLXGYNzkX3x+RCpUsccowERGFYUChiPAFFDS0BAf5+QIK3vmiBq/sPI4Gly+sLsGgwT2FOZg5Oh0alQydRoVEgxZ6LYMJERF9o0vX0VesWIErr7wS8fHxSE1Nxc0334zS0tKwGrfbjcWLFyMpKQkmkwmzZs1CbW1tWE1lZSVmzpwJg8GA1NRUPPLII/D7w9uYU//gDyg41eTBiYZWNLb68MGRWsxbswd/+PBYWDgx6lS4d2oe/nrvJNw8bhAMWjXSzHEYZNUznBARUQdduoKydetWLF68GFdeeSX8fj8ee+wxXHPNNTh8+DCMRiMA4OGHH8Y777yDN998ExaLBUuWLMGtt96KHTt2AAACgQBmzpwJm82G4uJi1NTUYM6cOdBoNFi+fHnkXyFdEgFFoMHlRZPbD0VRUPL1GazeXoGvT3dssnbruEG488osmPWa0JbheB2H+RER0YVJQghx8bLzO3XqFFJTU7F161Z85zvfQWNjI1JSUvDqq6/itttuAwB8+eWXGDlyJEpKSlBYWIhNmzbhhhtuQHV1NdLS0gAAzz//PB599FGcOnUKWq32os/rdDphsVjQ2NgIs9nc3eVTNwTOmZcjhMDnZ5usHWrXZE0lS7j+chvuKcpBsknHLcNERNSl9+8e3YPS2NgIAEhMTAQA7Nu3Dz6fD9OnTw/VjBgxAtnZ2aGAUlJSgoKCglA4AYAZM2Zg0aJFOHToEMaNG9fheTweDzweT9gLpN517iA/RQgcrW3Ci9vLsbuioUPt90ekYv7kXAxK0HPLMBERdUu3A4qiKHjooYcwZcoUXH755QAAu90OrVYLq9UaVpuWlga73R6qOTectB1vO3Y+K1aswBNPPNHdpVIPKIqA0x0MJgFF4ESDC2t2VJy3ydqkvEQsnJqHoakmAIApTo1Eg5ZThomIqMu6HVAWL16MgwcPYvv27ZFcz3ktW7YMS5cuDX3vdDqRlZV1yZ83lp07YTigCJxq8uDlkuPYdLAG7Xqs4fIMM+6dlofRmVYA4JZhIiLqsW4FlCVLlmDjxo345JNPkJmZGXrcZrPB6/XC4XCEXUWpra2FzWYL1ezevTvs97Xt8mmraU+n00Gn03VnqdRF504Y9isKGlt9eG13JTbsr4a3XZO1wSlG3Ds1D5PyEiFJEqcMExFRxHQpoAgh8MADD2D9+vX4+OOPkZeXF3Z8/Pjx0Gg02LJlC2bNmgUAKC0tRWVlJYqKigAARUVF+M1vfoO6ujqkpqYCADZv3gyz2Yz8/PxIvCbqBiEEmjzBYOILKGj1BvC3T0/gjT1VaPF2bLI2f0qwyZosSdCqg1OGDVq21SEiosjo0jvK4sWL8eqrr+Kf//wn4uPjQ/eMWCwW6PV6WCwWLFy4EEuXLkViYiLMZjMeeOABFBUVobCwEABwzTXXID8/H/fccw+eeuop2O12PP7441i8eDGvkvSRJrcPjrPBxOtXsPGLGqzb1bHJWqJRi3sKc3B9gQ0alQy1LCPBqEE8pwwTEVGEdWmb8YW2h65Zswbz5s0DEGzU9pOf/ASvvfYaPB4PZsyYgeeeey7s45vjx49j0aJF+Pjjj2E0GjF37lw8+eSTUKs7l5e4zTgymj1+NLR44QsoCCgCW47UYk1xBWqdnrA6k06NuyZm4ZZxgxCnUUGWJCQYtDDr2cuEiIg6ryvv3z3qg9JXGFB6xuX1o77FC69fgRACxWVn8OL2clSccYXV6dQyZl0xCHdcmYX4OA23DBMRUY/0Wh8U6l9avQHUu7zw+IL3lOyvcmDVtq9xuKYprE4lS7ihIB13F2YjyRT82I1bhomIqDcxoJxDUQQOVTtR7/Ii0aDFqAwz5AFwpcDtC6DB5UXr2Ztdv6ptwqpt5dh7PLzJmgTg6pGpmDs5F4OsegDcMkxERH2DAeWs4mOnsXJrGcrqmuELCGhUEoakmrDoqiGYPDS5r5fXLR5/AA0tPri8wUGMlfXBJmtbv+rYZK1wcLDJ2pCUYJM1ThkmIqK+xICCYDh5bP0BNHv8SDBooVXJ8AYUHKlpwmPrD2D5LQX9KqR4/QocLi+aPcFgcqrJg5dKKvDeQXuHJmsFgyy4b1oeLh9kAQBoVDISjFqYdPyjQUREfSfm34UURWDl1jI0e/ywmeNCu1LiZBVsZhl2pwcrt5ahcHBS1H/c4wsoaHB50ewOBpNGlw+v7q7Ehv0n4QuEJ5OhKSYsnJaLibnBJmsqWYLVoIU5jjtziIio78V8QDlU7URZXTMSDFoICDhagv1ANCoZFoMaVoMGZXXNOFTtREGmpa+Xe17+gAJHqw9Nbj+EEHB5/fjbvhN4Y+8JuNo1WRtk1WP+lFx8d3gKZEmCJEmw6DWw6jVRH8CIiCh2xHxAqXd54QsINLp9ON3kCfsIpLoRSI7XQYhgXbQJnDNhWAgBr1/B219UY93OSjhaw5usJZm0mFOYg+sut4V24sTHaZBg0HBnDhERRZ2YDyiJBi08/gCcZz8WOZcigDqnB+azW2yjhXJOMFGEQEAR2Hy4FmuLK1DXFN5kLT5OjbuuzMLNZ5usAYBRpw7ea6NmMCEiougU8wFleKoJTecJJ+dqcvsxPNXUSyu6sPYThoUQ2H7sDFbvKMfxdk3W4tQyZo3PxB0TsmCKC/5n5jA/IiLqL2I+oGz44iQu1kpXnK374YTs3lhSx+dvN2EYAD6tbMCqbeX40h7eZE0tS7hhdDruLsxBojF41UejCg7zM3JnDhER9RMx/471j31Vna7ri4By7iA/ACi1N2HVtq+xr9IRVtfWZG3+lFykW4JN1tSyDKtRAzOH+RERUT8T8wHl6KnmiNZFSosnOC+nLZhUnnFh9Y5yfHL0dIfayUOSsGBKLgafbbImSxKsBg0seg23DBMRUb8U8wHF51MiWtdT7efl1DrdeKn4OP51uGOTtTGZFtw7LQ+jMoLbnznMj4iIBoqYDyg6jQrwXDx86C7xjaVuXwD1LV64zwYTh8uLdbsq8dbn1R2brKWacO/UPFyZmxC6QsJhfkRENJDEfEAxaFUAfJ2si7z2g/xaPH68ue8E3tx7Aq2+8CZrmQl6LJiSi+9cFmyyFlwXh/kREdHAE/MBxRynBeDuZF3kePwBOFw+tJydl+P1K/jn59VYt/N4h54sySYt5hTl4tpRaaErJBzmR0REA1nMB5Qrc604UO3sVF0k+AIKGlq+GeQXUATeP2THyyXHOzRZM8epcdfEbNw8NiP0EROH+RERUSyI+Xe5+E5uwe1s3YX4AwoaXD40e4LzcoQQ+OToaazeXo6qhtaw2jiNjNvGZ+KHE7JCQYTD/IiIKJbEfEA51dy5GTudrWuv/SA/IQT2HW/Ai9srUFob3mRNo5Jw4+gM/GhSdqjJmnx2mJ+Fw/yIiCiGxHxAcXs7t324s3VtAoqAw+WF82wwAYAjNU68sK0c+6scYbWyBPwgPw1zi3Jhs8QBCG4Zjo8LzszhlmEiIoo1MR9QRmWa8Y/9JztV1xntB/kBQMWZFqzeXoHtxzo2WZsyNAkLpuQhL9kYesykUyPBqIWGW4aJiChGxXxAmZCTCFlChyZo55KlYN23URQBpzsYTAJnf5nd6cZLxRXYfLi2w+8fm2XFfdPyMDL9m+Cj16qQYOAwPyIiopgPKAWDLBiVYcaBkxfeyTMqw4yCQZbzHms/YRgAGlxerNtZibe/6Nhk7bK0YJO18TnfNFnjlmEiIqJwMR9QZFnCsutG4uE39uN0kwdCBKcXSwAkCUiO12HZdSM73KAqhECTxw9HyzcThps9fry5twpv7jsBd7vW+FkJeiyYmofvDEsOBRNuGSYiIjo/vjMCmDw0GU//cCye+/gYvrQ3wRcQ0KgkjLDF49+/OxSThyaH1befMOzxBfDPz6vx6q7KDk3WUkw6zJ2cgxmjbKGbXdumDMfruGWYiIjofBhQzpo8NBmFg5NwqNqJepcXiQYtRmWYw66cNHv8aDhnwnBAEXjvoB0vlVTgdLttyOY4NWZPysZNYwdBqw7e7No2Zdgcxy3DRERE34YBpRNcXj/qW7zw+oPBRBECn3x1Gqt3lONEuyZreo0Kt0/IxO3jM2E8+9ENpwwTERF1DQPKWcXHTn/zEY9fQKOWMCzVhB9NzMaoszfICiGw93gDVm0rx9G65rCf16gk/NuYDMyelA2r4Zu5PdwyTERE1HUMKAiGk7abZJWzN8kCQH1zPb6qbcJj14+ETq3Cqu1fY39VY9jPyhIwY5QNc4pykGaOCz3OLcNERETdF/MBRVEEVmw6glqnp+MxAGdafPj5Pw+hxRvocHzasGQsmJKLnKRvmqxp1TISjVoYtDF/aomIiLot5t9FD5xsxKGLTDNuH07GZ1uxcFoeRti+abKmlmUkGDU9HipIREREDCjYU3HmW7vInmu4LR73Tc3DFTkJocfaduZY9BpuGSYiIoqQmA8oO46d6VTdsFQjnvvRuFAI4c4cIiKiSyfmA4rSycsnVr02FE64M4eIiOjSivmA4vZ1vPn1fDz+AHfmEBER9ZKYDyiaTp4Bo05GukV/aRdDREREAIAuf0bxySef4MYbb0RGRgYkScKGDRvCjgsh8POf/xzp6enQ6/WYPn06jh49GlZTX1+P2bNnw2w2w2q1YuHChWhuDm981luq6t2dqqt2dNyGTERERJdGlwNKS0sLxowZgz/96U/nPf7UU0/hD3/4A55//nns2rULRqMRM2bMgNv9TRCYPXs2Dh06hM2bN2Pjxo345JNPcP/993f/VfRAoJP3oHS2joiIiHquyx/xXHfddbjuuuvOe0wIgWeeeQaPP/44brrpJgDAyy+/jLS0NGzYsAF33nknjhw5gvfeew979uzBhAkTAAB//OMfcf311+N3v/sdMjIyevByui5e17mM1tk6IiIi6rmIvuuWl5fDbrdj+vTpoccsFgsmTZqEkpISAEBJSQmsVmsonADA9OnTIcsydu3add7f6/F44HQ6w74iRurkKehsHREREfVYRN917XY7ACAtLS3s8bS0tNAxu92O1NTUsONqtRqJiYmhmvZWrFgBi8US+srKyorYmqvaTSPuaR0RERH1XL+4LLBs2TI0NjaGvqqqqiL2u3kPChERUfSJaECx2WwAgNra2rDHa2trQ8dsNhvq6urCjvv9ftTX14dq2tPpdDCbzWFfkTI8xXjxoi7UERERUc9FNKDk5eXBZrNhy5YtocecTid27dqFoqIiAEBRUREcDgf27dsXqvnwww+hKAomTZoUyeV0ymM35Ee0joiIiHquy7t4mpubcezYsdD35eXl2L9/PxITE5GdnY2HHnoIv/71rzFs2DDk5eXhZz/7GTIyMnDzzTcDAEaOHIlrr70W9913H55//nn4fD4sWbIEd955Z6/v4AGAK7ITIUv41oGBshSsIyIiot7R5YCyd+9efO973wt9v3TpUgDA3LlzsXbtWvzXf/0XWlpacP/998PhcGDq1Kl47733EBcXF/qZdevWYcmSJbj66qshyzJmzZqFP/zhDxF4OV13xN4Ek04Fp/vCLe9NOhWO2JtQkGnpxZURERHFLkkI0e/u/nQ6nbBYLGhsbOzx/Sgfldbh3pf2futNsCpZwqq5E/C94akXrCEiIqJv15X3736xi+dSqm/2XnSHTkARqG/29tKKiIiIKOYDSmNr54JHZ+uIiIio52I+oNgdnRsW2Nk6IiIi6rmYDyiQIlxHREREPRbzASXNEnfxoi7UERERUc/FfECx6rURrSMiIqKei/mAkmTUQnWRs6CSg3VERETUOxhQTDpY9VrIF7jHRJaCV0+STLreXRgREVEMi/mAMirDjPwMM+Lj1DBoZKjlYChRy4BBIyM+To38DDNGZURuQCERERF9u5gPKLIsYdFVQ5Bg0MKgUyPdokdmgh7pFj0MOjUSDFosumoI5AtdYiEiIqKIi/mAAgCThyZj+S0FGJluRkARaPUpCCgCI9PNWH5LASYPTe7rJRIREcWULg8LHKgmD03GxNxEvP1FDU46XBhkNeDG0elQq5nhiIiIehsDylnFx05j5dYylNU1wxcQ0Kgk/OOzE1h01RBeQSEiIuplvDyAYDh5bP0BHKlxwqhTIzVeB6NOjSM1TXhs/QEUHzvd10skIiKKKTEfUBRFYOXWMjR7/LCZ4xCnUUGWJcRpVLCZdWj2BLByaxmUi0w8JiIiosiJ+YByqNqJsrpmJBi0kKTwnTqSJMFq0KCsrhmHqp19tEIiIqLYE/P3oNS7vPAFBLQqGUIIuH0K/IoCtSwjTiNDp5LRqAjUu7x9vVQiIqKYEfMBJdGghUYlwdHqRWOrDx6/AiEASQJ0ahlmvQYaWUKiga3uiYiIekvMf8QzKsOMJJMWNY1utPoCAIC2D3pafQHYG91IMmnZSZaIiKgXxfwVlDZCAIoAAgi/GZb9Y4mIiHpfzF9BOVTtRLXDDVnqGEYkBOfyVDvcvEmWiIioF8X8FZQzzR443T4AgE4jQwiE7kGRJMDnV+B0+3Cm2dPHKyUiIoodMR9QGlw+KIqALEuQIEGSEHYpRZYlKIpAg8vXZ2skIiKKNTH/EY/VqAmGECEgRPj9J0IIKCIYXqxGTR+tkIiIKPbEfEBJNupgjlNDliT4FBEKKooQ8CkCsiTBHKdGslHX10slIiKKGTEfUEZlmJGfYYFeo0acWoYiBPxng0qcWoZeo0Z+hoXbjImIiHpRzAcUWZaw6KohSDRqoFPLSDBokWjQIsGghU4tI9GowaKrhkCWueGYiIiot8R8QAGAyUOTMXtSNhQEd/WcavHgTLMHCoDZk7IxeWhyXy+RiIgopsT8Lh4AKD52Gut2VUItS8iw6iFJwa3Grb4A1u2qxKgMC0MKERFRL4r5KyiKIrByaxmaPX7YzHGwGrSw6LWwGrSwmePQ7Alg5dYyKIq4+C8jIiKiiIj5gHKo2omyumYkGLSQpPD7TCRJgtWgQVldMzvJEhER9aKYDyj1Li98AQGt6vynQqeS4VME6l3eXl4ZERFR7Ir5gJJo0EKjkuANKOc97gko0MgSEg3aXl4ZERFR7Ir5gDIqw4whqSY0uHzn7STrcPkwJNXEPihERES9KOYDSlsfFJNOBbvTg1ZfAIoi0OoLwO70wKRTsQ8KERFRL4v5gAIE+6Asv6UAI9Pj4fL4Udfsgcvjx8j0eCy/pYBbjImIiHpZn/ZB+dOf/oTf/va3sNvtGDNmDP74xz9i4sSJfbKWyUOTUTg4CYeqnah3eZFo0GJUhplXToiIiPpAn11B+d///V8sXboUv/jFL/Dpp59izJgxmDFjBurq6vpqSZBlCQWZFlx1WQoKMi0MJ0RERH2kzwLK73//e9x3332YP38+8vPz8fzzz8NgMGD16tV9tSQiIiKKEn0SULxeL/bt24fp06d/sxBZxvTp01FSUtKh3uPxwOl0hn0RERHRwNUnAeX06dMIBAJIS0sLezwtLQ12u71D/YoVK2CxWEJfWVlZvbVUIiIi6gP9YhfPsmXL0NjYGPqqqqrq6yURERHRJdQnu3iSk5OhUqlQW1sb9nhtbS1sNluHep1OB51O11vLIyIioj7WJ1dQtFotxo8fjy1btoQeUxQFW7ZsQVFRUV8siYiIiKJIn/VBWbp0KebOnYsJEyZg4sSJeOaZZ9DS0oL58+f31ZKIiIgoSvRZQLnjjjtw6tQp/PznP4fdbsfYsWPx3nvvdbhxtjcpimCjNiIioiggifYT8voBp9MJi8WCxsZGmM2RGeJXfOw0Vm4tQ1ldM3wBAY1KwpBUExZdNYSt7omIiCKgK+/f/WIXz6VWfOw0Hlt/AEdqnDDq1EiN18GoU+NITRMeW38AxcdO9/USiYiIYkrMBxRFEVi5tQzNHj9s5jjEaVSQZQlxGhVsZh2aPQGs3FoGRel3F5qIiIj6rZgPKIeqnSira0aCQQtJCr/fRJIkWA0alNU141A1u9cSERH1lpgPKPUuL3wBAa3q/KdCp5LhUwTqXd5eXhkREVHsivmAkmjQQqOS4A0o5z3uCSjQyBISDdpeXhkREVHsivmAMirDjCGpJjS4fGi/oUkIAYfLhyGpJozKiMxuISIiIrq4mA8osixh0VVDYNKpYHd60OoLQFEEWn0B2J0emHQqLLpqCPuhEBER9aKYDygAMHloMpbfUoCR6fFwefyoa/bA5fFjZHo8lt9SwD4oREREvazPOslGm8lDk1E4OImdZImIiKIAA8o5ZFlCQaalr5dBREQU8/gRDxEREUUdBhQiIiKKOgwoREREFHUYUIiIiCjqMKAQERFR1GFAISIioqjDgEJERERRhwGFiIiIog4DChEREUWdftlJtm3qsNPp7OOVEBERUWe1vW+3vY9/m34ZUJqamgAAWVlZfbwSIiIi6qqmpiZYLN8+WkYSnYkxUUZRFFRXVyM+Ph6SFNlhfk6nE1lZWaiqqoLZbI7o745VPKeRx3MaeTynkcdzGnn9/ZwKIdDU1ISMjAzI8rffZdIvr6DIsozMzMxL+hxms7lf/sePZjynkcdzGnk8p5HHcxp5/fmcXuzKSRveJEtERERRhwGFiIiIog4DSjs6nQ6/+MUvoNPp+nopAwbPaeTxnEYez2nk8ZxGXiyd0355kywRERENbLyCQkRERFGHAYWIiIiiDgMKERERRZ2YCCiffPIJbrzxRmRkZECSJGzYsCHsuBACP//5z5Geng69Xo/p06fj6NGjYTX19fWYPXs2zGYzrFYrFi5ciObm5l58FdFlxYoVuPLKKxEfH4/U1FTcfPPNKC0tDatxu91YvHgxkpKSYDKZMGvWLNTW1obVVFZWYubMmTAYDEhNTcUjjzwCv9/fmy8laqxcuRKjR48O9TcoKirCpk2bQsd5PnvmySefhCRJeOihh0KP8Zx23S9/+UtIkhT2NWLEiNBxntPuOXnyJO6++24kJSVBr9ejoKAAe/fuDR2PyfcpEQPeffdd8X//7/8V//jHPwQAsX79+rDjTz75pLBYLGLDhg3i888/F//2b/8m8vLyRGtra6jm2muvFWPGjBE7d+4U27ZtE0OHDhV33XVXL7+S6DFjxgyxZs0acfDgQbF//35x/fXXi+zsbNHc3Byq+fGPfyyysrLEli1bxN69e0VhYaGYPHly6Ljf7xeXX365mD59uvjss8/Eu+++K5KTk8WyZcv64iX1ubfeeku888474quvvhKlpaXiscceExqNRhw8eFAIwfPZE7t37xa5ubli9OjR4sEHHww9znPadb/4xS/EqFGjRE1NTejr1KlToeM8p11XX18vcnJyxLx588SuXbvE119/Ld5//31x7NixUE0svk/FREA5V/uAoiiKsNls4re//W3oMYfDIXQ6nXjttdeEEEIcPnxYABB79uwJ1WzatElIkiROnjzZa2uPZnV1dQKA2Lp1qxAieA41Go148803QzVHjhwRAERJSYkQIhgcZVkWdrs9VLNy5UphNpuFx+Pp3RcQpRISEsSqVat4PnugqalJDBs2TGzevFlcddVVoYDCc9o9v/jFL8SYMWPOe4zntHseffRRMXXq1Asej9X3qZj4iOfblJeXw263Y/r06aHHLBYLJk2ahJKSEgBASUkJrFYrJkyYEKqZPn06ZFnGrl27en3N0aixsREAkJiYCADYt28ffD5f2HkdMWIEsrOzw85rQUEB0tLSQjUzZsyA0+nEoUOHenH10ScQCOD1119HS0sLioqKeD57YPHixZg5c2bYuQP4Z7Qnjh49ioyMDAwePBizZ89GZWUlAJ7T7nrrrbcwYcIE3H777UhNTcW4cePwwgsvhI7H6vtUzAcUu90OAGF/Wdq+bztmt9uRmpoadlytViMxMTFUE8sURcFDDz2EKVOm4PLLLwcQPGdarRZWqzWstv15Pd95bzsWiw4cOACTyQSdTocf//jHWL9+PfLz83k+u+n111/Hp59+ihUrVnQ4xnPaPZMmTcLatWvx3nvvYeXKlSgvL8e0adPQ1NTEc9pNX3/9NVauXIlhw4bh/fffx6JFi/Af//EfeOmllwDE7vtUvxwWSNFl8eLFOHjwILZv397XS+n3hg8fjv3796OxsRF/+9vfMHfuXGzdurWvl9UvVVVV4cEHH8TmzZsRFxfX18sZMK677rrQ/x49ejQmTZqEnJwcvPHGG9Dr9X24sv5LURRMmDABy5cvBwCMGzcOBw8exPPPP4+5c+f28er6TsxfQbHZbADQ4S7z2tra0DGbzYa6urqw436/H/X19aGaWLVkyRJs3LgRH330UdiEaZvNBq/XC4fDEVbf/rye77y3HYtFWq0WQ4cOxfjx47FixQqMGTMGzz77LM9nN+zbtw91dXW44ooroFaroVarsXXrVvzhD3+AWq1GWloaz2kEWK1WXHbZZTh27Bj/nHZTeno68vPzwx4bOXJk6KOzWH2fivmAkpeXB5vNhi1btoQeczqd2LVrF4qKigAARUVFcDgc2LdvX6jmww8/hKIomDRpUq+vORoIIbBkyRKsX78eH374IfLy8sKOjx8/HhqNJuy8lpaWorKyMuy8HjhwIOwv1ebNm2E2mzv8ZY1ViqLA4/HwfHbD1VdfjQMHDmD//v2hrwkTJmD27Nmh/81z2nPNzc0oKytDeno6/5x205QpUzq0afjqq6+Qk5MDIIbfp/r6Lt3e0NTUJD777DPx2WefCQDi97//vfjss8/E8ePHhRDB7VtWq1X885//FF988YW46aabzrt9a9y4cWLXrl1i+/btYtiwYf16+1ZPLVq0SFgsFvHxxx+HbTd0uVyhmh//+MciOztbfPjhh2Lv3r2iqKhIFBUVhY63bTe85pprxP79+8V7770nUlJSYna74U9/+lOxdetWUV5eLr744gvx05/+VEiSJP71r38JIXg+I+HcXTxC8Jx2x09+8hPx8ccfi/LycrFjxw4xffp0kZycLOrq6oQQPKfdsXv3bqFWq8VvfvMbcfToUbFu3TphMBjEX//611BNLL5PxURA+eijjwSADl9z584VQgS3cP3sZz8TaWlpQqfTiauvvlqUlpaG/Y4zZ86Iu+66S5hMJmE2m8X8+fNFU1NTH7ya6HC+8wlArFmzJlTT2toq/v3f/10kJCQIg8EgbrnlFlFTUxP2eyoqKsR1110n9Hq9SE5OFj/5yU+Ez+fr5VcTHRYsWCBycnKEVqsVKSkp4uqrrw6FEyF4PiOhfUDhOe26O+64Q6SnpwutVisGDRok7rjjjrB+HTyn3fP222+Lyy+/XOh0OjFixAjxl7/8Jex4LL5PcZoxERERRZ2YvweFiIiIog8DChEREUUdBhQiIiKKOgwoREREFHUYUIiIiCjqMKAQERFR1GFAISIioqjDgEJERERRhwGFiHrd2rVrYbVa+3oZRBTF2EmWiHpda2srmpqakJqa2umf+e53v4uxY8fimWeeuXQLI6Kooe7rBRBR7NHr9dDr9X29DCKKYvyIh4i67Lvf/S6WLFmCJUuWwGKxIDk5GT/72c/QdkG2oaEBc+bMQUJCAgwGA6677jocPXo09PPtP+L55S9/ibFjx+KVV15Bbm4uLBYL7rzzTjQ1NQEA5s2bh61bt+LZZ5+FJEmQJAkVFRUXXJ8QAkOHDsXvfve7sMf3798PSZJw7NixyJ0MIrokGFCIqFteeuklqNVq7N69G88++yx+//vfY9WqVQCCgWLv3r146623UFJSAiEErr/+evh8vgv+vrKyMmzYsAEbN27Exo0bsXXrVjz55JMAgGeffRZFRUW47777UFNTg5qaGmRlZV3wd0mShAULFmDNmjVhj69Zswbf+c53MHTo0AicASK6lBhQiKhbsrKy8PTTT2P48OGYPXs2HnjgATz99NM4evQo3nrrLaxatQrTpk3DmDFjsG7dOpw8eRIbNmy44O9TFAVr167F5ZdfjmnTpuGee+7Bli1bAAAWiwVarRYGgwE2mw02mw0qlepb1zdv3jyUlpZi9+7dAACfz4dXX30VCxYsiNg5IKJLhwGFiLqlsLAQkiSFvi8qKsLRo0dx+PBhqNVqTJo0KXQsKSkJw4cPx5EjRy74+3JzcxEfHx/6Pj09HXV1dd1eX0ZGBmbOnInVq1cDAN5++214PB7cfvvt3f6dRNR7GFCIKCpoNJqw7yVJgqIoPfqd9957L15//XW0trZizZo1uOOOO2AwGHr0O4modzCgEFG37Nq1K+z7nTt3YtiwYcjPz4ff7w87fubMGZSWliI/P7/bz6fVahEIBLr0M9dffz2MRiNWrlyJ9957jx/vEPUjDChE1C2VlZVYunQpSktL8dprr+GPf/wjHnzwQQwbNgw33XQT7rvvPmzfvh2ff/457r77bgwaNAg33XRTt58vNzcXu3btQkVFBU6fPt2pqysqlQrz5s3DsmXLMGzYMBQVFXX7+YmodzGgEFG3zJkzB62trZg4cSIWL16MBx98EPfffz+A4G6Z8ePH44YbbkBRURGEEHj33Xc7fIzTFf/5n/8JlUqF/Px8pKSkoLKyslM/t3DhQni9XsyfP7/bz01EvY+dZImoy/pTV9dt27bh6quvRlVVFdLS0vp6OUTUSewkS0QDksfjwalTp/DLX/4St99+O8MJUT/Dj3iIqF/68Y9/DJPJdN6vH//4x3jttdeQk5MDh8OBp556qq+XS0RdxI94iKhfqqurg9PpPO8xs9ncpUGERBR9GFCIiIgo6vAjHiIiIoo6DChEREQUdRhQiIiIKOowoBAREVHUYUAhIiKiqMOAQkRERFGHAYWIiIiiDgMKERERRZ3/DxUX8Lw37ByHAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.535485100159.121127(768, 100)
290768767.651288100141.949388(768, 100)
54100141.06513310098.515362(100, 100)
19810077.016393630588.210131(100, 630)
45314361440.84187210051.556799(1436, 100)
..................
16410088.722867365372.979942(100, 365)
16510089.014570365373.180964(100, 365)
19910078.555950630595.010237(100, 630)
13210082.467169365378.072813(100, 365)
50114361425.330135100101.271797(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.535485 100 159.121127 (768, 100)\n", + "290 768 767.651288 100 141.949388 (768, 100)\n", + "54 100 141.065133 100 98.515362 (100, 100)\n", + "198 100 77.016393 630 588.210131 (100, 630)\n", + "453 1436 1440.841872 100 51.556799 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 88.722867 365 372.979942 (100, 365)\n", + "165 100 89.014570 365 373.180964 (100, 365)\n", + "199 100 78.555950 630 595.010237 (100, 630)\n", + "132 100 82.467169 365 378.072813 (100, 365)\n", + "501 1436 1425.330135 100 101.271797 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.535485100159.121127(768, 100)
290768767.651288100141.949388(768, 100)
54100141.06513310098.515362(100, 100)
19810077.016393630588.210131(100, 630)
45314361440.84187210051.556799(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.535485 100 159.121127 (768, 100)\n", + "290 768 767.651288 100 141.949388 (768, 100)\n", + "54 100 141.065133 100 98.515362 (100, 100)\n", + "198 100 77.016393 630 588.210131 (100, 630)\n", + "453 1436 1440.841872 100 51.556799 (1436, 100)" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.225192\n", + "(100, 365) 0.409578\n", + "(100, 630) 0.674855\n", + "(768, 100) 0.920292\n", + "(768, 630) 1.254603\n", + "(1436, 100) 1.201001\n", + "(1436, 365) 1.517256\n", + "(1436, 630) 1.797223\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_24280\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_24280\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler\n", + " sc = StandardScaler()\n", + "\n", + " # Define the pipeline with polynomial features and Lasso\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.Lasso())\n", + "\n", + " # Define the parameter grid for GridSearchCV\n", + " param_grid = {\n", + " \"lasso__alpha\": [\n", + " 1e-15,\n", + " 1e-10,\n", + " 1e-8,\n", + " 1e-3,\n", + " 1e-2,\n", + " 1e-1,\n", + " 0.5,\n", + " 1,\n", + " 5,\n", + " 10,\n", + " 20,\n", + " 30,\n", + " 35,\n", + " 40,\n", + " 45,\n", + " 50,\n", + " 55,\n", + " 100,\n", + " ]\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " y_x = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, y_x, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X using GridSearchCV\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alpha for X: {grid_search.best_params_[\"lasso__alpha\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y using GridSearchCV\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alpha for Y: {grid_search.best_params_[\"lasso__alpha\"]}, R2 score : {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.022e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.344e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.883e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.207e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.153e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.009e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.240e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.458e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.241e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.186e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.338e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.154e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.437e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.159e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.064e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.277e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha for X: 1e-15, R2 score : 0.9983086290364169\n", + "-------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.632e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.723e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.023e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.972e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.028e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+03, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.398e+05, tolerance: 3.005e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha for Y: 1e-15, R2 score : 0.9768205711537207\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c30fb6db1e74cf707e41dc32a38830dc4418f9e4 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Fri, 5 Jul 2024 11:46:44 +0000 Subject: [PATCH 26/78] lasso CV grid search added --- .../test_lassoCV_regression_grid_search.ipynb | 2406 +++++++++++++++++ 1 file changed, 2406 insertions(+) create mode 100644 app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb new file mode 100644 index 00000000..c2f0b0a6 --- /dev/null +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb @@ -0,0 +1,2406 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAHwCAYAAABZvxc+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAADkv0lEQVR4nOzdd3hUVfoH8O+9UzNJJpVkEgi9E3oJsaBYQLCsZUWUJlUR/a1tl3V37WtZXfu6AoICAoruuq4FcBER3CWEXhKkt0AaqZNkMvXe3x+TmSSQkDY18/08Dw/PnJnMnCnvzH3vOec9gizLMoiIiIiIiIjII0R/d4CIiIiIiIioPWGiTURERERERORBTLSJiIiIiIiIPIiJNhEREREREZEHMdEmIiIiIiIi8iAm2kREREREREQexESbiIiIiIiIyIOYaBMRERERERF5EBPtZpJlGUajEbIs+7srRCGP8UgUOBiPRIGFMUkUGJhoN1NFRQWioqJQUVHh764QhTzGI1HgYDwSBRbGJFFgYKJNRERERERE5EFMtImIiIiIiIg8iIk2ERERERERkQf5NdHeunUrbr31ViQnJ0MQBHz11Vf1rr///vshCEK9fzfddFO925SUlGDKlCnQ6/WIjo7G7NmzUVlZWe82Bw4cwNVXXw2tVouUlBS89tpr3n5qREREREREFKL8mmhXVVVh8ODBeP/99xu9zU033YS8vDz3v08//bTe9VOmTEF2djY2btyIb7/9Flu3bsW8efPc1xuNRowbNw5dunTB7t278frrr+O5557DkiVLvPa8iIiIiIiIKHQp/fngEyZMwIQJEy57G41GA4PB0OB1v/zyCzZs2ICdO3dixIgRAID33nsPEydOxF//+lckJydj9erVsFqt+Oijj6BWqzFgwADs27cPb775Zr2EnIiIiIiIiMgTAn6N9k8//YSEhAT06dMH8+fPR3Fxsfu6jIwMREdHu5NsALjhhhsgiiIyMzPdtxkzZgzUarX7NuPHj8eRI0dQWlrquydCREQUwuwOCVa75O9uEBER+YRfR7SbctNNN+HOO+9Et27dcOLECfzhD3/AhAkTkJGRAYVCgfz8fCQkJNT7G6VSidjYWOTn5wMA8vPz0a1bt3q3SUxMdF8XExPT4GNbLBZYLBb3ZaPR6MmnRkQtwHgkChytiUdJkvG7fxxAicmKRVOHQ6tSeLOLRCGFv5FEgSmgR7QnT56M2267DQMHDsTtt9+Ob7/9Fjt37sRPP/3k9cd+5ZVXEBUV5f6XkpLi9cckooYxHokCR0vjUZZl/OnfWfhy73n8dOQCZn68E1UWu496S9T+8TeSKDAFdKJ9se7duyM+Ph7Hjx8HABgMBhQWFta7jd1uR0lJiXtdt8FgQEFBQb3buC43tvYbAJ566imUl5e7/+Xk5HjyqRBRCzAeiQJHS+PRLskoM1ndlzNOFmP6RztgNNu83VWikNCa38hqqwNrMs9ClmUf9JAoNAVVon3u3DkUFxcjKSkJAJCeno6ysjLs3r3bfZsff/wRkiQhLS3NfZutW7fCZqv9Qd+4cSP69OnT6LRxwFmETa/X1/tHRP7BeCQKHC2NR5VCxLuTh+LOoR3dbbvPlGLKh5korbJe5i+JqDlaGpMWuwMPrNqNP/zrIJ7/5hAkick2kTf4dY12ZWWle3QaAE6dOoV9+/YhNjYWsbGxeP7553HXXXfBYDDgxIkT+N3vfoeePXti/PjxAIB+/frhpptuwty5c7Fo0SLYbDY8/PDDmDx5MpKTkwEA9913H55//nnMnj0bCxcuRFZWFt555x289dZbHn0ukiQjO9eIEpMVsTo1BiTrIYqC+3q7XcI3B/JwvsyEjtE63DooCUql2ODfR4epAABl1Tb3fQHAwfPl2H2mBPtzylBYYUW4GiivsuFUsQlVVgdESUZVM+rMGCKU0KhV6BYXjqGdYzD/mh5Qq0NnvZzrtS6sNOPA2VLsOF2KsmobesXrICoEbDpUgApr4z86SgCd43UY2zsBtwxJxuBO0fXe62AkSTL25pTio59PYn1WARr7GGkA6LQKVJgdsAMQAIQpgC7xOvRKjESHSC0UoojESA3KzTYcLaiEKAi4rm8Cbh/Ssd5nPlg0FdutvZ9+hkhk5xmxN6cMsiRDH6ZCXLgaUToVjhdWYt+5MlRV22B3SNh9phgVFgkapYgkvQYl1TaUVFphdaDR98qlQ7gSs67ujit6dMDAjlFB/1l1sdsl/Ht/LnafLsbhgkpAkmCySYjRiTiUV4lyc+OvTFKkCjelGnDb0JSAj1+7XcIn20/ijf8cQ6X18u+2CEApAjIAtQh0ig3HvaNTcK7YjOx8I2QZMERoMKhzNIZ3ifXp58Fuk1BeWlyv7eD5cgx9ceMlt1UAWDZjGMb0MXgk1lobs0Tt1evrj2Dr0QsAgOXbTmP5ttON3nbayBj8elR/n31fMH6pPRFkP84Z+emnnzB27NhL2mfMmIEPPvgAt99+O/bu3YuysjIkJydj3LhxePHFF93FzACgpKQEDz/8ML755huIooi77roL7777LiIiIty3OXDgABYsWICdO3ciPj4ejzzyCBYuXNiivhqNRkRFRaG8vPySM4Xbjhfhgy0ncKKwEjaHDJVCQI+ECMy/pgeu6BmPD7eewPs/nUBFtQ0SnAdDkWEqLLi2B+aO6VHv76ssDlTbHBAEQKtSIFytQFyEGpUWO86WmODwQsFWUQDuHZmCl+4c5Pk7DzCu13p/TimMZodH7rNbvA4v3T4QV/SM98j9+dq240V4/PN9yDdamr5xG2hVIp64sTfmjunR5vu6XDx6UlOx3dr7kWQZZrsDVpsEuyTD9SUsAPDmF/LAjno8NaFf0H5WXT7cegJvbzqGKkvbYziQ4/fDrSfw0rrDXrlvhQj0T/LM56GpeJy9fAc2Hb7Q4vsVAKyek9amWGttzBIFs8vF5B+/PIDVO1q+/MoXvx+MX2pv/JpoB5PGvrS2HS/CH/51EJUWO2J0aqgVIqwOCaUmGyI0ClzZIw5rd52DQ5KhVAgQBUCSAbtDhkIUcM+ITvjfiWJUWuzQKEVcqLDAUTOFRxQERIWpUGyywhfv0pRR7TvZdr1XhUYzTDbPnrGI0anw/n3Dgu6HYNvxIsz7ZBcqPZCwNIcoAE9N6NvmZNsXiXZTsf3yHc1Lzi6+H6tDQk6JCf6aqZeo1+CtSUOC7rPq8uHWE3hl/WGPvn6BGL/eTLLr8sTn4XLx2Noku641zUy2PRWzRMGusZhsbZLt4s3fD8YvtUfBN48zgEiSjA+2nEClxQ6DXgutSgFRFKBVKWDQa1BhtrmTbLVSgFIUIQoilKIItVKAXZKxdtc5VJrtzqm21TY4ZOd6NpVShCTLKPFRkg0An+3MgdXqm4TL11zvlbHaimoPJ9kAUGay4f3Nx4NqnZMkyfjbj8d8lmQDzpNMf9t8AvYA30u3qdiutDjwwZYTTb7fF9+PRiWiqMLityQbAIorLfj7T033PRDZ7RL+tvm4x1+/UpMN728+FjCvid0u4a3/eD/JBrz7eTCb7W1OsgHg7Y2HWxxrrY1ZovbKanW0KckGgAKjBX//yfPHOoxfaq+YaLdBdq4RJworEaNTQxDqrx8RBGdibZdkKERAFOq/1M6E21mNVaEQYLHLsNglKEUBgiBAgABRFHx6QO6QgUVbT/ruAX3I9V6pFAqvTM2Vax4jOzd49q7MzjXiwHnf99dotuGbA3k+f9yWaCq2o3UqnCisbPL9vvh+zFYJFrt/T2bZJeBIfkVQfVZdvjmQh4pq72wLFUjx+82BPJh8tPuV5MXPw4vrfvHI/ew8U97iWKurJTFL1F556vjusBe+Lxi/1F4x0W6DEpMVNocMtaLhl1GqGYoW0HARB1erLMuwSxJkGY3c0nfOlFT5uQfe4XqvvDk7wOqQUGIKngq6ztfE9yPLsgycLzP5/HFboqnY1ihE2CS5yff74vuxS5JfR7Ndgu2z6nK+zNRk8bfWsjqafj99xZfxIcN7n4fTHvo9kYEWx9rFmhuzRO2Vp47vbF74rmT8UnvFRLsNYnVqqBQCrI0kK2LNWTm5kTFUdwGkmtFvQfBuIaTm6BIb7uceeIfrvRK8eCZDrRARq1N77wE8zPma+P4rQBCAjtE6nz9uSzQV2xaHBJUoNPl+X3w/zuUjHu9uiwXbZ9WlY7TOaz9aakXT76ev+DI+BHjv89DVQ78nAtDiWLtYc2OWqL3y1PGdygvflYxfaq+YaLfBgGQ9eiREoNRkw8U15Vyj1EpRgEMCJLn+l4ckS7BLqLlehkYpQKN0TjWXZRkyZEiS7NODcoUAPDimu+8e0Idc75XNIXll1oBQ8xiurdiCwYBkPQZ19H1/9VoVbh2U5PPHbYmmYrvMZEOPhIgm3++L70erFqFR+ncrPaUI9DFEBtVn1eXWQUmIDPPOrpSBFL+3DkqCzkebb4pe/Dw8PbGfR+5nZJeoFsdaXS2JWaL2ylPHd3298H3B+KX2iol2G4iigPnX9ECERoF8owXVNgckSUa1zYF8owWRWhXuGdEJClGA1S7XTBuVYJckWO0ylDVVxyM0ShRUWKEPU0EhADaHBJtdgig4z955cxS2rskjU9rtftqu90ofpkKYyvPPMVqnwoKxPYNqr0dRFPDwdb0QofHdey4KwMNjewT8ftpNxXaERoH51/Ro8v2++H7MNgnxkRq/jmrHR2jw0LVN9z0QKZUiHh7b0+OvX4xOhQVjewXMa6JUinhsXF+vP44A734etFolru/boc33M7Zv0/tpeypmidortVqBKaNS2nQfiXoNHrrW88c6jF9qr7i9VzM1ex9tSYZKbHwfbRnOg5tG99G2OlBt5T7a3sJ9tC/FfbQb11Rst/Z+JIn7aLcF99Fuu2DYR9tFpRDwt/uGYfwAQ5O39VTMEgUzb+yjrRQFvHxHKiaN7Oypbl6C8UvtDRPtZmrqQEKSZGTnGlFisiJWp8aAZH29M292u4RvDuThfJkJHaN1uHVQUr1Rvbp/Hx2mAgCUVdvc9wUAB8+XY/eZEuzPKUNhhRXhaqC8yoZTxSZUWR0QJRlVzUjEDRFKaNQqdIsLx9DOMZh/TY92O5LdENdrXVhpxoGzpdhxuhRl1Tb0itdBVAjYdKgAFdbGw0IJoHO8DmN7J+CWIckY3Ck66M+ySpKMvTml+Ojnk1ifVdBowSkNAJ1WgQqzA3Y4E8MwBdAlXodeiZHoEKmFQhSd29WZbThaUAlREHBd3wTcPqSjx0ayfZVoA03Hdmvvp58hEtl5RuzNKYMsydCHqRAXrkaUToXjhZXYd64MVdU22B0Sdp8pRoVFgkYpIkmvQUm1DSWVVlgdaLI4WIdwJWZd3R1X9OiAgR2jgv6z6mK3S/j3/lzsPl2MwwWVgCTBZJMQoxNxKK8S5ebGX5mkSBVuSjXgtqEpAR+/druET7afxBv/OYZK6+XfbRHOpQEyALUIdIoNx72jU3Cu2IzsfCNkGTBEaDCoczSGd4n12OehOfFoNtvx8Iqt+OFUdZP3pwDwu/G98O7mk6iq2XJSIQp4c9Jg/GpIxyb/3lMxSxSsmopJq9WBBR9swMYWbgASo1Phk9lpSO0Y5aGeXorxS+0JE+1m8uWBPRFdHuORKHB4Kx735ZRh+rJMGM3Ovc4EAfjLnYMwaWTbpr8StXeejMnPd+bg918ecO+YEalVYvnMkRjeJdYDPSVq3wJ7oSQRERGFpCEp0fh03mjEhjsrDcsy8Lt/HsDKjNP+7RhRCJk0MgXvTB4KZc2ocoXZjmnLduB/x4v83DOiwMdEm4iIiALSgOQorJ03GgmRGnfbM//OxuItJ/zYK6LQcuvgZCyaOty9z7XJ6sDM5Tvx4+ECP/eMKLAx0SYiIqKA1SsxEp8/kI6O0WHutlfWH8bbPxy9ZCsgIvKOG/on4qP7R7p3brHaJcxbuRvfHWjhQm+iEMJEm4iIiAJa1/hwrH1gNLrE6dxtb/9wDK+uP8xkm8hHruoVj5WzRyFCowQA2CUZj3y6B//Yfc7PPSMKTEy0iYiIKOB1itHh8wfS0TMhwt22eOtJPPd1NiSJyTaRL4zsGos1c9MQrXPukCPJwJNf7McnrJ1AdAkm2kRERBQUEvVafDZvNPol1VZSXpFxBk99eRAOJttEPjGoUzTWzktHfERt7YSnWTuB6BJMtImIiChoxEdo8OncNAzuVLuX79pdOXj8832wOZraWZ6IPKGPIRJfPJiO5Citu+2V9Yfx5kbWTiByYaJNREREQSVap8aqOWkY1bV2L99/78vFw2v2wGJ3+LFnRKGjW3w4Pn8wvV7thHc3HcPL635hsk0EJtpEREQUhCK1KiyfNRJX9Yx3t32fXYB5K3fDbGOyTeQLnWJ0+OKBdPSqUzvhw59P4U9fZbF2AoU8JtpEREQUlHRqJZbOGIHr+ya427YcvYD7P96BKovdjz0jCh0JNbUTBiTX1k5YnXkWT3yxH3Yu56AQxkSbiIiIgpZWpcAHU4dj4kCDu237yRJMW5YJo9nmx54RhY64CA3WzB2NYZ2j3W3/2nsej3y6F1Y7k20KTUy0iYiIKKiplSLenTwUdw7t6G7bc7YM9324HaVVVj/2jCh0RIWp8MnsNKR3j3O3rc/Kx7xPdnE5B4UkJtpEREQU9JQKEX+9ezDuHdXZ3ZZ13ojJS7bjQoXFjz0jCh3hGiU+njkS19VZzvHTEedyjkou56AQw0SbiIiI2gVRFPDyHamYdWU3d9uRggrcszgDeeXVfuwZUejQqhRY1MByjqlLM1Fu4nIOCh1+TbS3bt2KW2+9FcnJyRAEAV999ZX7OpvNhoULF2LgwIEIDw9HcnIypk+fjtzc3Hr30bVrVwiCUO/fq6++Wu82Bw4cwNVXXw2tVouUlBS89tprvnh6RERE5GOCIODpW/phwdge7raTRVWYtDgDOSUmP/aMKHS4lnPcNayTu21fThnu/XA7iis5w4RCg18T7aqqKgwePBjvv//+JdeZTCbs2bMHTz/9NPbs2YMvv/wSR44cwW233XbJbV944QXk5eW5/z3yyCPu64xGI8aNG4cuXbpg9+7deP311/Hcc89hyZIlXn1uRERE5B+CIOC34/viyXG93W05JdW4e1EGTlyo9GPPiEKHUiHi9V8PwtTRtcs5DuUZMWlxBvLLzX7sGZFvKP354BMmTMCECRMavC4qKgobN26s1/a3v/0No0aNwtmzZ9G5c23QRkZGwmAwXHwXAIDVq1fDarXio48+glqtxoABA7Bv3z68+eabmDdvnueeDBEREQWUh6/rBa1KgT9/9wsAIN9oxj2LM7BqThr6GvRN/DURtZUoCnjxV6nQqZVYsvUkAODEBecMk9Vz0pASq/NzD4m8J6jWaJeXl0MQBERHR9drf/XVVxEXF4ehQ4fi9ddfh91eW2whIyMDY8aMgVqtdreNHz8eR44cQWlpqa+6TkRERH4w5+ru+PPtqe7LRZVWTF6yHQfPlfuxV0ShQxAEPDWhLx67oXaGydkSE2eYULvn1xHtljCbzVi4cCHuvfde6PW1Z6H/7//+D8OGDUNsbCy2bduGp556Cnl5eXjzzTcBAPn5+ejWrVu9+0pMTHRfFxMT0+DjWSwWWCy1a0iMRqOnnxIRNRPjkShwBGM8Th3dBWEqBX77j/2QZKDMZMN9H27H8lkjMbxLrL+7R9QmwRCTgiDgNzf0gk6twEvr6s8w+WR2GvolcYYJtT9BMaJts9kwadIkyLKMDz74oN51jz/+OK699loMGjQIDz74IN544w2899579b5wWuOVV15BVFSU+19KSkqb7o+IWo/xSBQ4gjUe7xreCe/dOwxKUQAAVFjsmLZsB7adKPJzz4jaJphicu6YhmeY7M8p81+niLwk4BNtV5J95swZbNy4sd5odkPS0tJgt9tx+vRpAIDBYEBBQUG927guN7auGwCeeuoplJeXu//l5OS07YkQUasxHokCRzDH482DkrB42nCoFc7DH5PVgZkf78RPRwr93DOi1gu2mJw6ugveuHswas55obzahilLM7HjVIl/O0bkYQGdaLuS7GPHjuGHH35AXFxck3+zb98+iKKIhIQEAEB6ejq2bt0Km612376NGzeiT58+jU4bBwCNRgO9Xl/vHxH5B+ORKHAEezxe3y8Ry+4fAa3KeQhksUuYu3IXvs/O93PPiFonGGPyruGd8P59w6BSOLPtSosd0z/KxM/HLvi5Z0Se49dEu7KyEvv27cO+ffsAAKdOncK+fftw9uxZ2Gw2/PrXv8auXbuwevVqOBwO5OfnIz8/H1arFYCz0Nnbb7+N/fv34+TJk1i9ejUee+wxTJ061Z1E33fffVCr1Zg9ezays7Oxdu1avPPOO3j88cf99bSJiIjIj67u1QErZ6UhXK0AANgcMh5avQf/3nfezz0jCh0TBiZhybQR0Cid6YjZJmH28l34D096UTshyLIs++vBf/rpJ4wdO/aS9hkzZuC55567pIiZy+bNm3Httddiz549eOihh3D48GFYLBZ069YN06ZNw+OPPw6NRuO+/YEDB7BgwQLs3LkT8fHxeOSRR7Bw4cIW9dVoNCIqKgrl5eVBcaaQqD1jPBIFjmCOx71nSzHjox0wmp27lQgC8Jc7B2HSyMBd40rUlGCLyW0nijBnxS6YrA4AgEIU8OakwfjVkI5+7hlR2/g10Q4mwfalRdSeMR6JAkewx2N2bjmmLduBkiqru+2FXw3A9PSu/usUURsEY0zuqTnpVVHnpNcrdwzE5FGd/dwzotYL6DXaRERERN40IDkKa+eNRkJk7Uy4Z/6djcVbTvixV0ShZVjnGHw6dzRiw9UAAFkGfv/lQXz031N+7hlR6zHRJiIiopDWKzESax9IR3KU1t32yvrDeGvjUXDiH5FvpHa89KTXC98ewvubj/uxV0Stx0SbiIiIQl63+HB8/mA6usTp3G3vbDqGVzccZrJN5CO9EiPxxYPp6Bgd5m57/fsjeI1xSEGIiTYRERERgE4xOnz+QDp6JkS42xZvOYnnvs6GJPEgn8gXusSF44sH09E9Ptzd9vefTuD5bw4xDimoMNEmIiIiqpGo12LtvNHol1RbRGpFxhk89eVBOHiQT+QTydFhWPtAOvokRrrblm87jd9/eYBxSEGDiTYRERFRHXERGnw2dzQGp0S729buysFja/fB5pD81zGiENIhUoO1D4zG4E5R7rbPd53Do4xDChJMtImIiIguEqVTYdXsURjZNcbd9vX+XDy8Zg8sdocfe0YUOqJ1aqyak1YvDr/Zn4v5q/bAbGMcUmBjok1ERETUgEitCitmjcJVPePdbd9nF+CBT3bzIJ/IR1xxeHWv2jj84ZcCzF25Cyar3Y89I7o8JtpEREREjdCplVg6YwSu75vgbvvpyAXM/Hgnqiw8yCfyBVcc3tg/0d3287Ei3P/RTlSYbX7sGVHjmGgTERERXYZWpcAHU4fj5oFJ7raMk8WY/tEOGHmQT+QTGqUCf58yDLcNTna37ThdgilLM1FmsvqxZ0QNY6JNRERE1AS1UsQ7k4fgzqEd3W27z5RiyoeZKK3iQT6RL6gUIt66ZwjuGZHibjtwrhyTl2zHhQqLH3tGdCkm2kRERETNoFSI+Ovdg3FfWmd328Hz5bj3Qx7kE/mKQhTwyp0Dcf8VXd1th/MrMGlxBnLLqv3XMaKLMNEmIiIiaiZRFPDS7amYdWU3d9vh/ArcszgDeeU8yCfyBVEU8Oyt/bFgbA9326miKty9KANniqv82DOiWky0iYiIiFpAEAQ8fUu/egf5J4uqMGlxBnJKTH7sGVHoEAQBvx3fF78d38fddr6sGncvysCxggo/9ozIiYk2ERERUQu5DvKfHNfb3ZZT4jzIP3Gh0o89IwotC8b2xLO39ndfLqyw4J4l25GdW+7HXhEx0SYiImr3ZFlGpcXOCtle8PB1vfD0LbUH+flGM+5ZnIHD+UY/9oootMy8shv+ctdACILzckmVFfcu2Y49Z0v92zEKaUy0iYiI2imHJKO0yoqckmoUGs2w2iV/d6ldmn1VN7x0R6r7IL+o0orJS7bj4DmOqBH5yj0jO+Pte4ZAIToD0Wi2Y+rSTGw7UeTnnlGoYqJNRETUztgcEi5UWHC2xIRSkxV2iQm2t01J64I37h6MmmN8lJlsuO/D7dh9psS/HSMKIb8a0hEfTBkGtcKZ4pisDsz8eCc2Hy70c88oFDHRJiIiaicsdgcKjWbklJhQYbZBlmV/dymk3DmsE967dxiUNdl2hcWOact2YNtxjqgR+cq4AQYsnTECWpUzzbHYJcz7ZBfWH8zzc88o1DDRJiIiCnJmmwP55WacL61GpcXu7+6EtJsHJWHR1OH1R9SW78TmIxxRI/KVMb07YOWsNERolAAAm0PGgjV78OWec37uGYUSJtpERERBymS1I7esGrll1TBZmWAHihv6J2LZ/ReNqK3chQ1Z+X7uGVHoGNUtFqvnpCEqTAUAkGTg8c/3Y9X2M37uGYUKJtpERERBRJZlGM025JSYkF9uhtnm8HeXqAFX92p4RO3f+877uWdEoWNwSjQ+mzca8REad9ufvsrCh1tP+rFXFCqYaBMREQUBWZZRXm1DTkk1iiossDlaXuDMapdwttjkhd5RQ0Z1i8WqOWnQa53JtkOS8ejaffh8Z46fe0YUOvol6fH5A6ORFKV1t7207he8/cNR1rEgr/Jror1161bceuutSE5OhiAI+Oqrr+pdL8synnnmGSQlJSEsLAw33HADjh07Vu82JSUlmDJlCvR6PaKjozF79mxUVlbWu82BAwdw9dVXQ6vVIiUlBa+99pq3nxoREZFHuLboOltiQnGlpVUVxE9cqMTfNh/HhHd+xsOf7vFCL6kxQ1Ki8em80YgNVwMAZBn43T8PYMW20/7tGFEI6d4hAp8/kI7OsTp329s/HMPL635hsk1e49dEu6qqCoMHD8b777/f4PWvvfYa3n33XSxatAiZmZkIDw/H+PHjYTab3beZMmUKsrOzsXHjRnz77bfYunUr5s2b577eaDRi3Lhx6NKlC3bv3o3XX38dzz33HJYsWeL150dERNRaZpsDhRVm9xZdDqllB4OVFju+3p+L+av2YO7K3fhyz3mUV9tw4Fw5fskzeqnX1JAByVFYO280EiJrp68++3U2Fm054cdeEYWWlFgdvngwHT0TItxtH/58Cn/6KgtSC79fiZpDkAPkNI4gCPjXv/6F22+/HYBzNDs5ORlPPPEEnnzySQBAeXk5EhMTsXz5ckyePBm//PIL+vfvj507d2LEiBEAgA0bNmDixIk4d+4ckpOT8cEHH+CPf/wj8vPzoVY7zyb//ve/x1dffYXDhw83u39GoxFRUVEoLy+HXq/37JMnohZhPFJ7JcsyqqwOlFfbYGnF2mtJlnHgXDnWHczD1mNFsNovHf3WqkS8cudA3DG0kye6zHhsgdNFVZiyNBPny6rdbb+5vhcevaEXBEHwY8+oPWFMXl5xpQXTlu3AoTonHO8c2hGv/XoQlAquqiXPCdhP06lTp5Cfn48bbrjB3RYVFYW0tDRkZGQAADIyMhAdHe1OsgHghhtugCiKyMzMdN9mzJgx7iQbAMaPH48jR46gtLTUR8+GiIiocQ5JRpnJipySahQazS1Osi9UWLBq+xlM/2gHHv98P374pfCSJLuPIRJPTeiLHX+8wWNJNrVM1/hwrH1gNLrE1U5ffWfTMby6/jCnrxL5SFyEBp/OHY2hnaPdbV/uPY//+2xvgycniVpL6e8ONCY/37kFRmJiYr32xMRE93X5+flISEiod71SqURsbGy923Tr1u2S+3BdFxMT0+DjWywWWCwW92WjkdPsiPyF8UjtldUuobzahkqLvcWJls0hIeNEMdZl5WPX6RI0NPNRr1Xixv6JmJBqQPcOEdCHqaDXqtrUZ8Zj23SK0eHzB9IxZWkmjhc6a8os3noS1TYHnrt1AESRI9vUMozJlovSqfDJ7DTMWbET20+WAADWHcxHtXUXPpg6HFqVws89pPYgYEe0/e2VV15BVFSU+19KSoq/u0QUshiP1N6YbQ4UGM04V2pChdnWoiT7VFEV3t98HJMWb8dz3xzCjlP1k2wBwKiuMXjmlv74/IF0LBjbE907RDR6fy3FeGy7RL0Wa+eNRr+k2mm9KzPOYOE/D7R4LT4RY7J1IjRKLJ85Ctf26eBu23zkAmZ+vBNVFrsfe0btRcAm2gaDAQBQUFBQr72goMB9ncFgQGFhYb3r7XY7SkpK6t2mofuo+xgNeeqpp1BeXu7+l5PDrTiI/IXxSO1FlcWO82XVyC2rbtGBXKXFjm8P5OKh1Xswe8Uu/LOmsFldSVFazLyyKz6dm4ZX7xqEa/t0gFrp+Z95xqNnxEVo8Nnc0RicEu1u+2L3OTy6dl+rtm6j0MWYbD2tSoEl00ZgQmptTpBxshjTlmVe8h1L1FIBO3W8W7duMBgM2LRpE4YMGQLAORUmMzMT8+fPBwCkp6ejrKwMu3fvxvDhwwEAP/74IyRJQlpamvs2f/zjH2Gz2aBSOafLbdy4EX369Gl02jgAaDQaaDSaRq8nIt9hPFIwk2UZlRY7yky2FiVQsquwWVY+th69AEsDawfVShFjesVjQqoBg1OiIfqgoBbj0XOidCqsmj0Ks5fvwo7Tzumr3+zPhcXmwHv3DYVGyemr1DTGZNuolSLeu3cofvePA/hy73kAwJ6zZbjvw+1YOWsU4iL42lLr+LXqeGVlJY4fPw4AGDp0KN58802MHTsWsbGx6Ny5M/7yl7/g1VdfxYoVK9CtWzc8/fTTOHDgAA4dOgSt1rnp/IQJE1BQUIBFixbBZrNh5syZGDFiBNasWQPAWam8T58+GDduHBYuXIisrCzMmjULb731Vr1twJrCCo5EgYPxSMHAIcmoMNtgrLa3aO/rokoLvs/Ox/qsfOSWmRu8TZ/ESNyUasD1fRMQoW3+OXN9mArxHj5oZDy2nclqxwOf7MbPx4rcbdf07oDF07hWlFqOMdk6kiTj6X9nYXXmWXdbr4QIrJqThkS91o89o2Dl10T7p59+wtixYy9pnzFjBpYvXw5ZlvHss89iyZIlKCsrw1VXXYW///3v6N27t/u2JSUlePjhh/HNN99AFEXcddddePfddxERUbse7cCBA1iwYAF27tyJ+Ph4PPLII1i4cGGL+sovLaLAwXikQNaaAmc2h4SMk8XYkJV/yZprF71WiRv6JWLCQAN6tHLNNRPtwGW2OfDwmj344ZfaJXHp3eOwdMYIhGsCdgIiBSDGZOvJsoyX1/2CD38+5W7rHKvD6jlpSInVXeYviS4VMPtoBzp+aREFDsYjBSKT1Q5jtR0ma/PXXp8ursL6g/nYeKgAZQ2sBxQADO8Sg4kDDbiiR3yb11wz0Q5sNoeERz/bh+8O5rnbhnWOxsczRyEqrG3V4il0MCbbRpZlvP3DMbyz6Zi7LTlKi1Vz0jxaWJLaP4+fIjWZTNDpeMaHiIjaP1mWUWGxo7wF66+rLHZsPnIBG7LycCivosHbGPRa3JSaiPEDDB6dssiNowKbSiHinclDoFGK9daKTlm6HZ/MSkNMuNrPPSRq/wRBwGM39ka4RoGX1x0GAOSWmzFp8XasmjMKfQ08eUHN06pE+/rrr8fKlSvRsWPHeu07duzA1KlTcfToUY90joiIKBDZHRKMZjsqzLZmbcckyzIOni/H+qx8bDlyAeYGCpupFALG9OqACQMNGOLBwmZqpYhwtRJhagXX+wYBpULEX+8eDK1agTU1a0Wzzhsxecl2fDJnFBIiuVaUyBfmjemBMLUST3+VBcBZP2Pyku1YMXNUvd0CiBrTqkRbq9Vi0KBB+Pvf/4577rkHkiThhRdewMsvv4yHHnrI030kIiIKCBa7A+XVNlRZHM1af11cacH32QXYkJ2Pc6XVDd6mV0IEJg404Lq+CYjUemZ6sFalQLhaCZ1GAZUiYHfypEaIooCXbk+FVqnAR/9zrhU9UlCByYu3Y/XcNCRFhfm5h0ShYdroLghTKfC7f+yHJANlJhumLM3ExzNHYmTXWH93jwJcqxLt7777Du+//z5mzZqFf//73zh9+jTOnDmDb7/9FuPGjfN0H4koyDgkGRa7AxabhHCN0it7+RL5ktnmSrCbXn9td0jYfrIE67LyGi1sFllT2GxiqgE9Etq+5k8QBOjUipp/SihEThIPdoIg4Olb+iFMLeL9zScAACeLqjBpcQbWzBnNwkxEPvLr4Z2gUyvwf5/uhV1ybtc4bVkmPpw+Alf36uDv7lEAa/Ua7QULFuDcuXP4y1/+AqVSiZ9++glXXHGFJ/tGREHAYnfAYpdgtUuwOSTY7HK9rYzC1JyqSsFJlmVUWR0wVttgtjmavP2Z4iqsz3IWNis1NV7YbEKqAVf2bHthM4UoIKwmsdapFBCZXLc7giDgt+P7QqdW4vXvjwAAckqqcfeiDKyem9bq6vNE1DITByZBqxLx4Ko9sNolmG0SZi/fhfenDMON/RP93T0KUK1KtEtLSzFnzhxs2rQJixcvxpYtWzBu3Di89tprnDpO1I7JsgyLXYLZ5oDZ5vxf4sYF1M5IkowKsx3l1bYm9782We346cgFrDuYj0N5xgZvkxCpwYRUA8anGmBoY2EzjUoBnUrB9dYhZsHYntCqFHjx20MAgHyjGfcszsCqOWkszETkI9f1TcTymSMxZ8UumKwOWB0SHly1G2/dMwS3DU72d/coALVqe6+OHTuiW7du+OSTT9CtWzcAwNq1a/HQQw9h9OjR+O677zzeUX/jVgkUilxTwF1JtcUuNXtfYJfk6DCPJwSMR/KG5hY4k2UZWeeNWJeVhy1HL8Bsa7iw2VU94zEh1YBhXWLaVNhMq1IgXKNEuFoBZQCut2Y8+s6azLP441cH4foajtap8MmsNAzsFOXfjlFAYUx61+4zJbj/452oMDuXEgkC8Jc7B2HSyBQ/94wCTatGtB988EH88Y9/hCjW/uDfc889uPLKKzFz5kyPdY6IfMfukGB1OKeA150KTtTemW3O6eFV1ssXOCuutOA/hwqwPqvxwmY9O0TgplQDbuiXAH0r9z0WBAFalQidOnCTa/KP+9I6Q6sS8eQXtYWZ7vtwO5bPGonhXViYicgXhneJxadzR2P6RztQUmWFLAO/++cBmKx23H9lN393jwJIq0a0m+uhhx7CCy+8gPj4eG89hM/w7CC1Jxevq7bapWZtUdQaHNGmQCTLzoI2RrMdlsusv7Y7JGSeKsG6g/nIPFXcYGGzCI0S1/dLwIRUA3onRraqPyqFCJ3aOSU8TKWA4KGtvXyB8eh73x3Iw28+cxZmAgCdWoGlM0bgih7Bf7xFbceY9I1jBRWYsjQThRUWd9tvx/fBgrE9/dgrCiReTbT1ej327duH7t27e+shfIZfWhSsrPbakWqzzQGrXfLpumom2hRImjs9/GyxCRuy8/F9dn6Dhc0AYFjnaExITcJVPeOgacVnXKuqrRIezJX5GY/+8cOhAjy0eg+sNTOPNEoRi6cNx7V9EvzcM/I3xqTvnC6qwpSlmThfVjvLacHYHnhyXJ+gOmFK3tHqquPN4cUcnogaUHdNtcXu8OpINVEwMVntqDDbL7s9V7XVgZ+OFGJdVj6ycxsvbHbTAANuSjXAENWywmauLbjC1M49rrkFF7XFDf0T8dH9IzFn5c6a73wJc1fuwnv3DsNNqQZ/d48oJHSND8fnD6Zj6tJMnCqqAgC8v/kETFYHnrmlP5PtEOfVRJuIvEeSnBXAXdPALTapyQrJ/mBzOPsWpWvdelWi1rI7JFSYnQl2Y7EhyzKyc41Yn5WPzUcKGy1sdmWPeEwYaMCwzjEtSpAVouBca60JvinhFPiu6hWPlbPSMPPjHaiyOmBzyFiwZg/enDQYvxrS0d/dIwoJHaPDsPaB0Zi2dAeOFFQAAD7+32lUWx146Y6BPKkawphoEwUB1/RvW51p4IFSqMwhybhQYUG+0Yz88pp/RrP7clGlBeMHGPDB1OH+7iqFCLPNgfJq22VHr0uqrPjPoQJsyMrH2RJTg7fp3iEcE1MNuL5fIqJaUNhMKYoI1zgrhXMLLvK2Ud1isXruaExflgmj2Q6HJOPRtftgsUmsgkzkIwmRWnw2z1kg7eD5cgDAZztzYLI68MakwVCxqGVIYqJNFEBsjvoVv53/ZL8uw3BIMoorLcgzmlHgSqLLLcg3ViOv3IwLFZYGC0TVlVPacCJD5CmyLKPCYoex2garveGTUA5JxvaTxdiQlY+Mkw0XNgvXKHB930RMHGhAr4SIZo9AqxQiwjVK6Li/NfnBkJRofDpvNKYtq18FudrmwIwruvq7e0QhISZcjdVz0zB7+U7sPF0KAPh6fy7MNgfeu28oNEr+NoQaJtpEfmJ3SDDXFCjzduXvy5FkGcWVVhTUGYV2jUrnlZtRWGFpc7/ON7IVElFbmW0O99rrxor85ZSYsD4rH/85VICSKmuDtxnaORoTUg24umd8swubceSaAsmA5Ch8/sBo3PdhbRXkZ7/OhtnmwAPX9PBz74hCg16rwopZozBv5W7893gRAOA/hwowZ8UuLJk2AmFq/laEklYl2mfPnkVKSsolZ/plWUZOTg46d+4MAJg6dSqrHVLIk2UZVocEu0N2J9QWH079lmUZpSYb8sqrkV9ucSfUeeVmFBid/2yOtif4seFqGPRaJOo1SIrSwhClhUGvxeCUaHSND/fAMyFyckgyKs12VFgaH72utjrw09EL2JCVh4PnGy5s1iFCg/GpibhpgAHJ0WHNemyFKCBco0QEk2sKQD0TIvH5A+n1qiC/sv4wqm0O/Ob6XqwRQOQDOrUSS2eMwMNr9uCHXwoBAD8fK8KMj3Zg2f0jEKllzZpQ0artvRQKBfLy8pCQUH8LieLiYiQkJMDhaHxP0mDFrRKoKQ5Jhl1yjkpb7b5LqGVZRlm1Dfk1iXNezWh0QXlNMl1haTQZaYnoMJU7eTbUJNJJUVokRjqT68ZGAbm9F3mKyWpHpdmOKqujweUUsizjUF5NYbPDF1DdwP7YSlHAFT3jMDE1CcO7NK+wmWuPa45cN4zxGHjOlZowZWkmzhTXLtt5YEx3/H5CXybbIYAxGRhsDgmPrd2Hbw/kudsGd4rCilmjEK1T+7Fn5CutGtGWZbnBL+rKykpotS3b7oQoWLhGpm0OGTa7BFtNUm13yLBL3ltHLcsyjNX2egXG6k7xLjCaYfZAIq3XKpEUFYZEveaShDpRr0UYEwzyA6tdQqXFmWA3Vjm8pMqKH34pwPqD+TjTSGGzbvHhmJBqwI39EptVAV8QBIRrFNBrVUyuKeh0itG5R7aPF1YCABZvPYlqmwPP3ToAIqsgE3mdSiHinclDEaZS4Ivd5wAA+8+VY/KS7fhkdho6RGr83EPythYl2o8//jgA5wHI008/DZ1O577O4XAgMzMTQ4YM8WgHiXxFkmTYJOcUb7tDdifSNofzf2+tn5ZlGZUWe00CbUF+eTXyjRbklVejwGhBfrm5wZG5lorQKN0JdFJN8uz835lY69Qs2UCBQZJkVNaMXpsb+ew7JBk7TpVgXVYetp8saTA+w9UKXNcvARNSDeiTGNnkSJ5r5FqnVkKrEjnyR0EtUa/F2nmjMXXZDvyS51w+sTLjDKqtDrx61yBuOUTkAwpRwF/uGoRwjRLLt50GABzOr8A9izOwak5as5ctUXBq0ZH13r17ATgTg4MHD0Ktrp32oFarMXjwYDz55JOe7SGRB8iyc9TZOb3b+f/FibU396CucifSddZHl5vdlbyrrG1PpMPVCiRGaZGk1yKxTkJtqLkcoWEiTYGt2upAhcWGKkvDU8MB55TYDVn5+P5QAYorGy5sNiQlChNSk3B1r/gmR6OVoogIrXOfa1aEpfYmLkKDz+aOxvSPd2B/ThkA4Ivd52C2S3iTWw4R+YQoCnj21v4IUyvwwU8nAAAni6owaXEG1swZjc5xuibugYJVq9Zoz5w5E++8805IrfsIxPUukiQjO9eIEpMVsTo1BiTr600Ha+h6AMjONaKoyoKyKhtidCrEhKshyTL25pQht9SEfKMZh86Vo9BYjcrGt6FFQoQKOo0KQzrqMe3KbhiaEuPz6WiS5JrO7Rx1lmRnUu2QZfe0bme7d6t5V1sd7uncriJjdQuOVZgv80I2k1Yluqd2h6kU0KkVSI4Og8lqx/6ccpjMNoRplEiJ1uKXvDLklNkgAQhTCriyZywi1CpsO1mM0kobbA3cf98EHWJ0apwpNaHK7IBep8KoLrHokRiJWJ0a0To1eiaGQ2zhKF8ortFuLDYvF5MlJiuiw1SQZBn7z5VDlmREaJWoqLYht6waFyotMJltOFNqQk6xCdWNnJsRAKR1i8GT4/pgWJfYgJ8ianNIzsJml5kaXm1z4OejF7AuKx8HzpU3eJv4CDXGDzDgplQDOjYxQqAQBejUSkRqm15z7XrPiqosKDBWY/2BPOSWW5AcpcF1fTvg+4O52HPOCIsdUIpAXLgSXePCcSi3Akbrpc9HASBMrYBeq0BMuBoapQJhaiVuG5KMu4Z2glIZfIlPoMcjARVmG2Yv34Udp0vcbeP6J152yyFJkvHhf7Pwyrqzl73vaDXw02+vR3Qklw4GCsZk4Hp/83G8/v0R9+VEvQar54xGz4QId5skyVj001689p+8hu4CAoApaR1x1/AuGNwpOuB/50NZqxLtUBRoX1rbjhfhgy0ncKKwEjaHDJVCQI+ECMy/pgeu6Bnf4PVxEc4ZCLll1TCa7ZAk2RmtMuCBotMw6DV4c9IQXNEzvu13htqp3M7p2zLsDgn2mhFpqSZ59tV2WNU2hzN5diXR7tFo5xRvowcSaY1SvGRdtGtE2qDXQh+mxL6cMqzZkYOc4ipUmO2o9sDa7OYQAESFqdAjIQL3jUrB0M4xzf7bUEu0G4vNMb3isfVYUYMxWVxpRZXFgSqrHQ5Jhqc+1TE6Fd6/b5jHYtJTmjM1XJZlHM6vwPqsfPx4uBCmBmZ9KEUBV/SIw4SBBozoEnvZqbCCICBMpUCk1rnXdXOmhbvey0O55SipsnnsfWmMSiHgd+P7YO6Y4NqKKZDjkWqZrHY88Mlu/HysyN12Te8OWDxt+CXf0duOF+G+pZktuv+4cBV2Pz3OI32ltmFMBrZl/z2FF7895L4cF67GytmjMCA5qsWx1y1eh5duHxhwv/Pk1KpEu6qqCq+++io2bdqEwsJCSBeNQpw8edJjHQwUgfSlte14Ef7wr4OotNgRo1NDrRBhdUgoNdkQoVFgSlpnrM48W+/6smor8srNkGXUnPlyvu2eLogdoVFgybQRjQZ83SncsgznyHPNtG1Jrl+525d7SltsDhRUWGqrdtep4F1gNKPU1NAYcMuoFELt+uiL1koborSIDlNd9uB/79lSvLnxqDPhkGWUVrc9uW8JAUC4RoGoMBUev7F3s5PtUEq0G4vNAqMFJqsd4RoFEiK19WISAGJ1apRVW+GN8yZhKhHLZowMiB/haqsDFWZbo1XDAaDMZMXGQwVYn5WP08UNFzbrGqfDhIFJuLFfQpOVW1UKEXqtChFaZYvWpLrey5IqKyrMdq8n2S4CgD9M7BtUyXagxiNdymxz1NtyCABGd4/FshkjEV6zvKg1SbYLk+3AwJgMfJ/tOIun/nUQrp9CvVaJJ8b1xrNfH7r8HzYgUE+qUyurjs+ZMwdbtmzBtGnTkJSU5NWCMV27dsWZM2cuaX/ooYfw/vvv49prr8WWLVvqXffAAw9g0aJF7stnz57F/PnzsXnzZkRERGDGjBl45ZVXoFQG35pVSZLxwZYTqLTYYdBr3a+9VlTAoBeRV27G+z+dgFIU3NfLsozyameiKMOZzKoVgAfqa12i0uLAWxsPo2fCCABwT+GWJECSvT+FuzFWu4TCiosqdtcpPFZS1fBaz5ZQKQQkRNbZ9kqvgUEf5t5XOiZc3eJp1y6SLGPNjhyYrA7E6lQ4UdRwAuJNMgC7Q4LJ6sCaHTkYnBLd6ufTHjUWmxpBhKNOhXqNSgRkoLzaBkEAZMlZNdtbcxOqbRL+9uMxjO4e55fpZWabA1UWO6osjkanhjskGTtPl2B9Vj62nShu8CSbTq3A9X0TcFOqAX0Nly9sphRF6DSKVu917XovK8w22B2Sz5JswBln7206hplXdAvKaeQU2LQqBT6YOhyPrt2H72q2HNp+sgTTlmVi+axRiFAr8fy3+1t9/8VVNpRVmDmNnKgJk0d1Rphagcc/3w+HJMNotuO5ViTZAFBqsuH9zf77nafGtSrTXL9+Pb777jtceeWVnu7PJXbu3FlvX+6srCzceOONuPvuu91tc+fOxQsvvOC+fHE19JtvvhkGgwHbtm1DXl4epk+fDpVKhZdfftnr/fe07FwjThRWIkanvuRAUxAEhKkVyCurRnJ0mPt6s825n7NCFCDVzBGXIcBzE1Qv7mMF9pwpQ29DRNM39hC7Q0JhhaXB7a/yjGaUVFrb/GwVooCESGfS7Krenaiv3QYrLqL1iXRTjhdUIae4CnqtCpUW/+1Tb7XLiNaJyCmpwpkiE/ol6yEKAkTB+fkTBUAUBPdrrRAEKEPkS7+x2DTbJFgdEpQKAVaHBHPNul2LXYJSFCEJzqUR3rQvpxzZuUYM7BTl1cdxsdgdzqnwFvtl95E/X1btLGyWnY+iRgqbDeoUhYmpBozp3eGySbMoCAjXNG/ddVNc76VOrUSJB2aztJTR4sA3B/Jwx7COPn9sav9UChHv3DMEGqWIL/ecBwDsOVuG+z7cjqdu6ocj+eY23f+vl2Tihyeu8URXidq1Xw3pCK1KgUfW7IW1jSd1s3ONPv2dp+ZpVaIdExOD2NhYT/elQR06dKh3+dVXX0WPHj1wzTW1X+I6nQ4Gg6HBv//Pf/6DQ4cO4YcffkBiYiKGDBmCF198EQsXLsRzzz1Xr3J6IHKtRXYN8BRWmGG1S4gKE5yjwzXtruAUatZbS7LsPsA125zrsYWL7tdbbJKMcnPbR4jrckgyLtQk0nUrdrumeBdVWtDWpyQKcI9Iu0ahXUl0ol6L+AiN37ZDKTdbYZNk6BUCKi2+WZfdEBmAWimi2iZBqRS5LUUdJSYrbA4Z6ouq+NolCbIMKETnUg3XqK4sA76aEGBzSCgxeTYmL+YauTZZHZdNrs02B7YeK8KGrDzsy2m4sFlchBo3DTBg/IBEdIq5fDXWMLVz5DpCo/TY7CrXe6lSiPDpcHYd58t8P2uFQodSIeKvvx4MrUqBNZnOYmdZ541Y+OWBNt/3hYq2JepEoWT8AAM+nDECc1fsgrUN6zmtDtnrv/PUcq1KtF988UU888wzWLFiRb3RY2+zWq1YtWoVHn/88XoHVKtXr8aqVatgMBhw66231tvjOyMjAwMHDkRiYqL79uPHj8f8+fORnZ2NoUOHNvhYFosFFovFfdloNDarj3JNUuwq1CXVuSxLgIzay5LsXKfszJdrK2Zfbpq1zS5DFACT1QFNA9MKrZIEEXBvXwUAoiDWHtDX3KVQu0zb41SigChty05gOCQZRZWWeiPR+cbaddIXKjyTSMdHaNyj0a4E2qDXICkqDB0i/ZdIN8Q5UixAqOm3WiHWTPtXAA3WDfdFn5yfV5UoILaJtbGe1Np49KVYnRqqmlFrrVg7oqoUnfEn1STWStEZt4LguxxOpRC98n6ZbQ5UWuwwXWZaOOD8XjtSUIH1B52FzRrazk7hKmyWasDIrpcvbKZRKRCuViBco/TK9kSu91KSawtG+lrH6MDd7iUY4pGaJooCXro9FWEqBZb99xQA4FxpdZvvtwOnjfscYzK4XdO7A56/bQCe+tfBVt+HWuHb4zJqnlYl2m+88QZOnDiBxMREdO3aFSqVqt71e/bs8UjnLvbVV1+hrKwM999/v7vtvvvuQ5cuXZCcnIwDBw5g4cKFOHLkCL788ksAQH5+fr0kG4D7cn5+fqOP9corr+D5559vcR+LKq2oMHsvCeqZGI6UuHCcvFCJ+Ag1hDrj1DJkWGwSwrVKWOwSZMgQIECjEqBWOEchXbyZTvZJjEDPxPB6bZIso7jSWm8v6bpTvAsrLG0ufiYAiI1QI6lO5W53FW+9FgmRGij9tGeoUDOFWlHnn1IUILr+FwT3FGxREC5ZY9MxOgy9DZH4Ja8CCREq5PnpN1SjFFFtc6Bfkt69NZUvtDYefWlAsh49EiLwS14FDHqxtn6CSoRaIcJkdUCnVkCrdo6Sul5LWQJEwGtrtAHnvtKeer+qrc7q6E0l1wBQbrJh4y8F2JCVj5NFVQ3epkucDhNSDbixfyJiLnOQoFaKiNSooNMovL73r+u9PJRrRJhShMnm21kkeo0Ctw5K8uljtkQwxCM1jyAI+NPN/RCmUuBvm4975D7/MS/NI/dDzceYDH73jEzBe5uykWts3e/NgGTfHpdR87Sq6nhTwfzss8+2ukOXM378eKjVanzzzTeN3ubHH3/E9ddfj+PHj6NHjx6YN28ezpw5g++//959G5PJhPDwcKxbtw4TJkxo8H4aOjuYkpLSZAXHCxUWrybaQP3q05FaFdQKAVaHjAqzDTq1ArcOSsY3B3LrXW8023ChwrlOuW4O5+kZ5BqFgF+P6AStSoH8cot7VLrAaPbIOtTYcLU7ea4tOOa8nBCphdpHxYNcibMoClDUJMgQXGuSRYiic+SybkLdVrUVrR2QZQnFVb4d1RYBRIYpEaNT4+U7fLuVRGvj0dfqvkfROhU0ChEWh4TCOlXHO0RqoVGIKK12nngCAr/qeLW1ZuS6Zvuxy3FIMnafKcW6rDxsO14MeyOFzcb2ScCEVAP6JTVe2EwhCojUqhChUfostl1qq47bUGH2/tZeLsFQdTxY4pFa5uL9fVuDVcf9gzHZPrS24j+rjgeuoNlH+8yZM+jevTu+/PJL/OpXv2r0dlVVVYiIiMCGDRswfvx4PPPMM/j666+xb98+921OnTqF7t27Y8+ePY1OHb9Yc7dK8EWiDTiTbdd+yjZZhkoQkBIX7t7juKHro3XOmQeFFRZUWuyQJLinRfpv1W99MTqVc0p3ZO2otHsLLL3vE2mlwpk4qxTOUWiVQqxp98/IeN09msurbQ1OwfUGUQBidGr0T9a792r3p0DeuqTePtqSDJXYwD7aNe319tG2Otc4B8o+2q5p4a4+NSW3rBobsvPxfVYBLlRaGrzNwI5RmDjQWdgsrJGCZYIgQFez7rq5+117C/fRbp5AjkdqmaU/n8Sfv/ulVX/LJDtwMCaDF/fRbl+CZn+rjz/+GAkJCbj55psveztXQp2U5Jx2l56ejpdeegmFhYVISEgAAGzcuBF6vR79+/f3ap+9aWjnGAxOicbxgiqUm62I0qrRMzHcXfW6sesBZwXr0morjNU2RGlV0OtUcEgS9uWU4XRRFfKMZpwvMTm34/HwkaVeq0RSVBgSozRI0tfuIe2a3u3p/ZYbUnc0WuWexi1CoahJrv2YSDflip7xGN09Dtm5RpSYrNBrlTicb8TX+3JRWmlBuFaF7nFh2He2BCeLLZAA6FQixg3ogEiNGpsOF+JCuQUNlcsY3DEC8eEaHC+qQkW1HTHhalzTqwP6dYxChwgN4iI0GJCs59YRTbj4PYrVqd2v2+yrul/SDsDdFh2mgiTL2H+uHLIkI0KrREW1Dbll1bhQaYHJbMOZUhNyik2obuQciwAgrVsMnhzXB8O6xLbo/bI5JFSa7ahsolq4i6WmsNn6rHzsyylr8DZx4WqMG5CICamGyxY206gUiNQqEa5u2X7X3lT3vSyqsqDAWI31B/KQW25BcpQG1/XtgO8P5mLPOSMsdkApAnHhSnSNC8eh3AoYrZe+hgo4C7jptQrEhKuhUSoQplbitiHJuGtoJ27pRX415+ruCFMr8KevstDcYZhoNfDTb6/nll5EHnBFz3icfHkiXl23E0v+e6HB2wgApqR1xF3Du2Bwp2gelwWwZo9ox8bG4ujRo4iPj0dMTMxlRxlKSko81kEAkCQJ3bp1w7333otXX33V3X7ixAmsWbMGEydORFxcHA4cOIDHHnsMnTp1cu+t7XA4MGTIECQnJ+O1115Dfn4+pk2bhjlz5rRoe69AG9FuKVmWUWmxI89VZKxmH+m88moUGC3ILzej2gMba0dolO6k2TUS7Z7iHaWFTu39czt1R6MVNVO7lQrnOlmlQvD6+k7yPp6t9xyL3QGTxbnu2tqMueuyLONoQSXWZ+Vj0+ECVDWw3ZxCFDC6eywmpiZhVLfGC5spRRHhGoVziQsTzKDFeGx/vtxzDk9+sd+9vCxSo8TyWSMxvItvdpyhtmFMtg/55WZMWbodJy7U1jiZnt4Fz906gMl1kGh21vPWW28hMjISAPD22297qz8N+uGHH3D27FnMmjWrXrtarcYPP/yAt99+G1VVVUhJScFdd92FP/3pT+7bKBQKfPvtt5g/fz7S09MRHh6OGTNm1Nt3u72otNjrVeyuTaidbZ6YZhyuVlxStdu1DVZilBYRGt9MkhAEAWqlCFXN6LNCrE2u1QrRr9NNiQJdc7fiqqu82oYffinA+qx8nLzQcGGzzrG1hc1iwxsvbKZTO/e7DvfR9wURtcydwzpBo1TgN5/thV2SUWGxY9qyHVg6fQSnqBL5iCFKi7UPpGPash34paYK7sqMMzBZHfjLXYMCZvYXNS5o1mj7WyCMaJusdhTUjEI7C41VuwuO5ZebUWmxt/kxwlQKJEVpkVCz5ZVBr4EhKsy9r3SkVtX0nXiIoiaJVtWsjVYpRXflbqUoMJkOYTxb33Ku5LqqGdXCXRySjD1nS7HuYD62nShqsKBhmEqBsX06YMJAA/on6RuNS6UoIlLrTLADdWkGtQ7jsf3a9EsB5q/e457tolGKWDRtOMb2SfBzz+hyGJPtS7nJhhkf76i3ROvmQUl4a9IQzgYLcK1OtB0OB7766iv88ouzaMaAAQNw2223QaHw/hpbf/BFom22OertI51X7qzW7WozmtueSGuVIhIbGZE2RGmh1yp9msAKggBVzSi0Wik6i40pBKhEkdNiqFE8iGgeu0NClcUBo9nW7JFrAMgrr8aGrHx8n12AwoqGC5ulJusxIdWAa/skIEzd+Pd+mFqBqDCVT5aNkH8wHtu3/x4rwpyVO2Gu2eZOpRDw3r3DcFOqwc89o8YwJtufSosds5fvROap2uW51/dNwPtThvmkvhG1TqsS7ePHj2PixIk4f/48+vTpAwA4cuQIUlJS8N1336FHj+CqmNocnki0LTaHcz103X2kXWumjWaUmto+Eq5Wis5p3DVroi9eKx0VpvLbSLBSFKFSOkenNUpnYs1p3tQaPIhonMXuqNnr2gFLC+ouWGwO/Pd4EdZl5WPv2bIGbxOjU2H8AANuSjWgc2zjhc0EQUC4xplga5Q8AGjvGI/t345TJZi1fKd75pxCFPDmpMH41ZCOfu4ZNYQx2T5VWx14cNVubDlaWyTtyp5xWDJtBJdiBahWJdoTJ06ELMtYvXo1YmOdhTGKi4sxdepUiKKI7777zuMd9bfmfGlZ7A4cPFeOExcqnSPRNQXH8surkW+0oKSqoVrPLaNSCEjUa5EY6ZzSbYjSwKAPc45KR2kRo/NfIu3iWj9dN5nWKJlQk+fwIKK+1qy5BpyFzY4VVmL9wXxsOlzY4PITUQBGd4/DhFQD0rrFXnbat1opIlKjQoQ2cCqHk/cxHkPDvpwyTF+W6Z5dJwjAX+4chEkjU/zcM7oYY7L9stgd+L9P9+L77AJ32/AuMfjo/pGICvPd8k5qnlYl2uHh4di+fTsGDhxYr33//v248sorUVlZ6bEOBoqmvrQ2HynEzI93tvlxFKKARL3GOQJdU2Cs7tTu2HC1ewuvQHDxKLVGqeB6EfI6HkS0PrkGnIXNNv1SiPVZefWqmdbVKSYME1INGNc/EXERmsveX7hGiagwFaevhSjGY+jIzi3HtGU76g0cPH/bAMy4oqv/OkWXYEy2b3aHhCe/2I+v9uW621I76rFyVtplC5GS77VqnoFGo0FFRcUl7ZWVlVCrQ/MNTmzm/pGiAOeItN45Eu0qMpYY5Uys4yI0ATsSpBRFaNUitCqFc021guuoiXzJIcmoMNtQYW7ePtd1SbKM3WdKsSErH/893nBhM61KxLW9EzAh1YDUjo0XNgMAURAQqVVCH6bilnlEIWJAchQ+f2A07vsw012/4dmvs1Ftc+DBa9rfskGiQKRUiHhz0hCEqZX4dMdZAEDWeSMmL8nAqtlpSNBzT/tA0apE+5ZbbsG8efOwbNkyjBo1CgCQmZmJBx98ELfddptHOxgsOsaEAXAm0vERmtoiY1G1a6QNUVp0COBEuq660781SmdyzYNpIt+zOyRUWR0wWe2obsUWffnlZmzIzseGrPxGC5v1T9Jj4kADru3TocmiZSqFCL1WhUitkifaiEJQz4RIfP5AOqYszcT5smoAwKvrD6Pa6sCjN/TiMjEiHxBFAS/fkQqdWoFl/z0FADhaUIlJizOwak4aOsU0XkeFfKdVU8fLysowY8YMfPPNN1CpnOsBbDYbfvWrX2H58uWIioryeEf9rTnTcHJKTFCKAqpbUIAoELiSalflb62KRcoosLX3aXFmm6ugmd29rU5LWO0Sfj5WhA1ZedhztgwNfcnH6FS4sX8iJqQa0CUuvMn7DFMroNeqWHCFLtHe45Eadq7UhClLM3Gm2ORue2BMd/x+Ql8eP/gZYzJ0yLKMt344hnc3HXO3JUdpsXruaHSLb/q3nbyrTftoHz9+HIcOHQIA9O/fHz179vRYxwJNIOyj7Ql1k2qNioXKKDi1t4MIWZZhsUutXnPtcqygAuuznIXNKhrYDlAUgLRuzsJmo7tfvrAZ4KwZEaFRIlKrYu0FalR7i0dqvgKjGVOWZuJ4YW1tnunpXfDcrQM448WPGJOhZ9GWE3h1/WH35fgIDVbPSUMfQ6Qfe0WtHppYtmwZ3nrrLRw75jyD0qtXLzz66KOYM2eOxzpHbXNxUq3hdlpEAUOSZFTbnKPW1VYHHFLrznlWmJ2FzdZl5dc72K2rU0wYbhpgwLgBiYhvorAZ4Kwerg9TIVKj5PcFETUqUa/F2nmjMXXZDvySZwQArMw4A7PNgVfuHBQUS+WI2oMHr+kBnVqBZ/6dDQAoqrTgniUZWDlrFAZ1ivZv50JYqxLtZ555Bm+++SYeeeQRpKenAwAyMjLw2GOP4ezZs3jhhRc82klqHpU7oVa411bzIJkocNgcEkwWB0w2O8w2Ca2dUCTJMvadLcO6rHz8fOxCw4XNlCKu6dMBE1INGNgxqlnfBaweTkQtFRehwWdzR2P6xzuwP6cMAPD5rnMw2yS8MWkw67sQ+cj09K4IUymw8J8HIMlAmcmGKR9m4uOZIzGia6y/uxeSWjV1vEOHDnj33Xdx77331mv/9NNP8cgjj6CoqMhjHQwUgTZ13LWdlrrOllo8c0yhIlimxbmmhJuszq24Wjsl3KXAaMb32fnYkFWAfKO5wdv0T4rETalJGNunQ7PWUwuCgHCNAtFhak4Pp1YJlngk76ow2zB7+S7sOF3ibhvXPxHv3TcUGiVP3vkSYzK0fXsgF49+tg/2mplyYSoFPpw+Alf1ivdzz0JPq0a0bTYbRowYcUn78OHDYbdfui6Q2kYUBGhUIrRKBbQq52g11z4RBSZZrpkSbnFWCm/tlHAXq13C/44XYV1WPvacKW2wsFl0mLOw2U2phmYXPxEFARFaJaLDVE2u1SYiakqkVoUVs0Zh3ie78PMx54DLfw4VYN7K3Vg0dTjC1Ey2iXzhlkHJCFMpMH/1HljtEqptDsxavhN/nzIMN/RP9Hf3QkqrRrQfeeQRqFQqvPnmm/Xan3zySVRXV+P999/3WAcDhS9HtJWis/K3RqVw/s8zwUT1BNrZerPN4awUbnO0aUp4XScKK7E+Kx8//FIAYyOFzUZ1i8VNqQakd49r9vRMlUJElE6FCDW35yLPCLR4JP8y2xx4eM0e/PBLobttdPdYLJ0xEhHctcAnGJMEAP87XoQ5K3a5d0NSigLeumcIbh2c7OeehY5WJ9orV65ESkoKRo8eDcC5j/bZs2cxffp095ZfAC5JxoOVtxJtpeic/l13z2qOLhFdXiAcRFjszlFrT0wJd6k027HpcCHWZ+XhaEHDhc06RodhQmrzC5u5hKkViApTNblPNlFLBUI8UmCx2iU8tnYfvjuY524b1jkaH88chagw1WX+kjyBMUkuu06XYObHO1FhcZ6wFwXg1bsGYdKIFD/3LDS0KtEeO3Zs8+5cEPDjjz+2uFOByBOJtmsKeN1iZUyqiVrOXwcR1potuCo9mFxLsox9OWXYkJWPrceKGtw3W6MUcU3vDpgw0IBBzSxsBji/c8I1SujDlJwZQ17Dg3pqiN0h4Xf/PIAv95x3t6V21GPlrDTEhqv92LP2jzFJdR08V47pH2Wi1FSbnzx/2wDMuKKr/zoVItq0j3YoaU2i7Rqpdq2r5oEukWf48iDCbHO0eX/rhhQazfj+UAE2ZOUjr7zhwmb9kiJx0wADxvZNaNGUS5WidnsuTg8nb+NBPTVGkmQ8/e8srM48627rnRiBVXPSkBCp9WPP2jfGJF3saEEFpizNxIUKi7tt4U19Mf/aHn7sVfvHOYQeFqlVIlKr5NZaREHK7pBgsjlgtjrXXLe1mFldVruEbSeKsSErDztPN1zYLCpMhRv7J2BCalKzC5u56NTO7blYdIiIAoEoCvjz7anQKBX46H+nAABHCypxz+LtWD0nDcnRYX7uIVFo6J0YiS8eSMeUpZk4X1YNAPjLhsMwWe14/MbezFm8hIm2h3H/WaLgY7VLMFntqLI6YKkpGuJJJy84C5ttPNR4YbORXWMxIdWA9B7NL2wG1GzPpVYgSqfirBkiCjiCIODpW/pBp1bgb5uPAwBOFVVh0uIMrJkzGp3jdH7uIVFo6Bofjs8fTMeUD7fjdLEJAPDej8dhsjrwp5v7Mdn2AibaRBSSzDaHx/a3bkilxY4fDxdi/cF8HCmoaPA2SVFaTEg1YPwAAzpENr+wGeAspBihVUKvVbLWAxEFNEEQ8OT4PghTK/D690cAAOdKqzFpcQZWz01Djw4Rfu4hUWjoGB2Gzx9Mx9Slme6iq8v+ewomqx1/vn0gFFxu5lFMtIkoJEiSc39rk9WBaqsDdsnzybUkyzhwrhzrDuZdtrDZmN4dMCHVgEGdoiC28AyyVqWAPkyFcLWCZ5+JKKgsGNsTWpUCL357CACQbzTjnsUZWDUnDX0NXEtM5AsJkVp8Ni8d0z/KRNZ5IwDg0x05qLY68Ne7B/PkvQcx0Saidsshyag022Gy2T22v3VDLlRY8H12PtZfprBZH0MkJqa2vLCZS7jGuf6ay1OIKJjNvqobwlQK/PGrg5BloKjSislLtuOTWWkY2CnK390jCgmx4WqsmTsasz7eiV1nSgEAX+3LRbXNgXfvHcqlaB7CquPNxAqORIGjufFYbXUgr7zaK32wOZyFzdZn5WPX6RI0VDNNr1Xixv6JmJBqQPdWTI0UBAHhGgWiw9RQK3mGmQITfx+pNb7ccw5PfrHf/d0ZqVFi+ayRGN4l1r8dawcYk9RcJqsdc1fuwv+OF7vbrundAYumDmdhVQ9got1M/NIiChz+TLRPFVVhfVYeNh4qRHm17ZLrBQAju8ZgwsAkXNHCwmYuClFApFbF9dcUFPj7SK217mAe/u/TvbDXZNs6tQJLp4/AFT3j/dyz4MaYpJYw2xxYsHoPNh0udLeldYvFsvtHtmoGHtUK+CO45557DoIg1PvXt29f9/VmsxkLFixAXFwcIiIicNddd6GgoKDefZw9exY333wzdDodEhIS8Nvf/hZ2+6WVf4mIGlJpsePbA7l4aPUezF6xC//Yff6SJDspSouZV3bFp3PT8Opdg3BN7w4tTrI1KgU6RGrQOVaH2HA1k2wiatcmDkzC4mnD3TN2TFYHZi7fic11DviJyLu0KgUWTRuOmwcludsyT5VgytJMlJmsfuxZ8AuK0xQDBgzADz/84L6sVNZ2+7HHHsN3332HL774AlFRUXj44Ydx55134n//+x8AwOFw4Oabb4bBYMC2bduQl5eH6dOnQ6VS4eWXX/b5cyGi4CDXFDZbn5WPLUcvwNJAYTO1UsSYXvGYkGrA4JToFhc2c9GplYjWcf01EYWe6/sl4qMZIzF35S5U2xyw2CXM+2QX3rt3GG5KNfi7e0QhQaUQ8e7koQhTKfCP3ecAAPtzyjB5yXasmpOG+IiW7YxCTgE/dfy5557DV199hX379l1yXXl5OTp06IA1a9bg17/+NQDg8OHD6NevHzIyMjB69GisX78et9xyC3Jzc5GYmAgAWLRoERYuXIgLFy5ArVY3qx+chkMUOLw5dfxChQUbDxVgfVY+zpc1/Ld9EiMxYaAB1/VJQIS2decrBUFARE2BM66/pmDG30fyhJ2nSzDz452otDhnHCpEAW9OGoxfDeno554FH8YktZYkyXjum2yszDjjbuveIRxr5oyGIUrrx54Fp6AY0T527BiSk5Oh1WqRnp6OV155BZ07d8bu3bths9lwww03uG/bt29fdO7c2Z1oZ2RkYODAge4kGwDGjx+P+fPnIzs7G0OHDm3wMS0WCywWi/uy0Wj03hMkosvydjzaHBIyThZj/cF87LxMYbMb+iViwkBDm/Z8VYgC9FoV9GEq7ldJQYm/j+QNI7vGYtWcNExflgmj2Q6HJOPRtftgtjlwz8jO/u5eQGNMkqeIooDnbxuAMLUCi7ecBACcvFCFuxdvw+rZo9E5TufnHgaXgB9GSUtLw/Lly7FhwwZ88MEHOHXqFK6++mpUVFQgPz8farUa0dHR9f4mMTER+fn5AID8/Px6Sbbretd1jXnllVcQFRXl/peSkuLZJ0ZEzeateDxdXIUPfjqBexZvx3NfH0LmqfpJtgBgRJcYPH1zP3z+QDoevq5nq5NslUJEXIRz/XVMuJpJNgUt/j6StwxJican80YjLtw521CWgYX/PIgV2077t2MBjjFJniQIAn5/U188cWNvd1tOSTUmLc7A8cJKP/Ys+AT81PGLlZWVoUuXLnjzzTcRFhaGmTNn1juLBwCjRo3C2LFj8Ze//AXz5s3DmTNn8P3337uvN5lMCA8Px7p16zBhwoQGH6ehs4MpKSmchkPkB62Nx4amjpusdvx4+ALWZ+Xhl7yKBv/OoNdi/IBEjE81wKBv21QpjUqB6DAVwlm5k9oJ/j6Stx0rqMCUpZkorKj9nP1+Ql88eE0PP/YqcDEmyVuW/nwSf/7uF/fluHA1Ppmdhv7J/Fw1R9Ad+UVHR6N37944fvw4brzxRlitVpSVldUb1S4oKIDB4CygYTAYsGPHjnr34apK7rpNQzQaDTQaLvwnCgRtjUdZlnHwfE1hsyMXYG6gsJlKIeDqXh0wIdWAoZ1bX9jMhQXOqL3i7yN5W6/ESHz+QDqmLM1018p4df1hVFsdePSGXhDa+P3c3jAmyVvmXN0d4Rol/vCvg5BloLjKislLMrBi1igM7Rzj7+4FvICfOn6xyspKnDhxAklJSRg+fDhUKhU2bdrkvv7IkSM4e/Ys0tPTAQDp6ek4ePAgCgtrt4rYuHEj9Ho9+vfv7/P+E5HvFBrNWJN5FjM+3olH1+7H99kFlyTZvRIi8Mh1PfHFA+n40839MLxLTKuTbEFw7n/dKUYHQ5SWSTYRUSt1jQ/H2gdGo0udNaHvbDqGV9cfRpBNxiQKaveO6oy3Jg1xL3kzmu2YujQT208W+7lngS/gp44/+eSTuPXWW9GlSxfk5ubi2Wefxb59+3Do0CF06NAB8+fPx7p167B8+XLo9Xo88sgjAIBt27YBcG7vNWTIECQnJ+O1115Dfn4+pk2bhjlz5rRoey9WcCQKHE3FY4HRjD98eRA/Hb0ARwOVzSK1SlzfNwETByahZ0LrC5u5KERngq3XKrn3NYUc/j6SNxUYzZiyNLPe2tDp6V3w3K0DILLWRYMYk+QNG7Ly8cine2BzOI+rtCoRi6eNwDW9O/i5Z4Er4KeOnzt3Dvfeey+Ki4vRoUMHXHXVVdi+fTs6dHC+qW+99RZEUcRdd90Fi8WC8ePH4+9//7v77xUKBb799lvMnz8f6enpCA8Px4wZM/DCCy/46ykRkZdF61TYfba0XpItABjWJQYTUg24qme8R7bUUilE6MOcCTanMhIReV6iXou180Zj2rIdOJTnrKa9MuMMqq0OvHrXIBaWJPKRm1IN+HD6CDzwyW5Y7BLMNglzVuzknveXEfAj2oGCZweJAkdz4vG5r7OxfNtpJERqcFOqATcNMHhsD0i1UkS0To0IFjgj4u8j+US5yYbpH+/A/pwyd9utg5Px5qTBUHEmUT2MSfKm7SeLMXv5TlRZHQCcs/reuHswbh/KPe8vxkS7mfilRRQ4mhOPZ4qrcKygEl3jdW0ubObCCuJEl+LvI/lKhdmG2St2YcepEnfbuP6JeO++odAoWRPDhTFJ3rb3bClmfLQDRrMdACAIwMt3DMS9o7jnfV08BUhE7VKXuHBc2TPeI0m2Tq1EcnQYOkaHMckmIvKTSK0KK2aOwtW94t1t/zlUgHkrd6O6ZnSNiLxvaOcYfDYvvd6e9099eRDL/nvKzz0LLEy0iYgaEaFVomNMGCuIExEFiDC1Ah9OH4Eb+iW427YcvYCZy3egymL3Y8+IQkv/ZD3WPpCORH3t1nIvfnsI7206xp0BajDRJiKqw7VFV0qsDgmRWk5HJCIKMFqVAh9MHY6bByW527afLMG0ZZkor7b5sWdEoaVnQgS+eOAKpMSGudve2HgUf9lwhMk2mGgTEQFwJtj6MBVSYsLQIVLD4jpERAFMpRDxzj1DcOew2gJMe86WYcrS7SipsvqxZ0ShpXOcDp8/kI7uHcLdbYu2nMCzX2dDamCL1VDCI0kiCmkKUUCMTo3OsTrER2i4DzYRUZBQKkT89deDMSWttgBT1nkj7l2yHYUVZj/2jCi0JEWFYe28dPQ1RLrbVmacwe/+eaDeVquhhkeURBSSlKKIuHANUmJ0iAlXcy9WIqIgJIoC/nx7KmZf1c3ddqSgAvcs3o7csmo/9owotHSI1OCzeaMxOCXa3faP3efwm8/2wuaQ/NcxP2KiTUQhRaUQERehQUpsGKJ0KohMsImIgpogCPjTzf3wyHU93W2niqowaXEGckpMfuwZUWiJ1qmxek4aRnWLdbd9eyAP81fthtkWejsDMNEmopCgUojoEKlBp5gwRIWpIHhob20iIvI/QRDwxLg++O34Pu62c6XVuHtRBk5cqPRjz4hCS4RGiRUzR2FM7w7uth9+KcTsFTthsobWzgBMtImoXVMrRSTotUiJ1SFSywSbiKg9WzC2J56+pb/7cr7RjHsWZ+BwvtGPvSIKLc5t+IZj/IBEd9v/jhdj+rIdMJpDZ2cAJtpE1G5pVSI6xegQoVH6uytEROQjs6/qhpfvGAjXedWiSismL9mOA+fK/NovolCiUSrw/n3DcPuQZHfbrjOlmPJhZsjsDMBEm4jaLY5eExGFpvvSOuONuwfDVYajzGTDlA8zsftMiX87RhRClAoRb0wagntHpbjbDp4vx+QlGSGxMwATbSIiIiJqd+4c1gnv3TsMyppsu8Jix7RlO7DteJGfe0YUOhSigJfvGIhZV9buDHC0oBKTFmXgfDvfGYCJNhERERG1SzcPSsLiacOhVjoPeU1WB2Yu34nNRwr93DOi0CEIAp6+pf7OAKeLTZi0KAOni6r82DPvYqJNRERERO3W9f0S8dGMkQhTKQAAFruEeSt3YUNWvp97RhQ6XDsD/O6m2p0BzpdV4+7FGThaUOHHnnkPE20iIiIiateu6hWPFbNGuYtj2hwyFqzZg3/vO+/nnhGFloeu7YnnbxvgvnyhwoJ7Fmcg63y5H3vlHUy0iYiIiKjdG9UtFqvmpEGvdSbbDknGo2v3Ye3Os37uGVFomXFFV7z260HuYoWlJhvuXbK93RUrZKJNRERERCFhSEo0Pp03GrHhagCALAML/3kQK7ad9m/HiELMpBEpePfeoZcUK/xfOypWyESbiIiIiELGgOQofP7AaCREatxtz36djUVbTvixV0Sh55ZByVg0dTjUivrFCn88XODnnnkGE20iIiIiCik9EyLx+QPp6Bgd5m57df1hvLXxKGRZ9mPPiELLDf0T8dH9tcUKrXYJ81buxncH8vzcs7Zjok1EREREIadrfDjWPjAaXeJ07rZ3Nh3Dq+sPM9km8qGresXjk9mjEFlTrNAuyXjk0z34YleOn3vWNky0iYiIiCgkdYrR4fMH0tEzIcLdtnjrSTz7dTYkick2ka+M6BqLNXNHI1qnAgBIMvDbfxzAJxmn/duxNmCiTUREREQhK1Gvxdp5o9EvSe9uW5lxBr//8gAcTLaJfGZgpyisnZeO+Ija+glP/zt46ycEfKL9yiuvYOTIkYiMjERCQgJuv/12HDlypN5trr32WgiCUO/fgw8+WO82Z8+exc033wydToeEhAT89re/hd1u9+VTISIiIqIAFBehwWdzR2NwSrS77fNd5/DY2n2wOST/dYwoxPQxROKLB9ORHKV1t726/jDe/M+RoFvSEfCJ9pYtW7BgwQJs374dGzduhM1mw7hx41BVVVXvdnPnzkVeXp7732uvvea+zuFw4Oabb4bVasW2bduwYsUKLF++HM8884yvnw4RERERBaAonQqrZo/CqG6x7rav9+diweo9sNgdfuwZUWjpFh+Ozx9Mr1c/4d0fj+PP3/0SVMm2IAdTbwFcuHABCQkJ2LJlC8aMGQPAOaI9ZMgQvP322w3+zfr163HLLbcgNzcXiYmJAIBFixZh4cKFuHDhAtRqdZOPazQaERUVhfLycuj1+iZvT0Tew3gkChyMR2pvqq0OzPtkF34+Vruf7zW9O2DxtOHQ1lRGDmSMSWovCo1mTFmaiWOFle62e0d1xku3p0Ks2X87kAX8iPbFysvLAQCxsbH12levXo34+Hikpqbiqaeegslkcl+XkZGBgQMHupNsABg/fjyMRiOys7MbfByLxQKj0VjvHxH5B+ORKHAwHqm9C1Mr8OH0EbihX4K7bcvRC7j/4x2osgTeskPGJLVXCXot1j6QjgHJtSeMPt1xFk98sR/2IFjSEVSJtiRJePTRR3HllVciNTXV3X7fffdh1apV2Lx5M5566il88sknmDp1qvv6/Pz8ekk2APfl/Pz8Bh/rlVdeQVRUlPtfSkqKF54RETUH45EocDAeKRRoVQr8fcpw3Dwwyd22/WQJpi3LRHm1zY89uxRjktqz2HA11swdjeFdYtxt/9p7Hg+v2QurPbCT7aCaOj5//nysX78e//3vf9GpU6dGb/fjjz/i+uuvx/Hjx9GjRw/MmzcPZ86cwffff+++jclkQnh4ONatW4cJEyZcch8WiwUWi8V92Wg0IiUlhdNwiPyA8UgUOBiPFErsDgm/++cBfLnnvLsttaMeK2elITa86aWHvsCYpFBQZbFj7spd2Hai2N12bZ8OWDQ1cJd0BM2I9sMPP4xvv/0WmzdvvmySDQBpaWkAgOPHjwMADAYDCgoK6t3GddlgMDR4HxqNBnq9vt4/IvIPxiNR4GA8UihRKkT89deDcV9aZ3db1nkjJi/JQGGF2Y89q8WYpFAQrlHio/tH4rq+tUs6fjriXNJRGYBLOoAgSLRlWcbDDz+Mf/3rX/jxxx/RrVu3Jv9m3759AICkJOd0n/T0dBw8eBCFhYXu22zcuBF6vR79+/f3Sr+JiIiIKPiJooCXbk/F7Ktqj0GPFlTinsXbkVtW7ceeEYUWrUqBRVOHY+LA2oHS7SdLMHVpJspNgbWkAwiCRHvBggVYtWoV1qxZg8jISOTn5yM/Px/V1c4vthMnTuDFF1/E7t27cfr0aXz99deYPn06xowZg0GDBgEAxo0bh/79+2PatGnYv38/vv/+e/zpT3/CggULoNFoLvfwRERERBTiBEHAn27uh0eu6+luO1VUhUmLM3C22HSZvyQiT1IrRbw7eSjuHNbR3bYvpwyTP9yOokrLZf7S9wI+0f7ggw9QXl6Oa6+9FklJSe5/a9euBQCo1Wr88MMPGDduHPr27YsnnngCd911F7755hv3fSgUCnz77bdQKBRIT0/H1KlTMX36dLzwwgv+elpEREREFEQEQcAT4/rgt+P7uNvOlVZj0uIMHK+z/RAReZdrScfU0bVLOn7JM+KexRnILw+MJR1AkBVD8yfuSUgUOBiPRIGD8Uih6KP/nsIL3x5yX46PUGPVnDT0Nfg/BhiTFCpkWcar6w9j8daT7raU2DCsmTMaKbE6P/bMKeBHtImIiIiIAsmsq7rh5TsGQhCcl4sqrZi8ZDsOnCvza7+IQokgCPj9hL547Ibe7rackmrcvSgDJy74f5YJE20iIiIioha6L60z3pw0GGJNsl1msmHKh5nYfabEvx0jCiGCIOA3N/TCHyf2c7flG824Z3EGfskz+rFnTLSJiIiIiFrljqGd8Lf7hkFZk21XWOyYtmwHth0v8nPPiELL3DHd8dIdqZfMMtmXU+a3PjHRJiIiIiJqpYkDk7B42nColc7DapPVgZnLd2LzkcIm/pKIPGlKWhe8cXftLJPyahumLs1E5sliv/SHiTYRERERURtc3y8RH80YCa3KeWhtsUuYt3IXNmTl+blnRKHlzmGd8Pcpw6BSOLPtSosdMz7egS1HL/i8L0y0iYiIiIja6Kpe8Vg5Kw0RGiUAwOaQsWDNXny197yfe0YUWm5KTcKS6SOgqZllYrZJmLtiF77PzvdpP5hoExERERF5wKhusVg1Jw16rTPZdkgyHvt8H9buPOvnnhGFlrF9ErB85iiEqxUAAKtDwkOr9+Df+3x34ouJNhERERGRhwxJicZn89IRF64GAMgysPCfB7H8f6f83DOi0JLeIw6fXHTi69G1+/DZDt+c+GKiTURERETkQf2T9Vj7wGgkRGrcbc99cwiLtpzwY6+IQs+wzjH4dN5oxNY58fX7Lw9i2X+9f+JLkGVZ9vqjtANGoxFRUVEoLy+HXq9v8vaSJCM714gSkxXRYSoAQInJirIqG2J0KsRFaDAgWQ/RVRavht0u4d/7c7HnbAlMFgfiwlVw16mvQ5ZlFBrNOHmhCiXVNug1CnSODYcgAOdKTSipssBolgAAsToltKKM46W2S+4nTAEM6BSFG/sk4khhFcI0CgzrHINfDU6GUhk452EkScb+c2VYdzAP50pMUCkEiIKzsmeh0YIysw1alQKDOkahT2IklAoFBqVEAQD25pQht8SE3NJK/HS4CCap4cfQKoBxAzpAo1Bh3/lyyLKAMb3jcOvgjhjcKfqS98rX7HYJa3eewXubjiG/8tL3EgBu7huNNyanQVtz5q69ak48umKwqMrSYNw1dn0/QySy84zYm1MGQUa9z1F+aTUkyBAEAQa9BkazHQWlJuw7Xw6rXYJSISAmTA2IgFoUUFRpQUGFBZVmBxwSIAqArYFvXKUA9DWEIy48DKJCwOCUaDw0pgfUNdOd/MX1GuVXVOPH7AIcKTCivNoGWQZMNge0SgUGdYrClT3jUW2V4JAkHCkwIuN4EQorrLA0EmuAM960aiXSu8XAZHWgoNKKGJ0adwzriDuHdPLb948kydhzugRv/XAEmadLYb/Mc5h3VWc8Oa6/398nf2vp7yNRIKp73BarU1/yW1H3eK6s2oboMBUkWcbenDKcu1CB9ftzkFfd8H1H65QoM9kv+/gr56RiTM8uHnkujElqzy6XY0WFKVFebUeMToUonQr/O5KDv27KafFjPH1HEmanDWtzX5loN1NLvrS2HS/CB1tO4ERhJaqsDphtDjgkGTIAyDJEUYBeq0L/ZD3mX9MDV/SMBwB8uPUE3t50DFUWh/efUBMiNAr85vpemDumh7+7gm3Hi/DHrw7iVJHJb33oFq/DS7cPdL9Xvvbh1hN4ed1hNDdYr+/bAcvuH+XVPvlTU/HoisFDueUwmu2QpPpxN6ZXPLYeK7rk+jCVAg5ZhtUmwSHLkGU0+zX3NIUATB6ZgpfuHOSXx3e9hrtPl8Bku0y26QVqhYDfju/j8++fbceL8PCaPSgxNXwiqzFTRvnvfQoEPKinYFf3uM3mkKFSCOiREOH+rThRWIkqiwPVNgcEAVCKAix2CXbJ878Qp1+9uc33wZik9qpejlUTk7IsQwIg1+RarvFJT4RnW+ORiXYzNfdLa9vxIvzhXwdRabFDo1Sg0GiGQ3J+AABAKTqnLAiCgHCNAjE6NV6+YyCyc8vxyvrDHvlQeIpCAH4/oa9fk+1tx4uwYM0elLbwwNcbYnQqvH/fMJ8n2x9uPYGX1h1u8d+152T7cvHoisGSKiuqbQ5IsgxRcI5KCIIAjVKAxS5DoxRgdcju6x2O2jgFnKPPgRCP/kjiXK9hXlk1LA7/vAgCgD9M9N33z7bjRZi9YieqW3lSIZSTbR7UUzCre9wWo1NDrRBhdUgoMFpgstoRrlEgQqPEhQqLc9BEBrx96rGtB/eMSWqP6udYIi5UWGC/6NjNG9oSj4EzN7gdkCQZH2w5gUqLHYl6DcqrbZBk2XnE6LqNDKhqpkTaHTIqLXa8v/kY/vbj8YA4qK/LIQPvbz4B++XmTnqRJMl4f/OxgEiyAaDUZMP7m49B8uEbZbdLeHfTsVb97abDF2A2X36qWnvjisEKs815gksGVKIIpShCpRQhyzKqbc5RiGqbBEmWoRJFKAThkpHrQInHtbvOwWr13SwX12tYbrL4LckGnDMJ/rb5uE++fyRJxt9+PN7qJBsAVu/I8en7RERtV/e4zaDXQqtSQBQFaJQiHJIEhyTDZpdQVm2DQ3aOZPviW3Hr8TM+eBSi4FEvx4p05ljemFHSkGWZe1r9t0y0PSg714gThZWI0alhscmw2B0QRQGuOQMCnKPZsgwoRAFWh4QwlQLZuUYYAzQhMppt+OZAnl8eOzvXiOxco18euzG+7tM3B/JQ0YalBC+u+8WDvQl8rhjUqZWwOiQoRQFCzRwiAYJzvZ3snK0hyYAoOK+X4b8p4k2xSzIWbT3ps8dzvYaC4P+fB2O13SffP9m5Ruw7V9bm+/Hl+0REbVf3uE2oUw/HbJOcvyEK57Gaxeb8PUEDJ2W9YfrSLB88ClHwqJdj2WVY7BIUPjrx9eK/Wn8c4v8jqXakxGSFzSFDrRBhlyR3gl33QyDDNXW89n+rQw7Yg3xZBs6X+WdtdInJCqs9sF4Zq0NGicnqs8dr62t/uqTKQz0JDq4YFAXnCa4my9e5YjSwPmaXOOPD99H1Gjr8M5GlHhm++f5xPue2P2Ffvk9E1HZ1j9vqch3DuZYQybLsHiwhIt9rMMfyYx2d5mKi7UGxOjVUNWc/laLoXoxf92BfQP0kW5adhX/8W8+6cYIAdIzW+eWxY3VqqJWB9cqoFQJidWqfPV5bX/uuseEe6klwcMWgJMvO+GrqD1wxGlgfs0t08eH76HoNFQHw6yDAN98/zufc9ifsy/eJiNqu7nFbXa5jOKkm2XbNfAr03wqi9qrBHEtoxoCKnwXAoVT7MSBZjx4JESg12aBRCdAoFTUFmJzXu76kBcG5YbpaIaLa5sCAZL17I/VAo9eqcOugJL889oBkPQYkB1YRD1/36dZBSYjUtH7roKcn9vNgbwKfKwZNVkfNWU8ZrnqPMmRndXHBWX/AOVIhu0cqAvXLWikKeHBMd589nus1DIQ6mfowpU++fwYk6zGkU3Sb78eX7xMRtV3d47a633lalej8DakZQdOonL8nqPm98LaVc1J98ChEwaNejqV01VHwTTw+fUfrj0OYaHuQKAqYf00PRGgUKDBaERWmgnjRsJooALaa4j5KhYAIjRILxvbCw9f1hJ+3ab6EQgAWjO3ht/1sRVHAgrG9EKNT+eXxLxajU2HB2F4+3U9bqRTxf9f3atXfXt+3Q7vfT/tirhiM1CqhEEVnvEkS7JIEm12CIAgIU4lQis7/RUGATXJu5XXxuxoo8XjPiE4+3afZ9RpG6dTQ+HFYWwDw8NiePvn+EUUBD1/XE2Gq1j/WlFEpIb+fNlGwqXvclm+0OHeqkGSY7RIUogiFKEClFBEVpoJCcNbM8MVPg6f20yZqL+rlWBVW6MNUzroJPtCW/bSZaHvYFT3j8fIdA9EvKRKyLEOnUUKldB7YK2oKo4migKgwFQZ1isbLdzj3Zp47pgeemtAX4W0YvfSkCI3C71t7Ac7X8/37hqFbvH+mr7t0i9f5ZWsvAJg7pgf+OLFvi37c2/PWXk1xxeDglCjotUr31l6uuBvWJRYLb+qDYV1i612vUAjQa5UI1yigEl0F1PxHIfhvyyjXaziqeyx0bUg+W0utEHy6tRfgfM7LZoxEbCtO7IXy1l5Ewa7ucZvJYkdhpQUmix2DU6Kw8KY+GNQpGpCBMLXzBK5KKSJcrfDaQb4n9tEmao/qxqorJpUKEaqaHEuE87hNFDw3WMJ9tH2kpXsSSpKM7FwjSkxWRIc5D9xKTFaUVdkQo1MhLkKDAcn6S0ZH7XYJ/96fiz1nS2CyOBAXrmpwUZAsyyg0mnHyQhVKqm3QaxToHBsOQQDOlZpQUmWB0ewcOY/VKaEVZRwvvXSbrDAFMKBTFG7sk4gjhVUI0ygwrHMMfjU42W8j2Q2RJBn7z5Vh3cE8nCsxQaUQIAqAyepAodGCMrMNWpUCgzpGoU9iJJQKBQalRAEA9uaUIbfEhNzSSvx0uAimRmoeaRXAuAEdoFGosO98OWRZwJjecbh1cEcM7hTt05HshtjtEtbuPIP3Nh1DfmXDW57d3Dcab0xOa/cj2c2JR1cMFlVZGoy7xq7vZ4hEdp4Re3PKIMio9znKL62GBOd+3Aa9BkazHQWlJuw7Xw6r3VmhNiZMDYiAWhRQVGlBQYUFlWYHHFLNjJYGvnGVAtDXEI648DCICgGDU6Lx0Jgefh8hdb1G+RXV+DG7AEcKjCivtkGWAZPNAa1SgUGdonBlz3hUWyU4JAlHCozIOF6EwgorLJepL6ZVAFq1EundYmCyOlBQaUWMTo07hnXEnUM6+e37R5Jk7Dldgrd+OILM06W43O5i867qjCfH9ff7++Rv3LOX2oO6x22xOvUlvxV1j+fKqm2IDlNBkmXszSnDuQsVWL8/B3nVDd/363f3xY29O+K6lzahpJHHXzkn1WMj2YxJas8ul2NFhSlRXm1HjE6FKJ0K/zuSg79uyrnkPjpHKfDmfYPw6w/2NvgYT9+R1KaRbBcm2s3ELy2iwMF4JAocjEeiwMKYJAoMgTNkSURERERERNQOMNEmIiIiIiIi8iAm2kREREREREQe1L4rJnmQaym70Wj0c0+I2q/IyEgIDRT/uxjjkcj7GI9EgYUxSRQ4mhOPTLSbqaKiAgCQkpLi554QtV/NLdzCeCTyPsYjUWBhTBIFjubEI6uON5MkSThy5Aj69++PnJycdlPF0Wg0IiUlpV09J4DPK5jUfU4dO3Zs1tl6SZKQm5sLWZbRuXPnoH892sv7yucRWNr6PJo7euaKx+be3huC/T1j//0nmPoeTDHZlGB63S/GvvtHoPWdI9oeJIoiOnbsCADQ6/UB8QZ7Unt8TgCfVzDR6/XNPiAQRRGdOnVyT4trL68Hn0dg4fNoHlc8BoJgf8/Yf/8J5r5fLJBisinB/Lqz7/4RTH1nMTQiIiIiIiIiD2KiTURERERERORBTLRbQKPR4Nlnn4VGo/F3VzymPT4ngM8rmLTlObWX14PPI7DweQSfYH+u7L//BHPfg1kwv+7su38EY99ZDI2IiIiIiIjIgziiTURERERERORBTLSJiIiIiIiIPIiJNhEREREREZEHMdEmIiIiIiIi8iAm2s0kyzKMRiNYO47I/xiPRIGD8UgUWBiTRIGBiXYzVVRUICoqChUVFf7uClHIYzwSBQ7GI1FgYUwSBQYm2kREREREREQexESbiIiIiIiIyIOYaBMRERERERF5EBNtIiIiIiIiIg9iok1ERERERETkQUy0iYiIiIiIiDyIiTYRERERERGRBzHRJiIiIiIiIvIgJtpEREQhwm6X/N0FIiKikMBEm4iIKAQcL6zAze/9jF2nS/zdFSIionaPiTYREVE7dyi3HFM+zMSRgkrM/Hgnss6X+7tLRERE7RoTbSIionZs79lSTFmaiYIKCwCgwmLHTo5qExEReRUTbSLyGrtDwu4zPKAn8peME0WY8dEOlJps7rYXfjUAM6/s5sdeERERtX9MtInIK0xWO2Yt34l7Fm/Hz8cu+Ls7RCHnx8MFmL1iF4xmOwBAFIAXbx+A6eld/dsxIiKiEMBEm4g8rrjSgkmLM7D1WBHskowHP9mNowUV/u4WUchYdzAPD67aA5PVAQBQigJev3swpo3u6t+OERERhQilvztARO3L2WITpi7LxNkSk7vtql7x6Byr82OviELHP3efw8J/HoBdkgEAaqWIdycPwU2pSX7uGRERUehgok1EHpN1vhwzPtqB4iqru23q6M54/rZUKETBjz0jCg0rtp3G899koybHRphKgUVTh+GaPgn+7RgREVGIYaJNRB6x5Ugh5q+unaoKAL8d3wcPXdsDgsAkm8jb/v7Tcby24Yj7cqRWiWUzRmBUtzg/9oqIiCg0MdEmojb7cvc5/PafB+CoGUZTCAJevWsg7h6R4ueeEYWG178/jPc3n3BfjtWpsXzWSAzqFO2/ThEREYUwJtpE1GqyLOODLSfqjaKFqRX4+5RhGMupqkReJ8syXvjmED7edtrdlqjXYNXsNPRKjPRfx4iIiEIcE20iahVJkvHcN9lYmXHG3RYbrsbH94/E4JRo/3WMKERIkoyFXx7AF7vOuds6xYRhzZw0dI4L92PPiIiIiIk2EbWY2ebAo2v3YUNWvrutc6wOK2eNQtd4HuATeZvN7sCja/fju4N57rYeHcKxes5oGKK0fuwZERERAUy0iaiFyqttmLNiJ3aeLnW3DewYhY9njkR8hMaPPSMKDRabAw+u2o3NRy642/on6bF6zijEhDMGiYiIAgETbSJqtvxyM6Yty8Sxwkp325he8fhg6nCEa/h1QuRtJosds1bswvaTxe62YZ2jsWLWKERqVX7sGREREdXFI2MiapZjBRWYtmwH8o1md9udQzviL78eBJVC9GPPiEKDsdqK6R/txL6cMnfbFT3i8NH9I6FVKfzXMSIiIroEE20iatLO0yWYvXwnjGa7u23B2B54clwf7pFN5APFlRZMXZaJX/Iq3G039EvA36cMh1rJE11ERESBhok2EV3Whqx8/OazvbDYJQCAAOD5Xw3A9PSufu0XUagoKDfj3qXbcfJClbvttsHJeOueIVCIPNFFREQUiJhoE1GjVm47jWe/zoZcc1mtFPHu5CG4KTXJr/0iChU5JSbc++F2nCutdrfdN6oz/nx7KkQm2URERAGLiTYRXUKWZby24Qg+2HLC3abXKrF0xkiM6hbrx54RhY7jhRW478NMFFZY3G3zru6Opyb25ZINIiKiAMdEm4jqsTkkLPznAXy557y7zRClxcpZo9A7MdKPPSMKHVnnyzD9o50oqbK62x6/sTf+7/pefuwVERERNRcTbSJyq7LYMX/Vbmw9VuRu650YgRWzRiEpKsyPPSMKHbtPl+D+5TtRUaf44DO39Mesq7r5sVdERETUEky0iYKE3S5h7c4zePeHoyiosjd4m2itiOt6x+Pg+XKcLLZAAqBTiRg3oAMiNWpsOlyIC+UWWBv42y7RKpwrt8Eh128/WlCJ9Fd+BACoAYRrFagwO2CHszBamALoEq9Dr8RIdIjUQiGKSIzUoNxsw9GCSoiCgOv6JuD2IR2hZHVkCnCSJGPP6RK89cMRZJ4uRU0NwAZd0SUCsixib64RFjugFIG4cCW6xoXjUG4FjNZL/1gBIEytgF6rQEy4GhqlAmFqJW4bkoy7hnbC9tPFmLtiN6ptDgCAKAC3DDTgzf8cxgvfHrps30U4+yADUItAp9hw3Ds6BeeKzcjON0KWAUOEBoM6R2N4l1gM7Bjll3XedruEj7cdw0vrjl9ynVoE/jCxL6aO7sbviwD049FTmPVR/c9hjAbY/OT1iI7U+qlXwcVul/CbL7biu/1V9dpXzknFmJ5dfNaPsgozhry06ZL2lGglvnv4WugjND7rCzWsxFiNYS//WK9NCWDPn27g++NBXx08jEdXn6jX9vQdSZidNqzN9y3Isiw3fTMyGo2IiopCeXk59Hq9v7tDIebDrSfw8rrDCOZg1apEPHFjb8wd06PN98V4JG/YdrwID6/ZgxKTzS+PLwqAIAhwSLL7suSloFeIQP8kPZ6a0A9X9Ixv0321JB4/3HoCL6073Kz7/ePEvh75viDP6Pr77y57fVy4CrufHuej3gSn5nz+T796c5sfp6mYHP7if1BcdfnvueQoLbY9dX2b+0KtM+i5DTCaHY1ez/fHM5r6XmtrPPJ0MVGAc/0wB3OSDQBmm4RX1h/Gh1tPNH1jIh/bdrwIs1fs9FuSDTiTaleSrRQFryXZAOCQgIPnjXjs833Ydryo6T/wgJYk2QDw0jp+XwSKpg5GAaC4yobhL/7HB70JTs39/DfntW6L5iTZAJBbbsYVr1w64k3e11SSDfD98YTmxFpb45GJNlEAs9slvLvpmL+74TGSDPxt8wnYLzcfl8jHJEnG3348jmpb4HwuRR+dWiuutODvP52A5M2sHs7vstdakGS7vPfjcX5f+NmPR081+7bFVTaUVZi92JvgZLdLeGNj8z//W4+f8Uo/yirMzUqyXXLLzTBWWpq+IXlMibG6ySTbhe9P6311sPnxuCxzT6sfh4k2UQD75kAeKizN+8INFkazDd8cyPN3N4jcsnON2HeuzN/dqKeB5d1eIUnAkfwKZOcavfo43xzIQ2vmChjNdn5f+NnFa7Kb8uslmV7qSfD65kAezC0IgOlLs7zSj9a8N1M+3umFnlBjbvt7Rotuz/endS5ek305L/6r9b9BTLSJAtj5MpO/u+Bxstw+nxcFrxKTFTZHaI6aygCsDgklpoZKJHpOW2Ke3xfB5QJHtC8RKJ/h1rw3+eXVXugJNaakqmUj1Hx/AhsTbaIA1jFa5+8ueJwgtM/nRcErVqeGShGaP4cCALVCRKxO7dXHaUvM8/siuHRg9fFLBMpnuDXvjYFbe/pUbHjLqonz/QlsAX9kcf78eUydOhVxcXEICwvDwIEDsWvXLvf1sizjmWeeQVJSEsLCwnDDDTfg2LH6a1pLSkowZcoU6PV6REdHY/bs2aisrPT1UyFqsVsHJSFSo/B3NzxKr1Xh1kFJ/u4GkduAZD2GdIr2dzfcIlQCdD7afFMUgT6GSAxI9m71/lsHJUHVir/Ta5X8vvCzj2b1b9Ht/zEvzUs9CV63DkqCtgUBsHJOqlf60Zr3ZvXMkV7oCTXm64fSW3R7vj+t8/aU5u9o8fQdrf8NCuhEu7S0FFdeeSVUKhXWr1+PQ4cO4Y033kBMTIz7Nq+99hreffddLFq0CJmZmQgPD8f48eNhNtdOj5kyZQqys7OxceNGfPvtt9i6dSvmzZvnj6dE1CJKpYj/u76Xv7vhMaIAPDy2B/fHpYAiigIevq4nwlT+/1wKAH5zYx88Nq6vTx4rPkKDh67t4fX9tJVKEb+b2PLn9Mh1Pfl94WfX9e7W7NvGhau4n3YDlEoRT9zY/M+/t/bTjo7UIi68+Rl/cpSW+zX7WKw+DHpt8wZY+P603u0Dmx+PbdlPO6D30f7973+P//3vf/j5558bvF6WZSQnJ+OJJ57Ak08+CQAoLy9HYmIili9fjsmTJ+OXX35B//79sXPnTowYMQIAsGHDBkycOBHnzp1DcnJys/rCfXvJn7iPdn2MR/IGf++jrVII+N34Pu4Yael2WC3BfbSppbiPdttxH21qLu6j7Rve3kc7oBPt/v37Y/z48Th37hy2bNmCjh074qGHHsLcuXMBACdPnkSPHj2wd+9eDBkyxP1311xzDYYMGYJ33nkHH330EZ544gmUlpa6r7fb7dBqtfjiiy9wxx13NPjYFosFFkttQQKj0YiUlBQe2JPf2O0S1u48g3d/OIqCKnuDt4nWiriudzwOni/HyWILHHCOWtUNchFAQ2WfuseqYay2o6i68aJQagDhWgUqzA7Ya+47TAF0idehV2IkOkRqoRBFJEZqUG624WhBJURBwHV9E3D7kI6tHpliPJKvSJKMPadL8NYPR5B5uhSX21nqii4RkGURe3ONsNgBpQjEhSvRNS4ch3IrYLxM6XC1QkD3eB10aiXC1ErcNiQZdw3tdEmM2O0SPtl+Em/85xgqmyhFLsLZBxmAWgQ6xYbj3tEpOFdsRna+EbIMGCI0GNQ5GsO7xGJgx6hWjWS3NR7tdgkfbzuGl9Ydv+Q6tQj8YWJfTB3djSPZAejHo6cuqUIeowE2P3k9R7KbyW6X8JsvtuK7/VX12lfOSW31SHZrYrKswowhL126D3NKtBLfPXwtR0oDQImxGsNe/rFemxLAnj/dwPfHg746ePiSKuRP35HUppFsl4BOtLVa55f2448/jrvvvhs7d+7Eb37zGyxatAgzZszAtm3bcOWVVyI3NxdJSbXz5ydNmgRBELB27Vq8/PLLWLFiBY4cOVLvvhMSEvD8889j/vz5DT72c889h+eff/6Sdh7YU7DYfrIYc1fuQoW5Nil/5LqeePzG3hAE704T9TTGIwWrCrMNMz7agT1ny9xt1/TugMXThkOrCs76C4xHosDCmCQKTAF9uliSJAwbNgwvv/wyhg4dinnz5mHu3LlYtGiR1x/7qaeeQnl5uftfTk6O1x+TyFPWHczDtGWZ7iRbFIA/356KJ8b1CbokG2A8UnAqrbJi8pLt9ZLsCakGfDh9RNAm2QDjkSjQMCaJApOP6pq2TlJSEvr3r1/tsl+/fvjnP/8JADAYDACAgoKCeiPaBQUF7qnkBoMBhYWF9e7DbrejpKTE/fcN0Wg00Gg4LYOCz/L/ncLz3xxyTxfXKEW8e+9QjB/Q+Oc90DEeKdgUGs2498PtOHGhdnroXcM64rVfD4bCy4XHvI3xSBRYGJNEgSmgR7SvvPLKS6Z8Hz16FF26ONewdOvWDQaDAZs21a4xMRqNyMzMRHq6szx+eno6ysrKsHv3bvdtfvzxR0iShLQ0bkFB7Ycsy3h1/WE8VyfJjgpTYfWctKBOsomCzblSE+76YFu9JHvGFV3wejtIsomIiKh5AnpE+7HHHsMVV1yBl19+GZMmTcKOHTuwZMkSLFmyBAAgCAIeffRR/PnPf0av/2/vvsOjKNc2gN+zJZvegCQEQu81EEgIWEBQRNSDYAXpRRRURPwU9WDhKBwLokdFpQQUsB4BGzYEPNJJCIQWCAIJkBAgpJdt7/dHspNsOmQ3s+X+XReX5p2yz+zOMzPPzuz7duyItm3b4p///CfCw8MxatQoAKV3wG+//Xb5kXODwYDZs2fjwQcfrHeP40SOTm8047n/HsK3B87Lbc0DPPHplGh0DPVTMDIi9/L3pXw8tHw3LuaWd0w0a3B7zBvunD/bICIiouvj0IV2//79sWHDBsyfPx+vvvoq2rZti6VLl2LcuHHyPP/3f/+HgoICzJgxA9nZ2bjhhhvw888/yx2pAcC6deswe/ZsDB06FCqVCmPGjMF7772nxCYR2Vx+iRGPro3H/05eltu6hPlh9eRohAWwF1iixnIsPRfjVuxBVoFebvu/2zvjscEdFIyKiIiIlODQvY47Eo7bS47oUl4JJsftxeELuXLbgHbB+GRCP/h7ahWMzL6Yj+RoEtOyMWHlHuRW6OX/1bu7Y8LANsoF1UiYj0SOhTlJ5Bgc+o42EdXs9OUCTFi1B2lZRXLbnb2a4+37e0Oncd4ejYmcza5TVzB1zT4U6k0AALUk4c37emF035YKR0ZERERKYaFN5IQS07IxOW4vrhYa5LapN7TFC3d0hYqdLRE1mq3HMzFzbTxKjGYAgEYl4f2xfXF7D3ZASERE5M5YaBM5ma3HM/HYugQUGUxy2/N3dMGMm9orGBWR+/n+4AXM+TIRJnPpL7B0GhU+mdAPN3dqpnBkREREpDQW2kRO5Kt9aZi/IUm+sNeqJbx5b2+M6tNC4ciI3MuX+1Ix/9sklKUifHUarJrUH9Ftg5UNjIiIiBwCC20iJyCEwPt/pODt307Ibb46DT56OAo3dGyqYGRE7mfVX6fx6g9H5b8DvLT4bGo0erUMVC4oIiIicigstIkcnMks8NJ3h7F2d6rc1sxPh7hJ/dGjRYCCkRG5l+q+8Grq64H10wegE8erJyIiogpYaBM5sGKDCU98fgC/Hr0ot7Vr6oM1U6IREeytYGRE7kUIgUWbj+OTP/+W28IDPbF+2gC0aeqjYGRERETkiFhoEzmo7EI9pq3Zj/1nr8ptkRGBWDWpP4J9PBSMjMi9mM0C/9x0GOv2lD9V0qaJN9ZPH4DwQC8FIyMiIiJHxUKbyAGdzy7CxFV7kZKZL7fd0iUE74/tA28Ppi1RYzGazJj3zSFsPHBebusS5oe102LQ1FenYGRERETkyHjFTuRgjqXnYlLcXlzMLZHbHuwfgX+N6gGNWqVgZETupcRowuPrrX+6ERkRgDWTYxDgrVUwMiIiInJ0LLSJHMjOU5cx49N45JcY5bYnh3bEnGEdIUmSgpERuZcivQnTP92Pv1Iuy22x7ZpgxcR+8NHx1ElERES149UCkYP4/uAFzP0qEQZT6cC8Kgn416ieGBvTSuHIiNxLbrEBk+P2Ib5C/whDOjfDsoej4KlVKxgZEREROQsW2kQOoPK4vJ5aFf7zUF/c2i1UwaiI3E9WgR7jV+7BkQu5ctvIns2x9MFIaPnTDSIiIqonFtpECjKbBRb/bD1kUKCXFisn9UdU6yAFIyNyP5m5xRi7fA9SLpV3Qnh/v5ZYNLoX1Cr+dIOIiIjqj4U2kUL0RjOe+eYgNiVekNtaBHrh06nRaN/MV8HIiNxPWlYhxq3Yg9SsQrlt8sA2WHBXN/aPQERERNeMhTaRAvKKDXh0bYJVR0tdm/tjzeT+CPH3VDAyIveTkpmPcSt2W/X0//gtHTD31k4ssomIiOi6sNAmamSZecWYtGofjqaX/wZ0UIcm+OjhKPh5csggosZ09EIuHl65B1kFerlt/ogueOTm9gpGRURERM6OhTZRIzp1KR8TV+3FuatFctvdvcPx1n294aFhR0tEjSkh9SomrdqL3OLS4fQkAAtH9cDDA1orGxgRERE5PRbaRI0kIfUqpq7eh6uFBrlt+o1tMX9EV6jY0RJRo9qZchlT1+xHkcEEAFBLEt66vxfu6dNS4ciIiIjIFbDQJmoEvx+9iNmfJ6DYYJbbXhzZFdNubKdgVETu6Y/jFzHzswToTaX5qFVL+M9DfXF7jzCFIyMiIiJXwUKbyM6+2JuK5zckwSxK/9aqJbx9fyTu7h2ubGBEbuj7gxfw1JeJMJYlpJdWjU8mROHGjs0UjoyIiIhcCQttIjsRQuDdLSex9PeTcpuvToNPxkdhYIemCkZG5J6+3JeK5/6bhLLvvOCr0yBucn/0bxOsaFxERETkelhoE9mB0WTGPzcdxud70+S2ED8dVk+ORrdwfwUjI3JPq/46jVd/OCr/HeitxWdTYtCzZYCCUREREZGrYqFNZGNFehMe/zwBvx/LlNvaN/PBminRaBnkrWBkRO5HCIH3/0jB27+dkNua+emwbloMOoX6KRgZERERuTIW2kQ2lFWgx9Q1+3AgNVtui2odhBUT+iHIx0O5wIjckBACizcfx8d//i23tQz0wrrpMWjdxEfByIiIiMjVsdAmspG0rEJMjNuLvy8VyG23dgvFfx7qA0+tWsHIiNyP2Szwz02HsW5PqtzWrqkP1k2PQfMALwUjIyIiInfAQpvIBo5cyMGkuH24lFcit42NaYVX7+4OjVqlYGRE7sdoMuPprw9iU+IFua1rmB8+mxaDpr46BSMjIiIid8FCm6iBdqZcxozP4pFfYpTb5t7aCY/f0gGSJCkYGZH7KTGa8Pj6A/j16EW5rU9EIFZPjkaAt1bByIiIiMidsNAmaoDvDl7A018lwmAqHTBIrZLw+j098ED/VgpHRuR+CvVGzPg0Hn+lXJbbYts1wYqJ/eCj4+mOiIiIGg+vPIiu04r//Y1//XhM/ttTq8KH4/rili6hCkZF5J5yiw2YHLcP8Wevym1Du4bgg7F92UcCERERNToW2kTXyGwWeP2nY1jx12m5Lchbi1WT+qNPqyAFIyNyT1kFeoxfuQdHLuTKbXf1ao4lD0RCyz4SiIiISAEstImuQYnRhGe+PoTvDpZ3shQR7IU1k6PRrpmvgpERuaeLucUYt2IPUjLz5bYH+0fgtXt6Qq1iHwlERESkDBbaRPWUW2zAzM/isfPUFbmte7g/4ib3R4ifp4KREbmntKxCjF2xG2lZRXLb1Bva4sWRXdkRIRERESnKbs/U/f333/ZaNVGjy8wtxgMf77Yqsm/o0BRfPhLLIptIASmZ+bj3o51WRfaTQzuyyCYiIiKHYLdCu0OHDhgyZAjWrl2L4uJim6xz8eLFkCQJc+bMkduKi4sxa9YsNGnSBL6+vhgzZgwuXrxotVxqaipGjhwJb29vhISE4JlnnoHRaARRfaRk5uOeD3fiWHr57z9HRYZj1aT+8GVPxkSN7siFHNz/8S5czC0ft/75O7rgqVs7scgmIiIih2C3QjshIQG9evXC3LlzERYWhkceeQR79+697vXt27cPH3/8MXr16mXV/tRTT+H777/H119/je3bt+PChQsYPXq0PN1kMmHkyJHQ6/XYuXMn1qxZg9WrV2PBggXXHQu5j/izWbh32U6czy6/a/bITe2w5P5IeGjYyRJRY4s/exUPfrIbWQV6AIAE4LV7emDGTe2VDYyIiIioArtVCpGRkXj33Xdx4cIFrFq1Cunp6bjhhhvQo0cPLFmyBJcuXar3uvLz8zFu3DgsX74cQUHlvTrn5ORg5cqVWLJkCW655RZERUUhLi4OO3fuxO7duwEAv/76K44ePYq1a9ciMjISI0aMwMKFC/HBBx9Ar9fbfLvJdfx6JANjl+9BdpEBACBJwII7u2H+HV2hYidLRI1uR8pljF+5B3nFpU8kqVUS3nkgEuNiWiscGREREZE1u9+S02g0GD16NL7++mv8+9//RkpKCubNm4eIiAhMmDAB6enpda5j1qxZGDlyJIYNG2bVHh8fD4PBYNXepUsXtGrVCrt27QIA7Nq1Cz179kRoaPnYxsOHD0dubi6OHDlio60kV7Nuz1nMXBuPEqMZAOChVuE/D/XBlBvaKhwZkXv6/ehFTF69D4V6EwBAq5awbFxfjOrTQuHIiIiIiKqy+w9M9+/fj1WrVuGLL76Aj48P5s2bh6lTp+LcuXN45ZVX8I9//KPWR8q/+OILJCQkYN++fVWmZWRkwMPDA4GBgVbtoaGhyMjIkOepWGRbplum1aSkpAQlJeW//8vNza1xXnIdQgi889sJvPdHitzm56nBJ+P7IbZ9EwUjc2/MR/f2/cELeOrLRBjNAgDgpVXjkwlRuLFjM4Ujc0/MRyLHwpwkckx2K7SXLFmCuLg4JCcn44477sCnn36KO+64AypV6U30tm3bYvXq1WjTpk2N60hLS8OTTz6J3377DZ6ejduz86JFi/DKK6806muSsowmM17YcBhf7k+T20L9dVgzJRpdwvwVjIyYj+7ri72pmL8hCaK0xoavToPVk/ujX5tgZQNzY8xHoqqEEDCZBcwCEBAQAjCL0r/NQkCYS9vNcruABAnN/HQNfm3mJJFjkoSwXL7YVseOHTFlyhRMmjQJzZs3r3YevV6Pzz//HBMnTqx2+saNG3HPPfdArVbLbSaTCZIkQaVS4ZdffsGwYcNw9epVq7varVu3xpw5c/DUU09hwYIF+O6775CYmChPP336NNq1a4eEhAT06dOn2teu7tvBiIgI5OTkwN+fRZerKdQbMXv9AfxxPFNu6xDiizVTotEi0EvByAhgPrqrlX+dxsIfjsp/B3lr8dnUGPRoEaBgVMR8JGdnNgu5CK5cFItK/7UUxRCw+lsIVFju+i6l1SoJrZv4NHh7mJPkzsxmAQGU52VZTgOw+ttqHlEp71E6Q4C3FjqNuraXuyZ2u6N98uTJOufx8PDAnj17MHLkSDRt2rTK9KFDhyIpKcmqbfLkyejSpQueffZZREREQKvVYsuWLRgzZgwAIDk5GampqYiNjQUAxMbG4rXXXkNmZiZCQkIAAL/99hv8/f3RrVu3GmPT6XTQ6Rr+LSM5viv5JZiyZj8OpmXLbf1aB2HFxH4I9PZQLjCSMR/dixAC721JwTu/n5DbQvx0WDctBh1D/RSMjADmI9lWxcJWoLx4BSpeOJdOByDPU91yFYtjq2UqFdKu5npzMqfIgGKDCRIASZIgSYBKksr+Lm+Tp5e1A4AECRVHUyydr3x+VFqGQy+6huq+pLKQi9tqCltUyks5Z8sKXFFh+YpfZlVeL+TlLNNsm89+nlqbrk/xQYDXrl2LefPmVVto+/n5oUePHlZtPj4+aNKkidw+depUzJ07F8HBwfD398fjjz+O2NhYDBgwAABw2223oVu3bhg/fjzeeOMNZGRk4MUXX8SsWbN4oeBEzGaBg+ey8VNSOs5lFUKrlqCSgEK9CZm5JcguNsBTq0avFgHoHOoHjVqNXhGld70OpGXjQlYhLlzNx7bjl1Forvv19p+9ishXf6t2mgZAq6beGNIpBHdGhqN3y0D2Qk4uyWwWOHIhFxl5RfjjyEUkX8xFTpEBQgCFBhM8NWr0ahmAQR2aokhvhslsRvLFXOxKuYzMPD1Kask1TzXg6aFBbNsgFOpNuJivR5C3B0b1CcfJiwVY8ddped6WQV5YP20AWjXxltuMRjM2HbyA+DNXcPxiPmA2o9BgRpC3CkfT85FTXPOLN/fT4vYeYbi7TwTzt5EUFxsxe82f+P10UY3zzBvaDhMGtYem0tCJNV3QV/y7fN6K81l/ruUX/zXP4+wqXnTWtm1CLmRrvqgtnS4qXByj7IJYVFhP+cW05a6v1QV0hXksd5As01y16K1JfqEe45ftQk6l9tk3BGDuHYMa9Tik15vwdNxv+P60qcq0fm19sHBkH+h0trurZyFJpddulpxWqayL9yrzl2Wt/NZUKuYt+3jl3JYgWe2n8rosM1aYJKmqfjFQcRb5i4JKxyHAOlfqOpRYpte0y1csJit/gYQK02patrZMsqzLXGHG8kK10mtWKpxdOVfPXM7GlDUHq7QvHdceo3p2sclr2O3R8fry8/PDwYMH0a5du3rNP3jwYERGRmLp0qUAgOLiYjz99NP4/PPPUVJSguHDh+PDDz9EWFiYvMzZs2fx6KOPYtu2bfDx8cHEiROxePFiaDT1/54hNzcXAQEBfAxHATtTLuOFjUk4fblQ6VCq1bapN14b1RMDO1T9sojsg/lofztTLmPZ9lOIP5OFQkM9vp2yk/bNfLBu2gCEBZT307H8z1NYuuUkCkqqXiReK+Zvw9WVj1NX78WW4/Uf0nPmTe1wf/8IW4ZYq4oX1+VtZf+tMqW65Wtvr6ngr27+ykVpTao8HonqL4Qrb1tN81FpEWIyC5jNAqay/7f85try/6YK0yrPZ72M5f9Ll136WzJy6zherZ8WY7PjUG05+cK3h7Bub1oNS5aLbReE1+7pZZN4iBzNLW9vr3OeM4tHNvh1nK7QVgov7JWxM+UyZq1PwNVCg9Kh1CrIW4sPxvblxXojYT7a186Uy3h+QxLSs4tQYlLuFBHqp8NPT96IJr7lTx8t//MUFm0+DrMNw2L+Nkxt+XitRbZFYxfb7sbyiHWV4rFi4VilaCydz1xWPFa7jCgrQM0CpkoFauV1y39XKVBR72XKi91aYqptOyq0OQJbFds15WR9i2wLFtu2U/knDlY/dwCqPlpd6ekPoNLvi1FpeoXlUc1y1uuv5ucZFb68EzUsI0+3Wr7y49+W3z6XBmKu8kSL5e56helW21RhuqhmeVTz3ljaK2xbbe/TtwfO1/tza2ixrfij40Q1MZsFPth60uGLbADILjTgg60pGNCuCR9DJadmNgss234KOYUlihbZAFBkMCKgwu+ljEYz3t+aYtMiGwCuFhrwwdaTzF8bKy42XleRDQBrdp3GmL4toVbb5vMwWxWA9bhbWVOBV59is57L1HR3tHJMNReyVbej1rgrvQY5nsdX7MG+1++wy3FIrzddU5ENALv+voqPtp6CSi1V6WiqSkFW6be2Vo8rA1aPJlsVZLAUdFWLqCqFWVlRWqUwq1DYlb9+pSJUfp3qC97qtq/iMlbbUN02CpTFVjpv5WKUnNPGpOMNeoychTY5rCMXcnHkgnOMBSlQHm/PlgFKh0N03Y5cyMWpzHxIkgpAwx/Nboi8YhO+P5SOe/q2AAB8fygdeUVGu7wW89f2Fv507LqXLTIITP9sPwK9tXXeraz6d9Wik9e5ZAuqss7CNCoJKpUEtUoq/1sq+1sFqKXS6Rk5xSgx1v+nN1cAux2HPvrz7+ta7quEczaOhMh5zFl3CqMWs9AmF5RVqIfe6DyXR3qTGVmFeqXDIGqQrEI9DKbSRzGVJgCczy7vm+F8diHsFZbeJJi/NnYmq6Bhy18pLK08SDGlhSSgUankAlJdocC0/L9aJcnFZek8ZQWpukIBWrFALZu3pgLV0l5xebU8n/VyaglQq1Wl/1VVej2r17fejqqvL1ltn7ricmpVaSde19h53phlO6+p0AZgt+PQ2QbmIzkuq17iUd4/RHkP8mUdyJV16qaqOL3SchJKc1HudK7syyXr9VXfI72qrKFyB3Klr1fWEZ7V61mWK11A1cC4reKRqrarJOmaHhu3BbsV2qmpqYiIiKhyUBJCIC0tDa1atQIAPPzww/yNJVUr2NsDHhoJTvDkOADAQ61CMIcDIycX7O0BrVpyiEJbAtAisLyn8RaB3lABdim2PdQS89fG2gT7YIcTVcoSIBdmlQvC0v+WFWqWgq5yoVZHYVe1gJOgVlcq6CzzV15vHQWqdeFZeb2Vl6//dlDDNPPVXfPP3+x1HGodfH3jdbcK9kbvlgFVhuqqWLwAkL+IqFLswLposyrKYF1kWS9XQ5EGqdJ6qlkelmKzrDiTABWqKb7qUURaTb/GwrPya1W37bUVrBV7aK/43lRenurPZQrttm3bIj09XR672iIrKwtt27aFyVT6SOKyZcvsFQI5ue7h/uge7o8dp7KUDqVOEsrjJXJm3cP90T7EF4fPZSsdCvy9NLirV3P577t6NcfL3x9Gjh0eH2f+2t4/7+iKdXtTr2tZrQqYNaQjNOrqi8UaC9qaCtSKdyutCtTygpMXrGRrb43pibuX7ar3/E0Aux2HZt7UDkt+P3HNy308tq9dhvoiUsKqib2rHdKrJkvHtW/Q69mt0BZCVHvSys/Ph6enZzVLEFlTqSTMGtIRR9Mdv9fxQG8tZg3pwI6UyOmpVBIevbk9nt+QhEK9GSUK3dqWAMwe0sFqTGWNRoXZQzrYpdfxWUM6Mn9tzNNTg6Fdml1Xh2hTb2iHuyPD7RCV86ruLhlgfaes8qOSlrG9rOavvK5q7qhZ1ltZWd9OlcYul6zapEqvWXE1lcc9rrqN1vNVp/JgOVada1WI0WqeCkOhVVxO7qW5mteo3C4vL8rXWX185bMFeGkR6qfDxbySGrenov9Mi7HbccjDQ41x0RHX3Ou4qxTZlfdtyx1q+U51hbvflffd6vLCalxuVM2XynkmRDW5IZXnj2Ud1X36Ffc0CajXfl5b/lbXVrHH8Rr3f1H9fJbO4KrPpWqWqSEXK26vvQbFatM08Jrmb+h42jYf3mvu3LkAgHfffRfTp0+Ht3f5Y38mkwl79uyBWq3Gjh07bPmydsfhhJTDcbSpMuaj/Sk5jraHWsIzwztj+k3Vf5PMcbQdi7OPo32tpLLHVCv+TlBV6YK94mOnFYvZio+aAtUUzJUfE7W08267Uxu4aAsu5BTXOo+rjKNdJQ8q5whQ9li0ZJUjpc2lO3/FHKj4+DRQc3Fa03j1zB/nVt6rvPUXaRW/VCjvMb7CF27m6r8UsBT+0a9tqfO1HXIc7SFDhgAAtm/fjtjYWHh4lP/WxMPDA23atMG8efPQsWNHW76s3fHCXllms8DBc9n4KSkd57IKoVWXHnwL9SZk5pYgu9gAT60avVoEwEurxlfx51FkKL8I790yAOF+GmxPvoLCWmqGMF8Jmfmi1t+AagC0auqNIZ1CcGdkOHq3DOSdsEbGfGwcZrPAkQu5yMgrwh9HLiL5Yi5yigwQAig0mOCpUaNXywAM6tAURXozTGYzki/mYlfKZWTm6VFSR33uqVVhSMcmKNSbcDFfjyBvD9zTtwVGR7a0upNdHaPRjE0HLyD+zBUcv5gPmM0oNJgR5K3C0fR85BTX/OLN/bS4vUcY7u4Twfy1gfrkY3GxEbPX/InfTxfVuJ5HbmiFe/u1ue4hvaRqLtxVqvJiteJFfuWC1nIxb7nIV0mlj5hb/t9SJHBfoeuVm1+Cwf/6HZV/DDf7hgDMvWOQTfetunJSrzfh6bjf8P3pql9WDmjvi7fv6QcvL215PqnKvziq/FtlqxxiQUtO5ETGFdy2dHeV9qXj2jf4TraFzQtti8mTJ+Pdd991mYtgXtg7h1+OZOCJzw/IvXxKEvDyXd0xcWAbZQMjm2I+Opf9Z7IwefU+5BWX/rZakoDX7+mJh6JbKRwZ2cK15OPZKwUwVXjuv+KFu0plfWfYMk2+o6WyLnolqaz3aBbARFZ4jiRyDHb7jXZcXJy9Vk1Urc92n8VLmw7Lv9300Kjw7gORGNGzee0LEpHd/HXyMqZ/ul9+wkStkrDk/t74R2QLhSMjJYQFeMp3i1kcExGRK7NboV1QUIDFixdjy5YtyMzMhNls/Rjf33//ba+XJjcjhMDbv57A+1tT5DZ/Tw2WT+iHmHZNFIyMyL39eiQDs9YnwGAq/fZLq5bw4bgo3NotVOHISCk6jWt0rERERFQXuxXa06ZNw/bt2zF+/Hg0b96cv9sguzCYzJj/bRK+iT8ntzUP8MSaKdHoFOqnYGRE7m1T4nnM/eqg/Jiwl1aN5RP64YaO7HSMiIiIXJ/dCu3Nmzfjxx9/xKBBg+z1EuTmCkqMmLU+AduSy3uz7RTqi9WToxEe6KVgZETu7fO9qXh+Q5LcI6ifpwarJ/dHVOtgZQMjIiIiaiR2K7SDgoIQHMyLKrKPy/klmLJ6Hw6dy5HbotsEY/mEfgjw1ioYGZF7W/G/v/GvH4/Jfwd5a/HZ1Bj0aBGgYFREREREjav28VMaYOHChViwYAEKCx1z7GNyXmevFGDMsp1WRfaIHmH4dGo0i2wihQgh8M5vJ6yK7FA/Hb6eGcsim4iIiNyO3e5ov/322zh16hRCQ0PRpk0baLXWBVBCQoK9XppcWNK5HExevReX8/Vy28TY1lhwV3eo2YMtkSKEEHjtx2NY8ddpuS0iyAvrpw9ARLC3gpERERERKcNuhfaoUaPstWpyU9tPXMKja+NRqDfJbc/e3gUzb27HzvaIFGIyC7y4MQmf702T2zo088XaaTEIC/BUMDIiIiIi5dit0H7ppZfstWpyQ/+NP4dn/3sIxrIejDUqCW/c2wuj+7ZUODIi92UwmfH0Vwfx3cELclv3cH98OiUaTXx1CkZGREREpCy7FdpEtiCEwLLtp/DGz8lym7eHGh89HIWbOjVTMDIi91ZsMGH2+gP4/dhFuS2qdRBWTeqPAC/2lUBERETuzaaFdnBwME6cOIGmTZsiKCio1sd5s7KybPnS5IJMZoFXvz+CNbvOym1NfD2welI0erZk50pESikoMWLGp/ux49QVuW1QhyZYPqEfvD34/S0RERGRTa+I3nnnHfj5+QEAli5dastVk5spNpjw1JeJ2Hw4Q25r08Qba6ZEo3UTHwUjI3JvOUUGTFm9D/Fnr8ptt3YLxX8e6gNPrVrByIiIiIgch00L7YkTJ1b7/0TXIqfQgOmf7cfe0+VPPfRuGYCVk/qjKX/3SaSYK/klGL9yL46m58pt/+gdjrfu7w2t2m6jRRIRERE5Hbs+42cymbBx40YcO1Y6rmr37t1x9913Q63mXQ+q3oXsIkyK24sTF/PltsGdm+GDsX3ho+MjqURKycgpxrgVu3HqUoHc9lB0K/xrVA8OrUdERERUid0ql5SUFNxxxx04f/48OnfuDABYtGgRIiIi8OOPP6J9+/b2emlyUskZeZi4ai8ycovltnujWmLR6J68W0akoNQrhRi3YjfSrhbJbdNvbIvn7+jKofWIiIiIqmG36uWJJ55A+/btkZaWhoSEBCQkJCA1NRVt27bFE088Ya+XJSe15+8ruPejnVZF9uwhHfDmvb1YZBMp6OTFPNz30U6rInvurZ1YZBMRERHVwm53tLdv347du3cjODhYbmvSpAkWL16MQYMG2etlyQn9lJSOOV8kQm8yAwBUEvDKP3pg/IDWCkdG5N4On8/B+JV7cLXQILe9OLIrpt3YTsGoiIiIiByf3QptnU6HvLy8Ku35+fnw8PCw18uSk1mz8wxe/v4IhCj9W6dR4d0H++D2HmHKBkbk5vafycKkuH3ILzECACQAi0b3xIPRrZQNjIiIiMgJ2O2Z3DvvvBMzZszAnj17IISAEAK7d+/GzJkzcffdd9vrZclJCCHw75+P46XvyovsAC8t1k2LYZFNpLC/Tl7G+JV75SJbo5Lw7kN9WGQTERER1ZPdCu333nsP7du3R2xsLDw9PeHp6YmBAweiQ4cOePfdd+31suQEDCYznv76IJZtOyW3hQd44puZsejXJriWJYnI3n49koHJq/eiyGACAHhoVPjo4Sjc3Ttc4ciIiIiInIfdHh0PDAzEpk2bkJKSgqNHjwIAunXrhg4dOtjrJckJFJQY8ei6BPx54pLc1iXMD6snRyMswFPByIhoU+J5zP3qIEzm0sdMvDzUWDGhHwZ1aKpwZERERETOxa4DE69cuRLvvPMOTp48CQDo2LEj5syZg2nTptnzZclBXcorwZTV+5B0PkduG9AuGB+P74cAL62CkRHR+j2peGFDEsp+yQE/Tw1WT45GVOsgReMiIiIickZ2K7QXLFiAJUuW4PHHH0dsbCwAYNeuXXjqqaeQmpqKV1991V4vTQ7ozOUCTFi1F6lZhXLbyF7NseT+3tBp1ApGRkTL//wbr/10TP472McDn02NRvfwAAWjIiIiInJediu0ly1bhuXLl+Ohhx6S2+6++2706tULjz/+OAttN5KYlo2pq/fhSoFebps8qA3+ObIbVCqOw0ukFCEElv5+Eu9uOSm3hfrrsG7aAHQI8VUwMiIiIiLnZrdC22AwoF+/flXao6KiYDQa7fWy5GC2JmfisbUJcsdKAPD8HV0w/cZ2kCQW2URKEULgXz8ew8q/TsttEUFeWD99ACKCvRWMjIiIiMj52a3X8fHjx2PZsmVV2j/55BOMGzeu3utZtGgR+vfvDz8/P4SEhGDUqFFITk62mqe4uBizZs1CkyZN4OvrizFjxuDixYtW86SmpmLkyJHw9vZGSEgInnnmGRb8dvb1/jRMW7NfLrK1aglLH4jEjJvas8gmUpDJLDD/2ySrIrtDiC++eXQgi2wiIiIiG7B7Z2i//vorBgwYAADYs2cPUlNTMWHCBMydO1eeb8mSJTWuY/v27Zg1axb69+8Po9GI559/HrfddhuOHj0KHx8fAMBTTz2FH3/8EV9//TUCAgIwe/ZsjB49Gjt27AAAmEwmjBw5EmFhYdi5cyfS09MxYcIEaLVavP7663Z8B9yTEAIfbE3BW7+ekNt8PNT4eHw/3NCRvRcTKclgMmPul4n4/lC63NY93B+fTY1BsI+HgpERERERuQ5JCCHqnu3aDRkypH4BSBL++OOPeq/30qVLCAkJwfbt23HTTTchJycHzZo1w/r163HvvfcCAI4fP46uXbti165dGDBgADZv3ow777wTFy5cQGhoKADgo48+wrPPPotLly7Bw6Pui8vc3FwEBAQgJycH/v7+9Y7X3ZjMAi99dxhrd6fKbU19PbB6cjR6tGDHSmQbzMfrU2wwYdb6BGw5lim3RbUOQtzk/vD3ZM//dH2Yj0SOhTlJ5Bjsdkd769atdllvTk7p0FDBwcEAgPj4eBgMBgwbNkyep0uXLmjVqpVcaO/atQs9e/aUi2wAGD58OB599FEcOXIEffr0sUus7qbYYMKTXxzAL0fKH9tv29QHn06J5uOoRAorKDFi2pr92PX3Fbnthg5N8cmEKHh72PXhJiIiIiK341RXV2azGXPmzMGgQYPQo0cPAEBGRgY8PDwQGBhoNW9oaCgyMjLkeSoW2ZbplmnVKSkpQUlJifx3bm6urTbDJWUX6jFtzX7sP3tVbouMCMSqSf35OCo1GPOxYXKKDJgUtxcHUrPlttu6heI/Y/tweD26ZsxHIsfCnCRyTHbrDM0eZs2ahcOHD+OLL76w+2stWrQIAQEB8r+IiAi7v6azOp9dhHs/2mVVZA/tEoL10/mbT7IN5uP1u5xfggc/2WVVZP8jMhwfjOvLIpuuC/ORyLEwJ4kck9MU2rNnz8YPP/yArVu3omXLlnJ7WFgY9Ho9srOzrea/ePEiwsLC5Hkq90Ju+dsyT2Xz589HTk6O/C8tLc2GW+M6jmfkYvSHO5CSmS+3Pdg/Ah+P5+OoZDvMx+uTnlOE+z7ahWPpeXLb2OgIvHN/JLRqpzn8k4NhPhI5FuYkkWNy+EpICIHHH38cGzZswLZt29C2bVur6VFRUdBqtdiyZQvGjBkDAEhOTkZqaipiY2MBALGxsXjttdeQmZmJkJAQAMBvv/0Gf39/dOvWrdrX1el00Ol0dtwy57fr1BXM+Gw/8orLh0l7cmhHzBnWkcN3kU0xH6/d2SsFGLt8D85nF8lt029si+fv6Mr8pAZhPhI5FuYkkWNy+EJ71qxZWL9+PTZt2gQ/Pz/5N9UBAQHw8vJCQEAApk6dirlz5yI4OBj+/v54/PHHERsbKw8rdtttt6Fbt24YP3483njjDWRkZODFF1/ErFmzeGC6Tj8cuoCnvkyEwVTaab1KAv41qifGxrRSODIiOnExD+NW7MGlvPLf7D01rCOeGMovwYiIiIgag8MX2suWLQMADB482Ko9Li4OkyZNAgC88847UKlUGDNmDEpKSjB8+HB8+OGH8rxqtRo//PADHn30UcTGxsLHxwcTJ07Eq6++2lib4VJW/XUaC388CsvAcDqNCu+P7Ytbu4XWviAR2V3SuRyMX7UH2YUGuW3Bnd0w5Ya2tSxFRERERLZkt3G0XQ3HJATMZoF//3wcH//5t9wW6KXFykn9ENU6WMHIyN0wH6u370wWJsftQ35J6c85JAD/HtML9/dnxzhkP8xHIsfCnCRyDA5/R5scg95oxv99cxAbEy/IbS0CvbBmSjQ6hPgqGBkRAcD/Tl7C9DX7UWw0AwDUKglLH4jEXb3DFY6MiIiIyP2w0KY65ZcYMfOzePyVcllu69rcD6snRyPU31PByIgIAH45koHZ6xPkPhM81Cose7gvhnblzzmIiIiIlMBCm2qVmVeMyXH7cORCrtw2sH0TfDQ+Cv6eWgUjIyIA2HDgHOZ9dQimsl8BeWnVWDmxHwZ2aKpwZERERETui4U21ejvS/mYuGov0q6WDw90d+9wvHlfL+g0agUjIyIAWLfnLF7ccBiWjjb8PDVYMyUafVsFKRoXERERkbtjoU3VOpB6FVNW78PVCj0XT7uhdAxelYrDAxEp7ePtp7Bo83H57yBvLdZOi0H38AAFoyIiIiIigIU2VWPLsYuYtT4BxQaz3PbiyK6YdmM7BaMiIgAQQuCd307gvT9S5LZQfx3WTx+A9s3YMSERERGRI2ChTVa+2JuKFzYehslc+jCqVi3hrft64x+RLRSOjIiEEHj1h6OI23FGbosI9sL6aQMQEeytXGBEREREZIWFNgEovYB/b0sK3vn9hNzmo1Pjk/H9MIidKhEpzmQWmP/tIXy1/5zc1iHEF+unxSCEvf8TERERORQW2gSjyYwF3x3B+j2pclszXx1WT+nP33sSOQCDyYw5XyTix6R0ua17uD8+mxqDYB8PBSMjIiIiouqw0HZzRXoTHv88Ab8fy5Tb2jX1wZop0XwUlcgBFBtMeHRtPLYmX5LboloFIW5Kfw6xR0REROSgWGi7sasFekxbsx/xqVfltj4RgVg1qT+CeJeMSHEFJUZMXbMPu//OktsGdWiC5RP6wduDh28iIiIiR8UrNRsxmwWOXMhFVqEewd4e6Brmh2MZecgq1CPQSwuj2YyfD2fg/NUitAz0wohezdG7ZaA8VJZl+Ut5xTh4LhvJGbnILTIixM8DTXx1AIDL+SW4mFOMzLwSFBlMMBgMyC4SMNpoG/x0auSX6DE5bjdyCg24lFuM/FpWHuKrhbdOi8gW/hg/qC36RARx6C9yGJaculxQguwCA4K8tWjiq0P3cH+oVFKN07uG+eFIei4OpGVDEkCPlv44fakACWnZKCoxIthHC5VKhTB/HXKLjbh4tRCJ53OgN5qhUUsI8vKAkAT0RjOu5OuRU2xAUYkZJgFIAEw1xNvEW40ATx00akBSSdBp1EjJzEdhhd7/AWBHyhV0W/BLvd+HZj4aTLmxHQa2b4aeLQKYo6Qoo9GMDYnn8fOhc9hyIqvaeYK8NPjPg5EY2DHkuvbXyudjS87TtTGbBZb/dRiLfkqtdnqYnwfGRLXEk7d0goeHupGjs72a9puK7YFepU8RZRcZEOilhVkIHEjLxrlLedh8MA3pRdWv+837umBMn3YOux+azQI7/s7E+BX7q0wb3z8I90Z3a7TzB/O39D34aNsBvPFrerXTPdXAvf1aYExUa6tawl1Ul5NZhXpkFxgQ4KVBTpERQd5aBHhrsSM5DW9tSauyjlYBavzw+BD4l9VY9iIJIYRdX8FF5ObmIiAgADk5OfD397eatjPlMpZtP4VTmfkwmATMQsAkBNQSYDQD+cUGmCq9yxKANk298dqongCAZdtP4WBaNnKLbVU2N74wfx2W3B+Jgew8jeystnwEynPy6IUc5BYbYTYLqFQS/D216Bbuj5s6NsWfJy9Xme6lVcMkBPQGM0xCwOxiR8eeLfwxf0RX5ijZVF35aLH8z1N4+7cTVkNH1katAj6bEnNN+2vl87FWLaF9iC8evbk99/trsDPlMsau2FPv+cdFR+C10b3sGJF91bTfWM4VpzLzUVBiQpHBBEkCNCoJJUYzjNd4klg/7dr25+tV35wESrd9/Ko9MNWRlo1x/mD+XnvutS2rJdzp/bHsI5acFELADECYBQQAqex7h/qkZ3iAJ3bOH2q3eFlo11NNB62dKZfx/IYk5JcYEeTtAb3JjPNXi2Aqu3A3l33oNfHVqeGj0yC/2IgCfU33upyHb1lP5e6S8KSMur74en5DErIK9CgymGAWAiqpNBclSYJOI6HEKKDTSNCXfTGmkiSYTKUHagsJqDV3nVWovw7v8AsxsqH6XNQv//MUFv10HPUrsctJErBuav2Kk8rnYw+1CnqTGVcLDfDVqfH6Pe5zMdoQ13qhb+GsxXZN+83F3BIU6o3w0anhq9PgUl4JTGYBIXDN+3FFjVFs17fQ3plyGeNW7Kn3uc6e5w/m7/XnXpC3Fh+M7esW749lH9FpVLiUVwJjpWu362HPYltll7W6CbNZYNn2U8gvMSLM3xM6rQpX8vUAAA+NVHpArmMd+SUmXMnXo9AFimygdHve/+MEzK52K5CcgiUn84oNMJlL70hrVSpoVCpoNSoIIVBkKL0LUWQwwywEtCoV1JJUJVdddQ++kl+CD7edYo5SozEazXj/j5TruhgSAnhvS93nlMrnY0+tGiqVBE+tGmH+OuSXmLBsO/f7upjNAq/8cPC6ll2/Nw16J7uWqWm/0WlUMJnNMJkFDEYzsotKn0zUqKqeK67V4p8OO8R+aDYLvPPL0Wvanou5JfhwW4rN42f+lr4HL35b9dH9+rhaaMAHW0+6/Ptj2UdC/XTIKTJc8xMlNbmQU4zc/BKbrKsyFtoNcORCLk5l5iPI2wOSJKFYb0aJ0QS1SkLp/bD6MdajIHcmh87n4siFXKXDIDdkyUlvDw30JjM0KglS2TNEEqTSp0wEoJZKHylSSaXTBVy3sK7MaAaSM/KYo9Rovj+U3qCfRe07k13n/lr5fFyRJEkI9NbiVGY+9/s6HLmQi+SM4utaVgD46M+/bRuQndW03xQbzKXnELUEvcmMEkPp+QTVfCl7rQ5dKHCI/fDIhVwknMu75uWO2+H8wfwtfQ/+zrr+L6qOXHDta++K+0iJUaDEaIbaBl98WYyL22ejNVljod0AWYV6GEwCHurSt9FoNkOI0kfd3PmBfINJIKtQr3QY5IYsOamSpNJcrGuBsjx1t3zVm8zMUWo057MLG3QxZBJ1n1Mqn48r06lVMJh5bqpLQ9+fs1kFNoqkcdS031iu51RlX8oKIUp/TmSjc4Uj7IdZhfoq/QfVhz2u8Zi/Dd8n9C5+7V1xH7HkJ4TtbpJk5NTQk2EDsdBugGBvD2jLvu0EAI1KJRfZknt1AGhFq5YQ7M3hwajxWXLSLERpLta1QFmeulu+eqhVzFFqNC0Cva/hGa+q1FLd55TK5+PKSkxmaFU8N9Wloe9P62AfG0XSOGrabyzXc+ayYtvy5JOtzhWOsB8Ge3tAfR3bY49rPOZvw/cJDxe/9q64j1jyE9K1PD9cu7AALxutyRoL7QboHu6P9iG+uFpogBACnh4q6DRqmMzX9iCqVi3ZbEdxBL1a+KN7eO29XBLZgyUnC/Wmsm89BSz9PQqI0t7FJcAk36kQ8p0KV8rB2mhUQOcwP+YoNZq7ejWHv+f1jybav01gnftr5fNxRUIIZBca0D7El/t9HbqH+6NzmOd1LSsBmHlTO9sGZGc17TeeWlXpOaTsDppOW3o+Qdn5oiF6hfs4xH7YPdwffVv6XfNyXexw/mD+lr4H7YKvf5i87uGufe1dcR/RaSz9KDQ8Hy3WTe5vozVZY6HdACqVhEdvbg9fnRoZuSUoNpjRxLf02yS9UUCtqruA9tWpEezjAW8XGIMSKN2e2bd0crsx/cgxWHLSz1MDtUoFlQQYzGYYzWYYjGZIkgQvrQoaVel/VZIEg7l0KK/Ke6yr7sFNfXV4bHB75ig1Go1Ghdm3dLiuCw5JAp4YWvc5pfL5uMhggtksUGQwISO3BL46NR69mft9XVQqCS/d2fu6lh0bHeF042nXtN8UG81Qq1RQqyRoNSoEeGnLhmxt+IX9c3f0cIj9UKWS8NTwbte0PaH+Ojw2uIPN42f+lr4H/xrd77qWDfLWYtaQji7//lj2kYt5evh7aUv7TbCB8ABPu42nzeG96qne42ibS++acRxtIvvhONrXh+Nokz047DjaZgGtyv3G4bUFtx5Hu8J+YzWOtt6EIj3H0W60cbTdNH85jnbtrMbRLstJjqPtAuo6aJnNAkcu5CKrUI9gbw90DfPDsYw8ZBXqEeilhdFsxs+HM3D+ahFaBnphRK/m6N0yUP72ybL8pbxiHDyXjeSMXOQWGRHi54EmZd+yXM4vwcWcYmTmlX7bZzAYkF0k0JDSXALQoaknBrZrigPnclFiMsFLq0JOoQGXcouRX8vKQ3y18NZpEdnCH+MHtUWfiCCX/jaNHEd9LiIsOXW5oATZBQYEeWvRxFeH7uH+8hj31U3vGuaHI+m5OJCWDUkAPVr64/SlAiSkZaOoxIhgHy1UKhXC/HXILTbi4tVCJJ7Pgd5Y2kNtkJcHhCSgN5pxJV+PnGIDikrMMJV1zlZTn6JNvNUI8NRBowYklQQfDw2EECg2GHH6UiH0ZsiPvV/LQbuZjwZTbmyHge2boWeLAOYo2dy1XNQbjWZsSDyPnw+dw5YTWdXOE+SlwX8ejMTAjiHXtb9WPh9bcp6ujdkssPyvw1j0U2q108P8PDAmqiWevKWT093Jrk5N+03F9kAvLQAgu8iAQC8tzELgQFo2zl3Kw+aDaUivoT+lN+/rgjF92jXafngtOQmUbvuOvzMxfkXV4aXG9w/CvdHdGu38wfwtfQ8+2nYAb/yaXu10TzVwb78WGBPV2qqWcBfV5WRWoR7ZBQYEeGmQU2REkLcWAd5a7EhOw1tb0qqso1WAGj88PsRud7ItWGjX07UetBrbb0cvYvb6BJQYS7+SlAD8885umHJDW2UDI7IDR8/Hmny4NQVv/JIs/x3srcXaaTHoFh6gYFREDeOs+UjkqpiTRI7h+nsnIYexfk8qXtyYJD8ioVVLeOeBSNzZK1zZwIgIQGlnLm/+kowPt52S20L8dPh8xgC0b+arYGREREREZA8stJ2YEAJLfz+Bd7ekyG2+Og0+mRCFge3d47caRI7ObBZ46fsj+GzXWbmtZZAXvpgxAC2DvBWMjIiIiIjshYW2kzKazHhhw2F8ub/8dwfN/HT4dEo0ujbnY0JEjsBkFvi/bw7ivwnn5bb2zXzw+fQBCPG/viF0iIiIiMjxsdB2QkV6E2avT8CW45lyW/tmPvh0agxaBNpnwHUiujZ6oxmPf56AX45clNu6NffHumkxCPLxUDAyIiIiIrI3FtpOJqtAj8lxe3HwXI7cFtU6CCsn9kOgNy/eiRxBscGEGZ/ux58nL8ttfVsFYs2UaPh5ahWMjIiIiIgaAwttJ5KWVYjxK/fgzJVCue3WrqH4z9g+8NQ6/9AaRK4gv8SISXF7sf/MVbltYPsmWDmxP7xcYAgcIiIiIqobC20ncfh8DibF7cXlfL3cNja6FRaO6gG1m42fR+Sosgv1GL9yL5LOlz9xMrRrCD4c1xc6DYtsIiIiInfBQtsJ/O/kJTzyWTwK9Sa57enbOmH2kA6QJBbZRI7gUl4Jxi7fjZOZ+XLbXb2a450HIqFRqxSMjIiIiIgaGwttB7cp8Tye/uogjGWDZKslCYtG98T9/SMUjoyILC5kF+HBT3YjNav8Zx0P9o/A6/f0hIpPnBARERG5HRbaDkoIgU/+/BuLNh+X2zy1KiwbF4UhXUIUjIyIKjp9KR9jV+xBek6x3Db1hrZ4cWRXPnFCRERE5KZYaDsgs1lg4Y9HEbfjjNwW5K1F3ORoREYEKhYXEVk7np6LcSv24EpBed8Jc4Z1xJxhnRSMioiIiIiU5lY/HPzggw/Qpk0beHp6IiYmBnv37lU6pCpKjCbM/jzBqshuGeSFbx8bxCKbyIEkpl7F/R/vsiqyX7ijK4tsIiIiInKfQvvLL7/E3Llz8dJLLyEhIQG9e/fG8OHDkZmZqXRospwiA8av3IufkjLktm7N/bHhsUFo29RHwciIqKJdp65g7Io9yC02AgBUEvD6PT0w/aZ2CkdGRERERI7AbQrtJUuWYPr06Zg8eTK6deuGjz76CN7e3li1apXSoQEAMnKKcd9HO7H3dJbcdkOHJvhqZiya+ekUjIyIKvrj+EVMitsrjwKgUUlYcn8kxsa0VjgyIiIiInIUblFo6/V6xMfHY9iwYXKbSqXCsGHDsGvXLgUjK3XyYh5GfbADJy6WDws0KjIcqyZFw1fHn9ETOYrvD17AjE/jUWI0AwA8NCp8OK4vRvVpoXBkRERERORI3KKKu3z5MkwmE0JDQ63aQ0NDcfz48WqXKSkpQUlJifx3bm6uXWLbdyYLU1fvkx9BBYBHbmqH50Z0YY/FRGUaKx9r8+W+VMz/NgllI+3BS6vGx+P74qZOHAWA3Isj5CMRlWNOEjkmt7ijfT0WLVqEgIAA+V9EhO3Hrf75cDoervA7TwnAy3d1w/w7OCwQUUWNkY+1WfnXaTz73/Ii289Tg0+nRLPIJrekdD4SkTXmJJFjkoQQQukg7E2v18Pb2xvffPMNRo0aJbdPnDgR2dnZ2LRpU5Vlqvt2MCIiAjk5OfD3929wTJ/uPIOXvz8iX7h7qFV454HeGNkrvMHrJnI19s7H2rz7+wm88/tJ+e8gby0+nRqNni0C7fq6RI5KyXwkoqqYk0SOyS0eHffw8EBUVBS2bNkiF9pmsxlbtmzB7Nmzq11Gp9NBp7N9J2RCCLz1SzI+2HZKbvPVabByYj/EtGti89cjcgX2ysfaCCHw+k/HsPx/p+W2ED8d1k6LQadQv0aNhciRKJGPRFQz5iSRY3KLQhsA5s6di4kTJ6Jfv36Ijo7G0qVLUVBQgMmTJzdaDAaTGfP/m4RvEs7JbaH+Onw2lRfuRI7EbBZ4ceNhrN+bKre1DPTCumkxaM2h9oiIiIioDm5TaD/wwAO4dOkSFixYgIyMDERGRuLnn3+u0kGavRTqjXh0bQK2n7gkt7Vv5oPPpsYgPNCrUWIgorqZzAJzv0zEpoMX5LZ2TX2wbloMmjNXiYiIiKge3KbQBoDZs2fX+Ki4PV3JL8HEuL04fL68F8io1kFYNbE/Ary1jR4PEVVPbzTjsXXx+P1YptzWJcwPa6fFoKkvH8sjIiIiovpxq0JbCalXCvHwyj1IzSqU227tFor/PNQHnlq1gpERUUVFeiOmrdmPHaeuyG2REYFYM7k/Arw9FIyMiIiIiJwNC207SjqXg4lxe5FVoJfbxsa0wsJ/9IBaxeG7iBxFXrEBE1ftRUJqttw2oF0wVk7oDx9PHiaJiIiI6NrwCtJOtiVn4rF1CSjUm+S2ubd2wuO3dOAY2UQOJCu/BA+v3Iuj6eU/7RjSuRmWjYuCpwefOiEiIiKia8dC2w6+3p+G575NgqlskGy1SsLi0T1xX78IhSMjoooycooxbsVunLpUILfd0bM53n2gN7QaFtlEREREdH1YaNuQEAIfbE3BW7+ekNu8tGp8MK4vbukSomBkRFRZ6pUCjF2xB+euFslt90a1xL9H94RarVIwMiIiIiJydiy0bcRkFnj1+yNYs+us3BbkrcXqyf3ROyJIwciIqLKTF/Pw8Mo9uJhbIrdNjG2Nl+7qBpWKRTYRERERNQwLbRtZvPmYVZEdEeSFtdNi0LqJj4JREVFlh85lY9KqvcgqNMhtjw1uj/+7vYuCURERERGRK+GtGxuZENsGzfxKx9ntHu6PDbMGscgmcjD7Tmdh3Io9VkX2M7d1ZpFNRERERDbFQttGIoK9ETepP0b0CMNXj8Siqa9O6ZCIqILtyZmYsGov8oqNAACVBLx8VzfMuqWDwpERERERkavho+M21KNFAJY9HKV0GERUyeakdDz5ZSL0RjMAQKOS8Po9PXF/f44EQERERES2x0KbiFzaf+PP4dn/HoKxbLg9D40Kb9/XG3f1Dlc4MiIiIiJyVSy0ichlfb43FS9sSEJZjQ1vDzX+82AfDO0WqmxgREREROTSWGgTkUv6dNcZLNh0RP7bz1ODj8dHYWD7pgpGRURERETugIU2EbmcfWeyrIrsIG8tVkzsh6jWwQpGRURERETugr2OE5HL6dc6CNNuaAsACPHTYe20GBbZRERERNRoeEebiFyOJEl4YWRX6LQq3NOnBTqE+CkdEhERERG5ERbaROSSJEnCM8O7KB0GEREREbkhPjpOREREREREZEMstImIiIiIiIhsiIU2ERERERERkQ2x0CYiIiIiIiKyIRbaRERERERERDbEQpuIiIiIiIjIhlhoExEREREREdkQC20iIiIiIiIiG9IoHYCzEEIAAHJzcxWOhMh1+fn5QZKkOudjPhLZH/ORyLEwJ4kcR33ykYV2PeXl5QEAIiIiFI6EyHXl5OTA39+/zvmYj0T2x3wkcizMSSLHUZ98lITlay+qldlsRnJyMrp164a0tLR6HeicQW5uLiIiIlxqmwBulzOpuE0tWrSo17f1ZrMZFy5cgBACrVq1cvr3w1U+V26HY2nodtT37pklH+s7vz04+2fG+JXjTLE7U07WxZne98oYuzIcLXbe0bYhlUqFFi1aAAD8/f0d4gO2JVfcJoDb5Uz8/f3rfUGgUqnQsmVL+bE4V3k/uB2OhdtRP5Z8dATO/pkxfuU4c+yVOVJO1sWZ33fGrgxnip2doRERERERERHZEAttIiIiIiIiIhtioX0NdDodXnrpJeh0OqVDsRlX3CaA2+VMGrJNrvJ+cDscC7fD+Tj7tjJ+5Thz7M7Mmd93xq4MZ4ydnaERERERERER2RDvaBMRERERERHZEAttIiIiIiIiIhtioU1ERERERERkQ25faC9atAj9+/eHn58fQkJCMGrUKCQnJ8vTz5w5A0mSqv339ddfy/NVN/2LL75QYpOwbNky9OrVSx5nLjY2Fps3b5anFxcXY9asWWjSpAl8fX0xZswYXLx40WodqampGDlyJLy9vRESEoJnnnkGRqOxsTfFSm3blZWVhccffxydO3eGl5cXWrVqhSeeeAI5OTlW63Ckz8mirs9r8ODBVWKeOXOm1Toc7fOqbZvqyilLTtb0WTlLTrpKHrpK3rlKnjUktywc4fOoj7rOz4Bj55GrHAMAYPHixZAkCXPmzJHbHDn+l19+uco+3qVLF6eI3Zn9+eefuOuuuxAeHg5JkrBx40ar6UIILFiwAM2bN4eXlxeGDRuGkydPWs2TlZWFcePGwd/fH4GBgZg6dSry8/PtHrszH294rHGc+KsQbm748OEiLi5OHD58WCQmJoo77rhDtGrVSuTn5wshhDAajSI9Pd3q3yuvvCJ8fX1FXl6evB4AIi4uzmq+oqIiRbbpu+++Ez/++KM4ceKESE5OFs8//7zQarXi8OHDQgghZs6cKSIiIsSWLVvE/v37xYABA8TAgQPl5Y1Go+jRo4cYNmyYOHDggPjpp59E06ZNxfz58xXZHovatispKUmMHj1afPfddyIlJUVs2bJFdOzYUYwZM8ZqHY70OVnU9XndfPPNYvr06VYx5+TkyMs74udV2zbVlVOWnAQgXnnlFTF06FDRokULcerUKVFUVOQ0OekqeegqeecqedaQ3LJwhM+jPuo6Pwvh2HnkKseAvXv3ijZt2ohevXqJJ598Um535Phfeukl0b17d6t9/NKlS04RuzP76aefxAsvvCC+/fZbAUBs2LDBavrixYtFQECA2Lhxozh48KC4++67Rdu2ba2OP7fffrvo3bu32L17t/jf//4nOnToIB566CG7x+7Mxxseaxwj/uq4faFdWWZmpgAgtm/fXuM8kZGRYsqUKVZt1R1QHElQUJBYsWKFyM7OFlqtVnz99dfytGPHjgkAYteuXUKI0gOlSqUSGRkZ8jzLli0T/v7+oqSkpNFjr41lu6rz1VdfCQ8PD2EwGOQ2R/+cLCpu180332x1wKnMWT6v2j6r2nLKlXLSVfLQVfLOVfLsenPL2VQ+FjhjHjnbMSAvL0907NhR/Pbbb1Y54ujxv/TSS6J3797VTnP02F1F5eOM2WwWYWFh4s0335TbsrOzhU6nE59//rkQQoijR48KAGLfvn3yPJs3bxaSJInz5883WuxCOP/xhscax8hXt390vDLLI4/BwcHVTo+Pj0diYiKmTp1aZdqsWbPQtGlTREdHY9WqVRAOMHKayWTCF198gYKCAsTGxiI+Ph4GgwHDhg2T5+nSpQtatWqFXbt2AQB27dqFnj17IjQ0VJ5n+PDhyM3NxZEjRxp9G6pTebuqk5OTA39/f2g0Gqt2R/ycLGrarnXr1qFp06bo0aMH5s+fj8LCQnmao39edX1WdeVUp06dAADbtm2r9rNyhpx0lTx0lbxzlTxraG45yudRX5XPz86UR856DJg1axZGjhxpFSfgHO/9yZMnER4ejnbt2mHcuHFITU11mthd0enTp5GRkWH1vgcEBCAmJsbqfQ8MDES/fv3keYYNGwaVSoU9e/Y0arzOerzhsUaZ+GuiqXsW92E2mzFnzhwMGjQIPXr0qHaelStXomvXrhg4cKBV+6uvvopbbrkF3t7e+PXXX/HYY48hPz8fTzzxRGOEXkVSUhJiY2NRXFwMX19fbNiwAd26dUNiYiI8PDwQGBhoNX9oaCgyMjIAABkZGVY7q2W6ZZqSatquyi5fvoyFCxdixowZVu2O9jlZ1LZdY8eORevWrREeHo5Dhw7h2WefRXJyMr799lsAjvt51fezqi2nBg8ejOeffx5paWl4/fXXERgYWOWzcuScdJU8dJW8c5U8s0VuOcLncS2qOz9nZGQ4fB458zHgiy++QEJCAvbt21dlmqO/9zExMVi9ejU6d+6M9PR0vPLKK7jxxhtx+PBhh4/dVVnet+re14rve0hIiNV0jUaD4ODgRn3fnfF4w2ONY+YrC+0KZs2ahcOHD+Ovv/6qdnpRURHWr1+Pf/7zn1WmVWzr06cPCgoK8Oabbyp24dK5c2ckJiYiJycH33zzDSZOnIjt27crEost1bRdFS8yc3NzMXLkSHTr1g0vv/yy1fKO9jlZ1LZdFYuWnj17onnz5hg6dChOnTqF9u3bKxh17erzWdWVU48++ijS0tLw119/4ZNPPqnyWTl6TrpKHrpK3rlKntkitywc6ThYm7rOz47KWY8BaWlpePLJJ/Hbb7/B09NT6XCu2YgRI+T/79WrF2JiYtC6dWt89dVX8PLyUjAycgbOeLzhscYx8dHxMrNnz8YPP/yArVu3omXLltXO880336CwsBATJkyoc30xMTE4d+4cSkpKbB1qvXh4eKBDhw6IiorCokWL0Lt3b7z77rsICwuDXq9Hdna21fwXL15EWFgYACAsLKxKb36Wvy3zKKWm7bLIy8vD7bffDj8/P2zYsAFarbbW9Sn9OVnUtV0VxcTEAABSUlIAOO7nVZ9tqi2nKudkdZ+Vo+ekq+Shq+Sdq+RZQ3OrMkc5DtakpvOzM+SRsx4D4uPjkZmZib59+0Kj0UCj0WD79u147733oNFoEBoa6tDxVxYYGIhOnTohJSXF4d97V2V536p7Xyu+75mZmVbTjUYjsrKyGu19d9bjDY81jpmvbl9oCyEwe/ZsbNiwAX/88Qfatm1b47wrV67E3XffjWbNmtW53sTERAQFBUGn09ky3OtmNptRUlKCqKgoaLVabNmyRZ6WnJyM1NRU+Td+sbGxSEpKsjrY/fbbb/D396/28UQlWbYLKL2jdtttt8HDwwPfffddvb4Zc7TPyaLidlWWmJgIAGjevDkA5/m8qtum6nKqppys7rNytpx0lTx0lbxzlTyrb27VxFE+j8rqOj87Yx45yzFg6NChSEpKQmJiovyvX79+GDdunPz/jhx/Zfn5+Th16hSaN2/u8O+9q2rbti3CwsKs3vfc3Fzs2bPH6n3Pzs5GfHy8PM8ff/wBs9ksf/lpL652vOGxxkHyVale2BzFo48+KgICAsS2bdushoEoLCy0mu/kyZNCkiSxefPmKuv47rvvxPLly0VSUpI4efKk+PDDD4W3t7dYsGBBY22Gleeee05s375dnD59Whw6dEg899xzQpIk8euvvwohSrvJb9Wqlfjjjz/E/v37RWxsrIiNjZWXt3STf9ttt4nExETx888/i2bNmineTX5t25WTkyNiYmJEz549RUpKitVnaTQahRCO9znVZ7tSUlLEq6++Kvbv3y9Onz4tNm3aJNq1ayduuukmeXlH/Lzq2geFqDmnHn30UeHj4yPmzZsntm7dKnbu3CkWLVokvLy8rD4rR89JV8lDV8k7V8mzhuSWEI7zedRHfc7PjpxHrnIMsKjcM78jx//000+Lbdu2idOnT4sdO3aIYcOGiaZNm4rMzEyHj92Z5eXliQMHDogDBw4IAGLJkiXiwIED4uzZs0KI0uG9AgMDxaZNm8ShQ4fEP/7xj2qH9+rTp4/Ys2eP+Ouvv0THjh0bZXgvZz7e8FjjWPFX5PaFNoBq/8XFxVnNN3/+fBERESFMJlOVdWzevFlERkYKX19f4ePjI3r37i0++uijaudtDFOmTBGtW7cWHh4eolmzZmLo0KFWF2FFRUXiscceE0FBQcLb21vcc889Ij093WodZ86cESNGjBBeXl6iadOm4umnn7YarkcJtW3X1q1ba/wsT58+LYRwvM/JorbtSk1NFTfddJMIDg4WOp1OdOjQQTzzzDNW4/sK4XifV137oBA151RNn+OECROs5nX0nHSVPHSVvHOVPGtIbgnhOJ9HfdTn/OzIeeQqxwCLyhe/jhz/Aw88IJo3by48PDxEixYtxAMPPCBSUlKcInZnVtM5YeLEiUKI0iG+/vnPf4rQ0FCh0+nE0KFDRXJystU6rly5Ih566CHh6+sr/P39xeTJk0VeXp7dY3fm4w2PNY4Vf0WSEE4wpgcRERERERGRk3D732gTERERERER2RILbSIiIiIiIiIbYqFNREREREREZEMstImIiIiIiIhsiIU2ERERERERkQ2x0CYiIiIiIiKyIRbaRERERERERDbEQpuIiIiIiIjIhlhok90MHjwYc+bMqff8GzduRIcOHaBWq69pOYszZ85AkiQkJibWOt/LL7+MyMjIa14/kbOTJAkbN26s9/zbtm2DJEnIzs5u9DhWr16NwMBAm74ukSPhOZLIcfD8SPbAQpscxiOPPIJ7770XaWlpWLhwISZNmoRRo0bVe/mIiAikp6ejR48etc43b948bNmypYHREjmf9PR0jBgxwqbrvJ6L8vrE8cADD+DEiRMNiIzItfAcSWQ/PD+SPWiUDoAIAPLz85GZmYnhw4cjPDz8utahVqsRFhZW43QhBEwmE3x9feHr63u9oRI5Jb1eX2t+NKa64jAYDPDy8oKXl1cjRUTk2HiOJLIfnh/JXnhHmxpFSUkJ5s2bhxYtWsDHxwcxMTHYtm0bgNLHb/z8/AAAt9xyCyRJwuDBg7FmzRps2rQJkiRBkiR5/ppUfizO8ljP5s2bERUVBZ1Oh7/++qvKN4zbtm1DdHQ0fHx8EBgYiEGDBuHs2bN1vpZKpcL+/fut2pcuXYrWrVvDbDZf0/tDZGuDBw/G7NmzMWfOHDRt2hTDhw+v8kjazp07ERkZCU9PT/Tr1w8bN26s9tHS+Ph49OvXD97e3hg4cCCSk5MBlD6+9sorr+DgwYNynq5evbrO2CrGYcnbL7/8EjfffDM8PT2xbt26Ko/GHTx4EEOGDIGfnx/8/f0RFRVVJf8qKygogL+/P7755hur9o0bN8LHxwd5eXl1xkrUGFztHCmEwLBhwzB8+HAIIQAAWVlZaNmyJRYsWHBd7xGRrfD8WGrKlCno1asXSkpKAJR+4dCnTx9MmDChzmWpflhoU6OYPXs2du3ahS+++AKHDh3Cfffdh9tvvx0nT560OjD997//RXp6Or777jvcf//9uP3225Geno709HQMHDjwul77ueeew+LFi3Hs2DH06tXLaprRaMSoUaNw880349ChQ9i1axdmzJgBSZJqXWebNm0wbNgwxMXFWbXHxcVh0qRJUKmYWqS8NWvWwMPDAzt27MBHH31kNS03Nxd33XUXevbsiYSEBCxcuBDPPvtstet54YUX8Pbbb2P//v3QaDSYMmUKgNLH155++ml0795dztMHHnjgumJ97rnn8OSTT+LYsWMYPnx4lenjxo1Dy5YtsW/fPsTHx+O5556DVqutdZ0+Pj548MEHq83Te++9Vy5eiJTmaudISZKwZs0a7Nu3D++99x4AYObMmWjRogULbXII7n5+BID33nsPBQUFeO655+Rtyc7Oxvvvv39dcVJVfHSc7C41NRVxcXFITU2VH3mbN28efv75Z8TFxeH1119HSEgIACA4OFh+bMbLywslJSUNfpzn1Vdfxa233lrttNzcXOTk5ODOO+9E+/btAQBdu3at13qnTZuGmTNnYsmSJdDpdEhISEBSUhI2bdrUoHiJbKVjx4544403qp22fv16SJKE5cuXw9PTE926dcP58+cxffr0KvO+9tpruPnmmwGUnvBHjhyJ4uJieHl5wdfXFxqNpsF5OmfOHIwePbrG6ampqXjmmWfQpUsXedvqY9q0aRg4cCDS09PRvHlzZGZm4qeffsLvv//eoHiJbMVVz5EtWrTAxx9/jAkTJiAjIwM//fQTDhw4AI2Gl56kPJ4fAV9fX6xduxY333wz/Pz8sHTpUmzduhX+/v4NipfK8bYb2V1SUhJMJhM6deok//bL19cX27dvx6lTp+z++v369atxWnBwMCZNmoThw4fjrrvuwrvvvov09PR6rXfUqFFQq9XYsGEDgNLHhIYMGYI2bdrYImyiBouKiqpxWnJyMnr16gVPT0+5LTo6utp5K97lat68OQAgMzPTRlGWqi1PAWDu3LmYNm0ahg0bhsWLF9f72BEdHY3u3btjzZo1AIC1a9eidevWuOmmmxocM5EtuOo5EgDuu+8+3HPPPVi8eDHeeuutehcARPbG82Op2NhYzJs3DwsXLsTTTz+NG264oaHhUgUstMnu8vPzoVarER8fj8TERPnfsWPH8O6779r99X18fGqdHhcXh127dmHgwIH48ssv0alTJ+zevbvO9Xp4eGDChAmIi4uDXq/H+vXr5UeGiBxBXft+fVV8BM3yyKit+yGoK9aXX34ZR44cwciRI/HHH3+gW7du8pdcdZk2bZr827i4uDhMnjy5zkdfiRqLq54jAaCwsBDx8fFQq9U4efKkLcIlsgmeH0uZzWbs2LEDarUaKSkptgiXKmChTXbXp08fmEwmZGZmokOHDlb/anucxsPDAyaTqdFinD9/Pnbu3IkePXpg/fr19Vpu2rRp+P333/Hhhx/CaDTW+mgPkSPp3LkzkpKS5E5QAGDfvn3XvJ7GzNNOnTrhqaeewq+//orRo0dX+e11TR5++GGcPXsW7733Ho4ePYqJEyfaOVKi+nPlc+TTTz8NlUqFzZs347333sMff/xh50iJGs6dzo9vvvkmjh8/ju3bt8s/VyHbYaFNdtepUyeMGzcOEyZMwLfffovTp09j7969WLRoEX788ccal2vTpg0OHTqE5ORkXL58GQaDweaxnT59GvPnz8euXbtw9uxZ/Prrrzh58mS9f4PWtWtXDBgwAM8++yweeughDrdATmPs2LEwm82YMWMGjh07hl9++QVvvfUWAFzT3d42bdrg9OnTSExMxOXLl60uTGylqKgIs2fPxrZt23D27Fns2LED+/btq3eeBgUFYfTo0XjmmWdw2223oWXLljaPkeh6ueo58scff8SqVauwbt063HrrrXjmmWcwceJEXL161eZxEtmSu5wfDxw4gAULFmDFihUYNGgQlixZgieffBJ///23zeN0Vyy0qVHExcVhwoQJePrpp9G5c2eMGjUK+/btQ6tWrWpcZvr06ejcuTP69euHZs2aYceOHTaPy9vbG8ePH8eYMWPQqVMnzJgxA7NmzcIjjzxS73VMnToVer2ej42TU/H398f333+PxMREREZG4oUXXpB7A674u7S6jBkzBrfffjuGDBmCZs2a4fPPP7d5rGq1GleuXMGECRPQqVMn3H///RgxYgReeeWVeq+DeUqOzNXOkZcuXcLUqVPx8ssvo2/fvgCAV155BaGhoZg5c6bN4ySyJXc4PxYXF+Phhx/GpEmTcNdddwEAZsyYgSFDhmD8+PGNdife1UnCMsAhEV2XhQsX4uuvv8ahQ4eUDoWoQdatW4fJkycjJyfH5Z7O+Oyzz/DUU0/hwoUL8PDwUDocIiJyIq58fiT74RgLRNcpPz8fZ86cwfvvv49//etfSodDdM0+/fRTtGvXDi1atMDBgwfx7LPP4v7773epi4jCwkKkp6dj8eLFeOSRR1hkExFRndzh/Ej2x0fHyWm8/vrrVkOfVPw3YsQIm79e9+7da3y9devWYfbs2YiKisLgwYP5OCo5pYyMDDz88MPo2rUrnnrqKdx333345JNPGrTOdevW1Zg33bt3t1Hk5UaMGFHj673++ut444030KVLF4SFhWH+/Pk2f30iR+Fo50giZ+YO50eyPz46Tk4jKysLWVlZ1U7z8vJCixYtbPp6Z8+erbFzmdDQUPj5+dn09YhcQV5eHi5evFjtNK1Wi9atW9v09c6fP4+ioqJqpwUHByM4ONimr0fkqHiOJHJsPD+6HxbaRERERERERDbER8eJiIiIiIiIbIiFNhEREREREZENsdAmIiIiIiIisiEW2kREREREREQ2xEKbiIiIiIiIyIZYaBMRERERERHZEAttIiIiIiIiIhtioU1ERERERERkQ/8PRWtEGBcdo0MAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61363.50530881889, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54678.73897011955, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50269.74963784958, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47884.76380684636, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46603.934383086365, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62024.307753073794, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56490.87247515278, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52149.69994578107, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49616.16985890787, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48164.10928131883, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62493.216581327004, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56721.080799783784, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52648.89686642215, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50415.68380080664, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49207.59134460457, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65805.63830308433, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57923.24351563832, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53233.31006707088, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50799.131739411896, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49536.218900455904, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59007.70608710826, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53409.3580534048, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49395.31937919355, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47141.11926884786, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45888.828487440434, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.9983086270947197" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a lassoCV model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.LassoCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 778.48127854, 767.04348722, 137.50634772, 83.21529745,\n", + " 1455.41333396, 133.5823542 , 137.14194629, 83.8560983 ,\n", + " 772.06920896, 760.9532296 , 1407.30207245, 757.11533568,\n", + " 141.08267288, 94.14143674, 761.06824159, 780.41602556,\n", + " 132.32070365, 1422.6720953 , 80.94333059, 81.80828345,\n", + " 758.11565608, 759.62563439, 140.42528323, 1425.78515296,\n", + " 1451.38665456, 778.99768147, 73.08086834, 80.81048925,\n", + " 77.84871052, 89.0436865 , 761.72972039, 760.58419566,\n", + " 779.54102982, 762.87790117, 1460.85378801, 1425.81431481,\n", + " 1405.53616185, 1458.52180025, 1451.2255034 , 1441.32293854,\n", + " 768.51129813, 89.6590618 , 135.56755369, 1417.95676987,\n", + " 760.32441535, 132.83150268, 781.95270128, 1432.45558429,\n", + " 1426.7249187 , 131.7525834 , 1432.60778424, 1425.93225084,\n", + " 754.57412144, 1433.42431901, 80.77880402, 768.93550952,\n", + " 77.8649223 , 76.48449354, 135.04244013, 779.41827978,\n", + " 1420.11905903, 152.72002266, 134.51245983, 757.26584889,\n", + " 77.57875017, 94.89624771, 1422.53266418, 85.4307151 ,\n", + " 1429.58896627, 1437.49671432, 1425.5346257 , 764.60814942,\n", + " 137.322858 , 763.44935447, 77.54638924, 1420.07229191,\n", + " 1449.00007175, 779.68589031, 1414.76712742, 78.91169675,\n", + " 1427.14837263, 768.6823163 , 75.59675113, 765.62494876,\n", + " 144.72987143, 1433.33498747, 1470.02389046, 73.5248781 ,\n", + " 1469.74791094, 1423.47916386, 72.40031663, 137.28188093,\n", + " 80.09007763, 776.74423896, 95.64294598, 777.07784823,\n", + " 131.6023471 , 195.65361333, 1464.4668382 , 79.6538671 ,\n", + " 769.62334298, 1409.91355418, 775.52758542, 770.39912279,\n", + " 83.1786281 , 773.822501 , 73.20064316, 70.60121481,\n", + " 69.60121395, 1424.52221713, 81.64112263, 1353.13420125,\n", + " 1438.71046426, 99.77731722, 1425.99237255, 778.74222296,\n", + " 761.44411332, 131.80780938, 1459.10719644, 146.60383825,\n", + " 136.41358109, 758.979804 , 1439.04901805, 135.66377809,\n", + " 775.88408387, 133.02085838, 760.67007518, 1425.76600072,\n", + " 1437.69172132, 1452.92931588, 1431.57611863, 763.07975594,\n", + " 72.94068503, 1461.86270282, 780.66684308, 1411.05302499,\n", + " 85.0859176 , 150.49426696, 73.36648258, 91.2841013 ,\n", + " 90.39703864, 85.0961172 , 81.44547618, 1427.63448175])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7064.336851651082, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200159.05577759317, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249881.27993171554, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249513.53795295014, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 245341.35252676593, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242070.7765863414, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171270.0566077683, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246143.47668155946, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 251129.85143080176, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249045.20598711274, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246886.98673294307, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198408.61779734457, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254758.83851363405, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255745.8701724262, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252078.1847329364, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249080.8396489511, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3302.570484981872, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204417.00538329384, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262996.3810029083, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264448.62337668287, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260965.17792806384, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258077.10223143882, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3388.073061162373, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202273.39377483987, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258802.28148703428, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259630.2984787177, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255782.95718621058, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252602.58914670834, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.398e+05, tolerance: 3.005e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.9768205500574753" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a lassoCV model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.LassoCV(alphas=np.logspace(-6, 6, 13))\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([154.35544808, 137.93629756, 100.31473354, 588.58488656,\n", + " 52.48716382, 112.11305014, 108.20706359, 389.7900419 ,\n", + " 153.00838262, 665.20884841, 613.68708933, 690.65803162,\n", + " 89.52328036, 319.81054051, 664.04016349, 157.62424525,\n", + " 119.13254824, 377.05473569, 576.20044299, 590.07344208,\n", + " 692.34965184, 602.18014481, 80.00067828, 618.08358598,\n", + " 104.55656814, 147.84779838, 584.34273622, 582.63050174,\n", + " 372.13594548, 371.50913433, 662.68943017, 598.07634727,\n", + " 139.66930121, 613.11887179, 357.97036905, 691.15891145,\n", + " 614.99438068, 358.11170426, 358.82930581, 99.17479194,\n", + " 143.39800613, 372.27578689, 97.94593031, 644.38350125,\n", + " 635.57577427, 102.55310446, 159.38247766, 369.13177828,\n", + " 661.81289755, 106.55608822, 116.51647578, 627.8099384 ,\n", + " 672.49473217, 335.25386423, 372.17017728, 158.76976875,\n", + " 586.76834361, 575.78198488, 112.44660291, 155.35176762,\n", + " 114.64050937, 39.09126607, 106.0477728 , 659.20021131,\n", + " 592.30000103, 312.28675809, 657.1194534 , 370.57738964,\n", + " 112.30765397, 372.77522648, 638.7185935 , 668.52004661,\n", + " 105.44057683, 613.10132462, 372.45310812, 116.20320007,\n", + " 352.84095965, 156.17551707, 631.42315097, 579.29469596,\n", + " 114.42192949, 143.17553154, 589.23394488, 145.298486 ,\n", + " 81.93675711, 103.67175374, 354.48148916, 590.15495407,\n", + " 75.84257083, 656.21566816, 575.18899487, 104.80583718,\n", + " 578.80663677, 149.97573917, 401.8161623 , 154.85641011,\n", + " 108.54715635, -17.42321016, 357.09639731, 359.17339531,\n", + " 154.20326129, 612.48010958, 156.63103192, 150.86042382,\n", + " 590.46819404, 168.68804039, 576.74520262, 580.22733722,\n", + " 591.61463109, 657.03262926, 382.25521947, 51.69033846,\n", + " 367.47881226, 287.50808557, 107.57417577, 160.84804545,\n", + " 605.72065683, 98.06126501, 355.39243999, 80.36244683,\n", + " 100.831099 , 648.64453807, 363.14842993, 100.60996879,\n", + " 149.17472931, 101.69950378, 637.02677974, 654.65549126,\n", + " 334.47801516, 84.94335673, 642.3426637 , 659.28325502,\n", + " 577.46487406, 349.86963183, 150.30998619, 634.65993495,\n", + " 382.93077531, 24.11219858, 574.08329376, 374.18540157,\n", + " 373.74992035, 597.05038034, 381.84619234, 101.62086784])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768778.481279100154.355448(768, 100)
290768767.043487100137.936298(768, 100)
54100137.506348100100.314734(100, 100)
19810083.215297630588.584887(100, 630)
45314361455.41333410052.487164(1436, 100)
..................
16410091.284101365374.185402(100, 365)
16510090.397039365373.749920(100, 365)
19910085.096117630597.050380(100, 630)
13210081.445476365381.846192(100, 365)
50114361427.634482100101.620868(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 778.481279 100 154.355448 (768, 100)\n", + "290 768 767.043487 100 137.936298 (768, 100)\n", + "54 100 137.506348 100 100.314734 (100, 100)\n", + "198 100 83.215297 630 588.584887 (100, 630)\n", + "453 1436 1455.413334 100 52.487164 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 91.284101 365 374.185402 (100, 365)\n", + "165 100 90.397039 365 373.749920 (100, 365)\n", + "199 100 85.096117 630 597.050380 (100, 630)\n", + "132 100 81.445476 365 381.846192 (100, 365)\n", + "501 1436 1427.634482 100 101.620868 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768778.481279100154.355448(768, 100)
290768767.043487100137.936298(768, 100)
54100137.506348100100.314734(100, 100)
19810083.215297630588.584887(100, 630)
45314361455.41333410052.487164(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 778.481279 100 154.355448 (768, 100)\n", + "290 768 767.043487 100 137.936298 (768, 100)\n", + "54 100 137.506348 100 100.314734 (100, 100)\n", + "198 100 83.215297 630 588.584887 (100, 630)\n", + "453 1436 1455.413334 100 52.487164 (1436, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(143, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.221980\n", + "(100, 365) 0.409091\n", + "(100, 630) 0.673298\n", + "(768, 100) 0.912557\n", + "(768, 630) 1.249868\n", + "(1436, 100) 1.239896\n", + "(1436, 365) 1.509302\n", + "(1436, 630) 1.784008\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_27004\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_27004\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler\n", + " sc = StandardScaler()\n", + "\n", + " # Define the pipeline with PolynomialFeatures and LassoCV regression\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.LassoCV())\n", + "\n", + " # Define the hyperparameter grid for Ridge regression\n", + " param_grid = {\n", + " \"lassocv__alphas\": [[0.1, 1.0, 10.0], [0.01, 0.1, 1.0], [0.001, 0.01, 0.1]]\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for X\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alpha for X: {grid_search.best_params_[\"lassocv__alphas\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for Y\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alpha for Y: {grid_search.best_params_[\"lassocv__alphas\"]}, R2 score: {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.344e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.883e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.207e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.153e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.009e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27001.238514512777, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51669.78313600251, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25433.064175009727, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48750.54248090497, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23595.33234409988, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52085.33996937015, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23681.97113211453, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50940.47190261811, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25752.27994774282, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46825.854455829416, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27111.997629120946, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51021.09811788032, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27851.23576526344, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52659.1182658448, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27062.152402356267, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54258.13067814849, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26993.761432886124, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54005.42170735355, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 28141.9186540246, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50239.27852412552, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24325.282854616642, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48875.4093873589, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27048.152341887355, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52545.68592076785, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24891.686308681965, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52554.13622946837, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23696.900168433785, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52095.680344598964, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25748.89860931039, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48163.617058736774, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23165.87727586925, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51800.15520736135, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26684.938877493143, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54945.123784184296, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24874.763845279813, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53151.619686045786, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22539.789426013827, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54493.93782141632, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24919.619185760617, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49655.726728943766, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25539.17362704873, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46152.04097500274, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27845.552521407604, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49840.47878713764, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26942.847137898207, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48598.28082026785, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24988.611784487963, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49136.644468750754, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25041.87955003977, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49377.360949991154, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37193.377502700576, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63595.21057763434, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52679.31778976358, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39760.875704333564, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60294.94831218976, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49408.27055467501, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49457.874315577326, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63230.753291795256, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51123.56478545584, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46344.96769402514, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62178.58575046729, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50622.121905554755, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38819.10061960967, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58320.80237603167, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47503.63106274663, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37986.890800213325, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62919.14786270635, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51720.84143060726, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34240.854499590816, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64628.57952805885, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54295.29329061412, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45539.47532828315, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66646.82587328301, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54699.240534092154, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42524.96543007254, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66141.19028456513, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54629.841394628624, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34073.392419011216, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62137.2398138638, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51550.49088223381, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40939.74682639532, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60150.8185720223, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49602.06671067447, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 33808.04937170018, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380.413860191795, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54379.02295034827, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49014.39845109897, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64272.79310131351, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52729.33995481163, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44605.51267793213, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63302.962033356875, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52658.89533348087, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37694.46592193071, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59533.28507159174, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49452.52789970351, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50547.35884167315, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62597.57788583079, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50289.83319347684, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44165.22908349594, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67336.5034885758, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55504.78059453689, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51042.57248043457, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64856.506277178836, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52716.9946037113, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51253.97269166887, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64976.88761989483, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53181.8956843206, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47161.75666572072, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61419.48397788271, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49802.58604209638, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38874.00350949238, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57431.72102337918, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46839.74466162229, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31773.543228341907, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61630.55500765223, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51782.07520300847, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38911.76604615903, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60257.714285395, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49642.073785733344, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 43571.02769605149, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60718.71056878313, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50096.34789168997, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44747.592332329135, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61023.223430858656, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49839.03069800484, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.022e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53436.48430187529, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77098.22830657577, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62962.473745587515, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48825.09257898154, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80858.00629039922, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66884.57305832753, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52066.838120024244, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 78256.71113536177, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64893.42910026042, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61532.451442323596, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80315.30977154904, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65415.539096207984, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50087.105223755294, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 75614.82026594163, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62222.97321526852, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.336e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3299.8101275265217, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10845.021059398074, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10028.077613340341, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6103.15114655986, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+03, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6010.930996235227, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5024.27236861299, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6683.007060111151, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11404.760652294499, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha for X: [0.001, 0.01, 0.1], R2 score : 0.9983066806087357\n", + "-------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7540.261712116422, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8907.889932699385, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3665.987252778141, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145804.27080314362, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3299.8101275265217, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147710.04629368795, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10845.021059398074, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 174810.22727891142, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10028.077613340341, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166741.8990673345, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6103.15114655986, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160318.2330132673, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.023e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137778.47237434448, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122758.97077297635, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148382.43391371344, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144017.30019165823, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127018.48959432897, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6010.930996235227, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158932.8736311625, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136289.09177669926, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165573.70125127787, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5024.27236861299, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164104.78408577995, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156455.78729827385, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.972e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6683.007060111151, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166430.94864977512, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144037.14875146816, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147744.35308860906, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11404.760652294499, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186751.16317974965, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166374.26740222645, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7540.261712116422, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164346.773108571, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141532.80877918153, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145557.6555681755, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8907.889932699385, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 181902.02497356254, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3665.987252778141, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168282.45226544066, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.028e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144585.05900922802, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192008.17523541514, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2778.5474211804103, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 146686.9255644182, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 187427.51207353827, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10079.97370744677, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173907.4659622846, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211387.4911716713, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285.072762441123, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165853.41796351466, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202984.5866449888, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5434.551816466032, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159312.7749068756, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198851.1162001828, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136529.4067637401, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190552.52422892852, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122758.99074181131, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183512.30774619576, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147072.88033138518, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208123.49327046314, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 142718.91817563848, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202634.53417400707, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127019.08428033529, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194448.08313360647, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5340.944362295675, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 157921.87093700204, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 197441.0374932648, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134911.9408829766, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190453.0345442784, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164303.45219882295, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211153.43895284442, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4418.303120764438, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163119.0634495192, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206501.74178828715, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155106.5568488789, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203273.4460461305, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.632e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6078.075453025696, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165563.816403025, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206770.57611088033, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 142872.72595095512, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198799.46145068703, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 146511.2971749203, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 199312.8359719755, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10657.099400804902, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 185888.76200273272, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226881.91441898578, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165223.31735161654, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 214000.7903468631, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.723e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819.405477439752, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163335.46831019252, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201444.8976191769, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 140185.5351146482, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195061.45772995887, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144135.80540272713, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195258.6515898856, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8151.49479778501, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180915.29687128207, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222751.9604088086, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3144.2590368899982, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167289.73430093497, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213685.35554384018, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6275.61922646407, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198931.27304518648, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249335.40144705592, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171269.92069856432, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246143.4288674457, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196814.88602281915, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254093.29390393704, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204647.44464610174, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263090.58998221107, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202356.67748898745, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258838.46788680268, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.302e+05, tolerance: 3.005e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha for Y: [0.001, 0.01, 0.1], R2 score: 0.9767994846187407\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 6ed7da18cbae464adae41f7c0ac6b31435736d11 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Fri, 5 Jul 2024 15:16:53 +0000 Subject: [PATCH 27/78] elastic net notebook added --- .../test_elasticnet_regression.ipynb | 1646 +++++++++++++++++ 1 file changed, 1646 insertions(+) create mode 100644 app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb new file mode 100644 index 00000000..9a4327b0 --- /dev/null +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb @@ -0,0 +1,1646 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAHwCAYAAABZvxc+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAADksklEQVR4nOzdd3hUVfoH8O+9UzNJJj2ZBAKEIr2XEAuKhaa4ujakSrXh7lp2Xde1r2X1Z10LgiiIoOiuq6uCiqDiLhB6lxJqIJkE0ibJ9Ln398dkJgkkpE3NfD/Pw6M5c2fmzNx5Z+57z7nvEWRZlkFEREREREREPiEGuwNERERERERE7QkTbSIiIiIiIiIfYqJNRERERERE5ENMtImIiIiIiIh8iIk2ERERERERkQ8x0SYiIiIiIiLyISbaRERERERERD7ERJuIiIiIiIjIh5hoN5MsyzCZTJBlOdhdIYp4jEei0MF4JAotjEmi0MBEu5kqKysRFxeHysrKYHeFKOIxHolCB+ORKLQwJolCAxNtIiIiIiIiIh9iok1ERERERETkQ0y0iYiIiIiIiHwoqIn2+vXrMXHiRGRkZEAQBHzxxRf1br/jjjsgCEK9f+PGjau3TWlpKaZMmQK9Xo/4+HjMnj0bVVVV9bbZvXs3LrvsMmi1WmRmZuLFF1/090sjIiIiIiKiCBXURLu6uhoDBw7EW2+91eg248aNQ2Fhofffxx9/XO/2KVOmYN++fVizZg2+/vprrF+/HvPmzfPebjKZMGbMGHTu3Bnbtm3DSy+9hCeffBILFy702+siIiIiIiKiyKUM5pOPHz8e48ePv+A2Go0GBoOhwdt+/fVXfPvtt9iyZQuGDRsGAPjHP/6BCRMm4P/+7/+QkZGB5cuXw2634/3334darUbfvn2xc+dOvPLKK/USciIiIiIiIiJfCPlrtH/66SekpqaiZ8+euPvuu1FSUuK9bePGjYiPj/cm2QBw9dVXQxRF5ObmercZNWoU1Gq1d5uxY8fi4MGDKCsrC9wLISIiimB2pwSL3RXsbhAREQVEUEe0mzJu3Dj89re/RVZWFo4cOYK//OUvGD9+PDZu3AiFQgGj0YjU1NR691EqlUhMTITRaAQAGI1GZGVl1dsmLS3Ne1tCQkKDz22z2WCz2bx/m0wmX740ImoBxiNR6GhNPDpdEn738Q6Ume1YfMdwxGhC+vCDKKzwN5IoNIX0iPakSZNw/fXXo3///rjhhhvw9ddfY8uWLfjpp5/8/tzPP/884uLivP8yMzP9/pxE1DDGI1HoaGk8uiQZD362C9/uMyL3WCmmLc5FhcURoN4StX/8jSQKTSGdaJ+ra9euSE5ORl5eHgDAYDCguLi43jZOpxOlpaXe67oNBgOKiorqbeP5u7FrvwHgkUceQUVFhfdffn6+L18KEbUA45EodLQmHlWK2sONHSfLMXnRJpRW2/3ZTaKI0ZqYrLQ6sGj9UUiSHIAeEkWmsJq7derUKZSUlCA9PR0AkJOTg/Lycmzbtg1Dhw4FAKxbtw6SJCE7O9u7zaOPPgqHwwGVSgUAWLNmDXr27NnotHHAXYRNo9H4+RURUXMwHolCR0vjUSEKePGmAYhSKbBs0wkAwL4CE257dyOWz8lGql7rr64SRYSWxqTF7sLsJVux+XgpDhZV4oXf9odSEVZjb0RhIaiJdlVVlXd0GgCOHTuGnTt3IjExEYmJiXjqqadw0003wWAw4MiRI/jTn/6E7t27Y+zYsQCA3r17Y9y4cZg7dy4WLFgAh8OB+fPnY9KkScjIyAAATJ48GU899RRmz56Nhx9+GHv37sXrr7+OV1991aevRZJk7CswodRsR6JOjb4Zeoii4L3d6ZTw1e5CnC43o0O8DhMHpEOpFBu8f3yU+4RAucXhfSwA2HO6AttOlGJXfjmKK+2IVgMV1Q4cKzGj2u6CKMmolpruqyFGCY1ahaykaAzulIC7L+8GtVrh0/cjlHne6+IqK3afLMPm42UotzjQI1kHUSFg7f4iVNobP8OrBNApWYfRF6XiukEZGNgxvt6+DkeSJGNHfhne/+UoVu8tQmMfIw0AnVaBSqsLTgACgCgF0DlZhx5psUiJ1UIhikiL1aDC6sChoiqIgoAre6XihkEd6n3mw0VTsd3ax+ltiMW+QhN25JdDlmToo1RIilYjTqdCXnEVdp4qR7XFAadLwrYTJai0SdAoRaTrNSi1OFBaZYfdhUb3lUdKtBKzLuuKi7uloH+HuLD/rHo4nRK+3FWAbcdLcKCoCpAkmB0SEnQi9hdWocLa+DuTHqvCuH4GXD84M+Tj1+mUsGzTUbz8/WFU2S+8t0UAShGQAahFoGNiNG4fmYlTJVbsM5ogy4AhRoMBneIxtHNiQD8PdrsLBcb6s8sOF1dhxHNrz9tWAWDxjCEY1dPgk1hrbcwStVcvfXsAm4+XAgD+ue0U/rntVKPbThuegJtH9AnY9wXjl9oTQZbloM0Z+emnnzB69Ojz2mfMmIF33nkHN9xwA3bs2IHy8nJkZGRgzJgxeOaZZ7zFzACgtLQU8+fPx1dffQVRFHHTTTfhjTfeQExMjHeb3bt3495778WWLVuQnJyM++67Dw8//HCL+moymRAXF4eKigro9fp6t23IO4t3fj6CI8VVcLhkqBQCuqXG4O7Lu+Hi7slYtP4I3vrpCCotDkhwHwzFRqlw7xXdMHdUt3r3r7a5YHG4IAiAVqVAtFqBpBg1qmxOnCw1w9WMRLqlRAG4fXgmnv3tAN8/eIjxvNe78stgsvqm+m1Wsg7P3tAfF3dP9snjBdqGvLN44NOdMJpsTW/cBlqViAevuQhzR3Vr82NdKB59qanYbu3jSLIMq9MFu0OCU5Lh+RIWAPjzC7l/Bz0eGd87bD+rHovWH8Fraw+j2tb2GA7l+F20/gieXXXAL4+tEIE+6b75PDQVj7OXbMbaA2da/LgCgOVzstsUa62NWaJwdqGYfPTz3Vi+ueWXXwXi94PxS+1NUBPtcNLYl9aGvLP4y7/3oMrmRIJODbVChN0loczsQIxGgUu6JWHl1lNwSTKUCgGiAEgy4HTJUIgCbhvWEf87UoIqmxMapYgzlTa4aq6XEQUBcVEqlJjtCMRemjKifSfbnn1VbLLC7PDtGYsEnQpvTR4Sdj8EG/LOYt6yrajyQcLSHKIAPDK+V5uT7UAk2k3F9nM3Ni85O/dx7C4J+aVmBOuyuDS9Bq/eOijsPqsei9YfwfOrD/j0/QvF+PVnkl2XLz4PF4rH1ibZda1oZrLtq5glCneNxWRrk2wPf/5+MH6pPQq/eZwhRJJkvPPzEVTZnDDotdCqFBBFAVqVAga9BpVWhzfJVisFKEURoiBCKYpQKwU4JRkrt55CldXpnmprccAlu4vGqJQiJFlGaYCSbAD4ZEs+7O10jVPPvjJZ7LD4OMkGgHKzA2/9mBdWRUUkScab6w4HLMkG3CeZ3vzxCJxOP0zN8KGmYrvK5sI7Px9pcn+f+zgalYizlbagJdkAUFJlw9s/Nd33UOR0Snjzxzyfv39lZgfe+vFwyLwnTqeEV7/3f5IN+PfzYLU625xkA8Braw60ONZaG7NE7ZXd7mpTkg0ARSYb3v7J98c6jF9qr5hot8G+AhOOFFchQaeGINS/fkQQ3Im1U5KhEAFRqP9WuxNuuG9XCLA5ZdicEpSiAEEQIECAKAoBPSB3ycCC9UcD94QB5NlXKoXCL1Nz5Zrn2FcQPmtX7iswYffpwPfXZHXgq92FAX/elmgqtuN1Khwprmpyf5/7OFa7BJszuCeznBJw0FgZVp9Vj692F6LS4vTLY4dS/H61uxBm/7zM80h+/Dw8s+pXnzzOlhMVLY61uloSs0Ttla+O7w744fuC8UvtFRPtNig12+FwyVA3UqlRqhmKFtBwEQdPqyzLcEoSZBmNbBk4J0qrg9wD//DsK3/ODrC7JJSaw2e5Gvd7EviRZVkGTpebA/68LdFUbGsUIhyS3OT+PvdxnJIU1NFsj3D7rHqcLjc3WfytteyupvdnoAQyPmT47/Nw3Ee/JzLQ4lg7V3Njlqi98tXxncMP35WMX2qvmGi3QaJODZVCgL2RZEWsOSsnNzKG6i2AVDP6LQj+LYTUHJ0To4PcA//w7CvBj2cy1AoRiTq1/57Ax9zvSeC/AgQB6BCvC/jztkRTsW1zSVCJQpP7+9zHcV8+4vPutli4fVY9OsTr/PajpVY0vT8DJZDxIcB/n4cuPvo9EYAWx9q5mhuzRO2Vr47vVH74rmT8UnvFRLsN+mbo0S01BmVmB86tKecZpVaKAlwSIMn1vzwkWYJTQs3tMjRKARqle6q5LMuQIUOS5IAelCsE4K5RXQP3hAHk2VcOl+SXWQNCzXN4lmILB30z9BjQIfD91WtVmDggPeDP2xJNxXa52YFuqTFN7u9zH0erFqFRBncpPaUI9DTEhtVn1WPigHTERvlnVcpQit+JA9KhC9Dim6IfPw+PTejtk8cZ3jmuxbFWV0tilqi98tXxXS8/fF8wfqm9YqLdBqIo4O7LuyFGo4DRZIPF4YIkybA4XDCabIjVqnDbsI5QiALsTrlm2qgEpyTB7pShrKk6HqNRoqjSDn2UCgoBcLgkOJwSRMF99s6fo7B1TRqe2W7X0/bsK32UClEq37/GeJ0K947uHlZrPYqigPlX9kCMJnD7XBSA+aO7hfx62k3FdoxGgbsv79bk/j73cawOCcmxmqCOaifHaHDPFU33PRQplSLmj+7u8/cvQafCvaN7hMx7olSKuH9ML78/jwD/fh60WiWu6pXS5se5uHtqi2OttTFL1F6p1QpMGZHZpsdI02twzxW+P9Zh/FJ7xeW9mqnZ62hLMlRi4+toy3Af3DS6jrbdBYud62j7C9fRPh/X0W5cU7Hd2seRJK6j3RZcR7vtwmEdbQ+FKODlWwbihsEdmtzWVzFLFM78sY62QhTw1PV9MXVkZ1918zyMX2pvmGg3U1MHEpIkY1+BCaVmOxJ1avTN0Nc78+Z0SvhqdyFOl5vRIV6HiQPS643q1b1/fJQKAFBucXgfCwD2nK7AthOl2JVfjuJKO6LVQEW1A8dKzKi2uyBKMqqbkYgbYpTQqFXISorG4E4JuPvybu12JLshnve6uMqK3SfLsPl4GcotDvRI1kFUCFi7vwiV9sbDQgmgU7IOoy9KxXWDMjCwY3zYn2WVJBk78svw/i9HsXpvUaMFpzQAdFoFKq0uOOFODKMUQOdkHXqkxSIlVguFKLqXq7M6cKioCqIg4MpeqbhhUAefjWQHKtEGmo7t1j5Ob0Ms9hWasCO/HLIkQx+lQlK0GnE6FfKKq7DzVDmqLQ44XRK2nShBpU2CRikiXa9BqcWB0io77C40WRwsJVqJWZd1xcXdUtC/Q1zYf1Y9nE4JX+4qwLbjJThQVAVIEswOCQk6EfsLq1BhbfydSY9VYVw/A64fnBny8et0Sli26She/v4wquwX3tsi3JcGyADUItAxMRq3j8zEqRIr9hlNkGXAEKPBgE7xGNo50Wefh+bEo9XqxPyl6/HDMUuTj6cA8Oi1PfH6uqOosDgAuGs7PHdjf9w+olOT9/dVzBKFq6Zi0m534d53vsWaZiwAUvcEcKxGiQ9mDsewLok+7W9djF9qT5hoN1MgD+yJ6MIYj0Shw1/x+GuhCVPfy0VJdW2l4Scm9sHMS7J89hxE7ZEvY/Lr3QX4wyc74axZMiNKpcB7M4bhEo4wEzUptC+UJCIioojUO12PlXfmIE2v8bY99dV+vP1TXhB7RRRZrhuQgXenDYW6ZkaaxeHCzCVbsPbXoiD3jCj0MdEmIiKikNQ9NQaf3pmDDvFR3rYXvz2IV74/eF51YiLyj6t6p+H9GcO9xWTtTgl3LtuGr3cXBLlnRKGNiTYRERGFrM5J0fjsrhxkJdeuA/zGujw8t+pXJttEAXJpj2Qsmz0CsRr32oNOScbvPt6Bz7a2vLAaUaRgok1EREQhLSM+CivnjUSP1Bhv26JfjuGxL/dCkphsEwXCsC6JWDF3JOJ17qK9kgz88Z+78eHG48HtGFGIYqJNREREIS9Vr8Un80aiT3ptcaePNp3En/61Gy4m20QB0b9jHFbOy0FKbG3thMe/3Id3fz4SxF4RhSYm2kRERBQWkmI0+HjuSAzKjPe2/XPbKfz+kx1wuJqxviURtVlPQ+x5tROeX32AtROIzsFEm4iIiMJGnE6Fj+ZkY0RW7Vq+X+8uxD3Lt8PmdAWxZ0SRIys5GivvHIkuSTpv2xvr8vDsN6ydQOTBRJuIiIjCSoxGiaUzR+CyHrVr+a7ZX4Q5S7fCYmeyTRQIHRN0+PTOnHq1E9777zE8+gVrJxABTLSJiIgoDEWpFVg0fRiu7p3qbfvl8FnM+GAzqmzOIPaMKHKk6rVYeWcO+mbU1k5YkXsSD322C05ezkERjok2ERERhSWtSoF3pg7FtQPSvW2bj5Vi2uJcVFgcQewZUeRIjFZjxdyRGNIp3tv2+Y7TmL9iBy/noIjGRJuIiIjClkoh4o1Jg3HTkI7eth0nyzF50SaUVtuD2DOiyBEXpcKy2dm4uFuSt+3bfUbM+3AbrA4m2xSZmGgTERFRWFOIAl66eQCmZHfytu0rMGHSwo0oNlmD2DOiyBGtUeL9O4bjyl61l3P8fOgM7uDlHBShmGgTERFR2BNFAX+7oR/mXJrlbTtUVIXbFm5CQbkliD0jihxalQILpg7FhP4Gb9umozWXc5h5OQdFFibaRERE1C4IgoBHr+2N+67s7m07drYatyzYiJMl5iD2jChyqJUNX85x+6JNKKmyBbFnRIEV1ER7/fr1mDhxIjIyMiAIAr744gvvbQ6HAw8//DD69++P6OhoZGRkYPr06SgoKKj3GF26dIEgCPX+vfDCC/W22b17Ny677DJotVpkZmbixRdfDMTLIyIiogATBAEPjumJP47t6W07XW7BLe9uQF5xVRB7RhQ5lAoRL908ANNGdva27S804baFm2Cs4OUcFBmCmmhXV1dj4MCBeOutt867zWw2Y/v27Xjsscewfft2fP755zh48CCuv/7687Z9+umnUVhY6P133333eW8zmUwYM2YMOnfujG3btuGll17Ck08+iYULF/r1tREREVHw3Du6O56Y2Mf7d5HJhkkLN+LXQlMQe0UUOURRwNO/6Ys7R3X1tuUVV+HWdzciv5QzTKj9UwbzycePH4/x48c3eFtcXBzWrFlTr+3NN9/EiBEjcPLkSXTqVFvwJDY2FgaD4dyHAAAsX74cdrsd77//PtRqNfr27YudO3filVdewbx583z3YoiIiCikzLwkCxqlAo9+sQeyDJytsmPSwk1YNnsEBnSMD3b3iNo9QRDw5/G9oFMr8eoPhwAAJ0vNuPXdjfhoTja6pcQEuYdE/hNW12hXVFRAEATEx8fXa3/hhReQlJSEwYMH46WXXoLTWVvZcOPGjRg1ahTUarW3bezYsTh48CDKysoC1XUiIiIKgsnZnfDKrQMhCu6/KywOTFmUi63HS4PbMaIIIQgCfn91Dzw6obe3rbDCitve5QwTat+COqLdElarFQ8//DBuv/126PV6b/vvfvc7DBkyBImJidiwYQMeeeQRFBYW4pVXXgEAGI1GZGVl1XustLQ0720JCQkNPp/NZoPNVluwwWTiFwFRsDAeiUJHOMbjjYM7QqNU4Hcf74BTklFpc2La4s1YPGMYLu6eHOzuEbVJuMTk3FFdodMo8Ncv9tabYfLhrBEYmBkf7O4R+VxYjGg7HA7ceuutkGUZ77zzTr3bHnjgAVxxxRUYMGAA7rrrLrz88sv4xz/+Ue8LpzWef/55xMXFef9lZma26fGIqPUYj0ShI1zjcUL/dCycPhRqpfvQx+JwYeaSLfjxQHGQe0bUNuEUk1OyO+PlW86ZYfJeLjYf4wwTan9CPtH2JNknTpzAmjVr6o1mNyQ7OxtOpxPHjx8HABgMBhQVFdXbxvN3Y9d1A8AjjzyCiooK77/8/Py2vRAiajXGI1HoCOd4vLJXGj64YziiVAoAgM0pYd6yrfh2rzHIPSNqvXCLyd8O6Yi3Jg+BSuHOtqtsTkx/PxfrD50Jcs+IfCukE21Pkn348GH88MMPSEpKavI+O3fuhCiKSE1NBQDk5ORg/fr1cDgc3m3WrFmDnj17NjptHAA0Gg30en29f0QUHIxHotAR7vF4SfdkLJ01AjEa99VzDpeMe1dsx5c7Twe5Z0StE44xOb5/OhZOGwZNzQwTq0PCnKVb8f0+nvSi9iOoiXZVVRV27tyJnTt3AgCOHTuGnTt34uTJk3A4HLj55puxdetWLF++HC6XC0ajEUajEXa7HYC70Nlrr72GXbt24ejRo1i+fDnuv/9+TJ061ZtET548GWq1GrNnz8a+ffuwcuVKvP7663jggQeC9bKJiIgoiEZkJeKjOdnQa93JtkuS8YeVO/HpltAeCSRqT0b3SsWSmSOgU7tnmNhdEu5ezpNe1H4IsizLwXryn376CaNHjz6vfcaMGXjyySfPK2Lm8eOPP+KKK67A9u3bcc899+DAgQOw2WzIysrCtGnT8MADD0Cj0Xi33717N+69915s2bIFycnJuO+++/Dwww+3qK8mkwlxcXGoqKgIizOFRO0Z45EodIRzPO4vMGHa4lyUVNu9bU9d3xczLu4SvE4RtVG4xeT2k2WY8f5mVFrdqwYJAvD33w7ArcND91pzouYIaqIdTsLtS4uoPWM8EoWOcI/HvOJKTF6Ui+LK2iKqfx7fC3dd3i2IvSJqvXCMyb2nKzD9/c0orXPS64mJfTDzkoYH3YjCQUhfo01ERETkT91TY/HpnTnoEB/lbXth9QG8suYQOBZBFBj9OsRh5byRSI2tnZH61Ff78daPeUHsFVHbMNEmIiKiiNYlORqf3pWDLkk6b9sbaw/jhdUHmGwTBUiPtFh8dlf9k14vfXcQL33HOKTwxESbiIiIIl6H+Ch8emcOeqTGeNveXX8UT/xnHySJB/lEgdA5KRqf3ZWDrORob9tbPx7BU1/tZ7JNYYeJNhERERGAVL0Wn8wbiT7ptde1frjxBP78+W64mGwTBURGfBRW3jkSPdNivW1LNhzHI5/vYRxSWGGiTURERFQjKUaDj+eOxMDMeG/bp1tP4f6VO+FwScHrGFEESY11n/Qa0DHO2/bJlnw88CnjkMIHE20iIiKiOuJ0Knw0ewRGdEn0tv1nVwHuXb4dNqcriD0jihwJ0Wosn5ON4V0SvG1f7izAPYxDChNMtImIiIjOEatVYcms4bisR7K37fv9RZj34TZYHTzIJwqEWK0KS2eNqBeHa/YXYc7SrbDYGYcU2phoExERETVAp1Zi0fRhuLp3qrft50NnMPODLai2OYPYM6LIURuHad62Xw6fxYz3N6PS6ghiz4gujIk2ERERUSO0KgXenjIU1/ZP97ZtPFqCaYtzYeJBPlFAaFUKvDN1CCYOzPC2bT5eiqnv5aLcbA9iz4gax0SbiIiI6ALUShGvTxqE3w7p4G3bfrIckxdtQmk1D/KJAkGlEPHabYNw67CO3rZdpyowaeEmnKm0BbFnRA1jok1ERETUBKVCxP/dPBCTszt52/aeNuF2HuQTBYxCFPDCbwfgjou7eNsOGCtx27sbUVhhCV7HiBrARJuIiIioGURRwLM39MPsS7O8bQeLeJBPFEiiKOCJiX1wzxXdvG1Hz1bjlgUbcbLEHMSeEdXHRJuIiIiomQRBwF+v7Y35o7t7246ercat725EfikP8okCQRAE/GlcL/xxbE9v26kyC255dwPyiiuD2DOiWky0iYiIiFpAEAQ8NLYnHhpzkbctv9SCW9/diKNnqoLYM6LIcu/o7nj8uj7ev4tMNtz27ibsK6gIYq+I3JhoExEREbXC/Ct74LE6B/mFFVbc+u4mHDRyRI0oUGZdmoUXftsfguD+u6TajtsXbsKOk2XB7RhFPCbaRERE7ZxLklFWbUcZK2T73OxLs/DcjbUH+WerbJi0cCP2nOKIGlGgTBrRCa/dNggK0R2IJqsTU9/LxaajJUHuGUUyJtpERETtlN0p4UylDSdLzSgz2+GS5WB3qV2anN0JL98yEDXH+CgzOzB50SZsO1Ea3I4RRZDfDOqAt6cMgVrhTm+q7S7MeH8zfjpYHOSeUaRiok1ERNTOWOwuGCusOFVmRqXVAZkJtt/9dkhH/OP2IVDWZNuVNiemLd6MDUfOBrlnRJFjbF8DFs0YBq3KneLYnBLmfrgV3+4tDHLPKBIx0SYiImonqmxOnC63oLDCArPdGezuRJxrB6RjwdSh3hE1s92FmR9s4YgaUQBdflEKls4cgWi1AgDgcMm4d8UO/HvHqSD3jCINE20iIqIwJssyTFYH8kvNKDZZYXO4gt2liHZ1nzQsvqOhETVjkHtGFDmyuyZh+dyRiItSAXDXqXjg011YkXsyyD2jSMJEm4iIKAy5JBnlZjtOlppxttIGh0sKdpeoxmU9GhpR244vd54Ocs+IIsegzHh8Mm8kkqLVAABZBv7y7z1475ejQe4ZRQom2kRERGHE4ZJwtsqG/FIzSqvtcEm8/joUZXdNwkdzsqHXKgG4T4z8YeVOfLolP8g9I4ocvdP1WHlnDgx6rbftb9/8ijfWHmbtCvI7JtpERERhwOZ0odhkRX6pGSaLA1IrDhItdhfyiqv80DtqyOBOCfh43kgk1hlR+9O/duPDjceD2zGiCNI9NQaf3ZWDzMQob9sraw7hhW8PMNkmvwpqor1+/XpMnDgRGRkZEAQBX3zxRb3bZVnG448/jvT0dERFReHqq6/G4cOH621TWlqKKVOmQK/XIz4+HrNnz0ZVVf2DiN27d+Oyyy6DVqtFZmYmXnzxRX+/NCIiIp+w2F0orLDgdJkFVbaWFziTZRm/Fprw8veHMO619fjdxzv80EtqTN+MOKycNxKpsRpv2+Nf7sPC9UeC2CuiyJKZqMNnd16MbinR3rZ3fz6KJ/6zDxJnBZGfBDXRrq6uxsCBA/HWW281ePuLL76IN954AwsWLEBubi6io6MxduxYWK1W7zZTpkzBvn37sGbNGnz99ddYv3495s2b573dZDJhzJgx6Ny5M7Zt24aXXnoJTz75JBYuXOj310dERNRa1TYnTpWZUVhhgcXe8gJn5WY7Ptuaj9lLt+LeFTvwzZ5CVNtd2F9owt7TFX7oMTWmR1osPr0zBx3ia0fUnlt1AK//wOmrRIFiiNNi5Z056GWI9bZ9uPEE/vSv3bwEh/xCkEPkG14QBPz73//GDTfcAMB9Bj4jIwMPPvggHnroIQBARUUF0tLSsGTJEkyaNAm//vor+vTpgy1btmDYsGEAgG+//RYTJkzAqVOnkJGRgXfeeQePPvoojEYj1Gr31K0///nP+OKLL3DgwIFm989kMiEuLg4VFRXQ6/W+ffFE1CKMR2qv3BXEnTBZHK0qbuaSZGw5XorVe43YcKSkwYPHGI0Sz97YD78Z1MEXXWY8tsCpMjOmvJeLEyVmb9udl3fFn8f1giAIQewZtSeMyQsrN9sx44Mt2JVf7m27dkA6Xr11ENRKXlVLvhOyn6Zjx47BaDTi6quv9rbFxcUhOzsbGzduBABs3LgR8fHx3iQbAK6++mqIoojc3FzvNqNGjfIm2QAwduxYHDx4EGVlZY0+v81mg8lkqvePiIKD8UjtndMlobTaXUG8pKrlFcRPl1mw+L/HcPuiTfjLv/fil8Nnz0uyB3SMw5PX98GWR69uU5LNeGy9jgk6fHpnznnTV5/k9FVqA8Zky8Tr1Fg+JxsjshK9bd/sLsTdH22Dlcsjkg+FbKJtNLrXm0xLS6vXnpaW5r3NaDQiNTW13u1KpRKJiYn1tmnoMeo+R0Oef/55xMXFef9lZma27QURUasxHqm9crgknKm0Ib/MgnJzyyqIWx0ufL+/CPev3Ilp72/G8tyTOFtlr7dNUrQak0dk4sNZw/HabYNw3YAMRNUsOdVajMe2SdOfP3116cYTeOTzPZy+Sq3CmGy5GI0SS2eOwKiLUrxtaw8UY/bSLTDbW14Lg6ghIZtoB9sjjzyCiooK77/8fC7HQRQsjEdqb6wOF4pqKohXWh3Nvk5XlmUcMJrw6ppDuGXBRryw+gB2nap/vbVCFHBp92Q8d2M/fDJvJOZc1hUdE3Q+6zvjse2SYzT4ZN5IDOwY521buTUfD3y6k+uhU4sxJlsnSq3AoulDMbZv7YDc//JKMG3xZpisjiD2jNoLZbA70BiDwQAAKCoqQnp6ure9qKgIgwYN8m5TXFxc735OpxOlpaXe+xsMBhQVFdXbxvO3Z5uGaDQaaDSaRm8nosBhPFJ7UWVzotxsh93ZsmSqwuzADweKsHqPEUfPVje4TedEHcb3N+CaPmlI0Kkb3MYXGI++Ea9T46M52Zi1ZAu2HHdfyvblzgJYHS68cftgaJRtm3lAkYMx2XoapQJvTR6Chz7bhS92FgAAtp0ow+RFm/DhrGzv0nxErRGyI9pZWVkwGAxYu3att81kMiE3Nxc5OTkAgJycHJSXl2Pbtm3ebdatWwdJkpCdne3dZv369XA4as9MrVmzBj179kRCQkKAXg0REUUqd4EzB/JLzSg2WZudZLskGZuPleLJr/bhlnc34q0fj5yXZEepFJjQ34A3bx+M9+8YhluHZfo1ySbfitWqsHTWCFzaPdnb9t2+Ity5jNeKEgWKUiHi5VsH4fYRtVPu9542YdLCjSg2WS9wT6ILC2rV8aqqKuTl5QEABg8ejFdeeQWjR49GYmIiOnXqhL///e944YUXsHTpUmRlZeGxxx7D7t27sX//fmi1WgDA+PHjUVRUhAULFsDhcGDmzJkYNmwYVqxYAcBdqbxnz54YM2YMHn74YezduxezZs3Cq6++Wm8ZsKawgiNR6GA8UjiQJHeCXWFxtOja28IKC1bvNeK7vUU4U2VrcJv+HfQY3y8dl/dMQZSq+SOf+igVkmN8O/LFeGw7q8OFe5dvx9oDtbP0crom4b0ZwxCtCdnJhxSiGJOtI8synvn6V7z/v2Peti5JOiyfO7Le0nxEzRXURPunn37C6NGjz2ufMWMGlixZAlmW8cQTT2DhwoUoLy/HpZdeirfffhsXXXSRd9vS0lLMnz8fX331FURRxE033YQ33ngDMTEx3m12796Ne++9F1u2bEFycjLuu+8+PPzwwy3qK7+0iEIH45FCmdMleZfokpr5E2tzuPBL3lms3mvEjpPlDW6TFK3GmL5pGNfXgMzE1l1zzUQ7dNmdEv6wcgdW7akt1Dq0cwI+mDkceq0qiD2jcMOYbD1ZlvHKmkP4x7o8b1uH+Ch8NCcbWcnRF7gn0flCZh3tUMcvLaLQwXikUGRzulBhcaDa5mpWcTNZlnG4uAqr9hix9kARqm3nTxVWiAJGZiVifH8DsrOSoBDbttYyE+3Q5nRJ+NM/d+PzHae9bf07xOHDWSOQwGtFqZkYk233zk9H8PdvD3j/TonVYPmcbFyUFnuBexHV5/P5SGazGTqd76qbEhERhTKrw4Vys6PZS8JUWBxY+2sRVu014uiZhgubdUrUYVw/A8b0SWMxngiiVIj4v1sGQqNS4OPNJwEAe05X4PZFm7BsdjZSYlnwiigQ7r6iG3RqBZ74zz4AwJlKG257dyOWzc5Gvw5xTdybyK1VifZVV12FDz/8EB06dKjXvnnzZkydOhWHDh3ySeeIiIhCldnuRIXFAYu96aJVLknG9pNlWL3HiP8dOQuH6/wR7yiVAqN7pmBcPwP6ZughCG0bvT6XRqWAtgXXc1NwiKKA527sB61KxAf/Ow4AOGCsxG0LN2L5nGykx/FaUaJAmHFxF0SpFfjzv3ZDkoEyswO3L9yEJbOGY2jnxGB3j8JAqxJtrVaLAQMG4O2338Ztt90GSZLw9NNP47nnnsM999zj6z4SERGFBJcko8rqhMnqaNZ6x8YKK77da8S3+4wormy4sFnfDD0m9DPgip6piFL7LhEWBQFRagV0agV0amWbp51T4AiCgMev64MolQJv/3QEAHD0TDVufXcjVswZ2epr9ImoZW4dlokolQL3r9wJpySj0ubEtMWb8d70Ybi4zmoBRA1pVaL9zTff4K233sKsWbPw5Zdf4vjx4zhx4gS+/vprjBkzxtd9JKIw4pJk2J0S7E4JNqcLcToV14OlsGd1uGCyNu/6a7tTwi+Hz2L13kJsb6SwWYJOhTF90jC+Xzo6JfkuaVIrRejUSkSpFNCqRJ+PilPgCIKAP43rhSiVAi+vcc8UzC+14NZ33SPbXVNimngEIvKFiQMzEKVS4J7l22F3STDbXbhjyRa8M2UIruqdFuzuUQhrUzG0Rx55BH//+9+hVCrx008/4eKLL/Zl30IKC0sQnU+SZNhqEmpbTXJ97ihfRnyUz6erMh4pENzrX7urhzdn9PpwUSVW7TVi7a/FqLKdf722KAAjuyZhfD8DsrMSoVSIbe6jIAiIUimg0yigUyl88pgtxXj0v/d+OYq/ffOr9+/kGHdhpp4GFmai8zEm/eO/h89i7odbYalZ414pCnh90mBcOyA9yD2jUNWqEe2ysjLMmTMHa9euxbvvvouff/4ZY8aMwYsvvsip40TtmM3pgtVem1g3J/kgCjee5bkqrU2vf11pdeCHX4uxeo8ReWeqGtymY0IUxtcUNkvyQcVvpSjWmRKu4Kh1BJhzWVdoVQr89Yu9AICzVTZMWsjCTESBdGmPZHw4ewRmfbAFlTYnnJKM+z7eDotjIG4e2jHY3aMQ1KoR7Q4dOiArKwvLli1DVlYWAGDlypW45557MHLkSHzzzTc+72iw8ewgRRqnS6oZra5JrB1Ss9cErosj2hQumjs9XJJl7DhZjtV7jfjl8JkGC5tpVSIuvygFE/qlo1+Hthc206jcI9Y6jSLkLsVgPAbOP7edwp/+uQue8z+xWiWWzByBoZ0TgtsxCimMSf/ac6oC097PRbnZ4W175jd9MS2nS/A6RSGpVSPad911Fx599FGIYu0Utdtuuw2XXHIJZs6c6bPOEZH/ybIMh0uGw+We+m13SbA5JDgljlZT++dwSai2OVFlc8LuvPBn3miy4ruawmZFpoYLm/VJ12N8PwNG90qBTt36FTQ9U8Kj1ApEq4MzJZxCz81DO0KrEvGHT2oKM1mdmLY4F4tnDEdOt6Rgd48oIvTvGIeV83Iw5b1cnK1y/xY89uU+mO0u3Hl5tyD3jkJJm67Rbso999yDp59+GsnJ4V+Vj2cHqT1wSTJsTledYmUSnJLcZHGntuCINoUaWZZRZXOi0uqE1XHhpbnsTgn/yzuLVXuN2H6iDA1FSoJOhWv6pGF8PwM6J0W3ul+iIECnViBaowyrKeGMx8Bbs78I99YUZgIAjVLEwunDcPlFKUHuGYUCxmRgHDtbjSmLNqGgwupt+91VPXD/1T3C5vub/MuvibZer8fOnTvRtWtXfz1FwPBLi8KR3SnB6nTB6nBP/Q7GNdVMtClUuCQZJosDpmZce32kuAqr9hrxw69FqLQ2XNhsRFYixvdLR07X1hc2U4hCzah1eCXXdTEeg2P9oTOYt2wrrA7397paIeLNyYMxpq8hyD2jYGNMBs6pMjOmvJeLEyVmb9vcy7Lwlwm9w/L7nHyr9fPamsGfo2REVEuWZdhdEpw1U8CtDvd11U0lE0SRwOGSUGFxoNLqvODvUpXVibUHirBqjxGHixsvbDaurwFj+qYhuZWFzVQK0bu2NZfgotYadVEKlswcgdlLtqDa7oLdJeHu5dvx2m2DMHFgRrC7RxQROibo8Nmd7mnknt+NRb8cg9nuwjO/6QdR5Pd7JPNrok1EvuV0ua+hdjhrEmvJ/f+hfD212e6+/rW1SQlRa5nt7unhZnvjxc0kWcbO/HKs3mPEL3lnG7xOW6sUcXnPFIzvZ0D/DnGtSow1Kve11lHq0CtmRuFrZNckLJuTjRnvb0al1QmXJOP3n+yA1eHCLcMyg909ooiQqtfik3kjMf39zdhXYAIALM89CYvdhRdvHsAaGxGMiTZRCJIkGQ5Jchcpq7mW2u4MzQJldqcEY4UVRpMVhRVWFJms3r+NFVaUWxwY19eABdOGBrurFAFckoxKq3v0+kKXShSbrPhuXxG+3WdEYZ3r6+rqnR7rLmzWMxXRmpb/XKqVImI0SkRrlFDxQIv8ZEinBHw8dySmLc5FmdkBSQb++M/dsDpcrIJMFCBJMRqsmDsSMz/YjO0nywEAn+84DYvDhdcnDYZayd+ASMREmyiIXJJ7qrf7n1xToCy0pnzbnRKKK89Nom3eZLq02t7kY+SXmZvchqgtLHYXKq0OVF9g9NrulLDhSAlW7y3E1uMNFzaLi1Lhmj6pGN8vHVnJLS9s5hm51qmVPLCigOnXIQ4ra6avnqmsrYJsdUiYOyr86+QQhYO4KBWWzc7GnKVbsfFoCQBg9V4jLMu2YsHUoT6vV0Ohj4k2UQA4Xe7q3janVLuMlrN161L7o29FlTYUVbiTaaOp/qh0SZW9wYSkJYpMDY8YErWF0yV5q4dfaPT66JkqrN5rxJr9RTA1UthseJdEjO9nQE63pBaNPgs1lcKj1O51rjlFkILlorRYfHpnTr0qyM+u+hUWhwv3XdmdtQCIAiBao8QHM4fjnuXbse5AMQDgp4NncMcHm/HejOGIacXsKApfrdrbJ0+eRGZm5nlf2rIsIz8/H506dQIATJ06ldUOKWLIsgynJMPpcl8/7VmT2hHkhNolyThTafNO5fZO6675+2yVDW0dQBcFIDVWC0OcBgZ9VM1/tTDEaTEoMwGZiTrfvBgiuEevTVYHqm3nJ80eVTYnfjxQjFV7jThorGxwm4x4Lcb1NWBsXwNSYptfQ0AhCtCplYjWKBClCs9K4dQ+ZSVHe0e2T5a6ZxK9suYQLA4X/jS2Jz+rRAGgVSmwYOpQ/GHlDqzaYwQAbDpaimmLc7HkjhGI06mC3EMKlFYt76VQKFBYWIjU1NR67SUlJUhNTYXLdeF1ScMRl0ogoH4y7ZDcSbTDVTv9OxhckoyzVe5E+vxRaRuKK60+SaSTYzQwxGmRptciXa9FWpwW6XFaGPRapMRqoGiksiaX9yJfcEnuta9NFkejsSbJMnafqsCqPYVYf7jhwmYapYhRF7kLmw3oGAexmYmHUhSh0yjco9dMruthPIYeY4UVk9/bhKNnqr1td1zcBU9M7MPPbgRgTIYGp0vCn/61G59vP+1t65Oux7LZI5DEArERoVUj2rIsN/hFXVVVBa1W2+ZOEQWTJLlHpB11lsvyJNfBKEYmyTJKquz1RqKLKqworBmRLq60tfmabgFAUowa6TWJtKEmgfb8NzVWwymxFBTNufb6TKUN3+0zYvXexgub9TS4C5td2Su12VP3VAp3MTOdhpXCKbwY4rRYOS8H0xbn4kDNjI4lG47D5nThbzf0b/TEKBH5jlIh4v9uHgidWoGPNp0EAOwvNOG2hZuwfE420vTMmdq7FiXaDzzwAAD3NWmPPfYYdLra6aAulwu5ubkYNGiQTztI5C+eQmQ2p+S9htruDPzItCzLKDM7UFhhgbHChiJT/VHpIpMVDlfbp54nRqvrJM8aGOKiahJrDVJjtSzcRCHDUzncZHE2enLL4ZKw8UgJVu01Yuvx0gZnbei1SlzTJw3j+xnQNSWmWc+tEAVEa5SI1SqZXFNYS4nVeJcc2n2qAgDw8eZ8WOwu/N8tA3nylCgARFHAM7/pB51aiYXrjwIA8oqrcMuCjVg+J5uX1rVzLUq0d+zYAcCdGOzZswdqtdp7m1qtxsCBA/HQQw/5todEreCSZLgkGVLNVG9XzVRvT7vDJQWssrcsyyi3OM67PrrIU73bZG1wimtLJehU7qndsTXJ9Dmj0kykKZRJkgyzw4Vq24XXvT52thqr9xZizf5iVFgc590uABjeJQHj+6cjp2tSsz73oiBAp1EgVqOCViVyai21G/E6NT6ak41ZH2zB1hNlAIAvdhbA5pS45BBRgAiCgEfG94JOrcBrPxwGAJwsNePWd93JdnNPBFP4adU12jNnzsTrr78eUdd9hOL1LpIkY1+BCaVmOxJ1avTN0EOsMx2sodsBYF+BCWerbSivdiBBp0JCtBqSLGNHfjkKyswwmqzYf6oCxSYLqhqvNYTUGBV0GhUGddBj2iVZGJyZUO/5/UWWZThqpnE7Jdm95rTnb5c7sW7Fx7pN/TFZnfXXkj4nobb6IJHWa5VIj4tCml4DjVKBaI0C6fFamG0ubD9RhmqrA1EaJTLjtfi1sBz55Q5IAKKUAi7pnogYtQobjpagrMqB89MToFeqDgk6NU6UmVFtdUGvU2FE50R0S4tFok6NeJ0a3dOim31Nq0ckXqPdWGxeKCZLzXbER6kgyTJ2naqALMmI0SpRaXGgoNyCM1U2mK0OnCgzI7/EDEsjpTAEANlZCXhoTE8M6ZwYkJhsC6vDhUqrE9U2Z6NFA6ttTvx4sBir9xrxa2HDhc3S4zyFzdKQ2ozpeJ5q4Tq1AtFqZaPvk2efna22ochkwerdhSiosCEjToMre6Xguz0F2H7KBJsTUIpAUrQSXZKisb+gEib7+XGvABClVkCvVSAhWg2NUoEotRLXD8rATYM7QhmGiU+oxyO5Y2juh1ux4UiJt210zxS8c4ElhyRJxqL/7sXzq05e8LHj1cBPf7wK8bGcBhsqGJOha+H6I3hu1QHv38kxGnw0ZwR6GWr3kyTJWPDTDrz4fWGDjyEAmJLdATcN7YyBHeND/nc+krUq0Y5EofaltSHvLN75+QiOFFfB4ZKhUgjolhqDuy/vhou7Jzd4e1KMewZCQbkFJqsTkiS7o1UGfDAzGQa9Bq/cOggXd09u0f1k2T3K7JJlSBLgqvlbqhmR9rQ764xIB1ql1T0iXVh3JLqitnK3xdH2AoAxGqV39NlzrXR6nZHpKLUCO06WYcXmfOSXVKPS6oTFBwl8cwhwrw/ZLTUGk0dkYnCnhGbfN9IS7cZic1SPZKw/fLbBmCypsqPa5kK13QmXJLd5OTWPBJ0Kb00e0uKY9DfP1PALLcslyzJ2n67A6j1G/HzoDGwNfNbVShGjeiRjXD8DBmXGN3kSyJNcR2uU0KkUTR6cePbl/oIKlFY7fLZfGqNSCPjT2J6YO6qbn5/Jt0I5HqmW1eHC3R9tw48Hz3jbLu6WhPdmDINOXX+C44a8s5j8Xm6LHj8pWoVtj43xSV+pbRiToW3ZphN47Iu93r/jolT4cNYIDMyMb3HsZSXr8OwN/UPud57cWpVoV1dX44UXXsDatWtRXFwM6Zxr6I4ePeqzDoaKUPrS2pB3Fn/59x5U2ZxI0KmhVoiwuySUmR2I0SgwJbsTlueerHd7ucWOwgorZBk1B5fu3e7ry5Gj1SLenjIU2V2T3EmyJEOSUZs01yTU7kTanVSHwrmeKpvTOwrtvT66zqh0ta3tiXS0WlF/OnfNNO/0OHcF76YKNO04WYZX1hyC2e4CZBlllgtMN/ADAUC0RoG4KBUeuOaiZifbkZRoNxabRSYbzHYnojUK9/XwdWISABJ1apRb7PDHeZMolYjFM4YH/UdYlmVU212osjphcTQ+NfxslQ3f7yvC6r1GnC63NLhNz7RYjOuXhqt6pSFG2/QVUFE1yXXMBUauz+XZl6XVdlRanX5Psj0EAH+Z0Cusku1QjUc6n90p4Xcf78C3+4zetmGdE/D+zOHQa91LDrUmyfZgsh0aGJOh71/bTuGP/9zlrS8So1HigWt64Omvf23xY4XqSXVqZdXxOXPm4Oeff8a0adOQnp7u1+vZunTpghMnTpzXfs899+Ctt97CFVdcgZ9//rnebXfeeScWLFjg/fvkyZO4++678eOPPyImJgYzZszA888/D6Uy/BaNlyQZ7/x8BFU2Jwx6rfe914oKGPQiCiuseOunI1CKgvd2WZa91zLKcI8mqRWADwZhz1Ntl/DamoN46dZBLZ5i7E8Wu6smiW644Filte1Ja5RKgfQ4LVL1GqTHRXkLjhlq/m5OQtAYSZaxYnM+zHYXEnUqHDlrbnN/W0qGe6kKs92FT7bkY0SXJKiUIkTRfY2rAPeIoSjU/68qQgruNBabGkGEq2Y2htMlQ6MSARmosDggCIAsAaXVdvhrboLFIeHNdYcxsmtSUKaX2Zy1U8Mbm43icEnYeLQE3+41YvOxxgubXd3bXdisW2rT17NpVe4p4dEaRYuLPnn2ZaXVAadLCliSDbjj7B9rD2PmxVlhOY2cQptaKeLNyYPx0Ge78MXOAgDA1hNlmPpeLj6cNQJ6rQpPfb2r1Y9fUu1AeaWV08iJmnDT0I6IUivw+092wOFyL1/5TCuSbAAoMzvw1o/B+52nxrXqyH/16tX45ptvcMkll/i6P+fZsmVLvXW59+7di2uuuQa33HKLt23u3Ll4+umnvX+fWw392muvhcFgwIYNG1BYWIjp06dDpVLhueee83v/fW1fgQlHiquQoFOfd4JDEARoVSIKy63IiNdClt0JmtXugtXhgijAewAryfDbwePBoirkFVXjIkPgijtYHS5v8lwvia6wobDCPVW+rbRKsXbpqzpTuz3TvPVapd9OOuUVVSO/pBp6rQpVPhhdby2HS0aKRonTZRaUWxzo3zEuaH0JNY3FptUhwe6SoFQIsLskWGuu27U5JShFEZIg+6Sq/IXszK/AvgJTwPaX0yWh2uZCpc1xwUJ/J0qqsWqPEWv2F6G8kcJmQzsnYHw/Ay7pntxk4Satyj1yHa1ueXJdl2df6tRKlJobqmjgXyabC1/tLsSNQzoE/Lmp/VMqRLx86yBoVQp8siUfALD7VAUmLdyERyf0xkFjw0vkNdfNC3Pxw4OX+6KrRO3ahP7piFIpcOdH22B3tu2k7r4CU0B/56l5WpVoJyQkIDEx0dd9aVBKSkq9v1944QV069YNl19e+yWu0+lgMBgavP/333+P/fv344cffkBaWhoGDRqEZ555Bg8//DCefPLJepXTg0WWZcg1ia8syzVJcO11yZJcu83xkmrYHBJiNPBe2+iZgSnDvY0E95Rsz+02lwtyzeXYtc/pv9fjkGRUWO0+fUy7U/JeD+39b50R6TIfHAyrlSIMevdyV3WndXuS6gSdKijViAVBgMnmgFNy97HaFtgp43XJMiAI7n1cavbtPg53pWY7HC4Z6nMSPKckQZYBhei+VMOzXJXnvQwEh0vy+/6SJBnVdieqbE5Y7I2fDDLbnfjxwBms3mvE/kJTg9uk6TUY38+AMX0NMDRR2EytdK91HaNR+my5Is++VClE/52RbMLp8sDPWqHIoRAFPHdjf2hVCizZcBwAcMBYiT/9a3ebH/tMZdsSdaJIMrpXKpbcMRwzl2xpsBZJc9ldPC4LRa1KtJ955hk8/vjjWLp0ab3RY3+z2+346KOP8MADD9RLeJYvX46PPvoIBoMBEydOrLfG98aNG9G/f3+kpaV5tx87dizuvvtu7Nu3D4MHD27wuWw2G2w2m/dvk6nhA8JzWewu2JwuSLJ7irbsKeZVJ1n2jDR7EuuWUIkiFKJ7NEzTwOiOS5Yhon4irRDE2gP6mnah9jJtn1OJAuK0LTuBYXdKKK70JM82GCss9QqOlVa3/ctDpRCQpq8diU7Ta2DQu6t4p8dpkRCtDsh0d4UouKdaC+4p1wrR/U8lilAoBChr/lYIgncKUJXVCa3Kvb99fb1zSwiC+7OlEgUk6gJ3kqq18RhIiTo1VDWj1lqxdh8pRXf8STWJtVJ070dBCFwOp1KIftlfLkmG2e5Etc11weuuZVnG3tMmrNpbiJ8PnmmwCr9KIeDS7smY0D8dgztduLCZSiG6R641Cr+sde3Zl5LnDGUQku0O8aG7tmo4xCM1TRQFPDGxD7QqBRb8fAQAvHUj2iKF08YDjjEZ3i7unoy//aYf/tiGE11qRWCPy6h5WpVov/zyyzhy5AjS0tLQpUsXqFSqerdv377dJ5071xdffIHy8nLccccd3rbJkyejc+fOyMjIwO7du/Hwww/j4MGD+PzzzwEARqOxXpINwPu30WhEY55//nk89dRTLe5jlc2JSqv/php2T4tGZlI0jp6pQnKMGkKdcWoZMmwOCdFaJWxOCTJkCBCgUQlQK0RYHLUHt/5MJ3umxaB7WnS9NqdLQnGlzVtkrPCcUemSKnubj2UVooDUWI13KnfdNaQNcVokxfg3kRYEd5KsVLgTZaUo1vxXqPff1oyK983Qo1tqDH4trERqjKrpO/iJRinC4nChd7reuzRVILQ2HgOp7j4y6GvXYtaqRKgVIsx2F3RqBbRq9yip572UJUAE/HaNNgAMyozz2f7yTAs3O5ywOqQLniwsqbLh+/3uwmanyhoubNYjNQbj+xlwVe9UxGob/2wLgoBotQKxWhWi1P492eTZl/sLTIhSijA7/Ll3zqfXKDBxQHpAn7MlwiEeqXkEQcDD43pCp1bglTWHfPKY/5yX7ZPHoeZjTIa/m4Z2xGtr9uK0qXW/N30zAntcRs3TqqrjTQXzE0880eoOXcjYsWOhVqvx1VdfNbrNunXrcNVVVyEvLw/dunXDvHnzcOLECXz33XfebcxmM6Kjo7Fq1SqMHz++wcdp6OxgZmZmkxUcz1Ta/JpoA/WrT8dqVVArBNhd7uVydGoFJg7IwFe7C+rdbrI6cKbSnczWrZPg65WyNAoBNwzpAI1SUW+a99kqW5ufSxSA1FgtDHGaOqPStcl0cowGCj8VgXAX9apNnlUKAUqF2ODos7/UVrR2QZYllFQH9tpREUBslBIJOjWeuzGwS0m0Nh4Dre4+itepoFGIsLkkFNepOp4Sq4VGIaLMYocxTKqOe6aFV9tcMNsvfOmC0yUh91gpVu0xIvdYSYNxH6tV4qpeqRjfz4AeabEXfDyVQoReq0KMVum3+G5IbdVxByqt/l/ayyMcqo6HSzxSyyxafxTPrmpdMSYPVh0PDsZk+9Daiv+sOh66wmYd7RMnTqBr1674/PPP8Zvf/KbR7aqrqxETE4Nvv/0WY8eOxeOPP47//Oc/2Llzp3ebY8eOoWvXrti+fXujU8fP1dylEgKRaAOot56yQ5ahEgRkJkV71zhu6PZ4nXu0qLjShiqbE5IE77TIwI7XNEwUgOQYTW2hMb172StP1e6UWP8l0kDtiLRKIUKpcE/lVindswF8de1nW9Vdo7nC4kD1Ba6F9SVRABJ0avTJ0HvXag+mUF66pN462pIMldjAOto17fXW0ba7vJW5Q2Ed7brJ9YWmhXucLDFj9d5CfL+/qMGaCQKAIZ3iMb5/Oi5tRmEznVoJfZTyvPV9A4nraDdPKMcjtcyyjcfx2Jf7WnVfJtmhgzEZvriOdvsSNutbffDBB0hNTcW11157we08CXV6unvaXU5ODp599lkUFxcjNTUVALBmzRro9Xr06dPHr332p8GdEjAwMx55RdWosNoRp1Wje1q0d2p0Y7cD7grWZRY7TBYH4rQq6HUqOCUJ20+U4URJNYwmK06XWlBtd8HXxZCTYtRIb2Qd6dRYjd+Xgqo7vVspijVJtDu5VilaN607kC7unoyRXZOwr8CEUrMdeq0SB4wm/GdnAcqqbIjWqtA1KQo7T5biaIkNEgCdSsSYvimI1aix9kAxzlTY0NAV7wM7xCA5WoO8s9WotDiREK3G5T1S0LtDHFJiNEiK0aBvhp5LRzTh3H2UqFN737fZl3Y9rx2Aty0+SgVJlrHrVAVkSUaMVolKiwMF5RacqbLBbHXgRJkZ+SVmWBo5xyIAyM5KwENjemJI58QW7a+WJtdmuxM/HXQXNttX0PA1gamxGozrZ8C4vgYY4i587aZCFBCrVUGv9V1hs7aouy/PVttQZLJg9e5CFFTYkBGnwZW9UvDdngJsP2WCzQkoRSApWokuSdHYX1AJk/38U5gKuNf11msVSIhWQ6NUIEqtxPWDMnDT4I5c0ouCalpOF2hVCjz8r93NnoUWrwZ++uNVXNKLyAcu7p6Mo89NwIurt2LBL8UNbiMAmJLdATcN7YyBHeN5XBbCmj2inZiYiEOHDiE5ORkJCQkXTEhKS0t91kEAkCQJWVlZuP322/HCCy94248cOYIVK1ZgwoQJSEpKwu7du3H//fejY8eO3rW1XS4XBg0ahIyMDLz44oswGo2YNm0a5syZ06LlvUJtRLulZFlGmdlRv2q3qX7lbl8sMZQUra6p2u1ZR1pbU8nb/a+pUSxfUIjuad0qz3+9ibQY0Kmn5D88W+87sizDXDOaXm1vOrmWZRn7CkxYvdeIHw8Ww9rA9csqhYBLuiVjfH8DhnRKaDLuomquvY5WK0L+ZBedj/HY/ny1qwD3r9wJZ022Ha1WYPEdwzGya1KQe0bNwZhsH4orrZj23mYcLKr0tk0anolnb+zP49kw0ewR7VdffRWxse5r6V577TV/9adBP/zwA06ePIlZs2bVa1er1fjhhx/w2muvobq6GpmZmbjpppvw17/+1buNQqHA119/jbvvvhs5OTmIjo7GjBkz6q273R7IsowKi6OBdaRr1pautF1wPdvmio9S1Ssw5rlW2rMsliZAFbE910yraq6TVindo9Nqhcgze0RN8FQLt9hdMNtd7uraTSittrsLm+0pRH4jhc26pkRjQj8DruqdhrioCxftU4gCYjRK6KNUfp/JQkQtM3FgBjRKEfNX7IDdJaHa7sIdH2zGwmnDMOqilKYfgIjaLDVWi0/mjcT09zdjz+kKAMAnW/Jhcbjwf7cM5G9nGAiba7SDLdgj2rIsw2R1oqjOKHTdpLqowtrgkjktpdcqz5vWbaizlnRUgJeWUogC1Er3aLTnmmnP6DRFLp6tbzmHS4LZ5kK13elelaAZX/0uSUbusRKs3mPEpmOlcDUwlzRao8BVvdIwob8BPVJjmhyR1qoUiNW6173m6HX7wHhsv34+dAbzPtzqXd9XrRDx1pQhuKZPWhP3pGBiTLYvJqsDsz7Ygq0nyrxt1/RJw5uTB/tliUvynVYn2i6XC1988QV+/dVdobJv3764/vrroVC0zx0eiES7yupEoWf96Lqj0TXJtMXR9sJXMRp3Il13FLruCHWwCg/Vne6tVorufyFUhIxCCw8imsfqcHmnhTtczT8Rd7LUjG/3GvH9/qJG17Af3Cke4/sZcFn35CZnsoiCgFit0r0KAq9BbncYj+3bpqMlmLVkC8w1xTeVooDXJg3CdQMygtwzagxjsv0x252Y9+E2/DfvrLftsh7JWDhtmN+XvKTWa1WinZeXhwkTJuD06dPo2bMnAODgwYPIzMzEN998g27dwqtianP4ItGutjm910XXnd7tGaGutrU9kdapFfUSZ0PdZbD0WsRog1f/zjPdW10zIq1W1lb35nRvagkeRDTMc7212e6Cxe6CU2p+cm1xuPDTwTP4dm8h9pxuuLBZSowG4/qlYWxfAzLio5p8TI1n9FqtZIy3Y4zH9m/7yTLMeH8zKq3u5f1EAXjx5oG4eWjHIPeMGsKYbJ+sDhfmr9iOH36tLZI2oksiFt8xDLHaC1+uRcHRqkR7woQJkGUZy5cvR2JiIgCgpKQEU6dOhSiK+Oabb3ze0WBrzpdWtc2J3acqcPRM1XkFx4pMVpisF15/tjm0KhHpcVFIq1nyqt6ItF6LWG1oTMcUBfd105qaf2qlyOkt5DM8iKjlcEnexLo5lcLrkmUZvxZWYtXeQvx44EyDs2aUooBLuidjQjMLmwmC59prJWM+QjAeI8Pe0xWYtji33vJ9f7uhH6aO7BzEXlFDGJPtl8Ml4f6VO/H17kJv28DMeCydORzxOnUQe0YNaVWiHR0djU2bNqF///712nft2oVLLrkEVVVVPutgqGjqS2vdgSLMWrK1zc+jUYruKd1xNetIeyp4x2mQro+CPio0Eum6RKF2uremJqHm9FDyp0g/iGjtlHCPMrMda/YXYfUeI06UmhvcJis5GuP7GXBN7zTE6Zo+U65SiNBrVYjRKlkNNcJEejxGkoPGSkx5Lxdnq2zetr9e2xtzLusaxF7RuRiT7ZtLkvHnf+3GZ9tOedt6GWKxbHY2UmI1QewZnatV84g1Gg0qKyvPa6+qqoJaHZlnU9Ljmp5GCbiXvfFM406vW7W75l98lCrkEum6PFO+VYrakWoWJiPyL0mSYXa4vJXCGypK1hSXJGPL8VKs2mPExqMlDRc2UytwZe9UTOiXjovSmi5sphAFRGuUiNVy9JooEvQ0xOLTO0diynu5KKywAgD+9s2vsNhduO+qHkHuHVFkUIgC/n7TAERrlFiy4TgA4ICxErct3Ijlc7KbnZOQ/7Uq0b7uuuswb948LF68GCNGjAAA5Obm4q677sL111/v0w6Giw4J7g+1UhRq1ozWnHeNdHqcFgnRaoghnEh7eKp9a5QK93XVNcXJQvkkAFF7YndK7uW3HE5YHc2rEt6QU2Xuwmbf7S9CSVXDhc0GZcZhfL90XNYjGdpmrCygUysRo1Vy3WuiCNQ1JQaf3pmDye9tQn6pe6m/l9ccgsXhwh/H9uR3AlEAiKKAJyb2QZRagXd+OgIAOHqmGrcs2IgVc0aiU5IuyD0koJVTx8vLyzFjxgx89dVXUKncUwodDgd+85vfYMmSJYiLi/N5R4OtOdNwjBVWCIL7Wu1wohRFaFTuRNrzX1b7plDWHqfFybIMq0OC2e6E2e5q1ZRwD4vDhfWHzmD1XiN2n6pocJukGDXG9TVgXF+D90ThhShF0V3YTKvkLBaqpz3GIzXNWGHF5Pc24eiZam/bzEu64PHr+jDZDjLGZGR568c8vPTdQe/fBr0WH83JRvfUmCD2ioA2rqOdl5eH/fv3AwD69OmD7t27+6xjoSbY62j7ikIUvNdQc+o3hav2chDhkmRYHC6Ybe7kWmr91zFkWcYBYyVW7zVi3YFi71I8dSlFARd3S8L4/gYM65zYrGup1UoR8To1R6+pUe0lHqnlzlTaMG1xLg4Yay8nvH1EJzx7Qz+uNBBEjMnI8/5/j+Hpr/d7/06KVmPZ7Gz0yeD+D6ZWr/W0ePFivPrqqzh8+DAAoEePHvjDH/6AOXPm+Kxz1Hae6d9aFYuUEYUCz5TwarsTNmfrp4R7lJvtWPNrMVbvKcTxkoYLm3VO0mFCPwOu6ZPW7KqkUWoF4qPUXJ+TiBqVEqvBx3NHYsYHm72zZz7efBI2hwsv3jyAs+OIAmTWpVmIUivwl3/vgSwDJdV2TFq4EUtnjcDgTgnB7l7EalWi/fjjj+OVV17Bfffdh5ycHADAxo0bcf/99+PkyZN4+umnfdpJappQU/lbpRCgUdSOWPOMMlFw+XJKuIdLkrH1RClW7zFiw5ESOBsobKZTKzC6Zyom9DeglyG2WaPRgiAgWqNAXJSKxc2IqFkSotX4aE42Zn6wBdtOlAEAPt9xGlanC6/dNpgn+IkC5PYRnaBTK/DAp7vgkmSYrE5MfS8Xi+8YjpFdk4LdvYjUqqnjKSkpeOONN3D77bfXa//4449x33334ezZsz7rYKgIpanjdQuVeYqU8YeMIkmoT4tzSbK3Qnhbp4TXdbrc4i5sts+Is40UNhvQMQ7j+xkw6qIURDWjsBng/k7Ra1XQR6m4NBe1WKjHIwVGtc2JuR9uxYYjJd62K3ul4u0pQ5pVZJF8hzEZ2b7da8R9H2+Hw+U+9tAoRbw7bSiu6Jka5J5FnlaNaDscDgwbNuy89qFDh8LpDK9CYKHOs0a1RilCo1JAw2uqiUKSZ21ri8MFm+P866Pb8ri/HD6L1XsLsTO/kcJm0WqM7ZuGcf0M6JjQ/EqjKoWIOJ0KsRolr78mojaJ1ijx/h3DcddH2/DTwTMAgHUHijFn6VYsnD4UOnWrr1YkohYY18+ARdOH4c5l22BzSrA5Jcz9cCv+cftgjOuXHuzuRZRWjWjfd999UKlUeOWVV+q1P/TQQ7BYLHjrrbd81sFQEYgRbaFuUq3kNdVEjQmFs/Wy7C5kVm1zr2/dmrWtL/TYh4qqsGpvIdYdKEa17fzEXSEKGNk1ERP6pWNEVvMKm3lEa5TQa1W8/pp8IhTikUKHzenC7z7ege/2FXnbhndJwPt3DEesVhXEnkUOxiQBQO7REsxasgXVNcVRFaKAl28ZiBsGdwhyzyJHqxPtDz/8EJmZmRg5ciQA9zraJ0+exPTp071LfgE4LxkPV/5ItNU1Vb81SoU3ueaoElHTgnUQ4ZJkVNeZEt7WQmbnqjA78MOBIqzeY8TRs9UNbtM5UYdxNYXNEqObV9gMcP/AxmpV0GuVLFBEPsWDejqX0yXhwc924cudBd62gR3jsHTWiGYXZKTWY0ySx878ckxfnAuT1T3jWBCA527sj9tHdApyzyJDqxLt0aNHN+/BBQHr1q1rcadCUVsTbZWidpRao2JSTdQWgTyIsDvdhcyq7b6dEu7hkmRsP1mGVXuM2HDkrPeaqrqiVAqM7pWCCf3S0Tu9eYXNPFQKEfood4LN7xzyBx7UU0Nckoy/fL4HK7fme9t6GWLx0ZxsJMdogtiz9o8xSXXtLzBh2uJclFTX1nZ57Lo+mH1pVhB7FRnatI52JGlpoq1WitCqFO5/SpEjSEQ+5M+DCH9UCW9IYYWnsFkRiittDW7Tv4Me4/ql44qLUlo8zVurclcPj9bwukjyLx7UU2MkScbTX+/Hkg3HvW3dUqKxfM5IGOK0wetYO8eYpHPlFVdhynubUGSqPd548JqLMP/K7jwJ70c8AvOxeJ0KSdFqLqtFFEYkSYbZ4fJWCvfl9dZ12Rwu/DfvLFbtNWLHyfIGt0mMVmNMH3dhs06JzS9sBtQuz6XXqljll4iCThQFPDGxDzQqEe/+fBQAcORMNW5buBHL52S3qHgjEbVe99QY/POuizH5vU3IL7UAAF5ecwjVdhceHteTybafMNH2MVYEJwoPdqfkvtba4YTVIfn8emsPWZZxuLgKq/cYsfZAMaps56/MoBAFjMxKxPj+BmRnJbV4iS1RELzTwzl7hohCiSAI+PO4XtCplHj1h0MAgBMlZty6YCNWzB2JLsnRQe4hUWTITNTh0ztzMGVRrrcOzIKfj8Bid+KJiX05SOgHTLSJKCIEakq4h8niwA+/FmP13kIcOdNwYbPMhCiM75+OMS0sbOahFEXERakQq1XyB5KIQpYgCPj91T0QpRbx3KoDAICCCitufdc9st0jLTbIPSSKDOlxUVh5Zw6mLc7FAWMlAGDpxhOotrvw95sGtPhEP10YE20iarekc6qES34uSSHJMrafKMPqvUb8N6/hwmZalYjRPVMxvp8BfTP0rZqupVa6E+wYrn9NRGFk3qhu0KoUePzLfQCA4kobblu4Cctmj0DfjLgg944oMqTEavDJvJGY8cEW7MovBwD8c9spWBwuvHbbIM7O9SEm2kTUbtmcEs40UmjMl4wVVny7z4hv9xobLWzWN0OPCf0MuLxnCnTq1n31RqkViI9Sc/1rIgpb03O6QKtU4OHPd0OWgdJqO25fuAlLZ43A4E4Jwe4eUUSI16mxfE42Zi3Zgs3HSgEA3+wuhM3hwpuTh7DOi4+w6ngzsYIjUehobjxa7C4UVlj80ge7U8Ivh8/i272F2H6yHA19kSboVBjTJw3j+6WjU1Lriv4IgoBotQJxOhU0Sv7wUejh7yO1xpc7T+OBT3d5i09GqxV4/47hyO6aFOSehT/GJDWXxe7CnR9tw/pDZ7xtl3RPwqLpw1o9KEC1mGg3E7+0iEJHMBPtvOIqrNpTiLUHilFpPb+wmSgA2VlJmNDfgOysxFYXJxMEAbFaJeKiVJzGRSGNv4/UWt/tM2L+iu3ey2y0KhELpw3DqItSgtyz8MaYpJawOV24b8UOfL+/yNs2rHMC3p85HHqtKog9C38hf/T25JNPQhCEev969erlvd1qteLee+9FUlISYmJicNNNN6GoqKjeY5w8eRLXXnstdDodUlNT8cc//hFO5/kHyEREDam0OvDFjtOYt2wb5i3bhi92FpyXZHdMiMLcy7Kwct5IPHtjP1zSPblVSbZCFJCgU6NTog7JMRom2UTUbo3ta8Ci6cOgUbq/56wOCXOWbsUP+4uauCcR+YpGqcBbU4bgN4MyvG1bT5RhyqJclFbbg9iz8BcWcwL69u2LH374wfu3Ulnb7fvvvx/ffPMNPvvsM8TFxWH+/Pn47W9/i//9738AAJfLhWuvvRYGgwEbNmxAYWEhpk+fDpVKheeeey7gr4WIwoMky9hxshyr9xrxy+EzDRc2U4q4vGcKJvRLR78OrSts5qFSiIjTqRDLAmdEFEGu6JmKD2YOx5ylW2G2u2B3Sbjro214fdJgXDsgPdjdI4oIKoWIV24dBJ1agY835wMA9pyuwKSFG/HR7Gyk6rVB7mF4Cvmp408++SS++OIL7Ny587zbKioqkJKSghUrVuDmm28GABw4cAC9e/fGxo0bMXLkSKxevRrXXXcdCgoKkJaWBgBYsGABHn74YZw5cwZqdfOW1OE0HKLQ4c+p40UmK77da8S3+4woMjVc2KxPeizG9UvH6J4piNa07XylRqVAfJSqzY9DFCz8fSRf2HaiFHd8sMU7W0gUgJduHoibhnYMcs/CD2OSWkuWZTz99X588L/j3rYuSTosnzsSHeKjgtexMBUWR3aHDx9GRkYGtFotcnJy8Pzzz6NTp07Ytm0bHA4Hrr76au+2vXr1QqdOnbyJ9saNG9G/f39vkg0AY8eOxd133419+/Zh8ODBDT6nzWaDzVZ7kG0ymfz3Aonogvwdj3anhP/lncWqvUZsP1HWYGGz+CgVrumThnH9DMhKjm7zc+rU7uuvWUGcwg1/H8kfhnZOxIo5IzHt/VyUmx2QZODBz3bB6nRhSnbnYHcvpDEmyVcEQcDj1/VBtFqJN3/MAwAcLzHj1gXuNe+7+OD4J5KE/MV/2dnZWLJkCb799lu88847OHbsGC677DJUVlbCaDRCrVYjPj6+3n3S0tJgNBoBAEajsV6S7bndc1tjnn/+ecTFxXn/ZWZm+vaFEVGz+Ssej5ypwpvr8nDruxvxzDe/Yts5SbYoACO7JuKp6/vi0ztH4u4rurU5yY7RKNEhIQqGOC2TbApL/H0kf+nfMQ4r5+UgOUbjbXv033ux+L/Hgtir0MeYJF8SBAEPje2JP43r6W07XW7BLe9uxKGiyiD2LPyE/NTxc5WXl6Nz58545ZVXEBUVhZkzZ9Y7iwcAI0aMwOjRo/H3v/8d8+bNw4kTJ/Ddd995bzebzYiOjsaqVaswfvz4Bp+nobODmZmZnIZDFAStjceGpo5XWZ1Ye6AYq/cW4lBRVYP36xAfhfH9DBjTN63eAV9bxGiViI9SQ60M+fObRBfE30fytyNnqjBlUS6MJqu37Y9je+Le0d2D2KvQxZgkf1nyv2N48qv93r8TdCosm52Nfh3igtir8BEWU8frio+Px0UXXYS8vDxcc801sNvtKC8vrzeqXVRUBIPBAAAwGAzYvHlzvcfwVCX3bNMQjUYDjcY3B9hE1DZtjUdJlrEr313YbP3hs7A7pfOfQyni8otSML6fAQM6xvmkIJkgCIjRKBGv4xJd1H7w95H8rVtKDD67Kwe3L9qEU2Xuk6UvfXcQFrsLD465iAUjz8GYJH+545Is6NRK/Pnz3ZBkoMzswO2LNmHJzBEY2jkh2N0LeWF35FdVVYUjR44gPT0dQ4cOhUqlwtq1a723Hzx4ECdPnkROTg4AICcnB3v27EFxcbF3mzVr1kCv16NPnz4B7z8RBU5hhQXLNp3AtMWb8eBnu/HDr8XnJdm9DLF44Joe+OyuHPx5fC8MzIxv80GcKAiIi1IhMyEKKbFcoouIqKUyE3X47K4cdK1zuc6bP+bhma9/RZhNxiQKa7cOz8TrkwZDKbqPjSqtTkxbnIsNeWeD3LPQF/JTxx966CFMnDgRnTt3RkFBAZ544gns3LkT+/fvR0pKCu6++26sWrUKS5YsgV6vx3333QcA2LBhAwD38l6DBg1CRkYGXnzxRRiNRkybNg1z5sxp0fJerOBIFDqaikdjhRUP/2s31h8+g4a+4eKiVLi6dyom9E/3SWEzD6UoQh+lhF6rgihyxIUiA38fyZ/OVNow9b1cHKxzbejk7E7422/68Xu2EYxJ8oc1+4tw7/LtsLvcAxZqpYgFU4fgyl5pTdwzcoX81PFTp07h9ttvR0lJCVJSUnDppZdi06ZNSElJAQC8+uqrEEURN910E2w2G8aOHYu3337be3+FQoGvv/4ad999N3JychAdHY0ZM2bg6aefDtZLIiI/S4xWY8/pinpJtigAw7okYkI/A3K6Jfl0lJlrYBMR+UdKrAafzHNXI9972l1Ne0XuSVgdLrx40wAoOWOIKCCu6ZOGxXcMw7wPt8HicMHulDDvw2144/bBmNCfa943JORHtEMFzw4ShY7mxOPTX+3H+/87hvQ4Lcb3M2BsXwNSYn17DZtWpUC8TgWdOuTPWRL5DX8fKRAqLA7M/GAztp8s97Zd2z8dr00axMtzzsGYJH/acrwUMz/Ygiob17xvChPtZuKXFlHoaE48niwx49jZKnRIiILo41Fmndpd4Eyr4vJcRPx9pECptjkxe+kWbDpa6m27uncq3pw8hN/HdTAmyd92nyrH9Pc3o9zs8Lb97YZ+mDqSa97XxVOARNQudUrSYURWks+SbEEQEKNVomOCDoY4LQ/qiIgCLFqjxJKZI3D5RSneth9+LcbcD7fCYncFsWdEkWVAx3h8Mm9kvSVQ//rFXixcfySIvQo9TLSJiC5AEATEalXomBCF1Fgt18EmIgoirUqBhdOHYkyf2gJMvxw+ixkfbPZOZSUi/+tl0OPTO0ciPU7rbXtu1QG8uuYQVwaowSNGIqIGiIIAPZfoIiIKORqlAm9NGYKJAzO8bZuPlWLKe7moqDOVlYj8q2tKDD69MwedEnXettfXHsZzq7gMH8BEm4ioHoUoIEGnRmaiDskxGla0JSIKQSqFiNduG4Rb6hRg2pVfjkmLNqGkyhbEnhFFFs+a991TY7xti345hr9+sReSFNnJNo8giYjgPmhLjtWgU6IOCdFqKLg+KxFRSFOIAv5+0wBMz6ktwPRroQm3LdyEIpM1iD0jiixpei1WzhuJPum1xfeW557EQ5/tgrNm3e1IxESbiCKaRqVAml6LzEQd9FoV18EmIgojoijgqev7Yt6ort62vOIq3PruRpwqMwexZ0SRJSlGg4/njcTgTvHets93nMZ9H++A3RmZyTYTbSKKSDq1EhnxUegQH4VoDdfBJiIKV4Ig4JHxvfC7q3p4206UmHHbu5tw/Gx1EHtGFFniolRYNjsbOV2TvG2r9xoxb9lWWB2RtzIAE20iihhcoouIqH0SBAEPXHMR/jy+l7ftdLkFt767EXnFlUHsGVFkidEo8cHM4Rjds3YZvp8OnsEdEbgyABNtImr3REFAXBSX6CIiau/uurwbnrq+r/fv4kobbnt3E/YXmILYK6LIolUp8O60YRjfz+Bt23S0FNMW56LCEjkrA/Bok4jaLUEAkqLdBc6SYrhEFxFRJJhxcRf8/ab+8JTcKKm2Y9LCjdiZXx7UfhFFErVSxD9uH4zfDu7gbdtxshy3L4yclQF41ElE7ZZWpUCcTgWRFcSJiCLKbcM74bXbBnlXkDBZnZj6Xi42HysNcs+IIodSIeL/bhmIKdmdvG37I2hlACbaRERERNTu/GZQB7w1eTBUCneyXWVzYsb7m/Hfw2eD3DOiyCGKAv52Qz/MvSzL2xYpKwMw0SYiIiKidmlcv3QsnDbMW5vD4nBh1tItWPtrUZB7RhQ5BEHAXyb0xu/PWRnglgUbcfRMVRB75l9MtImIiIio3RrdKxUf3DEcUTUrTdidEu5ctg2r9hQGuWdEkUMQBNx/zUV4pM7KAIUVVtz67iYcMLbPYoVMtImIiIioXbukezKWzR6BWI0SAOCUZMxfsR3/3nEqyD0jiix3Xt4Nz/ymdmWAs1U2TFq4CbtPlQevU37CRJuIiIiI2r1hXRKxfG424qJUAABJBh74dBdW5J4Mcs+IIsu0nC54+ZaB8NSqLTc7MHlRLrYcb1/FCploExEREVFEGNAxHp/MG4nkGDUAQJaBv/x7D97/77Eg94wostw0tCPenDwESrG2WOG0xbn45fCZIPfMd5hoExEREVHE6J2uxyfzcmDQa71tT3+9H2/9mBfEXhFFngn907Fw+lBvsUKrQ8LsJVvxw/72UayQiTYRERERRZTuqTH49M4cdEyI8ra99N1BvPz9QciyHMSeEUWWK3ulYckdw6FT1xQrdEm466Nt+GpXQZB71nZMtImIiIgo4nRK0uHTO3OQlRztbfvHujw8+82vTLaJAuji7slYNjsbsdraYoW/+2QHPt2SH+SetQ0TbSIiIiKKSBnxUVh550hclBbjbXvvv8fw2Jd7IUlMtokCZWjnBHw8dyQSdO5ihbIM/Olfu7Hkf+FbP4GJNhERERFFrNRYLT6Zl4O+GXpv20ebTuKP/9wNF5NtooDp1yEOK+/MQUqsxtv25Ff78c5PR4LYq9YL+UT7+eefx/DhwxEbG4vU1FTccMMNOHjwYL1trrjiCgiCUO/fXXfdVW+bkydP4tprr4VOp0Nqair++Mc/wul0BvKlEBEREVEISoxWY8XckRjcKd7b9q/tp/D7T3bA4ZKC1zGiCHNRWiw+uzMHHeJr6yf8/dsDYVk/IeQT7Z9//hn33nsvNm3ahDVr1sDhcGDMmDGorq6ut93cuXNRWFjo/ffiiy96b3O5XLj22mtht9uxYcMGLF26FEuWLMHjjz8e6JdDRERERCEoLkqFZbOzkZ2V6G37ench7v5oO2xOVxB7RhRZuiRH49O7ctAlSedt+8e6PDz99f6wSrYFOZx6C+DMmTNITU3Fzz//jFGjRgFwj2gPGjQIr732WoP3Wb16Na677joUFBQgLS0NALBgwQI8/PDDOHPmDNRqdZPPazKZEBcXh4qKCuj1+ia3JyL/YTwShQ7GI7U3FrsL85ZtxS+Hz3rbLuuRjIXThiGqpjJyKGNMUntRbLJi2uLNOFhU6W2bNDwTz97YH4qa9bdDWciPaJ+roqICAJCYmFivffny5UhOTka/fv3wyCOPwGw2e2/buHEj+vfv702yAWDs2LEwmUzYt29fg89js9lgMpnq/SOi4GA8EoUOxiO1d1FqBd6bMQzX9Kk9bvzl8FnM+GAzqmyhd9khY5Laq1S9Fp/MG4n+HeK8bZ9syccDn+4Mi0s6wirRliQJf/jDH3DJJZegX79+3vbJkyfjo48+wo8//ohHHnkEy5Ytw9SpU723G43Gekk2AO/fRqOxwed6/vnnERcX5/2XmZnph1dERM3BeCQKHYxHigQapQJvTxmC6wake9s2HyvF1PdyUWF2BLFn52NMUnuWEK3G8rnZGNY5wdv25c4CzF8R+pd0hNXU8bvvvhurV6/Gf//7X3Ts2LHR7datW4errroKeXl56NatG+bNm4cTJ07gu+++825jNpsRHR2NVatWYfz48ec9hs1mg81m8/5tMpmQmZnJaThEQcB4JAodjEeKJC5JxsP/2o1/bjvlbeuTrsey2SOQFKO5wD0DhzFJkcBsd2Leh9vw37zaSzpGXZSCd6cODdlLOsJmRHv+/Pn4+uuv8eOPP14wyQaA7OxsAEBeXh4AwGAwoKioqN42nr8NBkODj6HRaKDX6+v9I6LgYDwShQ7GI0UShSjgxZsGYOrITt62/YUmTFq4CcUmaxB7VosxSZFAp1bivRnDcHXvVG/b+kNnQvaSDiAMEm1ZljF//nz8+9//xrp165CVldXkfXbu3AkASE93T/fJycnBnj17UFxc7N1mzZo10Ov16NOnj1/6TUREREThTxQFPPObfph7We0x6OHiKtz67kacLrcEsWdEkUWrUuCdqUPPu6Rjynu5KDfbg9izhoV8on3vvffio48+wooVKxAbGwuj0Qij0QiLxf3FduTIETzzzDPYtm0bjh8/jv/85z+YPn06Ro0ahQEDBgAAxowZgz59+mDatGnYtWsXvvvuO/z1r3/FvffeC40mNKb9EBEREVFoEgQBf5nQG7+7qoe37XiJGbcu2IgTJdUXuCcR+ZJKIeL1SYNx89DaGc678ssxaeEmnK2yXeCegRfyifY777yDiooKXHHFFUhPT/f+W7lyJQBArVbjhx9+wJgxY9CrVy88+OCDuOmmm/DVV195H0OhUODrr7+GQqFATk4Opk6diunTp+Ppp58O1ssiIiIiojAiCAIeuOYiPDyul7ftdLkFt767EXnFVUHsGVFk8VzSMSOns7ftgLESt767EcaK0LikAwizYmjBxDUJiUIH45EodDAeKRIt+d8xPPnVfu/fSdFqLJudjT4ZwY8BxiRFClmW8eJ3B/HOT0e8bZmJUVgxZyQyE3VB7JlbyI9oExERERGFkjsuycILv+0PQXD/XVJtx+2LNmFXfnlQ+0UUSQRBwMPjeuGhMRd52/JLLbh5wQbkFVcGsWduTLSJiIiIiFpo0ohOePXWQVCI7my7wuLAlPdyseV4aZB7RhRZ5l/ZA49dV1vgushkw23vbsL+AlMQe8VEm4iIiIioVW4Y3AFv3j4YKoU72a6yOTF98Wb8r85av0Tkf7MvzcJzN54/y2THybKg9YmJNhERERFRK43vn453pw2FWuk+rLY4XJi5ZAt+PFDcxD2JyJcmZ58/y2Tqe7nIPVoSlP4w0SYiIiIiaoMre6XhgzuGI0qlAADYnRLmLduK1XsKg9wzoshyw+AOeGvyEO8sk2q7CzM+2IyfD50JeF+YaBMRERERtdEl3ZPx4ewRiNEoAQAOl4z5H+/AFztOB7lnRJFlXD8DFk0fBk3NLBOrQ8KcpVvw7V5jQPvBRJuIiIiIyAeGd0nE8jnZiItSAQBckoz7P92JTzafDHLPiCLLFT1TsXTWCESr3bNMHC4Z967YHtATX0y0iYiIiIh8ZGBmPD6ZNxJJ0WoAgCwDf/58Dz7437Eg94wosozsmoSP5mRDr3XPMvGc+Po4QCe+mGgTEREREflQ73Q9Vt6ZgzS9xtv21Ff78fZPeUHsFVHkGdwpAZ/My6l34uuRz/dg8X/9f+JLkGVZ9vuztAMmkwlxcXGoqKiAXq9vcntJkrGvwIRSsx3xNdOHSs12lFc7kKBTISlGg74Zeog1VfE8nE4JX+4qwPaTpTDbXEiKVsFbp74OWZZRbLLi6JlqlFoc0GsU6JQYDUEATpWZUVptg8kqAQASdUpoRRl5ZY7zHidKAfTtGIdreqbhYHE1ojQKDOmUgN8MzIBSGTrnYSRJxq5T5Vi1pxCnSs1QKQSIAmC2u1BssqHc6oBWpcCADnHomRYLpUKBAZlxAIAd+eUoKDWjoKwKPx04C7PU8HNoFcCYvinQKFTYeboCsixg1EVJmDiwAwZ2jD9vXwWa0ylh5ZYT+MfawzBWnb8vAeDaXvF4eVI2tDVn7tqr5sSjJwbPVtsajLvGbu9tiMW+QhN25JdDkFHvc2Qss0CCDEEQYNBrYLI6UVRmxs7TFbA7JSgVAhKi1IAIqEUBZ6tsKKq0ocrqgksCRAFwNPCNqxSAXoZoJEVHQVQIGJgZj3tGdYO6ZrpTsHjeI2OlBev2FeFgkQkVFgdkGTA7XNAqFRjQMQ6XdE+GxS7BJUk4WGTCxryzKK60w9ZIrAHueNOqlcjJSoDZ7kJRlR0JOjVuHNIBvx3UMWjfP5IkY/vxUrz6w0HkHi+D8wKvYd6lnfDQmD5B30/B1tLfR6JQVPe4LVGnPu+3ou7xXLnFgfgoFSRZxo78cpw6U4nVu/JRaGn4sRN0SpSZnRd8/g/n9MOo7p198loYk9SeXSjHiotSosLiRIJOhTidCv87mI//W5vf4ud47MZ0zM4e0ua+MtFuppZ8aW3IO4t3fj6CI8VVqLa7YHW44JJkyAAgyxBFAXqtCn0y9Lj78m64uHsyAGDR+iN4be1hVNtc/n9BTYjRKPD7q3pg7qhuwe4KNuSdxaNf7MGxs+ag9SErWYdnb+jv3VeBtmj9ETy36gCaG6xX9UrB4jtG+LVPwdRUPHpicH9BBUxWJySpftyN6pGM9YfPnnd7lEoBlyzD7pDgkmXIMpr9nvuaQgAmDc/Es78dEJTn97yH246Xwuy4QLbpB2qFgD+O7Rnw758NeWcxf8V2lJobPpHVmCkjgrefQgEP6inc1T1uc7hkqBQCuqXGeH8rjhRXodrmgsXhgiAASlGAzSnBKfn+F+L4C9e2+TEYk9Re1cuxamJSlmVIAOSaXMszPumL8GxrPDLRbqbmfmltyDuLv/x7D6psTmiUChSbrHBJ7g8AAChF95QFQRAQrVEgQafGczf2x76CCjy/+oBPPhS+ohCAP4/vFdRke0PeWdy7YjvKWnjg6w8JOhXemjwk4Mn2ovVH8OyqAy2+X3tOti8Uj54YLK22w+JwQZJliIJ7VEIQBGiUAmxOGRqlALtL9t7uctXGKeAefQ6FeAxGEud5DwvLLbC5gvMmCAD+MiFw3z8b8s5i9tItsLTypEIkJ9s8qKdwVve4LUGnhlohwu6SUGSywWx3IlqjQIxGiTOVNvegiQz4+9RjWw/uGZPUHtXPsUScqbTBec6xmz+0JR5DZ25wOyBJMt75+QiqbE6k6TWosDggybL7iNGzjQyoaqZEOl0yqmxOvPXjYby5Li8kDurrcsnAWz8egfNCcyf9SJJkvPXj4ZBIsgGgzOzAWz8ehhTAHeV0Snhj7eFW3XftgTOwWi88Va298cRgpdXhPsElAypRhFIUoVKKkGUZFod7FMLikCDJMlSiCIUgnDdyHSrxuHLrKdjtgZvl4nkPK8y2oCXZgHsmwZs/5gXk+0eSZLy5Lq/VSTYALN+cH9D9RERtV/e4zaDXQqtSQBQFaJQiXJIElyTD4ZRQbnHAJbtHsgPxrbg+70QAnoUofNTLsWLdOZY/ZpQ0ZHHu9lbfl4m2D+0rMOFIcRUSdGrYHDJsThdEUYBnzoAA92i2LAMKUYDdJSFKpcC+AhNMIZoQmawOfLW7MCjPva/AhH0FpqA8d2MC3aevdheisg2XEjyz6lcf9ib0eWJQp1bC7pKgFAUINXOIBAju6+1k92wNSQZEwX27jOBNEW+KU5KxYP3RgD2f5z0UhOD/PJgszoB8/+wrMGHnqfI2P04g9xMRtV3d4zahTj0cq0Ny/4Yo3MdqNof79wQNnJT1h+nv7Q3AsxCFj3o5llOGzSlBEaATX8/8u/XHIcE/kmpHSs12OFwy1AoRTknyJth1PwQyPFPHa/9rd8khe5Avy8Dp8uBcG11qtsPuDK13xu6SUWq2B+z52vreHy+t9lFPwoMnBkXBfYKryfJ1nhgNrY/ZeU4EcD963kNXcCay1CMjMN8/7tfc9hccyP1ERG1X97itLs8xnOcSIlmWvYMlRBR4DeZYQayj01xMtH0oUaeGqubsp1IUvRfj1z3YF1A/yZZld+Gf4NazbpwgAB3idUF57kSdGmplaL0zaoWARJ06YM/X1ve+S2K0j3oSHjwxKMmyO76auoMnRkPrY3aezgHcj573UBECvw4CAvP9437NbX/BgdxPRNR2dY/b6vIcw0k1ybZn5lOo/1YQtVcN5lhCMwZUgiwEDqXaj74ZenRLjUGZ2QGNSoBGqagpwOS+3fMlLQjuBdPVChEWhwt9M/TehdRDjV6rwsQB6UF57r4ZevTNCK0iHoHu08QB6YjVtH7poMcm9PZhb0KfJwbNdlfNWU8ZnnqPMmR3dXHBXX/APVIhe0cqQvXLWikKuGtU14A9n+c9DIU6mfooZUC+f/pm6DGoY3ybHyeQ+4mI2q7ucVvd7zytSnT/htSMoGlU7t8T1Pxe+NuHc/oF4FmIwke9HEvpqaMQmHh87MbWH4cw0fYhURRw9+XdEKNRoMhkR1yUCuI5w2qiADhqivsoFQJiNErcO7oH5l/ZHUFepvk8CgG4d3S3oK1nK4oC7h3dAwk6VVCe/1wJOhXuHd0joOtpK5UifndVj1bd96peKe1+Pe1zeWIwVquEQhTd8SZJcEoSHE4JgiAgSiVCKbr/KwoCHJJ7Ka9z92qoxONtwzoGdJ1mz3sYp1NDE8RhbQHA/NHdA/L9I4oC5l/ZHVGq1j/XlBGZEb+eNlG4qXvcZjTZ3CtVSDKsTgkKUYRCFKBSioiLUkEhuGtmBOKnwVfraRO1F/VyrEo79FEqd92EAGjLetpMtH3s4u7JeO7G/uidHgtZlqHTKKFSug/sFTWF0URRQFyUCgM6xuO5G91rM88d1Q2PjO+F6DaMXvpSjEYR9KW9APf7+dbkIchKDs70dY+sZF1QlvYCgLmjuuHRCb1a9OPenpf2aoonBgdmxkGvVXqX9vLE3ZDOiXh4XE8M6ZxY73aFQoBeq0S0RgGV6CmgFjwKIXhLRnnewxFdE6FrQ/LZWmqFENClvQD3a148YzgSW3FiL5KX9iIKd3WP28w2J4qrbDDbnBiYGYeHx/XEgI7xgAxEqd0ncFVKEdFqhd8O8n2xjjZRe1Q3Vj0xqVSIUNXkWCLcx22i4LvBEq6jHSAtXZNQkmTsKzCh1GxHfJT7wK3UbEd5tQMJOhWSYjTom6E/b3TU6ZTw5a4CbD9ZCrPNhaRoVYMXBcmyjGKTFUfPVKPU4oBeo0CnxGgIAnCqzIzSahtMVvfIeaJOCa0oI6/s/GWyohRA345xuKZnGg4WVyNKo8CQTgn4zcCMoI1kN0SSZOw6VY5VewpxqtQMlUKAKABmuwvFJhvKrQ5oVQoM6BCHnmmxUCoUGJAZBwDYkV+OglIzCsqq8NOBszA3UvNIqwDG9E2BRqHCztMVkGUBoy5KwsSBHTCwY3xAR7Ib4nRKWLnlBP6x9jCMVQ0veXZtr3i8PCm73Y9kNycePTF4ttrWYNw1dntvQyz2FZqwI78cgox6nyNjmQUS3OtxG/QamKxOFJWZsfN0BexOd4XahCg1IAJqUcDZKhuKKm2osrrgkmpmtDTwjasUgF6GaCRFR0FUCBiYGY97RnUL+gip5z0yVlqwbl8RDhaZUGFxQJYBs8MFrVKBAR3jcEn3ZFjsElyShINFJmzMO4viSjtsF6gvplUAWrUSOVkJMNtdKKqyI0Gnxo1DOuC3gzoG7ftHkmRsP16KV384iNzjZbjQ6mLzLu2Eh8b0Cfp+Cjau2UvtQd3jtkSd+rzfirrHc+UWB+KjVJBkGTvyy3HqTCVW78pHoaXhx37pll645qIOuPLZtSht5Pk/nNPPZyPZjElqzy6UY8VFKVFhcSJBp0KcToX/HczH/63NP+8xOsUp8MrkAbj5nR0NPsdjN6a3aSTbg4l2M/FLiyh0MB6JQgfjkSi0MCaJQkPoDFkSERERERERtQNMtImIiIiIiIh8iIk2ERERERERkQ+174pJPuS5lN1kMgW5J0TtV2xsLIQGiv+di/FI5H+MR6LQwpgkCh3NiUcm2s1UWVkJAMjMzAxyT4jar+YWbmE8Evkf45EotDAmiUJHc+KRVcebSZIkHDx4EH369EF+fn67qeJoMpmQmZnZrl4TwNcVTuq+pg4dOjTrbL0kSSgoKIAsy+jUqVPYvx/tZb/ydYSWtr6O5o6eeeKxudv7Q7jvM/Y/eMKp7+EUk00Jp/f9XOx7cIRa3zmi7UOiKKJDhw4AAL1eHxI72Jfa42sC+LrCiV6vb/YBgSiK6Nixo3daXHt5P/g6QgtfR/N44jEUhPs+Y/+DJ5z7fq5QismmhPP7zr4HRzj1ncXQiIiIiIiIiHyIiTYRERERERGRDzHRbgGNRoMnnngCGo0m2F3xmfb4mgC+rnDSltfUXt4Pvo7QwtcRfsL9tbL/wRPOfQ9n4fy+s+/BEY59ZzE0IiIiIiIiIh/iiDYRERERERGRDzHRJiIiIiIiIvIhJtpEREREREREPsREm4iIiIiIiMiHmGg3kyzLMJlMYO04ouBjPBKFDsYjUWhhTBKFBibazVRZWYm4uDhUVlYGuytEEY/xSBQ6GI9EoYUxSRQamGgTERERERER+RATbSIiIiIiIiIfYqJNRERERERE5ENMtImIiIiIiIh8iIk2ERERERERkQ8x0SYiIiIiIiLyISbaRERERERERD7ERJuIiIiIiIjIh5hoExERRYhqqzPYXSAiIooITLSJiIgiwPaTpbji5Z/w3T5jsLtCRETU7jHRJiIiauc25J3F9MVbcKbShvtW7MAvh88Eu0tERETtmjLYHSAiIiL/WXegCPcs3w6rQwIAuGQZJVX2IPeKiIiofeOINhH5TbXNiW/3Fga7G0QR65vdBbjro9okWykKeGvyENwwuEOQe0ZERNS+MdEmIr84W2XDbQs34q6PtuPLnaeD3R2iiPPPbfn43Sc7YXe6k2yNUsQ7U4dgXD9DkHtGRETU/jHRJiKfO1lixm/f3oC9p00AgIc+24W9pyuC3CuiyPHhxuP40z93wyXJAACdWoHFM4bhmj5MsomIiAKB12gTkU/tOVWBGR9sRml17TWgU7I7o3e6Poi9IoocC346ghe+PeD9W69V4oOZIzC0c0IQe0VERBRZmGgTkc+sP3QGd320DWa7y9v2p7E9cfcV3SAIQhB7RhQZXv7+IP6xLs/7d2K0Gstmj0DfjLgg9oqIiCjyMNEmIp/4945T+ONnu+GsmaqqEAX8/ab+uHloZpB7RtT+ybKMp7/ajw82HPe2pek1WD4nG91TY4PXMSIiogjFRJuI2kSWZby7/iheWF07VTVKpcA7U4fgip6pQewZUWSQJBl//nw3Pt16ytuWmajDijnZyEzUBbFnREREkYuJNhG1miTJeObr+qNoCToVls4agQEd44PWL6JI4XRJuH/lTny1u3YZvW4p0VgxZyTS4rRB7BkREVFkY6JNRK1ic7rwwMpd+GZP7QF+x4QofDQ7G12So4PYM6LIYHdKuPujbVh7oNjb1jdDj49mj0BCtCaIPSMiIiIm2kTUYiarA3OXbkXusVJvW98MPZbMHIGUWB7gE/mb1eHCrCVbsOFIibdtSKd4LJ01ArFaVRB7RkRERAATbSJqIWOFFTPe34yDRZXetku7J+PdaUMRreFXCpG/VVodmPH+Zmw/We5tu7hbEhbPGI4otSJ4HSMiIiIvHhUTUbPlFVdi+uLNKKiwettuHNwBL948ACqFGMSeEUWGsmobpi7ejH0FJm/bVb1S8c7UoVArGYNEREShgok2ETXLthOlmLVkKyosDm/bXZd3xcPjenGNbKIAKDZZcfuiXBw5U+Vtu35gBl65dSCUPNFFREQUUphoE1GTvt9nxH0f74DNKQEABABPTOyDOy7JCm7HiCLEqVIzbl+0CfllFm/bpOGZeO7G/hBFnugiIiIKNUy0ieiClueewGNf7IUku/9WKQS8PmkwJvRPD27HiCLE0TNVuH3RJhSZbN62OZdm4dFre3M2CRERUYhiok1EDZJlGa+uOYQ31uV522I1SiyaMQwjuyYFsWdEkWN/gQnTFueipNrubbv/6h74/dUXBbFXRERE1BQm2kR0HqdLwqP/3ouVW/O9bWl6DZbOGoFeBn0Qe0YUOXacLMOM9zfDZHV62x69tjfmXtY1iL0iIiKi5mCiTUT1mO1OzF+xA+sOFHvbuqfE4MPZI5ARHxXEnhFFjo1HzmL20q0w210AAFEAnr2hP27P7hTknhEREVFzMNEmIq/SajtmLdmCnfnl3rZhnRPw3oxhiNepg9cxogjy44Ei3PXRdm/xQYUo4NVbB+L6QR2C3DMiIiJqLibaRGHC6ZSwcssJvPHDIRRVOxvcJl4r4sqLkrHndAWOltggAdCpRIzpm4JYjRprDxTjTIUN9gbumxmnwmmTw1v0zGPriTIMenoNAEANIFqrQKXVBSfc1cejFEDnZB16pMUiJVYLhSgiLVaDCqsDh4qqIAoCruyVihsGdYCS6/xSiJMkGduPl+LVHw4i93gZanLdBl3cOQayLGJHgQk2J6AUgaRoJbokRWN/QSVM9vPvrAAQpVZAr1UgIVoNjVKBKLUS1w/KwE2DO+K7/Ub8YeVOOFzuQFQrBPx2cDr+8vlu/O6TnRfsuwh3H2QAahHomBiN20dm4lSJFfuMJsgyYIjRYECneAztnIj+HeKCUrHc6ZTwwYbDeHZV3nm3qUXgLxN6YerILH5fhKB1h45h1vv767UlaIAfH7oK8bHaIPUqvDidEn7/2Xp8s6u6XvuHc/phVPfOAetHeaUVg55de157ZrwS38y/AvoYTcD6Qg0rNVkw5Ll19dqUALb/9WruHx/6Ys8B/GH5kXptj92YjtnZQ9r82IIsy3LTm5HJZEJcXBwqKiqg1/MaVQqsReuP4LlVBxDOwapViXjwmoswd1S3Nj8W45H8YUPeWcxfsR2lZkfTG/uBKACyDG+cqxSCN+H2NYUI9EnX45HxvXFx9+Q2PVZL4nHR+iN4dtWBZj3uoxN6+eT7gnyjy5+/ueDtSdEqbHtsTIB6E56a8/k//sK1bX6epmJy6DPfo6T6wt9zGXFabHjkqjb3hVpnwJPfwmR1NXo7949vNPW91tZ45OliohDn+WEO5yQbAKwOCc+vPoBF6480vTFRgG3IO4vZS7cELckGAKlOkq1Rin5LsgHAJQF7Tptw/6c7sSHvrN+ep66WJNkA8Owqfl+EiqYORgGgpNqBoc98H4DehKfmfv6b8163RXOSbAAoqLDi4ufPH/Em/2sqyQa4f3yhObHW1nhkok0UwpxOCW+sPRzsbviMJANv/ngEzgvNxyUKMEmS8ea6PFgcIfS5lALTl5IqG97+6Qikc68Z8TGnU8KLLUiyPf6xLo/fF0G27tCxZm9bUu1AeaXVj70JT06nhJfXNP/zvz7vhF/6UV5pbVaS7VFQYYWpyuaXvlDDSk2WJpNsD+6f1vtiT/PjcXHu9lY/DxNtohD21e5CVNqa94UbLkxWB77aXRjsbhB57SswYeep8mB3ox5bgHJLSQIOGiuxr8Dk1+f5anchWjNXwGR18vsiyM69JrspNy/M9VNPwtdXuwthbUEATH9vr1/60Zp9M+WDLX7oCTXm+rc3tmh77p/WOfea7At55t+t/w1iok0Uwk6Xm4PdBZ+T5fb5uih8lZrtcLgic9RUBmB3SSg1N1Qi0XfaEvP8vggvZziifZ5Q+Qy3Zt8YKyx+6Ak1prS6ZSPU3D+hjYk2UQjrEK8Ldhd8ThDa5+ui8JWoU0OliMyfQwGAWiEi0c/L97Ul5vl9EV5SWH38PKHyGW7NvjHERfmhJ9SYxOiWVRPn/gltIX9kcfr0aUydOhVJSUmIiopC//79sXXrVu/tsizj8ccfR3p6OqKionD11Vfj8OH617SWlpZiypQp0Ov1iI+Px+zZs1FVVRXol0LUYhMHpCNWowh2N3xKr1Vh4oD0YHeDyKtvhh6DOsYHuxteMSoBugAtvimKQE9DLPpm+Ld6/8QB6VC14n56rZLfF0H2/qw+Ldr+n/Oy/dST8DVxQDq0LQiAD+f080s/WrNvls8c7oeeUGP+c09Oi7bn/mmd16Y0f0WLx25s/W9QSCfaZWVluOSSS6BSqbB69Wrs378fL7/8MhISErzbvPjii3jjjTewYMEC5ObmIjo6GmPHjoXVWjs9ZsqUKdi3bx/WrFmDr7/+GuvXr8e8efOC8ZKIWkSpFPG7q3oEuxs+IwrA/NHduD4uhRRRFDD/yu6IUgX/cykA+P01PXH/mF4Bea7kGA3uuaKb39fTVipF/GlCy1/TfVd25/dFkF15UVazt02KVnE97QYolSIevKb5n39/racdH6tFUnTzM/6MOC3Xaw6wRH0U9NrmDbBw/7TeDf2bH49tWU87pNfR/vOf/4z//e9/+OWXXxq8XZZlZGRk4MEHH8RDDz0EAKioqEBaWhqWLFmCSZMm4ddff0WfPn2wZcsWDBs2DADw7bffYsKECTh16hQyMjKa1Reu20vBxHW062M8kj8Eex1tlULAn8b29MZIS5fDagmuo00txXW0247raFNzcR3twPD3OtohnWj36dMHY8eOxalTp/Dzzz+jQ4cOuOeeezB37lwAwNGjR9GtWzfs2LEDgwYN8t7v8ssvx6BBg/D666/j/fffx4MPPoiysjLv7U6nE1qtFp999hluvPHGBp/bZrPBZqstSGAymZCZmckDewoap1PCyi0n8MYPh1BU7Wxwm3itiCsvSsae0xU4WmJDQ1/RIoCGyj51TVTDZHHirKXxolBqANFaBSqtLjjhHhGLUgCdk3XokRaLlFgtFKKItFgNKqwOHCqqgigIuLJXKm4Y1KHVI1OMRwoUSZKx/XgpXv3hIHKPl+FCK0td3DkGsixiR4EJNiegFIGkaCW6JEVjf0ElTPbG76xRCuiarEOUSokotRLXD8rATYM7nhcjTqeEZZuO4uXvD6PqAo8HuGNbKboLnKlFoGNiNG4fmYlTJVbsM5ogy4AhRoMBneIxtHMi+neIa9VIdlvj0emU8MGGw3h2Vd55t6lF4C8TemHqyCyOZIegdYeOnVeFPEED/PjQVRzJbianU8LvP1uPb3ZV12v/cE6/Vo9ktyYmyyutGPTs+eswZ8Yr8c38KzhSGgJKTRYMeW5dvTYlgO1/vZr7x4e+2HPgvCrkj92Y3qaRbI+QTrS1WveX9gMPPIBbbrkFW7Zswe9//3ssWLAAM2bMwIYNG3DJJZegoKAA6em18+dvvfVWCIKAlStX4rnnnsPSpUtx8ODBeo+dmpqKp556CnfffXeDz/3kk0/iqaeeOq+dB/YULn7YX4T5H2+HtWZtYAHAX6/rg9mXNn8aYKhgPFK4KqmyYcp7uThgrPS2XTcgHa/eNihsC7AxHolCC2OSKDSF9K+8JEkYMmQInnvuOQwePBjz5s3D3LlzsWDBAr8/9yOPPIKKigrvv/z8fL8/J5GvfLL5JOYt2+pNslUKAf+YPDgsk2yA8UjhyVhhxS0LNtZLsm8blonXJw0O2yQbYDwShRrGJFFoClBd09ZJT09Hnz71q1327t0b//rXvwAABoMBAFBUVFRvRLuoqMg7ldxgMKC4uLjeYzidTpSWlnrv3xCNRgONhtMyKLzIsow31ubh1R8OedtiNEosnD4UF3dr23WYwcR4pHCTX2rGpIWbcLq8do3TWZd0wWPX9YEg+LfwmL8xHolCC2OSKDSF9Cn1Sy655Lwp34cOHULnzu5rWLKysmAwGLB2be01JiaTCbm5ucjJcZfHz8nJQXl5ObZt2+bdZt26dZAkCdnZXIKC2g+nS8KjX+ytl2Snxmrw6Z05YZ1kE4WbvOIq3PTOhnpJ9u+u7N4ukmwiIiJqnpAe0b7//vtx8cUX47nnnsOtt96KzZs3Y+HChVi4cCEAQBAE/OEPf8Df/vY39OjRA1lZWXjssceQkZGBG264AYB7BHzcuHHeKecOhwPz58/HpEmTml1xnCjUWR0u3PfxDqzZX+Rt65ocjQ9nj0DHBF0Qe0YUWfYVVGDqe7koq1O5/C8TemEeq2fT/7d33+FRlWkbwO+p6RVIAiH0XkJPCFhAWFGxIFgWkR5UBBQRV3FdLKjwqYvIqqBIlWYFKy5Kc4XQAqETCC0BEgKE9GTq+/0R5mQmPWQmZ8r9u65cMO9pz5mZ55zzzCkvERF5FKcutPv06YMNGzZg1qxZeOutt9CyZUssWLAAo0aNksb5xz/+gYKCAjz11FPIzs7Gbbfdht9++016kBoArFmzBlOnTsWgQYOgVCoxYsQILFy4UI5VIrK7GwV6TFy5DwdSs6W2ns2CsXRsH4T4aeULjMjDJF64gXHL9yKvuKRXAAWAt4d1wai+jukTl4iIiJyXUz913Jmw315yRhdvFGLssr04c7W0m5DBHcPxn5E94KNVyRiZYzEfydnsSrmGiSv3o8hQ0qmeSqHAvx+LxrAeTWWOzPGYj0TOhTlJ5Byc+ow2EVXu+OVcjFu+F5l5pX1njoyJwpyHukDtwk80JnI1fxzPwLNrDkJvKn3K/ydP9MTdnSt/4CYRERG5NxbaRC5o15lreGpVIvJ1Rqlt+uC2eH5QWz5siage/XToEqZ/dQgmc8nFYd4aJZaM6Y3b2zaSOTIiIiKSEwttIhfz8+HLeOGrJBhMJQf2SgXwzsNdMTKmmcyREXmWr/al4pXvjsBy/5W/lxorxvdB7xahssZFRERE8mOhTeRClv11DnN+OQ7LkxW8NUp8PLInBncKlzcwIg+z9H9nMeeXE9LrYB8NVsfHoktkkIxRERERkbNgoU3kAsxmgf/77SQ++/Os1Bbso8HScX3Qq3mIjJEReRYhBBZuSbHpr76RvxfWTopF2/AAGSMjIiIiZ8JCm8jJ6Y1m/OPbQ9iYdFlqiwz2wcoJMWgT5i9jZESeRQiBuZtO4nOrH7wig32wblJfNGvA/uqJiIioFAttIieWrzPimS8T8VfKNamtY+MArBgfg/BA7yqmJCJ7MpsFXtt4FGv3pkptLRv6Ye2kWDQO8pExMiIiInJGLLSJnFRmXjEmrNiHo5dypbZ+rRtg8eheCPTWyBgZkWcxmsyY+Y3tVSUdIgKwOj4WDf29ZIyMiIiInBULbSIndO5aAcYu24vUrEKp7f7oxvj3Y93gpVbJGBmRZ9EZTZi27iA2H7sitXWPCsLK8bEI8uUPXkRERFQxFtpETiYpLRsTlu9DVqFeaou/rSVeva8jlEr2kU1UX4r0Jjz15X7873TprRtxrRrgi7G94efF3ScRERFVjkcKRE5k28lMPLvmAIoMJqntn/d1xKQ7WskYFZHnySs2YPzyfdh/4YbUNrB9Iyx6she8NbyqhIiIiKrGQpvISXy9Pw2zvj8Ck7mkk2yNSoEPHu2Gh7pHyhwZkWe5UaDH6GV7bJ6PMLRrYyz4e3doVEoZIyMiIiJXwUKbSGZCCHyyLQUfbC7tl9dPq8Jno3vjtrYNZYyMyPNk5hVj1JI9OJ2ZL7U92qsp5o2Ihoq3bhAREVENsdAmkpHJLPDGj8fw5e4LUltDfy1WjI9Bl8ggGSMj8jyXsosw8vPdNg8hHNevBWbf34nPRyAiIqJaYaFNJJNigwnT1yfht2MZUluLBr74cmIsokJ9ZYyMyPOcu1aAkUt2IyOnWGqbOrANXry7HRQKFtlERERUOyy0iWSQU2hA/Kp92He+9EFL3aOCsWxcH4T6aWWMjMjznMzIxagv9uB6fumT/l++pwMmD2gtY1RERETkylhoE9Wzy9lFGLtsr809oHd1CMPHT/SAr5YpSVSfDqVlY/TSPcgtNkptcx7qjNFxLeQLioiIiFwej+qJ6lFyRh7GLtuLjNzSy1Mf690U7z7cFWo+zZioXu05ex0TVuxDgb6kOz2lAnjvkW54pFdTmSMjIiIiV8dCm6ie7Dl7HfGr9iPP6szZtLvaYMbfeA8oUX3bnpyJZ75MRLHRDABQKxVYOLIH7uvaWObIiIiIyB2w0CaqB5uOpOP5r5Kgv3lQr1QAbz3UBU/2bS5zZESeZ9ORdExbdxDGm33We6mVWDy6Fwa2D5M5MiIiInIXLLSJHGxVwnm8/uMxiJJjemjVSiz8ew/c0yVC3sCIPNB3iRfx0reHcLPGhq9WhWXj+qBvqwbyBkZERERuhYU2kYMIIfD+f5Px6fYzUluQjwZLx/ZG7xahMkZG5Jm+3H0B/9p4VHod5KPBygkx6B4VLF9QRERE5JZYaBM5gMFkxivfHcF3By5KbU2CvLFyQgzahgfIGBmRZ1q84wzmbTopvW7or8Xq+Fh0iAiUMSoiIiJyVyy0ieysQGfEs2sOYMepq1Jbh4gArBgfg4ggbxkjI/I8Qgj8e/MpfLwtRWprHOSNNfGxaNXIX8bIiIiIyJ2x0Cayo2v5OkxYsQ+HL+ZIbbEtQ/H5mN4I8tHIGBmR5zGbBd76+ThW7DovtTVv4Is18bFoGuIrX2BERETk9lhoE9nJhesFGLNsLy5cL5Ta7usagfmPdYe3RiVjZESex2QWmPX9YXy9v/T2jbZh/lgTH4uwQF5ZQkRERI7FQpvIDo5czMG4FXtxPV8vtY3r1wL/ur8TVEr2kU1Un/RGM174Kgm/HEmX2rpEBmLVhFiE+mlljIyIiIg8BQttojraceoqJq9ORKHeJLW9fE8HPHNnKygULLKJ6lOxwYRn1xzA1pOZUlufFiFYNq4PArx5+wYRERHVDxbaRHXwXeJFvPzdYRhvdsqrVirw3iPRGN6zqcyREXmeAp0RE1fux+6z16W229s2xOeje8NHy9s3iIiIqP6w0Ca6BUIILN5xFv/3W2l3Qb5aFRY92Qt3tmskY2REnimn0IBxy/fiYFq21DakczgWjuwBLzWLbCIiIqpfLLSJaslkFnjrp2NYmXBBamvgr8WKcTHo2jRIxsiIPNO1fB1Gf7EHJzLypLbhPSLx3iPRUKuUMkZGREREnoqFNlEtFBtMmPF1En49kiG1tWjgi5UTYtC8gZ+MkRF5pvScIoxasgdnrxVIbaNim2HOQ12g5IMIiYiISCYstIlqKKfIgEmr9mPvuSyprVvTICwd1wcN/b1kjIzIM124XoBRS/bgYnaR1Pb0Ha3wyr0d+CBCIiIikpXDrqk7e/aso2ZNVO8ycorx2OIEmyL7znaNsHZSXxbZRDI4fSUPjyxOsCmyX/xbOxbZRERE5BQcVmi3adMGAwcOxOrVq1FcXGyXec6bNw8KhQLTp0+X2oqLizFlyhQ0aNAA/v7+GDFiBK5cuWIzXWpqKoYOHQpfX1+EhYXhpZdegtFotEtM5P5OX8nD8E93IvlK6f2fI3o2xRdje8PPixeFENW3o5dy8NhnCbiap5Pa/nV/J0wb1JZFNhERETkFhxXaBw4cQHR0NGbMmIGIiAg8/fTT2Lt37y3Pb9++ffjss88QHR1t0/7CCy/gp59+wjfffIMdO3bg8uXLGD58uDTcZDJh6NCh0Ov12LVrF1auXIkVK1Zg9uzZtxwLeY5957PwyOIEXM4p/bFoysDW+ODRaGj4kCWierf/fBb+/vlu3Cg0AAAUAP5vRFdMvK2lvIERERERWXFYpdC9e3d89NFHuHz5MpYtW4b09HTcdttt6NKlC+bPn4+rV6/WeF75+fkYNWoUlixZgpCQEKk9JycHS5cuxfz583HXXXehV69eWL58OXbt2oXdu3cDADZv3ozjx49j9erV6N69O+69917MmTMHn3zyCfR6vd3Xm9zHb0cz8OQXe5BTdPOAXgG8+WBnvDSEl6YSyeGv09cweule5OtKrkhSKxX4aGQPPN6nmcyREREREdly+Ck5tVqN4cOH45tvvsH//d//ISUlBTNnzkRUVBTGjBmD9PT0aucxZcoUDB06FIMHD7ZpT0xMhMFgsGnv0KEDmjVrhoSEBABAQkICunbtivDwcGmcIUOGIDc3F8eOHbPTWpK7Wb37Ap5dkwid0QwA0KqV+OSJnhjbr4W8gRF5qM3HMjB+xV4UGUwASnJy8ZO98GC3JjJHRkRERFSew28w3b9/P5YtW4b169fDz88PM2fOxMSJE3Hx4kW8+eabeOihh6q8pHz9+vU4cOAA9u3bV25YRkYGtFotgoODbdrDw8ORkZEhjWNdZFuGW4ZVRqfTQacrvf8vNze32nUl1yeEwPzfT+E/W1OktgBvNb4Y0xuxrRrIGJlnYz56th+SLmHG14dgMgsAgI9GhS/G9kb/Ng1ljswzMR+JnAtzksg5OeyM9vz589G1a1f069cPly9fxqpVq3DhwgW8/fbbaNmyJW6//XasWLECBw4cqHQeaWlpeP7557FmzRp4e3s7KtQKzZ07F0FBQdJfVFRUvS6f6p/RZMbL3x22KbIjAr3x7TP9WGTLjPnoudbtTcX09UlSkR3grcbq+BgW2TJiPhLVjBACJrOAwWSGzmhCscGEIr0JBToj8ooNyCs22GU5zEki56QQQghHzLht27aYMGECxo0bh8aNG1c4jl6vx7p16zB27NgKh2/cuBEPP/wwVCqV1GYymaBQKKBUKvHf//4XgwcPxo0bN2zOajdv3hzTp0/HCy+8gNmzZ+PHH39EUlKSNPzcuXNo1aoVDhw4gB49elS47Ip+HYyKikJOTg4CAwNr8U6QKyjUGzF17UFsPZkptbUN88fKCTFoEuwjY2QEMB891Rf/O4u3fzkhvQ7x1eDLibHoEhkkY1TEfCRXYDm8FQIQN1+Lm68BQECU/r9Mm2V8swAgALM0bek8hBAwWcaxDBMlw8xCwFyDw2uVUoHmDfzqvK7MSfJkZnNJXpotOYjS3Lf8K1CSq9Z5bRKiXE4H+WrgpVZVtqhac9il46dPn652HK1Wiz179mDo0KFo2LD82YlBgwbhyJEjNm3jx49Hhw4d8PLLLyMqKgoajQZbtmzBiBEjAADJyclITU1FXFwcACAuLg7vvPMOMjMzERYWBgD4/fffERgYiE6dOlUam5eXF7y82D+yJ7ier8OElftxKC1bauvTIgRfjOmDIF+NfIGRhPnoWYQQWLglBR/+cUpqCw/wwppJsWgTFiBjZAQwH6l2pAIUpYWq5YDY5l8Awmx7QGwZF9L05Yth64Np64LYk9xqThbpTdCbzLA831UBQKFQ3PwXUECBqp79ahlHqQCUipJx+bBYz2b9Axdg+6OV5Qcok6UwNpffNkiFss30ZbYL0rzsn+sB3vY97pe9E+DVq1dj5syZFRbaAQEB6NKli02bn58fGjRoILVPnDgRM2bMQGhoKAIDAzFt2jTExcWhb9++AIC7774bnTp1wujRo/Hee+8hIyMDr732GqZMmcIDBRdiNgscupiNX4+k42JWITSqkg17od6EzFwdsosN8NaoEB0ZhPbhAVCrVIiOKjnrdTAtG5ezCnH5Rj62n7yGQnP1y9t3/ga6vbW5wmFqAM0a+mJguzDc370JujUNhlLJHQu5H7NZ4NjlXGTkFWHrsStIvpKLnCIDhAAKDSZ4q1WIbhqE/m0aokhvhslsRvKVXCSkXENmnh66KnLNWwV4a9WIaxmCQr0JV/L1CPHVYliPJjiVUYClO89J40aF+GBNfF80a+ArtRmNZvxw6DISz1/HySv5gNmMQoMZIb5KHE/PR05x5QtvHKDBPV0i8GCPKOZvPSkuNmLqyj/xx7miSseZPrAlRsW1gkatRMmhPgAFbIoAwPZA3vqTsz7csi4UpLYyH3PpfBXS67LLKDt/6SCyTDy1+QYpFOUPQiuOX1FuWEVnYytqLx3f9myN9UGt7XqVTm+9wErPAAPlilzr6StajqfLL9Rj9KIE5JRpn3pbEGbc179et0N6vQkvLv8dP50zlRvWu6Uf5gztAS+vWzurZ12oKxW2+Ve2iJdW2SoXrfO0bI7ZDitN2Opyt+p4S/6t7qtadnth3XYrrKetaNkV5VR1yl25UcU8rLcR5duspqtgGpO5pGA2m137x63z17IxYeWhcu0LRrXGsK4d7LIMh106XlMBAQE4dOgQWrVqVaPxBwwYgO7du2PBggUAgOLiYrz44otYt24ddDodhgwZgk8//RQRERHSNBcuXMDkyZOxfft2+Pn5YezYsZg3bx7U6pr/zpCbm4ugoCBehiODXSnX8M+NR3DuWqHcoVSoZUNfvDOsK/rxntF6w3x0vF0p17Boxxkkns9CoaEGv045SOtGflgT3xcRQaXP6Vjy5xks2HIaBbryB4m1xfytu+ryceKKvdhysuZdej5zRys81of3mFL9KikcSs62mW4WEZb/2/x7889c5rWpzPQm8815CIEPNycjt5rt1dr4WLtth6rKyX9+fxhr9qZVO4+4ViF45+Fou8RD5Gzu+veOasc5P29onZfjcoW2XHhgL49dKdcwZe0B3Ci0zwNDHCXEV4NPnujJg/V6wnx0rF0p1/DqhiNIzy6CziTfLiI8wAu/PH87GvqXXn205M8zmLvppHRfpD0wf+umqnysbZFtwWLbsSyXclZeIJYvFqsqME1VFKhm62VUNR+zVUyVjWvdZhYwWY9f2XjlCmSUibf0Ula52avYriwna1pkW7DYtq+yV5hY9mM2zwAQtpdKQwBmy5UqZacHyj9DoOz0KHv7RZn7ma1vx7i5LNt52i7X8uwB2+mtx7Fc2l0SnLnMbR4lzWXaazJdmUvLq3p+giU2mzitpvn+4KUaf2Z1LbZlv3ScqDJms8An2047fZENANmFBnyyLQV9WzXgZajk0sxmgUU7ziCnUCdrkQ0ARQYjgq3ulzIazfh4W4pdi2wAuFFowCfbTjN/7ay42HhLRTYArEw4hxE9m0Klss/nYVNYVnd2soozk5UXbxUVnqiwcC07jU1clcVS6bJh21bNelgXm+R8pn2xB/vevc8h2yG93lSrIhsAEs7ewGfbz0BxM56KihZzmQLIUtBZijuzdSEH23tsS+ZpW+jZ3Jt/c3iNC7Myy6qsELMu+iouHMs8O6DMNGWfH1DdOkrxkMvZeORknS4jZ6FNTuvY5Vwcu+wafUEKlMbbtWmQ3OEQ3bJjl3NxJjMfCoUSQN0vza6LvGITfjqcjod7RgIAfjqcjrwio0OWxfy1vzm/nqh+pEoUGQQmfbkfwb5aq4K0ijOTFRSbNmdleZRLdmB56JdKqYBSoYBapbB6DaiVSiiVgEqhgFKpQHpOMfTGmt96cx1w2HZo8Z9nb2m6rxIv2jkSItcxfc0ZDJvHQpvcUFahHnqj6xwd6U1mZBXq5Q6DqE6yCvUwmErOlMlNALiUXfpshkvZhXBUWHqTYP7a2fmsgrpNf70QuO6cz+bwFJYCUqVUQGVVYKpUtq/VypLCsqTARMlry3Clwva1dWGqUpafv9X/pXaVQipeS8dFJcsvuzzb5diMX8H0Kpv1sJru5ri1MWLRrloV2gActh26UMd8JOcmPWROUfqQOaX0EDlF6QPjFGXay0xneSCd9cPslFbTW89LefM/ypsPpis7veX/ypsLLjsPJUoWriw3b9t5KBWQllPuoXpVra/1vG4Or81l4/bgsEI7NTUVUVFR5R7zL4RAWloamjVrBgB48skneY8lVSjUVwutWgEXuHIcAKBVKRHqq5U7DKI6CfXVQqNSOEWhrQAQGVz6pPHIYF8oAYcU21qVgvlrZy1C/bAT1+UOo1bKF4xlCjSrM5jlC8OKCzmbglRlW3CWjAebIlJqr6RQVSkAlUpZ8q91ewXTlz3jWnUc5QtUdtVUN438vWp9+5ujtkPNQ2+tv+5mIT6IjgqusGgpV1zBthAr9/+aTCeNBwCW76F1cWU1vIoircLiDpAKsNoUkTWdprSAtF1HlJtnzaaTikXr9+7mupednmrGbQrtli1bIj09Xeq72iIrKwstW7aEyVRySeKiRYscFQK5uM5NAtG5SSB2nsmSO5RqKVAaL5Er69wkEK3D/HH0YrbcoSDQR40HohtLrx+Ibow3fjqKHAdcPs78tb9/3dcRa/am3tK0GiXw7IA2UKuUFRebSlRa0JaekSxb6JY/s1m28CSypw9GdMWDixJqPH4DwGHboWfuaIX5f5yq9XSfjep1y119ETmbZWO7VdilV2UWjGpdp+U5rNAWQlT4C0t+fj68vb0rmILIllKpwJSBbXE83fmfOh7sq8GUgW34ICVyeUqlApPvbI1XNxxBod4MnUynthUApg5sA7VaKbWp1UpMHdjGIU8dnzKwLfPXzry91RjUodEtPRBt4m2t8FCPSAdEJT/rs1OWwyTpQVKwOmtlOats1bfwzZN05S79LJnvzXGVKDf/ivr+tT5Es+5LvCxRTa6VfV532V7FazPPqp79XdPeysv2GWz93pZ9mJU0XmWLtV6kZX5l+hNHBfOzvPL3UiM8wAtX8nQ1iv0/8bEO2w5ptSqMiomq9VPHXb3ILtsHd7m+um/mk/VZ6rI5drNJGt9m/qWdgFfYB3hF8dQo7ipithdh9Z22fW0ZXiZPrAaWzSlYTWMZzfoBebbzLX2gnuXBcRXFZJ2v9tKiYXCtxq9rf9p2795rxowZAICPPvoIkyZNgq9v6WV/JpMJe/bsgUqlws6dO+25WIdjd0LyYT/aVBbz0fHk7Edbq1LgpSHtMemOin9JZj/azsWd+tG2PuC2FLrSa8swoHwBa7nEE7bjK6wO2KUDeZ459zj95m7B5ZziKsdxh360bS7Ptv6BSFF6L23ZH4Cky6BRJq+scqrsfAFUWCwrrOLgrQ/upaKnvls/IR5lXlsX6GULXSGAbm9urnaZTtmP9sCBAwEAO3bsQFxcHLTa0ntNtFotWrRogZkzZ6Jt27b2XKzD8cBeXmazwKGL2fj1SDouZhVCoyrZiBbqTcjM1SG72ABvjQrRkUHw06qxfv9FFBlKD8KjIwMRGajBjuTrKKyiZojwVyAzX1R5D6gaQLOGvhjYLgz3d2+Cbk2DeSasnjEf64fZLHDsci4y8oqw9dgVJF/JRU6RAUIAhQYTvNUqRDcNQv82DVGkN8NkNiP5Si4SUq4hM08PXTX1ubdaiYHtGqBQb8KVfD1CfLV4uGckhndvanMmuyJGoxk/HLqMxPPXcfJKPmA2o9BgRoivEsfT85FTXPnCGwdocE+XCDzYI4r5awc1ycfiYiOmrvwTf5wrqnQ+T9/WDI/0blFtl142Z5+sLgNXWh1cK6UD/NKDe+tpKjrA50E5OVJuvg4D3v4DZW+Gm3pbEGbc19+u26HqclKvN+HF5b/jp3Plf6yMbeWP9x/uDW9vlc2PS9b3Vkv5dvMWDhV/RCIXdCrjOu5esLtc+4JRret8JtvC7oW2xfjx4/HRRx+5zUEwD+xdw+/Hr2DaugMovnkGTqEAZt/fCeP7t5Q5MrIn5qNrOZh6A2OX7UVuccm91QoFMOehLniyb3OZIyN7qE0+Ztw8qyc9Rda6QC5z2bPtmeSb//JHEaJqcR9J5Bwcdo/28uXLHTVrogqt25uKf244It27qVUpMf/xbrg/uom8gRF5sF1nriF+5X4U6kvOnKiUCnzwaDQe7tFU5shIDhFBfEYLERF5BocV2gUFBZg3bx62bNmCzMxMmM22l/GdPXvWUYsmDyOEwII/TuOjLaeltgAvNT4f0xtxrRvIGBmRZ9t68gomrz4A3c1+ZDUqBf4zsifu6RIhc2REREREjuWwQjs+Ph47duzA6NGj0bhxY963QQ5hNJnxrx+OYp3Vgz3CA72wYnwMOjbm5VJEcvnlcDqeX38QxpuXmHhrlPhsdG/c2a6RzJEREREROZ7DCu1Nmzbhl19+Qf/+/R21CPJwRXoTpq07gD9OZEptrRv5YeWEGDQN8a1iSiJypK/3p+GV7w5Lt3H4eamwbGwfxLbiFSZERETkGRxWaIeEhCA0NNRRsycPd6NAjwkr9+FgarbU1qt5CL4Y0xshftrKJyQih1qx8xze+Om49DrYR4NVE2MQ3TRYvqCIiIiI6lnV/afUwZw5czB79mwUFjpn38fkutKyCjFi8S6bIvvuTuFYEx/LIptIRp9sS7Epshv5e+Grp+NYZBMREZHHcdgZ7X//+984c+YMwsPD0aJFC2g0GpvhBw4ccNSiyY0dv5yLccv3IjNPJ7WNim2Gtx7qAhW7fSGShRAC7/03GYu2n5HamgR7Y218X7Ro6CdjZERERETycFihPWzYMEfNmjzUrpRrePrLROTpjFLbjL+1w7S72vBhe0QyMZsF3vzpGFYmXJDaWjTwxZpJfREZ7CNjZERERETycVih/frrrztq1uSBfjp0GTO+ToLBVPJ0JaUCePfhrvh7TDOZIyPyXEaTGa98fwTfJl6U2tpHBODLiTEIC2B/yUREROS5HFZoE9nLF/87i7d/OSG99tYo8ckTPTGoY7iMURF5Nr3RjOfXH8SmoxlSW7emQVg5IQbBvnxWAhEREXk2uxbaoaGhOHXqFBo2bIiQkJAqL+fNysqy56LJDZnNAnM3ncCS/52T2kJ8NVg6rg96NguRMTIiz1ZsMOGZLxOx/dRVqS22ZSiWjusDfy/+fktERERk1yOiDz/8EAEBAQCABQsW2HPW5GH0RjP+8e0hbEy6LLU1DfHBygkxaN3IX8bIiDxbvs6I+JX7sPts6Y+lA9o3wuIne8Fbo5IxMiIiIiLnYddCe+zYsRX+n6g28ooNmLz6AP5KuSa1dWociBXj+yAskPd9Esklu1CPccv3ISktW2q7t0sEPvp7D2jVDustkoiIiMjlOPQaP5PJhI0bN+LEiZL7azt37owHH3wQKhXPelDFMvOKMW7ZPhxPz5Xa+rdpgMVP9kKAt6aKKYnIka7m6fDk0j1IzsiT2kb0bIr/G9EVahWLbCIiIiJrDiu0U1JScN999+HSpUto3749AGDu3LmIiorCL7/8gtatWztq0eSizl7Nx5hle3HxRpHU9mC3Jvjg0W48W0Yko0vZRRi1ZDfOXy+U2sbENccbD3SGkv3XExEREZXjsOrlueeeQ+vWrZGWloYDBw7gwIEDSE1NRcuWLfHcc885arHkog6m3sCIRbtsiuxJt7fEgse7s8gmktH5awV4dPEumyJ78oDWePNBFtlERERElXHYGe0dO3Zg9+7dCA0NldoaNGiAefPmoX///o5aLLmgrSev4Nk1B1BsMEttrw3tiPjbW8kYFRElZ+ThyS/24Gq+Tmp7aUh7TBnYRsaoiIiIiJyfwwptLy8v5OXllWvPz8+HVss+VqnE1/vSMGvDEZjMAgCgUSnwwaPd8FD3SJkjI/Jshy9mY8zSvcguMkhtrz/QCeP7t5QxKiIiIiLX4LBrcu+//3489dRT2LNnD4QQEEJg9+7deOaZZ/Dggw86arHkIoQQ+M+W0/jHd4elItvfS40V42NYZBPJbN/5LDyxZI9UZCsVwHuPRLPIJiIiIqohhxXaCxcuROvWrREXFwdvb294e3ujX79+aNOmDT766CNHLZZcgMks8NrGo/j376ektkYBXvjq6b7o36ahjJER0f9OX8XopXuQrzMCANRKBf4zsice6x0lc2RERERErsNhl44HBwfjhx9+QEpKCo4fPw4A6NSpE9q04b19nqzYYMJz6w5i8/ErUlurRn5YOT4GUaG+MkZGRL8dzcC0dQdgMJVcZeKlVmLRkz1xV4dwmSMjIiIici0O7Ud76dKl+PDDD3H69GkAQNu2bTF9+nTEx8c7crHkpLIL9YhfuR/7L9yQ2rpHBWPZuD4I9eN9+0Ry2nDwImZ+fRgmUVJk+2pV+GJsb/RrzatMiIiIiGrLYYX27NmzMX/+fEybNg1xcXEAgISEBLzwwgtITU3FW2+95ahFkxO6lF2Escv2IiUzX2ob1CEM/3miB3y1Dv29h4iqsWbPBby24SjEzdeB3mqsmBCDns1CZI2LiIiIyFU5rMJZtGgRlixZgpEjR0ptDz74IKKjozFt2jQW2h7kZEYuxi7biyu5pV0EPd47Cu883AVqFfvIJpLT53+ewbu/npReN/DT4suJsejUJFDGqIiIiIhcm8MKbYPBgN69e5dr79WrF4xGo6MWS05m99nrmLRqP/KKSz/z5wa1xQuD20KhUMgYGZFnE0Lgwz9OY+GW01JbRJA31sTHonUjfxkjIyIiInJ9DjudOHr0aCxatKhc++eff45Ro0bVeD5z585Fnz59EBAQgLCwMAwbNgzJyck24xQXF2PKlClo0KAB/P39MWLECFy5csVmnNTUVAwdOhS+vr4ICwvDSy+9xILfwX45nI7RS/dIRbZSAbzzcBfM+Fs7FtlEMhJC4O2fj9sU2VGhPvjm6TgW2URERER24PCHoW3evBl9+/YFAOzZswepqakYM2YMZsyYIY03f/78SuexY8cOTJkyBX369IHRaMSrr76Ku+++G8ePH4efnx8A4IUXXsAvv/yCb775BkFBQZg6dSqGDx+OnTt3AgBMJhOGDh2KiIgI7Nq1C+np6RgzZgw0Gg3effddB74DnmvFznN48+fjuPlcJXiplfjPyB64u3OEvIEReTiTWeDVDUfw1b40qa1tmD9Wx8ciPNBbxsiIiIiI3IdCCEspZF8DBw6sWQAKBbZu3Vrj+V69ehVhYWHYsWMH7rjjDuTk5KBRo0ZYu3YtHnnkEQDAyZMn0bFjRyQkJKBv377YtGkT7r//fly+fBnh4SXd1CxevBgvv/wyrl69Cq22+ide5+bmIigoCDk5OQgM5L2LlRFC4L3/JmPR9jNSW7CPBkvH9Uav5qEyRkbuhPl4awwmM2Z8lYSfDqdLbV2aBGLVxFg++Z9uGfORyLkwJ4mcg8POaG/bts0h883JyQEAhIaWFG2JiYkwGAwYPHiwNE6HDh3QrFkzqdBOSEhA165dpSIbAIYMGYLJkyfj2LFj6NGjh0Ni9TQGkxkvf3sY3x+8JLU1CfLGqokxaBMWIGNkRFRsMGHq2gP440Sm1NareQiWj++DQG+NjJERERERuR+X6lfJbDZj+vTp6N+/P7p06QIAyMjIgFarRXBwsM244eHhyMjIkMaxLrItwy3DKqLT6aDTlT4lOzc3116r4ZbydUZMXp2I/52+JrW1jwjAyvExiAji5ahUN8zHuinQGTFp1X7sOnNdarutTUN8PqYXu9ejWmM+EjkX5iSRc3KpvpWmTJmCo0ePYv369Q5f1ty5cxEUFCT9RUVFOXyZrupqng4jP99tU2T3bRWKr5+OY5FNdsF8vHU5RQaMXrrHpsj+W6dwLB3Xm0U23RLmI5FzYU4SOSeXKbSnTp2Kn3/+Gdu2bUPTpk2l9oiICOj1emRnZ9uMf+XKFUREREjjlH0KueW1ZZyyZs2ahZycHOkvLS2twvE83flrBRixaBeOXMqR2oZGN8bKCTEI8uHlqGQfzMdbcz2/5EewA6nZUttD3Zvg01E94aVWyRcYuTTmI5FzYU4SOSenP50hhMC0adOwYcMGbN++HS1btrQZ3qtXL2g0GmzZsgUjRowAACQnJyM1NRVxcXEAgLi4OLzzzjvIzMxEWFgYAOD3339HYGAgOnXqVOFyvby84OXl5cA1c32H0rIxfsU+ZBXopbbx/VvgX0M7Qalk911kP8zH2svIKcaoL3bjzNUCqe2J2GZ4+6EuzE+qE+YjkXNhThI5J6cvtKdMmYK1a9fihx9+QEBAgHRPdVBQEHx8fBAUFISJEydixowZCA0NRWBgIKZNm4a4uDipW7G7774bnTp1wujRo/Hee+8hIyMDr732GqZMmcIN0y3anpyJZ9ccQKHeJLW9cm8HPH1HK/aRTSSztKxCjFyyGxdvFEltk25viVfv68j8JCIiIqoHTl9oL1q0CAAwYMAAm/bly5dj3LhxAIAPP/wQSqUSI0aMgE6nw5AhQ/Dpp59K46pUKvz888+YPHky4uLi4Ofnh7Fjx+Ktt96qr9VwK98mXsQr3x2G0VzSM5xaqcD7j0bj4R5Nq5mSiBwtJTMPTyzZg8y80gfjTB/cFs8Passim4iIiKieOKwfbXfDPglLLuP/dPsZvP/fZKnNT6vCoid74Y52jWSMjDwN87FiRy/lYMyyvTa3c7w2tCPib28lY1Tk7piPRM6FOUnkHJz+jDY5B5NZ4K2fjmFlwgWprYGfFisnxKBLZJCMkRERACReuIFxy/YiT2cEACgAvPNwVzwR20zewIiIiIg8EAttqlaxwYQXvkrCpqOlfY43b+CLVRNi0LyBn4yREREA7Eq5hokr96PIUPLMBJVCgfmPd8ND3SNljoyIiIjIM7HQpirlFBkwadV+7D2XJbVFNw3CsnF90NCfD5IjktuWE1cwefUB6E1mAIBGpcAnT/TE3Z0r7rqQiIiIiByPhTZVKj2nCGOX7sWpzHypbUD7RvjkiZ7w8+JXh0huPx66jBe+SoLp5oMJfTQqfD6mF25vy2cmEBEREcmJ1RJV6NSVPIxZthcZOcVS2yO9mmLu8K7QqJQyRkZEALB+bypmfX8ElqdZ+nupsWJ8H/RuESprXERERETEQpsqsPdcFuJX7kNusVFqmzKwNWbe3Z7dAxE5gWV/ncVbP5+QXgf7arB6YiwfTEhERETkJFhok43fjqbjufVJ0BtL7vdUKIC3HuyM0XEt5A2MiCCEwH+2pGD+H6ektkYBXlgbH4u24QEyRkZERERE1lhok+TLhPOY/eMxWHpW16qVWPj3HrinCx+qRCQ3IQTmbjqJz/88K7U1CfbGukl9+fR/IiIiIifDQpsghMC/N5/Cx9tSpLZAbzW+GNsHMS15vyeR3Mxmgdc2HsXavalSW8uGflgTH4smwT4yRkZEREREFWGh7eEMJjNe/f4Ivkm8KLVFBHlj1YQYtOOlqESyM5rMmPnNIWxMuiy1dYgIwOr4WHaxR0REROSkWGh7sEK9Ec+uPoDtp65Kbe3C/bFyQgwaB/EsGZHcdEYTpq09iM3Hr0ht3ZoGYdWEWAT5amSMjIiIiIiqwkLbTsxmgWOXc5FVqEeorxYdIwJwIiMPWYV6BPtoYDSb8dvRDFy6UYSmwT64N7oxujUNhlKpsJn+al4xDl3MRnJGLnKLjAgL0KLBzbNW1/J1uJJTjMw8HYoMJhgMBmQXCRirCqwW/LQqmM1mTP5yH3IKDbiaW4z8KmYe5q+Br5cG3SMDMbp/S/SICpHWh0hulpy6VqBDdoEBIb4aNPD3QucmgVAqFZUO7xgRgGPpuTiYlg2FALo0DcS5qwU4kJaNIp0RoX4aKJVKRAR6IbfYiCs3CpF0KQd6oxlqlQIhPloIhYDeaMb1fD1yig0o0plhEoACgKmSeBv4qhDk7QW1ClAoFfBRq3DmWgHydLZTHLqYg25vba7x+9DIT40Jt7dCv9aN0DUyiDlKsjIazdiQdAm/Hb6ILaeyKhwnxEeN//y9O/q1Dbul72vZ/bEl56l2zGaBJX8dxdxfUyscHhGgxYheTfH8Xe2g1arqOTr7q+x7Y90e7FPyA2d2kQHBPhqYhcDBtGxcvJqHTYfSkF5U8bzff7QDRvRo5bTfQ7NZYOfZTIz+Yn+5YaP7hOCRmE71tv9g/pa8B4u3H8R7m9MrHO6tAh7pHYkRvZrb1BKeoqKczCrUI7vAgCAfNXKKjAjx1SDIV4OdyWn4YEtauXk0C1Lh52kDEejgKwMVQlgefUVVyc3NRVBQEHJychAYGGgzbFfKNSzacQZnMvNhMAmYhYBJCKgUgNEM5BcbYCrzLisAtGjoi3eGdQUALNpxBofSsm261HI1EYFemP9Yd/Rr01DuUMjNVZWPQGlOHr+cg9xiI8xmAaVSgUBvDTo1CcQdbRviz9PXyg330ahgEgJ6gxkmIWB2s61j18hAzLq3I3OU7Kq6fLRY8ucZ/Pv3Uyg2mGs0X5US+HJCbK2+r2X3xxqVAq3D/DH5ztb83tfCrpRreOKLPTUef1RMFN4ZHu3AiByrsu+NZV9xJjMfBToTigwmKBSAWqmAzmiGsZY7ibXxtfs+36qa5iRQsu6jl+2BqZq0rI/9B/O39rnX8mYt4Unvj+U7YslJIQTMAIRZQKCkxyQANTqGaxLkjV2zBjksXhbaNVTZRmtXyjW8uuEI8nVGhPhqoTeZcelGEUw3D9zNNz/0yvh7qeDnpUZ+sREF+srOdbkOfy8VPh/d22MSnuRR3Q9fr244gqwCPYoMJpiFgFJRkosKhQJeagV0RgEvtQL6mz+MKRUKmEwlG2oLBVBl7rqq8EAvfMgfxMiOanJQv+TPM5j760nUrMQupVAAaybWrDgpuz/WqpTQm8y4UWiAv5cK7z7sOQejdVHbA30LVy22K/veXMnVoVBvhJ+XCv5ealzN08FkFhACtf4eW6uPYrumhfaulGsY9cWeGu/rHLn/YP7eeu6F+GrwyRM9PeL9sXxHvNRKXM3TwVjm2O1WOLLYVjpkrh7CbBZYtOMM8nVGRAR6w0ujxPV8PQBAq1aUbJCrmUe+zoTr+XoUukGRDZSsz8dbT8HsbqcCySVYcjKv2ACTueSMtEaphFqphEathBACRYaSsxBFBjPMQkCjVEKlUJTLVXf9Bl/P1+HT7WeYo1RvjEYzPt6acksHQ0IAC7dUv08puz/21qigVCrgrVEhItAL+ToTFu3g9746ZrPAmz8fuqVp1+5Ng97FjmUq+954qZUwmc0wmQUMRjOyi0quTFQry+8ramver0ed4ntoNgt8+N/jtVqfK7k6fLo9xe7xM39v9i7yfflL92viRqEBn2w77fbvj+U7Eh7ghZwiQ62vKKnM5Zxi5Obr7DKvslho18Gxy7k4k5mPEF8tFAoFivVm6IwmqJQKlJwPqxljDQpyV3L4Ui6OXc6VOwzyQJac9NWqoTeZoVYqoLh5DZECipKrTASgUpRcUqRUlAwXcN/CuiyjGUjOyGOOUr356XB6nW6L2nc+u9rva9n9sTWFQoFgXw3OZObze1+NY5dzkZxRfEvTCgCL/zxr34AcrLLvTbHBXLIPUSmgN5mhM5TsT1DBj7K1dfhygVN8D49dzsWBi3m1nu6kA/YfzN+S9+Bs1q3/UHXssnsfe1t/R3RGAZ3RDJUdfviyGLV8n53mZIuFdh1kFephMAloVSVvo9FshhAll7p58gX5BpNAVqFe7jDIA1lyUqlQlORidRPczFNPy1e9ycwcpXpzKbuwTgdDJlH9PqXs/rgsL5USBjP3TdWp6/tzIavATpHUj8q+N5bjOeXNH2WFECW3E9lpX+EM38OsQn255wfVhCOO8Zi/df9O6N382Nv6O2LJTwj7nSTJyKnkSYZ1xEK7DkJ9tdDc/LUTANRKpVRkKzzrAYA2NCoFQn21codBHsiSk2YhSnKxuglu5qmn5atWpWSOUr2JDPatxTVe5akU1e9Tyu6Py9KZzNAouW+qTl3fn+ahfnaKpH5U9r2xHM+Zbxbbliuf7LWvcIbvYaivFqpbWB9HHOMxf+v+ndC6+bG39XfEkp9Q1Ob64apFOKhbYxbaddC5SSBah/njRqEBQgh4a5XwUqtgMtfuQlSNSmG3L4oziI4MROcmVT/lksgRLDlZqDfd/NVTwPK8RwFR8nRxBWCSzlQI6UyFO+VgVdRKoH1EAHOU6s0D0Y0R6H3rvYn2aRFc7fe17P7YmhAC2YUGtA7z5/e+Gp2bBKJ9hPctTasA8MwdrewbkINV9r3x1ihL9iE3z6B5aUr2J7i5v6iL6CZ+TvE97NwkED2bBtR6ug4O2H8wf0veg1aht95NXucm7n3sbf0d8VJbnqNQ93y0WDO+j53mZIuFdh0olQpMvrM1/L1UyMjVodhgRgP/kl+T9EYBlbL6AtrfS4VQPy183aAPSqBkfabe1c7j+vQj52DJyQBvNVRKJZQKwGA2w2g2w2A0Q6FQwEejhFpZ8q9SoYDBXNKVV9lvrLt+gxv6e+HZAa2Zo1Rv1Golpt7V5pYOOBQK4LlB1e9Tyu6PiwwmmM0CRQYTMnJ18PdSYfKd/N5XR6lU4PX7u93StE/ERLlcf9qVfW+KjWaolEqolApo1EoE+Whudtla9wP7V+7r4hTfQ6VSgReGdKrV+oQHeuHZAW3sHj/zt+Q9eHt471uaNsRXgykD27r9+2P5jlzJ0yPQR1Py3AQ7aBLk7bD+tNm9Vw3VuB9tc8lZM/ajTeQ47Ef71rAfbXIEp+1H2yygUXpeP7z24NH9aFt9b2z60dabUKRnP9r11o+2h+Yv+9Gumk0/2jdzkv1ou4HqNlpms8Cxy7nIKtQj1FeLjhEBOJGRh6xCPYJ9NDCazfjtaAYu3ShC02Af3BvdGN2aBku/Plmmv5pXjEMXs5GckYvcIiPCArRocPNXlmv5OlzJKUZmXsmvfQaDAdlFAnUpzRUA2jT0Rr9WDXHwYi50JhN8NErkFBpwNbcY+VXMPMxfA18vDbpHBmJ0/5boERXi1r+mkfOoyUGEJaeuFeiQXWBAiK8GDfy90LlJoNTHfUXDO0YE4Fh6Lg6mZUMhgC5NA3HuagEOpGWjSGdEqJ8GSqUSEYFeyC024sqNQiRdyoHeWPKE2hAfLYRCQG8043q+HjnFBhTpzDDdfDhbZc8UbeCrQpC3F9QqQKFUwE+rhhACxQYjzl0thN4M6bL32my0G/mpMeH2VujXuhG6RgYxR8nuanNQbzSasSHpEn47fBFbTmVVOE6Ijxr/+Xt39Gsbdkvf17L7Y0vOU+2YzQJL/jqKub+mVjg8IkCLEb2a4vm72rncmeyKVPa9sW4P9tEAALKLDAj20cAsBA6mZePi1TxsOpSG9Eqep/T+ox0wokerevse1iYngZJ133k2E6O/KN+91Og+IXgkplO97T+YvyXvweLtB/He5vQKh3urgEd6R2JEr+Y2tYSnqCgnswr1yC4wIMhHjZwiI0J8NQjy1WBncho+2JJWbh7NglT4edpAh53JtmChXUO13WjVt8QLWZiwYj9yigxS2zN3tsbL97Qv11UCkatz9nyszDf70/Dyd4elX1l9tSosG9cHfVs1kDcwojpw1XwkclfMSSLncOtPJyGnsflYBqatOwidseS6HwWA1x/ohHH9W8obGBFJVuw8hzd+Oi69DvRWY9XEWHSPCpYvKCIiIiJyCBbaLm7Nngv418aj0hkyjUqBBY/3wNDoxvIGRkSST7al4P3/JkuvQ/20WBMfi46NeaaBiIiIyB2x0HZRQgh8+PspLNyaIrUFeKuxZExvXoZK5CSEEHjvv8lYtP2M1BYR6I21k2LRqpG/jJERERERkSOx0HZBRpMZr244gq/3X5TawgK8sGpiDDpE8AwZkTMwmwVe//EYvtx9QWqLCvHBuqf6ommIr4yREREREZGjsdB2MUV6E55dk4htyVelttaN/LBqYiwig31kjIyILExmgZe+PYTvD1yS2lo38sO6SX0RFugtY2REREREVB9YaLuQrAI9JqzYi6S0HKmtV/MQLB3bG8G+WhkjIyILvdGM59YdxG/HMqS2zk0CsXpiLEL8mKdEREREnoCFtotIyyrE6KV7cP56odR2d6dwLBzZA94a1++/ksgdFBtMeGrVfvx5+prU1qt5CFaM74MAb42MkRERERFRfWKh7QKOXc7B2GV7cS1fL7U9GdsMbz7UBSoP66SeyFkV6IwYt3wv9p2/IbX1a90AS8f2gY+WP4YREREReRIW2k5uZ8o1PLVqPwr0Jqlt5t3tMGVgGygULLKJnEFOoQGjl+7B4Uult3UM7hiOT0b1gJeaRTYRERGRp2Gh7cR+SLqEF78+BOPNTrJVCgXeHd4Fj/dpJnNkRGRxLV+HJ5bsxqkr+VLbg92aYP5j3aBWKWWMjIiIiIjkwkLbSX3+5xm8++tJ6bWPRolPRvXEXR3CZYyKiKyl5xTh75/vxgWrZyf8vU8U3n24K5S8rYOIiIjIY7HQdjJms8A7v57A0r/OSW3BvhosH9cHPZqFyBgZEVk7fy0fI5fsQXpOsdQ28baWeG1oR97WQUREROThPOq6xk8++QQtWrSAt7c3YmNjsXfvXrlDsqE3mvHc+oM2RXZksA++n9yPRTaREzmVkYtHFifYFNnTB7VlkU1EREREADyo0P7qq68wY8YMvP766zhw4AC6deuGIUOGIDMzU+7QAAB5xQaMXbYXPx9Ol9o6Ng7Ahin90KqRv4yREZG1w2nZePSz3Ta9ALx6XwdM/1s7FtlEREREBMCDCu358+dj0qRJGD9+PDp16oTFixfD19cXy5Ytkzs0ZOYW49HFCUg4e11q69+6Ab5+Og5hAd4yRkZE1vacu46RS3Yjp8gAAFAAeGdYFzx1R2t5AyMiIiIip+IR92jr9XokJiZi1qxZUptSqcTgwYORkJBQ4TQ6nQ46nU56nZub65DYzlzNx+ile3A5u/QS1Ae7NcEHj3aDVu0xv4MQVam+8rEqO5Iz8fTqRBQbzAAAlVKBDx6NxsM9mtZ7LERycoZ8JKJSzEki5+QRldy1a9dgMpkQHm77xO7w8HBkZGRUOM3cuXMRFBQk/UVFRdk9rgOpNzDi0102RXb87S2x4PHuLLKJrNRHPlbl1yPpiF+1XyqytSolPn2iB4ts8khy5yMR2WJOEjknhRBCyB2Eo12+fBmRkZHYtWsX4uLipPZ//OMf2LFjB/bs2VNumop+HYyKikJOTg4CAwPrHNMfxzMwZe1B6Ixmqe21oR0Rf3urOs+byN04Oh+r8l1iGv7x7WGYbm4pvTVKfD66F+5oF+bQ5RI5KznzkYjKY04SOSePuHS8YcOGUKlUuHLlik37lStXEBERUeE0Xl5e8PLyckg86/ak4p8bj8B888Bdo1Lg3491x4PdmjhkeUSuzpH5WJVVCefx+g/HYPk10t9LjWXj+iCmZWi9x0LkLOTKRyKqGHOSyDl5xPXJWq0WvXr1wpYtW6Q2s9mMLVu22JzhdjQhBD78PRmzNpQW2f5eaqwcH8Mim8jJfLotBbOtiuxgHw3WToplkU1ERERE1fKIM9oAMGPGDIwdOxa9e/dGTEwMFixYgIKCAowfP75elm80mfHaxqNYvy9Namvk74WVE2LQqQkv6yFyFkIIvP/fZHy6/YzU1tBfi7XxfdEuIkDGyIiIiIjIVXhMof3444/j6tWrmD17NjIyMtC9e3f89ttv5R6Q5ghFehOmrD2ArSdL++xu2dAPqybEICrU1+HLJ6KaEULgjZ+OYeWuC1Jbk2BvrI3vixYN/WSMjIiIiIhciccU2gAwdepUTJ06tV6XeaNAj/Er9iEpLVtq6xYVhBXjYhDip63XWIiocmazwD++O4RvEy9Jbc0b+GL9pL5oHOwjY2RERERE5Go8qtCubxdvFGL00r04d61AarurQxg+eaInfLQqGSMjImtGkxnT1h3EpqOl3f21C/fH2vhYNAzwljEyIiIiInJFLLQd5ER6LsYs24ureaXdLTzeOwrvPNwFapVHPIOOyCXojCY8vSoR209dldq6RgZh9cQYBPnyqhMiIiIiqj0W2g6QcOYa4lftR4HOJLU9P6gtpg9uC4VCIWNkRGStQGfAhBX7sedcltTWp0UIVoyPgZ8XN49EREREdGt4JGlnPyZdxovfJMFgKukUSKkA5gzrglGxzWWOjIisZRfqMXbZXhy6mCO13d62IZaM6Q1vDW/tICIiIqJbx0Lbjpb9dQ5zfj4u9bvrpVbi4yd64m+dHP9kcyKquat5Ojy5dA+SM/Kktrs7heOTUT2h4a0dRERERFRHLLTt5OOtp/HB5lPS6yAfDZaN641ezUNljIqIyrp8oxBPfLEH568XSm0PdW+C+Y91h0rJWzuIiIiIqO546sZO7mjXCL43nyTeOMgb303uxyKbyMmcu5aPRz/bbVNkPxHbDAseZ5FNRERERPbDQttOopsG49NRPdGlSSA2TumPNmH+codERFZOZuTisc9241J2kdQWf1tLvDOsCx9SSERERER2xUvH7WhA+zDc0bYRlDwzRuRUklJvYPyKfbhRaJDapg9ui+mD28kYFRERERG5KxbadsYim8i57D5zHfGr9iNfZwQAKAC8OrQjJt3eSt7AiIiIiMhtsdAmIre141Qmnv4yEcUGM4CS7vbeHtYFT7C7PSIiIiJyIBbaROSWtp3MxDOrE6EzlhTZaqUCHzwajWE9msocGRERERG5OxbaROR2DqTewFNf7ofBVNKrvZdaiYUje2BI5wiZIyMiIiIiT8CnjhOR24mODMJdHcIAAL5aFT4f3YtFNhERERHVGxbaROR21KqSM9hDuzbG8vF9cGf7MLlDIiIiIiIPwkvHicgtealV+GRUT7nDICIiIiIPxDPaRERERERERHbEQpuIiIiIiIjIjlhoExEREREREdkRC20iIiIiIiIiO2KhTURERERERGRHLLSJiIiIiIiI7IiFNhEREREREZEdsdAmIiIiIiIisiO13AG4CiEEACA3N1fmSIjcV0BAABQKRbXjMR+JHI/5SORcmJNEzqMm+chCu4by8vIAAFFRUTJHQuS+cnJyEBgYWO14zEcix2M+EjkX5iSR86hJPiqE5WcvqpLZbEZycjI6deqEtLS0Gm3oXEFubi6ioqLcap0ArpcrsV6nyMjIGv1abzabcfnyZQgh0KxZM5d/P9zlc+V6OJe6rkdNz55Z8rGm4zuCq39mjF8+rhS7K+VkdVzpfS+LscvD2WLnGW07UiqViIyMBAAEBgY6xQdsT+64TgDXy5UEBgbW+IBAqVSiadOm0mVx7vJ+cD2cC9ejZiz56Axc/TNj/PJx5djLcqacrI4rv++MXR6uFDsfhkZERERERERkRyy0iYiIiIiIiOyIhXYteHl54fXXX4eXl5fcodiNO64TwPVyJXVZJ3d5P7gezoXr4XpcfV0Zv3xcOXZX5srvO2OXhyvGzoehEREREREREdkRz2gTERERERER2RELbSIiIiIiIiI7YqFNREREREREZEceX2jPnTsXffr0QUBAAMLCwjBs2DAkJydLw8+fPw+FQlHh3zfffCONV9Hw9evXy7FKWLRoEaKjo6V+5uLi4rBp0yZpeHFxMaZMmYIGDRrA398fI0aMwJUrV2zmkZqaiqFDh8LX1xdhYWF46aWXYDQa63tVbFS1XllZWZg2bRrat28PHx8fNGvWDM899xxycnJs5uFMn5NFdZ/XgAEDysX8zDPP2MzD2T6vqtapupyy5GRln5Wr5KS75KG75J275FldcsvCGT6Pmqhu/ww4dx65yzYAAObNmweFQoHp06dLbc4c/xtvvFHuO96hQweXiN2V/fnnn3jggQfQpEkTKBQKbNy40Wa4EAKzZ89G48aN4ePjg8GDB+P06dM242RlZWHUqFEIDAxEcHAwJk6ciPz8fIfH7srbG25rnCf+coSHGzJkiFi+fLk4evSoSEpKEvfdd59o1qyZyM/PF0IIYTQaRXp6us3fm2++Kfz9/UVeXp40HwBi+fLlNuMVFRXJsk4//vij+OWXX8SpU6dEcnKyePXVV4VGoxFHjx4VQgjxzDPPiKioKLFlyxaxf/9+0bdvX9GvXz9peqPRKLp06SIGDx4sDh48KH799VfRsGFDMWvWLFnWx6Kq9Tpy5IgYPny4+PHHH0VKSorYsmWLaNu2rRgxYoTNPJzpc7Ko7vO68847xaRJk2xizsnJkaZ3xs+rqnWqLqcsOQlAvPnmm2LQoEEiMjJSnDlzRhQVFblMTrpLHrpL3rlLntUltyyc4fOoier2z0I4dx65yzZg7969okWLFiI6Olo8//zzUrszx//666+Lzp0723zHr1696hKxu7Jff/1V/POf/xTff/+9ACA2bNhgM3zevHkiKChIbNy4URw6dEg8+OCDomXLljbbn3vuuUd069ZN7N69W/zvf/8Tbdq0ESNHjnR47K68veG2xjnir4jHF9plZWZmCgBix44dlY7TvXt3MWHCBJu2ijYoziQkJER88cUXIjs7W2g0GvHNN99Iw06cOCEAiISEBCFEyYZSqVSKjIwMaZxFixaJwMBAodPp6j32qljWqyJff/210Gq1wmAwSG3O/jlZWK/XnXfeabPBKctVPq+qPquqcsqdctJd8tBd8s5d8uxWc8vVlN0WuGIeudo2IC8vT7Rt21b8/vvvNjni7PG//vrrolu3bhUOc/bY3UXZ7YzZbBYRERHi/fffl9qys7OFl5eXWLdunRBCiOPHjwsAYt++fdI4mzZtEgqFQly6dKneYhfC9bc33NY4R756/KXjZVkueQwNDa1weGJiIpKSkjBx4sRyw6ZMmYKGDRsiJiYGy5Ytg3CCntNMJhPWr1+PgoICxMXFITExEQaDAYMHD5bG6dChA5o1a4aEhAQAQEJCArp27Yrw8HBpnCFDhiA3NxfHjh2r93WoSNn1qkhOTg4CAwOhVqtt2p3xc7KobL3WrFmDhg0bokuXLpg1axYKCwulYc7+eVX3WVWXU+3atQMAbN++vcLPyhVy0l3y0F3yzl3yrK655SyfR02V3T+7Uh656jZgypQpGDp0qE2cgGu896dPn0aTJk3QqlUrjBo1CqmpqS4Tuzs6d+4cMjIybN73oKAgxMbG2rzvwcHB6N27tzTO4MGDoVQqsWfPnnqN11W3N9zWyBN/ZdTVj+I5zGYzpk+fjv79+6NLly4VjrN06VJ07NgR/fr1s2l/6623cNddd8HX1xebN2/Gs88+i/z8fDz33HP1EXo5R44cQVxcHIqLi+Hv748NGzagU6dOSEpKglarRXBwsM344eHhyMjIAABkZGTYfFktwy3D5FTZepV17do1zJkzB0899ZRNu7N9ThZVrdcTTzyB5s2bo0mTJjh8+DBefvllJCcn4/vvvwfgvJ9XTT+rqnJqwIABePXVV5GWloZ3330XwcHB5T4rZ85Jd8lDd8k7d8kze+SWM3wetVHR/jkjI8Pp88iVtwHr16/HgQMHsG/fvnLDnP29j42NxYoVK9C+fXukp6fjzTffxO23346jR486fezuyvK+VfS+Wr/vYWFhNsPVajVCQ0Pr9X13xe0NtzXOma8stK1MmTIFR48exV9//VXh8KKiIqxduxb/+te/yg2zbuvRowcKCgrw/vvvy3bg0r59eyQlJSEnJwfffvstxo4dix07dsgSiz1Vtl7WB5m5ubkYOnQoOnXqhDfeeMNmemf7nCyqWi/roqVr165o3LgxBg0ahDNnzqB169YyRl21mnxW1eXU5MmTkZaWhr/++guff/55uc/K2XPSXfLQXfLOXfLMHrll4UzbwapUt392Vq66DUhLS8Pzzz+P33//Hd7e3nKHU2v33nuv9P/o6GjExsaiefPm+Prrr+Hj4yNjZOQKXHF7w22Nc+Kl4zdNnToVP//8M7Zt24amTZtWOM63336LwsJCjBkzptr5xcbG4uLFi9DpdPYOtUa0Wi3atGmDXr16Ye7cuejWrRs++ugjREREQK/XIzs722b8K1euICIiAgAQERFR7ml+lteWceRS2XpZ5OXl4Z577kFAQAA2bNgAjUZT5fzk/pwsqlsva7GxsQCAlJQUAM77edVknarKqbI5WdFn5ew56S556C555y55VtfcKstZtoOVqWz/7Ap55KrbgMTERGRmZqJnz55Qq9VQq9XYsWMHFi5cCLVajfDwcKeOv6zg4GC0a9cOKSkpTv/euyvL+1bR+2r9vmdmZtoMNxqNyMrKqrf33VW3N9zWOGe+enyhLYTA1KlTsWHDBmzduhUtW7asdNylS5fiwQcfRKNGjaqdb1JSEkJCQuDl5WXPcG+Z2WyGTqdDr169oNFosGXLFmlYcnIyUlNTpXv84uLicOTIEZuN3e+//47AwMAKL0+Uk2W9gJIzanfffTe0Wi1+/PHHGv0y5myfk4X1epWVlJQEAGjcuDEA1/m8KlqninKqspys6LNytZx0lzx0l7xzlzyraW5Vxlk+j7Kq2z+7Yh65yjZg0KBBOHLkCJKSkqS/3r17Y9SoUdL/nTn+svLz83HmzBk0btzY6d97d9WyZUtERETYvO+5ubnYs2ePzfuenZ2NxMREaZytW7fCbDZLP346irttb7itcZJ8lespbM5i8uTJIigoSGzfvt2mG4jCwkKb8U6fPi0UCoXYtGlTuXn8+OOPYsmSJeLIkSPi9OnT4tNPPxW+vr5i9uzZ9bUaNl555RWxY8cOce7cOXH48GHxyiuvCIVCITZv3iyEKHlMfrNmzcTWrVvF/v37RVxcnIiLi5Omtzwm/+677xZJSUnit99+E40aNZL9MflVrVdOTo6IjY0VXbt2FSkpKTafpdFoFEI43+dUk/VKSUkRb731lti/f784d+6c+OGHH0SrVq3EHXfcIU3vjJ9Xdd9BISrPqcmTJws/Pz8xc+ZMsW3bNrFr1y4xd+5c4ePjY/NZOXtOukseukveuUue1SW3hHCez6MmarJ/duY8cpdtgEXZJ/M7c/wvvvii2L59uzh37pzYuXOnGDx4sGjYsKHIzMx0+thdWV5enjh48KA4ePCgACDmz58vDh48KC5cuCCEKOneKzg4WPzwww/i8OHD4qGHHqqwe68ePXqIPXv2iL/++ku0bdu2Xrr3cuXtDbc1zhW/NY8vtAFU+Ld8+XKb8WbNmiWioqKEyWQqN49NmzaJ7t27C39/f+Hn5ye6desmFi9eXOG49WHChAmiefPmQqvVikaNGolBgwbZHIQVFRWJZ599VoSEhAhfX1/x8MMPi/T0dJt5nD9/Xtx7773Cx8dHNGzYULz44os23fXIoar12rZtW6Wf5blz54QQzvc5WVS1XqmpqeKOO+4QoaGhwsvLS7Rp00a89NJLNv37CuF8n1d130EhKs+pyj7HMWPG2Izr7DnpLnnoLnnnLnlWl9wSwnk+j5qoyf7ZmfPIXbYBFmUPfp05/scff1w0btxYaLVaERkZKR5//HGRkpLiErG7ssr2CWPHjhVClHTx9a9//UuEh4cLLy8vMWjQIJGcnGwzj+vXr4uRI0cKf39/ERgYKMaPHy/y8vIcHrsrb2+4rXGu+K0phHCBPj2IiIiIiIiIXITH36NNREREREREZE8stImIiIiIiIjsiIU2ERERERERkR2x0CYiIiIiIiKyIxbaRERERERERHbEQpuIiIiIiIjIjlhoExEREREREdkRC20iIiIiIiIiO2KhTQ4zYMAATJ8+vcbjb9y4EW3atIFKparVdBbnz5+HQqFAUlJSleO98cYb6N69e63nT+TqFAoFNm7cWOPxt2/fDoVCgezs7HqPY8WKFQgODrbrcomcCfeRRM6D+0dyBBba5DSefvppPPLII0hLS8OcOXMwbtw4DBs2rMbTR0VFIT09HV26dKlyvJkzZ2LLli11jJbI9aSnp+Pee++16zxv5aC8JnE8/vjjOHXqVB0iI3Iv3EcSOQ73j+QIarkDIAKA/Px8ZGZmYsiQIWjSpMktzUOlUiEiIqLS4UIImEwm+Pv7w9/f/1ZDJXJJer2+yvyoT9XFYTAY4OPjAx8fn3qKiMi5cR9J5DjcP5Kj8Iw21QudToeZM2ciMjISfn5+iI2Nxfbt2wGUXH4TEBAAALjrrrugUCgwYMAArFy5Ej/88AMUCgUUCoU0fmXKXhZnuaxn06ZN6NWrF7y8vPDXX3+V+4Vx+/btiImJgZ+fH4KDg9G/f39cuHCh2mUplUrs37/fpn3BggVo3rw5zGZzrd4fInsbMGAApk6diunTp6Nhw4YYMmRIuUvSdu3ahe7du8Pb2xu9e/fGxo0bK7y0NDExEb1794avry/69euH5ORkACWXr7355ps4dOiQlKcrVqyoNjbrOCx5+9VXX+HOO++Et7c31qxZU+7SuEOHDmHgwIEICAhAYGAgevXqVS7/yiooKEBgYCC+/fZbm/aNGzfCz88PeXl51cZKVB/cbR8phMDgwYMxZMgQCCEAAFlZWWjatClmz559S+8Rkb1w/1hiwoQJiI6Ohk6nA1Dyg0OPHj0wZsyYaqelmmGhTfVi6tSpSEhIwPr163H48GE8+uijuOeee3D69GmbDdN3332H9PR0/Pjjj3jsscdwzz33ID09Henp6ejXr98tLfuVV17BvHnzcOLECURHR9sMMxqNGDZsGO68804cPnwYCQkJeOqpp6BQKKqcZ4sWLTB48GAsX77cpn358uUYN24clEqmFslv5cqV0Gq12LlzJxYvXmwzLDc3Fw888AC6du2KAwcOYM6cOXj55ZcrnM8///lP/Pvf/8b+/fuhVqsxYcIEACWXr7344ovo3LmzlKePP/74LcX6yiuv4Pnnn8eJEycwZMiQcsNHjRqFpk2bYt++fUhMTMQrr7wCjUZT5Tz9/Pzw97//vcI8feSRR6TihUhu7raPVCgUWLlyJfbt24eFCxcCAJ555hlERkay0Can4On7RwBYuHAhCgoK8Morr0jrkp2djY8//viW4qTyeOk4OVxqaiqWL1+O1NRU6ZK3mTNn4rfffsPy5cvx7rvvIiwsDAAQGhoqXTbj4+MDnU5X58t53nrrLfztb3+rcFhubi5ycnJw//33o3Xr1gCAjh071mi+8fHxeOaZZzB//nx4eXnhwIEDOHLkCH744Yc6xUtkL23btsV7771X4bC1a9dCoVBgyZIl8Pb2RqdOnXDp0iVMmjSp3LjvvPMO7rzzTgAlO/yhQ4eiuLgYPj4+8Pf3h1qtrnOeTp8+HcOHD690eGpqKl566SV06NBBWreaiI+PR79+/ZCeno7GjRsjMzMTv/76K/744486xUtkL+66j4yMjMRnn32GMWPGICMjA7/++isOHjwItZqHniQ/7h8Bf39/rF69GnfeeScCAgKwYMECbNu2DYGBgXWKl0rxtBs53JEjR2AymdCuXTvp3i9/f3/s2LEDZ86ccfjye/fuXemw0NBQjBs3DkOGDMEDDzyAjz76COnp6TWa77Bhw6BSqbBhwwYAJZcJDRw4EC1atLBH2ER11qtXr0qHJScnIzo6Gt7e3lJbTExMheNan+Vq3LgxACAzM9NOUZaoKk8BYMaMGYiPj8fgwYMxb968Gm87YmJi0LlzZ6xcuRIAsHr1ajRv3hx33HFHnWMmsgd33UcCwKOPPoqHH34Y8+bNwwcffFDjAoDI0bh/LBEXF4eZM2dizpw5ePHFF3HbbbfVNVyywkKbHC4/Px8qlQqJiYlISkqS/k6cOIGPPvrI4cv38/Orcvjy5cuRkJCAfv364auvvkK7du2we/fuauer1WoxZswYLF++HHq9HmvXrpUuGSJyBtV992vK+hI0yyWj9n4OQXWxvvHGGzh27BiGDh2KrVu3olOnTtKPXNWJj4+X7o1bvnw5xo8fX+2lr0T1xV33kQBQWFiIxMREqFQqnD592h7hEtkF948lzGYzdu7cCZVKhZSUFHuES1ZYaJPD9ejRAyaTCZmZmWjTpo3NX1WX02i1WphMpnqLcdasWdi1axe6dOmCtWvX1mi6+Ph4/PHHH/j0009hNBqrvLSHyJm0b98eR44ckR6CAgD79u2r9XzqM0/btWuHF154AZs3b8bw4cPL3XtdmSeffBIXLlzAwoULcfz4cYwdO9bBkRLVnDvvI1988UUolUps2rQJCxcuxNatWx0cKVHdedL+8f3338fJkyexY8cO6XYVsh8W2uRw7dq1w6hRozBmzBh8//33OHfuHPbu3Yu5c+fil19+qXS6Fi1a4PDhw0hOTsa1a9dgMBjsHtu5c+cwa9YsJCQk4MKFC9i8eTNOnz5d43vQOnbsiL59++Lll1/GyJEj2d0CuYwnnngCZrMZTz31FE6cOIH//ve/+OCDDwCgVmd7W7RogXPnziEpKQnXrl2zOTCxl6KiIkydOhXbt2/HhQsXsHPnTuzbt6/GeRoSEoLhw4fjpZdewt13342mTZvaPUaiW+Wu+8hffvkFy5Ytw5o1a/C3v/0NL730EsaOHYsbN27YPU4ie/KU/ePBgwcxe/ZsfPHFF+jfvz/mz5+P559/HmfPnrV7nJ6KhTbVi+XLl2PMmDF48cUX0b59ewwbNgz79u1Ds2bNKp1m0qRJaN++PXr37o1GjRph586ddo/L19cXJ0+exIgRI9CuXTs89dRTmDJlCp5++ukaz2PixInQ6/W8bJxcSmBgIH766SckJSWhe/fu+Oc//yk9Ddj6vrTqjBgxAvfccw8GDhyIRo0aYd26dXaPVaVS4fr16xgzZgzatWuHxx57DPfeey/efPPNGs+DeUrOzN32kVevXsXEiRPxxhtvoGfPngCAN998E+Hh4XjmmWfsHieRPXnC/rG4uBhPPvkkxo0bhwceeAAA8NRTT2HgwIEYPXp0vZ2Jd3cKYengkIhuyZw5c/DNN9/g8OHDcodCVCdr1qzB+PHjkZOT43ZXZ3z55Zd44YUXcPnyZWi1WrnDISIiF+LO+0dyHPaxQHSL8vPzcf78eXz88cd4++235Q6HqNZWrVqFVq1aITIyEocOHcLLL7+Mxx57zK0OIgoLC5Geno558+bh6aefZpFNRETV8oT9IzkeLx0nl/Huu+/adH1i/XfvvffafXmdO3eudHlr1qzB1KlT0atXLwwYMICXo5JLysjIwJNPPomOHTvihRdewKOPPorPP/+8TvNcs2ZNpXnTuXNnO0Ve6t577610ee+++y7ee+89dOjQAREREZg1a5bdl0/kLJxtH0nkyjxh/0iOx0vHyWVkZWUhKyurwmE+Pj6IjIy06/IuXLhQ6cNlwsPDERAQYNflEbmDvLw8XLlypcJhGo0GzZs3t+vyLl26hKKiogqHhYaGIjQ01K7LI3JW3EcSOTfuHz0PC20iIiIiIiIiO+Kl40RERERERER2xEKbiIiIiIiIyI5YaBMRERERERHZEQttIiIiIiIiIjtioU1ERERERERkRyy0iYiIiIiIiOyIhTYRERERERGRHbHQJiIiIiIiIrKj/wcvgHmhpHLYxAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9523077664938892" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNet regression model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5)\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 747.02218614, 737.42924978, 279.88344694, 230.54044098,\n", + " 1309.30767492, 278.41914017, 277.03634549, 238.78249632,\n", + " 741.60500798, 729.82005295, 1309.11697423, 725.7708785 ,\n", + " 283.22159251, 245.85116235, 729.26935504, 748.71908107,\n", + " 273.70202144, 1313.88438104, 232.14734217, 229.64929264,\n", + " 726.55354494, 728.91464129, 285.18215641, 1315.61023111,\n", + " 1307.45528605, 747.59055299, 229.28881811, 231.43617598,\n", + " 235.44182376, 239.5300476 , 730.80986415, 729.70893204,\n", + " 747.7969688 , 731.19087 , 1331.1203785 , 1322.01493109,\n", + " 1307.47001122, 1330.88422484, 1327.71129565, 1307.77941948,\n", + " 739.05924954, 240.28238704, 280.71725394, 1312.38413871,\n", + " 729.1727232 , 277.70446184, 749.85190945, 1318.43210968,\n", + " 1319.06845813, 278.30397737, 1304.40871971, 1318.17948896,\n", + " 723.57129236, 1316.16997109, 236.52877322, 738.93216736,\n", + " 231.27588717, 229.8455486 , 277.78279306, 747.85284925,\n", + " 1299.13362219, 295.13439951, 278.09686026, 726.53926277,\n", + " 232.69573111, 245.59552806, 1318.81541478, 238.32120295,\n", + " 1301.73514316, 1318.6272491 , 1319.21778272, 732.65105957,\n", + " 278.7234045 , 731.84106104, 237.13855167, 1299.39441485,\n", + " 1328.16669933, 747.89848157, 1311.46482228, 230.73602777,\n", + " 1300.05569908, 739.18857753, 229.44405547, 736.57368308,\n", + " 287.18662115, 1304.62354064, 1335.37752742, 226.99296668,\n", + " 1319.06963996, 1317.54309471, 228.91207535, 279.45513804,\n", + " 231.76712682, 745.78693604, 246.0720484 , 746.2362892 ,\n", + " 277.67972933, 331.09915806, 1334.87584136, 236.01751051,\n", + " 739.32244242, 1308.97690697, 744.46516167, 740.23075744,\n", + " 231.06068186, 743.25455083, 228.20381357, 227.8524838 ,\n", + " 227.5186283 , 1318.33420456, 235.1441785 , 1162.692433 ,\n", + " 1317.64165203, 248.71382359, 1302.74841894, 747.35589658,\n", + " 730.0502556 , 275.25255461, 1329.96397674, 288.18453711,\n", + " 278.13795065, 727.72966284, 1317.89954611, 279.13739563,\n", + " 744.68763952, 279.68791021, 729.48338688, 1319.44560975,\n", + " 1318.68750438, 1312.46717238, 1318.1450041 , 731.62362041,\n", + " 228.25090443, 1331.94714861, 749.09926775, 1311.7232039 ,\n", + " 239.95729342, 294.8194464 , 227.30009557, 240.51905358,\n", + " 241.36231465, 231.40717573, 238.19798205, 1303.41153226])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.901287618717771" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNet regression model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5)\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([201.74917125, 185.23221683, 142.1986381 , 516.90506635,\n", + " 108.01383572, 154.01956202, 151.01190587, 358.19497598,\n", + " 198.31700416, 561.12947779, 595.78324198, 582.5134014 ,\n", + " 134.56978357, 309.5565218 , 561.72553749, 202.20531574,\n", + " 160.62435362, 429.83495112, 512.78998158, 509.84960589,\n", + " 584.42561482, 526.94341393, 124.61335133, 606.18542394,\n", + " 170.5342504 , 197.639494 , 515.80270602, 513.72590626,\n", + " 342.39977669, 350.20572588, 554.89136981, 518.00374096,\n", + " 193.77385374, 538.15755241, 411.01714275, 647.11559949,\n", + " 602.58028061, 422.54014636, 416.90526083, 165.82479817,\n", + " 190.83257017, 349.00574088, 139.89150645, 608.86756376,\n", + " 551.41432302, 144.82192418, 203.92553112, 426.51546804,\n", + " 628.71531415, 149.52645348, 186.22496506, 609.96307602,\n", + " 568.49275178, 393.84477015, 354.4503328 , 202.96107636,\n", + " 513.1383704 , 509.42505411, 153.65983788, 202.78872567,\n", + " 183.92755529, 90.20019376, 149.55184482, 560.57667309,\n", + " 507.30415964, 310.44190701, 626.51898851, 346.0753966 ,\n", + " 181.20362739, 428.52662446, 615.41112984, 562.07222261,\n", + " 148.12395858, 536.80608316, 353.96719968, 185.78147604,\n", + " 417.01285354, 205.31240627, 605.21030072, 510.79636841,\n", + " 183.7079199 , 192.7073409 , 518.88643302, 196.12980701,\n", + " 128.04387639, 170.65530931, 419.85766726, 509.85854476,\n", + " 135.69080568, 616.47289985, 506.23977645, 147.56524142,\n", + " 518.15700707, 198.36749571, 374.98348823, 202.44814876,\n", + " 148.21303901, 39.91755786, 418.90530107, 336.91869628,\n", + " 198.20015075, 604.28484187, 201.10330065, 198.85233735,\n", + " 511.57755392, 211.98931155, 513.46591069, 511.13016839,\n", + " 508.60034563, 623.00910399, 348.38189758, 93.07055017,\n", + " 424.73465207, 292.75494825, 175.76136814, 202.95733901,\n", + " 535.50495828, 144.35736504, 416.45212406, 127.30311496,\n", + " 142.61551777, 558.04601979, 421.65060191, 143.46187585,\n", + " 197.08824855, 143.83700192, 557.04616062, 630.90789552,\n", + " 396.39169738, 147.86418367, 608.56271497, 551.61579495,\n", + " 503.23510096, 415.01660822, 199.86749586, 610.4226928 ,\n", + " 349.75337516, 75.81977023, 506.50881804, 356.48761564,\n", + " 358.35952529, 513.82902386, 354.05057477, 168.69758897])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768747.022186100201.749171(768, 100)
290768737.429250100185.232217(768, 100)
54100279.883447100142.198638(100, 100)
198100230.540441630516.905066(100, 630)
45314361309.307675100108.013836(1436, 100)
..................
164100240.519054365356.487616(100, 365)
165100241.362315365358.359525(100, 365)
199100231.407176630513.829024(100, 630)
132100238.197982365354.050575(100, 365)
50114361303.411532100168.697589(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 747.022186 100 201.749171 (768, 100)\n", + "290 768 737.429250 100 185.232217 (768, 100)\n", + "54 100 279.883447 100 142.198638 (100, 100)\n", + "198 100 230.540441 630 516.905066 (100, 630)\n", + "453 1436 1309.307675 100 108.013836 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 240.519054 365 356.487616 (100, 365)\n", + "165 100 241.362315 365 358.359525 (100, 365)\n", + "199 100 231.407176 630 513.829024 (100, 630)\n", + "132 100 238.197982 365 354.050575 (100, 365)\n", + "501 1436 1303.411532 100 168.697589 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768747.022186100201.749171(768, 100)
290768737.429250100185.232217(768, 100)
54100279.883447100142.198638(100, 100)
198100230.540441630516.905066(100, 630)
45314361309.307675100108.013836(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 747.022186 100 201.749171 (768, 100)\n", + "290 768 737.429250 100 185.232217 (768, 100)\n", + "54 100 279.883447 100 142.198638 (100, 100)\n", + "198 100 230.540441 630 516.905066 (100, 630)\n", + "453 1436 1309.307675 100 108.013836 (1436, 100)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(27, 5)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(768, 100) 0.754771\n", + "(768, 630) 1.245229\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_9604\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_9604\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and ElasticNet model\n", + " # with 2-degree polynomial features and alpha=1.0 and l1_ratio=0.5\n", + " sc = StandardScaler()\n", + " model = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5)\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "prefix = \"e2e_test3\"\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 618300d38cd976f89a13064349fbd96ab6ec03ee Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Fri, 5 Jul 2024 23:11:18 +0000 Subject: [PATCH 28/78] elastic net grid search added --- ...st_elasticnet_regression_grid_search.ipynb | 2584 +++++++++++++++++ 1 file changed, 2584 insertions(+) create mode 100644 app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb new file mode 100644 index 00000000..20f8fa63 --- /dev/null +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb @@ -0,0 +1,2584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9523077664938892" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNet regression model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5)\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 747.02218614, 737.42924978, 279.88344694, 230.54044098,\n", + " 1309.30767492, 278.41914017, 277.03634549, 238.78249632,\n", + " 741.60500798, 729.82005295, 1309.11697423, 725.7708785 ,\n", + " 283.22159251, 245.85116235, 729.26935504, 748.71908107,\n", + " 273.70202144, 1313.88438104, 232.14734217, 229.64929264,\n", + " 726.55354494, 728.91464129, 285.18215641, 1315.61023111,\n", + " 1307.45528605, 747.59055299, 229.28881811, 231.43617598,\n", + " 235.44182376, 239.5300476 , 730.80986415, 729.70893204,\n", + " 747.7969688 , 731.19087 , 1331.1203785 , 1322.01493109,\n", + " 1307.47001122, 1330.88422484, 1327.71129565, 1307.77941948,\n", + " 739.05924954, 240.28238704, 280.71725394, 1312.38413871,\n", + " 729.1727232 , 277.70446184, 749.85190945, 1318.43210968,\n", + " 1319.06845813, 278.30397737, 1304.40871971, 1318.17948896,\n", + " 723.57129236, 1316.16997109, 236.52877322, 738.93216736,\n", + " 231.27588717, 229.8455486 , 277.78279306, 747.85284925,\n", + " 1299.13362219, 295.13439951, 278.09686026, 726.53926277,\n", + " 232.69573111, 245.59552806, 1318.81541478, 238.32120295,\n", + " 1301.73514316, 1318.6272491 , 1319.21778272, 732.65105957,\n", + " 278.7234045 , 731.84106104, 237.13855167, 1299.39441485,\n", + " 1328.16669933, 747.89848157, 1311.46482228, 230.73602777,\n", + " 1300.05569908, 739.18857753, 229.44405547, 736.57368308,\n", + " 287.18662115, 1304.62354064, 1335.37752742, 226.99296668,\n", + " 1319.06963996, 1317.54309471, 228.91207535, 279.45513804,\n", + " 231.76712682, 745.78693604, 246.0720484 , 746.2362892 ,\n", + " 277.67972933, 331.09915806, 1334.87584136, 236.01751051,\n", + " 739.32244242, 1308.97690697, 744.46516167, 740.23075744,\n", + " 231.06068186, 743.25455083, 228.20381357, 227.8524838 ,\n", + " 227.5186283 , 1318.33420456, 235.1441785 , 1162.692433 ,\n", + " 1317.64165203, 248.71382359, 1302.74841894, 747.35589658,\n", + " 730.0502556 , 275.25255461, 1329.96397674, 288.18453711,\n", + " 278.13795065, 727.72966284, 1317.89954611, 279.13739563,\n", + " 744.68763952, 279.68791021, 729.48338688, 1319.44560975,\n", + " 1318.68750438, 1312.46717238, 1318.1450041 , 731.62362041,\n", + " 228.25090443, 1331.94714861, 749.09926775, 1311.7232039 ,\n", + " 239.95729342, 294.8194464 , 227.30009557, 240.51905358,\n", + " 241.36231465, 231.40717573, 238.19798205, 1303.41153226])" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.901287618717771" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNet regression model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5)\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([201.74917125, 185.23221683, 142.1986381 , 516.90506635,\n", + " 108.01383572, 154.01956202, 151.01190587, 358.19497598,\n", + " 198.31700416, 561.12947779, 595.78324198, 582.5134014 ,\n", + " 134.56978357, 309.5565218 , 561.72553749, 202.20531574,\n", + " 160.62435362, 429.83495112, 512.78998158, 509.84960589,\n", + " 584.42561482, 526.94341393, 124.61335133, 606.18542394,\n", + " 170.5342504 , 197.639494 , 515.80270602, 513.72590626,\n", + " 342.39977669, 350.20572588, 554.89136981, 518.00374096,\n", + " 193.77385374, 538.15755241, 411.01714275, 647.11559949,\n", + " 602.58028061, 422.54014636, 416.90526083, 165.82479817,\n", + " 190.83257017, 349.00574088, 139.89150645, 608.86756376,\n", + " 551.41432302, 144.82192418, 203.92553112, 426.51546804,\n", + " 628.71531415, 149.52645348, 186.22496506, 609.96307602,\n", + " 568.49275178, 393.84477015, 354.4503328 , 202.96107636,\n", + " 513.1383704 , 509.42505411, 153.65983788, 202.78872567,\n", + " 183.92755529, 90.20019376, 149.55184482, 560.57667309,\n", + " 507.30415964, 310.44190701, 626.51898851, 346.0753966 ,\n", + " 181.20362739, 428.52662446, 615.41112984, 562.07222261,\n", + " 148.12395858, 536.80608316, 353.96719968, 185.78147604,\n", + " 417.01285354, 205.31240627, 605.21030072, 510.79636841,\n", + " 183.7079199 , 192.7073409 , 518.88643302, 196.12980701,\n", + " 128.04387639, 170.65530931, 419.85766726, 509.85854476,\n", + " 135.69080568, 616.47289985, 506.23977645, 147.56524142,\n", + " 518.15700707, 198.36749571, 374.98348823, 202.44814876,\n", + " 148.21303901, 39.91755786, 418.90530107, 336.91869628,\n", + " 198.20015075, 604.28484187, 201.10330065, 198.85233735,\n", + " 511.57755392, 211.98931155, 513.46591069, 511.13016839,\n", + " 508.60034563, 623.00910399, 348.38189758, 93.07055017,\n", + " 424.73465207, 292.75494825, 175.76136814, 202.95733901,\n", + " 535.50495828, 144.35736504, 416.45212406, 127.30311496,\n", + " 142.61551777, 558.04601979, 421.65060191, 143.46187585,\n", + " 197.08824855, 143.83700192, 557.04616062, 630.90789552,\n", + " 396.39169738, 147.86418367, 608.56271497, 551.61579495,\n", + " 503.23510096, 415.01660822, 199.86749586, 610.4226928 ,\n", + " 349.75337516, 75.81977023, 506.50881804, 356.48761564,\n", + " 358.35952529, 513.82902386, 354.05057477, 168.69758897])" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768747.022186100201.749171(768, 100)
290768737.429250100185.232217(768, 100)
54100279.883447100142.198638(100, 100)
198100230.540441630516.905066(100, 630)
45314361309.307675100108.013836(1436, 100)
..................
164100240.519054365356.487616(100, 365)
165100241.362315365358.359525(100, 365)
199100231.407176630513.829024(100, 630)
132100238.197982365354.050575(100, 365)
50114361303.411532100168.697589(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 747.022186 100 201.749171 (768, 100)\n", + "290 768 737.429250 100 185.232217 (768, 100)\n", + "54 100 279.883447 100 142.198638 (100, 100)\n", + "198 100 230.540441 630 516.905066 (100, 630)\n", + "453 1436 1309.307675 100 108.013836 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 240.519054 365 356.487616 (100, 365)\n", + "165 100 241.362315 365 358.359525 (100, 365)\n", + "199 100 231.407176 630 513.829024 (100, 630)\n", + "132 100 238.197982 365 354.050575 (100, 365)\n", + "501 1436 1303.411532 100 168.697589 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768747.022186100201.749171(768, 100)
290768737.429250100185.232217(768, 100)
54100279.883447100142.198638(100, 100)
198100230.540441630516.905066(100, 630)
45314361309.307675100108.013836(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 747.022186 100 201.749171 (768, 100)\n", + "290 768 737.429250 100 185.232217 (768, 100)\n", + "54 100 279.883447 100 142.198638 (100, 100)\n", + "198 100 230.540441 630 516.905066 (100, 630)\n", + "453 1436 1309.307675 100 108.013836 (1436, 100)" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(27, 5)" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(768, 100) 0.754771\n", + "(768, 630) 1.245229\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_26840\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_26840\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and ElasticNet model\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.ElasticNet())\n", + "\n", + " # Define the parameter grid for GridSearchCV\n", + " param_grid = {\n", + " \"elasticnet__alpha\": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0],\n", + " \"elasticnet__l1_ratio\": [0, 0.01, 0.2, 0.5, 0.8, 1],\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " y_x = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, y_x, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X using GridSearchCV\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alpha for X: {grid_search.best_params_[\"elasticnet__alpha\"]}, Best l1_ratio for X: {grid_search.best_params_[\"elasticnet__l1_ratio\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y using GridSearchCV\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alpha for Y: {grid_search.best_params_[\"elasticnet__alpha\"]}, Best l1_ratio for Y: {grid_search.best_params_[\"elasticnet__l1_ratio\"]}, R2 score : {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.077e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.076e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.061e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.025e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.116e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.023e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.195e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.194e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.120e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.180e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.157e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.593e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.720e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.559e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.588e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.560e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.606e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.714e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.467e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.509e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.335e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.320e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.441e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.189e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.146e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.022e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.220e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.196e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.251e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.418e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.193e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.799e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.232e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.285e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.764e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.240e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.845e+06, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.869e+06, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.860e+06, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.906e+06, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.855e+06, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.344e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.883e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.207e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.153e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.009e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.021e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.575e+07, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.618e+07, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.579e+07, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.609e+07, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.586e+07, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.154e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.437e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.159e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.064e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.277e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.381e+07, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.619e+07, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.363e+07, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.453e+07, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.427e+07, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.511e+07, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.840e+07, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.478e+07, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.588e+07, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.571e+07, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.690e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.717e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.810e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.765e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.689e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.717e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.809e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.765e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.610e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.715e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.807e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.763e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha for X: 0.001, Best l1_ratio for X: 0.8, R2 score : 0.9982355264998006\n", + "-------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.684e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.607e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.712e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.804e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.760e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.680e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.605e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.709e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.801e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.757e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.678e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.603e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.707e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.799e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.772e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.668e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.793e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.884e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.839e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.771e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.668e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.792e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.884e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.838e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.753e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.654e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.776e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.867e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.822e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.725e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.632e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.749e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.841e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.796e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.694e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.610e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.720e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.812e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.768e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.672e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.595e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.700e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.792e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.748e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.163e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.001e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.163e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.260e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.207e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.064e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.012e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.131e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.087e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.501e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.759e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.486e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.471e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.618e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.295e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.568e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.651e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.697e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.515e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.692e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.786e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.746e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.632e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.723e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.057e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.860e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.026e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.089e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.055e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.023e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.972e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.028e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.174e+06, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.146e+06, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+06, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+06, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+06, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+03, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.679e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.604e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.482e+06, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.417e+06, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.506e+06, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.463e+06, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.464e+06, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.009e+07, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.013e+07, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.005e+07, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.171e+07, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+07, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.200e+07, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\base.py:1473: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", + " return fit_method(estimator, *args, **kwargs)\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.398e+05, tolerance: 3.005e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha for Y: 0.0, Best l1_ratio for Y: 0, R2 score : 0.9768205711537207\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "prefix = \"e2e_test3\"\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1b7cc8266916f03806f46c808fde908348b3da4e Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Fri, 5 Jul 2024 23:25:16 +0000 Subject: [PATCH 29/78] minor changes in cv grid search notebooks --- .../test_lassoCV_regression_grid_search.ipynb | 4 ++-- .../test_ridgeCV_regression_grid_search.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb index c2f0b0a6..548daf7b 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb @@ -1689,7 +1689,7 @@ " r2_score_x = r2_score(y_test_x, y_pred_x)\n", " print(\"-------------------MODEL RESULT FOR X------------------\")\n", " print(\n", - " f'Best alpha for X: {grid_search.best_params_[\"lassocv__alphas\"]}, R2 score : {r2_score_x}'\n", + " f'Best alphas for X: {grid_search.best_params_[\"lassocv__alphas\"]}, R2 score : {r2_score_x}'\n", " )\n", " print(\"-------------------------------------------------------\")\n", "\n", @@ -1715,7 +1715,7 @@ " r2_score_y = r2_score(y_test_y, y_pred_y)\n", " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", " print(\n", - " f'Best alpha for Y: {grid_search.best_params_[\"lassocv__alphas\"]}, R2 score: {r2_score_y}'\n", + " f'Best alphas for Y: {grid_search.best_params_[\"lassocv__alphas\"]}, R2 score: {r2_score_y}'\n", " )\n", " print(\"-------------------------------------------------------\")\n", "\n", diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb index 53fc2086..c84ecc62 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb @@ -1567,7 +1567,7 @@ " r2_score_x = r2_score(y_test_x, y_pred_x)\n", " print(\"-------------------MODEL RESULT FOR X------------------\")\n", " print(\n", - " f'Best alpha for X: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score : {r2_score_x}'\n", + " f'Best alphas for X: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score : {r2_score_x}'\n", " )\n", " print(\"-------------------------------------------------------\")\n", "\n", @@ -1593,7 +1593,7 @@ " r2_score_y = r2_score(y_test_y, y_pred_y)\n", " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", " print(\n", - " f'Best alpha for Y: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score: {r2_score_y}'\n", + " f'Best alphas for Y: {grid_search.best_params_[\"ridgecv__alphas\"]}, R2 score: {r2_score_y}'\n", " )\n", " print(\"-------------------------------------------------------\")\n", "\n", From 84e1c4213a2f53b02c29b78eecd5a8ab69a782ff Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Mon, 8 Jul 2024 15:17:10 +0000 Subject: [PATCH 30/78] elastic net CV model added --- .../test_elasticnetCV_regression.ipynb | 1654 +++++++++++++++++ 1 file changed, 1654 insertions(+) create mode 100644 app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb new file mode 100644 index 00000000..1d249490 --- /dev/null +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb @@ -0,0 +1,1654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9966165885337552" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNetCV model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.ElasticNetCV(alphas=(0.1, 1.0, 10.0), l1_ratio=0.5),\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 774.21757722, 762.32695009, 160.82949933, 92.4830288 ,\n", + " 1415.65994118, 158.77776525, 156.94515186, 103.83019647,\n", + " 767.50804767, 752.95912389, 1414.86308469, 747.9448386 ,\n", + " 165.42635101, 113.68221387, 752.29436934, 776.31466822,\n", + " 152.329388 , 1419.92937548, 94.66378085, 91.23633019,\n", + " 748.92120668, 751.82596039, 168.08421862, 1421.72975029,\n", + " 1413.71481463, 774.91539079, 90.61874144, 93.68298702,\n", + " 99.15313706, 104.9303305 , 754.17935532, 752.81450251,\n", + " 775.18148952, 754.67200875, 1437.94819259, 1428.25272319,\n", + " 1413.15643443, 1437.67918868, 1434.36710776, 1413.92382468,\n", + " 764.33373976, 105.97272448, 161.93670039, 1418.33768333,\n", + " 752.15757025, 157.79311846, 777.72304334, 1424.6911829 ,\n", + " 1425.26592981, 158.59419713, 1410.36405828, 1424.35031864,\n", + " 745.20672642, 1422.39779083, 100.6889481 , 764.19503218,\n", + " 93.42135941, 91.43280158, 157.93198776, 775.24391419,\n", + " 1404.79835479, 181.78774493, 158.35222129, 748.8819082 ,\n", + " 95.37161037, 113.34207161, 1424.95418811, 103.21832112,\n", + " 1407.58541887, 1424.95407606, 1425.40265649, 756.48738014,\n", + " 159.24665866, 755.47506804, 101.48176305, 1405.06552956,\n", + " 1434.80107403, 775.30798386, 1417.3580004 , 92.69268308,\n", + " 1405.83186151, 764.49471413, 90.86755549, 761.25221992,\n", + " 170.87340087, 1410.59318198, 1442.37759309, 87.46230202,\n", + " 1425.80546373, 1423.67040961, 90.09040701, 160.2428607 ,\n", + " 94.12855724, 772.67880784, 114.00651433, 773.22795236,\n", + " 157.74151761, 231.04592912, 1441.79930351, 99.97015579,\n", + " 764.68637888, 1414.75358535, 771.05422066, 765.80300755,\n", + " 93.1988608 , 769.54532705, 89.12600362, 88.60585547,\n", + " 88.13200424, 1424.48977401, 98.79695056, 1260.05467549,\n", + " 1423.96506335, 117.68888932, 1408.58020893, 774.62580815,\n", + " 753.25129761, 154.43637462, 1436.75320046, 172.25761867,\n", + " 158.43581357, 750.37157209, 1424.23219019, 159.78651598,\n", + " 771.33294841, 160.49777266, 752.54344315, 1425.63760766,\n", + " 1425.01793577, 1418.86079248, 1424.38746011, 755.20169628,\n", + " 89.18725578, 1438.7992014 , 776.77766916, 1417.5739756 ,\n", + " 105.46179679, 181.32724508, 87.88328857, 106.32066442,\n", + " 107.46666896, 93.7024473 , 102.99294324, 1409.28043019])" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGxCAYAAAB89YyPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABUeUlEQVR4nO3deViU9f4//ufMwAzrDJtsCoq7IAppKmpmyRFzSdNSlMxOfvVkkmumZlpW5lJHEzPNOp/sdzJRSy0t9XDUJBMREWRREUkFlwEVmWFnlvv3R8cbxw3UgWHg+bgurnN43e8ZXvd9IfPsntd9j0QQBAFEREREVkRq6QaIiIiIHhYDDBEREVkdBhgiIiKyOgwwREREZHUYYIiIiMjqMMAQERGR1WGAISIiIqvDAENERERWx8bSDdQVo9GIK1euwNnZGRKJxNLtEBERUS0IgoDi4mL4+vpCKr3/eZZGG2CuXLkCPz8/S7dBREREjyAvLw8tWrS47/ZGG2CcnZ0B/HUAlEqlhbshIiKi2tBqtfDz8xNfx++n0QaYW28bKZVKBhgiIiIrU9P4B4d4iYiIyOowwBAREZHVYYAhIiIiq8MAQ0RERFaHAYaIiIisDgMMERERWR0GGCIiIrI6DDBERERkdRhgiIiIyOo02jvxEhERkfnp9UbsSruKy0VlaO7igGFdfGBjU//nQxhgiIiIqFa+is9BzP5sFFcaxNqin9IxbUA7TOrXpl57YYAhIiKiGn0Vn4Mlv565q15caRDr9RliOANDRERED6TXG7Fs793h5XbL9p6BXm+sp44YYIiIiKgGP5zIg6GGbGIw/rWuvjDAEBER0QNtSsw16zpzYIAhIiKiByoorjTrOnNggCEiIqIHsjHqzLrOHBhgiIiI6IGulNRuOLe268yBAYaIiIgeSDDzOnNggCEiIiKr89ABJj4+HsOGDYOvry8kEgl27tx537Wvv/46JBIJPvvsM5N6YWEhoqKioFQq4eLigokTJ6KkpMRkTVpaGp566inY2dnBz88PK1aseNhWiYiIqJF66ABTWlqKrl27Yu3atQ9ct2PHDhw9ehS+vr53bYuKikJmZibi4uKwe/duxMfHY/LkyeJ2rVaLgQMHomXLlkhOTsYnn3yC999/Hxs2bHjYdomIiOgxSWq5rj7f1nnojxJ47rnn8Nxzzz1wzeXLl/Hmm29i3759GDJkiMm206dPY+/evUhKSkL37t0BAGvWrMHgwYPx6aefwtfXF5s2bUJVVRX+7//+D3K5HEFBQUhNTcXKlStNgg4RERHVHUEQsC35Uq1nW6S1TTpmYPawZDQaMX78eMyZMwdBQUF3bU9ISICLi4sYXgAgPDwcUqkUiYmJ4pp+/fpBLpeLayIiIpCVlYWbN2/e8+dWVlZCq9WafBEREdGj0ZTp8MamE3j7h7RaP8bDSV7zIjMxe4BZvnw5bGxsMG3atHtuV6vV8PT0NKnZ2NjAzc0NarVaXOPl5WWy5tb3t9bcaenSpVCpVOKXn5/f4+4KERFRk5SQcwMRn8VjT8a9X3Pv56Vuzeuoo7uZNcAkJydj9erV2LhxIySSejyPBGD+/PnQaDTiV15e/X0eAxERUWNQpTdi2Z7TGPfVUai1FWL9CX+XWj3+zWc71FFndzNrgPn9999RUFAAf39/2NjYwMbGBhcvXsTs2bPRqlUrAIC3tzcKCgpMHqfX61FYWAhvb29xTX5+vsmaW9/fWnMnhUIBpVJp8kVERES1k3OtBC988QfWH/pTnHmxkUow62/tse313ojq8eB3NqJ6+EEul9V9o//z0EO8DzJ+/HiEh4eb1CIiIjB+/Hj8/e9/BwCEhYWhqKgIycnJ6NatGwDgwIEDMBqN6Nmzp7hmwYIF0Ol0sLW1BQDExcWhQ4cOcHV1NWfLRERETZogCIhNysPiXZmo0FXfSdffzQExkSEI8f/rdXfJyC4AgE3H7n6HI6qHn7i9vjx0gCkpKcG5c+fE78+fP4/U1FS4ubnB398f7u7uJuttbW3h7e2NDh3+Oq3UqVMnDBo0CJMmTcL69euh0+kQHR2NyMhI8ZLrcePGYfHixZg4cSLmzp2LjIwMrF69GqtWrXqcfSUiIqLbFJZWYd6PafjPKdN3PV7s1gIfDA+Cg9w0JiwZ2QXvDQ3C+vg/cbGwFC3dHPF6v9b1eubllocOMMePH8czzzwjfj9r1iwAwIQJE7Bx48ZaPcemTZsQHR2NAQMGQCqVYtSoUYiJiRG3q1Qq/Oc//8HUqVPRrVs3eHh4YNGiRbyEmoiIyEx+z76GWVtP4tptnyCtsrfFxyM7Y0jw3fdwu0Uul2FaeLv6aPGBJIIg1OdHF9QbrVYLlUoFjUbDeRgiIqL/qdQbsGJvFv51+LxJvWeAG1ZHhsBbZW+hzv5S29dvs87AEBERUcOVnV+MNzen4Iy6WKzZyiSYFd4e/3i6DaT1eSe6x8QAQ0RE1MgJgoDvjl7ER7+cRqW+elA3wMMRa8aGonNzlQW7ezQMMERERI3Y9ZJKzNl2EgezrpnUx/bww3vDgmBnW/8DuObAAENERNRI/ZZVgNnbTuJGSZVYc3WwxfJRXTAw6N73VbMWDDBERESNTIXOgGV7zmDjkQsm9T5t3fHZmFA0c1ZYpjEzYoAhIiJqRM6otZi2OQVn80vEmlwmxduDOmBi34B6/6ifusIAQ0RE1AgYjQI2HrmAZXvOoMpQPajb1tMJn48NRUefxnVLEQYYIiIiK1egrcDsbSfxe/Z1k/r4Xi2xYEgnqx3UfRAGGCIiIisWdyofb/9wEjfLdGLN3VGOT17qgmc7elmws7rFAENERGSFyqsM+OiXU9iUmGtS79++Gf45uivcnax/UPdBGGCIiIisTMZlDabFpuDPa6ViTWEjxTuDO+KVsFaNZlD3QRhgiIiIrITRKODrw39ixd4s6I3VH2XYwdsZn48NRTsvZwt2V78YYIiIiKyAWlOBWVtTcSTnhliTAHitbwDmDuoIuY3Ucs1ZAAMMERFRA7c34yrm/pgOTXn1oG4zZwX++VJX9GvfzIKdWQ4DDBERUQNVWqnHB7tPYUtSnkn9b508seLFrnB1lFuoM8tjgCEiImqATuYVYXpsCi7cKBNr9rYyLBzaCWN7+DeJQd0HYYAhIiJqQAxGAesP5WBV3FmTQd1AHyU+HxeK1s2cLNhdw8EAQ0RE1EBcLirHrC2pSDxfKNYkACb3a423IjrAVta0BnUfhAGGiIioAdh18gre2ZGO4gq9WPNSKrB6TCh6tXG3YGcNEwMMERGRBRVX6PDeT5nYnnLZpD6oszeWj+wClYOthTpr2BhgiIiILORE7k1M35yCvJvlYs1BLsPi54PwYrcWTX5Q90EYYIiIiOqZ3mDE2oM5iNmfDYNQPajbtYUKa8Y+AX93Bwt2Zx0YYIiIiOpRXmEZpsem4kTuTbEmlQBv9G+DGeHtYcNB3VphgCEiIqonO1Mu492dGSiprB7U9VXZYXVkCJ4MsI5BXaNRQOYVLQrLquDmIEeQrxJSaf2/1cUAQ0REVMe0FTos3JGBn05eMak/39UXH73QGUo76xjUPXLuOtYdykFOQQl0BgG2MgnaeDphytNt0LutR732wgBDRERUh5IuFGJGbCouF1UP6jopbPDhiM54IbS5BTt7OEfOXcc7O9JRUqmHq4MccpkUVQYjTl8txjs70vHxC8H1GmIYYIiIiOqAzmDEmv3Z+PzgOdx2Q12E+rtgzdhQtHC1nkFdo1HAukM5KKnUw1tpJ14dZSeVwVsphVpbiXWHctCrtXu9vZ3EAENERGRmF2+UYnpsKlLzisSaTCLBtAFtEf1sO8gsMDPyODKvaJFTUAJXB/ldl3ZLJBK4ONgip6AEmVe0CG6hqpeeGGCIiIjMRBAE/JB8Ce/9nImyKoNY93O1x+rIUDzR0tWC3T26wrIq6AwC5Pe5Qkohk0JjFFBYVlVvPTHAEBERmYGmTIf5O9Lwa7rapD7yieb4YHhnOCms9yXXzUEOW5kEVQYj7KSyu7ZXGoywlUrg5iCvt56s92gSERE1EAk5NzBzSyrU2gqx5mxng6UvBGNoV18LdmYeQb5KtPF0wumrxfBWSk3eRhIEAUVlOnTycUaQr7LeeuLdcoiIiB5Rld6IZXvOYNxXR03Cy5OtXLFvRr9GEV4AQCqVYMrTbeCkkEGtrUS5zgCjUUC5zgC1thJOChmmPN2mXu8HwzMwREREjyDnWgmmx6Yg47JWrMmkEsz+W3v84+k2VjeoW5PebT3w8QvB4n1gNEYBtlIJOvk48z4wREREDZ0gCIhNysMHu06hXFc9qNvS3QGfj32i3q7CsYTebT3Qq7U778RLRERkTW6WVmHe9jTsy8w3qY950g/vDQuEg7zxv6xKpZIGEdIa/5EmIiIyg8PZ1zFrayoKiivFmsreFite7IKIIG8LdtY0McAQERE9QKXegE/3ZeGr38+b1Hu3cceqMSHwUtpZqLOmjQGGiIjoPrLzizEtNgWnrxaLNVuZBG9HdMTEvgEWmf2gvzz0ZdTx8fEYNmwYfH19IZFIsHPnTnGbTqfD3LlzERwcDEdHR/j6+uKVV17BlSumn75ZWFiIqKgoKJVKuLi4YOLEiSgpKTFZk5aWhqeeegp2dnbw8/PDihUrHm0PiYiIHpIgCPh3wgUMXXPYJLy0aeaIn6b2xaR+rRleLOyhA0xpaSm6du2KtWvX3rWtrKwMJ06cwMKFC3HixAls374dWVlZeP75503WRUVFITMzE3Fxcdi9ezfi4+MxefJkcbtWq8XAgQPRsmVLJCcn45NPPsH777+PDRs2PMIuEhER1d71kkpM/PY4Fv6UiUq9UaxH9fTHL9OeQmA93qyN7k8iCIJQ87L7PFgiwY4dOzBixIj7rklKSkKPHj1w8eJF+Pv74/Tp0wgMDERSUhK6d+8OANi7dy8GDx6MS5cuwdfXF+vWrcOCBQugVqshl/91W+J58+Zh586dOHPmTK1602q1UKlU0Gg0UCr5y0ZERDU7mFWAt7aexI3S6s/0cXOU45MXu2BAJy8LdtZ01Pb1u87vxKvRaP76pEoXFwBAQkICXFxcxPACAOHh4ZBKpUhMTBTX9OvXTwwvABAREYGsrCzcvHmzrlsmIqImpkJnwPs/Z+Lv3ySZhJd+7Tywb0Y/hpcGqE6HeCsqKjB37lyMHTtWTFFqtRqenp6mTdjYwM3NDWq1WlwTEBBgssbLy0vc5up696d5VlZWorKy+tI2rVZ71xoiIqI7nVFrMW1zCs7mV89iymVSzH+uI17t08rkc3+o4aizAKPT6TB69GgIgoB169bV1Y8RLV26FIsXL67zn0NERI2DIAjYeOQClu45g6rbZl3aeTlh7bgn0N7L2YLdUU3q5C2kW+Hl4sWLiIuLM3kPy9vbGwUFBSbr9Xo9CgsL4e3tLa7Jzze9y+Gt72+tudP8+fOh0WjEr7y8PHPuEhERNSIFxRV49ZskLN51yiS8/L1PK+yK7svwYgXMfgbmVnjJzs7GwYMH4e7ubrI9LCwMRUVFSE5ORrdu3QAABw4cgNFoRM+ePcU1CxYsgE6ng62tLQAgLi4OHTp0uOfbRwCgUCigUCjMvTtERNTI/PdUPub8cBI3y3RizcNJjn+ODsHT7ZtZsDN6GA99BqakpASpqalITU0FAJw/fx6pqanIzc2FTqfDiy++iOPHj2PTpk0wGAxQq9VQq9WoqvprKKpTp04YNGgQJk2ahGPHjuGPP/5AdHQ0IiMj4ev718eOjxs3DnK5HBMnTkRmZia2bNmC1atXY9asWebbcyIialLKqwx4d2c6/t//d9wkvDzb0RP7ZvRjeLEyD30Z9W+//YZnnnnmrvqECRPw/vvv3zV8e8vBgwfRv39/AH/dyC46Ohq7du2CVCrFqFGjEBMTAycnJ3F9Wloapk6diqSkJHh4eODNN9/E3Llza90nL6MmIqJbMi5rMD02BTnXSsWawkaKRUMDMa6nPwd1G5Davn4/1n1gGjIGGCIiMhoFfH34T6zYlwW9ofrlrpOPM9aMfQJtPZ0e8GiyhNq+fvOzkIiIqFFSayowe2sq/si5IdYkACb1a423BnaA3KbOb4VGdYgBhoiIGp29GWrM+zENReXVsy6ezgp8NiYEvdt6WLAzMhcGGCIiajRKK/X4cPcpxCaZ3kojIsgLy0Z2gauj/D6PJGvDAENERI1C2qUiTNucggs3ysSana0U7w8Lwpgn/Tio28gwwBARkVUzGAWsP5SDVXFnoTdWD+oGN1chZmwoAjwcLdgd1RUGGCIislpXisoxc0sqEs8XijUJgNf7t8Gsv7WHrYyDuo0VAwwREVml3WlX8M72dGgr9GLNW2mHzyJD0Ku1+wMeSY0BAwwREVmVkko93vspEz+euGRSHxzsg6Ujg6Gyt7VQZ1SfGGCIiMhqnMi9iemxKcgrLBdrjnIZPhjeGSOfaM5B3SaEAYaIiBo8vcGItQdzEHMgG4bbBnVD/FwQExkKf3cHC3ZHlsAAQ0REDVpeYRlmbknF8Ys3xZpUAkQ/0xbTBrSDDQd1myQGGCIiarB2plzGuzszUFJZPajb3MUeMWND0K2lmwU7I0tjgCEiogZHW6HDwp0Z+Cn1ikl9eIgvPhrRGc52HNRt6hhgiIioQUm6UIgZsam4XFQ9qOuksMGSFzpjeEhzC3ZGDQkDDBERNQg6gxFr9mfj84PncNucLrq3dMVnkSFo4cpBXarGAENERBZ34XopZmxJRWpekViTSSSY8bd2eKN/W8ikvDyaTDHAEBGRxQiCgB+SL+G9nzNRVmUQ635u9oiJDEWov6sFu6OGjAGGiIgsQlOmwzs70vFL+lWT+ovdWmDx80FwVPAliu6Pvx1ERFTvEnJuYObWVKg1FWJNaWeDZaO6YHCwjwU7I2vBAENERPWmSm/Eyriz+DI+B8Jtg7q9AtywKjIEPip7yzVHVoUBhoiI6kXOtRJMj01BxmWtWLORSvBWRAdMfqo1pBzUpYfAAENERHVKEATEJuXhg12nUK6rHtQN8HBETGQogluoLNgdWSsGGCIiqjM3S6swb3sa9mXmm9Qjn/TDomGBcJDzZYgeDX9ziIioThzOvo7ZW1ORX1wp1lzsbbHixS4YGORtwc6oMWCAISIis6rUG/Dpvix89ft5k3qftu5YOToEXko7C3VGjQkDDBERmU12fjGmx6bi1NXqQV1bmQRzB3XEa30COKhLZsMAQ0REj00QBHx39CI++uU0KvVGsd6mmSNixoYiyJeDumReDDBERPRYrpdU4u0f0nDgTIFJfXyvlnhncCfYy2UW6owaMwYYIiJ6ZAezCvDW1pO4UVol1twc5fjkxS4Y0MnLgp1RY8cAQ0RED61CZ8CyPWew8cgFk/rT7Zvh05e6opmzwjKNUZPBAENERA/ljFqLaZtTcDa/RKzJZVK8M7gjJvRuBYmEg7pU9xhgiIioVoxGARuPXMCyvWdQddugbnsvJ6wZ+wQ6eDtbsDtqahhgiIioRgXFFXhrWxriz14zqf+9dyvMfa4j7Gw5qEv1iwGGiIge6L+n8vH2j2kovG1Q18NJjn+ODsHT7ZtZsDNqyhhgiIjonsqrDFjy6yl8dzTXpD6goydWvNgF7k4c1CXLYYAhIqK7ZFzWYHpsCnKulYo1OxspFgwNxMs9/TmoSxbHAENERCKjUcDXh//EJ/uyoDMIYr2TjzPWjA1FW08O6lLDwABDREQAALWmArO3peKPczdM6pP7tcbsge2hsOGgLjUcDDBERIS9GWrM+zENReU6sebprMCqMSHo09bDgp0R3Zv0YR8QHx+PYcOGwdfXFxKJBDt37jTZLggCFi1aBB8fH9jb2yM8PBzZ2dkmawoLCxEVFQWlUgkXFxdMnDgRJSUlJmvS0tLw1FNPwc7ODn5+flixYsXD7x0RET1QaaUe835Mw+vfJZuEl4ggL+yb0Y/hhRqshw4wpaWl6Nq1K9auXXvP7StWrEBMTAzWr1+PxMREODo6IiIiAhUVFeKaqKgoZGZmIi4uDrt370Z8fDwmT54sbtdqtRg4cCBatmyJ5ORkfPLJJ3j//fexYcOGR9hFIiK6l5N5RRgS8ztik/LEmr2tDCtGdcH6l7vB1VFuwe6IHkwiCIJQ87L7PFgiwY4dOzBixAgAf5198fX1xezZs/HWW28BADQaDby8vLBx40ZERkbi9OnTCAwMRFJSErp37w4A2Lt3LwYPHoxLly7B19cX69atw4IFC6BWqyGX//UPaN68edi5cyfOnDlTq960Wi1UKhU0Gg2USuWj7iIRUaNjMApYfygHq+LOQm+sfgno3FyFNWNDEeDhaMHuqKmr7ev3Q5+BeZDz589DrVYjPDxcrKlUKvTs2RMJCQkAgISEBLi4uIjhBQDCw8MhlUqRmJgorunXr58YXgAgIiICWVlZuHnz5j1/dmVlJbRarckXERGZulJUjnFfHcUn+7LE8CIB8Eb/Ntg+pTfDC1kNswYYtVoNAPDyMv0IdS8vL3GbWq2Gp6enyXYbGxu4ubmZrLnXc9z+M+60dOlSqFQq8cvPz+/xd4iIqBHZnXYFgz6LR+L5QrHmo7LD5sm98PagjpDbmPUlgahONZrf1vnz50Oj0YhfeXl5NT+IiKgJKKnUY/bWk4j+PgXaCr1YHxrsg73T+6FXa3cLdkf0aMx6GbW3tzcAID8/Hz4+PmI9Pz8fISEh4pqCggKTx+n1ehQWFoqP9/b2Rn5+vsmaW9/fWnMnhUIBhYK3tSYiut2J3JuYEZuK3MIyseaokOGD5ztj5BPNeUddslpmPQMTEBAAb29v7N+/X6xptVokJiYiLCwMABAWFoaioiIkJyeLaw4cOACj0YiePXuKa+Lj46HTVV/SFxcXhw4dOsDV1dWcLRMRNUp6gxGr/5uNl9YnmISXUH8X7JnWD6O6tWB4Iav20AGmpKQEqampSE1NBfDX4G5qaipyc3MhkUgwY8YMfPTRR/j555+Rnp6OV155Bb6+vuKVSp06dcKgQYMwadIkHDt2DH/88Qeio6MRGRkJX19fAMC4ceMgl8sxceJEZGZmYsuWLVi9ejVmzZplth0nImqs8grLELnhKFb99ywM/xvUlUqA6QPaYds/wuDv7mDhDoke30NfRv3bb7/hmWeeuas+YcIEbNy4EYIg4L333sOGDRtQVFSEvn374osvvkD79u3FtYWFhYiOjsauXbsglUoxatQoxMTEwMnJSVyTlpaGqVOnIikpCR4eHnjzzTcxd+7cWvfJy6iJqCnamXIZ7+7MQEll9axLcxd7xIwNQbeWbhbsjKh2avv6/Vj3gWnIGGCIqCnRVuiwcGcGfkq9YlJ/IbQ5Fg8PgtLO1kKdET2c2r5+87OQiIisXNKFQsyITcXlonKx5qSwwZIXOmN4SHMLdkZUdxhgiIislM5gxJr92fj84DncdkNddG/pilVjQuDnxlkXarwYYIiIrNDFG6WYHpuK1LwisSaTSDDzb+0wpX9byKS8wogaNwYYIiIrIggCfki+hPd/zkRplUGs+7s5YHVkCEL9easJahoYYIiIrISmTId3dqTjl/SrJvXR3Vtg0bAgOCn4J52aDv62ExFZgYScG5i5NRVqTYVYU9nbYunIYAwO9nnAI4kaJwYYIqIGrEpvxMq4s/gyPge33/SiV2t3rBrTFT4qe8s1R2RBDDBERA1UzrUSzIhNQfplrVizkUrwVkQHTHqqNQd1qUljgCEiamAEQUBsUh4+2HUK5brqQd0AD0fERIYiuIXKgt0RNQwMMEREDUhhaRXm/ZiG/5zKN6mP6+GHd4cGwkHOP9tEAAMMEVGD8Xv2NczeehIFxZVizcXBFstHdUFEkLcFOyNqeBhgiIgsrFJvwCd7s/D14fMm9T5t3bFydAi8lHYW6oyo4WKAISKyoOz8YkyLTcHpq8VizVYmwdxBHfFanwBIOahLdE8MMEREFiAIAr47ehEf/XIalXqjWG/r6YSYyFAE+t7/U3iJiAGGiKjeXS+pxNs/pOHAmQKT+ithLfHO4E6ws5VZqDMi68EAQ0RUj37LKsBb207iekmVWHN3lOOTl7rg2Y5eFuyMyLowwBAR1YMKnQHL9pzBxiMXTOpPt2+GT1/qimbOCss0RmSlGGCIiOrYGbUW0zan4Gx+iViT20ixYHAnvBLWEhIJB3WJHhYDDBFRHTEaBWw8cgHL9p5B1W2Duh28nBEzNhQdvJ0t2B2RdWOAISKqAwXaCrz1Qxriz14zqb/WJwBvD+rAQV2ix8QAQ0RkZnGn8vH2Dydxs0wn1po5KfDp6K54un0zC3ZG1HgwwBARmUl5lQEf/XIKmxJzTeoDOnlixagucHfioC6RuTDAEBGZQcZlDabHpiDnWqlYs7OVYuHQQIzr4c9BXSIzY4AhInoMRqOArw//iU/2ZUFnEMR6kK8SqyND0dbTyYLdETVeDDBERI9IranA7K2p+CPnhliTAJjUrzVmD2wPhQ0HdYnqCgMMEdEj2Juhxrwf01BUXj2o6+WswKoxIejd1sOCnRE1DQwwREQPobRSjw93n0JsUp5JfVCQN5aODIaro9xCnRE1LQwwRES1dDKvCNNjU3DhRplYs7eV4f3nAzG6ux8HdYnqEQMMEVENDEYBX8bnYOV/zkJvrB7UDW6uQszYUAR4OFqwO6KmiQGGiOgBLheVY+aWVBw7XyjWJBLgjf5tMCO8PWxlUgt2R9R0McAQEd3HrpNX8M6OdBRX6MWaj8oOq8aEoFdrdwt2RkQMMEREdyip1GPRTxnYfuKySX1oFx8sGREMlYOthTojolsYYIiIbnMi9yZmxKYit7B6UNdRIcOHwzvjhdDmHNQlaiAYYIiIAOgNRqw9mIOYA9kw3Dao+4S/Cz4bEwp/dwcLdkdEd2KAIaImL6+wDDO3pOL4xZtiTSoBop9th2nPtoUNB3WJGhwGGCJq0namXMa7OzNQUlk9qNvC1R6rI0PQraWbBTsjogdhgCGiJklbocPCnRn4KfWKSX1kaHO8PzwISjsO6hI1ZAwwRNTkJF0oxIzYVFwuKhdrTgobLHmhM4aHNLdgZ0RUWwwwRNRk6AxGrNmfjc8PnsNtc7p4spUrVo0JQQtXDuoSWQuzT6YZDAYsXLgQAQEBsLe3R5s2bfDhhx9CEKr/WgiCgEWLFsHHxwf29vYIDw9Hdna2yfMUFhYiKioKSqUSLi4umDhxIkpKSszdLhE1ERdvlOKl9QmIOVAdXmRSCd4a2B6xk8MYXoisjNkDzPLly7Fu3Tp8/vnnOH36NJYvX44VK1ZgzZo14poVK1YgJiYG69evR2JiIhwdHREREYGKigpxTVRUFDIzMxEXF4fdu3cjPj4ekydPNne7RNTICYKAbcfz8Nzq35GaVyTW/d0c8OOU3oh+th1kUt7bhcjaSITbT42YwdChQ+Hl5YV//etfYm3UqFGwt7fHd999B0EQ4Ovri9mzZ+Ott94CAGg0Gnh5eWHjxo2IjIzE6dOnERgYiKSkJHTv3h0AsHfvXgwePBiXLl2Cr69vjX1otVqoVCpoNBoolUpz7iIRWQlNmQ7v7EjHL+lXTeqju7fAe8OC4Kjgu+hEDU1tX7/Nfgamd+/e2L9/P86ePQsAOHnyJA4fPoznnnsOAHD+/Hmo1WqEh4eLj1GpVOjZsycSEhIAAAkJCXBxcRHDCwCEh4dDKpUiMTHxnj+3srISWq3W5IuImq6EnBuIWB1vEl5U9rb4IuoJrHixK8MLkZUz+7/gefPmQavVomPHjpDJZDAYDFiyZAmioqIAAGq1GgDg5eVl8jgvLy9xm1qthqenp2mjNjZwc3MT19xp6dKlWLx4sbl3h4isTJXeiJVxZ/FlfA5uP78c1todK8d0hY/K3nLNEZHZmD3AbN26FZs2bcL333+PoKAgpKamYsaMGfD19cWECRPM/eNE8+fPx6xZs8TvtVot/Pz86uznEZFlFGrL8fwXCSgsrYSbowI/vxEGN+VfoeTPayWYFpuCjMvVZ2BtpBK8FdEBk55qzVkXokbE7AFmzpw5mDdvHiIjIwEAwcHBuHjxIpYuXYoJEybA29sbAJCfnw8fHx/xcfn5+QgJCQEAeHt7o6CgwOR59Xo9CgsLxcffSaFQQKFQmHt3iKgB6fL+XmgrDOL3ZUXleOLjA3BWSPHOkCB8sOsUynXV2wM8HBETGYrgFipLtEtEdcjsMzBlZWWQSk2fViaTwWg0AgACAgLg7e2N/fv3i9u1Wi0SExMRFhYGAAgLC0NRURGSk5PFNQcOHIDRaETPnj3N3TIRWYE7w8vtiiuNmL893SS8jOvpj1+m9WV4IWqkzH4GZtiwYViyZAn8/f0RFBSElJQUrFy5Eq+99hoAQCKRYMaMGfjoo4/Qrl07BAQEYOHChfD19cWIESMAAJ06dcKgQYMwadIkrF+/HjqdDtHR0YiMjKzVFUhE1LgUasvvG17u5OJgi+WjuiAi6N5na4mocTB7gFmzZg0WLlyIN954AwUFBfD19cU//vEPLFq0SFzz9ttvo7S0FJMnT0ZRURH69u2LvXv3ws7OTlyzadMmREdHY8CAAZBKpRg1ahRiYmLM3S4RWYEhnx+u1Tq5TIJ9M/rBS2lX82Iismpmvw9MQ8H7wBA1Hq3n/QJjLdZJAJxfNqSu2yGiOmSx+8AQEZlbba8d4h80oqaD/96JqMGT1fIvlY2sbvsgooaDAYaIGrSDWQWoqs37RwCYX4iaDt5Lm4gapAqdAcv2nMHGIxdq/Zha5hwiagQYYIiowTmj1mLa5hSczS95qMdV1e5KayJqBBhgiKjBMBoFbDxyAcv2nkGV/uHPp/AMDFHTwRkYImoQCrQVePWbY/hg9ymT8PJanwDIa/mXSsEhGKImg2dgiMji/nsqH3N+OImbZTqx1sxJgU9Hd8XT7ZvhalEJ9mReq/F5nu3YrC7bJKIGhAGGiCymvMqAj345hU2JuSb18E6eWD6qC9yd/vqA1klPta1VgJn0VNs66ZOIGh4GGCKyiIzLGkyPTUHOtVKxZmcrxcKhgRjXwx8SSfXt60L8XeGtVECtrbzv83krFQjxd63Tnomo4eAMDBHVK6NRwIb4HIz44g+T8BLkq8TuN59CVM+WJuEFAKRSCVaODoHTfYZcnBQyrBwdAqm0tvfsJSJrxzMwRFRv1JoKzNqaiiM5N8SaBMDkp1tj9t86QG5z//+m6t3WAxvGd8fnB7KRdkULnd4IWxspuvgqEf1sO/Ru61EPe0BEDQUDDBHVi70Zasz9MQ2a8upBXS+lAqtGh9Q6fPRu64Ferd2ReUWLwrIquDnIEeSr5JkXoiaIAYaI6lRppR4f7j6F2KQ8k/qgIC8sHdkFro7yh3o+qVSC4BYqc7ZIRFaIAYaI6szJvCJMj03BhRtlYs1eLsPiYUF4qXuLu2ZdiIhqiwGGiMzOYBSw/lAOVsWdhd4oiPXg5irEjA1FgIejBbsjosaAAYaIzOpyUTlmxqbi2IVCsSYBMKV/G8z8W3vYynjxIxE9PgYYIjKbXSevYMGOdGgr9GLNR2WHVWNC0Ku1uwU7I6LGhgGGiB5bcYUO7/2Uie0pl03qQ7v4YMmIYKgcbC3UGRE1VgwwRPRYTuTexPTNKci7WS7WHBUyfDi8M14Ibc5BXSKqEwwwRPRI9AYj1h7MQcz+bBiE6kHdUH8XrB4TCn93Bwt2R0SNHQMMET20vMIyTI9NxYncm2JNKgHefLYd3ny2LWw4qEtEdYwBhogeys6Uy3h3ZwZKKqsHdVu42GP12BB0a+lmwc6IqClhgCGiWtFW6LBwRwZ+OnnFpP5CaHMsHh4EpR0HdYmo/jDAEFGNki4UYkZsKi4XVQ/qOtvZ4KMRnTE8pLkFOyOipooBhojuS2cwImZ/NtYePIfbbqiL7i1d8VlkCFq4clCXiCyDAYaI7unC9VJMj03ByUsasSaTSDAjvB3eeKYtZPwEaCKyIAYYIjIhCAJ+SL6E937ORFmVQaz7uzlgdWQIQv1dLdgdEdFfGGCISKQp02H+jjT8mq42qY/u3gKLhgXBScE/GUTUMPCvEREBABJybmDmllSotRViTWVvi49fCMaQLj4W7IyI6G4MMERNXJXeiFX/PYv1v+XgtjldhLV2x8oxXeGjsrdYb0RE98MAQ9SE5VwrwfTNKci4ohVrNlIJ3orogElPteagLhE1WAwwRE2QIAiITcrDB7tOoVxXPagb4OGImMhQBLdQWbA7IqKaMcAQNTE3S6swb3sa9mXmm9TH9fDDu0MD4SDnnwUiavj4l4qoCfk9+xpmbT2Ja8WVYs3FwRYrRnXBwCBvC3ZGRPRwGGCImoBKvQGf7M3C14fPm9T7tvXAP0d3hZfSzkKdERE9GgYYokYuO78Y0zan4LS6WKzZyiSYO6gjXusTACkHdYnICjHAEDVSgiDgu6MX8dEvp1GpN4r1tp5OiIkMRaCv0oLdERE9HmldPOnly5fx8ssvw93dHfb29ggODsbx48fF7YIgYNGiRfDx8YG9vT3Cw8ORnZ1t8hyFhYWIioqCUqmEi4sLJk6ciJKSkrpol6jRuV5Sidc2JmHhT5km4eWVsJbY/WZfhhcisnpmDzA3b95Enz59YGtriz179uDUqVP45z//CVfX6s9PWbFiBWJiYrB+/XokJibC0dERERERqKiovgNoVFQUMjMzERcXh927dyM+Ph6TJ082d7tEjc7BrAJErIrHwaxrYs3NUY7/e7U7PhjeGXa2Mgt2R0RkHhJBEISal9XevHnz8Mcff+D333+/53ZBEODr64vZs2fjrbfeAgBoNBp4eXlh48aNiIyMxOnTpxEYGIikpCR0794dALB3714MHjwYly5dgq+vb419aLVaqFQqaDQaKJX8r01q/Cp0BizbcwYbj1wwqT/dvhk+fakrmjkrLNMYEdFDqO3rt9nPwPz888/o3r07XnrpJXh6eiI0NBRfffWVuP38+fNQq9UIDw8XayqVCj179kRCQgIAICEhAS4uLmJ4AYDw8HBIpVIkJiaau2Uiq3dGrcXznx82CS9ymRSLnw/Cxr8/yfBCRI2O2Yd4//zzT6xbtw6zZs3CO++8g6SkJEybNg1yuRwTJkyAWv3Xp9x6eXmZPM7Ly0vcplar4enpadqojQ3c3NzENXeqrKxEZWX1vS20Wu091xE1JkajgI1HLmDZnjOoMlTPunTwckbM2FB08Ha2YHdERHXH7AHGaDSie/fu+PjjjwEAoaGhyMjIwPr16zFhwgRz/zjR0qVLsXjx4jp7fqKGpqC4Am9tS0P82Wsm9Yl9WmHOoI6cdSGiRs3sbyH5+PggMDDQpNapUyfk5uYCALy9/7rbZ36+6W3M8/PzxW3e3t4oKCgw2a7X61FYWCiuudP8+fOh0WjEr7y8PLPsD1FDFHcqHxGr4k3CSzMnBb59rQcWDgtieCGiRs/sAaZPnz7IysoyqZ09exYtW7YEAAQEBMDb2xv79+8Xt2u1WiQmJiIsLAwAEBYWhqKiIiQnJ4trDhw4AKPRiJ49e97z5yoUCiiVSpMvosamvMqABTvSMen/O46bZTqxHt7JE3tnPIWn2zezYHdERPXH7G8hzZw5E71798bHH3+M0aNH49ixY9iwYQM2bNgAAJBIJJgxYwY++ugjtGvXDgEBAVi4cCF8fX0xYsQIAH+dsRk0aBAmTZqE9evXQ6fTITo6GpGRkbW6AomoMcq4rMG02BT8ea1UrNnZSLFwWCDG9fCHRMI76hJR02H2y6gBYPfu3Zg/fz6ys7MREBCAWbNmYdKkSeJ2QRDw3nvvYcOGDSgqKkLfvn3xxRdfoH379uKawsJCREdHY9euXZBKpRg1ahRiYmLg5ORUqx54GTU1FkajgK8P/4kVe7OgN1b/cw30USJmbCjaetbu3wQRkTWo7et3nQSYhoABhhoDtaYCs7am4kjODbEmATC5X2vMHtgBcps6uZk2EZHF1Pb1m5+FRNRA7c24irk/pkNTXj3r4qVUYOXoEPRp62HBzoiILI8BhqiBKa3UY/GuTGw9fsmkHhHkhWUju8DVUW6hzoiIGg4GGKIG5GReEabFpuDijTKxZm8rw+Lng/BS9xYc1CUi+h8GGKIGwGAUsP7QOayKyzYZ1A1ursLqyBC0bsZBXSKi2zHAEFnY5aJyzIhNQdKFm2JNAuCNZ9pgRnh72Mo4qEtEdCcGGCIL2nXyCt7Zno7iSr1Y81HZ4bMxIejZ2t2CnRERNWwMMEQWUFyhw6KfMrEj5bJJfWiwD5aMDIbK3tZCnRERWQcGGKJ6diL3JqZvTkHezXKx5qiQ4cPhnfFCaHMO6hIR1QIDDFE90RuMWHvwHGL2n4PhtvtHhvq7YPWYUPi7O1iwOyIi68IAQ1QP8grLMC02BSm5RWJNKgGin22Hac+2hQ0HdYmIHgoDDFEd25lyGe/uzEDJbYO6zV3sETM2BN1aulmwMyIi68UAQ1RHNOU6LNyZjp9PXjWpvxDaHIuHB0Fpx0FdIqJHxQBDVAeOnS/EjC0puFJUIdac7Wzw0YjOGB7S3IKdERE1DgwwRGakMxix+r/Z+OK3c7jthrro3tIVq8aEwM+Ng7pERObAAENkJheul2JabArSLmnEmkwiwcy/tcOU/m0hk/LyaCIic2GAIXpMgiBgW/IlvPdTJsp1BrHu7+aAmLGhCPFzsVxzRESNFAMM0WPQlOkwb3sa9mSoTeovdmuBxc8HwVHBf2JERHWBf12JHlFCzg3M2JKCfG2lWFPa22DZyC4YHOxjwc6IiBo/Bhiih1SlN+Kf/8nChvg/cducLnq1dsOqMSHwUdlbrDcioqaCAYboIeRcK8G0zSnIvKIVazZSCWYP7IDJ/VpzUJeIqJ4wwBDVgiAI2HwsFx/sPoUKnVGst3J3wJqxTyC4hcqC3RERNT0MMEQ1KCytwts/pOG/p/NN6mN7+GHh0EA4yPnPiIiovvEvL9ED/J59DbO2nMS1kupBXRd7Wyx/sQsigrwt2BkRUdPGAEN0D5V6A5bvOYP/++OCSb1PW3esHB0CL6WdZRojIiIADDBEd8nOL0b05hRkqYvFmq1MgrmDOuK1PgGQclCXiMjiGGCI/kcQBPw74SKW/HoalfrqQd22zZwQMzYUgb5KC3ZHRES3Y4AhAnC9pBKzt57EobPXTOrje7XEgiGdYGcrs1BnRER0Lwww1OQdzCrA7K0nUVhaJdbcHOX49KUueLajlwU7IyKi+2GAoSarQmfAkl9P498JF03q/do3wz9f6opmzgoLdUZERDVhgKEm6Yxai+jvU3CuoESsyWVSzB/cEa/2bgWJhIO6REQNGQMMNSlGo4Bv/jiP5XuzUGWoHtTt4OWMNeNC0d7L2YLdERFRbTHAUJNRoK3ArK0ncfjcdZP6a31a4e1BHTmoS0RkRRhgqEn4T6Yab/+YhqIynVjzcJJj5egQ9GvfzIKdERHRo2CAoUatvMqAD3ZnYvOxPJP6gI6e+OSlrnBzlFuoMyIiehwMMNRoZVzW4M3NKTh/vVSsKWykWDQ0EON6+nNQl4jIijHAUKNjNArY8Puf+HRfFvRGQax38lFizdhQtPV0smB3RERkDgww1KioNRWYsSUVR/+8IdYkACb1a423BnaA3EZqueaIiMhsGGCo0diTfhXztqdDU149qOvprMBnY0LQu62HBTsjIiJzY4Ahq1daqcd7P2fih+RLJvWBgV5YPqoLXDmoS0TU6NT5+fRly5ZBIpFgxowZYq2iogJTp06Fu7s7nJycMGrUKOTn55s8Ljc3F0OGDIGDgwM8PT0xZ84c6PX6um6XrEzapSI8t/p3k/BibyvD8lHB+HJ8N4YXIqJGqk7PwCQlJeHLL79Ely5dTOozZ87EL7/8gm3btkGlUiE6OhojR47EH3/8AQAwGAwYMmQIvL29ceTIEVy9ehWvvPIKbG1t8fHHH9dly2QlDEYBX/x2Dp/9NxuG2wZ1g5srETP2CQR4OFqwOyIiqmt1dgampKQEUVFR+Oqrr+Dq6irWNRoN/vWvf2HlypV49tln0a1bN3zzzTc4cuQIjh49CgD4z3/+g1OnTuG7775DSEgInnvuOXz44YdYu3Ytqqqq7vcjqYm4XFSOMV8m4J//OSuGFwmAN/q3wfY3+jC8EBE1AXUWYKZOnYohQ4YgPDzcpJ6cnAydTmdS79ixI/z9/ZGQkAAASEhIQHBwMLy8vMQ1ERER0Gq1yMzMvOfPq6yshFarNfmixmfXySsY9Fk8jl+8Kda8lXaIndwLbw/qCFsZrzIiImoK6uQtpNjYWJw4cQJJSUl3bVOr1ZDL5XBxcTGpe3l5Qa1Wi2tuDy+3tt/adi9Lly7F4sWLzdA9NUTFFTq8uzMDP6VeMakPDvbG0pFdoLK3tVBnRERkCWYPMHl5eZg+fTri4uJgZ2dn7qe/r/nz52PWrFni91qtFn5+fvX286nunMi9iWmbU3DpZrlYc5TL8MHwzhj5RHPeUZeIqAkye4BJTk5GQUEBnnjiCbFmMBgQHx+Pzz//HPv27UNVVRWKiopMzsLk5+fD29sbAODt7Y1jx46ZPO+tq5RurbmTQqGAQqEw896QJekNRqw5kI3PD+TAIFQP6nZtocKasU/A393Bgt0REZElmX1gYMCAAUhPT0dqaqr41b17d0RFRYn/39bWFvv37xcfk5WVhdzcXISFhQEAwsLCkJ6ejoKCAnFNXFwclEolAgMDzd0yNUB5hWV46csErN5/TgwvUgkw7dm2+HFKb4YXIqImzuxnYJydndG5c2eTmqOjI9zd3cX6xIkTMWvWLLi5uUGpVOLNN99EWFgYevXqBQAYOHAgAgMDMX78eKxYsQJqtRrvvvsupk6dyrMsTcD25EtY+FMGSqsMYs3XxQ5rxoaiW0s3C3ZGREQNhUXuxLtq1SpIpVKMGjUKlZWViIiIwBdffCFul8lk2L17N6ZMmYKwsDA4OjpiwoQJ+OCDDyzRLtUTTbkOC3akY3faVZP681198dELnaG046AuERH9RSIItw0XNCJarRYqlQoajQZKpdLS7VANEv+8gRlbUnFVUyHWnBQ2WDKiM4aHNrdgZ0REVJ9q+/rNz0Iii9IZjFgVdxbrD+XgthvqoltLV6yODEELV866EBHR3RhgyGIuXC9F9OYTyLhcfdNBmUSC6QPaYuqz7SCT8vJoIiK6NwYYqneCIGDr8UtYvCsTZbcN6vq52mPNuCcQ4udiueaIiMgqMMBQvSoqq8K8H9OxN9P0jsqjnmiOD4Z3hqOCv5JERFQzvlpQvUnIuY4ZsanIL64Ua0o7G3z8QjCGdvW1YGdERGRtGGCozlXpjfhk3xl8/ft53H7J25OtXLE6MhS+LvYW642IiKwTAwzVqZxrJYj+/gROXy0WazZSCWb9rT3+8XQbDuoSEdEjYYChOiEIAjYl5uKjX06hQmcU6y3dHfD52CcQ3EJlwe6IiMjaMcCQ2RWWVmHOtpPYf6bApD66ewu8/3wQHOT8tSMiosfDVxIyq0NZBZi97SSul1SJNZW9LVaM6oKIzvf+JHEiIqKHxQBDZlGpN2Dpr2ew8cgFk3pYG3d8NiYEXko7yzRGRESNEgMMPbbs/GJM/f4EzuaXiDVbmQRvDeyASU+1hpSDukREZGYMMPTIBEHAt0cu4OM9Z1Clrx7Ube3hiDXjQhHky0FdIiKqGwww9Eiul1Ri1pZUxGdfN6lH9fTHwqGBsLOVWagzIiJqChhg6KHtP52POdtOorBMJ9ZcHWzxyYtdER7oZcHOiIioqWCAoVqr0Bnw0e5T+C4x16Tet60HVo0JQTNnhYU6IyKipoYBhmrl9FUNor9PQc61UrEml0kx97mOeK1PK0gkHNQlIqL6wwBDD2Q0CvjX4fP4ZF8WqgzVg7ptPZ3w+dhQdPRRWrA7IiJqqhhg6L4KtBWYsSUVR3JumNRfCWuJdwZ34qAuERFZDAMM3dPejKuY92M6isqrB3XdHeX49KWueKajpwU7IyIiYoChO5RXGfD+z5nYcjzPpN6/QzP886WucHfioC4REVkeAwyJMi4XIfr7FFy4USbWFDZSvDO4E14Ja8lBXSIiajAYYAhGo4B1h3KwKu4s9EZBrHfwdsbasaFo6+Vswe6IiIjuxgDTxKk1FZgem4LE84ViTQLgtb6tMHdQJ8htpJZrjoiI6D4YYJqw3WlX8M72dGgr9GKtmZMCK8d0xVPtmlmwMyIiogdjgGmCSiv1WPRTBn48cdmkHt7JE5+82BWujnILdUZERFQ7DDBNTEruTUyLTUFeYblYs7eV4d2hnTCuhz8HdYmIyCowwDQRBqOAzw9kI+bAORhuG9QN9FXi87GhaN3MyYLdERERPRwGmCbg0s0yTNucghO5RWJNAmByv9Z4K6IDbGUc1CUiIuvCANPI7Uy5jHd3ZqCksnpQ10upwKrRIejd1sOCnRERET06BpiHYDQKyLyiRWFZFdwc5AjyVUIqbZgzIyWVeryzPR0/n7xiUo8I8saKF7tAZW9roc6IiIgeHwNMLR05dx3rDuUgp6AEOoMAW5kEbTydMOXpNg3uTMbxC4WYHpuKy0XVg7oOchneGxaI0d39OKhLRERWjwGmFo6cu453dqSjpFIPVwc55DIpqgxGnL5ajHd2pOPjF4IbRIjRG4z47L9nse63HBiq53QR3FyFz8eFoqW7o+WaIyIiMiMGmBrcus1+SaUe3ko78eyFnVQGb6UUam0l1h3KQa/W7hZ9OymvsBTR36fg5CWNWJNKgNefboNZf2sPGw7qEhFRI8IAU4PMK1rkFJTA1UF+11svEokELg62yCkoQeYVLYJbqCzS47bjeXj/50yUVhnEmo/KDqvHhKBHa3eL9ERERFSXGGBqUFhWBZ1BgPw+ZzAUMik0RgGFZVX13BmgrdBh3o9p+DVdbVIf2sUHS0cGw9mOg7pERNQ4McDUwM1BDluZBFUGI+yksru2VxqMsJVK4OZQv7ffT8i5jplbTkKtrRBrTgobvP98IF7s5levvRAREdU3BpgaBPkq0cbTCaevFsNbKTV5G0kQBBSV6dDJxxlBvsp66UdnMOLTfVn46vc/cdsNdRHi54I1kaHwc3eolz6IiIgsyeyTnUuXLsWTTz4JZ2dneHp6YsSIEcjKyjJZU1FRgalTp8Ld3R1OTk4YNWoU8vPzTdbk5uZiyJAhcHBwgKenJ+bMmQO9Xo/6JpVKMOXpNnBSyKDWVqJcZ4DRKKBcZ4BaWwknhQxTnm5TLwO856+V4IW1f+DL+OrwIpUA0wa0w49TejO8EBFRk2H2AHPo0CFMnToVR48eRVxcHHQ6HQYOHIjS0lJxzcyZM7Fr1y5s27YNhw4dwpUrVzBy5Ehxu8FgwJAhQ1BVVYUjR47g22+/xcaNG7Fo0SJzt1srvdt64OMXgtHJxxlllXoUlFSirFKPTj7O9XIJtSAI+D7xIgbHHEbGFa1Yb+5ij23/CMOsv7WHrIHeUI+IiKguSARBEGpe9uiuXbsGT09PHDp0CP369YNGo0GzZs3w/fff48UXXwQAnDlzBp06dUJCQgJ69eqFPXv2YOjQobhy5Qq8vLwAAOvXr8fcuXNx7do1yOU1z5totVqoVCpoNBooleZ5e8cSd+ItKqvCnG1piDtteoZqeIgvPn4hGI4KvgtIRESNR21fv+v85iAazV/3JXFzcwMAJCcnQ6fTITw8XFzTsWNH+Pv7IyEhAQCQkJCA4OBgMbwAQEREBLRaLTIzM+u65fuSSiUIbqHC0+2bIbiFqs7Dy+9nr2HgqniT8OJsZ4PPxoRgdWQowwsRETVZdfoKaDQaMWPGDPTp0wedO3cGAKjVasjlcri4uJis9fLyglqtFtfcHl5ubb+17V4qKytRWVkpfq/Vau+57nHU1xmYKr0Ry/acwTd/nMftp8e6tXRFTGQImrty1oWIiJq2Og0wU6dORUZGBg4fPlyXPwbAX8PDixcvrrPnP3LuOr747RzOqIuh0wuwtZGgo7cz3ujf1qwzMNn5xXhzcwrOqIvFmo1UgmkD2mHqM20560JERIQ6fAspOjoau3fvxsGDB9GiRQux7u3tjaqqKhQVFZmsz8/Ph7e3t7jmzquSbn1/a82d5s+fD41GI37l5eWZbV+OnLuOmVtTkXi+EEVlOpRW6VFUpkPi+ULM3JqKI+euP/bPEAQB3x45j2GfHzYJL/5uDvjh9TBMG9CO4YWIiOh/zB5gBEFAdHQ0duzYgQMHDiAgIMBke7du3WBra4v9+/eLtaysLOTm5iIsLAwAEBYWhvT0dBQUFIhr4uLioFQqERgYeM+fq1AooFQqTb7MwWgUsHTPaVwrroRREAAIEP73v0ZBwLXiSizdcxpG46PPQheWVOK1jUl47+dTqNAZxfqL3Zpj74ynEOLv+vg7QkRE1IiY/S2kqVOn4vvvv8dPP/0EZ2dncWZFpVLB3t4eKpUKEydOxKxZs+Dm5galUok333wTYWFh6NWrFwBg4MCBCAwMxPjx47FixQqo1Wq8++67mDp1KhQKhblbfqD0yxpkqUsgCDC5cdyt4RQJgCx1CdIva9DVz+Whn//AmXzM2ZaGG6XVH0WgsrfFkhGdMbSr72P1TkRE1FiZPcCsW7cOANC/f3+T+jfffINXX30VALBq1SpIpVKMGjUKlZWViIiIwBdffCGulclk2L17N6ZMmYKwsDA4OjpiwoQJ+OCDD8zdbo1S8oqgMxhxv/MrAv66O25KXtFDBZhKvQEf7T6Ffx/NNan3DHDDZ2NC4ONi/8g9ExERNXZmDzC1ua2MnZ0d1q5di7Vr1953TcuWLfHrr7+as7VHYjTeP7zcIvxvXW2dvqrBtM2pyC4oEWu2MglmhrfH6/V0V18iIiJrxhuJ1EBTpjPbOkEQ8K/D57FiXxaq9NWBJ8DDETGRIQhu4fKobRIRETUpDDA1OJNfu/vJ1LSuQFuBWVtP4vAdVyyN6e6HxcODYGd79yddExER0b0xwNSg4GZJzYtqWBd3So25P6aj8LZBXVcHWywdGYxBnX0eu0ciIqKmhgGmBulXyx55XUWVHot3n8LmY6b3pOndxh2rRofAS2Vnlh6JiIiaGgaYGuhreXuXO9elX9Jg+pYU/Hmt+lO4bWUSzInogElPtYZEwkFdIiKiR8UAUwMbKaCvxQVGNv+7JaDRKGB9fA5WxZ2FzlCdato0c0TM2FAE+arqqFMiIqKmgwGmBmNDvfHv5Ht/gOSd664WlWPm1lQc/bPQZFtUT38sHBrIQV0iIiIzYYCpwcierWsVYLzclRi0+ndoyqsvp3ZzlGPFi10Q3snrAY8kIiKih8UAU4OuLVwQ4OGA89fvP8zrpJDh0/+cNan1a+eBf77UFc2UHNQlIiIytzr7NOrGQiqVYMmIYLg62N57uwQoqTSI3ytspFg4tBO+fa0HwwsREVEdYYCphd5tPfBG/zZQKu6eYbn9Ax7beznhp+g+mNiXVxkRERHVJb6FVAtHzl1HzP5zKL7tTMvtJADGh7XEgsGdoOCgLhERUZ1jgKmB0Shg1tZUFFfq77vGxd4G7w8L4ocwEhER1RO+hVSD1NybUGsrH7jmZrkeqbk366kjIiIiYoCpwXeJuWZdR0RERI+PAaYG14pr91lItV1HREREj48BpgaXbpabdR0RERE9PgaYGhSW6mpe9BDriIiI6PExwNSg7D6XTj/qOiIiInp8DDA10Ak1r3mYdURERPT4GGCIiIjI6jDAEBERkdVhgKnBPT7+6LHWERER0eNjgKnB38NamXUdERERPT4GmBrMGtjRrOuIiIjo8THA1EAulyGqh98D10T18INczveQiIiI6gs/jboWlozsAgCITcqD4bbLpWUSIPJJP3E7ERER1Q+JIAiN8g4mWq0WKpUKGo0GSqXSLM9ZVWXA+vg/cbGwFC3dHPF6v9Y880JERGRGtX395hmYhyCXyzAtvJ2l2yAiImryOANDREREVocBhoiIiKwOAwwRERFZHQYYIiIisjoMMERERGR1GGCIiIjI6jDAEBERkdVhgCEiIiKrwwBDREREVqfR3on31ickaLVaC3dCREREtXXrdbumTzpqtAGmuLgYAODn9+BPkiYiIqKGp7i4GCqV6r7bG+2HORqNRly5cgXOzs6QSCSWbsfstFot/Pz8kJeXZ7YPq7RmPB7VeCxM8XhU47EwxeNRrSEdC0EQUFxcDF9fX0il9590abRnYKRSKVq0aGHpNuqcUqm0+C9bQ8LjUY3HwhSPRzUeC1M8HtUayrF40JmXWzjES0RERFaHAYaIiIisDgOMlVIoFHjvvfegUCgs3UqDwONRjcfCFI9HNR4LUzwe1azxWDTaIV4iIiJqvHgGhoiIiKwOAwwRERFZHQYYIiIisjoMMA3I0qVL8eSTT8LZ2Rmenp4YMWIEsrKyTNZUVFRg6tSpcHd3h5OTE0aNGoX8/HyTNbm5uRgyZAgcHBzg6emJOXPmQK/X1+eumN2yZcsgkUgwY8YMsdbUjsXly5fx8ssvw93dHfb29ggODsbx48fF7YIgYNGiRfDx8YG9vT3Cw8ORnZ1t8hyFhYWIioqCUqmEi4sLJk6ciJKSkvrelcdiMBiwcOFCBAQEwN7eHm3atMGHH35octvxxnws4uPjMWzYMPj6+kIikWDnzp0m282172lpaXjqqadgZ2cHPz8/rFixoq537ZE86HjodDrMnTsXwcHBcHR0hK+vL1555RVcuXLF5Dkay/Go6Xfjdq+//jokEgk+++wzk7pVHQuBGoyIiAjhm2++ETIyMoTU1FRh8ODBgr+/v1BSUiKuef311wU/Pz9h//79wvHjx4VevXoJvXv3Frfr9Xqhc+fOQnh4uJCSkiL8+uuvgoeHhzB//nxL7JJZHDt2TGjVqpXQpUsXYfr06WK9KR2LwsJCoWXLlsKrr74qJCYmCn/++aewb98+4dy5c+KaZcuWCSqVSti5c6dw8uRJ4fnnnxcCAgKE8vJycc2gQYOErl27CkePHhV+//13oW3btsLYsWMtsUuPbMmSJYK7u7uwe/du4fz588K2bdsEJycnYfXq1eKaxnwsfv31V2HBggXC9u3bBQDCjh07TLabY981Go3g5eUlREVFCRkZGcLmzZsFe3t74csvv6yv3ay1Bx2PoqIiITw8XNiyZYtw5swZISEhQejRo4fQrVs3k+doLMejpt+NW7Zv3y507dpV8PX1FVatWmWyzZqOBQNMA1ZQUCAAEA4dOiQIwl//GG1tbYVt27aJa06fPi0AEBISEgRB+OsXWCqVCmq1Wlyzbt06QalUCpWVlfW7A2ZQXFwstGvXToiLixOefvppMcA0tWMxd+5coW/fvvfdbjQaBW9vb+GTTz4Ra0VFRYJCoRA2b94sCIIgnDp1SgAgJCUliWv27NkjSCQS4fLly3XXvJkNGTJEeO2110xqI0eOFKKiogRBaFrH4s4XKXPt+xdffCG4urqa/DuZO3eu0KFDhzreo8fzoBftW44dOyYAEC5evCgIQuM9Hvc7FpcuXRKaN28uZGRkCC1btjQJMNZ2LPgWUgOm0WgAAG5ubgCA5ORk6HQ6hIeHi2s6duwIf39/JCQkAAASEhIQHBwMLy8vcU1ERAS0Wi0yMzPrsXvzmDp1KoYMGWKyz0DTOxY///wzunfvjpdeegmenp4IDQ3FV199JW4/f/481Gq1yfFQqVTo2bOnyfFwcXFB9+7dxTXh4eGQSqVITEysv515TL1798b+/ftx9uxZAMDJkydx+PBhPPfccwCa1rG4k7n2PSEhAf369YNcLhfXREREICsrCzdv3qynvakbGo0GEokELi4uAJrW8TAajRg/fjzmzJmDoKCgu7Zb27FotJ+FZO2MRiNmzJiBPn36oHPnzgAAtVoNuVwu/sO7xcvLC2q1Wlxz+wv2re23tlmT2NhYnDhxAklJSXdta2rH4s8//8S6deswa9YsvPPOO0hKSsK0adMgl8sxYcIEcX/utb+3Hw9PT0+T7TY2NnBzc7Oq4zFv3jxotVp07NgRMpkMBoMBS5YsQVRUFAA0qWNxJ3Ptu1qtRkBAwF3PcWubq6trnfRf1yoqKjB37lyMHTtW/LyfpnQ8li9fDhsbG0ybNu2e263tWDDANFBTp05FRkYGDh8+bOlWLCIvLw/Tp09HXFwc7OzsLN2OxRmNRnTv3h0ff/wxACA0NBQZGRlYv349JkyYYOHu6tfWrVuxadMmfP/99wgKCkJqaipmzJgBX1/fJncsqPZ0Oh1Gjx4NQRCwbt06S7dT75KTk7F69WqcOHECEonE0u2YBd9CaoCio6Oxe/duHDx40OQTtb29vVFVVYWioiKT9fn5+fD29hbX3Hklzq3vb62xBsnJySgoKMATTzwBGxsb2NjY4NChQ4iJiYGNjQ28vLyazLEAAB8fHwQGBprUOnXqhNzcXADV+3Ov/b39eBQUFJhs1+v1KCwstKrjMWfOHMybNw+RkZEIDg7G+PHjMXPmTCxduhRA0zoWdzLXvjemfztAdXi5ePEi4uLiTD5tuakcj99//x0FBQXw9/cX/6ZevHgRs2fPRqtWrQBY37FggGlABEFAdHQ0duzYgQMHDtx1mq5bt26wtbXF/v37xVpWVhZyc3MRFhYGAAgLC0N6errJL+Gtf7B3vgA2ZAMGDEB6ejpSU1PFr+7duyMqKkr8/03lWABAnz597rqk/uzZs2jZsiUAICAgAN7e3ibHQ6vVIjEx0eR4FBUVITk5WVxz4MABGI1G9OzZsx72wjzKysoglZr+6ZLJZDAajQCa1rG4k7n2PSwsDPHx8dDpdOKauLg4dOjQwWreLrnlVnjJzs7Gf//7X7i7u5tsbyrHY/z48UhLSzP5m+rr64s5c+Zg3759AKzwWNT72DDd15QpUwSVSiX89ttvwtWrV8WvsrIycc3rr78u+Pv7CwcOHBCOHz8uhIWFCWFhYeL2W5cODxw4UEhNTRX27t0rNGvWzCovHb7T7VchCULTOhbHjh0TbGxshCVLlgjZ2dnCpk2bBAcHB+G7774T1yxbtkxwcXERfvrpJyEtLU0YPnz4PS+fDQ0NFRITE4XDhw8L7dq1s4pLh283YcIEoXnz5uJl1Nu3bxc8PDyEt99+W1zTmI9FcXGxkJKSIqSkpAgAhJUrVwopKSniVTXm2PeioiLBy8tLGD9+vJCRkSHExsYKDg4ODe6yYUF48PGoqqoSnn/+eaFFixZCamqqyd/V26+iaSzHo6bfjTvdeRWSIFjXsWCAaUAA3PPrm2++EdeUl5cLb7zxhuDq6io4ODgIL7zwgnD16lWT57lw4YLw3HPPCfb29oKHh4cwe/ZsQafT1fPemN+dAaapHYtdu3YJnTt3FhQKhdCxY0dhw4YNJtuNRqOwcOFCwcvLS1AoFMKAAQOErKwskzU3btwQxo4dKzg5OQlKpVL4+9//LhQXF9fnbjw2rVYrTJ8+XfD39xfs7OyE1q1bCwsWLDB5QWrMx+LgwYP3/DsxYcIEQRDMt+8nT54U+vbtKygUCqF58+bCsmXL6msXH8qDjsf58+fv+3f14MGD4nM0luNR0+/Gne4VYKzpWPDTqImIiMjqcAaGiIiIrA4DDBEREVkdBhgiIiKyOgwwREREZHUYYIiIiMjqMMAQERGR1WGAISIiIqvDAENERERWhwGGiBqkjRs3wsXFxdJtEFEDxTvxElGDVF5ejuLiYnh6etb6Mf3790dISAg+++yzumuMiBoEG0s3QER0L/b29rC3t7d0G0TUQPEtJCKqE/3790d0dDSio6OhUqng4eGBhQsX4tZJ35s3b+KVV16Bq6srHBwc8NxzzyE7O1t8/J1vIb3//vsICQnBv//9b7Rq1QoqlQqRkZEoLi4GALz66qs4dOgQVq9eDYlEAolEggsXLjywxw8++AC+vr64ceOGWBsyZAieeeYZGI1G8x0MIjI7BhgiqjPffvstbGxscOzYMaxevRorV67E119/DeCvwHH8+HH8/PPPSEhIgCAIGDx4MHQ63X2fLycnBzt37sTu3buxe/duHDp0CMuWLQMArF69GmFhYZg0aRKuXr2Kq1evws/P74H9LViwAK1atcL/+3//DwCwdu1aHDlyBN9++y2kUv55JGrI+BYSEdUZPz8/rFq1ChKJBB06dEB6ejpWrVqF/v374+eff8Yff/yB3r17AwA2bdoEPz8/7Ny5Ey+99NI9n89oNGLjxo1wdnYGAIwfPx779+/HkiVLoFKpIJfL4eDgAG9v71r1J5PJ8N133yEkJATz5s1DTEwMvv76a/j7+5vnABBRneF/YhBRnenVqxckEon4fVhYGLKzs3Hq1CnY2NigZ8+e4jZ3d3d06NABp0+fvu/ztWrVSgwvAODj44OCgoLH6rF169b49NNPsXz5cjz//PMYN27cYz0fEdUPBhgishq2trYm30skErPMqsTHx0Mmk+HChQvQ6/WP/XxEVPcYYIioziQmJpp8f/ToUbRr1w6BgYHQ6/Um22/cuIGsrCwEBgY+8s+Ty+UwGAwP9ZgtW7Zg+/bt+O2335Cbm4sPP/zwkX8+EdUfBhgiqjO5ubmYNWsWsrKysHnzZqxZswbTp09Hu3btMHz4cEyaNAmHDx/GyZMn8fLLL6N58+YYPnz4I/+8Vq1aITExERcuXMD169drPDtz6dIlTJkyBcuXL0ffvn3xzTff4OOPP8bRo0cfuQciqh8MMERUZ1555RWUl5ejR48emDp1KqZPn47JkycDAL755ht069YNQ4cORVhYGARBwK+//nrX20QP46233oJMJkNgYCCaNWuG3Nzc+64VBAGvvvoqevTogejoaABAREQEpkyZgpdffhklJSWP3AcR1T3eiZeI6gTviktEdYlnYIiIiMjqMMAQUaP1+uuvw8nJ6Z5fr7/+uqXbI6LHwLeQiKjRKigogFarvec2pVL5UB8USUQNCwMMERERWR2+hURERERWhwGGiIiIrA4DDBEREVkdBhgiIiKyOgwwREREZHUYYIiIiMjqMMAQERGR1WGAISIiIqvz/wNBGWzQEmN28QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9544525609675586" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNetCV model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.ElasticNetCV(alphas=(0.1, 1.0, 10.0), l1_ratio=0.5),\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([163.62920734, 144.35081307, 93.70953005, 556.7896188 ,\n", + " 58.44975618, 107.73288393, 104.18291045, 358.45979342,\n", + " 159.75427595, 616.49409602, 640.6188396 , 643.3361415 ,\n", + " 84.77862831, 297.17071678, 616.89348864, 164.34964657,\n", + " 115.54948765, 428.21129208, 550.2961131 , 549.76418449,\n", + " 645.6263344 , 569.605286 , 72.93158039, 651.6663148 ,\n", + " 126.06120853, 158.68051212, 554.8815117 , 552.43400951,\n", + " 339.13121777, 347.45218426, 609.56524314, 559.63960144,\n", + " 153.90761289, 583.14379649, 406.17829791, 708.14334659,\n", + " 647.47993132, 416.86507696, 411.86245919, 121.09142846,\n", + " 150.88625209, 346.26945602, 90.96919913, 660.25860343,\n", + " 600.95243354, 96.83547563, 166.36521787, 423.23666311,\n", + " 683.53222 , 102.42712849, 143.36592422, 657.56154953,\n", + " 625.45758081, 384.88128067, 352.06682834, 165.25977488,\n", + " 552.57831271, 546.76511473, 107.29955997, 164.83878947,\n", + " 141.03976837, 32.736619 , 102.46679039, 614.80628555,\n", + " 547.50729734, 297.23174806, 680.32548692, 342.89971922,\n", + " 137.85385811, 425.97661593, 665.33950839, 618.08195697,\n", + " 100.76053401, 581.75598615, 351.59220917, 143.04948458,\n", + " 410.49397369, 167.65519318, 653.78496993, 548.81351804,\n", + " 140.77499224, 152.95640108, 558.94590232, 156.8569158 ,\n", + " 77.10260427, 126.29893833, 413.5024409 , 549.78786578,\n", + " 88.43019782, 670.33329703, 543.37756379, 100.10009692,\n", + " 556.26928137, 159.619804 , 378.04103911, 164.43080845,\n", + " 100.80699064, -25.3643385 , 413.28482191, 331.57181989,\n", + " 159.69597592, 648.53176619, 163.06678307, 160.21095107,\n", + " 551.62628023, 175.89288057, 551.08904881, 549.32621466,\n", + " 548.73726498, 676.89488231, 346.89038224, 44.00286515,\n", + " 421.18995673, 275.2444689 , 132.0508005 , 165.38954106,\n", + " 579.03848442, 96.39693611, 410.61131098, 76.278694 ,\n", + " 94.2019219 , 610.22199913, 417.29210715, 95.2378525 ,\n", + " 158.15883957, 95.66288071, 606.98946296, 684.00301472,\n", + " 387.05598964, 101.53336042, 659.50518039, 605.54211706,\n", + " 540.67636537, 407.92584076, 161.28860203, 659.57887575,\n", + " 348.46142146, 15.68219139, 543.45597717, 354.51025812,\n", + " 356.43215345, 555.12946446, 352.95138943, 124.23288811])" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768774.217577100163.629207(768, 100)
290768762.326950100144.350813(768, 100)
54100160.82949910093.709530(100, 100)
19810092.483029630556.789619(100, 630)
45314361415.65994110058.449756(1436, 100)
..................
164100106.320664365354.510258(100, 365)
165100107.466669365356.432153(100, 365)
19910093.702447630555.129464(100, 630)
132100102.992943365352.951389(100, 365)
50114361409.280430100124.232888(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 774.217577 100 163.629207 (768, 100)\n", + "290 768 762.326950 100 144.350813 (768, 100)\n", + "54 100 160.829499 100 93.709530 (100, 100)\n", + "198 100 92.483029 630 556.789619 (100, 630)\n", + "453 1436 1415.659941 100 58.449756 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.320664 365 354.510258 (100, 365)\n", + "165 100 107.466669 365 356.432153 (100, 365)\n", + "199 100 93.702447 630 555.129464 (100, 630)\n", + "132 100 102.992943 365 352.951389 (100, 365)\n", + "501 1436 1409.280430 100 124.232888 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768774.217577100163.629207(768, 100)
290768762.326950100144.350813(768, 100)
54100160.82949910093.709530(100, 100)
19810092.483029630556.789619(100, 630)
45314361415.65994110058.449756(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 774.217577 100 163.629207 (768, 100)\n", + "290 768 762.326950 100 144.350813 (768, 100)\n", + "54 100 160.829499 100 93.709530 (100, 100)\n", + "198 100 92.483029 630 556.789619 (100, 630)\n", + "453 1436 1415.659941 100 58.449756 (1436, 100)" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.235302\n", + "(100, 365) 0.408093\n", + "(100, 630) 0.667081\n", + "(768, 100) 0.919394\n", + "(768, 630) 1.232948\n", + "(1436, 100) 1.202288\n", + "(1436, 365) 1.532106\n", + "(1436, 630) 1.802787\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_19664\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_19664\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 5 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACRpklEQVR4nOzde1yUdd7/8dc1HAZFOZgoWmiQkuIphbVIO3iktnW3Yrvv7l9W5ikNbT3UZrsd1Ex0y9Z01dSs7G6re8tqVylRwUOWZkq45hENpFKMcgHROM3M74+RSQSVGWYYwPfz8ZgHznV9v9f1udCY3lzf6/s1bDabDREREREREak1k7cLEBERERERaWwUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpEREpFG6+uqrGTFihLfLEBGRy5SClIiINDhHjhzh4YcfJioqioCAAIKCgujXrx8vv/wyP//8s8fPf+bMGaZPn86mTZs8fi4REWmcfL1dgIiIyLlSUlK45557MJvNPPDAA3Tv3p2ysjK2bt3K448/zt69e1m2bJlHazhz5gwzZswA4NZbb/XouUREpHFSkBIRkQYjOzube++9l44dO5Kenk67du0c+5KSkjh8+DApKSlerLBuTp8+TWBgoLfLEBERN9DQPhERaTD+8pe/UFxczIoVK6qEqEqdOnXiD3/4Q419p0+fjmEY1ba/8cYbGIZBTk6OY9vOnTtJSEigdevWNGvWjMjISEaOHAlATk4OYWFhAMyYMQPDMDAMg+nTpzv6HzhwgN///ve0atWKgIAA4uLi+Ne//lXjeTdv3swjjzxCmzZtuOqqqwA4deoUkyZN4uqrr8ZsNtOmTRuGDBlCRkaGU98vERHxHt2REhGRBmP16tVERUVx4403euwcP/zwA0OHDiUsLIxp06YREhJCTk4OH3zwAQBhYWEsWbKE8ePHc9ddd3H33XcD0LNnTwD27t1Lv379uPLKK5k2bRqBgYH84x//4M4772TVqlXcddddVc73yCOPEBYWxjPPPMPp06cBGDduHO+//z4TJkwgJiaGn376ia1bt7J//3769OnjsWsXERH3UZASEZEGoaioiO+//57f/e53Hj3P559/zn/+8x/WrVtHXFycY/usWbMACAwM5Pe//z3jx4+nZ8+eDB8+vEr/P/zhD3To0IEvv/wSs9kM2MNS//79eeKJJ6oFqVatWpGWloaPj49jW0pKCmPGjGHevHmObX/84x/dfq0iIuI5GtonIiINQlFREQAtW7b06HlCQkIAWLNmDeXl5U71PXnyJOnp6fzXf/0Xp06d4scff+THH3/kp59+IiEhgaysLL7//vsqfcaMGVMlRFXW8MUXX3Ds2LE6XYuIiHiPgpSIiDQIQUFBgP35IU+65ZZbSExMZMaMGbRu3Zrf/e53vP7665SWll6y7+HDh7HZbDz99NOEhYVVeT377LOAfejguSIjI6sd5y9/+Qtff/01ERER9O3bl+nTp/PNN9+45wJFRKReaGifiIg0CEFBQbRv356vv/7apf41TTQBYLFYqrV7//332b59O6tXryY1NZWRI0cyb948tm/fTosWLS54DqvVCsBjjz1GQkJCjW06depU5X2zZs2qtfmv//ovbrrpJj788EPWrVvHCy+8wNy5c/nggw+4/fbbL3qdIiLSMOiOlIiINBi/+c1vOHLkCNu2bXO6b2hoKAAFBQVVth89erTG9jfccAPPP/88O3fu5O9//zt79+7l3XffBS4cyqKiogDw8/Nj8ODBNb5qOzSxXbt2PPLII3z00UdkZ2dzxRVX8Pzzz9eqr4iIeJ+ClIiINBh//OMfCQwMZPTo0Zw4caLa/iNHjvDyyy/X2Peaa64BYMuWLY5tp0+fZuXKlVXa/ec//8Fms1XZdt111wE4hvc1b94cqB7K2rRpw6233srSpUs5fvx4tRry8/MvcnV2FouFwsLCasdt3759rYYXiohIw6ChfSIi0mBcc801vP322/z3f/83Xbt25YEHHqB79+6UlZXx+eef89577zFixIga+w4dOpQOHTowatQoHn/8cXx8fHjttdcICwsjNzfX0W7lypUsXryYu+66i2uuuYZTp06xfPlygoKC+PWvfw3Yh+PFxMTwf//3f0RHR9OqVSu6d+9O9+7dWbRoEf3796dHjx6MGTOGqKgoTpw4wbZt2/juu+/YvXv3Ra/x1KlTXHXVVfz+97+nV69etGjRgg0bNvDll19WmcVPREQaNgUpERFpUH7729/y73//mxdeeIF//vOfLFmyBLPZTM+ePZk3bx5jxoypsZ+fnx8ffvghjzzyCE8//TTh4eFMmjSJ0NBQHnroIUe7W265hR07dvDuu+9y4sQJgoOD6du3L3//+9+rTAzx6quvMnHiRCZPnkxZWRnPPvss3bt3JyYmhp07dzJjxgzeeOMNfvrpJ9q0aUPv3r155plnLnl9zZs355FHHmHdunV88MEHWK1WOnXqxOLFixk/fnzdv4EiIlIvDNv54xtERERERETkovSMlIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESVpHCrBarRw7doyWLVtiGIa3yxERERERES+x2WycOnWK9u3bYzJd+L6TghRw7NgxIiIivF2GiIiIiIg0EN9++y1XXXXVBfcrSAEtW7YE7N+soKAgL1cjIiIiIiLeUlRUREREhCMjXIiCFDiG8wUFBSlIiYiIiIjIJR/50WQTIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJMUpERERERERJykICUiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESQpSIiIiIiIiTlKQEhERERERcZKClIiIiIiIiJN8vV2AiIiIiIg0USX5cGQFnEiH8kLwC4bwQRA1EgLCvF1dnShIiYiIiIiIe1lKYNcke4iyWQHrL/vy0mD3U3DNaIidDz5mLxVZNwpSIiIiIiLiPpYSSE+A/K1UCVAOVnu4OrwMivbDgNRGGab0jJSIiIiIiLjPrskXCVHnssIPn9rvXDVCClIiIiIiIuIeJflw5FUuHaIqWe3tS370ZFUeoSAlIiIiIiLu8c1rZ5+JcoLNCt+s8Ew9HqQgJSIiIiIi7pGXRu3vRlWyQl66J6rxKK8GqauvvhrDMKq9kpKSACgpKSEpKYkrrriCFi1akJiYyIkTJ6ocIzc3lzvuuIPmzZvTpk0bHn/8cSoqKrxxOSIiIiIil7fyQhf7Fbi1jPrg1SD15Zdfcvz4ccdr/fr1ANxzzz0ATJ48mdWrV/Pee++xefNmjh07xt133+3ob7FYuOOOOygrK+Pzzz9n5cqVvPHGGzzzzDNeuR4RERERkcuaX7CL/ULcWkZ9MGw2m83bRVSaNGkSa9asISsri6KiIsLCwnj77bf5/e9/D8CBAwfo2rUr27Zt44YbbuCTTz7hN7/5DceOHaNt27YAvPLKKzzxxBPk5+fj7+9fq/MWFRURHBxMYWEhQUFBHrs+EREREZEmbd9cyPwTzg3vM8F1syHmCU9V5ZTaZoMG84xUWVkZb731FiNHjsQwDHbt2kV5eTmDBw92tOnSpQsdOnRg27ZtAGzbto0ePXo4QhRAQkICRUVF7N2794LnKi0tpaioqMpLRERERETqKGokGE5GDMMHokZ5ph4PajBB6qOPPqKgoIARI0YAkJeXh7+/PyEhIVXatW3blry8PEebc0NU5f7KfReSnJxMcHCw4xUREeG+CxERERERuVwFhME1o6l9zDDBNaMgoLUnq/KIBhOkVqxYwe2330779u09fq4nn3ySwsJCx+vbb7/1+DlFRERERC4LsfMhrD+XjhomaHOTvX0j1CCC1NGjR9mwYQOjR492bAsPD6esrIyCgoIqbU+cOEF4eLijzfmz+FW+r2xTE7PZTFBQUJWXiIiIiIi4gY8ZBqZCp7Fg+FI9cpjs2zuNhQGp9vaNUIMIUq+//jpt2rThjjvucGyLjY3Fz8+PtLQ0x7aDBw+Sm5tLfHw8APHx8ezZs4cffvjB0Wb9+vUEBQURExNTfxcgIiIiIiK/8AmAax+FsJvsf8ZkfxbK/wro8ge46xj0XdJoQxSAr7cLsFqtvP766zz44IP4+v5STnBwMKNGjWLKlCm0atWKoKAgJk6cSHx8PDfccAMAQ4cOJSYmhvvvv5+//OUv5OXl8dRTT5GUlITZ3Hj/UkREREREGq2yAlh3IxTtr7rdBpT9BAf+CsdSYeg28G+8I8O8HqQ2bNhAbm4uI0eOrLbvr3/9KyaTicTEREpLS0lISGDx4sWO/T4+PqxZs4bx48cTHx9PYGAgDz74IDNnzqzPSxAREREREbCHqH9efemFeYv2wT87wO9yG22YalDrSHmL1pESEREREXGD1V3h1IHatw+Kgd9ceNkib2h060iJiIiIiEgjdjLTuRAF9jtThQc9Uo6nKUiJiIiIiEjdbfmta/0yJrm1jPqiICUiIiIiInVTkg9nXFyb9acv3VtLPVGQEhERERGRuvnmNdf7WkrcV0c9UpASEREREZG6OfaJ6319AtxXRz3y+vTnIiIiIiLSyBUfcfzx0HFYlg47v4HCnyG4GcRFwdiBEN2uhr5X/Kr+6nQjBSkREREREambijPsPgpT/g7pe8HHBBbrL7u3HoJ5H8OgbjDvPujV8Zy+febXd7VuoaF9IiIiIiJSJ2l7DeKnw+b99vfnhqhz32/aD/HTIe3ryj0+EHxt/RTpZgpSIiIiIiList27dzNs9klKyqsHqPNZrFBaDsPmwe6jQPOr6qVGT1CQEhERERERl02ZMoWyChs2W+3aW21QVgFT/w6U/cejtXmSgpSIiIiIiLjk0KFDpKenX/JO1PksVkjbC1nfFUHJj54pzsMUpERERERExCXLXlmMj4uJwscES9OAb1a4tab6oiAlIiIiIiIu2bnp/5y+G1XJYoVd2UBeultrqi8KUiIiIiIi4rySfApP5tXpEAVngPICt5RT3xSkRERERETEeYcWENysbocIaQ74hbijmnqnICUiIiIiIs7LeYe4KOr0jFRsJBA+0K1l1RcFKRERERERcd7pXMYOvPTaURdiscLDg4CoUW4tq74oSImIiIiIiHNOfwu2cqLbwcBuzt+V8jHB4G7QuZ0fBLT2TI0epiAlIiJNXD4wBxgKXH/269yz20VExGmWElh/k+PtS/eBvy+YjNp1Nxn29i/eB/jU8SErL1KQEhGRJqoEeBAIB54E1gM7zn79E9AeGA+UeqtAEZHGaddkOHPU8bZXR1g9Fcx+l74z5WOyt1s91d6PgLaerdWDFKRERKQJKgCigDeBmgbvW4EKYBmQgMKUiEgtleTD4eXVNg/qDtumw61d7e/PD1SV7wd0tbcb1P3sjqvv9VSlHufr7QJERETcqwSIAY7Xoq0V+BSYBCzxYE0iIk3EN69R8y+o7HeYNvwJsvJgaZp9sd2CM/YpzmMj7RNLdA4/r1P0ox4v2VMUpEREpIkZR+1CVCUr8CrwHNA4H3gWEak3eWmA7aJNOoefff7pUgKubLQTTYCG9omISJOSD/yvC/2swAo31yIi0gSVF7rvWNHj3XcsL1CQEhGRJuTCQ04uzgqku7kWEZEmyC/YTQfyhU4Pu+lY3qEgJSIiTUhaHfoWuKsIEZGmK3yQGw5igk6jG/WwPlCQEhGRJqUuQ05C3FWEiEjTFTWyjgcwQZubIHa+O6rxKgUpERFpQuoy5GSg26oQEWmyAsLAHOZiZwM6jYUBqeBjdmtZ3qAgJSIiTcggwHChnwkY5eZaRESaqJCervVrcwv0XdIkQhQoSImISJMyEvBxod8DaOpzEZFaajcE539pZYL2t3miGq9RkBIRkSYkDBjhZJ92wCvuL0VEpKmKGgmGk7+0Mnwgqmnd+VeQEhGRJqQEOOBE+/Cz7ZvGMBMRkXoREAbXjKb2UcIE14xq9LP0nU9BSkREmpDJwOdOtB8GBHmoFhGRJix2PoT159JxounM0nc+BSkREWki8oFXcW5B3uXAAGDu2f4iIlIrPmYYmGqfhc/wpXqsMNm3N6FZ+s5n2Gw2m7eL8LaioiKCg4MpLCwkKEi/mRQRaZzmAn/CuSBVyXT2NRqYj4b6iYg4oSQfvnkN8tKhvAD8QiB8oP1ZqgBXp0r3ntpmAwUpFKRERJqGocD6Oh7DBNwEpKIwJSJyeaptNtDQPhERaSIK3XAMK/ApMMkNxxIRkaZMQUpERJqIYDcdx4r9Wasf3XQ8ERFpihSkRESkiRiE+z7WrMAKNx1LRESaIgUpERFpIkbi3iCV7qZjiYhIU+Tr7QJERETcIwz7rHtLgarzKB06BMuWwc6dUFgIwcEQFwdjx0J09IWOV+DRakVEpHHTrH1o1j4RkaajFAinMgTt3g1TpkB6Ovj4gMXyS8vK94MGwbx50KvX+ccain32PhERuZxo1j4REbkMmYFOAKSlQXw8bN5s33NuiDr3/aZN9nZpaefuNQEDPVuqiIg0agpSIiLSxISyezcMGwYlJdUD1PksFigttbffvfvcPaM8WaSIiDRyClIiItLEDGLKFCgrg9oOXrda7e2nTq3c0hZo7aH6RESkKVCQEhGRJuXQoVtIT7/0najzWSz24X1ZWaCPRxERuRR9UoiISJOybNn7+Pi41tfHB5YuBShxZ0kiItIEeT1Iff/99wwfPpwrrriCZs2a0aNHD3bu3OnYb7PZeOaZZ2jXrh3NmjVj8ODBZNl/Xehw8uRJ7rvvPoKCgggJCWHUqFEUFxfX96WIiEgDsHPnTqfvRlWyWGDXLoBm7ixJRESaIK8Gqf/85z/069cPPz8/PvnkE/bt28e8efMIDQ11tPnLX/7CggULeOWVV/jiiy8IDAwkISGBkpJfflt43333sXfvXtavX8+aNWvYsmULY8eO9cYliYiIlxUWFtapf0EBVM78JyIiciFeXZB37ty5RERE8Prrrzu2RUZGOv5ss9mYP38+Tz31FL/73e8AePPNN2nbti0fffQR9957L/v372ft2rV8+eWXxMXFAbBw4UJ+/etf8+KLL9K+ffv6vSgREfGq4ODgOvUPCQG4zR2liIhIE+bVO1L/+te/iIuL45577qFNmzb07t2b5cuXO/ZnZ2eTl5fH4MGDHduCg4O5/vrr2bZtGwDbtm0jJCTEEaIABg8ejMlk4osvvqjxvKWlpRQVFVV5iYhI0xAXF4ePiw9J+fhAbCxo6nMREbkUrwapb775hiVLltC5c2dSU1MZP348jz76KCtXrgQgLy8PgLZt21bp17ZtW8e+vLw82rRpU2W/r68vrVq1crQ5X3JyMsHBwY5XRESEuy9NRES8ZOzYsVhcfEjKYoGHH9bU5yIicmleDVJWq5U+ffowe/ZsevfuzdixYxkzZgyvvPKKR8/75JNPUlhY6Hh9++23Hj2fiIjUn+joaAYOHIiPj+FUPx8fGDwYOnee6KHKRESkKfFqkGrXrh0xMTFVtnXt2pXc3FwAwsPDAThx4kSVNidOnHDsCw8P54cffqiyv6KigpMnTzranM9sNhMUFFTlJSIiTcdLL72Ev38Aplp+yplM4O8PL77oAzzs0dpERKRp8GqQ6tevHwcPHqyy7dChQ3Ts2BGwTzwRHh5OWlqaY39RURFffPEF8fHxAMTHx1NQUMAu+3y1AKSnp2O1Wrn++uvr4SpERKSh6dWrF6tXr8Zs9rnkmlI+PmA2w+rV0KvXSDSsT0REasOrQWry5Mls376d2bNnc/jwYd5++22WLVtGUlISAIZhMGnSJGbNmsW//vUv9uzZwwMPPED79u258847AfsdrNtuu40xY8awY8cOPvvsMyZMmMC9996rGftERC5jgwYNYtu2bdx6q30Wv/MDVeX7AQNg2zYYNAhgH1Ban2WKiEgjZdhsNps3C1izZg1PPvkkWVlZREZGMmXKFMaMGePYb7PZePbZZ1m2bBkFBQX079+fxYsXEx0d7Whz8uRJJkyYwOrVqzGZTCQmJrJgwQJatGhRqxqKiooIDg6msLBQw/xERJqch8jKeoOlS+2L7RYU2Kc4j42Fhx+Gzp3Pbz8GWFbvVYqISMNQ22zg9SDVEChIiYg0VflAm0u2qsoAfkBD/ERELk+1zQZeHdonIiLiWS+50McGLHB3ISIi0sQoSImISBO22MV+77q1ChERaXoUpEREpInKB4pc7Hvi0k1EROSypiAlIiJN1GveLkBERJowBSkREWmi0i7d5ILauq0KERFpmhSkRESkifpPHfre67YqRESkaVKQEhGRJiqvDn0fdVsVIiLSNClIiYhIE5QPHHOx76/QGlIiInIpClIiItIEvYZ9PShXfOjOQkREpIny9XYBcp6ycsj7EQpOQYUFfH0gpCWEtwZ/P29XJyLSSKThWpC68uxLRETk4hSkGgqrFQ7n2kPU+Z/9/ymCnO8hPAw6RYBJNxJFRC6u0MV+7dxahYiINF0KUg2B1Qq7D0FR8YXb2IDj+XDmZ+gZrTAlInJRwS72C3FnESIi0oTp/8Ybgqzci4eocxUWw8Ecj5YjItL4DcK1j7hT7i5ERESaKN2R8rbKZ6LOOvTtUZat/pCdB/dTeLqY4MAWxF3blbHD7iI6oqO90Q8n4ep20KyZl4oWEWnoRgJ/dqHfLuBHNGufiIhcimGz2Vyd1qjJKCoqIjg4mMLCQoKCgur35NnfQ+5xdh8+xJTF80nP+BIfkw8Wq8XRpPL9oD6/Yt4jk+jVKdo+CUW/3vVbq4hIo3ID8IWTfUzAbOAJ95cjIiKNQm2zgYb2eduJH0nbtYP4pJFsztwFUCVEnft+U+Yu4pNGkrZrh31GvzM/13u5IiKNRwsX+liBdHcXIiIiTZCClDdZrezeu5dhf5pCSVkZFqv1os0tViulZWUM+9MUdh8+ZJ/lT0RELsDV550K3FmEiIg0UQpS3nToKFMWz6esvJzajrC02myUlZczdfF8+I8eihYRuTDN3CciIp6jIOUtZeUc2plBesaXl7wTdT6L1UpaxpdkfZcL5eUeKlBEpLFzZeY+EzDQA7WIiEhToyDlLbnHWbb6Q3xMPi519zGZWPqvD+D4j5duLCJyWRqJ8x9zPsAoD9QiIiJNjYKUt5z4iZ0H91ebWKK2LFYruw4dgJOFbi5MRKSpCANGU/uPOhP2EKWpz0VE5NIUpLylwkLh6VouwnsBBcWn4OdSNxUkItIUzQf6c+mPOxNw09n2IiIil6Yg5UXBga5MzfuLkBYtwcnnq0RELi9mIBUYi30N+vM/9kxnt489285cr9WJiEjjpSDlRXHXdq3TM1Kx0V3ApL9CEZGLCwCWAMewL7Y7FOh79uvss9uXoBAlIiLO0P+Fe4uvD2OH3VWnZ6Qe/u3d0Ewf/CIitRMGPIH9ztMXZ78+cXa7iIiIcxSkvMXsR3RERwb2+RU+Tt5V8jGZGBz7Kzpf1QFaubpOioiIiIiIuEpBylv8/QF46ZFJ+Pv5YTKMWnUzGQb+fn68OH4SGAa00+xSIiIiIiL1TUHKW0JaAtCrUzSrZ7+E2d//knemfEwmzP7+rJ79Er06RUN4a/Dzq49qRURERETkHApS3hL+y52kQbF92bboNW69Lhag2gQUle8H9I5l26LXGBTbF4JbQKeI+qtXREREREQcfL1dwGXL3w9aNodTZwD7nakNLy0m67tclv7rA3YdOkBB8SlCWrQkNroLD//2bvszUQAtA6FntGbsExERERHxEsNms9m8XYS3FRUVERwcTGFhIUFBQfV34pJS+GKPc30MIL6XhvSJiIiIiHhAbbOBbml4U4C5yhC/WgkPU4gSEREREfEyBSlv69zB/rxTbei5KBERERGRBkFByttMJvvzTu3C7MP2amJg36/nokREREREGgRNNtEQmEwQ3RGubg95P0LBKaiwgK+PfZr08Nb2ySlERERERKRBUJBqSPz9oEM7+0tERERERBosjRMTERERERFxkoKUiIiIiIiIkxSkREREREREnKQgJSIiIiIi4iQFKREREREREScpSImIiIiIiDhJQUpERERERMRJWkdKRERqzWKxUF5e7u0ypA78/f0xmfR7VBGRulKQEhGRS7LZbOTl5VFQUODtUqSOTCYTkZGR+Pv7e7sUEZFGTUFKREQuqTJEtWnThubNm2MYhrdLEhdYrVaOHTvG8ePH6dChg/4eRUTqwKtBavr06cyYMaPKtmuvvZYDBw4AUFJSwtSpU3n33XcpLS0lISGBxYsX07ZtW0f73Nxcxo8fz8aNG2nRogUPPvggycnJ+PoqI4qIuIPFYnGEqCuuuMLb5UgdhYWFcezYMSoqKvDz8/N2OSIijZbX00a3bt3YsGGD4/25AWjy5MmkpKTw3nvvERwczIQJE7j77rv57LPPAPuH+x133EF4eDiff/45x48f54EHHsDPz4/Zs2fX+7WIiDRFlc9ENW/e3MuViDtUDumzWCwKUiIideD1IOXr60t4eHi17YWFhaxYsYK3336bgQMHAvD666/TtWtXtm/fzg033MC6devYt28fGzZsoG3btlx33XU899xzPPHEE0yfPl3jv0VE3EjDwJoG/T2KiLiH16ftycrKon379kRFRXHfffeRm5sLwK5duygvL2fw4MGOtl26dKFDhw5s27YNgG3bttGjR48qQ/0SEhIoKipi7969FzxnaWkpRUVFVV4iIiIiIiK15dUgdf311/PGG2+wdu1alixZQnZ2NjfddBOnTp0iLy8Pf39/QkJCqvRp27YteXl5gP3h53NDVOX+yn0XkpycTHBwsOMVERHh3gsTEZEq8vNhzhwYOhSuv97+de5c+3YREZHGyKtD+26//XbHn3v27Mn1119Px44d+cc//kGzZs08dt4nn3ySKVOmON4XFRUpTImIeEBJCUyaBCtWgNVqf1VKS4OnnoLRo2H+fDCbvVOjYRh8+OGH3Hnnnd4pQEREGiWvD+07V0hICNHR0Rw+fJjw8HDKysqqrVly4sQJxzNV4eHhnDhxotr+yn0XYjabCQoKqvISERH3KimBhARYvhwqKqqGKLC/r6iAZcvs7UpL3V9DXl4eEydOJCoqCrPZTEREBMOGDSMtLc39JwM2bdqEYRgeXW/r+eef58Ybb6R58+bVRm2IiEj9aVBBqri4mCNHjtCuXTtiY2Px8/Or8mF38OBBcnNziY+PByA+Pp49e/bwww8/ONqsX7+eoKAgYmJi6r1+ERH5xeTJsHVr9QB1PqsVPv3UfufKnXJycoiNjSU9PZ0XXniBPXv2sHbtWgYMGEBSUpJ7T+ZmNpuNioqKGveVlZVxzz33MH78+HquSkREzuXVIPXYY4+xefNmcnJy+Pzzz7nrrrvw8fHhf/7nfwgODmbUqFFMmTKFjRs3smvXLh566CHi4+O54YYbABg6dCgxMTHcf//97N69m9TUVJ566imSkpIwe2uMiIiIkJ8Pr7566RBVyWq1t//xR/fV8Mgjj2AYBjt27CAxMZHo6Gi6devGlClT2L59e419arqjlJmZiWEY5OTkAHD06FGGDRtGaGgogYGBdOvWjY8//picnBwGDBgAQGhoKIZhMGLEiLPXZyU5OZnIyEiaNWtGr169eP/996ud95NPPiE2Nhaz2czWrVtrrHHGjBlMnjyZHj161P2bJCIiLvPqM1Lfffcd//M//8NPP/1EWFgY/fv3Z/v27YSFhQHw17/+FZPJRGJiYpUFeSv5+PiwZs0axo8fT3x8PIGBgTz44IPMnDnTW5ckIiLAa6/VPkRVslrtz1I98UTdz3/y5EnWrl3L888/T2BgYLX9dRkSl5SURFlZGVu2bCEwMJB9+/bRokULIiIiWLVqFYmJiRw8eJCgoCDH877Jycm89dZbvPLKK3Tu3JktW7YwfPhwwsLCuOWWWxzHnjZtGi+++CJRUVGEhoa6XKOIiHieV4PUu+++e9H9AQEBLFq0iEWLFl2wTceOHfn444/dXZqIiNRBWpprQSo93T1B6vDhw9hsNrp06VL3g50nNzeXxMRExx2hqKgox75WrVoB0KZNG0dYKy0tZfbs2WzYsMExND0qKoqtW7eydOnSKkFq5syZDBkyxO01i4iI+3l9QV4REWl6Cgtd6+euORpsNpt7DlSDRx99lPHjx7Nu3ToGDx5MYmIiPXv2vGD7w4cPc+bMmWoBqaysjN69e1fZFhcX55GaRUTE/RSkRETE7YKDXevnrknoOnfujGEYHDhwwKl+JpP90eFzg1h5eXmVNqNHjyYhIYGUlBTWrVtHcnIy8+bNY+LEiTUes7i4GICUlBSuvPLKKvvOf563pmGIIiLSMDWoWftERKRpGDQITE5+wphMMHCge87fqlUrEhISWLRoEadPn662/0LTk1c+o3v8+HHHtszMzGrtIiIiGDduHB988AFTp05l+fLlAPj7+wNgsVgcbWNiYjCbzeTm5tKpU6cqL61hKCLSeClIiYiI240c6XyQ8vGBUaPcV8OiRYuwWCz07duXVatWkZWVxf79+1mwYIHjWaXzVYab6dOnk5WVRUpKCvPmzavSZtKkSaSmppKdnU1GRgYbN26ka9eugP25XcMwWLNmDfn5+RQXF9OyZUsee+wxJk+ezMqVKzly5AgZGRksXLiQlStXOn1dubm5ZGZmkpubi8ViITMzk8zMTMedLxERqR8KUiIi4nZhYTB6dO3DlMlkD1GtW7uvhqioKDIyMhgwYABTp06le/fuDBkyhLS0NJYsWVJjHz8/P9555x0OHDhAz549mTt3LrNmzarSxmKxkJSURNeuXbntttuIjo52zCh75ZVXMmPGDKZNm0bbtm2ZMGECAM899xxPP/00ycnJjn4pKSlERkY6fV3PPPMMvXv35tlnn6W4uJjevXvTu3dvdu7c6fSxRETEdYbNk0/kNhJFRUUEBwdTWFhIUFCQt8sREWlQSkpKyM7OJjIykoCAgFr3Ky2FoUMvvSivyQQ33QSpqaAlAD3P1b9PEZHLRW2zge5IiYiIR5jN9nA0diz4+la/O2Uy2bePHasQJSIijY9m7RMREY8JCIAlS2DmTPsivenp9inOQ0LsE0uMHGkfBigiItLYKEiJiIjHhYXZF9p1x2K7IiIiDYGG9omIiIiIiDhJQUpERERERMRJClIiIiIiIiJOUpASERERERFxkoKUiIiIiIiIkxSkRETE8/LzYc4c+wq9119v/zp3rn27lxmGwUcffeTtMkREpJFRkBIREc8pKYFx46B9e/jzn2H9etixw/71T3+ybx8/HkpLPXL6vLw8Jk6cSFRUFGazmYiICIYNG0ZaWppHzrdp0yYMw6CgoMAjx8/JyWHUqFFERkbSrFkzrrnmGp599lnKyso8cj4REbkwrSMlIiKeUVICCQmwdStYrdX3W63217JlsH8/pKaC2ey20+fk5NCvXz9CQkJ44YUX6NGjB+Xl5aSmppKUlMSBAwfcdi53s9lsWCwWfH2rfkwfOHAAq9XK0qVL6dSpE19//TVjxozh9OnTvPjii16qVkTk8qQ7UiIi4hmTJ184RJ3LaoVPP4VJk9x6+kceeQTDMNixYweJiYlER0fTrVs3pkyZwvbt22vsU9MdpczMTAzDICcnB4CjR48ybNgwQkNDCQwMpFu3bnz88cfk5OQwYMAAAEJDQzEMgxEjRpy9RCvJycmOO0m9evXi/fffr3beTz75hNjYWMxmM1u3bq1W32233cbrr7/O0KFDiYqK4re//S2PPfYYH3zwgXu+aSIiUmu6IyUiIu6Xnw+vvnrpEFXJarW3f+45aN26zqc/efIka9eu5fnnnycwMLDa/pCQEJePnZSURFlZGVu2bCEwMJB9+/bRokULIiIiWLVqFYmJiRw8eJCgoCCaNWsGQHJyMm+99RavvPIKnTt3ZsuWLQwfPpywsDBuueUWx7GnTZvGiy++SFRUFKGhobWqp7CwkFatWrl8PSIi4hoFKRERcb/XXqt9iKpktcKKFfDEE3U+/eHDh7HZbHTp0qXOxzpfbm4uiYmJ9OjRA4CoqCjHvspA06ZNG0dYKy0tZfbs2WzYsIH4+HhHn61bt7J06dIqQWrmzJkMGTKk1rUcPnyYhQsXalifiIgXKEiJiIj7paW5FqTS090SpGw2W52PcSGPPvoo48ePZ926dQwePJjExER69ux5wfaHDx/mzJkz1QJSWVkZvXv3rrItLi6u1nV8//333Hbbbdxzzz2MGTPGuYsQEZE6U5ASERH3Kyx0rZ+bZrvr3LkzhmE4PaGEyWR/dPjcIFZeXl6lzejRo0lISCAlJYV169aRnJzMvHnzmDhxYo3HLC4uBiAlJYUrr7yyyj7zeZNr1DQMsSbHjh1jwIAB3HjjjSxbtqxWfURExL002YSIiLhfcLBr/erw7NK5WrVqRUJCAosWLeL06dPV9l9oevKwsDAAjh8/7tiWmZlZrV1ERATjxo3jgw8+YOrUqSxfvhwAf39/ACwWi6NtTEwMZrOZ3NxcOnXqVOUVERHh9LV9//333HrrrcTGxvL66687wp+IiNQv/fQVERH3GzQInP0ffJMJBg50WwmLFi3CYrHQt29fVq1aRVZWFvv372fBggWOZ5XOVxlupk+fTlZWFikpKcybN69Km0mTJpGamkp2djYZGRls3LiRrl27AtCxY0cMw2DNmjXk5+dTXFxMy5Yteeyxx5g8eTIrV67kyJEjZGRksHDhQlauXOnUNVWGqA4dOvDiiy+Sn59PXl4eeXl5rn2TRETEZQpSIiLifiNHOh+kfHxg1Ci3lRAVFUVGRgYDBgxg6tSpdO/enSFDhpCWlsaSJUtq7OPn58c777zDgQMH6NmzJ3PnzmXWrFlV2lgsFpKSkujatSu33XYb0dHRLF68GIArr7ySGTNmMG3aNNq2bcuECRMAeO6553j66adJTk529EtJSSEyMtKpa1q/fj2HDx8mLS2Nq666inbt2jleIiJSvwybJ5/IbSSKiooIDg6msLCQoKAgb5cjItKglJSUkJ2dTWRkJAEBAbXvOH68fbHd2kw6YTLB2LFwgYAj7uPy36eIyGWittlAd6RERMQz5s+H/v0vfWfKZIKbbrK3FxERaSQUpERExDPMZkhNtd9p8vWtHqhMJvv2sWPt7c6bwU5ERBqh/HyYMweGDoXrr7d/nTvXvr2J0fTnIiLiOQEB9uF6M2faF+lNT7dPcR4SYp9YYuRIODtTnoiINGIlJTBpkn1hdau16rDu9eth2jR7sHr/fbjqKq+V6U4KUiIi4nlhYfaFdt2w2K6IiDQwJSWQkABbt178udgvvoAOHWD0aFi4sNGPRNDQPhERERERcd3kyZcOUZVsNnj1VXvwKi31fG0epCAlIiIiIiKuyc+3B6PahKhKNhts2WIfCtiIKUiJiIiIiIhrXnvNuRBVqfLO1I8/ur+meqIgJSIiIiIirklLcy1IAVRU2CenaKQUpERERERExDWFhXXr//zzjXaKdAUpERHxuPzT+czZOoeh/zuU65dfz9D/HcrcrXPJP+39D03DMPjoo4+8XYaISOMUHFyrZoeAx4Bbgd5nvz4GHDp16pfp0du0gRtugO++81Cx7qUgJSIiHlNSUcK4NeNo/1J7/pz+Z9Z/s54dx3aw/pv1/Cn9T7R/qT3jU8ZTWuGZmZvy8vKYOHEiUVFRmM1mIiIiGDZsGGlpaR4536ZNmzAMg4KCAo8cH+C3v/0tHTp0ICAggHbt2nH//fdz7Ngxj51PROSiBg2qvuD6OXYDg4BrgfnAZiDz7Nf5Z7cPPtsOsE+RHhFhX6y9gc/qpyAlIiIeUVJRQsJbCSzPWE6FtQKrreoYeqvNSoW1gmW7lpHwVoLbw1ROTg6xsbGkp6fzwgsvsGfPHtauXcuAAQNISkpy67nczWazUVFRUeO+AQMG8I9//IODBw+yatUqjhw5wu9///t6rlBE5KyRIy8YpNKAeOyhCcBy3v7K95vOtqvyK67ly2HIkAYdphSkRETEIyanTmZr7tZqAep8VpuVT3M/ZVLqJLee/5FHHsEwDHbs2EFiYiLR0dF069aNKVOmsH379hr71HRHKTMzE8MwyMnJAeDo0aMMGzaM0NBQAgMD6datGx9//DE5OTkMGDAAgNDQUAzDYMSIEfZrtFpJTk4mMjKSZs2a0atXL95///1q5/3kk0+IjY3FbDazdevWGmucPHkyN9xwAx07duTGG29k2rRpbN++nfLy8rp/00REnBUWZl9g9zy7gWFACdUD1PksQOnZ9rvP3fHppzBxonvq9AAFKRERcbv80/m8mvHqJUNUJavNyqsZr/LjGfdMg3vy5EnWrl1LUlISgYGB1faHhIS4fOykpCRKS0vZsmULe/bsYe7cubRo0YKIiAhWrVoFwMGDBzl+/Dgvv/wyAMnJybz55pu88sor7N27l8mTJzN8+HA2b95c5djTpk1jzpw57N+/n549e9bqOv/+979z44034ufn5/I1iYjUyfz5YBhVNk0BygBbLQ9hPdt+6vk7GvAU6QpSIiLidq999VqtQ1Qlq83Kigz3TIN7+PBhbDYbXbp0ccvxzpWbm0u/fv3o0aMHUVFR/OY3v+Hmm2/Gx8eHVq1aAdCmTRvCw8MJDg6mtLSU2bNn89prr5GQkEBUVBQjRoxg+PDhLF26tMqxZ86cyZAhQ7jmmmscx6rJE088QWBgIFdccQW5ubn885//dPt1iojUmtkM4eGOt4eAdC59J+p8FuzD+7LO3WizwYIFda3QIxSkRETE7dKy01wKUunZ6W45v81W29+BOu/RRx9l1qxZ9OvXj2effZZ///vfF21/+PBhzpw5w5AhQ2jRooXj9eabb3LkyJEqbePi4mpVw+OPP85XX33FunXr8PHx4YEHHvDoNYuIXFJ0tOOPywAfFw/jAyw9f+O777p4NM/y9XYBIiLS9BSWuLauSEFpgVvO37lzZwzD4MCBA071M519YPrcUHL+s0ejR48mISGBlJQU1q1bR3JyMvPmzWPiBcbxFxcXA5CSksKVV15ZZZ/ZbK7yvqZhiDVp3bo1rVu3Jjo6mq5duxIREcH27duJj4+vVX8REbe7/XY4O1x5J87fjapkAXadv/HECdfr8iDdkRIREbcLDqjduiLnCzGHuOX8rVq1IiEhgUWLFnH69Olq+y80PXlYWBgAx48fd2zLzMys1i4iIoJx48bxwQcfMHXqVJYvXw6Av78/ABbLL/8LERMTg9lsJjc3l06dOlV5RUREuHqJDlar/c5faQOe2UpELgMjR4Kv/R5NHZfopaDOxdQPBSkREXG7QZGDMBnOfcSYDBMDIwe6rYZFixZhsVjo27cvq1atIisri/3797NgwYIL3rmpDDfTp08nKyuLlJQU5s2bV6XNpEmTSE1NJTs7m4yMDDZu3EjXrl0B6NixI4ZhsGbNGvLz8ykuLqZly5Y89thjTJ48mZUrV3LkyBEyMjJYuHAhK1eudOqavvjiC/72t7+RmZnJ0aNHSU9P53/+53+45pprdDdKRLyrcvY+kwnXfpX2i5DzN7RtW8cjeoaClIiIuN3I3iOdDlI+hg+j+oxyWw1RUVFkZGQwYMAApk6dSvfu3RkyZAhpaWksWbKkxj5+fn688847HDhwgJ49ezJ37lxmzZpVpY3FYiEpKYmuXbty2223ER0dzeLFiwG48sormTFjBtOmTaNt27ZMmDABgOeee46nn36a5ORkR7+UlBQiIyOduqbmzZvzwQcfMGjQIK699lpGjRpFz5492bx5c7VhgiIi9W7+fOjfnzjq9oxU7Pkb7723LlV5jGFrIE+nzpkzhyeffJI//OEPzJ8/H4CSkhKmTp3Ku+++S2lpKQkJCSxevJi256TS3Nxcxo8fz8aNG2nRogUPPvggycnJ+PrW/vGvoqIigoODKSwsJCgoyN2XJiLSqJWUlJCdnU1kZCQBAQG17jc+ZTzLdi2r1aQTJsPE2NixLLmj5oAj7uPq36eISK2UlHDooYe4tg4TRBwCOp+7IT8fWreua2W1Vtts0CDuSH355ZcsXbq02poZkydPZvXq1bz33nts3ryZY8eOcffddzv2WywW7rjjDsrKyvj8889ZuXIlb7zxBs8880x9X4KIiJxnfsJ8+nfof8k7UybDxE0dbmJ+wvz6KUxERDwnIIDod95h4E034XPe2lKX4gMM5rwQdeWV9RqinOH1IFVcXMx9993H8uXLCQ0NdWwvLCxkxYoVvPTSSwwcOJDY2Fhef/11Pv/8c8eK9OvWrWPfvn289dZbXHfdddx+++0899xzLFq0iLKyMm9dkoiIAGZfM6nDUxkbOxZfk2+1QGUyTPiafBkbO5bU4amYfTU0TUSkqXhp4UL8AwJqHTZMgD/w4vk7xo93a13u5PUglZSUxB133MHgwYOrbN+1axfl5eVVtnfp0oUOHTqwbds2ALZt20aPHj2qDPVLSEigqKiIvXv3XvCcpaWlFBUVVXmJiIj7BfgGsOSOJRybcozZA2czNGoofa/sy9CoocweOJtjU46x5I4lClEiIk1Mr169WL16NeZmzS75vJQPYAZWA73O3WEywcMPe6rEOvPqOlLvvvsuGRkZfPnll9X25eXl4e/vT0hISJXtbdu2JS8vz9Gm7XmzeFS+r2xTk+TkZGbMmFHH6kVEpLbCAsN4ov8TPNH/CW+XIiIi9WTQoEFs27aNqRMmkLZ1Kz5UXV+q8v0A7Heiep1/gAceaLDD+sCLQerbb7/lD3/4A+vXr6/3h12ffPJJpkyZ4nhfVFTklrU8REREREQE+wQRK1bQKz2dDWVlZLVty9ITJ9iFfZ2oEOyz8z3Mec9EVWrXDl55pd7KdYXXgtSuXbv44Ycf6NOnj2ObxWJhy5Yt/O1vfyM1NZWysjIKCgqq3JU6ceIE4eHhAISHh7Njx44qxz1xduXjyjY1MZvNmiZWRERERMTdSkrsw/Heegusv8za2pkann+6kHbt4MABaOD/v+61Z6QGDRrEnj17yMzMdLzi4uK47777HH/28/MjLS3N0efgwYPk5uY6Fh2Mj49nz549/PDDD44269evJygoiJiYmHq/JhERERGRy1ZBAURFwZtvVglRtWYywYMPQnY2NIIlibx2R6ply5Z07969yrbAwECuuOIKx/ZRo0YxZcoUWrVqRVBQEBMnTiQ+Pp4bbrgBgKFDhxITE8P999/PX/7yF/Ly8njqqadISkrSHScRERERkfpSUgIxMXD8eO3aG4Z9avP27SEkBAYOhJEjISzMo2W6k1cnm7iUv/71r5hMJhITE6ssyFvJx8eHNWvWMH78eOLj4wkMDOTBBx9k5syZXqxaREREROQyM25c7UMUgM0GeXnw1VcNekKJizFsNpvN20V4W21XL26Izj7HR3o6FBZCcDAMGtToAr2INGAlJSVkZ2cTGRlZh8mB8oEVQDpQCAQDg4CRgHd/WBmGwYcffsidd97p1Trqi3v+PkVEzpGfD+Hhzg/nM5lg9mx4omHN6FrbbOD1daTENSUl9uDfvj38+c+wfj3s2GH/+qc/2bePHw+lpd6uVEQubyXAOKA98GdgPbDj7Nc/nd0+HvDMD6u8vDwmTpxIVFQUZrOZiIgIhg0bVuX5W3fatGkThmFQUFDgkeOfq7S0lOuuuw7DMMjMzPT4+URELui111x7Jspqtd8NaKQa9NA+qVlJCSQkwNatNf+btVrtr2XLYP9+SE1t8JOeiEiTVAIkAFuBmj5grWdfy4D9QCr2JRndIycnh379+hESEsILL7xAjx49KC8vJzU1laSkJA4cOOC2c7mbzWbDYrHg63vhj+k//vGPtG/fnt27d9djZSIiNajLL6fq4RdPnqI7Uo3Q5MkXDlHnslrh009h0qR6KUtE5DyTuXCIOpcV+BSY5NazP/LIIxiGwY4dO0hMTCQ6Oppu3boxZcoUtm/fXmOfmu4oZWZmYhgGOTk5ABw9epRhw4YRGhpKYGAg3bp14+OPPyYnJ4cBAwYAEBoaimEYjBgxwn6FVivJyclERkbSrFkzevXqxfvvv1/tvJ988gmxsbGYzWa2bt16wWv75JNPWLduHS++WOvJhEVEPKew0PW+5yxz1NjojlQjk58Pr75a+7unVqu9/XPPNdrn+ESkUcoHXuXSIaqS9Wz754C6/7A6efIka9eu5fnnnycwMLDa/pA6fHAnJSVRVlbGli1bCAwMZN++fbRo0YKIiAhWrVpFYmIiBw8eJCgoiGbNmgGQnJzMW2+9xSuvvELnzp3ZsmULw4cPJywsjFtuucVx7GnTpvHiiy8SFRVFaGhojec/ceIEY8aM4aOPPqJ58+YuX4eIiNsEB7ved+BA99VRzxSkGhlXhqBarfYJKRrYc3wi0qS9Ru1DVCUr9gkp6v7D6vDhw9hsNrp06VLnY50vNzeXxMREevToAUBUVJRjX6tWrQBo06aNI6yVlpYye/ZsNmzY4FgHMSoqiq1bt7J06dIqQWrmzJkMGTLkgue22WyMGDGCcePGERcX57hLJiLiVYMGwYYN9pn4nGEywahRnqmpHmhoXyOTluZakGrEz/GJSKOUhmtByj0/rDw5Ie2jjz7KrFmz6NevH88++yz//ve/L9r+8OHDnDlzhiFDhtCiRQvH68033+TIkSNV2sbFxV30WAsXLuTUqVM8+eSTdb4OERG3GTkSfHyc7/fAA416yJSCVCPj6hDURvwcn4g0Sq6Oly9wy9k7d+6MYRhOTyhhMtk/Fs8NYuXl5VXajB49mm+++Yb777+fPXv2EBcXx8KFCy94zOLiYgBSUlLIzMx0vPbt21flOSmgxmGI50pPT2fbtm2YzWZ8fX3p1KkTYA9gDz74YO0vVETEncLCYPRo+yK7tdWuHbzyiudqqgcKUo2Mq0NQG/FzfCLSKLk6Xj7ELWdv1aoVCQkJLFq0iNOnT1fbf6HpycPOLsB3/JxFJWuaWjwiIoJx48bxwQcfMHXqVJYvXw6Av78/ABaLxdE2JiYGs9lMbm4unTp1qvKKiIhw6roWLFjA7t27HWHs448/BuD//u//eP755506loiIW82fDzfdVLsw1a4dHDjQ6KeVVpBqZAYNsg8ndYbJ1Kif4xORRmkQzn/EmAD3/bBatGgRFouFvn37smrVKrKysti/fz8LFixwPKt0vspwM336dLKyskhJSWHevHlV2kyaNInU1FSys7PJyMhg48aNdO3aFYCOHTtiGAZr1qwhPz+f4uJiWrZsyWOPPcbkyZNZuXIlR44cISMjg4ULF7Jy5UqnrqlDhw50797d8YqOjgbgmmuu4aqrrnLhuyQi4iZms33NnYcfBl/fmgOVyQQPPgjZ2XCRhW4bCwWpRmbkSOeDlI9Po36OT0QapZE4/xHjA7jvh1VUVBQZGRkMGDCAqVOn0r17d4YMGUJaWhpLliypsY+fnx/vvPMOBw4coGfPnsydO5dZs2ZVaWOxWEhKSqJr167cdtttREdHs3jxYgCuvPJKZsyYwbRp02jbti0TJkwA4LnnnuPpp58mOTnZ0S8lJYXIyEi3Xa+IiNcFBMCSJXDsGCQnw9Ch0Lev/eucOZCXB2+80ejvRFUybJ58IreRKCoqIjg4mMLCQoIaaDrOz7fPvJeeDpmZ9ve1YTLB2LH2f9MiIq4oKSkhOzubyMhIAgICnOg5Hvtiu7WZdMIEjAX0w8rTXP/7FBG5PNQ2G2j68waupMS+oO6KFfbZ95yZsc9ksg9VnT/fU9WJiFzMfGAfl16U1wTcdLa9iIhI46ChfQ1YSQkkJMDy5VBRUfsQZTLZh6aOHWsfqtpE7p6KSKNjBlKx32nypfpHjuns9rFn2+mHlYiINB66I9WATZ4MW7c6dxcqJAQee8weos5OPiUi4kUB2IfrzcS+SG869inOQ7BPLDES0A8rERFpfBSkGqj8fHj1VecX3y0ogKNHFaJEpKEJA544+xIREWn8NLSvgXrtNftwPle8+ir8+KN76xERERERkV8oSDVQn3ziel+bDRYscF8tIiIiIiJSlYJUA7V9e936v/uue+oQEREREZHqFKQaoP37obS0bsc4ccI9tYiIiIiISHUKUg3QH/7g7QpERERERORiNGtfA7Rly4X2HAKWATuBQiAYiMO+Bkt0lZZt23quPhERZ52xnmFv2V6+K/+OUlspZsNMhF8EMf4xNDc192pthmHw4Ycfcuedd3q1DhERaVx0R6qByc+vaVjfbmAQcC0wH9gMZJ79Ov/s9sFn29n99reerlRE5NIqbBWkn05nReEKPv/5c3IrcjlhOUFuRS6f/fwZKwpXkH46nQqbi9OUXkJeXh4TJ04kKioKs9lMREQEw4YNIy0tzSPn27RpE4ZhUFBQ4JHjA1x99dUYhlHlNWfOHI+dT0REaqY7Ug3Ma6+dvyUNGAaUnX1vOW9/5ftNQDywGhhEc+/+gldEhApbBR8Wf8jxiuPYsNXYxoqVr8u+5qT1JHe2uBNfw30fSzk5OfTr14+QkBBeeOEFevToQXl5OampqSQlJXHgwAG3ncvdbDYbFosFX9+avx8zZ85kzJgxjvctW7asr9JEROQsp+9I/fzzz2zdupV9+/ZV21dSUsKbb77plsIuV1V/Sbobe4gqoXqAOp8FKD3bfjdffOGR8kREam3LmS0XDVGVbNj4vuJ7tpy54LhmlzzyyCMYhsGOHTtITEwkOjqabt26MWXKFLZfYGrUmu4oZWZmYhgGOTk5ABw9epRhw4YRGhpKYGAg3bp14+OPPyYnJ4cBAwYAEBoaimEYjBgxAgCr1UpycjKRkZE0a9aMXr168f7771c77yeffEJsbCxms5mtW7de8NpatmxJeHi44xUYGFi3b5aIiDjNqSB16NAhunbtys0330yPHj245ZZbOH78uGN/YWEhDz30kNuLvJwUFp77bgr2O1EX/5+QX1jPtp+KB0eViIhcUuUzUZcKUefaW7aXn60/u+X8J0+eZO3atSQlJdUYMkJCQlw+dlJSEqWlpWzZsoU9e/Ywd+5cWrRoQUREBKtWrQLg4MGDHD9+nJdffhmA5ORk3nzzTV555RX27t3L5MmTGT58OJs3b65y7GnTpjFnzhz2799Pz549L1jDnDlzuOKKK+jduzcvvPACFa6u4C4iIi5zagzFE088Qffu3dm5cycFBQVMmjSJfv36sWnTJjp06OCpGi8rzZpV/ukQkO7CESxAGn5+WUBnd5UlIuKUfWX7sGJ1qo8VK3vL9hIXEFfn8x8+fBibzUaXLl3qfKzz5ebmkpiYSI8ePQCIiopy7GvVqhUAbdq0cYS10tJSZs+ezYYNG4iPj3f02bp1K0uXLuWWW25x9J85cyZDhgy56PkfffRR+vTpQ6tWrfj888958sknOX78OC+99JI7L1NERC7BqSD1+eefs2HDBlq3bk3r1q1ZvXo1jzzyCDfddBMbN27U0AI3+CVILQN8uPSQvpr4YDItBV50V1kiIk75tvxbl/u5I0jZbLW/E+asRx99lPHjx7Nu3ToGDx5MYmLiRe8eHT58mDNnzlQLSGVlZfTu3bvKtri4S1/7lClTHH/u2bMn/v7+PPzwwyQnJ2M2m528GhERcZVTQ/t+/vnnKg++GobBkiVLGDZsGLfccguHDh1ye4GXm58do1p24lqIArBgte5yT0EiIi4otbm2qrir/c7XuXNnDMNwekIJk8n+sXhuECsvL6/SZvTo0XzzzTfcf//97Nmzh7i4OBYuXHjBYxYXFwOQkpJCZmam47Vv374qz0kBLv1C8vrrr6eiosLxDJeIiNQPp4JUly5d2LlzZ7Xtf/vb3/jd737HbzXndp39EqQKL9bskk6fLqhrKSIiLjMbrt0ZcbXf+Vq1akVCQgKLFi3i9OnT1fZfaHrysLAwgCrP/2ZmZlZrFxERwbhx4/jggw+YOnUqy5cvB8Df3x8Ai+WXX4TFxMRgNpvJzc2lU6dOVV4RERGuXmKV+kwmE23atKnzsUREpPacClJ33XUX77zzTo37/va3v/E///M/Hh1OcTn4ZWhfcJ2OU5cHqUVE6irCz7WA4Gq/mixatAiLxULfvn1ZtWoVWVlZ7N+/nwULFjieVTpfZbiZPn06WVlZpKSkMG/evCptJk2aRGpqKtnZ2WRkZLBx40a6du0KQMeOHTEMgzVr1pCfn09xcTEtW7bkscceY/LkyaxcuZIjR46QkZHBwoULWblypVPXtG3bNubPn8/u3bv55ptv+Pvf/+6YuCI0NNS1b5SIiLjEqSD13//936xZs+aC+xcvXozV6tzDxVLVL0EqDvszUs7z8fEhNjbWXSWJiDgtxj8Gk5MrbJgw0c2/m9tqiIqKIiMjgwEDBjB16lS6d+/OkCFDSEtLY8mSJTX28fPz45133uHAgQP07NmTuXPnMmvWrCptLBYLSUlJdO3aldtuu43o6GgWL14MwJVXXsmMGTOYNm0abdu2ZcKECQA899xzPP300yQnJzv6paSkEBkZ6dQ1mc1m3n33XW655Ra6devG888/z+TJk1m2bJkL3yEREakLw+bELSQfHx+OHz/uGD7w3//93yxYsIC2bdt6rMD6UFRURHBwMIWFhQQFBXm1lltvBftsuIeAa10+zqFDh+jcWbP2iUjdlZSUkJ2dTWRkJAEBAbXul346na/Lvq71FOg9/HswMHCgq2VKLbn69ykicrmobTZw6teF52eujz/+uMax5+K6X56RigYG4uxdKR8fHwYPHqwQJSJed3Pzm2nn2w4D45Jtr/S9kpub31wPVYmIiLiHc+MuxOOCqzwa9RLgT23/mkwmE/7+/rz4oqY9FxHv8zV8uavFXXT3737BYX4mTPTw78GdLe7E13BqRQ4RERGvcupTyzAMDMOotk3cZ9AgSEsD+6NmvYDVwDCgjItNh24YPpjN/qxevZpevXrVS60iIpfia/gyMHAgNzS7gX1l+/i2/FtKbaWYDTMRfhHE+MfQ3NTc22WKiIg4zakgZbPZGDFihGPBv5KSEsaNG1dt3YsPPvjAfRVeZkaOhKeeqgxSAIOAbcBUII3qi/Ta39900wAWLHhRIUpEGqTmpubEBcS5ZbFdERGRhsCpIPXggw9WeT98+HC3FiMQFgajR8OyZeeGqV7ABiALWArsAgqAECCWe+99mHfe0TNRIiIiIiL1xakg9frrr3uqDjnH/Pmwbx9s3XpumALoDPzy/JPJBDfdBG+8Ub/1iYiIiIhc7jTZRANkNkNqKowdC76+9sB0LpPJvn3sWHu7syMtRURERESknmiKpAYqIACWLIGZM+G11yA9HQoKICQEBg60P0sVFubtKkVERERELk8KUg1cWBg88YT9JSIiIiIiDYOClIiIeNzp/NN8teIrstOzKSksISA4gMhBkfQe2ZvAsMBLH8CDDMPgww8/5M477/RqHSIi0rjoGSkREfGYipIK1oxbw0vtXyL9z+l8s/4bju04xjfrvyH9T+m81P4lUsanUFFa4ZHz5+XlMXHiRKKiojCbzURERDBs2DDS0tI8cr5NmzZhGAYFBQUeOX6llJQUrr/+epo1a0ZoaKhCoIiIF+iOlIiIeERFSQVvJbxF7tZcbFZbtf02qw2b1cauZbvI35/P8NTh+Jrd97GUk5NDv379CAkJ4YUXXqBHjx6Ul5eTmppKUlISBw4ccNu53M1ms2GxWPD1rf79WLVqFWPGjGH27NkMHDiQiooKvv76ay9UKSJyedMdKRER8YjUyakXDFHnsllt5H6aS+qkVLee/5FHHsEwDHbs2EFiYiLR0dF069aNKVOmsH379hr71HRHKTMzE8MwyMnJAeDo0aMMGzaM0NBQAgMD6datGx9//DE5OTkMGDAAgNDQUAzDYMSIEQBYrVaSk5OJjIykWbNm9OrVi/fff7/aeT/55BNiY2Mxm81s3bq1Wn0VFRX84Q9/4IUXXmDcuHFER0cTExPDf/3Xf7nnmyYiIrWmINWI5efD009D584QHGx/de5s35af7+3qRORydjr/NBmvZlwyRFWyWW1kvJrBmR/PuOX8J0+eZO3atSQlJREYWP0ZrJCQEJePnZSURGlpKVu2bGHPnj3MnTuXFi1aEBERwapVqwA4ePAgx48f5+WXXwYgOTmZN998k1deeYW9e/cyefJkhg8fzubNm6sce9q0acyZM4f9+/fTs2fPaufOyMjg+++/x2Qy0bt3b9q1a8ftt9+uO1IiIl6goX2NUEkJTJwIK1aA7bz/Rykqglmz4PnnYfRoWLhQ60yJSP376rWvah2iKtmsNjJWZND/if51Pv/hw4ex2Wx06dKlzsc6X25uLomJifTo0QOAqKgox75WrVoB0KZNG0dYKy0tZfbs2WzYsIH4+HhHn61bt7J06VJuueUWR/+ZM2cyZMiQC577m2++AWD69Om89NJLXH311cybN49bb72VQ4cOOc4vIiKe59U7UkuWLKFnz54EBQURFBREfHw8n3zyiWN/SUkJSUlJXHHFFbRo0YLExEROnDhR5Ri5ubnccccdNG/enDZt2vD4449TUeGZh5YbgpISGDIEXn21eog6l80Gy5fb25aW1l99IiIA2WnZLgWp7PRst5zfdrEfkHX06KOPMmvWLPr168ezzz7Lv//974u2P3z4MGfOnGHIkCG0aNHC8XrzzTc5cuRIlbZxcXEXPZbVagXgz3/+M4mJicTGxvL6669jGAbvvfde3S5MRESc4tUgddVVVzFnzhx27drFzp07GThwIL/73e/Yu3cvAJMnT2b16tW89957bN68mWPHjnH33Xc7+lssFu644w7Kysr4/PPPWblyJW+88QbPPPOMty7J4yZPhs8+q337Tz+FSZM8Vo6ISI1KCktc6lda4J7f/HTu3BnDMJyeUMJksn8snhvEysvLq7QZPXo033zzDffffz979uwhLi6OhQsXXvCYxcXFgH2mvczMTMdr3759VZ6TAmochniudu3aARATE+PYZjabiYqKIjc3txZXKCIi7uLVIDVs2DB+/etf07lzZ6Kjo3n++edp0aIF27dvp7CwkBUrVvDSSy8xcOBAx2/dPv/8c8dDwuvWrWPfvn289dZbXHfdddx+++0899xzLFq0iLKysguet7S0lKKioiqvxiA//9J3omqyfDn8+KNnahIRqUlAcIBL/cwh7hmL3KpVKxISEli0aBGnT5+utv9C05OHhYUBcPz4cce2zMzMau0iIiIYN24cH3zwAVOnTmX58uUA+Pv7A/Zf9FWKiYnBbDaTm5tLp06dqrwiIiKcuq7KiSgOHjzo2FZeXk5OTg4dO3Z06lgiIlI3DWayCYvFwrvvvsvp06eJj49n165dlJeXM3jwYEebLl260KFDB7Zt2wbAtm3b6NGjB23btnW0SUhIoKioyHFXqybJyckEBwc7Xs5+kHnLa6/BOZ/NtWa12p+nEhGpL5GDIjFMhlN9DJNB5MBIt9WwaNEiLBYLffv2ZdWqVWRlZbF//34WLFjgeFbpfJXhZvr06WRlZZGSksK8efOqtJk0aRKpqalkZ2eTkZHBxo0b6dq1KwAdO3bEMAzWrFlDfn4+xcXFtGzZkscee4zJkyezcuVKjhw5QkZGBgsXLmTlypVOXVNQUBDjxo3j2WefZd26dRw8eJDx48cDcM8997jwXRIREVd5PUjt2bOHFi1aYDabGTduHB9++CExMTHk5eXh7+9fbWaltm3bkpeXB9gXWjw3RFXur9x3IU8++SSFhYWO17fffuvei/KQtDTn70aBvU96uvvrERG5kN4jezsfpHwM+ozq47YaoqKiyMjIYMCAAUydOpXu3bszZMgQ0tLSWLJkSY19/Pz8eOeddzhw4AA9e/Zk7ty5zJo1q0obi8VCUlISXbt25bbbbiM6OprFixcDcOWVVzJjxgymTZtG27ZtmTBhAgDPPfccTz/9NMnJyY5+KSkpREY6HxxfeOEF7r33Xu6//35+9atfcfToUdLT0wkNDXX6WCIi4jrD5skncmuhrKyM3NxcCgsLef/993n11VfZvHkzmZmZPPTQQ5SeN1NC3759GTBgAHPnzmXs2LEcPXqU1NRf1h45c+YMgYGBfPzxx9x+++21qqGoqIjg4GAKCwsJCgpy6/W50/XXw44drvXt2xe++MK99YjI5aGkpITs7GwiIyMJCKj9kL2U8SnsWrarVpNOGCaD2LGx3LHkjrqUKrXg6t+niMjlorbZwOt3pPz9/enUqROxsbEkJyfTq1cvXn75ZcLDwykrK6s2jv3EiROEh4cDEB4eXm0Wv8r3lW2akuBg1/vWYckUERGXJMxPoEP/Dpe8M2WYDDrc1IGE+Qn1VJmIiEjdeT1Inc9qtVJaWkpsbCx+fn6kpaU59h08eJDc3FzH2Pb4+Hj27NnDDz/84Gizfv16goKCqsxo1FQMGgSGcyNlAHufgQPdX4+IyMX4mn0Znjqc2LGxmHxN1QKVYTIw+ZqIHRvL8NTh+Jq1tKGIiDQeXv3UevLJJ7n99tvp0KEDp06d4u2332bTpk2kpqYSHBzMqFGjmDJlCq1atSIoKIiJEycSHx/PDTfcAMDQoUOJiYnh/vvv5y9/+Qt5eXk89dRTJCUlYW6Cq9COHAlPPQXOLpPl4wOjRnmmJhGRi/EN8OWOJXdw68xb+eq1r8hOz6a0oBRziJnIgZH0HtmbwLCLT/ktIiLSEHk1SP3www888MADHD9+nODgYHr27ElqaqpjVfe//vWvmEwmEhMTKS0tJSEhwfFAL4CPjw9r1qxh/PjxxMfHExgYyIMPPsjMmTO9dUkeFRYGo0fD0qXOTToxejS0bu25ukRELiUwLJD+T/Sn/xP9vV2KiIiIW3h9somGoLFMNgFQWgqDB8PWrbVrf9NNsH49NMEbdCJSTzQ5QdOiv08RkYtrNJNNiHPMZnswGjPm0s9L/epX8PbbClEiIiIiIu6mINUIBQTAsmVw4gRMmwYXCsq7dkFkJIwfb7+TJSIiIiIi7qEg1Yi1bAmffw7FxTXvt1rtE1MsWwYJCQpTIiIiIiLuoiDViE2ebH9Wymq9eDurFT79FCZNqpeyRERERESaPAWpRio/H1599dIhqpLVam//44+erUtEpEYl+bB3DqQPhdTr7V/3zbVv9zLDMPjoo4+8XYaIiDQyClKN1Guv1T5EVbJaYcUKz9QjIlIjSwnsGAcftofdf4a89fDTDvvXzD/Zt+8YDxbPjD3Oy8tj4sSJREVFYTabiYiIYNiwYVUWe3enTZs2YRgGBQUFHj1+Ta8vv/zSI+cUEZGaKUg1UmlprgWp9HTP1CMiUo2lBNIT4PBysFUA5//Qstq3H14GGxPcHqZycnKIjY0lPT2dF154gT179rB27VoGDBhAUlKSW8/lbjabjYoaVl+/8cYbOX78eJXX6NGjiYyMJC4uzguViohcvhSkGqnCQtf6eeiXpCIi1e2aDPlbqR6gzmeFHz6FXZPcevpHHnkEwzDYsWMHiYmJREdH061bN6ZMmcL27dtr7FPTHaXMzEwMwyAnJweAo0ePMmzYMEJDQwkMDKRbt258/PHH5OTkMGDAAABCQ0MxDIMRI0bYr9BqJTk5mcjISJo1a0avXr14//33q533k08+ITY2FrPZzNYaFgz09/cnPDzc8briiiv45z//yUMPPYRxqTUxRETErXy9XYC4JjjYtX4hIW4tQ0SkZiX5cORVLh2iKlnt7Xs+BwGt63z6kydPsnbtWp5//nkCAwOr7Q+pww/DpKQkysrK2LJlC4GBgezbt48WLVoQERHBqlWrSExM5ODBgwQFBdGsWTMAkpOTeeutt3jllVfo3LkzW7ZsYfjw4YSFhXHLLbc4jj1t2jRefPFFoqKiCA0NvWQt//rXv/jpp5946KGHXL4eERFxjYJUIzVokPPD+0wmGDjQczWJiDh88xrYnBx/bLPCNysg5ok6n/7w4cPYbDa6dOlS52OdLzc3l8TERHr06AFAVFSUY1+rVq0AaNOmjSOslZaWMnv2bDZs2EB8fLyjz9atW1m6dGmVIDVz5kyGDBlS61pWrFhBQkICV111VV0vS0REnKShfY3UyJH2YOQMHx8YNcoz9YiIVJGXRu3vRlWyQp57HuS02WxuOU5NHn30UWbNmkW/fv149tln+fe//33R9ocPH+bMmTMMGTKEFi1aOF5vvvkmR44cqdLWmeecvvvuO1JTUxmlH+wiIl6hINVIhYXB6NG1D1Mmkz1Eta77iBkRkUsrd/FBzvICt5y+c+fOGIbBgQMHnOpnOvtD9dwgVl5eXqXN6NGj+eabb7j//vvZs2cPcXFxLFy48ILHLD67anpKSgqZmZmO1759+6o8JwXUOAzxQl5//XWuuOIKfvvb39a6j4iIuI+CVCM2fz7073/pMGUywU032duLiNQLPxcf5PQLccvpW7VqRUJCAosWLeL06dPV9l9oevKwsDAAjh8/7tiWmZlZrV1ERATjxo3jgw8+YOrUqSxfvhywTwYBYLFYHG1jYmIwm83k5ubSqVOnKq+IiAiXrs9ms/H666/zwAMP4Ofn59IxRESkbhSkGjGzGVJTYexY8PWtHqhMJvv2sWPt7cxm79QpIpeh8EE4/xFjgnD3Pci5aNEiLBYLffv2ZdWqVWRlZbF//34WLFjgeFbpfJXhZvr06WRlZZGSksK8efOqtJk0aRKpqalkZ2eTkZHBxo0b6dq1KwAdO3bEMAzWrFlDfn4+xcXFtGzZkscee4zJkyezcuVKjhw5QkZGBgsXLmTlypUuXVt6ejrZ2dmMHj3apf4iIlJ3ClKNXEAALFkCx47B7NkwdCj07Wv/Onu2ffuSJQpRIlLPokaC4eRHjOEDUe573icqKoqMjAwGDBjA1KlT6d69O0OGDCEtLY0lS5bU2MfPz4933nmHAwcO0LNnT+bOncusWbOqtLFYLCQlJdG1a1duu+02oqOjWbx4MQBXXnklM2bMYNq0abRt25YJEyYA8Nxzz/H000+TnJzs6JeSkkJkZKRL17ZixQpuvPFGj0ymISIitWPYPPlEbiNRVFREcHAwhYWFBAUFebscEZEGpaSkhOzsbCIjIwkICKh9xx3j7Yvt1mrSCRN0Ggt9aw444j4u/32KiFwmapsNdEdKREQ8I3Y+hPXn0h81Jmhzk729iIhII6EgJSIinuFjhoGp9jtNhi/VP3JM9u2dxsKAVHt7ERGRRkIL8oqIiOf4BNiH6/WcaV+kNy/dPsW5X4h9YomokRAQ5u0qRUREnKYgJSIinhcQBjFP2F8iIiJNgIb2iYiIiIiIOElBSkRERERExEkKUiIiIiIiIk5SkBIREREREXGSgpSIiIiIiIiTFKRERMTzysoh9zj8+xBk7Ld/zT1u3+5lhmHw0UcfebsMERFpZBSkRETEc6xWOJQD23dD9vfwnyI4ddr+Nft7+/ZDR+3tPCAvL4+JEycSFRWF2WwmIiKCYcOGkZaW5pHzbdq0CcMwKCgo8MjxAQ4dOsTvfvc7WrduTVBQEP3792fjxo0eO5+IiNRMQUpERDzDarXfeTr+I9gu0MYGHM+3t3NzmMrJySE2Npb09HReeOEF9uzZw9q1axkwYABJSUluPZe72Ww2Kioqatz3m9/8hoqKCtLT09m1axe9evXiN7/5DXl5efVcpYjI5U1BSkREPOPwt1BYXLu2hcX29m70yCOPYBgGO3bsIDExkejoaLp168aUKVPYvn17jX1quqOUmZmJYRjk5OQAcPToUYYNG0ZoaCiBgYF069aNjz/+mJycHAYMGABAaGgohmEwYsQIAKxWK8nJyURGRtKsWTN69erF+++/X+28n3zyCbGxsZjNZrZu3Vqtvh9//JGsrCymTZtGz5496dy5M3PmzOHMmTN8/fXX7vnGiYhIrfh6uwAREWmCysohL9+5Pnn5ENke/PzqfPqTJ0+ydu1ann/+eQIDA6vtDwkJcfnYSUlJlJWVsWXLFgIDA9m3bx8tWrQgIiKCVatWkZiYyMGDBwkKCqJZs2YAJCcn89Zbb/HKK6/QuXNntmzZwvDhwwkLC+OWW25xHHvatGm8+OKLREVFERoaWu3cV1xxBddeey1vvvkmffr0wWw2s3TpUtq0aUNsbKzL1yQiIs5TkBIREffLu8hwvguxYR8G2KFdnU9/+PBhbDYbXbp0qfOxzpebm0tiYiI9evQAICoqyrGvVatWALRp08YR1kpLS5k9ezYbNmwgPj7e0Wfr1q0sXbq0SpCaOXMmQ4YMueC5DcNgw4YN3HnnnbRs2RKTyUSbNm1Yu3ZtjcFLREQ8R0FKRETcr+CU6/3cEKRsNmdTXO09+uijjB8/nnXr1jF48GASExPp2bPnBdsfPnyYM2fOVAtIZWVl9O7du8q2uLi4i57bZrORlJREmzZt+PTTT2nWrBmvvvoqw4YN48svv6Rdu7p/70REpHb0jJSIiLhfhaV++52nc+fOGIbBgQMHnOpnMtk/Fs8NYuXlVadoHz16NN988w33338/e/bsIS4ujoULF17wmMXF9ufEUlJSyMzMdLz27dtX5TkpoMZhiOdKT09nzZo1vPvuu/Tr148+ffqwePFimjVrxsqVK526VhERqRsFKRERcT9fn/rtd55WrVqRkJDAokWLOH36dLX9F5qePCwsDIDjx487tmVmZlZrFxERwbhx4/jggw+YOnUqy5cvB8Df3x8Ai+WXQBgTE4PZbCY3N5dOnTpVeUVERDh1XWfOnAF+CXyVTCYTVg9NIS8iIjVTkBIREfcLaVm//WqwaNEiLBYLffv2ZdWqVWRlZbF//34WLFjgeFbpfJXhZvr06WRlZZGSksK8efOqtJk0aRKpqalkZ2eTkZHBxo0b6dq1KwAdO3bEMAzWrFlDfn4+xcXFtGzZkscee4zJkyezcuVKjhw5QkZGBgsXLnT6LlJ8fDyhoaE8+OCD7N69m0OHDvH444+TnZ3NHXfc4do3SkREXKIgJSIi7hfeGgwn+xgGtGvtthKioqLIyMhgwIABTJ06le7duzNkyBDS0tJYsmRJjX38/Px45513OHDgAD179mTu3LnMmjWrShuLxUJSUhJdu3bltttuIzo6msWLFwNw5ZVXMmPGDKZNm0bbtm2ZMGECAM899xxPP/00ycnJjn4pKSlERkY6dU2tW7dm7dq1FBcXM3DgQOLi4ti6dSv//Oc/6dWrlwvfJRERcZVh8+QTuY1EUVERwcHBFBYWEhQU5O1yREQalJKSErKzs4mMjCQgIKD2HQ8dtS+2W1vtwiC6o/MFilNc/vsUEblM1DYb6I6UiIh4RqcICG5Ru7bBLeztRUREGgkFKRER8QyTCXpG2+80XWiYn4F9f89oe3sREZFGQutIiYiI55hM9uF6V7e3L9JbcMo+xbmvj31iifDW4O/n7SpFREScpiAlIiKe5+9nX2jXDYvtioiINAQaRyEiIiIiIuIkBSkREREREREnKUiJiIiIiIg4SUFKRERERETESZpsQkREPO7QoUMsW7aMnTt3UlhYSHBwMHFxcYwdO5bo6GhvlyciIuI0r96RSk5O5le/+hUtW7akTZs23HnnnRw8eLBKm5KSEpKSkrjiiito0aIFiYmJnDhxokqb3Nxc7rjjDpo3b06bNm14/PHHqaioqM9LERGRGuzevZtBgwZx7bXXMn/+fDZv3kxmZiabN29m/vz5XHvttQwePJjdu3d7u9QGY9OmTRiGQUFBwQXbvPHGG4SEhNRbTSIiUp1Xg9TmzZtJSkpi+/btrF+/nvLycoYOHcrp06cdbSZPnszq1at577332Lx5M8eOHePuu+927LdYLNxxxx2UlZXx+eefs3LlSt544w2eeeYZb1ySiIiclZaWRnx8PJs3bwbsP6/PVfl+06ZNxMfHk5aW5pE68vLymDhxIlFRUZjNZiIiIhg2bJhbz3frrbcyadIktxzrxhtv5Pjx4wQHB7vleCIi4hleHdq3du3aKu/feOMN2rRpw65du7j55pspLCxkxYoVvP322wwcOBCA119/na5du7J9+3ZuuOEG1q1bx759+9iwYQNt27bluuuu47nnnuOJJ55g+vTp+Pv7e+PSREQua7t372bYsGGUlJRgs9ku2tZisVBaWsqwYcPYtm0bvXr1clsdOTk59OvXj5CQEF544QV69OhBeXk5qampJCUlceDAAbed61JsNhsWiwVf34t/9Pr7+xMeHl5PVYmIiKsa1GQThYWFALRq1QqAXbt2UV5ezuDBgx1tunTpQocOHdi2bRsA27Zto0ePHrRt29bRJiEhgaKiIvbu3VvjeUpLSykqKqryEhER95kyZQplZWWXDFGVrFYrZWVlTJ061a11PPLIIxiGwY4dO0hMTCQ6Oppu3boxZcoUtm/fDkBBQQGjR48mLCyMoKAgBg4cWGWo4fTp07nuuuv43//9X66++mqCg4O59957OXXqFAAjRoxg8+bNvPzyyxiGgWEY5OTkOIboffLJJ8TGxmI2m9m6dSulpaU8+uijtGnThoCAAPr378+XX37pOF9NQ/veeOMNOnToQPPmzbnrrrv46aefqlzn7t27GTBgAC1btiQoKIjY2Fh27tzp1u+liIhU1WCClNVqZdKkSfTr14/u3bsD9uEY/v7+1caBt23blry8PEebc0NU5f7KfTVJTk4mODjY8YqIiHDz1YiIXL4OHTpEenp6taF8l2KxWEhLSyMrK8stdZw8eZK1a9eSlJREYGBgtf2Vny333HMPP/zwA5988gm7du2iT58+DBo0iJMnTzraHjlyhI8++og1a9awZs0aNm/ezJw5cwB4+eWXiY+PZ8yYMRw/fpzjx49X+VyZNm0ac+bMYf/+/fTs2ZM//vGPrFq1ipUrV5KRkUGnTp1ISEiocr5zffHFF4waNYoJEyaQmZnJgAEDmDVrVpU29913H1dddRVffvklu3btYtq0afj5+dX1WygiIhfRYIJUUlISX3/9Ne+++67Hz/Xkk09SWFjoeH377bceP6eIyOVi2bJl+Pj4uNTXx8eHpUuXuqWOw4cPY7PZ6NKlywXbbN26lR07dvDee+8RFxdH586defHFFwkJCeH99993tLNarbzxxht0796dm266ifvvv9/xjFVwcDD+/v40b96c8PBwwsPDq1z/zJkzGTJkCNdccw1ms5klS5bwwgsvcPvttxMTE8Py5ctp1qwZK1asqLHGl19+mdtuu40//vGPREdH8+ijj5KQkFClTW5uLoMHD6ZLly507tyZe+65x61DJEVEpLoGEaQmTJjAmjVr2LhxI1dddZVje3h4OGVlZdVmLjpx4oRj/Hh4eHi1Wfwq319ojLnZbCYoKKjKS0RE3GPnzp1O342qZLFY2LVrl1vqqM2wwt27d1NcXOyYGbbylZ2dzZEjRxztrr76alq2bOl4365dO3744Yda1REXF+f485EjRygvL6dfv36ObX5+fvTt25f9+/fX2H///v1cf/31VbbFx8dXeT9lyhRGjx7N4MGDmTNnTpXaRUTEM7wapGw2GxMmTODDDz8kPT2dyMjIKvtjY2Px8/OrMrPSwYMHyc3NdXyIxMfHs2fPniofaOvXrycoKIiYmJj6uRAREXGofN7VVReb9tsZnTt3xjCMi04oUVxcTLt27cjMzKzyOnjwII8//rij3fnD5AzDwGq11qqOmoYVutv06dPZu3cvd9xxB+np6cTExPDhhx96/LwiIpczrwappKQk3nrrLd5++21atmxJXl4eeXl5/Pzzz4B9uMSoUaOYMmUKGzduZNeuXTz00EPEx8dzww03ADB06FBiYmK4//772b17N6mpqTz11FMkJSVhNpu9eXkiIpeluk7b7a71kVq1akVCQgKLFi2qsqxGpYKCAvr06UNeXh6+vr506tSpyqt169a1Ppe/v3+t7sJdc801+Pv789lnnzm2lZeX8+WXX17wl39du3bliy++qLKtcqKMc0VHRzN58mTWrVvH3Xffzeuvv17r+kVExHleDVJLliyhsLCQW2+9lXbt2jle//d//+do89e//pXf/OY3JCYmcvPNNxMeHs4HH3zg2O/j48OaNWvw8fEhPj6e4cOH88ADDzBz5kxvXJKIyGUvLi6uTs9IxcbGuq2WRYsWYbFY6Nu3L6tWrSIrK4v9+/ezYMEC4uPjGTx4MPHx8dx5552sW7eOnJwcPv/8c/785z87Nevd1VdfzRdffEFOTg4//vjjBe9WBQYGMn78eB5//HHWrl3Lvn37GDNmDGfOnGHUqFE19nn00UdZu3YtL774IllZWfztb3+rsnzIzz//zIQJE9i0aRNHjx7ls88+48svv6Rr167OfbNERMQpXl1Hqjbj1wMCAli0aBGLFi26YJuOHTvy8ccfu7M0ERFx0dixY5k3b55LfS0WCw8//LDbaomKiiIjI4Pnn3+eqVOncvz4ccLCwoiNjWXJkiUYhsHHH3/Mn//8Zx566CHy8/MJaxtG7A2xDPIdxKGfDnGq9BQ2Lv559dhjj/Hggw8SExPDzz//THZ29gXbzpkzB6vVyv3338+pU6eIi4sjNTWV0NDQGtvfcMMNLF++nGeffZZnnnmGwYMH89RTT/Hcc88B9vD5008/8cADD3DixAlat27N3XffzYwZM1z/xomIyCUZttou8tGEFRUVERwcTGFhoSaeEBE5T0lJCdnZ2URGRhIQEFCrPoMGDWLz5s1OTTrh4+PDgAEDWL9+vaulusxqs/Jt4bf8eObHGkOTgUHr5q2JCI7AZDSIeZpc5srfp4jI5aS22aBxfxqIiEiD9NJLL+Hv74/JVLuPGZPJhL+/Py+++KKHK6vOarNy6KdD5J/Jv+CdJxs28s/kk/VTFlZb7SaZEBGRpk1BSkRE3K5Xr16sXr0as9l8yeelfHx8MJvNrF692itrH31b+C3FZcW1anuq7BTfFmrtQRERUZASEREPGTRoENu2bePWW28FqBaoKt8PGDCAbdu2MWjQoPoukXJLOT+e+dGpPj+e+ZFyS7mHKhIRkcbCq5NNiIhI09arVy82bNhAVlYWS5cuZdeuXRQUFBASEkJsbCwPP/wwnTt39lp9F3om6mJs2PjxzI+0a9nOQ1WJiEhjoCAlIiK1Upe5iTp37uyV558u5VTZKZf6/XD6B9q2aNsoJ57QHFMiIu7R+D4BRESkXvn5+QFw5swZL1fifhZr7WcVPFe5tZw9J/a43N+bysrKgOpDLUVExDm6IyUiIhfl4+NDSEgIP/zwAwDNmzfHMAwvV1V3VpuVspIyqozsqwB+PvvVBhjYPymbUe0Ts5xy9ny/h+jW0Y3mzpTVaiU/P5/mzZvj66v/BRARqQv9FBURkUsKDw8HcISpxs5ms3Hi9AlKK0rtGyxACfYAdSG+QABw3o2cUz+conXz1h6p0xNMJhMdOnRoEmFYRMSbFKREROSSDMOgXbt2tGnThvLyxj9j3fRN0/nH3n/Y14T6DliDPUxd7PEhA3uI+g1w1S+bTYaJz0Z+RmizUA9W7D7OrO8lIiIXpiAlIiK15uPj0+ifrck/nc+8L+dRYa2APOBVLn4n6lwGsAgYDYT/svnNvW/yRP8n3F2qiIg0YPqVlIiIXFaW7lxqD1EAqdjvRNWW7Wz71Kqb07PT3VOciIg0GgpSIiJy2SipKGHe9nn2Nz8C2Vx8OF9NbGf7/fTLpoLSAneUJyIijYiClIiIXDYmp06moKTA/mYX9qF6rjCAnb+8DTGH1KkuERFpfBSkRETkspB/Op9XM179ZcMxnL8bVckGHP/l7cDIgXWoTEREGiMFKRERuSy89tVr9ln6KpXW8YAlv/xxVJ9RdTyYiIg0NgpSIiJyWUjLTqsapMx1PGCA/Ut4YHijWkdKRETcQ0FKREQuC4UlhVU3tKduz0i1s/9xQt8JdahKREQaK60jJSIil4XggOCqG2KBbS4ezAbE2f/4cNzDdahKRKRxOZ1/mq9WfEV2ejZnfjxD2akyAPxa+hHYOpDIQZH0HtmbwLBAL1fqeQpSIiJyWRgUOajq8L7WQCSQg3OTThhn+10BrZu11rA+EbksVJRUsHbSWr5a8RVWi/WCPze/2fANG5/aSJ/RfUiYn4CvuenGDQ3tExGRy8LI3iMxGed97CUAPtR+iJ9xtv1Q+9vrwq9zV3kiIg1WRUkF/zvkf9m1bBfWiguHKABsYK2wsmvZLt5KeIuK0op6q7O+KUiJiMhlISwwjNF9RlfdGA78P2oXpipD1P+z9zMwGBw12AOViog0LJ88+gm5W3Oduntvs9rI/TSX1EmpnivMyxSkRETksvHn/n+uvjEKGA1cffb9+YGq8n3k2XZR9re+Jl9Ney4iTd7p/NNkLM9wqa/NaiPj1QzO/HjGzVU1DE130KKIiMh5/r7n7zXvCAceBH4CdmJfbLcE+xTn7bBPLHHFL81NholRfUbp+SgRafK+WPBFnfrbrDYyVmTQ/4n+bqqo4VCQEhGRy0ZadtrFG1yB/bmpizAZJm7qcBPzE+a7qywRkQZrz9/31Km/zWojOz27SQYpDe0TEZHLRrW1pJxgMkz4mnwZGzuW1OGpmH3ruqKviEjDd+rYqTofo7Sg1A2VNDy6IyUiIpeNamtJ1VKrgFb8sd8fGdl7JGGBYW6uSkSk4bJWWOt8DHNI0/zFk+5IiYjIZWNQ5KDqU6BfgoHBH/v9kSf6P6EQJSKXHZNv3eKCYTKIHBjppmoaFgUpERG5bNS4ltQlaHY+EbmctWzfsk79DR+DPqP6uKmahkVBSkRELhuVa0nVNkxpdj4Rudz1uK9HnfqHRobi39LfTdU0LApSIiJyWZmfMJ/+HfpfMkxpdj4REbj+0esvvWD5Rfx06CfeSniLitIK9xXVQChIiYjIZcXsayZ1eCpjY8fia/KtFqg0O5+IyC8CwwLrfFcq99NcUieluqmihsOw2Ww2bxfhbUVFRQQHB1NYWEhQUJC3yxERkXqSfzqf1756jfTsdApKCwgxhzAwcqBm5xMROUdFaQXJLZOxlrs+g5/J18TU41Np3rq5GyvzjNpmAwUpFKRERERERC5mduxs1mSs4RjHKKUUM2ba055YYmlNLZ4jNWBQ8qBGsTBvbbOB1pGSKs5Yz7C3bC/flX9Hqa0Us2Emwi+CGP8Ympsa/m8QRERERMR9du/ezZQpU0jPSMfAwMYv92ByyWUb24gkkgQSCCf8wgeyQXZ6dqMIUrWlICUAVNgq2HJmC3vL9mKl6m3b3Ipctv28jW7+3bi5+c34GvpnIyIiItLUpaWlMWzYMMrKygCqhKhz3+eQw6u8yv/j/xFF1AWPdyb/jOeK9QJNNiFU2Cr4sPhDvi77ulqIqmTFytdlX/NR8UdU2JrerCsiIiIi8ovdu3czbNgwSkpKsFgsF21rw4YFC2/zNnnkXbBdWXGZu8v0KgUpYcuZLRyvOF7ttwzns2Hj+4rv2XJmSz1VJiIiIiLeMGXKFMrKyqjtdAqVYSqVpjc734UoSF3mKp+JulSIOtfesr38bP3Zg1WJiIiIiLccOnSI9PT0S96JOp8NG9lk8xM/1bi/qS3MqyB1mdtXtu+Cw/kuxIqVvWV7PVSRiIiIiHjTsmXL8PHxcamvgcFOdta4rzFMfe4MBanL3Lfl37rU74ufv6DUWurmakRERETE23bu3On03ahKNmwc53i17YbJIHJgZF1La1AUpC5zpTbXwlAFFbxW+JrClIiIiEgTU1hYWKf+JZRU22b4GPQZ1adOx21oFKQuc2bD7HLfMsr4x6l/uLEaEREREfG24ODgOvUPIKDqBgP6jOqjoX3StET4RdSp/0nrSf5T8R83VSMiIiIi3hYXF1enZ6Ta0a7adqvFSkVp01pCR0HqMhfjH4Opjv8MNv+82U3ViIiIiIi3jR07tk7PSMURd/5GvlrxFW8lvNWkwpSC1GWuuak53fy71ekYeRUXXnhNRERERBqX6OhoBg4c6PRdKQODKKK4giuq7bNZbeR+mkvqpKazzpRXg9SWLVsYNmwY7du3xzAMPvrooyr7bTYbzzzzDO3ataNZs2YMHjyYrKysKm1OnjzJfffdR1BQECEhIYwaNYri4uJ6vIrG7+bmN9PCaOFy/1I04YSIiIhIU/LSSy/h7++PyVS7uGBg4IMPQxl6wTY2q42MVzM48+MZd5XpVV4NUqdPn6ZXr14sWrSoxv1/+ctfWLBgAa+88gpffPEFgYGBJCQkUFLyy0wg9913H3v37mX9+vWsWbOGLVu2MHbs2Pq6hCbB1/DFZKvbPwUt0CsiIiLSdPTq1YvVq1djNpsveWeqMkT9P/4f4YRftK3NaiNjRYY7S/Uaw2az2bxdBIBhGHz44YfceeedgP1uVPv27Zk6dSqPPfYYYJ+KsW3btrzxxhvce++97N+/n5iYGL788kvi4uxjMdeuXcuvf/1rvvvuO9q3b1+rcxcVFREcHExhYSFBQUEeub6GbuF/Fjq9MO+5+jXrR1xA3KUbioiIiEijsXv3bqZOnUpaWho+Pj5Vnp0yGSasNitRRDGUoZcMUZWihkZxf+r9niq5zmqbDXzrsSanZGdnk5eXx+DBgx3bgoODuf7669m2bRv33nsv27ZtIyQkxBGiAAYPHozJZOKLL77grrvuqvHYpaWllJb+MhytqKjIcxfSCJyxnqlTiALIKctRkBIRERFpYnr16sWGDRvIyspi6dKl7Nq1i4KCAkJCQvA74EeXvC41PhN1MaUFTeOxkAYbpPLy7BMYtG3btsr2tm3bOvbl5eXRpk2bKvt9fX1p1aqVo01NkpOTmTFjhpsrbrz2le2r8zEKrAV1L0REREREGqTOnTvz4osvVtn2v0P/l2/yvnH6WL7NG2wEcUrTuAonPfnkk0yZMsXxvqioiIiIuq2n1Jh9W/7tJdv8cPgHtq3cRm5mLiVFJQQEBdDhug7EPxhPm05t+NmmZ6RERERELieRgyLJTsvGZnXuSaHcLbmkjE8hYX4CvubGG0cabOXh4fYxlidOnKBdu18W9Tpx4gTXXXedo80PP/xQpV9FRQUnT5509K+J2WzGbDa7v+hGqtR24dur33/9PR899RFZW7Iw+ZiwWn4ZApi9PZuNizbS+ebO3DnrTn7u9zPNTM3qo2QRERER8bLeI3uz8amNTgcpm9XGrmW7yN+fz/DU4Y02TDXYdaQiIyMJDw8nLS3Nsa2oqIgvvviC+Ph4AOLj4ykoKGDXrl2ONunp6VitVq6//vp6r7mxMhs1h8pDmw8xP2E+hz87DFAlRJ37/vBnh5mfMJ/XU1/3bKEiIiIi0mAEhgXSZ3QfDJPhdN+msK6UV4NUcXExmZmZZGZmAvYJJjIzM8nNzcUwDCZNmsSsWbP417/+xZ49e3jggQdo3769Y2a/rl27cttttzFmzBh27NjBZ599xoQJE7j33ntrPWOfQIRf9WGN33/9Pcv/33IqSiqwWS7+WwabxUZFaQWTEyeze/duT5UpIiIiIg1MwvwEOvTv4HKYaszrSnk1SO3cuZPevXvTu3dvAKZMmULv3r155plnAPjjH//IxIkTGTt2LL/61a8oLi5m7dq1BAQEOI7x97//nS5dujBo0CB+/etf079/f5YtW+aV62msYvxjMKj6j/+jpz6ioqyC2s6Ob7PaqCirYOrUqZ4oUUREREQaIF+zL8NThxM7Ntb1MNVI15VqMOtIeZPWkYL00+nsKdsD2CeWmN13tsvHOnToEJ07d3ZXaSIiIiLSCLxx6xsc3XzU6X4NbV2p2maDBvuMlNSvm5vfTDsf+6Qe21Zuw+Tj2j8Nk4+JJa8scWdpIiIiItIIlP9c7lK/xrqulIKUAOBr+HJ3y7u51vdacjNzq00sUVtWi5WUHSmUWhvnfxAiIiIi4pqA4IBLN6qBOaRxzqatICUOvoYvt7W8jWbFdZvC/HThad4sepMKW4WbKhMRERGRhi5yUKTTz0kZJoPIgZEeqsizFKSkmitCrqhT/2bBzThjO0P66XQ3VSQiIiIiDV3vkb2dD1I+Bn1G9fFQRZ6lICXVxMXF4ePj41Jfk4+JiF726dQPlB/gZ+vP7ixNRERERBooZ9eVMkz2ENW8dXMPV+YZClJSzdixY7FYLC71tVqs3DjiRgBs2NhbttedpYmIiIhIA1bbdaUMk0GHmzqQMD+hnipzPwUpqSY6OpqBAwc6PXOf4WMQfUs0YdeEObZ9W/6tu8sTERERkQbq3HWlTL6maoHKMBmYfE3Ejo1leOpwfM2+Xqq07hpv5eJRL730EvHx8ZSUlmCzXnqpMcNk4Ovvy++e+12V7aU2zd4nIiIicjnxDfDljiV3cOvMW/nqta/ITs+mtKAUc4iZyIGR9B7Zm8CwQG+XWWdakBctyHshaWlp/HrYrykvK8dmufA/E8PHHqLGvD2G6Fuiq+zr4NuBu1re5elSRURERETcQgvySp0NGjSI5A3JdO7fGaDaUL/K9537d2ZS6qRqIQogwi/C84WKiIiIiNQzDe2Ti7q5z834fOhD/pF8Pn/jc77d/S0/F/5Ms+BmRPSK4MYRN1Z5JupcJkx08+9WzxWLiIiISH07nX+ar1bYh/GVFJYQEBxA5KCmM4yvJhrah4b2XcwZ6xlWFK7AitXpvj38ezAwcKAHqhIRERGRhqCipIK1k9by1YqvsFltVZ6tN0yGfYrz0X1ImJ/QaCaW0NA+cYvmpuZ08++GgXOLq13peyU3N7/ZQ1WJiIiIiLdVlFTwVsJbZCzPwFphrTZBmc1qw1phZdeyXbyV8BYVpRVeqtQzFKTkkm5ufjPtfNvVOkx18+/GnS3uxNdoHL91EBERERHnpU5OJXdr7iVneLZZbeR+mkvqpNR6qqx+KEjJJfkavtzV4i66+3fHdJF/MuE+4YwMGsngwMEKUSIiIiJN2On802S8mlGrZXLAHqYyXs3gzI9nPFxZ/dH/7Uqt+Bq+DAwcyA3NbmBf2T6+Lf+WUlspZsNMhF8EMf4xNDc193aZIiIiIlIPvnrtq1qHqEo2q42MFRn0f6K/h6qqXwpS4pTmpubEBcQRFxDn7VJERERExEuy07JdClLZ6dlNJkhpaJ+IiIiIiDilpLDEpX6lBaVursR7dEdKRERERC5rl+MaSHUVEBzgUj9ziNnNlXiPgpSIiIiIXJYutgZSdlo2G5/a2OjWQKovkYMinR7eZ5gMIgdGerCq+qWhfSIiIiJy2bnc10Cqq94je2OYnFtn1PAx6DOqj4cqqn8KUiIiIiJy2bnc10Cqq8CwQPqM7lPrMGWY7CGqeeumM8uzgpSIiIiIXFa0BpJ7JMxPoEP/DpcMU4bJoMNNHUiYn1BPldUPBSkRERERuazUZQ0k+YWv2ZfhqcOJHRuLyddULVAZJgOTr4nYsbEMTx3e5J4za1pXI151xnqGvWV7+a78Oy3WKyIiIg2W1kByH98AX+5Ycge3zryVr16zz3xYWlCKOcRM5MCmPfOhgpTUWYWtgi1ntrC3bC9WrFX25Vbksu3nbXTz78bNzW/G19A/OREREfEurYHkfoFhgfR/ov9lFTT1f7VSJxW2Cj4s/pDjFcexUfNvdqxY+brsa05aT3JnizsVpkRERMSrtAaSuIOekZI62XJmy0VDVCUbNr6v+J4tZ7bUU2UiIiIiNYscFOn81N1NbA0kqTsFKXFZ5TNRlwpR59pbtpefrT97sCoRERGRi9MaSOIOClLisn1l+6o9E3UpVqzsLdvroYpERERELk1rIIk7KEiJy74t/7Ze+4mIiIi4y+W+BpLUnYKUuKzU5trMNa72ExEREXGXy30NJKk7/YsQl5kN12aucbWfiIiIiDtdzmsgSd0pSInLIvwiyK3IdamfiIiISENxOa6BJP+/vXsNjqo+4zj+22XZTZaYcCsJCRtIqyPNpYhEMaDpWDNFyoBgRyuT0kD7hooK4iBQR33RoVA706qj4uWFfVGr1hmglfEyNKCRNnIPmNAijBQiEqJiLpiYC/v0hWZluYUN2T2b3e9nZmfI+f/PznPyZJfzm3P2v5ePW/vQZ/nefLkj/BNyy60Cb0GUKgIAAABigyCFPvO7/RGHogJvgVLdqVGqCAAAAIgNghQuy5TUKfLKe0lzvfJqaurUKFcEAAAARB9BCpfl3+3/Vpe6Lmlupzr1r/Z/RbkiAAAAIPoIUuiztmCb6jrrZLJL3qeus07twfYoVgUAAABEH0EKfba/c7+CCka0T1BB1XXWRakiAAAAIDYIUuiz+q76mO4HAAAAxAuCFPqswzpiuh8AAAAQLwhS6DOfyxfT/QAAAIB4QZBCnwUGB2K6HwAAABAvCFLos3xvvtwR/gm55Y74S3wBAACAeEOQQp/53X4VeAvkkuuS9ynwFijVnRrFqgAAAIDoS5gg9fTTT2vcuHFKSUnR5MmTtX37dqdLSgql/lKN9oy+pDCV48lRqb80BlUBAAAA0ZUQQerVV1/V0qVL9eijj2r37t2aMGGCpk2bpsbGRqdLS3gel0dz0uao0Ft4wdv83HKryFuk2Wmz5XF5YlwhAAAA0P9cZmZOF3G5Jk+erOuuu05PPfWUJCkYDCoQCOjee+/VihUret2/paVFGRkZam5uVnp6erTLTVhtwTbt79yv+q56dViHfC6fAoMDyvfmy+/2O10eAAAA0KtLzQYD/vJAZ2endu3apZUrV4a2ud1ulZWVqbq6+rz7dHR0qKPj2+8yamlpiXqdycDv9qs4pVjFKcVOlwIAAABE1YC/te+zzz7T6dOnlZmZGbY9MzNTDQ0N591n9erVysjICD0CAZbjBgAAAHDpBnyQ6ouVK1equbk59Kivr3e6JAAAAAADyIC/tW/kyJEaNGiQTpw4Ebb9xIkTysrKOu8+Pp9PPp8vFuUBAAAASEAD/oqU1+vVpEmTVFlZGdoWDAZVWVmpkpISBysDAAAAkKgG/BUpSVq6dKkqKipUXFys66+/Xo8//ri+/PJLLViwwOnSAAAAACSghAhSP/vZz/Tpp5/qkUceUUNDg6655hq99dZb5yxAAQAAAAD9ISG+R+py8T1SAAAAAKRLzwYD/jNSAAAAABBrCXFrH5JLW7BNdZ11+rjrY3VYh3wunwKDA8r35svv9jtdHgAAAJIAQQoDRrd1q6qtSnWddQoqGDZ2tPuoqturVeAtUKm/VB4Xf9oAAACIHs42MSB0W7fWn1qv493HZTr/x/qCCqq2s1Yngyc1O202YQoAAABRw2ekMCBUtVVdNET1MJmOdR9TVVtVjCoDAABAMiJIIe71fCaqtxB1prrOOrUH26NYFQAAAJIZQQpxb3/n/nM+E9WboIKq66yLUkUAAABIdgQpxL36rvqY7gcAAAD0hiCFuNdhHTHdDwAAAOgNQQpxz+fyxXQ/AAAAoDcEKcS9wOBATPcDAAAAekOQQtzL9+bLHeGfqltuFXgLolQRAAAAkh1BCnHP7/arwFsgl1yXvE+Bt0Cp7tQoVgUAAIBkRpDCgFDqL9Voz+hLClM5nhyV+ktjUBUAAACSFUEKA4LH5dGctDkq9BZe8DY/t9wq8hZpdtpseVyeGFcIAACAZMLZJgYMj8ujHw35kW5IvUH7O/ervqteHdYhn8unwOCA8r358rv9TpcJAACAJECQwoDjd/tVnFKs4pRip0sBAABAkuLWPgAAAACIEEEKAAAAACJEkAIAAACACBGkAAAAACBCBCkAAAAAiBBBCgAAAAAiRJACAAAAgAgRpAAAAAAgQgQpAAAAAIgQQQoAAAAAIkSQAgAAAIAIeZwuIB6YmSSppaXF4UoAAAAAOKknE/RkhAshSElqbW2VJAUCAYcrAQAAABAPWltblZGRccFxl/UWtZJAMBjUJ598oiuuuEIul8vpcpJCS0uLAoGA6uvrlZ6e7nQ5OAv9iW/0J77Rn/hGf+Ib/YlvydIfM1Nra6uys7Pldl/4k1BckZLkdrs1ZswYp8tISunp6Qn9Qhzo6E98oz/xjf7EN/oT3+hPfEuG/lzsSlQPFpsAAAAAgAgRpAAAAAAgQgQpOMLn8+nRRx+Vz+dzuhScB/2Jb/QnvtGf+EZ/4hv9iW/0JxyLTQAAAABAhLgiBQAAAAARIkgBAAAAQIQIUgAAAAAQIYIUAAAAAESIIIV+s3r1al133XW64oorNGrUKM2ePVsHDhwIm/PVV19p0aJFGjFihNLS0vTTn/5UJ06cCJtz9OhRzZgxQ36/X6NGjdKyZcvU3d0dy0NJCmvWrJHL5dKSJUtC2+iPs44dO6af//znGjFihFJTU1VUVKSdO3eGxs1MjzzyiEaPHq3U1FSVlZXp4MGDYc9x8uRJlZeXKz09XUOHDtWvfvUrnTp1KtaHknBOnz6thx9+WHl5eUpNTdX3vvc9/fa3v9WZ6zXRn9ipqqrSzJkzlZ2dLZfLpQ0bNoSN91cv9u3bp5tuukkpKSkKBAJ67LHHon1oCeFi/enq6tLy5ctVVFSkIUOGKDs7W7/4xS/0ySefhD0H/Yme3l4/Z1q4cKFcLpcef/zxsO305xsG9JNp06bZiy++aLW1tVZTU2M/+clPLDc3106dOhWas3DhQgsEAlZZWWk7d+60G264waZMmRIa7+7utsLCQisrK7M9e/bYG2+8YSNHjrSVK1c6cUgJa/v27TZu3Dj7wQ9+YIsXLw5tpz/OOXnypI0dO9bmz59v27Zts48++sjefvttO3ToUGjOmjVrLCMjwzZs2GB79+61WbNmWV5enrW3t4fm3HrrrTZhwgR7//337b333rMrr7zS5s6d68QhJZRVq1bZiBEjbOPGjXb48GF77bXXLC0tzZ544onQHPoTO2+88YY99NBDtm7dOpNk69evDxvvj140NzdbZmamlZeXW21trb388suWmppqzz33XKwOc8C6WH+ampqsrKzMXn31Vfvvf/9r1dXVdv3119ukSZPCnoP+RE9vr58e69atswkTJlh2drb96U9/ChujP18jSCFqGhsbTZK9++67Zvb1m+fgwYPttddeC835z3/+Y5KsurrazL5+cbvdbmtoaAjNWbt2raWnp1tHR0dsDyBBtba22lVXXWWbNm2yH/7wh6EgRX+ctXz5crvxxhsvOB4MBi0rK8v+8Ic/hLY1NTWZz+ezl19+2czM9u/fb5Jsx44doTlvvvmmuVwuO3bsWPSKTwIzZsywX/7yl2Hbbr/9disvLzcz+uOks08E+6sXzzzzjA0bNizsvW358uV29dVXR/mIEsvFTtR7bN++3STZkSNHzIz+xNKF+vPxxx9bTk6O1dbW2tixY8OCFP35Frf2IWqam5slScOHD5ck7dq1S11dXSorKwvNGT9+vHJzc1VdXS1Jqq6uVlFRkTIzM0Nzpk2bppaWFtXV1cWw+sS1aNEizZgxI6wPEv1x2j/+8Q8VFxfrjjvu0KhRozRx4kS98MILofHDhw+roaEhrD8ZGRmaPHlyWH+GDh2q4uLi0JyysjK53W5t27YtdgeTgKZMmaLKykp9+OGHkqS9e/dq69atmj59uiT6E0/6qxfV1dUqLS2V1+sNzZk2bZoOHDigL774IkZHkxyam5vlcrk0dOhQSfTHacFgUPPmzdOyZctUUFBwzjj9+ZbH6QKQmILBoJYsWaKpU6eqsLBQktTQ0CCv1xt6o+yRmZmphoaG0JwzT9J7xnvGcHleeeUV7d69Wzt27DhnjP4466OPPtLatWu1dOlS/eY3v9GOHTt03333yev1qqKiIvT7Pd/v/8z+jBo1Kmzc4/Fo+PDh9OcyrVixQi0tLRo/frwGDRqk06dPa9WqVSovL5ck+hNH+qsXDQ0NysvLO+c5esaGDRsWlfqTzVdffaXly5dr7ty5Sk9Pl0R/nPb73/9eHo9H991333nH6c+3CFKIikWLFqm2tlZbt251uhR8o76+XosXL9amTZuUkpLidDk4SzAYVHFxsX73u99JkiZOnKja2lo9++yzqqiocLg6/O1vf9NLL72kv/71ryooKFBNTY2WLFmi7Oxs+gP0UVdXl+68806ZmdauXet0OdDXd6c88cQT2r17t1wul9PlxD1u7UO/u+eee7Rx40Zt2bJFY8aMCW3PyspSZ2enmpqawuafOHFCWVlZoTlnrxLX83PPHPTNrl271NjYqGuvvVYej0cej0fvvvuunnzySXk8HmVmZtIfB40ePVr5+flh277//e/r6NGjkr79/Z7v939mfxobG8PGu7u7dfLkSfpzmZYtW6YVK1borrvuUlFRkebNm6f7779fq1evlkR/4kl/9YL3u+jqCVFHjhzRpk2bQlejJPrjpPfee0+NjY3Kzc0NnSscOXJEDzzwgMaNGyeJ/pyJIIV+Y2a65557tH79em3evPmcS7qTJk3S4MGDVVlZGdp24MABHT16VCUlJZKkkpISffDBB2Ev0J432LNPMhGZW265RR988IFqampCj+LiYpWXl4f+TX+cM3Xq1HO+LuDDDz/U2LFjJUl5eXnKysoK609LS4u2bdsW1p+mpibt2rUrNGfz5s0KBoOaPHlyDI4icbW1tcntDv8vc9CgQQoGg5LoTzzpr16UlJSoqqpKXV1doTmbNm3S1VdfnTC3JTmlJ0QdPHhQ//znPzVixIiwcfrjnHnz5mnfvn1h5wrZ2dlatmyZ3n77bUn0J4zTq10gcfz617+2jIwMe+edd+z48eOhR1tbW2jOwoULLTc31zZv3mw7d+60kpISKykpCY33LK/94x//2Gpqauytt96y73znOyyvHSVnrtpnRn+ctH37dvN4PLZq1So7ePCgvfTSS+b3++0vf/lLaM6aNWts6NCh9ve//9327dtnt91223mXdJ44caJt27bNtm7daldddRXLa/eDiooKy8nJCS1/vm7dOhs5cqQ9+OCDoTn0J3ZaW1ttz549tmfPHpNkf/zjH23Pnj2hVd/6oxdNTU2WmZlp8+bNs9raWnvllVfM7/cn3PLN0XCx/nR2dtqsWbNszJgxVlNTE3a+cOYKb/Qnenp7/Zzt7FX7zOhPD4IU+o2k8z5efPHF0Jz29na7++67bdiwYeb3+23OnDl2/PjxsOf53//+Z9OnT7fU1FQbOXKkPfDAA9bV1RXjo0kOZwcp+uOs119/3QoLC83n89n48ePt+eefDxsPBoP28MMPW2Zmpvl8PrvlllvswIEDYXM+//xzmzt3rqWlpVl6erotWLDAWltbY3kYCamlpcUWL15subm5lpKSYt/97nftoYceCjvxoz+xs2XLlvP+f1NRUWFm/deLvXv32o033mg+n89ycnJszZo1sTrEAe1i/Tl8+PAFzxe2bNkSeg76Ez29vX7Odr4gRX++5jI742vZAQAAAAC94jNSAAAAABAhghQAAAAARIggBQAAAAARIkgBAAAAQIQIUgAAAAAQIYIUAAAAAESIIAUAAAAAESJIAQAAAECECFIAAAAAECGCFAAgqcyfP18ul+ucx6FDh1RVVaWZM2cqOztbLpdLGzZscLpcAECcIkgBAJLOrbfequPHj4c98vLy9OWXX2rChAl6+umnnS4RABDnPE4XAABArPl8PmVlZZ2zffr06Zo+fboDFQEABhquSAEAAABAhAhSAICks3HjRqWlpYUed9xxh9MlAQAGGG7tAwAknZtvvllr164N/TxkyBAHqwEADEQEKQBA0hkyZIiuvPJKp8sAAAxg3NoHAAAAABHiihQAAN84deqUDh06FPr58OHDqqmp0fDhw5Wbm+tgZQCAeEOQAgDgGzt37tTNN98c+nnp0qWSpIqKCv35z392qCoAQDxymZk5XQQAAAAADCR8RgoAAAAAIkSQAgAAAIAIEaQAAAAAIEIEKQAAAACIEEEKAAAAACJEkAIAAACACBGkAAAAACBCBCkAAAAAiBBBCgAAAAAiRJACAAAAgAgRpAAAAAAgQv8HT1yRltJxQHsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and ElasticNetCV model\n", + " # with polynomial features of degree 2\n", + " sc = StandardScaler()\n", + " model = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.ElasticNetCV(alphas=(0.1, 1.0, 10.0), l1_ratio=0.5),\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From dd013a5c327637b8e1f7d088a6ac1b50838cbb60 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Mon, 8 Jul 2024 19:23:36 +0000 Subject: [PATCH 31/78] elastic net cv grid search added --- ..._elasticnetCV_regression_grid_search.ipynb | 4610 +++++++++++++++++ 1 file changed, 4610 insertions(+) create mode 100644 app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb new file mode 100644 index 00000000..a8063317 --- /dev/null +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb @@ -0,0 +1,4610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9966165885337552" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNetCV model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.ElasticNetCV(alphas=(0.1, 1.0, 10.0), l1_ratio=0.5),\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 774.21757722, 762.32695009, 160.82949933, 92.4830288 ,\n", + " 1415.65994118, 158.77776525, 156.94515186, 103.83019647,\n", + " 767.50804767, 752.95912389, 1414.86308469, 747.9448386 ,\n", + " 165.42635101, 113.68221387, 752.29436934, 776.31466822,\n", + " 152.329388 , 1419.92937548, 94.66378085, 91.23633019,\n", + " 748.92120668, 751.82596039, 168.08421862, 1421.72975029,\n", + " 1413.71481463, 774.91539079, 90.61874144, 93.68298702,\n", + " 99.15313706, 104.9303305 , 754.17935532, 752.81450251,\n", + " 775.18148952, 754.67200875, 1437.94819259, 1428.25272319,\n", + " 1413.15643443, 1437.67918868, 1434.36710776, 1413.92382468,\n", + " 764.33373976, 105.97272448, 161.93670039, 1418.33768333,\n", + " 752.15757025, 157.79311846, 777.72304334, 1424.6911829 ,\n", + " 1425.26592981, 158.59419713, 1410.36405828, 1424.35031864,\n", + " 745.20672642, 1422.39779083, 100.6889481 , 764.19503218,\n", + " 93.42135941, 91.43280158, 157.93198776, 775.24391419,\n", + " 1404.79835479, 181.78774493, 158.35222129, 748.8819082 ,\n", + " 95.37161037, 113.34207161, 1424.95418811, 103.21832112,\n", + " 1407.58541887, 1424.95407606, 1425.40265649, 756.48738014,\n", + " 159.24665866, 755.47506804, 101.48176305, 1405.06552956,\n", + " 1434.80107403, 775.30798386, 1417.3580004 , 92.69268308,\n", + " 1405.83186151, 764.49471413, 90.86755549, 761.25221992,\n", + " 170.87340087, 1410.59318198, 1442.37759309, 87.46230202,\n", + " 1425.80546373, 1423.67040961, 90.09040701, 160.2428607 ,\n", + " 94.12855724, 772.67880784, 114.00651433, 773.22795236,\n", + " 157.74151761, 231.04592912, 1441.79930351, 99.97015579,\n", + " 764.68637888, 1414.75358535, 771.05422066, 765.80300755,\n", + " 93.1988608 , 769.54532705, 89.12600362, 88.60585547,\n", + " 88.13200424, 1424.48977401, 98.79695056, 1260.05467549,\n", + " 1423.96506335, 117.68888932, 1408.58020893, 774.62580815,\n", + " 753.25129761, 154.43637462, 1436.75320046, 172.25761867,\n", + " 158.43581357, 750.37157209, 1424.23219019, 159.78651598,\n", + " 771.33294841, 160.49777266, 752.54344315, 1425.63760766,\n", + " 1425.01793577, 1418.86079248, 1424.38746011, 755.20169628,\n", + " 89.18725578, 1438.7992014 , 776.77766916, 1417.5739756 ,\n", + " 105.46179679, 181.32724508, 87.88328857, 106.32066442,\n", + " 107.46666896, 93.7024473 , 102.99294324, 1409.28043019])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9544525609675586" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ElasticNetCV model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.ElasticNetCV(alphas=(0.1, 1.0, 10.0), l1_ratio=0.5),\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([163.62920734, 144.35081307, 93.70953005, 556.7896188 ,\n", + " 58.44975618, 107.73288393, 104.18291045, 358.45979342,\n", + " 159.75427595, 616.49409602, 640.6188396 , 643.3361415 ,\n", + " 84.77862831, 297.17071678, 616.89348864, 164.34964657,\n", + " 115.54948765, 428.21129208, 550.2961131 , 549.76418449,\n", + " 645.6263344 , 569.605286 , 72.93158039, 651.6663148 ,\n", + " 126.06120853, 158.68051212, 554.8815117 , 552.43400951,\n", + " 339.13121777, 347.45218426, 609.56524314, 559.63960144,\n", + " 153.90761289, 583.14379649, 406.17829791, 708.14334659,\n", + " 647.47993132, 416.86507696, 411.86245919, 121.09142846,\n", + " 150.88625209, 346.26945602, 90.96919913, 660.25860343,\n", + " 600.95243354, 96.83547563, 166.36521787, 423.23666311,\n", + " 683.53222 , 102.42712849, 143.36592422, 657.56154953,\n", + " 625.45758081, 384.88128067, 352.06682834, 165.25977488,\n", + " 552.57831271, 546.76511473, 107.29955997, 164.83878947,\n", + " 141.03976837, 32.736619 , 102.46679039, 614.80628555,\n", + " 547.50729734, 297.23174806, 680.32548692, 342.89971922,\n", + " 137.85385811, 425.97661593, 665.33950839, 618.08195697,\n", + " 100.76053401, 581.75598615, 351.59220917, 143.04948458,\n", + " 410.49397369, 167.65519318, 653.78496993, 548.81351804,\n", + " 140.77499224, 152.95640108, 558.94590232, 156.8569158 ,\n", + " 77.10260427, 126.29893833, 413.5024409 , 549.78786578,\n", + " 88.43019782, 670.33329703, 543.37756379, 100.10009692,\n", + " 556.26928137, 159.619804 , 378.04103911, 164.43080845,\n", + " 100.80699064, -25.3643385 , 413.28482191, 331.57181989,\n", + " 159.69597592, 648.53176619, 163.06678307, 160.21095107,\n", + " 551.62628023, 175.89288057, 551.08904881, 549.32621466,\n", + " 548.73726498, 676.89488231, 346.89038224, 44.00286515,\n", + " 421.18995673, 275.2444689 , 132.0508005 , 165.38954106,\n", + " 579.03848442, 96.39693611, 410.61131098, 76.278694 ,\n", + " 94.2019219 , 610.22199913, 417.29210715, 95.2378525 ,\n", + " 158.15883957, 95.66288071, 606.98946296, 684.00301472,\n", + " 387.05598964, 101.53336042, 659.50518039, 605.54211706,\n", + " 540.67636537, 407.92584076, 161.28860203, 659.57887575,\n", + " 348.46142146, 15.68219139, 543.45597717, 354.51025812,\n", + " 356.43215345, 555.12946446, 352.95138943, 124.23288811])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768774.217577100163.629207(768, 100)
290768762.326950100144.350813(768, 100)
54100160.82949910093.709530(100, 100)
19810092.483029630556.789619(100, 630)
45314361415.65994110058.449756(1436, 100)
..................
164100106.320664365354.510258(100, 365)
165100107.466669365356.432153(100, 365)
19910093.702447630555.129464(100, 630)
132100102.992943365352.951389(100, 365)
50114361409.280430100124.232888(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 774.217577 100 163.629207 (768, 100)\n", + "290 768 762.326950 100 144.350813 (768, 100)\n", + "54 100 160.829499 100 93.709530 (100, 100)\n", + "198 100 92.483029 630 556.789619 (100, 630)\n", + "453 1436 1415.659941 100 58.449756 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.320664 365 354.510258 (100, 365)\n", + "165 100 107.466669 365 356.432153 (100, 365)\n", + "199 100 93.702447 630 555.129464 (100, 630)\n", + "132 100 102.992943 365 352.951389 (100, 365)\n", + "501 1436 1409.280430 100 124.232888 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768774.217577100163.629207(768, 100)
290768762.326950100144.350813(768, 100)
54100160.82949910093.709530(100, 100)
19810092.483029630556.789619(100, 630)
45314361415.65994110058.449756(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 774.217577 100 163.629207 (768, 100)\n", + "290 768 762.326950 100 144.350813 (768, 100)\n", + "54 100 160.829499 100 93.709530 (100, 100)\n", + "198 100 92.483029 630 556.789619 (100, 630)\n", + "453 1436 1415.659941 100 58.449756 (1436, 100)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.235302\n", + "(100, 365) 0.408093\n", + "(100, 630) 0.667081\n", + "(768, 100) 0.919394\n", + "(768, 630) 1.232948\n", + "(1436, 100) 1.202288\n", + "(1436, 365) 1.532106\n", + "(1436, 630) 1.802787\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_15712\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_15712\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and ElasticNetCV model\n", + " # with polynomial features up to degree 2\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.ElasticNetCV())\n", + "\n", + " # Define the parameter grid for GridSearchCV\n", + " param_grid = {\n", + " \"elasticnetcv__alphas\": [[0.1, 1.0, 10.0], [0.01, 0.1, 1.0], [0.001, 0.01, 0.1]],\n", + " \"elasticnetcv__l1_ratio\": [0, 0.01, 0.2, 0.5, 0.8, 1],\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " y_x = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, y_x, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X using GridSearchCV\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alphas for X: {grid_search.best_params_[\"elasticnetcv__alphas\"]}, Best l1_ratio for X: {grid_search.best_params_[\"elasticnetcv__l1_ratio\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y using GridSearchCV\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alphas for Y: {grid_search.best_params_[\"elasticnetcv__alphas\"]}, Best l1_ratio for Y: {grid_search.best_params_[\"elasticnetcv__l1_ratio\"]}, R2 score : {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52241392.984785445, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20759230.312280465, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3073560.367979618, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50794387.93326712, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20507794.362768695, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3060470.4693500516, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50770482.40803474, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20593789.582115255, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3093226.380059079, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50541672.20234161, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20588687.99689006, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3090290.788971961, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50765688.99489567, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20509155.341858782, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3061400.841675672, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.845e+06, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52466824.0485671, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20799456.239972703, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3077654.571616266, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52916278.793447345, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20897609.28436407, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3080861.236155312, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53169590.21618326, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21027911.57943716, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3115784.538845005, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53009932.32325099, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21033421.570985653, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3112869.97696479, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53107705.46953778, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20939345.586630933, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3085964.0244875127, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.869e+06, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49958735.22221848, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20410203.167066377, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3067901.89130264, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51926185.63902524, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20751757.51378552, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3077481.9454401773, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51607111.05819739, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20788197.204152156, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3110474.6497256486, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50250665.346671775, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20609998.665319398, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3103847.8855926623, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50607179.6040795, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20555696.29225641, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3076525.3913657996, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.860e+06, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895166.86883311, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20726126.261070747, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3116130.5899213115, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52911807.73920385, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21062064.202052414, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3123135.9434957756, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52137774.20354035, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959191.098321006, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3127864.5467754183, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50597527.25026663, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20735369.761270344, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3131438.2272928082, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51539720.47328845, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20866950.017541334, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3123772.5167936576, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.906e+06, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50555349.99247141, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20474277.570002355, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3062274.663896205, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52472641.51994393, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20807154.024277393, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3072303.468695589, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51893698.64493438, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20733490.720915385, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3076512.6632144093, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50792242.62827226, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20622235.451482527, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3092514.801586809, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51194366.17624749, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20767418.248338334, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3112690.6299800905, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.855e+06, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.344e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.883e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.207e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.153e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.009e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20759230.312280465, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3073560.3679796834, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410861.0294770871, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20507794.362768687, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3060470.469350014, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411930.87242159975, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20593789.582115248, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3093226.3800591337, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428484.09298036, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20588687.996890083, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3090290.788971976, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425468.8039250055, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20509155.34185881, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3061400.841675754, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410145.36803073436, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.220e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20799456.23997269, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3077654.5716162245, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411768.4115463066, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20897609.28436409, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3080861.2361552855, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409380.4053543313, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21027911.57943716, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3115784.5388450315, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425171.2967426144, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21033421.570985682, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3112869.976964696, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422627.0428141533, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20939345.586630933, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3085964.024487523, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408340.3700514909, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.196e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20410203.167066395, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3067901.891302694, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416797.39060822065, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20751757.51378554, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3077481.945440165, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409309.68733474426, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20788197.204152174, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3110474.649725619, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 431168.8412055088, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20609998.665319376, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3103847.8855926753, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428320.6629958892, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20555696.29225641, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3076525.3913657977, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413653.97421997617, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.251e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20726126.261070747, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3116130.589921285, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 434478.2787742772, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21062064.20205241, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3123135.943495717, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425874.9684875888, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959191.098321006, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3127864.546775431, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 435858.2107405147, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20735369.761270378, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3131438.2272928213, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 439656.7917579747, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20866950.017541334, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3123772.516793645, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 430844.64036634436, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.418e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20474277.570002336, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3062274.6638962044, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410708.86903277267, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20807154.024277385, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3072303.4686955595, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403453.8035094354, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20733490.72091538, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3076512.663214506, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412673.91513746034, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20622235.451482534, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3092514.801586878, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422295.3546966252, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20767418.24833836, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3112690.6299799276, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 426421.3109854809, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.193e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.799e+04, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.232e+04, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.285e+04, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.764e+04, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.240e+04, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27001.238514512777, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51669.78313600251, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25433.064175009727, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48750.54248090497, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23595.33234409988, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52085.33996937015, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23681.97113211453, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50940.47190261811, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25752.27994774282, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46825.854455829416, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27111.997629120946, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51021.09811788032, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27851.23576526344, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52659.1182658448, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27062.152402356267, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54258.13067814849, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26993.761432886124, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54005.42170735355, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 28141.9186540246, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50239.27852412552, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24325.282854616642, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48875.4093873589, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27048.152341887355, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52545.68592076785, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24891.686308681965, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52554.13622946837, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23696.900168433785, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52095.680344598964, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25748.89860931039, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48163.617058736774, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23165.87727586925, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51800.15520736135, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26684.938877493143, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54945.123784184296, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24874.763845279813, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53151.619686045786, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22539.789426013827, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54493.93782141632, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24919.619185760617, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49655.726728943766, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25539.17362704873, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46152.04097500274, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27845.552521407604, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49840.47878713764, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26942.847137898207, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48598.28082026785, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24988.611784487963, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49136.644468750754, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25041.87955003977, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49377.360949991154, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3073560.367979779, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410861.0294771909, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121759.06119833616, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3060470.469350123, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411930.8724213932, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122949.32643986364, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3093226.3800590807, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428484.09298057586, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134840.64704374727, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3090290.7889720704, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425468.80392499117, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132479.58815528097, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3061400.8416757127, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410145.3680308145, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120783.39549805383, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.593e+05, tolerance: 1.533e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3077654.571616318, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411768.41154609324, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121914.95627698078, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3080861.236155271, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409380.4053543772, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120322.74002676901, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3115784.5388449216, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425171.29674242897, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131168.23753793177, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3112869.976964704, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422627.04281403293, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129308.22628810685, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3085964.0244875653, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408340.37005136814, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 118212.94244870468, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+05, tolerance: 1.601e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3067901.8913026145, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416797.39060835866, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126388.88247207573, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3077481.9454402733, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409309.68733479583, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120509.14125550221, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3110474.6497256896, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 431168.84120551625, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136113.2008904689, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3103847.8855926897, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428320.662995939, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134248.7256697031, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3076525.391365785, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413653.97422007803, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123014.65593263641, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+05, tolerance: 1.525e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3116130.5899213245, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 434478.27877430926, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137882.9547467864, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3123135.9434958026, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425874.9684879448, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131637.0012168145, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3127864.5467755133, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 435858.21074047254, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138949.0666419515, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3131438.2272928874, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 439656.79175778467, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141644.9136422769, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3123772.516793755, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 430844.6403663381, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134104.90830278082, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.720e+05, tolerance: 1.548e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3062274.663896218, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410708.86903295084, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120855.29093348929, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3072303.468695548, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403453.80350947997, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 115133.66331495703, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3076512.663214506, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412673.9151374414, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122304.79888960392, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3092514.801586932, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422295.3546965064, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129169.87617713091, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3112690.6299799546, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 426421.31098556286, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131131.08328559282, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.559e+05, tolerance: 1.545e+04 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121267.11813709466, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122445.9389892054, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134311.75377975026, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131951.28121902433, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120280.01286111191, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.588e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121420.12653025604, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 119835.56175688925, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130658.55258930728, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 128799.25409093755, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 117723.60074637274, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.560e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125870.41826413336, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120019.062220802, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135581.04357348004, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 133712.10122664977, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122502.87214659582, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.606e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137336.95712796247, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131125.32748179723, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138409.10620523067, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141090.82546967515, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 133565.87806103445, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.714e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120347.39863705443, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 114652.86717462502, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121802.06018197251, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 128645.71954268016, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130599.44468212995, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112374.36385725543, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113332.87840818237, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124635.0006258501, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122338.86077849465, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 111161.76855088581, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112457.320345864, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 111055.65286171433, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121348.64939958067, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 119548.14697694493, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 108886.4797849406, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.467e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 116504.17954093085, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 111223.8658575544, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125840.51134487573, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124003.17269449012, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113272.1150251159, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.509e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127345.59517926854, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121819.03460433248, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 128499.97068425381, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130976.5367827544, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123716.04539748735, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 111149.18371908777, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 106011.70334853735, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112674.30674720835, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 119112.9511222277, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120921.07136823965, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98310.59305226034, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98770.36066434573, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 108736.60205182082, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 106697.51616306292, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 96577.85594135737, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.335e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98216.6716022065, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 97306.59376829061, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 106265.62108180033, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104697.97075070915, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94984.0946222174, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.320e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 101506.07151661778, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 97529.72188743124, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 109845.25208496163, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 108338.5391032718, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98599.43267980257, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 110846.16955294048, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 106842.47125308847, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112116.0582750817, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 114322.32187585343, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 107551.55969597175, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.441e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 96421.58838910311, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 92583.45301344446, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98053.90288672187, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 103667.41233231586, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105178.80081920291, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79963.27924302113, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79363.8355036785, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86810.4221721606, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85258.01742225855, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77143.10528430519, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79547.69599981882, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79613.55903396462, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86032.4766367895, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84915.17592976356, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77026.550489888, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81282.61574243417, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79929.50472040477, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 87810.79321971687, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86897.97951144163, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79034.72818191642, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.189e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 87835.03012864725, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86789.51422377418, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89343.77045089871, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91073.85721991418, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85209.06119170724, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76744.94403654819, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 75525.94335760656, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 78611.762468278, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82669.96025243071, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83637.93165785522, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.146e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37193.377502700576, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63595.21057763434, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52679.31778976358, tolerance: 12614.073443478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39760.875704333564, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60294.94831218976, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49408.27055467501, tolerance: 12195.495930434785\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49457.874315577326, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63230.753291795256, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51123.56478545584, tolerance: 12172.093421739135\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46344.96769402514, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62178.58575046729, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50622.121905554755, tolerance: 12092.549143478263\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38819.10061960967, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58320.80237603167, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47503.63106274663, tolerance: 12187.25048695652\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+05, tolerance: 1.533e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37986.890800213325, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62919.14786270635, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51720.84143060726, tolerance: 12680.036991304354\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34240.854499590816, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64628.57952805885, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54295.29329061412, tolerance: 12802.88003468835\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45539.47532828315, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66646.82587328301, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54699.240534092154, tolerance: 12860.925433062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42524.96543007254, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66141.19028456513, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54629.841394628624, tolerance: 12800.703332249328\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34073.392419011216, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62137.2398138638, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51550.49088223381, tolerance: 12858.74873062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+05, tolerance: 1.601e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40939.74682639532, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60150.8185720223, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49602.06671067447, tolerance: 11946.677547826084\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 33808.04937170018, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380.413860191795, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54379.02295034827, tolerance: 12511.685619512193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49014.39845109897, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64272.79310131351, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52729.33995481163, tolerance: 12400.673795121951\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44605.51267793213, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63302.962033356875, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52658.89533348087, tolerance: 11993.630439024393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37694.46592193071, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59533.28507159174, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49452.52789970351, tolerance: 12125.20000867209\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+05, tolerance: 1.525e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50547.35884167315, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62597.57788583079, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50289.83319347684, tolerance: 12178.883786956529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44165.22908349594, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67336.5034885758, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55504.78059453689, tolerance: 12762.0064\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51042.57248043457, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64856.506277178836, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52716.9946037113, tolerance: 12529.099239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51253.97269166887, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64976.88761989483, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53181.8956843206, tolerance: 12082.875239024392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47161.75666572072, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61419.48397788271, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49802.58604209638, tolerance: 12357.381602168025\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+05, tolerance: 1.548e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38874.00350949238, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57431.72102337918, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46839.74466162229, tolerance: 12128.441073913045\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31773.543228341907, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61630.55500765223, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51782.07520300847, tolerance: 12680.25913062331\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38911.76604615903, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60257.714285395, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49642.073785733344, tolerance: 12503.946233062337\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 43571.02769605149, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60718.71056878313, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50096.34789168997, tolerance: 12172.12003902439\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44747.592332329135, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61023.223430858656, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49839.03069800484, tolerance: 12270.071648780484\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.022e+05, tolerance: 1.545e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102415.51957595888, tolerance: 15325.272264347834\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102050.16710112226, tolerance: 16008.310198698491\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104078.4842133789, tolerance: 15254.665933188713\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 110241.06574463885, tolerance: 15479.229855097614\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99677.43130724251, tolerance: 15449.029879392636\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+05, tolerance: 1.939e+04\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3531564.962902579, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 918487.882544352, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7987188.322268448, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3605598.7599645886, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 943648.0657479146, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8158133.761291219, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3595481.7791378186, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 950542.06640342, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8204966.607159814, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3619393.1901538, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 940476.6740443246, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7946145.543969339, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3572359.233428018, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 938975.6847374665, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.174e+06, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8128127.361377727, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3548354.7820301894, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 921202.0033719868, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7904797.135069206, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3521622.3607484167, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 913611.8940331229, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188840.476270991, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3530224.62361696, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 921761.6160162778, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8228532.289476067, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3552575.6708189603, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 914149.079502091, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7984550.592155035, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3507848.8457871317, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 910605.3246771228, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.146e+06, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3640380.2244999614, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 946990.8978974203, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7916581.677477786, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3533367.163620577, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 912518.1308948789, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8193004.064165882, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3607664.803375752, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 950103.2402830238, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214538.675409523, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3637257.097300348, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 938496.9080861458, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8015833.5208386835, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3598791.179358662, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 936554.7018831597, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+06, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8293585.402200199, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3597301.2849009847, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 927714.6266264089, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8054522.204081177, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3491853.2243667943, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 892450.8239801002, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8216450.603461849, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3597285.701128345, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 929045.7721452892, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8322863.834520293, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3604416.2429041094, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 940120.7842818964, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8169536.422423176, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3558619.268107924, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 918740.4695406758, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+06, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8053928.908424687, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3596461.5372820953, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 945209.9562765381, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3488634.190225013, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 908257.9458747033, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7994091.006725936, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3598425.2012433074, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 946458.798213183, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8140466.648163852, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3595687.4510898693, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 947863.5800904727, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8155616.743963005, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3569955.704646599, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 927165.5353919524, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+06, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alphas for X: [0.001, 0.01, 0.1], Best l1_ratio for X: 0.8, R2 score : 0.9982355264998006\n", + "-------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3299.8101275265217, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10845.021059398074, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10028.077613340341, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6103.15114655986, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+03, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6010.930996235227, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5024.27236861299, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6683.007060111151, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11404.760652294499, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7540.261712116422, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8907.889932699385, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3665.987252778141, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3531564.9629025795, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 918487.8825443431, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388772.38063755364, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3605598.75996459, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 943648.0657478964, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394197.268900038, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3595481.779137817, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 950542.0664034153, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420728.6798068598, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3619393.1901538, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 940476.6740443362, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412893.2486176593, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3572359.2334280163, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 938975.6847374653, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402552.5848347669, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.057e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3548354.7820301894, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 921202.003371995, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388299.9024517876, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3521622.3607484205, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 913611.8940331386, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375012.0069634465, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3530224.623616959, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 921761.6160162697, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400083.4602152473, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3552575.670818961, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 914149.0795020738, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394956.76063674083, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3507848.845787134, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 910605.3246771253, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382616.1720809747, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.860e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3640380.224499964, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 946990.8978974337, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405021.60830119485, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3533367.1636205795, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 912518.1308948857, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380288.72900856065, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3607664.8033757517, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 950103.2402830275, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412970.60463897756, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3637257.097300346, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 938496.9080861283, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408839.25614713354, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3598791.1793586677, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 936554.7018831738, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398796.2420386522, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.026e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3597301.284900984, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 927714.6266264163, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411842.5800890705, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3491853.2243667934, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 892450.8239801042, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385634.48172392405, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3597285.701128344, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 929045.7721452874, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398703.0508654977, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3604416.2429041094, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 940120.7842819052, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 429380.1090312622, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3558619.268107924, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 918740.4695406577, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407071.6393059233, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.089e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3596461.5372820953, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 945209.9562765338, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408051.7410142496, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3488634.1902250107, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 908257.945874695, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382469.4161488358, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3598425.2012433116, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 946458.7982131679, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395484.9290610949, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3595687.4510898665, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 947863.5800904552, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 423344.3636388456, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3569955.7046466004, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 927165.5353919447, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409800.0360160737, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.055e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145804.27080314362, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3299.8101275265217, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147710.04629368795, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10845.021059398074, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 174810.22727891142, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10028.077613340341, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166741.8990673345, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6103.15114655986, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160318.2330132673, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.023e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137778.47237434448, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122758.97077297635, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148382.43391371344, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144017.30019165823, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127018.48959432897, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6010.930996235227, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158932.8736311625, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136289.09177669926, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165573.70125127787, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5024.27236861299, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164104.78408577995, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156455.78729827385, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.972e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6683.007060111151, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166430.94864977512, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144037.14875146816, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147744.35308860906, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11404.760652294499, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186751.16317974965, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166374.26740222645, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7540.261712116422, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164346.773108571, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141532.80877918153, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145557.6555681755, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8907.889932699385, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 181902.02497356254, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3665.987252778141, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168282.45226544066, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.028e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 918487.882544335, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388772.3806375701, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238881.97768874164, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 943648.0657479076, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394197.2689000469, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 239385.5865803242, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 950542.0664034316, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420728.67980686115, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 269186.6962691041, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 940476.6740443392, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412893.2486176548, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261524.492639996, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 938975.6847374927, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402552.5848347632, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252184.89676408487, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.163e+05, tolerance: 2.385e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 921202.0033720008, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388299.902451754, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235842.29396668926, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 913611.8940331296, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375012.0069634171, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225506.50887778657, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 921761.6160162771, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400083.46021524846, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252461.18364590796, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 914149.0795020866, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394956.76063675387, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247760.1575976184, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 910605.3246771215, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382616.1720809989, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235866.52268561083, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.001e+05, tolerance: 2.407e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 946990.8978974292, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405021.60830117547, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252523.37620980132, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 912518.1308948816, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380288.72900858964, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234394.973417678, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 950103.2402830273, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412970.60463899653, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261440.16543481906, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 938496.9080861281, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408839.25614716613, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260713.01323792586, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 936554.7018831815, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398796.2420386353, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 251507.31885626426, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.163e+05, tolerance: 2.395e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 927714.626626414, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411842.58008909185, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262348.9784342649, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 892450.8239801001, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385634.48172390996, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 243359.09577997218, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 929045.7721452819, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398703.05086551234, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247232.57668360608, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 940120.7842818924, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 429380.1090312657, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 285095.37532695686, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 918740.4695406536, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407071.6393059103, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263183.2095373859, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.260e+05, tolerance: 2.446e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 945209.9562765367, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408051.74101427535, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 256678.92860060884, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 908257.945874702, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382469.41614885547, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238494.72148849253, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 946458.798213176, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395484.9290610922, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242126.68013218584, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 947863.5800904641, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 423344.36363885924, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277959.156117901, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 927165.5353919442, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409800.036016083, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264803.8888429678, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.207e+05, tolerance: 2.376e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234743.94045869616, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234866.1982271196, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265684.10474168253, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 257305.34772855413, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248414.40010164757, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.064e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 231076.89828884622, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220100.56053695484, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247902.18098879734, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242349.85548103234, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 230563.61300794533, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247879.21219277842, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 229371.85799363762, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 257166.14040609298, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255639.25375995727, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246756.8332456822, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.012e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 257887.6929843454, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238610.7184230909, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241683.871702821, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281188.02461934043, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258738.77776034948, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.131e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252910.86071317564, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234323.81100344704, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 237279.65995299883, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274391.5247992188, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260635.83779081932, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.087e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201854.9462045866, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200357.2300735049, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 236139.60099156993, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224195.15584459237, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217671.39293637784, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.501e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194766.69000815816, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180728.3101320557, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211845.7115883509, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202514.3708864695, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191021.00466119417, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.759e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 212600.40104315642, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191910.4784417847, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223032.96329804452, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217986.25218725184, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210496.81632175052, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223348.21990051615, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202392.13703781372, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201595.9405855805, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249110.42642527726, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223854.96971166527, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.486e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222159.6287370833, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200992.0566569237, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200525.14666808603, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244239.93561595207, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 227243.8770792173, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.471e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201557.21448673253, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201263.4656391183, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 233340.17039819126, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223500.3231732847, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215402.3876891057, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.618e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195918.68199393412, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183268.6036507309, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211979.1946914916, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 205083.2636547875, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192979.39861851116, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.295e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213882.74960479347, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 193946.63485132204, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222925.5404200333, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220478.07903361012, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 212121.8446640784, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.568e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223044.46653979603, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203561.98206021648, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204771.3627974359, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247576.1788381551, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224401.39276617134, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220136.4711715886, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200839.74411847824, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202100.24267684392, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241716.29141859149, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 227050.45850144615, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.651e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203019.05886709454, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202547.74074176082, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 231840.16141757014, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223399.29036176973, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215901.56631438094, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.697e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 199052.12222655292, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189033.4097485694, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215570.91158886484, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210119.01189684792, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 199004.43750624298, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.515e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215008.15986889886, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198356.41278586112, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224336.9469738894, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222813.53769662618, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215036.3842564835, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.692e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224282.38926282295, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 207124.28808289216, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 209181.4511212778, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247236.25412369345, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226356.24034119098, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.786e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219976.32143921254, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203412.1021930209, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 205339.2058204916, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241316.3625303731, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 228151.91333432257, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.746e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144585.05900922802, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192008.17523541514, tolerance: 1900.7884714673914\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2778.5474211804103, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 146686.9255644182, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 187427.51207353827, tolerance: 1878.2897554347828\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10079.97370744677, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173907.4659622846, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211387.4911716713, tolerance: 1935.4620652173908\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285.072762441123, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165853.41796351466, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202984.5866449888, tolerance: 1944.7554279891306\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5434.551816466032, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159312.7749068756, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198851.1162001828, tolerance: 1871.038260869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+05, tolerance: 2.385e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136529.4067637401, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190552.52422892852, tolerance: 1933.324782608696\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122758.99074181131, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183512.30774619576, tolerance: 1867.2998780487799\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147072.88033138518, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208123.49327046314, tolerance: 1957.7359756097565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 142718.91817563848, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202634.53417400707, tolerance: 1965.0820054200547\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127019.08428033529, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194448.08313360647, tolerance: 1894.704756097561\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+05, tolerance: 2.407e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5340.944362295675, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 157921.87093700204, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 197441.0374932648, tolerance: 1923.859673913044\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134911.9408829766, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190453.0345442784, tolerance: 1867.908875338754\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164303.45219882295, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211153.43895284442, tolerance: 1945.2134688346887\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4418.303120764438, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163119.0634495192, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206501.74178828715, tolerance: 1944.6425338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155106.5568488789, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203273.4460461305, tolerance: 1887.5109756097563\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.632e+05, tolerance: 2.395e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6078.075453025696, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165563.816403025, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206770.57611088033, tolerance: 1977.5970652173912\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 142872.72595095512, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198799.46145068703, tolerance: 1920.4348915989165\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 146511.2971749203, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 199312.8359719755, tolerance: 1954.9954878048782\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10657.099400804902, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 185888.76200273272, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226881.91441898578, tolerance: 1986.7775338753393\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165223.31735161654, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 214000.7903468631, tolerance: 1944.2999728997295\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.723e+05, tolerance: 2.446e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819.405477439752, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163335.46831019252, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201444.8976191769, tolerance: 1900.883885869565\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 140185.5351146482, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195061.45772995887, tolerance: 1846.3275338753392\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144135.80540272713, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195258.6515898856, tolerance: 1882.1822493224934\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8151.49479778501, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180915.29687128207, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222751.9604088086, tolerance: 1928.8847289972905\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3144.2590368899982, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167289.73430093497, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213685.35554384018, tolerance: 1937.9055013550137\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+05, tolerance: 2.376e+03\n", + " model = cd_fast.enet_coordinate_descent(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6275.61922646407, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198931.27304518648, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249335.40144705592, tolerance: 2384.657804347826\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171269.92069856432, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246143.4288674457, tolerance: 2406.87428416486\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196814.88602281915, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254093.29390393704, tolerance: 2394.65726681128\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204647.44464610174, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263090.58998221107, tolerance: 2446.4501084598687\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202356.67748898745, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258838.46788680268, tolerance: 2376.225108459869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.302e+05, tolerance: 3.005e+03\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alphas for Y: [0.001, 0.01, 0.1], Best l1_ratio for Y: 1, R2 score : 0.9767994846187407\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9YAAAHqCAYAAADhztZNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAC4EklEQVR4nOzdd3wUdf7H8dcm2XSSTUIKPUDoVVAgKoKKcAhWPE/Pgr0hd/Z2KiAqHmfnLOf9PLvH2QsWRMAOqPQiSAtFSECSTSCQPr8/Zmt2k2yy6byfPPYxs/P9zne+G3Zn9rPf73y/FsMwDERERERERESkTkKaugIiIiIiIiIiLZkCaxEREREREZEgKLAWERERERERCYICaxEREREREZEgKLAWERERERERCYICaxEREREREZEgKLAWERERERERCYICaxEREREREZEgKLAWERERERERCYICaxERadG++uorLBYLX331VVNXpUbp6elcdtllrufNse6V69gYXn75ZSwWC1lZWY163Or84x//oFu3boSGhjJ48OCmrk6zMX36dCwWS1NXQ0Sk2VFgLSLSwlgsloAezSlYaw6cwZvzERkZSc+ePbnxxhvJyclp6urVyqeffsr06dObtA6ef8uQkBDat2/P2LFjG/19t2HDBqZPn16vQfkXX3zBHXfcwQknnMBLL73Eww8/XG9lV1ZUVERGRga9e/empKTEJ338+PHEx8ezZ8+eBqtDfbLb7bRr144TTjgBwzB80pcuXUpISAi33357E9RORKThhDV1BUREpHZee+01r+evvvoqCxYs8Nnep0+fxqxWi/HAAw/QtWtXioqK+O6773juuef49NNPWbduHdHR0Y1al5NOOokjR44QHh5eq/0+/fRTnnnmmSYPrk877TQuvfRSDMNg+/btPPvss5xyyil88sknjB8/PuByLrnkEi644AIiIiJqXYcNGzYwY8YMRo8eTXp6eq3392fRokWEhITw4osv1vr/prYiIyN57rnnGDt2LLNmzWLatGmutLlz5/L5558zZ84c2rdv36D1qC82m40nn3ySCy64gH//+99cc801rrSysjKuu+46unTpwowZM5qwliIi9U+BtYhIC3PxxRd7PV+6dCkLFizw2V7Z4cOHGz1wbI7Gjx/PscceC8BVV11FUlISjz/+OB9++CEXXnih330KCwuJiYmp97qEhIQQGRlZ7+U2lp49e3q978455xwGDhzIk08+WavAOjQ0lNDQ0IaoYp3s27ePqKioeguqDcOgqKiIqKgov+mnnXYaf/7zn5k1axYXXnghPXv2xG63c/PNN3Pcccdxww031Es9Gsuf/vQnXnnlFe666y7OOussUlNTAXjqqadYvXo1n376qc5FItLqqCu4iEgrNHr0aPr378/y5cs56aSTiI6O5p577gHMLrz+Wjr93Vtrt9u56aab6NSpExEREWRkZPD3v/+dioqKGuvw4YcfMmHCBNq3b09ERATdu3dn5syZlJeX+63rhg0bOPnkk4mOjqZDhw7Mnj3bp8zdu3dz9tlnExMTQ0pKCjfffDPFxcWB/2H8OOWUUwDYvn07AJdddhmxsbFs3bqV008/nTZt2nDRRRcBUFFRwZNPPkm/fv2IjIwkNTWVa6+9lry8PK8yDcPgwQcfpGPHjkRHR3PyySezfv16n2NXdY/1smXLOP3000lISCAmJoaBAwfy1FNPuer3zDPPAN7dsZ3qu461MWDAANq2bev6W4LZ+jty5EhiYmKw2WycddZZ/PLLL177+bvHOj09nYkTJ/Ldd98xbNgwIiMj6datG6+++qrXfn/84x8BOPnkk31ug/j5558ZN24cbdu2JSoqiq5du3LFFVdU+xosFgsvvfQShYWFrvJefvllwGxxnTlzJt27dyciIoL09HTuuecen/egs+7z58/n2GOPJSoqin/961/VHveJJ54gOjqa6667DoC77rqL/fv3869//YuQkJq/rj366KMcf/zxJCUlERUVxdChQ3nnnXf8vr4bb7yRDz74gP79+xMREUG/fv34/PPPffJ+9913HHfccURGRtK9e/caX4OnZ599luLiYm655RYAdu3axfTp0/nTn/5Uqx9dRERaCrVYi4i0UgcOHGD8+PFccMEFXHzxxa5Wo0AdPnyYUaNG8dtvv3HttdfSuXNnfvjhB+6++2727t3Lk08+We3+L7/8MrGxsdxyyy3ExsayaNEi7r//fgoKCvjHP/7hlTcvL48//OEPnHvuuZx//vm888473HnnnQwYMMD1JfzIkSOceuqp7Ny5k7/85S+0b9+e1157jUWLFtXqdVW2detWAJKSklzbysrKGDduHCeeeCKPPvqoq3Xt2muv5eWXX+byyy/nL3/5C9u3b+ef//wnK1eu5Pvvv8dqtQJw//338+CDD3L66adz+umns2LFCsaOHev3HtrKFixYwMSJE2nXrh1//etfSUtL45dffmHevHn89a9/5dprr2XPnj1+u/83Vh2rkpeXR15eHhkZGQB8+eWXjB8/nm7dujF9+nSOHDnCnDlzOOGEE1ixYkWNXbe3bNnCeeedx5VXXsnkyZP5z3/+w2WXXcbQoUPp168fJ510En/5y194+umnueeee1y3P/Tp04d9+/YxduxYkpOTueuuu7DZbGRlZfHee+9Ve8zXXnuNF154gR9//JH/+7//A+D4448HzB4Or7zyCueddx633nory5YtY9asWfzyyy+8//77XuVs2rSJCy+8kGuvvZarr76aXr16VXvclJQUHnnkEa699lqmTp3KCy+8wE033cQxxxxT7X5OTz31FGeeeSYXXXQRJSUlzJ07lz/+8Y/MmzePCRMmeOX97rvveO+997jhhhto06YNTz/9NJMmTWLnzp2uz8HatWtdf7/p06dTVlbGtGnTAj6PpKenM2PGDG6//XYuu+wynn32WcLCwmo8b4iItFiGiIi0aFOmTDEqn85HjRplAMbzzz/vkx8wpk2b5rO9S5cuxuTJk13PZ86cacTExBi//vqrV7677rrLCA0NNXbu3FltvQ4fPuyz7dprrzWio6ONoqIin7q++uqrrm3FxcVGWlqaMWnSJNe2J5980gCMt956y7WtsLDQyMjIMABj8eLF1dbnpZdeMgDjyy+/NPbv32/s2rXLmDt3rpGUlGRERUUZu3fvNgzDMCZPnmwAxl133eW1/7fffmsAxhtvvOG1/fPPP/favm/fPiM8PNyYMGGCUVFR4cp3zz33GIDX33jx4sVedS8rKzO6du1qdOnSxcjLy/M6jmdZ/v7PG6qOVQGMK6+80ti/f7+xb98+Y9myZcapp55qAMZjjz1mGIZhDB482EhJSTEOHDjg2m/16tVGSEiIcemll7q2Of9vtm/f7trWpUsXAzC++eYb17Z9+/YZERERxq233ura9vbbb/v9/3///fcNwPjpp59qfC2VTZ482YiJifHatmrVKgMwrrrqKq/tt912mwEYixYt8qn7559/XqvjVlRUGCeccIIBGJ06dTIOHjwY8L6VP28lJSVG//79jVNOOcVrO2CEh4cbW7ZscW1bvXq1ARhz5sxxbTv77LONyMhIY8eOHa5tGzZsMEJDQ/2+9/wpLS01Bg8ebCQmJhqA8a9//Svg1yMi0tKoK7iISCsVERHB5ZdfXuf93377bUaOHElCQgK///676zFmzBjKy8v55ptvqt3f837SgwcP8vvvvzNy5EgOHz7Mxo0bvfLGxsZ63asbHh7OsGHD2LZtm2vbp59+Srt27TjvvPNc26Kjo70GRwrEmDFjSE5OplOnTlxwwQXExsby/vvv06FDB698119/vdfzt99+m/j4eE477TSvv8fQoUOJjY1l8eLFgNlKW1JSwtSpU726aN9000011m3lypVs376dm266CZvN5pUWyBRHjVFHTy+++CLJycmkpKQwfPhwvv/+e2655RZuuukm9u7dy6pVq7jssstITEx07TNw4EBOO+00Pv300xrL79u3LyNHjnQ9T05OplevXl7vi6o4/37z5s2jtLS0Vq/LH2d9nV2bnW699VYAPvnkE6/tXbt2Zdy4cbU6hsVicf2tMjMziY2NDXhfz89bXl4e+fn5jBw5khUrVvjkHTNmDN27d3c9HzhwIHFxca6/a3l5OfPnz+fss8+mc+fOrnx9+vSp1WsKCwvjhRdeIDc3lxEjRnD11VcHvK+ISEujruAiIq1Uhw4dghp8afPmzaxZs4bk5GS/6fv27at2//Xr13PvvfeyaNEiCgoKvNLy8/O9nnfs2NEncExISGDNmjWu5zt27CAjI8MnX01dbCt75pln6NmzJ2FhYaSmptKrVy+fe1jDwsLo2LGj17bNmzeTn59PSkqK33Kdf48dO3YA0KNHD6/05ORkEhISqq2bs1t6//79A39BjVxHT2eddRY33ngjFouFNm3a0K9fP9cgb85j+Pv/6dOnD/Pnz69xUDjPoM4pISHB535xf0aNGsWkSZOYMWMGTzzxBKNHj+bss8/mz3/+c51GH9+xYwchISGubu5OaWlp2Gw21+t16tq1a62P8d577/Hxxx/Tv39/3n77bW688UavHxaqM2/ePB588EFWrVrldc+3vx9kavq77t+/nyNHjvi8P8D8/wzkRxGn4447DoChQ4dq/msRadUUWIuItFJVjUBclcqDilVUVHDaaadxxx13+M3fs2fPKsuy2+2MGjWKuLg4HnjgAbp3705kZCQrVqzgzjvv9Bn8rKoRoQ0/8+AGa9iwYa5RwasSERHhE2xXVFSQkpLCG2+84Xefqn6AaEyNXceOHTsyZsyYei3TUzDvC4vFwjvvvMPSpUv5+OOPmT9/PldccQWPPfYYS5curVVrcOVyA1Hbz9/Bgwf5y1/+wtChQ1m8eDEDBw7k+uuvZ+XKla774qvy7bffcuaZZ3LSSSfx7LPP0q5dO6xWKy+99BJvvvmmT/7G/LyJiBwtFFiLiBxlEhISsNvtXttKSkrYu3ev17bu3btz6NChOgVOX331FQcOHOC9997jpJNOcm33HC26trp06cK6deswDMMruNm0aVOdy6yN7t278+WXX3LCCSdUGzR16dIFMFuPu3Xr5tq+f//+Gltand1z161bV+3fvargrjHqGCjnMfz9/2zcuJG2bdvWyxRmNQW6I0aMYMSIETz00EO8+eabXHTRRcydO5errrqqVsfp0qULFRUVbN682WuO+JycHOx2u+v11tW9997L3r17+fDDD2nTpg1z5szhjDPO4LHHHuOuu+6qdt93332XyMhI5s+f79Ua/9JLL9WpLsnJyURFRbF582aftMb6vImItDS6x1pE5CjTvXt3n/ujX3jhBZ8W6/PPP58lS5Ywf/58nzLsdjtlZWVVHsPZIubZAlZSUsKzzz5b53qffvrp7Nmzx2sKocOHD/PCCy/UuczaOP/88ykvL2fmzJk+aWVlZa4fK8aMGYPVamXOnDlerz+Q0ZCHDBlC165defLJJ31+/PAsyxmQVs7TGHUMVLt27Rg8eDCvvPKKVz3XrVvHF198wemnn14vx6nqb5GXl+fTAjt48GCAOk3R5qxv5b/R448/DuAz8nZtLF++nGeeeYYbb7yRoUOHAjBx4kTOOeccZs6c6dPNvLLQ0FAsFovXZzgrK4sPPvigTvUJDQ1l3LhxfPDBB+zcudO1/ZdffvF7PhAREbVYi4gcda666iquu+46Jk2axGmnncbq1auZP38+bdu29cp3++2389FHHzFx4kTXFEeFhYWsXbuWd955h6ysLJ99nI4//ngSEhKYPHkyf/nLX7BYLLz22mtBdTW9+uqr+ec//8mll17K8uXLadeuHa+99pprKqyGNmrUKK699lpmzZrFqlWrGDt2LFarlc2bN/P222/z1FNPcd5555GcnMxtt93GrFmzmDhxIqeffjorV67ks88+q/Lv5RQSEsJzzz3HGWecweDBg7n88stp164dGzduZP369a6gxhl8/eUvf2HcuHGEhoZywQUXNEoda+Mf//gH48ePJzMzkyuvvNI13VZ8fLzfudTrYvDgwYSGhvL3v/+d/Px8IiIiOOWUU3jzzTd59tlnOeecc+jevTsHDx7k3//+N3FxcXUK6gcNGsTkyZN54YUXXLc6/Pjjj7zyyiucffbZnHzyyXWqf3l5Oddccw1paWk8+OCDXmlPPfUUffv2ZerUqXz00UdVljFhwgQef/xx/vCHP/DnP/+Zffv28cwzz5CRkeE1TkFtzJgxg88//5yRI0dyww03UFZWxpw5c+jXr1+dyxQRadWaajhyERGpH1VNt9WvXz+/+cvLy40777zTaNu2rREdHW2MGzfO2LJli890W4ZhGAcPHjTuvvtuIyMjwwgPDzfatm1rHH/88cajjz5qlJSUVFuv77//3hgxYoQRFRVltG/f3rjjjjuM+fPn+0yNVFVdJ0+ebHTp0sVr244dO4wzzzzTiI6ONtq2bWv89a9/dU0lFeh0WzVNv+RvqiVPL7zwgjF06FAjKirKaNOmjTFgwADjjjvuMPbs2ePKU15ebsyYMcNo166dERUVZYwePdpYt26dz9+48nRbTt99951x2mmnGW3atDFiYmKMgQMHek2FVFZWZkydOtVITk42LBaLz/9/fdaxKoAxZcqUGvN9+eWXxgknnGBERUUZcXFxxhlnnGFs2LDBK09V021NmDDBp7xRo0YZo0aN8tr273//2+jWrZtrKqjFixcbK1asMC688EKjc+fORkREhJGSkmJMnDjR+Pnnn2usc1XvgdLSUmPGjBlG165dDavVanTq1Mm4++67vaaPq67u/jzxxBMGYLzzzjt+0x999FEDMN57771qy3nxxReNHj16GBEREUbv3r2Nl156yZg2bZrPe6Oq/zd//+9ff/21MXToUCM8PNzo1q2b8fzzz/stsyaBvldERFoyi2FopAoRERERERGRutI91iIiIiIiIiJBUGAtIiIiIiIiEgQF1iIiIiIiIiJBUGAtIiIiIiIiEgQF1iIiIiIiIiJBUGAtIiIiIiIiEoSwpq5Ac1BRUcGePXto06YNFoulqasjIiIiIiIizYBhGBw8eJD27dsTElJ1u7QCa2DPnj106tSpqashIiIiIiIizdCuXbvo2LFjlekKrIE2bdoA5h8rLi6uiWsjIiIiIiIizUFBQQGdOnVyxYxVUWANru7fcXFxCqxFRERERETES023DGvwMhEREREREZEgKLAWERERERERCYICaxEREREREZEgKLAWERERERERCYICaxEREREREZEgKLAWERERERERCYICaxEREREREZEgNGlgnZ6ejsVi8XlMmTIFgKKiIqZMmUJSUhKxsbFMmjSJnJwcrzJ27tzJhAkTiI6OJiUlhdtvv52ysrKmeDkiIiIiIiJyFGrSwPqnn35i7969rseCBQsA+OMf/wjAzTffzMcff8zbb7/N119/zZ49ezj33HNd+5eXlzNhwgRKSkr44YcfeOWVV3j55Ze5//77m+T1iIiIiIiIyNHHYhiG0dSVcLrpppuYN28emzdvpqCggOTkZN58803OO+88ADZu3EifPn1YsmQJI0aM4LPPPmPixIns2bOH1NRUAJ5//nnuvPNO9u/fT3h4eEDHLSgoID4+nvz8fOLi4hrs9YmIiIiIiEjLEWis2GzusS4pKeH111/niiuuwGKxsHz5ckpLSxkzZowrT+/evencuTNLliwBYMmSJQwYMMAVVAOMGzeOgoIC1q9f3+ivQURERERERI4+YU1dAacPPvgAu93OZZddBkB2djbh4eHYbDavfKmpqWRnZ7vyeAbVznRnWlWKi4spLi52PS8oKKiHVyAiIiIiIiJHo2YTWL/44ouMHz+e9u3bN/ixZs2axYwZMxr8OCIiIs2RvchOlj2LgqIC4iPj6WLrgi3S1tTVEhGRRqbrQf1pFoH1jh07+PLLL3nvvfdc29LS0igpKcFut3u1Wufk5JCWlubK8+OPP3qV5Rw13JnHn7vvvptbbrnF9bygoIBOnTrVx0sRERFp1rblbuPxpY+zNmctpRWlWEOsDEwdyM0jbqZbYremrp6IiDSSbbnbePrHp9mat9W1LSMhg6nDpup6UAfN4h7rl156iZSUFCZMmODaNnToUKxWKwsXLnRt27RpEzt37iQzMxOAzMxM1q5dy759+1x5FixYQFxcHH379q3yeBEREcTFxXk9REREWjt7kZ3Z38/m082fsvHARrbmbWXjgY18svkTZn8/G3uRvamrKCIijcBeZPcJqgG25G1hzo9zdD2ogyYPrCsqKnjppZeYPHkyYWHuBvT4+HiuvPJKbrnlFhYvXszy5cu5/PLLyczMZMSIEQCMHTuWvn37cskll7B69Wrmz5/Pvffey5QpU4iIiGiqlyQiItIsbT6wmS+2fUFhaaHX9sLSQr7Y9gWbD2xuopqJiEhjyrJn+QTVTlvytpBlz2rcCrUCTd4V/Msvv2Tnzp1cccUVPmlPPPEEISEhTJo0ieLiYsaNG8ezzz7rSg8NDWXevHlcf/31ZGZmEhMTw+TJk3nggQca8yWIiIi0CDmFOT5BtVNhaSE5hTmNXCMREWkKBUXVD95cU7r4avLAeuzYsVQ1lXZkZCTPPPMMzzzzTJX7d+nShU8//bShqiciItJqRIZFBpUuIiKtQ1xk9bfC1pQuvpq8K7iIiIg0jtSYVPon9/eb1j+5P6kxqX7TRESkdUm3pZORkOE3LSMhg3RbeuNWqBVQYC0iInKU6BTfiauHXO0TXPdP7s/VQ66mU7xmyBARORrYIm1MHTbVJ7jOSMhg6vCpmnKrDixGVf2wjyIFBQXEx8eTn5+vEcJFRKRV2567nQXbF1BeUU5RWRGRYZGEhoRyWrfT6JrQtamrJyIijchzHuu4yDjSbekKqisJNFZUYI0CaxERObroi5SIiEhgAo0Vm3zwMhEREWkcngF1fGQ8A9MGKqAWERGpBwqsj1obgaHA4VruNxpYXO+1ERGRhrUtdxtP//i0a97SB076hjbWAioqICTgEVc6AauBhAaqpYiINI7ZwL1AaS32iQLeBiY0SI1aOgXWR5WpwD+DLOMrwOJYtwD7gLZBlikiIg3JXmTn6R9n8OiYVwkNNbdZHKdywzAflTnTve0CEj2evwZcXK91FRGRhtIB2BPE/keAiR7PR6MGNzfdY01rv8f6dyC5mvSOwEpqDo6fB24EyqtI7wzsqHXtRESkoaVQUbEfi8UMlp1X/YoKOFwSwucb7mD+b/vJKcxx7ZGRkMHUYVPpltjNsSUPGAD8VsUxwjB/aFVLtohI83ID8FwVaaHAHcDDAZRzLvABUFXo+F/ggtpWrkXQ4GW10HoDa3/NDTHAoXooex5whp/tk4B36qF8EREJzghgGeAOpsvL4Yq3TyDPEQAPbTeURdsXUVhaSERoBDHhMUSGRQJmcD1t9LRq7sGOBIorbauva4yIiATnK+BkP9vvJrBAuiYnO45RWR5gq4fym49AY0XNY90qDcA3qP4F8xem+vrCM9FRngGc6LH9Xcexf6+n44iISO0sxzwPL3Nt2bXvIs6cO5Fz3p7oCqoLigqItkazYu8KDMMg90guu/J3UVBUAMCWvC1k2bOqOU4R5jXgS49thY5j31ifL0hERGolFO+gugvu7+31EVSD2QXcWWakx/YEjtbeSwqsW5XfMb/QrPPY9i3mG753rUtzdhv0fACEhVXe/i133GEA8R57J+P9IfM2dar/sgOpR2Ji1WnVlSMi0vqFAcd6PB8OGMTF/5OMhAzX1qKyIvKK8jhYfJBQSyg5hTn8euBXsg9lk1eUR1FZEfP+/BHHtBvkc36NjKx83j2Vhx4ygCc8jvsM/ntNme69t+7XAM+8lbdbrYH/pUREWp8JmOfeCsfzMMw4IKvKPexFdlZlr+KbrG9Ynb0ae5HdlVbV+Tc6uvL2I8ye7dkJ2u6ox3+qPO4DD9T9OpCeXnVaVFTV5TQ0dQWntXQFfx643uN5T2BTUCVaLOZIseXlkJYGOY7b7yIioLTU3P7ddzBypLnd/U6q/MnwfYt5DpoTFwcHD8Ixx8CKFd75wsPNY3XsCLt2ee/3+++QnOyuo7OcSZPgHfVGF5Gjjue5N4TKY2Jsy93GnB/nsCN/B33a9iEyLJLebXuz7LdlJEUlcbDkINZQKzvsO9iVv4tFl31JWFgZpaVWuneHbdvM821sLBQVQXExLF8OxzriePc1IAXY73HkrUA3r7p4nstTUmD/fhg9GhZXGgPntNPgyy+hXz9Yt848vudga2efDR984FumiMjRxwbkezzfDqRXu0fl2SLAe4wNi8X83l9UBL17w6ZNEBpqfuc+csR8rFplfocH5/l3NnCnx1FG42+AM89zdvv2sHcvjB0L8+d754uPh4ICGDgQVq/23u/cc+H99yEmBg4d8r1G1BfdY10LLT+wPgPznmen+vkv9Qysnc/B983qf/s44Isq61R5n0DLHjYMfvrJ/AB/8YUZWDvTQ0PNwXieew6uuy7AFyki0uLl4T1S9/PAtX5z2ovsbD6wmSeXPsmanDXcecKdPL/8eZbtXkZEWASFpYUMSh3EFcdcwV9HTHUF1lDba0DlOnmPHu5sWaioqK4MOOEE+OEH80tcfn71wbMCaxE5eoXibqVOxhxMsno77DvYYd9BXlEeJeUlRFmj+GX/LyzZvYQu8V2YNnoaCVE2V2ANtb0OVF+nusYZEybAp5+aQfiiRVBW5r2Ps/dSSUmNf4KA6R7ro8YwGiKoriw83Fz26eO9/eqrffOaX5jmY7FUeDwMLBazhdupY8eajxsRYS4jHb3Kf/rJXK5aBW09BjL3/IKmoFpEjh6VA9hcqgqqnd5Y+wa/HviVvil9eXzJ4+wr3Ee0NZrismIiQiNYnbOaT379BACLxQyqY2PNfYcP9y7rttt8yzevAQmO877zGnARFovB8uXufL0DuEPp++/NZUGB+8uV83rkfTxz3XnNEBE5enh2/b6YQILq1dmrWbB1AR9t+ojle5ezInsFq7NXExYSxlk9z2JH/g6fMTbiHXd8nniid1n33OOnRhawWMo9rgE5rlhg1Sp3vv79a351CQney08/NZeLF8MFjkHIK08jWVqbqbnrkeaxbtGeBxyRpteHqv5UVHjf87Bhg8fRn4f/+z9z3fOXIu8u4ZWn+6pd4F9UZB6/uNj3vrp5Hr8nGIY7fcAAWLu2VocREWmhPIPqms+vWfYstuZtJSIsgj5t+/DW+rcIIYTU2FRSY1OJskZRXFbMkbIjgPnlxPPcu3Spe/311+GxxxxH9nsNAPM64NlFPZfaDGrzpz+Zy+hoKCw061JSYh774ou9j+e8VoiIHD08B5b4O+bUWdXbc3APn/76KTHhMXy5/UtW56x2pR3b7lhuOO4GhrUf5hrIsvJ38G+/da/PnQuzZpnrVccCxwE/O54PBNzHC0Rurnl8u927HqGh8Npr5vWgcrzSVNRi3aJ53lNd/0E1uO9VcApz/BTzxhtwvePw+/d77+M9iEBbr5br774b6Mq3e3dgdXDeK2EY0Lmzue2GG+CMM9zpnst163zLEBFpfTy/RQT2o6Xzi1JseCwWx78KKth7aC+bczdzpPQIh0sPE22NBszudJ7XAGfvoQ8/hEsuMddzcyvVymewG3fL9fLl7oHVNm6sub5vvWUuCwsdr9JRlylTfPM6004+ueZyRURavkuAMsf6iwQSVANsPrCZLgldeGnVS15BNcDPe3/mPyv/Q5/kPsRFml2eIyK8rwPR5uWBTz6BCy801/PyvI/hfQ34ySMWWMUqjybrQL+ze8YCgweb2+6+2zftxRfNbUlJgZVb3xRYt1i1/0IVDM85UOfNc7cU7N/v3SXbmdf7YcEwrsEwQjjxxLVU/uA7b1WoqjtIp07m8rvvYOdOc33OHOjmGAvH2S3Q+UFPCLwxRESkhRrhsf5zlbkqc35RirZG0zOpJ73a9qJHYg96JfWiY1xHYqwx9EjsgTXUe3ht5zWguBgWLjQHDQMzqK58zvW9BjivAyEMHboNZ2c5Z5kpKeby+ON96+v8Mdd5fQhxfGuZMcOsQ8+e7rzO1or33gv4zyEi0kLZgdcd66nAFQHvmXckDwsWn6DaaU3OGiqMCtJt6V7bnefsI0fgq69g4kRHeXlgs+GT1981wDBCGDz4GFc+522c7duby9Gj/dd50CBzuWqVuyv5/fd7pwFceaW5/L2JZv1VYN0ieUay+6vMVd+c91Q4W4rBHDwssGmu/o37zoN/cNVV5prFYo7kDe7u25XL273bfO4cfXz2bHO51TGAobOr4hGz56JP64mISOuyDfcc1T2AoQHvmW5Lp3dSb8ZnjGfj7xuJDY8ly57FtrxtFJcVExcZR3R4tNfUXE4TJpjLMWPc2xITazPV4WuOZTm33mo2RVss7l5PzvupPctz3ie3fr25zfnF7qabYNky2LzZO7/Vqh9XReRo4Hmiy67dnlEJFJWZo5FZ/EyLaLFYCLGEYIu0+aSdc4659OwZlJAQ6HXAsyEwxNXibLGYI4KDe2aIyuWtWWM+d44+7myZ9kxz5v/jH2uqR8PRqOC0xFHBne+0Y4AV1WVshhq3pV1EpPUJ7jy65cAWbvr8JtbsW8N1x17H+xvfZ/2+9URbo4mLiGNCjwncnHkz3RK61VxYrbUFDjjWdQ0QEam93rin1K39eXTPwT18s+Mbrpt3HQXFBV5p1hAraW3SeOeP73Bch+OCr6qPVZjxC8B/gQsa4Bj1L9BYUYOXtTihHustLagGs4U9ucZcIiLij8ew2nUMTA+VHqLMKKOrrStfbP2Ccd3HcdmgyygpLyEyLJITO5/YQEE1mANaOn8YGIz5JUtERALnDKoD7/7tZC+y83vh7/RK6sW47uOYv3W+K7i2hlpJjUllfMZ4eiT1qMf6ehoMxACFwIW0lMA6UAqsWxznIGWvV5ur+fLsxh5CQw26JiLSOjkH/6r7nVwFRQVYQ62u+6hX56z2uteuX3K/YCoYgAnAJ9R2ZFgREbnEY/3FKnP5sy13G0//+DRb87YSHx7PNUOvIdoazYb9GwgLCWNU+ih6JfUiNTaVHfYdYMNvd/DgHcL9A6sdaIhjNA0F1i2K57QqFzVZLYL3LTASdQMUEakNz2FXy+tcinMAs7qmB28e7i9VNwL/bODjiYi0Fs6Gtb612steZHcF1QD5Jfm8sPwFzup9FpP6TCImPIZXVr/CT3t+IjLMnP4hIyGDqcOm0i2xIXowWTDjgARaUzygwctaFOeXqsYJqp0DAbzxhu+2ynNK+xu0wHfKFWcez5nlq/4C529fz+lZKqdVNcXKHXf45u3rOB9t3Oib9vzzgfx1REQaW/3MH5JuSycjIQNriJURHUZwesbpnJJ+Cqf3OJ0zep7hGgnWeU788EP3vvVzDQDo4Fg+U2U9/e27bZuZFhnpm/bQQ1W/Zv91qK5+IiLNzSce6+trtWeWPcsVVIN5L/WYbmN4ccWLfLjpQ2Z8PYPPtnzG1tytHC45DMCWvC10T+qKxWLwicehqzuXhlSKLKu+DgQ20rC/fbOyqs53/vmBl2O3V51WVwqsW6TG7QbunFqrrioPuW9y/jhwsMr9LBaIiTH3mT7d3NanjzsNYNIkd5lffeW/nH/8w1zu3+/O+8sv3uU995x7ZNrrr0dEpBlynkC3VpurJrZIG38Z9hfO73s+C7cv5N5F9zLzm5nM+nYWy/csJ/ew9xce59RadeX/GrC7xv3Cwsy5SA0DnnjC3Na9u7mMiDCnWjQM+Nkx29i99/ovx3m9uPVW9w+nzm0WC/TrZ5bj/EKm4FpEmqezHcvah28FRd6DlA1tN5S56+aybv860m3prMlZA0BhaSHb7dspLS/1yu+cWqsmVQ2J7XsdsHmk3lJleeHhkJpq7vNPR+emrl298wRyzr7+eti+3Sznv/81tzlnkAgJMefGNgx3zFPX64AC6xZjZFNXgHHjfLdFRJhL5/xznlNxVa/mHwcqKuDQIXN92jT/ed55x1wG8gF45x1zDm5/rrvOdz5uEZHmKfhueQnRCSzOWkzekTzSYtPoENeBaGs0X27/ktnfz8ZeZPfZ509/8i2nTRtz2cMxzs0ll/jmqdk2v1tLS91zkd50k3dafr45pzbA0ABnG3v0Ubj2Wu9tFRWwbp25/r//BVaOiEjTKHMs/12rvexFdgwMV8+kER1GkBydzLr95smvpLzEa9qtwtJCjpQe8SjBHS37O8c757B29gZ1ziVdsxjH8okqcxQXQ7ZjNrEpU3zTj3MMXF5To9izz0J6urm+YIF3Wnk5rFxprr/2GkHRPdYtxneOZUSjHjUkxPzi0aED7Nnjm15SYi5/+80Mbv0FrpWDXsNwbqt64LLKv3h16eI/X5cusHy5O/+wYfDjj955pk83H54fOmeLtdMZZ8AFrWtgQhFpVRbWa2mbD2xmwbYFFJYW+qR9se0LrjxwJXAcVqsZ4Pbsac4ZXZnzx89ffzXP66+/7vvFpD6uAf37m8vQUN+8t91WZTG1EuP4juf88iUi0jwFPhq4c8Cy9fvX83vh75SUl9AvpR/XDb2OuPA4CkoKiLJGERrifXItrXC3WFvDyyktCaFfP9iwwfcY+fnmcv1689z+n/94zzMNvt3GKyrAYikAP/NoO1W+DjjnsA7ziF6dPZaefdbsfVqd0FDzuE55eb55nD8WZ2RUX1ZV1GLd4tTcfS4omzbBF184fro3wDA/WM6gOsIjrncG0eHh1RfprxuguR7ieAypoqug6YwzYOdO936ey507Idlj9q7UVN/jO7uRT5/u/tA5u4A7u3/Pmxd8l3cRkYYzvl5LyynM8RtUU1ZOYWEeOQd24HkNcAbVntN3LnTE+rGx1R+r6mvA1R7XgaqvAZdcYn5hAygr8057/XV47DF3mXXVtSscNm8rZPv2upcjItJcOAcsW5uzlq25W9lVsIvt9u18uOlDHvn+ER44+QESIhM4UnqEASkDzJ3KyqGkBGtJmetLsjP0dQbVzi7U4L4NMz6++rp4nt+dwa37/O97Dah8Pr/ySli1ylwvdcT8zmA90HN/ebmZ90THUE+Jid7pPXq4fyz290NyIBRYtzgN2F952TKzn9y4ce7mWwPaJx52ZSkqcmd3dvsuKfH+JSosgH4Q5uAAFY7H8ioHDTjvPHcAX/mD4+/D9/HHVR9z2jSzy7entm2r/hCLiDQfztaDv9VLac5RX70PUQoHCyDPTuQhRz/rsnJ6dHCPheFsmQAYM8ZcHjrkfd6O9FN0Zea5/v88rgP+rwFXXWUGz+B7jv7wQ3e3xNzAxsHxq2dP92A4ug6ISPM0odZ7ZNmz2Pj7Rrbbt1NYWkhoSCix4bHEWmP5ec/P5B7J5YL+F/DDrh84p/c59EnshVFcRExRBVHbd5Oxx/zSb6lwd/MG7/Otc+Dg/Hzvc3d0dM31844Fqh5A7IYbzFZw8H+O9sz/9tvejYD+fPutb1n9+sGWLVUfI1AKrMW0aRPceSd8/XWlBIPfBpxOTV32PAPT8gBmgTHzn1Jta8XFF8O777rzV9apk7mcOrXm44F5r57zfj2nGTNgpOP29QEDAitHRKTpPFgvpaTGpNI/ub97Q1m5GSGXlNK/3SBSC5wncoNfM84AKqocy6Lyudt573N13PtUfQ248UZ3d8LK14CFC92DquXmeregVOW22+Bf//Le1revu2VCQbWINF/La71HQVEBR0qPePVOslgshIWGYQ21crj0MCkxKazZt4bZ38yiR3RHpp/2EE+f+28eybiOaXt7mTuVlbH+vU2A76jfTpXP30eO+M/nu0/1Lda33OLubVpdA5sz7Y9/9H8Nio93/4B6ww3eaYMGuVvjg70O6B5rMe3Y4Seodvj6a4z5X8LYsT5JzvsdnJz3ZHtOWeXv/jrT6cBXVVap8jRflfffvdt7u7Nbt2d+wzAHNvjpJ+8u48779V591Zy+xV/5IiKtVaf4Tlw95Gr+veLf5gA2FeWuoPrqgZPpNPcLwNEcXM01YPRo7+fh4WYvJmcrM1R3DajeMx4zcVU+Rztby8G7O5+zbIvFvJ+urMx9T7ezy7hnPs/xNnQdEJHmyzmqd+BtonGRcV73SnsyDIMKo4KosCimj5pO0eECIvMLyV3xA9uzt/O3XV2xvfoWMBeogB07MIxefsuqfGmIjDR7uM6d695W83XAjvdI4SbnjBCVywjkHG2xmL1oS0uhoMB3NHHnPdZr1tT9GJUpsBaTnzv4Dc8Pr0d6dW80z9bqyt2ufbWpNrWmN3R16Z5plQcz87Q1uFlrRERaJFukjeEdhlNQUsB5FedRdMhO5MEjhB4qZPiGg9hefwcDj183A7wGeLYU1MdUjXVJ85de3RQwIiKtUbotnb7Jfb3mr3bqm9yXLblbiIuI49Mtn8Jve2DFCld61ol/YPCrHrFA3lyv/as7d3q2Vgc+MLAdf4F1bc7R1Z33g7me1IYCazHV1I8ukH52taa5TUREmkrXxK6cH30+WfYsCnZvI+6XVaQv243tf8/49qVrkGuAiIgEJg44QnW3ZlZmi7Rxa+atZB/K5qc9P7m290/uzzm9z2Her/NIt6WbG8OtXvsWhFRq6W7wa0B6A5ffOBRYi6lLFxg1yn938FGjqp7vKijLGqBMEZHW7F7q6z5rML94DU4bDPlR8NbTjXwNEBGRwAwCvqj1Xn2S+/DkuCf5YdcPHDhygMiwSHKP5LJu/zom9Z3ER5s+oqC4gNJIA+vQfkTl5GLdk0NchUegrWtAwBRYtzi/0yAjg/fqBX//u+8AZqNGwezZZnq9c/YVOabaXCIiYsUcGfwh6jOwdmmSa8BVDVCmiEhrNJ/q5nyuTkZSBm1j2pq9k4oKiIuMIyUmhYe/eZj1+9e7BzcrLSUm1sLY0RNJX+LoPt6g14DWx2IYusOooKCA+Ph48vPzifOcpLNZcX6YIoCi6jIGZ9MmcyCzvDyz20eXLg34YXK+pqP+LSgiUoOFgHPErgY8ZzbJNSAa8DOntoiIeAj8e7O9yM7mA5vJKcwhKiyK1JhUOsZ3xBZpc6V/tvkz/m/F/5kDWDr0T+rD1QMv5w9FnbAVljXwNSALcI4o1rxjgUBjRbVYtxgnAt8BAcxjEoxevfSrlIhIs3Nqg5W85+AeNh/YTN6RPBKjEsnI7E/7Nu0b7Hi+1jbisUREWrr/AFdUmbotdxuzv5/NVzu+YmTnkaTb0okIi6BP2z70SupFRlIGWfYs3t7wNqd0PYXz+p5HUVmRq5v4W1s+pPeo+83bhBpU/5qztDAKrFuMb6lrF5Dm6eqmroCISAuVB9TPQDKrsldx78J7+Wmve2Cb49ofx4OnPNgIX6qcujXScUREWrIwoAzzO7T/wNpeZOfxpY/z1Y6vuHTQpby/8X1eXfMqAG3C23Bq11OZPno6B4sPUlpRytLflvotp6CowO/2+uXsqXRzIxyrcQQ+GZo0I0HOYdIs/J9jWf2UWyIi4uT8cTWx2lyB2nNwj09QDfDTnp+4d9G97Dm4p16O41/nBixbRKQ1+sCxrHpk8Cx7Fmtz1jKy80je3/g+a3LckzQfLDnIht838NiSx1xdwqsSF9nQt8baPdYfb+BjNR4F1i2Ks4XijWpzNX/feaw3xi9iIiKtwYF6LW3zgc0+QbXTT3t+YvOBzfV6PG+7HMspDXgMEZHWZILH+iC/OQqKCiitKCXdlu4VVDtVVFSwYf8GisqKyEjI8FtGRkKGexquBpPUwOU3DXUFb1FycbdYvAFc1IR1CcbIpq6AiEgL5Nn9OxQoD6q0/KJ8zux5Jum2dErKS4gIi2B73nbmb51PcXkxeUfygiq/ahM91v/ZQMcQEWmN/gi8DfgGzWC2NFtDrJSUl/hNDwkJwRpi5UjpEaYOm8qcH+ewJW+LKz0jIYOpw6fW2KIdPGer+/YGPk7jUmDd4oRgvhkvpmUG1r97rDfvEQBFRJqfn4Fjqa4rYKCSopNYlb2KV1a/AoAFCwPTBnLdsdfx/M/PkxBVP/dx+/rEsfTf4iIiIlV5C3cj25XAi16p6bZ0BqYOJMoa5bNnm/A2WLAQZY0iLjKObondmDZ6mtc0XOm29EYIqj1vA01v4GM1ribvCv7bb79x8cUXk5SURFRUFAMGDODnn392pRuGwf3330+7du2IiopizJgxbN7s3T0tNzeXiy66iLi4OGw2G1deeSWHDh1q7JfSSDxbKIY1WS3qLrmpKyAi0oIN9Viv+4CWWw5s4bmfn2O7fTtlFWXmwyhjdfZq3t/4PtcMvYYeST2Cr66PFI/1VQ1QvohIa+ecvec/Pim2SBs3j7iZNtY2DEkb4treJrwNXeK7kBydTJ+2fVxdvW2RNganDeak9JMYnDa4EYLqVYAzRvtvAx+r8TVpYJ2Xl8cJJ5yA1Wrls88+Y8OGDTz22GMkJLh/JZ89ezZPP/00zz//PMuWLSMmJoZx48ZRVOSey/miiy5i/fr1LFiwgHnz5vHNN99wzTXXNMVLaiTOv89PeLcAN3fhHutqrRYRqZutHut9a723vcjOD7t+YNH2RXRP7E5ilDkYmmEYlBvl7LTvZGTnkQ0w5dbrwH7H+vn1XLaIyNFio8e67w+s3RK7cdGgi3jyD09yft/zGZI2hH4p/UiOSWZg6sBG6updlWM81i9oojo0HIthGE0W4dx11118//33fPvtt37TDcOgffv23Hrrrdx2220A5Ofnk5qayssvv8wFF1zAL7/8Qt++ffnpp5849thjAfj88885/fTT2b17N+3b1/zFINBJv5sXzw9SYBPFO7t6xEfG08XWpZE/VFNx30t3IzCnEY8tItLaHAssd6z/jHdLdvVWZa9i8fbFzP5+NglRCdgibZRVlFFeUU5oSCjR1mgeGP0AJ6WfVM91dl63QjGnjBERkbqx425oawf4n8XB8/t/43X1rkoI7pilZTWwBRorNuk91h999BHjxo3jj3/8I19//TUdOnTghhtu4OqrzTmOt2/fTnZ2NmPGjHHtEx8fz/Dhw1myZAkXXHABS5YswWazuYJqgDFjxhASEsKyZcs455xzGv11NQ4D95cUC9W9QbflbuPpH59ma567lSMjIYOpw6bSLbEx5g/9HXdQbUVBtYhIsH7GfQ04ltp8SSkoKiDKGkVKTAq/HfzN69rQJrwNvdv2boCpVjx/DFZQLSISHBvugcz2YnYL953b2tnVu+ll4r5OrWzKijSoJu0Kvm3bNp577jl69OjB/Pnzuf766/nLX/7CK6+YA6lkZ2cDkJqa6rVfamqqKy07O5uUlBSv9LCwMBITE115KisuLqagoMDr0TI957Hu/7/SXmT3CaoBtuRtYc6Pc7AX2RuueoAZVHveV+1/lEIREaktz2A68Put4yLjiIuIIzEqkYMlB73SDpYcpE1EG1JiUqrYuy4865Zbj+WKiBzN3sLdRnolMLsJ61Kd44CljvW+wOCmq0oDa9LAuqKigiFDhvDwww9zzDHHcM0113D11Vfz/PPPN+hxZ82aRXx8vOvRqVOnBj1ew7kO881qdpsvK7PwTdY3rM5ejb3Ijr3Izurs1Szfs5wDhw/we+Hv5vx25aWAGVxn2bMasH7f4R1Ut6xuHyIizZ9noGoBap4iK92WTkVFBad1P42BqQO90oakDeHsXmezv3B/FXvXlmdQ/SDeU4aJiEhwSj3W7wQuaaqKVKETZg8rgFhgfRPWpeE1aVfwdu3a0bev98Arffr04d133wUgLS0NgJycHNq1a+fKk5OTw+DBg1159u3b51VGWVkZubm5rv0ru/vuu7nllltczwsKClpwcP0jeXkjsdm+IzQUTuw8ilEvn0gXWxeuGnIV9iI7m3M3k3skl1BLqDnEfkQc3RO6Ex0eTUFRQ7XWnwHM83iuoFpEpP4lYAbXiY7nicBrmFMy+meLtNE2pi0PfvsgIzuP5OxeZ1NSXkKUNYojpUeYv3U+fdr2CbJeeR51AngeuDbIMkVExJeB+/7l14GFVHXPdeMKxT01ZBIta8DlumnSwPqEE05g06ZNXtt+/fVXunTpAkDXrl1JS0tj4cKFrkC6oKCAZcuWcf311wOQmZmJ3W5n+fLlDB1qDt6yaNEiKioqGD58uN/jRkREEBER0UCvqnHZi+zMWDaUbrF7mXrCVkJC4JvLv2PFb99xz+L9XDroUg6XHiYiNILDZYcpKymjpLyECqOCvsl9G+A+OvDtkqigWkSk4STgPe7GJcBlVHcvc1psGum2dH498Cvr96/HGmIlyhqFNdQKEOS1oR3geSvWVqAxxvMQETlaVQDxQAHmPdcWYDtNM0/0M5gDFTudCPgfqLq1adKu4DfffDNLly7l4YcfZsuWLbz55pu88MILTJkyBQCLxcJNN93Egw8+yEcffcTatWu59NJLad++PWeffTZgtnD/4Q9/4Oqrr+bHH3/k+++/58Ybb+SCCy4IaETwli7LnsXWvK18mpVBt6diqXD8MDSkA3x20ReEG+vpmdiTCqOC8NBwnIPA5xTm0KFNB9c8dvUjEe+gOhwF1SIijcXAbCEAKMc8H5/gN2e6LZ0+bfsQFxlHUnQScZFxrqA6IyGjjteGJx3H9AyqDRRUi4g0hnxgrMfzrpiDBjcmC95B9YscLUE1NHFgfdxxx/H+++/z3//+l/79+zNz5kyefPJJLrroIleeO+64g6lTp3LNNddw3HHHcejQIT7//HMiIyNded544w169+7Nqaeeyumnn86JJ57ICy+80BQvqdE5u3IfKT1CQXE4UQ+Fs+o3My0kBM4bOItlVy1nYGp7Qi2hVBgVhFhC6Nu2L5cMvKSehtw/Gd97+/YDxfVQtoiIBK4M+NLj+Q+Y5+d7vXLZIm1MHTaVjIQMr+0ZCRl1mON0oeMYN3tsuxL9sCoi0tjm4+4aDuY1wULD/8AZjXfjWryjHr4jlbdmTTqPdXPRMuexNq3KXsV9i+/jwOED/PL7LxQUFxBiCaGsoozSv0FoKFgsYBjm43BpGC+s+DvL9yzn0kGXMi5jXB2PPA/zPurKJgIf1/0FiYhIPfGc69pTLs5BxIKb4zQS3x9Qo4HCOtZXRETqz1eYjV+V3Q08XA/ljwO+8LM9D3M6sNYj0FhRgTUtO7C2F9mZ8dUMft7zM3sO7iEsJAwDAwsW+qf0Z2BSAn8b/SKhjt6BnkG2YUBoaDfM+99q8gbV37PXEdhVD69IRETqV1vgQBVpbYDFwNAaysgDBlH1eT4M2IdG/RYRaW5uwHuKXk9hwH3A/QGUcwnwJu4BySr7L3BBrWvXEiiwroWWHFgDbMvdxsPfPcznmz/nhC4n0CupF1FhUfRK6kW72HY8+9OzrN63mjtG7OTPg/KxOHpqWAKf9rQKFswvUm2DLUhERBrcNqB7PZdZ/QjkIiLSnKQBOfVY3ghgST2W1zwFGis26ajgUj8SoxMZlDKIM3ueycurX+bdDe9ypOwI9iI7Q9sN5bbjb2PI/iFszDvAMz+lMihtEBY2MbLLX4EjtTza0TOyn4hI69IN7/ueq+oqXp0OwFrUMi0i0hJ5Di75ADCT6maQ8BUF/IfW2jIdLLVY0/JbrFdlr+KdDe+wZPcSUqNTyUjKoKyiDGuolV9//5WIUHNqsfc2voeBwR+6/4E7jr+D4zoe18Q1FxERERERab7UYn0UKSgqoENsB0Z2HskHGz/gv+v/60oblDqISwZeQnJ0Mv9d919CQ0JZs28Nh0oPNWGNRUREREREWo8mnW5L6kdcZBwJ0Ql8sPEDVues9kpbnbOadza8Q9uYtpRUlBBiCcEaYuVw6eEmqq2IiIiIiEjrosC6FUi3pRMRGuETVAOEWcJYmb2SigpzBL824W2ItkYTGRbpk1dERERERERqT4F1K2CLtBEWEkZCpPdgMmEhYUSERRARGkFhaSEJkQmuOUpTYlKaqLYiIiIiIiKti+6xbiWSY5LpntCd3QW7KSgpwIKFCqOCsJAwOsZ1pLSilLbRbWnfpj1/7PtHOsV3auoqi4iIiIiItAoKrFuJ3m17MyhtEAdLDhIfGU+5UU4IIVgsFo5tfyxRoVH8fczfyT2cy/COw7FF2pq6yiIiIiIiIq2CAutWwhZp454T7+HRJY+yKnsVJeUlWEOs9Evpx5XHXInVYsUaZuXkricrqBYREREREalHmsealj+PtSd7kZ0sexYFRQXERca57qkWERERERGR2tE81kcpW6SNwWmDm7oaIiIiIiIiRw2NCi4iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFQYC0iIiIiIiISBAXWIiIiIiIiIkFo0sB6+vTpWCwWr0fv3r1d6UVFRUyZMoWkpCRiY2OZNGkSOTk5XmXs3LmTCRMmEB0dTUpKCrfffjtlZWWN/VJERERERETkKBXW1BXo168fX375pet5WJi7SjfffDOffPIJb7/9NvHx8dx4442ce+65fP/99wCUl5czYcIE0tLS+OGHH9i7dy+XXnopVquVhx9+uNFfi4iIiIiIiBx9mjywDgsLIy0tzWd7fn4+L774Im+++SannHIKAC+99BJ9+vRh6dKljBgxgi+++IINGzbw5ZdfkpqayuDBg5k5cyZ33nkn06dPJzw8vLFfjoiIiIiIiBxlmvwe682bN9O+fXu6devGRRddxM6dOwFYvnw5paWljBkzxpW3d+/edO7cmSVLlgCwZMkSBgwYQGpqqivPuHHjKCgoYP369VUes7i4mIKCAq+HiIiIiIiISF00aWA9fPhwXn75ZT7//HOee+45tm/fzsiRIzl48CDZ2dmEh4djs9m89klNTSU7OxuA7Oxsr6Dame5Mq8qsWbOIj493PTp16lS/L0xERERERESOGk3aFXz8+PGu9YEDBzJ8+HC6dOnCW2+9RVRUVIMd9+677+aWW25xPS8oKFBwLSIiIiIiInXS5F3BPdlsNnr27MmWLVtIS0ujpKQEu93ulScnJ8d1T3ZaWprPKOHO5/7u23aKiIggLi7O6yEiIiIiIiJSF80qsD506BBbt26lXbt2DB06FKvVysKFC13pmzZtYufOnWRmZgKQmZnJ2rVr2bdvnyvPggULiIuLo2/fvo1efxERERERETn6NGlX8Ntuu40zzjiDLl26sGfPHqZNm0ZoaCgXXngh8fHxXHnlldxyyy0kJiYSFxfH1KlTyczMZMSIEQCMHTuWvn37cskllzB79myys7O59957mTJlChEREU350kREREREROQo0aSB9e7du7nwwgs5cOAAycnJnHjiiSxdupTk5GQAnnjiCUJCQpg0aRLFxcWMGzeOZ5991rV/aGgo8+bN4/rrryczM5OYmBgmT57MAw880FQvSURERERERI4yFsMwjKauRFMrKCggPj6e/Px83W8tIiIiIiIiQOCxYrO6x1pERERERESkpVFgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdZHvdMASwCPaODDJqqjiIiIiIjUr51ARwKLBQYD+U1Sy5ZCgfVRp/KH58sA9zsCnF1p39cboH4iIiIiIlL/dgKhuL/LdwF+C3Df1YDNY9+YBqhfy6bA+qhwG+4Pge+Hp6QkklXZj/FN1teszl6FvSgPMDweE6oo9xJHmVYgryEqLiIiIiIiQXE2rHUBKvykDwXseH//dz52AJ387HMYd3wxqt5r3BI1m8D6kUcewWKxcNNNN7m2FRUVMWXKFJKSkoiNjWXSpEnk5OR47bdz504mTJhAdHQ0KSkp3H777ZSVlTVy7ZsrZ+D7WKXtV+L8sGzL3codi67lvsWL+ceSf3Dv4nuZ8dUMtuVu88g/D98PWZRHehmQ6DiWiIiIiIg0vWh8G9ZCMINlz+/1PwPxVZTRGbOl2zP/55XyfOM4To/6qniL1CwC659++ol//etfDBw40Gv7zTffzMcff8zbb7/N119/zZ49ezj33HNd6eXl5UyYMIGSkhJ++OEHXnnlFV5++WXuv//+xn4JzUwevl21k3F/GP4PAHuRnad/fJqteVu99t6St4U5P87BXmSv5hiHHWVV7kpuAVKCqbyIiIiIiNTZxZjfyY94bJuF+d29HDNYDsY43HHFRI/tWxzHfS3I8lumJg+sDx06xEUXXcS///1vEhISXNvz8/N58cUXefzxxznllFMYOnQoL730Ej/88ANLly4F4IsvvmDDhg28/vrrDB48mPHjxzNz5kyeeeYZSkpKmuolNbFIzNZjp06Yb/p9Pjmz7Fk+QbXTlrwtZNmzAjjeqY7ycz227cf8UD0UUI1FRERERCRYOzG/g7/hse1zzO/qdzXQMT92lH+dx7ZLORp7sjZ5YD1lyhQmTJjAmDFjvLYvX76c0tJSr+29e/emc+fOLFmyBIAlS5YwYMAAUlNTXXnGjRtHQUEB69evr/KYxcXFFBQUeD1aBwtQ7PHcwPyA+VdQVP3rPqbdICwWvB4AkZH4bH/ooQTH8W71KOFeoLvfsu+917eMKl9VpXwdO1adVl05IiIiIiKt0yOY91A7DcX8bj6uziVW9T07NtZ3+5NPPuc4XpxnCVQVi9Tm+3t4uHfeAQMCS2tsTRpYz507lxUrVjBr1iyftOzsbMLDw7HZbF7bU1NTyc7OduXxDKqd6c60qsyaNYv4+HjXo1MnfzfktyTOrt9Or2G+sasXFxlXYx6rFQwDunUzn4eGQkSE+SY2DPj5Z3P7vfc693jUcWyr4/k2j3W3hxyN2YYBycnm+skn+x4/NtZc9ulj5gX4rdL4a846Oh8iIiIiIkeP4cDdHs+d900HLyrK/H7dv7/5PDzc3BYdbW5fs8bcfvPNzj3yMQdCc+qCGfR7i46GSmGeXzt3QmmpGTQ7v+evW1dzWlNossB6165d/PWvf+WNN94gMjKyUY999913k5+f73rs2rWrUY9fv7bh3fU7F/O+Cl/2IjurslfxTdY3rM5eTUpMChkJGX7zVt6+1dFjvKIC8vOh2NEwPnRoVfUqAfo51svw91Zz/jq1z9FL/auvfEspLDSXGzaYS2cHBn9BuIiIiIjI0aUX8KNjPZJAGtfqYu1ac1laCvv3u7+j+28hjq9Uj7uBm7xyFBZCXgCTCvVwjIdW4RjM/K9/NZcvvFB9WlNossB6+fLl7Nu3jyFDhhAWFkZYWBhff/01Tz/9NGFhYaSmplJSUoLdbvfaLycnh7S0NADS0tJ8Rgl3Pnfm8SciIoK4uDivR8vl2dXaABL85tqWu43pX03nvsX3uUb/fmLJE0wePNkniM5IyGDq8Kl4toI7W46HD/cu97bbfI/l7o6xDoulwvEox2IxWL7cna9375pfnfO/xrn80jFW2nffufM4f6myWGDEiJrLFBERERFp+U4AfnWst8N7sLL6lehoxzvlFO/t06f75nXHAoZHLPAEFovhCtADVXnYrCefNJcPPFB9WlMIa5rDwqmnnsraSn/Zyy+/nN69e3PnnXfSqVMnrFYrCxcuZNKkSQBs2rSJnTt3kpmZCUBmZiYPPfQQ+/btIyXFHIl6wYIFxMXF0bdv38Z9QU3Cs/t31b9OVTX698YDG3ljzRvcfsLt7CvcR0FRAXGRcaTb0rFF2gB30OrkGDcOgNdfh8ccM3l5dsH27o5twbzX2jmQ2VnAB4G8OMBsHbdY4OBB73qEOH4S6tABdu+GDz+Es8+GZcsCLlpEREREpIXaCfzgWM8ANjfIUY4c8f4OvnChe/3dd2HGDHO9+lggHCh1pjZIPZuDJgus27RpQ39nZ32HmJgYkpKSXNuvvPJKbrnlFhITE4mLi2Pq1KlkZmYywtEsOXbsWPr27csll1zC7Nmzyc7O5t5772XKlClEREQ0+mtqXLEe69W/Qasb/XvjgY3sK9zH4LTBftOtVvPXIM+By4qKzED2kkvMbbm53vv4DkDwIDATgJ9/zmDo0DwggY0bq622i+eHs39/WL8ebr/dfL57t7k86yzzPuxffgmsTBERERGRlstzoLKGCarBvJ/68GHvgcsOHYL58+G888xtlToY+4kFSnDGK2vWdGHAgB0BHz883Ltl+qabzOX998PUqVWnNYUmC6wD8cQTTxASEsKkSZMoLi5m3LhxPPvss6700NBQ5s2bx/XXX09mZiYxMTFMnjyZB5qq/b/RPAQ4bmzwGoXbv9KyUs7ocQaJUYmEhISQX5RPSXkJvx/+neV7l9c4OjiYwa3FYt5bvXCh2ToMZlCdkOCb15cFdwt7ImC48jk6G3D88f6P3beveY/18uVmUA3w4INmN/QtW+CDD8xtCqpFREREpPUL9VhvnBZgZyxQWGjekvmHP5jb7XaIj/fN66sAsDnWuwLbAzru5s3QpYvZW7WiAp56ytx+zTVmHapKawoWw9A4ygUFBcTHx5Ofn99C7rd2BqgRQFG1ObfnbufTLZ/SNrot2+3bKS4rZmf+TtbkrMEWaeP8fuczrMMwBqUN8j2Kxd1iDTBxInzySdXHCuydZNb9ttse4rHH7vG7v/NXrsrPnZ5/Hq69Ftq2hQMHvNNSUqDSbfciIiIiIq3EdMDR/5pvgRMb7EgWi7vFGuCCC+B//6s6f82xwAXA/xxll1N5uK/ISO+u587ywsKgvNydr2dP2LSp5rT6EmisqMCalhZYDwZWO9ar/6+zF9n5bPNn/G/9//h6x9fkF+UDMCh1EOf1PY//rf8fneM788IZL9C+TfsGrbXbv3BPIH/Uv/VERERERGrB2eLUAdjdlBWpI4vHsqIpKxKwQGPFZt0VXPxxBtUTasy5K38X/7fi/9h3eB+FJYVYLBYMw2B1jlnG2G5jWZm9kn2F+xoxsL4Wd2DdHfB/77eIiIiIiHjymBYniKDaXmQny55FQVEB8ZHxdLF1cQ1c3PDsmF3CW18DmwLrFuVGj/V5NebOKcxh3f51tI1qi8ViIYwwDIv5Jl6/fz2TB09mU+6mgO6xrl8XA69jzsEtIiIiIiI1G+lYRta5hG2523xmC8pIyGDqsKl0S+wWZP0C4XlDdgzucaNaviabx1rq4hnHslNAuY+UHqGsvMzVUg1gsViwhlrpmdST9m3ac16f8yitKGXPwT0++zvnoPvwQ99tnvc9+9tWebt3ntc8cp1dZf397bvNEYunpPimTZxY/d/j2GP91xPgoYeqThMRERERaVr5Hut1m6+6qil4t+RtYc6Pc7AX2V35VmWvcsxDbfDux3ZX3pCQqmOB0FCvYquJBT535DhcbX27dKk6zvCsh/PhnMe6skce8VcHU3Jy1Wm1pcC6RdpZY45tudsorSgFC+QX5xNtjabMKCPUEkrftn3Zc3APG/Zv4Lp513H+2+dz9UdXsyp7ld+ynCOA15VheD9Mzl/EPqxiL3MwgqQkc58nnjC3de9uLmfONEcG9yyzuoHVwBxVvCr33lvTqxARERERaSrdgy6huil4t+RtYYd9B9tytzH9q+nct/g+V9p5Z7ZhW67ZulXd6FwVVdwy7RsLjPNInV9leUOG+Abr1ZXtnG6rsrvvduft5GifPOMMc/n77+60U04x1wcMqP6YVQk4sC4sbD3N9C3TkwHntBfZmf39bL7e8TUndzmZwpJCUmNTibXGkm5LZ2veVjrHd+bX338lIiyC/OJ8FmUt4p6F9/htuXb60598t7VpYy579DCXzrmta1bzvdWlpe43e+UPyrXXwt/+Zq5XFzA7Vffrk1qpRURERKR5c06Fc1GdS6jq9k9riJURHUZwuPQwD3zzACv3rqS0vNSRagAhzPlxDpdcZvfZNynJXA5yTDB0/fWB1sYZMZ9VZY7334eysvr5ru4M0Hc62ifnedxV66z7woXmct26uh0j4MB64MCBfPfddzVnlAZyi2NZ823xmw9s5ottX7B4+2KObX8sIzqOwIKFrgldaRPehm4J3Ti3z7l8tuUzDpcexhpqpaS8hK+yvmLT797j01ut5rJnT3jrLd9jHTpkLn/91Vy+/rpvHn/dK8z1Csej5i4Y/fubS89frV5/3cx77LHm8/PP9//3cM6T/eCDvmnO1vihQ/3vKyIiIiLSfPj5sh2guEjfEa2tIVbGZ4xn0fZF/LTnJxZtX0TPpJ6MTh8NQFi4GWD/37XX8forvvvn5prLVavM5fPP+x7X8zu+87u8xVLiiAOOBBQLVCXQfQYPrjpttWNs6I4dAz+uPwEH1pMmTeKUU07h9ttvp8Q5sbE0Ime/i3dqzJlTmENhaSEGBg9/9zAZiRmc3+98Tu9xOnedeBd92vbh4W8f5mDJQUorSgm1hGIYBmUVZRw4/Dt88YVjkjoDDPPDtHmzWbbnCPPOX3ViY2uouZ+u4OZ6iOPxUBXdxU2XXALr15vrZWXu7RdfbOZ9zXHLtr/APy8P9u8378NwtnB7ct4//vPP1b8GEREREZGWLN2WTkZChte2oe2GMnfdXLbbt1NRXsZ1Q65h1Z6VTFtkdgUvqygBDA7t7AFYaNvWva+zzTUxsfrjen7Hd8457Y4DQnzigEAmg772WrjiCjNvnz7mtrq0bHfu7N73t99qv7+ngAPr2bNn88033/DJJ58wZMgQVq5cGdyRpY6q7i7hFBlmjhRYUVFBcXkxn2/5nAe/eZDZ38+mqKyI9ze+T3F5se+OhkFcSBSMG2fOAA9QVk6PDgddWfI9xk0YM8ZcHjrk/UaODGCgQu8W63uq/JXqqqvcreBVfcguvrjqD9J1jpm9Kip8B1moXJ+q0kREREREms5d9VKKLdLG1GFTvYLrxKhEttu30zW6PV0j0vjkl4/omdCdu080b0wODbFi67Id5/zT+/e7yxvpGKQ8N9f7+7PzVtHq1Lb3amXPPQcvvmiub9hQc35ni3plO3bULqCvTq0GLxsxYgQrV65k/PjxHH/88Zx55pmce+65Xg9peqkxqfRP7k9IiPnfG2Jx/zcfLjnMsA7D/O53fKfj6XC4cldzg18zzgAqqnyTV34zFvuJ2f3vM7bKX6oAbrzR/YGp/EZv18494Ni2bVV/EP73P/8fFu+Wc/9pIiIiIiJN77/1VlK3xG5MGz2NmSfP5PbM2+kU34mesV2IXreJ4uIjnN77DDbkbuKBr2cCUF5eSsY9FwAGIaHlfsus/H3aeatodbx7r9a+xfrYY933S1c1aJknZ0u5s4V67FhzecIJ7nKcsY5z0OTaqvWo4MXFxezbtw+LxUJ8fLzPQ5pep/hOXD3kavq27cvFAy5m8qDJ/G3k3/jHaf8gNiKWqcOmcmKnEzEwCAsJo9wo58TOJ3Bf5l10+Pf/fAv8+muM+V/6Helv9Gjv5+Hh5tLzXuuqf4GqtHMlzzzjXq+8f3a2e4os50jhnnWxWMz7wkVEREREWjbnoGM1DJEdIFukjcFpgzkp/SQ6xnXEWngE9v9OaGgon235lPX71nvlX79vHaNfOpWlWSsdU3IZrsfY8Qe98kZHm8t333Vvq7k1Ot/fRtc0Wc5A23P/5cvd03E99ZS5zW43l4MHex9n2jT3/rt2mevzHYOR//CDuxwwb3ENJFD3x2IYgbfPLViwgCuuuIJ27drxyiuv0MfZob2FKygoID4+nvz8fOLifG/Kbx6c747A/ruycrPYlLuJV1e/ysrslUSFRZFfnE9iVCIX9LuA+Ih44qPiKSkvISwkjHAjlMHf/Ur6TTP8NznPnet/WPCgLAQc/ckDfF0iIiIiIkeXGMw5n61A/Y51ZS+yM+OtKWz5/E0uv/0N/vzeRYRZIwgJDQMLhBkhhFlCsYSH8+a5b/Lxrx97TdmVHp/OxQMvJq8oD1uEjZKKEkrLS2kb3ZYuti7YIm3VHN0Z36wB6jjHVSMINFaseYhph2uvvZZXXnmFe+65h7/97W+E1jSpmDQpW7SNj5d+zO6C3SRFJVFulJNmTaPCqGB1zmpuHHYjZRVl5BXlkRydTI99ZdhuuqjqftwJCQ1Qy/rr1iIiIiIi0jpFYwbWZTVlrDVbpI2pQ65jzob15B/cT2xEHAeLCwitKCc6JILQgwchwUZ0WDR7D+31CqpLy0opqyhj2W/LSI5OZuPvG1mbs5YN+zcQbY1mYOpApg6bSrfEbjXUovkG1bURcGD9/fff88MPPzBkyJCGrI/Ukyx7Fjvyd/gdVj+3KBdrqJXjOhzn3nhoE4wYAV9/7VvYqFFmH4l6t7gByhQRERERaU06AL/TUD08u4WnMi32DFZbU+mdkMHOQ79RdMiOpbAQwq3ERMbRIb4jh0o8bp424JRupzB33Vw++vUjyivK2Ve4j8Fpg/nzgD/z/i/vs3T3Ug6WHOSBkx+gfZv2DVL35iTgwHrFihWEO2+glWavqgngq0zv1Qv+/ne4807v4HrUKJg920yvd9scy5QGKFtEREREpDX4GrA1XPE9e2I7bSKDFszjuMMGsWUhHEnoTKktDGubeKKi42kX2464iDhOzzidorIiuti68MLyF1iTs4buCd357eBvWCwWfj3wK3PXzaVfcj/m/TqPbfZtnNT5JE7sfGIALdctW8CBtYLq5mIwsKrGXP5aqmtMHz4c/vUvc9z5vDyz+3eXLg0UVHva2MDli4iIiIi0VI0wQPTw4dgSEphaMpY5K55ni30rREVBbCwZCRlcOOBC7l90PytzzCmXbxx2I0t3L6VP2z6MTh9N25i2lJSXYA2x8svvv9AzqSeGo4X9wJEDzPlxDtNGT6vhnuuWLeDAWppaB+A3YHVAuZ0TwG/J2+KTlpGQQbot3f+OvXo1QiBdWUPcvy0iIiIi0trsBDo3TNE9e9KNnkzLGECWPYuCogLiIuNIiUlh9vez+TX3V1fW0vJSisuKOb/f+czfOp8lu5dgwUK5Uc6g1EH8sc8fiQyNpJxyIsMi2ZK3hSx7FoPTBjtKaOx4o+HVerotaSpra5Xb3wTwYAbVU4dPbQa/Fi1s4uOLiIiIiLQUzhG0Bzb4kTyn4xqcNph9hfvYmreVrrauxFhjAIgIjeCs3mfx3i/vsX7/ekIs7rBydc5q3lj7Bqf3PJ3+yf3JPZILVL4V1RmkH9/gr6exqMW6xfBs1V0ODK1xD+cE8J6/OKXb0ptBUA3uabY0uryIiIiISPX+AjxFVXM+NyRnQBwdHk3PpJ4cKT3CoZJDHN/peN7Z8A6RYZFEhEZQVlFGeXk58RHxLN+7nNuPv53hHYbz2ZbPgKpuVf2+EV9Jw6pTi/W3337LxRdfTGZmJr/99hsAr732Gt999129Vk4qc/53HRvwHpV/cWoeQbWn/U1dARERERGRZu5Jj/VHGvXIngGxNdRKXGQcmw5sIikqCVukDWuoFWuIlRhrDO1i29EzqSe2SBux4bF8tuUzSitKK92K2jpHCK91YP3uu+8ybtw4oqKiWLlyJcWOeY/z8/N5+OGH672C4qncYz2vyWoRPM+3ne6vFhERERGpWQfH8u5GPapz7CZPpRWlHCw5SPs27emf3J8+yX3on9Kf3m17c+DIAXIKcygqK3IF1d63ou51LJ9ozJfR4CyGYdRqQrRjjjmGm2++mUsvvZQ2bdqwevVqunXrxsqVKxk/fjzZ2dkNVdcGU1BQQHx8PPn5+cTFVT+adtOzeKw3zFx2Dc/5GrYCrXvYfRERERGR+uP8Hv0tcGKjHXVb7jbm/DjHa2DkM3qewYq9K/jt4G9eeUvLS+kY15Frh1yLNcxa6VbUU4FFjvWWEcsEGivW+h7rTZs2cdJJJ/lsj4+Px26317Y4qbVcINGxvhDzzdmSeLZWK6gWEREREQlcBFAMjKQxA9Oqxm4a03WMT8Ddp20fpg6fSrcEf9/1nUH12Y1R7UZV68A6LS2NLVu2kJ6e7rX9u+++o1s3BUoNz7Pr9Bhayi89pom46/tBE9ZDRERERKQlKsLdah0LHKqXUu1FdrLsWRwsPkhCZAJHyo5wpPQI8ZHxdLF1wRZpc43d5MkWaavFYMmePW/fr5d6Nye1Dqyvvvpq/vrXv/Kf//wHi8XCnj17WLJkCbfddhv33XdfQ9RRfBi435gWWkZwnQd84lhPA85qwrqIiIiIiLRUTwA3A4XAdMej7rblbuPpH59mZ/5OxmeMZ+66uWy3b6errSvR4dHmPdLDptIt0X8jqr+A21dXj3V7UPVtrmp9j7VhGDz88MPMmjWLw4cPAxAREcFtt93GzJkzG6SSDa1l3WPttBz36OCDgFVNV5WAtIZ7w0VEREREmoNE3IMZ1/27tb3IzvSvprM1bysjOoxg0fZFrNu/DoAYaww9k3piDbWSkZDBtNHT6jjD0HzgD471PwFz61zfphBorFjrUcEtFgt/+9vfyM3NZd26dSxdupT9+/e32KC65RoK9HOsr8bsZt1cKagWEREREak/uR7rlipz1STLnsXWvK0AJEYluoJqgMLSQo6UHgFgS94WsuxZdTjCd7iDaistLaiujVp3BXcKDw+nb9++9VkXqbV1mG/QMsxu1scCPzdpjXx5ftBfa7JaiIiIiIi0LpVvD7UD8bUqoaCowLVeVFbkk15aUeo3b2DeBc7zeF5Sy/1blloH1ieffDIWS9W/iixatKjKNGkIpUAk5uiAy4Fo4HCT1siUh3v0coAvaXkjmIuIiIiINGeewbUN+BdwTcB7x0W6uzZHhkX6pFtDrH7z1mwc8EWlerZute4KPnjwYAYNGuR69O3bl5KSElasWMGAAQMaoo5SoyLMAcEAjmB+uPKqzt7gjsU7qN6KgmoRERERkYbgGbReixlgBybdlk5GQgYAuUdy6Z/c35UWY40hyhoFQEZCBum29ABLDcUdVIdyNATVUIcW6yeeeMLv9unTp3PoUP0M9y51sRe4CnjR8TwRCMdsyW4sCzGnAPN0dHyQRERERESajoH53b8UyMdsaPsr8GS1e9kibUwdNpU5P85h+d7lXND/Aq9RwZ0Dl00dPjWAgcsGY4795NQZ2FG3l9MC1XpU8Kps2bKFYcOGkZubW3PmZqZljgpencpd9c+iqnmjnXPWFRQVeM1TV3sheAfRo4HFdShHRERERETq5gXMVmtPdmq699pzHmtbpI2isiKOlB6pYV5qJ89Rv512YAbWLV+gsWKdBy+rbMmSJURG+vbLl6ZgAK8Dlzief4g72N4KmHPQOeesc44ECNQ4T523ibjnpnYKxRxMTUREREREGtc1jkdXIMuxzeZYZgCb/e4V2FzUlcVizqXtqeVNp1Vfah1Yn3vuuV7PDcNg7969/Pzzz9x33331VjEJ1sWOR0+8P0DdASgrC2HFrmPZmpfitdeWvC3M+XFOFfPU5QHDqeoD6Rm0i4iIiIhIU9nuWIYCFY71Lbgb29oBS6ldq/J3mA1r+X7SYoGDta9mK1LrruCXX3651/OQkBCSk5M55ZRTGDt2bL1WrrG0vq7g/lQepRs8/+ed657bQkMDKbcf5rRfIiIiIiLSPN0EPNUA5b4DTGqAcpuPQGPFWgXW5eXlfP/99wwYMICEhIR6qWhzcHQE1p4eoqzsPkJCDDxnTqtmFrVKkjBbrVvPe0BERERE5OhwAvBDHfcNAS7EvO306NAg91iHhoYyduxYfvnll1YVWB99/sa63yfwwNcPMD5jPHPXzWXd/m+5fTgMSC2jR1I4CZFdSIgZDJyB2aVcRERERERavu/9bPsOs/V5LXAA6ITZM/UGWssgZA2t1vdY9+/fn23bttG1a9eGqI80knRbOn/I+IMjqF4HJPCPZWZajDWGCT3GMvOUmXUcIVxERERERFqOEx0PqauQ2u7w4IMPcttttzFv3jz27t1LQUGB10NaBlukjX7J/dhu3+61PcYaQ1dbV7Lys8iyZzVN5URERERERFqQgFusH3jgAW699VZOP/10AM4880wsHjflGoaBxWKhvLy8/mspDaK8opyeST05UnqE0opSrCFWoqxRWEOtABQU6YcSERERERGRmgTcYj1jxgwKCwtZvHix67Fo0SLXw/m8Np577jkGDhxIXFwccXFxZGZm8tlnn7nSi4qKmDJlCklJScTGxjJp0iRycnK8yti5cycTJkwgOjqalJQUbr/9dsrKNI9yIOIi47CGWomLjCMpOsn13DNdREREREREqhdwi7Vz8PBRo0bV28E7duzII488Qo8ePTAMg1deeYWzzjqLlStX0q9fP26++WY++eQT3n77beLj47nxxhs599xz+f5784b78vJyJkyYQFpaGj/88AN79+7l0ksvxWq18vDDD9dbPVurdFs6GQkZbMnb4pOWkZBBui298SslIiIiIiLSwgQ83VZISAg5OTkkJyc3aIUSExP5xz/+wXnnnUdycjJvvvkm5513HgAbN26kT58+LFmyhBEjRvDZZ58xceJE9uzZQ2pqKgDPP/88d955J/v37yc8PDygYx590225bcvdxpwf53gF1xkJGUwdPpVuCd2asGYiIiIiIiJNq0Gm2+rZs6fXfdX+5Obm1qZIl/Lyct5++20KCwvJzMxk+fLllJaWMmbMGFee3r1707lzZ1dgvWTJEgYMGOAKqgHGjRvH9ddfz/r16znmmGPqVJejSbfEbkwbPY0sexYFRQXERcaRbkvXaOAiIiIiIiIBqlVgPWPGDOLj4+u1AmvXriUzM5OioiJiY2N5//336du3L6tWrSI8PBybzeaVPzU1lezsbACys7O9gmpnujOtKsXFxRQXF7ueH+2jmdsibQxOG9zU1RAREREREWmRahVYX3DBBaSkpNRrBXr16sWqVavIz8/nnXfeYfLkyXz99df1eozKZs2axYwZMxr0GCIiIiIiInJ0CHhU8Jq6gNdVeHg4GRkZDB06lFmzZjFo0CCeeuop0tLSKCkpwW63e+XPyckhLS0NgLS0NJ9Rwp3PnXn8ufvuu8nPz3c9du3aVb8vSkRERERERI4aAQfWAY5xFrSKigqKi4sZOnQoVquVhQsXutI2bdrEzp07yczMBCAzM5O1a9eyb98+V54FCxYQFxdH3759qzxGRESEa4ov50NM9iI7q7JX8U3WN6zOXo29yN7UVRIREREREWnWAu4KXlFRUe8Hv/vuuxk/fjydO3fm4MGDvPnmm3z11VfMnz+f+Ph4rrzySm655RYSExOJi4tj6tSpZGZmMmLECADGjh1L3759ueSSS5g9ezbZ2dnce++9TJkyhYiIiHqvb2u3LXcbT//4NFvztrq2ZSRkMHXYVLolaoRwERERERERf2p1j3V927dvH5deeil79+4lPj6egQMHMn/+fE477TQAnnjiCUJCQpg0aRLFxcWMGzeOZ5991rV/aGgo8+bN4/rrryczM5OYmBgmT57MAw880FQvqcWyF9l9gmqALXlbmPPjHKaNnqaRwkVERERERPwIeB7r1uxonsfaaVX2Ku5bfF+V6TNPnqmRw0VERERE5KgSaKwY8D3W0roVFFU/5VhN6SIiIiIiIkcrBdYCQFxk9S31NaWLiIiIiIgcrRRYCwDptnQyEjL8pmUkZJBuS2/cComIiIiIiLQQCqwFAFukjanDpvoE1xkJGUwdPlUDl4mIiIiIiFRBg5ehwcs82YvsZNmzKCgqIC4yjnRbuoJqERERERE5KgUaKzbpdFvS/NgibRr9W0REREREpBbUFVxEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCAqsRURERERERIKgwFpEREREREQkCE0aWM+aNYvjjjuONm3akJKSwtlnn82mTZu88hQVFTFlyhSSkpKIjY1l0qRJ5OTkeOXZuXMnEyZMIDo6mpSUFG6//XbKysoa86WIiIiIiIjIUapJA+uvv/6aKVOmsHTpUhYsWEBpaSljx46lsLDQlefmm2/m448/5u233+brr79mz549nHvuua708vJyJkyYQElJCT/88AOvvPIKL7/8Mvfff39TvCQRERERERE5ylgMwzCauhJO+/fvJyUlha+//pqTTjqJ/Px8kpOTefPNNznvvPMA2LhxI3369GHJkiWMGDGCzz77jIkTJ7Jnzx5SU1MBeP7557nzzjvZv38/4eHhNR63oKCA+Ph48vPziYuLa9DXKCIiIiIiIi1DoLFis7rHOj8/H4DExEQAli9fTmlpKWPGjHHl6d27N507d2bJkiUALFmyhAEDBriCaoBx48ZRUFDA+vXrG7H2IiIiIiIicjQKa+oKOFVUVHDTTTdxwgkn0L9/fwCys7MJDw/HZrN55U1NTSU7O9uVxzOodqY70/wpLi6muLjY9bygoKC+XoaIiIiIiIgcZZpNi/WUKVNYt24dc+fObfBjzZo1i/j4eNejU6dODX5MERERERERaZ2aRWB94403Mm/ePBYvXkzHjh1d29PS0igpKcFut3vlz8nJIS0tzZWn8ijhzufOPJXdfffd5Ofnux67du2qx1cjIiIiIiIiR5MmDawNw+DGG2/k/fffZ9GiRXTt2tUrfejQoVitVhYuXOjatmnTJnbu3ElmZiYAmZmZrF27ln379rnyLFiwgLi4OPr27ev3uBEREcTFxXk9REREREREROqiSe+xnjJlCm+++SYffvghbdq0cd0THR8fT1RUFPHx8Vx55ZXccsstJCYmEhcXx9SpU8nMzGTEiBEAjB07lr59+3LJJZcwe/ZssrOzuffee5kyZQoRERFN+fJERERERETkKNCk021ZLBa/21966SUuu+wyAIqKirj11lv573//S3FxMePGjePZZ5/16ua9Y8cOrr/+er766itiYmKYPHkyjzzyCGFhgf1uoOm2REREREREpLJAY8VmNY91U1FgLSIiIiIiIpW1yHmsRURERERERFoaBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQVBgLSIiIiIiIhIEBdYiIiIiIiIiQQhsomcRERE5qtiL7GTZsygoKiA+Mp4uti7YIm1NXS0REZFmSYG1iIiIeNmWu42nf3yarXlbXdsyEjKYOmwq3RK7NWHNREREmid1BRcREREXe5HdJ6gG2JK3hTk/zsFeZG+aiomIiDRjCqxFRETEJcue5RNUO23J20KWPatxKyQiItICqCu4iIiIuBQUFXg9t4ZYGdpuKIlRiRSVFXHg8AHsRXbdby0iIuJBgbWIiIi4RFmjKCgqoLSilBhrDBP7TuSt9W+xbv86DMMgIzGDXkm9mHLcFDKSMhRgi4iIoK7gIiIi4rAtdxsrslcQFhLG9rzt9Enuw2trXmNF9gpKy0sJDw2nuLyYn/b8xEPfPsS7699lW+62pq62iIhIk1NgLSIiItiL7Dz787PkHc7jtuNvY0y3MaTFprHst2UcKjkEQFJ0Ehiwu2A3P+z6gcToRJ77+TkNaCYiIkc9BdYiIiLCDvsOeiX1YsG2BXy46UPaxbYjLTaNrrau9EjsQVRYFOty1rHdvp2utq7Yi+zsyN+BLdKmAc1EROSop3usRUREhJKyEt7d8C7ptnRGdRnFuv3riAqLorCkkIPFBzmj1xl0T+xOaUUpSVFJ/Pr7rxwuOUxXW1efAc9ERESONgqsRUREhCPlRzgp/STe2fAO3+z4hu327VzQ/wKuHHIlQ9sN5T8r/8P/1v2PQyWHKCguYHjH4YzoNIIKKoiLjGvq6ouIiDQpdQUXERERwkPC+XDTh6zKXkWIJYSI0Ah6JPYgMiySB795kHmb5/Fr7q8cKjlEt8Ru2Ivs/HPZP+nQpgPptvSmrr6IiEiTUmAtIiIiHCk7wq8HfiXGGkNJRQnn9zuf9ze+T3FZMWv3rSU2PJZoazQVRgX7Du0jxBLCNvs2sKApt0RE5KinwFpEROQoty13G78d/I0QSwhF5UXsKdhDv5R+rN+/npKKEorLiymvKKekrAQDg9+P/I4FC10TulJRUdHU1RcREWlyusdagEeB26tJTwB+AVIbpzoiItJo7EW/khLdlwv7lXNhP7BYzO2GcTs3D4fy8kf4eTP8aA8lLCSMkvISIkMjKa0opU14G3MKLhERacEmA28CZX7SIoD7gXsatUYtkQLro04OkFbLffL87JMG7K2XGomISGOKBQpdz+Ij3MG0P6Gh8MW1AObI34YB5eWlnPiyle4J3XV/tYhIi3I/MLMW+YuBvzkenr4ETq2vSrUKFsMwjKauRFMrKCggPj6e/Px84uJa68im0cCRGvLEAylAb8wvUFsxg+fyGva7H5gRbAVFRKTBnAu87zelosIMlgtL4HBJHKuyy8g+dJiTurTBFnmQ+Cgz8PYMvi0Wcx/DgJCQDsDuRnkVIiJSF7uBLkB1t+7EA8cA3YDuwCZgM7ASKKpmv1jgYP1Us5kKNFZUYE1rDqyra51eDQysY7l9MbuGV5YA5NaxTBERqX9W/HftOwd4D4BV2au46qOrWJm9kvCQcM7sfSa9knphsVgY2Xkkz/70LN/v/J7RXUfTK6kXfePW8cfB7xPiMUqLO+g+6r9SiIg0Iw/j29IMZuC8pY5lFgAd8R9MLwFG1LHc5ivQWFFdwVutcKC00rYzgI/qoewNHuuhuH/9ygMswPPAtfVwHBERqZsrgf9U2hYFHPbJmRKTQp+2fVixdwXF5cW8vf5tV9rIziP567C/cs2Qa/jt4G/EhMewvzCZY17YQnJ0Mv8ct5ReKYc9AmsLulVIRKSpFWC2QHsKAw4AwTYixuG8NQgWAmM80jIdx6kcgxwdNCp4qzMZ84uN5xs6G7MVoT6C6srKHWUP9th2naMOIiLSuPZhnn89g+obMc/TvkE1wP7C/UwePJmTupzktX1Q6iBO7noy/1n9Hw4cPsCrq1/l6WVP886Gd2jfpj3R4dHcsfgUvtv5NbDWY89sRx3eq8fXJSIigUnFO6hOx7wGlBJ8UF3ZqY6yDSDSsa0M8xpwcj0fq/lTV3BaU1fwysHspcArdS/NT2xsGP63X3cdPPecvzr4b72ePBlefdW37EDqER8PdnvNaSIiR5fK91GHYw46U71vsr7hheUvcP1x17Ni7wqyD2VjDbUy4+RplXIaTHzzLOb9+T0qd3i78UYLc+aAeYuRZ5AdB+T7HPPKK+E/lRrUA70GtGkDBQX+06orR0SkdfPXSh3cCbGqc2x8vPs87PTMM3DDDRuAfgHVoXLZ1Z27IyOh2ONyNmQILF9ec1p9CTRWVIt1q+H57gzFfBPXPaj25PwAgfkhCA11b//iC3P9+edduTHv33a6DnMgBG/OoNowICLCXO/Z0/fYa9Z41wMgP7/mNBGRo0sC3kG1QSBBNUBcZBz5Jfk8vexpkqKTGNp+KF1tXR2pZazL3oA1xg5YmPfn9yGkDChj4ptnMvHhRwD45z+dpa3B+0tUAf56MDmDasOAqChzvW9f37qFOeL3lBT3ef6g47a+444zlyEhCqZF5Gi3FO+g+j/U15gXsbHmOXbIEPN5ZCRER5s/choGrF9vbp8yBcxxmAxgkkcJFtxdx93atIG2bWs+/u7dZuDsea5fsaLmtKagwLpV8PzScgb+B6oJjmcrcFmZ+8172mn+cg/E+8O8CuhUZdlFjoEGN2/2TRs0yFw6jzd2rLm87bbq00REjh4RgN3jee2+TKXb0slIyOBw2WH+u+6/vLTqJd755R1Xeof4Dmzcmed4ZmHi6+cz8c1zyUjI4Knr/oT/W38MzBFoce1XlcOOHuq/+BkTs9wxKUVOjrkcMMBc9u0LP//sncd5Lahu6jARkdZnLua9zU4GcHm9H8XZClxcDHv3ulus/f0oCu8Auzyex1M5uC4ogP37az5ujx7m0nmuv+MOc/nSS9WnNQV1BaeldwX3/AbxBeA30q1byY6iDcNspa6ogM6dYccOd54//Qneesudz3M/x1avMr/4wsJpp5l5kpLg9999j1VVHTy3hYdDSUnVacWBNdSIiLRwnqN+131mhm2525jz4xy25LlHiZ3354+AMgzDSlgYlJcbdOhcyJtfryAuMo50WzpTrrTx5ptm/kCuAQsXWjjlFDNPSoo7YK7qGmC1mj/mhoVBaal32SNHwrffuvfzTNM3GxE5OnwEnOXxvH5PfhaL2WJ98KB5zt6/H/7wB/jsM3eehx+GvzkGHg/kOrB+vcUrGK/q/F9dusUC6emQlVV12vbtNb++QGlU8KOC57s2mOmzajiKx2E8g+pHH/UNqiuvm3UMwz0Xdv0G/yIiR68rcQfVwc0l3S2xG9NGTyPLnkVBUQFxkXHMA8DqNeL37h2xgDnI2ZNP4hNUV143rwEzgfsdz/2PTF4VZzBdVubbEv3NN+5taqUWkaOTM6i2UP0c1XV36JD3OdYzqP7oI9+guvK6WTfPk3Tr/eVTXcFbrGM91v9BQwXV4PsrEMC//gW3326uZ2d757dYKj/KsFgqsFgqWLDA3U/7wIHa12XcOHM5dWrt0kREWh/n6F8hBBNUO9kibQxOG8xJ6ScxOG0wzi9CntcA59zVL74IN99srjtbnZ18rwH3ua4BixZ1B54EYN++wOplGO5HWpq57YorfNMeeMDcFh5em1ctItJSeQarDRNUg/sea2cM4Bx3aeFCOMsR11ce48j3OmC4rgMbNgyq1fEjI72f33mnubz//urTmoK6gtMSu4LnYM4TCua9dUUNcpTKXS+cz996C84/31zPzobUVP/724vsrtaP+Mh4BqYO9mhdMFxlO0fz69oVtm3zLmPNGu97qT3rVF2aiEjr1vC//ld1DXj3XZjkGJcmJ8fsHhhgiR5lu68B0dFw5AhkZPgfayM11TzOokVw6qnedXKm+auviEjrNQBY51jPp/6n0TJ5dgV3PgdYsgQyHbd15+dDYOHT48CtjvXHgFu8yqzq3L17N3TqZP6wW17unb+6tPoUaKyowJqWGFg3TneKym/OY46BVauqzu/5TtqWu42nf3yarXlbXdtOSDC4c9wnWCzwpz/9j7feOt/v/lV9mXPy9wH3lyYi0jpFAI5BJsgBAo5sa6Xyufi449wDhvkT2LcJs9CLLnqWN9+83u/+NV0DHngA7rvPf9rw4bB0aSD1EBFpqXbjHhR4EuZAYTWr3ODVxdYFW6Styu3gG1j7my7XU83XgUQgz1G2b+aYGO+u587ywsPNW4Oc+veHtWtrTqsvCqxroWUF1tdjzg0NzfUeBXuRnelfTfcKqp1ePWs+tqhSxwemedZfRKR5c0aTpwOfNGVF6mAf4Ozm1HA/CoiItF7Oa0AI7jGMqrctdxuPL32ctTlrsVgsnNj5RHok9iApOgl7kZ0d9h0s37uc0opSMhIymDpsKt0SuzVw/VOB7OoyNhsKrGuhZQXWzjejFXeLRfOyKnsV9y2+D2uIlcGpg7FF2ThSdoTosGjyjuRx36hpjsD6fOB/TVxbEZGWJAH31Fot9fJ9dAxiIyLSMJzn0MDOn/YiO3ctuIsvtn1BWUUZ1x17He9vfJ/1+9aTFJVEuzbt6BzXmSuGXMGa7DX8uOdHusR3Ydroaa6W6/r1EnBFrV5DU9Oo4K3SAo/15hlUAxQUFWANsXJat9N4dfWrrMh2z9Q+JG0Id4wIJzKyBHgLBdYiIrVhdyxPb8pKBCkHd6u1iIgEzlrrPTYf2MwX276gsLSQM3ueyfsb32dtzlrCQ8PJKcyhTUQbPt78MTsKdnB6xumMzxjPZ1s+I8ue5RjIsr5djjuwHgl82wDHaBoaFbxFGduoR3OO5Pevf/lu87yvrfK2uMg4hrYbyg3DrmPp1T9Qcl+R67H06u+56pM/etyD8XaNx/d8rFlTfbo/11/vm69Ll6rLmDat1n8qEZFGMMJjveG7gDvPiS++6LutumuAv+3eeTy7f1c9T5a/fdc5xuo599yaz/3VlVNVmohI8+acYnFXwHvkFOZQWFoIQLotnTU5awgLCaOorIjSilIqDHNE8TU5awgPDWfuurkMbTeUgqIC17lx4UJ3eaGhVZ9Lwyo12VZ9HXAG1t9VW3fPYzkfux2TYHTv7ps2eXL1f4vTTqv5OmCxQEFB9eVURYF1i9S43Sauu652+dNt6Rzb3jkdmEGff/aj15w+nDN3Eq+sfJ0Nv2+gzHle4PwqSjGFh5sDF9zqGETQOQq48wNxxhk1D5TwvOOW9Oxsd96dO83lpZea6YYBq1eb25xTtoiINC/LHMu0anPVt6uuCm5/zymx3OfrhdXt4hIRYe7jnEJlwABzecwxtavDyJFmOc4efBER5tJmq105IiJNZ6THeseA94oMc89JVVJeQkRoBJP6TOKuE+/irhPv4qohV3Fen/OICI2gpLyEdfvXkRiVSFyku8vzmDHu8iqqmdmrvIpbvn2vAx6/2FJ1FBsVBenp5j7/ccww2ckxbttDD8Ezz3hfW6obWA3gyy99t91xB+zaZZbx4YfmNueUYrWlwLrFWFNzlgZ2/PG+25y/TCUkmMvRo83l62ted+XZfGAzIZYQsuxZfLrlU576w1Ms+e2tGgNiwzCn4QJ49FH/eX76KfD6v/YavF2pgfyVV+Daa831mj6MIiLNw94mOepJJ/luszp6JbZtay49v3xV75QacxgGFDlmk3zkEe+0++4z06Oiaj6SYcA335jrzrlWSxx3U+XlaXouEWkpnK27/Wu1V2pMKv2TzX2iwqK4JfMW1u1bx8xvZvLoD49y/+L72bB/A7dk3kJUmHlSDbGEkG5L9yjFYFX2Ks6+OAuzgc994nROu3vccebyppsCrZmz2bhXlTkOHYLt2831yy/3TrvgArjhBnN9w4YAjlZFr6S//x06On6nWLSo5nKqo3usW4zaTaZe3xITzS8glTl/mcrNNd+wX39tBtKbDmxy5LBQNq2EXxzPVlJOr8UzeeDkaVQ3mX3lLzqV50l1zludne3+oFT169Ktt8Jjj8Htt7u3OVunwT2PttM//lFltUREjkrJyfD7777bnb2P9u/37S7oVPnLjPP87b4GGFTuEl75GtCuXR0q7YezLn/+c/2UJyLS+Go3l1Sn+E5cPeRq/r3i38SEx/DJhk9YnbOasJAwIkIjKC0vZd/hfazLWcfFAy/mlsxbOLb9sa6By6KiSzlyOIyTTgjj4LbOHiWb5+59+8xnP/5onmOfegqefNK7Dp7XgdBQ89phsZRR3e1Ala8DIx0N9pHuBng++gjOOsv9/HrvWRxdunc3l888A1Om+KZXnrLL+SNsbTVpi/U333zDGWecQfv27bFYLHzwwQde6YZhcP/999OuXTuioqIYM2YMmzdv9sqTm5vLRRddRFxcHDabjSuvvJJDhw414qtobGc0yVGdQXVoqHubs/XXcxuY93KUVTj7ehu8ve5d/rv2f7z/y4d8sWUxmR0y6fhYF8rLQ6ioCMEwFlTRVdA0erT5pQ3cac4PVUKCe1tVH4LHHjOXt97qDpoHefxOUVRklnGG40/rGYCLiDQPPZr06M6g2vP+uffe893mj7/zu7kejmGEOB5VXwPGjDF/RHXuV1chHt943nij7uWIiLQktkgbwzsM50/9/0THuI4UlxXTOb4zF/a7kNuPv537Rt3HDcfeQEx4DL/8/gvP//Q8L696mW252wCD0opSwODgtr6ABattn6vsL76yA74NYJV5nt+dP8i6z/++14DK5/qbboLvHA32R464t595pncX7uee8z12QQFs22beAuRs4a6spMQs5w9/MJ8nJlb/eqrSpIF1YWEhgwYN4plnnvGbPnv2bJ5++mmef/55li1bRkxMDOPGjaPI2TcMuOiii1i/fj0LFixg3rx5fPPNN1xzzTWN9RKawEcNW/ymTfDFF/C//2H+ElXq6uYNeNwbDec7bo8uL/f+JeqMXuM8Amu4Zt41XPjuhZzzv3O4fcHtbLdvZ/etOwgNrSAkpAKLZUyVA8iMG2e2goP3h8zZwpyb612XqrqMO9Nuu63q9I8a+E8rIlJ3WxzLAPo+B8PPNcDZzRu8f9GfNMlcmi0P7u0hAXyzMM/1JVgsFY6H/2vAhAnuVvBgguqwMO+gXkSkZQlsXIqqdE3syvn9zscaaiXGGsNtmbeRlZ/FE0uf4MFvHmTaV9NYm7OWXkm96NUmnc3bf+bhRTMACCGE9r124WxdHvese+CNcSe3AWDfPu9zdyDjV5jne//XAM+y7rzTbAWHqs/fZ57pvjWpsrvuMpfFxb6DllX22Wfmsqp7xWvSpIH1+PHjefDBBznnnHN80gzD4Mknn+Tee+/lrLPOYuDAgbz66qvs2bPH1bL9yy+/8Pnnn/N///d/DB8+nBNPPJE5c+Ywd+5c9uzZ08ivphVYtsy84XjcOPPGBYfcz5ZVs5O/X5fCuOfEe1zpBcUFhDjeamty1vDv5f/mni/vI6cgodrWijPPNL/fOY/hz5lnmsu33jKX1QXOOTnmw1N4uHvU8+qCchGR5iGr4Yqu4hqwf17trgGBBK5mfku114Bzz4VPPw28zKqEhbm/JCmoFpGW6SbHsu7TF9gibXSM68iYbmN495d32XRgEyGWECqMCkIsIazbv5a5a95geMIAfs3ZwMfLzMGHSsqK6X33FYABoZWn+zW/31c+fwfSldrMX32L9f33w+zZ7vyeevaExx8313fv9v7h19Ozz/pvCXeup6S4Rxp3DpRZV8128LLt27eTnZ3NGI+RUOLj4xk+fDhLliwBYMmSJdhsNo499lhXnjFjxhASEsKyZdV/EZBKNm0y303O5mFPd96JsXGT3y8kParonfi//3P2obBQPq2UiullML0CY3oZK3NWEhoSyoHDPaut0scfu9cr/4Ll7Bb48cf+f3HyzNunj7lMSzMfAF27msvSUnPUc4vF3QXcOQK5iEjzU0N/u7qq4zXAeX6tzPP+urpOafX++/7L8Nzm7BLoLy052Vz3bHmoXI7V6n/aFRGR5sXZ/Tq40C3dlk6nuE6s27/Otc1isWAB4kJjWL3rZ6JsbSk84j1S97q9qznl5dN4cJFvK5Sz95JTG7MR26snaF2vAzNn+i8DYPNm8zu7xeIeKdyzLhYLDBtW8zH27zf3t1jcQXxd77FutoOXZTsip1TnUHMOqamprrTs7GxSKnXqDwsLIzEx0ZXHn+LiYoo9RqsqqOtkZa3Jjh0+X6gM54f3a0d6L3PUvup+8c87Yue2L26jW0I3tve5gzU5azAcIwdaHP8qgNKKUnbsHUKf1KqH9a7uOKmp1ad7plU3UqBaL0REqLdrgGda4CPD1lxWbdP9tUr4U1ULh4hI8+K8sTi40M0WaSMlNoUYawyFpYVYHC3gbcLb0CE8iT2H9nKk1H0T8z8XzOKxb/5OYUUI6/av47y+5wEw8c0zyUjIYNroaa5Bzpw8w6rAv2dvAPr6bA30PF+b9Mrb6zMWaLYt1g1p1qxZxMfHux6dnD9zHM38Dfldm3SHLHsWuwt2E2oJJTTEHNXM+aE1MLA4fmaKCI2gZ7v1da+viIjUn3q6BoiISENwjq9RVm2uQKTFptEzqSe9k3rTM6knfdv2pW24jX17t1JuVBAeYt6s3L/dIOyb1tA1IpWYsGgAisrMca4yEjKYOnyqT1Bdd75BdUvUbAPrNEef3ZxKN8Xm5OS40tLS0ti3b59XellZGbm5ua48/tx9993k5+e7Hrt27arn2rdAniOU1SXdoaCoAGuolcOlh+lu644t0ubdYm2xMLTdUACSY34NqsoiIkeffTVnqYt6ugaIiEhDcPbQrXqq2kCl29Lp07YPcZFxtI1pS2JUIiXlJVQYFQxMHUjWvk30bzeIC7qdyfIfPyD6l630bNOV3km9GZA6gJknz2Ta6Gl0S+gWdF1am2YbWHft2pW0tDQWekyKWVBQwLJly8jMzAQgMzMTu93O8uXLXXkWLVpERUUFw4cPr7LsiIgI4uLivB5HvS5dYNQo/2mjRpnpAYiLNP+Wa/et5dRupzKhxwQSI837rQ0MBqYM5LLBlzE4dTBRkeYXRHXFExEJVHrDFFtP14DaaaAfCUREWp2HHMvg+y3bIm1MHTaVjIQMAKLDo+mZkMEZA87jocy/MapdJqeE9+Kz92dTWloMCTasYVaGtBvCse2PZXDa4HpsqW5dLIbRdHeZHjp0iC1bzClEjjnmGB5//HFOPvlkEhMT6dy5M3//+9955JFHeOWVV+jatSv33Xcfa9asYcOGDUQ6JjIeP348Of/f3p3HRVXufwD/zDAwMwjDgAiIsgYlFuFuWLmFa9dMK7vmgkp1LZcwNe1VLmWK3dui8CuxRcRbqV1Tb2llLqFS5g5JKQKCWBewkmERkWWe3x8j44wsss7G5/16zYuZ85w553t4mOF8z/Oc5ykoQHx8PCorKzF9+nT06dMHn332WaPjKC4uhouLC4qKiiw4ya65038M2mzKraNHaw9eM2iQ7k7+xtz9D0BTrsFrSa8hszATEEBwx2Dc73M/qrRVUMgUcJG7IK0gDd9mfYsvJ+6GRAK8uedhzBz8CT+kRET1CgSQfeN5G/3bboX/AU1jj5vdGjngBRFRw2pygdb5vtSUa5CjyUFxeTFUChX8CwXUC17FhYjeiCv4Epm5qUBHNyCkO7oF3Yd/9PkHSitKUVxeDBeFC/zUfq1w7t66x9RWGpsrmjWxTkpKwpAhQ2otj4yMxMaNGyGEwLJly/DBBx9Ao9HggQcewPvvv48777w5mvSVK1cwe/ZsfPXVV5BKpXjssccQGxsLJyenRsdhXYk10KZ/fOnpukFqCgt1Xf/8/PQD1jTWhSsXEHcsDpmFmSirKEO2Jhu+Lr4YfsdwxJ+IR7BbMP5+z9/xdC/dfOOPbPkbVgxZgR5ePdrggIiIbIUJTkBa4X9A49Ucz4MADrXRPoiIbIWJ/gdcugSNAshx1qJYVg2Xjt5wsHPA+pPrkVWYpV81yDUIc/rNQaBbc7uERwHYcOM5E2ubYR2J9c8Awm48t/wqM7wKprRXokpbheO/H4e9nT2KyosASQEW3b8GABB/8n308eqDvl37mjdoIiKLZh1X9hvP1o6HiKgt1XxnhgFIMdleNeUaLE9abpRU16hvZPDGkUL3/e8FIK9lQbaxxuaKFnuPNd3qXnMH0CRqhRo9vHpgoP9A9O3SF0p7JZIuJqGovAj3et2L+f3XQCIBtFqgoKQAZ/44g8y/Ms0dNhGRFehs7gBawQFzB0BEZGUeuPEz1aR7zdHk1JlUA0BmYSZyNDnN3HLNRdX0Zr7f8jCxtkpNmFndQvir/TGh+wRcq7qGlYdXQibTzRv31Fbg3aPvYvG+xYjeE42zf5w1d6hERBaqz42f+WaNonU8ZO4AiIiszGGD57+ZbK/F5cUtKq9blMFzS+0t3HRMrK3Kd+YOoEWScpKgtFfi26d+BKBLrLdlSlFRXQEAOP6/43j7yNvQlGvMGCURkaU6bvD8YbNF0XKGo4GzGzgRUePJbvz0Mdkea2b8aUy5plyDlPwUHMo5hNT81AbO6WvurX6gnnLrJLv9KmQ5hhk8dwBQYa5AmixHk4PfS39HcMdgOMl1yzaeBGQSGbRCi2ptNWR2Mvz6x6/I0eRwIDMiojqpABQD+NrcgbSAp7kDICKyUn8BcDHpHv3V/ghyDdLN+HOLINcg+Kv9AegGL449FtuIAc4SDJ4btsJbP7ZYW52ZN35a1+TPNd1Enun5qn7Zc3sc9L3axY1WC3upfTO7lBARtQdFBs+tsdXasLW6wGxREBFZJ8PWYzuT7PHWea9rBLkGYU7/OVAr1NCUa2ol1YDuHuy4Y3G3tFzPuPHT9i6yssXa6qwDEH/juQTW0o1OpVChq6wAzopyAEDqLYP/SSBBB/sOUNorb9vlhIiofavpsfQ1dImqh3nDaRLDEylripuIyFJcgq4ruBbA4wC2tfkeA90CsWzwMuN5r9X++tHAGzPAma43akeDElsYL8QYE2urlA/d0PQAoABQbsZYGsdf7Y/3HtPdH1hdDcz7bjAA3b3WdhI7qOQqBKgDEOIeou9SQkREdbmOm4NYesJaLrAaD7xpLTETEVmargDuAZAG4Avobg9q+0apmhl/6tK4Ac7eAXDlxpK3WzM0i8HE2ip5AugN4CR0J1hvAVhg1ohuR61whRC6Acsm7ZiAuf3/jk4dOuHcn+fgLHeGs4MzQtxD9F1KiIioIQI3E1UZgCozxtIYAw2ev2u2KIiIbMMZ3Pwf4AJzX6xs3ABn82+8cgPwYluHZBZMrK3WCdz8QC0EMByWO9e1AwBAIgGKrn+Olx8MRsn1EiwZuATlVeW4VnmtVpcSIiK6nRnQjaxaDV0LhummX2maFbg5QI0SQLT5QiEishmGF1il0HUNN4/bDXB2r0dPgyV/mS4wE5MIIdp9f6zi4mK4uLigqKgIKpW13d9r2LXuOxiPHG4J7HDzg94JxgPXEBFRy9jjZmu1K252s7MUcwD8n8Hrdn/KQUTUir4EMNbgtfm+Yy9cuYC4Y3FGyXWQaxDeHr4GUv1w2UWwxnmrG5srMrGGtSfWgHFyPQa6D5klMIyrK3SDLRARUesyTK4lMGerhTF/ABcNXrf70w0iojawBcBEg9fm+67VlGsMBjgrRpjnGEj06YB1JtVA43NFTrdlEww/QF/B/D38f4ZxUt0DTKqJiNpKJQD1jeeGXQPNSQIm1UREpvB3AEcMXktgPFe06dQMcDbQPxY9vGwjqW4KJtY2w/CkpRq6D1WkGeKQAAgzeB0P4LQZ4iAiak8KAYwzeC0BIDdDHPfCOLFXgUk1EVFbuw+65LXGDJjnIutvN/b7hcEygfaQVANMrG2MADDV4PUm6P64C0yw756o/QEWAP5hgn0TERGwHcbf9xXQfS/PMcG+027s64zBsi9gfKJHRERtp+ZCpofBMgmAO0y0fyV082vXGIz2dmGVibXNSYTuj9jeYJkXdB+sR9pgf3Y3tp1isCwe7e2DRERkGTyg+/6dYbDs/6D7nnZqg/3VtFCH1hHD+DbYHxERNawAxhc1L0D3PW0P3ZzXzacp1yAlPwWHcg4hNT8VmvLvbmxbAqD8xloy6P4HfN+ifVkjc9+MS22mAroPlpfBsq9ws1U5Fc2fnqs7gLN1LLfEEWmJiNqjj288DAc2u4qb/wPGQdfC3dxtP11PGS+qEpHtMByMy0XhAj+1n5VMDVvTer0KwCs3llVBN+c1oGvFrj01VkMuXLmA2GOxyCo8jU/HJcNZXtdAmUeg65bePnFUcNjCqOCNoQBw/TbruEDX0tATuqQ8C0AedPdsN2QpgNdaGiAREbWZ8QB23GYdOQBvACE3np8D8Dtu38LRBZY7hzYRUfPcTCSz9MuCXIMwp98cBLoFmjGy5vgNgB8anjXCBbocIBC6xDsdQAaAFGi11wwGIgMkEqAmgyyvtMN17QWoFb5tErkl4HRbTdA+Eusat7ZiN5cXdEk3ERFZFyfoWq9bqt2fPhCRjdKUa7A8ablRUl0jyDUIywYvs5KW67osBbCiSe8wzBaF0D0WftsTGcVdAAArhqxAD68erReihWlsrsiu4O2OJ2qfDL0FYGED73GFruu3Z1sFRUREJlN6y+vL0LVQ1Jds2wH4HLxnmojaixxNTp1JNQBkFmYiR5NjxYnk6zcehiIBfIq6e6nKkfHnBMzfV1jvFovLW3bvtq1gYk0AFtx4EBFR++OB2sk2EVHb02q1qKioMHcYtZRcLYG7vXuD5eXl5fWWW5/1Nx51u1p+Fu72sfWWO9s5W/Xvw97eHnZ2di3eDruCo711BSciIiIiMq+KigpkZ2dDq23ovl/zqKiugKZcU2+5WqGGg52D6QIyM63QQlOuQZW2qlaZTCqDWqGGVGLdk02p1Wp4eXlBIqk9/ze7gpPVsd6RF4mIiIiosYQQyMvLg52dHXx8fCCVWlZSVqWtwh9X/8D1qtoD/8plcnTq0AkyaftKoyqqKvDXtb+MfidymRwdlR3hILPeiwxCCJSVleHy5csAgM6dOzd7W+3rL4Islm2NvEhERERE9amqqkJZWRm8vb3h6Oho7nDqZO9gj8tXL6O86mYXZ4VMAY8OHpDL5GaMzDwUUMDR0REV1RWo1lbDTmoHBzsHm7jAoFQqAQCXL1+Gh4dHs7uFW9blIWqXNOWaWkk1oBscIu5YXINdcYiIiIjIulRX6wbJcnCw3JZOuUyOzs6d0UXVBV5OXuii6oLOzp3bZVJdQyaVwdHeEc5yZzjaO9pEUl2j5gJPZWVls7fBxJrMrjEjLxIRERGRbanrflZLYsuJJBlrjb9FJtZkdrcbop9D+BMRERERtR5/f3+sWbPG3GHYFCbWZHYqRcMjsd+unIiIiIioLUkkkgYfy5cvN0kcoaGhmDlzZp1l//73vyGXy/Hnn3+aJBYyxsSazM5f7Y8g16A6y4Jcg+Cv9jdtQEREREREBvLy8vSPNWvWQKVSGS1bsGCBfl0hBKqqak9N1RqioqKwZcsWXLt2rVZZQkICHnnkEbi71z8HN7UdJtZkdmqFGnP6zamVXAe5BmFO/zmccouIiIiIzMrLy0v/cHFxgUQi0b8+d+4cnJ2d8c0336B3796Qy+VITk7GtGnT8OijjxptJzo6GoMHD9a/1mq1iImJQUBAAJRKJcLCwrBt27Z645g8eTKuXbuGL774wmh5dnY2kpKSEBUVhaysLIwdOxaenp5wcnJC3759sW/fvnq3mZOTA4lEgpSUFP0yjUYDiUSCpKQk/bK0tDSMGjUKTk5O8PT0xJQpU4xax7dt24bQ0FAolUp07NgRERERuHr1asO/WBvCO/DJIrg5uuGp0KdQcLUASpkSHh084OPiw6SaiIiIiOqm0QA5OUBxMeDiAvj5AWq12cJZvHgx3nrrLQQGBsLV1bVR74mJicEnn3yC+Ph4BAcH49ChQ5g8eTI6deqEQYMG1Vrf3d0dY8eOxYYNGzB58mT98o0bN6Jr164YPnw4zpw5g9GjR2PlypWQy+XYtGkTxowZg/T0dPj6+jbr2DQaDYYOHYqnn34a7777Lq5du4ZFixZhwoQJOHDgAPLy8jBx4kT885//xLhx41BSUoLDhw9DCNGs/VkjJtZkdg3NYc3EmoiIiIhquXABiI0FsgxmlgkKAubMAQIDzRLS66+/jmHDhjV6/evXr2PVqlXYt28fwsPDAQCBgYFITk7G+vXr60ysAV138FGjRiE7OxsBAQEQQiAxMRGRkZGQSqUICwtDWFiYfv0VK1Zgx44d+PLLLzF79uxmHdv//d//oWfPnli1apV+2YYNG+Dj44Pz58+jtLQUVVVVGD9+PPz8/ADo7gdvT9gVnMyKc1gTERERUZNoNLWTagDIzATi4nTlZtCnT58mrZ+ZmYmysjIMGzYMTk5O+semTZuQdeuxGRg2bBi6du2KhIQEAMD+/fuRm5uL6dOnAwBKS0uxYMEChISEQK1Ww8nJCWfPnkVubm6zjy01NRXff/+9UZzdunUDAGRlZSEsLAwPPfQQQkND8cQTT+DDDz9EYWFhs/dnjdhiTWbVmDmse3j1MG1QRERERGS5cnJqJ9U1MjN15T16mDAgnQ4dOhi9lkqltbpCV1ZW6p+XlpYCAHbv3o0uXboYrSeXy+vdj1QqxbRp05CYmIjly5cjISEBQ4YMQeCNlvoFCxZg7969eOuttxAUFASlUonHH38cFRUV9W4PgFGshnHWxDpmzBi8+eabtd7fuXNn2NnZYe/evfjxxx/x3XffIS4uDq+88gqOHj2KgICAeo/FlrDFmsyKc1gTERERUZMU3+b88HblJtKpUyfk5eUZLTMcIKx79+6Qy+XIzc1FUFCQ0cPHx6fBbU+fPh2XLl3C9u3bsWPHDkRFRenLfvjhB0ybNg3jxo1DaGgovLy8kJOT02CcAIxiNYwTAHr16oVffvkF/v7+tWKtuaAgkUhw//3347XXXsPp06fh4OCAHTt2NHgctoSJNZkV57AmIiIioiZR3eb88HblJjJ06FCcOHECmzZtQkZGBpYtW4a0tDR9ubOzMxYsWIB58+YhMTERWVlZOHXqFOLi4pCYmNjgtgMCAjB06FA8++yzkMvlGD9+vL4sODgY27dvR0pKClJTU/HUU09Bq9XWuy2lUon77rsPq1evxtmzZ3Hw4EG8+uqrRuvMmjULV65cwcSJE3H8+HFkZWVhz549mD59Oqqrq3H06FGsWrUKJ06cQG5uLrZv344//vgDISEhzfztWR8m1mRWnMOaiIiIiJrE3183UFldgoJ05RZgxIgRWLJkCV566SX07dsXJSUlmDp1qtE6K1aswJIlSxATE4OQkBCMHDkSu3fvblT36aioKBQWFuKpp56CQqHQL3/nnXfg6uqKAQMGYMyYMRgxYgR69erV4LY2bNiAqqoq9O7dG9HR0XjjjTeMyr29vfHDDz+guroaw4cPR2hoKKKjo6FWqyGVSqFSqXDo0CGMHj0ad955J1599VW8/fbbGDVqVBN+Y9ZNItrTGOj1KC4uhouLC4qKiqCykCtc7cmFKxcQdywOmYWZ+mU1c1gHuppnVEciIiIiahvl5eX6Ea0NE8ImuXBBN1BZ5s3zR3OPCk7Wq6G/ycbmihy8jMwu0C0QywYvQ44mB8XlxVApVPBX+3OqLSIiIiKqW2AgsGzZzXmsVSpdS7UZ57Gm9o2JNVkEtULN0b+JiIiIqPHUarOM/k1UF95jTURERERERNQCTKyJiIiIiIiIWsBmEuv33nsP/v7+UCgU6N+/P44dO2bukIiIiIiIiKgdsInEeuvWrXjxxRexbNkynDp1CmFhYRgxYgQuX75s7tCIiIiIiIjIxtlEYv3OO+/gmWeewfTp09G9e3fEx8fD0dERGzZsMHdoREREREREZOOsPrGuqKjAyZMnERERoV8mlUoRERGBI0eO1Pme69evo7i42OhBRERERERE1BxWn1j/+eefqK6uhqenp9FyT09P5Ofn1/memJgYuLi46B8+Pj6mCJWIiIiIiIhskNUn1s3x8ssvo6ioSP+4dOmSuUMiIiIiIiICAEybNg2PPvqo/vXgwYMRHR1t8jiSkpIgkUig0WjadD8SiQQ7d+5s0320NatPrN3d3WFnZ4eCggKj5QUFBfDy8qrzPXK5HCqVyuhBRERERERUn2nTpkEikUAikcDBwQFBQUF4/fXXUVVV1eb73r59O1asWNGodU2VDFdUVMDd3R2rV6+us3zFihXw9PREZWVlm8ZhKaw+sXZwcEDv3r2xf/9+/TKtVov9+/cjPDzcjJEREREREZEtGTlyJPLy8pCRkYH58+dj+fLl+Ne//lXnuhUVFa22Xzc3Nzg7O7fa9lqDg4MDJk+ejISEhFplQghs3LgRU6dOhb29vRmiMz2rT6wB4MUXX8SHH36IxMREnD17Fs899xyuXr2K6dOnmzs0IiIiIiKyEXK5HF5eXvDz88Nzzz2HiIgIfPnllwBudt9euXIlvL29cddddwEALl26hAkTJkCtVsPNzQ1jx45FTk6OfpvV1dV48cUXoVar0bFjR7z00ksQQhjt99au4NevX8eiRYvg4+MDuVyOoKAgfPzxx8jJycGQIUMAAK6urpBIJJg2bRoAXeNjTEwMAgICoFQqERYWhm3bthnt5+uvv8add94JpVKJIUOGGMVZl6ioKJw/fx7JyclGyw8ePIgLFy4gKioKx48fx7Bhw+Du7g4XFxcMGjQIp06dqnebdbW4p6SkQCKRGMWTnJyMBx98EEqlEj4+Ppg7dy6uXr2qL3///fcRHBwMhUIBT09PPP744w0eS0vZRGL95JNP4q233sLSpUvRo0cPpKSk4Ntvv601oBmRplyDlPwUHMo5hNT8VGjKNeYOiYiIiIiawRLO65RKpVHL9P79+5Geno69e/di165dqKysxIgRI+Ds7IzDhw/jhx9+gJOTE0aOHKl/39tvv42NGzdiw4YNSE5OxpUrV7Bjx44G9zt16lRs3rwZsbGxOHv2LNavXw8nJyf4+Pjgiy++AACkp6cjLy8Pa9euBaAbwHnTpk2Ij4/HL7/8gnnz5mHy5Mk4ePAgAN0FgPHjx2PMmDFISUnB008/jcWLFzcYR2hoKPr27VtrmuOEhAQMGDAA3bp1Q0lJCSIjI5GcnIyffvoJwcHBGD16NEpKSpr2yzaQlZWFkSNH4rHHHsPPP/+MrVu3Ijk5GbNnzwYAnDhxAnPnzsXrr7+O9PR0fPvttxg4cGCz99cYsjbdugnNnj1b/4skqsuFKxcQeywWWYVZ+mVBrkGY028OAt0CzRgZERERETWFuc/rhBDYv38/9uzZgzlz5uiXd+jQAR999BEcHBwAAJ988gm0Wi0++ugjSCQSALqkU61WIykpCcOHD8eaNWvw8ssvY/z48QCA+Ph47Nmzp959nz9/Hp9//jn27t2rn3I4MPDmMbu5uQEAPDw8oFarAehauFetWoV9+/bpb5cNDAxEcnIy1q9fj0GDBmHdunW444478PbbbwMA7rrrLpw5cwZvvvlmg7+LqKgoLFiwALGxsXByckJJSQm2bduG2NhYAMDQoUON1v/ggw+gVqtx8OBB/O1vf2tw2/WJiYnBpEmT9K34wcHBiI2N1R9Hbm4uOnTogL/97W9wdnaGn58fevbs2ax9NZZNtFgT3Y6mXFPryxcAMgszEXcsji3XRERERFbCnOd1u3btgpOTExQKBUaNGoUnn3wSy5cv15eHhobqk2oASE1NRWZmJpydneHk5AQnJye4ubmhvLwcWVlZKCoqQl5eHvr3769/j0wmQ58+feqNISUlBXZ2dhg0aFCj487MzERZWRmGDRumj8PJyQmbNm1CVpbu93j27FmjOAA0asyqiRMnorq6Gp9//jkAYOvWrZBKpXjyyScB6AaVfuaZZxAcHAwXFxeoVCqUlpYiNze30fHfKjU1FRs3bjQ6lhEjRkCr1SI7OxvDhg2Dn58fAgMDMWXKFHz66acoKytr9v4aw2ZarIkakqPJqfXlWyOzMBM5mhz08Oph2qCIiIiIqMnMeV43ZMgQrFu3Dg4ODvD29oZMZpxOdejQweh1aWkpevfujU8//bTWtjp16tSsGJRKZZPfU1paCgDYvXs3unTpYlQml8ubFUcNlUqFxx9/HAkJCZgxYwYSEhIwYcIEODk5AQAiIyPx119/Ye3atfDz84NcLkd4eHi9g7tJpbq2X8P7zG8dWby0tBT/+Mc/MHfu3Frv9/X1hYODA06dOoWkpCR89913WLp0KZYvX47jx4/rW/FbGxNraheKy4tbVE5ERERElsGc53UdOnRAUFBQo9fv1asXtm7dCg8Pj3qn+O3cuTOOHj2qvwe4qqoKJ0+eRK9evepcPzQ0FFqtFgcPHtR3BTdU02JeXV2tX9a9e3fI5XLk5ubW29IdEhKiH4itxk8//XT7g4SuO/jgwYOxa9cu/Pjjj0Yjpf/www94//33MXr0aAC6e7n//PPPerdVc8EhLy8Prq6uAHSt9IZ69eqFX3/9tcG6kMlkiIiIQEREBJYtWwa1Wo0DBw7ou9y3NnYFp3ZBpWh4rvLblRMRERGRZbCm87pJkybB3d0dY8eOxeHDh5GdnY2kpCTMnTsXv/32GwDghRdewOrVq7Fz506cO3cOzz//fINzUPv7+yMyMhIzZszAzp079dus6Yrt5+cHiUSCXbt24Y8//kBpaSmcnZ2xYMECzJs3D4mJicjKysKpU6cQFxeHxMREAMDMmTORkZGBhQsXIj09HZ999hk2btzYqOMcOHAggoKCMHXqVHTr1g0DBgzQlwUHB+Pf//43zp49i6NHj2LSpEkNtroHBQXBx8cHy5cvR0ZGBnbv3q2/77vGokWL8OOPP2L27NlISUlBRkYG/vvf/+rH3Nq1axdiY2ORkpKCixcvYtOmTdBqtfqR2tsCE2tqF/zV/ghyrfuKVpBrEPzV/qYNiIiIiIiaxZrO6xwdHXHo0CH4+vpi/PjxCAkJQVRUFMrLy/Ut2PPnz8eUKVMQGRmJ8PBwODs7Y9y4cQ1ud926dXj88cfx/PPPo1u3bnjmmWf0U0116dIFr732GhYvXgxPT099srlixQosWbIEMTExCAkJwciRI7F7924EBAQA0HWh/uKLL7Bz506EhYUhPj4eq1atatRxSiQSzJgxA4WFhZgxY4ZR2ccff4zCwkL06tULU6ZMwdy5c+Hh4VHvtuzt7bF582acO3cO9957L95880288cYbRuvce++9OHjwIM6fP48HH3wQPXv2xNKlS+Ht7Q0AUKvV2L59O4YOHYqQkBDEx8dj8+bNuPvuuxt1PM0hEbdOktYOFRcXw8XFBUVFRfV20SDrd+HKBcQdi0NmYaZ+WZBrEOb0n4NAV44KTkRERGQK5eXlyM7ORkBAABQKRbO2wfM6ak0N/U02NlfkPdbUbgS6BWLZ4GXI0eSguLwYKoUK/mp/qBVqc4dGRERERE3A8zqyNEysqV1RK9Qc/ZuIiIjIBvC8jiwJ77EmIiIiIiIiagEm1kREREREREQtwMSaiIiIiIiIqAWYWBMRERERkclxciKyFK3xt8jEmoiIiIiITMbOzg4AUFFRYeZIiHTKysoA6ObQbi6OCk5ERERERCYjk8ng6OiIP/74A/b29pBK2dZH5iGEQFlZGS5fvgy1Wq2/6NMcTKyJiIiIiMhkJBIJOnfujOzsbFy8eNHc4RBBrVbDy8urRdtgYk1ERERERCbl4OCA4OBgdgcns7O3t29RS3UNJtZERERERGRyUqkUCoXC3GEQtQre0EBERERERETUAkysiYiIiIiIiFqAiTURERERERFRC/Aea9ycELy4uNjMkRAREREREZGlqMkRa3LG+jCxBlBSUgIA8PHxMXMkREREREREZGlKSkrg4uJSb7lE3C71bge0Wi3+97//wdnZGRKJpN71iouL4ePjg0uXLkGlUpkwQmptrEvbwvq0HaxL28L6tB2sS9vBurQtrM+2J4RASUkJvL29IZXWfyc1W6yhG+q/a9eujV5fpVLxD9dGsC5tC+vTdrAubQvr03awLm0H69K2sD7bVkMt1TU4eBkRERERERFRCzCxJiIiIiIiImoBJtZNIJfLsWzZMsjlcnOHQi3EurQtrE/bwbq0LaxP28G6tB2sS9vC+rQcHLyMiIiIiIiIqAXYYk1ERERERETUAkysiYiIiIiIiFqAiTURERERERFRCzCxboL33nsP/v7+UCgU6N+/P44dO2bukMhATEwM+vbtC2dnZ3h4eODRRx9Fenq60Trl5eWYNWsWOnbsCCcnJzz22GMoKCgwWic3NxcPP/wwHB0d4eHhgYULF6KqqsqUh0K3WL16NSQSCaKjo/XLWJfW5ffff8fkyZPRsWNHKJVKhIaG4sSJE/pyIQSWLl2Kzp07Q6lUIiIiAhkZGUbbuHLlCiZNmgSVSgW1Wo2oqCiUlpaa+lDaterqaixZsgQBAQFQKpW44447sGLFChgO18K6tFyHDh3CmDFj4O3tDYlEgp07dxqVt1bd/fzzz3jwwQehUCjg4+ODf/7zn219aO1OQ3VZWVmJRYsWITQ0FB06dIC3tzemTp2K//3vf0bbYF1ajtt9Ng3NnDkTEokEa9asMVrO+rQAghply5YtwsHBQWzYsEH88ssv4plnnhFqtVoUFBSYOzS6YcSIESIhIUGkpaWJlJQUMXr0aOHr6ytKS0v168ycOVP4+PiI/fv3ixMnToj77rtPDBgwQF9eVVUl7rnnHhERESFOnz4tvv76a+Hu7i5efvllcxwSCSGOHTsm/P39xb333iteeOEF/XLWpfW4cuWK8PPzE9OmTRNHjx4VFy5cEHv27BGZmZn6dVavXi1cXFzEzp07RWpqqnjkkUdEQECAuHbtmn6dkSNHirCwMPHTTz+Jw4cPi6CgIDFx4kRzHFK7tXLlStGxY0exa9cukZ2dLf7zn/8IJycnsXbtWv06rEvL9fXXX4tXXnlFbN++XQAQO3bsMCpvjborKioSnp6eYtKkSSItLU1s3rxZKJVKsX79elMdZrvQUF1qNBoREREhtm7dKs6dOyeOHDki+vXrJ3r37m20Ddal5bjdZ7PG9u3bRVhYmPD29hbvvvuuURnr0/yYWDdSv379xKxZs/Svq6urhbe3t4iJiTFjVNSQy5cvCwDi4MGDQgjdPxp7e3vxn//8R7/O2bNnBQBx5MgRIYTui00qlYr8/Hz9OuvWrRMqlUpcv37dtAdAoqSkRAQHB4u9e/eKQYMG6RNr1qV1WbRokXjggQfqLddqtcLLy0v861//0i/TaDRCLpeLzZs3CyGE+PXXXwUAcfz4cf0633zzjZBIJOL3339vu+DJyMMPPyxmzJhhtGz8+PFi0qRJQgjWpTW59eS9teru/fffF66urkbfs4sWLRJ33XVXGx9R+9VQIlbj2LFjAoC4ePGiEIJ1acnqq8/ffvtNdOnSRaSlpQk/Pz+jxJr1aRnYFbwRKioqcPLkSUREROiXSaVSRERE4MiRI2aMjBpSVFQEAHBzcwMAnDx5EpWVlUb12K1bN/j6+urr8ciRIwgNDYWnp6d+nREjRqC4uBi//PKLCaMnAJg1axYefvhhozoDWJfW5ssvv0SfPn3wxBNPwMPDAz179sSHH36oL8/OzkZ+fr5Rfbq4uKB///5G9alWq9GnTx/9OhEREZBKpTh69KjpDqadGzBgAPbv34/z588DAFJTU5GcnIxRo0YBYF1as9aquyNHjmDgwIFwcHDQrzNixAikp6ejsLDQREdDtyoqKoJEIoFarQbAurQ2Wq0WU6ZMwcKFC3H33XfXKmd9WgYm1o3w559/orq62ugEHQA8PT2Rn59vpqioIVqtFtHR0bj//vtxzz33AADy8/Ph4OCg/6dSw7Ae8/Pz66znmjIynS1btuDUqVOIiYmpVca6tC4XLlzAunXrEBwcjD179uC5557D3LlzkZiYCOBmfTT0HZufnw8PDw+jcplMBjc3N9anCS1evBh///vf0a1bN9jb26Nnz56Ijo7GpEmTALAurVlr1R2/ey1PeXk5Fi1ahIkTJ0KlUgFgXVqbN998EzKZDHPnzq2znPVpGWTmDoCoLcyaNQtpaWlITk42dyjUDJcuXcILL7yAvXv3QqFQmDscaiGtVos+ffpg1apVAICePXsiLS0N8fHxiIyMNHN01BSff/45Pv30U3z22We4++67kZKSgujoaHh7e7MuiSxQZWUlJkyYACEE1q1bZ+5wqBlOnjyJtWvX4tSpU5BIJOYOhxrAFutGcHd3h52dXa0RhwsKCuDl5WWmqKg+s2fPxq5du/D999+ja9eu+uVeXl6oqKiARqMxWt+wHr28vOqs55oyMo2TJ0/i8uXL6NWrF2QyGWQyGQ4ePIjY2FjIZDJ4enqyLq1I586d0b17d6NlISEhyM3NBXCzPhr6jvXy8sLly5eNyquqqnDlyhXWpwktXLhQ32odGhqKKVOmYN68efqeJaxL69VadcfvXstRk1RfvHgRe/fu1bdWA6xLa3L48GFcvnwZvr6++nOiixcvYv78+fD39wfA+rQUTKwbwcHBAb1798b+/fv1y7RaLfbv34/w8HAzRkaGhBCYPXs2duzYgQMHDiAgIMCovHfv3rC3tzeqx/T0dOTm5urrMTw8HGfOnDH6cqr5Z3RrYkBt56GHHsKZM2eQkpKif/Tp0weTJk3SP2ddWo/777+/1tR358+fh5+fHwAgICAAXl5eRvVZXFyMo0ePGtWnRqPByZMn9escOHAAWq0W/fv3N8FREACUlZVBKjU+dbCzs4NWqwXAurRmrVV34eHhOHToECorK/Xr7N27F3fddRdcXV1NdDRUk1RnZGRg37596Nixo1E569J6TJkyBT///LPROZG3tzcWLlyIPXv2AGB9Wgxzj55mLbZs2SLkcrnYuHGj+PXXX8Wzzz4r1Gq10YjDZF7PPfeccHFxEUlJSSIvL0//KCsr068zc+ZM4evrKw4cOCBOnDghwsPDRXh4uL68Zoqm4cOHi5SUFPHtt9+KTp06cYomC2A4KrgQrEtrcuzYMSGTycTKlStFRkaG+PTTT4Wjo6P45JNP9OusXr1aqNVq8d///lf8/PPPYuzYsXVO89OzZ09x9OhRkZycLIKDgzlFk4lFRkaKLl266Kfb2r59u3B3dxcvvfSSfh3WpeUqKSkRp0+fFqdPnxYAxDvvvCNOnz6tHym6NepOo9EIT09PMWXKFJGWlia2bNkiHB0dOaVPK2uoLisqKsQjjzwiunbtKlJSUozOiQxHhGZdWo7bfTZvdeuo4EKwPi0BE+smiIuLE76+vsLBwUH069dP/PTTT+YOiQwAqPORkJCgX+fatWvi+eefF66ursLR0VGMGzdO5OXlGW0nJydHjBo1SiiVSuHu7i7mz58vKisrTXw0dKtbE2vWpXX56quvxD333CPkcrno1q2b+OCDD4zKtVqtWLJkifD09BRyuVw89NBDIj093Widv/76S0ycOFE4OTkJlUolpk+fLkpKSkx5GO1ecXGxeOGFF4Svr69QKBQiMDBQvPLKK0Yn66xLy/X999/X+X8yMjJSCNF6dZeamioeeOABIZfLRZcuXcTq1atNdYjtRkN1mZ2dXe850ffff6/fBuvSctzus3mruhJr1qf5SYQQwhQt40RERERERES2iPdYExEREREREbUAE2siIiIiIiKiFmBiTURERERERNQCTKyJiIiIiIiIWoCJNREREREREVELMLEmIiIiIiIiagEm1kREREREREQtwMSaiIiIiIiIqAWYWBMRERERERG1ABNrIiIiKyWRSBp8LF++3CRxXL16FXfccQdefPFFo+U5OTlQqVT48MMPTRIHERGRuUiEEMLcQRAREVHT5efn659v3boVS5cuRXp6un6Zk5MTnJycAABCCFRXV0Mmk7VJLIcOHcJDDz2EAwcO4MEHH4QQAkOHDoVCocA333zTJvskIiKyFGyxJiIislJeXl76h4uLCyQSif71uXPn4OzsjG+++Qa9e/eGXC5HcnIypk2bhkcffdRoO9HR0Rg8eLD+tVarRUxMDAICAqBUKhEWFoZt27Y1GMvAgQMxZ84cTJ8+HVevXsXatWuRkpKCjz76qA2OnIiIyLK0zWVrIiIisgiLFy/GW2+9hcDAQLi6ujbqPTExMfjkk08QHx+P4OBgHDp0CJMnT0anTp0waNCget+3cuVKfP3115g8eTL27NmDDz74AF26dGmtQyEiIrJYTKyJiIhs2Ouvv45hw4Y1ev3r169j1apV2LdvH8LDwwEAgYGBSE5Oxvr16xtMrJVKJdauXYuRI0di1KhRmDx5covjJyIisgZMrImIiGxYnz59mrR+ZmYmysrKaiXjFRUV6Nmz523f//HHH8PR0RFnzpxBUVERXFxcmrR/IiIia8TEmoiIyIZ16NDB6LVUKsWt45ZWVlbqn5eWlgIAdu/eXasbt1wub3BfW7duxa5du3DkyBFMnDgR8+bNw4YNG1oSPhERkVVgYk1ERNSOdOrUCWlpaUbLUlJSYG9vDwDo3r075HI5cnNzG+z2fauCggLMmjULb7zxBsLCwrBx40YMGDAATzzxBEaNGtWqx0BERGRpOCo4ERFROzJ06FCcOHECmzZtQkZGBpYtW2aUaDs7O2PBggWYN28eEhMTkZWVhVOnTiEuLg6JiYn1bvfZZ59FSEgIoqOjAQD9+vXDwoUL8eyzz6KoqKitD4uIiMismFgTERG1IyNGjMCSJUvw0ksvoW/fvigpKcHUqVON1lmxYgWWLFmCmJgYhISEYOTIkdi9ezcCAgLq3OamTZuwb98+JCQkQCq9eWrx2muvQa1WY968eW16TEREROYmEbfeaEVEREREREREjcYWayIiIiIiIqIWYGJNRERERERE1AJMrImIiIiIiIhagIk1ERERERERUQswsSYiIiIiIiJqASbWRERERERERC3AxJqIiIiIiIioBZhYExEREREREbUAE2siIiIiIiKiFmBiTURERERERNQCTKyJiIiIiIiIWoCJNREREREREVEL/D/m+fmsF0+LsQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 869aab35f57f2eb4f0b20ecf64c0f58209383df5 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Mon, 8 Jul 2024 19:28:23 +0000 Subject: [PATCH 32/78] comments updated --- .../elasticnet_regression/test_elasticnetCV_regression.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb index 1d249490..135f5ba4 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb @@ -1306,7 +1306,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Create a KMeans model with 5 clusters\n", + "# Create a KMeans model with 8 clusters\n", "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", "\n", "# Fit the data to the model\n", From ea0405aa8384a6229a44af13110a9deda30d5942 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 9 Jul 2024 18:57:18 +0000 Subject: [PATCH 33/78] bayesian ridge model added --- .../test_bayesian_ridge_regression.ipynb | 1646 +++++++++++++++++ 1 file changed, 1646 insertions(+) create mode 100644 app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb new file mode 100644 index 00000000..f9119a6f --- /dev/null +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb @@ -0,0 +1,1646 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA2XElEQVR4nO3de3RU1cH38d8kISFckkAgCZGAVLGQChZBYRTaB8hDxFRFqL7ypkhblr5iULmIlKViLyoUexGsYNVW7VMR5RFvVEUEBKqRSxQFtIiKBg0TrJiEKCQh2e8f04xMyGVOMpc94ftZa1bIOXvm7H2Sw/lln7P3cRljjAAAACwSE+kKAAAANERAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYJy7SFWiNuro6lZSUqGvXrnK5XJGuDgAACIAxRkeOHFFmZqZiYprvI4nKgFJSUqKsrKxIVwMAALTCgQMH1Lt372bLRGVA6dq1qyRvA5OSkiJcGwAAEIiKigplZWX5zuPNicqAUn9ZJykpiYACAECUCeT2DG6SBQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsE5UTtUWTMUvGaGPZRt/3o1NGa8NNGyJYI6B94NgCQsOWY8tljDFh32obVVRUKDk5WeXl5VbPJOv6VdMz5Zk7om63A9bg2AJCI9THlpPzN5d4QqS5H3Ig6wE0jmMLCA3bji0CSgiMWTImqOUAeHFsAaFh47FFQAmBE6/dBaMcAC+OLSA0bDy2CCgAAMA6BBQAAGAdAkoIjE4ZHdRyALw4toDQsPHYYphxiARytzPDIQHnOLaA0AjHscUwYwu09EPkP1CgdTi2gNCw7dgioISQucOc1B02OmU0/4ECbcSxBYSGTccWl3gAAEBYcIkHAABENQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6jgPK559/rp/85CdKTU1VYmKiBg0apB07dvjWG2O0YMEC9erVS4mJicrJydG+ffv8PuPw4cPKz89XUlKSUlJSNG3aNFVWVra9NQAAoF1wFFC++uorXXjhherQoYNeeuklvffee/r973+vbt26+cosXrxYS5cu1QMPPKCtW7eqc+fOys3N1bFjx3xl8vPztWfPHq1bt05r1qzR5s2bde211wavVQAAIKo5mur+F7/4hV5//XVt2bKl0fXGGGVmZmrOnDm6+eabJUnl5eVKT0/Xo48+qquuukrvv/++srOztX37dg0bNkyS9PLLL+viiy/WZ599pszMzBbrwVT3AABEn5BNdf/8889r2LBhuuKKK5SWlqYhQ4booYce8q3fv3+/PB6PcnJyfMuSk5M1fPhwFRYWSpIKCwuVkpLiCyeSlJOTo5iYGG3durXR7VZVVamiosLvBQAA2i9HAeXjjz/W8uXL1b9/f61du1bTp0/XjTfeqMcee0yS5PF4JEnp6el+70tPT/et83g8SktL81sfFxen7t27+8o0tHDhQiUnJ/teWVlZTqoNAACijKOAUldXp3PPPVd33323hgwZomuvvVbXXHONHnjggVDVT5I0f/58lZeX+14HDhwI6fYAAEBkOQoovXr1UnZ2tt+ygQMHqri4WJKUkZEhSSotLfUrU1pa6luXkZGhQ4cO+a0/fvy4Dh8+7CvTUEJCgpKSkvxeAACg/XIUUC688ELt3bvXb9kHH3ygvn37SpL69eunjIwMrV+/3re+oqJCW7duldvtliS53W6VlZWpqKjIV2bDhg2qq6vT8OHDW90QAADQfsQ5KTxr1ixdcMEFuvvuu3XllVdq27ZtevDBB/Xggw9Kklwul2bOnKk777xT/fv3V79+/XT77bcrMzNTEyZMkOTtcbnooot8l4Zqamo0Y8YMXXXVVQGN4AEAAO2fo2HGkrRmzRrNnz9f+/btU79+/TR79mxdc801vvXGGN1xxx168MEHVVZWppEjR2rZsmU666yzfGUOHz6sGTNm6IUXXlBMTIwmTZqkpUuXqkuXLgHVgWHGAABEHyfnb8cBxQYEFAAAok/I5kEBAAAIBwIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWcRRQfvnLX8rlcvm9BgwY4Ft/7NgxFRQUKDU1VV26dNGkSZNUWlrq9xnFxcXKy8tTp06dlJaWprlz5+r48ePBaQ0AAGgX4py+4Xvf+55effXVbz8g7tuPmDVrlv7xj39o1apVSk5O1owZMzRx4kS9/vrrkqTa2lrl5eUpIyNDb7zxhg4ePKirr75aHTp00N133x2E5gAAgPbAcUCJi4tTRkbGScvLy8v1l7/8RStWrNCYMWMkSY888ogGDhyoN998UyNGjNArr7yi9957T6+++qrS09P1/e9/X7/5zW80b948/fKXv1R8fHzbWwQAAKKe43tQ9u3bp8zMTH3nO99Rfn6+iouLJUlFRUWqqalRTk6Or+yAAQPUp08fFRYWSpIKCws1aNAgpaen+8rk5uaqoqJCe/bsaXKbVVVVqqio8HsBAID2y1FAGT58uB599FG9/PLLWr58ufbv369Ro0bpyJEj8ng8io+PV0pKit970tPT5fF4JEkej8cvnNSvr1/XlIULFyo5Odn3ysrKclJtAAAQZRxd4hk/frzv34MHD9bw4cPVt29fPfXUU0pMTAx65erNnz9fs2fP9n1fUVFBSAEAoB1r0zDjlJQUnXXWWfrwww+VkZGh6upqlZWV+ZUpLS313bOSkZFx0qie+u8bu6+lXkJCgpKSkvxeAACg/WpTQKmsrNRHH32kXr16aejQoerQoYPWr1/vW793714VFxfL7XZLktxut3bt2qVDhw75yqxbt05JSUnKzs5uS1UAAEA74ugSz80336xLLrlEffv2VUlJie644w7FxsZq8uTJSk5O1rRp0zR79mx1795dSUlJuuGGG+R2uzVixAhJ0rhx45Sdna0pU6Zo8eLF8ng8uu2221RQUKCEhISQNBAAAEQfRwHls88+0+TJk/Xll1+qZ8+eGjlypN5880317NlTkvTHP/5RMTExmjRpkqqqqpSbm6tly5b53h8bG6s1a9Zo+vTpcrvd6ty5s6ZOnapf//rXwW0VAACIai5jjIl0JZyqqKhQcnKyysvLuR8FAIAo4eT8zbN4AACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwTpsCyqJFi+RyuTRz5kzfsmPHjqmgoECpqanq0qWLJk2apNLSUr/3FRcXKy8vT506dVJaWprmzp2r48ePt6UqAACgHWl1QNm+fbv+/Oc/a/DgwX7LZ82apRdeeEGrVq3Spk2bVFJSookTJ/rW19bWKi8vT9XV1XrjjTf02GOP6dFHH9WCBQta3woAANCutCqgVFZWKj8/Xw899JC6devmW15eXq6//OUv+sMf/qAxY8Zo6NCheuSRR/TGG2/ozTfflCS98soreu+99/T3v/9d3//+9zV+/Hj95je/0f3336/q6urgtAoAAES1VgWUgoIC5eXlKScnx295UVGRampq/JYPGDBAffr0UWFhoSSpsLBQgwYNUnp6uq9Mbm6uKioqtGfPnka3V1VVpYqKCr8XAABov+KcvmHlypV66623tH379pPWeTwexcfHKyUlxW95enq6PB6Pr8yJ4aR+ff26xixcuFC/+tWvnFYVAABEKUc9KAcOHNBNN92kxx9/XB07dgxVnU4yf/58lZeX+14HDhwI27YBAED4OQooRUVFOnTokM4991zFxcUpLi5OmzZt0tKlSxUXF6f09HRVV1errKzM732lpaXKyMiQJGVkZJw0qqf++/oyDSUkJCgpKcnvBQAA2i9HAWXs2LHatWuXdu7c6XsNGzZM+fn5vn936NBB69ev971n7969Ki4ultvtliS53W7t2rVLhw4d8pVZt26dkpKSlJ2dHaRmAQCAaOboHpSuXbvq7LPP9lvWuXNnpaam+pZPmzZNs2fPVvfu3ZWUlKQbbrhBbrdbI0aMkCSNGzdO2dnZmjJlihYvXiyPx6PbbrtNBQUFSkhICFKzAABANHN8k2xL/vjHPyomJkaTJk1SVVWVcnNztWzZMt/62NhYrVmzRtOnT5fb7Vbnzp01depU/frXvw52VQAAQJRyGWNMpCvhVEVFhZKTk1VeXs79KAAARAkn52+exQMAAKxDQAEAANYhoAAAAOsQUE5UXi6NHCn16eP9Wl4e6RoBAHBKCvoonqh15pnSRx99+/2BA1JKinTGGdKHH0asWgAAnIroQZFODicn+ugj73oAABA2BJTy8qbDSb2PPuJyDwAAYURAyc0NrFyDJzRHQmWldPnl0uDB3q+VlZGuEQAAocE9KO++G3jZ4mLvDbQRcP750vbt336/a5fUtat03nnStm0RqRIAACFDD4qTiXT79g1dPZrRMJycaPt273oAANoTelD69ZPefz/w8p9/Lp12WsDFxywZo41lG33fj04ZrQ03bQj4/ZWVTYeTetu3e8t16RLwxwIA0Ki2nreChWfxLF4szZsXePmuXaWKioCKun7lanKduSOw3f6jH0n/+EfL5eLjpaqqgD6ybaqrpWXLvDcOn3GGdP313o1bprZW2rJFOnhQ6tVLGjVKio2NdK0AwG7BOG81x8n5m4BSXS0lJDh7TwC7rLkfsu9jAvhhd+smlZUFUinpyy+l7t0DK9sqc+dKv/td48sXLw7hhp1ZvVq68UZvZ1e9zp2lhx+WrroqcvUCAJsF67zVHB4W6EQI/vofs2RM0ModPRr4dlNTAy/r2IQJjYcTSbrnHumWW0K48cCtXi1NmuQfTiTp66+lyZO5XwcAGhPM81awEFCcCqCL4sRrd20t5/SyxMUXOysfkCeflJ57rvky99zj7Y2KoNpaaerU5sts3y5ddll46gMA0SKY561gIaBI0syZgZfdsydk1WiM06tPL73krNelRbW1gV8XifBlng0bApsb5vnng7yPAABBR0CRpN/+NrBynTpJGRmhrUsD/fo5f8/cuUGswEsvBV72978P4oade+yxwMvOmBG6egAA2o6AInnvQ2nprB4X572RIQCjU0YHrdyddwb0UX727XP+niZdd13gZQO9mzdENjroeXzySQcfXF0t3XuvdMMN3q8RvpQFAMEWzPNWsBBQ6i1e3HRIueYaqaYm4I8KdLx4IOXGjQt4sz79+zt/T5Ma3m1qqdpaqaQk8PLHjgVYcPZs73W2WbOkP/3J+zUhIaI3BX/4odShg+RyeV/nnsujogC0TTDPW8HCMOOGgjjPR7DGk//qV9Ivfxn4dr/5RkpMDLx8s1wtDzvzE6Ffp9dek0Y7CPadOgXQIdbcFL5SRIZXx8Q0vYvPOMMbXgCgtWyaB4UelIbi4703zd53n/drG4YhmzvMSd1ho1NGO/4h33FH4GXHjQtiOIkiTjt6xo9vocCcOS1P4RvmkUvNhRPJm6nPPDNs1QHQDgXrvBUM9KBEkZY6M1wuqa4uzBttKEK/Tvfe6736Eqi1a5u5fOZk8r6LLw5sqt82+vDDwC/dlZVJyckhrQ4AtAo9KO2UMd57dRsTGxuCcBJFevZ0Vn7s2GZW3nNP4B/04ovONtxKTu4ryskJXT0AIFwIKA198YXUu7e3Pz0uTho+3Ko7EGtqpM8+806BHxfn/frZZ9Lx4yHa4L33Bl62qfQUBg6e3yiphQnwli5tU10ibceOSNcAANqOpxmfqHNn7x2m9WprpW3bpJQUq+5APO006fDhMG3snHMCL5udHbp6tGDUqMDLtjg771dftakuAIC2owelnsvlH04aOlXvQBw1yttNE4i77gptXZoRGxv4KJ6rr26hQJTfZRzl1QcASQQUr0BvBP3oI6su94RFbKz04IMtl+vQIYChMaEV6L2q99/fQoErrgh8o5MmBV62Dbp0CbzsX/8aunoAQLgQUJyOwGjNzGnR7sc/bnmm3ZUrnT/ZMMgSE1t+EOBllwXQw3DffYFvdMWKwMu2wccfB17WSb4CAFsRUH70I2fl33orNPWw3eLF0lNPnXy5p1cv6emnpYkTI1OvBp59tumQctll3vUtCiTpSN7Q1oZ5cpzo2TOwocNPPx3xnAgAQcE8KE7n+ZAiNtfH0aPex8E895x3SPEFF0hPPOGs+7/NamulLVukgwe94WTUKCvPiEePevPDvn3eIbr33NOKezMmTPDu7MZEYBZZyXu/dlNXGS3KiQDQKCfnbwKK04CSkODgQS7BUV4unX5608/iO+8872AjhMDRo97n8Wzc6L3PZsqUNs8w3FZffOH9mZeWesPpsmXeYGJhTgQAP07O3wwzduqss8K6uTPP9N6b25zt272PjSGkBFllpTeQfPSRNHCg9D//E+buKn9Hj0o33ui9TFVb651sbuXKiFYJAEKGe1BmznRWvl+/kFSjMYGEk3rbt3vPpwiS88+Xunb1poFdu7xfu3b1Lo+ACRO8Dzh8+GHp3//2TtXyj39EtEoAEFIElN/+1ln5H/4wNPVooLw88HBS7//8n9DU5ZTT3FOM67urwqi5W2GkiFQJAEKOe1Ak6eabpd//vuVyMTHefvYw3H+QnS29/76z93Ts6K0e2qCy0tst0ZIjR8JybeXoUW/PSSDCVCUAaDUeFujU734X2LDSOXPCdnOk03Aihf3e3fYp0G6oQEJMELQ0/cyJuncPXT0AINwIKPWefVZ68snGA4jLFbFhpQizjRsDLxuGZzN98EHgZWtqwviMJgAIMUbxnOjKK71Tl69f7x2xUVkpjRzpnXwkgsNKA9WxY6Rr0A5UVQVe9qyzvBPShFDnzs7Kjxol7dkTmroAQDgRUBqKjfVOZx/hKe2vuUZ66CFn7wn0YXloRlycVF0dWFljvON9QzgByYQJAc5++x8HDoSqJgAQXlzisdSf/uT8PTk5wa/HKcftdlZ+y5bQ1OM/+vZ1Vj4Y94wDgA0IKJaKj/decQpUTIw0Y0bo6nPKuPlmZ+UPHgxNPf5j8GBn5ZcuDU09ACDcCCgWW7Ei8PtKwjjAqH37+mtn5Xv1Ck09/uPSS52VD2QwGgBEAwKKxWJjpccfb74MA4yC7Oc/d1Z+1KjQ1OM/Pv008LKJiTyPB0D7QUCx3MSJ3qfUnnaa//LOnaWf/tQ79wnhJIi++SbwspdfHvJEUFMTeNkwTc0CAGHBKJ4oMHGit+t+yxbvLQ+9enn/cOev5QgrKAj5JrKzvU8tDsSIEaGtCwCEEz0ozdm503sNpf61c2fEqhIbK/3Xf0mTJ3u/Ek5CJDc3sHIxMd4fRIhlZwdetqXLgQAQTQgoTXG5pCFD/JcNGeJdfir64gvvdSaXy3tyHjiwfU5b+r//G1i5v/0tLCnxnnsCKzd0KM/hAdC+EFAa01IIOdVCSkqKlJYmlZR4vzdG+te/pNRU7wNgamsjWr161dXeE/rIkd7XPfcEPueaT5cu0nnnNV/mjDOk/PxW19OJxMSWR+YkJ0s7doSlOgAQNgSUhgINHxG83BNWKSlSeXnT67/6yjv76urVYatSY+bOlRISpFtukV5/3fu65RbvsjFjHAaVbduaDinDhoXlGTwnevbZpkNKXp5UVhbO2gBAeLiMMSbSlXDKyeOaHfF4nM1rEX27zpkvvvD2nATq6ae9d/SG2YQJ0nPPtVzO8XDsykppyhTpo4+8vSb/8z8RvY5y9Ki3Dfv2Sf37e3uIEhMjVh0AcMzJ+ZuAcqKePaV//zvw8sePt++7VXv0kL78MvDysbHeh+2FcZ88+aR01VWBl2fOGACIHCfnby7xnMhJOJFC/hyWiHMSTiTvvSgvvRSaujSxuZ/9zNl7WrwvpbLSO7/J4MHer5WVbapjWx096n2EQW6u9+vRoxGtDgCEDQGlLUL8HJbmWHvimjUrbJvasqV17f7975tYcf753tnOnn1W2rXL+7VrV+/yCLjoIqlTJ+n++6VXXvF+7dTJe0kLANo7RwFl+fLlGjx4sJKSkpSUlCS3262XTviL+dixYyooKFBqaqq6dOmiSZMmqbTBLFPFxcXKy8tTp06dlJaWprlz5+r48ePBaU1btGYkSmufw1JbK732mvTEE96vDrf9ox81fuKy4jksn30Wtk21Nh8uWdLIwvPPl7Zvb/wN27eHPaTExkpr1za+7rnnCCkA2j9HAaV3795atGiRioqKtGPHDo0ZM0aXXXaZ9uzZI0maNWuWXnjhBa1atUqbNm1SSUmJJp5w02Rtba3y8vJUXV2tN954Q4899pgeffRRLViwILitao3WXK5pzXNYVq+WTj9dGj1a+r//1/v19NMDHgWTkiL94x+Nr3v++SCfR0ePdv6eMD6xsLX58KSrNpWVTYeTetu3h+1yT8eOUl1d82Wee86iXjMACAXTRt26dTMPP/ywKSsrMx06dDCrVq3yrXv//feNJFNYWGiMMebFF180MTExxuPx+MosX77cJCUlmaqqqoC3WV5ebiSZ8vLytlb/WytWGOMdlxPYKyvL+TaeftoYl+vkz3K5vK+nn2727T16BFa1FStauQ8a+uYbZ/tEMubqq4O08ZYdP+68epIxZ5zR4IMCfeN//3fI23TwYODVufbakFcHAILKyfm71feg1NbWauXKlfr666/ldrtVVFSkmpoa5eTk+MoMGDBAffr0UWFhoSSpsLBQgwYNUnp6uq9Mbm6uKioqfL0wjamqqlJFRYXfK+ic/jk+fbqz8rW10k03NT40uX7ZzJlNXu656abA7+G95pogzZ2WmChdeqmz91xxRRA2HJjYWO9wW6f87kG5/vrA37hunfONOeTk17CpnjQAaA8cB5Rdu3apS5cuSkhI0HXXXadnnnlG2dnZ8ng8io+PV0pKil/59PR0eTweSZLH4/ELJ/Xr69c1ZeHChUpOTva9srKynFa7ZaNGOZtUondvZ5+/ZUvz92cYIx040OilpupqaenSwDf19ddBHGD03HOSk/392GNB2nBg/vQnZ+VdLu89PJK8O3b58qDXqbWcXkFyOsgKAKKJ44Dy3e9+Vzt37tTWrVs1ffp0TZ06Ve+9914o6uYzf/58lZeX+14HDhwI/kZiY6W//jXw8k7PDoHe0dlIubPOcrYpJ5sLiJOZU9euDevU92PHeu/ZCNTll58wTcu994aiSq12+eXOyofxdh8ACDvHASU+Pl5nnnmmhg4dqoULF+qcc87RkiVLlJGRoerqapU1mHe7tLRUGRkZkqSMjIyTRvXUf19fpjEJCQm+kUP1r5C46iqpX7/Ayvbs6eyzA+27b1DuySelTz91tiknmwtIfLx05ZWBlT1yJKzzw8TGeid4DZTf04HD3NvTkldfdVb+Bz8ITT0AwAZtngelrq5OVVVVGjp0qDp06KD169f71u3du1fFxcVyu92SJLfbrV27dunQoUO+MuvWrVNSUpKynTxXPpQC7UX56CNnnztqlPeyUFPP+nG5vJdSThgZVFsrTZ3qbDOS9/kzrRlg1KwVK6QOHQIrG+b5YS6/PPBelP/6rxO++eQTZxsaPtxZ+RB74olI1wAAQsdRQJk/f742b96sTz75RLt27dL8+fP12muvKT8/X8nJyZo2bZpmz56tjRs3qqioSD/72c/kdrs1YsQISdK4ceOUnZ2tKVOm6J133tHatWt12223qaCgQAkJCSFpoGP1QaIld9zh7AF5sbHfTsDRMKTUf3/vvX7TxL/2mnfmeKduvjkEs83Hxkq33RZY2aB237Rsyxbp2LGWyyUlNQgoTuffcdrF4YDTsHHuuRF9LBAAhJ6T4UE///nPTd++fU18fLzp2bOnGTt2rHnllVd8648ePWquv/56061bN9OpUydz+eWXm4MHD/p9xieffGLGjx9vEhMTTY8ePcycOXNMTU2Nk2qEZpjxiZoaDtxwaHBWlnesq9PP7t375CHLjQwxvu221g2jdVqlgB0/bkxqavD3SRsFOkJ85swGb4yLC3ynxsaGrP5PP+38ZwwA0cjJ+ZuHBTbl17/29pK0ZOPGBn+WB6C21vtn/8GD3t6GUaMa7fK4/XbpzjudffTf/y7l57d6ky1bvVqaNOnk5fW9QP/7v2F/ovFrrwU2p5zfj6q62nstLFCXXhrYI5Ob0djPQJL69JFKSgL/nLQ0qcGtXAAQFXhYYDAEOsFGa+63iI31niknT/Z+bSIpOM09Z5zReDhp4+S1/iZOlJ5++uTLYL17RyScSC3f3iM1uL1n9Wrvk5qdePzxVtevfpMNfwZ9+0r//d/Owolk1choAAgZelCa0qo/y4Orttb7rLpApjRPTJS++ebk5atXSz/+8cnzw7W5wyNoXTLBUd9Oyb+tfu28rFa6667AesZO1K2bdPhwm+sWrCPt+PGI7moAaDV6UIKhFaNugi02VrrllsDKxsWdPP1IGyevbblyAfQChcvEid4Qctpp/st9HTta7e2ycBpOJGnlylbXq7mfQWs8+WTEdzUAhAUBpSmtGHUTCrffHtgEt0eOeDsHTtSGyWuj0sSJ3pHDGzd6R0Vv3Cjt3/+fcPLjH0uff+78QxMTvbPBtVJLPwMnLrkk8OloACDaEVCa0+Kf5aG/3yI2Vvp//y+wskuW+PeGtGHy2qh1UseO2tiF8be/tSmEBmvfDhvmfVo1AJwqCCgtafLP8vDdDHrZZYGVO3zYvzeklZPXti9t6cK4+eZvb2xppWDs21mzpO3b2/45ABBN4iJdgahQ/2d5hIwaJXXvHth9mif+xV5/G83nnzfegeByedeH8DaayGttF8acOdI997R58y39DJqTlCR5PM6eYQkA7QU9KFEgNtZ7lSIQJ/7FbsltNJHltAujUyfpqaek3/0uKJtv7mfQHJdLeuQRwgmAUxcBJUrcequUmtr0+qYGFVlwG01kBfroAsnbZfHVV9IVVwS1Ck39DJqSmnqK/GwAoBnMgxJF2jKJq2XTloRXoBORPP10SFPBiT+Dffukhx7yvz2me3dvT9mtt55CPxsApxQn528CSpRZvdp7EjvxxJaV5b1Uw1/czWhsx9WL0A48pUMjgFMSAaWd48TWSvU77vPPpS++kHr29F53YQcCQFg4OX8ziicKRXhQUfRixwFA1OAmWQAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1HAWUhQsX6rzzzlPXrl2VlpamCRMmaO/evX5ljh07poKCAqWmpqpLly6aNGmSSktL/coUFxcrLy9PnTp1UlpamubOnavjx4+3vTUAAKBdcBRQNm3apIKCAr355ptat26dampqNG7cOH399de+MrNmzdILL7ygVatWadOmTSopKdHEiRN962tra5WXl6fq6mq98cYbeuyxx/Too49qwYIFwWsVAACIai5jjGntm7/44gulpaVp06ZN+sEPfqDy8nL17NlTK1as0I9//GNJ0r/+9S8NHDhQhYWFGjFihF566SX96Ec/UklJidLT0yVJDzzwgObNm6cvvvhC8fHxLW63oqJCycnJKi8vV1JSUmurDwAAwsjJ+btN96CUl5dLkrp37y5JKioqUk1NjXJycnxlBgwYoD59+qiwsFCSVFhYqEGDBvnCiSTl5uaqoqJCe/bsaXQ7VVVVqqio8HsBAID2q9UBpa6uTjNnztSFF16os88+W5Lk8XgUHx+vlJQUv7Lp6enyeDy+MieGk/r19esas3DhQiUnJ/teWVlZra02AACIAq0OKAUFBdq9e7dWrlwZzPo0av78+SovL/e9Dhw4EPJtAgCAyIlrzZtmzJihNWvWaPPmzerdu7dveUZGhqqrq1VWVubXi1JaWqqMjAxfmW3btvl9Xv0on/oyDSUkJCghIaE1VQUAAFHIUQ+KMUYzZszQM888ow0bNqhfv35+64cOHaoOHTpo/fr1vmV79+5VcXGx3G63JMntdmvXrl06dOiQr8y6deuUlJSk7OzstrQFAAC0E456UAoKCrRixQo999xz6tq1q++ekeTkZCUmJio5OVnTpk3T7Nmz1b17dyUlJemGG26Q2+3WiBEjJEnjxo1Tdna2pkyZosWLF8vj8ei2225TQUEBvSQAAECSw2HGLper0eWPPPKIfvrTn0ryTtQ2Z84cPfHEE6qqqlJubq6WLVvmd/nm008/1fTp0/Xaa6+pc+fOmjp1qhYtWqS4uMDyEsOMAQCIPk7O322aByVSCCgAAESfsM2DAgAAEAoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsE5cpCvQ3o1ZMkYbyzb6vh+dMlobbtoQwRoB7QPHFhAathxbjntQNm/erEsuuUSZmZlyuVx69tln/dYbY7RgwQL16tVLiYmJysnJ0b59+/zKHD58WPn5+UpKSlJKSoqmTZumysrKNjXERq5fufx+yJK0sWyjXL9yRahGQPvAsQWEhk3HluOA8vXXX+ucc87R/fff3+j6xYsXa+nSpXrggQe0detWde7cWbm5uTp27JivTH5+vvbs2aN169ZpzZo12rx5s6699trWt8JCLf0w+Y8UaB2OLSA0bDu2HAeU8ePH684779Tll19+0jpjjO69917ddtttuuyyyzR48GD97W9/U0lJia+n5f3339fLL7+shx9+WMOHD9fIkSN13333aeXKlSopKWlzg2wwZsmYoJYD4MWxBYSGjcdWUG+S3b9/vzwej3JycnzLkpOTNXz4cBUWFkqSCgsLlZKSomHDhvnK5OTkKCYmRlu3bm30c6uqqlRRUeH3slnD7rG2lgPgxbEFhIaNx1ZQA4rH45Ekpaen+y1PT0/3rfN4PEpLS/NbHxcXp+7du/vKNLRw4UIlJyf7XllZWcGsNgAAsExUDDOeP3++ysvLfa8DBw5EukoAACCEghpQMjIyJEmlpaV+y0tLS33rMjIydOjQIb/1x48f1+HDh31lGkpISFBSUpLfy2ajU0YHtRwAL44tIDRsPLaCGlD69eunjIwMrV+/3resoqJCW7duldvtliS53W6VlZWpqKjIV2bDhg2qq6vT8OHDg1mdiAl0vDhzNgDOcGwBoWHjseU4oFRWVmrnzp3auXOnJO+NsTt37lRxcbFcLpdmzpypO++8U88//7x27dqlq6++WpmZmZowYYIkaeDAgbrooot0zTXXaNu2bXr99dc1Y8YMXXXVVcrMzAxm2yLK3GHatB5A4zi2gNCw7dhyHFB27NihIUOGaMiQIZKk2bNna8iQIVqwYIEk6ZZbbtENN9yga6+9Vuedd54qKyv18ssvq2PHjr7PePzxxzVgwACNHTtWF198sUaOHKkHH3wwSE2yh7nDnNQdNjplNP+BAm3EsQWEhk3HlssYE3VHdEVFhZKTk1VeXm79/SgAAMDLyfk7KkbxAACAUwsBBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwTlykK9Aa9ZPfVlRURLgmAAAgUPXn7UAmsY/KgHLkyBFJUlZWVoRrAgAAnDpy5IiSk5ObLROVz+Kpq6tTSUmJunbtKpfL1WL5iooKZWVl6cCBA6fks3tO9fZL7APaT/tP5fZL7ANb2m+M0ZEjR5SZmamYmObvMonKHpSYmBj17t3b8fuSkpJOyV/Meqd6+yX2Ae2n/ady+yX2gQ3tb6nnpB43yQIAAOsQUAAAgHVOiYCSkJCgO+64QwkJCZGuSkSc6u2X2Ae0n/afyu2X2AfR2P6ovEkWAAC0b6dEDwoAAIguBBQAAGAdAgoAALAOAQUAAFjnlAgo999/v04//XR17NhRw4cP17Zt2yJdpTZbuHChzjvvPHXt2lVpaWmaMGGC9u7d61fm2LFjKigoUGpqqrp06aJJkyaptLTUr0xxcbHy8vLUqVMnpaWlae7cuTp+/Hg4mxIUixYtksvl0syZM33LToX2f/755/rJT36i1NRUJSYmatCgQdqxY4dvvTFGCxYsUK9evZSYmKicnBzt27fP7zMOHz6s/Px8JSUlKSUlRdOmTVNlZWW4m+JYbW2tbr/9dvXr10+JiYk644wz9Jvf/MbvGR/tqf2bN2/WJZdcoszMTLlcLj377LN+64PV1nfffVejRo1Sx44dlZWVpcWLF4e6aQFrbh/U1NRo3rx5GjRokDp37qzMzExdffXVKikp8fuMaN4HLf0OnOi6666Ty+XSvffe67c8qtpv2rmVK1ea+Ph489e//tXs2bPHXHPNNSYlJcWUlpZGumptkpubax555BGze/dus3PnTnPxxRebPn36mMrKSl+Z6667zmRlZZn169ebHTt2mBEjRpgLLrjAt/748ePm7LPPNjk5Oebtt982L774ounRo4eZP39+JJrUatu2bTOnn366GTx4sLnpppt8y9t7+w8fPmz69u1rfvrTn5qtW7eajz/+2Kxdu9Z8+OGHvjKLFi0yycnJ5tlnnzXvvPOOufTSS02/fv3M0aNHfWUuuugic84555g333zTbNmyxZx55plm8uTJkWiSI3fddZdJTU01a9asMfv37zerVq0yXbp0MUuWLPGVaU/tf/HFF82tt95qVq9ebSSZZ555xm99MNpaXl5u0tPTTX5+vtm9e7d54oknTGJiovnzn/8crmY2q7l9UFZWZnJycsyTTz5p/vWvf5nCwkJz/vnnm6FDh/p9RjTvg5Z+B+qtXr3anHPOOSYzM9P88Y9/9FsXTe1v9wHl/PPPNwUFBb7va2trTWZmplm4cGEEaxV8hw4dMpLMpk2bjDHeg7VDhw5m1apVvjLvv/++kWQKCwuNMd5f9piYGOPxeHxlli9fbpKSkkxVVVV4G9BKR44cMf379zfr1q0zP/zhD30B5VRo/7x588zIkSObXF9XV2cyMjLMPffc41tWVlZmEhISzBNPPGGMMea9994zksz27dt9ZV566SXjcrnM559/HrrKB0FeXp75+c9/7rds4sSJJj8/3xjTvtvf8OQUrLYuW7bMdOvWze/3f968eea73/1uiFvkXHMn6Hrbtm0zksynn35qjGlf+6Cp9n/22WfmtNNOM7t37zZ9+/b1CyjR1v52fYmnurpaRUVFysnJ8S2LiYlRTk6OCgsLI1iz4CsvL5ckde/eXZJUVFSkmpoav7YPGDBAffr08bW9sLBQgwYNUnp6uq9Mbm6uKioqtGfPnjDWvvUKCgqUl5fn107p1Gj/888/r2HDhumKK65QWlqahgwZooceesi3fv/+/fJ4PH77IDk5WcOHD/fbBykpKRo2bJivTE5OjmJiYrR169bwNaYVLrjgAq1fv14ffPCBJOmdd97RP//5T40fP15S+2//iYLV1sLCQv3gBz9QfHy8r0xubq727t2rr776KkytCZ7y8nK5XC6lpKRIav/7oK6uTlOmTNHcuXP1ve9976T10db+dh1Q/v3vf6u2ttbvBCRJ6enp8ng8EapV8NXV1WnmzJm68MILdfbZZ0uSPB6P4uPjfQdmvRPb7vF4Gt039etst3LlSr311ltauHDhSetOhfZ//PHHWr58ufr376+1a9dq+vTpuvHGG/XYY49J+rYNzf3+ezwepaWl+a2Pi4tT9+7drd8Hv/jFL3TVVVdpwIAB6tChg4YMGaKZM2cqPz9fUvtv/4mC1dZoPyZOdOzYMc2bN0+TJ0/2PRyvve+D3/72t4qLi9ONN97Y6Ppoa39UPs0Y/goKCrR7927985//jHRVwubAgQO66aabtG7dOnXs2DHS1YmIuro6DRs2THfffbckaciQIdq9e7ceeOABTZ06NcK1C72nnnpKjz/+uFasWKHvfe972rlzp2bOnKnMzMxTov1oWk1Nja688koZY7R8+fJIVycsioqKtGTJEr311ltyuVyRrk5QtOselB49eig2NvakkRulpaXKyMiIUK2Ca8aMGVqzZo02btyo3r17+5ZnZGSourpaZWVlfuVPbHtGRkaj+6Z+nc2Kiop06NAhnXvuuYqLi1NcXJw2bdqkpUuXKi4uTunp6e26/ZLUq1cvZWdn+y0bOHCgiouLJX3bhuZ+/zMyMnTo0CG/9cePH9fhw4et3wdz58719aIMGjRIU6ZM0axZs3w9au29/ScKVluj/ZiQvg0nn376qdatW+frPZHa9z7YsmWLDh06pD59+vj+T/z00081Z84cnX766ZKir/3tOqDEx8dr6NChWr9+vW9ZXV2d1q9fL7fbHcGatZ0xRjNmzNAzzzyjDRs2qF+/fn7rhw4dqg4dOvi1fe/evSouLva13e12a9euXX6/sPUHdMMTn23Gjh2rXbt2aefOnb7XsGHDlJ+f7/t3e26/JF144YUnDS3/4IMP1LdvX0lSv379lJGR4bcPKioqtHXrVr99UFZWpqKiIl+ZDRs2qK6uTsOHDw9DK1rvm2++UUyM/39hsbGxqqurk9T+23+iYLXV7XZr8+bNqqmp8ZVZt26dvvvd76pbt25hak3r1YeTffv26dVXX1Vqaqrf+va8D6ZMmaJ3333X7//EzMxMzZ07V2vXrpUUhe0P+225YbZy5UqTkJBgHn30UfPee++Za6+91qSkpPiN3IhG06dPN8nJyea1114zBw8e9L2++eYbX5nrrrvO9OnTx2zYsMHs2LHDuN1u43a7fevrh9mOGzfO7Ny507z88sumZ8+eUTPMtqETR/EY0/7bv23bNhMXF2fuuusus2/fPvP444+bTp06mb///e++MosWLTIpKSnmueeeM++++6657LLLGh16OmTIELN161bzz3/+0/Tv39/KYbYNTZ061Zx22mm+YcarV682PXr0MLfccouvTHtq/5EjR8zbb79t3n77bSPJ/OEPfzBvv/22b4RKMNpaVlZm0tPTzZQpU8zu3bvNypUrTadOnawYYmtM8/ugurraXHrppaZ3795m586dfv8vnjgiJZr3QUu/Aw01HMVjTHS1v90HFGOMue+++0yfPn1MfHy8Of/8882bb74Z6Sq1maRGX4888oivzNGjR831119vunXrZjp16mQuv/xyc/DgQb/P+eSTT8z48eNNYmKi6dGjh5kzZ46pqakJc2uCo2FAORXa/8ILL5izzz7bJCQkmAEDBpgHH3zQb31dXZ25/fbbTXp6uklISDBjx441e/fu9Svz5ZdfmsmTJ5suXbqYpKQk87Of/cwcOXIknM1olYqKCnPTTTeZPn36mI4dO5rvfOc75tZbb/U7GbWn9m/cuLHRY37q1KnGmOC19Z133jEjR440CQkJ5rTTTjOLFi0KVxNb1Nw+2L9/f5P/L27cuNH3GdG8D1r6HWiosYASTe13GXPCtIsAAAAWaNf3oAAAgOhEQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdf4/g2uRra49qkkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9960664324743589" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a BayesianRidge regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge())\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 776.55387798, 766.05848214, 117.34559919, 115.33575667,\n", + " 1425.04998602, 108.36698771, 128.08678891, 86.83669419,\n", + " 770.9199631 , 769.74106758, 1419.36598347, 766.93911555,\n", + " 116.72431774, 95.96186806, 770.37920861, 777.76049966,\n", + " 123.77602587, 1431.55769606, 101.94879346, 113.81389966,\n", + " 767.83117278, 768.19749663, 105.21141129, 1434.30972717,\n", + " 1423.47245431, 775.95551615, 85.44834333, 104.0988384 ,\n", + " 78.40106289, 102.37668531, 769.91134443, 769.10039085,\n", + " 778.39738741, 771.76394562, 1458.50843574, 1438.6455693 ,\n", + " 1417.32350228, 1458.08444569, 1453.49733502, 1427.32725656,\n", + " 764.43066969, 101.6709869 , 105.97528733, 1428.07335138,\n", + " 769.31679646, 108.5697784 , 780.42694993, 1439.34194643,\n", + " 1437.30647773, 101.59770332, 1423.56790181, 1436.27499643,\n", + " 764.71619656, 1437.54041693, 84.66741803, 768.34588635,\n", + " 94.59715221, 95.17186813, 116.87661832, 777.07353148,\n", + " 1416.68936779, 109.26543808, 113.44622415, 766.60778873,\n", + " 88.20794411, 99.699148 , 1434.73824726, 94.27748863,\n", + " 1420.08484249, 1441.03735844, 1436.76219485, 773.32104219,\n", + " 121.64705279, 772.11481107, 70.40012199, 1416.99274449,\n", + " 1453.54612084, 778.67913792, 1425.63744304, 100.19867365,\n", + " 1417.94035109, 764.69346394, 93.60920638, 762.40441892,\n", + " 113.48062208, 1423.80924617, 1464.04022748, 95.57276183,\n", + " 1436.66309027, 1434.51920325, 84.50108563, 118.33361244,\n", + " 100.43092674, 773.19187128, 100.50950761, 772.01914038,\n", + " 103.80879898, 115.08416843, 1463.40418746, 82.63892241,\n", + " 770.17194218, 1421.14079982, 774.40025383, 769.20455806,\n", + " 113.38805298, 771.20539643, 89.9709598 , 82.24059822,\n", + " 80.00426352, 1435.61906916, 93.12060161, 1033.41224307,\n", + " 1440.25045956, 104.98922183, 1421.47512283, 775.88833752,\n", + " 770.40478416, 115.26633657, 1456.97819554, 116.50136073,\n", + " 120.65029095, 768.40048081, 1440.59232112, 113.39652589,\n", + " 775.26831528, 100.4092378 , 769.60780216, 1437.04042145,\n", + " 1441.14523502, 1431.86651058, 1438.76878188, 771.66524899,\n", + " 88.89357373, 1459.59442129, 776.54520105, 1423.30074829,\n", + " 86.61396817, 101.40014605, 93.902762 , 106.45921553,\n", + " 100.13138503, 118.5012867 , 80.42540785, 1422.36578485])" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9807764683415322" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a BayesianRidge regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge())\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([132.12340639, 118.80427889, 107.18570023, 584.37931972,\n", + " 55.32894366, 113.06673122, 108.46759432, 404.01956916,\n", + " 133.79882581, 666.91806681, 611.82046055, 690.51898517,\n", + " 92.04670205, 331.7968981 , 664.92313111, 138.52862798,\n", + " 118.04996274, 372.05949062, 569.34591881, 591.12151365,\n", + " 691.87883843, 597.86874749, 89.08714835, 621.14191144,\n", + " 132.42907122, 123.71929895, 579.0385398 , 577.78356325,\n", + " 391.55978633, 378.6291887 , 667.26910094, 597.3563262 ,\n", + " 112.20824719, 607.24360219, 349.05181173, 693.42266845,\n", + " 616.87130752, 363.08588597, 355.63337588, 102.36857756,\n", + " 122.97913791, 381.82980231, 105.89876107, 640.37760946,\n", + " 631.23707916, 107.08700151, 139.99199684, 367.50676851,\n", + " 662.90861326, 107.11125099, 127.78533248, 629.48116724,\n", + " 672.92277948, 327.52119155, 374.37264241, 139.99554413,\n", + " 584.03031899, 570.55413896, 115.16865657, 132.91510743,\n", + " 114.9261265 , 46.57813741, 105.33836445, 658.81785502,\n", + " 596.4682104 , 314.40835399, 658.47900016, 382.61438396,\n", + " 123.75701408, 370.08808346, 639.77965888, 671.05014514,\n", + " 107.33669023, 607.84471345, 375.52104464, 118.42280938,\n", + " 356.1803266 , 131.73973953, 629.10238712, 574.73909466,\n", + " 115.87656638, 119.97824027, 584.19111053, 120.34561497,\n", + " 84.84590204, 117.22686479, 359.94544305, 591.23776 ,\n", + " 89.58811998, 652.76651994, 571.59095678, 106.78564414,\n", + " 570.36974963, 127.18764447, 405.10603042, 132.30860944,\n", + " 117.79194372, -8.00932547, 358.20836991, 372.7539651 ,\n", + " 136.69388866, 616.62994315, 137.89260653, 128.42955213,\n", + " 590.48340792, 149.17342776, 569.75547184, 575.86955574,\n", + " 594.39461337, 656.4849587 , 403.47001831, 96.76905711,\n", + " 365.31062782, 288.31895272, 106.40295977, 144.71731494,\n", + " 598.58882764, 93.02575583, 355.11853112, 81.30063804,\n", + " 107.73593931, 645.6814726 , 361.46851908, 104.36884116,\n", + " 127.10531354, 107.0662245 , 630.80916657, 659.6563803 ,\n", + " 330.60330595, 94.77913931, 638.69201945, 664.52233694,\n", + " 576.93161606, 353.89590043, 126.13345747, 634.1731852 ,\n", + " 402.70130592, 38.82835491, 569.83611366, 375.6834875 ,\n", + " 372.64214934, 598.73206049, 393.9367473 , 105.65590744])" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768776.553878100132.123406(768, 100)
290768766.058482100118.804279(768, 100)
54100117.345599100107.185700(100, 100)
198100115.335757630584.379320(100, 630)
45314361425.04998610055.328944(1436, 100)
..................
164100106.459216365375.683488(100, 365)
165100100.131385365372.642149(100, 365)
199100118.501287630598.732060(100, 630)
13210080.425408365393.936747(100, 365)
50114361422.365785100105.655907(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 776.553878 100 132.123406 (768, 100)\n", + "290 768 766.058482 100 118.804279 (768, 100)\n", + "54 100 117.345599 100 107.185700 (100, 100)\n", + "198 100 115.335757 630 584.379320 (100, 630)\n", + "453 1436 1425.049986 100 55.328944 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.459216 365 375.683488 (100, 365)\n", + "165 100 100.131385 365 372.642149 (100, 365)\n", + "199 100 118.501287 630 598.732060 (100, 630)\n", + "132 100 80.425408 365 393.936747 (100, 365)\n", + "501 1436 1422.365785 100 105.655907 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768776.553878100132.123406(768, 100)
290768766.058482100118.804279(768, 100)
54100117.345599100107.185700(100, 100)
198100115.335757630584.379320(100, 630)
45314361425.04998610055.328944(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 776.553878 100 132.123406 (768, 100)\n", + "290 768 766.058482 100 118.804279 (768, 100)\n", + "54 100 117.345599 100 107.185700 (100, 100)\n", + "198 100 115.335757 630 584.379320 (100, 630)\n", + "453 1436 1425.049986 100 55.328944 (1436, 100)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.210945\n", + "(100, 365) 0.418064\n", + "(100, 630) 0.685232\n", + "(768, 100) 0.904307\n", + "(768, 630) 1.260747\n", + "(1436, 100) 1.202940\n", + "(1436, 365) 1.518347\n", + "(1436, 630) 1.799418\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_22324\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_22324\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and BayesianRidge model\n", + " # with 2-degree polynomial features\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge())\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "prefix = \"e2e_test3\"\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 106416c8bc692ee4b6c51dfb4ab82f52416a8eb0 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 11 Jul 2024 16:52:08 +0000 Subject: [PATCH 34/78] sgd regression model added --- .../sgd_regression/test_sgd_regression.ipynb | 1655 +++++++++++++++++ 1 file changed, 1655 insertions(+) create mode 100644 app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb new file mode 100644 index 00000000..ae556f78 --- /dev/null +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb @@ -0,0 +1,1655 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.997505913985049" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SGD regression model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.SGDRegressor(random_state=42, penalty=\"elasticnet\"),\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 779.94150317, 767.73121684, 143.68963566, 72.85602179,\n", + " 1429.93895354, 141.49591683, 139.75017057, 84.38549774,\n", + " 773.05925323, 758.51852956, 1429.46608792, 753.47523395,\n", + " 148.44601855, 94.6708371 , 757.93414329, 782.07870155,\n", + " 134.93549616, 1434.45399846, 75.00434616, 71.55080054,\n", + " 754.49079864, 757.31797299, 151.11235811, 1436.24253279,\n", + " 1428.00814705, 780.62872138, 70.67611648, 74.0053766 ,\n", + " 79.46983054, 85.65043199, 759.72210333, 758.33811849,\n", + " 780.95611876, 760.32308564, 1452.31019321, 1442.79370547,\n", + " 1427.76279144, 1452.05419641, 1448.77361761, 1428.28787937,\n", + " 769.71564457, 86.72521704, 144.7501754 , 1432.88592571,\n", + " 757.71111735, 140.47746462, 783.55987706, 1439.17436619,\n", + " 1439.78876569, 141.25479438, 1424.76527943, 1438.87426038,\n", + " 750.66109946, 1436.86360512, 81.11152212, 769.66536801,\n", + " 73.65601933, 71.59897576, 140.68484057, 780.98573529,\n", + " 1419.25058566, 165.33513618, 141.09378058, 754.34506236,\n", + " 75.6264621 , 94.3479742 , 1439.50277652, 83.81033728,\n", + " 1421.98907679, 1439.40585122, 1439.93376497, 762.19380368,\n", + " 142.08343327, 761.1237607 , 81.82250195, 1419.51995754,\n", + " 1449.22291381, 781.09039245, 1431.92282335, 72.94645957,\n", + " 1420.24067212, 769.88351847, 71.00015447, 766.56602362,\n", + " 154.06300674, 1424.99082915, 1456.70144508, 67.48555571,\n", + " 1440.05337546, 1438.20727976, 70.12066273, 143.08961872,\n", + " 74.436971 , 778.31446392, 95.04313865, 778.84164272,\n", + " 140.38804012, 216.3718145 , 1456.15376195, 80.35024797,\n", + " 770.20536627, 1429.33815027, 776.70940728, 771.30313969,\n", + " 73.58145194, 775.1152214 , 69.16479105, 68.56311367,\n", + " 68.05386191, 1439.02347882, 79.21732083, 1272.78461583,\n", + " 1438.4041329 , 98.89554668, 1423.01619279, 780.33587881,\n", + " 758.84835038, 137.05155418, 1451.12110756, 155.51931207,\n", + " 141.23587337, 755.91845984, 1438.67038275, 142.57908283,\n", + " 777.01143391, 143.21795734, 758.10564932, 1440.16822831,\n", + " 1439.46875483, 1433.17738324, 1438.8749366 , 760.82701589,\n", + " 69.21956503, 1453.15836935, 782.51605758, 1432.16491311,\n", + " 86.0753242 , 164.80094342, 67.90833246, 87.12484877,\n", + " 88.26135074, 74.14699875, 83.46709568, 1423.70945451])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9726494616458502" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SGD regression model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.SGDRegressor(random_state=42, penalty=\"elasticnet\"),\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([158.92473735, 140.8327875 , 93.56914114, 580.88108033,\n", + " 53.39485601, 107.34424052, 103.65373494, 376.84493023,\n", + " 156.13962536, 652.00393796, 623.41964251, 678.43536216,\n", + " 84.13033461, 309.24312429, 651.44805481, 160.76101231,\n", + " 115.08030473, 393.36468221, 570.6579886 , 578.8364957 ,\n", + " 680.38187615, 594.38233666, 72.48800111, 628.92269529,\n", + " 106.08334598, 153.26819822, 577.50988016, 575.47524714,\n", + " 357.5135996 , 361.95354772, 647.60770452, 587.80673378,\n", + " 146.86568172, 606.49092276, 373.98916524, 697.65432179,\n", + " 625.70503867, 374.90218035, 375.32079387, 104.78272484,\n", + " 146.94215167, 361.66851237, 90.84830193, 651.40561631,\n", + " 627.46005753, 96.56913373, 162.6746681 , 385.71276519,\n", + " 669.74658349, 101.89679636, 122.3128838 , 637.82007319,\n", + " 660.09427924, 351.40889859, 364.76782225, 161.77510074,\n", + " 578.05234154, 568.99251358, 107.08905986, 160.03891935,\n", + " 122.05042811, 31.76824487, 101.80824745, 647.62516166,\n", + " 579.15047907, 305.86445272, 665.41745721, 358.96987564,\n", + " 117.6869734 , 389.28297877, 647.97457801, 654.67598974,\n", + " 100.33166238, 605.94583043, 364.65663624, 123.44219045,\n", + " 369.56767767, 161.9258323 , 639.98636861, 571.96927935,\n", + " 121.63364371, 148.02973635, 582.14652841, 151.11073197,\n", + " 76.37322002, 107.84578662, 371.2549907 , 578.89484901,\n", + " 77.01241644, 662.68903232, 567.24630143, 99.6666392 ,\n", + " 574.59875856, 154.74793336, 393.37402133, 159.59104223,\n", + " 101.05189789, -26.09025096, 373.78443893, 347.18020267,\n", + " 156.59942834, 623.82202917, 159.59532748, 155.47047388,\n", + " 579.86984058, 172.14785502, 571.30772076, 572.74665771,\n", + " 579.29487084, 664.68687185, 366.67721615, 41.88513013,\n", + " 384.02931053, 281.95726106, 114.63417123, 162.71657086,\n", + " 600.25349424, 95.22406824, 372.02323887, 75.41594719,\n", + " 94.07593881, 639.2466358 , 379.72794594, 94.87102911,\n", + " 153.58031996, 95.44268531, 630.64933625, 664.09323431,\n", + " 350.86032017, 87.91766242, 649.66997005, 643.86322638,\n", + " 567.46861904, 366.58827096, 155.8042439 , 643.66860065,\n", + " 367.90678268, 14.55888948, 566.60927581, 367.06809405,\n", + " 367.84741984, 585.27399927, 369.97027976, 107.30435295])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.941503100158.924737(768, 100)
290768767.731217100140.832788(768, 100)
54100143.68963610093.569141(100, 100)
19810072.856022630580.881080(100, 630)
45314361429.93895410053.394856(1436, 100)
..................
16410087.124849365367.068094(100, 365)
16510088.261351365367.847420(100, 365)
19910074.146999630585.273999(100, 630)
13210083.467096365369.970280(100, 365)
50114361423.709455100107.304353(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.941503 100 158.924737 (768, 100)\n", + "290 768 767.731217 100 140.832788 (768, 100)\n", + "54 100 143.689636 100 93.569141 (100, 100)\n", + "198 100 72.856022 630 580.881080 (100, 630)\n", + "453 1436 1429.938954 100 53.394856 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 87.124849 365 367.068094 (100, 365)\n", + "165 100 88.261351 365 367.847420 (100, 365)\n", + "199 100 74.146999 630 585.273999 (100, 630)\n", + "132 100 83.467096 365 369.970280 (100, 365)\n", + "501 1436 1423.709455 100 107.304353 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.941503100158.924737(768, 100)
290768767.731217100140.832788(768, 100)
54100143.68963610093.569141(100, 100)
19810072.856022630580.881080(100, 630)
45314361429.93895410053.394856(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.941503 100 158.924737 (768, 100)\n", + "290 768 767.731217 100 140.832788 (768, 100)\n", + "54 100 143.689636 100 93.569141 (100, 100)\n", + "198 100 72.856022 630 580.881080 (100, 630)\n", + "453 1436 1429.938954 100 53.394856 (1436, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.224959\n", + "(100, 365) 0.406354\n", + "(100, 630) 0.670418\n", + "(768, 100) 0.920260\n", + "(768, 630) 1.250042\n", + "(1436, 100) 1.201567\n", + "(1436, 365) 1.522869\n", + "(1436, 630) 1.803532\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_17660\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_17660\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Parameters:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Parameters:\n", + " - group: pandas.DataFrame\n", + " A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " - float\n", + " The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACS2UlEQVR4nOzdeVyVZf7/8dd9WA6KsqggamiQkuKW4ViklbhR0zhTwzTfZrJy1ExFHVAb7TstaiUt2liNmltl36b6TWnNJCkquGRpLqRj7hhIk2CUCS6xnvP748hJBJUD53BY3s/H4zzo3Pd13ffnBvP45rru6zasVqsVERERERERqTaTuwsQERERERFpaBSkREREREREHKQgJSIiIiIi4iAFKREREREREQcpSImIiIiIiDhIQUpERERERMRBClIiIiIiIiIOUpASERERERFxkIKUiIiIiIiIgxSkRESkQbr22msZOXKku8sQEZEmSkFKRETqnWPHjvHII48QHh6Oj48Pfn5+9O/fn5dffpmffvrJ5ec/f/48M2fOZNOmTS4/l4iINEye7i5ARETkYsnJydx7772YzWYefPBBevToQXFxMVu3buXRRx9l//79LFmyxKU1nD9/nlmzZgEwcOBAl55LREQaJgUpERGpNzIzM7nvvvvo1KkTaWlptGvXzr4vPj6ejIwMkpOT3Vhh7Zw7dw5fX193lyEiIk6gqX0iIlJvvPDCC5w9e5bly5dXCFHlOnfuzJ///Ocq+86cORPDMCptf/PNNzEMg6ysLPu2Xbt2ERsbS5s2bWjWrBlhYWGMGjUKgKysLIKCggCYNWsWhmFgGAYzZ8609z906BC/+93vaNWqFT4+PvTt25d///vfVZ538+bNTJgwgeDgYK655hoAzpw5Q0JCAtdeey1ms5ng4GCGDh1Kenq6Q98vERFxH41IiYhIvfHxxx8THh7OLbfc4rJzfPfddwwbNoygoCBmzJhBQEAAWVlZrFq1CoCgoCAWLVrE+PHjueeee/jtb38LQK9evQDYv38//fv3p0OHDsyYMQNfX1/++c9/cvfdd7Ny5UruueeeCuebMGECQUFBPPnkk5w7dw6AcePG8cEHHzBx4kQiIyP54Ycf2Lp1KwcPHuTGG2902bWLiIjzKEiJiEi9UFBQwLfffstvfvMbl57n888/58cff2TdunX07dvXvv2ZZ54BwNfXl9/97neMHz+eXr16MWLEiAr9//znP9OxY0d27tyJ2WwGbGFpwIABTJ8+vVKQatWqFampqXh4eNi3JScn8/DDDzNv3jz7tr/85S9Ov1YREXEdTe0TEZF6oaCgAICWLVu69DwBAQEArF69mpKSEof6njp1irS0NH7/+99z5swZvv/+e77//nt++OEHYmNjOXr0KN9++22FPg8//HCFEFVewxdffMGJEydqdS0iIuI+ClIiIlIv+Pn5Abb7h1zp9ttvJy4ujlmzZtGmTRt+85vf8MYbb1BUVHTVvhkZGVitVp544gmCgoIqvJ566inANnXwYmFhYZWO88ILL/DVV18RGhpKv379mDlzJl9//bVzLlBEROqEpvaJiEi94OfnR/v27fnqq69q1L+qhSYAysrKKrX74IMP2L59Ox9//DEpKSmMGjWKefPmsX37dlq0aHHZc1gsFgCmTZtGbGxslW06d+5c4X2zZs0qtfn973/Prbfeyocffsi6det48cUXef7551m1ahV33nnnFa9TRETqB41IiYhIvfGrX/2KY8eOsW3bNof7BgYGAnD69OkK248fP15l+5tvvplnn32WXbt28Y9//IP9+/fz3nvvAZcPZeHh4QB4eXkxZMiQKl/VnZrYrl07JkyYwEcffURmZiatW7fm2WefrVZfERFxPwUpERGpN/7yl7/g6+vLmDFjOHnyZKX9x44d4+WXX66y73XXXQfAli1b7NvOnTvHihUrKrT78ccfsVqtFbbdcMMNAPbpfc2bNwcqh7Lg4GAGDhzI4sWLycnJqVRDXl7eFa7OpqysjPz8/ErHbd++fbWmF4qISP2gqX0iIlJvXHfddbzzzjv8z//8D926dePBBx+kR48eFBcX8/nnn/P+++8zcuTIKvsOGzaMjh07Mnr0aB599FE8PDx4/fXXCQoKIjs7295uxYoVLFy4kHvuuYfrrruOM2fOsHTpUvz8/PjlL38J2KbjRUZG8v/+3/8jIiKCVq1a0aNHD3r06MGCBQsYMGAAPXv25OGHHyY8PJyTJ0+ybds2/vvf/7J3794rXuOZM2e45ppr+N3vfkfv3r1p0aIFGzZsYOfOnRVW8RMRkfpNQUpEROqVX//61/znP//hxRdf5F//+heLFi3CbDbTq1cv5s2bx8MPP1xlPy8vLz788EMmTJjAE088QUhICAkJCQQGBvKnP/3J3u72229nx44dvPfee5w8eRJ/f3/69evHP/7xjwoLQyxbtoxJkyaRmJhIcXExTz31FD169CAyMpJdu3Yxa9Ys3nzzTX744QeCg4Pp06cPTz755FWvr3nz5kyYMIF169axatUqLBYLnTt3ZuHChYwfP77230AREakThvXS+Q0iIiIiIiJyRbpHSkRERERExEEKUiIiIiIiIg5SkBIREREREXGQgpSIiIiIiIiDFKREREREREQcpCAlIiIiIiLiID1HCrBYLJw4cYKWLVtiGIa7yxERERERETexWq2cOXOG9u3bYzJdftxJQQo4ceIEoaGh7i5DRERERETqiW+++YZrrrnmsvsVpICWLVsCtm+Wn5+fm6sRERERERF3KSgoIDQ01J4RLkdBCuzT+fz8/BSkRERERETkqrf8aLEJERERERERBylIiYiIiIiIOEhBSkRERERExEEKUiIiIiIiIg5SkBIREREREXGQgpSIiIiIiIiDFKREREREREQcpCAlIiIiIiLiIAUpERERERERBylIiYiIiIiIOEhBSkRERERExEGe7i5AREREREQap3N55/hy+ZdkpmVSmF+Ij78PYYPD6DOqD75Bvu4ur1bcOiJ17bXXYhhGpVd8fDwAhYWFxMfH07p1a1q0aEFcXBwnT56scIzs7GzuuusumjdvTnBwMI8++iilpaXuuBwREREREQFKC0tZPW41L7V/ibS/pvH1+q85seMEX6//mrT/TeOl9i+RPD6Z0qKG++92twapnTt3kpOTY3+tX78egHvvvReAxMREPv74Y95//302b97MiRMn+O1vf2vvX1ZWxl133UVxcTGff/45K1as4M033+TJJ590y/WIiIiIiDR1pYWlvB37NulL07GUWrBarBX2Wy1WLKUWdi/ZzduxbzfYMGVYrVbr1ZvVjYSEBFavXs3Ro0cpKCggKCiId955h9/97ncAHDp0iG7durFt2zZuvvlm1qxZw69+9StOnDhB27ZtAXjttdeYPn06eXl5eHt7V+u8BQUF+Pv7k5+fj5+fn8uuT0RERESksUsen8zuJbsrBaiqGCaDqLFR3LXorjqorHqqmw3qzWITxcXFvP3224waNQrDMNi9ezclJSUMGTLE3qZr16507NiRbdu2AbBt2zZ69uxpD1EAsbGxFBQUsH///sueq6ioiIKCggovERERERGpnXN550hfll6tEAW20an0Zemc//68iytzvnoTpD766CNOnz7NyJEjAcjNzcXb25uAgIAK7dq2bUtubq69zcUhqnx/+b7LSUpKwt/f3/4KDQ113oWIiIiIiDRRX77+ZbVDVDmrxUr68nQXVeQ69SZILV++nDvvvJP27du7/FyPPfYY+fn59tc333zj8nOKiIiIiDR2mamZNQpSmWmZLqrIderF8ufHjx9nw4YNrFq1yr4tJCSE4uJiTp8+XWFU6uTJk4SEhNjb7Nixo8Kxylf1K29TFbPZjNlsduIViIiIiIhIYX5hjfoVnS5yciWuVy9GpN544w2Cg4O5666fbzKLiorCy8uL1NRU+7bDhw+TnZ1NdHQ0ANHR0ezbt4/vvvvO3mb9+vX4+fkRGRlZdxcgIiIiIiL4+PvUqJ85oOENcrh9RMpisfDGG2/w0EMP4en5czn+/v6MHj2aKVOm0KpVK/z8/Jg0aRLR0dHcfPPNAAwbNozIyEgeeOABXnjhBXJzc3n88ceJj4/XiJOIiIiISB0LGxzm8PQ+w2QQNijMhVW5htuD1IYNG8jOzmbUqFGV9v3tb3/DZDIRFxdHUVERsbGxLFy40L7fw8OD1atXM378eKKjo/H19eWhhx5i9uzZdXkJIiLSgJy3nGd/8X7+W/JfiqxFmA0zoV6hRHpH0tzU3N3liYg0aH1G9WHj4xsdC1IeBjeOvtGFVblGvXqOlLvoOVIiIo1fqbWULee38FXxV1ip/NFnYNDDuwe3Nb8NT8Ptv2cUEWmw9BwpERGRRqLUWsqqM6vYV7yvyhAFYMXKvuJ9fHjmQ0qtpXVcoYhI4xE7P5aOAzpimIwrtjNMBh1v7Ujs/Ng6qsy5FKRERKTR23R+EzllOdVqe6LsBJvOb3JtQSIijZin2ZMRKSOIGhuFydNUKVAZJgOTp4mosVGMSBmBp7lhzgLQ1D40tU9EpDE7bznP0vylDvcb6z+WZqZmLqhIRKTpOJd3ji9f/5LMtEyKThdhDjATNiiMPqP64Bvk6+7yqlTdbKAghYKUiEhj9vlPn7OzcKfD/fr59CO6WbQLKhIRkfqsutmgYY6jiYiIVNOR4iM16ne4+LCClIhILZzLO8eXy22jUYX5hfj4+xA2uH6PRjlCQUpERBq1nyw/1Wk/EZGmrrSwlLUJa/ly+ZdYLdYKq/dlpmay8fGN3DjmRmLnxzbY+6NAQUpERERERJyktLCUt2PfJntrdpXLn5cHq91LdpN3MK9BLzahVftERKRRq+lDdvVwXhERx6UkpnD80+NXfYaU1WLl+JbjpCSk1FFlzqcgJSIijVqEd0Sd9hMRaarO5Z1j15JdXOZxfZVZYffS3Zz//rxL63IVBSkREWnUept716jfDeYbnFuIiEgjt2vxLrA41sdaZiV9ebprCnIxBSkREWnUmpua092ru0N9unt11zOkREQctHOB44+aAMhYm+HkSuqGgpSIiDRqpdZSMKrfvr1Hewb6DnRZPSIijdG5vHOcyz1Xo74/HP7BydXUjYa5RIaIiEg1lFpL+fDsh+SU5lSrfXev7gz0HYinoY9HERFHfPHKFzXuW5hf6MRK6o4+KUREpNHacn4LOaU5WKt55/PXpV9z5uwZQr1CifSO1Mp9IiLVtP//7a9xX2tZdVenqF8UpEREpFE6bznP/uL91Q5RAD9ZfyK7NJvs0my2/bSN7t7dua35bRqhEhG5inMnf57W9z3fs5vdnOAERRRhxkx72hNFFG1oU6mvh7dHXZbqNPpkEBGRRulA8QEsji4fdRELFr4q/opTllPc3eJuhSkRkavIJZcUUsgkEwOjwi+ysslmG9sII4xYYgkhxL6vRUgLd5Rba1psQkREGqVvSr6p9TGsWPm29Fu2nN/ihIpERBqn0sJSjpYeZRnLyCILoNJsgPL3WWSxjGV8zdf2fT3u61FntTqTgpSIiDRKRdYipx1rf/F+frL85LTjiYg0FqWFpczpP4e3zr9FKaVXnU5txUoZZbzDO+SSCwbcNPmmOqrWuRSkRESkUTIbZqcdy4KF/cU1v5FaRKSxSklM4c30NymjrNp9ysNUCin0GtGL5m0a5sI+ClIiItIohXqFOvV4zpgqKCLSmJzLO8e6pevIJNOhhX3AFqYyyaRLYhcXVed6ClIiItIoRXpHYnLix5wzpwqKiDQGX77+JbvKdmE48tTzixgYPD7icSdXVXcUpEREpFFqbmpOd+/uTjueM6cKiog0BpmpmXzLtw6PRpWzYmXvgb2c//68kyurGwpSIiLSaN3W/DZamlo65VjOniooItLQFeYXUkTtRusLKSR9ebqTKqpbeiiGiIg0Wp6GJwGmAM5YzlTa913Gd2xbsY3sPdkUFhTi4+dDxxs6Ev1QNMGdgyu1v87zurooWUSkwfBq5oWZ2o3W++BDZlomA6YPcFJVdUdBSkREGrVia3GF999+9S0fPf4RR7ccxeRhwlL280N7M7dnsnHBRrrc1oW7n7mbDj062PcdKz1GX8++dVa3iEh959nMk/a0J5vsGk3vMzBoRzuKTjfMe1A1tU9ERBq1i+9tOrL5CPNj55PxWQZAhRB18fuMzzKYHzufI5uP2Pdp1T4RkYqKCoqIIqpW90j1pS/mgIZ5D6qClIiINGrl9zZ9+9W3LP3jUkoLS7GWXeWBkWVWSotKWfrHpXz71beAVu0TEblUQXYBbWhDGGEOr9xnYBBOOK2N1oQNCnNRha6lICUiIo1apHckBgYfPf4RpcWlWK3V+82p1WKltLiUfz3xL0Cr9omIXKrkfAkAscTigUe1w5SBgQceDGMYhmFw4+gbXVmmyyhIiYhIo9bc1Jwzx85wdMvRq45EXcpaZuXI5iPkHcvTqn0iIpfwbG5bbiGEEP7IH6sVpspD1B/5IyGE0KJ9C5q3aV4X5TqdgpSIiDR621Zsw+RRs488k4eJbW9uc+ozqUREGoNW17Wy/3c44YxhDNdyLUClQFX+PowwxjCGcMIBaBninEdUuINW7RMRkUYva09WpYUlqstSZuGH//xAM1MzJ1clItKwhQ8J5/jm4/b3IYTwEA/xAz+wi13kkEMhhfjgQzva0Ze+tKZ1hWP4tPKp67KdRkFKREQavZIzJbXqb5xx7CZqEZHGrrSwlKPJR6vc15rWxBJ71WMYJqPBLjQBmtonIiJNQNuAtrXqHxAQ4JxCREQaiZTEFL7d8W2tjmF4NNyFJkBBSkREmoB+ffvV6h6pqKgoJ1ckItJwncs7R/qydKyWmj0/qly7G9s12IUmQEFKRESagLFjx9bqHqlHHnnEyRWJiDRcX77+Za1DFIB3S28nVOM+ClIiItLoRUREcP1t12N4OPjASA+D62+/ni5durioMhGRhiczNdMpQaq4oNgJ1biPgpSIiDQJ9z97P57enhimaj4w0mTg6e3J/c/c7+LKREQalsL8QqccxxzQsB90riAlIiJNwpCoITz8zsN4mj2vOjJleBh4mj15+J2HGRI1pI4qFBFpGHz8a79keUNfsQ8UpEREpInobe5NxO0RJKQk0GWAbarepQtQlL/vMqALCSkJRNwewQ3mG+q6VBGRei1scO0DUENfsQ/0HCkREWkimpua092rO/SACR9OIO9YHp+/+Tnf7P2Gn/J/opl/M0J7h3LLyFsIui4IgO5e3fUgXhGRS/QZ1YfUGak17m+YbCGqIa/YBwpSIiLShAz0HcipM6fIKcsh6LogfvP0by7btr1Hewb6Dqy74kREGgjfIF/8Qv0o+KagRv073tqR2PlXf2BvfaepfSIi0mR4Gp78tuVv6endE4PL3ycVZAriDt878DT0+0YRkarc9/F9NerX/b7ujEgZgae54f/9qiAlIiJNiqfhyW3NbyPCM+KybfIsebxe8Dobzm2g1Fpah9WJiDQM7Xq3o03XNg71aR3Rmt+9+7tGEaJAQUpERJqYUmspq86s4nDp4au23V+8nw/PfKgwJSJShdFfjMbsX70lzM3+ZsbsHOPiiuqWgpSIiDQpW85vIacsp9rtT5SdYMv5LS6sSESkYfLx8yEhK4GgyKArtguKDCIhOwEfv9ovm16fNI5xNRERkWo4bznPV8VfOdzvq+KviG4WrRX8REQu4RPgw4T9E8g7mEfKlBRO7DxBaWEpnj6etP9Fe2JfiiWo25WDVkOlICUiIk3GgeIDWLE63M+Klf3F++nr09cFVYmINHxB3YIYsWaEu8uoU5raJyIiTcY3Jd+4pa+IiDQ+bg9S3377LSNGjKB169Y0a9aMnj17smvXLvt+q9XKk08+Sbt27WjWrBlDhgzh6NGjFY5x6tQp7r//fvz8/AgICGD06NGcPXu2ri9FRETquZ8sP9W4b5G1yImViIhIQ+fWIPXjjz/Sv39/vLy8WLNmDQcOHGDevHkEBgba27zwwgu88sorvPbaa3zxxRf4+voSGxtLYWGhvc3999/P/v37Wb9+PatXr2bLli2MHTvWHZckIiL1WAklNe5rNqq3MpWIiDQNhtVqdXyyuJPMmDGDzz77jE8//bTK/Varlfbt2zN16lSmTZsGQH5+Pm3btuXNN9/kvvvu4+DBg0RGRrJz50769rXNXV+7di2//OUv+e9//0v79u0rHbeoqIiiop9/s1hQUEBoaCj5+fn4+fm54EpFRKQ+ePP0m+Rb82vUt3+z/rpHSkSkCSgoKMDf3/+q2cCtI1L//ve/6du3L/feey/BwcH06dOHpUuX2vdnZmaSm5vLkCFD7Nv8/f256aab2LZtGwDbtm0jICDAHqIAhgwZgslk4osvvqjyvElJSfj7+9tfoaGhLrpCERGpT2ozPa+7d3cnViIiIg2dW4PU119/zaJFi+jSpQspKSmMHz+eyZMns2LFCgByc3MBaNu2bYV+bdu2te/Lzc0lODi4wn5PT09atWplb3Opxx57jPz8fPvrm290A7GISGN33nKeQgqv3rAKLYwWWvpcREQqcOvy5xaLhb59+zJnzhwA+vTpw1dffcVrr73GQw895LLzms1mzOZ6PNe9uARyv4fTZ6C0DDw9IKAlhLQBby93Vyci0iAdKD5Q4749zT2dWImIiDQGbh2RateuHZGRkRW2devWjezsbABCQkIAOHnyZIU2J0+etO8LCQnhu+++q7C/tLSUU6dO2ds0GBYLHMmC7Xsh81v4sQDOnLN9zfzWtv3IcVs7ERFxSG2WL1eQEhGRS7k1SPXv35/Dhw9X2HbkyBE6deoEQFhYGCEhIaSmptr3FxQU8MUXXxAdHQ1AdHQ0p0+fZvfu3fY2aWlpWCwWbrrppjq4CiexWOA/RyDney77rEgrkJNna6cwJSLikJoufe6Dj6b1iYhIJW4NUomJiWzfvp05c+aQkZHBO++8w5IlS4iPjwfAMAwSEhJ45pln+Pe//82+fft48MEHad++PXfffTdgG8G64447ePjhh9mxYwefffYZEydO5L777qtyxb5663AW5Ffz2Vf5ZyFD93WJiDiipkufa9lzERGpilvvkfrFL37Bhx9+yGOPPcbs2bMJCwtj/vz53H///fY2f/nLXzh37hxjx47l9OnTDBgwgLVr1+Lj42Nv849//IOJEycyePBgTCYTcXFxvPLKK+64JMdZLLYQ9d0px/rl5kFYe/DSPVMiItVhvexw/1UYzq1DREQaB7c+R6q+qO5a8U5nscDeI1Dw80jUkW+Os+TjD9l1+CD5587i79uCvtd3Y+zwe4gI7VSx/7XtoVMDGnUTEXGjd/LfIc+S53C/IFMQf/T/owsqEhGR+qi62cCtI1JN3tFse4jam3GEKQvnk5a+Ew+TB2WWMnuzrfv2Mu+f/2Dwjb9g3oQEeneOsO04kacgJSJSTc1MzaAGt5fq/igREamKW++RatLKlzgHUnfvIDp+FJv32BbMuDhEXfx+057dRMePInX3jp+PUVKzOf8iIk1NqFfNHr5e034iItK4KUi5y7e2Jdv3Zhxh+P9OobC4mLKrrMRXZrFQVFzM8P+dwt6MI7aNOd+7ulIRkUYh0jsSk4MfeyZMdPfu7qKKRESkIVOQcpcLi0tMWTif4pISqnurmsVqpbikhKkL59s2nD7jogJFRBqX5qbmdPfujuHA6hHdvbtrap+IiFRJQcpdSko48s1x0tJ3XnUk6lJlFgup6Ts5+t9sKC27egcREQHgtua30c6zXbXCVAfPDtzW/LY6qEpERBoiBSl3scKSjz/Ew+RRo+4eJhOL/70KPGvWX0SkKfI0PLmnxT308O5x2Wl+Jkz09O7J3S3uxtPQmkwiIlI1fUK4i8lg1+GDlRaWqK4yi4XdRw5BS18nFyYi0rh5Gp4M8h3Ezc1u5kDxAb4p+YYiaxFmw0yoVyiR3pE0NzV3d5kiIlLPKUi5i4cH+efOXr3dFZw+ewZq+oBJEZEmrrmpOX19+tLXp6+7SxERkQZIU/vcxdMDf98WtTpEQIuWcOa8kwoSEREREZHqUpByF08P+l7frVb3SEVFdNViEyIiIiIibqAg5S4mE2OH31Ore6Qe+fVvtdiEiIiIiIgbKEi5i8VCRGgnBt34CzxMjv0YPEwmhkT9gi7XdISAli4qUERERERELkdByl0stkUiXpqQgLeXFyajeg+INBkG3l5ezB2fYNvQro2LChQRERERkctRkHKXC1PyeneO4OM5L2H29r7qyJSHyYTZ25uP57xE784R0C4IvLzqoloREREREbmIgpS7XDQlb3BUP7YteJ2BN0QBVFqAovx9TJ8oti14ncFR/cDPFzqH1l29IiIiIiJiZ1it1ib/IKKCggL8/f3Jz8/Hz8+vbk5aXALb91Z6DNTR/2az+N+r2H3kEKfPniGgRUuiIrryyK9/a7snCqBta4joBA7eWyUiIiIiIldW3WygIIWbghTAkeOQk+dYn6hu0MLXNfWIiIiIiDRx1c0GGtJwp86h4F/Nh/KaDIjupRAlIiIiIlIPKEi5k8kEvS4sGnGlRfuCAqF/H/D2rrPSRERERETk8jzdXUCTZzLZ7ne6tj3kfg+nz0BpmW1Vv4CWENIGvLUyn4iIiIhIfaIgVV94e0HHdraXiIiIiIjUa5raJyIiIiIi4iAFKREREREREQcpSImIiIiIiDhIQUpERERERMRBClIiIiIiIiIOUpASERERERFxkIKUiIiIiIiIgxSkREREREREHKQgJSIiIiIi4iAFKREREREREQcpSImIiIiIiDhIQUpERERERMRBClIiIiIiIiIOUpASERERERFxkIKUiIiIiIiIgxSkREREREREHKQgJSIiIiIi4iAFKREREREREQcpSImIiIiIiDhIQUpERERERMRBClIiIiIiIiIOUpASERERERFxkIKUiIiIiIiIgxSkREREREREHKQgJSIiIiIi4iAFKREREREREQe5NUjNnDkTwzAqvLp27WrfX1hYSHx8PK1bt6ZFixbExcVx8uTJCsfIzs7mrrvuonnz5gQHB/Poo49SWlpa15ciIiIiIiJNiKe7C+jevTsbNmywv/f0/LmkxMREkpOTef/99/H392fixIn89re/5bPPPgOgrKyMu+66i5CQED7//HNycnJ48MEH8fLyYs6cOXV+LSIijV1ZWRklJSXuLkNqwdvbG5NJE1JERGrL7UHK09OTkJCQStvz8/NZvnw577zzDoMGDQLgjTfeoFu3bmzfvp2bb76ZdevWceDAATZs2EDbtm254YYbePrpp5k+fTozZ87E29u7ynMWFRVRVFRkf19QUOCaixMRaSSsViu5ubmcPn3a3aVILZlMJsLCwi77GSkiItXj9iB19OhR2rdvj4+PD9HR0SQlJdGxY0d2795NSUkJQ4YMsbft2rUrHTt2ZNu2bdx8881s27aNnj170rZtW3ub2NhYxo8fz/79++nTp0+V50xKSmLWrFkuvzYRkcaiPEQFBwfTvHlzDMNwd0lSAxaLhRMnTpCTk0PHjh31cxQRqQW3BqmbbrqJN998k+uvv56cnBxmzZrFrbfeyldffUVubi7e3t4EBARU6NO2bVtyc3MB2wf7xSGqfH/5vst57LHHmDJliv19QUEBoaGhTroqEZHGpayszB6iWrdu7e5ypJaCgoI4ceIEpaWleHl5ubscEZEGy61B6s4777T/d69evbjpppvo1KkT//znP2nWrJnLzms2mzGbzS47vohIY1J+T1Tz5s3dXIk4Q/mUvrKyMgUpEZFaqFd3mwYEBBAREUFGRgYhISEUFxdXmo9/8uRJ+z1VISEhlVbxK39f1X1XIiJSc5oG1jjo5ygi4hz1KkidPXuWY8eO0a5dO6KiovDy8iI1NdW+//Dhw2RnZxMdHQ1AdHQ0+/bt47vvvrO3Wb9+PX5+fkRGRtZ5/SIiIiIi0jS4dWrftGnTGD58OJ06deLEiRM89dRTeHh48Ic//AF/f39Gjx7NlClTaNWqFX5+fkyaNIno6GhuvvlmAIYNG0ZkZCQPPPAAL7zwArm5uTz++OPEx8dr6p6ISD2SlwfLl0NaGuTng78/DB4Mo0ZBUJC7qxMREXGcW4PUf//7X/7whz/www8/EBQUxIABA9i+fTtBFz5V//a3v2EymYiLi6OoqIjY2FgWLlxo7+/h4cHq1asZP3480dHR+Pr68tBDDzF79mx3XZKIiFyksBASEmwhymKxvcqlpsLjj8OYMTB/Prjr91+GYfDhhx9y9913u6cAERFpkAyr1Wp1dxHuVlBQgL+/P/n5+fj5+bm7HBGReqWwsJDMzEzCwsLw8fFxoB/ExsLWrRUD1KVMJrj1VkhJcX6Yys3N5dlnnyU5OZlvv/2W4OBgbrjhBhISEhg8eDDg3CC1adMmYmJi+PHHHyutOuss5dezZ88evL29HX62V01/niIiTUV1s0G9ukdKREQaj8TEq4cosO3/9FPbyJUzZWVlERUVRVpaGi+++CL79u1j7dq1xMTEEB8f79yTOZnVaqW0tLTKfcXFxdx7772MHz++jqsSEZGLKUiJiIjT5eXBsmVXD1HlLBZb+++/d14NEyZMwDAMduzYQVxcHBEREXTv3p0pU6awffv2Kvts2rQJwzAqjPLs2bMHwzDIysoC4Pjx4wwfPpzAwEB8fX3p3r07n3zyCVlZWcTExAAQGBiIYRiMHDnywvVZSEpKIiwsjGbNmtG7d28++OCDSudds2YNUVFRmM1mtm7dWmWNs2bNIjExkZ49e9b+myQiIjXm1nukRESkcXr99eqHqHIWi+1equnTa3/+U6dOsXbtWp599ll8fX0r7a/NtLv4+HiKi4vZsmULvr6+HDhwgBYtWhAaGsrKlSuJi4vj8OHD+Pn52Z+JmJSUxNtvv81rr71Gly5d2LJlCyNGjCAoKIjbb7/dfuwZM2Ywd+5cwsPDCQwMrHGNIiLiegpSIiLidKmpNQtSaWnOCVIZGRlYrVa6du1a+4NdIjs7m7i4OPuIUHh4uH1fq1atAAgODraHtaKiIubMmcOGDRvsj+8IDw9n69atLF68uEKQmj17NkOHDnV6zSIi4nwKUiIi4nT5+TXr5+C6CZflynWUJk+ezPjx41m3bh1DhgwhLi6OXr16XbZ9RkYG58+frxSQiouL6dOnT4Vtffv2dUnNIiLifApSIiLidP7+NevnrIXuunTpgmEYHDp0yKF+JpPt1uGLg1hJSUmFNmPGjCE2Npbk5GTWrVtHUlIS8+bNY9KkSVUe8+zZswAkJyfToUOHCvsufeZhVdMQRUSkftJiEyIi4nSDB9uWNXeEyQSDBjnn/K1atSI2NpYFCxZw7ty5Svsvt2R4+XMMc3Jy7Nv27NlTqV1oaCjjxo1j1apVTJ06laVLlwLg7e0NQFlZmb1tZGQkZrOZ7OxsOnfuXOEVGhpa00sUERE3U5ASERGnGzXK8SDl4QGjRzuvhgULFlBWVka/fv1YuXIlR48e5eDBg7zyyiv2e5UuVR5uZs6cydGjR0lOTmbevHkV2iQkJJCSkkJmZibp6els3LiRbt26AdCpUycMw2D16tXk5eVx9uxZWrZsybRp00hMTGTFihUcO3aM9PR0Xn31VVasWOHwdWVnZ7Nnzx6ys7MpKytjz5497Nmzxz7yJSIidUNBSkREnC4oCMaMqX6YMplsIapNG+fVEB4eTnp6OjExMUydOpUePXowdOhQUlNTWbRoUZV9vLy8ePfddzl06BC9evXi+eef55lnnqnQpqysjPj4eLp168Ydd9xBREQECxcuBKBDhw7MmjWLGTNm0LZtWyZOnAjA008/zRNPPEFSUpK9X3JyMmFhYQ5f15NPPkmfPn146qmnOHv2LH369KFPnz7s2rXL4WOJiEjNGVZX3pHbQFT36cUiIk1RYWEhmZmZhIWF4ePjU+1+RUUwbNjVH8prMsGtt0JKClxyy5C4QE1/niIiTUV1s4FGpERExCXMZls4GjsWPD0rj06ZTLbtY8cqRImISMOjVftERMRlfHxg0SKYPdv2kN60NNsS5wEBtoUlRo2yTQMUERFpaBSkRETE5YKCbA/adcbDdkVEROoDTe0TERERERFxkIKUiIiIiIiIgxSkREREREREHKQgJSIiIiIi4iAFKREREREREQcpSImIiOvl5cFzz9me0HvTTbavzz9v2+5mhmHw0UcfubsMERFpYBSkRETEdQoLYdw4aN8e/vpXWL8eduywff3f/7VtHz8eiopccvrc3FwmTZpEeHg4ZrOZ0NBQhg8fTmpqqkvOt2nTJgzD4PTp0y45flZWFqNHjyYsLIxmzZpx3XXX8dRTT1FcXOyS84mIyOXpOVIiIuIahYUQGwtbt4LFUnm/xWJ7LVkCBw9CSgqYzU47fVZWFv379ycgIIAXX3yRnj17UlJSQkpKCvHx8Rw6dMhp53I2q9VKWVkZnp4VP6YPHTqExWJh8eLFdO7cma+++oqHH36Yc+fOMXfuXDdVKyLSNGlESkREXCMx8fIh6mIWC3z6KSQkOPX0EyZMwDAMduzYQVxcHBEREXTv3p0pU6awffv2KvtUNaK0Z88eDMMgKysLgOPHjzN8+HACAwPx9fWle/fufPLJJ2RlZRETEwNAYGAghmEwcuTIC5doISkpyT6S1Lt3bz744INK512zZg1RUVGYzWa2bt1aqb477riDN954g2HDhhEeHs6vf/1rpk2bxqpVq5zzTRMRkWrTiJSIiDhfXh4sW3b1EFXOYrG1f/ppaNOm1qc/deoUa9eu5dlnn8XX17fS/oCAgBofOz4+nuLiYrZs2YKvry8HDhygRYsWhIaGsnLlSuLi4jh8+DB+fn40a9YMgKSkJN5++21ee+01unTpwpYtWxgxYgRBQUHcfvvt9mPPmDGDuXPnEh4eTmBgYLXqyc/Pp1WrVjW+HhERqRkFKRERcb7XX69+iCpnscDy5TB9eq1Pn5GRgdVqpWvXrrU+1qWys7OJi4ujZ8+eAISHh9v3lQea4OBge1grKipizpw5bNiwgejoaHufrVu3snjx4gpBavbs2QwdOrTatWRkZPDqq69qWp+IiBsoSImIiPOlptYsSKWlOSVIWa3WWh/jciZPnsz48eNZt24dQ4YMIS4ujl69el22fUZGBufPn68UkIqLi+nTp0+FbX379q12Hd9++y133HEH9957Lw8//LBjFyEiIrWmICUiIs6Xn1+zfk5a7a5Lly4YhuHwghImk+3W4YuDWElJSYU2Y8aMITY2luTkZNatW0dSUhLz5s1j0qRJVR7z7NmzACQnJ9OhQ4cK+8yXLK5R1TTEqpw4cYKYmBhuueUWlixZUq0+IiLiXFpsQkREnM/fv2b9anHv0sVatWpFbGwsCxYs4Ny5c5X2X2558qCgIABycnLs2/bs2VOpXWhoKOPGjWPVqlVMnTqVpUuXAuDt7Q1AWVmZvW1kZCRms5ns7Gw6d+5c4RUaGurwtX377bcMHDiQqKgo3njjDXv4ExGRuqW/fUVExPkGDwZH/4FvMsGgQU4rYcGCBZSVldGvXz9WrlzJ0aNHOXjwIK+88or9XqVLlYebmTNncvToUZKTk5k3b16FNgkJCaSkpJCZmUl6ejobN26kW7duAHTq1AnDMFi9ejV5eXmcPXuWli1bMm3aNBITE1mxYgXHjh0jPT2dV199lRUrVjh0TeUhqmPHjsydO5e8vDxyc3PJzc2t2TdJRERqTEFKREScb9Qox4OUhweMHu20EsLDw0lPTycmJoapU6fSo0cPhg4dSmpqKosWLaqyj5eXF++++y6HDh2iV69ePP/88zzzzDMV2pSVlREfH0+3bt244447iIiIYOHChQB06NCBWbNmMWPGDNq2bcvEiRMBePrpp3niiSdISkqy90tOTiYsLMyha1q/fj0ZGRmkpqZyzTXX0K5dO/tLRETqlmF15R25DURBQQH+/v7k5+fj5+fn7nJEROqVwsJCMjMzCQsLw8fHp/odx4+3PWy3OotOmEwwdixcJuCI89T45yki0kRUNxtoREpERFxj/nwYMODqI1MmE9x6q629iIhIA6EgJSIirmE2Q0qKbaTJ07NyoDKZbNvHjrW1u2QFOxERkfpMy5+LiIjr+PjYpuvNnm17SG9amm2J84AA28ISo0bBhZXyREREGhIFKRERcb2gINuDdp3wsF0REZH6QFP7REREREREHKQgJSIiIiIi4iAFKREREREREQcpSImIiIiIiDhIQUpERERERMRBWrVPRERcLu9cHsu/XE5aZhr5hfn4+/gzOGwwo/qMIsjXvcufG4bBhx9+yN133+3WOkREpGHRiJSIiLhMYWkh41aPo/1L7flr2l9Z//V6dpzYwfqv1/O/af9L+5faMz55PEWlRS45f25uLpMmTSI8PByz2UxoaCjDhw8nNTXVJefbtGkThmFw+vRplxwf4Ne//jUdO3bEx8eHdu3a8cADD3DixAmXnU9ERKqmICUiIi5RWFpI7NuxLE1fSqmlFIvVUmG/xWqh1FLKkt1LiH071ulhKisri6ioKNLS0njxxRfZt28fa9euJSYmhvj4eKeey9msViulpaVV7ouJieGf//wnhw8fZuXKlRw7dozf/e53dVyhiIgoSImIiEskpiSyNXtrpQB1KYvVwqfZn5KQkuDU80+YMAHDMNixYwdxcXFERETQvXt3pkyZwvbt26vsU9WI0p49ezAMg6ysLACOHz/O8OHDCQwMxNfXl+7du/PJJ5+QlZVFTEwMAIGBgRiGwciRI23XaLGQlJREWFgYzZo1o3fv3nzwwQeVzrtmzRqioqIwm81s3bq1yhoTExO5+eab6dSpE7fccgszZsxg+/btlJSU1P6bJiIi1aZ7pERExOnyzuWxLH3ZVUNUOYvVwrL0ZTwd8zRtmrep9flPnTrF2rVrefbZZ/H19a20PyAgoMbHjo+Pp7i4mC1btuDr68uBAwdo0aIFoaGhrFy5kri4OA4fPoyfnx/NmjUDICkpibfffpvXXnuNLl26sGXLFkaMGEFQUBC33367/dgzZsxg7ty5hIeHExgYWK3r/Mc//sEtt9yCl5dXja9JREQcpyAlIiJO9/qXr1c7RJWzWC0sT1/O9AHTa33+jIwMrFYrXbt2rfWxLpWdnU1cXBw9e/YEIDw83L6vVatWAAQHB9vDWlFREXPmzGHDhg1ER0fb+2zdupXFixdXCFKzZ89m6NChV61h+vTp/P3vf+f8+fPcfPPNrF692lmXJyIi1aSpfSIi4nSpmak1ClJpmWlOOb/VanXKcaoyefJknnnmGfr3789TTz3Ff/7znyu2z8jI4Pz58wwdOpQWLVrYX2+99RbHjh2r0LZv377VquHRRx/lyy+/ZN26dXh4ePDggw+69JpFRKQyjUiJiIjT5Rfm16jf6aLTTjl/ly5dMAyDQ4cOOdTPZLL9fvHiUHLpvUdjxowhNjaW5ORk1q1bR1JSEvPmzWPSpElVHvPs2bMAJCcn06FDhwr7zGZzhfdVTUOsSps2bWjTpg0RERF069aN0NBQtm/fbh/xEhER16s3I1LPPfcchmGQkJBg31ZYWEh8fDytW7emRYsWxMXFcfLkyQr9srOzueuuu2jevDnBwcE8+uijl13pqFHKy4PnnoNhw+Cmm2xfn3/etl1ExE38ffxr1C/AHOCU87dq1YrY2FgWLFjAuXPnKu2/3PLkQUG2Z1rl5OTYt+3Zs6dSu9DQUMaNG8eqVauYOnUqS5cuBcDb2xuAsrIye9vIyEjMZjPZ2dl07ty5wis0NLSml2hnsdhG/oqKXLOEvIiII/LO5fHc1ucY9n/DuGnpTQz7v2E8v/V58s41vn+b1osRqZ07d7J48WJ69epVYXtiYiLJycm8//77+Pv7M3HiRH7729/y2WefAbYPqrvuuouQkBA+//xzcnJyePDBB/Hy8mLOnDnuuJS6U1gICQmwfDlYLLZXudRUePxxGDMG5s+HS37jKSLiaoPDBjs8vc9kmBgUNshpNSxYsID+/fvTr18/Zs+eTa9evSgtLWX9+vUsWrSIgwcPVupTHm5mzpzJs88+y5EjR5g3b16FNgkJCdx5551ERETw448/snHjRrp16wZAp06dMAyD1atX88tf/pJmzZrRsmVLpk2bRmJiIhaLhQEDBpCfn89nn32Gn58fDz30ULWv6YsvvmDnzp0MGDCAwMBAjh07xhNPPMF1112n0SgRcavC0kIS1iaw/MvlWKyWCn//p2am8vjGxxlz4xjmx87H7Nk4/m3q9hGps2fPcv/997N06dIKKxTl5+ezfPlyXnrpJQYNGkRUVBRvvPEGn3/+uX3Z2nXr1nHgwAHefvttbrjhBu68806efvppFixYQHFx8WXPWVRUREFBQYVXg1JYCLGxsHQplJZWDFFge19aCkuW2Nrpt5QiUsdG9RmFyXDsI8bD8GD0jaOdVkN4eDjp6enExMQwdepUevTowdChQ0lNTWXRokVV9vHy8uLdd9/l0KFD9OrVi+eff55nnnmmQpuysjLi4+Pp1q0bd9xxBxERESxcuBCADh06MGvWLGbMmEHbtm2ZOHEiAE8//TRPPPEESUlJ9n7JycmEhYU5dE3Nmzdn1apVDB48mOuvv57Ro0fTq1cvNm/eXGmaoIhIXXH3cwPdxbC6+e7Uhx56iFatWvG3v/2NgQMHcsMNNzB//nzS0tIYPHgwP/74Y4Vlajt16kRCQgKJiYk8+eST/Pvf/64w7SIzM9P+4dmnT58qzzlz5kxmzZpVaXt+fj5+fn7OvkTnGz/eFpIuDVBVMZlg7Fi4zD8aRESuprCwkMzMTMLCwvDx8al2v/HJ41mye0m1RqVMhomxUWNZdJf+rnK1mv48RUQux5G/7w0MHuz9IG/e/abrC6uhgoIC/P39r5oN3Doi9d5775Genk5SUlKlfbm5uXh7e1d61kfbtm3Jzc21t2nbtm2l/eX7Luexxx4jPz/f/vrmm29qeSV1KC8Pli2rXogCW7tly+D7711bl4jIJebHzmdAxwFXHZkyGSZu7Xgr82Pn101hIiLiNI4+N9CKlRV7V/Cnj/7U4Eem3BakvvnmG/785z/zj3/8o85/I2Y2m/Hz86vwajBef736IaqcxWK7l0pEpA6ZPc2kjEhhbNRYPE2elQKVyTDhafJkbNRYUkakNJo58yIiTUlNnhsIsGLvigY/zc9tQWr37t1899133HjjjXh6euLp6cnmzZt55ZVX8PT0pG3bthQXF1daWenkyZOEhIQAEBISUmkVv/L35W0andTUmgWpNOc8m0VExBE+nj4sumsRJ6acYM6gOQwLH0a/Dv0YFj6MOYPmcGLKCRbdtUghSkSkgarJcwPBNjL1afanJKQkOL+oOuK2VfsGDx7Mvn37Kmz705/+RNeuXZk+fTqhoaF4eXmRmppKXFwcAIcPHyY7O9u+MlF0dDTPPvss3333HcHBwQCsX78ePz8/IiMj6/aC6kp+zZ7NwmWW+hURqQtBvkFMHzCd6QOmu7sUERFxopo+NxBsi1AsS1/G0zFP06Z5GydWVTfcFqRatmxJjx49Kmzz9fWldevW9u2jR49mypQptGrVCj8/PyZNmkR0dDQ333wzAMOGDSMyMpIHHniAF154gdzcXB5//HHi4+Mb7+pF/jV7NguX3GsmIiIiIlJbNX1uYLlSSynL05c3yF+0uX358yv529/+xq9+9Svi4uK47bbbCAkJYdWqVfb9Hh4erF69Gg8PD6KjoxkxYgQPPvggs2fPdmPVLjZ4sG0lPkeYTDDIec9mEREREREB23MDHX3cxaXWZqx1UjV1y+3Ln9cH1V3i0K3y8mwLRqxdC1u2gCM/Ni8vOHEC2jS8IVMRcT8tl9246OcpIs6Udy6P9i+1p9RSeuWG3wO7gRNAEWAG2gNRcE34NXyTWH9W0a5uNnDb1D6ppsJCSEiwhSiLxfGFJkwmGD1aIUpEREREnC7IN4gxN45h8a7FWKniF/25QAqQCRhQoUk2sA3+G/Zf/hz4Zx6/93GCfIPqomynqNdT+5q8wkKIjYWlS6G0tGYh6tZbYf58l5QnIiIiIjI/dj4tzS0r7/gaWAZkXXh/ac4qf58Frzz8CsETgrl52c38N/+/LqrUuRSk6rPERNi6tWYBytMTxo6FlBRorAtviEgDkgc8BwwDbrrw9fkL293LMAw++ugjd5chItJgmT3NtPS+JEjlAu8ApVQOUJeyAmW29l/s/oKO8zsy9uOx9f4ZUwpS9VVeHixb5niIGjgQ5syx3RO1aJFClIi4WSEwDttE+L8C64EdF77+74Xt47FNmHe+3NxcJk2aRHh4OGazmdDQUIYPH05qaqpLzrdp0yYMw6j0DERXKCoq4oYbbsAwDPbs2ePy84mIXMlPpT9V3JCCLRxVV3mYSrE9Y2pp+lKG/t/Qeh2mFKTqq9dfr9lI1B13wPTpENRw5peKSGNVCMQCS7H9SvLSv9MsF7YvudDOuR+WWVlZREVFkZaWxosvvsi+fftYu3YtMTExxMfHO/Vczma1WiktvfKN23/5y19o3759HVUkInJlzb2a//zme2z3RDm6pJ31Qr8fbG8/zf6USWsmOaU+V1CQqq9SUx0PUhYLbNjgmnpERByWCGylcoC6lAX4FEhw6tknTJiAYRjs2LGDuLg4IiIi6N69O1OmTGH79u1V9qlqRGnPnj0YhkFWVhYAx48fZ/jw4QQGBuLr60v37t355JNPyMrKIiYmBoDAwEAMw2DkyJG2K7RYSEpKIiwsjGbNmtG7d28++OCDSudds2YNUVFRmM1mtm7detlrW7NmDevWrWPu3Lm1+yaJiDjJdYHX/fxmN7aFJWrCAHb9/HZZ+jK+P/99LSpzHa3aV1/9+GPN+m3fDkVFmtInIm6Wh+0O4+r+Qshyof3TQO1XGT116hRr167l2WefxdfXt9L+gFo8pDw+Pp7i4mK2bNmCr68vBw4coEWLFoSGhrJy5Uri4uI4fPgwfn5+NGvWDICkpCTefvttXnvtNbp06cKWLVsYMWIEQUFB3H777fZjz5gxg7lz5xIeHk5gYGCV5z958iQPP/wwH330Ec2bN6+yjYhIXbuz851sPr7Z9uYEjo9GlbMCORe/tfLKF68wO6b+PSdWI1L1UWEhHDlSs75nz8Kk+jsEKiJNxetUP0SVswDLnXL2jIwMrFYrXbt2dcrxLpadnU3//v3p2bMn4eHh/OpXv+K2227Dw8ODVq1aARAcHExISAj+/v4UFRUxZ84cXn/9dWJjYwkPD2fkyJGMGDGCxYsXVzj27NmzGTp0KNddd539WBezWq2MHDmScePG0bdvX6dfm4hITY3qMwpP04UxmtrO1C6s+Pa9r96r5QFdQ0GqPkpMhIKCmvdftgy+r59DoCLSVKRSsyCV5pSzu/JZ85MnT+aZZ56hf//+PPXUU/znP/+5YvuMjAzOnz/P0KFDadGihf311ltvcezYsQptrxaOXn31Vc6cOcNjjz1W6+sQEXGm8udJmQyT7WG7tXHJs8JPnjtZywO6hoJUfVO+Wl9tWK3wyivOqUdEpEbya9jvtFPO3qVLFwzD4NChQw71M5lsH4sXB7GSkpIKbcaMGcPXX3/NAw88wL59++jbty+vvvrqZY959uxZAJKTk9mzZ4/9deDAgQr3SQFVTkO8WFpaGtu2bcNsNuPp6Unnzp0BWwB76KGHqn+hIiIuMD92PgM6DoAO1O4eqXZOLMqFFKTqm9dfhzJH1oq8jPfq5xCoiDQV/jXsF+CUs7dq1YrY2FgWLFjAuXPnKu2/3PLkQRdWPM3J+XmCflVLi4eGhjJu3DhWrVrF1KlTWbp0KQDe3t4AlF3093hkZCRms5ns7Gw6d+5c4RUaGurQdb3yyivs3bvXHsY++eQTAP7f//t/PPvssw4dS0TE2cyeZlJGpHDfQ/fV7h6pSwbn2/q2rW1pLqHFJuqb1FTbiNJlHMG2UPAubL/v9cf2Z20sEHFxw5P1cwhURJqKwTg+vc8EDHJaBQsWLKB///7069eP2bNn06tXL0pLS1m/fj2LFi3i4MGDlfqUh5uZM2fy7LPPcuTIEebNm1ehTUJCAnfeeScRERH8+OOPbNy4kW7dugHQqVMnDMNg9erV/PKXv6RZs2a0bNmSadOmkZiYiMViYcCAAeTn5/PZZ5/h5+fn0EhSx44dK7xv0aIFANdddx3XXHONo98iERGn8/H04d2x7/LtO9+y9dOtWC0OJCoDCANaV9x8X/f7nFmi02hEqr7Jr3o6zF5s/yy5HpgPbAb2XPg6/8L2IRfaAVcMYyIirjcKxz9iPIDRTqsgPDyc9PR0YmJimDp1Kj169GDo0KGkpqayaNGiKvt4eXnx7rvvcujQIXr16sXzzz/PM888U6FNWVkZ8fHxdOvWjTvuuIOIiAgWLlwIQIcOHZg1axYzZsygbdu2TJw4EYCnn36aJ554gqSkJHu/5ORkwsLCnHa9IiL1yasvv4qP2QfDqOYcPwPbx8Cwyrsm3zzZmaU5jWF15R25DURBQQH+/v7k5+fj5+fn3mKGDYP16ytsSgWGA8Vc+QHRHoA38DEwuHVrLTghIk5RWFhIZmYmYWFh+Pj4XL2D3XhsY+jVGZUyYRtbrzrgiPPU/OcpIuKY1NRUhg8fzk+FP115ql95iPojEF5xV4eWHfjvlP+6rsgqVDcbaESqvrnppgpv92ILUYVcOURxYX/RhfZ79eEoIm43HxjA1T9qTMCtF9qLiEhDl3cuj+e2PsfT3zxNywktMcIujEpdOjhV/j4MGEOlEAUwvu941xVaS7pHqp6bgm0kqrrDhpYL7afm57PBZVWJiFSHGUgBEvn54bwXj06ZLrzGYAtRepC4iEhDVlhaSMLaBJalL6PMemEIoCXwIPADtpv8c7CNEPhgW52vL5XuiSrnZfLikb6PuLzumlKQqm+++ML+n0eo2RNVyoDUs2c5evQoXbp0cVZlIiI14INtut5sbA/pTcO2xHkAtoUlRgFBbqpNREScpbC0kNi3Y/n0+KdYqxoCaA3EVv94JsPE6BtH06Z5G6fV6Gya2lffXLTYxBJs00VrwsMwWLx4sVNKEhGpvSBgOrYRqi8ufJ2OQpSISOOQmJLI1uytVYcoB5kME7d2vJX5sfNrX5gLKUjVN/4/P3tlF1e/L+pyyqxWdu/e7ZSSREREREQuJ+9cHsvSl2GxOvLIi8pMhglPkydjo8aSMiIFs2f9nvKtqX31zeDB9lX7ql4Ivfou98BJERERERFnef3L12sVoq7xu4bINpEMChvEqD6jCPJtGLMVFKTqm1//GmbMAGwP262NgICAWpcjIiIiInIlqZmptQpSkW0iSXkgxYkV1Q1N7atv/v1v+3/2pRb3SHl4EBUV5ZSSREREREQuJ7+wdvOoTheddk4hdUxBqr5JTbX/51hqcY9UWRmPPFJ/l4sUERERkcbB36d286gCzAHOKaSOKUjVNxet2heBbXFgR0elPDw8GDJkiJY+F5F647zlPDsLd/LhmQ95r+A9PjzzIbsKd3Hect7dpWEYBh999JG7yxARabAGhw3GZNQsVpgME4PCBjm5orqhIFXf+FdM9C8B3lT/B2UymfD29mbu3LnOrkxExGGl1lLSzqWxPH85n//0Odml2ZwsO0l2aTaf/fQZy/OXk3YujVJrqUvOn5uby6RJkwgPD8dsNhMaGsrw4cNJvWj035k2bdqEYRguXezn2muvxTCMCq/nnnvOZecTEbmaUX1G1ThIeRgejL5xtJMrqhsKUvXN4MFg+vnH0hv4GDBz9ZEpDw8PzGYzH3/8Mb1793ZhkSIiV1dqLeXDsx/yVfFXWKj6JmQLFr4q/oqPzn7k9DCVlZVFVFQUaWlpvPjii+zbt4+1a9cSExNDfHy8U8/lbFarldLSy38/Zs+eTU5Ojv01adKkOqxORKSiIN8gxtw4xuEw1RAeunslDgepn376ia1bt3LgwIFK+woLC3nrrbecUliTNWpUhSAFMBjYBgy88P7SQFX+PiYmhm3btjF48GCXligiUh1bzm8hpzTnqg9ntGLl29Jv2XJ+i1PPP2HCBAzDYMeOHcTFxREREUH37t2ZMmUK27dvr7JPVSNKe/bswTAMsrKyADh+/DjDhw8nMDAQX19funfvzieffEJWVhYxMTEABAYGYhgGI0eOBMBisZCUlERYWBjNmjWjd+/efPDBB5XOu2bNGqKiojCbzWzduvWy19ayZUtCQkLsL19f39p9s0REaml+7HwGdByAgVGt9g3lobtX4lCQOnLkCN26deO2226jZ8+e3H777eTk5Nj35+fn86c//cnpRTYpQUEwZkylMNUb2AAcARKwhaobLnxN6N2bI0eOsH79eo1EiUi9cN5ynv3F+x16wv3+4v38ZPnJKec/deoUa9euJT4+vsqQUZvHQ8THx1NUVMSWLVvYt28fzz//PC1atCA0NJSVK1cCcPjwYXJycnj55ZcBSEpK4q233uK1115j//79JCYmMmLECDZv3lzh2DNmzOC5557j4MGD9OrV67I1PPfcc7Ru3Zo+ffrw4osvXnH0SkSkLpg9zaSMSOGRvo/gYVx5HlVDeujulTj0HKnp06fTo0cPdu3axenTp0lISKB///5s2rSJjh07uqrGpmf+fDhwALZuBUvF6TBdAPvdTyYT3HorpKSAueH+IRSRxudA8YHLTue7HAsW9hfvp69P31qfPyMjA6vVSteuXWt9rEtlZ2cTFxdHz549AQgPD7fva9WqFQDBwcH2sFZUVMScOXPYsGED0dHR9j5bt25l8eLF3H777fb+s2fPZujQoVc8/+TJk7nxxhtp1aoVn3/+OY899hg5OTm89NJLzrxMERGH+Xj6sOiuRcweOJvXv3ydtRlryfgxg59KfqKZVzM6B3bmjs53NKiH7l6JQ0Hq888/Z8OGDbRp04Y2bdrw8ccfM2HCBG699VY2btyoqQXOYjbbwlFiIixbZgtTFwcqk8n2GjPGFroUokSknvmm5Jsa93NGkLJaqz8S5qjJkyczfvx41q1bx5AhQ4iLi7vi6FFGRgbnz5+vFJCKi4vp06dPhW19+1792qdMmWL/7169euHt7c0jjzxCUlISZn0eiEg9EOQbxPQB05k+YLq7S3Eph6b2/fTTT3h6/py9DMNg0aJFDB8+nNtvv50jR444vcAmy8cHFi2CEydgzhwYNgz69bN9nTPHtn3RIoUoEamXiqxFddrvUl26dMEwDA4dOuRQP9OFadUXB7GSkpIKbcaMGcPXX3/NAw88wL59++jbty+vvvrqZY959uxZAJKTk9mzZ4/9deDAgQr3SQE1+oXkTTfdRGlpqf0eLhERqRsOjUh17dqVXbt20a1btwrb//73vwPw61//2nmViU1QEEyfbnuJiDQQZqNmv+Spab9LtWrVitjYWBYsWMDkyZMrBZTTp09XeZ9UUJBtqklOTg6BgYGAbbGJS4WGhjJu3DjGjRvHY489xtKlS5k0aRLe3t6A7aHo5SIjIzGbzWRnZ1eYxucse/bswWQyERwc7PRji4jI5Tk0InXPPffw7rvvVrnv73//O3/4wx9cOp1CREQahlCv0DrtV5UFCxZQVlZGv379WLlyJUePHuXgwYO88sor9nuVLtW5c2dCQ0OZOXMmR48eJTk5mXnz5lVok5CQQEpKCpmZmaSnp7Nx40b7Lxg7deqEYRisXr2avLw8zp49S8uWLZk2bRqJiYmsWLGCY8eOkZ6ezquvvsqKFSscuqZt27Yxf/589u7dy9dff80//vEP+8IV5cFPRETqhkNB6n/+539YvXr1ZfcvXLgQi8Wxm4tFRKTxifSOxOTgEzZMmOju3d1pNYSHh5Oenk5MTAxTp06lR48eDB06lNTUVBYtWlRlHy8vL959910OHTpEr169eP7553nmmWcqtCkrKyM+Pp5u3bpxxx13EBERwcKFCwHo0KEDs2bNYsaMGbRt25aJEycC8PTTT/PEE0+QlJRk75ecnExYWJhD12Q2m3nvvfe4/fbb6d69O88++yyJiYksWbKkBt8hERGpDcPqwBCSh4cHOTk59ukD//M//8Mrr7xC27ZtXVZgXSgoKMDf35/8/Hz8/PzcXY6ISL1SWFhIZmYmYWFh+Pj4VLtf2rk0vir+qtpLoPf07skg30E1LVOqqaY/TxGRpqK62cChXxdemrk++eQTzp07V7MKRUSkUbut+W2082xXrYczdvDswG3Nb6uDqkRERJzDsXkXIiIi1eRpeHJPi3vo4d3jstP8TJjo6d2Tu1vcjafh0PpHIiIibuXQp5ZhGBiGUWmbiIhIVTwNTwb5DuLmZjdzoPgA35R8Q5G1CLNhJtQrlEjvSJqbmru7TBEREYc5FKSsVisjR460P/CvsLCQcePGVVpWdtWqVc6rUK4sLw+WL4e0NMjPB39/GDwYRo2yLZ0uIlIPNDc1p69PX6c8bFdERKQ+cChIPfTQQxXejxgxwqnFiAMKCyEhwRaiLBbbq1xqKjz+OIwZA/Pn66G9IiIiIiJO5lCQeuONN1xVhziisBBiY2Hr1ooBqlx5sFqyBA4ehJQUhSkRERERESfSYhMNUWIifPpp1SHqYhYLbNliG7kSERERERGnUZBqaPLyYOlSqO7jv6xWW/vvv3dtXSIiIiIiTYiCVEPz+utQVuZYn7Iy271UIiIiIiLiFHpoR0OzZk3N+q1dC9OnO7cWEZFqOpd3ji+Xf0lmWiaF+YX4+PsQNjiMPqP64Bvke/UDuJBhGHz44Yfcfffdbq1DREQaFo1INTTHjtWsX0aGc+sQEamG0sJSVo9bzUvtXyLtr2l8vf5rTuw4wdfrvybtf9N4qf1LJI9PprSo1CXnz83NZdKkSYSHh2M2mwkNDWX48OGkpqa65HybNm3CMAxOnz7tkuOXS05O5qabbqJZs2YEBgYqBIqIuIFGpBqa8+dr1u+nn5xbh4jIVZQWlvJ27Ntkb83Gaql8X6fVYsVqsbJ7yW7yDuYxImUEnmbnfSxlZWXRv39/AgICePHFF+nZsyclJSWkpKQQHx/PoUOHnHYuZ7NarZSVleHpWfn7sXLlSh5++GHmzJnDoEGDKC0t5auvvnJDlSIiTZtbR6QWLVpEr1698PPzw8/Pj+joaNZcNHWtsLCQ+Ph4WrduTYsWLYiLi+PkyZMVjpGdnc1dd91F8+bNCQ4O5tFHH6W01DW/2awXmjevWb9mzZxbh4jIVaQkplw2RF3MarGS/Wk2KQkpTj3/hAkTMAyDHTt2EBcXR0REBN27d2fKlCls3769yj5VjSjt2bMHwzDIysoC4Pjx4wwfPpzAwEB8fX3p3r07n3zyCVlZWcTExAAQGBiIYRiMHDkSAIvFQlJSEmFhYTRr1ozevXvzwQcfVDrvmjVriIqKwmw2s3Xr1kr1lZaW8uc//5kXX3yRcePGERERQWRkJL///e+d800TEZFqc2uQuuaaa3juuefYvXs3u3btYtCgQfzmN79h//79ACQmJvLxxx/z/vvvs3nzZk6cOMFvf/tbe/+ysjLuuusuiouL+fzzz1mxYgVvvvkmTz75pLsuyfWuu65m/Tp3dm4dIiJXcC7vHOnL0q8aospZLVbSl6Vz/vsajrpf4tSpU6xdu5b4+Hh8fSvfgxUQEFDjY8fHx1NUVMSWLVvYt28fzz//PC1atCA0NJSVK1cCcPjwYXJycnj55ZcBSEpK4q233uK1115j//79JCYmMmLECDZv3lzh2DNmzOC5557j4MGD9OrVq9K509PT+fbbbzGZTPTp04d27dpx5513akRKRMQN3Dq1b/jw4RXeP/vssyxatIjt27dzzTXXsHz5ct555x0GDRoE2B4I3K1bN7Zv387NN9/MunXrOHDgABs2bKBt27bccMMNPP3000yfPp2ZM2fi7e1d5XmLioooKiqyvy8oKHDdRTrbnXfCJR+81XLHHc6vRUTkMr58/ctqh6hyVouV9OXpDJg+oNbnz8jIwGq10rVr11of61LZ2dnExcXRs2dPAMLDw+37WrVqBUBwcLA9rBUVFTFnzhw2bNhAdHS0vc/WrVtZvHgxt99+u73/7NmzGTp06GXP/fXXXwMwc+ZMXnrpJa699lrmzZvHwIEDOXLkiP38IiLievVmsYmysjLee+89zp07R3R0NLt376akpIQhQ4bY23Tt2pWOHTuybds2ALZt20bPnj1p27atvU1sbCwFBQX2Ua2qJCUl4e/vb3+Fhoa67sKcbdQoqGLO/BV5ecHo0a6pR0SkCpmpmTUKUplpmU45v7W6z9qrgcmTJ/PMM8/Qv39/nnrqKf7zn/9csX1GRgbnz59n6NChtGjRwv566623OHbJAkJ9+/a94rEsFx7E/te//pW4uDiioqJ44403MAyD999/v3YXJiIiDnF7kNq3bx8tWrTAbDYzbtw4PvzwQyIjI8nNzcXb27vS9Iu2bduSm5sL2FZjujhEle8v33c5jz32GPn5+fbXN99849yLcqWgIBgzBkzV/NGZTLYQ1aaNa+sSEblIYX5hjfoVnS66eqNq6NKlC4ZhOLyghOnC360XB7GSkpIKbcaMGcPXX3/NAw88wL59++jbty+vvvrqZY959uxZwLbS3p49e+yvAwcOVLhPCqhyGuLF2rVrB0BkZKR9m9lsJjw8nOzs7GpcoYiIOIvbg9T111/Pnj17+OKLLxg/fjwPPfQQBw4ccOk5zWazfYGL8leDMn8+DBhw9TBlMsGtt9rai4jUIR9/nxr1MweYnXL+Vq1aERsby4IFCzh37lyl/ZdbnjwoKAiAnJwc+7Y9e/ZUahcaGsq4ceNYtWoVU6dOZenSpQD2KeVlFz04PTIyErPZTHZ2Np07d67wcnRGRPlCFIcPH7ZvKykpISsri06dOjl0LBERqR23Bylvb286d+5MVFQUSUlJ9O7dm5dffpmQkBCKi4srfdidPHmSkJAQAEJCQiqt4lf+vrxNo2Q2Q0oKjB1rm+Z3aaAymWzbx461tTM75x8mIiLVFTY4DMNkONTHMBmEDQpzWg0LFiygrKyMfv36sXLlSo4ePcrBgwd55ZVX7PcqXao83MycOZOjR4+SnJzMvHnzKrRJSEggJSWFzMxM0tPT2bhxI926dQOgU6dOGIbB6tWrycvL4+zZs7Rs2ZJp06aRmJjIihUrOHbsGOnp6bz66qusWLHCoWvy8/Nj3LhxPPXUU6xbt47Dhw8zfvx4AO69994afJdERKSm3B6kLmWxWCgqKiIqKgovL68KD008fPgw2dnZ9g/A6Oho9u3bx3fffWdvs379evz8/CpMe2iUfHxg0SI4cQLmzIGBA+Gaa6B1a2jfHvr3h2uvhYa0kIaINBp9RvVxPEh5GNw4+kan1RAeHk56ejoxMTFMnTqVHj16MHToUFJTU1m0aFGVfby8vHj33Xc5dOgQvXr14vnnn+eZZ56p0KasrIz4+Hi6devGHXfcQUREBAsXLgSgQ4cOzJo1ixkzZtC2bVsmTpwIwNNPP80TTzxBUlKSvV9ycjJhYY4HxxdffJH77ruPBx54gF/84hccP36ctLQ0AgMDHT6WiIjUnGF15R25V/HYY49x55130rFjR86cOcM777zD888/T0pKCkOHDmX8+PF88sknvPnmm/j5+TFp0iQAPv/8c8D2YXbDDTfQvn17XnjhBXJzc3nggQcYM2YMc+bMqXYdBQUF+Pv7k5+f3/Cm+RUWQkICLF8OFovtVc5ksr3GjLFN79PIlIjUQGFhIZmZmYSFheHjU/0pe8njk9m9ZHe1Fp0wTAZRY6O4a9FdtSlVqqGmP08RkaaiutnArcuff/fddzz44IPk5OTg7+9Pr1697CEK4G9/+xsmk4m4uDiKioqIjY21/9YPwMPDg9WrVzN+/Hiio6Px9fXloYceYvbs2e66pLpVWAixsbB1a8UAVa48WC1ZAgcPapqfiNSp2Pmx5B3Iu+pDeQ2TQcdbOxI7P7YOqxMREakdt45I1RcNdkRq/HhbSKoqRF3KZLLdM3WZ6SwiIpdTmxGM0sJSUhJT7A/nvThQGSYDw2Rw45gbiZ0fi6fZrb/bazI0IiUicmXVzQYKUjTQIJWXZ7sXqrS0+n08PSEnR0uhi4hDnPEP73N55/jy9S/JTMuk6HQR5gAzYYPC6DOqD75BV17yW5xLQUpE5MoaxNQ+qYXXX6/eSNTFLBbbvVTTp7umJhGRy/AN8mXA9AEMmD7A3aWIiIg4Rb1btU+qKTW1ZkEqLc019YiIiIiINCEKUg1Vfn7N+l3mIZQiIiIiIlJ9ClINlb9/zfoFBDi1DBERERGRpkhBqqEaPNi2Ep8jTCYYNMg19YiIiIiINCEKUg3VqFGOBykPDxg92jX1iIiIiIg0IQpSDVVQEIwZU/0wZTLZQpSWPhcRdyjMg/3PQdowSLnJ9vXA87btbmYYBh999JG7yxARkQZGQaohmz8fBgy4epgymeDWW23tRUTqUlkh7BgHH7aHvX+F3PXwww7b1z3/a9u+YzyUFbnk9Lm5uUyaNInw8HDMZjOhoaEMHz6c1NRUl5xv06ZNGIbBaRct7FN+/KpeO3fudMk5RUSkagpSDZnZDCkpMHas7WG7lwYqk8m2fexYWzuz2T11ikjTVFYIabGQsRSspcClj2yw2LZnLIGNsU4PU1lZWURFRZGWlsaLL77Ivn37WLt2LTExMcTHxzv1XM5mtVopreKB67fccgs5OTkVXmPGjCEsLIy+ffu6oVIRkaZLQaqh8/GBRYvgxAmYMweGDYN+/Wxf58yxbV+0SCFKROre7kTI20rlAHUpC3z3KexOcOrpJ0yYgGEY7Nixg7i4OCIiIujevTtTpkxh+/btVfapakRpz549GIZBVlYWAMePH2f48OEEBgbi6+tL9+7d+eSTT8jKyiImJgaAwMBADMNg5MiRtiu0WEhKSiIsLIxmzZrRu3dvPvjgg0rnXbNmDVFRUZjNZrZu3VqpPm9vb0JCQuyv1q1b869//Ys//elPGIbhnG+ciIhUi6e7CxAnCQqC6dNtLxERdyvMg2PLuHqIKmexte/1NPjU/l7OU6dOsXbtWp599ll8fX0r7Q+oxaMg4uPjKS4uZsuWLfj6+nLgwAFatGhBaGgoK1euJC4ujsOHD+Pn50ezZs0ASEpK4u233+a1116jS5cubNmyhREjRhAUFMTtt99uP/aMGTOYO3cu4eHhBAYGXrWWf//73/zwww/86U9/qvH1iIhIzShIiYiI8339OlirG6IusFrg6+UQWftfCGVkZGC1WunatWutj3Wp7Oxs4uLi6NmzJwDh4eH2fa1atQIgODjYHtaKioqYM2cOGzZsIDo62t5n69atLF68uEKQmj17NkOHDq12LcuXLyc2NpZrrrmmtpclIiIOUpASERHny02l+qNR5SyQm+aUIGW1Wmt9jMuZPHky48ePZ926dQwZMoS4uDh69ep12fYZGRmcP3++UkAqLi6mT58+FbY5cp/Tf//7X1JSUvjnP//p2AWIiIhTKEiJiIjzleTXsN9pp5y+S5cuGIbBoUOHHOpnurBoz8VBrKSkpEKbMWPGEBsbS3JyMuvWrSMpKYl58+YxadKkKo959uxZAJKTk+nQoUOFfeZL7l+tahri5bzxxhu0bt2aX//619XuIyIizqPFJkRExPm8/GvYL8App2/VqhWxsbEsWLCAc+fOVdp/ueXJg4KCAMjJybFv27NnT6V2oaGhjBs3jlWrVjF16lSWLl0K2BaDACgrK7O3jYyMxGw2k52dTefOnSu8QkNDa3R9VquVN954gwcffBAvL68aHUNERGpHQUpERJwvZDCOf8SYIGSQ00pYsGABZWVl9OvXj5UrV3L06FEOHjzIK6+8Yr9X6VLl4WbmzJkcPXqU5ORk5s2bV6FNQkICKSkpZGZmkp6ezsaNG+nWrRsAnTp1wjAMVq9eTV5eHmfPnqVly5ZMmzaNxMREVqxYwbFjx0hPT+fVV19lxYoVNbq2tLQ0MjMzGTNmTI36i4hI7SlIiYiI84WPAsPBjxjDA8JHO6+E8HDS09OJiYlh6tSp9OjRg6FDh5KamsqiRYuq7OPl5cW7777LoUOH6NWrF88//zzPPPNMhTZlZWXEx8fTrVs37rjjDiIiIli4cCEAHTp0YNasWcyYMYO2bdsyceJEAJ5++mmeeOIJkpKS7P2Sk5MJCwur0bUtX76cW265xSWLaYiISPUYVlfekdtAFBQU4O/vT35+Pn5+fu4uR0SkXiksLCQzM5OwsDB8fHyq33HHeNvDdqu16IQJOo+FflUHHHGeGv88RUSaiOpmA41IiYiIa0TNh6ABXP2jxgTBt9rai4iINBAKUiIi4hoeZhiUYhtpMjyp/JFjsm3vPBZiUmztRUREGggtfy4iIq7j4WObrtdrtu0hvblptiXOvQJsC0uEjwKfIHdXKSIi4jAFKRERcT2fINuDdp3wsF0REZH6QFP7REREREREHKQgJSIiIiIi4iAFKREREREREQcpSImIiIiIiDhIQUpERERERMRBClIiIuJ6xSWQnQP/OQLpB21fs3Ns293MMAw++ugjd5chIiINjIKUiIi4jsUCR7Jg+17I/BZ+LIAz52xfM7+1bT9y3NbOBXJzc5k0aRLh4eGYzWZCQ0MZPnw4qampLjnfpk2bMAyD06dPu+T4AEeOHOE3v/kNbdq0wc/PjwEDBrBx40aXnU9ERKqmICUiIq5hsdhGnnK+B+tl2liBnDxbOyeHqaysLKKiokhLS+PFF19k3759rF27lpiYGOLj4516LmezWq2UlpZWue9Xv/oVpaWlpKWlsXv3bnr37s2vfvUrcnNz67hKEZGmTUFKRERcI+MbyD9bvbb5Z23tnWjChAkYhsGOHTuIi4sjIiKC7t27M2XKFLZv315ln6pGlPbs2YNhGGRlZQFw/Phxhg8fTmBgIL6+vnTv3p1PPvmErKwsYmJiAAgMDMQwDEaOHAmAxWIhKSmJsLAwmjVrRu/evfnggw8qnXfNmjVERUVhNpvZunVrpfq+//57jh49yowZM+jVqxddunThueee4/z583z11VfO+caJiEi1eLq7ABERaYSKSyA3z7E+uXkQ1h68vGp9+lOnTrF27VqeffZZfH19K+0PCAio8bHj4+MpLi5my5Yt+Pr6cuDAAVq0aEFoaCgrV64kLi6Ow4cP4+fnR7NmzQBISkri7bff5rXXXqNLly5s2bKFESNGEBQUxO23324/9owZM5g7dy7h4eEEBgZWOnfr1q25/vrreeutt7jxxhsxm80sXryY4OBgoqKianxNIiLiOAUpERFxvtwrTOe7HCu2aYAd29X69BkZGVitVrp27VrrY10qOzubuLg4evbsCUB4eLh9X6tWrQAIDg62h7WioiLmzJnDhg0biI6OtvfZunUrixcvrhCkZs+ezdChQy97bsMw2LBhA3fffTctW7bEZDIRHBzM2rVrqwxeIiLiOgpSIiLifKfP1LyfE4KU1epoiqu+yZMnM378eNatW8eQIUOIi4ujV69el22fkZHB+fPnKwWk4uJi+vTpU2Fb3759r3huq9VKfHw8wcHBfPrppzRr1oxly5YxfPhwdu7cSbt2tf/eiYhI9egeKRERcb7Ssrrtd4kuXbpgGAaHDh1yqJ/JZPtYvDiIlZRUXKJ9zJgxfP311zzwwAPs27ePvn378uqrr172mGfP2u4TS05OZs+ePfbXgQMHKtwnBVQ5DfFiaWlprF69mvfee4/+/ftz4403snDhQpo1a8aKFSsculYREakdBSkREXE+T4+67XeJVq1aERsby4IFCzh37lyl/ZdbnjwoKAiAnJwc+7Y9e/ZUahcaGsq4ceNYtWoVU6dOZenSpQB4e3sDUFb2cyCMjIzEbDaTnZ1N586dK7xCQ0Mduq7z588DPwe+ciaTCYuLlpAXEZGqKUiJiIjzBbSs235VWLBgAWVlZfTr14+VK1dy9OhRDh48yCuvvGK/V+lS5eFm5syZHD16lOTkZObNm1ehTUJCAikpKWRmZpKens7GjRvp1q0bAJ06dcIwDFavXk1eXh5nz56lZcuWTJs2jcTERFasWMGxY8dIT0/n1VdfdXgUKTo6msDAQB566CH27t3LkSNHePTRR8nMzOSuu+6q2TdKRERqREFKREScL6QNGA72MQxo18ZpJYSHh5Oenk5MTAxTp06lR48eDB06lNTUVBYtWlRlHy8vL959910OHTpEr169eP7553nmmWcqtCkrKyM+Pp5u3bpxxx13EBERwcKFCwHo0KEDs2bNYsaMGbRt25aJEycC8PTTT/PEE0+QlJRk75ecnExYWJhD19SmTRvWrl3L2bNnGTRoEH379mXr1q3861//onfv3jX4LomISE0ZVlfekdtAFBQU4O/vT35+Pn5+fu4uR0SkXiksLCQzM5OwsDB8fHyq3/HIcdvDdqurXRBEdHK8QHFIjX+eIiJNRHWzgUakRETENTqHgn+L6rX1b2FrLyIi0kAoSImIiGuYTNArwjbSdLlpfga2/b0ibO1FREQaCD1HSkREXMdksk3Xu7a97SG9p8/Yljj39LAtLBHSBry93F2liIiIwxSkRETE9by9bA/adcLDdkVEROoDzaMQERERERFxkIKUiIiIiIiIgxSkREREREREHOTWIJWUlMQvfvELWrZsSXBwMHfffTeHDx+u0KawsJD4+Hhat25NixYtiIuL4+TJkxXaZGdnc9ddd9G8eXOCg4N59NFHKS0trctLERERERGRJsStQWrz5s3Ex8ezfft21q9fT0lJCcOGDePcuXP2NomJiXz88ce8//77bN68mRMnTvDb3/7Wvr+srIy77rqL4uJiPv/8c1asWMGbb77Jk08+6Y5LEhGRKhw5coRp06YxcOBA+vTpw8CBA5k2bRpHjhxxd2kiIiI1YlitVqu7iyiXl5dHcHAwmzdv5rbbbiM/P5+goCDeeecdfve73wFw6NAhunXrxrZt27j55ptZs2YNv/rVrzhx4gRt27YF4LXXXmP69Onk5eXh7e191fNW9+nFIiJNUWFhIZmZmYSFheHj4+NQ37179zJlyhTS0tLw8PCgrKzMvq/8/eDBg5k3bx69e/d2dukN0qZNm4iJieHHH38kICCgyjZvvvkmCQkJnD592uHj1+bnKSLSFFQ3G9Sre6Ty8/MBaNWqFQC7d++mpKSEIUOG2Nt07dqVjh07sm3bNgC2bdtGz5497SEKIDY2loKCAvbv31/leYqKiigoKKjwEhER50pNTSU6OprNmzcDVAhRF7/ftGkT0dHRpKamuqSO3NxcJk2aRHh4OGazmdDQUIYPH+7U8w0cOJCEhASnHOuWW24hJycHf39/pxxPRERco94EKYvFQkJCAv3796dHjx6A7cPP29u70m/k2rZtS25urr3NxSGqfH/5vqokJSXh7+9vf4WGhjr5akREmra9e/cyfPhwCgsLKwWoS5WVlVFUVMTw4cPZu3evU+vIysoiKiqKtLQ0XnzxRfbt28fatWuJiYkhPj7eqee6GqvVWq37d729vQkJCcEwjDqoSkREaqreBKn4+Hi++uor3nvvPZef67HHHiM/P9/++uabb1x+ThGRpmTKlCkUFxdT3dnjFouF4uJipk6d6tQ6JkyYgGEY7Nixg7i4OCIiIujevTtTpkxh+/btAJw+fZoxY8YQFBSEn58fgwYNqhDoZs6cyQ033MD//d//ce211+Lv7899993HmTNnABg5ciSbN2/m5ZdfxjAMDMMgKyuLTZs2YRgGa9asISoqCrPZzNatWykqKmLy5MkEBwfj4+PDgAED2Llzp/185f0unrb35ptv0rFjR5o3b84999zDDz/8UOE69+7dS0xMDC1btsTPz4+oqCh27drl1O+liIhUVC+C1MSJE1m9ejUbN27kmmuusW8PCQmhuLi40hzwkydPEhISYm9z6Sp+5e/L21zKbDbj5+dX4SUiIs5x5MgR0tLSrjoSdamysjJSU1M5evSoU+o4deoUa9euJT4+Hl9f30r7y2c73HvvvXz33XesWbOG3bt3c+ONNzJ48GBOnTplb3vs2DE++ugjVq9ezerVq9m8eTPPPfccAC+//DLR0dE8/PDD5OTkkJOTU2Gmw4wZM3juuec4ePAgvXr14i9/+QsrV65kxYoVpKen07lzZ2JjYyuc72JffPEFo0ePZuLEiezZs4eYmBieeeaZCm3uv/9+rrnmGnbu3Mnu3buZMWMGXl5etf0WiojIFbg1SFmtViZOnMiHH35IWloaYWFhFfZHRUXh5eVVYR774cOHyc7OJjo6GoDo6Gj27dvHd999Z2+zfv16/Pz8iIyMrJsLERERuyVLluDh4VGjvh4eHixevNgpdWRkZGC1Wunatetl22zdupUdO3bw/vvv07dvX7p06cLcuXMJCAjggw8+sLezWCy8+eab9OjRg1tvvZUHHnjA/tnk7++Pt7c3zZs3JyQkhJCQkArXP3v2bIYOHcp1112H2Wxm0aJFvPjii9x5551ERkaydOlSmjVrxvLly6us8eWXX+aOO+7gL3/5CxEREUyePJnY2NgKbbKzsxkyZAhdu3alS5cu3HvvvVq8Q0TExdwapOLj43n77bd55513aNmyJbm5ueTm5vLTTz8Btg+n0aNHM2XKFDZu3Mju3bv505/+RHR0NDfffDMAw4YNIzIykgceeIC9e/eSkpLC448/Tnx8PGaz2Z2XJyLSJO3atcvh0ahyZWVl7N692yl1VGda4d69ezl79qz9WYXlr8zMTI4dO2Zvd+2119KyZUv7+3bt2lX4Bd6V9O3b1/7fx44do6SkhP79+9u3eXl50a9fPw4ePFhl/4MHD3LTTTdV2Fb+y8RyU6ZMYcyYMQwZMoTnnnuuQu0iIuIanu48+aJFiwDbakcXe+ONNxg5ciQAf/vb3zCZTMTFxVFUVERsbCwLFy60t/Xw8GD16tWMHz+e6OhofH19eeihh5g9e3ZdXYaIiFykfAXWmqrJkt5V6dKlC4ZhcOjQocu2OXv2LO3atWPTpk2V9l280NGl0+QMw8BisVSrjqqmFTrbzJkz+eMf/0hycjJr1qzhqaee4r333uOee+5x+blFRJoqtwap6vy20MfHhwULFrBgwYLLtunUqROffPKJM0sTEZEaqu2y3Zd7dpKjWrVqRWxsLAsWLGDy5MmVAs3p06e58cYbyc3NxdPTk2uvvbbG5/L29q7WKNx1112Ht7c3n332GZ06dQKgpKSEnTt3Xnb59G7duvHFF19U2Fa+UMbFIiIiiIiIIDExkT/84Q+88cYbClIiIi5ULxabEBGRxqNv3761ukcqKirKabUsWLCAsrIy+vXrx8qVKzl69CgHDx7klVdeITo6miFDhhAdHc3dd9/NunXryMrK4vPPP+evf/2rQ6veXXvttXzxxRdkZWXx/fffX3a0ytfXl/Hjx/Poo4+ydu1aDhw4wMMPP8z58+cZPXp0lX0mT57M2rVrmTt3LkePHuXvf/87a9eute//6aefmDhxIps2beL48eN89tln7Ny5k27dujn2zRIREYcoSImIiFONHTu2VvdIPfLII06rJTw8nPT0dGJiYpg6dSo9evRg6NChpKamsmjRIgzD4JNPPuG2227jT3/6ExEREdx3330cP3680jMKr2TatGl4eHgQGRlJUFAQ2dnZl2373HPPERcXxwMPPMCNN95IRkYGKSkpBAYGVtn+5ptvZunSpbz88sv07t2bdevW8fjjj9v3e3h48MMPP/Dggw8SERHB73//e+68805mzZpV/W+UiIg4zLBW9yEfjVhBQQH+/v7k5+drKXQRkUsUFhaSmZlJWFgYPj4+1eozePBgNm/e7FCg8vDwICYmhvXr19e0VKcrKYHvv4czZ6CsDDw8oGVLaNMGGurq4jX5eYqINCXVzQZuvUdKREQap5deeono6GiKioqqtSiDyWTC29ubuXPn1kF1V2exwDff2ELUpb9uLCiAEydsYSo0FEya2yEi0iTpr38REXG63r178/HHH2M2m696v5SHhwdms5mPP/64Xjz7yGKBI0cgL69yiCpntdr2Hz1qay8iIk2PgpSIiLjE4MGD2bZtm/0RF5cGqvL3MTExbNu2jcGDB9d1iVX65hs4e7Z6bc+csbUXEZGmR1P7RETEZXr37s2GDRs4evQoixcvZvfu3Zw+fZqAgACioqJ45JFH6NKli7vLtCu/J8oR338P7ds33HumRESkZhSkRESkWmqzNlGXLl3qzf1PV1LVPVFXY7XCd99Bhw6uqcnZtMaUiIhzaGqfiIhckdeFoZbz58+7uRLXO3OmZv1ycuD48YZxv1RxcTFQeaqliIg4RiNSIiJyRR4eHgQEBPDdd98B0Lx5cwzDcHNVzmexwLlzNe+flwfnz0OnTvV3JT+LxUJeXh7NmzfH01P/BBARqQ39LSoiIlcVEhICYA9TjY3VCidPQlHRpXtKgLNAEWAFDMAMtAAq3xT1/ffwww/QurVr660Nk8lEx44dG2UYFhGpSwpSIiJyVYZh0K5dO4KDgykpKXF3OU43cyb8858XT807BDwHbAc8gIsfLFz+PhqYDnStcCxPT/j0UwgMdHHRNeTt7Y2pvg6ZiYg0IApSIiJSbR4eHo3u3pq8PJg3D0pLy7ekAsOBYioGqEv9F/gX8DHw89LthgFvvQXTp7umXhERqR/0KykREWnSXn8dyux5aS+2EFXIlUMUF/YXXWi/177VaoW0NOfXKSIi9YuClIiINGmpqRcveT4F20hUdZcIt1xoP7XC1tOnnVSciIjUWwpSIiLSpP28fsYRII2rj0RdqgzbdMCj9i3NmzujMhERqc8UpEREpMkqLITDh8vfLcG2kERNeACL7e98fGpXl4iI1H8KUiIi0mQlJtrClM0uHB+NKlcG7La/+/mYIiLSWClIiYhIk5SXB8uWXbwlv5ZHPG3/r/Pna3koERGp9xSkRESkSXr99YufGwXgX8sjBvz8XwGXbSQiIo2EgpSIiDRJqamXBqm+1O4eqSj7u0GDal6XiIg0DApSIiLSJOVXmsk3ltrdI/WI/d3o0TU8jIiINBgKUiIi0iT5V5rJFwEMwvFRKQ9gCNAFAD8/aNOmttWJiEh9pyAlIiJN0uDBYKr0KfgS4E31Px5NF9rPtW8JD3dGdSIiUt8pSImISJM0alRVQao38DFg5uojUx4X2n18oZ9NcLDzahQRkfpLQUpERJqkoCCIiqpqz2BgGzDwwvtLA1X5+5gL7Qbb95hMWmhCRKSp8HR3ASIiIu7SosXl9vQGNgBHgcXYHrZ7GtsS51HYFpboUqmXh4cWmhARaSoUpEREpMk6c+ZqLbpw8f1PV2Iy2UKUFpoQEWkaNLVPRESarMor99WMyQS33grz5zvneCIiUv8pSImISJNV9cp91WcygacnjB0LKSlgNjuvNhERqd80tU9ERJqsUaPg8cfBYql+H8OAPn1sU/gGDbIdIyjIdTWKiNQ7hXlwbDmcTIOSfPDyh5DBED4KfJrOX4gKUiIi0mQFBcGYMbBkSfXClMlkG31atMj1tYmI1DtlhbDjEcj8P8BacV/uetgzA1rfBAM+AN9r3FJiXdLUPhERadLmz4cBA64+xU/3QYlIk1Z8Gj4Kg8y3qBSiLvbDF/CvUPhiLJQV1VV1bqEgJSIiTZrZbLu/aexY2/1OlwYq3QclIk1eWSF83A2Kcqvf59hS2BjbqMOUYbVarxApm4aCggL8/f3Jz8/Hz8/P3eWIiIib5OXB669DWhqcPg0BAboPSkSEbSMhc0UNOhrQ+RHo17DmQ1c3GyhIoSBVUR6wHEgD8gF/YDAwCtC/IkRERESalMI8WNWWK07nuxLDE+7JAZ+G85C96mYDTe2TCwqBcUB74K/AemDHha//e2H7eKDxDs+KiIiIyCW+fp0ahygAqwW+Xu60cuoTBSnBFqJigaVAKXDp0lWWC9uXXGinMCUiIiLSJOSm1vIAFshNc0op9Y2ClACJwFYqB6hLWYBPgQRXFyQiIiIi9UFJvhOOcbr2x6iHFKSavDxgGVcPUeUs2EauvndZRSIiIiJST3j5O+EYAbU/Rj2kINXkvU71Q1S5MqA7UOD8ckRERESk/ggZ7IRjDKr9MeohBakmLxXHgxTAd0BHFKZEREREGrHwUYBH7Y5x7QinlFLfeLq7AHG32sx7zQeigf1OqkVERERE6hWfIOj8MEc+fY0labDra8j/CfybQd9wGDsIItpd5Rif/QEGrQePxvVEcwWpJq+2814PAIeB651Qi4iIiIjUJ3v37mXKjEOkbQQPE5RdNJFp6xGY9wkM7g7z7ofenS5zkLxPYdckuGlJndRcVzS1r8kbTO3/GCQ4oQ4RERERqU9SU1OJjo5m85ZPgYoh6uL3mw5C9ExI/eoKBzu2DAob12JlClJN3ihq/8dguzMKEREREZF6Yu/evQwfPpzCwkLKysqu2LbMAkUlMHwe7D1+uVZWOPKK0+t0JwWpJi8IGEPt/iicdk4pIiIiIlIvTJkyheLiYqxWa7XaW6xQXApT/3GFRlnvOae4esKtQWrLli0MHz6c9u3bYxgGH330UYX9VquVJ598knbt2tGsWTOGDBnC0aNHK7Q5deoU999/P35+fgQEBDB69GjOnj1bh1fRGMwHBtTyGI1rqFZERESkqTpy5AhpaWlXHYm6VJkFUvfD0dzLNCg6Wfvi6hG3Bqlz587Ru3dvFixYUOX+F154gVdeeYXXXnuNL774Al9fX2JjYyksLLS3uf/++9m/fz/r169n9erVbNmyhbFjx9bVJTQSZuBftTxG4xqqFREREWmqlixZgodHzZY89zDB4lQnF1RPuXXVvjvvvJM777yzyn1Wq5X58+fz+OOP85vf/AaAt956i7Zt2/LRRx9x3333cfDgQdauXcvOnTvp27cvAK+++iq//OUvmTt3Lu3bt6+za2n4Hqtl/3eB2c4oRERERETcaNeuXQ6PRpUrs8DuzMvs9Glb86LqoXp7j1RmZia5ubkMGTLEvs3f35+bbrqJbdu2AbBt2zYCAgLsIQpgyJAhmEwmvvjii8seu6ioiIKCggqvpi0PWFbLY2Q5oQ4RERERcbf8/No8ZxROn7/Mjo731eq49U29DVK5ubbJlW3bVkyubdu2te/Lzc0lODi4wn5PT09atWplb1OVpKQk/P397a/Q0FAnV9/QvA5YrtrqykrRfVIiIiIiDZ+/f+2eMxrQvKqtJrh+cq2OW9/U2yDlSo899hj5+fn21zfffOPuktwsleoEqSNHYNo0GDgQ+vSxfZ02zbbdZrnrShQRERGROtG3b99a3SMVFVbFDr/rwatl7QqrZ+ptkAoJCQHg5MmKq3ucPHnSvi8kJITvvvuuwv7S0lJOnTplb1MVs9mMn59fhVfTduXh2717YfBguP56mD8fNm+GPXtsX+fPt20fMgT27v2gLooVERERERcaO3Zsre6RemRwFTsKDsPGWCgrql1x9Ui9DVJhYWGEhISQmvrzsh8FBQV88cUXREdHAxAdHc3p06fZvXu3vU1aWhoWi4WbbrqpzmtuuC4/fJuaCtHRttAEcOn/U+XvN22C6OjdFX5eIiIiItLwREREMGjQIIdHpTxMMKQ7dKlyPMMC330KuxOcUWK94NYgdfbsWfbs2cOePXsA2wITe/bsITs7G8MwSEhI4JlnnuHf//43+/bt48EHH6R9+/bcfffdAHTr1o077riDhx9+mB07dvDZZ58xceJE7rvvPq3Y55DBVPVHYe9eGD4cCgsrB6hLlZVBUZGV4cOHs3fvXteUKSIiIiJ14qWXXsLb2xuTqXpxwWSAtyfMvf9KrSxwbBkUNo776t0apHbt2kWfPn3o06cPYHuCcp8+fXjyyScB+Mtf/sKkSZMYO3Ysv/jFLzh79ixr167Fx8fHfox//OMfdO3alcGDB/PLX/6SAQMGsGTJErdcT8M1iqr+KEyZAsXFUM0HWmOxQHFxIVOnTnVueSIiIiJSp3r37s3HH3+M2Wy+6siUhwnMXvDxVOjd6SoHtlrg68ZxX71htVb3n8mNV0FBAf7+/uTn5zfh+6XGA4sB2x+HI0ds9z7V1JEjR+jSpYtTKhMRERER99i7dy9Tp04lNTUVDw+PCvdOeZgMyixWhnS3jURdNUSVCx4IQza6pF5nqG42cOsDeaU+mQ8cALYAsGQJeHhcfUpfVTw8YPHihcyd+zdnFigiIiIidax3795s2LCBo0ePsnjxYnbv3s3p06cJCAggqvUhHrkl9zL3RF3Bd5tgx3iImg8eZhdUXTc0IoVGpH5WCEwGljJw4M8LTNTEwIHebNyYBzTl76eIiIhII5Y2DHLX17CzCYJvhZiUehemqpsN6u2qfeIOPsASIJv8/Jo9O6Dc6dPFQFeg8SxxKSIiIiIXCal6wbLqafir+ClISRVC8fe/pVZHCAgAyAHGOaEeEREREal3wkeBUZs40bBX8VOQkir17dsPD4+a/fHw8ICoqPJ3bwEN838OEREREbkCnyC4bgy1ihQNeBU/BSmpku2J1pYa9S0rg0ceKX9nARrm/xwiIiIichVR8yFoALWa4peb5sSC6o6ClFTp5ydaGw718/CAIUOg4srnDfN/DhERERG5Cg8zDEqBzmMBx/7daFdy2pkV1RkFKbks2xOtPanmA60xmcDbG+bOvXTPaSdXJiIiIiL1hocP9FsEwbfVrL9XgFPLqSsKUnJZtida98Rsto00XYmHB5jN8PHH0Lv3pXsDXFShiIiIiNQb7e/E8XhhgpBBrqjG5RSk5IoGD/Zk2zYYOND2/tJAVf4+Jga2bYPBgy89gglomP9ziIiIiIgDarKKn+EB4aNdU4+Lebq7AKnv/OndGzZsgKNHYfFi2L0bTp+2LXEeFWVbWKLiPVEX8wAa5v8cIiIiIuKA8lX8MpZgW3Dsakxw3WjwaePqylxCQUquYjCQCljo0qWq+5+uxIQtRDXM/zlERERExEFR8yH/AORt5cphygTBt9raN1Ca2idXMYqa/TExgFuB+U6tRkRERETqsYtX8TM8qfzvSJNte+exEJNia99AaURKriIIGANUd4gWbCHqEWwhquH+zyEiIiIi1VSYB8eWw8k0KMkHL3/oPgOswA87bEucewXYFpYIH2WbBtjAKUhJNcwHDgBXG6IFCAO2ANe4uCYRERERcbuyQtidYAtRVgsV/q2Ym2pbfOK6MbYpfA149Kkqmton1WAGUoCx2LJ3FUO0eALjgIMoRImIiIg0AWWFkBYLGUvBWkrlX7hbbNszlsDGWCgrckeVLqMgJdXkAywCTgBzgGFAvwtf51zYvghN5RMRERFpInYnVmNRCWz7v/vUNnLViBhWq9Xq7iLcraCgAH9/f/Lz8/Hz83N3OSIiIiIi9VthHnzY/sJIVDUZnnBPTr1f7ry62UAjUiIiIiIi4pivX79wT5QDrBb4erlr6nEDLTYhIiIiIlLVqnMhgxvNCnNOl2t7zqhjLJCbBpHTXVFRnVOQEhEREZGm62qrzu19vNGuOlcrJfk17HfaqWW4k4KUiIiIiDRN5avOXXbBBIstXGUsgYKDDf4Bsk7l5V/DfgFOLcOddI+UiIiIiDRNTXzVuVoJGYzjUcJkeyBvI6EgJSIiIiJNT2EeHFtG9e/zsdjaF37vyqoajvBRtoftOsLwgPDRrqnHDRSkRERERKTp0apzteMTZLt3rNpxwgTXja73S587QkFKnCwPeA7bg3pvuvD1+QvbRUREROqJ2qw6JzZR8yFoAFePFCYIvtXWvhFRkBInKQTGAe2BvwLrgR0Xvv7vhe3jgSJ3FSgiIiLyM606V3seZhiUAp3H2h62WylamGzbO49tlAt1aNU+cYJCIBa4woo3WIAlwEEgBWhc/yOJiIhIA6NV55zDwwf6LYJes23TJXPTbGHTK8C2sEQjfg6XgpQ4QSKXD1EXswCfAgnAIhfXJCIi/7+9+4+t6q7/OP66l9LbArYFCu3KWqiODDcQkY7awTSTxg7J5q9MJRXLmBImc3Qs2JF9tyVfM0GXKFOxOnXMxDnmEoZK5gi2OCDfjkqhsO5HBxkCTtqC2B9sAwr3/f2j7ZE7fpTT++PcH89HcpP1nM9t3of36W1fO+e+L4AryJ87hNv7kmvqXERljOv7oN0k+bDdq8GtfQjTcUkuJ97o15KYeAMAADzE1DmEiSCFMD2lIb1RU0y8AQAAHmLqHMJEkEKYhjjxRky8AQAAHkvxqXMID0EKYRrixBt1RrIIAAAA91J86hzCw7AJhGmIE2+UE8kiAAAAhiaFp84hPAQphGmu3N/e55fExBsAABBHUnDqHMLDrX0I02K5P42GSWLiDQAAABIXQQphGidpkYv1fvWFKCbeAAAAIHERpBCm05LedLF+tqS10SkFAAAAiBGCFMJ0v6T/c7F+iiQm3gAAACCxEaQQhuOSfi13gybWSzoRnXIAAACAGCFIIQxPaWgfxvubKNQCAAAAxA5BCmFwO/Zc/evro1ALAAAAEDsEKYSha4jP64xkEQAAAEDMEaQQhuwhPi8nkkUAAAAAMUeQQhjmyv0p5Jf0mSjUAgAAAMQOQQphWCz3p9Aw9X0gLwAAAJC4CFIIwzhJ39TVn0Z+9YWo3KhVBAAAAMRC0gSpdevWadKkScrIyFBpaakaGxu9LilFrJU0R4OfSn5Jt/SvBwAAABJbUgSp5557TitWrNCjjz6qPXv2aPr06aqoqFBHR4fXpaWAgKQtkpZIStPFp5S/f/uS/nWBmFYHAAAARIPPzMzrIsJVWlqqm266ST/72c8kScFgUIWFhfrOd76jBx98cNDnd3d3Kzs7W11dXcrKyop2uUnsuPo+pLdefSPOc9Q3WGKx+m4DBAAAAOLb1WaDtBjWFBVnz55VU1OTVq1a5Wzz+/0qLy9XQ0PDJZ9z5swZnTlzxvm6u7s76nWmhnGSavofAAAAQPJK+Fv7Tpw4ofPnzysvLy9ke15entra2i75nNWrVys7O9t5FBYWxqJUAAAAAEki4YPUUKxatUpdXV3O4+jRo16XBAAAACCBJPytfbm5uRo2bJja29tDtre3tys/P/+SzwkEAgoEGHoAAAAAYGgS/opUenq6Zs6cqbq6OmdbMBhUXV2dysrKPKwMAAAAQLJK+CtSkrRixQpVVVWppKREs2bN0tq1a/Xuu+/qrrvu8ro0AAAAAEkoKYLUV7/6VR0/flyPPPKI2tra9PGPf1wvvfTSRQMoAAAAACASkuJzpMLF50gBAAAAkK4+GyT8e6QAAAAAINaS4tY+pJrjkn4jqV5Sl6RsSXMlLVbfhwIDAAAA0UWQQgI5LalafSEq2P8YUCfpfyR9U9JaSYy3BwAAQPQQpJAgTkuqkLRToQFqwECwelLSG5K2iDAFAACAaOE9UkgQ9+vyIepCQUk71HflCgAAAIgOghQSwHFJv9bgIWpAsH/9iahVBAAAgNRGkEICeEpXH6IGBNX3XioAAAAg8ghSSAB1GlqQqo9CLQAAAABBCgmha4jP64xkEQAAAICDIIUEkD3E5+VEsggAAADAQZBCApgr96eqX9JnolALAAAAQJBCQlgs96fqMEl3R6EWAAAAgCCFhDBO0jd19aerX30hKjdqFQEAACC1EaSQINZKmqPBT1m/pFv61wMAAADRQZBCgghI2iJpiaQ0XXzq+vu3L+lfF4hpdQAAAEgtaV4XAFy9DEm1kv5XfR/SW6++Eec56hsssVh9twECAAAA0UWQQgIaJ6mm/wEAAADEHrf2AQAAAIBLBCkAAAAAcIkgBQAAAAAuEaQAAAAAwCWCFAAAAAC4RJACAAAAAJcIUgAAAADgEkEKAAAAAFwiSAEAAACASwQpAAAAAHCJIAUAAAAALqV5XUA8MDNJUnd3t8eVAAAAAPDSQCYYyAiXQ5CS1NPTI0kqLCz0uBIAAAAA8aCnp0fZ2dmX3e+zwaJWCggGg2ptbdUNN9ygo0ePKisry+uSoL7/G1BYWEhP4gx9iT/0JP7Qk/hEX+IPPYlPqd4XM1NPT48KCgrk91/+nVBckZLk9/s1YcIESVJWVlZKnjDxjJ7EJ/oSf+hJ/KEn8Ym+xB96Ep9SuS9XuhI1gGETAAAAAOASQQoAAAAAXCJI9QsEAnr00UcVCAS8LgX96El8oi/xh57EH3oSn+hL/KEn8Ym+XB2GTQAAAACAS1yRAgAAAACXCFIAAAAA4BJBCgAAAABcIkgBAAAAgEsEKUnr1q3TpEmTlJGRodLSUjU2NnpdUtJavXq1brrpJn3oQx/S+PHj9YUvfEGtra0ha06fPq1ly5Zp7NixGjVqlL785S+rvb09ZM2RI0c0f/58jRgxQuPHj9fKlSt17ty5WB5K0lqzZo18Pp+qq6udbfTEG++8846+/vWva+zYscrMzNS0adO0e/duZ7+Z6ZFHHtE111yjzMxMlZeX68CBAyHf4+TJk6qsrFRWVpZycnJ0991369SpU7E+lKRw/vx5PfzwwyouLlZmZqY+8pGP6Hvf+54unNlET6Jv+/btuv3221VQUCCfz6dNmzaF7I9UD/bv369bbrlFGRkZKiws1A9/+MNoH1rCulJPent7VVNTo2nTpmnkyJEqKCjQN77xDf3rX/8K+R70JPIG+1m50NKlS+Xz+bR27dqQ7fRlEJbiNmzYYOnp6fbUU0/Za6+9Zt/61rcsJyfH2tvbvS4tKVVUVNj69eutpaXFmpub7XOf+5wVFRXZqVOnnDVLly61wsJCq6urs927d9snP/lJu/nmm539586ds6lTp1p5ebnt3bvXXnzxRcvNzbVVq1Z5cUhJpbGx0SZNmmQf+9jHbPny5c52ehJ7J0+etIkTJ9qiRYts165d9vbbb9uWLVvs4MGDzpo1a9ZYdna2bdq0yfbt22d33HGHFRcX2/vvv++sue2222z69On2yiuv2I4dO+y6666zBQsWeHFICe+xxx6zsWPH2ubNm+3QoUP2/PPP26hRo+yJJ55w1tCT6HvxxRftoYceso0bN5oke+GFF0L2R6IHXV1dlpeXZ5WVldbS0mLPPvusZWZm2i9/+ctYHWZCuVJPOjs7rby83J577jl78803raGhwWbNmmUzZ84M+R70JPIG+1kZsHHjRps+fboVFBTYj3/845B99OXKUj5IzZo1y5YtW+Z8ff78eSsoKLDVq1d7WFXq6OjoMEn28ssvm1nfC+7w4cPt+eefd9a88cYbJskaGhrMrO+Fwe/3W1tbm7OmtrbWsrKy7MyZM7E9gCTS09NjkydPtq1bt9qnP/1pJ0jRE2/U1NTYnDlzLrs/GAxafn6+Pf744862zs5OCwQC9uyzz5qZ2euvv26S7O9//7uz5i9/+Yv5fD575513old8kpo/f74tXrw4ZNuXvvQlq6ysNDN64oUP/nEYqR78/Oc/t9GjR4e8ftXU1Nj1118f5SNKfFf6g31AY2OjSbLDhw+bGT2Jhcv15Z///KdNmDDBWlpabOLEiSFBir4MLqVv7Tt79qyamppUXl7ubPP7/SovL1dDQ4OHlaWOrq4uSdKYMWMkSU1NTert7Q3pyZQpU1RUVOT0pKGhQdOmTVNeXp6zpqKiQt3d3XrttddiWH1yWbZsmebPnx/yby/RE6/86U9/UklJie68806NHz9eM2bM0K9+9Stn/6FDh9TW1hbSl+zsbJWWlob0JScnRyUlJc6a8vJy+f1+7dq1K3YHkyRuvvlm1dXV6a233pIk7du3Tzt37tS8efMk0ZN4EKkeNDQ06FOf+pTS09OdNRUVFWptbdV//vOfGB1N8urq6pLP51NOTo4keuKVYDCohQsXauXKlbrxxhsv2k9fBpfSQerEiRM6f/58yB9/kpSXl6e2tjaPqkodwWBQ1dXVmj17tqZOnSpJamtrU3p6uvPiOuDCnrS1tV2yZwP74N6GDRu0Z88erV69+qJ99MQbb7/9tmprazV58mRt2bJF99xzj+677z799re/lfTff9crvX61tbVp/PjxIfvT0tI0ZswY+jIEDz74oL72ta9pypQpGj58uGbMmKHq6mpVVlZKoifxIFI94DUtek6fPq2amhotWLBAWVlZkuiJV37wgx8oLS1N99133yX305fBpXldAFLXsmXL1NLSop07d3pdSko7evSoli9frq1btyojI8PrctAvGAyqpKRE3//+9yVJM2bMUEtLi37xi1+oqqrK4+pS0x/+8Ac988wz+v3vf68bb7xRzc3Nqq6uVkFBAT0BrkJvb6++8pWvyMxUW1vrdTkprampSU888YT27Nkjn8/ndTkJK6WvSOXm5mrYsGEXTR9rb29Xfn6+R1WlhnvvvVebN2/Wtm3bdO211zrb8/PzdfbsWXV2doasv7An+fn5l+zZwD6409TUpI6ODn3iE59QWlqa0tLS9PLLL+snP/mJ0tLSlJeXR088cM011+iGG24I2fbRj35UR44ckfTff9crvX7l5+ero6MjZP+5c+d08uRJ+jIEK1eudK5KTZs2TQsXLtT999/vXMmlJ96LVA94TYu8gRB1+PBhbd261bkaJdETL+zYsUMdHR0qKipyfvcfPnxYDzzwgCZNmiSJvlyNlA5S6enpmjlzpurq6pxtwWBQdXV1Kisr87Cy5GVmuvfee/XCCy+ovr5excXFIftnzpyp4cOHh/SktbVVR44ccXpSVlamV199NeSHe+BF+YN/eGJwc+fO1auvvqrm5mbnUVJSosrKSue/6UnszZ49+6KPBnjrrbc0ceJESVJxcbHy8/ND+tLd3a1du3aF9KWzs1NNTU3Omvr6egWDQZWWlsbgKJLLe++9J78/9NfmsGHDFAwGJdGTeBCpHpSVlWn79u3q7e111mzdulXXX3+9Ro8eHaOjSR4DIerAgQP661//qrFjx4bspyext3DhQu3fvz/kd39BQYFWrlypLVu2SKIvV8XraRde27BhgwUCAXv66aft9ddftyVLllhOTk7I9DFEzj333GPZ2dn2t7/9zY4dO+Y83nvvPWfN0qVLraioyOrr62337t1WVlZmZWVlzv6BUduf/exnrbm52V566SUbN24co7Yj6MKpfWb0xAuNjY2WlpZmjz32mB04cMCeeeYZGzFihP3ud79z1qxZs8ZycnLsj3/8o+3fv98+//nPX3LM84wZM2zXrl22c+dOmzx5MqO2h6iqqsomTJjgjD/fuHGj5ebm2ne/+11nDT2Jvp6eHtu7d6/t3bvXJNmPfvQj27t3rzMBLhI96OzstLy8PFu4cKG1tLTYhg0bbMSIESkz0tmtK/Xk7Nmzdscdd9i1115rzc3NIb/7L5z0Rk8ib7CflQ/64NQ+M/oymJQPUmZmP/3pT62oqMjS09Nt1qxZ9sorr3hdUtKSdMnH+vXrnTXvv/++ffvb37bRo0fbiBEj7Itf/KIdO3Ys5Pv84x//sHnz5llmZqbl5ubaAw88YL29vTE+muT1wSBFT7zx5z//2aZOnWqBQMCmTJliTz75ZMj+YDBoDz/8sOXl5VkgELC5c+daa2tryJp///vftmDBAhs1apRlZWXZXXfdZT09PbE8jKTR3d1ty5cvt6KiIsvIyLAPf/jD9tBDD4X8MUhPom/btm2X/D1SVVVlZpHrwb59+2zOnDkWCARswoQJtmbNmlgdYsK5Uk8OHTp02d/927Ztc74HPYm8wX5WPuhSQYq+XJnP7IKPZAcAAAAADCql3yMFAAAAAENBkAIAAAAAlwhSAAAAAOASQQoAAAAAXCJIAQAAAIBLBCkAAAAAcIkgBQAAAAAuEaQAAAAAwCWCFAAAAAC4RJACAKSURYsWyefzXfQ4ePCgtm/frttvv10FBQXy+XzatGmT1+UCAOIUQQoAkHJuu+02HTt2LORRXFysd999V9OnT9e6deu8LhEAEOfSvC4AAIBYCwQCys/Pv2j7vHnzNG/ePA8qAgAkGq5IAQAAAIBLBCkAQMrZvHmzRo0a5TzuvPNOr0sCACQYbu0DAKScW2+9VbW1tc7XI0eO9LAaAEAiIkgBAFLOyJEjdd1113ldBgAggXFrHwAAAAC4xBUpAAD6nTp1SgcPHnS+PnTokJqbmzVmzBgVFRV5WBkAIN4QpAAA6Ld7927deuutztcrVqyQJFVVVenpp5/2qCoAQDzymZl5XQQAAAAAJBLeIwUAAAAALhGkAAAAAMAlghQAAAAAuESQAgAAAACXCFIAAAAA4BJBCgAAAABcIkgBAAAAgEsEKQAAAABwiSAFAAAAAC4RpAAAAADAJYIUAAAAALj0/y7zOCgTJ6mPAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Parameters:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns:\n", + " - None\n", + " \"\"\"\n", + "\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Parameters:\n", + " - df: DataFrame\n", + " The input DataFrame containing the data for analysis.\n", + " - ax: AxesSubplot, optional\n", + " The subplot to plot the analysis results on.\n", + " - title: str, optional\n", + " The title of the plot.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Initialize the StandardScaler and SGD regression model\n", + " # with 2-degree polynomial features\n", + " sc = StandardScaler()\n", + " model = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.SGDRegressor(random_state=42, penalty=\"elasticnet\"),\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "prefix = \"e2e_test3\"\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 51c5e9467f5e63dccb0f91ce4121a301acb1b4dc Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 1 Aug 2024 23:17:40 +0000 Subject: [PATCH 35/78] models/session file formatted --- app/models/session.py | 62 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 49 insertions(+), 13 deletions(-) diff --git a/app/models/session.py b/app/models/session.py index a61784bf..22f07699 100644 --- a/app/models/session.py +++ b/app/models/session.py @@ -1,5 +1,35 @@ class Session: - def __init__(self, id, title, description, user_id, created_date, website_url, screen_record_url, webcam_record_url, heatmap_url, calib_points, iris_points): + """ + Represents a session in the eye tracking application. + + Attributes: + id (int): The unique identifier of the session. + title (str): The title of the session. + description (str): The description of the session. + user_id (int): The user ID associated with the session. + created_date (datetime): The date and time when the session was created. + website_url (str): The URL of the website being tracked. + screen_record_url (str): The URL of the screen recording for the session. + webcam_record_url (str): The URL of the webcam recording for the session. + heatmap_url (str): The URL of the heatmap image for the session. + calib_points (list): The calibration points used in the session. + iris_points (list): The iris tracking points recorded in the session. + """ + + def __init__( + self, + id, + title, + description, + user_id, + created_date, + website_url, + screen_record_url, + webcam_record_url, + heatmap_url, + calib_points, + iris_points, + ): self.id = id self.title = title self.description = description @@ -13,16 +43,22 @@ def __init__(self, id, title, description, user_id, created_date, website_url, s self.iris_points = iris_points def to_dict(self): + """ + Converts the session object to a dictionary. + + Returns: + dict: A dictionary representation of the session object. + """ return { - u'id': self.id, - u'title': self.title, - u'description': self.description, - u'user_id': self.user_id, - u'created_date': self.created_date, - u'website_url': self.website_url, - u'screen_record_url': self.screen_record_url, - u'webcam_record_url': self.webcam_record_url, - u'heatmap_url': self.heatmap_url, - u'callib_points': self.calib_points, - u'iris_points': self.iris_points - } \ No newline at end of file + "id": self.id, + "title": self.title, + "description": self.description, + "user_id": self.user_id, + "created_date": self.created_date, + "website_url": self.website_url, + "screen_record_url": self.screen_record_url, + "webcam_record_url": self.webcam_record_url, + "heatmap_url": self.heatmap_url, + "callib_points": self.calib_points, + "iris_points": self.iris_points, + } From 43f583e115b780008ad580595b0f4134017f4c85 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 1 Aug 2024 23:24:43 +0000 Subject: [PATCH 36/78] eyeinfo file docs added --- app/services/calib_validation/eyeInfo.py | 177 ++++++++++++++++++----- 1 file changed, 141 insertions(+), 36 deletions(-) diff --git a/app/services/calib_validation/eyeInfo.py b/app/services/calib_validation/eyeInfo.py index 69851fa3..f4db0f56 100644 --- a/app/services/calib_validation/eyeInfo.py +++ b/app/services/calib_validation/eyeInfo.py @@ -1,12 +1,45 @@ -# from screeninfo import get_monitors +import numpy as np +import pandas as pd import seaborn as sns import matplotlib.pyplot as plt -import pandas as pd -import numpy as np + +# from screeninfo import get_monitors class EyeInfo: - def __init__(self, calib_points=[], dataset='./data.csv', screen_width=0, screen_height=0, is_right=False, is_left=False): + """ + Class representing eye information for calibration and prediction. + + Attributes: + calib_points (list): List of calibration points. + dataset (str): Path to the dataset file. + screen_width (int): Width of the screen. + screen_height (int): Height of the screen. + is_right (bool): Flag indicating if the eye is the right eye. + is_left (bool): Flag indicating if the eye is the left eye. + right_eye_df (pandas.DataFrame): DataFrame containing right eye data. + left_eye_df (pandas.DataFrame): DataFrame containing left eye data. + prediction_df (pandas.DataFrame): DataFrame containing prediction data. + calib_df (pandas.DataFrame): DataFrame containing calibration data. + palette (list): List of colors for plotting. + + Methods: + init_eye(): Initializes the calibration points and eye points. + init_calib_points(): Initializes the calibration points DataFrame. + init_points(): Initializes the eye points DataFrame. + plot(): Plots the eye data. + + """ + + def __init__( + self, + calib_points=[], + dataset="./data.csv", + screen_width=0, + screen_height=0, + is_right=False, + is_left=False, + ): self.is_right = is_right self.is_left = is_left self.dataset = dataset @@ -22,52 +55,96 @@ def __init__(self, calib_points=[], dataset='./data.csv', screen_width=0, scree self.screen_height = screen_height self.palette = [ - 'blue', - 'red', - 'green', - 'yellow', - 'lightgreen', - 'purple', - 'orange', - 'pink', - 'turquoise' + "blue", + "red", + "green", + "yellow", + "lightgreen", + "purple", + "orange", + "pink", + "turquoise", ] def init_eye(self): + """ + Initializes the calibration points and eye points. + """ self.init_calib_points() self.init_points() def init_calib_points(self): + """ + Initializes the calibration points DataFrame. + """ if self.calib_points: post_calib = [] for point in self.calib_points: - calibrated_point = { - "screen_x": point["x"], - "screen_y": point["y"] - } + calibrated_point = {"screen_x": point["x"], "screen_y": point["y"]} post_calib.append(calibrated_point) df = pd.DataFrame(post_calib) self.calib_df = df def init_points(self): + """ + Initializes the eye points DataFrame. + """ try: data = pd.read_csv(self.dataset) if self.is_right: - self.prediction_df = data[[ - 'screen_x', 'screen_y', 'right_iris_x', 'right_iris_y']] + self.prediction_df = data[ + ["screen_x", "screen_y", "right_iris_x", "right_iris_y"] + ] elif self.is_left: - self.prediction_df = data[[ - 'screen_x', 'screen_y', 'left_iris_x', 'left_iris_y']] + self.prediction_df = data[ + ["screen_x", "screen_y", "left_iris_x", "left_iris_y"] + ] else: - self.prediction_df = data[[ - 'screen_x', 'screen_y', 'right_iris_x', 'right_iris_y', 'left_iris_x', 'left_iris_y']] + self.prediction_df = data[ + [ + "screen_x", + "screen_y", + "right_iris_x", + "right_iris_y", + "left_iris_x", + "left_iris_y", + ] + ] except FileNotFoundError: print(f"File {self.dataset} not found.") except Exception as e: print(f"An error occurred while reading the CSV file: {str(e)}") - def plot(self, datasets, keys_x, keys_y, is_subset, subset_size, lock_plot, eyes_only, ax, display_centroid, colors=[]): + def plot( + self, + datasets, + keys_x, + keys_y, + is_subset, + subset_size, + lock_plot, + eyes_only, + ax, + display_centroid, + colors=[], + ): + """ + Plots the eye data. + + Args: + datasets (list): List of DataFrames containing eye data. + keys_x (list): List of x-axis keys for each dataset. + keys_y (list): List of y-axis keys for each dataset. + is_subset (bool): Flag indicating if the data is a subset. + subset_size (int): Size of each subset. + lock_plot (bool): Flag indicating if the plot should be locked to screen dimensions. + eyes_only (bool): Flag indicating if only eye points should be plotted. + ax (matplotlib.axes.Axes): Axes object for plotting. + display_centroid (bool): Flag indicating if centroid should be displayed. + colors (list): List of colors for each dataset. + + """ sns.set(style="whitegrid") for i in range(len(datasets)): if is_subset: @@ -82,31 +159,59 @@ def plot(self, datasets, keys_x, keys_y, is_subset, subset_size, lock_plot, eyes centroid_y = subset_df[keys_y[i]].mean() distances = np.sqrt( - (x_values - centroid_x)**2 + (y_values - centroid_y)**2) + (x_values - centroid_x) ** 2 + (y_values - centroid_y) ** 2 + ) max_distance = max_distance = np.max(distances) - centroid_df = pd.DataFrame( - {'x': [centroid_x], 'y': [centroid_y]}) + centroid_df = pd.DataFrame({"x": [centroid_x], "y": [centroid_y]}) if not eyes_only: sub_calib_df = self.calib_df.iloc[[j]] - sns.scatterplot(data=sub_calib_df, x='screen_x', - y='screen_y', marker='*', color=self.palette[j], s=300) + sns.scatterplot( + data=sub_calib_df, + x="screen_x", + y="screen_y", + marker="*", + color=self.palette[j], + s=300, + ) if display_centroid: - sns.scatterplot(data=centroid_df, x='x', y='y', markers='x', - s=max_distance, color=self.palette[j], alpha=0.5) + sns.scatterplot( + data=centroid_df, + x="x", + y="y", + markers="x", + s=max_distance, + color=self.palette[j], + alpha=0.5, + ) circle = plt.Circle( - (centroid_x, centroid_y), max_distance, color=self.palette[j], fill=False) + (centroid_x, centroid_y), + max_distance, + color=self.palette[j], + fill=False, + ) plt.gca().add_patch(circle) sns.scatterplot( - data=subset_df, x=keys_x[i], y=keys_y[i], color=self.palette[j], ax=ax, s=10) + data=subset_df, + x=keys_x[i], + y=keys_y[i], + color=self.palette[j], + ax=ax, + s=10, + ) else: sns.scatterplot( - data=datasets[i], x=f'{keys_x[i]}', y=f'{keys_y[i]}', color=colors[i], ax=ax) + data=datasets[i], + x=f"{keys_x[i]}", + y=f"{keys_y[i]}", + color=colors[i], + ax=ax, + ) if lock_plot: plt.xlim(0, self.screen_width) plt.ylim(0, self.screen_height) - ax.set_xlabel('') - ax.set_ylabel('') + ax.set_xlabel("") + ax.set_ylabel("") ax.grid(True) From 2932a8458c81faf1eb0218c8c0dd0077a32e3920 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 20:04:17 +0000 Subject: [PATCH 37/78] minor change to doc string --- app/services/calib_validation/eyeInfo.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/app/services/calib_validation/eyeInfo.py b/app/services/calib_validation/eyeInfo.py index f4db0f56..5bb32262 100644 --- a/app/services/calib_validation/eyeInfo.py +++ b/app/services/calib_validation/eyeInfo.py @@ -28,7 +28,6 @@ class EyeInfo: init_calib_points(): Initializes the calibration points DataFrame. init_points(): Initializes the eye points DataFrame. plot(): Plots the eye data. - """ def __init__( @@ -143,7 +142,6 @@ def plot( ax (matplotlib.axes.Axes): Axes object for plotting. display_centroid (bool): Flag indicating if centroid should be displayed. colors (list): List of colors for each dataset. - """ sns.set(style="whitegrid") for i in range(len(datasets)): From 05c901d158fc4ae61244efa34f325ed8fe7d9b13 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 20:31:03 +0000 Subject: [PATCH 38/78] parameters to args --- .../calib_validation/data_visualize.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/app/services/calib_validation/data_visualize.ipynb b/app/services/calib_validation/data_visualize.ipynb index f951f385..23017e4f 100644 --- a/app/services/calib_validation/data_visualize.ipynb +++ b/app/services/calib_validation/data_visualize.ipynb @@ -385,12 +385,12 @@ " \"\"\"\n", " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", "\n", - " Parameters:\n", - " d (float): The distance.\n", - " h (float): The bandwidth.\n", + " Args:\n", + " - d (float): The distance.\n", + " - h (float): The bandwidth.\n", "\n", " Returns:\n", - " float: The quartic KDE value.\n", + " float: The quartic KDE value.\n", " \"\"\"\n", " dn = d / h\n", " P = (15 / 16) * (1 - dn**2) ** 2\n", @@ -497,12 +497,12 @@ " \"\"\"\n", " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", "\n", - " Parameters:\n", - " d (float): The distance.\n", - " h (float): The bandwidth.\n", + " Args:\n", + " - d (float): The distance.\n", + " - h (float): The bandwidth.\n", "\n", " Returns:\n", - " float: The quartic KDE value.\n", + " float: The quartic KDE value.\n", " \"\"\"\n", " dn = d / h\n", " P = (15 / 16) * (1 - dn**2) ** 2\n", From 6677662e4e66416c243942d714641022b98b471d Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 20:43:58 +0000 Subject: [PATCH 39/78] mix notebook added --- app/services/calib_validation/mix.ipynb | 344 ++++++++++++++++++------ 1 file changed, 255 insertions(+), 89 deletions(-) diff --git a/app/services/calib_validation/mix.ipynb b/app/services/calib_validation/mix.ipynb index 0647702b..1382ad4d 100644 --- a/app/services/calib_validation/mix.ipynb +++ b/app/services/calib_validation/mix.ipynb @@ -1,60 +1,82 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mix Notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "from sklearn import linear_model\n", + "# Necessary imports for the program\n", "import csv\n", - "import matplotlib.pyplot as plt\n", - "from eyeInfo import EyeInfo\n", "import numpy as np\n", - "import seaborn as sns" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "from eyeInfo import EyeInfo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# coloque o prefixo do nome na variavel prefix para analisar\n", - "prefix = ''\n", - "# quantidade de dados por ponto de calibração\n", + "# set the name prefix in the variable prefix for analysis\n", + "prefix = 'e2e_test3'\n", + "# number of data points per calibration point\n", "subset_size = 100\n", - "# divide os clusters em cores\n", + "\n", + "# configuration for the program\n", "is_subset = True\n", - "# determina se o grafico deve ou nao ficar travado nas dimensoes upper_lim_x e upper_lim_y\n", "lock_plot = False\n", - "# determina se o grafico vai mostrar ou nao a distancia do centroide\n", - "display_centroid = True\n", "\n", + "# display configuration\n", + "display_centroid = True\n", "display_train_data = True\n", - "display_predict_data = True\n", - "display_cost = True\n", "display_prediction = True\n", "display_label = True" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ + "# Set the name of the csv files\n", "output = './csv/output/output.csv'\n", "output_right = './csv/output/outright.csv'\n", "output_left = './csv/output/outleft.csv'\n", "train_input = f'./csv/data/{prefix}_fixed_train_data.csv'\n", - "predict_input = f'./csv/data/{prefix}_predict_train_data.csv'\n", "\n", + "# Column names for the csv files\n", "fieldnames = ['screen_x', 'screen_y','left_iris_x','left_iris_y','right_iris_x','right_iris_y']\n", "fieldnames_left = ['screen_x', 'screen_y', 'left_iris_x','left_iris_y']\n", "fieldnames_rigth = ['screen_x', 'screen_y', 'right_iris_x','right_iris_y']\n", "\n", + "# Color palette for the clusters\n", "full_palette = {\n", " 'calib_df': 'black',\n", " 'first': 'blue',\n", @@ -68,6 +90,7 @@ " 'ninth': 'turquoise'\n", "}\n", "\n", + "# Legend for the clusters\n", "full_legend_dict = {\n", " full_palette['first']: 'Cluster 1',\n", " full_palette['second']: 'Cluster 2',\n", @@ -81,89 +104,165 @@ "}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "def train_to_validate_calib(output, fieldnames, isLeft, isRight):\n", + "def train_to_validate_calib(output, fieldnames, isLeft, isRight, test_size=0.2):\n", + " \"\"\"\n", + " Train the model to validate calibration and write the predictions to a CSV file.\n", + "\n", + " Args:\n", + " - output (str): The path to the output CSV file.\n", + " - fieldnames (list): The field names for the CSV file header.\n", + " - isLeft (bool): Flag indicating whether the calibration is for the left eye.\n", + " - isRight (bool): Flag indicating whether the calibration is for the right eye.\n", + " - test_size (float): The proportion of the dataset to include in the test split.\n", "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + "\n", + " # Set the path for the csv files\n", " dataset_train_path = train_input\n", - " dataset_predict_path = predict_input\n", + "\n", + " # Read the data from the train csv file\n", " data = pd.read_csv(dataset_train_path)\n", + "\n", + " # Set the X and y values for the model to train\n", " if isLeft:\n", - " X = data[['left_iris_x', 'left_iris_y']]\n", - " y = data[['point_x', 'point_y']]\n", + " X = data[[\"left_iris_x\", \"left_iris_y\"]]\n", + " y = data[[\"point_x\", \"point_y\"]]\n", " elif isRight:\n", - " X = data[['right_iris_x', 'right_iris_y']]\n", - " y = data[['point_x', 'point_y']]\n", + " X = data[[\"right_iris_x\", \"right_iris_y\"]]\n", + " y = data[[\"point_x\", \"point_y\"]]\n", " else:\n", - " X = data[['left_iris_x', 'left_iris_y', 'right_iris_x', 'right_iris_y']]\n", - " y = data[['point_x', 'point_y']]\n", + " X = data[[\"left_iris_x\", \"left_iris_y\", \"right_iris_x\", \"right_iris_y\"]]\n", + " y = data[[\"point_x\", \"point_y\"]]\n", "\n", + " # Split the data into training and testing sets\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n", + "\n", + " # Train the model\n", " model = linear_model.LinearRegression()\n", - " model.fit(X, y)\n", - " dados_teste = pd.read_csv(dataset_predict_path)\n", - " if isLeft:\n", - " dados_teste.drop(['right_iris_x', 'right_iris_y'],\n", - " axis=1, inplace=True)\n", - " eye_positions = dados_teste[['left_iris_x', 'left_iris_y']].values\n", - " elif isRight:\n", - " dados_teste.drop(['left_iris_x', 'left_iris_y'], axis=1, inplace=True)\n", - " eye_positions = dados_teste[['right_iris_x', 'right_iris_y']].values\n", - " else:\n", - " eye_positions = dados_teste[[\n", - " 'left_iris_x', 'left_iris_y', 'right_iris_x', 'right_iris_y']].values\n", + " model.fit(X_train, y_train)\n", "\n", - " previsoes = model.predict(dados_teste)\n", - " combined_predictions = np.concatenate((previsoes, eye_positions), axis=1)\n", + " # Make predictions on the test set\n", + " predictions = model.predict(X_test)\n", + " combined_predictions = np.concatenate((predictions, X_test), axis=1)\n", "\n", - " with open(output, mode='w', newline='') as file:\n", + " # Write the predictions to the output csv file\n", + " with open(output, mode=\"w\", newline=\"\") as file:\n", + " # Write the header of the csv file\n", " writer = csv.DictWriter(file, fieldnames=fieldnames)\n", - "\n", " writer.writeheader()\n", "\n", + " # Iterate through the predictions and write them to the csv file\n", " for row in combined_predictions:\n", " if isLeft:\n", " writer.writerow(\n", - " {'screen_x': row[0], 'screen_y': row[1], 'left_iris_x': row[2], 'left_iris_y': row[3]})\n", + " {\n", + " \"screen_x\": row[0],\n", + " \"screen_y\": row[1],\n", + " \"left_iris_x\": row[2],\n", + " \"left_iris_y\": row[3],\n", + " }\n", + " )\n", " elif isRight:\n", " writer.writerow(\n", - " {'screen_x': row[0], 'screen_y': row[1], 'right_iris_x': row[2], 'right_iris_y': row[3]})\n", + " {\n", + " \"screen_x\": row[0],\n", + " \"screen_y\": row[1],\n", + " \"right_iris_x\": row[2],\n", + " \"right_iris_y\": row[3],\n", + " }\n", + " )\n", " else:\n", - " writer.writerow({'screen_x': row[0], 'screen_y': row[1], 'left_iris_x': row[2],\n", - " 'left_iris_y': row[3], 'right_iris_x': row[4], 'right_iris_y': row[5]})\n", + " writer.writerow(\n", + " {\n", + " \"screen_x\": row[0],\n", + " \"screen_y\": row[1],\n", + " \"left_iris_x\": row[2],\n", + " \"left_iris_y\": row[3],\n", + " \"right_iris_x\": row[4],\n", + " \"right_iris_y\": row[5],\n", + " }\n", + " )\n", "\n", "\n", "def extract_calib(csv_path, entries):\n", + " \"\"\"\n", + " Extracts unique calibration points from a CSV file.\n", + "\n", + " Args:\n", + " csv_path (str): The path to the CSV file.\n", + " entries (int): The number of unique points to extract.\n", + "\n", + " Returns:\n", + " list: A list of dictionaries representing the unique points, each containing the 'x' and 'y' coordinates.\n", + " \"\"\"\n", + "\n", + " # Read the data from the csv file\n", " df = pd.read_csv(csv_path)\n", "\n", + " # Initialize the unique points list\n", " unique_points = []\n", " seen = set()\n", " change = 0\n", "\n", + " # Iterate through the data and extract the unique points\n", " for index, row in df.iterrows():\n", - " point_x, point_y = row['point_x'], row['point_y']\n", + " # Extract the point x and y values\n", + " point_x, point_y = row[\"point_x\"], row[\"point_y\"]\n", + "\n", + " # Check if the point is not seen before\n", " if (point_x, point_y) not in seen:\n", - " change = change+1\n", - " unique_points.append({'x': point_x, 'y': point_y, 'order': change})\n", + " # Increment the change value\n", + " change = change + 1\n", + "\n", + " # Add the point to the unique points list and seen set\n", + " unique_points.append({\"x\": point_x, \"y\": point_y, \"order\": change})\n", " seen.add((point_x, point_y))\n", + "\n", + " # Break if we have enough unique points\n", " if len(unique_points) == entries:\n", " break\n", "\n", + " # Return the unique points\n", " return unique_points\n", "\n", "\n", "def extract_hw(csv_path):\n", + " \"\"\"\n", + " Extracts the screen height and width from a CSV file.\n", + "\n", + " Args:\n", + " csv_path (str): The path to the CSV file.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the screen height and width.\n", + " \"\"\"\n", + "\n", + " # Read the data from the csv file\n", " df = pd.read_csv(csv_path)\n", " interest = df.iloc[0]\n", - " return interest['screen_height'], interest['screen_width']" + "\n", + " # Return the screen height and width\n", + " return interest[\"screen_height\"], interest[\"screen_width\"]" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -172,54 +271,114 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ + "# Extract the calibration points and screen height and width\n", "calib_points = extract_calib(train_input, subset_size)\n", "h, w = extract_hw(train_input)\n", - "legend_dict = {key: full_legend_dict[key] for key in list(full_legend_dict)[\n", - " :len(calib_points)]}\n", - "eye = EyeInfo(calib_points=calib_points, dataset=output,\n", - " screen_height=h, screen_width=w)\n", + "\n", + "# Legend dictionary\n", + "legend_dict = {\n", + " key: full_legend_dict[key] for key in list(full_legend_dict)[: len(calib_points)]\n", + "}\n", + "\n", + "# Initialize the EyeInfo object\n", + "eye = EyeInfo(\n", + " calib_points=calib_points, dataset=output, screen_height=h, screen_width=w\n", + ")\n", "eye.init_eye()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizations" + ] + }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def full_plot(df, eyes_only):\n", + " \"\"\"\n", + " Plots the eye data from the given dataframe.\n", + "\n", + " Args:\n", + " df (pandas.DataFrame): The dataframe containing the eye data.\n", + " eyes_only (bool): If True, only plots the eye data. If False, plots both eye and centroid data.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", "\n", + " # Create the subplots\n", " fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", - " eye.plot(datasets=[df, df], keys_x=['left_iris_x', 'right_iris_x'],\n", - " keys_y=['left_iris_y', 'right_iris_y'], is_subset=is_subset, subset_size=subset_size,\n", - " lock_plot=lock_plot, eyes_only=eyes_only, ax=axs[0], display_centroid=display_centroid)\n", + "\n", + " # Both Eyes plot\n", " axs[0].set_title(\"Both Eyes - Fixed Train\")\n", - " eye.plot(datasets=[df], keys_x=['left_iris_x'], keys_y=['left_iris_y'],\n", - " is_subset=is_subset, subset_size=subset_size, lock_plot=lock_plot, eyes_only=display_train_data, ax=axs[1], display_centroid=display_centroid)\n", - " axs[1].set_title(\"Right Eye - Fixed Train\")\n", - " eye.plot(datasets=[df], keys_x=['right_iris_x'], keys_y=['right_iris_y'],\n", - " is_subset=is_subset, subset_size=subset_size, lock_plot=lock_plot, eyes_only=display_train_data, ax=axs[2], display_centroid=display_centroid)\n", - " axs[2].set_title(\"Left Eye - Fixed Train\")\n", + " eye.plot(\n", + " datasets=[df, df],\n", + " keys_x=[\"left_iris_x\", \"right_iris_x\"],\n", + " keys_y=[\"left_iris_y\", \"right_iris_y\"],\n", + " is_subset=is_subset,\n", + " subset_size=subset_size,\n", + " lock_plot=lock_plot,\n", + " eyes_only=eyes_only,\n", + " ax=axs[0],\n", + " display_centroid=display_centroid,\n", + " )\n", + "\n", + " # Left Eyes plot\n", + " axs[1].set_title(\"Left Eyes - Fixed Train\")\n", + " eye.plot(\n", + " datasets=[df],\n", + " keys_x=[\"left_iris_x\"],\n", + " keys_y=[\"left_iris_y\"],\n", + " is_subset=is_subset,\n", + " subset_size=subset_size,\n", + " lock_plot=lock_plot,\n", + " eyes_only=display_train_data,\n", + " ax=axs[1],\n", + " display_centroid=display_centroid,\n", + " )\n", + "\n", + " # Right Eye plot\n", + " axs[2].set_title(\"Right Eye - Fixed Train\")\n", + " eye.plot(\n", + " datasets=[df],\n", + " keys_x=[\"right_iris_x\"],\n", + " keys_y=[\"right_iris_y\"],\n", + " is_subset=is_subset,\n", + " subset_size=subset_size,\n", + " lock_plot=lock_plot,\n", + " eyes_only=display_train_data,\n", + " ax=axs[2],\n", + " display_centroid=display_centroid,\n", + " )\n", + " \n", " if display_label:\n", " for color, label in legend_dict.items():\n", " plt.scatter([], [], c=color, label=label)\n", - " plt.legend()\n", + " plt.legend(bbox_to_anchor=(1, 1))\n", + "\n", + " # Display the plot\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -229,17 +388,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABcwAAAHkCAYAAAAD/WxfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVhUZfsH8O/MsA8IKuCCC8imLO77iqOFqW28vm5pEfi6IS4VbpVaryRpVO6KGwruaZlpWUk/xdwq34xFBRH3FIQAWWSb+f0xzcQMAwwwAwx8P9fVZTNzlueMnnnOuc/93I9AJpPJQERERERERERERETUxAnruwFERERERERERERERA0BA+ZERERERERERERERGDAnIiIiIiIiIiIiIgIAAPmREREREREREREREQAGDAnIiIiIiIiIiIiIgLAgDkREREREREREREREQAGzImIiIiIiIiIiIiIADBgTkREREREREREREQEgAFzomqRyWT13QQiIiKqA+zziYiIGh72z0RUF4zquwFE+jB16lRcvnxZ5T0rKyt4eHhgzpw56Nu3b7W29+jRIyxbtgzLli1Du3btAAASiQR9+/ZFWFhYrdtWVrdu3XDo0KFqbbM+ubu7V/iZhYUF/ve//+H+/fsYMWIEVq1aBT8/P72259KlS3j99dexZ88e9OvXr9znVX3/APDqq69W+++1rMWLF+Py5cuIiYmp8TaIiJoiTb/RAoEAFhYWcHR0xBtvvIGXX35Z+Zm7uzvmzJmD4OBgrfehzTq//fYbtmzZgm3btlW4jKK/qcy2bdswdOhQrdumT1X1f1988QW8vb1rfH1TE5X9Xaxfvx4bNmyodH0HB4da9bVHjx7FkiVLcPr0aeX1HRER1c7UqVMBAFFRUbXeVmRkJLZt24anT59i5syZ6NevH/tn9s9EdYIBc2q0PDw8sHz5cgBAaWkp/vrrL+zfvx+BgYE4evQoXF1dtd7W+fPncebMGb20TZ1YLNbZfurKuHHj8O9//7vc+0KhfBCLvb09Dh48iA4dOtR108pZvnw5cnNzla8/+OAD5fsKLVq0qNU+Zs+eXeVFGhERaabeR5aWluLRo0eIjIzEwoULYWNjg2HDhgEADh48iNatW+u8DYcPH0ZKSopWyy5btgyenp4aP3N2dtZls2qtsusPRVs3bNgAS0vLumyWRv/+978xZMgQ5evDhw/jiy++wMGDB5XvmZiY1GofPj4+OHjwIOzt7Wu1HSIi0r3c3Fx8/PHH8PHxQUBAANq1a4e1a9eyf65n7J+pqWDAnBotS0tLdO/eXeW9gQMHYsCAATh69CgWLVpUPw2D5rYZstatW1d6PCYmJg3meF1cXFReKy46dNm+hvBggIjIUFXURw4dOlTZhysC5g2hb3FxcWkQ7dCGNtcfHh4eddOYKrRu3VrlYUhsbCwA3f6dt2jRotYPyYmISD+ys7MhlUoxcuRI9OnTp9rrs3/WD/bP1FSwhjk1Kebm5jA1NYVAIFB5/+TJk/Dz80OPHj0waNAgLFu2DNnZ2QD+GQ4EACNGjMDixYuV6xUXF2P16tUYNGgQunfvjoCAANy5c0cnbf3444/RtWtXPH36VOX9TZs2oVevXigoKAAAJCUlYcaMGejZsyd69uyJoKAg3Lt3T2Wd3bt3Y9SoUfD29saQIUOwYsUKlSxrfbt//z7c3d1x9OhRlJaWYty4cejXrx8yMzOVyyxevBjdu3fHrVu3AABSqRQRERF47rnn4OXlBV9fX43D+g4cOABfX1907doVU6ZMwcOHD3XS5vXr1+O5557Dhg0b0LdvXwwePBjZ2dl49uwZwsPD8fzzz8PLyws9e/bEm2++iWvXrqkci0QiUb6WSCRYt24dPv74YwwcOBBdu3ZFYGAgbt++rZO2EhE1BaampjAxMVHpw93d3bF+/Xrl65SUFPznP/9Bz549MXDgQHz22WdYsmSJcni4Qm5uLt5991307dsXPXr0wNy5c/HkyRMA8t/wL7/8Eg8ePFD2XbWRlZUFb29vfPrppyrvFxQUoFevXti8eTMA7fs9fZNIJMprnVWrVsHd3R0XL15Ufn706FG4u7vjq6++Ur73448/ws/PD97e3hg0aBBWrlyJ/Px8le1evnwZEyZMQLdu3eDr64vz58/rpL2XLl2Cu7s7Dhw4gOHDh6Nnz574+eefAciz3vz8/NC9e3d07doVL7/8Mr799ttyx3L//n0A8r97f39/HDlyBL6+vvDy8sLLL7+Ms2fP6qStRET0j19//RVTpkxBt27d0LdvXyxatEh5f3j06FHl/dTSpUvh7u7O/pn9M/tnqlMMmFOjJZPJUFJSgpKSEhQXFyM9PR3h4eEoKirCv/71L+VymzZtwltvvYXu3btj3bp1CAoKwqlTpzB16lQ8e/YMPj4+mDVrFgD5MKjZs2cr1z158iSSk5MRFhaG5cuXIz4+HgsWLKhW29T/U0xiMm7cOBQWFuK7775TWffYsWMYPXo0zM3NkZqaiokTJyIjIwMff/wxQkNDce/ePUyaNAkZGRkAgG+++QZr1qzBa6+9hh07diAoKAjHjh3Df//731p/xwpSqVTjsWgiEokQFhaG/Px8fPzxxwDkHfmXX36JhQsXolOnTgCAFStWYN26dXjppZewZcsWjBo1Ch999BE2btyo3FZ0dDSWL1+OYcOGYdOmTejWrRvef/99nR3Xw4cPcebMGWXAxdraGgsXLsSRI0cwffp07Ny5E0uWLEFycjLefvvtSieg2bNnD27duoVVq1Zh5cqViI+Pr9dRDkREDZV6H1lYWIhbt25hyZIlyMvLU6lhXlZmZiamTJmCP//8E6tWrcJ7772H7777Dt988025Zffs2YPi4mKsXbsWb7/9NmJiYvDhhx8CkJfVGjZsGOzs7HDw4EH4+PhU2t6K+sDS0lIAgI2NDUaOHInjx4+r9BM//PAD8vPz8corrwDQrt+rrYquPyrqvxYsWABHR0csX74cRUVFePjwIUJDQ/HCCy8o2338+HEEBQWhU6dO2LhxI+bMmYOvv/4as2fPVm43ISEBAQEBsLKywrp16/D666/jrbfe0tlxAfJrtEWLFmHZsmXo0aMH9u7di2XLlmHkyJHYunUrPvnkE5iYmOCdd97Bo0ePKtxOfHw8duzYgblz52Ljxo0QiUQIDg5WJlIQEVHt/fLLL/D394eZmRk+//xzLF26FJcvX8brr7+uvAdX1MmeNWsWDh48yP65DPbP7J9J/1iShRqtX375RWPNsrfeektZByw7OxubN2/G+PHjsWzZMuUybm5ueO2113DkyBG89tpryhIbXbp0UZl0olWrVti0aROMjY0BAHfu3MHmzZuRm5tbaX2xitoGAGvXrsWoUaPg7OyMHj164NixY8r64FeuXMHt27eVE31s2LAB5ubmiIyMVO5vwIABGDlyJLZv345Fixbh8uXLaNeuHV577TUIhUL07dsXFhYWOu1YNm3ahE2bNpV7/9y5c7Czsyv3vouLC4KDgxEeHo6RI0dixYoV8PHxweTJkwEAqampOHToEN566y1Mnz4dADB48GAIBAJs3boVkydPho2NDTZt2oTRo0dj6dKlymVyc3Nx4MABnRxXSUkJFi1ahN69ewMAioqKkJeXh/feew+jR48GAPTt2xe5ubkICwvDkydPNB4vADRr1gybNm2CSCQCANy9exfr16/HX3/9hebNm+ukvUREjYGmPlIgEMDNzQ1r167F8OHDNa4XFRWFvLw8fPXVV2jVqhUAKLOl1Hl7e2P16tUA5P3m1atXlXOVdOjQAS1atNC6nJi/v7/G911dXZXB+n/96184efIkLl26hP79+wMAvvrqKwwcOBBt2rTRqt/TRV9R0fXHp59+ijFjxpR738zMDGFhYZg8eTIiIiJw5coVWFpaKuf/kMlk+OSTTzBkyBB88sknyvUcHR3h7++PM2fOwMfHB1u3bkXLli2xefNm5TVT8+bNtUoy0NbkyZMxatQo5et79+4hMDBQJdHBwcEBfn5++O233zQeLwA8ffoUR48eVV77WVhYYMqUKbh48aLGf0tERFR94eHhcHJywtatW5X3R926dcOYMWOU9+BdunQBIO+XFf0x+2c59s/sn0n/GDCnRsvT01Olw8jJycHZs2fx2WefIT8/HwsWLMDvv/+OoqIijB07VmXd3r17w8HBAZcvX8Zrr71W4T66du2q7FgAKIPpOTk5lQbMy7ZNXdn61//617/w/vvv48GDB3BwcMCXX34JJycn9OjRAwBw8eJF9O3bF2ZmZsqMbktLS/Tu3Vs5lKp///44ePAg/Pz8MHLkSAwbNgwvvvhiubI0ZalnhwuFQuUEnpqMHz8e48ePL/d+ZRcPgYGB+PHHHzF37lw0b94cH330kfKzixcvQiaTQSKRqLRFIpFg8+bN+O233+Dk5ISMjIxygZMXXnhBZwFzAMoLNUBei33Hjh0AgMePHyM1NRW3b9/GTz/9BEAeUK+It7e38mIQgLLuW0FBAQPmRERllO0j09LS8Pnnn6O4uBiff/65chSSJhcvXkSPHj2UwXJAfgOm6DPL6tWrl8rrdu3aIScnp0bt/eCDDzTe5JqZmSn/f+DAgWjbti2OHTuG/v3749GjR7hw4QLWrFmjbHtV/d7IkSPL7aO0tFQl+0wgEKj0Neoquv6obO6NHj16wN/fHxs3boRMJsOuXbtgbW0NALh16xYePXqEGTNmqLS7T58+sLS0xM8//wwfHx/89ttvGD58uMo10/PPP19pW6urbH8NQDlsPScnB7du3cKdO3dw6dIlAJX31y1atFD5Psr210REVHsFBQW4evUqAgMDlZnVANC+fXs4Ozvj559/rvQeXFvsn9k/E9UGA+bUaInFYnh7e6u8N3jwYOTn52P79u14/fXXlVnWtra25da3tbUtVz9cnYWFhcprRVBZKpVWu22ajB49Gh999BGOHTuGwMBAfPvtt8on24C87trJkydx8uTJcusqJskYPXo0pFIp9u3bh02bNmH9+vVwcHDAO++8o8ySVqd+YTFnzhwEBwdX2E57e3utjqcskUiEl156CVevXkXXrl3RsmVLleMCUOHT5cePHyuPTz3YXFGGd02JxWKV17Gxsfjoo49w69YtiMVidO7cWfnvoLKSLObm5iqvtf23QkTU1Kj3kd26dcNLL72EgIAAHD16tMJJoDIzMzXeGNva2irrkyto6r8r+w2vjJOTU5V9oFAohJ+fH3bt2oXly5fj2LFjsLS0xHPPPQdAu35PE39/f1y+fFn5um/fvpXWVdX2+kPdq6++ip07d8LOzg7dunVTvq9o9wcffKDxRj8tLQ2AfESfen9tZGSk0wfG6n+nd+/exbJly3DhwgUYGxujU6dO6Ny5M4Dq9deKBAP210REupGTkwOpVIpt27Zh27Zt5T43NTXVyX7YP7N/JqoNBsypyfHy8sLhw4dx//595RPYJ0+elMtaS09PR/v27eujiUpisRijRo3Ct99+Czc3N+Tn56vUbrWyssLAgQPx5ptvllvXyOif03vs2LEYO3Ysnj59inPnzmHbtm0ICQlBr169VDLxFL744guV1/b29jo8Krn09HSsX78eXbp0wU8//YTvvvtOOVSrWbNmAOSTlaoHrAGgbdu2ykxARa12BcXFgT7cvXsXQUFBynpr7du3h0AgwN69e5WzgxMRkW7Z2tpi2bJlmDdvHkJDQxEeHq5xudatW5cLjAPl+4n64ufnh40bN+Ls2bP49ttvMXr0aGVQQJt+T5MPPvgAeXl5ytea1q0tqVSKFStWoEOHDnjy5AnWrFmD5cuXq7R74cKF6Nu3b7l1FddZNjY25f5uZDKZ3uqOSqVSTJ8+HcbGxvjiiy/QpUsXGBkZ4ebNmzh27Jhe9klERNoRi8UQCATw9/fXGIhWD4zqG/tn9s9EmnDST2py/vjjD4hEIrRv3x7dunWDiYlJuQnBfv31Vzx8+BA9e/YEgErLkejbuHHjkJSUhN27d2PgwIEqAe6+ffvi5s2b6NKlC7y9veHt7Q0vLy9ERkbihx9+AADMnz8fQUFBAOQB9hdeeAGzZ89GSUmJ8smyOsW2FP9pCqrX1rJlyyASiRAZGYkRI0bggw8+UM6KrqgZ/tdff6m0IzMzE2vXrkVWVhYcHR3Rpk2bcpOiKsqj6EN8fDwKCwsxffp0dOjQQflUWxEsr2l2IhERVW7UqFEYMmQIvvnmG5WMrbL69OmD33//Henp6cr30tLS8Pvvv1d7f/ro9x0cHDBgwADs2bMH165dg5+fn/Izbfo9TTp16qSyfGUla2pq9+7duHLlCj766CPMmzcP+/fvx4ULF5T7b9myJe7fv1/uuiE8PByJiYkA5HXiz549qzJsOjY2FsXFxTpvLyD/HlNTUzFu3Dh4e3srkwjOnj0LgNloRET1ydLSEh4eHrh165ZK3+Hq6or169cry3Nowv75H+yfifSLGebUaOXm5qrcJBcVFSEmJgZHjhzBhAkTlEO6p0+fjo0bN8LY2BjDhw/H/fv3sXbtWri4uODVV18F8M8T2h9++AFDhw5VThqqq7apK1vvulevXnBycsLly5fx2WefqSw3e/ZsTJw4ETNmzMCkSZNgamqKgwcP4scff8S6desAyGuYL1++HB9//DGGDh2KnJwcbNiwAY6OjsqhT3Xtq6++QkxMDMLDw2FjY4Nly5Zh9OjRyhnI3d3d8dJLLynrt3t5eSE1NRWfffYZ2rVrB0dHRwgEArzzzjt4++238d5772HUqFH4/fffsX//fr2129PTE0ZGRlizZg0CAgJQVFSEo0eP4v/+7/8AAPn5+XrbNxFRU7d06VK89NJLWLlyJb788sty9TVff/117N27F4GBgcoHxZs2bUJxcXGl83Zo0qxZMzx58gRnzpxBly5dKh1pdfPmzQqHj9vZ2cHBwUH5ety4ccrJx8sOndam36sPqamp+PzzzzF+/Hj06dMHPXv2xPHjx/Huu+/i+PHjEIvFWLBggfIh+PDhw5GTk4NNmzbh8ePHyhI5QUFB+PHHHxEYGIhp06YhMzMTn3/+uUrNVF1q2bIlHBwcsHfvXrRu3RrNmjVDbGws9uzZA4D1TomI9O3Ro0eIjIws976bmxsGDhyonETz7bffxksvvYTS0lLs3LkTV69eVZkMUh37Zzn2z0T6x4A5NVqJiYmYMGGC8rWpqSk6dOiABQsWIDAwUPl+cHAwbG1tER0djYMHD8LGxgajRo3C/PnzlfW2+vXrh4EDByI8PBwXLlxARESETtum7pdfflEG6QHAx8cHmZmZ5SYU6dy5M/bu3YvPPvsMCxcuhEwmg5ubGzZu3IgRI0YAACZOnIji4mIcOHAA+/btg5mZGQYMGICQkBC9dYSVefz4MUJDQzFs2DDlZKutW7fGggULsHLlSnzzzTcYO3YsVq1aha1bt+LAgQN49OgRWrZsidGjR2P+/PnKIMnYsWMhFAqxadMmHDt2DG5ubvjwww/x1ltv6aXtHTt2RHh4ODZs2IBZs2bB2toa3bt3R1RUFKZOnYpff/0V7u7uetk3EVFT16lTJ0ydOhU7d+7E/v37MWXKFJXPmzVrhj179iA0NBQLFy6EWCzG5MmTYW5uXq5+ZlX8/Pxw5swZBAUFYe7cuSrzh6j78MMPK/zs9ddfx7vvvqt8PWzYMAgEApXsNQVt+r26JJVKsWTJElhZWSEkJASAfP6R//73vxg3bhw+/vhjfPjhh/j3v/8NsViM7du34+DBg7CwsEDPnj3xySefKEvbOTo6Ijo6GmFhYViwYAFatmyJRYsWISwsTG/t37RpE0JDQ7F48WKYmJjAxcUFmzdvxkcffYRff/0VU6dO1du+iYiaurt372LVqlXl3h83bhwGDhyIwYMHY8eOHdiwYQPmzp0LY2NjeHp6YteuXejevXuF22X/zP6ZqK4IZKwhQNSgyWQyjBkzBoMHD8bSpUvruzlEREQN0tWrV5GVlYVhw4Yp3yspKYGPjw/GjBmDJUuW1GPr5E6ePImFCxfizJkzKpNdExERUf1h/0xE6phhTtRA5ebmIjIyEnFxcbh37x6ftBIREVXi4cOHWLBgAYKCgtC3b18UFBTg4MGDePr0KcaPH1+vbfvxxx8RFxeHAwcOwM/PjzfjREREDQD7ZyKqCAPmRA2UmZkZDhw4AKlUio8++kg5bIqIiIjKe+GFF5CVlYV9+/Zhx44dMDY2Rrdu3RAdHV3ruUdq6/79+9i9ezd69eqlHD5NRERE9Yv9MxFVhCVZiIiIiIiIiIiIiIgACOu7AUREREREREREREREDQED5kREREREREREREREYMCciIiIiIiIiIiIiAhAI5j083//+x9kMhmMjY3ruylERES1UlxcDIFAgB49etR3U/SG/TYRETUW7LeJiIgMR3X6bYPPMJfJZMr/ioqK0FTmMOXxNm5N6Xib0rECPN7GTBfHqujPGrP6Psam9G9Sn/g91h6/Q93g91h7/A5rrr77tLrQFI5RE54X1cPvq3r4fVUPv6/q4fdVser0aQafYa540u3s7Ixr167BxcUFFhYW9dwq/cvPz+fxNmJN6Xib0rECPN7GTBfHGhcXp+NWNTyKftvb27te9t+U/k3qE7/H2uN3qBv8HmuP32HNsd9uvHheVA+/r+rh91U9/L6qh99XxarTbxt8hjkRERERERERERERkS4wYE5EREREREREREREBAbMiYiIiIiIiIiIiIgAMGBORERERERERERERASAAXMiIiIiIiIiIiIiIgCAUX03gIiIiIiIiIiIiKi+lJaWori4uL6bUWuFhYXKP4XCppMnbWxsDJFIpLPtMWBORERERERERERETY5MJsOjR4+QlZVV303RCalUCiMjIzx8+LBJBcwBwMbGBq1bt4ZAIKj1thgwJyIiIiIiIiIioiZHESy3t7eHhYWFToKt9am0tBSFhYUwNTXVacZ1QyaTyZCfn4+0tDQAQJs2bWq9TQbMiYiIiIiIiIiIqEkpLS1VBstbtmxZ383RidLSUgCAmZlZkwmYA4C5uTkAIC0tDfb29rU+9qaVm09ERERERERERERNnqJmuYWFRT23hHRB8feoi1r0DJgTERERERERERFRk2ToZVhITpd/jwyYExERERERERERERGBAXMiIiIiIiIiIiIig1ZSUoKoqCi89tpr6N27N/r374+AgABcvHhRZTl3d3ccPXpUZ/v97bff8Ouvv+pse+qkUimmTZuG9evX620f6hgwJyIiIiIiIiIiIjJQhYWFeP311xEZGYmJEyfiiy++QGRkJJydnfHmm2/i+PHjetv35MmTcffuXb1su6ioCEuXLkVsbKxetl8RozrdGxEREREREREREVEjVFoKxMYCf/4JtGkDDBkCiET63+/atWtx48YNHDt2DM2bN4eZmRlEIhHeffdd5ObmYuXKlZBIJBCLxfpvjI5cuXIFy5Ytw7Nnz9CsWbM63TczzHVAJpPh9K3TWHdpHU7fOg2ZTFbfTSIiIiJqNHitRUREVH3sP4nq1tGjgKMjMHw4MHmy/E9HR/n7+lRcXIwjR47Az88Pbdq0Kff5/PnzsW3bNpiZmZX7bP369ZBIJJW+d+bMGfj5+aFbt24YMGAAFi9ejOzsbADy8i4AsGTJEixevBgA8PjxYyxYsAC9e/dGv379MHPmTNy+fVu5vcWLF2Pu3LkICAhAz549sW3bNo3HdebMGQwZMgRfffUVrKysqvel1FK1M8wzMjIQFhaG2NhYFBYWok+fPli0aBGcnZ0BAO+99x4OHz6sso6DgwNiYmIAyL+0oUOHltvuqlWr4OfnV5NjqFcymQwH4g/gRPIJiE3EWP7Tcnwx/guM6DSivptGRERE1CjEpMbAN9oXpbJSiAQinJpyitdaRNQo8X6btCGTyRCTGoOE9AR42nlC4iSBQCAotxz7T6K6c/QoMG4coP5c6sED+ftffAHo62f43r17yMrKQs+ePTV+3qpVK7Rq1apG287MzMScOXOwePFi+Pj44NGjR1i4cCFWr16N0NBQnDt3DoMHD8bSpUvh5+eH/Px8TJ06FZ6enoiOjoZQKMSuXbswfvx4HD9+XNmOU6dOISQkBO+//77GQD4ALFiwoEZt1oVqB8yDgoIglUoREREBsViMtWvXwt/fH99//z3Mzc1x48YNzJw5E1OmTFGuIyoz9uD69eswNTXFjz/+qPKDXtdPCnTl7O2zuJd9Dy3NW8LBygHTekxDYnoiOyEiIiIiHUlIT0CprBQAUCor5bUWETVavN8mbWgKhA93HI6DCQcRlxYHb3tvTPCcwP6TqI6UlgLz5pUPlgPy9wQCYP584OWX9VOeRZHtbW1trfNtP378GEVFRWjbti0cHBzg4OCALVu2oLRU/ttiZ2cHQN7PWFlZ4fDhw8jJycGaNWtgZCQPO4eGhuLSpUs4dOgQgoODlW2dNm2azturK9UKmGdnZ8PBwQEzZsyAm5sbAGD27Nl4+eWXkZycDG9vb9y8eRPTp09XfmHqkpKS4OjoCHt7+9q3vgG4kXkDS2OWKjuqz3w/g4edR303i4iIiJq4qrLPtM1O07R85xadYSfUfK1X3XZow9POEyKBSHm9xWstImqMeL9N2tIUCE/LS8PUL6cq+0qZTMb+k6iOxMYC9+9X/LlMBty7J1/Ox0f3+2/RogUAICsrS+fb7tKlC8aOHYuZM2fCzs4OgwYNgo+PD5577jmNyycmJiI7Oxt9+vRReb+wsBApKSnK1x07dtR5W3WpWgFza2trhIeHK19nZmYiMjISrVu3houLC+7evYv8/Hx06tSpwm3cuHFDOZysMbiffR+BPQORW5QLSxNLpOWlYU7fOfXdLCIiImriqhqGrc0w7bLBbjsLO5Ub8f2j98Md7uX2qx4gB1Dr4eASJwlOTTmFxPREeNh5QOIkqXolIiIDw/tt0pZ6INzL3gtXH13FBK8JsDSxxIH4A0hIT8Ak70nsP4nqwJ9/6na56mrfvj1sbW1x5coV+Pr6lvs8JSUFoaGhWLJkCVxdXavcXklJicrr8PBwBAUF4ezZszh//jxCQkLQq1cv7N69u9y6UqkUTk5O2Lx5c7nPLCwslP9fURmWhqLaJVkU3n//fRw6dAgmJibYvHkzLCwskJSUBACIiorC2bNnIRQKMXToUCxYsEA5BCwpKQnNmzfHa6+9htTUVHTs2BGzZs3SWGdNWzKZDAUFBQCg/FOfZDIZfn74MxKfJKKDTQd8dO4jZUcVMTYCBQUFKst42HpgUNtB1c6mqkxdHm9DwONtvJrSsQI83sZMF8cqk8l02ldQ01bVMGxthmmXDaq/5v2ayvL3cu/h3INzuJ55XSVzXD0Qv9xnea2HgwsEAozoNILDyImoyWhI99vU8Kg/SJbKpHjnh3eUfW9gj0B42Xux/ySqIxrm2azVctUlFAoxbtw4REVFwd/fH82bN1f5fPv27YiLi4ODg0O5dY2NjZGXl6fy3p07d5T/f/XqVZw4cQJLly5Fp06d4O/vj6+//hohISHIyMhAy5YtVdZ1c3PDsWPHYGVlpcx8Ly4uxttvv41Ro0Zh9OjRujpsvapxwPyNN97AhAkTsHfvXgQFBWHfvn1ISkqCUCiEvb09tmzZgrt372L16tVITk7G7t27IZVKcevWLbi4uGDx4sWwtLTEiRMnMH36dOzatQsDBgyoUVuKi4uVs62WnXVVX1KRioknJqJUVgr/bv6qN49Z93Djxg2kSFOUyyiysDoJKs4EqKm6ON6GhMfbeDWlYwV4vI1ZbY/VxMRENw2hJq+qYdjaDNMuG1QXm4hVlm9p1RKj948ulzmuHoi3s7DTuB9dl4whImpMGtL9tkwmQ35+vo6PsGGri6SP2ibZDWg9AANay/9OI65GqPS9rS1b40WnF+vs703T96XvJEJD1pSSinRBn99XYWEhpFIpSktLlTW5a2LgQKBdOyEePABksvL/zgUCGRwcgIEDpajFbir1n//8B7GxsZgyZQpmzZqFPn36ICcnBwcOHMCxY8cQHh4OU1NT5XEqjrtr167IysrCtm3b4Ovri3PnzuHs2bOwtrZGaWkpzM3NsXfvXhgZGWHcuHEoKirCyZMn0bFjRzRr1gylpaWwsLDAzZs3kZGRgTFjxiAiIgLBwcF4++23YWVlhc2bN+Ps2bOYM2cOSktLIZPJIJPJqvWdy2QyZZsrUlpaCqlUioKCAkilUo3b0PZ3qMYBcxcXFwDywu1Xr15FdHQ0QkNDMXnyZOWTDDc3N9jZ2WH8+PGIi4tDt27dcOnSJYhEImXqvZeXF5KTk7Fjx44ad+DGxsZwcHDA7du34ejoCHNz85oellZir8YqOyMTIxOVm8A/8/9EWvs03H9yX6XDelj8EGO6jdFZGwoKCurseBsCHm/j1ZSOFeDxNma6ONabN2/quFXUlFVVxkSbMidlg+qH4g9hzyt7kFGQAfcW7rhy74rGzHH1QLx7S3eN+9G2ZIzYRIxJXpNw/v55DGw3kIFzImoSGtL9dnFxMa5du6abAzMw+kz6KJuIV9sku3Ym7VT63k7NOiE5OVljwEifyn5fujy+xqopJRXpgr6+LyMjIxQWFtZ6Ox9/LMKUKaYQCGQqQXOBQPb354UoLi5FcXGtd6WRQCBAREQE9uzZg8jISKxatQpmZmbo3LkzIiIi0LNnTzx79ky5fHFxMZ49e4Zu3bph5syZ2LVrFzZs2ICBAwdixowZ2L9/P549ewYHBwd88skniIiIwL59+yASidCnTx+sW7cORUVFAIApU6Zgz549SE5Oxueff45t27bhs88+w3/+8x9IpVJ07twZmzZtgoODA549e6YMbJdtT1VkMhlKSkoqXaewsBAlJSW4detWhctom6BWrYB5ZmYmLly4AF9fX+VMp0KhEC4uLkhLS4NQKCyX9q+ojfPo0SN069YNYrG43HZdXV1x7ty56jRFhUAgUAYnzM3NVWri6IN3K2+Vm8eNozfi7N2zsDSxxP74/fC081RZRiQQwauVl17aVRfH25DweBuvpnSsAI+3MavNsTIISLpU1TBsbYZpawqqCwQC5OfnIycnR2PmuPo6Po4+yn2VpW3JmIleE7H9yvZa1UAnIjIEDfV+29jYWBnAbyrqIumjbCJebZPsOss6Y7fRbnyb8i3EJmLM/2E+DvzrAIY4DNFlkyuk6fvS5fE1Nk0pqUgX9Pl9FRYW4uHDhzA1Na11Te0JEwATEykWLBCqTADq4AB89pkUr75qDMC4dg2ugpmZGebOnYsZM2bA1NS0wvvLxMRElddz587F3LlzVd6bNm2a8v+ff/55PP/88xXud/78+Zg/f77ytbOzMzZs2FDh8h9//HFlh6HR6dOntVrOyMgIHTp0gKmpabnPqpOgVq2A+ZMnT/DWW29h+/btGDJE/sNbXFyMxMRESCQSLFy4EGlpaYiMjFSuExcXB0D+hDw5ORkTJkzA5s2b0a9fP+Uy8fHxBtUBq98ICgVCBJ0MUrlhlDhJcHrqadzIvIGUzBSk5aVBKpVCKBTWd/OJiIiIKqSpFIqmALWz0BknJp1AUmaSSua4tvVStS0Zk1uUW+sa6EREhqCh3m8LBIImk/igTp9JH7pOsst4loG9cXuVr5Myk+DrWn7yP30q+33VVRKhIWtKSUW6oI/vSygUQigUQiQSQSQS1Xp748YBr74KxMbKJ/hs0wYYMkSgk21rS1GyRCCo2/02BCKRCEKhEObm5hofgFQnQa1aAXM3NzcMHToUK1euxMqVK2FtbY2tW7ciJycH/v7+uHbtGmbPno0NGzbgpZdeQmpqKj788EOMHTsWzs7OkEql6NSpEz788EN88MEHaN68OQ4dOoTff/8dR44cqU5T6pX6jaBMJtOYffUw9yFmn5it7CBkMhkmd51cz60nIiIiqpimUikSJ4lKEL1/q/6QSqUY4jCkxjfj2paMSc5MxsH4g5XWWiciagx4v920aFMarTq0mZukLpUbcdbRB/vj9iMuLQ7e9t6Y4DmBCYXUKIlEgI9PfbeCaqvaNcw//fRThIeHY8GCBXj69Cl69+6NvXv3om3btmjbti0+//xzREREYNu2bbCyssKLL76oTMsXCoXYsmULwsPDMX/+fOTk5MDDwwO7du2Cm5ubro+tzlSUSRWXFqeSEZWQnlAfzSMiIiLSmqZSKQBUgugnJp2ALWxrtR9NCQinb50ul9kucZLAtYWrzgIKREQNGe+3mw5tR2RpS9cB+NpSP779cfsx9cupTCgkIoNQ7YC5lZUVVqxYgRUrVmj8/IUXXsALL7xQ4fq2trZYtWpVdXdrkLzt1YYg2XvVd5OIiIiIKqUpQ009iH79yXUMNhms0/1WNAmorgMKREQNGe+3qaYaen/JhEIiMiTVDpiTKk11PhU1cSZ4ToBMJkNCegK87L0wwXNCPbeWiIiIqHIVZaiVDaJ3se0C5Oh2v1VNAkpERESGiwmFRGRIGDCvpYqyoQD5kDgOMSIiIiJDoilDTT2I3r9Vf1zPua7T/Ta02qtERESGoLIkvoaECYVEZEgYMK8l9WyoC/cvICkjCTZmNkjPT2/QHRYRERGRNtSD6Pn5+TrfR0OrvUpERGQIKkvia0iYUEhEhoQB81pSz4YyEZrg+pPr2PjLxgbfYRERERE1FA299ioREVFDxJJmRES6x4B5LUmcJNjzyh6cvHkSYhMxVp1bhbcHvq3SYcWkxiiXZaY5EREREREREemCrkua1aTEi0wmQypSEXs1Ft6tvBn7ICKDx4B5LQkEAjwpeIK9cXuV72UUZKh0WE8KnsA32peZ5kRERERERESkM+olzYY7DsfpW6drXNO8JiVefn74MyaemMhR9kTUaDBgrgPe9t6Y0WsGnhY9hZWJFbradcWeV/YgIT0BTwqe4ED8AQ6NIiIiIiIiIiKdUi9pdvrW6VrVNK9JiZfEJ4ksC0PUAJSUlCA6OhpfffUV7ty5A1NTU3h4eGD69Ono37+/cjl3d3esWrUKfn5+Otnvb7/9BplMht69e+tkewrJyclYs2YNrl69CqFQiD59+mDx4sVo27atTvejiVDve2gCpDIptl/Zjn1x+7D9yna0t2mPyV0nQ+IkwY4rO5BTmKOToVFERERERERERBXRFPCuDkWJFwBaxzE8bD2qvY5MJsPpW6ex7tI6nL51GjKZrFrtJCJVhYWFeP311xEZGYmJEyfiiy++QGRkJJydnfHmm2/i+PHjetv35MmTcffuXZ1u86+//sKbb74JMzMzREVFYdu2bcjMzMS0adNQWFio031pwgxzHVDvkK6lX8PITiPLDY2SOEnquaVERERERERE1BjJZDLYWdjhNe/XIDYR41D8oWon7tUkjjGo7SDsH70fD4sfwquVl1br1KT0C5FBKC0FYmOBP/8E2rQBhgwBRCK973bt2rW4ceMGjh07hubNm8PMzAwikQjvvvsucnNzsXLlSkgkEojFYr23RRd+/PFH5OfnY/Xq1TAzMwMArFmzBj4+Prhy5QoGDBig1/0zYK4DdhZ2KjXLLU0sse7SOmW9MP7oExEREelHTSYnIyIiaoxiUmMw9cupytjEnlf2VDtxT73Ei7brdBJ0wphuY2BhYaHVOjUp/ULU4B09CsybB9y//8977doBa9cCOip/oklxcTGOHDkCPz8/tGnTBs+ePVP5fP78+Zg0aZIy8FzW+vXr8eWXXyImJqbC986cOYO1a9ciJSUFFhYWGDZsGJYsWQJra2u4u7sDAJYsWYLLly8jLCwMjx8/RlhYGGJjYyESidCjRw8sXrwYjo6OAIDFixcjPz8fubm5+P333zFr1iz85z//UWnXgAEDsGnTJpU2C4XyQik5OTm1/9KqwJIsOpD1LAsBPQIw2XsyAnsG4ue7P2Ped/PgG+2LmNSYqjdAREREZMDqc1i1IkON115ERNTUqQehMwoyGuxD5JqUfiFq0I4eBcaNUw2WA8CDB/L3jx7V267v3buHrKws9OzZU+PnrVq1QteuXSGqQaZ7ZmYm5syZg3/96184efIkNmzYgF9++QWrV68GAJw7dw4AsHTpUrz77rvIz8/H1KlTAQDR0dGIiopC8+bNMX78eDx+/Fi53VOnTmHgwIE4cuQIxo4dW26/7dq1U6m7DgAREREwMzNDnz59qn0c1cUMcx1wa+mG4G+DlU9xA3sGAuBTUiIiImoaajusujZZ4sxQIyIiklMf/W5rYVvfTaoQS9hSo1JaKs8s15Q0IpMBAgEwfz7w8st6Kc+SnZ0NALC2ttb5th8/foyioiK0bdsWDg4OcHBwwJYtW1BaKr/+trOzAwBYWVnBysoKhw8fRk5ODtasWQMjI3nYOTQ0FJcuXcKhQ4cQHBysbOu0adO0bkdUVBSio6Px3nvvoUWLFjo+yvIYMK8lRQbV8mHLYW1mDSGEKC4thoOVAx7lPuJTUiIiIjJI1Qli1zZoXZuAuyJDTbEur72IiKipUox+zyvOg6WJJbKfZddqe/ose1aT0i9EDVZsbPnM8rJkMuDePflyPj46370igJyVlaXzbXfp0gVjx47FzJkzYWdnh0GDBsHHxwfPPfecxuUTExORnZ1dLgu8sLAQKSkpytcdO3bUav8ymQxr167F5s2bMWvWLGX2ur4xYF5GTToD9Ru8gB4B2Pm/ndjwwgZAAAgFQshksgY7DIqIiIhIk+oEsWsbtK5NwJ0ZakRERHLqo99PTTlVq+1xYk4iLf35p26Xq6b27dvD1tYWV65cga+vb7nPU1JSEBoaiiVLlsDV1bXK7ZWUlKi8Dg8PR1BQEM6ePYvz588jJCQEvXr1wu7du8utK5VK4eTkhM2bN5f7rOwcB5rqqasrLi7GkiVL8M0332DJkiXw9/evch1dYcC8jJp0Buo3eHnFeSiVleLak2tYd3kdOxUiIiIySNUJYtc2aF2bgDsz1IiIiOR0/RCZZc+ItNSmjW6XqyahUIhx48YhKioK/v7+aN68ucrn27dvR1xcHBwcHMqta2xsjLy8PJX37ty5o/z/q1ev4sSJE1i6dCk6deoEf39/fP311wgJCUFGRgZatmypsq6bmxuOHTsGKysrZeZ7cXEx3n77bYwaNQqjR4/W+rgWLlyIH374AeHh4RgzZozW6+kCA+Zl1KQzUL/BszSxhEggQlurttXaDhEREVFDoimIrRiNF/c4Du1M2qGzrDOA2getmSVORERUPRWNkK+oP1Ysf/7+edhZ2MG9pTt8HH0qHQ3PsmdEWhoyBGjXTj7Bp6Y65gKB/PMhQ/TWhJkzZyI2NhZTpkzBrFmz0LdvXzx9+hT79+/HV199hc8++0wlw1uhe/fuyMrKwo4dO+Dr64tz587h7NmzsLGxAQBYWlpi3759MDY2xvjx41FYWIiTJ0/C0dFRGZi3sLBASkoK/vrrL7z00kuIiIjA3LlzERISAktLS2zatAlnz57FvHnztD6eo0eP4uTJk1i4cCH69u2L9PR05WdWVlZaZajXBgPmZdSkM1Dc4CWkJ8DKxAp3s+5i24vbsPyn5QA42zMREREZJk1BbPXReCeanYCva/lhn9XFLHEioqZBnzWxm5rqjpBXXz6wRyCkMmml61T0QFv977F/q/46Pz4igyISAWvXAuPGyYPjZYPmit+4zz/Xy4SfCubm5oiOjsb27dsRGRmJVatWwdzcHB4eHoiKikLv3r01rte/f38EBwdj586dWLduHYYOHYq5c+diz549AABnZ2esX78eGzZswL59+yAUCtG/f39s27YNQqEQABAQEIDt27cjJSUFW7ZsQXR0NFavXo3AwECUlpbC09MTO3fuhLOzs9bH88033wAAVq9ejdWrV6t8tmrVKvj5+dXka9IaA+ZlDHccjqhXoxCXFgdve28Mdxxe5ToCgUDZaSSkJ2Bwx8EY7jgcHaw7MEuKiIiIDJamILb6aLzrT65rFTBngISIiADWxNal6o6QV18+tzi3ynUqeqBd7gH6pBOwha0OjorIgPn5AV98AcybpzoBaLt28mC5ngO8gDzTOygoCIGBgTAzM4OoggD9jRs3VF7PmTMHc+bMUXmvbL3w4cOHY/jwimOkwcHBCA4OVr5u37491q9fX+HyYWFhlR0GAGDnzp1VLqNPDJiX8dPtnzD1y6nKH317sX2VnbdMJsOB+AM4kXwCYhMxlv+0HF+M/4JZUkRERNToeNp5wsbUBuO9xiOvKA+2YlutJjdngISIiADWxNal6o6QV1/eysSqxqPhNT1AH2wyuEbbImpU/PyAl18GYmPlE3y2aSMvw6LHzHLSDwbMy6hJ5x2TGqMSZA/oEcBOn4iIiBoliZMEm8ZsUl77HIg/gNaWras9STqvlYiImibWxNad6s7/IXGS4Lsp3+Hi/YuwtbBV1jCvCfW/xy62XYCcGm2KqPERiQAfn/puBdUSA+ZlqP/oG4uMcfrW6UqHDavfAOYX57PTJyIiokZJIBAgPT+91pOk81qJiKhp4iTPuqPNBJ/qpdBGdhqJkZ1G1nrf6n+P/Vv1x/Wc67XeLhFRQ8GAeRmKH/0L9y/gfs59LPpxEfKK8iodNqx+AzjCaYRWtc+JiIiIGqKq6o3XZpJ0BkiIiJouzmehexV9p/ouhaYerM/Pz9fZtomIGgIGzMtQ/OgnpCdg629ble9XljklcZJgzyt7cPLmSYhNxHjr1FvoYN2Bw4yJiIjIIFV2ky2TySAUCLHuhXXIystCxxYdkZCeAACVBj4qy4IjIqKmgfNZ6F5F3ylLoRER1Y6wvhvQECkypwBUmTklEAjwpOAJ9sbtRcRvEcgqzEJiemJdNZWIiIhIpzTdZCvEpMbguajnEHQyCHdz7+KNr97AvO/mwTfaFzGpMfXVZCIiMgCV9S9UMxV9p9WJaRARUXnMMNegusOGWZeTiIiIGovKrmvK3pg/LXrK7DUiItKaru6bWdrlHxV9pyyFRkRUOwyYa1DdYcPsjIiIiKixqOy6puyNuZWJFRMGiLTA4B6RnK7um1na5R+avlP+5hAR1R4D5pXQtqNhXU4iIiJqLCq7rlHcmMc/jkcHsw541e1VJP2VxIQBokowuEckp6v7Ztbn/oem7/T0rdP8zSEiqiUGzDVQBMrP3z+PBzkPsD9+P/KK8vDdlO8ggIBPaomIiKhJUtyYD2g9ANeuXUOXdl3g6+Zb380iatAY3CPSLZZErRx/c4iarpKSEkRHR+Orr77CnTt3YGpqCg8PD0yfPh39+/dXLufu7o5Vq1bBz89PJ/v97bffIJPJ0Lt3b51sTyEhIQGrV6/GH3/8AVNTUzz//PMICQmBlZWVTvejCSf91ECRBbLsp2XYfmU7JnhOQKmsFBfvX4RvtG+5ya1kMhlO3zqNdZfW4fSt05DJZPV8BERERERE1BCUnXzPxtQGtha2vG8gqgXFaKd1o9bh1JRTHOGkhhN+EjVNhYWFeP311xEZGYmJEyfiiy++QGRkJJydnfHmm2/i+PHjetv35MmTcffuXZ1u88mTJ3jzzTfh4OCAo0ePYtOmTfjtt9+wePFine6nIsww1yAhPQFiEzEmek1EblEuOtp0hI2pDSyNLRHYMxAH4g8gpzAHF+5fgMRJwmGWRERERESkUdkaw7YWtpj65VTeNxDVAkuiVo5zrBHVr1JpKWLvxuLPp3+ijVUbDOkwBCKhSO/7Xbt2LW7cuIFjx46hefPmMDMzg0gkwrvvvovc3FysXLkSEokEYrFY723RhQcPHmDw4MH48MMPYWRkBCcnJ4wfPx6fffZZneyfAXMNPO08MclrErZf2a68mA2VhGL5meXIK8pDQI8A7PzfTtzPuY/TqaeRmJ7IIU9ERERE4ASHROrKBvfWXVrH+wYi0is+UCCqP0evHcW87+bhfs595XvtmrXD2lFr4ddFN+VPNCkuLsaRI0fg5+eHNm3a4NmzZyqfz58/H5MmTYKZmVm5ddevX48vv/wSMTExFb535swZrF27FikpKbCwsMCwYcOwZMkSWFtbw93dHQCwZMkSXL58GWFhYXj8+DHCwsIQGxsLkUiEHj16YPHixXB0dAQALF68GPn5+cjNzcXvv/+OWbNm4T//+Y9Ku7p164ZPP/1U+TolJQXHjh3DoEGDdPKdVYUlWTSQOEng0MxB5WL2j7Q/kFOYg1JZKcyNzBHYMxD74/fj4v2LsLOww2ver2F6r+mwMbXhkCciIiJqshQj79RL2BERSyUQ1QWWTCWi+nD02lGMOzROJVgOAA9yHmDcoXE4eu2o3vZ97949ZGVloWfPnho/b9WqFbp27QqRqPqZ7pmZmZgzZw7+9a9/4eTJk9iwYQN++eUXrF69GgBw7tw5AMDSpUvx7rvvIj8/H1OnTgUAREdHIyoqCs2bN8f48ePx+PFj5XZPnTqFgQMH4siRIxg7dmylbfD19cXo0aORlZWFd999t9rHUBPMMNdAIBBgYLuBKhOJdLPvBngDViZWMBYaY93ldRAJRLA2tVYZVrnnlT0c8kRERERNFicbI6oYSyUQ1V5VI5nqo2QqR1cRNW2l0lLM+24eZCj/gE4GGQQQYP538/Gy+8t6Kc+SnZ0NALC2ttb5th8/foyioiK0bdsWDg4OcHBwwJYtW1BaKr/et7OzAwBYWVnBysoKhw8fRk5ODtasWQMjI3nYOTQ0FJcuXcKhQ4cQHBysbOu0adO0asMnn3yCgoICrFmzBq+//jqOHTum99IyDJhXoOzFrKWJJd469RayCrMgEoiwefRmhAwIga2FLZqZNFO5KcwoyGDHSEREZCB4g1tzQqEQ5x6cw/XM6yrfnSKDVhGoYAYt0T9YKoGo9qoKiNfHg9uGMq8Zr2uI6kfs3dhymeVlySDDvZx7iL0bCx9HH53vv0WLFgCArKwsnW+7S5cuGDt2LGbOnAk7OzsMGjQIPj4+eO655zQun5iYiOzsbPTp00fl/cLCQqSkpChfd+zYUes2eHt7AwA2bNiAYcOG4YcffsArr7xS/YOpBgbMK1D2YnbRD4uQVZgFQN7hnrl7BhbGFvj04qeIejWKN4VERET1QBc3hQ3lBtcQpUhTMHH/xHLfHTNoieQYuCLSD/WAeHJGsvJ9TztPeNt71/k9ekMZXcXrGqL68efTP3W6XHW1b98etra2uHLlCnx9fct9npKSgtDQUCxZsgSurq5Vbq+kpETldXh4OIKCgnD27FmcP38eISEh6NWrF3bv3l1uXalUCicnJ2zevLncZxYWFsr/11RPvaxbt27h7t278PHxUb7XqlUr2NjYqJR20RcGzLXg2sJVpcMVm4hhJjJDYM9AZD3L4k0hERFRPdDFTWFDucE1RMnZyRq/O2bQEskxcEWkH+ojmazNrFXOte+mfKf1PbouHmzJZDLYWdhVGKSvy4dnvK4hqh9trNrodLnqEgqFGDduHKKiouDv74/mzZurfL59+3bExcXBwcGh3LrGxsbIy8tTee/OnTvK/7969SpOnDiBpUuXolOnTvD398fXX3+NkJAQZGRkoGXLlirrurm54dixY7CyslJmvhcXF+Ptt9/GqFGjMHr0aK2O6fz581i9ejXOnTuHZs2aAQDu3r2Lv/76C87OzlptozYYMNeCucgcoZJQxKXFQWwixqH4QxjvNR47ruxAxIsRvCkkIiKqB7q4Kazr8iGNKePU1dqVo+yIKqH+G3Xh/oVy53xj+k0gqivqI5nUz7Vr6dcQ3C9Yq2sCXTzYikmNwewTszGj1wyYGJmgk00nCAVCyGQyCASCOnl4pvgtyS7MxoxeM7A/fj/yivLYNxPVkSEdhqBds3Z4kPNAYx1zAQRo16wdhnQYorc2zJw5E7GxsZgyZQpmzZqFvn374unTp9i/fz+++uorfPbZZyoZ3grdu3dHVlYWduzYAV9fX5w7dw5nz56FjY0NAMDS0hL79u2DsbExxo8fj8LCQpw8eRKOjo7KwLyFhQVSUlLw119/4aWXXkJERATmzp2LkJAQWFpaYtOmTTh79izmzZun9fGMHTsWERERCAkJwTvvvIPs7GysXLkSXbt2xfDhw3XynVWGAXMtJGQkYOMvGzHBcwJyi3Lx7tB38d+z/0WprBS3/rpV380jIiJqknQR7K7r8iGNKePUWeiME5NOICkziaPsiDRQ/426n3MfMakxKud8Y/pNIKormkYy1fR6QBcP3xPSE5BVmIUSWQm2Xtpa7nyui6xv9d+SDS9sgGtLV/bNRHVEJBRh7ai1GHdoHAQQqATNBZA/CP981Od6mfBTwdzcHNHR0di+fTsiIyOxatUqmJubw8PDA1FRUejdu7fG9fr374/g4GDs3LkT69atw9ChQzF37lzs2bMHAODs7Iz169djw4YN2LdvH4RCIfr3749t27ZBKBQCAAICArB9+3akpKRgy5YtiI6OxurVqxEYGIjS0lJ4enpi586d1coMt7Gxwe7duxEWFoZJkyZBJBJhxIgRWLx4MUQi/X2PCgyYa8Hb3ht5RXnYdmUbRAIRBrQbgIleE3Eo/hA87Tzru3lERERNki6C3XVdPiQhPQFiEzEmek1EblEukjOTDTajVCqVYojDEPi6lq+TSETy36j1L6zHuXvnYGliif3x++Fp51nvkxM2CDIZEBMDJCQAnp6ARAIY4O8gNQy1uR6o6OG7VCpFPOJx5MIReNt7Y4LnBGVgqKJt5Bblajyf62I0m/pvSbG0uGn8lhA1IH5d/PDF+C8w77t5KhOAtmvWDp+P+hx+Xfz03gYLCwsEBQUhMDAQZmZmFQaWb9y4ofJ6zpw5mDNnjsp7/v7+yv8fPnx4pVndwcHBCA4OVr5u37491q9fX+HyYWFhlR2GkpOTE7Zu3arVsrrGgLkWJnhOgEwmwx9pf6CFWQuEnQvDo9xHiHgxQvkZh1ISERHVLX0Gu/XVt3vaeWKS1yRsv7IdpbJSHIw/CNcWrrypJWqEBAIB3Fq6Ifjb4AoDZXVdFqrBiIkBfH2B0lJAJAJOnQJG8HeQaqY21wMVBduP3TqGwBOBynNTKpPita6vabw+UGwjOTMZB+MPKtfxsvfC6VunkZCegKhXo5D9LFtvWd9N9reEqIHx6+KHl91fRuzdWPz59E+0sWqDIR2G6DWznPSDAXMtCIVCTO46GU8uPcG87/6pt/Pw6cM6q0lGREREdaeivr22gXSJkwTn759vmhmlRE1QVZmv2mbGNroEnYQEebAckP+ZmKgaMGcGOtWRioLtfzz+Q6WvjkuLA1Dx9YHi3F3usxx2FnZwb+kOqUxaZ3GCui4xR0QVEwlF8HH0qe9mUC0xYK4lxczXr3m/ppz4837OfZxOPY3E9ETe+BIRETUiFZVJUL9R/mHqD5DKpFoHsQQCAQa2G1jtLLBGFywjaiKqynyt6HP1cx5A40rQ8fSUZ5YrMsw91H4HmYFOdUz9nHNtoTqxdafmnQBUfX0gNhFjktckpOenw8bUBmITMXIKc/QeJ6jrEnNERI0dA+ZaikmNwdQvpyo7zFBJKD469xHaNWuHAe0GcPgTERFRI1LR0Gb1G+UbGTcw5+QcrYNYMpkMQoEQ619Yj4yCDAxoN0CrLDCOZiNqWtTP+eU+yxtXgo5EIg+CJybKg+UStd/ByjLQmX1OeqB+zs3rOw+hklA8fPoQbi3d4N7CHUDV1wcTvSYqy66JBCIE9ghExJUIxgmIiAwMA+ZaUr9B/iPtD+QV5cHWwpbDn4iIiBqZivp29Rvl9Pz0agWxNAW+tckUb7ITAxI1AjUZIaJ+zttZ2DWuBB2BQB4AryhrvLIMdGafkx6on3OP8h/h00ufAgD+O/y/GOo4FEDV1wfqE39aGFtgsvdkDOkwhHECIiIDwoC5ljQ9SZ7WcxrcW7pz+BMREVEjU1Hfrn6jDKDc9UFlwbHqBr4V28ouzMaMXjOwP34/8ory6jRYpul4iEh7mh6USZwklQbR1e893Fu6N60Encoy0Kuqf05UA+rnnJWJFQB5Hz+g3QDl+VnV9YH6xJ/5Jfk4GH8QAd0DWEqNiMiAMGCuJUUHmJCeACsTK9zNuothHYdhWMdh9d00IiIiqiPqN8oymaxcEKuy8imedp6wMbXBeK/xypFqMpmswpvomNQYjDs0Trn8589/DjNjMySkJwBAndQy13Q8A1oP0Os+iQyVpgdMmh6UAZXXJFd/OOfj6KP8/WkSKstAr6r+OVENqJ9zMqkMjhaO6Nm+Z6UPqNTP+ek9p8O1hSsS0xNha2GL7GfZyodkRERkOBgw11LZC9SyF7f2Yvumc+FKREREKjRlmlWWRS5xkmDTmE3KeVEOxB+AndgOAgjKZZrKZDIkZybjnUHvYPlP8vrFliaWKrVR66KWuabjYcCcSDNND5g0jVSt7HeiSU3yW5N65FXVPyeqAfX+PD8/H82zm6OLQ5dKz7+KHpIrHqCn56fX1SFUqEn9phAR6QgD5tXEGqJERGRIMjIyEBYWhtjYWBQWFqJPnz5YtGgRnJ2dyy373nvv4fz584iJiVG+9/jxYwwdOrTcsqtWrYKfn59e226oKpoQDJDfkKvXPY+9E4uUv1IgNhFj+U/L8cX4LzCi0wjEpMZgzsk5mOA1Qbn806KndX4dUtnxEJGcIiB1OvV0uXN0Tt85KpmrQoEQJiKTCs+rJjXJb03qkVdV/7winCyU9KCi+EBDOo8bUluIiAwFA+bVxJtGIiIyJEFBQZBKpYiIiIBYLMbatWvh7++P77//Hubm5srlfvzxRxw+fBgODg4q61+/fh2mpqb48ccfVbKRrKys6uwYDE1Vk4GrX0s8znuMvXF7IRKIENAjQHmzrbgJtzSxVC5vZWJVrZrp+jqegoICnW2fyNDJZDIciD+AqV9ORWDPQJVz1NbCFgCUmaunb53Gc1HPQWwiRmCPQLS3bo8B7Qao/E40iQQdRfD69GkgMBA4cADIydGuHnlNA9+cLJSqSSgU4tyDc7ieeb3C/lVTfOD0rdMaH57V13ncJH5TiIh0jAHzaqrqJpiIiKihyM7OhoODA2bMmAE3NzcAwOzZs/Hyyy8jOTkZXbt2BQCkpaXh/fffR9++ffHgwQOVbSQlJcHR0RH29vZ13n5DVdVk4GWvJYxFxlj04yIA8pvY/OJ85cN4xU34gfgDCOwRCAsTCxgLjLFqxCo8yX+C7q27Y7jjcL1njnFyc2qqKnsYVfYzOws7nEg+oSyzFNAjAOZG5nhW+gyzT8xWKeGoCFzlFOYg4koE1o1aV+7cahIJOurB64AAYOdO7eqR1zTwzclCqZpSpCmYuH9ipf2renxAKBDiuajnyj08q8/zuEn8phARAKCkpATR0dH46quvcOfOHZiamsLDwwPTp09H//79lcu5u7vrdMTwb7/9BplMht69e+tke5p8/fXXCAkJwenTp9GuXTu97UeBAfNq4k0jEREZCmtra4SHhytfZ2ZmIjIyEq1bt4aLiwsAedBn8eLFePnllyEWi/Hll1+qbOPGjRsay7dQzZW9ljh96zTyivIAACKBCGNcxygfxmsKrE/0mohPL36qMpcKM8eI9KOyh1FlP3vN+zWITcQQCUTIKczBzv/tRGDPQET8FgEAKuekNoGrJpGgox68trOTB761qUde08A3JwulakrOTq6yf1WPD6y7tE7l4ZmdhR0kTpJ6PY+bxG8KEaGwsBBvvvkmHj58iJkzZ6JPnz4oLi7GkSNH8Oabb2L16tV48cUX9bLvyZMnY9WqVXoLmD948AAffvihXrZdEQbMa4GTZxARkaF4//33cejQIZiYmGDz5s2wsLAAAERGRiI9PR1btmzB1q1by62XlJSE5s2b47XXXkNqaio6duyIWbNmaaxrri2ZTIb8/Pwar6/N9n9++DMSnyTCw9YDg9oOUvbPilIiDaWkSP9W/XFi0glcf3IdXWy7YFDbQSptG9B6AAa0HoBzD84hrygPuUW5Kjfv8Y/j0cW2i0oAzr2Fu16/X6DhfY+GiN+hbujze4x7HFfufFNMeFv2M7GJGIfiDyGgRwDyi/MhcZLg7VNvA0C5c1L9nO/fqj/y8vLK/WYpzv1qH5tMBpOff4YgMREyDw8UDRpUZbkSrb/DGmy7IiadO8OoTPC6ZNgwFA0YAGhxrOXWdXdHkTa/ef37w+TECQiuX4esSxcU9e8P1PK3UiaT8f6vEXO1dq12ZrbioZji4VlDqBfOpD+iulYKIBbAnwDaABgCQKT3va5duxY3btzAsWPH0Lx5c5iZmUEkEuHdd99Fbm4uVq5cCYlEArFYrPe26JJUKkVISAg8PT1x8eLFOtsvA+a1wMkziIjIULzxxhuYMGEC9u7di6CgIOzbtw8ikQgbNmzA3r17YWJiUm6dkpIS3Lp1Cy4uLli8eDEsLS1x4sQJTJ8+Hbt27cKAAQNq1Jbi4mJcu3attodUoVSkYuKJf4ZQ7x+9H50EnVSWuX37tvL/hUIhUqQpSM5Ohqu1K5yFzpBKpXprX1lCoRBPpU+RX5CPnJwcJOUmady3ndAO+0fvR1pJGg7GH1Qem4OJA+xy5Z+l5KTAxdoFdrl2ev1+yyr7PVLN8DvUDX18j+1M2qkEyxxMHJTnVtnPDsUfwmcjP8OjnEdwsXaBi8AFESMjKjwnbWGLwSaDgRzges71cr9ZB8YeAGSo0W+SU2oqLCZOVAaTc/bvR2qnTlWviKq/w9psW53Qzg4d9++HSUoKilxccMfODlItf7dqsy5sbYHBg+X/f/16jdquTlP/SY2Ds9AZJyadQFJmktaZ2czmJmrqjgKYB+B+mffaAVgLQDflTzRRZJL7+fmhTZs2ePbsmcrn8+fPx6RJk2BmZlZu3fXr1+PLL79ETExMhe+dOXMGa9euRUpKCiwsLDBs2DAsWbIE1tbWcHd3BwAsWbIEly9fRlhYGB4/foywsDDExsZCJBKhR48eWLx4MRwdHQEAixcvRn5+PnJzc/H7779j1qxZ+M9//qPx2LZs2YLi4mLMmTOnYQfMMzIylAddWFiIPn36YNGiRcrh2u+99x4OHz6sso6Dg4PyS5ZKpdiwYQMOHz6Mp0+fok+fPli2bBnat2+vg8OpWxwCTUREhkJRgiU0NBRXr15FdHQ04uLiMGvWLHTu3FnjOkZGRrh06RJEIpHy4srLywvJycnYsWNHjQPmxsbGyvboQ+zVWJX++WHxQ4zpNgaAPIPy9u3bcHR0VE56eu7BOZUapScmncAQhyF6a19Z1dm3O9whk8ng0tIF159cR2fbzjASGOFM+hl42HoguEtwnWU6avoeqXr4HeqGPr/HzrLOONFMdQSI4hyr7DMAcIOb1vtR/8269vQaPjjzgfJ34bu+6+AjdEHhwIH4+c/zGkfPKJjGxqqUK7F++BBdxoypdP/afoc12Xal/r7BNgPgXpfr6tDNmzfrce+kb1KpFEMchsDX1VfrdRTZ3BInCWJSY7D+8nqORidqMo4CGAdApvb+g7/f/wL6Cprfu3cPWVlZ6Nmzp8bPW7VqhVatWtVo25mZmZgzZw4WL14MHx8fPHr0CAsXLsTq1asRGhqKc+fOYfDgwVi6dCn8/PyQn5+PqVOnwtPTE9HR0RAKhdi1axfGjx+P48ePK9tx6tQphISE4P3339cYyAeAP/74Azt37sQXX3yBx48f16j9NVXtgHlQUBCkUikiIiIgFouxdu1a+Pv74/vvv4e5uTlu3LiBmTNnYsqUKcp1RKJ/hh5s2rQJ+/btQ1hYGFq3bo01a9Zg2rRpOH78uME9nefkGURE1JBlZmbiwoUL8PX1hZGRvMsXCoVwcXHB/fv3kZycjA0bNmDjxo0A5JkJJSUl6NGjB7Zt24bevXtrHLLn6uqKc+fO1bhdAoFAWRJGH7xbeav0z16tvMrtz9zcXPne9czrKsGqpMykat0c10ZKVgoCewYitygXliaWSM1KrXLfvq6+8HX1xelbp+t9pFvZ75Fqht+hbujre1Scb9X9rDrUf7PsxHaqAfSUixg5by7OxEZh9A9TKz/nvb1V6nSLvMr//lWkyu+wFtturBgApYpwNDpRU1MKeWa5erAcf78nADAfwMvQR3mW7OxsAPI5rHTt8ePHKCoqQtu2beHg4AAHBwds2bIFpX8/RLezswMAWFlZwcrKCocPH0ZOTg7WrFmjvAcNDQ3FpUuXcOjQIQQHByvbOm3atAr3m5+fj3feeQfvvPMOHB0dG3bAPDs7Gw4ODpgxYwbc3ORZE7Nnz8bLL7+M5ORkeHt74+bNm5g+fbryCyurqKgIO3fuxDvvvAMfHx8AwGeffYYhQ4bg+++/x9ixY2t/RHWIw62IiKghe/LkCd566y1s374dQ4bIs5aLi4uRmJgIiUSClStXqiwfFRWF77//HlFRUWjVqhWSk5MxYcIEbN68Gf369VMuFx8fr9cM8dqqbv9cnw/AbcxssOPKDuW+97yyR+t1OdKNqHFQ/80Spmeq/SZ5AqWlSEiPq/qcl0jkk2cmJsontdRmEk2tG6rHbRM1MuyjiZqaWKiWYVEnA3Dv7+V8dL73Fi1aAACysrJ0vu0uXbpg7NixmDlzJuzs7DBo0CD4+Pjgueee07h8YmIisrOz0adPH5X3CwsLkZKSonzdsWPHSve7cuVKODk5YeLEibU/iBqoVsDc2toa4eHhyteZmZmIjIxE69at4eLigrt37yI/Px+dKqhld/36deTl5akM4W7WrBk8PDzwyy+/GFzAnJNnEBFRQ+bm5oahQ4di5cqVWLlyJaytrbF161bk5OTA398fbdu2VVne2toaRkZGyosXZ2dndOrUCR9++CE++OADNG/eHIcOHcLvv/+OI0eO1MchaaW6/XN9PgBPz09XuaHOKMjQel2OdCNqHNR/s2SnNuOUcQASLfLgUWCJ4bF3cTpsBrLFxpjRawb2x+9HXlGe5nNeIABGjJD/p/uG6m/bRI0M+2iipuZPHS9XPe3bt4etrS2uXLkCX9/yo99SUlIQGhqKJUuWwNXVtcrtlZSUqLwODw9HUFAQzp49i/PnzyMkJAS9evXC7t27y60rlUrh5OSEzZs3l/us7Mi0isqwKBw5cgQmJibo0aMHACgz2hXB+5kzZ1Z5HLVR40k/33//fRw6dAgmJibYvHkzLCwskJSUBECeoXb27FkIhUIMHToUCxYsgJWVFR49egQAaNOmjcq27O3tlZ81JDKZDDGpMUhIT2DdMSIiMkiffvopwsPDsWDBAjx9+hS9e/fG3r17ywXLNREKhdiyZQvCw8Mxf/585OTkwMPDA7t27VKONGsM6vMBeG1uqDnSjahhqs49hMZl3dwwwjcYI/4ufXL6203wvTAbpefkvxMbXtgA15auVZ/zMhkQEwMkJACenvKMcN7LENUJ9tFETU2bqhep1nLVIxQKMW7cOERFRcHf3x/NmzdX+Xz79u2Ii4uDg4NDuXWNjY2Rl5en8t6dO3eU/3/16lWcOHECS5cuRadOneDv74+vv/4aISEhyMjIQMuWLVXWdXNzw7Fjx2BlZaXMfC8uLsbbb7+NUaNGYfTo0Vod0/fff6/y+urVqwgJCUFERESd3IvWOGD+xhtvYMKECdi7dy+CgoKwb98+JCUlQSgUwt7eHlu2bMHdu3exevVqJCcnY/fu3SgoKABQfiZxU1NTZb2dmpDJZMptK/7UhXMPzmH0/tH1MglYVfRxvA0Zj7fxakrHCvB4GzNdHKtMJtP5g1krKyusWLECK1asqHLZ4OBgZU05BVtbW6xatUqnbaJ/1OaGmiPdiBombWoXKwLlyZnJmHNyjuqyaqVPEixVSzsUS4u1O+9jYgBfX2XNcZw6xexwojrCPpqoqRkCoB3kE3xqqmMu+Ptz/cUUZ86cidjYWEyZMgWzZs1C37598fTpU+zfvx9fffUVPvvsM41zj3Tv3h1ZWVnYsWMHfH19ce7cOZw9exY2NjYAAEtLS+zbtw/GxsYYP348CgsLcfLkSTg6OioD8xYWFkhJScFff/2Fl156CREREZg7dy5CQkJgaWmJTZs24ezZs5g3b57Wx6NeskWRaN22bVtl2/SpxgFzRe3S0NBQXL16FdHR0QgNDcXkyZOVX5ibmxvs7Owwfvx4xMXFKdPti4qKVFLvCwsLazWrfXFxMW7fvg0Ayj914bfs35QXp60tW+N29m38dPsndGnRBd1E3ZTDAeqTLo/XEPB4G6+mdKwAj7cxq+2xGtoE2FQ7vKEmanwqql1cNpvczsIOs0/Mxmi30cplxSZiJGcmy7PNnTwhkcyBQCCA5y3UbCRKQoI8WA7I/0xMrJuAOTPbiXSGo96JDIUIwFoA4yAPjpcNmivO2c+hjwk/FczNzREdHY3t27cjMjISq1atgrm5OTw8PBAVFYXevXtrXK9///4IDg7Gzp07sW7dOgwdOhRz587Fnj3yuZWcnZ2xfv16bNiwAfv27YNQKET//v2xbds2CIVCAEBAQAC2b9+OlJQUbNmyBdHR0Vi9ejUCAwNRWloKT09P7Ny5E87Ozno7fl2rVsA8MzMTFy5cgK+vr3KmU6FQCBcXF6SlpUEoFJZL+1fUxnn06JGyFEtaWho6dOigXCYtLQ3u7u41PghjY2M4ODjg9u3bcHR0rFXwvayMBxnKi9O5feci6GSQ8kI1YmwEJnSeUG+dVUFBgc6PtyHj8TZeTelYAR5vY6aLY71586aOW0VERHWtolJL6pnnAT0CIBAIlMtO8ppUPtu804iaj0Tx9JRnlisyzD3qqIYyM9uJdEabEStE1FD4AfgCwDyoTgDaDvJguZ/eW2BhYYGgoCAEBgbCzMwMIpHmAP2NGzdUXs+ZMwdz5sxRec/f31/5/8OHD8fw4cMr3K/6SOX27dtj/fr1FS4fFhZW2WFo1K9fv3Lt1qdqBcyfPHmCt956C9u3b8eQIfJhBMXFxUhMTIREIsHChQuRlpaGyMhI5TpxcXEA5Bnp7du3h6WlJS5duqQMmOfk5CAxMRFTpkyp8UEIBAJlcMLc3FzjEIOaeM7lOfww9QfcyLiBm5k3VTJFrj6+io42Heu9s9Ll8RoCHm/j1ZSOFeDxNma1OVZmDBERGb6KAtzqmef5xfk4nnQcgT0C0d7EDsYm5gjsGYjcolxYmlgiOSMZIzqNqPlIFLXSLpDUUQ3l+spsJ2qEKhqxQkQNlR+AlwHEQj7BZxvIy7DoL7Oc9KNaAXM3NzcMHToUK1euxMqVK2FtbY2tW7ciJycH/v7+uHbtGmbPno0NGzbgpZdeQmpqKj788EOMHTtWmXY/ZcoUfPLJJ2jRogUcHBywZs0atG7dGs8//7xeDrA2BAIBpDIp5pycg1BJqEqmSFurtuysiIiIiIhIRUUB7rKZ5zamNhjhNAIdLB3gXdISE6atw8Gtc/HulR3K+409r+ypbUPkgeq6DlbXV2Z7I5GRkYGwsDDExsaisLAQffr0waJFi5T30++99x4OHz6sso6DgwNiYmIAAFKpFBs2bMDhw4fx9OlT9OnTB8uWLUP79u3r/Fio9mozOTgR1RcRAJ/6bgTVUrVrmH/66acIDw/HggUL8PTpU/Tu3Rt79+5F27Zt0bZtW3z++eeIiIjAtm3bYGVlhRdffBHz589Xrj937lyUlJTgvffew7Nnz9CnTx/s2LEDxsbGujwunVE80V1/eT1CJaFIy0uDvdgeGy9vxK5XdtV384iIiIiIqI7VpK5w2cxzWwtbTP1yqjIIZj87AOmp8SqZpBkFGdq3I+U8PKV2kJi4Q+DjU781w+srs72RCAoKglQqRUREBMRiMdauXQt/f398//33MDc3x40bNzBz5kyVEdplh9xv2rQJ+/btQ1hYGFq3bo01a9Zg2rRpOH78OOdJMUC1mRyciIhqrtoBcysrK6xYsQIrVqzQ+PkLL7yAF154ocL1RSIRQkJCEBISUt1d1wvFE90HTx/g3Zh3EfVqFJ7kP8GuV3axsyIiIiKDxwnFiKqvJnWFBQKB8v7hdOppBPYMxIH4A8gpzEGMhzm8jF0gyqheJmm5dpgGYoRUWr8lUOors70RyM7OhoODA2bMmAE3NzcAwOzZs/Hyyy8jOTkZ3t7euHnzJqZPnw47O7ty6xcVFWHnzp1455134OPjAwD47LPPMGTIEHz//fcYO3ZsXR4O6QAnByciqh/VDpg3NZqe6PImkoiIiBoLTihGVH01rSusaeLPnf/biSeiZ5j9xyrseWU3Mgoyy2WSKh5sJWUkwcbMBun56fC08yzfDrNcjGDNcINlbW2N8PBw5evMzExERkaidevWcHFxwd27d5Gfn49OnTppXP/69evIy8vDgAEDlO81a9YMHh4e+OWXXxgwJyIi0hID5lXgE10iIiJqzDihGFH1aaorrM1oDfXzzcbMRiXTPKMgE8H9gv9ZQSYDYmIQU5IM30tzENgzEDvK1DmPejVKtR3PrIDerHHcGLz//vs4dOgQTExMsHnzZlhYWCApKQkAEBUVhbNnz0IoFGLo0KFYsGABrKys8OjRIwBAmzZtVLZlb2+v/KwmZDIZ8vPza34wdUgmk+Hnhz8j8UkiPGw9MKjtoBolvBUUFKj8SZXj91U9/L6qR5/fV2FhIaRSKUpLS1GqmLDawMlkMuWfjeWYtFVaWgqpVIqCggJIpdJyn8tkMq37BAbMiYiIiJowXU4oxvIu1FRoGoWqzWgN9fOtR+seKrXMy51/MTGAry+S1r+BwJ6BMBOZqQTcs59l49SU75CYchEeMltIjN2Bv0txkGF74403MGHCBOzduxdBQUHYt28fkpKSIBQKYW9vjy1btuDu3btYvXo1kpOTsXv3bmUwSb1WuampKbKzs2vcluLiYly7dq1Wx1NXUpGKiScmKs+p/aP3o5NAc0a+Nm7fvq27xjUB/L6qh99X9ejr+zIyMkJhYaFetl2fGuMxVaWwsBAlJSW4detWhctoO58HA+ZERERETZguJxRjeRdqKjSNQtVmtIb6+TbccTjsxfYVn38JCUBpKWw6umHHr+8isGegSsDdtaXr3+0Yqfdjprrl4uICAAgNDcXVq1cRHR2N0NBQTJ48Gc2bNwcAuLm5wc7ODuPHj0dcXBzMzMwAyGuZK/4fkAcQzM3Na9wWY2NjZXsautirsSrn4cPihxjTbUy1t1NQUIDbt2/D0dERZmZmuPjnRST9lYQn+U/Qt21fDG47mA+Eyyj7fdXm31pTwe+revT5fRUWFuLhw4cwNTVV+d00ZDKZDIWFhTA1NW2Sv1NGRkbo0KEDTE1Ny3128+ZN7bejy0YRERERkWHRZfk5lnehpkyb0RqazrdKzz9PT0AkQnqq/Nw6EH8AAT0CYGdhB4mTpFYPuKjhyczMxIULF+Dr6wsjI/mtulAohIuLC9LS0iAUCpXBcgVXV1cAwKNHj5SlWNLS0tChQwflMmlpaXB3d69xuwQCASwsLGq8fl3ybuWtch56tfKqVdvNzc1x4dEFHL5+GNuvbK/0gTBHWcm/L0P5t9IQ8PuqHn18X0KhEEKhECKRCCKRSKfbri+KMiwCgaDRHJO2RCIRhEIhzM3NNT4Aqc5vslCXDSMiIiKipksRMARQ6/IuRIZGkT2+btQ6nJpySjfBbIkEOHUKnq4DIRKIkFOYg53/2wmJkwQjOo1ocsG4xu7Jkyd46623cOHCBeV7xcXFSExMhLOzMxYuXAh/f3+VdeLi4gDIM9I7d+4MS0tLXLp0Sfl5Tk4OEhMT0adPnzo5hvqmj/MwIT0BT4uelnsgrE4xymred/PgG+2LmNSYWu+biIjqBzPMDZhQKMS5B+dwPfN6k32CTUREZKgaYyaaLsu7EBkaXY7WKLNRYMQISGQSnHJxlZ9bUltITiUBbpAH1A38d4P+4ebmhqFDh2LlypVYuXIlrK2tsXXrVuTk5MDf3x/Xrl3D7NmzsWHDBrz00ktITU3Fhx9+iLFjx8LZ2RkAMGXKFHzyySdo0aIFHBwcsGbNGrRu3RrPP/98PR9d3ajsPKxpv+tp54nE9MQqR5BwlBUR1beSkhJER0fjq6++wp07d2BqagoPDw9Mnz4d/fv3Vy7n7u6OVatWwc/PTyf7/e233yCTydC7d2+dbE/h66+/RkhISLn3T58+jXbt2ul0X+oYMDdgKdIUTNw/kXVCiYiIDFBV9b4NMaCul4AhESnPLckdIWIuH8R686fw/PUqJEIhBMOH13fzSIc+/fRThIeHY8GCBXj69Cl69+6NvXv3om3btmjbti0+//xzREREYNu2bbCyssKLL76I+fPnK9efO3cuSkpK8N577+HZs2fo06cPduzYAWNj4/o7qAaipvNsSJwkEAqE6N6qO54UPMGAdgM0PhDW5STaRETVVVhYiDfffBMPHz7EzJkz0adPHxQXF+PIkSN48803sXr1arz44ot62ffkyZOxatUqnQfMb9y4gb59++LTTz9Veb9FixY63Y8mDJgbsOTsZD7BJqJGyxCDhUTVUVUmGifQJGq4quqj9NWHxRTdgG/hdpQ++/t3oag7RoAB88bEysoKK1aswIoVKzR+/sILL+CFF16ocH2RSISQkBCNGXlNXU0zwAUCAYY7Dcdwp8rPNY6yIiIAkMqkeFjyEHmyPIgFYrQ1aguhQP8VsdeuXYsbN27g2LFjaN68OczMzCASifDuu+8iNzcXK1euhEQigVgs1ntbdCUpKQnu7u6ws7Or830zYG7AXK1d+QSbiBotBgupsasqE41Du4karqr6KH31YQnCdOXvgthEjORmxUi4tI4Plom0oO8McI6yIqKbRTdxJv8McmW5yvcsBZYYZjEMLiYuetuvIpPcz88Pbdq0wbNnz1Q+nz9/PiZNmqRxIsz169fjyy+/RExMTIXvnTlzBmvXrkVKSgosLCwwbNgwLFmyBNbW1spJpZcsWYLLly8jLCwMjx8/RlhYGGJjYyESidCjRw8sXrwYjo6OAIDFixcjPz8fubm5+P333zFr1iz85z//Kde2GzduQCKpn4ePDJgbMBeRC3a/shsJ6QnwtvfGcEdmlxCR4VBk3yVlJMHGzAbp+ekqN/wMFlJjV1UmGod2EzVcVfVR+urDPJ0HQnRB/rswyWsS5vywoHpBeZkMiIkBEhIAT095DXSiJoIZ4ESkTzeLbuJE3oly7+fKcnEi7wTGYIzegub37t1DVlYWevbsqfHzVq1aoVWrVjXadmZmJubMmYPFixfDx8cHjx49wsKFC7F69WqEhobi3LlzGDx4MJYuXQo/Pz/k5+dj6tSp8PT0RHR0NIRCIXbt2oXx48fj+PHjynacOnUKISEheP/99zUG8rOzs/H48WP8+uuv2LdvH/766y907doVISEhcHJyqtGxVAcD5gbsZulNvPH1G8qLZHuxPYNJRGQwFNl3gT0DcSj+EMZ7jcdvD3/Drb9u4c+cP9Hepj2DhdSoVZWJxht7ooarqgda+nrgVfZ3Iaswq/pB+ZgYwNcXKC0FRCLg1ClgwACdtI2ooWMGOBHpi1QmxZn8M5Uucyb/DDoZd9JLeZbs7GwAgLW1tc63/fjxYxQVFaFt27ZwcHCAg4MDtmzZgtJS+TWIolyKlZUVrKyscPjwYeTk5GDNmjUwMpKHnUNDQ3Hp0iUcOnQIwcHByrZOmzatwv0mJycDkCfarVq1Cs+ePcPmzZsxefJkHD9+HLa2tjo/1rIYMDdgrGFORIZMkX2XW5SL8V7jlUHzM3fOwNveG8t/Wo6IsRHIK85jsJCaJN7YEzVcVT3Q0tcDr7K/C6dvna5+UD4hQR4sB+R/JiYyYE5ERFRLD0seqpRh0SRXlouHJQ/RzridzvevmAQzKytL59vu0qULxo4di5kzZ8LOzg6DBg2Cj48PnnvuOY3LJyYmIjs7G3369FF5v7CwECkpKcrXHTt2rHS/vXv3xoULF9C8eXNlybkNGzbAx8cHR48exfTp02t5ZJVjwNyAsYY5ERkyRfadpYkl8oryMN5rPHZc2QGxiRiWJpaY3HUysgqz0KN1D/g4+rAuKxk8TmRL1HhU9UCrLh541Sgo7+kpzyxXZJh78P6BiIiotvJkeTpdrrrat28PW1tbXLlyBb6+vuU+T0lJQWhoKJYsWQJXV9cqt1dSUqLyOjw8HEFBQTh79izOnz+PkJAQ9OrVC7t37y63rlQqhZOTEzZv3lzuMwsLC+X/ayrDok7xIEDB3Nwc7dq1w+PHj6tct7YYMDdgzkJnnJh0AkmZScy+JCKDo7jRT85IhpHICP93+/9QKivFRK+J2H5lu/Jh4Ac+H+BR7iNM9JrI4CIZNE5kS0RV0lRjvIK+r0ZBeYlEXoYlMVEeLJdIgIICHTWeiIioaRILxDpdrrqEQiHGjRuHqKgo+Pv7o3nz5iqfb9++HXFxcXBwcCi3rrGxMfLyVAP5d+7cUf7/1atXceLECSxduhSdOnWCv78/vv76a4SEhCAjIwMtW7ZUWdfNzQ3Hjh2DlZWVMuBdXFyMt99+G6NGjcLo0aO1OqaDBw/i008/xU8//aQMtOfm5uL27dsYN26cVtuoDd0XzqE6I5VKMcRhCIL7BWNEpxEMJBGRQVHc6M/oPQOuzV0xtONQvOb9GiyMLVTLTT1JxNQvpyImNaaKLRI1bJomASxLJpPh9K3TWHdpHU7fOg2ZTFYfzSSi+iKTAQcOALt2yQPm48bJg+e6JBAAI0YAwcHyP3n/QEREVGttjdrCUmBZ6TKWAku0NWqrtzbMnDkTjo6OmDJlCr755hvcvXsXf/zxB5YsWYKvvvoK//3vf1UyvBW6d++OrKws7NixA/fv38eBAwdw9uzZf9ptaYl9+/ZhzZo1uHPnDpKSknDy5Ek4OjoqA/MWFhZISUnBX3/9hZdeegnW1taYO3curl69ipSUFCxevBhnz56Fu7u71sczdOhQSKVSLFy4EMnJyYiLi0NwcDBatGgBPz+/2n9hVWDA3MDx5pqIDF1Magz2J+zH7BOzsTduLwqKCyASiABAWbJFU3CRyNAoyhAB0FhKTZGBPu+7efCN9uVDIqKmJiYGmDoV2LsX2LED+Pe/5ZngRERE1KAJBUIMsxhW6TLDLIbpZcJPBXNzc0RHR8PPzw+RkZF49dVXMWPGDKSlpSEqKgqjRo3SuF7//v0RHByMnTt3YsyYMfj5558xd+5c5efOzs5Yv349Ll68iFdeeQWTJk2CSCTCtm3bIBTKjycgIADR0dFYsmQJrKysEB0djebNmyMwMBDjxo3D48ePsXPnTjg7O2t9PG3atEFkZCTy8/MxadIk+Pv7w8rKCnv27IGpqWntviwtsCSLgZLJZEhFKi4nXkbwt8Ec3k1EBishPQFPi54qM2/3x+/H+hfW4+HTh0jPT8f++P2cp4EaharqDWvKQGefTtSEqE/ImZ/PGuNERERaqu/5glxMXDAGY3Am/4zKBKCWAksMsxgGFxMXvbfBwsICQUFBCAwMhJmZGUQikcblbty4ofJ6zpw5mDNnjsp7/v7+yv8fPnw4hg8fXuF+g4ODERwcrHzdvn17rF+/vsLlw8LCKjsMJU9PT+zcuVOrZXWNAXMD9fPDnzHxxERM8JrAm2siMmiedp5ITE9UTmKcV5QHGzMbLD29FP/2/DdedHsRY1zHcJ4GMnhV1RtWZKBzMm+iJkp9Qs4xY+Q1xomIiKhKDWG+IBcTF3Qy7oSHJQ+RJ8uDWCBGW6O2es0sJ/1gwNxAJT5JRKmsFJYmlry5JiKDJnGSQCgQonur7nhS8AQD2g1AUkYSsgqzsO3KNgBAP4d+nKeBDEJtMluqykAnokZO04Sc7PuIiIi00lBGawoFQrQzblfn+yXdYsDcQHnYekAkEOFA/AEE9ghEe+v2GNBuAG+uicjgCAQCDHcajuFOqkO8+DCQDFFtMluqykAnokZOMSHnCP4GEBERVRdHa5IuMWBuoAa1HYT9o/fjYfFDeLXyqvPaTERE+sRMWzJUDSWzhYiIiIioKeE9JOkSA+YGTCAQQAZZfTeDiEjnmGlLhsrb3hszes3A06KnsDKxgpe9V303iYiIiIioyWCcjHSBAXMDpZj0sz4nMyAiIiJVUpkU269sV/bP4zzG1XeTiIiIiIgavdqURpTJGGRvDHT598hpWg1UcmYyAnsGYrL3ZAT2DERyRnJ9N4mIiKjJUy/Jci39Wj23iIiIiIio8dNUGrEqxsbGAID8/Hy9to3qhuLvUfH3WhvMMDdQNuY22HFlh/LJ2SfPf4Ktv26FW0s3+Dj6sJ45ETUKMpkMMakxSEhPgKedJ+droAaPkw0REREREdW9mlyHi0Qi2NjYIC0tDQBgYWFh8PebpaWlKCwsBCA/vqZAJpMhPz8faWlpsLGx0clxM2BuoNJz01WenP3y8BccjD+IwB6BkMqkLM9CRAZHU3C8NsPqiOoDJxsiIiIiIqp7Nb0Ob926NQAog+aGTiqVoqSkBEZGRhAKm1ZhERsbG+XfZ20xYG6g7CztVJ6cWZpYolRWitziXCSmJzKgREQGRz04vueVPYhPj0dgz0AciD+AnMIc/r5Rg8cJa4mIiIiI6l5l1+GVjVwWCARo06YN7O3tUVxcXNfN1rmCggLcunULHTp0gLm5eX03p84YGxvrNKOeAXMDlVWQhYAeARCbiFFQXID98fshEohgZWKlediJTAbExAAJCYCnJyCRAAY+zISIGg+ZTIbkzGRM8JoASxNLHIg/gJM3T2Jv3F6IBCIE9AjAzv/tZHkLIiIiIiIiqhZtRi6LRKJGUcJEKpUCAExNTWFmZlbPrTFcDJgbKNcWrph3ah7EJmJM9JyIuX3non2z9nBt6QofR5/yK8TEAL6+QGkpIBIBp04BI5j9RkQNQ0xqDOacnKO8gAnsEQj8/UyvVFYKOws7nJpyiuUtiIiIiIiImqiaznGlaUJQjgilyjBgbqAGthmIHWN24EbWDXjbe2OCx3gI/+//gPNxgKe0fAZ5QoI8WA7I/0xMZMCciBoM9QsYe7E9dv++GwAgEoggcZLwgoaIiIiIiKgJq+kcVzWZEJSaNgbMDZBMJsOxW8cQeCJQebLb58owwvf1ijPIPT3l7ys+9+CPAxE1HOoXMOn56Xhn0DswEZnAvaW75pEzRERERERE1GTUNFO8phOCUtPFgLkBikmNwbc3v1X9kUhLwIjKMsglEnkQPTFRHiyX8MeBiBoOiZMEW8ZsweP8x3iS/wTFpcW4+ugqdl/djVNTTmk1zI6IiIiIiIgar5pmilc2ISiRJgyYG6CE9ASITcQqPxLG9q1xeu18CNt3QFz+bXg6WEIilUIgFMpXEgjkAXSWYSGiBkggEEBsIsbyE8uVv2uhklDWlyMiIiIiIiIAzBSnusOAuQHytPPE8p+WI6BHAPKL89G/XX9cy0jGkvw9WNluJUqLHfHmxeUIE8swqfebzMwkIoOQnp+uMnImIT2B9eWIiIiIiIgIADPFqe4wYG6AJE4SHPjXAVy4dwGP8h/h3Zh3kVeUh4AeAbhw7wLExmIE9Q3CyXsxaFVihhH9J6lOAEpE1IAoZjovKC7AxyM+Rnx6PCyMLdCvbT+80e0NZg0QERERERERgH/uHxPSE+Bp5wmJk4SJoqRzDJgbIIFAgCEOQ3Dl3hVs/W2r8v384nyITcTILcrFw6cPITYRI/GXkxiR34qlWIiowVLMdB7YMxA7ruxQlmQZ1nEYMweIiIiIiIhISXH/qLhvPDXlFO8bSeeE9d0AqrnOzTtjRq8ZmOw9GTN6zUCv1r1wKP4QrEys4NbSDYfiD8EjXyyf6LMsmQw4fRpYt07+p0xWPwdARIR/ZjrPLcpVKckSnxZfJ/uXyWQ4fes01l1ah9O3TkPG30QiIiIiIqIGSTGv3/Re0zHBawKSM5N5D0c6xwxzAyaVSbH9ynblU7VPn/8Uy4Ytg9hYjIz0e/gC4yFZfQg4PF4eFI+JARISADs7YOpUoLQUEImAU6eYgU5E9UYx07mliaXKZMZe9l51sn9mKBBRk1L2mtDTE5BIWLqPiIiIDIannScmeU1SxsMOxh+EawtXSJwkGku1aCrhAoBlXahSDJgbsKSsJJVszMsPL8PC2AI7/7cTp56LwgixvTxYLpHIb4x8feVB8tdek/8JyP9MTGTAnIjqjWKm8+SMZES8GIFbf92Cp50nJnhOqJP9KzLcAflvaWJ6IgPmRNR4lb0mZOIEERERGRiJkwTn758vdw8HQGMilKYEqYqWJVJgwNyAuVq7qmRjett741nxM5yackr+xGxAmadjCQn/BMnFYvkNklgMTJoEZGXJS7Mww4iI6kF9z3Tube+NGb1m4GnRU1iZWNVZZjsRUb0oe03IxAkiIiIyMAKBAAPbDVSJh9la2CIpI0ljIpSmBCkZZEyaokoxYG7AnIXOiBgbgZjbMRCbiBF2LgwbR4RDcioJMd3TkCBM/2doiacnYGMjzy43NQU2bgSMjICffgLu3wfGjQO++II3TERUr+pjxnP18lbjPMbpdX9ERPXK01OeOKHIMPfwqO8WEREREVWLxEmCPa/swcmbJyE2EWP2idnYOHqjShDdw05+jaMoAar+vqb3iBQYMDdgUqkUOYU52Bu3V/lexuUziHlqBt8fdqgOLZFIgE2bVGuXBwYCe/fK/z8ggBlGRFTv6qOeuHrGwbX0axjZaaRe90lEVG8kEnkZlsREebBcIqnvFhERERFVi0AgwJOCJ6rxsIIMnJpyConpifCw81DWKleUAFV/X9N7jR7nstEaA+YGrotdF9WnYs+skGDxF0oL1YaWpAKIi5MHyQ8cAHJygNxc+UZKS4H8fGYYEVG9kslkSM5MxgSvCbA0scSB+AN1MjSuoowDIqJGSSCQJ0gwSYKIiIgMTNkRyXYWdrAxtUFWYZbyPk5Tqc+KSoDWZ1nQesO5bLTGgLmBMxIYIbBHIHKLc2FlYoUMY2d4J9xWDf5IbVVPiIAAYOdOwMpKvhGRCBgzhhlGRKQTiouYpIwkNDNthnvZ95DxIAPPuTxXaXmVmNQYzDk5R/nbFdgjsE6C1xVlHBAREREREVHDoT4iec8re5BRkKGf+7jGmI3NuWy0xoC5ARMKhbiZdRMWJhboYNMBablpuCJ6DN/u3XDKfA8ShX//aJxKUj0hWrSQl2exsZGf9IrhuIZ+4hNRg6C4iAnsGYgdV3ZoXV5FvTRKe+v2eg9e10fNdCIiIiIiIqqaTCbDuQfn8Fv2b8h4kIHrmddV7hkzCjIQ3C9YPztvjNnYnMtGawyY1xcdPKlKkaYg6GSQSjZmW6u2+F2Yha5ogzl9giAQCgE3qJ4Qzz1n+Cc5ETVYisB3blFuuZnHFZ9rCk6rl0YZ0G6A3oPX6hkKG0dvhFtLN/g4+jBwTkREREREVI9iUmMwev9o5f1a1KtRdVdOszFmY3MuG60xYF5favGkSvGE7dfMX1WCUbnFuUhIT0BLi5YIubQQpyDDiAGTeUIQUZ1SBL4tTSxVLmZsLWwrndCzPkqjqGe1x96NxZU/r0Aqk5bPhm+MQ/KIiIiIiIgaKPX7texn2bW6Z6zWCOPKsrEN9d6Qc9lojQHz+lKLJ1WKJ2yBPQNVglFWJvKa5MWlxX9ncyZgBCA/IRRB8oQE+Z+GcjITkcFRBL6TM5Kx++XduJlxE/ZW9sgvzkdry9YY4zYGuUW5SM5MVrlAqWgyFn1Sz2oXm4iRW5SrebLRxjgkj4iIiIiIqIFSv19zbemq1T1jRYFx9RHGp6acgsRJonFZ2fDhiImNQkJ6HLxb9YDUFki4tA52FnbISr8Lt19TIfloPwR5ecCpU5BJNG+HDBMD5vWlFnWDFE/YDsQfQECPALQwb4G2Vm0BGZDyVwoir0b+PTTF65+VGOghojpSNvD9ffL3eOPYG8oLkrW+azHv1DyUykpxMP4gXFu41uvM5BInCfa8sgcnb56E2ESMQ/GHMMFrAmwtbCGTyVQvcBISALEYmDgRyM0FkpP58JGI6p6uM5oMNUOKiIiIGj2JkwQnJp3A/+79Dz3b99Q6o1xTYHxEpxHlMtYVZUM1LRtz+yf4/jAVpbJSzOg1A9tPbVcuE9AjAMGFO3FqcQBGLN0GJCYixknzdsgwMWBe18relOzZA9y/DzRrBgiF8s+0uEFRPGHLKczBzv/txKkpp5CQnoD3/+99TPCcgLFuYzGkTX9I+k34Z6XGWHuJiBq8xCeJKhckt7JuIbBnIA7EH0BOYQ5iUmMAoN6evgsEAkzyngR7S3vE3olFyKAQPM59jNknZsNObIeRnUb+s7CnJzBpErB9u/x39OBBwNWVv6VEVD1/XwuaxsXBqV07oHPn6q2v6yQIJlUQERFRAyUQCDDEYQhsc2zRxaGL1veMmgLjIzqNKJex7mHnUeGyZd9/WvRUZZm84jz5shZ5GPF3EmxF21HBRAWDwYB5XVO/KQkMBCIiqnWDInGS4LvJ3+HGkxvILMwEAHSz74aA7gEwNTKFW0s3iE2tINu5EwInJ/kJyJlwiageeNh6qFyQ5BbnYseVHQjoEYCd/9uJ7MJs+Eb71vvTdwEEeFbyDI/yHimD+RfvX1QNmEskwPnzzDInotr5+1pQVFqKFiIRSk6ckF8baqsmSRCKm7Pz5wE7O8DdHfDxkf92MamCiIiIGglFKZbswmzM6DUD++P3I68oTzk5aEXzZmmaSNTOwk75vpWJlcoyivm6PLoMAU4FABIJPFM1b0dFY0pUaOTBfwbM60LZf0QmJvJgS06O/ATJzZUvU40bFIFAAKlMinnfz1OeiHte2YP1l9crXwf2CISoTSdM9p0pPwE58ScR1YNBbQdh++jt+F/6//Cs9BkOxB9AqawU5kbmmNZzGpxtnCE2ESuHwtVHvbf/u/1/OJx4GE+LnsLKxApvdH0Dm37dBFsLW9UFBQJg4EBmmRNR7agFqAXXr1cvYF6TJAhNCRtSqfy3i0kVRERE1Eiol2LZ8MIGuLZ0VQbGFeVDFXXL119eD297b3w35TtcS7+mEkTPepaFgB4ByCvOg6nIFBtHb0RRaRFsLWyVk49KnP4JElcUjFfRmBIVGlPwX4NqB8wzMjIQFhaG2NhYFBYWok+fPli0aBGcnZ3LLfvee+/h/PnziImJUb73+PFjDB06tNyyq1atgp+fX3WbYxgqyyq3kk/UWd0bFPUyBwmP/lB5nVuciwSzNHlwXnECciZcIqpjAoEA3UTdIG0txfTj05UXLp1sOsHYyBjpuelYMXQFWotb10u9N5lMhuTMZOQW5cLSxBIH4w/i3SHvYlrPaXBv6V5+BUWWeWO5yCGiuqcWoJZ16aJ5uYqydmqSBKF+c5ab+89vF5MqiIiIqJFQL4tSLC3WeF+pqcZ5cL9glWXcWroh+Ntgre9Ry87lVaH6SlTQRzZ4Ywr+a1DtgHlQUBCkUikiIiIgFouxdu1a+Pv74/vvv4e5ublyuR9//BGHDx+Gg4ODyvrXr1+HqakpfvzxR5XsQStF4LgxUv9H1L49sG6d/MQQCuVDY5s3Bx4+/OekqYJ6mQMvqa3KaysTK3gaOQBLlwJeXlVuj4hIX6RSKQpKChDQIwBiEzEKiguw7Mwy5BXlIaBHAP4b+19sHL2x6npvehCTGoPZJ2arTN6S+SwTwzoOw7COw1QXVlxk2NmpXuQYGwOnTwP9++u9vUTUCPwdoC6Nj0e2gwPMBg3SfEFeUdaOImgOyK8xFdus7KZH/ebMyuqfGzSBgEkVRERE1ChoqlGuiTb1xrXKGK+u+kpU0Ec2eCMfpVitgHl2djYcHBwwY8YMuLm5AQBmz56Nl19+GcnJyejatSsAIC0tDe+//z769u2LBw8eqGwjKSkJjo6OsLe319EhGAD1f0T29vIh/D4+QGQksGrVP58VFwMBAVVuclDbQdg/ej8eFj+EV44phsfcQsG4LUjOTkFLi5YQQQSIrIG4GKBXL70fIhFRZVxbuGLeqXmY4DUB++L2Kd9XTJby8OlD2JjaIKswq9ILG107f++8yoVSfnE+8ovzMfXLqbAX26teNP3f/wGHDwOFhcDatcCTJ8CffwKLFgF5eTA5cQKwtdW8IyIihb8D1IUDBiD12jV0qSjQXVnWTlU3PepZRMOHA999B1y8KP+dUtQwJyIiImpEKgpyK2qbK0qAett7VxlY1ypjvLrqK1FBH9ngNQ3+G0jt82oFzK2trREeHq58nZmZicjISLRu3RouLi4A5P8IFy9ejJdffhlisRhffvmlyjZu3LihsXxLoyaRAHv2ACdPykukLFoEjB8vrx2ZmKj6j/b6da02KRAI0EnQCWO6jYHFhQvAg3O4llGKTy59qlwmpN9bmCwWA9euASNHVrI1IiL9GtR2EE5NOYWE9AQcjD+ocmEyo9cMZD3LwsbRG5FRkKG7p/eVUFwwNTNtpnKhNKj9IFxLv6Y5y+DGjX9ql0dFAStWAFu3Kj8WXL8ODB6s13YTURNSWdZOVTc9mgLqI0fyepCIiIgatYqC3OolWL6b8p3us8c1UA/U1+VcXSq0yQbXlHDx008VB7YrCv5XFRA3kNrnNZ708/3338ehQ4dgYmKCzZs3w8LCAgAQGRmJ9PR0bNmyBVvLBBIUkpKS0Lx5c7z22mtITU1Fx44dMWvWLI11zRsNgUCeibh37z/v5ebK/5G4uwM2NkBWlvwfSkV1LCsjkQBpaehq/RQzes1QTlzn3dITOPS2PDhPRFSPFBcuSRlJmNFrBqzNrJFZkInVP69GXlEeZvSagYyCjHJ14/RFccEU/nw4AnsEIrc4F1YmVigpLUErq1awMbWRZxmU7ewFAtVJmzMytKtDTERUE5Vl7VR109PIa0oSERERVYd6CZZr6dcQ3C+49tnjVQSHNdVKr4vSo+Vokw2uHsjeswd4/fXqB7arCogbyHVqjQPmb7zxBiZMmIC9e/ciKCgI+/btg0gkwoYNG7B3716YmJiUW6ekpAS3bt2Ci4sLFi9eDEtLS5w4cQLTp0/Hrl27MGDAgBq1RSaToaCgAACUfzY0Jp07w0i9duSTJ8DHH0O6eTMEycmQde6MZ+PGAfn5VW6v3PG+9BJaXzmC7Ve2K0/Efw3pi5KDB1HUv79W22zIGvrfr641peNtSscKNJ3jlclk+Pnhz4hPi0dLs5Y4f/U83G3d4drcFcHfBpcrzZJdmA33Fu7Ir6PfqrjHcSiVlUIqlaq8XyItwfKflmP3CxEY9u11SG0eQThnzj8PNctO2lxcDAQGQtquHaT9+iGnVy/g9u1a/d3KZLL6yTggooansiG7Vd30NPKakkRERETVoW1t82qrIjisTa30OqFNKRj1QHZNA9tVrWcg16k1DpgrSrCEhobi6tWriI6ORlxcHGbNmoXOnTtr3pmRES5dugSRSAQzMzMAgJeXF5KTk7Fjx44aB8yLi4tx+/ZtAFD+2dAI7ezgeOAALOPjIbK2hvD2bXn98tJSlD54gPhJk+SBm6Skam237PEmPElUORETspLQokM3rcu8GIKG+verL03peJvSsQKN/3hTkYqJJyaqTKa54IcF2DF2BwJ7BKKDTQeVC5bnOj4Hu1w7XLt2rU7a186kHUQCEcIvhiO4bzDS8tLQqXknrDq3CqWyUiRl3YIoLBKCPx/J55XYtg0oLUVxmzYoWrUKRm3aoDgjA0UdOuCOs7P891tH/ZCmB85ERCqquumprwmliIiIiBqgsrXNbS1skZSRpHxfkbBUo/IpVQSH9Rao1+TvbHfTuDg4tWsHVBCbrZB6INvLq2aB7aoC4gZynVqtgHlmZiYuXLgAX19fGBnJVxUKhXBxccH9+/eRnJyMDRs2YOPGjQDkgeySkhL06NED27ZtQ+/evSEWi8tt19XVFefOnavxQRgbG8PBwQG3b9+Go6MjzM3Na7wtnZLJYPLzzxAkJkLm4YGiF15A8ejREJw7B2FIiPIfj/GjR/BOS0PRkCFab7qgoKDc8WYWeUMU98+J6NnaG10aSYkATcfbmDWl421Kxwo0neONvRqr8gBPMbln8l/JgAC4lXkLoZJQPMp9hIKSAjhYO8C9rXudta+zrDNOWJ3A5T8vw9rUGnYWdnj7+7eVk44+LnyCmM/nYcS4hf+M0BGJIBg4EILBg1EKQAjADICi1br4u71586YOjo6Imrz6mlCKiIiIqAFSlAgFUGGJlBqVT6kiOFzRJKR68Xe2u6i0FC1EIpScOCHPfteWeiB7+HDA3r76ge2qAuIGcp1arYD5kydP8NZbb2H79u0Y8ndwt7i4GImJiZBIJFi5cqXK8lFRUfj+++8RFRWFVq1aITk5GRMmTMDmzZvRr18/5XLx8fHKjPWaEAgEyuCEubm5sp56vTt9Ghg9WnniGH36KWBiIq9bvmWLvHi+pSWwfz+MPD1hVJ1/yH8re7wj+03CKQiQmJ4ADzsvSPpNgEAo1PVR1asG9fdbB5rS8TalYwUa//F6t1KdddzSxBIigQjOLZwRdjxM+X6oJBSLTy+Gp50nRrrU7WR0vm6+8HWT/+7+lPoTFg5aiLT8NNha2CItNw3JRrkYIRIBY8YA/foBHh4wkkhgVEWWQW3+blmOhYiIiIiISDcUWePn75+HnYUdjIXGEJuIkVOYg1JZKWJSYwDIA9vVLZ8ik0oRY5GGhENvw9PKGRITdwjU5mesaBJSvVDLdhdcv169gLmmQHZNAtsGEhCvSrUC5m5ubhg6dChWrlyJlStXwtraGlu3bkVOTg78/f3Rtm1bleWtra1hZGSEjh07AgCcnZ3RqVMnfPjhh/jggw/QvHlzHDp0CL///juOHDmiu6NqKBIS5BPETZwon+Tz6VPgk0/kk3AOGwYcPKjTmj0CoRAjBkyGYf+TJKLGQPEkPe5xHJqbNEdeSR5OTTmFpIwk1dJR6Qn6H5pWCcUFVHxaPGwtbLH659XKLPM9w9cBUVHAhAlAI3v4SERERERElahiMkcyDOpZ44E9AjHRcyIirkRAJBDhScET+Eb74tSUU9UunxJz6SB8f5j6T0b6c3swoj7/jahlu8saScWJ+lLtGuaffvopwsPDsWDBAjx9+hS9e/fG3r17ywXLNREKhdiyZQvCw8Mxf/585OTkwMPDA7t27YKbm1uNDqBB8/QEJk0Ctm//JzAeECAPnmdkGETNHiKimlA8SR/QegCuXbuGLt5dlFnXZS9CBrYfiDe6vaHfoWmVUL+ACugRgG1XtqFUVoqMR7eAqZ/Lh6EZ+NNxIiIiIiKqhiomc6S6I5PJkIpUxF6NhXcrb+1qi/9NPWs8tzgXXWy7YOngpXhS8AQH4g8os8nn9J1TrfIpCWlxahnpCfWbwPp3KZTS+HhkOzjAbNCgmk9cSdX/7qysrLBixQqsWLGiymWDg4MRHBys8p6trS1WrVpV3d0aJokEOH9edQKA/HzAykoeJG8EQxSIiKpDUw23+ixDkpCeALGJGFO7ToWxyBgOVg6Y23cu9lzdA4+7z/6ZuAVgdgkRERERUVNRxWSOVHd+fvgzJp6YWL3a4n9Tzxq3MrHCgHYDAKjWMvew81Apn6LNBKCe9t5qGeleOj/2avm7FErhgAFIvXYNXSq6Z+XoCa3wYYM+CQTAwIGqEwAMHQq4ugI+PvXdOiKiOlenNdy04GnniUlek7Dl1y0qddXX+nwMiW+I/Hfb1pbZJURERERETUkVkzlS3Ul8klit2uJlSZwk+G7Kd7h4/yJsLWzh3tIdPo4+AFBpNrk2E4BK+k3AKchU5hE0CBw9oRUGzPVNMTtscjJgbQ2kpwNSaX23ioiIIL+AunjvIgJ7BiK3KBeWJpZIzkiGta03BCtXyi+Mk5IMOrskIyMDYWFhiI2NRWFhIfr06YNFixbB2dm53LLvvfcezp8/j5iYGOV7UqkUGzZswOHDh/H06VP06dMHy5YtQ/v27evyMIjqHrNviIiIGh9t+3dFLKeiMrq8TqgzHrYe1aotXpZAIMDITiMxstPIcp9VlsilzQSgBjuPIEdPaIUBc31TzA4L8AkOEVEDohhmBwDONs6IT4+HAAJ42nnifv5jnO7tDUm/4RAABp1dEhQUBKlUioiICIjFYqxduxb+/v74/vvvYW5urlzuxx9/xOHDh+Hg4KCy/qZNm7Bv3z6EhYWhdevWWLNmDaZNm4bjx4/DxMSkrg+HqO5Uln2jfpM8fDjw00+8aSYiImrotM2uVcRyKorbMEu3zgxqOwj7R+/Hw+KH8GrlVS4bXJvyKdXlbe+NGb1m4GnR/7N35+FRVmf/wL8zSYYlCQFMQiSCCCEhawEBQQiGYclbl9eWWgKyNghUAlbQCFZRbIPSYgAB2YM0GIhgbf1ZqKJEX9kFFwpZyLAqUkhIzErINs/vj+NMZiaTZfbt+7muXElmy3OezDznnPucc59K+Cv8ERMcY/TvAGj9bzvjwApXT7QLA+b2whEcIiKjbNHAaQ/NMrvZg2cj45sM7YyFP435E8rqKpB4bDo+lgHjlJNddpPm8vJyhIaGYt68edrNtefPn4/HH38cKpUKcXFxAICioiIsW7YMw4YNw48//qh9fl1dHXbs2IHnn38eCT+nEluzZg3i4+Nx8OBBPProo3YvE5HdtNZ2M+wkZ2YCM2aw00xERGQr1go8Wis2wxiP3chkMvSV9cUjv3gEnTt3bnZ/e9KnmEotqbH9m+3a13wi6gmjfwdA63/bGQdW2lo9QQAYMLcfjuAQERlliwZOe2g2/Ozs01lvuV1lbSV2/WcXGqVGnCjPxbi2Zpc4sYCAAKSnp2t/Ly0txc6dOxESEoKwsDAAYsBi6dKlePzxx+Hr64t//OMf2scXFBSguroaI0aM0N7WpUsXREVF4dSpUwyYk3trre1m2Elmp5mIiMi2rBV4tFZshjEep9Ge9CktamEgxvA184vzIUFq9nea3XbxOMb+S+e1nLGN6ML9W3tiwNxeWhvBccYlGkREdmLYGDl+7bhdZptrNvysqa/Ry4lXVVeFitoKeMm8ENilh03+tiMsW7YMe/fuhUKhwKZNm7SzM3bu3Ini4mJs3rwZW7Zs0XvOjRs3AAB333233u3BwcHa+4jcVmttN8NOckwMO81ERES2ZK3Ao7Vm13KWrtOIDoo2O8d5SwMxLb2m7m1+Cj/07tJb/3HfXAOWbml6Ld02Y9euQGAgsG4dY38ugAFze2ltBMcZl2gQEdmJYWPkWsU1bPl6i81nmyvvU+LYtWN489ibSB6UjOr6atwfcj+ull3Fk7FPwl/hj4iQGJv8bUeYOXMmkpKSkJWVhZSUFOzevRteXl7YsGEDsrKyjOYjr6mpAYBm93Xo0AHl5eVmH4skSbh9+7bZz7eEpkya72QejzmPI0aILwDQLevw4VDs3w9ZQQGkyEjUPfig/u/DhwNtvMc95hzaGM+j5XgOzSdJktUH9i3drPvmzZsYPXp0s8e+8cYbmDhxolWPlciurDWj21qzaw1fR5KAQ4eAy5fF8alUQGwskJQEyOWW/S1qlfI+JT6Z9gnyivMQFRTVLMd5q1oYiNF7TXUglJ8UAgMGYOtjW5FzOQe+Cl8s/mQxNj+yuelxZT5QTlqi/1oLFjQNrAQGAtOnM/bnIhgwdwbOuESDiMhOdBsjPl4+WPKZaGSYvJzORDKZDA/e8yCq66qx7Ztt8JJ5oVvHbqhrrAMADLwrGgl9Emzytx1Bk4JlxYoVOHPmDN59912cPXsWTz/9NAYMGGD0OR07dgQgcplrfgaA2tpavQ1DTVVfX4/8/Hyzn28NV65ccejfdxcefR4DA4FRo8TP58/r/15Q0O6X8ehzaEU8j5bjOTSPtTfAtnSz7oKCAnTo0AGfffaZXjDf39/fqsdJZHeGM7rHjBEB6pZW6tt7Jb9mIuSKFcBLLzUFRSUJePJJ2/1dgkwmw9i+Y83rN7YwEKN9zcvQm+B6/t+LkXU2S/v0b29+i5XjVoq/fegQUF0t7tC8lu7Ayrp1jP25EAbMnQFzXxGRh2hpg09NA+fQpUOorhONDJOX05lBN1gf2DkQ8/fPR1ltGbxkXkj+RbJdNh+1pdLSUhw/fhyJiYnw9hZVvlwuR1hYGK5duwaVSoUNGzbg7bffBiAC2Q0NDRg0aBC2bdumTcVSVFSE3r17a1+3qKgIERERZh+Xj4+PNoBvbzU1Nbhy5Qr69OljUdDf0/E8Wo7n0Dp4Hi3Hc2i+CxcuWPX1LN2sGwAKCwvRp08fBAcHW/XYiBzOcEb3oUOtr9S390p+zUTI69eb73NCzqut1DoGE1z7Bdyntzq6b7e+7X8txv5cCgPmzoC5r4jIQxhu8Jn5q0xMiZ2iDUxbtJzOAhIkBN+WYcugV3DmzhXE+IRizDVvoPnqZ5dy69YtLF68GNu3b0d8fDwAERTPy8uDUqlEWlqa3uN37dqFgwcPYteuXejRowfkcjn8/Pxw8uRJbcC8oqICeXl5mDZtmtnHJZPJjO5wb0+dOnVy+DG4A486jzaaqeZR59CGeB4tx3NoOmsPrFu6WTcAnD9/3mj6FiK309ZKfVNW8lujjtcEQ3v2bL7PCTmvtlL0GAS5I2QhWDVuFRrRiFu3b6GLoktTeq62XouxP5fCgLkz4A61ROQhDDf4PHDhAHr49dAun7NoOZ2JJElC9rlsTP/HdG0A//f3/x71XvXYf+c/QNeumCyNdulZ5uHh4Rg9ejTS0tKQlpaGgIAAbNmyBRUVFZg1axZ69uyp9/iAgAB4e3vj3nvv1d42bdo0vPnmm+jevTtCQ0OxatUqhISEYMKECfYuDpFjcc8ZIrIjczbrBsQM827dumHq1Km4fPky7r33Xjz99NNG85q3lyP3HnEU5vY3jSPOl2LAAHjrBDIbIiJQp/M+bet+vdc6cgTeDz/c9Nj9+1H382STdtPsb/L995Bt3QpcuABER+POY48129eE7y/TOPR8Gexb82CVD67X1GH66Ze0fchuHbshPrSd75eW9sexIr6/WmbK3iMMmBMRkd0YbvDpq/C1aZ7y1uRczsF+1X69AL6Ptw82n9yMRqkR2eeyEVzZiLHe4S69g/nq1auRnp6ORYsWobKyEkOGDEFWVlazYHlLnnnmGTQ0NODll1/GnTt3MHToUGRkZMDHx8fGR05kY6bOJuOeM0RkR+Zs1t3Q0IBLly4hLCwMS5cuhZ+fH/bv34+5c+finXfewQhNkMZEzrD3iKMwt79p7Hm+5EFBuHfPHiguXkRdWBiuBgVBrfM+bet+XRFffw0/nTr+zrff4nxgoOkHFRgovgDgF78Q38+fb/HhfH+ZxmHnS2efmoiDB1Hse06vD/ntD98isMKM94uN8f1lXHv3HmHA3BnYezMKIiIHUd6nROavMnHgwgH4Knyx99xeTJo0ySHHklucC1+Fr14AP6hzkF7jJ6/gCMY+t9ClZ5L6+/tj+fLlWL58eZuPXbhwIRYuXKh3m5eXF1JTU5GammqjIyRyEFNnjEdHA127ApMmiQ2dAgNFG45tNiKyAXM26/b29sbJkyfh5eWl3aw7JiYGKpUKGRkZZgfMHbn3iKMwt79pzDpfkgTF0aOQ5eVBiopC3ciRptepP++p0xGA0d112rr/Z4qSEr20Gx0HD0ZkZKT1j/dnfH+ZxpnOl6KkBNHfXtXrQw7uNRiRoZFtP9lOnOl8ORtT9h5hwNzWWguGa+47dgz48Udgzx7RAXPhwAwRUUs0G36W15bj4bCHUVJTgkmTJtktT7mh6KBovPr5q0gelIzb9bfxSP9H4C3z1mv8RNX4cSYpkbsydca4Ugls3AhMny4en50NBAfz2kBEVmPpZt1DhgyBr69vs9ft378/jhw5YvZxOcPeI47C3P6mMel8HToE6KRB8XZkHGT8eL3c0t5KJbwNg+E2OF6+v0zjFOdr/HgofXzwSd1A5MlvIarfCCjvUzplGk+nOF9OxpT/EwPmttba7CXD+5KTgW3bGJghIrdkuOHnJ9M+cUgqFg3lfUq8P+n9pg1Gr8qBggJ0H5+JvKrLiPrmByhf38MdzInclcEmTm1+zmUyoLhYP8h+/DhXCBKR1Vi6WbdKpUJSUhI2bdqEBx54QPu4c+fOedwMcXIB1tiU01qr9duzrxxTsxEAyGSQjRmDsRgD/vfdGwPmtmZ4UVWpxM+FhUBpKTB7NlBVBfj5AQEBwLx53EWZiNyS4Yafx68dd+hovN4Go59/Drz3HlBZibFn/DE2KQkYogbSormDOZG7Uir1ZpO163NuGGS/dg3YsoWbgBKRVVi6WXe/fv3Qt29f/OlPf8Jrr72Gbt26Ye/evfjuu+/w97//3RFFImqZsYHrlgLgLU1EtOeG3KYOtBORS2PA3NYML6oBAeKCPns20K8fkJHRdN+KFcDq1cATTzj6qImIrM5ww89rFdeQcznHobPMtc6fB7Zvb7oeDxwI/P73DH4RubP2zCYzpBtk9/EBliwRt3OmGRFZiSWbdcvlcmzevBnp6el49tlnUVFRgaioKLzzzjsIDw+3w9ETmcDYwHVLAfCWZnfbc9a3OQPtROSyGDC3Nd2LakyMCMokJQGdOwPnzulf3DUX+/x8YNw4xx43EZGVKe9TYv0v1+PID0fgp/DDnnN7EB0U7RwBc8M0C7duOfZ4iMgx2lrarRtkP3RI7D0DcKYZEVmNpZt1BwYG4o033rDR0RFZkbGB65YC4C3N7rbnrG9zBtqJyGUxYG5rhh2rBQvExTw1FXjgAUCtBnx9gb17gU6d2OEiIrek2fDzVs0t+Cv8sefcHlTXVSMqyEmudw8+qN/YHjHC0UdERI5gytJuzjQjIiKyrpYC4C3VuayLichGGDC3J93R0qAg4A9/aKoINm0SP3/yCS/yROR2jl4/iof3PKxNx7LhlxvQ/67+UN7nJNc7NraJCDBtaTdnmhEREVlXS21yY3WutTb8JCIyggFze9BcyBUKERz39RUX8qQksdlndjZw9aqYbR4QIB7PCz0RuRFVqQqzB89GVV0V/BR+AOAcqVg0GPgiIoAbehERETmSKW1ye274SUQehwFze9BcyH19xWafMTHAokXiwt61K5CWBvz4I1BVBcyfLwLmTz7p6KMmIrKarp26IuObDO0M88xfZTr6kIiImrPXahNJguLIEUR8/TUUJSXA+PG2nSzBWXhERORu7LnhJxF5HAbM7UFzIa+oALZuBV58senCPmlSU/DcywtITga++w7o0YOdGSJyG8VVxWiUxHWvUWpESU2Jg4+IiMgIe602ycmB98MPw89es+I4C4+IiNwNV4URkQ3JHX0AHkFzIQfE99jYpt+rq/VHRW/fBsrLRacmJ8cxx0tEZGVRQVHwkonrnpfMy3k2+yQicgRjs+Lc6e8RERHZmmZV2Lp13AuOiKyOM8ztwXB575gxYmnsqVPA3XeLHOaaUdGBA4E//5lLiojIrYzsORKfTPsEecV5iAqKcp7NPomIHMHYrDhbpk3hLDwiInI3rrIHkbH6nYicHgPm9qB7IddcLM+dA3bsAGbOBJYvF/nLq6uBH34QqVvYmSEiNyKTyTC271jn2uiTiMhRlEo07N+PO99+i46DB8NbqbRt2hR75WYnIiIifcbq9xEjHH1URNQGpmSxN83FsqREBMjXrxcB859+AjZsAIKCuKSIiIiIyN3JZGIihYYt06ZoJm8sXCi+c48cIiIi+2BaNCKXxBnm9qJWA++9B3zzDTB7NrB/P7BiBXDjBlBTA+zZI0YbR4xw/iVFRERERGQ+Y5t+Mm0KERGR+2H9TuSSGDC3l/feA6ZPb7pIzp8vfi4pERfQJ54Ahg7lrHIiIiIid2dsttmCBUybQkRE5G6MpUWrqXH0URFRGxgwtwdJAr77Tr9jdN99QGpqUwB99mygf38ukSUiIiJyd8Zmm7nK5mVERETUfqzfiVwSA+b2kJMD3HWXfseorEw/gN6rF2cSEREREXkCY5t+EhERERGRU2DA3B5yc8VGnitWANevi1lF/frpB9BHjODsciIiIiJPIJOhLj4e5wMDERkZCW+2AYmIiIiInAYD5vYQHQ1UVwOXLonv/v7AmDHAp58C588DxcXicZLEoDkRERERGSdJYuVibq5oXyqVbDsSERHZmzn1sc5zFAMGQB4UZJ9jJSKzMGBuD0olsHFj06af2dniYllRITZ40swy/+QT5rUiIjIXA0lE5O5ycoDERONtR14DiYjI1blKXdZafdyO53h7eeHePXuAiAj7HC8RmYwBc3uQycQsct2c5QcOiMpA97a8PAbMiYjMZU7DlYjIleTmttx25DWQiIhcnavUZa3Vx+18juLiRdseIxFZRO7oA/AY0dFA167A3LnA1KlAbCzQrZuoBADxPSrKoYdIROTSjDVciYjcSXR0y21HXgOJiMjV2boukyTg0CGxx9yhQ+J3cx6nqY+7dAHmzQPKylp/PSPP6XDnDhRHjrT+HCJyGM4wtwdJAuRyYOVKICWlabR0/nxg9mygVy+x6adS6egjJSJyXZpGqOYay0FIIrKUsy0NVyrFbLu8PHGN0207WnINdLZyEhGRZ7J1e769M9jbepymPlap2p9m1+A5Ps4+i57IwzFgbg+ai21Skv5oqUwGJCQAkyezU0JEZKnWAklEROZwtqXhMpn4+8aOwZJroG45u3YVe+8UFzN4TkRE9mXr9nx7U6m09ThNfWxKahZznkNEDsOAuT1oLoh+fvqjpcOHM1hORGQtrQWSiIjMYdipVamabne2YLIl10Ddck6a1LRRvTMMEhARkeewpC5rz2qp9s5gj40VqVYqKwF/fyAmxvjjzJkRz1WxRC6BAXN70FwQs7Obp2Bxlk4WEREREekz7NQGBDjXjHNr0S1ndTVnvhERketpz6qw9s5gV6uB7dubXmv0aBGQN4zfmDMjXqlEw/79uPPtt+g4eDC8uSqWyCkxYG4Pxi6iMlnTRhLOOEuJiIiIyNMZtuGcYRm1LfKN65YzMFBM8uDMNyIiciRT67v21NHtncFu+FoHDgA9epj/egbPqYuPx/nAQERGRsKbMSAip8SAuT20dBF1tryYRESuyLAxLZcDZ89yIJKILGesDefoZdS2aD/qllOSgOBg7gdBRESOZWp9Z81UJ4YpWTp0aN8geWtBfp37FAMGwDs4GIojR4CCAvZbiJwQA+aO5AyzlIiIXJFuYzQoCJg/HygrE43j2bOBrVs5EElE1mfrzcjaM5vO1u1H7gdBRETOwNT6zpp1tGFKlt//vvUAvKb+VqmABQuMB/l1BgC8vbwwYPt2eD/1FCdQEjkpBswdiZs9EBGZx3DGSXIysG1b0wbLK1cC69dzIJKIrMtwJra1U6PoXtu6dgU2bgSKi/Vfn+1HIiLyBKbWd9Yc8DUM1oeEtB6A19TfSUktB/kNXtOroMC0AQFbtDuIqEUMmDuSrWcpERG5K8NGbKdOwNy5wN69QFUV8NZbwIoVDCQRke3YIjWK7rVt0iRg+vTmry+Xi5U0VVVimbhcbnlZiIiInI0j4yWGwfrAQFHvtxSk1tTffn4tB/kNXrMxMhLepgwIMKUvkV0xYG5rkgR88QVw/ryYIfTgg00XWS55JSIyj2Ej9s4dICNDBMlff13c/tNPHIgkItuxRWoU3WtbdbXx1z97VqSd0n3OmDGW/V0iIiJn48h4iSZYf/w4cO0asGSJqJdbClJr6u/sbDGoHRICjB6t3xfRGQBoiIhAQXAwovfvh3dhYfsGBJjSl8iuGDC3tZwcYN8+/fxXHAkkIrKMpsGZkwPcuiUap42NwPXrwOTJYqb5L37BZYpEZDu2SI2iO5suMLDp2qb7+kzJQkREZFuaYH1uLrBlS9PtLQWplUogMxM4cED8vm6dCJjr9kV0BgDqbt9GQ34+6uLj4Z2Y2L5jYv1PZFcMmNtabq7YWZkjgURE1qNpcAL6SxM1M823bhU5BImIbEWpBD7+GDhxQgS35XKxstCSgTrDHOnBwc2XojOlHxERkWnMzf9tLEjd0mvdugVkZTU919pxH9b/RHbFgLmtRUeLCxpHAomIrE8zm+PkSREs18zGrK5mXl8isq6WOsjLl9tmFWFLS9GZ0o+IiDydqQFwc/N/GwtSt/Ratp4BzvqfyK4YMLc1pVIEbQYOFCOOI0ZwJJCIyFpkMmDKFPFdd3M8DkwSkbUZ6yCbkk/UsHM/fLj5x2LuTDkiIiJ3oFsnd+0KbNwo9oxrqU40N/+3sSB1S6/FGeBEboUBc3tQq4G6uqZgOTs0RETWI5OJvOXGUhcQEQHWCTAb6yCbMpvMIODe8W9/Q8T330NRUgKMH9/8eHSPOSgIKCsDwsNbn91GRETkCXTr5EmT9CfOGKsTrTH7W1Mvl5cD8+YBe/aIVa2a1+IMcCK3woC5rbFDQ0Rke2ygElFrrNEeM9bZNmU2mUHAXf7vf8MvK6vl4zE85uRkYOFC02e2ExERuTrDgW/dOrm6uu060Rqzvw3r5Q0bgP79OVGHyE0xYG5rxjo0mplBXEZLREREZHvWCDAb62ybMlhnGHD39W39eAyPWRMQMHVmOxERkaszDFZ/+mlTnRwY2LSPUUt1ojUm1xjWy/X1HKwmcmMMmNuSJAF33y2W61RWAv7+QEwMZ50TERER2ZM1AszmdLY1M+KOHQN69gR27RJ72gQGAvPni8e0dDyGx+znZ97MdiIiIldnGKw+d06suho7VtS19kjNyMFqIo9icsC8pKQEK1euxOHDh1FbW4uhQ4diyZIl6NevX7PHvvzyyzh27BhycnK0t6nVamzYsAH79u1DZWUlhg4dildeeQW9evWyrCTOKCcH+PJLYPv2povqE0+ICzmX0RIRmYab3BGRuWwdYNZcnwoLxeZjmo3HAP1JErNni1yrSiUaunfHnW+/RcfBg+FteDySJDaNX78eKC0F7rsPqKgQZTB1ZjsREZGr0w1Wd+0qBp7XrWvqE9ijTuRgNZFHMTlgnpKSArVaja1bt8LX1xdvvfUWZs2ahYMHD6JTp07ax3322WfYt28fQkND9Z6/ceNG7N69GytXrkRISAhWrVqFp556Ch999BEUCoXlJXImhYWAt7d+cPzECbH5J0cmiYhMY7g65+23xQZ4CQkMnBNR62wdYNZcn2bPBjIymq5Tr76q3w6sqtJOlKiLj8f5wEBERkbC2/AalpMjJllMmiRSsfTtK1Ys8lpHRESeSDdYHRjY9iaf5mhrco6mLaFJsbt+PSfxELkxkwLm5eXlCA0Nxbx58xAeHg4AmD9/Ph5//HGoVCrExcUBAIqKirBs2TIMGzYMP/74o/b5dXV12LFjB55//nkkJCQAANasWYP4+HgcPHgQjz76qJWK5SS6dgVqavSD44GBHJkkIjKH4VLMw4eBb74B1GrOsiQix9Jcn6qq9K9TQUH67UB///ZNlMjNFcFyTfA9O1ssN+e1joiIPJHuwPe6dbZZsd/e1LlMsUvkEUwKmAcEBCA9PV37e2lpKXbu3ImQkBCEhYUBACRJwtKlS/H444/D19cX//jHP7SPLygoQHV1NUaMGKG9rUuXLoiKisKpU6fcL2BeXAzs2QMkJ4vZQTExQEQEl9ESEZnD2IZ5OrM1iYgcRnN90uQZ11ynIiKAjz8WKwwDA8XvP08aafP1vvqKKfyIiIgMWTOXuO6s8vLy9tW71thInIicntmbfi5btgx79+6FQqHApk2b0LlzZwDAzp07UVxcjM2bN2PLli16z7lx4wYA4O6779a7PTg4WHufOSRJQk1NDQBovzsDxYAB8K6uBrZtA7y8oH74YdwZNgy4fdvi13bG8toSy+u+PKmsAMtrkeHD0fFvf4P83/8WwfK9e4GkJDRERKDOCtdVS1mjrJIkQcYlnUSuR7N6UKUCMjOBkhLRgdekjBo3zvTXKyoSM8s1AQEfH+DQofYt/eaeD0RE5K5MXbHfWp2oO1t83rz2BeK5+SeRRzA7YD5z5kwkJSUhKysLKSkp2L17N7y8vLBhwwZkZWUZzUeuCSIY3tehQweUl5ebeyior6/HlStXAED73RnIg4LQJzsbfufOAYGBqOrcGVcKC6FWq632N5ypvPbA8rovTyorwPKaSx4VhT4dOojraloaqnr2xJWgIKjz863y+tZgaVndbj8PIk9g7dWDMhkwebJIw3L8OHDtGrBkiVix2J6l31wuTkRE7sJYwNuUOre1OlF3tviePcCGDUB9feuBeKbYJfIIZgfMNSlYVqxYgTNnzuDdd9/F2bNn8fTTT2PAgAFGn9OxY0cAIpe55mcAqK2t1dsw1FQ+Pj4IDQ3FlStX0KdPH4tey9oUxcXwTksDGhvRzcsL/vv3oy4+3uLXrampccry2grL6748qawAy2sVERGof/hhAEAHABHWeVWLWaOsFy5csPJREZHL0gThc3MB3VWb7Vn6zeXiRETkLiwdBG6tTtSdLV5dDfTv3/ZrM8UukUcwKWBeWlqK48ePIzExEd7e4qlyuRxhYWG4du0aVCoVNmzYgLfffhuAmPnd0NCAQYMGYdu2bdpULEVFRejdu7f2dYuKihARYX7IQyaTaYMTnTp10qaHcQoFBXoXZ+/CQngnJlrt5Z2uvDbG8rovTyorwPKazQXSDFhSVqZjIaJmzFn6zeXiRETkLiwdBG6tTuRscSJqgUkB81u3bmHx4sXYvn074n+eJV1fX4+8vDwolUqkpaXpPX7Xrl04ePAgdu3ahR49ekAul8PPzw8nT57UBswrKiqQl5eHadOmWalIToYdFiIiy2kC5SoVsGAB0wwQkecwpzPPAAAREbkLS2MqrdWJnC1ORC0wKWAeHh6O0aNHIy0tDWlpaQgICMCWLVtQUVGBWbNmoWfPnnqPDwgIgLe3N+69917tbdOmTcObb76J7t27IzQ0FKtWrUJISAgmTJhgnRI5G3ZYiIgsp1mKOXMmMHs2UFUF+PmJADobuETkzszpzDMAQERE7sLSmIqt6kQXWPlKROYzOYf56tWrkZ6ejkWLFqGyshJDhgxBVlZWs2B5S5555hk0NDTg5Zdfxp07dzB06FBkZGTAx8fH5IN3duL6KUNu7lhER4+FcowEGS+oRESm0yzFDA8HXnqpaYZJZqajj4yI3JErd4Jd+diJiIgMOesgMDfYJnJrJgfM/f39sXz5cixfvrzNxy5cuBALFy7Uu83LywupqalITU019U+7HMPr54+7ctBjOi+oREQm0yzFNMxhePmyY4+LiNyTK3eCXfnYiYiIXAU32CZya3JHH4A7M7x+SmeNXFCJiKhtSiXw8cfA/feLABAgvt91l2OPi4jck7FOsCQBhw4B69aJ75Lk2GNsibFjJyIiIuvSTOgBuF8dkRsyeYY5tZ/h3hSyWG4ASkRkFplMfC1fDiQnA7dvA7GxQESEo4+MiNyRsQ3GXGXmNjecJyIisj3uV0fk1hgwtyHD62fwGCUQzAsqEZFZcnOBsjJg2zbx+5//DCQkOPKIiMhdGesEr1/vGkuv2YEnIiJ34Ox7cjhrbnUisgoGzG1IJgPGKiWMxc8Xefx8kecFlYjIdIazJkeMcK5GMxG5D2OdYFeZuc0OPBERuSrdIHlQEDB9uvOv7CIit8SAua25yvJdIiJnZzhrcswYkUdYM+tkzBjg88+ddxYKEbk2ztwmIiKyLd34ydSprrGyi4jcEgPmVtZs1VBuLmS8yBMRWc5w1uShQ/oDkrt2cRYKEdkOZ24TERHZlu7G1b6+rrGyi4jcEgPmVmY4ofzHXdHowYs8EZH1FRYCs2cDVVWAn59+A5sDlERka86eW5WIiMjV6KY/27sXyMwESkq4souI7I4BcyszjNf8s1yJeVy+S0RkfV27AhkZTQOS27ZxFgoR2Q/T7hEREVmXsfRnHIwmIgdgwNzKDPeDCuvP5btERDZRVKQ/QnnnDvMLE5H9cFULERGRdTH9GRE5CQbMrUypBD79FDh/HiguFrdJEgdFiYisLjhYf4Sya1c2sInIfgxnSXBVCxERkfmY6oyInAgD5lYmkwFqNbBggeg/de0KbNwogue85hMRWVFZGZCcDFRXixzm5eWOPiIi8iTGlo0TERGReZjqjIicCAPmNqBZodulC7B0KTB9Oq/5RERWFx4OLFyof4ElIrIXLhsnIiKyHkekOuOsdiJqAQPmNhAdLWaWL10KnD3L9JZERDahmd2pUgEBAaKhq7mdDV0iai92lomIiOzPsP7VpDrz9QWmTBGrSQ8dsm29zFntRNQCBsxtQKkUaVj27xfXeqa3JCKyAc3sTgB44glg0iTgq6/EZqCTJzPgRUTt01JnmYF0InKAkpISrFy5EocPH0ZtbS2GDh2KJUuWoF+/fs0e+/LLL+PYsWPIycnR3qZWq7Fhwwbs27cPlZWVGDp0KF555RX06tXLnsUgapth/fvpp0BmpmjLP/+8fYLY3MCbiFogd/QBuCOZTOQs9/UF9u4VKXanTgV27WJ6SyIiq8vNFcHyjAwgK0vkwdLpOBIRtcpYZ1mSgOxs0ZH/wx/Ed15XiMgOUlJScPXqVWzduhXvv/8+OnbsiFmzZqGmpkbvcZ999hn27dvX7PkbN27E7t278ec//xnZ2dlQq9V46qmnUFdXZ68iEBknSWLG+Lp14nthoX79e/QoMGMGcOpU83rZVjSz2gHOcCQiPQyY20h0NPDvfwMvvQR07w4MGwaEhDj6qIiIXJhhI1uSxO3R0WLjT3s1rInIvRjrLOfkiKWCvK4QkR2Vl5cjNDQUaWlpiIuLQ79+/TB//nwUFRVBpVJpH1dUVIRly5Zh2LBhes+vq6vDjh078MwzzyAhIQEDBgzAmjVrcOPGDRw8eNDexSHSp5lRrhmIDgjQr38DA0V96+dnvyC2JsXjunXiO2c4EtHPmJLFRpRK4C9/0d/wc/ZsQK3mCh8iIrO0lDZBqRRLN7Ozm3Ie3rwJ7NkDJCUBco4NE1ErNJ3lvDzRKVcqgfXr25dXj2lbiMiKAgICkJ6erv29tLQUO3fuREhICMLCwgAAkiRh6dKlePzxx+Hr64t//OMf2scXFBSguroaI0aM0N7WpUsXREVF4dSpU3j00UftVxgiQ4YrukpK9OtfuVzUt9nZInjSqxcwYoRtg9jcwJuIWsCAuY1o0rLo1gdVVUyJRURkttxcEcCaPFlcUFWqpuDU5MlAcLC4yC5a1BTgkiTgyScdfeRE5MyMdZajo4FXXxV59W7fBh55xHiHnZuFEZGNLFu2DHv37oVCocCmTZvQuXNnAMDOnTtRXFyMzZs3Y8uWLXrPuXHjBgDg7rvv1rs9ODhYex+Rw2hWdOkOROvWv5LUfACbg9BE5CAMmNuQYX3g78+UWEREZouOFrPHt28XF9b33gP69xeNbE3A69Ah/ZHK3FzHHjMRuSalEnj//bY77dwsjIhsZObMmUhKSkJWVhZSUlKwe/dueHl5YcOGDcjKyoJCoWj2HE2ec8P7OnTogPLycrOPRZIk3L592+znuyLNuTTMHU/Gtet8DR8Oxf79kBUUQIqMRN3w4WJQWteIEeJLvJiNjtbx+P4yDc+XaXi+WiZJEmTtHIhjwNxGJEmsKHr7beD6deDuu4GICCAhwdFHRkTkopRK4Nix5sEppbIpJUL//kDXrkBZmRipjIlx5BETkbMyTKUyZgzw+ef6qVXas0Tb2Gw5IiIr0KRgWbFiBc6cOYN3330XZ8+exdNPP40BAwYYfU7Hjh0BiFzmmp8BoLa2Fp06dTL7WOrr65Gfn2/2813ZlStXHH0ILqXN8xUYCIwaJX4uKIBcLse9Fy9CoVKhrn9/XO3XD2q12ubH6Sz4/jINz5dpeL6MMzbgbAwD5jZibIXumDGOPioiIhcmkwEPPtgUnOraVTS609KAH38UOcurq4GtW4GLF0WwPCnJ0UftcCUlJVi5ciUOHz6M2tpaDB06FEuWLEG/fv0AAAcOHMCmTZtw9epVBAcHIykpCU899ZR25P3mzZsYPXp0s9d94403MHHiRLuWhchqDBtqmZnAjBmmp1Yxlv+ciMhMpaWlOH78OBITE+HtLbrqcrkcYWFhuHbtGlQqFTZs2IC3334bgAhkNzQ0YNCgQdi2bZs2FUtRURF69+6tfd2ioiJERESYfVw+Pj7aAL6nqKmpwZUrV9CnTx+LBhs8hbnnS3HkCLwnT9bWv13270ddfLwNj9Q58P1lGp4v0/B8tezChQvtfiwD5jbCFbpERDYwZowIbOXmAmFhwPz5wKRJIlD+xz8Cr78ufl6xwtFH6jRSUlKgVquxdetW+Pr64q233sKsWbNw8OBBnD59Gs8//zxefPFFJCQkID8/H0uWLIFCocDMmTMBiA3EOnTogM8++0xv+Zq/v7+jikRkOcOGmrkNN24WRkRWdOvWLSxevBjbt29H/M9Bw/r6euTl5UGpVCItLU3v8bt27cLBgwexa9cu9OjRA3K5HH5+fjh58qQ2YF5RUYG8vDxMmzbN7OOSyWTaHOqeplOnTh5bdnOYfL4KCvTqX+/CQngnJtrm4JwQ31+m4fkyDc9Xc+1NxwIwYG4zXKFLRGQDn3/eNAt06lQRLM/IaLrYzp4tZp0TAKC8vByhoaGYN28ewsPDAQDz58/H448/DpVKheLiYsydOxfTp08HAPTq1Qsffvghjh49qg2YFxYWok+fPggODnZYOYiszrChFhPDhhsROVx4eDhGjx6NtLQ0pKWlISAgAFu2bEFFRQVmzZqFnj176j0+ICAA3t7euPfee7W3TZs2DW+++Sa6d++O0NBQrFq1CiEhIZgwYYK9i0PUNgZOiMhJMWBuI1yhS0RkA7qzQH19xWxy3VmhnTsDFmxq5W4CAgKQnp6u/b20tBQ7d+5ESEgIwsLCEBcXp71PrVbjxIkTOHXqFFJSUrS3nz9/Xpu+hchtGDbUxowBgoPZcCMih1u9ejXS09OxaNEiVFZWYsiQIcjKymoWLG/JM888g4aGBrz88su4c+cOhg4dioyMDPj4+Nj4yInMwMAJETkpBsxNZbhJlFIpluMa0F2h286nEBFRW3RnoezdC6xeDWRnN81KqakRG39SM8uWLcPevXuhUCiwadMmveV5169fx/jx49HQ0IBRo0ZhypQp2vsKCwvRrVs3TJ06FZcvX8a9996Lp59+2mhe8/aSJAm3b9+2qDzm4q7x1uEW53HECPEFAHfu6P9uh3K5xTl0AjyPluM5NJ8kSSYt724Pf39/LF++HMuXL2/zsQsXLsTChQv1bvPy8kJqaipSU1OtelxElmgxJmKL1GYMwBCRFTBgbipju3m2cXE34ylERGSMXC7SrlRVAf7+QN++wMcfAydOiFQsERFAQoKjj9IpzZw5E0lJScjKykJKSgp2796N6OhoAECXLl2wb98+XL16FWlpaXjhhRewdu1aNDQ04NKlSwgLC8PSpUvh5+eH/fv3Y+7cuXjnnXcwQhNcNFF9fT3y8/OtWTyTcdd463C38yiXy3HvxYtQqFSo698fV/v1g1qttunfdLdz6Cg8j5bjOTSPQqFw9CEQOT27xkQYgCEiK2DA3FRmbArFDUCJiCygO0tEJhMzyv39gYULgQMHgMhIoEsX4L//5ezyVoSFhQEAVqxYgTNnzuDdd9/FG2+8AQDw8/NDVFQUoqKi0NjYiOeeew6pqakIDQ3FyZMn4eXlhY4dOwIAYmJioFKpkJGRYXbA3MfHR3s89sZd463DXc+j4sgReE+erO1kd9m/H3U/b7xnbe56Du2N59FyPIfmu3DhgqMPgcgl2DUmwgAMEVkBA+am0qQD8PUFpkwBysqAQ4daXebDfSyIiCxgOEtk9mwxs/yll8Rt8+YB27dzFokRpaWlOH78OBITE+HtLap8uVyOsLAwFBUV4fTp01AoFHq5zCMiIgAARUVFCA0Nha+vb7PX7d+/P44cOWL2cclkMofv2M5d463D7c5jQYFeJ9u7sBDeiYk2/ZNudw4dhOfRcjyHprN2OhYid2XXmAgDMERkBXJHH4DLGTMGyMwEXn9d/F5QAOzbB3zxRYtP0exjsW6d+M59LIiITGA4S6RzZ6CoqOm2ysrms0gIAHDr1i0sXrwYx48f195WX1+PvLw89OvXD5mZmXhdU5/97MyZM/D29kafPn2gUqkwePBgnDx5Uu8x586dc9gMcSKb0nSyAXayiYiIrMSuMREGYIjICjjD3FSffw7MmAG89pr+jMaBA0Uw3QD3myAispDhLJEHHhA5zDW3+ftzFkkLwsPDMXr0aKSlpSEtLQ0BAQHYsmULKioqMGvWLNy4cQPTpk3DmjVrMHHiROTl5WHVqlWYMWMGunXrhoCAAPTt2xd/+tOf8Nprr6Fbt27Yu3cvvvvuO/z97393dPGIrE/Tyc7LE9cSdrKJiIgsZou9PZ3jjxGRu2LA3FSFhUBKipjhmJQE+PmJfLq3bhl9OPebICKykGEAq7QUqK4WqVkCAoABA8RmnyUlQEwMA1wGVq9ejfT0dCxatAiVlZUYMmQIsrKy0LNnT/Ts2RNbtmzB2rVrsXPnTnTv3h3JycmYM2cOAJG+ZfPmzUhPT8ezzz6LiooKREVF4Z133kF4eLiDS0ZkA+xkExEROQZnGxKRE2HA3FRduwK1tUBqalMUfMUKYNgwow/nfhNERBYyDGD98Y8iYL51K5CWBsyd23Q93rCBDWsD/v7+WL58OZYvX270/vj4eMS3sqlhYGCgdnNQIiIiIiKb4GxDInIizGFuquLi5vlyz55t+t2AbirMrl3FhPR168Q+oZJkn0MmInIrsbFAaKi4uJaUiOtvly5ixvnly7zAEhERERHZgCSJprZNYhrGZhsSETkIZ5ibKjpaXLh18+X6+gL5+cC4cc0erlSKPUIPHBAxnjlzOGBKRGSRpCTgn/8ULXUvL/E1eTKQkSEusOnpvMASEREREVmZTSeBG+5bxH2JiMiBGDA3lVIJyOXAoEHA4cMiWL53LzBpktGHy2QivXlWFvDkk0zPQkRkMbkcmDhR/LxpE5CcDHTsyAssEREREZEN2TTlLDfeJiInwoC5qWQyYMwYICEBCAsTF/NJk1q9mGsGSv38OGBKRGRV4eHAwoUiHQsvsERERERENmPTSeDceJuInAgD5uYy4WKuGShVqUR6lpISDpgSEVkFL7BERERERHbR0iRwSQIuX74Phw93QGysuF0mc+yxEhFZggFzO+BAKRGRFUiSSJyYmyumt2ha4mPHip9zckQOLCIiIiIisrqWYhtHjyoweXJni3Kbt9TUJyJyBAbMiYjINeTkAE88IdJgffUVUFQkNvuUyWy8AxEREREREbUkL09mcW5zNueJyJnIHX0AnkaSgEOHgHXrxHdJcvQRERG5iNxcESzPyBA7KU+fLlrWmvsMW+lERERERGRzUVESvLzEz23lNm8pJsLmPBE5E84wtzOOmhIRmSk6WswsNzZ9xaY7EBERERERUUtGjqzDnj0VuH49ADExXlAqW06x0lJMhM15InImDJjbmbFRUwbMiYjaQakUaViys5u3pFvagYiIiIiIiGxKJgP69r2MRx6JROfOnQGI2ePGAuMtxUTYnCciZ8KAubkkCfjiC+DcOeDmTTEcmpQEyFvPcsNRUyIiM8lkImd5cHDzljR3VyYiIiIichotBcZbiomwOU9EzoQBc3Pl5AD79gHbtzdd6QFgypRWn8ZRUyIiK+AGEETUkpbWgBMREZHdtBQY142JxMQAarXIZx4UBJSVAeHhrLqJyPEYMDdXbi5QWak/ZHrunOiktXJl56gpEZEFuBEEEbWF1wkiIiKHa2myoG5MxDBtS3IysHAhq24icrzW84dQy6KjAX9/6G0F3b276KQREZFtGFvbSUSki9cJIiIih9MExhcuFN+NzSs0rLKrq1l1E5Fz4AxzcymVQEkJ0KsXcOsW0NAAXL8OKBStDoVylTARkQW4EQQRtYXXCSIiIpdgWGX7+bHqJiLnwIC5uWQy4Le/BfbsAfLzAV9f4N13gfffb/VpXCVMRGQBbgRBRG3hdYKIiMgl6FbZgYFAebn4nVU3ETkaA+aWkMmAyZPFz7m5wMaNwJgxrT6lpZ2iiYioFYbLcxYs4PIcIjKOG8YQERE5hCSJvOTtXVHPKpuInBUD5pb6/HNgxgztlPGbUjCCp4xtsVLgKmEiIjPk5ABPPAFMmgR89RVQVCQGLBk0JyIiIiJyCkePKvDww1xRT0SujwFzSxlMGfc6dRz/d7cSCWOMB3G4SpiIyAy5ucDUqcDmzeKam50NBAUB48Y5+siIiIiIiAhAXp6MK+qJyC0wYG4pgynjgTXX0CPvEKSEcUYnPnLJERGRGaKjgcuXxX4RkycDVVVi/4ixYznLnIiIiIjICURFSVxRT0RugQFzS40ZA/Wb6ZCf+kps6bxnD3p1uwc5OeMYFCcishalUkxRmTIF2L5dtMLfe0+0wnmxJSIiIiJyuJEj6/DJJ95cUU9ELk9u6hNKSkqQmpqK4cOHY9CgQZg7dy4uXryovf/AgQN47LHHEBcXh3HjxmHbtm2QJEl7/82bNxEREdHs64MPPrBOiezt888hv10tAjdbtwLV1ajzD0RenqMPjIjIjUgS0LUr0KlT852TiYiIiIjI4TQr6hcutN5CUM1GouvWie864SUiIpsxeYZ5SkoK1Go1tm7dCl9fX7z11luYNWsWDh48iNOnT+P555/Hiy++iISEBOTn52PJkiVQKBSYOXMmAKCgoAAdOnTAZ599BpnO1dPf3996pbKnY8eAykpgxQrg+nUgNBQnyiMR9YCjD4yIyI289x7wzDPA0qXcOZmIiIiIXIckiQ3sc3NFmkGlkikFTZCTAyQmciNRIrIvkwLm5eXlCA0Nxbx58xAeHg4AmD9/Ph5//HGoVCoUFxdj7ty5mD59OgCgV69e+PDDD3H06FFtwLywsBB9+vRBcHCwlYviID17Aj/9JII4P1/BB2z6GJch6kXWg0REVnD2LFBWBrz+OpCcLGabJyZynScREREROTdGfC2Sm9t8gSlPHxHZmkkpWQICApCenq4NlpeWlmLnzp0ICQlBWFgYJk6ciGeffRYAoFarcezYMZw6dQojR47Uvsb58+fRr18/65XA0Tp3Br77Tu8K3vFyPhITRb0IcAkREZHFYmNFB6OiAtixAxg0iBt+EhEREZHzMxbxpXaLjhbdAIALTInIfsze9HPZsmXYu3cvFAoFNm3ahM6dO2vvu379OsaPH4+GhgaMGjUKU6ZM0d5XWFiIbt26YerUqbh8+TLuvfdePP300xg9erRlJXGU4mLA11cvRUBpSJTeyCcHlImILJSUJEYbc3OBmBjxOxERERGRs9NEfNubUpApXPQolSKGwo1EiciezA6Yz5w5E0lJScjKykJKSgp2796N6OhoAECXLl2wb98+XL16FWlpaXjhhRewdu1aNDQ04NKlSwgLC8PSpUvh5+eH/fv3Y+7cuXjnnXcwYsQIs45FkiTU1NQAgPa7vSgGDID3q6+KFAG3bwNxcVB0lMPLC+jeXY3bt+/g7NkOaGwUQ6KNjcC5c40YMaLWor/rqPI6CsvrvjyprADLa5HHH4ciMBCyvDxIn32GupEjnarzYI2ySpKkt78HEREREbkwSQLkcmD9eqCkBBgxonnE1zBALpdzxp0OzUaiHnwKiMgBzA6Yh4WFAQBWrFiBM2fO4N1338Ubb7wBAPDz80NUVBSioqLQ2NiI5557DqmpqQgNDcXJkyfh5eWFjh07AgBiYmKgUqmQkZFhdsC8vr4eV65cAQDtd3uRBwUh5s9/hs/x44CfH7BiBfxT0pCSMgY3b95Gfv553HPPffDy6q6t70JDy5Gff9kqf9/e5XU0ltd9eVJZAZbXHPddvozOkydrOw8Ve/bgct++lh+clVlaVoVCYZ0DISIiIiLHMrbc3HByhOFj1q/32KTdnFxPRM7CpIB5aWkpjh8/jsTERHh7i6fK5XKEhYWhqKgIp0+fhkKhQFxcnPY5ERERAICioiKEhobC19e32ev2798fR44cMbsQPj4+CA0NxZUrV9CnTx906tTJ7NcylSQBP35bij7vPaut4A6XRuGOBMTEdERkZCQGDAD2729AQYEMkZESRo7sCJks0qK/W1NT45DyOgrL6748qawAy2uJDocP63UeAq5fR+Qjj1jhKK3DGmW9cOGClY+KiIiIiBympR0rdSPDCoVI81pRIR5TUmJaChc3wnS2ROQsTAqY37p1C4sXL8b27dsRHx8PQMzuzsvLg1KpRGZmJoqKipCdna19zpkzZ+Dt7Y0+ffpApVIhKSkJmzZtwgMPPKB9zLlz57Qz1s0hk8m0wYlOnTrp5VO3tUOHgN8+p8TG5E/Q53YevGKjMOd1JVJSgPJyb/ztb94IDwcmTBAXfsHsif3N2Lu8jsbyui9PKivA8ppFs/Hnzy1or5gYpzyHlpSV6ViILMOZaURE5FSio4GuXYFJk4DqaiAwsKmy0o0Mz54NbN0qfh4xAvj0U+D8ebFnGiCe4wEVWkvjC0RE9mZS5DY8PByjR49GWloa0tLSEBAQgC1btqCiogKzZs3CjRs3MG3aNKxZswYTJ05EXl4eVq1ahRkzZqBbt24ICAhA37598ac//QmvvfYaunXrhr179+K7777D3//+d1uV0aZyc4GfymSYsm0sgLF48klRDwYEAFOmiNTmCxdyZJSIyGLc8YeI2sCZaURE5FSUSmDjRmD6dFE5ZWcDwcHNI8O9egHr1jW1cXNygAULPK5CM3V/VEMcOCciazF5qvPq1auRnp6ORYsWobKyEkOGDEFWVhZ69uyJnj17YsuWLVi7di127tyJ7t27Izk5GXPmzAEg0rds3rwZ6enpePbZZ1FRUYGoqCi88847CA8Pt3rh7MHwgj5kCDBwIPDWW+K26mqOjBIRWQV3/CGiNnBmGhERORWZTMwSN6ycDAMJI0boV1geWqEZzo8ZM0as6m9vAJwD50RkLSYHzP39/bF8+XIsX77c6P3x8fHadC3GBAYGajcHdQeGF3S5HBg/Xn9EdN48ICbG0UdKRERE5N4snZlGRERkdcYqp7ZWTnpohWY4P+bQIdMC4B46zkBENmC9ZNoeyvCCLkniIn78OHDtGvDXv4pZ5k884djjJCIiInJ3zNxEREROx1jl1NbKSVZoAEwPgHvoOAMR2QAD5lamqfdyc4EtW5puz88Hxo1z3HERERERuTtmbiIiIqdjTuXECg2A6QFwjjMQkbUwYG4jHNkkIiIiIiIiIjKPqQFwjjMQkbUwYG4jHNkkIiIiIiIiIjIPA+BE5CgMmNsIL+xERERERERERERErkXu6AMgIiIiIiIiIiIiInIGnGFuA5IE5OSIjT+jo5s2wSYiIiKi9mF7ioiIiKyF7QoiMgUD5jaQkwMkJjZt+Pnxx8C4cY4+KiIiF8SWLZHHMmxPffIJU90RERGRediuICJTMGBuA8eOiYswIL6fOMGAORGRWdiyJfJYubn67am8POMff91xtaAgoKwMCA/n+BoREbkoThixifa2K4iIAAbMbSIoSMR1NPGdgABg3TrH1XWsb4nIZbFlS+SxoqP121NRUcYfZziutmIF8MQTwPvv83JBREQuSLdi69oVWL0aUKmA2FggKQmQcys6c7S3XUFEBDBgbhMREUBKChAcDNy6BdTWAn/+M1Bd7ZjJkZygSUQuiy1bIutwwdFzpVK0WfLyxEdfqTT+OMNxtbNngd/+luNrRETkonQrtkmTgDlzmtrCxcUuU487m/a2K4iIAAbMbSIhQSwHvnIFCA0VQfPly8WXIzpvnKBJRC6LLVsi62jv6LkTBdZlMnGIbbVZDMfVfH3FJAWOrxERkUvSrdiqq/U78ydPAosXG6/HnagOd0btbVcQEQEMmNuETAbcvAmcPw9s397UgZs92zGdN07QJCKXxZYtkXUYjp4fP268I/3FF8C+fUBlpRioksuBMWPsfrimUCqBzEzgwAERLN+7F9i4keNrRETkonQnjPj5AdnZTZ15P7+WZ8FxaTkRkdUwYG4jly6JvqZu37RXL8d03jhBk4iIyMMZjp5fuyY61oYdacPR/oEDnT5gLpMBU6YAPXqIts6kSZxUR0RELkx3wohaDXToICq4W7eAPXtangXHpeUt4uR7IjIVA+Y2EhkJVFTo901HjLDvRdmwUkhJAT7/HFi/npUEERGRR1EqRQPgyBExO+1f/xLBcMOeY3Gxfmf71i2HHnZ7cTEKERG5JbkcePLJps59dHTLs+C4tLxFnHxPRKZiwNxGZswQ3/v3B376SWz8efkycOiQbQPVmnq0sBDw8RH94JAQ4He/A/7yF2D6dFYSREREHsFw5HzAAGDhQtEQmDcPWLCgeaPgwQebj/YTERGRY7VnZJhLy1vEyfdEZCoGzG3Ey0ukZHn++abbnnwS+P3vbRuo1oyczp4NZGQ09XdXrADOnmUlQURE5DEMp1N9/HFTR7qszHijwEM621yaTUREbsOwUluwABJkrOd0cPI9EZmKAXMbMrwot7Y/h7VoRk4bGkTQvKpK/N2SEmDQIFYSREREbk/TcT50SD8onp8vZpiPHSvuM9Yo8JDcJlyaTUREbsNIpfZ/ciVK9uVgWGUuvs+Lxv/JlUgY47kRcw+ZD0BEVsSAuQ1pLsrHj4u9tVrbn8NaNEH68HDgpZcAX1+xEZZCIVKzfPyx6C+zkiAiInJTusvNWhop94CeY2uzyLk0m4iI3IaRSu0eBZCwXQTRh3t54cKgj4Ex4xx7nA7kIfMBiMiKGDC3Ic1FWalse38Oa9H0fw8eFMHyP/5RBM4bG4GVK4HMTJGy1JOXYxEREbk1Tcc5OxtITgaCgkQDQbcB4gE9x9ZmkXNpNhERuQ0jlVrvL4/pBdF7Xz+BQ4fGMUULEVE7MWBuB/bsk2r+VlGRmFlumLf8wAGgRw+37h8TkRthnmEiM2g6zhUVwI4dHptvpLVZ5B4wwZ6IiDyFbqUWGAgUFsInsKt+ED0wkKnIiIhMwIC5G9ENLP3iF0BwsLhNt5709RUpYjTBJ7lcBNUZiCIiZ8Q8w0RmYDQYgP6Eu65dRQxh3bqmNo+bT7AnIiJP4+MDTJ8ONDZC9swzTZua+fvj+84RTEVGRGQCBszdiCawFBICPPMM0K0bsH69WI19+zYQGyvSsiQlAVu2iE7k7NnA1q0MRBGRc2KeYSIzeEC6lfYwnHD3cwyBbR4icmolJSVYuXIlDh8+jNraWgwdOhRLlixBv379AAAHDhzApk2bcPXqVQQHByMpKQlPPfUUZD/PfLp58yZGjx7d7HXfeOMNTJw40a5lITvRBAKSkpoazjt3An/5C1BfD8TEwP+GGiemrsMV32g8vVeJqCjOlCMiag0D5m5EE1hauFDkLvf1BSZPBu66CwgIAFQqUWemporHNzYCNTXA3Lli4Fml4ixzInIuzDNMRObSHTdYt46Db0TkGlJSUqBWq7F161b4+vrirbfewqxZs3Dw4EGcPn0azz//PF588UUkJCQgPz8fS5YsgUKhwMyZMwEABQUF6NChAz777DNtEB0A/P39HVUksjVNIMDPr6nhXF2NQvTHjzFjEXXjEHpMT0SPxkY84OWFhMxPEKxkJUhE1BoGzN2IJrB0/bqoIysqxH5fy5cDxcWiY9izJ1BdLR7v5QXExIjgemMj8N57QP/+7EASkfNgZgkisoagoGapXImInE55eTlCQ0Mxb948hIeHAwDmz5+Pxx9/HCqVCsXFxZg7dy6mT58OAOjVqxc+/PBDHD16VBswLywsRJ8+fRAcHOywcpCdxcYC8+aJZeUrVqCuQYb/+NyPzaeUCK8A/HJz0UNn1LhHSR4ga7nTzz2EiIgYMHcrmsDSxYtNncIpU8SMck0HcdMmYM8eoKRE3PbDD5xxRUTOi5kliMgayspEirrqajEBr7zc0UdERNRcQEAA0tPTtb+XlpZi586dCAkJQVhYGOLi4rT3qdVqnDhxAqdOnUJKSor29vPnz2vTt5CHUKuB7du1nf5rmz7G8KfHamMAx1eYtmTTmfcQYjCfiOyFAXM3ogksjRkjOoPffQd06tS010dgoOgo5uUBw4aJAeiyMqY7ICIiIvcWHi5S1ul2/omInNmyZcuwd+9eKBQKbNq0CZ07d9bed/36dYwfPx4NDQ0YNWoUpkyZor2vsLAQ3bp1w9SpU3H58mXce++9ePrpp43mNW8vSZJw+/Zti8rjampqavS+O7MOZ8/CS2cWnOJiHnx9x2Lr5BzcW5ULeMXi7/M+Rmh5Pvr8cgC6DB8uggEGJAk4elSBL76Qo7FRrnk5nDvXiBEjals9BnudryNHFHj4YW9tfb5/fwPi4+ts+jdtwZXeX86A58s0PF8tkyRJL11Zaxgwd0NyOfDkk8C5c0DXrsDNm+L20FDg+eebOou7dgEdOoiULSUlIojOdAdERETkbpjeiYhczcyZM5GUlISsrCykpKRg9+7diI6OBgB06dIF+/btw9WrV5GWloYXXngBa9euRUNDAy5duoSwsDAsXboUfn5+2L9/P+bOnYt33nkHI0aMMOtY6uvrkZ+fb83iuYwrV644+hDadN8996C7ziw4KbI/tk3JwaTtP08Tf88Lfmv3I+/uafivHChYJ6F//1r063cVarVa+zqXL9+HyZM7Y/Zs/Ul1oaHlyM+/3K5jsfX5+vrrCDQ2+gEQx/ftt3cQGHjepn/Tllzh/eVMeL5Mw/NlnEKhaNfjGDB3U5IERESIQLhmdZZMpp9+5dYt4H//V3QeR4zgciYiIiJyT0zvRJ7C0nQFTHfgPMLCwgAAK1aswJkzZ/Duu+/ijTfeAAD4+fkhKioKUVFRaGxsxHPPPYfU1FSEhobi5MmT8PLyQseOHQEAMTExUKlUyMjIMDtg7uPjoz0eT1FTU4MrV66gT58+6NSpk6MPp3UDBqBh/37ICgogRUai+4MjMeH77Xqd/67Xz+NOt/GYOVOuDYT/7W9d8atf3dF+xg8f7oDGRrEPWnIyEBioRkKCGiNHdoRMFtnqIdjrfJWUKPSC+YMHd0RkZOvH5oxc6v3lBHi+TMPz1bILFy60+7EMmLupnByRikWz/BgAfH31R4p9fMTtKSnA558D69ezYUxEzoEddiIH8/APoYcXn1yYpbmHnTl3sScoLS3F8ePHkZiYCG9v0VWXy+UICwtDUVERTp8+DYVCoZfLPCIiAgBQVFSE0NBQ+Pr6Nnvd/v3748iRI2Yfl0wm00sJ40k6derkGmVPTIQ0IVHUXTuApLAYvc7/lyXR+Pe/5XoT6P79bzlCQjprP+OxseIpFRXAjh3AJ5/IMXasHKaEjWx9vsaPN1wx5g2ZzHXDWi7z/nISPF+m4flqrr3pWAAGzN2SJAEqFZCUJNKwaOrJvXuBlSuBn34SM8+XLBE5zTMzgRkz2DAmIueh6bCHhADPPAMcPAgMHCiua3K5o4+OyAN4eNTMWPGVSgbRyfnl5uqvKM3LM+2ja+nzyTK3bt3C4sWLsX37dsTHxwMQ6VDy8vKgVCqRmZmJoqIiZGdna59z5swZeHt7o0+fPlCpVEhKSsKmTZvwwAMPaB9z7tw5j5sh7ol0667lXZXIz/wE6tw8fHkrCnOzlZg8WX8Cna+v/mfcFdKXccUYEdkLA+ZuwHAWFAAsWCAqwgMHgDVrgOvXgV69gPPngbg44PXXm57PhjEROUwL0zg116VnnwWWLm1q2AOAzr5WRGQrbtA4sGSWuLHiA5aPIege04ABCgQFcQSQrCs6Wj8gFhVl3+eTZcLDwzF69GikpaUhLS0NAQEB2LJlCyoqKjBr1izcuHED06ZNw5o1azBx4kTk5eVh1apVmDFjBrp164aAgAD07dsXf/rTn/Daa6+hW7du2Lt3L7777jv8/e9/d3TxyMZ0666fymTYWzIWUcqxmJrYNIFu61ZRD/n6it8nTWp6flvBaFPrVa7WIiJXxoC5GzCcBfXqq00VZVkZcOIE8N57wFtviZzlcrl+Qzgmhg1jInKQFmaxajrst27pB61ycx17uEQeww2iZpZMkjdWfGuMIegfkzf27LkXP2dTILIKS2eIusIMU3e3evVqpKenY9GiRaisrMSQIUOQlZWFnj17omfPntiyZQvWrl2LnTt3onv37khOTsacOXMAiPQtmzdvRnp6Op599llUVFQgKioK77zzDsLDwx1cMrI1Y3WX4Wd6zBjg3nvF75MmmfYZN7Ve9fDFakTk4hgwdwOGHbigIP2K0s9P/Hz1KjBgAPDQQ/qVplwuguxBQWKj0IQEhxaHiDxJbq7Iu7JwoVgKc/EiMGYMlEo5PvkEOHdO/3rWo4ejD5jIQ4wZI3K25eaKkfUxYxx9RCazJMDdUtDQ0jEEw2O6eFFh+osQtcLSdAVMd+B4/v7+WL58OZYvX270/vj4eG26FmMCAwO1m4OSZzFWdxn7TJv7GTe1Xi0sFPuqVVWJmIRKxWsLEbkOBszdgOFI8l13AR9/DBw+DNy8CfzrX8C8eWKTz3/+UwTINZXkoUNi4wzdUV8ukyIiuwkKEknK//hHvVE+2ZNPYuxYcb3SNLT9/UXcjojs4PPP9Tc4CQ52uV6uJZPkjQUYrDHz1vCYwsLqAHQ0/YWIiIgM2HrAy9R6tWtXICOj6fGZmbY5LiIiW2DA3A0olcDu3cCFC0BxMfDll8DEiWLW+M6dQFiYuO/KFRFwunRJTBSTJODYMZdPUUpErqysTHwZTj/5WUICoFY3Bai4AobITtwgh7m1U0tYIxChe0wREQ0ICroKgDlZiIjI+ZlarxYX6zclSkpsf4xERNbCgLkbkMmA0lLglVeaRm/79hVBpqoqoLYW2L696T7NxqA5OcCPP7p8ilIiclWSJKaeyOXAX//adCHaulX7EE2ASqkU16z167lpEJFdGJtGprN7l2LAAMiDghx9lK1yxtQSusd0+3Yd8vPVjj4kIiKidjG1XnWD7VCIyIMxYO4mDEdvi4qAJ54ANm4ELl/Wv6+8XPycmwvs2QMkJwPV1UB8PDf2ISI7yskBpk8XX7oXqaoqow/lpkFEdmRsGpnOB9Hbywv37tkD7lipT2dMgYN7RETk0biJMBG5MgbM3cSDD+qP3lZVAb/9rQiODxumf9+IEeI50dEiUL5tm7g9OZmdOiKyI01OKIVC/yKlWQbzM6aPInIAY9PIDNK0KC5edMyxOTFTBvckCbh8+T4cPtwBsbEMrhMRkXtxxpVeRETtxYC5m1AqRaqCI0dECuDMTOCxx4D+/cV9wcHNR3Y54ktEDhUUJCJK2dkih3lQkNhgweBixPRRlikpKcHKlStx+PBh1NbWYujQoViyZAn69esHADhw4AA2bdqEq1evIjg4GElJSXjqqacg+zlyp1arsWHDBuzbtw+VlZUYOnQoXnnlFfTq1cuRxSJ7MJwubbC2ui4sjNtVGjAl9fvRowpMntyZK2eIiIiIiJwMA+ZuQiYDwsOBhQubAkqPPCJiTy0tDeaILxE5VERE02afmgvSmDHNHlZYCHToACxfLjYLiozkAJ8pUlJSoFarsXXrVvj6+uKtt97CrFmzcPDgQZw+fRrPP/88XnzxRSQkJCA/Px9LliyBQqHAzJkzAQAbN27E7t27sXLlSoSEhGDVqlV46qmn8NFHH0GhUDi4dGRThtOlP/1UO9LeEBGBq0FBTdtVMhcJANPyteblybhyhgi8fBAREZHzYcDcjbSRbpSzl4jIuSQkiN2JNRethASjD+vaFXj77abrWGYmO9LtVV5ejtDQUMybNw/h4eEAgPnz5+Pxxx+HSqVCcXEx5s6di+nTpwMAevXqhQ8//BBHjx7FzJkzUVdXhx07duD5559Hws//nzVr1iA+Ph4HDx7Eo48+6qiikT0YTpc+d06MzI8di7rbt6HOz296LBscAExbvRcVJXHlDBF4+SAiIiLnw4C5G2lHulHOXiIi59HOZS4XL+pfxy5ftsOxuYmAgACkp6drfy8tLcXOnTsREhKCsLAwxMXFae9Tq9U4ceIETp06hZSUFABAQUEBqqurMUKz+QWALl26ICoqCqdOnWLA3N2ZMl2aDQ4Apq3eGzmyDnv2VOD69QDExHhx5Qx5FM2s8sJCcXl58kmgUyeRpc1DLx9ELk8ul+PIEQUKCrhahIhcHwPmbs6Uvi4RkTMKDNS/jt11l6OPyDUtW7YMe/fuhUKhwKZNm9C5c2ftfdevX8f48ePR0NCAUaNGYcqUKQCAGzduAADuvvtuvdcKDg7W3kdurLXp0pKE+y5fRofDh4HYWDY4zCCTAX37XsYjj0TqfR6JPIFmVvns2UBGRtOlY/ZsXj6IXJEkAf/5TxRmz/bmahEicgsMmLs5buxJRK5ON9W5v7/4nUw3c+ZMJCUlISsrCykpKdi9ezeio6MBiFnj+/btw9WrV5GWloYXXngBa9euRU1NDQA0y1XeoUMHlJeXm30skiTh9u3b5hfGApoyab5TG0aMEF8AoHPOvL78Et0nT9ZGuRo+/hjYvx+yggJIkZGoGz4ccND/2FXwvWgdPI+Wc8Q5PHu2AxobvVBVpb845Z571HjggTs4eFCBvDwZoqIkjBxZ57SzVCVJ0m6STeTJjh5V4NNPvU1ebMY9DIjIWTFg7kZaqmy4sScRubJ2pjq3GndtuIeFhQEAVqxYgTNnzuDdd9/FG2+8AQDw8/NDVFQUoqKi0NjYiOeeew6pqano2LEjAKCurk77MwDU1taiU6dOZh9LfX098nXzXzvAlStXHPr3XV3Ed9+hg06v+M7p0zg/fjwwapS4raAAcrkc9168CIVKhbr+/XG1Xz+o1WrHHbST4nvROjzlPMrlcly8eC9UKgX6969Dv35Xrfa5suQcmnpc99xzH7y8usPPT39xSmRkGT7+GJg8ubP2tj17KtC3r/PmY+MG2ERiI2tfX9MXm3EPAyLrkiTgyBEFvv46AiUlCowf7x59WUdgwNyNGFY2H38sPhjuFvQhIjfUSpRaM/Cn2ch4/XrbXtMMr6Xr1wPh4a55DS0tLcXx48eRmJgIb29R5cvlcoSFhaGoqAinT5+GQqHQy2Ue8fMU/qKiIm0qlqKiIvTu3Vv7mKKiIu3jzOHj46MN4NtbTU0Nrly5gj59+lgU9Pd0XkVFer3ijoMHIzIyUu8xiiNH4K0zC73L/v2oi4930BE7H74XrcPTzuORIwpMntyU8mD//i6Ij68z+lhJErM+25qp3Z5z2NZrmXJcADBgALB/fwMuXpThb3+TobhY+vl1O2Lbtg56s1SvXw/AI49EtvhajnThwgVHHwKRU4iKkvDaa8D8+UBICFBbK26XpNbbz9wChci6cnKAhx/2RmOjHwehLMSAuRsxrGxOnACWL+doLRG5gHZMLzF8SGYmMGWK9YPYhtfSI0eAhQtd8xp669YtLF68GNu3b0f8z4HK+vp65OXlQalUIjMzE0VFRcjOztY+58yZM/D29kafPn3g6+sLPz8/nDx5Uhswr6ioQF5eHqZNm2b2cclkMofnbO7UqZPDj8GV3R49Gj9lZyMgPx/yoCB4+/jAu1Mn/Q9kQYHeh8m7sBDeiYmOOWAnxveidXjKeTT4WOHwYW+UlnqjuLj5YPKhQ8DDD+tWrd6t1mOtncO2XsvwuAoLvZGY2HpX0/jlwBuxsfqzVGNivJz2f8t0LETCyJF12Lq1AiUlXbFggVz7+d29G6isFFnaysrEQjS1ummOjOHnnXsYEFmGg1DWw4C5GzHcbyswkB8UInIRrdTsmsnnhw41PcTXF7hyBUhLAx580Lqzvw2vpX5+rnsNDQ8Px+jRo5GWloa0tDQEBARgy5YtqKiowKxZs3Djxg1MmzYNa9aswcSJE5GXl4dVq1ZhxowZ6NatGwBg2rRpePPNN9G9e3eEhoZi1apVCAkJwYQJExxcOnIomQwSAPlrr7U80MWNQImszvBj5e8PTJ9u/GNozU5zW69lzY8792Aicj2ajax//HGg3rXiwgXg+++B7dvF7/PmNf2sWRXPzzuR9bD5bT0MmLsRw8alXM4PChG5iFZqds3M8tmzmx4yZQrwyiu2WUGjuZYePw5cuwbs2ePa19DVq1cjPT0dixYtQmVlJYYMGYKsrCz07NkTPXv2xJYtW7B27Vrs3LkT3bt3R3JyMubMmaN9/jPPPIOGhga8/PLLuHPnDoYOHYqMjAz4+Pg4sFTkUJIExZEjUJw+Dfz+9+IDcusW6vJU2FGoRP9wGZRjJMjkcpHTqKREbBwqlwPr1ulNg3XXPQOIbEVTR+XkALduAYWFLQeyrdlpbu21JEl8f/VVIChIbM5tuN+IKZ917sFE5LqioiS9a0VxsZhhrrlO6f7c2Ajk54uVnPy8E1mHUilSnn377R0MHtwRSiXDvubimXMjho1LSeJoLRG5CLlcRMSrqsR0Oblce5dmVlt2NpCcDHTqJO621Qoaw5zp0dGufQ319/fH8uXLsXz5cqP3x8fHa9O1GOPl5YXU1FSkpqba6AjJ5eTkwFs3N8Ps2cDu3VC89x66JvdH4sKx+HH3F+iR857oGfv7QyophezJKdrn3Mz8BMFTxmoHxHx9xUDYsWPWXzViKgbxyRkZvi8nTBADun37thzItuZM7dZey1hWNcPPDDf2I/IMI0fW4ZNPvJGXJ1a8Hzsmmvaa65Tuz648IYXI3trbPpXJgPj4OgQGnkdkZCRksvaHfdkG1seAuRvj7AwichlnzwJbtzb9Hh0NjBmj/dHLC6ioAHbsAHbtAsrLbd/Y5jWUqAWGuRmqqrQ/96nOQ2PjWHT58bzemmspIgIynedcPpCHcz3Gal9q8mT9JdqODKYxsEfOyPB9uXWr+Mz4+ooxq169gJEjRW5g3YUc5tRjLXWYW3qt9qR+YU5VIs+ge62QJLEB6KVL4lpSXi6uU088IWaWa1bFGyw+02LwjlyNLd+z9mifsg2sjwFzN8NKhYhcUmysSGr482xUxMRo72ppVlv//lxBQ+QQxpIoA4CXF676RcHLC5CVFOtFx9SlZZDrPOeqbxSK8ppeqqrKeYJpDOyRMzJ8X545I75XVIjg+bp1IlhujY6uqR1mY+laDh3S748wpyqR55HJxPyXn+fA6Bk3Tlwnxo9v+VrD4B25GlPfs4bxO7lczCMzFsuzR/uUbWB9JgfMS0pKsHLlShw+fBi1tbUYOnQolixZgn79+gEADhw4gE2bNuHq1asIDg5GUlISnnrqKe0O4mq1Ghs2bMC+fftQWVmJoUOH4pVXXkGvXr2sWzIPxUqFiFySWq0/vfSJJ7R3GUs3lZMj8rYGBoqKHeAAIZHdKJVo2L8fd779Fh0HDUJpuQJXa0Tuos9USuzaBVwpfRADdKJjF0JGo9PW0fhvTh6u+kbh6b1K7JvUNCCmUgHvveccwTQG9siRWpr8Yvi+7Nmz+fvUWh1dU1/H2D5KhkEwbuRJRIbautYweEeuxtT3rGH8bvZsMQhuLJZnj/Yp28D6TA6Yp6SkQK1WY+vWrfD19cVbb72FWbNm4eDBgzh9+jSef/55vPjii0hISEB+fj6WLFkChUKBmTNnAgA2btyI3bt3Y+XKlQgJCcGqVavw1FNP4aOPPoJCobB6AT1Nbq5Ymjl5spitpVIxiERELsCwdZGfL6aeGKG7CWhGBgcIiexOJkNdfDzOBwYiMjISQZ064+xdY5CXB0yeItod//eFEv+Z/Ql6V+Xhe/8ofHVJiYwdMrz99liUlEAbLNfdM8BZVo0wsEf2phskDwoCpk9vXrcplUBmJnDggGjrv/22+L2kRLxPx4wBioqAqVPF/Xv3mt/RNbXDbDiwvW6d8YAB05wRka62rjUM3pGrMfU9a9gFbmgA5s41HsuzR/uUbWB9JgXMy8vLERoainnz5iE8PBwAMH/+fDz++ONQqVQoLi7G3LlzMX36dABAr1698OGHH+Lo0aOYOXMm6urqsGPHDjz//PNI+Hnr9DVr1iA+Ph4HDx7Eo48+at3SeaDYWOCll4A//lF84N57T3RA2TglIqcWFKTfuvDzExEEI6N9moaFvVM4MOUVkXHGchs/lCBDjnosPsoZK/YcaAAeflikj1iwoPlnx5n2DHCmYyHPoDvDbOpU43WbTCY2xu3RQ9w2aZJ+PXTokH6gPTPT/I6upR1mBrmIqD3autYweEeuxtT3rGF9GRHRcizPcH8AW/RL2QbWZ1LAPCAgAOnp6drfS0tLsXPnToSEhCAsLAxxcXHa+9RqNU6cOIFTp04hJSUFAFBQUIDq6mqMGDFC+7guXbogKioKp06dYsDcCtRq4D//4dIlInIxZWVAcjJQXS2C5UePAr17G714aRoWfn727ZB/+SXw9dfAjz8Cd+4A3t7AQw/Z9m8SuSpNgxsA9u0DNm/mQD5RS3RnmPn6tly3mbLxZklJy51nw4728OH691vaYWaQi4jao61rDYN35GpMfc8qlcD69cCRI0C3bsDNm+2L5RmmcsnMFIPquvW+XC7HkSMKFBRwspe5zN70c9myZdi7dy8UCgU2bdqEzp07a++7fv06xo8fj4aGBowaNQpTpkwBANy4cQMAcPfdd+u9VnBwsPY+sowmJQtndRCRSwkPBxYu1E/g1kILQTfnse5ydFt3yM+fbxrx9/ISy+EZMCdqnVIJHDvGdHFErdGdYbZ3L7BihZgA4+8v8oGb+hpttf8NO9r79ysQGGidsgAMchEREbWHTKbfDZ43r6ku79pV7Ne1bl3zgLfhIPmBA2IFmm69e/HivZg82ZvpSy1gdsB85syZSEpKQlZWFlJSUrB7925ER0cDELPG9+3bh6tXryItLQ0vvPAC1q5di5qaGgBolqu8Q4cOKC8vN7sQkiRpX1vz3d21VN7oaAVUKm8sXCgyHPTrp8bw4Xdw+7YjjtJ6+P91X55UVoDlbdHw4ei4dSvkOTna5Kvq0aNxp4WL14gR4kv/b1njiJuTJODoUQUuXPBCY6NopTQ2At9/L6G6ukbbcLHG/1aSJO0m2UTuQCYDHnxQzHrR7OvLWebkyYwto9adke3jAyxZItIXAcBdd4kVpG0NMo0ZIwaRc3OBmBjxe0sMO9oFBTKMGmW9MhIREXkKS9Oj6LYBYmKAJ54Q23kFBhrf0wRoPkgeGdl8QopKpbBK5glrpn9xtRSnZgfMw8LCAAArVqzAmTNn8O677+KNN94AAPj5+SEqKgpRUVFobGzEc889h9TUVHTs2BEAUFdXp/0ZAGpra9GpUyezC1FfX48rV64AgPa7pzAsb1lZX2za1E37wcnIqEdh4Xmo1WrHHKCVefr/1515UlkBlteYiJ9+gl/nzmIK6qRJaLx6FefP2/f6JZfLcelSH5w964fAQKB37yqUlfni4EFvPPigGOkvKxPX15ISGQ4cuIO+fS/rvYal/1tugE3uRjPLvK1Gu6s1oonMYTi7W9MB1nwdOiSykwHi/lu3xOPbmhn2+efAjBlNrxsc3PLjm3e0JesXlIiIyAO0VK+3xFh713BV1rhxLW+gDTRtBH7qlJg09te/iraD7oSU/v3rrJJ5wtTy2eu17MGkgHlpaSmOHz+OxMREeHuLp8rlcoSFhaGoqAinT5+GQqHQy2UeEREBACgqKtKmYikqKkLv3r21jykqKtI+zhw+Pj4IDQ3FlStX0KdPH4uC766ipqbGaHkPH+6g96H69NMOCAmJRXx8nYOO1DpaKq+78qTyelJZAZa3NYpbt8S0up9rUJ+nnkJsURHq4uPtdLTA4cMKJCU1LV1bsaIbXnpJHFJ2NvDWWyLw5+cH7NkDREYG4JFHIgFY53974cIFaxaHyCloZpm31Wh3tUY0kTkMZ3cbDh5pZpodOQJ07CgeP3u2mDlmuNFXbKyYfZ6bC5SXt38PI8Mc48OH16GgwLblJiIickdt1euGNO1dX1+xAvPYMdFONpwo0lqqNc1G4BcvAq+80nS77t/u1+8q9u/vgsJCb4vSl5paPlNfS6l03gkzJgXMb926hcWLF2P79u2I/zmAUV9fj7y8PCiVSmRmZqKoqAjZ2dna55w5cwbe3t7o06cPfH194efnh5MnT2oD5hUVFcjLy8O0adPMLoRMJtMGJzp16qSXT93dGZY3Nlb/Q+XrCxQWeiMx0ezFBE7F0/+/7syTygqwvEZ17gysWgWcPq2NSHtHR8M7MdE+B4nms2CvX9f//cIFkU5Cc42NifFqVi5L/rdMx0Luqj2bAJraIOeMdHJFbeUa1+T/LirSX4q9a5eYfX7liphRVlkpNqB+4w2x8kk376nh67Y1m83VUzcSERE5iil7iABN7d3Jk5vSFRqbKNJW27mtCSlqtRrx8XUWxwJNLV9rgoL0Xysw0LknzJh05sLDwzF69GikpaUhLS0NAQEB2LJlCyoqKjBr1izcuHED06ZNw5o1azBx4kTk5eVh1apVmDFjBrp16wYAmDZtGt588010794doaGhWLVqFUJCQjBhwgSbFNCTSJLYGOjtt4HDh7VpgDFpkqOPjIiobVJ+PmTff68Xka7oGIhvPpfwUIKIgtkyOCZJQPfu+pV4aKj+7/37A3/4g9jFfMQI2280SuQu2rMJoKkNcmduYBO1pD2DR4AImOsOIF28KALor72mvx9AcjKwbZtY9bRhA1Bf3/x1+VkhIiKyjfbW6xqa9m5VVesTRVprO+sOhO/aJVaZ9e9vm76pqeVrTVmZaLdUV4v5cT/8ABQXW28Gu7WZPNSwevVqpKenY9GiRaisrMSQIUOQlZWFnj17omfPntiyZQvWrl2LnTt3onv37khOTsacOXO0z3/mmWfQ0NCAl19+GXfu3MHQoUORkZEBHx8fqxbME+ku7Zg8GejVC3j/fQZ0iMg1SBcuQta7t1h7XlUF+Pujy7kTKD0TjBy1qDVt2eHPyQEuXQIWLAA6dRIbrfXoAWzeDHzxhbi2vvQS8NvfAn36OE9FTuQuTG2QW3OJKJG9tGfwCBA5yHUHkLp0ET/fuqX/vtfMDjfMXaqLnxUiIiLbaG+9rqFp76pU+iuX21oZBjTdFhSkvwrt44/F/evXi8cPHy7SZx85okBBgXh8WRkQHm76pDNTy9ea8HBg4cKm437qKeChh6w3g93aTA6Y+/v7Y/ny5Vi+fLnR++Pj47XpWozx8vJCamoqUlNTTf3T1AZNY7iiAti6VWwSwMYwEbmKqn4D0SXvhLiAaTz5JH4RnIcDeWMhSbbt8OfmAjt3An/8I7R5y728gOXLgayspsfV1jpXRU7kLkxtkFtziSiRI7SWVshwFpaXl/iSJP33/aBB4vGjR7c8yGT4WYmJEeldNH93+HC7FJeIiMjjadq7SqUY6DY2UcTYyjCg6bbZs5vmmPn5ASdPAvn5YoLXq68C2dkKVFbei8mTm/bmSk4WwWpjk87sleZQqRRB/SNHmvYEGzjQejPYrc09ElsTAHYcici1fdgxCU9GV8NL90Lm74/ioCjt9cxW1zi1WqRQf+wx4MYN/cB8SYn+301IcK6KnMhTmbNElHnPyZm0lipFMwtLsylYWRmwYoV4nM5CLDQ2ik5n//4tv5cNPytqtf7f3b9fgcBAuxWbiIjI47U2UcTYyjDdyWP9+4sJXpo2QmVlU0rm554DLl+Wo7paofca1dUtTzqzV+o2maz5LHPN6jhnnOzLgLkbsWZuISIie7untxy7riRj3Epf9LiVi8agHvghIAbXAhLwm5+vZ7a6xr33HvD73zelXZk6tanRUV8vghOdO4ul77W1DLAROQNzlogylzM5k9ZSpegu2V6woOk9u3Sp/kKsP/5RpApLSBC/tzQopPtZWbdO/+8WFMgwapRdikxEREQ6jNXbupNhu3YVm2NeuCA2+N6zR7QXjG0cmpwsZpq/954cGRk+epO+NKvVjE06s2fqNleKWzJg7kasmVuIiMjeEhKAXbvk+H+dp+CKGrivM+DXAZj2RFOA2lbXuLNnmxodf/xj02j9ggVASAhQWAjs2CFG5jVL4ojI9TCXMzmT1laHatr1hu/Ze+7Rf45Sqf8ebs+gkOHfjYyUbF9YIiIiasZYva0bVA4M1M9XvmEDEBAgUoYabhx6+7YIjDc2Apcve2H//gYUFnojMFBsDKp5bUP2zFbhSnFLBsyJiMgpyGSAj4+Y4T1pEnD0qH0qUkkC+vbV363ccLQ+MxMIC3P+UXAiah3T15Ezac8sK8P3bERE689pz6CQ4d8dPrwOBQW2KSMRERE1MZxRXlhovN7WfBmuCquvF33V4ODmG4fGxTWlbwsKkhAfX4/ExLbDvpbM+rZlukNHp1JkwJyIiJxGcbEIlmdkiIo/Oxvo3du2gfOcHCA1VSxh69NHP3AONOUxX7jQdsdARPbhSstAybW1p5PXnllWhu/ZhISm5xnTnkEh3b8rScCnnyrw9dcRKClRYPx4ph0jIiKyFcMZ5ZmZrdfbsbEiFUtlpdi7JCRE3G64cWhgIHDiBPDoo+JxPXtWAejQrmOyZNa3LdMdOjqVIgPmRETkNKKjga++0g9WHz9u29Hk3Fyxmdq2bUCXLsDKleJv6Y7WcxYqkXtwpWWg5Nqs1ckz9T1r6qBQTg7w8MPeaGz0Y15/IiLyeLae1Wy4EqykpPV6W63WX/ns7Q107y7qasMB8OBg8ToREQ0ICroCIMJ6B97O8lgz3aGjUykyYE5ERE5DqQSKisTMck2j4No1YPNmsaO2LQLnurPx5HLxXaUS+eGuXQPGjOEsVCIiMo2jOnmmBtgd3RklIiJyJrae1WxsJVhr9bZhPf3TT8brat36//btOuTnq6130K2wZbpDR6dSZMCciIichkwmcrKVlQFHjohNS/bsEUvQFi60zcw3zWy8Tz4B7rpL5FBvbAR27gRmz246LiJyHY7OeUjk6E5ee7nKcRIREdmDrQeSTV0JZlhP+/u3r66Wy+U4ckSBggLbtoWtle7QWNvd0akUGTAnIiKnIpOJ2eQLFzY1DDS7fdti5ptMJipflQrIzxd/p0sXEbjv2FHczmAbkfPTbWgHBQHTpzsu5yGRozt57aVUAvv3N+Dbb+9g8OCOUCrZPSQiIs9lbCDZmhMxzEm19vHHIj95YKDY/DshQdxn7LgA4MgRBY4di8GNG97YsweorrZdW9ha6Q5bmtnvyFSKbBEREZHT0QQajh8XaVH27BEVp48PcOiQ9QPYOTnAggViRrmXlwiWazYe9fISm6kw2Ebk3HQb2lOnNt8LgbPNyZ4M84o664oHmQyIj69DYOB5REZGQiYT3UNnPmYiIiJb0fRDVSogIEDUg0VFwPz5YhW0vSdiyGTAuHHiy5CxILNcDnzwgbd2k9CZM4H1650/5ZozpohjwJyIiJyOJtCgVIqGwD33iMD5kiW2GSHXVNDZ2UByctOMdsB5KmwiT9ae4N2xY02fW19f0XHw9QWmTBEbKv3wA/Dqq8Dbb4vbGPwjWzF8v8rlts2H2t7jMCXobescrkRERM5I0w8F9OvB5GRg27b29Q3tNehsLMjs46O/Sejy5Y5LuWbKeXDGFHEMmBMRkdPSNFhyc4EtW5put3YAW1NBV1QAO3YAr7/ufBU2kSdrT/AuKKgpSN6xI/Dmm+L3RYv0OzsHDgA9ejD4R7Zj+H5dv976g7Dt6YRaEvR2xpleRERE9mJYD96+LX728hKpUdats039awpjQWbdCSSNjcCdO+LvOyI1nCnnwRlT2TFgTkRETs9WI86agENhoRiJP3MGCA0Vy+7mzRPL7oYPB8aMsc7fIyLztCd4FxEh0ir17i1mkhtLzXL7tgioM/hHtmT4fi0paV6HHTpk2cyz9nRCLQl6O+NMLyIiInsxrAcfeQR44AERLG8rPYu9Bp1bCjLrHveYMY5r85pyHqyVC92aGDAnIiKnZ6sRZ92AQ9euIlVDRQWwdKn+TuSff+5clTeRp2lP8C4hAVCrRSDSMDWL5nmxscDKlcCkSXY9fPIwhu/XESP06zC5HBg/3rKZZ+3phFoS9HbGmV5ERET2YqwelMnEzPKyMvEYW9S/pjAWZHamzbxdffCdAXMiInJ6thpx1g04lJWJWYALFoiZ50eOiFzme/aIyp4BcyLHaU/wTjfn5F//Kj7be/cCmZnisx0YKGaYb9woPvua12Uuc7K2ljrZmvfnunWWb0rbnk6oJUFvZ5zpRUREZC8t1YO2rn/bq6XUbC1t5u0Irj74zoA5ERE5LUkCvvgCOH8eKC4GHnzQugEuYw0emQwIDwcWLnTd0XAid2NK8K6lYCUgZp9zI0Oytbber4Z1z7VrYp8OU96Tpgwi8T1ORERkHc5S/7rC5tyu3g5hwJyIiJxWTg7w738DVVVAZSXw449iKbu1coorlcDHHwMnTojZp3K5CNIrlcCnnzYF6gFxO2eiEjm/lhrnkgSoVEBSklg9kp3NXObkGLqdbR8fYMkScbuvr3iPtme2uat3QomIiFyRs9S/3Jzb9hgwJyIipyRJwJdfAnffLYIJmtHz6GjrbsJZXAwUFIhAxYsvAh98IPIgq1QiPYszj9oTUfvl5Oh/pmfP5uoRcgzdzvahQ0B1tbh9yhTWO0RERNQ2V88P7goYMCciIqeUkwPcvAnU1uqPnt+4Yd2/MX16U0MjORk4dw5YtEjMQuWoPZH7MJyJ06uX6+VSJNfTUo5RDd3Z5mVlrHeIiIioba6eH9wVMGBOREROKTdXbLiZnq4/en7ffdb9G7rBidu3RdqXxkaRsoGj9kTuw3AmzogRTLNEttdWjlHD2easd4iIiKgtzpIaxp0xYE5ERE4pOlosU//hB+CNN4CSEiAoSKRLsVY+ccMAWmwsoFCIn7OzRcqGXr1EYI2j9kSujTNxyBFMyTHK9ygRERGRc2DAnIiInJImcPD99yLP+LVrwE8/AXv3Av36WWc0XakEMjOBAwdEDvOVK4EZM4AVK4CzZ4GEBGDyZM5CJXIHnIlDjmBKjlG+R4mIiJxXW2nWyL0wYE5ERE5JEzg4dAiYM0c/z7i18rrKZGKTtcpKsfHn888DRUXAnTvA737HRhARNWEniczBWeNERETuoa00a9bEdqfjMWBOREROzViecWvndVUogPXrmxo/mZmc4UdE+uzZSSL3wVnjRERE7sGUNGuWYrvT8eSOPgAiIqLWaJazA+L7I49Yd4ZeTo6Yxa7b+Ckpsd7rE5F7MNZJIiIiIiLPYNgvteXm3Gx3Oh5nmBMRkVMztpzdmsvRcnNF/vL25pglIs9kSi5qIiIiInIv9kyzxnan4zFgTkRETk13ObstcrlFRwOvvipyo9++bf0Z7ETkHpiLmmyFeUqJiIicnz3TrLHd6XgMmBMRkcuwRS43pRJ4/33bzWAnIvfAXNRkK+2t2xhYJyIi8gxsdzoeA+ZEROQybLHRChsjRETkSO2t27gBGBERkfPjALd7YMCciIhchq1yubFRQ0REjtLeus0Wg8ZERERkXRzgdg9yRx8AERFRe2lyua1bJ75bK5dbTg7wxBMiGPHOO0B2tgiiExER2Vp76zZNYB3gBmDurKSkBKmpqRg+fDgGDRqEuXPn4uLFi9r7Dxw4gMceewxxcXEYN24ctm3bBkmn0aJWq7Fu3TrEx8dj4MCBmDNnDn744QdHFIWIyCMZG+Am18MZ5kRE5DJslT4lNxeYNAnIyBCNmuxsIDiYMwGIiMj22lu3cQMwz5CSkgK1Wo2tW7fC19cXb731FmbNmoWDBw/i9OnTeP755/Hiiy8iISEB+fn5WLJkCRQKBWbOnAkA2LhxI3bv3o2VK1ciJCQEq1atwlNPPYWPPvoICoXCwaUjInJ/mgFumqk9qgAAITVJREFUX19gyhSgrAz4/HNAreaKZlfCgDkREXm86Gjgq6+41J2IiJwX99xwf+Xl5QgNDcW8efMQHh4OAJg/fz4ef/xxqFQqFBcXY+7cuZg+fToAoFevXvjwww9x9OhRzJw5E3V1ddixYweef/55JCQkAADWrFmD+Ph4HDx4EI8++qijikZE5DE0A9wqFbBggehbzpsHbN/ONC2uhClZiIjI4ymVwCOPcKk7EREROU5AQADS09O1wfLS0lLs3LkTISEhCAsLw8SJE/Hss88CEKlXjh07hlOnTmHkyJEAgIKCAlRXV2PEiBHa1+zSpQuioqJw6tQpu5eHiMgTaQa46+qaJmRVVjJNi6vhDHMiIvJ4MhkwebJIw8Kl7kRE5Gy4ObXnWbZsGfbu3QuFQoFNmzahc+fO2vuuX7+O8ePHo6GhAaNGjcKUKVMAADdu3AAA3H333XqvFRwcrL2PiIjsQ3dTb3//9m3wTc6DAXMiIiJwqTsRETmvnBwgMZFLuT3JzJkzkZSUhKysLKSkpGD37t2Ijo4GIGaN79u3D1evXkVaWhpeeOEFrF27FjU1NQDQLFd5hw4dUF5ebvaxSJKE27dvm18YF6Q5l5rv1DqeL9PwfJnGlc6XJAFHjyqQlydDZKSEjz8GcnNliImR8OtfAwUF4vbhw+tgq8uqK50ve5MkCbJ2zjhgwJyIiAicvUdERM4rN5f7bHiasLAwAMCKFStw5swZvPvuu3jjjTcAAH5+foiKikJUVBQaGxvx3HPPITU1FR07dgQA1NXVaX8GgNraWnTq1MnsY6mvr0d+fr4FpXFdV65ccfQhuBSeL9PwfJnGFc7X5cv3YfLkztoB7j17SjFq1GUAgFwuR2Xlvfj6awUqKurQr99VqNVqmx2LK5wvR2jvBtgMmBMREYGz94iIyHnFxooNwyorxbLumBhHHxHZQmlpKY4fP47ExER4e4uuulwuR1hYGIqKinD69GkoFArExcVpnxMREQEAKCoq0qZiKSoqQu/evbWPKSoq0j7OHD4+PtoAvqeoqanBlStX0KdPH4sGGzwFz5dpeL5M40rn6/DhDnoD3NevB+CRRyIBAEeOKDB5sre2v7l/fxfEx9dZ/Rhc6XzZ24ULF9r9WAbMiYiIABQWArNnA1VVgJ+f2NWcAXMiInIGajWwfTvg6wtMmQIcOSJu42oo93Lr1i0sXrwY27dvR3x8PAAxuzsvLw9KpRKZmZkoKipCdna29jlnzpyBt7c3+vTpA19fX/j5+eHkyZPagHlFRQXy8vIwbdo0s49LJpPp5VD3JJ06dfLYspuD58s0PF+mcYXzFRurn6s8JsZLe8wFBfqrxQoLvZGYaLuwrCucL3trbzoWgAFzIiIiAEBgYFPQQSYDund37PEQERFpaFKyTJ4sAudcDeWewsPDMXr0aKSlpSEtLQ0BAQHYsmULKioqMGvWLNy4cQPTpk3DmjVrMHHiROTl5WHVqlWYMWMGunXrBgCYNm0a3nzzTXTv3h2hoaFYtWoVQkJCMGHCBAeXjojI/SmVom7OyxMbeyqVTffpbgLKjT+dHwPmREREAEpL9YMQgwY5+oiIiMhTGe6roZmxVlXFXObubvXq1UhPT8eiRYtQWVmJIUOGICsrCz179kTPnj2xZcsWrF27Fjt37kT37t2RnJyMOXPmaJ//zDPPoKGhAS+//DLu3LmDoUOHIiMjAz4+Pg4sFRGRezK2D9bYscbr5taC6eR8GDAnIiICcP06DPLNOfZ4iIjIcxnuq/Hxx6KTrVIB773H2WnuzN/fH8uXL8fy5cuN3h8fH69N12KMl5cXUlNTkZqaaqMjJCIiDVP2wZLJWg6mk/NhwJyIiAjA3XfrL5H7ed8sIiIiu9OkYAHE9/x8YOFCMRutf3/OTiMiInIGhvU1V365DwbMiYiIAERENG366e8vficiInKElvKccnYaERGR82BecvfFgDkRERGAhARArW6atZeQ4OgjIiIiT8U8p0RERM6P9bX7YsCciIgInLVHRETOg3USERGR82N97b7kjj4AIiIiIiIiIiIiIiJnwIA5EREREREREREREREYMCciIiIiIiIiIiIiAsCAORERERERERERERERAAbMiYiIiIiIiIiIiIgAMGBORERERERERERERASAAXMiIiIiIiIiIiIiIgAMmBMRERERERERERERATAjYF5SUoLU1FQMHz4cgwYNwty5c3Hx4kXt/Tk5OfjNb36DQYMGQalU4i9/+Qvu3Lmjvf/rr79GREREs6+TJ09ap0RERERERERERERERGbwNvUJKSkpUKvV2Lp1K3x9ffHWW29h1qxZOHjwIHJzc7FgwQI888wz+J//+R9cvXoVr7zyCsrKyvDGG28AAM6fP4/evXtj9+7deq8bEBBgnRIREREREREREREREZnBpBnm5eXlCA0NRVpaGuLi4tCvXz/Mnz8fRUVFUKlUyM7OxgMPPIDf//736NOnDx566CEsWrQIH330Eerq6gAAhYWFCAsLQ1BQkN6XQqGwSQGJiIiIiIiIiIiIiNrDpBnmAQEBSE9P1/5eWlqKnTt3IiQkBGFhYUhOToZcrh+Dl8vlqK+vR1VVFbp3747z58/j/vvvt87RExERERERERERERFZickpWTSWLVuGvXv3QqFQYNOmTejcuTOioqL0HlNfX4+dO3ciJiYG3bt3BwCoVCp069YNEydOxM2bNxEeHo5FixYhLi7O7EJIkoSamhoA0H53dyyve/Ok8npSWQGW151Zo6ySJEEmk1nrkIiIiIiIiIjIRGYHzGfOnImkpCRkZWUhJSUFu3fvRnR0tPb+hoYGvPDCC1CpVMjKygIA/Pe//0VlZSVu376Nl19+GV5eXnj33Xcxbdo0fPDBBwgLCzPrWOrr63HlyhUA0H73FCyve/Ok8npSWQGW151ZWlamKCMiIiIiIiJyHJkkSZIlL6BWq/Hoo4/iF7/4hXZjz6qqKjz77LP46quvsH79ejz00EPax1dUVKBTp07w8fHRPv+xxx7DkCFD8Nprr5n897/55htIkgRvb280NDTA29vbI2bnSZLE8roxTyqvJ5UVYHndmTXKWl9fD5lMhsGDB1v56JyHpt521MCAJEmor6+Hj4+P278nbYnn0XI8h9bB82g5nkPz1dXVsd52U/xcmIbnyzQ8X6bh+TINz1fLTKm3TZphXlpaiuPHjyMxMRHe3uKpcrkcYWFhKCoqAgAUFRVhzpw5+PHHH5GRkYGhQ4fqvUaXLl30fpfL5ejXrx9u3rxpyqFoaf75crncoypxmUzG8roxTyqvJ5UVYHndmTXKKpPJ3L5R4+jyedJ70pZ4Hi3Hc2gdPI+W4zk0H+tt98XPhWl4vkzD82Uani/T8Hy1zJR626SA+a1bt7B48WJs374d8fHxAMRsuLy8PCiVSpSXl2PmzJmoqqpCVlYWIiIi9J7/5Zdf4g9/+AP+3//7f+jVqxcAkbqloKAAEyZMMOVQtAYNGmTW84iIiMj+WG8TERG5DtbbRETkiUxOyTJnzhx8//33SEtLQ0BAALZs2YLDhw/jn//8J9atW4d//etf2L59O/r166f3vO7du6Ompgb/+7//i5CQEPzxj3+Ej48Ptm7disOHD+PAgQMIDAy0auGIiIiIiIiIiIiIiNrL5IB5ZWUl0tPT8dlnn6GyshJDhgzB0qVL0bdvXwwaNAi1tbVGn3fo0CHcc889+P777/Hmm2/i5MmTqK2txf33348lS5YgPDzcKgUiIiIiIiIiIiIiIjKHxZt+EhERERERERERERG5A7mjD4CIiIiIiIiIiIiIyBkwYE5EREREREREREREBAbMiYiIiIiIiIiIiIgAMGBORERERERERERERASAAXMiIiIiIiIiIiIiIgAMmBMRERERERERERERAWDAnIiIiIiIiIiIiIgIAAPmREREREREREREREQAXCBgfvPmTURERDT7+uCDDwAA+fn5mDZtGgYOHAilUonMzEy956vVaqxbtw7x8fEYOHAg5syZgx9++MERRWmXtsqbk5OD3/zmNxg0aBCUSiX+8pe/4M6dO9rnf/3110aff/LkSUcVqVVtlffll19udp9SqdQ+35X+v62Vdfr06Ubvi4iIwD//+U8AQGNjI+Li4prdv379escWrBX//Oc/8fDDDyM2NhaPPPII/v3vf2vvu3btGubNm4fBgwdj1KhRWLt2LRobG/Wen5WVhbFjxyIuLg5PPvkk8vLy7F0Ek7RW3m+++QbTp0/H/fffj/j4eLz00ksoKyvT3t/WZ8EZtVbeTZs2GS2PLlf6/7ZU1qVLl7b42d2wYYP2+RMmTGh2/9KlSx1VHLflSe9JW7LkPLpiXWULrZ3DqqoqvPrqqxg+fDjuv/9+/P73v2/Wdvn3v/+Nhx9+GHFxcfjVr36F48eP27sIDmfpOeR1V7C0/uJ7kdxNe9vcP/30E0aNGtWsH+1K/U9rsPR8uWIfxxKWxnNqa2vx2muvYcSIERg0aBCee+45lJaWOqo4Nudp8S9LtXW+Dhw4gMceewxxcXEYN24ctm3bBkmStM/3tOuXxSQn98UXX0ixsbHSzZs3paKiIu1XTU2NVFpaKj3wwAPSiy++KF24cEF6//33pdjYWOn999/XPn/9+vXSAw88IH3++edSfn6+lJycLE2YMEGqra11YKla1lp5T506JUVGRkqbNm2SLl++LH3xxRfS6NGjpaVLl2qfn5WVJY0bN07vuUVFRS5ZXkmSpCeeeEJavXq13n0lJSXa57vS/7e1sv700096t928eVN68sknpUceeUSqqqqSJEmSLly4IIWHh0v5+fl6j9Xc72z++c9/SlFRUdK7774rXb16Vdq4caM0YMAA6ZtvvpHq6uqkCRMmSHPnzpXOnz8vffrpp9KwYcOkt956S/v8Dz74QIqLi5M+/PBDSaVSSampqdKwYcP0/v/OpLXyXrp0SRo4cKD05z//Wbpw4YJ06tQp6dFHH5VmzJihfX5bnwVn01p5JUmS/vCHP0ipqanNrkUarvT/ba2sFRUVzcq4aNEiaeTIkdKNGzckSZKk6upqacCAAdLnn3+u97iKigoHl8y9eNJ70pYsPY+uVlfZQlvncNasWVJiYqJ0+vRpKT8/X1vfNzY2SpIkScePH5eio6Olv/3tb9KFCxeklStXSjExMdKFCxccWSy7svQc8rorWFp/8b1I7qg9be4bN25Iv/71r6Xw8HDpxIkTes93pf6nNVh6vlytj2MpS+M5S5culcaNGyedOnVKOnPmjPSrX/1Kmjp1qgNLZFueFv+yVGvn68svv5QiIyOlzMxM6fvvv5c++eQTaeDAgdLOnTu1z/e065elnD5gvnXrVumxxx4zet/mzZulUaNGSfX19drb0tPTpQkTJkiSJEm1tbXSoEGDpKysLO395eXlUlxcnPTRRx/Z9sDN1Fp5n3vuOWnWrFl6t/3jH/+QoqOjtW/wV199Vfr9739v8+O0ltbKq1arpYEDB0oHDx40er+r/X9bK6uhXbt2STExMdLFixe1t+3fv18aPHiwrQ7PqtRqtTRmzBhp5cqVercnJydLmzdvlj766CMpJiZGKisr096XnZ0tDR48WPtenjBhgvTXv/5Ve399fb300EMPSZs3b7ZPIUzQVnlXr14tTZgwQVKr1dr7Tp06JYWHh0vff/+9JEmmvT8cra3ySpIk/fKXv5TeeeedFl/DVf6/7SmrrkOHDkkRERF6nYUzZ85I4eHheu93si5Pek/akjXOoyvVVbbQ1jk8ceKEFBERIRUUFGjvU6lUUkJCgrbOT05Olv7whz/oPT8pKUlatmyZzY/fGVjjHPK6a536y9Pfi+Se2mpz79u3Txo2bJjRALCr9T+twZLz1Z7nuxtL4jk3btyQBgwYIH3xxRfa+y9duiSFh4drB4zdjafFvyzV2vn6+9//Lq1Zs0bvtvnz50tz5syRJMkzr1+W8nb0DPe2nD9/Hv369TN63+nTpzFs2DB4ezcVY/jw4diyZQtu3bqF69evo7q6GiNGjNDe36VLF0RFReHUqVN49NFHbX78pmqtvMnJyZDL9bPoyOVy1NfXo6qqCt27d8f58+dx//332+NQraK18n7//fe4ffs2+vbta/T+goICl/r/tlZWXaWlpVi7di2efvppvbK39/nO4PLly/jxxx/x2GOP6d2ekZEBAFi+fDmio6MREBCgvW/48OGoqqpCfn4+7rnnHly5ckXvf+vt7Y0hQ4bg1KlTmDdvnn0K0k5tlffixYsYM2YMZDKZ9j7Nz+Xl5ejVq5db/X/r6upw5cqVFj+7JSUlLvP/bausumpra7FixQr85je/wQMPPKC9/fz58wgMDNR7v5N1edJ70pYsPY+Aa9VVttDWOUxPT0d4eLheGpuwsDB8/vnnAMRS2W+++aZZ6pAHHngABw8etPHROwdLzyHA6y5gef3F9yK5q7bqqU8//RSLFi3CyJEjMW7cOL37XK3/aQ2WnK/2PN/dWBLP+frrrwGIfrHGfffdhx49euDUqVMYNGiQ7Q7cQTwt/mWp1s7XxIkTtT+r1WqcOHECp06dQkpKCgDPvH5ZyulzmBcWFqK0tBRTp07Fgw8+iClTpuDLL78EANy4cQMhISF6jw8ODgYA/Pe//8WNGzcAAHfffXezx2juczatlTcqKgoDBgzQPra+vh47d+5ETEwMunfvDgBQqVS4dOkSJk6ciJEjR+J3v/sd/vOf/zikLO3RWnkLCwsBALt27YJSqcS4cePwpz/9CZWVlQDgcv/f1sqqa9u2bejYsSNmz57d7PkNDQ2YPXs2Ro4ciYkTJ+LDDz+01+Gb5PLlywCA27dvY/bs2RgxYgR++9vfIicnB4D7fXbbKm+/fv0wcOBAveds27YNQUFB2g5/e98fzqCt8l64cAGNjY345JNPkJiYiISEBKSmpqKoqAiAa3122yqrrn379uHWrVt49tln9W4/f/48OnfujGeeeQajRo3CY489hp07d0KtVtujCB7Bk96TtmTpeQRcq66yhbbO4eXLl3Hvvfdi9+7deOSRRxAfH49nn30WN2/eBABUVFTg9u3bRutIT3kvWnoOAV53AcvrL74XyV211ebesmULJk+erDfRRcMT2wuWnK/2PN/dWBLPuXnzJrp164YOHTrovaanvr/cMf5lqfZ8nq5fv47Y2Fj87ne/Q2xsLKZMmQLAM69flnLqgHlDQwMuXbqE8vJyLFy4EFu3bsXAgQMxd+5cHD9+HHfu3IFCodB7jubiUltbi5qaGgAw+pja2lr7FMIEbZXX8LEvvPACVCoVXn31VQAi0FhZWYnbt2/j5ZdfxsaNGxEYGIhp06bhwoULjihSq9oqb2FhIeRyOYKDg7F582YsXboUR44cwfz586FWq13q/9ve/21VVRX27t2L2bNnN6soVSoVysrKMH36dGRkZCAxMREvvvgi3n//fXsXp01VVVUAgCVLluDRRx/Fjh07MHLkSMyfP98tP7ttldfQX/7yF3zxxRdYvnw5fHx8TPrsO4O2yqsZ7OrUqRPeeustrFixApcuXcKMGTNw584dl/r/tvd/q1ar8be//Q2//e1vERQUpPcaKpUKFRUVSExMREZGBqZMmYK33nrL4zZBtCVPek/akqXnEXCtusoW2jqHVVVVOHHiBA4cOIDXXnsNa9aswY0bNzBjxgzU1tZqz6MnvxctPYcAr7uA5fUX34vkjixtc3tae8HS8+VqfRxLWRrPqampafbeAvj+0jzW1eNflmrv+erSpQv27duHtWvXoqCgAC+88AIAz7t+WYNTp2Tx9vbGyZMn4eXlhY4dOwIAYmJioFKpkJGRgY4dO6Kurk7vOZp/dOfOnbXPqaur0/6seUynTp3sVIr2a6u8mqUTVVVVePbZZ/HVV19hw4YNiIuLAyBGik6dOoVOnTrBx8cHABAbG4u8vDzs2rULr732mmMK1oK2yrt161Y8+eST6NatGwAgPDwcQUFBmDRpEs6ePetS/9/2/m8/++wz1NXV4Te/+U2z1/jXv/6FxsZG+Pr6AgAGDBiA69evIyMjA0888YT9CtMOmvff7Nmz8etf/xoAEBkZiby8PLzzzjsmfXYNH+Ns/1ug7fJq/r/19fV45ZVX8M9//hN//vOftcsW2/v+cBZtlXfr1q0YPXq0duQfAPr374/Ro0cjJycHvXv3BuAa/9/2/m+/+eYbfP/999oRfF3btm1DbW0t/P39AQARERGoqqrCpk2bsHDhwmZLDcl0nvSetCVLz+PDDz/sUnWVLbR1Dr29vVFbW4u3335bmy5kw4YNiI+PR05OjnYZtie/Fy09h7/85S953YXl9ZdmIoMnvxfJ/Vja5nal/qc1WHq+XK2PYylL4znG+sgA31/uEv+yVHvPl5+fH6KiohAVFYXGxkY899xzSE1N9bjrlzU4fWvR19dX758JiM7ZzZs3ERISorcMGID29x49emiXGhh7TI8ePWx41OZrrbyAOPapU6fiu+++Q0ZGBh566CG9x3bp0kV7sQBEjqd+/frpLVN1Jq2VVy6Xa4PluvcBYjmJq/1/2/rfAiJg/tBDD6FLly7Nnt+xY0dtAEIjPDzcKZfPaM5/eHi43u1hYWG4du2a23122yovICr6OXPm4KOPPsLq1avx29/+Vu+x7Xl/OIv2lFc3oAaIpV5du3Z1uc9ue8oKiPyNUVFRRnPKKRQKbdBGIzw8HLdv30Z5ebkNjtrzeNJ70pYsPY+Aa9VVttCe+q9Hjx56ubUDAwPRtWtXXLt2DV27dkXnzp09+r1o6TkEeN0FLK+/+F4kd2VJm9sT2wuW9lFcqY9jDZbEc0JCQlBWVtYsaO7J7y93i39ZqrXzdfr06WbpaDTpX4uKijzy+mUppw6Yq1QqDB48GCdPntS7/dy5cwgLC8PQoUPx9ddfo7GxUXvfiRMncN999+Guu+7CgAED4Ofnp/f8iooK5OXlYejQoXYrR3u1Vd7y8nLMnDkTpaWlyMrKalaGL7/8EoMGDcIPP/ygva2hoQEFBQUICwuzSxlM0VZ5X3jhBcyaNUvvvrNnzwIQjX1X+v+2VVaN06dPGx1pr6iowLBhw/DBBx/o3X727FntIIIziY6Ohq+vL86cOaN3e2FhIXr37o2hQ4ciLy9Pu1wYEJ9dX19fDBgwAHfddRfuu+8+vfPV0NCA06dPO93/Fmi7vHV1dZg3bx7+85//ICMjA7/85S/1Htfe94ezaKu8a9asQWJiIiRJ0t537do1/PTTTwgLC3Op/29bZdU4deqU0c+uJEkYN24cNmzYoHf72bNnERQU1GxQkMzjSe9JW7L0PLpaXWUL7an/rl+/rtdZKSoqwk8//YR7770XMpkMgwcPxldffaX3/JMnT2LIkCF2KYOjWXoOed0VLK2/+F4kd2Rpm9uV+p/WYOn5crU+jqUsjefcf//9UKvV2s0/AbEfxc2bNz3y/eVu8S9LtXW+MjMz8frrr+vdd+bMGXh7e6NPnz4ed/2yCsmJNTY2Sr/5zW+khx9+WDp16pR04cIF6fXXX5diYmKk8+fPS7du3ZKGDh0qLVmyRFKpVNLf//53KTY2Vvrggw+0r7F69Wpp2LBh0meffSbl5+dLycnJ0oQJE6S6ujoHlsy4tsq7ZMkSKTo6Wjp+/LhUVFSk99XQ0CBVVlZKY8aMkaZMmSKdPXtWKigokBYvXiwNHTpUKi4udnTxmmmrvJ999pkUHh4urV+/Xrp69ar0xRdfSEqlUlq8eLH2NVzl/9tWWSVJkq5fvy6Fh4dLp0+fNvoaCxculEaNGiV98cUX0uXLl6UtW7ZIkZGR0pdffmnPorTb22+/LQ0aNEj66KOPpKtXr0obN26UBgwYIJ04cUK6c+eONG7cOGn27NlSfn6+9Omnn0rDhg2T1q9fr33+e++9J8XFxUkffPCBpFKppNTUVOmBBx6QSkpKHFiqlrVW3nXr1kkRERHSv/71r2af3dra2na9P5xNa+U9e/asFB0dLb3yyivSpUuXpK+++kr61a9+JU2ePFlSq9WSJLnW/7e1skqSJDU0NEjR0dHShx9+aPT5K1eulAYOHCjt379funr1qpSdnS3FxcVJ7733nj2L4fY86T1pS5aeR1erq2yhtXNYW1srPfzww9LkyZOls2fPSrm5udLUqVOl//mf/5Fqa2slSZKkw4cPS5GRkdKOHTukCxcuSH/5y1+kuLg46cKFCw4umf1Yeg553RUsrb/4XiR3Y0qb+4cffpDCw8O1nxcNV+l/WoOl58sV+ziWsDSeI0mStHjxYkmpVEonTpyQzpw5I/3qV7+Spk2b5uCS2Yanxb8s1db5+vrrr6XIyEhp9erV0pUrV6QDBw5Iw4YNk1auXKl9DU+6flmDUwfMJUmSiouLpaVLl0ojR46UYmNjpaSkJOnUqVPa+8+cOSNNmjRJiomJkcaMGSPt2rVL7/kNDQ3SX//6V2n48OHSwIEDpTlz5kg//PCDvYvRbi2Vt6GhQYqNjZXCw8ONfmnKdPXqVWnhwoXSsGHDpF/84hdScnKyU1dGbf1/Dxw4IP3qV7+S4uLipJEjR0orV66U7ty5o73flf6/7Xkvh4eHt9gJqayslF5//XXpoYcekmJiYqTHH39c+vTTT+11+GbZsWOHpFQqpejoaOl///d/9Y73ypUr0u9+9zspNjZWGjVqlLR27VqpsbFR7/nbt2+XRo8eLcXFxUlPPvmklJeXZ+8imKSl8k6YMKHFz66mUdnW+8MZtfb/PXbsmJSUlCQNHDhQGjZsmPTiiy9KZWVles93pf9va2W9deuWFB4eLv3f//2f0efW19dLGzZskMaOHStFR0dLiYmJHhe0sRdPek/akiXn0RXrKlto7RwWFRVJixcvlgYPHiwNGjRIWrBggXTjxg295//jH/+Qxo8fL8XGxkq//vWvpWPHjtm7CA5nyTnkdbeJJfWXJPG9SO6nvW3ulgLmrtT/tAZLz5cr9nEsYWk8p7q6WnrppZekIUOGSEOGDJEWL14slZaWOrhUtuNp8S9LtfV5+vLLL6WJEydKcXFxUkJCgrR582a9GIunXb8sJZMknXW1REREREREREREREQeyqlzmBMRERERERERERER2QsD5kREREREREREREREYMCciIiIiIiIiIiIiAgAA+ZERERERERERERERAAYMCciIiIiIiIiIiIiAsCAORERERERERERERERAAbMiYiIiIiIiIiIiIgAMGBORERERERERERERASAAXMiIiIiIiIiIiIiIgAMmBMRERERERERERERAfj/7diBAAAAAICg/akXKYyEOQAAAAAAVMIcAAAAAACqGizlNB4h/aacAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAG1CAYAAADXxB39AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAADPfElEQVR4nOzdeViUVfvA8e/MsMuwuKGgAqIimgvuS5pSpm+ZC5qVpilaUipppYFWb4tar2al+EsTNRcys8SltMzSXHNfS9xQxBU3FpGdmd8fj4yMMyjKDAjen+uaa5jnPPPMmcMs95znPueo9Hq9HiGEEEIIIcoAdWlXQAghhBBCiKKS4FUIIYQQQpQZErwKIYQQQogyQ4JXIYQQQghRZkjwKoQQQgghygwJXoUQQgghRJkhwasQQgghhCgzJHgVQgghhBBlhgSvQohHjqzNIoQQZZcEr0KI+zZw4ED8/f2NLo899hidOnXio48+IiUlxSqPGxMTg7+/P+fOnQMgMjISf3//It//0qVLvPbaa5w/f96wLSgoiPDwcIvXVQghhHXYlHYFhBBlU4MGDfjvf/9ruJ2Tk8O///7LF198QWxsLN9//z0qlcqqdXj++efp0KFDkfffvn07mzZtMto2c+ZMnJ2dLV01IYQQViLBqxDigTg7O9O0aVOjbS1btuTmzZvMmDGDgwcPmpRbWrVq1ahWrVqxjtGgQQML1UYIIURJkLQBIYRFPfbYYwBcuHCBgQMH8s477xAWFkbTpk0ZMmQIAFlZWUyZMoUnnniCxx57jOeee461a9caHUen0/H111/TqVMnmjRpwhtvvGGSjmAubWDlypX07t2bJk2a0KlTJ6ZNm0Z2djYxMTFEREQA8OSTTxpSBe5MG7hx4waffvopTz31FI0aNaJ79+789NNPRo8RFBTEjBkz+N///ke7du1o3LgxQ4cOJT4+vvgNKIQQ4q6k51UIYVGnT58GoGbNmgD8+uuv9OjRg1mzZqHT6dDr9YwYMYJ9+/YRFhaGn58f69evZ8yYMWRnZ9OrVy8Apk6dyqJFi3j99ddp0qQJv/76K9OmTbvrY3/33Xd8/PHHPP/887z11lucPXuWKVOmkJKSwujRo3n99deZNWsWM2fONJsrm5mZSf/+/bl27RphYWF4eXnxxx9/MGHCBK5evUpoaKhh30WLFtG8eXM+/fRTUlJSmDRpEu+++y4//PCDhVpSCCGEORK8CiEeiF6vJzc313A7JSWFXbt2MWvWLAIDAw09sLa2tnz00UfY2dkBsG3bNrZs2cKXX37JM888A0CHDh3IyMjg888/p3v37qSnp7N48WKGDBnCyJEjDftcvnyZLVu2mK2PTqfj//7v/3jqqaeYOHGiYXtGRgZr1qxBq9VSq1YtAAICAqhRo4bJMWJiYjh+/DhLly4lMDDQ8Li5ubl8/fXXvPjii7i5uQHg4uLC119/jUajASAhIYHIyEiSkpJwd3d/4HYVQghxd5I2IIR4ILt376Zhw4aGS7t27Xjrrbd47LHHmDZtmmGwVu3atQ2BK8Dff/+NSqXiiSeeIDc313AJCgriypUrnDhxggMHDpCTk0Pnzp2NHvM///lPofU5ffo0165do0uXLkbbhw4dSkxMDLa2tvd8Trt27cLLy8sQuObr0aMHWVlZHDx40LCtUaNGhsAVMOTeZmRk3PNxhBBCPDjpeRVCPJCGDRvy0UcfAaBSqbC3t6d69eomI/crVKhgdDs5ORm9Xk+zZs3MHvfy5cukpqYCmPRgVqlSpdD6JCcnA1CpUqX7eh4FpaSkmH2MypUrAxjqBeDo6Gi0j1qt9AXodLoHfnwhhBD3JsGrEOKBVKhQgUaNGt33/bRaLU5OTixatMhsube3N4cOHQLg2rVr1K5d21CWH6Ca4+LiAsD169eNticlJXHkyBGT3lRzXF1dOXPmjMn2K1euAKbBtBBCiJInaQNCiBLVqlUr0tPT0ev1NGrUyHA5fvw4//d//0dubi6BgYE4ODjw22+/Gd1348aNhR63du3auLu7m+yzatUqXnvtNXJycgy9o4Vp2bIl58+fZ//+/UbbV69eja2tLY0bN77PZyuEEMLSpOdVCFGinnjiCVq2bMkbb7zBG2+8gZ+fH4cOHWLGjBl06NCBihUrAvDGG2/w1Vdf4ejoSJs2bdi0adNdg1eNRsOoUaP4+OOPqVSpEkFBQZw+fZoZM2YwYMAAXF1dDb2z69evp2PHjvj5+RkdIzg4mCVLljBixAjCwsKoUaMGGzZsYPny5YwcOdJwfyGEEKVHglchRIlSq9XMmTOH6dOn880333Dt2jU8PDwYMmQII0aMMOw3fPhwnJycWLhwIQsXLiQwMJB3332XDz/8sNBjDxgwACcnJ+bNm8cPP/xAtWrVePXVV3n11VcBaN26Ne3atWPatGn8/fffzJkzx+j+jo6OLF68mGnTpjF9+nTS0tKoXbs2kyZNom/fvlZpDyGEEPdHpdfr9aVdCSGEEEIIIYpCcl6FEEIIIUSZIcGrEEIIIYQoMyR4FUIIIYQQZYYEr0IIIYQQosyQ4FUIIYQQQpQZxQpev/nmGwYOHGi0bcOGDfTp04fAwECCgoL43//+R2ZmpqF87969+Pv7m1x27txp2Ofvv/8mODiYJk2a0K1bN9asWVOcagohhBBCiHLiged5/e677/jqq69o0aKFYduePXsYOXIkYWFhdOvWjTNnzvDBBx+QnJzMp59+CsCxY8eoVasWS5YsMTqeq6srAHFxcQwfPpwhQ4YwdepU/vrrL8aNG0fFihVp27btA9V1//796PV6bG1tH/DZCiGEEKKk5eTkoFKpirS8s3h03HfwmpiYyH//+1927tyJj4+PUdnSpUtp3bo1oaGhAPj4+DBmzBjee+89PvroI+zs7Dh+/Dh16tShSpUqZo+/cOFC/P39GTNmDAB+fn4cOXKEuXPnPnDwqtfreRSms9Xr9eTk5GBra4tKpSrt6jwUpE2MSXuYkjYxJu1hTNrDVEm2yaPw3S3u330Hr//++y+2trasXr2a//u//+P8+fOGspCQEJO1w9VqNTk5OaSlpVGxYkWOHTtG8+bNCz3+nj17eOqpp4y2tWnThkmTJqHX6x/ojZLf49qoUaP7vm9Zkp6eTmxsLHXq1MHJyam0q/NQkDYxJu1hStrEmLSHMWkPUyXZJocPH7bq8UXZdN/Ba1BQEEFBQWbLGjRoYHQ7JyeHBQsW8NhjjxnWKz9x4gTu7u4EBweTmJhIvXr1GDNmDI0bNwbg0qVLVKtWzeg4VatWJSMjg6SkJMNx7pderyc9Pf2B7ltWZGRkGF0LaZM7SXuYkjYxJu1hTNrDVEm2yYN2Wony7YFzXu8lNzeXcePGceLECb777jsALl68yI0bN0hPT+e9995Do9EQHR3Nyy+/TExMDHXq1CEzMxM7OzujY+Xfzs7OfuD65OTkEBsb++BPqAyJj48v7So8dKRNjEl7mJI2MSbtYUzaw1RJtcmdMYEQVgle09LSGD16NLt27WLmzJmGXtXq1auze/duHB0djU7lHzlyhMWLF/PRRx9hb29vEqTm33Z0dHzgOtna2lKnTp0Hvn9ZkJGRQXx8PD4+PsVqq/JE2sSYtIcpaRNj0h7GpD1MlWSbnDx50qrHF2WTxYPXy5cv8+qrr3L+/HnmzZtHy5YtjcpdXFyMbqvVavz8/EhMTASUAPfy5csmx3RyckKr1T5wvVQq1SOTr+To6PjIPNeikjYxJu1hStrEmLSHMWkPUyXRJpIyIMyx6CIFKSkpvPLKK1y/fp3vvvvOJHDdvHkzgYGBnD171rAtNzeXo0ePGnpFW7Rowa5du4zut2PHDpo1a2YyGEwIIYQQQjxaLNrz+umnn3L27Fnmzp1LxYoVuXLliqGsYsWKNGvWDHd3d959913Gjx+Pra0tc+bMITk5mcGDBwMwcOBAevfuzeeff07v3r3ZtGkTv/32G3PnzrVkVYUQQghRTuTl5ZGTk1Pa1RDFYGtri0ajKdK+Fgte8/LyWLt2LTk5Obzyyism5X/++Sc1atRgwYIFfP755wwdOpSsrCyaN29OdHQ0lStXBqBu3bp8/fXXTJ06lYULF1KjRg2mTp36wHO8CiGEEKJ80uv1XLp0ieTk5NKuirAANzc3qlWrds90kWIFr5999pnhb41Gw6FDh+55n1q1ajFjxoy77tOxY0c6duxYnKoJIYQQopzLD1yrVq2Kk5OT5MiWUfnTmeaPeapevfpd97faVFlCCCGEENaSl5dnCFwrVapU2tURxZQ/c8Xly5epWrXqXVMIZASUEEIIIcqc/BxXmQWi/Mj/X94rf1mCVyGEEEKUWZIqUH4U9X8pwasQQgghhCgzJHgVQgghhChlubm5LFy4kODgYAIDA2nTpg0hISHs2LHDaD9/f39iYmIs9rh79+5lz549FjvenXQ6HcOGDSMyMtJix5TgVQghhBCiFGVlZTFo0CAWLFjAwIEDWbFiBQsWLMDPz48hQ4bw888/W+2x+/fvT0JCglWOnZ2dzfjx49myZYtFjyuzDQghSk5WFly7plySkuDmTcjIgPR089f5f2dmQl4e6HSg1yvXOp1yTLUaVCrjawcHcHQEJ6d7Xqvs7bFNSoJatZRtkj8nxCMrLw+2bIGLF6F6dejQAYo4b36xTJ8+nWPHjvHLL78YTRM1YcIE0tLSmDhxIkFBQVSoUMH6lbGQffv28cEHH5CZmYmLi4tFjy3BqxDiwel0kJgIZ8/CuXNw/jxcvaoEp/nXBS83b979eIUFmQ4OYGNzOzjND1ThdjBbMKjNzCw8KM4Pem9xBBrn37C3h0qVzF8qV1a+zWrWhBo1wMsLbG0t3aJCiFISEwNvvql8lOWrUQOmT4fgYOs9bk5ODsuXLyc4ONjs/KajR4/mpZdewsHBwaQsMjKSFStWsGHDhkK3bdq0ienTpxMXF4eTkxNPPPEEERERuLq64u/vD0BERAS7du3is88+IzExkc8++4wtW7ag0WgIDAwkPDwcHx8fAMLDw0lPTyctLY0DBw7w+uuv8+qrr5rUbdOmTXTo0IERI0bQo0cPSzSVgQSvQojCpabCyZOQkHA7QD179vbl/HnIzb29v729EuQVDPr8/EyDwEqVwN0dnJ1vB6n29tbv9dTrISfndjB78yaZly5x9sABalWogH1ammnAHR+vXF+5Yhx8q1RQrdrtYLZmTeO/69SBKlWkJ1eIMiAmBvr2VT4iCjp/Xtn+00/WC2DPnj1LcnIyzZo1M1vu4eGBh4fHAx37+vXrjBw5kvDwcDp16sSlS5cYN24cU6ZMYdKkSWzdupXHH3+c8ePHExwcTHp6OgMHDqRhw4ZER0ejVqv59ttv6devHz///LOhHuvWrWPs2LG8//77ZoNqgDFjxjxQnYtCglchHnU5OXDqFBw/rlyOHbv998WLt/ezs1MCsxo1wNsbHn/cNHCrVKlIwVpenpI1cO0aXDtjHCumpd3uJC0smyD/OjfXuNNVr1cud3bQqtVKbOzkpMLR0Q4nJzscHd1wcgI7Ox8yM/3x9HTDxcUWJydwCzDf+eqUm4rqfIEAvmAwv26dcl0wwHVzg3r1lIu//+3rOnWgDJ3+E6I8y8tTelzvDFzh9ufJ6NHQs6d1UghSUlIAcHV1tfixExMTyc7OxtPTEy8vL7y8vJg9ezZ5eXkAVKlSBQCtVotWq+XHH38kNTWVqVOnYmOjhIiTJk1i586dLFu2jFGjRhnqOmzYMIvXt6gkeBXiUZGbiyo2FvfffsM2OlrpUT12TAlcb32Q4eR0O8Dq2PF24OXtrfQiqu8+xjMlxTimy79OTDQOUJOTzX9RODuDi4v5zAFnZ6UK+bcdHZWz9nemu8LtIDY/i0CnU9Jt7wyAk5MhLU1FUpIT//yjITNTiT2Tk407lPPZ27tQuXIDKlVqYOhE9vSEml1vxfA19NR0SaF6TgI2p08Y/yD49VflyeerUUNpZ39/aNIEAgPhsceUJyaEKDFbthinCtxJr1c+x7ZsgU6dLP/4FStWBCA5Odnixw4ICKB79+6EhoZSpUoV2rdvT6dOnejSpYvZ/Y8cOUJKSgotW7Y02p6VlUVcXJzhtre3t8Xrej8keBWiPLp5Ew4dggMHlMv+/XD4MI6ZmdQGdLVqKYFS9+7GPYOennftOU1Kut0xe+qUaaB648btfVUqJUW0Rg3l7HqDBuazB/IvFSsqnbslLT09i9jYowQEBBhWd9HrlYyJOzMI7rxcvQr//KM8/7Q0ABXghlrtRvXqjQ2d0jVaQ63noWG1awRojuOZdhxN3K2g9q+/YPZsJcJWq6F+fWjaVAlmmzZVLpUrl3zDCPGIKHiCyRL73a+aNWtSuXJl9u3bxzPPPGNSHhcXx6RJk4iIiKBu3br3PF7uHb+8p02bxogRI9i8eTPbt29n7NixNG/enIULF5rcV6fT4evry6xZs0zKCq5kVliqQEmR4FWIsi47G/btg+3bYdcuJVg9flyJwGxslKgxMBD69yezfn2O2ttTr3XrQpdUzMyEuDjjDIL866tXb++Xn+5ZsyZ06WKa8lm9etkdz6RSgaurcqld+9776/VKr/M5MxkF587B4cNK2nBGRiWgLTY2bfHzu/Wb4RkIeCODpjb/4HfjAC6nDqA6sB9WrlS6h0EZHBYYCM2bQ/v20Lq10kUthCg2M2OkirXf/VKr1fTt25fFixczdOhQk0Fbc+fO5fDhw3h5eZnc19bWlpt3DIQ9c+aM4e+DBw+yZs0axo8fT+3atRk8eDCrV69m7NixXLt2jUqVKhndt169eqxatQqtVmvoEc7JyeHtt9+mW7duZoPr0iDBqxBlzdWrSqC6fTts2wa7dyvnxB0dleDm6afh3XeVHrsGDZRkz1t06enkxcYCSsCVkHC7c/bAATh4UBmflH9K38Xldqpmt263O2nr1lVO4wuFSqWkt7q5KR3a5uh0yuCPO38UxMRAfLwjOl1LoCUuLhAQAIED8niiRhzNNQfwST6A7T/7ITISPvpIecBGjZRAtl075drHRwaHCfEAOnRQfnSfP28+nUmlUso7dLBeHUJDQ9myZQv9+/fnzTffpFmzZiQnJ/P999+zcuVKvvzyS7MdDk2bNiU5OZl58+bRtWtXtm7dyubNm3FzcwPA2dmZJUuWYGtrS79+/cjKymLt2rX4+Pjg7u4OKD2qcXFxJCUl0aNHD+bMmUNYWBhjx47F2dmZr7/+ms2bN/Pmm29arwHukwSvQjzM9Holwtm6VQlUt29Xoh5QTvG3bw+ffaZcN21aaFdnTg4cPQo7d2rYuLEG587Zc/iwkgYAymn7wEBlNG39+rcD1qpVJR6yFLX6du/0k08al2VlGfd2//MPbP1bQ1RsPfLy6qFW96NePQh8Wk+Q1zHa6Lfjd2kbjhs3Qv7pvWrVjIPZ5s2VnnchxF1pNMp0WH37Kp93BQPY/M+/r76y7nyvjo6OREdHM3/+fKKiorhw4QIODg40aNCAxYsX06JFC7P3a9OmDaNGjWL+/PnMmDGDjh07EhYWxqJFiwDw8/MjMjKSmTNnsmTJEtRqNW3atCEqKgr1rUECISEhzJ07l7i4OGbPnk10dDRTpkxh6NCh5OXl0bBhQ+bPn4+fn5/1GuA+qfR6c78zypfDhw8D0KhRo1KuiXWlp6cTGxtrlLv3qCuTbZKYCH/8AevXK9fnzyuRT5MmtwOTdu2USfULiSwvXLgd627frvSqZmcrZTVqZNKihQ3Nm9sYUivvkeparj3Mr5HMTPj339tpy/m940p+rfJ/eyrwGr2q/U2rnO1UP7UN9Z5dyh1dXKBzZyWno0sXpbu8CP/kh7k9SoO0h6mSbJO7fX9nZmZy+vRpfH19LZKDaW6e15o1lcDVmvO8ituK+j+Vn+VClLb0dNi8+XaweuiQsr1xY3jxRXjqKSVg1WrN3j03V+mpyw9Wt22D/JQnX1/lrgMGKEFqnTrpXLiQ/6Ujb/+HnYOD0oHavPntbTqdMljuwAHYuxf+/rsSA5Z0JyOjO/b20LZ5Nn199xLEn/idWo/d6NHKi6RWLeW11KWL0vV7a4ocIYQiOFiZDqs0VtgS90e+vYQoaXq9Em3+8gv8/rsScWZnK4NyunSBceOUIKOQSamzs2HnTvjzTyWbYOdOpSfO1haaNYM+fZSO2XbtTAcYpKcrvbKi7FKrlWli69RRTnOCkhZy4ED+jxc7Jm9sy8gLbYH3aFonjUHem+iqXk/drX9gO3++cqemTZXX27PPKr9wJMVACDQa60yHJSxLPq2EKAm5uUpksXIlrFqldJ05OyufklOnKkFE/fpmT+vq9RAbq3TMrl8PmzYpwWrFiso6Ae+9dzvFUaYIfTTZ2kLLlsolf7L1hIT8tBFnFm19lrcPPoteDx38LvCq7x90zl2P58JFqKdOVZKeu3dXup0ef7y0n44QQtyVBK9CWEt6utKzumoV/PyzMjFo9erQowf06qXkIxaYCaCgixeVntX8TIILF5Q5UB9/HCZMUGLdwMB7rhkgHlEqlbKuhLc3vPSSsu3aNdiwAdav9+SD9YOIjx+ErUbHK433MMh1Fc03rcRp4UIcHRzwa9kSzUsvKV27kl4ghHjISPAqhCUlJSm9qytXKpFnRoYy79Frrym9Wi1bmo04dTplxqv8OPeff5TtTZpA//5KFkGHDsrqUkI8iEqV4PnnlYter8xu8Mcfatavb0WPDa1ITp5E0wonCPNeQdCZH3EdMQJGjlTyT3r2VAJZH5/SfhpCCCHBqxDFlpGh5K9+9x2sXaukCLRrBx9/rHzpF7IiSlYWbNyoxLmrVyu9rZUqKSmIERHKmJpC0l6FKBaV6nbebGiosjrwnj2wfn1dvvnlbYbGjqUqlxlTZw19k1bh+977qMeOVV7XAwZAv36y6pcQotRI8CrEg8jLU87BLlkCy5cr66K2bKnkr/brV+hSLMnJSny7apWy1P2NG8oKTi++qGQStGsn42ZEydNolEW7WreGt97KYsuWE5w8WZ+1a0P4758haLJuMqrmKkIuf0fdsDB4801UTz+tBLI9e0KFCqX9FIQQjxD5mhSiqPR6pXtqyRJYuhQuXVK6rt56Szm3X6+e2bslJyvx7Q8/KD2tubnK4Kpx45Tv/ccee3TnWBUPp8qVc+nQIY8RI5QfWOvWVWDVqv60/qU/NnlXeM3tR4bu/47aawegr1ABVa9eynugS5eyuyawEKLMkOBViHu5fBkWLIB585QlkDw8lK7SAQOgRQuzkWdmJqxZo2QSrFmjBKydOimTXffooUx8LURZoNUq6a59+ypTcm3ZUoWVK9/g8Z/ewJ7TjLRfwsB131H1u++UVIKBA5Uc7/r1S7vqQohySsYqC2GOTqcM93/hBWVR6w8+UNIC1q1Tll/56ivldoHANS9PuUtIiBLf9u2rTFf02WfKXf78E0aMkMBVlF22thAUBDNmwNmzMPcPX/7tOYG6Wf/SlP1EqweRPmexMkjxiSeUX2+ZmaVdbSFEOSPBqxAFXb4MU6aAv78yxP/wYeX2hQsQHQ1PP22UlKrXw759SuZAzZrKXTZvhtGj4ehRJctgzJhCU2CFKLM0GmVQ4fz5kHhZxfs/NSWm/TQ8ss/RX/U9Bw6p4eWX0Xl6KW+Qo0dLu8pCPNRyc3NZuHAhwcHBBAYG0qZNG0JCQtixY4fRfv7+/sTExFjscffu3cuePXssdrx8J06c4LXXXqN169a0bduWsLAwLlholRwJXoXQ6ZTBVwV7Wdu0UaLQf/9VItGKFY3ucuMGfPPN7aU7v/tO6WndsQNOnICPPlLiXyEeBQ4OyspuMTGQkGhP0JwXGdN0I/4cY0ZaCDdmKb2x+ieeUHLGpTdWCCNZWVkMGjSIBQsWMHDgQFasWMGCBQvw8/NjyJAh/Pzzz1Z77P79+5OQkGDRYyYlJTFkyBAcHBxYvHgxUVFRXL9+nWHDhpGVlVXs40vOq3h0pafDokXw5ZdKLmtAgNLLOmiQSbCab88emDNH+f7NyFCmtfr4Y+jWTWYJEALA3R2GDVMuZ8/W49tvpxIYNZEW51bw9r5vaDlgALqKlVC/8bqSR1OtWmlXWYjb8vJgyxZl7sLq1ZUJtjUaqz/s9OnTOXbsGL/88gvVC5yqmzBhAmlpaUycOJGgoCAqlJGZPf744w/S09OZMmUKDg4OAEydOpVOnTqxb98+2rZtW6zjS8+rePQkJsL770OtWsqXZ+PGRe5lbdlSmeJq7FiIj1fmZ+3eXQJXIcypWVM5kXEs3p6Bv7zIxKCN1FcdY1bqy2R+9iW6Wt7oQ4beXpVDiNIUE6MsxNG5szJ7RufOym0LnqI3Jycnh+XLlxMcHGwUuOYbPXo0UVFRhiCwoMjISIKCgu66bdOmTQQHB9OkSRPatm1LeHg4KSkpgJKCABAREUF4eDgAiYmJjBkzhhYtWtC6dWtCQ0OJj483HC88PJywsDBCQkJo1qwZUVFRJvVq27YtX3/9tVGd1bcW6ElNTS1q0xRKglfx6Pj3Xxg6VAlav/xSmS3g5En48Ufl1/UdswYcOKAMmq5eHd54A7y8lNWv4uPhv/+VgVdCFJVGo5ylWLUK/kiox7X3v6KFxznCcz7hcvQ6aNSI3C7dlFXp9PrSrq54FMXEKLlf584Zbz9/XtluxQD27NmzJCcn06xZM7PlHh4eNG7cGM0D9ABfv36dkSNH0qdPH9auXcvMmTPZvXs3U6ZMAWDr1q0AjB8/ngkTJpCens7AgQMBiI6OZvHixbi7u9OvXz8SExMNx123bh3t2rVj+fLldO/e3eRxa9SoQZs2bYy2zZkzBwcHB1q2bHnfz+NOEryK8k2vR7tzJ/a9eikTqq5bB598onxATZ8Ovr5Gu+t0yiICTz4JgYHme1lL4AySEOVWflr5wTNuPPHLON7oeopBqsUc2ZgITz9NzmNNYeFCyM4u7aqKR0VeHrz5pvkfTvnbRo9W9rOC/F5QV1dXix87MTGR7OxsPD098fLyonnz5syePdsQoFapUgUArVaLVqtlzZo1pKamMnXqVOrXr0+9evWYNGkSzs7OLFu2zHBcV1dXhg0bhq+vr9ne4jstXryY6Oho3nnnHSoWkpZ3P+RkpyifdDr46SccJk6k3uHD6Bo3hsWLldWv7OxMds/MVIq//BJiY6FVK2VRgeBgSQkQwhrye2OffdaO+PiXmf7VAI5/s5GRsdP4z+DB5IyNwHbcW/D667KCl7CuLVtMe1wL0uuVueG2bFEm7Law/GAuOTnZ4scOCAige/fuhIaGUqVKFdq3b0+nTp3o0qWL2f2PHDlCSkqKSe9oVlYWcXFxhtve3t5Feny9Xs/06dOZNWsWr7/+uiFoLi7peRXli04Hy5YpeawvvIC+alWOz5pF5vbt8PLLJoHrlSvKzAC1asHw4coMAVu2KLMG9OsngasQJcHHB778SsV3F4P4d8oaOnscYeGVZ8gdF0G2ly/6qZ/DzZulXU1RXl28aNn97lPNmjWpXLky+/btM1seFxdHSEgIJ06cKNLxcnNzjW5PmzaNX3/9lWHDhpGUlMTYsWMZOnSo2fvqdDp8fX1ZuXKl0eXXX39lwoQJhv3M5d/eKScnh7FjxzJ79mwiIiIYPXp0kepfFBK8ivLhjqCVGjVg+3ayVq/mxh2LCQAcO6YEq7VqKRMM9OunbFuxAh5/XJZrFaI0uLnBO+/A72cDcPpuLr0anGBBSi9yx0WQUd2X3M8kiBVWUNSJuK00YbdaraZv377ExMRw0UyAPHfuXA4fPoyXl5dJma2tLTfveE+cOXPG8PfBgweZPHkytWvXZvDgwcyZM4fJkyezY8cOrl27ZnK8evXqceHCBbRaLd7e3nh7e+Pp6cm0adPYvXv3fT2vcePG8dtvvzFt2jQGDx58X/e9FwleRdlWSNDKb7+Bmak4YmOVQaQBAUoO6/vvK2eDZs6EunVLof5CCBO2tsr79OfDPtTbOIfQoBMsvtELfYQEscIKOnRQvjsK67VQqZQRuh06WK0KoaGh+Pj40L9/f1auXElCQgKHDh0iIiKClStX8sknn+Dk5GRyv6ZNm5KcnMy8efM4d+4cS5cuZfPmzYZyZ2dnlixZwtSpUzlz5gzHjx9n7dq1+Pj44O7uDoCTkxNxcXEkJSXRo0cPXF1dCQsL4+DBg8TFxREeHs7mzZsNMxMURUxMDGvXrmXMmDG0atWKK1euGC6ZFpjnWYJXUTbdZ9B69KiK/v2hYUPYuhVmzVIGYY0fX+iUrkKIUqZSKSmG8/704fF/5/D2cyeIliBWWJpGowzgBdMANv/2V19ZdbSuo6Mj0dHR9OnTh6ioKHr27Mnw4cO5fPkyixcvplu3bmbv16ZNG0aNGsX8+fN59tln2bZtG2FhYYZyPz8/IiMj2bFjB7169eKll15Co9EQFRVlmLoqJCSE6OhoIiIi0Gq1REdH4+7uztChQ+nbty+JiYnMnz8fPz+/Ij+fX375BYApU6bw+OOPG13Wrl1bjJZSqPT68j8vyeHDhwFo1KhRKdfEutLT04mNjSUgIMDsL7RyY8MG5dzi/v3Qtasyb1UhEx7v25fB+PEZ/P67OzVqqJgwAQYPBnv7kq3yw+KReY3cB2kTYw97e8TGwqx342n082QG8y3ZrlWwnzIRm6GvWCW4eNjbozSUZJvc7fs7MzOT06dP4+vrW6QczHuKiVFmHSg4eKtmTSVwDQ4u/vHFPRX1fyo9r6LsiI2F555T5rGys1NGVt0jPaBFCwcOHHBm+vQcTpxQ8lwf1cBViPIgIABmrPbh8SNzGPvcMVanPIHN8KFc8w4kZ83vpV09UZYFByun5DZuVJZR3LgRTp+WwPUhVKzg9ZtvvjGZ9iA2NpaXX36Zpk2bEhQUxKJFi4zKdTodM2bMoEOHDjRt2pRXX32Vs2fP3tcxxCMmMVGZLqdRI2WhgR9+gL//VkZW3eH0aWXtgfz0gOnTc1ix4h+GDs2VoFWIciQgAL5aXZumR77n/S47OHLeFdvuXTnXqBt5Bw6XdvVEWaXRKLkqL72kXMvE3g+lBw5ev/vuO7766iujbUlJSQwZMoRatWqxfPlyRowYweeff87y5csN+3z99dcsWbKETz75hKVLl6LT6Rg2bBjZtyakLsoxxCMiPR0mTYI6dWDpUmVagNhYZWqAO/KSkpKUxQTq14e//oKvv4YTJ2Do0Fzs7Mp9ZowQj6yAAPjk99ZU/ncz09rHkPFPHAQ25fx/hsGFC6VdPSGEFdx38JqYmEhoaCiff/45Pj4+RmXLli3D1taWjz/+GD8/P/r06WOYmgEgOzub+fPnExYWRqdOnahfvz5ffvklly5d4vfffy/SMcQjQKeDRYuUSVc/+ghefRXi4uCtt0zO+WdnK3n2deoog7Deew+OH4fQUEkPEOJREtBAxdtbe5O89V8ia3+F/W8ryaxVl8TXP5RBXUKUM/cdvP7777/Y2tqyevVqmjRpYlS2Z88eWrVqhU2Bmd3btGlDfHw8V69e5ejRo9y8eZO2BXIUXVxcaNCggWH+sHsdQ5Rzhw4p6QCvvKLkssbGwhdfmEwJoNfD8uXQoIES0/btCydPKlNfyWI8Qjy6Wra3482To9ix+CSLtCNxm/0p1zwacP3bVaVdNSGEhdz3+kFBQUEEBQWZLbt06RL16tUz2la1alUALl68yKVLlwBM1sGtWrWqoexex6hcufL9VhlQlihLT09/oPuWFRkZGUbXZcqNG9hOmoTN11+jr1eP7HXr0OXntN7xf9u1S01EhC07dmjo2jWPpUuzadBAb27Xst0mViDtYUraxFh5aY+gYDuyu/+XxVOG4P35W3QJ6UXsp89SdennONavVeTjlJf2sKSSbBO9Xo9KVo0Rd7Do4peZmZnY3bH8pv2tc7dZWVmGF7q5fVJSUop0jAeVk5NDbGzsA9+/LImPjy/tKhSdXo/bhg3UnDYNTUoK519/ncsDBqC3tVV6XQu4etWGL7+sybp1FalXL53/+79ztG59AzDZ1USZapMSIO1hStrEWHlpj8A+cKPLJCI/Cqb3pg9xaN6cHU+PxPWjYLAt+ldgeWkPSyqpNrkzJhDCosGrg4ODYeBVvvyA08nJyTBnV3Z2ttH8XVlZWTg6OhbpGA/K1taWOnXqPPD9y4KMjAzi4+Px8fExtOfDTBUfj91bb6FZt468//yHrGnTqOjtzZ1rBuTlQVSUDR99ZIudHcyalcWAAaDR1LjnY5S1NrE2aQ9T0ibGymt7tFrrz9kjPdj34mSe+X0a57b+Qm7kV3i+2P6u9yuv7VEcJdkmJ0+etOrxRdlk0eC1WrVqXL582Whb/m0PDw9yc3MN22rVqmW0T/6yY/c6xoNSqVSPzATTjo6OD/dzzc6Gzz+HTz6BKlVgxQo0PXviaObU0J49yuCrffvgtdfg00/B3f3+R2I99G1SwqQ9TEmbGCuP7eHfwgn/k1+x7f8G4/j26zQb+jT7ZgwmYPUUHGtVuet9y2N7FFdJtImkDAhzLLpIQcuWLdm7dy95eXmGbTt27MDX15dKlSpRv359nJ2d2blzp6E8NTWVI0eO0LJlyyIdQ5Rxe/dCYCB88AGMGgVHjkCvXiZTXyUnw8iR0KqV0vP6998wezbcWopZCCEeWPsRTQm4to1Vz36Dz8FVZPrW5+D4H0q7WuIRl5uby8KFCwkODiYwMJA2bdoQEhLCjh07jPbz9/cnJibGYo+7d+9e9uzZY7Hj5fv333955ZVXDM/lgw8+4MaNGxY5tkWD1z59+pCWlsaECRM4efIkMTExLFiwgOHDhwNK3srLL7/M559/zp9//snRo0cZM2YM1apV4+mnny7SMUQZlZ2tLOPaurUyh9W+fcq8rc7ORrvp9crCJvXrw8KFykQDu3crdxNCCEtxrKCm5y+vcX3bUQ5WfpImn77I9povcPGwzGojSl5WVhaDBg1iwYIFDBw4kBUrVrBgwQL8/PwYMmQIP//8s9Ueu3///iQkJFj0mFevXmXIkCF4eXkRExPD119/zd69ewkPD7fI8S2aNlCpUiXmzp3LpEmT6N27N1WqVGHcuHH07t3bsE9YWBi5ubm89957ZGZm0rJlS+bNm4etrW2RjyHKmMOHlamvDh9W5rIaPx5u/b8LOnVKSQ348094/nn48kvw8iqF+gohHhl12lXF79Iytob9QIP/e4OcJo/xy6tzeGZWD9SygPojJ0+Xx5aELVy8cZHq2up0qNUBjdr6q2xNnz6dY8eO8csvvxjNyDRhwgTS0tKYOHEiQUFBVCgjc0GeP3+exx9/nI8//hgbGxt8fX3p168fX375pUWOX6zg9bPPPjPZ1rhxY374ofDTLxqNhrFjxzJ27NhC97nXMUQZkZen5LZ+8IGyisCOHdC8ucluej3MmQNvv62kwP76K3TrVgr1FUI8klQqeDzyBVJGdOR8t9foPqcnv60eTMC6r6hSx/SHtiifYmJjePO3NzmXes6wrYZLDaZ3m05wQLDVHjcnJ4fly5cTHBxsMpUowOjRo3nppZeMBrrni4yMZMWKFWzYsKHQbZs2bWL69OnExcXh5OTEE088QUREBK6urobxRhEREezatYvPPvuMxMREPvvsM7Zs2YJGoyEwMJDw8HDDwlTh4eGkp6eTlpbGgQMHeP3113n11VeN6tWkSRO++OILw+24uDhWrVpF+/Z3HyBZVPK7UljH8ePKYgMRETB6tJLraiZwPXdOCVRDQ2HAAGWNAglchRClwbV+dRqdXk3s2Pm0T1yOpulj/PHuRvSywnS5FxMbQ99lfY0CV4Dzqefpu6wvMbGWyzG909mzZ0lOTqZZs2Zmyz08PGjcuDEazf33AF+/fp2RI0fSp08f1q5dy8yZM9m9ezdTpkwBYOvWrQCMHz+eCRMmkJ6ezsCBAwGIjo5m8eLFuLu7069fPxITEw3HXbduHe3atWP58uV07979rnXo2rUrzzzzDMnJyUyYMOG+n4M5ErwKy9LrITISmjaFq1dhyxb43//gjl+Mer2yAuxjj8E//yi9rd98A1pt6VRbCCEAUKkImDIEDv9DanV/eszsTkL3WVyKK9+L3DzK8nR5vPnbm+gx/ZWSv230b6PJ0+WZlFtC/jz3rq6uFj92YmIi2dnZeHp64uXlRfPmzZk9e7YhQK1SRZllQ6vVotVqWbNmDampqUydOpX69etTr149Jk2ahLOzM8uWLTMc19XVlWHDhuHr62u2t7igzz//nMWLF1OpUiUGDRrETQss1yzBq7Ccq1ehRw8IC4OQEDhwAMycIkhMhN69lTTY555TglfpbRVCPEy0DWvR4OzvHHj1K7omfk9O046s+d8/0gtbDm1J2GLS41qQHj1nU8+yJWGLVR6/4q3lz5OTky1+7ICAALp3705oaCiPP/447777LidPnix03vsjR46QkpJCy5YtCQwMJDAwkObNm3Pp0iXi4uIM+3l7exe5Do0aNaJVq1bMnDmTc+fOsX79+mI/LwlehWVs3qz0tv79N/zyC8ycCWYSy3/6CRo2hO3bISYGFi+W6a+EEA8ptZp6X73KoXmLcdBqCApvyTfN53DlskSw5cnFGxctut/9qlmzJpUrV2bfvn1my+Pi4ggJCeHEiRNFOl7+nPr5pk2bxq+//sqwYcNISkpi7NixDB061Ox9dTodvr6+rFy50ujy66+/Gp3yN5d/W9CpU6f466+/jLZ5eHjg5uZmlH7woCR4FcWTl6csNtC5szIo6+BBePZZk93S0mDIEGUWgSeegH//VXpfhRDiYWfXxJsqcX9xoctgQvcPZ3utF9m4MqW0qyUspLr27qe973e/+6VWq+nbty8xMTFcvGgaIM+dO5fDhw/jZWb6HVtbW5PT8GfOnDH8ffDgQSZPnkzt2rUZPHgwc+bMYfLkyezYsYNr166ZHK9evXpcuHABrVaLt7c33t7eeHp6Mm3aNHbv3l3k57R9+3bCwsJITU01bEtISCApKQk/P78iH6cwEryKB3flCjzzjDJ/6/vvK3NcmXlzHToELVrAsmXw7bdK72uVuy9mI4QQDxdHR/x+n0XSnGU8lfcbNXq3YMbQg9zRySXKoA61OlDDpQYqzK/mpUJFTZeadKjVwWp1CA0NxcfHh/79+7Ny5UoSEhI4dOgQERERrFy5kk8++cTsamZNmzYlOTmZefPmce7cOZYuXcrmzZsN5c7OzixZsoSpU6dy5swZjh8/ztq1a/Hx8cH91mlPJycn4uLiSEpKokePHri6uhIWFsbBgweJi4sjPDyczZs3G2YmKIru3bvj5ubG2LFjOXHiBHv27CEsLIzGjRvTuXPnYreXBK/iwWzfrqyUtX8//P47fPgh3DESUq9XVsVq1er2ugSDB5sspiWEEGWG+6vP4/jvXlyqO/Pq/DZMqT8fC8/vLkqYRq1herfpACYBbP7tr7p9ZdX5Xh0dHYmOjqZPnz5ERUXRs2dPhg8fzuXLl1m8eDHdChkY0qZNG0aNGsX8+fN59tln2bZtG2FhYYZyPz8/IiMj2bFjB7169eKll15Co9EQFRWF+tZExiEhIURHRxMREYFWqyU6Ohp3d3eGDh1K3759SUxMZP78+ffVY+rm5sbChQsBeOmllxgxYgQNGjRg3rx5DzRrwp1Uen35Tz8/fPgwoCQNl2fp6enExsYSEBBgvfWm9XqYPh3GjoU2bWDpUrO9rTduwKuvwg8/wIgRynSv90iRsYoSaZMyRNrDlLSJMWkPY4W2R0YGl158k2qro1hiN5iKS7+mW2/H0qtoCSrJ18jdvr8zMzM5ffo0vr6+98zBLApz87zWdKnJV92+suo8r+K2ov5PLbrClijnsrKUCVkXLFBWFPj0U7MrZf3zD/TtCxcuKKkCzz9f8lUVQgircnSk2qo5pH39OH1GDedA8BE+HbWSsV9Ux0a+Wcuk4IBgevr3LJUVtsT9kbeYKJrLl5URVnv3QnS0sqKAGYsWKfFt3bqwZw/Uq1fC9RRCiBLk/MYgdC0a0KBLT7wiWzJi+yo++qU51aqVds3Eg9CoNXTy6VTa1RD3IDmv4t4OHoSWLeHUKdi0yWzgmpsLb76pzN364ovKSrASuAohHgXqVi3Qxu7Gpb4nX+3twH8b/sjevaVdKyHKLwlexd2tWKEsNFC5MuzeDa1bm+ySlKRMOvB//6dc5s8Hx0cj9UsIIRSenrjs24S+d2++ud6PdW3+y48/6Eq7VkKUSxK8CvP0epg0CYKDlch0yxaoUcNkt+PHlXFbe/Yokw688UYp1FUIIR4Gjo44LY8m56PJjM/9GNWL/Zg0/iY6iWGFsCgJXoWpjAwlNeC99+Cjj5QpA8yMKF2/XumIVath1y4ICiqFugohxMNEpcL2gwj0MSvoYfcbz3z6OK93P4sFlnMXQtwiwaswlpIC3brBypXKVAEffGAyMater6z++p//KL2uO3Yoi2sJIYRQqHr3wm73dvyrXOf939oxsEUsZ8+Wdq2EKB8keBW3XbqkrN16+LCyWpaZOa5ycuD112HUKBg9Gn75BVxdS76qQgjx0GvcGKcDf1PJz415xx/ntaY7+fvv0q6UEGWfBK9CERenDMy6elXJb23b1mSXtDTo3l0ZkDV/vrLwgAUWyhBCiPLL0xPHXZup0CKAmOQgJj2xjpUrS7tSQpRtErwKOHBACVxtbGDbNmjY0GSXq1fhySfh779h3ToYMqTkqymEEGWSuzt2G3/HrmtnVuY+x4/B3zNvXmlXSoiyS4LXR92mTUqqQM2asHUreHub7JKQAB06wOnT8Ndf0LlzyVdTCCHKNCcnNKtWoH75JRbrB3BgWCT/+58yhkAIgNzcXBYuXEhwcDCBgYG0adOGkJAQduzYYbSfv78/MTExFnvcvXv3smfPHosdz5zVq1fj7+/PuXPn7r1zEUjw+ihbtQq6dlUWINiwAapUMdklNlbplM3MVDplmzUrhXoKIUR5YGuLesG3qN56i0jCyAr/gHfe1stUWoKsrCwGDRrEggULGDhwICtWrGDBggX4+fkxZMgQfv75Z6s9dv/+/UlISLDa8c+fP8/HH39s0WPK8rCPqiVLYOBAZR7X6GiwtzfZZedOZYpXT08lVcDTsxTqKYQQ5YlajWra5+BRlQ/efZfIL5MZcnU6c+epsLUt7coJyAO2ABeB6kAHwPqDO6ZPn86xY8f45ZdfqF69umH7hAkTSEtLY+LEiQQFBVGhQgWr18WSdDodY8eOpWHDhiY9yMUhPa+PoqVLlcB10CDlbzOB67p1yrytAQGwebMErkIIYVHjxsHs2YwikubfjaF3Lz3p6aVdqUddDOADdAb637r2ubXdenJycli+fDnBwcFGgWu+0aNHExUVhYODg0lZZGQkQXdMsn7ntk2bNhEcHEyTJk1o27Yt4eHhpKSkAEoKAkBERATh4eEAJCYmMmbMGFq0aEHr1q0JDQ0lPj7ecLzw8HDCwsIICQmhWbNmREVFFfrcZs+eTU5ODsOHDy96gxSBBK+Pmh9/hJdfVi5z55qdLmD5cnjuOSW39fffwd29FOophBDl3fDh8PXXhOmm8/Tv79D1aT03bpR2pR5VMUBf4M6czPO3tlsvgD179izJyck0KyQvz8PDg8aNG6N5gOl9rl+/zsiRI+nTpw9r165l5syZ7N69mylTpgCwdetWAMaPH8+ECRNIT09n4MCBAERHR7N48WLc3d3p168fiYmJhuOuW7eOdu3asXz5crp37272sQ8dOsT8+fOZOnXqA9X9biRt4FESEwMvvQQvvqjMdWXmxbRihVL8/POwcCFyGksIIazp9dchL4+wUaPQ7bbhmf98xq+/qXB2Lu2KPUrygDcBc6Pn9IAKGA30xBopBPm9oK5WmDQ9MTGR7OxsPD098fLywsvLi9mzZ5OXlwdAlVtjXbRaLVqtlh9//JHU1FSmTp2KjY0SIk6aNImdO3eybNkyRo0aZajrsGHDCn3c9PR03nnnHd555x18fHyMAl9LkOD1UbFyJbzwghKVLlhgNnBdtQr69YM+fWDRImXmLCGEEFY2ciTk5TF69Ghyd2t49plJrP1VRRlLbyzDtmDa41qQHjh7a79OFn/0ihUrApCcnGzxYwcEBNC9e3dCQ0OpUqUK7du3p1OnTnTp0sXs/keOHCElJYWWLVsabc/KyiIuLs5w29vMzEQFTZw4EV9fX1588cXiPwkzJDx5FPz8sxKVBgfD4sVmo9JfflHi2l69lPFbErgKIUQJevNNyMvjnbffJm+Hhue6f8wva1Q4OZV2xR4FFy283/2pWbMmlStXZt++fTzzzDMm5XFxcUyaNImIiAjq1q17z+Pl5uYa3Z42bRojRoxg8+bNbN++nbFjx9K8eXMWLlxocl+dToevry+zZs0yKXMq8GI0l39b0PLly7GzsyMwMBDA0NObH0iHhobe83ncjYQo5d3atUpXao8ehUal+bt0765MQiCBqxBClIK33oK8PN4dN468bRp69vyQ1avB0bG0K1bemQ6SKt5+90etVtO3b18WL17M0KFDTQZtzZ07l8OHD+Pl5WVyX1tbW27evGm07cyZM4a/Dx48yJo1axg/fjy1a9dm8ODBrF69mrFjx3Lt2jUqVapkdN969eqxatUqtFqtoUc4JyeHt99+m27dupkNrs35/fffjW4fPHiQsWPHMmfOHOrVq1ekY9yNDNgqz7ZtU6LSZ5+F7783m8C6bp3SIfuf/ygTD0iOqxBClKKxY2HyZMbnfESTTTPo1UuZZ1tYUwegBkpuqzkqoOat/awjNDQUHx8f+vfvz8qVK0lISODQoUNERESwcuVKPvnkE6Oez3xNmzYlOTmZefPmce7cOZYuXcrmzZsN5c7OzixZsoSpU6dy5swZjh8/ztq1a/Hx8cH91mhsJycn4uLiSEpKokePHri6uhIWFsbBgweJi4sjPDyczZs3G2YmKApvb2+ji4eHBwCenp64ubkVr7GQ4LX8OnpUmTKgdetCo9I//oCePaFLF1i2DOzsSqGeQgghjEVEwNtvMzV3NBX/Wk7v3hLAWpcGmH7r7zsD2PzbX2HN+V4dHR2Jjo6mT58+REVF0bNnT4YPH87ly5dZvHgx3bp1M3u/Nm3aMGrUKObPn8+zzz7Ltm3bCAsLM5T7+fkRGRnJjh076NWrFy+99BIajYaoqCjUaiUEDAkJITo6moiICLRaLdHR0bi7uzN06FD69u1LYmIi8+fPx8/Pz2rP/36p9Pryvzjd4cOHAWjUqFEp18S60tPTiY2NJcDNDacnnwRnZ2XJVzO/cnbuVKbC6tRJmWHAzFSv5YKhTQICzP5qfdRIe5iSNjEm7WGs1NpDp4MBA8hbvoKuqvW49+jA0qVmx9qWuJJsk7t9f2dmZnL69Gl8fX3vmYNZNDEosw4UHLxVEyVwDbbA8cW9FPV/Kj2v5Yw6LQ2H4GDIzYVffzUbuJ44oeS3BgYqc7qW18BVCCHKLLUaFixA83g71tr2IHb5Ed56C8p/d1NpCgbigY3AklvXp5HA9eEjQ3PKk+xs/MaNQ3XmDGzZAjVrmuySmAjdukGlSshAACGEeJjZ28OKFdh16MDf57pRf8bf1KzpxTvvlHbFyjMN1pgOS1iW9LyWF3o9dq+/jvP+/WQtXQpmTrGkpSk9runp8NtvSgArhBDiIebqCr/+itYZ9lR9hk/GprBkSWlXSojSJcFreTF+PDZLlxL/0UfoOnY0Kc7JUaZ6PXpUmRrLx6fkqyiEEOIBeHnBr79SLTuBv6v15tVXstmwobQrJUTpkeC1PPjuO/jsM7InTybp6adNivV6CA2F9euVFWJvzRkshBCirGjYENWqVQRc38bSaqPp3RsOHSrtSglROiR4Lev274dXX4VBg8gtMD1GQR99BPPnK5dCVoQTQgjxsOvYEdXMmTx3bhZvuc3jP/+BhITSrpQQJU+C17Ls6lXo3RsaNIDZs0FlOsHyjz8qweukSTBwYCnUUQghhOW8+iq89hofXHqDFrpd9O4NGRmlXSkhSpYEr2VVbi68+CLcvKnkApiZNuCff2DIEGW3iIhSqKMQQgjLmzEDVfPm/KgL5tqRREJDZQot8WiR4LWsioiAv/5SlsaqVcukOCkJevUCPz+YO9dsp6wQQoiyyN4efvoJO3Ueu7yf5/tF2URGlnalhCg5Fp/ndefOnQwaNMhsWY0aNfjzzz+ZNWsWX331lUn5sWPHDH9/9913zJ8/nytXrvDYY4/x3nvv0aBBA0tXt2xauhQ+/xy+/FJZJusOeXkwYABcvw6//w4VKpRCHYUQQliPpyf89BNVO3dmQ9O36fRWJE2awBNPlHbFhLA+i/e8BgYGsnXrVqPLzJkzUalUvPHGG4ASpPbs2dNkv3wrVqxgypQpvPnmm8TExFCjRg2GDBnC9evXLV3dsufQIQgJUaLTN980u8snn9iybp0S49auXcL1E0IIUTLat4cZM3j8wEwm11vA88/LAK6yLDc3l4ULFxIcHExgYCBt2rQhJCSEHTt2GO3n7+9PTEyMxR5379697Nmzx2LHy7d69Wr8/f1NLufOnbv3ne/B4sGrnZ0dVapUMVwqVKjAp59+Su/evenTpw8Ax48fp0GDBkb7ValSxXCM2bNn8/LLL9OjRw/q1KnD5MmTcXR05Mcff7R0dcuWmzeVyVrr1YM5c8zmAvz5pxtTp9oyeTKYmTVLCCFEeTJ8OISEMDb+DR6zOUpwsAzgKouysrIYNGgQCxYsYODAgaxYsYIFCxbg5+fHkCFD+Pnnn6322P379yfBCr96jh07RqtWrUw6KqtXr17sY1t9edjZs2eTkZHBu+++C0B2djbx8fHULqRL8Nq1a8THx9O2bdvblbSxoUWLFuzevZvhw4dbu8oPr7ffVn5W79sHTk4mxbGxKj780Ifg4FzGjZOVf4UQotxTqSAyEtW2bfxCf6r/8zdvvGHP/Pky1uFB6PQ6LuRe4Kb+JhVUFfC08UStsv7woOnTp3Ps2DF++eUXo+BuwoQJpKWlMXHiRIKCgqhQhvIAjx8/jr+/v1HnpKVY9T9y/fp1FixYQGhoKG5ubgCcPHmSvLw81q1bR9euXenUqRNjx47l8uXLAFy6dAnAJDKvWrWqoeyRtGoVfPONkudav75JcWYmDBpkT/Xq2cyalS0fWkII8ahwcoLvv8fp1D9se/J9FixQ1q4R9+dk9km+TfmW5WnL+e3mbyxPW863Kd9yMvukVR83JyeH5cuXExwcbLZXcvTo0URFReHg4GBSFhkZSVBQ0F23bdq0ieDgYJo0aULbtm0JDw8nJSUFUFIQACIiIggPDwcgMTGRMWPG0KJFC1q3bk1oaCjx8fGG44WHhxMWFkZISAjNmjUjKirK7PM6duwYfn5+99cYRWTV7rklS5ag1Wp54YUXDNuOHz8OgKOjI9OnT+fatWt88cUXDBo0iJUrV5Jx63yHnZ2d0bHs7e3Jysp64Lro9XrS09Mf+P6l6uJFHIcOJa97d7JffhnMPI+xY22Ji7Nh4cJTaDTVzO3ySMp/PWXIeTRA2sMcaRNj0h7Gykx7+Ptj8+GHPDZhAp90eoo33uhCs2aZ+PhYfg6tkmwTvV6PqgR6Y05mn2TNzTUm29P0aay5uYZneZY6dnWs8thnz54lOTmZZs2amS338PDAw8PjgY59/fp1Ro4cSXh4OJ06deLSpUuMGzeOKVOmMGnSJLZu3crjjz/O+PHjCQ4OJj09nYEDB9KwYUOio6NRq9V8++239OvXj59//tlQj3Xr1jF27Fjef/99s0F1SkoKiYmJ7NmzhyVLlpCUlETjxo0ZO3Ysvr6+D/RcCrJq8Lpy5Up69epl9MR69epFx44dqVixomFb3bp16dixIxs2bKDWrWmfsrOzjY6VlZWFo5m5TIsqJyeH2NjYB75/qdHpqDtqFLnAv6NHk3f0qMku27e78PXXdXn77bPUqZNp9AtJKKRNjEl7mJI2MSbtYaxMtEeXLtRduZKx/wxkaYX9vPSSC3PmHMPGSt/0JdUmd3ZmWZpOr2NT+qa77rMpfRO1bWtbJYUgvxfU1dXV4sdOTEwkOzsbT09PvLy88PLyYvbs2eTl5QEYTulrtVq0Wi0//vgjqampTJ06FZtbL5xJkyaxc+dOli1bxqhRowx1HTZsWKGPe+LECUD58fHpp5+SmZnJrFmz6N+/Pz///DOVK1cu1vOyWvB69OhRzp49y3PPPWdSVjBwBSUlwM3NjUuXLtG6dWsALl++bNTdfPny5Qf+5QFga2tLnTrW+dVkTTaRkdjt3EnmqlXUK5AHnO/KFZg40ZEuXfIID3fizBnw8fEpVqBfnmRkZBAfHy9tcou0hylpE2PSHsbKWnuolizBrlUrNgSMoPrW5fzySyMiInIt+hgl2SYnT1r3lD3AhdwLpOnT7rpPmj6NC7kXqGFbw+KPnx8TJScnW/zYAQEBdO/endDQUKpUqUL79u3p1KkTXQpZK/7IkSOkpKTQsmVLo+1ZWVnExcUZbnt7e9/1cVu0aMHff/+Nu7u7oed85syZdOrUiZiYGF577bViPS+rBa979uyhUqVK1L8jP/PLL7/kt99+47fffjM8oXPnzpGUlESdOnWoVKkSvr6+7Ny50zBoKzc3lz179tC/f/8Hro9KpcLJzCCnh9rBg/DBBzBmDA49epgU6/UwahTodLBokQYnJ+VDxNHRsew9VyuTNjEm7WFK2sSYtIexMtMedepAVBRV+/YlpscC+nwawjPP2GGm76PYSqJNSiJl4Kb+pkX3u181a9akcuXK7Nu3j2eeecakPC4ujkmTJhEREUHdunXvebzcXOMfK9OmTWPEiBFs3ryZ7du3M3bsWJo3b87ChQtN7qvT6fD19WXWrFkmZQX/1+ZSBe50Z0elo6MjNWrUIDEx8Z73vRerDdg6cuSIIRG4oC5dunD+/Hk+/PBDTp8+ze7duxk1ahTNmjWjQ4cOAISEhPDtt9+yYsUKTp48yfjx48nMzKRv377Wqu7DJycHBg1SBmdNnmx2l2++gZ9/hvnzoVq1Eq6fEEKIh1OfPjB0KD3+DKN74wQGDIDU1NKu1MOrgqpoI/iLut/9UqvV9O3bl5iYGC5evGhSPnfuXA4fPoyXl5dJma2tLTdvGgfVZ86cMfx98OBBJk+eTO3atRk8eDBz5sxh8uTJ7Nixg2vXrpkcr169ely4cAGtVou3tzfe3t54enoybdo0du/eXeTn9MMPP9C6dWujsUZpaWnEx8db5Cy41YLXK1euGGYYKOixxx4jKiqKY8eOERwczMiRIwkICGD27NmGX1j9+vUjLCyMr776ij59+nD+/Hm+/fZbkyi+XPviC/jnH/j2WzDzCyc2Ft56C15/HcxkZgghhHiUffEFKjc3vnMfwdUrem6lKgozPG08cVY533UfZ5UznjaeVqtDaGgoPj4+9O/fn5UrV5KQkMChQ4eIiIhg5cqVfPLJJ2Z7uZs2bUpycjLz5s3j3LlzLF26lM2bN9+ut7MzS5YsYerUqZw5c4bjx4+zdu1afHx8cHd3B5Qe1bi4OJKSkujRoweurq6EhYVx8OBB4uLiCA8PZ/PmzWY7JAvTsWNHdDod48aN48SJExw+fJhRo0ZRsWJFgoODi91eVksbKGzqBIC2bdsazeNqztChQxk6dKilq1U2xMXBhx/C6NFgZvRhXh4MGQLe3soqsUIIIYQRFxeIjKRCcDCrRy6n88y+9O0rnR3mqFVqnnB6wuxsA/mecHrCqvO9Ojo6Eh0dzfz584mKiuLChQs4ODjQoEEDFi9eTIsWLczer02bNowaNYr58+czY8YMOnbsSFhYGIsWLQLAz8+PyMhIZs6cyZIlS1Cr1bRp04aoqCjUauX5hISEMHfuXOLi4pg9ezbR0dFMmTKFoUOHkpeXR8OGDZk/f/59TXtVvXp1FixYwLRp03jppZfQ6/W0b9+eRYsWYW9vX+z2Uun1esvPo/GQOXz4MACNGjUq5ZoUgV4PXbvC8eNKz6uz6a/BmTOVXNetW5XVAfOlp6cTGxtLQEBA2cjNKgHSJsakPUxJmxiT9jBW5tujd2/0O3fSt0Esu4+7cuSI2a+V+1KSbXK37+/MzExOnz6Nr69vkXIw7+Vk9kk2pW8yGrzlrHLmCacnrDZNljBW1P+pLMP0sFmyBNavhzVrzH7CnD8P48dDaKhx4CqEEEKYiIxE1aAB31aLoPrfX/P++8paN8JUHbs61LatXSorbIn7I/+Rh8m1a0qqQL9+YGbEISg9rhUqwKeflmzVhBBClEE1asCkSbh8N4uoIduZMQP27CntSj281Co1NWxr4G/nTw3bGhK4PqTkv/IwGTtWmWVg+nSzxStXwooVMGMGmBkLJ4QQQph64w1o1YqX/nqNZo9l8+qrkGvZqV+FKFESvD4sNm9WZhaYMsXsvFepqTByJDz7LDxKM4YJIYQoJo0G5sxBdfQoKx6fxqFDhfaRCFEmSPD6MNDpYMwYaNUKCllu7b33IDkZ/u//oATmbBZCCFGeNGkCb79NDfUFPghN5IMPoCyseCuEOTJg62GwZAns2wdbtoDa9PfEvn3KDAPTpinTYwkhhBD37cMPwdGR935ey9INz7BwIbi7Q8OGEBQkHSOi7JDgtbRlZMCECdC7Nzz+uEmxXg9vvw0BAcgk00IIIR6co7KEuKZubSYPjOX5DwLIy1OyCtatgyefLOX6CVFEkjZQ2mbMgAsX4LPPzBavWQN//aWkwtrITw0hhBDFVb8+LXbNJC9PuZmXB0eOlG6VhLgfEryWpqtXYfJkZdLWevVMinNzYdw45XROITNnlbrULFkwW5RPWfqs0q6CEJaXq0SsNUO68m//iSx97U/c3fQ0aFDK9RLiPkjwWpo++US5/uADs8Xz5kFsLEyd+vDmIo1bP660qyCEVWxL31baVRDC8k6dU659fGiw5H1emNeVPydsoF270q2WgNzcXBYuXEhwcDCBgYG0adOGkJAQduzYYbSfv78/MTExFnvcvXv3sscKk//m5OQwbdo0OnToQNOmTXn55ZeJjY21yLEleC0tJ07A119DRARUqWJSfOMG/Pe/8PLL0KxZKdSvCOKT45mzdw5nks+UdlWEsKjUvFQOZx8mNU/OLIhyJDMLLl5Rrhs3hjp1IC+P3MNHiIws7co92rKyshg0aBALFixg4MCBrFixggULFuDn58eQIUP4+eefrfbY/fv3JyEhweLH/fDDD4mJiWHy5MksX76cihUr8uqrr3Ljxo1iH1uC19Ly3/8q87m++abZ4s8/V6bGmjSpZKt1P2JiY9CjJ+ao5X4BCvEwOJlzEoC4nLhSrokQFnQ16dZ1snLdpw9oNGhb1WfyZCWT7VGny9MR/1c8h78/TPxf8ejydCXyuNOnT+fYsWMsWbKE3r174+PjQ/369ZkwYQK9evVi4sSJ3Lx5s0TqYglnz55l+fLlTJo0iQ4dOuDn58fEiROxs7Pjn3/+KfbxJXgtDSdPwg8/KL2ut0Z/FnThghK8jh4NtWqVfPWK6qcjPxldC1EWqVQqHB0dURXIzTmZrQSvJ7JPlFa1hLC8K0lG1xmvj4EPPiDpci4NGiipao+y2JhYpvtMZ2HnhcT0j2Fh54VM95lObIxlTnUXJicnh+XLlxMcHEz16tVNykePHk1UVBQODg4mZZGRkQQFBd1126ZNmwgODqZJkya0bduW8PBwUlJSACUFASAiIoLw8HAAEhMTGTNmDC1atKB169aEhoYSX2BS4PDwcMLCwggJCaFZs2ZERUWZ1Gvbtm1otVo6duxo2Obi4sKGDRto27btfbSOeRK8loYpU5RUgSFDzBb/739gb6/Etg+r86nn2XFOycP5++zfXLhxoZRrJMSDcXR0pEGDBjje+iGZpkvjYt5FAC7mXSRNl1aa1RPCMrKyIfVWz11qGmRl4+jtAR98QNuP/sP27fDuu6VbxdIUGxPLsr7LSD1nnCqUej6VZX2XWTWAPXv2LMnJyTQrJEfQw8ODxo0bo9Fo7vvY169fZ+TIkfTp04e1a9cyc+ZMdu/ezZQpUwDYunUrAOPHj2fChAmkp6czcOBAAKKjo1m8eDHu7u7069ePxMREw3HXrVtHu3btWL58Od27dzd53NOnT1OzZk1+//13goODad++Pa+++ipxcZY5myWTL5W0c+dgwQIlH8BMr+vlyxAVpXyIuLqWfPXutOfCHm5kGeen6NEzZ+8c9OgNt99a9xavNX8NFcYjy7T2Wlp4tiix+gpRVIm5iWTrs022/5NlfEprS/oWHrN/zGQ/O5UdHjYeVqufEA/kxk0Mc2Dl06PkuhYUdxaqV4E7BwNrNKCtYM0aPnR0eTp+e/M3bn2lGdMDKvht9G/49/RHrbF8n19+L6irFb70ExMTyc7OxtPTEy8vL7y8vJg9ezZ5t14jVW6NudFqtWi1Wn788UdSU1OZOnUqNrfm55w0aRI7d+5k2bJljLo14byrqyvDClkRFCAtLY0zZ87w9ddfM27cOFxcXJg1axb9+/dn7dq1VKpUqVjPS4LXkjZtGlSooEyPZcb06coiW6W9IIFer0elUuHm4EboL6Hsvbj3rvv/8O8P/PDvD0bbWni24Ps+3xsdT4jSlv9atFfZsyF9A5fzLt91/+M5xzmec9xom4fGg24VuhkdT4hSpdcr09LYaOD4GUhLv/v+V5JupxHk0zqhr19biWfzj/cISNiSYNLjakQPqWdTSdiSgE8nH4s/fsWKFQFITk62+LEDAgLo3r07oaGhVKlShfbt29OpUye6dOlidv8jR46QkpJCy5YtjbZnZWUZ9Zp632O5TxsbG9LS0vjyyy/x8/MD4Msvv+SJJ55gxYoVdw18i0LSBkrSlSswZw6EhYFWa1KcnKwsA/v663DrtVxq8r+M61Ssw99D/+addu+Y9KoWel9UvNPuHbaHbKdOxTpGxxOitOW/Ft00brygfYFm9vc3nUcz+2b00/bDTeNmdDwhSlX+69DRAQIDoMZ9nhWo4YG+aX1UTg6cOsUjE7gC3LhYtNHvRd3vftWsWZPKlSuzb98+s+VxcXGEhIRw4kTRcvBzc3ONbk+bNo1ff/2VYcOGkZSUxNixYxk6dKjZ++p0Onx9fVm5cqXR5ddff2XChAmG/czl3xZUrVo1bGxsDIFr/n1q1qzJuXPnivQ87kaC15I0Y4bygRAWZrb4668hKwveequE63UPNmobpnaZym8v/4ZHhbt/IHpU8OC3l39japep2KilY1883FSo6ODUgV7OvXBSOd11XyeVE72ce9HBqUORf8gJUSpUgF9NaFQXbO/xOWxro+znVxOVXs8vv0DLlpD6CM0Sp61u2plUnP3ul1qtpm/fvsTExHDx4kWT8rlz53L48GG8vLxMymxtbU1mIThz5vb0lQcPHmTy5MnUrl2bwYMHM2fOHCZPnsyOHTu4du2ayfHq1avHhQsX0Gq1eHt74+3tjaenJ9OmTWP37t1Ffk4tW7YkNzeXw4cPG7ZlZmZy9uzZe/baFoUEryUlNRUiI5V0ATO5Hunp8OWXEBICZgYbliqVSoVer+dpv6fZ9equQoNSG7UNu17dxdN+T8upVFEmqFQqdDod3rbevOjyIupCPhLVqHnR5UW8bb3ltS0efiqVctq/ois0Cyi8F1WlUsorusLNm9C+Pa3ztnPjBsyeXbJVLk21OtTCpYaLaf5vPhW41HShVgfrTf8TGhqKj48P/fv3Z+XKlSQkJHDo0CEiIiJYuXIln3zyCU5Opj+wmzZtSnJyMvPmzePcuXMsXbqUzZs3G8qdnZ1ZsmQJU6dO5cyZMxw/fpy1a9fi4+ODu7s7AE5OTsTFxZGUlESPHj1wdXUlLCyMgwcPEhcXR3h4OJs3bzbMTFAULVq0oF27drz77rvs2bOHkydPMm7cODQaDT179ix2e0nwWlIWLFA+HArpVp07F5KSYOzYkq1WUen//hsArZ0Wvd5cVruS++di7wLIqVRRdmRlKcvA2mJrGIR4Jz167LAD5LUtyoj816lGowSy5uj16PNHsDs6QnIyVb77isGD4YsvICOjRGpa6tQaNd2mKznsJgHsrdvdvupmlcFa+RwdHYmOjqZPnz5ERUXRs2dPhg8fzuXLl1m8eDHdunUze782bdowatQo5s+fz7PPPsu2bdsIK3B218/Pj8jISHbs2EGvXr146aWX0Gg0REVFoVYrzyckJITo6GgiIiLQarVER0fj7u7O0KFD6du3L4mJicyfP98oBaAoIiMjadWqFSNHjqRv376kpaWxaNEiQ45vcaj0hUUi5Uh+t3WjRo1KpwJ6PTRsCI89BsuWmRTn5EDt2tC5Myxa9OAPk56eTmxsLAEBAWZ/oT0QvR79hg3kJadi06c3Cw8sZPCqwYXuvrDXQgY1GWSZx7YAq7RJGSbtYSorKwt7e3uOZB1hffr6Qvd72ulpAuwDSrBmpUNeI8bKfHtcugrH4gsv9/eBapWVv6dPh3fe4fSWc9Rp70FkJLzxhuldSrJN7vb9nZmZyenTp/H19b1nDmZRxMbE8tubvxkN3nKp6UK3r7oREFz+3/sPg6L+T6XntSRs3QqxsYXOMPDzz8oMWm+/XcL1KooNG1B17YqNm9Kjujx2uaFoUJNB7By2k4GNBxq2FSwXoizInw4mf1UtgAC7AF7QvkB9u/qGbQXLhSgzrhaYUcCjkjKYy6NA6lr+alsAgwaBjQ2+G+fTq5cyDqP8d2/dFhAcwJvxb/LKxlcIXhLMKxtf4c3Tb0rg+hCSETUlYfZsqFtX6VotpLhtW2jSpITrVRT//gtaLfpOT5CWdYPf435Ha6dl1rOzGNB4AACLei+iq19XXl/zOr/H/U5adhoVbCvI6VVRJqjVarL12STkJGCHHZ2dOlPfXglaq9lUw9vGm43pGzmTc4ZsfTa22MprWzz89HrI08H1VNCooa737aDVxRfcXeDEGUhKQZ+Xh0qtBnd3ePFFmDOH1795ly5d1WzbBo8/XrpPpSSpNWqrTIclLEt6Xq3tyhX46ScYPtxs0nxcHKxfrxQ/lBo2hF69UGlsWHNiDU2qNeFA6AEGNB6ALiOTjL92oM/OYkDjAewfvp9GVRux5vga+XIXZYZKpeJ0zmkqayrT36U/9e3rk3sjnaS/DqC/epX69vV5yeUlKmsqE58TL69tUTaoVHA9BZydoHlDJXC91Y2q1+vBoxL65g2gghOqaym3v59CQyE+nqDc3/Hzg2++KcXnIEQhJHi1tgULlA+FV14xWzxnDri5Qb9+JVqrogsKQn9rGTlPrSdbh2yltnttuHYNtaMDjp3aoLKzB8Cvoh/bQrZRXfuQTZcgxD04q5x5XtsPV40r/PEHNvX8cH+qBddXbSb9+FncNG48r+1HBdWjtfKQKOPsbKFpfXBUPqPzA9T8H2AqRwel3M729n1atYKmTVHPmc3w4fDjj3D1aklXXIi7k+DVmnQ65Wfr889D5comxVlZMH8+DB5sdqXYh4NKZah7R++O2KCGEycKXUXBRm1DR++OAIXOSiDEwyL/Nepl64VKryLh+63w9NNw6RLk5XFi43mc69ckIgJUehVetl5G9xPioZT/+nTTFj79Uz7Vrf3y76dSKacCf/6ZkKfPodfDwoXWrKwQ90+CV2vasEHJCygkJyAmRvlF+9CmDNyS/ytdr9ej0miU/N1CTp3mzwlb8H5CPKzyX6OZWZnKrANkKeszA2g01OjSgH9m/Elo9gyOLdlDRoa8tkUZkP/6LMoSr/lzwha834AB4OREpRVz6dtX6YOR32viYSIDtqwpOhr8/aF9e7PF33wDnTpB/fpmix86Rf3Cli92UZbk5uYSeyQWf39/PF4Mgqrr4MgRaNCA7AQ1DUZ3gbw80GiIy/4Dv5BOpV1lIYqmqJ/Fd+6n1cILL8B33xE6778sWaJi40YICrJ8FYV4ENLzai1ZWbBypfIBYOYD5Phx2LTp4e91FaK8y87ORqfTKTdUKnjySRg1Cp58Erujh5XAFSAvD/vjhws/kBDlyQsvwMmTPO58gAYNICqqtCskxG0SvFrL+vWQklLoSKxly8DZGSywSpoQwoL0evjzT5gxA/LqN1RWKALQaLBt0qB0KydESencGSpVQvXjMl5+GVavVpYxF+JhIMGrtfz4IzRooEw1ZcayZdCjx0M8UEuIR9SGDdC1K7z5JnT8bxBn5qzj0vgZJC5eR9UX5LypeETY2EBwMCxbxvN99aSnw9q1pV0pIRQSvFpDfsrA88+bLT56FA4fLrRYCFGK/v33dqZAwlkVq28+SbVJo/B46UlUasnnFo+Qfv3g1CnqpO4jMNDs6ubCgnJzc1m4cCHBwcEEBgbSpk0bQkJC2LFjh9F+/v7+xMTEWOxx9+7dy549eyx2PIDIyEj8/f3NXiIiIop9fAlereH33yE1tdDo9McflZSBbt1KuF5CiLvS66FKFWWw9WuvKXMwN5BMAfGo6tRJmSpx2TL69YM1a+DmzdKuVPmUlZXFoEGDWLBgAQMHDmTFihUsWLAAPz8/hgwZws8//2y1x+7fvz8JCQkWPWZISAhbt241ugwdOhQnJycGDx5c7OPLbAPWsGyZki5wl5SBnj3BwaGE6yWEuKsNG2DgQMPkAixaJCOsxSMsP3Xgxx95/vfPiIhQsXYtPPtsaVfMinR5cGULZFwEx+pQpQOoNVZ/2OnTp3Ps2DF++eUXqle/vdDPhAkTSEtLY+LEiQQFBVGhQtlYKKVChQpGdT1y5AiLFi3ik08+wd/fv9jHl55XS8vKUjLbCxmodeQI/PPPQ7yilhCPsIIpA3l5cO1a0WcbEqJc6tcPTp/GL2kPzZuX89SBszGw2gf+7Azb+yvXq32U7VaUk5PD8uXLCQ4ONgpc840ePZqoqCgczPR4RUZGEnTHL+w7t23atIng4GCaNGlC27ZtCQ8PJyUlBcAQSEZERBAeHg5AYmIiY8aMoUWLFrRu3ZrQ0FDi4+MNxwsPDycsLIyQkBCaNWtGVBGmovj4449p0aIFvXv3vneDFIEEr5a2dauSMtCrl9niH38EFxdlER8hxMOlofHkApIyIMQTTyj5M2vXGlIH0tJKu1JWcDYGtvSF9HPG29PPK9utGMCePXuW5ORkmjVrZrbcw8ODxo0bo9Hcfw/w9evXGTlyJH369GHt2rXMnDmT3bt3M+XWsu9bt24FYPz48UyYMIH09HQGDhwIQHR0NIsXL8bd3Z1+/fqRmJhoOO66deto164dy5cvp3v37netw8aNG9m/fz/vvvvufde/MBK8Wtoff4CHBzRqZLY4Jgaee05SBoR4GAUFwbp1yjRZ69ZJyoAQ2Ngob4Q//qBvX8jIgPXrrX8avUTp8mDvm4C5ZcRubds7WtnPCvJ7QV1dXS1+7MTERLKzs/H09MTLy4vmzZsze/ZsQ4BapUoVALRaLVqtljVr1pCamsrUqVOpX78+9erVY9KkSTg7O7OsQLe7q6srw4YNw9fX12xvcUHffvstnTt3JiAgwGLPS3JeLe2PP+Cpp8yea7x0CQ4dgnHjSqFeQoh7yl+j4MknS7smQjxEnnoKwsKoXeUG9epp2bhRXWZWhiySK1tMe1yN6CH9rLKfRyeLP3zFihUBSE5OtvixAwIC6N69O6GhoVSpUoX27dvTqVMnunTpYnb/I0eOkJKSQsuWLY22Z2VlERcXZ7jt7e1dpMe/cOECO3fuZM6cOQ/+JMyQnldLunYN9u5V3uhmbNigXBdSLIQQQjx8nnoKcnNh82a6dIGNG8tZz2vGRcvud59q1qxJ5cqV2bdvn9nyuLg4QkJCOHHiRJGOl5uba3R72rRp/PrrrwwbNoykpCTGjh3L0KFDzd5Xp9Ph6+vLypUrjS6//vorEyZMMOxnLv/WnD/++IOKFSvSvn37Iu1fVFYJXhMTE83O7ZU/L1lsbCwvv/wyTZs2JSgoiEWLFhndX6fTMWPGDDp06EDTpk159dVXOXv2rDWqalkbNypz7RTSbfPHH0o2gYdHCddLCCGEeFB16kCtWvDHHzz1FJw6peb8ebvSrpXlON79tPd973ef1Go1ffv2JSYmhosXTQPkuXPncvjwYby8vEzKbG1tuXnH/GVnzpwx/H3w4EEmT55M7dq1GTx4MHPmzGHy5Mns2LGDa9eumRyvXr16XLhwAa1Wi7e3N97e3nh6ejJt2jR27959389tz549tGrVChsby57ot0rwevToUezt7dmyZYvRHF/PPPMMSUlJDBkyhFq1arF8+XJGjBjB559/zvLlyw33//rrr1myZAmffPIJS5cuRafTMWzYMLKzs61RXcv54w/w94eaNU2K9HplxVjpdRVCCFGmqFTQpQusX0+nTqBW69m1y6W0a2U5VTqAUw2gsKlFVOBUU9nPSkJDQ/Hx8aF///6sXLmShIQEDh06REREBCtXruSTTz7BycnJ5H5NmzYlOTmZefPmce7cOZYuXcrmzZsN5c7OzixZsoSpU6dy5swZjh8/ztq1a/Hx8cHd3R0AJycn4uLiSEpKokePHri6uhIWFsbBgweJi4sjPDyczZs3P9AUV0eOHKG+FXJMrBK8Hj9+HB8fH6pWrUqVKlUMFwcHB5YtW4atrS0ff/wxfn5+9OnTx/BrACA7O5v58+cTFhZGp06dqF+/Pl9++SWXLl3i999/t0Z1Lecu0enx43DunASvQgghyqCnnoJ//8Ut4yItWujYtUtb2jWyHLUGmk+/dePOAPbW7eZfWXW+V0dHR6Kjo+nTpw9RUVH07NmT4cOHc/nyZRYvXky3QlY1atOmDaNGjWL+/Pk8++yzbNu2jbCwMEO5n58fkZGR7Nixg169evHSSy+h0WiIiopCrVZCwJCQEKKjo4mIiECr1RIdHY27uztDhw6lb9++JCYmMn/+fPz8/O77eV25cgU3N7cHapO7scqArWPHjhX6JM11Ibdp04ZvvvmGq1evcuHCBW7evEnbtm0N5S4uLjRo0IDdu3ffc0qGUnP6NJw6VWh0+scfYGsLHTuWcL2EEEKI4sqfeuOPP+jc+UVmz3ZBp3vIz4bej5rB0OEnZdaBgoO3nGoogWvNYKtXwcnJiZEjRzJy5Mi77nfs2DGj2+buU3AVq86dO9O5c+dCjzdq1ChGjRpluF2zZk0iIyML3f+zzz67a/0KOnjwYJH3vR9WCV6PHz+Ou7s7AwYM4PTp03h7e/P666/TsWNHLl26RL169Yz2r1q1KgAXL17k0qVLACZTL1StWtVQ9iD0ej3p6ekPfP970WzYgD2Q3qIFmHmcdevsaN1ahVqdZa7YIjIyMoyuhbTJnaQ9TEmbGJP2MCbtcYuzMw4BAeRt3ky753rwv/+5snt3Gq1bW/dh9Xo9qpJaKaRmMHj1LJUVtsT9sXjwmpuby6lTp6hTpw7h4eE4OzuzZs0aXnvtNb799lsyMzOxszNO9La3tweUqRjyPyDM7ZM/F9qDyMnJITY29oHvfy81/vwTNy8vYhMTocBEvqDku/71VxNeeimR2FjrjFYsqOBKGEIhbWJM2sOUtIkxaQ9j0h7gU7s2Dtu3U3VoHPb2TVm9+gYuLpet/rh3xgNWpdZYZTosYVkWD15tbGzYuXMnGo3GMJXCY489xokTJ5g3bx4ODg4mA6+ysrIApcs8/z7Z2dlGUzFkZWXh6Oj4wPWytbWlTp06D3z/e7FPSEDfurXZSXjj4lTcuGHDf/5TkYAAN6vVISMjg/j4eHx8fIrVVuWJtIkxaQ9T0ibGpD2MSXvcZtOpE7Z//EFdH0/8/dNJSKhKQEAlqz7myZMnrXp8UTZZJW2gQoUKJtvq1q3L1q1bqVatGpcvG/9Sy7/t4eFhmJ/s8uXL1KpVy2ifBxnplk+lUpkdqWcROh0cPAjvvYeNmcc4ckS5bt/eAWtVoSBHR0frPdcyStrEmLSHKWkTY9IexqQ9gLZtISsLp9OnCQjwY9++yjg52Vv1IUssZUCUKRafbeDEiRM0a9aMnTt3Gm3/559/qFOnDi1btmTv3r3k5d1eZm3Hjh34+vpSqVIl6tevj7Ozs9H9U1NTOXLkiMmKDw+N48eVxZ6bNzdbvHevMnvWrVXYhBBCiLKnaVNQq1Hv309AwE1OnFCTmlralRKPIosHr35+ftSuXZuPP/6YPXv2EBcXx6effsqBAwd4/fXX6dOnD2lpaUyYMIGTJ08SExPDggULGD58OKDktrz88st8/vnn/Pnnnxw9epQxY8ZQrVo1nn76aUtX1zL27lWumzUrtLiQuFYIIYQoGypUgPr1Ue/fT/36ysjj/ftLuU7ikWTxtAG1Ws3s2bOZNm0ao0ePJjU1lQYNGvDtt98aZhmYO3cukyZNonfv3lSpUoVx48bRu3dvwzHCwsLIzc3lvffeIzMzk5YtWzJv3jxsbW0tXV3L2LsXfH3h1vrEBen1sG8fvP12KdRLCCGEsKTmzVHv34/PsEwcHfXs3aviiSdKu1LiUWOVnNfKlSvz6aefFlreuHFjfvjhh0LLNRoNY8eOZezYsdaonuXdpWv11ClITpaeVyGEEOVA8+aoly3DhlwaN9axd69MIyVKnlVW2HrkHD6s5AKZkZ9RIMGrEEKIMq9pU1RZWTgkJNC0qc7wHSdESZLgtbiSkyEpCQqZhuvAAfDyglvrMAghiis7G65fh8uXlTmVL19WbmeXo9V+hHhY3fqusz9/nqZNdRw/DjdvlnKdxCPHKmkDj5TTp5Xr2rXNFp88CcWY4UsIkZOjBKlJScqsHrfmhS5Ir1dmrNPZ2qN3dgY3d6jmga2jLTLTjhAWVL06ent77M6fp243PXq98jX42GOlXbGyLzc3l++++45Vq1Zx+vRp7O3tadCgAa+99hpt2rQx7Ofv78+nn35KcLBllqzdu3cver2eFi1aWOR4+a5du8bkyZPZtm0ber2edu3aER4ejoeHR7GPLcFrcZ06pVwXEryeOgWBgSVYHyHKixs34Px5pWdVp1OC2Bs3IC0NfeoNMlJzyMrUk52jIk9ti9pVi8bNGY2bFrXdNfT/nCLHvSr66l7YuGuxs1MGS6vlfJMQD06tRu/jg/358/j46ADle67cBK96PaTcgOwcsLMFVy0l8Qs4KyuLIUOGcPHiRcLCwggMDCQzM5Ply5czZMgQpkyZwnPPPWeVx+7fvz+ffvqpxYPX0aNHk5uby7fffoter+ejjz5ixIgR/PTTT8U+tgSvxXXqFLi4mJ1pIL+4T58SrpMQZVluLpw4ofS26vVw7ZoSxCYlkZtriF/Jy1OKc3KUztjs7CvodMo2h+ruOPt7UcE7D5vLl8it7EFajbok2dpgZwfu7mBv3bnVhSi38oNX52rg4HC7D6fMu5IEJxOUwDWfnS3UqQVV3K360NOnT+fYsWP88ssvVK9e3bB9woQJpKWlMXHiRIKCgswuAvUwSk1NZdeuXcyaNcuw8uhrr73GG2+8QXJyMm5ubsU6vvRBFNepU8o0WWZ+mSUlKZdCOmWFEHe6dg127VIC14sXYccO+Ocf9NeTSEqCc+cgJQXS0+HKFUhIgAsXlLvduKHk3qWnw/W4JBLW/sPxRTu4sO8iGacTUe/ZRW7iNTIzldOcp08rHbo2NjaycpIQ90F3K3hVqZTvt3IRvF5JgiNxxoErKLePxCnlVpKTk8Py5csJDg42ClzzjR49mqioKBwcHEzKIiMjCQoKuuu2TZs2ERwcTJMmTWjbti3h4eGkpKQAGFYujYiIIDw8HIDExETGjBlDixYtaN26NaGhocTHxxuOFx4eTlhYGCEhITRr1oyoqCiTejk4OFChQgVWrlxJWloaaWlprFq1Cl9fX1xcXO6/ke4gwWtxnTpVaHR6j3TYMkuvhz//hBkzlGu9vrRrJMqFU6eUmTtu3IBDh+DYMcjKIitLCVBTUiAjQ/n74kUlUL3Xay/vZhbXth0jcf0hsq7eQP3vYW7+cwq9XhnftW8fpKWpcHV1RaORKX+EKAq9jw9258+DXl8+gle9XulxvZu4BKt92Z09e5bk5GSaFbLQkYeHB40bN36gz6jr168zcuRI+vTpw9q1a5k5cya7d+9mypQpAGzduhWA8ePHM2HCBNLT0xk4cCAA0dHRLF68GHd3d/r160diYqLhuOvWraNdu3YsX76c7t27mzyunZ0dn332Gbt27aJFixa0bNmSgwcPEhUVhdoCuVuSNlBcp05Bz55mi8pr8LphA3Ttqpy21Whg3Tp48snSrpUo006cUFIDrl2D2FgldQAljr12TekhvX5dSRd4EBnnr3Nh1W4qdwigApCalUe2d10cHeHgQTWVKtlSpYr8lheiKPS+vmgyM+HyZWrX9uWPP0q7RsWUn+N6N1k5yn5uxe81NHn4W72grq6uFj92YmIi2dnZeHp64uXlhZeXF7NnzyYvLw+AKrfWrddqtWi1Wn788UdSU1OZOnUqNjZKiDhp0iR27tzJsmXLGDVqlKGuw4YNK/Rx9Xo9sbGxBAYGMmzYMPLy8vjyyy954403+P7773F2di7W85LgtTj0ejhzBnx8zBbfIx22zPr3XyVwdXSEAQPg+HEJXkUxnDp1e2BWbKyhdyM1VQlYs7KUoluftQ9Ml53L5Y3/UKVDAC6+kHxWw/XKtbGzgzNnHHBxURX2VhZCFKCrVQsAdUICtWv7cuqU8rYtszN73Ctwvd/97lPFW0FCcnKyxY8dEBBA9+7dCQ0NpUqVKrRv355OnTrRpUsXs/sfOXKElJQUWrZsabQ9KyuLuLg4w21vb++7Pu6vv/5KdHQ0GzduNASqs2fPpnPnzvz0008MHjy4WM9LgtfiuHFDGS1SubLZ4rukw5ZpDRsqPa4REfD++7d7mIW4b9euKYmr+T2utwLXGzeUwDUz8/a4LYvQ6bmyJRaVjQY3IMvelZsObmRk5BEXp8LeHsyknAkhCsr/zrt2DV9f5X166VIZfu/YFXHp+aLud59q1qxJ5cqV2bdvH88884xJeVxcHJMmTSIiIoK6deve83i5t85c5Zs2bRojRoxg8+bNbN++nbFjx9K8eXMWLlxocl+dToevry+zZs0yKSs4NsBc/m1Be/bswdfX16iH1dXVFV9fX86cOXPP53Avcp6sOPJ/JbmbH4V45Ur5XJwgqLOe84v/JKKCkvTq4y1Jr+IB5OQY8loLBq5ZWUosm5Vl4cA1n07P1S2x5N7MotK1Y6jyctHrIS1Nz/HjSuAshCic/tZIcVVKiuE77sqV0qtPsblq7x2Y2t+aNssK1Go1ffv2JSYmhosXL5qUz507l8OHD+Pl5WVSZmtry807VokoGBwePHiQyZMnU7t2bQYPHsycOXOYPHkyO3bs4Nq1aybHq1evHhcuXECr1eLt7Y23tzeenp5MmzaN3bt3F/k5VatWjTNnzpBVYF7u9PR0zp07h48FTnFJ8FocSbdGHxYSvCYlFVpUZhQcnPX99zBrFlz+YQMeA7ti8/ab0LUrqg1/lnY1RVl08qQyaurYMUOOq14PV68qOa6XL1tvMKAuO5dr249hk5dNpaQ4nJw0JCcr8fTRo8rjC1Gexf+wg4UL9Lz3Hnz77X2m5Tg5obOxQZWcbPiOS7LeYHzrU6mU6bDuxq+WVU+jhoaG4uPjQ//+/Vm5ciUJCQkcOnSIiIgIVq5cySeffGJ2VpSmTZuSnJzMvHnzOHfuHEuXLmXz5s2GcmdnZ5YsWcLUqVM5c+YMx48fZ+3atfj4+OB+65/n5OREXFwcSUlJ9OjRA1dXV8LCwjh48CBxcXGEh4ezefNmw8wERdGrVy9AmSnh6NGjHD16lLfeegt7e3uLLK4gwWtxPALBa/7grDffhIEDYf9+sNm1/fYnXV6eMp2REPfjxo3b02Fdv27YnB9AXr9e/BzXe8k4f50bJy7inHYJ++w0qlZVceqUMouBBc5qCfFwykkFwMfxMg2u/IVWC2+9BWbOIBdOpSLPxQXKS/AKyjyuDfxMe2DtbZXtVp7n1dHRkejoaPr06UNUVBQ9e/Zk+PDhXL58mcWLF9OtWzez92vTpg2jRo1i/vz5PPvss2zbto2wsDBDuZ+fH5GRkezYsYNevXrx0ksvodFojEb9h4SEEB0dTUREBFqtlujoaNzd3Rk6dCh9+/YlMTGR+fPn4+fnV+TnU7VqVZYsWYJer+eVV15hyJAh2NrasmTJErTa4vdgS85rcZT34FWvp9qRDWx94V/OODfktaVB3LypQu9dRUl6zZ9uoJCcXyEKdWuaHQrMHZibe3s6rAedVeB+JR+Ip0JtD9zSL3HDraohcFapwNNTFjIQ5dD+cdBqNjRrRuDfs8nT6qj4vyDi4lT8+ScEBRWtgzFPq0WTlFR+gldQAtTKbqWywhYoPaAjR45k5MiRd93v2LFjRrfN3afggKjOnTvTuXPnQo83atQowywCoOTgRkZGFrr/Z599dtf65fPz82P27NlF2vd+SfBaHPnv1kJWiijzweuGDTQco8yJ1UajQTV0HX/yJPvT/ekydKgSYWi1UOsep1uEKCgnR8kJyE9svSU/17QkvwTzbmaRfvYqLp553NTl4uWlIS5OmSHkwgVlwKUQ5UZaPJycAw0joIY3Nj/9QJvTn5EwdB1X9E/StWvRpz7MdXHBJjkZG1tl2eVyEbyCEqhaYTosYVmSNlAcSUlK8GZj+htAp1Om+inTwes//xilB3Sq8i9160Ju+07Qrx94eyu9Z2V+hmpRohITlTfI+fOGTcqAqfxlXku2Oqmx51DpdWhvJuLmpjx+aqqS0SC5r6JcORcD6OFsjHK7Tx/Iy6NW2hFu3lQ+7o8cKdqh8rRaw6Bld/fb45eFKAkSvBZHcnKh0WlKivKFXBaDV70edu6EHCcXJS0AQKNB66nlu++g4WMq5af5X3/BnDnKSC4hiiopSel9LdBVk56ufHGmppZ8dTIuJJGXlYNDZjKg/B69cEEJYq9eLfn6CGE1CT8ZX7/wAjRtSkr9Vvj7Q9Om0KpV0Q6V6+KCqkDwWm56XkWZIGkDxZGZCYXMdXaPdNiHmkoFrVsDv56BkBBlBIuzM45XEti/X9lHf/48qvyBWn//rXzbe3qWWp1FGZKWZjIfVWam8qMpPb10qpR1NRVnN6VOWq0y9ay/v/L7tDxOdycePfr086iu3vrMvvo3pF+AwEDYv5+uQFfggw/u43h2dobBlhK8ipImwWtx5A9YMiP/S9jMzBZlh78/TJx4+3lGRcHGjaDXo5oz5/Y8Rnq9Mlz1tddME9u1WmjRouTrLh5O2dlKbsAdI7KyspTOWGtNjXUvmVdScfXJQp2XjbOzHXl5yntY5nwVZc61PZB7xwtXr0cVNwfIf4PpYf9b4GfmM9tGC5Xu/Zmtzx+0i/I9V1o/PEFZilSUD0X9X0rwWhx3CV7zc+UKKX6oHTgANWroqfzCC+izs1EdOADVqinLaRXIUzTyww/KpaAWLW6nFJTptQOFxeQHrQWiQr1eCVwLjN0qcZlXlXwFu+w0KlRQlmq8cUP57aXTgVoSrMTDLv8z1s4NdofC9b133//MD8qloIotoH0RP7NVKlS3gleNpnTyw21ujTe5c0UpUXbl/y9tzIwlKkg+kotDpys0Os0f51QWg9cmTSA2VsX5i2pUr7yCfsoUJfnvwoWiHUClgnfege3boU6d29uEyP+Sybm9Rnh+j2tJD9QqSJel1EetyzO8Z2/cUN7iGRmlVy8hiiz/M1ZbB57+GwLeAYr6uatS9n96u3L/gscrhL5AxFqgE7ZEaTQaNBoNqaWRLC+sIjU11fB/vRvpeS2Ou3Rvl+XgVaWCDh2Up3fyJNSubYNq6lTo0gUGDVJGixfGwwMWLYKnny69c8Di4VUw1eSW/B6b0hzZr89T6qPS6wzf2fnxtXTqiDJHZQOBU6FaF/h7EGTe5TPbwQPaLoLqD/CZfWt/jcbo92iJUalUVK1alYsXL2Jvb0+FChVQSUdJmaTX67l58yapqalUr179nv9HCV6L4y4/N8tD3KZS5XecqpQn9PTTsGsX+PmZ/0a3sVHKa9WSNAFhXv5rosBrw0w8W+JUGqU+epXapD4yXZYoc1S3PrOrPw1dd8FqP9Cb+cxW2SjlFe7/M1t1R9pcab1/XV1dycjI4OrVq1y5cqV0KiEsQqVS4ebmhqur6z33leC1OO4SvOa/p0vjVIpVGE5JaQv/lNLrwcXFeH8hCsrPY7K9vQSjmXi2xKntlfro1BrDeza/PpLvKsqk/BewrZbbA7XupAfbB/zM1uuV1AHuOvzD6lQqFdWrV6dq1arklEb3r7AYW1vbe6YL5JPgtTiKELyWu16b1asLj8jz8pTyQYNKtk6i7HB2Vq61WrjVS5IfHJZmkOhQWfkCz7Zz5maysi0/vr7HuAEhHm7nVoO+kM9sfR6cXw2+9/+ZXbDn9S7DP0pMUfIkRfkhfQrF4eCgTFBpRrnrec23fPntvwcNUlYzGDjQfLkQd7KzA3v720EsSpCoUilFpcWhigt5NvboNHaGCRG0WiWgdnQsvXoJUWxnC3wm+w6CrjvBt8BndsKDfWarsrOV9zKl2/MqHk0SvBaHm1uhMzPfek+X6vQ/FqXXo79xA37/XflWj46GhQuV5VgWLVJua7VKeVpa+Uj6Fdbh7Ky8Vm5RqZQANv89UxrsK7uQZafUKX8WL61WWbNd0gZEmaTXQ84NuPi7Mndru2houxAqtVIGaLWLVrZf+h1y7v8z2yY1Fb2bG6B8z5Xmj0/x6JGP5eJwd1e+6cwMXrr1ni4/q46oVKjWrIEmTdAfOAADBpCWpufkyVuTCg8YAPv3Q6NGsGaN5LyKwrm7K9FqgeXn7O1v98CWNEdPdzT2tmQ6uAHKW1qjUSZeLxBjC1G2qFRwYQ24N4FnDoDPAOPRkT4D4D/7wa2Rst99vvk0N24YvuiSksrmapKi7JLgtTjy36231ncuqNwFrwCenui3bkVVuzYbN+rJ+GUDddbOQLVhg/Jh6OcH27ZB9eqlXVPxMPPwULozvbwMmxwclO/O0liRziWgBnqVmhsVPEhOVnqRqlRR6pP/PhaiTHL0hC5bwbm2cvvO0ZFaP+iyDRzv/zO7YM+rBK+ipMlQhOLIf7cmJUHlykZFtrbK2dFyEbzmT6HSsSN6nZ7t26DBpQ1UGdj1drLTunXw5JPK6JaOHY3vJ0RBtrZQtary2rG3h6wsnJyUl5GLC9y8WXJV0VSwx6lmZa7auaNT2xgWkPP0VE6D3vG2FqJsyP/srdrx3ukAKhtlv4L3KwLNjRvob30HSvAqSpr0vBbHPbpX3d3LSfCa/2Gm16NWq2jfHjyu/Ht7NFpeHhw5cnvf/A9LCVxFYby8lNeHjw+g/OnsrMSyJZk759bUB1SQ7FSN7GxlITmtVgmiq1eXfFdRRhX4zL7n5/CDfGbr9WhSU8HVlbw8SEmR4FWULPloLo6CPa+FFJeL4DVfwQ+2hg1vDy/VaKBBA/P7CWGOVqukD1SvDhUrGjZByX0JOnpVRFu3OmnO1ciyc+bcOT16Pfj6Ki9hT8+SqYcQVlPUz+L7/cxOT0edm4vezY2UFGWTBK+iJEnaQHHcJec1v7hcBa8FBQUpqQJHjiiBa1BQaddIlDV16ypvEH9/2L0bG3LJX1jF2RnDlFXWoLazoVI7f3I1dlxz9yPtagYJCXpq1FBi6Vq1Snf2AyEeZqpbEavezc3wHSfBqyhJ0vNaHFqtkr939arZ4nIdvKpUSo7rqFHKtfS2ivtlY6MErvb2EBAAKhVubspbqmJFK84bqVZRuUMANhXsuVbJH53ahv37M7G1VcYcVqgA3t5WemwhyoP877xKlSR4FaVCgtfiUKmUb7n4eLPFFSsWGtcKIQAqVVK6OStVgoAAVGoVlSsruaZVq1rhN5FaRZUOATjVrESyay0ynCpx5oyetDQdjRursLWF+vUl11WIu1EnJACgq1XL8B13K/tHiBIhH9HFVbs2nDpltsjHp9C4VgiRr3ZtZQBX1arw2GPYV7ChUiWlQ9bDw3IBrNrOhqqdH6OCb1VStV4ku9cmKQkSEvS0auWIVquiXj2Z21WIe1GdPk2egwNUrUp8vHKWpMDMd0JYneS8Fpevr7JEqhm1ayvLt9+4IV+IQtxV3bq38wRatkR77Bh6/XWuX4dq1eDy5eIttezoVZFK7fyxqWBPsmstkt1rk5oKJ05A06YqNBob/Pz0MkWxEEWgio8n29MTVCpOnVJOQNpINCFKkPS8Fld+z6uZufRq35oX+vTpEq6TEGVR7drKCm1aLTRujEtLfyp52WNvr/TqODvf/yE1Feyp1N4fjy6NwUVLYtVGhh7XxERo1gxcXFR4e2fi5SVLGgtRFOrTp8m61dV66tTt7zohSor8Viqu2rUhNRWuX1fy9u4oAuXN3bhxKdRNiLKmUiVo1UrpEgW01arhcOka1w6fR61OokIFZRBkdvbdD+Pg6Y5LfS8ca1ZCpVKR5lyN6xXrkKey4dpVpZO3bl1lcJafn47MzBx0Ol0JPEEhyj5VfDxZzZphh/L91rJladdIPGosHrwmJyfzxRdf8Ndff5GWloa/vz9vv/02LVq0AGDIkCFs377d6D6tWrVi8eLFAGRlZfHZZ5/x22+/kZmZSVBQEBMmTKDiw5oNXjBCvSN4rVpVWe6ykJRYIYQ5NjbK7AM1asD589hqNHhUq0zGjRxuXkrDPfkGGVducON6Luk3daBSo7a3wb6SFrtKWuwqOaOxt0WnUpNWoSo3tF7kOGhRqyEnQ1k1S6VSxol5e0Nurp7ExBTcZC1YIe5Np1OC1+eeQ4vy/fbCC6VdKfGosXjw+tZbb3HlyhW++OILKlWqxOLFixk6dCgrVqygdu3aHDt2jA8//JCnnnrKcB9bW1vD3x9++CF79uwhMjISOzs7/vvf/xIWFkZ0dLSlq2oZBYPXO35+qlRKsaQNCPEAtFpl6L+fH6rERJySk3GodIOsVHccs8Fdr2Tr6HRKPmxuLuRq7Mm215Lu5EaWmwe2Tra4ayAzUzlB4uysrIvg6Xl7Htfc3FzS09NL97kKUVZcvIgqK4tsLy+SkpTVtSRtQJQ0iwavZ86cYdu2bSxZsoTmzZsD8P7777NlyxZ+/vlnXn75Za5du0aTJk2oUqWKyf0TExNZuXIls2fPNvTUfvHFF3Tr1o39+/cTGBhoyepahpubMsHdyZNmi+8yGYEQoihsbZVe2Bo1UAOO2dk43EgjIy2P7EwdeblqcnQach2c0dvaYYPywabOVBY6UKmUONjLC8M0XEKIBxQXB0CWlxeJ8cpUIBK8ipJm0eDV3d2dOXPm0KhRI8M2lUqFSqUiNTWVY8eOoVKp8PX1NXv/vXv3AtCmTRvDNl9fXzw8PNi9e/fDGbyCMsjk4EGzRbVrw5o1JVwfIcozOztUlSriVAmcbm3S6ZQ82Nzc27MS2NiAo6MEq0JY1IED6O3tyaxVi5NHlTdXIV/pQliNRYNXFxcXnnjiCaNt69at48yZM4wfP57jx4+j1Wr5+OOP2bZtG05OTnTr1o033ngDOzs7EhMTcXd3x/6OdRmrVq3KpUuXilU3vV5vtVODto0bo1mzhkwzx/f31zB9uh2JiRlWny4rIyPD6FpIm9ypvLVH/o9j5QJ2drcnhdXr9eTkKNf5F3PKW5sUl7SHMWkPY3Y7dypLgtvYsG+fDi8vHQ4OmVgr80av16OSFRzFHaw628C+ffuIiIjg6aefplOnTowfP56srCwaN27MkCFDiI2NZcqUKVy4cIEpU6aQkZGBnZ2dyXHs7e3JysoqVl1ycnKIjY0t1jEKU9HDA9/Tpzm+cyd5Li5GZW5ujuj1DVi9+izNmllxsfYC4mVlBBPSJsakPUxJmxiT9jAm7aFosGMHabfOgu7YkUOdOlnExsZZ9THNxQXi0Wa14PWPP/7gnXfeoVmzZnz++ecAfPzxx7z77ru4uroCUK9ePWxtbRkzZgzjxo3DwcGBbDNz4GRlZeHo6Fis+tja2lKnTp1iHaMwKrUa3n+f+unp6Fq3NiqrWxccHPRcv+5LQECuVR4/X0ZGBvHx8fj4+BS7vcoLaRNj0h6mpE2MSXsYk/Yo4OZNHOLjyRs1Cr0ejh93ZtSoXAICAqz2kCcLGU8iHm1WCV6jo6OZNGkS3bp143//+5/hV5ONjY0hcM1Xt25dAC5dukS1atVITk4mOzvb6JfW5cuX8fDwKFadVCoVTk5O997xQTRuDBUq4PDvv/DssybFTZrA4cN2ODmVzK9HR0dH6z3XMkraxJi0hylpE2PSHsakPYADB0CnQ9OqFRcu2JGcrKZNG+t+t0nKgDDH4kMZlixZwieffMKAAQP44osvjILQgQMHEhERYbT/4cOHsbW1xcfHh+bNm6PT6QwDtwBOnz5NYmIiLR/mWZA1GggMhAL1Lqh580KLhBBCiLJh716ws0PfoAGxsUogf2tiISFKlEWD19OnTzN58mS6dOnC8OHDuXr1KleuXOHKlSvcuHGDrl27smrVKr7//nvOnj3L2rVrmTJlCkOHDsXZ2RkPDw+effZZ3nvvPXbu3MmhQ4d46623aNWqFU2bNrVkVS3vLhFq8+Zw7BjcuFHCdRJCCCEsZe9e5UyjnR1Hjzrh6amjmCdFhXggFk0bWLduHTk5Oaxfv57169cblfXu3ZvPPvsMlUrF4sWLmTx5MlWqVGHw4MG89tprhv0++eQTJk+ezMiRIwHo2LEj7733niWraR3Nm8P06crale7uJkV6vXLGpUOH0qmeEEIIUSx79sDjjwMQG1uBwEAdVjiBK8Q9WTR4DQ0NJTQ09K77DBgwgAEDBhRa7uTkxMSJE5k4caIlq2Z97dsr15s3Q8+eRkUNGoCDA+zeLcGrEEKIMujKFfj3Xxg3Dp0Ojh51YtQoXWnXSjyi5CeTpdSurVz++MOkyNYW2raFjRtLoV5CCCFEcW3YoFw/9RSHD6tISbGhfXsJXkXpkODVkp56ymzwml/011+Qk1OyVRJCCCGKbf16aNgQPD3ZuFGDvb2O1q0leBWlQ4JXS3rqKTh6FM6dM1uUlga7dpVCvYQQQogHpdcrwetTTwGwcaOGZs1ucMdimEKUGAleLSkoCFQq+PNPk6LmzcHNrdCOWSGE+P/27js8iupr4Ph3Nj0hQChJqFITWkhCiUDoXUWlCUoTka50JYAgTX9IsQBKExAFAQUpKkgXfKUTOgFCqKEltBAgPZn3j0sCS4K0TXY3OZ/n2WeTububM5dh9+ydO+cKYZlOn4YLF6BxY+LiYPt2Ay+/HG3uqEQOJsmrKeXPD1WqqG+oj7CxUbmtJK9CCCGsyqZN6kOsXj127oTYWI2AAKn9KMxHkldTS533qusZNu3aJfVehRBCWJGNG6FGDXB1ZdMmKFBAp0yZWHNHJXIwSV5NrXFjiIiAo0czbEpKgm3bzBCXEEII8aySklSlgfvzXTduhPr1kzFI9iDMSA4/U6tdG1xdYdWqdE1lykDx4rB+fdaHJYQQQjyzf/6BqCh49VVu3FDrFDRsmGzuqEQOJ8mrqTk6whtvwK+/pmvSNLV+wYoVkCIVRoQQQli6X3+FEiWgenVWrlSfY82bS/IqzEuS18zQrp2aNhASkmHT5cuwc6cZ4hJCCCGeVlIS/PYbvPUWaBrLlkG9euDhYe7ARE4nyWtmaNoUcueGZcvSNdWqBYULZzgwK4QQQliObdvg+nVo147r11UVyHbtzB2UEJK8Zg5HRzU/IIMM1WBQX2KXLZOpA0JYFF1Xn87Tpqm1nHXdaNPmzRkWEREi+/r1VyhZEqpWZeVKdfy3bm3uoISQ5DXzvPWWmjZw7Fi6pnbt4MoV2L7dDHEJITJkv307NGsGAwbAjh2gaZw//2BTs2YPlncXIttLnTLQrh1oGr/+Cg0agLu7uQMTQpLXzJM6dSCD0dcaNaBo0ew9dUBGrITVCQmB5PsXorRtC0DhPPf4t/00lvbcjIuLzpYtcjyLHOLvv+HGDWjXjmvX1Bc3mTIgLIUkr5nFwQFatoRffkn3SWcwqM/G33578FmZ3WzZIiNWwrpczO2j1nAeMwa8vQGwd3OhxsHZtJ/XjO/f3sL163I8ixzil1+gdGnw90+rMtCqlbmDEkKR5DUzdewIJ0+qU5CPSJ06sHVr1odlStGPLG+t6+oL+5Ej0L499OwJLi4ZFl4QwmIYDAZ+jaxPyv8mPLT1/sHdpg0kJ1PLLYSlS9UXTjmehbWKj45/8oPu3FHJa8eOoGksXqymDBQsmPnxCfE0JHnNTI0bQ6lSMGtWuqYaNaBcOZgzxwxxmdDQoQ9+1nVYuhSCg+HECbVN06BrVyhQQE61Csuk63D4cAUaGrZiCD350BUp9w/utm3Bxoar+SoQHa2WeK9QwWzhCvFCNg7d+OQHLV4MMTHQvTsnTqiiA926ZX5sQjwtSV4zk8EAvXqp0gI3bhg1aRr07g0rV6rVZK3RuXMq+T5//kHiumYNODtDgfw6H1XZzKi80+hfaTOfjdflVKuwSNu327NxowP60WPg5gY+PsA5YA5wHipXJmbJSmJebsi0aWqFvIYNzRuzEM8j6lwUwXOCiTof9fgH6boacGnRAooVY84cNfggVQaEJZHkNbO9956qifXjj+maunRRozg//GCGuExgxQr1PrdiBWzbqmOzbTMDDNNocXcJw2wn4X9qGUVmjqJ0n2YseHeLnGoVFikkRMPFBc46V3xo6wpAv38Pzm1bULeeRr9+0KiR+vIphLU5vuI46HBixYnHP2jvXjh4EHr3JjYWFixQo64ODlkVpRBPJslrZitYUJ12nD073XlzNzc1L3T2bOus+bp8+YP7wie30G5uM6ovHEDxEZ3JdeU0zJ0LI0aAiwt5L4fIqVZhkQICdI4dg7MlGnKv56D7W5cb30u2KrKBkOUhRvcZ+vNPdVVi06Zs3Aj37kGPHlkUoBBPSZLXrNC7N4SGZnh1Vu/e6vT7hg1ZHtULuXQJdu1SP+/cCQUjjj0onZCcrN7xkpPVlVtvv429XwU51SosUq1atvz7LwQN03DxdEXXLwH3D252ApfNGJ0QphF9KZqLuy4CEL4znDuX72T8wHHjYN06sLHhjTdgzx4oUyYLAxXiKdiaO4AcoU4dKF9ezSNq0MCo6eWXwddXNTVvbqb4/sO+ferC04fpuprrmjqQrOsQal+Rl21sVMJqYwO5cql7Fxfu5S/G+VINeUkGr4RF2Qc8+gGuo2lzUFMGuH8/GOgJPHoAuwLVMjVCIZ7V5X2Xib/zSEUBHYLnBBsd1usHr6dqz6rpDmsHVwcKVyuc9ruNTebGK8TzkOQ1K2iaunDro4/g8mUoXDhd04cfwsWLavECS6DrKra8edXocHDwfz++6RcN+fuL9bhdCaFQ2Vw47tsO778Pv/6KPnIChU9ugboN5fSrsAA66hM7L9AbeMLBzS/3bw+rBix55PWEMB9d19E0Dce8jvzZ+0+uBF/5z8cf++UYx34xXgGycLXCtFnSBoA7d3T69tWYPz/TQhbiucm0gazStasqePr11+maOnZUTd9+m/VhPU5qjlmmjJoW8NFH/513RkdrNBjfiClx/Rh7rith1d/hunMxaN+eXOOCKNNXKrsLS5F6IJdBTQv4iKdPPrX7j99x//kPv54Q5qPdf4POVyYf7+98n1of1Xqmw7rWR7XotqMb+crkIzER/P01SpYEO7vMi1mI5yXJa1bJk0cNr86cCTdvGjXlzg19+sCMGRAVZZ7w/outLUyerKZBeXg8+bHOLhpLIhsRFplbXY0WHS2V3YWFsgUmA+uAJxzceNx/3GTkpJWwZAZbA00mN6HTuk64eLj852NdPFzotK4TTSY3wXA/2Z0wQZ0k/PDDLAhWiOcgyWtWGjBAlRWYPj1d06BBkJBgWaOvqTRNTSNo2lRN3rd9zOd2hw7w3Xfw6afqgq4LrhUfTJiSyu7CImmkpKQATdH1PTw+KbUF9gBNkWkCwtJpmsbt2zqlm5am+54eGGwz/qg32BrosacHpZuWRj9zBi14H3fuwDffqAoD7u5ZG7cQT0uS16xUsKBaL3Xq1HRXQXl6qimi33yjLtS3NKlTBlxdH79S1p07DwoOLFkCd6s3JPTb9dwePw1dKrsLCxUfn3pxiysPrmh5lA7kvv+zJK7C8h07po5TB1d79Me8aeu6jkNuVcBVGzwYAgKYPRvu3lVTxYSwVJK8ZrUhQ9Q7w+zZ6ZqGDoXbt+H7780Q11P6/fcHCeqjXFweDLTeuwcvldDw6t2IPCP7oUlld2GhDAb1NqhpvwOPObhJBn7PqpCEeGE1a6r7k7+fRE9+TPKarHPy95Pql08/JS5e48sv1QI6xYplUaBCPAdJXrNasWLqneHLLyEuzqjppZfUxVtTpkB8/GOeb2a//fbg5y5dYPdu6NxZ/b50KYwahSyhKayKbdo8mIcObroAu4HOD217uF0Iy5Y6VnD8t+Np23y7+NJ9d3cqd66cti2t3d+fBQsgMhKCgrIwUCGegySv5hAUpN4hMlgXdtgwNVF+4UIzxPUfdF1NC9iwQU0dWLRIrXgbEAA//aR+13X44gu1Im5DqYolrITBYEDX7wAbUFMHFgE/AgHAT/d/d73ffpfHTy0QwnLouk78nXhObziNvas9rRa1ouWPLSkSUIRWP7Wi1aJW2Lvac3rDaRLuJqCjrid+6y0oW9bc0Qvx3yR5NYeyZaFdO3VJZ2ysUVO5ctCmjUoCExPNFF8GNA3WrFELKhw8qEaIH16koGNHOHAAfHzU4yRxFdZC0zQ0bQ3gCxwEOmK8SEFH4ADgA6xB5rwKa6BpGqfWnMLT15PeB3tTuWNlkmMTYPt29PgEKnesTK8DvXD3cSd0TSiapuHrq1b0FsLSSfJqLmPHwpUr6hz7I0aOhDNnYO5cM8T1HwoXhn//hVKl1O+pCWrqfenSsH07FCpknviEeH6F0fV/gfsHd1qCmnpfGtgOyMEtrIdrYVfe+/c93Eq5EX/1BgYHOwgMRHOwh6go8pXOR7ft3XAt5Aqo0liVKz/hRYWwAJK8mouXl1q66n//g+vXjZp8fdV80jFjVIlUc0sdYa1b9/FlslLZ2qrHPfw8ISxX6kFalyfXbrW9/7iHnyeE5UmtLvBS3ZcwkAJDh+JQzBPt7y2pD4BXXoFp0zDYGnip7ksAVKsmx7WwDpK8mtOnn6r78ePTNX32mUpcJ03K4pgykDqymrpk7JMeq+vqtmWLGljevFkSWWGp1AEdHx/3UMms/3qs/tDPQlim1NW29KgotMBAtcpMUtKDhWKWLYNdu6BSJaKjNc6eVce1wSDHtbAOkryaU8GCMHy4Wlrr1CmjpqJF1cIFX32lCv5bgqedx6ppKnFt1kyty9BMVoYVFiwpKYmQkOP3Fyt4EvlwF9ZB1+HaxoOwf7/akLpQTHy8+tx57TVo2JAxY6B2be3R4jdCWDRJXs1twAA1SXT48HRNw4ZBrlyq/JS1OXbsQT1YWRlWWLKEhISnTFyFsB5btkC5nvVY2m09uzpOI2Lh/fqFM2bAuXMwaRKnT6tVET/4ABwdzR2xEE9Pkldzc3KCzz9XBVS3bzdqyp0bRo+GBQvg0CHzhPe8KsrKsEKIx9F1NZ9I5hVlmmPH4FaUxjvfN6Lmz/349XojiIpS09S6d4cKFRg+XC0BO3CguaMV4tlI8moJOnYEf3+1Ht8jI0A9e6rKWkOHmim2Z3X/Q6nhsWlcWriZWTN1WbBACGFM5hVlugwHED7/HBISYOxYdu5UU18//xycnc0aqhDP7EmX14qsYDCoya0NGsD8+epb8X12djBxIrRqpZZmfeMNM8b5NO5/KGnJyXjY2NBr/Xpo1MjcUQkhLElG84rkfcKkGjZUKx2GhKjEtWGBwzB1KowZQ3JBTwa8oSrbdOpk7kiFeHYWOfKakpLCtGnTqFOnDn5+fvTo0YPw8HBzh5W56teHd9+Fjz+Gq1eNmt58U1U1+eADtcqVRZPJrkKIJ5F5RZlO09T3gX79oFH9ZLRePVWJxo8/ZsYM2LtXTX9N/WcQwppYZPI6Y8YMFi9ezPjx41m6dCkpKSl0796dhIQEc4eWuaZMUe8kgwYZbdY09SZz86YVXLwlH0pCiCdJHRacNg2ZV5QFZs2C3bth9mzCI+wZMQL69IFatcwdmBDPx+KS14SEBObPn0///v2pX78+5cqV4+uvv+bq1ats2LDB3OFlrgIF4OuvYelS+Osvo6YSJWDcOPVev3evecJ7KvKhJIR4EqNhwUaynnRmunRJVbPp1Qtq16ZfP3B1VauTC2GtLC55PXHiBPfu3aNmzZpp23Lnzk2FChXYa9FZm4l06qTezPv0gXv3jJoGDAA/P3URV1KSecJ7IvlQEkKILPfYAg79+oGLC3zxBStXwurV6jF58pg1XCFeiMUlr1fvz/csVMh4DXF3d/e0tmxN09QpnogItT7sQ2xtYc4cOHwYvvnGLNEJIYSwQBkWcFi9GlauhKlTiTbkpV8/eP11aNPG3NEK8WIsrtpAbGwsAPb29kbbHRwcuH379nO/rq7rxMTEvFBsWaZwYWyHDcNu/HjiWrdG9/VNa6pQAfr0sWP0aFtefTWOEiUe1EdM7bvUeyF98ijpj/SkT4xJfxizlv44csSB5GR1vUFyMpzcF0WD6R+gN29O/GuvEfRRIlFRtkyeHEds7IvV1c3KPtF1PW25WyFSWVzy6nh/mY+EhIS0nwHi4+NxcnJ67tdNTEzk+PHjLxxflmnenAoLF6J37cqJBQvQH0rm27c3sHx5Rbp1S2T69FMYHhk/P3fuXNbGagWkT4xJf6QnfWJM+sOYpfdH0aIlsbHJR3Kyula2+cYB6DduEPLBB+xZGs7s2d4MGnSRu3cjMdVHYVb1yaODWUJYXPKaOl0gMjKS4sWLp22PjIzE29v7uV/Xzs6OMmXKvHB8WWrhQpzq1cNn6VISH5ldP3t2Ci1b5mbbNh/69lUTYGNjYzl37hwlSpR4oUQ/O5E+MSb9kZ70iTHpD2PW0h/lysGaNUmcOKFR7+ZKSo37ifjvvqNgtYaMr+HIyy+nMGZMfmxs8r/w38rKPgkLC8vU1xfWyeKS13LlypErVy52796dlrxGR0cTEhJCpxeopqxpGs7WtoxIzZowYQJ2H32E3euvQ+PGaU1vvqnmNo0caU+zZvb4+Dx4mpOTk8Xuq66ruVjHjqmqWg0bZs01XZbcJ+Yg/ZGe9Ikx6Q9j1tAfzZpBM5/L4NMLWrXCoU8fhr6rcfMm/P03uLqaNv6s6BOZMiAyYnHJq729PZ06dWLKlCnky5ePIkWKMHnyZDw9PWnatKm5w8t6gwbBunXQpQscOQL5H3xr/uILdVVphw4WXj7rIakXFaSe2pIFuIQQwkRSUtRiN46O8P33LFmqsXAhLFwIJUuaOzghTMfiqg0A9O/fn7Zt2zJy5EjeeecdbGxsmDdvHnZ2duYOLesZDPDjjxAfDz16PFT/RL0/LV4Mp05BUJAZY3wGsgCXEEJkkm++gU2b4McfOX83P336wDvvQMeO5g5MCNOyuJFXABsbGz7++GM+/vhjc4diGQoXhrlzoXVrdd+jR1qTjw9MmqSmENSvb+ChacIWKXUBrtSRV1mASwghTODgQbUYwZAhJDdoTKf6kDevWp1RzryL7MYiR15FBlq1UknrwIFw8qRRU79+0Lw59O7twM2bFvl9JI0swCWEECYWE6Pmj5UvD59/zhdfwI4darpA3rzmDk4I05Pk1Zp8/TUULarOAz1UX0/T4Icf1HSnsWNLkJJixhifQBbgEkIIExs4EM6ehcWL2XXAgdGjYcQIqFPH3IEJkTkkebUmLi7wyy9w4gT07m00/9XTE+bMiWfHjtxMnGjZo69CCCFMZM4c+P57+O47ruarQJs28PLL8Omn5g5MiMwjyau18fNT815/+gmmTzdqatYshV69LvPZZ/b8+ad5whNCCJFFdu6EDz+Evn1J6NSNt95SYxrLl0NOvL5Z5BwyRGeNOnSA4GAYPBh8faFevbSmbt2ucumSBx072rJ3L3h5mTFOIYQQmePKFWjTBgIC4OuvGTwYdu+GrVvh/lo/QmRbMvJqrSZOVEnrW29BeHjaZoMB5sxJoHBhaNkSoqPNF6IQQohMkJAAbduqiwaWL+eHn+357jv49luoVcvcwQmR+SR5tVa2trB0KTg5qRJacXFpTblzw6pVcPGiqldtyRdwCSGEeEYDB6qVaX77jT0XPOndWxWj6dnT3IEJkTUkebVmBQvCypVw9Cj06WN0AZe3NyxapJLYCRPMF6IQQggTmjcPZs6E774jomQNWrcGf/90l0AIka1J8mrtqlRRV5suWIDtd98ZNb3xBoweDaNGwR9/mCk+IYQQpvHvv9C3L/TqRXyXHrz1llrw5bffwMHB3MEJkXUkec0OOneGoUOxGzaMvJs2GTV9+qma+/r227Bnj3nCE0II8YKOH1cjErVqkfL1VLp2Ve/py5dDkSLmDk6IrCXJa3YxYQLJb71FyVGjMPz7b9pmgwF+/llV2HrtNQgLM1+IQgghnsPly2oZxSJFYOVKho5y4Jdf1Ht7YKC5gxMi60nyml0YDCTMns1df38c2reHY8fSmpyc4PffIX9+9f4XGWnGOIUQQjy96Gh45RV15e1ff/HNgrx8+SVMnaoqZQmRE0nymp3Y23N60iT0okVVlnrxYlpT/vywbh3cuwctWqh7IYQQFiwhQVWTOX8e1q1j2c6iDB4MQ4eqJbaFyKkkec1mUnLlIn7VKjVf4NVX4fbttLYSJWDtWjV1ql07SEoyW5hCCCH+S0oKdOsG//d/sHo1265XpFMntUaNVJAROZ0kr9mQXqiQGma9eBFatYL4+LQ2f391ZeqGDdC7t1F1LSGEEJZi+HBYvBgWLuRo/nq8+SbUqQPz56uxCSFyMvkvkF2VL68muu7Yob6qJyamNTVtqkoFzpsHI0ZIAiuEEBZl0iR1++orzlRrxyuvwEsvqYEHe3tzByeE+dmaOwCRiWrXVnVUWreGjh3Vt3hb9U/epQtcvw5DhoCdHYwbZ+ZYhRBCwJdfQlAQjBrFuZYDaVAPHB3hr78gTx5zByeEZZDkNbtr0QKWLVPrYHfuDAsXpiWwgwerea9BQWrTp5+aOVYhhMjJvvkGPvoIRozgwvtjaVBfDS78/TcULmzm2ISwIJK85gRvvgm//KKu0jIY4KefwMYGUFetJier6QM2NvDJJ2aOVQghcqLp02HQIBg6lPBen9GggYamqcS1aFFzByeEZZHkNado3RqWLlVLbdnYwA8/pCWww4erEdiRI9WmYcPMHKsQQuQkM2ZA//4wZAiX+n1Bg/oaycmwbRsUK2bu4ISwPJK85iRt26olWTp0UFnqvHlpl62OGqVGYIcPV00ff2zmWIUQIieYMwc++AAGDuTK4Mk0qK+RmAhbt6qLtIQQ6UnymtO0b6+y1M6dVZY6Z05aAjt6tGoaOlQ1DR5s5liFECI7mzsXevWCfv24OvQrGjbUiI1ViWvJkuYOTgjLJclrTtShgyqA/e67cPeumgNrb4+mqaoDycmqCkF0tEpoNc3cAQshRDYzZYo6xdW3L2cGTKVpHZW4btsGpUubOzghLJskrzlVp07g4gLvvKMqEqxYAblyoWnw+eeQO7eaQnDtGkybljY9VgghxIvQdVXiZfJk+OQTDrUdT/PaGq6usH27WglRCPHfZJGCnKxVK7US165d0LChKvyKGmkdNgy+/x5mzVIDtQ8t0iWEEOJ5JCWpJV8nT4ZvvuGfpp9Rt55G4cLw77+SuArxtCR5zenq11fnqc6fV4saXLiQ1tS9u1rRZfVqNTh75475whRCCKsWGwtt2sCiRbBoEatLDKBpU6hWTZXDcnc3d4BCWA9JXgX4+6vzVQkJEBgIISFpTS1bqsHZ3bvV4Oy1a+YLUwghrFJUFDRrBhs3wu+/80NCR1q3VoMCa9aoaVpCiKcnyatQypRRCWy+fFCnjppKcF/q4OyFC6rp/HnzhSmEEFblyhWoVw+OHoXNm5l05BW6dVNntn75RS39KoR4NpK8igcKFVJZaoUK0KiRuojrvocHZ2vWhL17zRinEEJYgyNHoFYtuHGDpL//j36LaxIUpBaEmTVLLoQV4nlJ8iqM5c0LGzbA66+r+VmffaaujkUNzu7YAcWLQ926asEuIYQQGfj9d5W45slD1F87eOWjisycCTNnwvjxUoJQiBchyatIz8kJlixRRV9HjVLlBmJjAfD0VAW027RRVbZGjVIlY4UQQqC+7E+cqC4YaNKEk/P+JaBNcfbvV1Nee/c2d4BCWD9JXkXGNE1lpsuXqxGEunXh0iVAzdFauBAmTFA1Yd96C+7dM3O8QghhbnFx0KWLqjU4ciTreyzn5Ua5sLWFPXugQQNzByhE9iDJq/hvbdqoAoQREVC9unoH5kEt2FWr1CyDwECjKltCCJGzXLmirm5dvhx98RKm5h/Hqy0MBAbCzp2yapYQpiTJq3gyf3+VtL70krpqdsmStKY33lDzYG/fVrntjh1mjFMIIcxh/34ICIALF0jc/A89t7zNwIEwaJA6cZUnj7kDFCJ7keRVPB1PT1VJu107NQc2KEitFgP4+Kjc1ttbnRabPTvtGi8hhMjefv5ZLfDi6cmV3/fSMKg6P/0EP/wAU6ZIRQEhMoMkr+LpOTrCggXqHfnLL9WqBZcvA1CwIGzaBO+/ry5I6NJF5sEKIbKxuDjo1Qs6dYK2bfl77D/4vlqEs2fV9/yuXc0doBDZlySv4tloGgwZokoOnD6tphRs2QKAvT3MmKEGIlauVGfRjh83b7hCCGFyp0+rMlg//kjKrDl8VvZHGrVwws8PDhxQTUKIzCPJq3g+tWurd+nKlaFJExg7FpKTATWrIHURg+rV4aefzBinEEKY0m+/QdWqEB3NjT938sqKHnw6WuPTT+Gvv9RZKCFE5pLkVTw/d3dYtw4+/VTVhG3cOG0aQfnyah5s27bw7rvqdveumeMVQojnFRcHffuqN7XGjdn25T4qdfbn4EFYvx7GjJH5rUJkFUlexYuxsYHRo2HzZggNBV9fNfwAuLioKbI//aQGK6pVg0OHzBuuEEI8K4dz53CsVw/mzyf525mM9F5Gg1Z5qVRJvac1aWLuCIXIWUyevF65coXBgwcTGBhI9erVef/99zl16pTRY5o2bYq3t7fRbdiwYWntt27dYsiQIVSvXp2AgADGjh1L7P0VnoSFql8fDh5UE11ffRUGDICYGAA6d4bgYHW9V0AATJ6cNsNACCEsl65jO3cu5Tt3hsREzv26h8CFvfliosZnn6kRV09PcwcpRM5ja8oXS0hIoGfPnuTNm5dZs2bh6OjI9OnTeffdd/nzzz/Jly8fMTExhIeHM3v2bCpWrJj2XEdHx7Sf+/fvT2xsLAsWLCA6OppPPvmEmJgYJk6caMpwhakVLAh//AHTp6sVDP76C378EWrWxNsbdu1Si3YFBanFDRYsgLJlzR20EEJkIDwcunfHfsMGIlu1YnHVeQxv70bRovB//wc1a5o7QCFyLpOOvO7bt4/Q0FCmTJmCj48PZcuWZfLkycTExLDl/hXpYWFhpKSk4O/vT8GCBdNurq6uABw4cIA9e/YwceJEKlasSM2aNRk3bhyrV68mIiLClOGKzGAwqFHXgwchXz51Ydfw4RAfj6OjGnX95x+1YJevL3z7LaSkmDtoIYS4T9fVl24fHzh2jPOzV/PK+R8ZNNKNnj3VNAFJXIUwL5Mmr2XLlmXOnDl4eHg8+AMG9Seio6MBOHnyJAUKFCDPY5Yc2bdvHwULFqT0Q2vpBQQEoGkawcHBpgxXZCZvb7Ws7GefqZqw1aqp6gSofPbQIejWDfr1g6ZNZWlZIYQFuHoVWraErl3R33yTHz8+SqUhr3Plij1r18YxdSo4O5s7SCGESacNFCxYkHr16hltW7hwIXFxcQQGBgIqeXV2dqZ///7s378fNzc32rRpQ5cuXTAYDERERFCoUCGj17C3tydv3rxcuXLluWPTdZ2Y+3Mws6vUecEWNT94wAC0hg1x6NEDLSCAxGHDSProIzQ7OyZNgubNDfTpY0+lShoTJybQpUsymma6P2+RfWJG0h/pSZ8Yy6n9YbNiBfYDB4LBwNUZS3l3ZVs2DrShc+c43n//OJUqFScmRk4TQdYeI7quo5nyQ0FkC8+UvF68eJFGjRo9tn3nzp3ky5cv7feNGzfy5Zdf0rVrV7y9vQE4deoU0dHRNGvWjA8++IDg4GAmT57M7du3GTBgALGxsdjb26d7bQcHB+Lj458lXCOJiYkczyEV88+dO2fuEIzZ2qLNmUOhuXPxnDCBxN9+49zo0cSVKUOhQrBokYEvvyxG374FWLw4imHDLuDpmWjSECyuT8xM+iM96RNjOaU/bKKiKD5pEvk2bOBmw4bMqzqR8UF+ODgkM3XqGQID1VnDnNIfzyKr+iSjnEDkbM+UvHp4eLB27drHtj88FWDJkiWMHz+eN954g6FDh6Zt//7774mPj0+b4+rt7c3du3eZOXMm/fr1w9HRkYSEhHSvHR8fj/MLnK+xs7OjTJkyz/18axAbG8u5c+coUaIETk5O5g4nvWnTiH/3XZx69qRCp04k9e9P4rBh4OLC0qXw119xfPhhbtq39+GTTxLp2zcJO7sX+5MW3ydZTPojPekTYzmmP1JSsPnpJ+xHjQJd5+znC+i6riP/TLbl7beTmDw5iXz5ihAbmy9n9MczyMpjJCwsLFNfX1inZ0pe7ezsjOaiPs7kyZOZO3cu7733HkFBQUZD/vb29um+RXl5eRETE8Pt27fx9PRk06ZNRu0JCQlERUXh7u7+LOEa0TTthZJfa+Lk5GS5+1qnjrqYa9Ik7D7/HLvffoNp0+CNN2jTRtVLHDUKRo60Z8kSe2bNMs1SixbdJ2Yg/ZGe9ImxbN0fhw9Dnz6wYwdJHbowscBkxo5xp0QJ2LgRGje25dGPx2zdH88pK/pEpgyIjJi8zmtq4hoUFMSwYcOMDjxd12ncuDHffvut0XOOHDlCwYIFcXNzo3r16ly9epXz58+nte/ZsweAqlWrmjpcYQ4ODipDPXZMLcX15pvqIokLF8idG6ZOVcvLOjpCYCD06AE3bpg7aCGE1bt7Fz76CKpUgVu32DlhK2V3/Mj42e6MHKly2saNzR2kEOJJTJq87t69m7lz59K5c2def/11rl27lna7d+8emqbRpEkT5s2bx9q1a7lw4QK//PILc+fOpX///gD4+vpSpUoVBg0axOHDh9m1axeffvopLVu2NKpiILKB0qVh7Vr49VeVrZYvD5MmQWIiVarAzp0wYwYsWwblysEPP6gqNkII8Ux0HVauVO8x331H1JDxtPM6SK3h9fDygqNH1SrXD5UbF0JYMJMmr3/++SegKgzUrl3b6DZ//nwAhgwZQvfu3fnqq6949dVX+eGHH/jkk09o164doE4RfPvttxQtWpR3332XgQMHUrduXcaMGWPKUIWl0DR46y04fhx69lQ1YatUgX//xcZGndk7eRKaN1elterWhSNHzB20EMJqnD0Lr78OrVuT4uPL94NCKPrdcP7dY88vv8C6dZDNL4cQItsxaams8ePHM378+P/+g7a2fPDBB3zwwQePfUz+/PmZNm2aKUMTli53bvj6a+jSRWWsdepA+/YwYQIeJUuycCG89x707Qt+fiqRHTcOHqmqJoQQyu3b8MUX8PXX6AULsuvjFXRZ0ZIz6zU+/BDGj1dvO0II62PyOa9CvBB/f9ixA+bPV2swliun5qjdukXDhmpO2tdfw4oVamnZsWPh3j1zBy2EsBiJifDdd2o4depULnYcStOix6k1uRVlvTQOHVLz6iVxFcJ6SfIqLI/BoIZZQ0Nh5EiYNUvNj/3mG+xJoH9/OH1ajcL+738qiZ03D5KTzR24EMJsdB1Wr4ZKlaBfP+7Uf50+jU9RbP44ImNysWGDmmJfqZK5AxVCvChJXoXlcnFRVQnCwtS82CFDoEIFWL6cvHl0Jk1S82Hr14fu3dWg7fr15g5aCJHl9u5VbwQtW5JYqDhTOh0g/+r5/B5chB9+gP37VRk+IUT2IMmrsHyenjB7tpoz4O2tEtnAQNi5kxIlYPFi2L0b8uRRF3Y1a6Y+rIQQ2dy5c9CxIwQEkHL9Bit6/IXHoQ2MWeHLqFHq5E3XrmBjY+5AhRCmJMmrsB4VK8KaNaqKeEyMWr3gtddgzx4CAuCff1Q1nHPnoGpVVTr24EEpcC1EtnP+PPTuDV5e6Ju3sKn99xS/cZB285vT9i2NsDB10sbFxdyBCiEygySvwvo0bgzBwbBkiSqD8/LL8NpraHv30LKlWvvgp58gJAQCA50YMqS0JLFCZAepSWvZsui//ca/zT+jjH6K5su70+QVW06cgDlz1MkaIUT2JcmrsE42NvD226ro6yNJrO3+PXTurJLX77+P5+xZRwIDnWjZEg4cMHfgQohnllHSajhL/bVDqftqLk6cUIuYSL1WIXIGSV6FdXtCEtuhQzK//nqM77+PJyRErX8gSawQVkKSViFEBiR5FdnDY5JYh1atyHtwHx3eSSIk5MF0gipV4NVXYfNmWXJWCItz8qRaca9sWVKW/8bmBp9RWpOkVQihSPIqspdHkljt4kW8e/fGsXZtbH9dTOe3EwkJgYUL4fJlNX3W318ltQkJ5g5eiBxM12HrVnjjDShXjqSVv7Oy6ng87p2lxT9DadZGklYhhCLJq8ie7iexcXv2EPrtt+j586uSOqVKYfvNFDq9fpsDB2DTJihcGN59F0qWVKtJ3rpl7uCFyEESE1W9u2rVoEED7h09yzS/+eS6fp7eZ4LoPyIX4eEwc6YkrUIIRZJXkb1pGndq1CD+999VndjGjWHECChaFG3wIBqVPsfatapCwauvwpgxUKwY9O8PZ86YO3ghsrHbt2HKFLV6XseOXEkqQL+y68h19jCzE97ju7kOnD+vSl4VKGDuYIUQlkSSV5Fz+Pioc47nz6vs9Mcf1Qdn+/ZUuLWd7+fonD+vFvJaskQtO9uqFaxbBykp5g5eiGwiLAwGD4ZixdBHjOBQwUY09TxM4cPrOV68GWvXahw9Cu+/D46O5g5WCGGJJHkVOU+hQvD55xAeDtOnq+W4atcGHx88fpnG2IG3uHABZsxQo6+vvAKlSqmnXL5s7uCFsEIJCfDrr9CoEZQtS8LcH/mtcD+Kp5yn5vEfKNLcJ20azyuvgCZlmYUQ/0GSV5FzubhA377qyuYNG6B8eTXsWrgwTr3fpZfPDg4e0Nm1Cxo2VMlr8eLQurWMxgrxVMLCICgIihaF9u0JP53AoAILyXPnImPsPifom0JcvqxOiPj5mTtYIYS1kORVCIMBmjSBZcvg4kUYPRr+/RcCA9F8K/PynunM//IWly/D1Knq81hGY4V4jNRR1saNoWxZEmd8z5+5O1DZ5hjekf9HVItObNnhxOHD8OGHkDevuQMWQlgbSV6FeJiHBwwbBqdOqdHYcuXU/LzChck74F0+8N7Eof3J7NxpPBr76qvw889w9665d0AIM9B1dUHk0KFpo6xnT8YzMP9P5L57ieFO39Dzmwppo6w1a8rUACHE85PkVYiMPDwaGx6uRmN37IAmTdCKF6PGr4OZ/0Ewly/pTJumLpzu1Enlvh07wtq1qgKQENnauXMwYYK6GNLXl9jv5vFzyjtU5CjVYv6PuLad2bxdRlmFEKYlyasQT+LpqUZjQ0Nh92546y01zFqtGnlrlqfv9XFs/zGMM2fgk0/g4EF47TVVP/aDD1TOK6t4iWzj+nVVdLV2bShZksTR49kc6UML/sAj+Qp/NJ7KhNUVuXIFZs2CWrVklFUIYVqSvArxtDQNAgLUxNdLl9RVWwEBMHkylC1LybdfZkSuaRzddJUDB+C99+D33yEwUM2P/eQTCA6WRFZYoXv3VP24Fi3QCxUi5cN+BJ9ypavNQvInRTLJfwntfmzBpWv2LF2qFsmytzd30EKI7EqSVyGeh60tNGum1pWNiIClS9WcgSFD0IoWwa9fHSZ5fMn5zWH8/beagTBzplpEqHhxdQp140ZZklZYsGvX1ATVli1JKVAQOnQg5P9uMDDlazxTLtO3xF9U+aoTp67kYv166NIFXF3NHbQQIiewNXcAQlg9Z2do317dbtyAlSth9Wr45BMMH31E/YoVqf/mm8xY05L/i6nKqt8NrF4N330HefKoi73efFNVMMid29w7I3K006exXbYMr19/xenQIXRd55BzTRbHjWG1TRtK1CjNm2/C/jfUdVlCCGEOkrwKYUr580P37up2966qWLB6Ncycie3//keDIkVo8MYbfDO7JYfz1WfVWntWrVJnZO3sVAWDN9+Epk3V4l9CZCpdV3NZVq0iZdVqDMeOotk4sNu2IUtT+vK3y+tUb+FBy5bwySvqy5YQQpibJK9CZJZcudSKBq1bQ1KSqh27ahWsXo02cya+rq74NmjA6PebcKlCE3474sXq3zX69YPkZChZUk03aNJEJbX58pl7h0S2cPUqbNqEvmEjies2YX/tMtG2bvyR0oIVjOFQgSb414mjU6fcfNfcEQcHcwcshBDGJHkVIivY2kL9+ur29deqJuYff6j1MAcPpkhiIv2LFaN/kybEdGjMNrvGrN1bkI0bYc4cda1Y1aoPktlatZCkQjyde/fgn39g40YS/tqE/YkjAByzqcy65HfY7PAatvVr06CpHWOaQKlSMZw4EU758uXlGBNCWCRJXoXIapoGvr7qNnKkml5wP7lg40ac58/nFeAVPz94owmRlRuz7k4g6/7PhblzVVlNJyeoW1fdatVSRQ+cnc29Y8IiJCbCgQPoGzcR98dG7IN3YJOUwFXbIvyV1ITN2jBu+DWi6qseNG4M/WoafxGKiTFf6EII8TQkeRXC3HLlUldtvfqq+v3KFTUiu3EjLFqE+5XJdLGxoYufH3r7WpwvGsj6O7VYua8YEydCdLQa2PXzU2W5atVSN7mgJoe4dQt27iTpnx3c27gd5yN7sEuM4a7myt96fTYyhTOlmlCssTdNmmpMbwhubuYOWgghnp8kr0JYmkKFoHNnddN1OH5czZfdvh1t3V+UCJtOL6BX0aKkNA/kasla7NAC+TPclz//tGXqVPUyxYurJDYwUI3MVqpk1r0SpqDrcOoU+vYd3NuwnZTtO8gdHgLADdzZTiB7bccS7VeLfM2qU6OOHWNrynxpIUT2IsmrEJZM06BCBXXr2VNti4xUy3bt2IFh+3YKr/qYtgkJtHV2hmrVuNfAnxNO/myN8mN1aHmGrLAnIUGteOvt7chLL5Wgdm1bAgLUaG3BgmbdQ/E4KSlw5gxJ+w5yc8tBEvYcIG/oHnLFXkdH4ywV2U4djrsFodcMpHSTUgTW1njdV1WuEEKI7EqSVyGsjbs7tGypbgDx8arc0Y4dsHs3LtvWUvXUVKoCQ+zsSKlQkZvF/Tnl4sf2e5VYecabCRPyce+eenrhwuDvrxJZX18oVw7KlFHzakUWiYtDP3qM29sOcnf7QQyHDpAv/BCOiXexBZIoxCH8OZO3D1FVAnGo+zLla+alhT/0KiLLrwohchZJXoWwdg4ODya6prpzR1U0OHgQw8GDFDhwgAJHF1MzPp6PgJQSJYgJqMQlF29CkrzYdc2LP7/35vNIT0BD09S0A29v8PJSt9SfixUDGxtz7ayVi4ri3v6TRP4byr0DoWihJ3G9dJzCt09gSxK50biCN4c1P654vkGyjx+56/jiXdeD2r7wmtRZFUIISV6FyJZcXdVk18DAB9sSE4k9eJCr69ZR9No1cp0+jffxVXifPUurlBQmAikuuYgp4kWEmzdnbb04fMOLXWu8WDH7JS4nFgA0HBygbFm1iEKxYupWtOiD+yJFcu669roOt85Hcy34Anf2nyIpJBS7s6G4Xj5JwahQ3BKv4QKUBC5RmLN23pzKX5ttFT8Af3/c6lSidGUXWpXOuX0ohBBPIsmrEDmFnR16xYrcNBjwKF8eu9TaWgkJcPo0hIZiCA0l18mT5AoNpXToFhpHRKQ9PcXBkdj8RbnpXJTLCcU4e6QYoXuKEXy7KD/HFCOcYtzCDU3T8PB4kNAWKwYeHmrxsYdvBQqoe0uvJZqSArdvq5V/b4XfJSb0Iolnwkm5EI7N5YvYXgnH+UY4ee+G455wkXxEk3p9VDSunLXz5mweLw56N4WyXrhU8cajdlnK+OWidl5z7pkQQlgnSV6FyOns7aF8eXV7VFQUhIXBhQsYwsNxuXgRl/BwioWf5uWL2yDykloO7L5kBydiXQpwh/zcupyfaxfzc2Vrfi7G5edEXH6u6/m5TgFukJ+b5OMeLhicnXDK70zu/HbkL6Dh6qrm2zo7//e9k5O6MEnT1MVoD9+npKhR0JSUBz/HxUFsLMTeSSIpOobE6FiSomNIvn6N+Mvn2ZlyAqeYKBzv3cD+zg0c7t7AJe4GuRNvkI8bFOQ6ZYg26p5Igwc3nIpyJ08xrhZvxJWixbAvVRTXCsXIF1CGgpU88LXT8M3kf0IhhMhJJHkVQjxe3rxQrZq6ZSQ5WS03Gh4OFy9ic/EiuW7cINeNGxS6fh1uXIcbJ+HGDfQbN9BiY9O/Roy6pVw0EG/jTLzBiTjNmVjNiVicicGJmBQnElJsSE7WSNINpGBARyMJDQMp6KSgoaOTgoEUHInjwbON7+1IeuzuJmp23LHPT4xTfuJz5yexWH50N18SC+Tnmkd+ol4qjLN3MVzLF8WxdBHcHRxwN0lHCyGEeFqSvAohnp+NjZrkWqTIEx+qgRr6vHFD3W7eVMs5xcZCTAyG2FicYmJwuv+7GiZ96Of7w6h6ik5yYgrJiSnoKSnoNjaALbpmQNc0dM0Ajo7gqIZp9fv3NrmcIK8zei4nNJcHw7exTk6E3bxJ6YAAnN3dyadpSFlUIYSwXJK8CiGyjpOTmgz7Ast/aag3LlO9eekxMSQcP64ucpOaU0IIYfEM5g5ACCGEEEKIpyXJqxBCCCGEsBomT16Dg4Px9vZOd9u9e3faY3bu3Enr1q3x9fWlefPmrFmzxug14uPjGTt2LDVr1sTf358hQ4Zw8+ZNU4cqhBBCCCGsjMnnvJ48eZLixYuzePFio+158qilYU6fPk2vXr147733mDx5Mlu3bmXo0KHky5ePmjVrAjBmzBj27dvH9OnTsbe3Z/To0fTv359FixaZOlwhhBBCCGFFTJ68hoaGUqZMGQoWLJhh+48//oi3tzeDBg0CoHTp0oSEhDB37lxq1qxJREQEq1atYtasWVS7X57nq6++onnz5hw4cAB/f39ThyyEEEIIIayEyacNnDx5ktKlSz+2fd++fWkjrKlq1KhBcHAwuq4THBycti1VyZIl8fDwYO/evaYOVwghhBBCWBGTj7yeOnUKNzc3WrduTUREBF5eXgwaNIjKlSsDcPXqVTw9PY2e4+7uTmxsLLdu3SIiIgI3NzccHlkz0t3dnatXrz53XLquExMT89zPtwax9wvAx2ZUCD6Hkj4xJv2RnvSJMekPY9If6WVln+i6jiYl7MQjnil5vXjxIo0aNXps+9atW7lz5w4xMTGMHDkSGxsbFi1aRKdOnVixYgVlypQhLi4Oe3t7o+el/p6QkEBsbGy6dgAHBwfi4+OfJVwjiYmJHD9+/Lmfb03OnTtn7hAsjvSJMemP9KRPjEl/GJP+SC+r+iSjnEDkbM+UvHp4eLB27drHtru7u7N3716cnJyws7MDwMfHh5CQEBYuXMjYsWNxcHAgISHB6Hmpvzs5OeHo6JiuHVQFAicnp2cJ14idnR1lypR57udbg9jYWM6dO0eJEiVeqK+yE+kTY9If6UmfGJP+MCb9kV5W9klYWFimvr6wTs+UvNrZ2f3nfFaA3LlzG/1uMBgoXbo0ERERABQqVIjIyEijx0RGRuLs7Iyrqyuenp5ERUWRkJBg9G0rMjISDw+PZwnXiKZpODs7P/fzrYmTk1OO2denJX1iTPojPekTY9IfxqQ/0suKPpEpAyIjJr1g659//sHf35/w8PC0bUlJSZw4cSJt1LNatWrs2bPH6Hm7du2iSpUqGAwGqlatSkpKStqFWwBnz54lIiKC6tWrmzJcIYQQQghhZUyavFapUgU3NzeCgoI4evQoJ0+eJCgoiKioKLp27QpA586dOXz4MFOmTOH06dPMnz+fdevW0b17d0BNTXjttdcYOXIku3fv5vDhwwwePJiAgAD8/PxMGa4QQgghhLAyJk1ec+XKxYIFCyhQoADvv/8+7du3JyoqikWLFlGgQAEAypYty4wZM9i2bRstW7Zk2bJlTJ482ah81vjx46lZsyYffvgh77//PqVKlWLatGmmDFUIIYQQQlghk5fKKl68+BMTzbp161K3bt3Htjs7O/PZZ5/x2WefmTo8IYQQQghhxTRd13VzB5HZ9u/fj67r2b7chq7rJCYmYmdnJ5Pc75M+MSb9kZ70iTHpD2PSH+llZZ8kJCSgaRpVqlTJ1L8jrIvJR14tUU55w9E0Ldsn6M9K+sSY9Ed60ifGpD+MSX+kl5V9omlajvkMF08vR4y8CiGEEEKI7MGkF2wJIYQQQgiRmSR5FUIIIYQQVkOSVyGEEEIIYTUkeRVCCCGEEFZDklchhBBCCGE1JHkVQgghhBBWQ5JXIYQQQghhNSR5FUIIIYQQVkOSVyGEEEIIYTUkeRVCCCGEEFZDklchhBBCCGE1JHnNJlJSUpg2bRp16tTBz8+PHj16EB4ebu6wMkVUVBSffvopdevWpUqVKrzzzjvs27cvrf29997D29vb6Na5c+e09vj4eMaOHUvNmjXx9/dnyJAh3Lx50xy7YjIRERHp9tnb25sVK1YAcPz4cTp16oSfnx8NGzbkp59+Mnp+djt+du/enWF/eHt706hRIwBmzpyZYfvDfv75Zxo1akTlypXp0KEDISEh5tidFzZ79myj/wNgmmPiSa9hqTLqjy1bttCmTRv8/f1p2LAhEydOJC4uLq09ODg4w+Nl9+7daY/ZuXMnrVu3xtfXl+bNm7NmzZos26cXlVGfjBw5Mt3+NmzYMK09Ox8jwsLpIluYPn26/vLLL+t///23fvz4cb1bt25606ZN9fj4eHOHZnLvvfee3qJFC33v3r36mTNn9LFjx+qVK1fWT58+reu6rtesWVNfvHixHhkZmXa7detW2vOHDRumN27cWN+7d69+6NAhvWXLlnrHjh3NtDemsXXrVt3Hx0ePiIgw2u/Y2Fj95s2b+ssvv6wPHz5cDwsL05cvX677+Pjoy5cvT3t+djt+4uPjjfohMjJS37Bhg+7t7Z223wMGDNA//vjjdI9LtWLFCr1y5cr66tWr9VOnTukff/yxHhAQoN+4ccNcu/VcFi1apJcrV07v1KlT2jZTHBNP8xqWKKP+2Lt3r16+fHl95syZ+tmzZ/WtW7fqdevW1YcNG5b2mJ9//llv3LhxuuMltT/CwsJ0Hx8f/auvvtLDwsL0uXPn6hUqVNB37NiR5fv4rDLqE13X9bZt2+pfffWV0f4+fPxn12NEWD5JXrOB+Ph43d/fX//555/Ttt2+fVuvXLmy/scff5gxMtM7d+6c7uXlpe/bty9tW0pKit64cWP9m2++0a9fv657eXnpx44dy/D5V69e1cuVK6dv3bo1bduZM2d0Ly8vff/+/Zkef2aZM2eO/vrrr2fYNmvWLL127dp6YmJi2rYvv/xSb9q0qa7rOeP4uXfvnt6gQQOjZOSVV17Rf/jhh8c+p2nTpvqkSZPSfk9MTNTr1aunz5o1KzNDNZmrV6/qvXr10v38/PTmzZsbJSamOCae9BqW5r/6Y8iQIXrXrl2NHr9y5Uq9YsWKaYnY6NGj9d69ez/29UeNGqW3bdvWaNvgwYP1bt26mXAvTOu/+iQlJUX38/PTN2zYkOFzs+MxIqyHTBvIBk6cOMG9e/eoWbNm2rbcuXNToUIF9u7da8bITM/NzY05c+bg4+OTtk3TNDRNIzo6mpMnT6JpGiVLlszw+cHBwQDUqFEjbVvJkiXx8PCw6r46efIkpUuXzrBt3759BAQEYGtrm7atRo0anDt3juvXr+eI42fWrFnExsYSFBQEQEJCAufOnaNUqVIZPv7GjRucO3fOqE9sbW2pVq2a1fTJsWPHsLOz4/fff8fX19eozRTHxJNew9L8V39069Yt7dhIZTAYSExM5O7du8B//x8D1R8P9xeo/ggODkbXdRPthWn9V59cuHCBmJiYx/4fyY7HiLAetk9+iLB0V69eBaBQoUJG293d3dPasovcuXNTr149o23r16/n/PnzjBgxgtDQUFxdXRk3bhzbt2/H2dmZ5s2b07dvX+zt7YmIiMDNzQ0HBwej17D2vgoNDcXNzY2OHTty9uxZXnrpJfr06UPdunW5evUqXl5eRo93d3cH4MqVK9n++Ll58yYLFixgyJAh5M2bF4CwsDCSk5NZv349n3/+OfHx8VSvXp2PP/7YaL8z6pMTJ05k9S48l4YNGxrNT3yYKY6JJ71GgQIFXnwnTOi/+qNChQpGvycmJrJgwQIqVapEvnz5ADh16hRubm60bt2aiIgIvLy8GDRoEJUrVwZUf3h6ehq9jru7O7Gxsdy6dSvtdSzJf/VJaGgoAAsXLuSff/7BYDBQt25dBg0ahKura7Y8RoT1kJHXbCA2NhYAe3t7o+0ODg7Ex8ebI6Qss3//foYPH07Tpk2pX78+oaGhxMfHU7lyZebOnUufPn1YtmwZI0eOBFRfPdpPYN19lZSUxJkzZ7h9+zb9+vVjzpw5+Pn50bNnT3bu3ElcXFyGxwaoi9ey+/GzePFiXF1dad++fdq21A9mJycnpk6dyueff86ZM2fo0qULcXFx2b5PTHFMPOk1rFVSUhJDhw7l1KlTjB49GlCJ1p07d4iJiWHkyJHMmDGDAgUK0KlTJ8LCwoCM+yP194SEhKzdCRMIDQ3FYDDg7u7OrFmzGDZsGP/++y99+/YlJSUlRx8jwvxk5DUbcHR0BNQbZOrPoN4cnJyczBVWptu0aRMfffQRVapUYcqUKQCMGzeOoKAg8uTJA4CXlxd2dnYMGjSIoUOH4ujomOEHiTX3la2tLbt378bGxibt379SpUqcOnWKefPmZbjPqR8czs7O2f74WbVqFS1btjTat5YtW1K3bl2j0bCyZctSt25dtmzZQvHixYH0SUd26RNTHBNPeg1rdPfuXQYOHMiePXv49ttv00ZVCxUqxN69e3FycsLOzg4AHx8fQkJCWLhwIWPHjsXBwSFdf6T+bo3HTJ8+fejQoQNubm6Aei8tWLAg7dq148iRIzn2GBGWQUZes4HU0zaRkZFG2yMjI/Hw8DBHSJlu0aJF9OvXjwYNGjBr1qy0b/O2trZpiWuqsmXLAg9O60VFRaV7Q7X2vnJxcTH6AAG13xEREXh6emZ4bAB4eHhk6+PnxIkThIeH8/rrr6dre/Q0rru7O3nz5uXq1avZuk8AkxwTT3oNaxMZGUnHjh05ePAg8+bNSzc9KXfu3GmJK6g5saVLlyYiIgJQ78MZ9YezszOurq6ZvwMmZjAY0hLXVA+/l+bEY0RYDkles4Fy5cqRK1cuo3qD0dHRhISEUL16dTNGljkWL17M+PHj6dixI1999ZXRaanOnTszfPhwo8cfOXIEOzs7SpQoQdWqVUlJSUm7cAvg7NmzREREWG1fnTp1iipVqhj9+wMcPXqUMmXKUL16dYKDg0lOTk5r27VrFyVLliR//vzZ+vjZt29f2j4+7Ouvv6ZZs2ZGF9JcvHiRW7duUaZMGfLnz0/JkiWN+iQpKYl9+/ZZfZ8AJjkmnvQa1uT27du8++673Lx5k59//jndv/E///yDv7+/UQ3TpKQkTpw4QZkyZQCoVq0ae/bsMXrerl27qFKlCgaD9X3UDh06lK5duxptO3LkCABlypTJcceIsCzW9z9KpGNvb0+nTp2YMmUKmzdv5sSJEwwaNAhPT0+aNm1q7vBM6uzZs/zvf/+jSZMm9OrVi+vXr3Pt2jWuXbvGnTt3aNasGatXr2bJkiWEh4ezdu1aJk2axPvvv0+uXLnw8PDgtddeY+TIkezevZvDhw8zePBgAgIC8PPzM/fuPZfSpUtTqlQpxo0bx759+zh9+jQTJkzg4MGD9OnThzZt2nD37l0++eQTwsLCWLFiBQsWLKBXr15A9j5+QkJC0i08ANCkSRMuXbrEmDFjOHv2LHv37qVfv35UqVKFOnXqAOoK9B9++IGVK1cSFhbGiBEjiIuLo23btlm9GyZnimPiSa9hTSZMmEB4eDiTJ08mX758ae8p165dIzk5mSpVquDm5kZQUBBHjx7l5MmTBAUFERUVlZbgde7cmcOHDzNlyhROnz7N/PnzWbduHd27dzfvzj2nZs2asXPnTr799lsuXLjAtm3bGDFiBC1atKB06dI57hgRFsbctbqEaSQlJemTJk3Sa9Soofv5+ek9evTQw8PDzR2Wyc2cOVP38vLK8BYUFKTruiq4/corr+iVKlXSGzRooM+cOVNPTk5Oe4179+7pn3zyiV6tWjW9WrVq+uDBg/WbN2+aa5dM4tq1a/qwYcP0wMBA3cfHR2/fvr2+d+/etPZDhw7p7dq1S+uThQsXGj0/ux4/3bt31wcOHJhh244dO/T27dvrfn5+ekBAgD58+HA9KirK6DFz587V69atq1euXFnv0KGDHhISkhVhm1xQUFC6AvSmOCae9BqW6uH+SEpK0n18fB77vpK6z+fPn9f79eunBwQE6L6+vnq3bt30kydPGr3utm3b9BYtWuiVKlXSmzdvrq9ZsybL9+15ZXSMrF27Vm/ZsqVeuXJlPTAwUP/iiy/0uLi4tPbsfIwIy6bpuoUWoBNCCCGEEOIRMm1ACCGEEEJYDUlehRBCCCGE1ZDkVQghhBBCWA1JXoUQQgghhNWQ5FUIIYQQQlgNSV6FEEIIIYTVkORVCCGEEEJYDUlehRBCCCGE1ZDkVQghhBBCWA1JXoUQQgghhNWQ5FUIIYQQQlgNSV6FEEIIIYTV+H8r6MZ2gtE22QAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -249,23 +398,40 @@ } ], "source": [ + "# Prediction data\n", "prediction_df = eye.prediction_df\n", + "\n", + "# Read the train data from the csv file\n", "fixed_train_df = pd.read_csv(train_input)\n", - "predict_train_df = pd.read_csv(predict_input)\n", + "\n", + "# Display the plots based on the configuration\n", "if display_train_data:\n", " full_plot(fixed_train_df, True)\n", - "if display_predict_data:\n", - " full_plot(predict_train_df, True)\n", + "\n", "if display_prediction:\n", + " # Display the prediction plot\n", " fig, ax = plt.subplots(1, 1)\n", - " eye.plot(datasets=[prediction_df], keys_x=['screen_x'], keys_y=['screen_y'],\n", - " is_subset=is_subset, subset_size=subset_size, lock_plot=lock_plot,\n", - " eyes_only=not display_prediction, ax=ax, display_centroid=display_centroid)\n", + " eye.plot(\n", + " datasets=[prediction_df],\n", + " keys_x=[\"screen_x\"],\n", + " keys_y=[\"screen_y\"],\n", + " is_subset=is_subset,\n", + " subset_size=subset_size,\n", + " lock_plot=lock_plot,\n", + " eyes_only=not display_prediction,\n", + " ax=ax,\n", + " display_centroid=display_centroid,\n", + " )\n", + "\n", + " # Set the title of the plot\n", " ax.set_title(\"Prediction\")\n", + "\n", " if display_label:\n", " for color, label in legend_dict.items():\n", " plt.scatter([], [], c=color, label=label)\n", - " plt.legend()\n", + " plt.legend(bbox_to_anchor=(1, 1))\n", + "\n", + " # Display the plot\n", " plt.show()" ] } @@ -286,7 +452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.8" } }, "nbformat": 4, From 7dcb250f618cc2bbfb3012815e8c0a1e274a5c82 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 21:15:15 +0000 Subject: [PATCH 40/78] database docs added --- app/services/database.py | 44 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/app/services/database.py b/app/services/database.py index e17e4fce..d7fc65fb 100644 --- a/app/services/database.py +++ b/app/services/database.py @@ -2,27 +2,71 @@ def create_document(collection, doc_id, data): + """ + Creates a new document in the specified collection with the given document ID and data. + + Args: + collection (str): The name of the collection to create the document in. + doc_id (str): The ID of the document to be created. + data (dict): The data to be stored in the document. + """ db = firestore.client() return db.collection(collection).document(doc_id).set(data) def get_documents(collection, field, op, value): + """ + Retrieves documents from a Firestore collection based on the provided field, operator, and value. + + Args: + collection (str): The name of the Firestore collection. + field (str): The field to filter the documents by. + op (str): The operator to use for the filtering operation. + value: The value to compare against the field. + """ db = firestore.client() + + # Check if field, op, and value are not None if field is not None and op is not None and value is not None: + # Filter documents based on the provided field, operator, and value return db.collection(collection).where(field, op, value).stream() + + # Return all documents from the collection return db.collection(collection).stream() def get_document(collection, doc_id): + """ + Retrieves a document from the specified collection in the Firestore database. + + Args: + collection (str): The name of the collection to retrieve the document from. + doc_id (str): The ID of the document to retrieve. + """ db = firestore.client() return db.collection(collection).document(doc_id).get() def delete_document(collection, doc_id): + """ + Deletes a document from the specified collection in the Firestore database. + + Args: + collection (str): The name of the collection where the document is located. + doc_id (str): The ID of the document to be deleted. + """ db = firestore.client() return db.collection(collection).document(doc_id).delete() def update_document(collection, doc_id, data): + """ + Update a document in the specified collection with the given data. + + Args: + collection (str): The name of the collection. + doc_id (str): The ID of the document to be updated. + data (dict): The data to be updated in the document. + """ db = firestore.client() return db.collection(collection).document(doc_id).update(data) From b5099d847e4d704d4e80c1485b6d15fa33cb44ac Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 21:19:19 +0000 Subject: [PATCH 41/78] heatmap to do for later --- app/services/heatmap.py | 1 + 1 file changed, 1 insertion(+) diff --git a/app/services/heatmap.py b/app/services/heatmap.py index e69de29b..a7c18b24 100644 --- a/app/services/heatmap.py +++ b/app/services/heatmap.py @@ -0,0 +1 @@ +# To be added later on the project \ No newline at end of file From 5d41c9e8be8b704893351ada078ae06cfa50e8f3 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 21:30:10 +0000 Subject: [PATCH 42/78] storage file docs added --- app/services/storage.py | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/app/services/storage.py b/app/services/storage.py index 064f137b..2d47fe5d 100644 --- a/app/services/storage.py +++ b/app/services/storage.py @@ -1,17 +1,33 @@ +import os +from pathlib import Path from flask import Flask from werkzeug.utils import secure_filename -from pathlib import Path -import os -UPLOAD_FOLDER = f'{Path().absolute()}/public/videos' +UPLOAD_FOLDER = f"{Path().absolute()}/public/videos" + +# Initialize Flask app app = Flask(__name__) -app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER +app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER def save_file_locally(file, folder): + """ + Save a file locally in the specified folder. + + Args: + file: The file to be saved. + folder: The folder where the file will be saved. + + Returns: + The path of the saved file relative to the specified folder. + """ # Create folder if does not exists - os.makedirs(UPLOAD_FOLDER+folder, exist_ok=True) + os.makedirs(UPLOAD_FOLDER + folder, exist_ok=True) + + # Save file filename = secure_filename(file.filename) - file.save(os.path.join(app.config['UPLOAD_FOLDER']+folder, filename)) - return f'{folder}/{filename}' + file.save(os.path.join(app.config["UPLOAD_FOLDER"] + folder, filename)) + + # Return file path + return f"{folder}/{filename}" From 49b76d047804070c2d579fa8809f278f88890980 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 21:36:07 +0000 Subject: [PATCH 43/78] mix notebook docs modified --- app/services/calib_validation/mix.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/app/services/calib_validation/mix.ipynb b/app/services/calib_validation/mix.ipynb index 1382ad4d..e3f9d8a4 100644 --- a/app/services/calib_validation/mix.ipynb +++ b/app/services/calib_validation/mix.ipynb @@ -122,11 +122,11 @@ " Train the model to validate calibration and write the predictions to a CSV file.\n", "\n", " Args:\n", - " - output (str): The path to the output CSV file.\n", - " - fieldnames (list): The field names for the CSV file header.\n", - " - isLeft (bool): Flag indicating whether the calibration is for the left eye.\n", - " - isRight (bool): Flag indicating whether the calibration is for the right eye.\n", - " - test_size (float): The proportion of the dataset to include in the test split.\n", + " output (str): The path to the output CSV file.\n", + " fieldnames (list): The field names for the CSV file header.\n", + " isLeft (bool): Flag indicating whether the calibration is for the left eye.\n", + " isRight (bool): Flag indicating whether the calibration is for the right eye.\n", + " test_size (float): The proportion of the dataset to include in the test split.\n", "\n", " Returns:\n", " None\n", From 7f9d0e5ade04ae1e360d25e50db6a7d786883227 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 21:41:59 +0000 Subject: [PATCH 44/78] data viz docs updated --- app/services/calib_validation/data_visualize.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/app/services/calib_validation/data_visualize.ipynb b/app/services/calib_validation/data_visualize.ipynb index 23017e4f..b45df06a 100644 --- a/app/services/calib_validation/data_visualize.ipynb +++ b/app/services/calib_validation/data_visualize.ipynb @@ -386,8 +386,8 @@ " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", "\n", " Args:\n", - " - d (float): The distance.\n", - " - h (float): The bandwidth.\n", + " d (float): The distance.\n", + " h (float): The bandwidth.\n", "\n", " Returns:\n", " float: The quartic KDE value.\n", @@ -498,8 +498,8 @@ " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", "\n", " Args:\n", - " - d (float): The distance.\n", - " - h (float): The bandwidth.\n", + " d (float): The distance.\n", + " h (float): The bandwidth.\n", "\n", " Returns:\n", " float: The quartic KDE value.\n", From feb72b25bce7e80ddd923bd5143a7235c9fada1e Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 6 Aug 2024 22:18:16 +0000 Subject: [PATCH 45/78] wsgi file docs added --- wsgi.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/wsgi.py b/wsgi.py index 1b27f453..b22c4d6b 100644 --- a/wsgi.py +++ b/wsgi.py @@ -1,7 +1,18 @@ -from app.main import app +""" +This script is the entry point for running the web application. + +It imports the `app` object from the `main` module and starts the Flask development server. + +Debug: + - The server runs in debug mode if the `debug` argument is set to `True`. + +Environment Variables: + - PORT: The port number on which the server should listen. Defaults to 5000 if not provided. +""" + import os +from app.main import app if __name__ == "__main__": app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 5000))) - From 5482515b674e01867c44a2ef36a80be226e2314e Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 11:46:53 +0000 Subject: [PATCH 46/78] docs change little --- app/models/session.py | 22 +++++------ app/services/calib_validation/eyeInfo.py | 50 ++++++++++++------------ app/services/database.py | 28 ++++++------- app/services/storage.py | 4 +- wsgi.py | 4 +- 5 files changed, 54 insertions(+), 54 deletions(-) diff --git a/app/models/session.py b/app/models/session.py index 22f07699..5a6f1fd8 100644 --- a/app/models/session.py +++ b/app/models/session.py @@ -3,17 +3,17 @@ class Session: Represents a session in the eye tracking application. Attributes: - id (int): The unique identifier of the session. - title (str): The title of the session. - description (str): The description of the session. - user_id (int): The user ID associated with the session. - created_date (datetime): The date and time when the session was created. - website_url (str): The URL of the website being tracked. - screen_record_url (str): The URL of the screen recording for the session. - webcam_record_url (str): The URL of the webcam recording for the session. - heatmap_url (str): The URL of the heatmap image for the session. - calib_points (list): The calibration points used in the session. - iris_points (list): The iris tracking points recorded in the session. + - id (int): The unique identifier of the session. + - title (str): The title of the session. + - description (str): The description of the session. + - user_id (int): The user ID associated with the session. + - created_date (datetime): The date and time when the session was created. + - website_url (str): The URL of the website being tracked. + - screen_record_url (str): The URL of the screen recording for the session. + - webcam_record_url (str): The URL of the webcam recording for the session. + - heatmap_url (str): The URL of the heatmap image for the session. + - calib_points (list): The calibration points used in the session. + - iris_points (list): The iris tracking points recorded in the session. """ def __init__( diff --git a/app/services/calib_validation/eyeInfo.py b/app/services/calib_validation/eyeInfo.py index 5bb32262..ee2670ba 100644 --- a/app/services/calib_validation/eyeInfo.py +++ b/app/services/calib_validation/eyeInfo.py @@ -11,23 +11,23 @@ class EyeInfo: Class representing eye information for calibration and prediction. Attributes: - calib_points (list): List of calibration points. - dataset (str): Path to the dataset file. - screen_width (int): Width of the screen. - screen_height (int): Height of the screen. - is_right (bool): Flag indicating if the eye is the right eye. - is_left (bool): Flag indicating if the eye is the left eye. - right_eye_df (pandas.DataFrame): DataFrame containing right eye data. - left_eye_df (pandas.DataFrame): DataFrame containing left eye data. - prediction_df (pandas.DataFrame): DataFrame containing prediction data. - calib_df (pandas.DataFrame): DataFrame containing calibration data. - palette (list): List of colors for plotting. + - calib_points (list): List of calibration points. + - dataset (str): Path to the dataset file. + - screen_width (int): Width of the screen. + - screen_height (int): Height of the screen. + - is_right (bool): Flag indicating if the eye is the right eye. + - is_left (bool): Flag indicating if the eye is the left eye. + - right_eye_df (pandas.DataFrame): DataFrame containing right eye data. + - left_eye_df (pandas.DataFrame): DataFrame containing left eye data. + - prediction_df (pandas.DataFrame): DataFrame containing prediction data. + - calib_df (pandas.DataFrame): DataFrame containing calibration data. + - palette (list): List of colors for plotting. Methods: - init_eye(): Initializes the calibration points and eye points. - init_calib_points(): Initializes the calibration points DataFrame. - init_points(): Initializes the eye points DataFrame. - plot(): Plots the eye data. + - init_eye(): Initializes the calibration points and eye points. + - init_calib_points(): Initializes the calibration points DataFrame. + - init_points(): Initializes the eye points DataFrame. + - plot(): Plots the eye data. """ def __init__( @@ -132,16 +132,16 @@ def plot( Plots the eye data. Args: - datasets (list): List of DataFrames containing eye data. - keys_x (list): List of x-axis keys for each dataset. - keys_y (list): List of y-axis keys for each dataset. - is_subset (bool): Flag indicating if the data is a subset. - subset_size (int): Size of each subset. - lock_plot (bool): Flag indicating if the plot should be locked to screen dimensions. - eyes_only (bool): Flag indicating if only eye points should be plotted. - ax (matplotlib.axes.Axes): Axes object for plotting. - display_centroid (bool): Flag indicating if centroid should be displayed. - colors (list): List of colors for each dataset. + - datasets (list): List of DataFrames containing eye data. + - keys_x (list): List of x-axis keys for each dataset. + - keys_y (list): List of y-axis keys for each dataset. + - is_subset (bool): Flag indicating if the data is a subset. + - subset_size (int): Size of each subset. + - lock_plot (bool): Flag indicating if the plot should be locked to screen dimensions. + - eyes_only (bool): Flag indicating if only eye points should be plotted. + - ax (matplotlib.axes.Axes): Axes object for plotting. + - display_centroid (bool): Flag indicating if centroid should be displayed. + - colors (list): List of colors for each dataset. """ sns.set(style="whitegrid") for i in range(len(datasets)): diff --git a/app/services/database.py b/app/services/database.py index d7fc65fb..76cc4f94 100644 --- a/app/services/database.py +++ b/app/services/database.py @@ -6,9 +6,9 @@ def create_document(collection, doc_id, data): Creates a new document in the specified collection with the given document ID and data. Args: - collection (str): The name of the collection to create the document in. - doc_id (str): The ID of the document to be created. - data (dict): The data to be stored in the document. + - collection (str): The name of the collection to create the document in. + - doc_id (str): The ID of the document to be created. + - data (dict): The data to be stored in the document. """ db = firestore.client() return db.collection(collection).document(doc_id).set(data) @@ -19,10 +19,10 @@ def get_documents(collection, field, op, value): Retrieves documents from a Firestore collection based on the provided field, operator, and value. Args: - collection (str): The name of the Firestore collection. - field (str): The field to filter the documents by. - op (str): The operator to use for the filtering operation. - value: The value to compare against the field. + - collection (str): The name of the Firestore collection. + - field (str): The field to filter the documents by. + - op (str): The operator to use for the filtering operation. + - value: The value to compare against the field. """ db = firestore.client() @@ -40,8 +40,8 @@ def get_document(collection, doc_id): Retrieves a document from the specified collection in the Firestore database. Args: - collection (str): The name of the collection to retrieve the document from. - doc_id (str): The ID of the document to retrieve. + - collection (str): The name of the collection to retrieve the document from. + - doc_id (str): The ID of the document to retrieve. """ db = firestore.client() return db.collection(collection).document(doc_id).get() @@ -52,8 +52,8 @@ def delete_document(collection, doc_id): Deletes a document from the specified collection in the Firestore database. Args: - collection (str): The name of the collection where the document is located. - doc_id (str): The ID of the document to be deleted. + - collection (str): The name of the collection where the document is located. + - doc_id (str): The ID of the document to be deleted. """ db = firestore.client() return db.collection(collection).document(doc_id).delete() @@ -64,9 +64,9 @@ def update_document(collection, doc_id, data): Update a document in the specified collection with the given data. Args: - collection (str): The name of the collection. - doc_id (str): The ID of the document to be updated. - data (dict): The data to be updated in the document. + - collection (str): The name of the collection. + - doc_id (str): The ID of the document to be updated. + - data (dict): The data to be updated in the document. """ db = firestore.client() return db.collection(collection).document(doc_id).update(data) diff --git a/app/services/storage.py b/app/services/storage.py index 2d47fe5d..75185843 100644 --- a/app/services/storage.py +++ b/app/services/storage.py @@ -16,8 +16,8 @@ def save_file_locally(file, folder): Save a file locally in the specified folder. Args: - file: The file to be saved. - folder: The folder where the file will be saved. + - file: The file to be saved. + - folder: The folder where the file will be saved. Returns: The path of the saved file relative to the specified folder. diff --git a/wsgi.py b/wsgi.py index b22c4d6b..6100c8f7 100644 --- a/wsgi.py +++ b/wsgi.py @@ -4,10 +4,10 @@ It imports the `app` object from the `main` module and starts the Flask development server. Debug: - - The server runs in debug mode if the `debug` argument is set to `True`. + The server runs in debug mode if the `debug` argument is set to `True`. Environment Variables: - - PORT: The port number on which the server should listen. Defaults to 5000 if not provided. + PORT: The port number on which the server should listen. Defaults to 5000 if not provided. """ import os From e37ae48aefbd566123b1faf2f07f8bc027ccc5ac Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 11:51:49 +0000 Subject: [PATCH 47/78] notebooks doc strings modified --- .../calib_validation/data_visualize.ipynb | 8 ++++---- app/services/calib_validation/mix.ipynb | 18 +++++++++--------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/app/services/calib_validation/data_visualize.ipynb b/app/services/calib_validation/data_visualize.ipynb index b45df06a..dfc3d444 100644 --- a/app/services/calib_validation/data_visualize.ipynb +++ b/app/services/calib_validation/data_visualize.ipynb @@ -386,8 +386,8 @@ " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", "\n", " Args:\n", - " d (float): The distance.\n", - " h (float): The bandwidth.\n", + " - d (float): The distance.\n", + " - h (float): The bandwidth.\n", "\n", " Returns:\n", " float: The quartic KDE value.\n", @@ -498,8 +498,8 @@ " Calculate the quartic kernel density estimate (KDE) for a given distance and bandwidth.\n", "\n", " Args:\n", - " d (float): The distance.\n", - " h (float): The bandwidth.\n", + " - d (float): The distance.\n", + " - h (float): The bandwidth.\n", "\n", " Returns:\n", " float: The quartic KDE value.\n", diff --git a/app/services/calib_validation/mix.ipynb b/app/services/calib_validation/mix.ipynb index e3f9d8a4..842c1ac2 100644 --- a/app/services/calib_validation/mix.ipynb +++ b/app/services/calib_validation/mix.ipynb @@ -122,11 +122,11 @@ " Train the model to validate calibration and write the predictions to a CSV file.\n", "\n", " Args:\n", - " output (str): The path to the output CSV file.\n", - " fieldnames (list): The field names for the CSV file header.\n", - " isLeft (bool): Flag indicating whether the calibration is for the left eye.\n", - " isRight (bool): Flag indicating whether the calibration is for the right eye.\n", - " test_size (float): The proportion of the dataset to include in the test split.\n", + " - output (str): The path to the output CSV file.\n", + " - fieldnames (list): The field names for the CSV file header.\n", + " - isLeft (bool): Flag indicating whether the calibration is for the left eye.\n", + " - isRight (bool): Flag indicating whether the calibration is for the right eye.\n", + " - test_size (float): The proportion of the dataset to include in the test split.\n", "\n", " Returns:\n", " None\n", @@ -204,8 +204,8 @@ " Extracts unique calibration points from a CSV file.\n", "\n", " Args:\n", - " csv_path (str): The path to the CSV file.\n", - " entries (int): The number of unique points to extract.\n", + " - csv_path (str): The path to the CSV file.\n", + " - entries (int): The number of unique points to extract.\n", "\n", " Returns:\n", " list: A list of dictionaries representing the unique points, each containing the 'x' and 'y' coordinates.\n", @@ -309,8 +309,8 @@ " Plots the eye data from the given dataframe.\n", "\n", " Args:\n", - " df (pandas.DataFrame): The dataframe containing the eye data.\n", - " eyes_only (bool): If True, only plots the eye data. If False, plots both eye and centroid data.\n", + " - df (pandas.DataFrame): The dataframe containing the eye data.\n", + " - eyes_only (bool): If True, only plots the eye data. If False, plots both eye and centroid data.\n", "\n", " Returns:\n", " None\n", From 45b0470d264258a9fa3d4343f8bfbab7b45bf303 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 11:55:17 +0000 Subject: [PATCH 48/78] main file docs added --- app/main.py | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/app/main.py b/app/main.py index 3015479c..67f8eba2 100644 --- a/app/main.py +++ b/app/main.py @@ -1,11 +1,17 @@ +# Necessary imports +import os from flask import Flask, request, Response from flask_cors import CORS + +# Local imports from app from app.routes import session as session_route -import os + +# Initialize Flask app and enable CORS app = Flask(__name__) CORS(app) + # @app.route('/', methods=['GET']) # def welcome(): # return Response(f'Welcome to EyeLab!', status=200, mimetype='application/json') @@ -51,8 +57,18 @@ # return Response('Invalid request method for route', status=405, mimetype='application/json') -@app.route('/api/session/calib_validation', methods=['POST']) +# Route for validating calibration +@app.route("/api/session/calib_validation", methods=["POST"]) def calib_validation(): - if request.method == 'POST': + """ + Validates the calibration request. + + Returns: + If the request method is 'POST', it calls the `calib_results` function from the `session_route` module. + Otherwise, it returns a `Response` object with an error message and status code 405. + """ + if request.method == "POST": return session_route.calib_results() - return Response('Invalid request method for route', status=405, mimetype='application/json') + return Response( + "Invalid request method for route", status=405, mimetype="application/json" + ) From fb67f97c45eca0ad03ed2d7b4c48bfdad015d3dd Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 12:07:49 +0000 Subject: [PATCH 49/78] yaml file formatted --- .github/workflows/main.yml | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index f58a0ca4..dd51045c 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -2,13 +2,13 @@ name: CI -# Controls when the action will run. +# Controls when the action will run. on: # Triggers the workflow on push or pull request events but only for the main branch push: - branches: [ main ] + branches: [main] pull_request: - branches: [ main ] + branches: [main] # Allows you to run this workflow manually from the Actions tab workflow_dispatch: @@ -19,7 +19,7 @@ jobs: build: # The type of runner that the job will run on runs-on: ubuntu-latest - + # Steps represent a sequence of tasks that will be executed as part of the job steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it @@ -29,5 +29,3 @@ jobs: heroku_api_key: ${{secrets.HEROKU_API_KEY}} # Located in GitHub secrets heroku_app_name: "web-eye-tracker-1204" # Must be unique in Heroku heroku_email: "karine.pistili@gmail.com" - - From 68fa335e6d637d7d5c590fde125f7256ffc5f61b Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 21:01:54 +0000 Subject: [PATCH 50/78] minor doc strings change --- app/services/calib_validation/mix.ipynb | 4 ---- 1 file changed, 4 deletions(-) diff --git a/app/services/calib_validation/mix.ipynb b/app/services/calib_validation/mix.ipynb index 842c1ac2..f9062bfe 100644 --- a/app/services/calib_validation/mix.ipynb +++ b/app/services/calib_validation/mix.ipynb @@ -131,7 +131,6 @@ " Returns:\n", " None\n", " \"\"\"\n", - "\n", " # Set the path for the csv files\n", " dataset_train_path = train_input\n", "\n", @@ -210,7 +209,6 @@ " Returns:\n", " list: A list of dictionaries representing the unique points, each containing the 'x' and 'y' coordinates.\n", " \"\"\"\n", - "\n", " # Read the data from the csv file\n", " df = pd.read_csv(csv_path)\n", "\n", @@ -251,7 +249,6 @@ " Returns:\n", " tuple: A tuple containing the screen height and width.\n", " \"\"\"\n", - "\n", " # Read the data from the csv file\n", " df = pd.read_csv(csv_path)\n", " interest = df.iloc[0]\n", @@ -315,7 +312,6 @@ " Returns:\n", " None\n", " \"\"\"\n", - "\n", " # Create the subplots\n", " fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", "\n", From 8f8be6113e18aec6a3549d2b9ccf699e7ccf3b93 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 21:12:11 +0000 Subject: [PATCH 51/78] remove not used import --- app/main.py | 1 - 1 file changed, 1 deletion(-) diff --git a/app/main.py b/app/main.py index 67f8eba2..4d1b63c1 100644 --- a/app/main.py +++ b/app/main.py @@ -1,5 +1,4 @@ # Necessary imports -import os from flask import Flask, request, Response from flask_cors import CORS From ad09e7507c687bf20cb562c64b1603f5b8aef202 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Wed, 7 Aug 2024 21:47:04 +0000 Subject: [PATCH 52/78] linear reg docs strings update --- .../test_linear_regression.ipynb | 47 ++++++++----------- 1 file changed, 19 insertions(+), 28 deletions(-) diff --git a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb index cf6e48f6..1e8cf50e 100644 --- a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb +++ b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb @@ -1196,11 +1196,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1209,13 +1209,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1389,18 +1387,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1505,18 +1501,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and LinearRegression model\n", " # with 2-degree polynomial features\n", " sc = StandardScaler()\n", From 01c5ad78ac02b60c743925db60ec935bc277d968 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:21:38 +0000 Subject: [PATCH 53/78] lasso reg doc fixes --- .../test_lassoCV_regression.ipynb | 47 ++++++++----------- .../test_lassoCV_regression_grid_search.ipynb | 47 ++++++++----------- .../test_lasso_regression.ipynb | 47 ++++++++----------- .../test_lasso_regression_grid_search.ipynb | 47 ++++++++----------- 4 files changed, 76 insertions(+), 112 deletions(-) diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb index 525bcaf9..a53764cb 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb @@ -1322,11 +1322,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1335,13 +1335,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1515,18 +1513,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1631,18 +1627,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and LassoCV model\n", " # with polynomial features of degree 2 and alphas set to logspace(-6, 6, 13)\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb index 548daf7b..c1866bd0 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb @@ -1324,11 +1324,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1337,13 +1337,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1517,18 +1515,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1633,18 +1629,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler\n", " sc = StandardScaler()\n", "\n", diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb index f809e373..119d1216 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb @@ -1204,11 +1204,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1217,13 +1217,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1397,18 +1395,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1513,18 +1509,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and Lasso model\n", " # with polynomial features of degree 2 and alpha=0.1\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb index 55409809..b9cc534b 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb @@ -1206,11 +1206,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1219,13 +1219,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1399,18 +1397,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1515,18 +1511,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler\n", " sc = StandardScaler()\n", "\n", From ab84525d927bc0b950362403f450a68ed5e0b626 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Thu, 8 Aug 2024 15:28:30 +0000 Subject: [PATCH 54/78] ridge reg doc strings fix --- .../test_ridgeCV_regression.ipynb | 47 ++++++++----------- .../test_ridgeCV_regression_grid_search.ipynb | 47 ++++++++----------- .../test_ridge_regression.ipynb | 47 ++++++++----------- .../test_ridge_regression_grid_search.ipynb | 47 ++++++++----------- 4 files changed, 76 insertions(+), 112 deletions(-) diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb index c9ac3cbe..434c39c8 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb @@ -1200,11 +1200,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1213,13 +1213,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1393,18 +1391,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1509,18 +1505,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and RidgeCV model\n", " # with polynomial features of degree 2 and alphas set to logspace(-6, 6, 13)\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb index c84ecc62..8bac9000 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb @@ -1202,11 +1202,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1215,13 +1215,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1395,18 +1393,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1511,18 +1507,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler\n", " sc = StandardScaler()\n", "\n", diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb index 46a434b5..e80ce590 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb @@ -1196,11 +1196,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1209,13 +1209,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1389,18 +1387,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1505,18 +1501,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and Ridge regression model\n", " # with polynomial features of degree 2 and alpha = 0.5\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb index ee99624f..39ab9355 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb @@ -1198,11 +1198,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1211,13 +1211,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1391,18 +1389,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1507,18 +1503,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler\n", " sc = StandardScaler()\n", "\n", From 5235cec2e39db362387d93a6292e2185027e103e Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 00:07:33 +0000 Subject: [PATCH 55/78] elastic net doc strings added --- .../test_elasticnetCV_regression.ipynb | 47 ++++++++----------- ..._elasticnetCV_regression_grid_search.ipynb | 47 ++++++++----------- .../test_elasticnet_regression.ipynb | 47 ++++++++----------- ...st_elasticnet_regression_grid_search.ipynb | 47 ++++++++----------- 4 files changed, 76 insertions(+), 112 deletions(-) diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb index 135f5ba4..7fff896a 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb @@ -1202,11 +1202,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1215,13 +1215,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1395,18 +1393,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1511,18 +1507,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and ElasticNetCV model\n", " # with polynomial features of degree 2\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb index a8063317..97eaa675 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb @@ -1204,11 +1204,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1217,13 +1217,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1397,18 +1395,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1513,18 +1509,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and ElasticNetCV model\n", " # with polynomial features up to degree 2\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb index 9a4327b0..6ac7c844 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb @@ -1194,11 +1194,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1207,13 +1207,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1387,18 +1385,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1503,18 +1499,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and ElasticNet model\n", " # with 2-degree polynomial features and alpha=1.0 and l1_ratio=0.5\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb index 20f8fa63..9253de1a 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb @@ -1196,11 +1196,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1209,13 +1209,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1389,18 +1387,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1505,18 +1501,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and ElasticNet model\n", " sc = StandardScaler()\n", " model = make_pipeline(PolynomialFeatures(2), linear_model.ElasticNet())\n", From 065438e054f466473e048f829768f038d2fbd9cc Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 13:01:35 +0000 Subject: [PATCH 56/78] bayesian ridge grid search added --- .../test_bayesian_ridge_regression.ipynb | 47 +- ...ayesian_ridge_regression_grid_search.ipynb | 1680 +++++++++++++++++ 2 files changed, 1699 insertions(+), 28 deletions(-) create mode 100644 app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb index f9119a6f..7ff44053 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb @@ -1196,11 +1196,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1209,13 +1209,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1389,18 +1387,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1505,18 +1501,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and BayesianRidge model\n", " # with 2-degree polynomial features\n", " sc = StandardScaler()\n", diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb new file mode 100644 index 00000000..e58e9909 --- /dev/null +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb @@ -0,0 +1,1680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9960664324743589" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a BayesianRidge regression model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge())\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 776.55387798, 766.05848214, 117.34559919, 115.33575667,\n", + " 1425.04998602, 108.36698771, 128.08678891, 86.83669419,\n", + " 770.9199631 , 769.74106758, 1419.36598347, 766.93911555,\n", + " 116.72431774, 95.96186806, 770.37920861, 777.76049966,\n", + " 123.77602587, 1431.55769606, 101.94879346, 113.81389966,\n", + " 767.83117278, 768.19749663, 105.21141129, 1434.30972717,\n", + " 1423.47245431, 775.95551615, 85.44834333, 104.0988384 ,\n", + " 78.40106289, 102.37668531, 769.91134443, 769.10039085,\n", + " 778.39738741, 771.76394562, 1458.50843574, 1438.6455693 ,\n", + " 1417.32350228, 1458.08444569, 1453.49733502, 1427.32725656,\n", + " 764.43066969, 101.6709869 , 105.97528733, 1428.07335138,\n", + " 769.31679646, 108.5697784 , 780.42694993, 1439.34194643,\n", + " 1437.30647773, 101.59770332, 1423.56790181, 1436.27499643,\n", + " 764.71619656, 1437.54041693, 84.66741803, 768.34588635,\n", + " 94.59715221, 95.17186813, 116.87661832, 777.07353148,\n", + " 1416.68936779, 109.26543808, 113.44622415, 766.60778873,\n", + " 88.20794411, 99.699148 , 1434.73824726, 94.27748863,\n", + " 1420.08484249, 1441.03735844, 1436.76219485, 773.32104219,\n", + " 121.64705279, 772.11481107, 70.40012199, 1416.99274449,\n", + " 1453.54612084, 778.67913792, 1425.63744304, 100.19867365,\n", + " 1417.94035109, 764.69346394, 93.60920638, 762.40441892,\n", + " 113.48062208, 1423.80924617, 1464.04022748, 95.57276183,\n", + " 1436.66309027, 1434.51920325, 84.50108563, 118.33361244,\n", + " 100.43092674, 773.19187128, 100.50950761, 772.01914038,\n", + " 103.80879898, 115.08416843, 1463.40418746, 82.63892241,\n", + " 770.17194218, 1421.14079982, 774.40025383, 769.20455806,\n", + " 113.38805298, 771.20539643, 89.9709598 , 82.24059822,\n", + " 80.00426352, 1435.61906916, 93.12060161, 1033.41224307,\n", + " 1440.25045956, 104.98922183, 1421.47512283, 775.88833752,\n", + " 770.40478416, 115.26633657, 1456.97819554, 116.50136073,\n", + " 120.65029095, 768.40048081, 1440.59232112, 113.39652589,\n", + " 775.26831528, 100.4092378 , 769.60780216, 1437.04042145,\n", + " 1441.14523502, 1431.86651058, 1438.76878188, 771.66524899,\n", + " 88.89357373, 1459.59442129, 776.54520105, 1423.30074829,\n", + " 86.61396817, 101.40014605, 93.902762 , 106.45921553,\n", + " 100.13138503, 118.5012867 , 80.42540785, 1422.36578485])" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGxCAYAAAB89YyPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABUVklEQVR4nO3deVhU9f4H8PcMwwzDMsM+MAKKG4uimKRiZhuJS5ZpFkVmN69evWCSS2qmaeWSlVuLZvfe7HdbXEpNrSxSE01ERXYVcUVFQEUYFoFZzu+PrgfHXFAHhoH363l4nuZzvjN8zhGYd2fO93wlgiAIICIiIrIhUms3QERERHSnGGCIiIjI5jDAEBERkc1hgCEiIiKbwwBDRERENocBhoiIiGwOAwwRERHZHAYYIiIisjkyazfQUEwmEwoKCuDi4gKJRGLtdoiIiKgeBEFAeXk5tFotpNKbn2dptgGmoKAA/v7+1m6DiIiI7sKZM2fg5+d30+3NNsC4uLgA+PMAqFQqK3dDRERE9aHT6eDv7y++j99Msw0wVz82UqlUDDBEREQ25naXf/AiXiIiIrI5DDBERERkcxhgiIiIyOYwwBAREZHNYYAhIiIim8MAQ0RERDaHAYaIiIhsDgMMERER2RwGGCIiIrI5zfZOvERERGR5BoMJmzPP41xpFVq5OmJwF1/IZI1/PoQBhoiIiOrl86TjWLYtD+U1RrE264csvPpYB4zu265Re2GAISIiotv6POk45v505C/18hqjWG/MEMNrYIiIiOiWDAYTFmz9a3i51oKtR2AwmBqpIwYYIiIiuo3vDp6B8TbZxGj6c1xjYYAhIiKiW/o6Jd+i4yyBAYaIiIhuqbi8xqLjLIEBhoiIiG7J21lu0XGWwABDREREt/Ts/X4WHWcJDDBERER0S6cuVlp0nCUwwBAREdEtfVXPi3PrO84SGGCIiIjolmoMlh1nCQwwREREZHMYYIiIiOiW6rvuUGOuT8QAQ0RERDdlMgkw3n4YAEBo0E7McTFHIiIiuqErtQYs23as3sGk8VZCuoszMElJSRg8eDC0Wi0kEgk2btx407Fjx46FRCLBkiVLzOolJSWIjY2FSqWCq6srRo0ahYqKCrMxmZmZePDBB+Hg4AB/f38sXLjwTlslIiKiu3S0sBwv/CsFy3cer/dz7O0asKHr3HGAqaysRNeuXfHJJ5/cctyGDRuwd+9eaLXav2yLjY1FTk4OEhMTsWXLFiQlJWHMmDHidp1Oh379+qF169ZITU3F+++/j9mzZ2PlypV32i4RERHdAYPRhK/2nsawFXuQll96R8/t09a9YZq6gTv+CGnAgAEYMGDALcecO3cO48ePxy+//IJBgwaZbTt8+DC2bt2K/fv3IyIiAgDw0UcfYeDAgfjggw+g1Wrx9ddfo7a2Fv/5z38gl8vRqVMnpKenY9GiRWZBh4iIiCynWFeNWZtysDW70Kz+QDsP/HH80m2f//HzEQ3V2l9Y/CJek8mEESNGYMqUKejUqdNfticnJ8PV1VUMLwAQFRUFqVSKlJQUcUzfvn0hl9etqRAdHY3c3Fxcvnz5ht+3pqYGOp3O7IuIiIhuTxAEbDtchKc++cMsvCjt7TAlOghvP9UJIT7Ot3yNbv5qODraN3SrIosHmPfeew8ymQyvvvrqDbcXFhbC29vbrCaTyeDu7o7CwkJxjEajMRtz9fHVMdebP38+1Gq1+OXv73+vu0JERNTsVdbo8fbmQxj9fwdwvqxarIf6umDlS90xoLMP5DI7bIzvg27+6hu+Rjd/NTbE9WmslgFYeBZSamoqli5dioMHD0IikVjypW9r+vTpmDhxovhYp9MxxBAREd1CzrkyvP59JnIK6j61kEqAF3u2xojI1rCTSqBS2sPDSQ6JRIINcX1QVaXHxO8zkV9SiQB3Jywa1qVRz7xcZdEAs2vXLhQXFyMgIECsGY1GTJo0CUuWLMGpU6fg4+OD4uJis+cZDAaUlJTAx8cHAODj44OioiKzMVcfXx1zPYVCAYVCYcndISIiapb0BiO+TD6ND389iiv6uru8+KodMH1AMDq3UkMmlcLLRQGl3HxqkaOjPVaM6N7YLf+FRQPMiBEjEBUVZVaLjo7GiBEj8Le//Q0AEBkZidLSUqSmpqJ79z8PwPbt22EymdCzZ09xzIwZM6DX62Fv/2eqS0xMRFBQENzc3CzZMhERUYtSUHoFb2zIwu+5F8zq/UI1GP9oezgpZHBWyODhrICdtHE/TbkTdxxgKioqcOzYMfHxyZMnkZ6eDnd3dwQEBMDDw8NsvL29PXx8fBAUFAQACAkJQf/+/TF69GisWLECer0e8fHxiImJEadcv/DCC5gzZw5GjRqFqVOnIjs7G0uXLsXixYvvZV+JiIhaLEEQsDW7ELN+yMGFihqx7qyQ4bWoDngk2BtSiQQeznK4ODT+R0J36o4DzIEDB/DII4+Ij69edzJy5EisWrWqXq/x9ddfIz4+Ho899hikUimGDRuGZcuWidvVajV+/fVXxMXFoXv37vD09MSsWbM4hZqIiOgulFfrMe+nw1i974zZXXXD/dWY1j8Y3ioHKOV28HJWQGZnG6sMSQRBaMylCxqNTqeDWq1GWVkZVCqVtdshIiKyirTTl/H695nIK667471MKsErD7TB8Ah/yOykcHeUQ22FC3FvpL7v31wLiYiIqBnSG4z4LOkElm0/hlpD3SpF/m5KzBgUgo4aF8hlf16oq5A14hoAFsIAQ0RE1MzkX6rE1O8zkXyixKz+ZFctxj7UFg72dlAr7eH+v+nRtogBhoiIqJkQBAE/pBdgzuYcXK7Si3VXpT0mR3dE73aeN50ebWsYYIiIiJqBsiu1mLPpENannTOr9wh0x+vRQXB3ksPZQQZPJwWkTXh6dH0xwBAREdm4lBOX8Pp3mThdUiXW5DIp/tG3LYaEayGzk8LDWQFnRfN5228+e0JERNTC1OqNWLbjGFb8fhwGU92k4rZeTpgxMASBnk42Nz26vhhgiIiIbNCJCxWYvC4DB/NLzerDu/thVJ9AKOzt4O4kh1rZNKZHWxoDDBERkQ0RBAFrDpzB3B8Po7zaINY9nOWY1j8Y3Vu7QS6TwtvFAXJZ8zrrci0GGCIiIhtxqbIGb27Ixs/ZhWb1vh088drjHaFW2sPVUQ43R3ubnR5dXwwwRERENmDX0QuY+n0mCsqqxZqDvRTjH+2A/p00kMvs4OWigIO9bU+Pri8GGCIioiasutaADxOP4t+7T+Ka63QR4uuCNwaEoJWbEi4O9vBwkjeL6dH1xQBDRETUROUW6jBxbQZyCnRiTSoBXuzZGi/2CoDC3g6ezgo4NaPp0fXV8vaYiIioiTOZTPi/vaexcGsuqmqNYt1H5YA3Bgajcys1HOUyeLkoYNeCzrpciwGGiIioCSkur8a077KwPbfYrP54qAavPtoeLg72cHeWQ+XQPKdH1xcDDBERURORmFOENzZm4UJ5jVhzVsjwWlQHPBLsDYX9nzela87To+uLAYaIiMjKqmoMmPfzEXy99zSuuU4XXf3UmD4gGBq1Eq5Ke7g5ya3WY1PDAENERGRF2edKMXFtBo4WVYg1O6kErzzQBs9G+MPBvmVNj64vBhgiIiIrMJlMWLnrJBYnHkWNwSTW/d2UmDEoBB01LlAp/5we3dxvSnc3GGCIiIgaWUFpFSavy8Se45fM6oO7+mLsQ+3gorCHp4scjnK+Td8MjwwREVEj+jGjAG/+kI3LVXqxplbaY0p0R/Ru5wknhQyezi13enR9McAQERE1gopqPWZvPoTvUs+a1Xu0ccPr/YPh6ayAh7McLi18enR9McAQERE1sNTTJZi0NgOnLlWJNXs7Cf7Rtx2e7qaF8n83pbO34/To+mKAISIiaiBGowkf7TiGT3Ycg95YN0G6rZcTZgwMQVsvZ7g5/rmCNN0ZBhgiIqIGkF9SidfWZCD19GWz+vDufhjVJxBOChm8VQooZJwefTcYYIiIiCzs+9SzmLM5B7pqg1jzcJJjav8gRLRxh1ppD3dOj74nDDBEREQWUlZVizc3ZmNz5nmzep/2npjUryM8nBTwclFAKedZl3vFAENERGQByccvYvK6DJwrrRZrDvZSxD/SHgM6+8DFwR4enB5tMQwwRERE90BvMOLDxKNYmXQCpmsWMgr2ccEbA4MR4O4ETxcFnBV8y7UkHk0iIqK7dKy4HAlr0pF9TifWpBLghZ4BeKlXa7go7eHlrICM06MtjgGGiIjoDgmCgK/35mPez4dRVWsU6xqVAm8MCEEXf1e4O8qhduRN6RoKAwwREdEduFRRg6nfZ+K3w8Vm9agQb7z6WAe4O8nh7eIAuYxnXRoSAwwREVE9/Z5bjNe/y0RxeY1Yc1LY4bWojng02BuujnK4OdpzenQjYIAhIiK6jRq9EfN+Poz/23Ma11yniy5+akwfEAw/N0d4uSjgYM/p0Y2FAYaIiOgWDp0vQ8LqdBwtqhBrdlIJ/ta7DZ673x9qR3t4Oikg5fToRsUAQ0REdAOCIOBfu07ig19zUWMwiXU/NyVmDAxBqFYFT2cFnDg92ip41ImIiK5TVHYFk9ZlYvexi2b1wV18MfbhdvBwUsDTWc7p0VbEAENERHSNX3LOY/r3WSip0os1tdIek/t1RJ8OXnB3kkOt5PRoa7vj6JiUlITBgwdDq9VCIpFg48aN4ja9Xo+pU6ciLCwMTk5O0Gq1eOmll1BQUGD2GiUlJYiNjYVKpYKrqytGjRqFiooKszGZmZl48MEH4eDgAH9/fyxcuPDu9pCIiKgertQa8Pp3GfjHfw+ahZf727jh3yMj8GiIBq1clQwvTcQdB5jKykp07doVn3zyyV+2VVVV4eDBg5g5cyYOHjyI9evXIzc3F08++aTZuNjYWOTk5CAxMRFbtmxBUlISxowZI27X6XTo168fWrdujdTUVLz//vuYPXs2Vq5ceRe7SEREdGsZZy5jwNJdWHvgrFizt5Mg/pF2mD80DO28nKFV894uTYlEEATh9sNu8mSJBBs2bMCQIUNuOmb//v3o0aMHTp8+jYCAABw+fBihoaHYv38/IiIiAABbt27FwIEDcfbsWWi1WixfvhwzZsxAYWEh5HI5AGDatGnYuHEjjhw5Uq/edDod1Go1ysrKoFKp7nYXiYioGTOZBHyy4xiWbc+D3lj3dtjW0wkzBoWgo8aF06MbWX3fvxs8SpaVlUEikcDV1RUAkJycDFdXVzG8AEBUVBSkUilSUlLEMX379hXDCwBER0cjNzcXly9fvuH3qampgU6nM/siIiK6mXOXr+C5lXvxYeJRs/DyTPdW+DT2PnTxc0UrVyXDSxPVoAGmuroaU6dOxfPPPy+mqMLCQnh7e5uNk8lkcHd3R2FhoThGo9GYjbn6+OqY682fPx9qtVr88vf3t/TuEBFRM7Ex7SwGLE3C/lMlYs3DSY6Fw8Iw/tEOCPD488Z0vLdL09VgAUav1+PZZ5+FIAhYvnx5Q30b0fTp01FWViZ+nTlzpsG/JxER2ZaKaj1e/TYNCWsyoKs2iPU+7T3xr5ci8FCQN/zcHOEo5yTdpq5B/oWuhpfTp09j+/btZp9h+fj4oLjYfAEsg8GAkpIS+Pj4iGOKiorMxlx9fHXM9RQKBRQKhSV3g4iImpF9Jy/htTUZOFd6Raw5yKSIf7Q9BoX5wsNFAZUDZxjZCoufgbkaXvLy8vDbb7/Bw8PDbHtkZCRKS0uRmpoq1rZv3w6TyYSePXuKY5KSkqDX101jS0xMRFBQENzc3CzdMhERNWMGowkLtx5BzMq9ZuElyMcFK1/qjqH3+cHP3ZHhxcbc8RmYiooKHDt2THx88uRJpKenw93dHb6+vnjmmWdw8OBBbNmyBUajUbxmxd3dHXK5HCEhIejfvz9Gjx6NFStWQK/XIz4+HjExMdBqtQCAF154AXPmzMGoUaMwdepUZGdnY+nSpVi8eLGFdpuIiFqCUxcr8erqNGSeLRNrUgnwfI8AvNy7DbxcFHB1lN/iFaipuuNp1L///jseeeSRv9RHjhyJ2bNnIzAw8IbP27FjBx5++GEAf97ILj4+Hps3b4ZUKsWwYcOwbNkyODs7i+MzMzMRFxeH/fv3w9PTE+PHj8fUqVPr3SenURMRtVyCIGDN/jN4e8shVNUaxbpGpcAbA0JwX2s3To9uour7/n1P94FpyhhgiIhaprKqWkz5LhO/HjK/ljIqxBuvPtYBWlclPJzkkEg4w6gpqu/7Ny+zJiKiZmNX3gVMWpuB4vIaseaksEPCYx0R3ckHXi4KKOU869IcMMAQEZHNqzWYsODnI/jij5O49mOFLn5qTBsQjPZezvBwVsCO93VpNhhgiIjIpuUVlSP+2zTkFpaLNTupBH/r3QbP9wiAt0oBF84wanYYYIiIyCYJgoBVe05hwc9HUGMwiXU/NyVmDAxBeIArvJwVkNlxAcbmiAGGiIhszsXyakxcl4GkoxfN6k908cU/H26PVq5KqB151qU5Y4AhIiKbknioENO+z8KlylqxplbaY3K/jngk2BteLgooZLxQt7ljgCEiIptQrTfi7c2H8M2+fLP6/W3c8Hp0ENp6OcOd06NbDAYYIiJq8nLOlWH8t2k4cbFSrNnbSTCmb1sM7+4PjcqB06NbGAYYIiJqskwmAZ8lHceixKPQG+smSLf1dMKMQSEI81PD00kBKadHtzgMMERE1CQVll3BhNXpSDlZYlZ/pnsrjOnbFlpXRzgr+DbWUvFfnoiImpwtmQV4Y30WdNUGsebhJMfr/YPQt6MXp0cTAwwRETUdlTUGvLkxGxvSzpnV+7T3xOR+HRHo5Qy1ktOjiQGGiIiaiNRTJZiwJh1nL18Raw4yKeIeaY8h3bTQqJSQy3jWhf7EAENERFZlNAlYuu0oPtl+DNdcp4sgHxfMGBiMzq1c4eZoz+nRZIYBhoiIrOZMSSXiv0lDxtkysSaVAM/3CMDf+wTC11UJB3tOj6a/YoAhIiKrWHfgDGZvykFlrVGsaVQKTB8QjAfae8HDSc7p0XRTDDBERNSoyq7oMe37TPycXWhWjwrxxmuPd0QbDyc4cXo03QZ/QoiIqNEkH7+E19ako1BXLdac5HZIiOqAwV1bwctFATuedaF6YIAhIqIGV2sw4f1fjuBfu07imut0EdZKjTcGhqBTKxVUDpweTfXHAENERA3qWHE5xn+bhsPny8WanVSCl3u3xsjegfBROXB6NN0xBhgiImoQgiDgv3tPY95Ph1GtN4l1Pzcl3hwUgp6BHnDl9Gi6SwwwRERkcZcqajB5XQZ25F4wqw8K88WExzogwMOR06PpnjDAEBGRRe04UozJ6zJwqbJWrKkcZJjcLwgDu/jCw0nOsy50zxhgiIjIIqr1Rrz74yF8tTffrN69tRtmDAxBsK8LHOV82yHL4E8SERHds0MFZRj/bRqOX6gUa/Z2Eox+sC1GRLaGt4sDp0eTRTHAEBHRXTOZBHy+6wQ++DUX+msWMgr0dMLMQSG4P9AdLpweTQ2AAYaIiO5Kka4aE1anY++JS2b1ofe1wvhH28PPzRH2dpweTQ2DAYaIiO7YT1nnMe37TOiqDWLN3UmOqf2DEd1JA1dHuRW7o5aAAYaIiOqtqtaAt37IwbrUs2b1B9p7YFr/YHT0cYFCxunR1PAYYIiIqF7S8y/j1dXpyC+pEmsOMiniHmmP53v4w8NZwenRLYDJJCCnQIeSqlq4O8rRSauyyqrhDDBERHRLRpOAj7bn4aPtx2A01V2oG6RxwawnQnFfazco5Tzr0hLsOXYRy3cex/HiCuiNAuztJGjn7YxxD7VD7/aejdoLAwwREd3UmZIqvPptGtLOlIo1CYAXegYg7uF20KiVnB7dQuw5dhFvbMhCRY0Bbo5yyO2kqDWacPh8Od7YkIV5T4c1aohhgCEiohv6PvUMZm3KQWWNUax5uyjw5qAQPBqigbOCbyEthckkYPnO46ioMcBH5SB+VOggtYOPSopCXQ2W7zyOXm09Gu3jJP70ERGRmbIreryxIQs/Zp43qz8W7I2pA4LQ1tMZMk6PblFyCnQ4XlwBN8e/LgMhkUjg6miP48UVyCnQIcxP3Sg9McAQEZFo74lLSFidjkJdtVhzktthQlRHPBfhD7Ujb0rXEpVU1UJvFCC/SXBV2ElRZhJQUlV7w+0NgQGGiIhQazDhw19zsTLpBIRr6mGtVJg1OBRd/dwgl/GsS0vl7iiHvZ0EtUYTHKR/vWC7xmiCvVQC90a8/w8DDBFRC3f8QgXGf5OGQ+d1Ys1OKsHIyNYY93B7eDpz9eiWrpNWhXbezjh8vhw+KqnZz4MgCCit0iPE1wWdtKpG6+mO43RSUhIGDx4MrVYLiUSCjRs3mm0XBAGzZs2Cr68vlEoloqKikJeXZzampKQEsbGxUKlUcHV1xahRo1BRUWE2JjMzEw8++CAcHBzg7++PhQsX3vneERHRTQmCgK/2nsagZbvMwoufmxLLY+/D6/2D4eXCe7sQIJVKMO6hdnBW2KFQV4MreiNMJgFX9EYU6mrgrLDDuIfaNer9YO44wFRWVqJr16745JNPbrh94cKFWLZsGVasWIGUlBQ4OTkhOjoa1dV1n6fGxsYiJycHiYmJ2LJlC5KSkjBmzBhxu06nQ79+/dC6dWukpqbi/fffx+zZs7Fy5cq72EUiIrpeSWUtRn15AG9uzEa13iTWB4X54qu/90BUiAYO9ry3C9Xp3d4T854OQ4ivC6pqDCiuqEFVjQEhvi6NPoUaACSCIAi3H3aTJ0sk2LBhA4YMGQLgzzSv1WoxadIkTJ48GQBQVlYGjUaDVatWISYmBocPH0ZoaCj279+PiIgIAMDWrVsxcOBAnD17FlqtFsuXL8eMGTNQWFgIufzPz9OmTZuGjRs34siRI/XqTafTQa1Wo6ysDCpV453SIiJq6n7PLcaktRm4VFl3waXKQYYp0UEYep8fnDg9mm6hoe/EW9/3b4tekXXy5EkUFhYiKipKrKnVavTs2RPJyckAgOTkZLi6uorhBQCioqIglUqRkpIijunbt68YXgAgOjoaubm5uHz5siVbJiJqMar1Rsz6IRsvf7HfLLx0b+2Gr//eC8/3CGB4oduSSiUI81PjoY5eCPNTW2UZAcDCF/EWFhYCADQajVldo9GI2woLC+Ht7W3ehEwGd3d3szGBgYF/eY2r29zc3P7yvWtqalBTUyM+1ul0fxlDRNRSHSnUIf6bNBwrrrve0N5OgjF92+LvD7aFG1ePJhvTbKL2/PnzMWfOHGu3QUTUpJhMAv69+wQW/pILvbHuioE2Ho6Y81RnRLb14PRoskkW/an18fEBABQVFZnVi4qKxG0+Pj4oLi42224wGFBSUmI25kavce33uN706dNRVlYmfp05c+bed4iIyIYV6arx4r9TMPenI2bhZeh9rbB6TC/07eDJ8EI2y6I/uYGBgfDx8cG2bdvEmk6nQ0pKCiIjIwEAkZGRKC0tRWpqqjhm+/btMJlM6NmzpzgmKSkJer1eHJOYmIigoKAbfnwEAAqFAiqVyuyLiKil2pp9Hv0WJ2HP8Utizd1Jjg+Gd8G8p8Pgo1ZyejTZtDsOMBUVFUhPT0d6ejqAPy/cTU9PR35+PiQSCRISEvDuu+9i06ZNyMrKwksvvQStVivOVAoJCUH//v0xevRo7Nu3D3/88Qfi4+MRExMDrVYLAHjhhRcgl8sxatQo5OTkYM2aNVi6dCkmTpxosR0nImqOKmsMmLIuA2O/OoiyK3X/E/hAOw+sHt0LQ7v5cXo0NQt3PI36999/xyOPPPKX+siRI7Fq1SoIgoC33noLK1euRGlpKfr06YNPP/0UHTt2FMeWlJQgPj4emzdvhlQqxbBhw7Bs2TI4OzuLYzIzMxEXF4f9+/fD09MT48ePx9SpU+vdJ6dRE1FLk3GmFOO/TUN+SZVYc5BJEfdoe7zyQBs4KbiOETV99X3/vqf7wDRlDDBE1FIYTQI+2Z6HpduPwWiq+5PeUeOMuUPCcF9rN9hZaaor0Z2q7/t3s5mFRETUEp29XIUJq9ORerruHlkSAC/0DMDExzvCw1lhveaIGhADDBGRjfoh7Rze2JiFyhqjWPN2UeCtwaHo18kH9nacYUTNFwMMEZGN0VXrMWNDFjZnnDerPxrsjdlPhiLA3clKnRE1HgYYIiIbsu9kCRJWp6GgrG6BXCe5HSY+3hGxvVpzhhG1GAwwREQ2QG80YdGvR/FZ0nFcc50uOmtVmDc0DGGt1LyvC7UoDDBERE3cyYuVGP/tQWSfq1vjzU4qwd96t0FCVEc4O/BPObU8/KknImqiBEHA6n1nMGdLDqr1JrHu56bEu091xoMdvTg9mlosBhgioiaopLIWr3+Xgd8Om68dNzDMB289EQqNWmmlzoiaBgYYIqImZlfeBUxck44LFbViTeUgw/QBIRge4QcZp0cTMcAQETUV1XojFvx8BKv2nDKrR7R2w/yhYeigcbFOY0RNEAMMEVETkFtYjvHfHsTRogqxZm8nwbiH2mHcw+2glPPPNdG1+BtBRGRFgiDgiz9OYcHPR1BrrLtQt42HIxYMDUPPth6cHk10AwwwRERWUqyrxsS1Gdh97KJZfdh9fnhzUAjcnORW6oyo6WOAISKygl9zCjH1+0xcrtKLNTdHe8x+shMGd9FCyunRRLfEAENE1Iiqag14Z/MhfLv/jFm9dzsPLBjWBQHujlbqjMi2MMAQETWSrLNlGP/tQZy6VCXWFDIpJkR1wOg+gbCXcR0jovpigCEiamBGk4Dlvx/D4t/yYLxmIaMOGme8/0xXhPu7Wq85IhvFAENE1IDOlV7BhNVpOHDqsliTAIjtFYBp/UO4jhHRXeJvDhFRA/kh/RxmbMhGRY1BrHm7KDD36c6ICtFwejTRPWCAISKyMF21HjM3ZuOH9AKz+qPB3pg/NAwalYOVOiNqPhhgiIgsaP+pEkxYnYaC0mqx5ii3w9T+wXixZwDsuI4RkUUwwBARWYDeaMLS3/Lw6e/HcM11ughrpcIHw7siyEdlveaImiEGGCKie3TyYiUmrE5D5tkysSaVAH9/sC0mPd4RCntOjyayNAYYIqK7JAgC1h44g9mbDuGK3ijWta4OWPhMF/Rp72XF7oiaNwYYIqK7cLmyFtPWZ+KXnCKz+hNdfPHOU525jhFRA2OAISK6Q7vzLmLi2nQUl9eINZWDDLOeCMWw7n6cHk3UCBhgiIjqqVpvxMKtR/CfP06Z1SNau+HDZ7uitYeTdRojaoEYYIiI6uFoUTnGf5OG3KJysWZvJ8E/H26PVx9tz+nRRI2MAYaI6BYEQcCqPacw/6cjqDWaxHobD0cseq4r7gtwt2J3RC0XAwwR0U0Ul1dj8toMJOVdNKs/G+GHtwZ3gpOCf0KJrIW/fUREN5B4qAivf5eBy1V6sebmaI+5T3fGwDCtFTsjIoABhojITFWtAe9sOYRv950xq/dp74kPnu0CH5XSSp0R0bUYYIiI/ifrbBnGf3sQpy5ViTWFTIop0UEY1SeQ06OJmhAGGCJq8YwmAZ8lHceiX4/CcM1CRh01zlgSE45QX7UVuyOiG2GAIaIW7VzpFby2Jh37TpaINQmAkb3bYPqAYK5jRNREMcAQUYu1KaMAMzZkobzaINa8XRR4/5kueCjI24qdEdHtMMAQUYujq9Zj1sZsbEwvMKs/HuKN957pCneuY0TU5Fn81pFGoxEzZ85EYGAglEol2rVrh3feeQeCUPe5siAImDVrFnx9faFUKhEVFYW8vDyz1ykpKUFsbCxUKhVcXV0xatQoVFRUWLpdImphDpwqwYAlu8zCi6PcDvOGhmHlSxEML0Q2wuIB5r333sPy5cvx8ccf4/Dhw3jvvfewcOFCfPTRR+KYhQsXYtmyZVixYgVSUlLg5OSE6OhoVFdXi2NiY2ORk5ODxMREbNmyBUlJSRgzZoyl2yWiFkJvNOHDX3Px7GfJOFd6Rax38VPjx/F98EKPAM4yIrIhEuHaUyMW8MQTT0Cj0eDf//63WBs2bBiUSiW++uorCIIArVaLSZMmYfLkyQCAsrIyaDQarFq1CjExMTh8+DBCQ0Oxf/9+REREAAC2bt2KgQMH4uzZs9Bqb38TKZ1OB7VajbKyMqhUKkvuIhHZmFMXKzFhdRoyzpaJNakEGPtQO0x8vCNkXMeIqMmo7/u3xX9re/fujW3btuHo0aMAgIyMDOzevRsDBgwAAJw8eRKFhYWIiooSn6NWq9GzZ08kJycDAJKTk+Hq6iqGFwCIioqCVCpFSkqKpVsmomZKEASs3X8GA5btMgsvrVyVWD2mF17vH8zwQmSjLH4R77Rp06DT6RAcHAw7OzsYjUbMnTsXsbGxAIDCwkIAgEajMXueRqMRtxUWFsLb23wGgEwmg7u7uzjmejU1NaipqREf63Q6i+0TEdmey5W1mLY+E7/kFJnVn+qqxbtPd4aLg72VOiMiS7B4gFm7di2+/vprfPPNN+jUqRPS09ORkJAArVaLkSNHWvrbiebPn485c+Y02OsTUdNQVaXHxO8zkV9SiQB3Jywa1gWOjuZhZHfeRUxcm47i8rr/qXFxkOHdpzrjqW6tGrtlImoAFg8wU6ZMwbRp0xATEwMACAsLw+nTpzF//nyMHDkSPj4+AICioiL4+vqKzysqKkJ4eDgAwMfHB8XFxWavazAYUFJSIj7/etOnT8fEiRPFxzqdDv7+/pbcNSKysqc/2Y20M3UfBR06X46tOYXo5q/Ghrg+qDEY8cEvufh810mz593fxg1Ln+sGrRvXMSJqLiweYKqqqiCVmn+mbGdnB5PJBAAIDAyEj48Ptm3bJgYWnU6HlJQUjBs3DgAQGRmJ0tJSpKamonv37gCA7du3w2QyoWfPnjf8vgqFAgqFwtK7Q0RNxPXh5VppZ8rQf8lOABIcKSwX6/Z2Ekx4rCP++XA7SKWcYUTUnFg8wAwePBhz585FQEAAOnXqhLS0NCxatAivvPIKAEAikSAhIQHvvvsuOnTogMDAQMycORNarRZDhgwBAISEhKB///4YPXo0VqxYAb1ej/j4eMTExNRrBhIRNS9VVfqbhperjhSa3ycq0NMJy2LCEebn2oCdEZG1WDzAfPTRR5g5cyb++c9/ori4GFqtFv/4xz8wa9Yscczrr7+OyspKjBkzBqWlpejTpw+2bt0KBwcHcczXX3+N+Ph4PPbYY5BKpRg2bBiWLVtm6XaJyAbEfbPvjsbH3O+P2U92ggPXMSJqtix+H5imgveBIWo+2kz7sd5jP38pAo+Ham4/kIiaJKvdB4aIyJoYXohaBgYYIiIisjkMMERERGRzGGCIqEkrKL1S7z9UjvyLRtRi8NediJqszRkF6L8kCaZ6jl85MuL2g4ioWbD4NGoiontVXq3HW5tysP7guXo/x04K9O7gffuBRNQs8AwMETUpqadLMGDpLrPw4ii3wz8eDLzpcyQS4L+v9OTddolaEJ6BIaImwWA0Ydn2Y/h4ex5M19ydqqufGh893w0BHk54KMgby7Ydxf5TpTAKAuwkEtzfxhWvPtYRvdt7Wq95Imp0DDBEZHWnL1Viwup0pJ8pFWt2EgnGPdwOCVEdILP782Rx7/ae6NXWAzkFOpRU1cLdUY5OWhXPvBC1QAwwRGQ1giBgXepZzN6Ug6pao1hv5arEsphwdG/j/pfnSKUShPmpG7NNImqCGGCIyCpKq2oxbX0WtmYXmtWf7tYK7wzpDGcF/zwR0c3xLwQRNbo/jl3ExLXpKNLViDWVgwzzng7DE1254jwR3R4DDBE1mhqDER/8kovPd500q/cIdMfSmHD4qpVW6oyIbA0DDBE1iqNF5ZiwOg2Hz5eLNZlUgkn9OuIffdvxQlwiuiMMMETUoARBwP8ln8a8nw6jxlB3T91ATyd8/EI3dNLyglwiunMMMETUYC6U1+D17zKwI/eCWf2FngGYOSgUSrmdlTojIlvHAENEDWLb4SJM+S4TJZW1Ys3dSY4PnumCR0M0VuyMiJoDBhgisqgrtUbM/ekQvtqbb1Z/qKMXPny2KzydFVbqjIiaEwYYIrKY7HNlmLA6DccvVIo1hUyKGQNDMCKyNSQSXqhLRJbBAENE98xoEvD5rhP48Ndc6I11CxkF+7jg4xe6ob23ixW7I6LmiAGGiO5JQekVTFybjr0nSsSaBMDfHwzElOhgyGVc9J6ILI8Bhoju2pbMAryxPgu6aoNY06gUWPxcOHq34+rQRNRwGGCI6I6VV+vx1qYcrD94zqzev5MP3hvWBWpHeyt1RkQtBQMMEd2R1NMlSFidjjOXr4g1R7kd3n6yE4Z19+OFukTUKBhgiKheDEYTlm0/ho+358FUd50uwv1dsSymGwI8HK3XHBG1OAwwRHRbpy9VImF1OtLOlIo1O4kE8Y+2x/hH20Nmxwt1iahxMcAQ0U0JgoDvUs9i9qYcVNYaxbqfmxJLY7qhe2s3K3ZHRC0ZAwwR3VBpVS3e2JCFn7IKzepD72uFt5/qDGcF/3wQkfXwLxAR/cWeYxcxcW0GCnXVYk3lIMP8oV0wqIuvFTsjIvoTAwwRiWoMRnzwSy4+33XSrN6rrTsWPxcOX7XSSp0REZljgCEiAEBeUTleXZ2Gw+fLxZpMKsHk6CCMebAtpFJOjyaipoMBhqiFEwQB/917GnN/PIwag0mst/VywrKYbujcSm3F7oiIbowBhqgFu1Beg9e/y8CO3Atm9RG9WuONgSFQyu2s1BkR0a0xwBC1UNsOF2HKd5koqawVa+5OcnwwvAseDdZYsTMiottjgCFqYa7UGjH3p0P4am++Wf3hIC+8/0xXeLkorNQZEVH9McAQtSDZ58owYXUajl+oFGsKmRRvPhGKF3sGcB0jIrIZDDBELYDJJGDlrhP48Ndc6I11CxmF+Ljgoxe6ob23ixW7IyK6cwwwRM1cQekVTFqbgeQTl8SaBMDovm0xqV9HKGS8UJeIbE+DrMB27tw5vPjii/Dw8IBSqURYWBgOHDggbhcEAbNmzYKvry+USiWioqKQl5dn9holJSWIjY2FSqWCq6srRo0ahYqKioZol6jZ+jHzPPovSTILLxqVAl+P7ok3BoYwvBCRzbJ4gLl8+TIeeOAB2Nvb4+eff8ahQ4fw4Ycfws2tbtG3hQsXYtmyZVixYgVSUlLg5OSE6OhoVFfX3bY8NjYWOTk5SExMxJYtW5CUlIQxY8ZYul2iZqmixoBJazMQ981B6KoNYn1gZx/8ktAXvdt5WrE7IqJ7JxEEQbj9sPqbNm0a/vjjD+zateuG2wVBgFarxaRJkzB58mQAQFlZGTQaDVatWoWYmBgcPnwYoaGh2L9/PyIiIgAAW7duxcCBA3H27Flotdrb9qHT6aBWq1FWVgaVSmW5HSRq4lJPX8Zra9KRX1Il1hzldpjzZCc8092PF+oSUZNW3/dvi5+B2bRpEyIiIjB8+HB4e3ujW7du+Pzzz8XtJ0+eRGFhIaKiosSaWq1Gz549kZycDABITk6Gq6urGF4AICoqClKpFCkpKTf8vjU1NdDpdGZfRC2JwWjCkt+O4tnPks3CS7i/K36e8CCGR/gzvBBRs2HxAHPixAksX74cHTp0wC+//IJx48bh1VdfxZdffgkAKCwsBABoNOY3ytJoNOK2wsJCeHt7m22XyWRwd3cXx1xv/vz5UKvV4pe/v7+ld42oycq/VIVnP0vGkt/yYDT9eVLVTiJBwmMd8N3YSLT2cLJyh0RElmXxWUgmkwkRERGYN28eAKBbt27Izs7GihUrMHLkSEt/O9H06dMxceJE8bFOp2OIoWZPEAR8f/Ac3vohG5W1RrHu56bE0phu6N7a7RbPJiKyXRYPML6+vggNDTWrhYSE4PvvvwcA+Pj4AACKiorg6+srjikqKkJ4eLg4pri42Ow1DAYDSkpKxOdfT6FQQKHgHUSp5SitqsWMjdn4MfO8WX3Yfa0w56nOcFbwLglE1HxZ/COkBx54ALm5uWa1o0ePonXr1gCAwMBA+Pj4YNu2beJ2nU6HlJQUREZGAgAiIyNRWlqK1NRUccz27dthMpnQs2dPS7dMZHP2HL+I/kt2mYUXlVKGT164Dx8+G87wQkTNnsX/yr322mvo3bs35s2bh2effRb79u3DypUrsXLlSgCARCJBQkIC3n33XXTo0AGBgYGYOXMmtFothgwZAuDPMzb9+/fH6NGjsWLFCuj1esTHxyMmJqZeM5CImqsagxGLfj2KlbtO4Nr5g73aemDxc13hq1ZarzkiokZk8WnUALBlyxZMnz4deXl5CAwMxMSJEzF69GhxuyAIeOutt7By5UqUlpaiT58++PTTT9GxY0dxTElJCeLj47F582ZIpVIMGzYMy5Ytg7Ozc7164DRqam6OFZdjwup05BTUzbCTSSWYEh2E0Q+2hVTKGUZEZPvq+/7dIAGmKWCAoeZCEAR8tfc03v3xMGoMJrHezssJS2O6oXMrtRW7IyKyrPq+f/ODcqIm7EJ5DaZ+n4ntR8wvah/RqzXeGBgCpZxLARBRy8QAQ9RE7ThSjMnfZeBSRa1Yc3eS4/1nuuCxEM0tnklE1PwxwBA1MdV6I+b9dBj/l3zarP5IkBcWPtMVXi68XQAREQMMUROSU1CGCavTcay4buV1hUyKGYNCMKJXay4FQET0PwwwRE2AySTgX7tP4P1fcqE31l1XH+LrgmUx3dBB42LF7oiImh4GGCIrO192BZPWZmDP8Utm9TF922JSv45QyHihLhHR9RhgiKzox8zzeGNDFsqu6MWaRqXAomfD8UB7Tyt2RkTUtDHAEFlBRY0Bszfl4LvUs2b1AZ19MH9oGFwd5VbqjIjINjDAEDWyg/mXkbA6HfklVWLNUW6H2U92wvDufrxQl4ioHhhgiBqJwWjCxzuO4aPtx2A01V2oG+7viqUx4Wjt4WTF7oiIbAsDDFEjyL9UhYQ1aTiYXyrWpBIg/tEOGP9oe9jbWXxheCKiZo0BhqgBCYKA7w+ew+xNOaioMYh1fzcllsSEo3trdyt2R0RkuxhgiBpIWZUeb2zIwo9Z583qQ+9rhTlPdoKLg72VOiMisn0MMEQNYM/xi5i0NgPny6rFmspBhnlDw/BEF60VOyMiah4YYIgsqNZgwoeJuViZdAJC3XW66NXWHYueDYfWVWm95oiImhEGGCILOVZcjgmr05FToBNr9nYSTOoXhNEPtoWdlNOjiYgshQGG6B4JgoCv9p7G3J8Oo1pvEuttPZ2w7Plu6NxKbcXuiIiaJwYYontwsaIGr3+Xie1His3qsT0D8OagUCjlXMeIiKghMMAQ3aUdR4ox5bsMXKyoFWvuTnIsHNYFUaEaK3ZGRNT8McAQ3aFqvRHzfjqM/0s+bVZ/OMgL7z/TFV4uCit1RkTUcjDAEN2BnIIyJKxOR15xhVhTyKR4Y2AIXopszXWMiIgaCQMMUT2YTAL+tfsE3v8lF3pj3fzoEF8VlsaEo6PGxYrdERG1PAwwRLdRWFaNSevS8cexS2b10Q8GYnJ0EBQyXqhLRNTYGGCIbuGnrPOYvj4LZVf0Yk2jUuDD4eHo08HTip0REbVsDDBEN1BRY8DsTTn4LvWsWX1AZx/MezoMbk5yK3VGREQAAwzRXxzMv4yE1enIL6kSa45yO8we3AnDI/x4oS4RURPAAEP0PwajCZ/sOI5l2/NgNNVdqNvV3xVLnwtHG08nK3ZHRETXYoAhApB/qQqvrU1H6unLYk0qAeIfaY/xj3WAvZ3Uit0REdH1GGCoRRMEAesPnsNbm3JQUWMQ635uSix5LhwRbdyt2B0REd0MAwy1WGVVeryxMQs/Zp43qw+9rxXmPNkJLg72VuqMiIhuhwGGWqQ9xy9i0toMnC+rFmsqBxnmPh2GwV21VuyMiIjqgwGGWpRagwkfJuZiZdIJCHXX6aJnoDsWPxcOravSes0REVG9McBQi3GsuAIJa9KQfU4n1uztJJj4eBDG9G0LOymnRxMR2QoGGGr2BEHA1yn5ePfHQ6jWm8R6Wy8nLH2uG8L81FbsjoiI7gYDDDVrFytqMO37TPx2uNisHtszAG8OCoVSznWMiIhsEQMMNVs7cosxZV0mLlbUiDV3JzneG9YFj4dqrNgZERHdKwYYanaq9UbM/+kwvkw+bVZ/qKMX3h/eBd4uDlbqjIiILKXBby+6YMECSCQSJCQkiLXq6mrExcXBw8MDzs7OGDZsGIqKisyel5+fj0GDBsHR0RHe3t6YMmUKDAYDiG7lUIEOgz/abRZe5DIpZg8Oxaq/3c/wQkTUTDToGZj9+/fjs88+Q5cuXczqr732Gn788UesW7cOarUa8fHxGDp0KP744w8AgNFoxKBBg+Dj44M9e/bg/PnzeOmll2Bvb4958+Y1ZMtko0wmAf/efRLv/5KLWmPdhbrBPi5YGtMNQT4uVuyOiIgsrcHOwFRUVCA2Nhaff/453NzcxHpZWRn+/e9/Y9GiRXj00UfRvXt3fPHFF9izZw/27t0LAPj1119x6NAhfPXVVwgPD8eAAQPwzjvv4JNPPkFtbW1DtUw2qrCsGiP+k4K5Px02Cy+j+gRiY9wDDC9ERM1QgwWYuLg4DBo0CFFRUWb11NRU6PV6s3pwcDACAgKQnJwMAEhOTkZYWBg0mroLLaOjo6HT6ZCTk3PD71dTUwOdTmf2Rc3f1uzz6L80CX8cuyTWNCoFvhrVEzOfCIWDPWcZERE1Rw3yEdLq1atx8OBB7N+//y/bCgsLIZfL4erqalbXaDQoLCwUx1wbXq5uv7rtRubPn485c+ZYoHuyBZU1BszZnIO1B86a1ft38sH8oWFwc5JbqTMiImoMFg8wZ86cwYQJE5CYmAgHh8a7YHL69OmYOHGi+Fin08Hf37/Rvj81nrT8y0hYk47Tl6rEmqPcDrMHd8LwCD9IJLyjLhFRc2fxAJOamori4mLcd999Ys1oNCIpKQkff/wxfvnlF9TW1qK0tNTsLExRURF8fHwAAD4+Pti3b5/Z616dpXR1zPUUCgUUCoWF94aaEoPRhE9/P46l2/JgNNUtZBTu74olz4WjjaeTFbsjIqLGZPFrYB577DFkZWUhPT1d/IqIiEBsbKz43/b29ti2bZv4nNzcXOTn5yMyMhIAEBkZiaysLBQX1909NTExESqVCqGhoZZumWzAmZIqxKzci0WJR8XwIpUArz7aHuvGRjK8EBG1MBY/A+Pi4oLOnTub1ZycnODh4SHWR40ahYkTJ8Ld3R0qlQrjx49HZGQkevXqBQDo168fQkNDMWLECCxcuBCFhYV48803ERcXx7MsLYwgCNiQdg6zfshBRU3dfYD83JRY8lw4Itq4W7E7IiKyFqvciXfx4sWQSqUYNmwYampqEB0djU8//VTcbmdnhy1btmDcuHGIjIyEk5MTRo4cibffftsa7ZKVlFXpMWNjFrZknjerP92tFeY81QkqB3srdUZERNYmEQRBuP0w26PT6aBWq1FWVgaVSmXtdugOJR+/hElr01FQVi3WXBxkmPt0GJ7sqrViZ0RE1JDq+/7NtZCoSak1mLAo8Sg+SzqOa6N1j0B3LH4uHK1cldZrjoiImgwGGGoyjhVXIGFNGrLP1d2EUCaVYGK/jvhH33awk3J6NBER/YkBhqxOEAR8sy8f72w5hGp93VIAbT2dsDSmG8L81FbsjoiImiIGGLKqSxU1mPp9Jn47XGxWf6FnAN4cFAJHOX9EiYjor/juQFazI7cYU9Zl4mJFjVhzd5LjvWFd8Hio5hbPJCKilo4Bhhpdtd6IBT8fwao9p8zqfTt64YPhXeDt0nhLUBARkW1igKFGdahAh4Q1aThaVCHW5DIppg8IxsjINpDyQl0iIqoHBhhqFCaTgP/8cRILt+ai1lh3oW6wjwuWxnRDkI+LFbsjIiJbwwBDDa5IV41JazOw+9hFs/orDwTi9f5BcLC3s1JnRERkqxhgqEFtzT6PaeuzUFqlF2veLgp8MLwr+nb0smJnRERkyxhgqEFU1hgwZ3MO1h44a1aP7qTB/KFd4O4kt1JnRETUHDDAkMWlnylFwuo0nLpUJdaU9nZ4a3AonrvfHxIJL9QlIqJ7wwBDFmMwmvDp78exdFsejKa6hYy6+qmxJKYbAj2drNgdERE1JwwwZBFnSqrw2pp0HDh9WaxJJcA/H26PCVEdYG8ntWJ3RETU3DDA0D0RBAEb0s5h1g85qKgxiPVWrkosfi4cPQLdrdgdERE1VwwwdNfKrujx5sZsbM4oMKsPCdfi7SGdoXKwt1JnRETU3DHA0F3Ze+ISJq5JR0FZtVhzUcjw7tOd8VR4Kyt2RkRELQEDDN2RWoMJi387ihU7j0Oou04XPdq4Y9FzXeHn5mi95oiIqMVggKF6O36hAgmr05F1rkysyaQSvPZ4R4x9qB3suI4RERE1EgYYui1BEPDNvny8s+UQqvV16xi19XTCkphwdPFztV5zRETUIjHA0C1dqqjB1O+z8NvhIrP68z38MfOJUDjK+SNERESNj+8+dFO/5xZj8rpMXKyoEWtujvZ4b1gX9OvkY8XOiIiopWOAob+o1hux4OcjWLXnlFm9b0cvfPBMF3irHKzTGBER0f8wwJCZQwU6JKxJw9GiCrEml0kxfUAwRka2gZQX6hIRURPAAEMAAJNJwH/+OImFW3NRa6y7UDdI44Klz4cj2Edlxe6IiIjMMcAQinTVmLQ2A7uPXTSrv/JAIF7vHwQHezsrdUZERHRjDDAt3Nbs85i2PgulVXqx5u2iwAfDu6JvRy8rdkZERHRzDDAtVGWNAW9vPoQ1B86Y1fuFarBgWBe4O8mt1BkREdHtMcC0QOlnSpGwOg2nLlWJNaW9Hd4aHIrn7veHRMILdYmIqGljgGlBjCYBn+44hiXb8mA01S1k1MVPjSXPhaOtl7MVuyMiIqo/BpgW4kxJFV5bk44Dpy+LNakEGPdwOyREdYS9ndSK3REREd0ZBpgWYGPaOczcmI3yGoNYa+WqxOLnwtEj0N2KnREREd0dBphmrOyKHjM3ZmNTRoFZfUi4Fm8P6QyVg72VOiMiIro3DDDNVMqJS5i4NgPnSq+INReFDO8+3RlPhbeyYmdERET3jgGmmak1mLDkt6NYvvM4hLrrdNGjjTsWPdcVfm6O1muOiIjIQhhgmpHjFyqQsDodWefKxJpMKsFrj3fE2IfawY7rGBERUTPBANMMCIKA1fvP4O3Nh3BFbxTrgZ5OWBoTji5+rtZrjoiIqAFYfO7s/Pnzcf/998PFxQXe3t4YMmQIcnNzzcZUV1cjLi4OHh4ecHZ2xrBhw1BUVGQ2Jj8/H4MGDYKjoyO8vb0xZcoUGAwGkLmSylqM+W8qpq/PMgsvz/fwx4+v9mF4ISKiZsniAWbnzp2Ii4vD3r17kZiYCL1ej379+qGyslIc89prr2Hz5s1Yt24ddu7ciYKCAgwdOlTcbjQaMWjQINTW1mLPnj348ssvsWrVKsyaNcvS7dq0nUcvIHpJEhIP1YU/N0d7fDaiO+YP7QJHOU+wERFR8yQRhGsv9bS8CxcuwNvbGzt37kTfvn1RVlYGLy8vfPPNN3jmmWcAAEeOHEFISAiSk5PRq1cv/Pzzz3jiiSdQUFAAjUYDAFixYgWmTp2KCxcuQC6//To9Op0OarUaZWVlUKlUDbmLja5ab8R7W4/giz9OmdUf7OCJD4d3hbfKwTqNERER3aP6vn83+O1Xy8r+vKDU3f3PG6alpqZCr9cjKipKHBMcHIyAgAAkJycDAJKTkxEWFiaGFwCIjo6GTqdDTk5OQ7fcpB0p1OGpj/8wCy9ymRQznwjFl3/rwfBCREQtQoN+xmAymZCQkIAHHngAnTt3BgAUFhZCLpfD1dXVbKxGo0FhYaE45trwcnX71W03UlNTg5qaGvGxTqez1G40CSaTgC/2nMJ7Px9BrdEk1oM0Llj6fDiCfZrXWSYiIqJbadAAExcXh+zsbOzevbshvw2APy8enjNnToN/H2so0lVj8roM7Mq7aFb/2wNtMLV/MBzs7azUGRERkXU02EdI8fHx2LJlC3bs2AE/Pz+x7uPjg9raWpSWlpqNLyoqgo+Pjzjm+llJVx9fHXO96dOno6ysTPw6c+aMBffGerZmF6L/kiSz8OLlosCXr/TAW4M7MbwQEVGLZPEAIwgC4uPjsWHDBmzfvh2BgYFm27t37w57e3ts27ZNrOXm5iI/Px+RkZEAgMjISGRlZaG4uFgck5iYCJVKhdDQ0Bt+X4VCAZVKZfZlyyprDJj2fSbGfpWKy1V6sf54qAZbJzyIhzp6WbE7IiIi67L4R0hxcXH45ptv8MMPP8DFxUW8ZkWtVkOpVEKtVmPUqFGYOHEi3N3doVKpMH78eERGRqJXr14AgH79+iE0NBQjRozAwoULUVhYiDfffBNxcXFQKBSWbrnJyThTioQ16Th5sW7qudLeDrMGhyLmfn9IJLyjLhERtWwWn0Z9szfXL774Ai+//DKAP29kN2nSJHz77beoqalBdHQ0Pv30U7OPh06fPo1x48bh999/h5OTE0aOHIkFCxZAJqtf5rLFadRGk4Dlvx/Dkt/yYDDV/bOEtVJjaUw42no5W7E7IiKihlff9+8Gvw+MtdhagDl7uQoT12Rg36kSsSaRAOMeaoeEqI6Qyxp8xjsREZHV1ff9m7dqbQI2pp3DmxuzUVFTt1RCK1clFj3bFT3belixMyIioqaJAcaKyq7oMeuHbPyQXmBWf7KrFu8M6Qy10t5KnRERETVtDDBWsu9kCRLWpKGgtFqsuShkeGdIZwzp1sqKnRERETV9DDCNTG80YclvR/Hp78dx7dVH97dxw6Jnw+Hv7mi95oiIiGwEA0wjOnGhAglr0pF5tkysyaQSJER1wLiH28NOyunRRERE9cEA0wgEQcDq/Wfw9uZDuKI3ivU2Ho5YEtMN4f6u1muOiIjIBjHA3AGTSUBOgQ4lVbVwd5Sjk1YF6W3OmpRU1mLq95lIPGS+NELM/f6Y+UQonBT8JyAiIrpTfPespz3HLuKTHXnIKdCh1iBALpOgk1aFuEc6oHd7zxs+J+noBUxal4EL5XWrZLsq7bFgWBf073zjNZ2IiIjo9hhg6mHPsYuI++ag2ZpEVXrgj+MlOHT+ID554T6zEFOtN+K9rUfwxR+nzF6nT3tPfPhsV2hUDo3VOhERUbPE27vehskkYMbGLLPwcq3LVXrM2JgF0/9u/X+kUIenPvnDLLzI7aSY+UQo/u+VHgwvREREFsAzMLeRcbYUJy9W3XLMyYtVSDtzGelnyvDe1iOoNZjEbUEaFyyJCUeIb9NfzoCIiMhWMMDcxpa0s/UaN/6bNBSUVZvVXu7dBtMGBMPB3q4hWiMiImqxGGBu47fDhfUad2148XJR4IPhXfFQR6+GaouIiKhFY4C5jTOltXc0/vFQDRYMDYOHs6KBOiIiIiIGmNsw3X6IaN7TYXi+hz8kEt5Rl4iIqCExwFjQCz0DrN0CERFRi8Bp1ERERGRzGGCIiIjI5jDAEBERkc1hgCEiIiKbwwBDRERENocB5jbs63mE6juOiIiI7h3fdm/DoZ7JpL7jiIiI6N7xXfc2uvq7WnQcERER3TsGmNsY1EVr0XFERER07xhgbsNXpbToOCIiIrp3DDC34eGsgIeT/NZjnORcvJGIiKgRMcDcRietCqFaFdRKGRRS4OoyjRIACimgVsoQqlWhk1ZlzTaJiIhaFAaY25BKJRj3UDu4Ocrh4iiHn5sSAe5K+Lkp4eIoh5ujHOMeageplCtQExERNRYGmHro3d4T854OQ4ivCkaTgCt6E4wmASG+Ksx7Ogy923tau0UiIqIWRWbtBmxF7/ae6NXWAzkFOpRU1cLdUY5OWhXPvBAREVkBA8wdkEolCPNTW7sNIiKiFo8fIREREZHNYYAhIiIim8MAQ0RERDaHAYaIiIhsDgMMERER2RwGGCIiIrI5DDBERERkcxhgiIiIyOYwwBAREZHNabZ34hUEAQCg0+ms3AkRERHV19X37avv4zfTbANMeXk5AMDf39/KnRAREdGdKi8vh1p98+V7JMLtIo6NMplMKCgogIuLCySS5rfgok6ng7+/P86cOQOVSmXtdqyOx6MOj4U5Ho86PBbmeDzqNKVjIQgCysvLodVqIZXe/EqXZnsGRiqVws/Pz9ptNDiVSmX1H7amhMejDo+FOR6POjwW5ng86jSVY3GrMy9X8SJeIiIisjkMMERERGRzGGBslEKhwFtvvQWFQmHtVpoEHo86PBbmeDzq8FiY4/GoY4vHotlexEtERETNF8/AEBERkc1hgCEiIiKbwwBDRERENocBpgmZP38+7r//fri4uMDb2xtDhgxBbm6u2Zjq6mrExcXBw8MDzs7OGDZsGIqKiszG5OfnY9CgQXB0dIS3tzemTJkCg8HQmLticQsWLIBEIkFCQoJYa2nH4ty5c3jxxRfh4eEBpVKJsLAwHDhwQNwuCAJmzZoFX19fKJVKREVFIS8vz+w1SkpKEBsbC5VKBVdXV4waNQoVFRWNvSv3xGg0YubMmQgMDIRSqUS7du3wzjvvmN12vDkfi6SkJAwePBharRYSiQQbN240226pfc/MzMSDDz4IBwcH+Pv7Y+HChQ29a3flVsdDr9dj6tSpCAsLg5OTE7RaLV566SUUFBSYvUZzOR63+9m41tixYyGRSLBkyRKzuk0dC4GajOjoaOGLL74QsrOzhfT0dGHgwIFCQECAUFFRIY4ZO3as4O/vL2zbtk04cOCA0KtXL6F3797idoPBIHTu3FmIiooS0tLShJ9++knw9PQUpk+fbo1dsoh9+/YJbdq0Ebp06SJMmDBBrLekY1FSUiK0bt1aePnll4WUlBThxIkTwi+//CIcO3ZMHLNgwQJBrVYLGzduFDIyMoQnn3xSCAwMFK5cuSKO6d+/v9C1a1dh7969wq5du4T27dsLzz//vDV26a7NnTtX8PDwELZs2SKcPHlSWLduneDs7CwsXbpUHNOcj8VPP/0kzJgxQ1i/fr0AQNiwYYPZdkvse1lZmaDRaITY2FghOztb+PbbbwWlUil89tlnjbWb9Xar41FaWipERUUJa9asEY4cOSIkJycLPXr0ELp37272Gs3leNzuZ+Oq9evXC127dhW0Wq2wePFis222dCwYYJqw4uJiAYCwc+dOQRD+/GW0t7cX1q1bJ445fPiwAEBITk4WBOHPH2CpVCoUFhaKY5YvXy6oVCqhpqamcXfAAsrLy4UOHToIiYmJwkMPPSQGmJZ2LKZOnSr06dPnpttNJpPg4+MjvP/++2KttLRUUCgUwrfffisIgiAcOnRIACDs379fHPPzzz8LEolEOHfuXMM1b2GDBg0SXnnlFbPa0KFDhdjYWEEQWtaxuP5NylL7/umnnwpubm5mvydTp04VgoKCGniP7s2t3rSv2rdvnwBAOH36tCAIzfd43OxYnD17VmjVqpWQnZ0ttG7d2izA2Nqx4EdITVhZWRkAwN3dHQCQmpoKvV6PqKgocUxwcDACAgKQnJwMAEhOTkZYWBg0Go04Jjo6GjqdDjk5OY3YvWXExcVh0KBBZvsMtLxjsWnTJkRERGD48OHw9vZGt27d8Pnnn4vbT548icLCQrPjoVar0bNnT7Pj4erqioiICHFMVFQUpFIpUlJSGm9n7lHv3r2xbds2HD16FACQkZGB3bt3Y8CAAQBa1rG4nqX2PTk5GX379oVcLhfHREdHIzc3F5cvX26kvWkYZWVlkEgkcHV1BdCyjofJZMKIESMwZcoUdOrU6S/bbe1YNNu1kGydyWRCQkICHnjgAXTu3BkAUFhYCLlcLv7iXaXRaFBYWCiOufYN++r2q9tsyerVq3Hw4EHs37//L9ta2rE4ceIEli9fjokTJ+KNN97A/v378eqrr0Iul2PkyJHi/txof689Ht7e3mbbZTIZ3N3dbep4TJs2DTqdDsHBwbCzs4PRaMTcuXMRGxsLAC3qWFzPUvteWFiIwMDAv7zG1W1ubm4N0n9Dq66uxtSpU/H888+L6/20pOPx3nvvQSaT4dVXX73hdls7FgwwTVRcXByys7Oxe/dua7diFWfOnMGECROQmJgIBwcHa7djdSaTCREREZg3bx4AoFu3bsjOzsaKFSswcuRIK3fXuNauXYuvv/4a33zzDTp16oT09HQkJCRAq9W2uGNB9afX6/Hss89CEAQsX77c2u00utTUVCxduhQHDx6ERCKxdjsWwY+QmqD4+Hhs2bIFO3bsMFtR28fHB7W1tSgtLTUbX1RUBB8fH3HM9TNxrj6+OsYWpKamori4GPfddx9kMhlkMhl27tyJZcuWQSaTQaPRtJhjAQC+vr4IDQ01q4WEhCA/Px9A3f7caH+vPR7FxcVm2w0GA0pKSmzqeEyZMgXTpk1DTEwMwsLCMGLECLz22muYP38+gJZ1LK5nqX1vTr87QF14OX36NBITE81WW24px2PXrl0oLi5GQECA+Df19OnTmDRpEtq0aQPA9o4FA0wTIggC4uPjsWHDBmzfvv0vp+m6d+8Oe3t7bNu2Tazl5uYiPz8fkZGRAIDIyEhkZWWZ/RBe/YW9/g2wKXvssceQlZWF9PR08SsiIgKxsbHif7eUYwEADzzwwF+m1B89ehStW7cGAAQGBsLHx8fseOh0OqSkpJgdj9LSUqSmpopjtm/fDpPJhJ49ezbCXlhGVVUVpFLzP112dnYwmUwAWtaxuJ6l9j0yMhJJSUnQ6/XimMTERAQFBdnMxyVXXQ0veXl5+O233+Dh4WG2vaUcjxEjRiAzM9Psb6pWq8WUKVPwyy+/ALDBY9Holw3TTY0bN05Qq9XC77//Lpw/f178qqqqEseMHTtWCAgIELZv3y4cOHBAiIyMFCIjI8XtV6cO9+vXT0hPTxe2bt0qeHl52eTU4etdOwtJEFrWsdi3b58gk8mEuXPnCnl5ecLXX38tODo6Cl999ZU4ZsGCBYKrq6vwww8/CJmZmcJTTz11w+mz3bp1E1JSUoTdu3cLHTp0sImpw9caOXKk0KpVK3Ea9fr16wVPT0/h9ddfF8c052NRXl4upKWlCWlpaQIAYdGiRUJaWpo4q8YS+15aWipoNBphxIgRQnZ2trB69WrB0dGxyU0bFoRbH4/a2lrhySefFPz8/IT09HSzv6vXzqJpLsfjdj8b17t+FpIg2NaxYIBpQgDc8OuLL74Qx1y5ckX45z//Kbi5uQmOjo7C008/LZw/f97sdU6dOiUMGDBAUCqVgqenpzBp0iRBr9c38t5Y3vUBpqUdi82bNwudO3cWFAqFEBwcLKxcudJsu8lkEmbOnCloNBpBoVAIjz32mJCbm2s25tKlS8Lzzz8vODs7CyqVSvjb3/4mlJeXN+Zu3DOdTidMmDBBCAgIEBwcHIS2bdsKM2bMMHtDas7HYseOHTf8OzFy5EhBECy37xkZGUKfPn0EhUIhtGrVSliwYEFj7eIdudXxOHny5E3/ru7YsUN8jeZyPG73s3G9GwUYWzoWXI2aiIiIbA6vgSEiIiKbwwBDRERENocBhoiIiGwOAwwRERHZHAYYIiIisjkMMERERGRzGGCIiIjI5jDAEBERkc1hgCGiJmnVqlVwdXW1dhtE1ETxTrxE1CRduXIF5eXl8Pb2rvdzHn74YYSHh2PJkiUN1xgRNQkyazdARHQjSqUSSqXS2m0QURPFj5CIqEE8/PDDiI+PR3x8PNRqNTw9PTFz5kxcPel7+fJlvPTSS3Bzc4OjoyMGDBiAvLw88fnXf4Q0e/ZshIeH47///S/atGkDtVqNmJgYlJeXAwBefvll7Ny5E0uXLoVEIoFEIsGpU6du2ePbb78NrVaLS5cuibVBgwbhkUcegclkstzBICKLY4Ahogbz5ZdfQiaTYd++fVi6dCkWLVqEf/3rXwD+DBwHDhzApk2bkJycDEEQMHDgQOj1+pu+3vHjx7Fx40Zs2bIFW7Zswc6dO7FgwQIAwNKlSxEZGYnRo0fj/PnzOH/+PPz9/W/Z34wZM9CmTRv8/e9/BwB88skn2LNnD7788ktIpfzzSNSU8SMkImow/v7+WLx4MSQSCYKCgpCVlYXFixfj4YcfxqZNm/DHH3+gd+/eAICvv/4a/v7+2LhxI4YPH37D1zOZTFi1ahVcXFwAACNGjMC2bdswd+5cqNVqyOVyODo6wsfHp1792dnZ4auvvkJ4eDimTZuGZcuW4V//+hcCAgIscwCIqMHwfzGIqMH06tULEolEfBwZGYm8vDwcOnQIMpkMPXv2FLd5eHggKCgIhw8fvunrtWnTRgwvAODr64vi4uJ76rFt27b44IMP8N577+HJJ5/ECy+8cE+vR0SNgwGGiGyGvb292WOJRGKRa1WSkpJgZ2eHU6dOwWAw3PPrEVHDY4AhogaTkpJi9njv3r3o0KEDQkNDYTAYzLZfunQJubm5CA0NvevvJ5fLYTQa7+g5a9aswfr16/H7778jPz8f77zzzl1/fyJqPAwwRNRg8vPzMXHiROTm5uLbb7/FRx99hAkTJqBDhw546qmnMHr0aOzevRsZGRl48cUX0apVKzz11FN3/f3atGmDlJQUnDp1ChcvXrzt2ZmzZ89i3LhxeO+999CnTx988cUXmDdvHvbu3XvXPRBR42CAIaIG89JLL+HKlSvo0aMH4uLiMGHCBIwZMwYA8MUXX6B79+544oknEBkZCUEQ8NNPP/3lY6I7MXnyZNjZ2SE0NBReXl7Iz8+/6VhBEPDyyy+jR48eiI+PBwBER0dj3LhxePHFF1FRUXHXfRBRw+OdeImoQfCuuETUkHgGhoiIiGwOAwwRNVtjx46Fs7PzDb/Gjh1r7faI6B7wIyQiaraKi4uh0+luuE2lUt3RQpFE1LQwwBAREZHN4UdIREREZHMYYIiIiMjmMMAQERGRzWGAISIiIpvDAENEREQ2hwGGiIiIbA4DDBEREdkcBhgiIiKyOf8PWIlXmuoVSzsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9807764683415322" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a BayesianRidge regression model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge())\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([132.12340639, 118.80427889, 107.18570023, 584.37931972,\n", + " 55.32894366, 113.06673122, 108.46759432, 404.01956916,\n", + " 133.79882581, 666.91806681, 611.82046055, 690.51898517,\n", + " 92.04670205, 331.7968981 , 664.92313111, 138.52862798,\n", + " 118.04996274, 372.05949062, 569.34591881, 591.12151365,\n", + " 691.87883843, 597.86874749, 89.08714835, 621.14191144,\n", + " 132.42907122, 123.71929895, 579.0385398 , 577.78356325,\n", + " 391.55978633, 378.6291887 , 667.26910094, 597.3563262 ,\n", + " 112.20824719, 607.24360219, 349.05181173, 693.42266845,\n", + " 616.87130752, 363.08588597, 355.63337588, 102.36857756,\n", + " 122.97913791, 381.82980231, 105.89876107, 640.37760946,\n", + " 631.23707916, 107.08700151, 139.99199684, 367.50676851,\n", + " 662.90861326, 107.11125099, 127.78533248, 629.48116724,\n", + " 672.92277948, 327.52119155, 374.37264241, 139.99554413,\n", + " 584.03031899, 570.55413896, 115.16865657, 132.91510743,\n", + " 114.9261265 , 46.57813741, 105.33836445, 658.81785502,\n", + " 596.4682104 , 314.40835399, 658.47900016, 382.61438396,\n", + " 123.75701408, 370.08808346, 639.77965888, 671.05014514,\n", + " 107.33669023, 607.84471345, 375.52104464, 118.42280938,\n", + " 356.1803266 , 131.73973953, 629.10238712, 574.73909466,\n", + " 115.87656638, 119.97824027, 584.19111053, 120.34561497,\n", + " 84.84590204, 117.22686479, 359.94544305, 591.23776 ,\n", + " 89.58811998, 652.76651994, 571.59095678, 106.78564414,\n", + " 570.36974963, 127.18764447, 405.10603042, 132.30860944,\n", + " 117.79194372, -8.00932547, 358.20836991, 372.7539651 ,\n", + " 136.69388866, 616.62994315, 137.89260653, 128.42955213,\n", + " 590.48340792, 149.17342776, 569.75547184, 575.86955574,\n", + " 594.39461337, 656.4849587 , 403.47001831, 96.76905711,\n", + " 365.31062782, 288.31895272, 106.40295977, 144.71731494,\n", + " 598.58882764, 93.02575583, 355.11853112, 81.30063804,\n", + " 107.73593931, 645.6814726 , 361.46851908, 104.36884116,\n", + " 127.10531354, 107.0662245 , 630.80916657, 659.6563803 ,\n", + " 330.60330595, 94.77913931, 638.69201945, 664.52233694,\n", + " 576.93161606, 353.89590043, 126.13345747, 634.1731852 ,\n", + " 402.70130592, 38.82835491, 569.83611366, 375.6834875 ,\n", + " 372.64214934, 598.73206049, 393.9367473 , 105.65590744])" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB4V0lEQVR4nO3dd3hUVf7H8fdMeiEVkhBIIPQAAREUAiqCgVBEFNSVRQRlrRQRC+Cigiiw9i7qTwFdlV0VXEVF6UoR6dJrICiEUFJJT+7vj2sGhgQIEDKT4fN6nnlgzrn3zrk3U773VIthGAYiIiIiLsrq6AKIiIiIXEoKdkRERMSlKdgRERERl6ZgR0RERFyagh0RERFxaQp2RERExKUp2BERERGXpmBHREREXJqCHREREXFpCnZEnMiSJUuwWCwsWbLE0UU5p/r16zNkyBDbc2cs++llrAozZszAYrGwb9++Kn3ds3nxxRdp0KABbm5uXHHFFY4ujtOYMGECFovF0cWQKqBgRxzKYrFU6OFMP6DOoPQHtfTh7e1NkyZNGD58OIcPH3Z08c7L999/z4QJExxahlOvpdVqJTIyku7du1f5+27r1q1MmDChUgOln376iSeeeIJOnToxffp0Jk+eXGnHPl1eXh6NGjWiWbNmFBQUlMnv2bMngYGBHDx48JKVoTKlp6dTu3ZtOnXqRHkrK/36669YrVYef/xxB5ROzoe7owsgl7dPPvnE7vnHH3/M/Pnzy6THxsZWZbGqjWeffZaYmBjy8vJYtmwZ7777Lt9//z2bN2/G19e3Ssty3XXXkZubi6en53nt9/333/P22287PODp1q0bd911F4ZhkJSUxDvvvEPXrl357rvv6NmzZ4WPM2jQIO644w68vLzOuwxbt25l4sSJXH/99dSvX/+89y/PokWLsFqtfPjhh+f9tzlf3t7evPvuu3Tv3p0pU6bwzDPP2PJmzZrFvHnzePPNN4mMjLyk5agsQUFBvPbaa9xxxx188MEH3Hfffba8oqIiHnjgAerVq8fEiRMdWEqpCAU74lB33nmn3fNff/2V+fPnl0k/XU5OTpX/mDujnj170q5dOwD+8Y9/EBoayiuvvML//vc/BgwYUO4+J06cwM/Pr9LLYrVa8fb2rvTjVpUmTZrYve9uueUWWrVqxWuvvXZewY6bmxtubm6XoogXJDU1FR8fn0oLdAzDIC8vDx8fn3Lzu3Xrxt///nemTJnCgAEDaNKkCenp6TzyyCNcddVVPPTQQ5VSjqryt7/9jZkzZzJ27Fj69u1LeHg4AK+//jobN27k+++/13dRNaBmLHF6119/PS1btmTt2rVcd911+Pr68uSTTwJm80N5NQLl9dVIT09n1KhRREVF4eXlRaNGjfjXv/5FSUnJOcvwv//9j969exMZGYmXlxcNGzZk0qRJFBcXl1vWrVu30qVLF3x9falTpw4vvPBCmWP+8ccf3Hzzzfj5+REWFsYjjzxCfn5+xS9MObp27QpAUlISAEOGDMHf3589e/bQq1cvatSowcCBAwEoKSnhtddeo0WLFnh7exMeHs79999PWlqa3TENw+C5556jbt26+Pr60qVLF7Zs2VLmtc/UZ2fVqlX06tWL4OBg/Pz8aNWqFa+//rqtfG+//TZg35RUqrLLeD7i4uKoWbOm7VqCWUty7bXX4ufnR1BQEH379mXbtm12+5XXZ6d+/frceOONLFu2jKuvvhpvb28aNGjAxx9/bLffbbfdBkCXLl3KNOGuWbOGxMREatasiY+PDzExMdxzzz1nPQeLxcL06dM5ceKE7XgzZswAzJqJSZMm0bBhQ7y8vKhfvz5PPvlkmfdgadl//PFH2rVrh4+PD++9995ZX/fVV1/F19eXBx54AICxY8dy5MgR3nvvPazWc//svPTSS3Ts2JHQ0FB8fHxo27YtX375ZbnnN3z4cL7++mtatmyJl5cXLVq0YN68eWW2XbZsGVdddRXe3t40bNjwnOdwqnfeeYf8/HxGjx4NwIEDB5gwYQJ/+9vfzisQFsdRzY5UC8eOHaNnz57ccccd3Hnnnba7q4rKycmhc+fO/Pnnn9x///1ER0ezYsUKxo0bx6FDh3jttdfOuv+MGTPw9/dn9OjR+Pv7s2jRIp5++mkyMzN58cUX7bZNS0ujR48e9OvXj9tvv50vv/ySMWPGEBcXZ/tizM3N5YYbbiA5OZmRI0cSGRnJJ598wqJFi87rvE63Z88eAEJDQ21pRUVFJCYmcs011/DSSy/Z7kLvv/9+ZsyYwd13383IkSNJSkrirbfeYv369SxfvhwPDw8Ann76aZ577jl69epFr169WLduHd27dy+3T8bp5s+fz4033kjt2rV5+OGHiYiIYNu2bcydO5eHH36Y+++/n4MHD5bbdFlVZTyTtLQ00tLSaNSoEQALFiygZ8+eNGjQgAkTJpCbm8ubb75Jp06dWLdu3TmbnXbv3s2tt97K0KFDGTx4MB999BFDhgyhbdu2tGjRguuuu46RI0fyxhtv8OSTT9qabmNjY0lNTaV79+7UqlWLsWPHEhQUxL59+5g9e/ZZX/OTTz7h/fff57fffuP//u//AOjYsSNg1gTOnDmTW2+9lUcffZRVq1YxZcoUtm3bxpw5c+yOs2PHDgYMGMD999/PvffeS9OmTc/6umFhYUydOpX777+fESNG8P777zNq1CjatGlz1v1Kvf7669x0000MHDiQgoICZs2axW233cbcuXPp3bu33bbLli1j9uzZPPTQQ9SoUYM33niD/v37k5ycbPscbNq0yXb9JkyYQFFREc8880yFv0fq16/PxIkTefzxxxkyZAjvvPMO7u7u5/zeECdiiDiRYcOGGae/LTt37mwAxrRp08psDxjPPPNMmfR69eoZgwcPtj2fNGmS4efnZ+zcudNuu7Fjxxpubm5GcnLyWcuVk5NTJu3+++83fH19jby8vDJl/fjjj21p+fn5RkREhNG/f39b2muvvWYAxn//+19b2okTJ4xGjRoZgLF48eKzlmf69OkGYCxYsMA4cuSIceDAAWPWrFlGaGio4ePjY/zxxx+GYRjG4MGDDcAYO3as3f6//PKLARiffvqpXfq8efPs0lNTUw1PT0+jd+/eRklJiW27J5980gDsrvHixYvtyl5UVGTExMQY9erVM9LS0uxe59Rjlfc3v1RlPBPAGDp0qHHkyBEjNTXVWLVqlXHDDTcYgPHyyy8bhmEYV1xxhREWFmYcO3bMtt/GjRsNq9Vq3HXXXba00r9NUlKSLa1evXoGYPz888+2tNTUVMPLy8t49NFHbWlffPFFuX//OXPmGICxevXqc57L6QYPHmz4+fnZpW3YsMEAjH/84x926Y899pgBGIsWLSpT9nnz5p3X65aUlBidOnUyACMqKsrIysqq8L6nf94KCgqMli1bGl27drVLBwxPT09j9+7dtrSNGzcagPHmm2/a0m6++WbD29vb2L9/vy1t69athpubW7nvvfIUFhYaV1xxhRESEmIAxnvvvVfh8xHHUzOWVAteXl7cfffdF7z/F198wbXXXktwcDBHjx61PRISEiguLubnn38+6/6n9k/Iysri6NGjXHvtteTk5LB9+3a7bf39/e36fnh6enL11Vezd+9eW9r3339P7dq1ufXWW21pvr6+dh0gKyIhIYFatWoRFRXFHXfcgb+/P3PmzKFOnTp22z344IN2z7/44gsCAwPp1q2b3fVo27Yt/v7+LF68GDBrMwoKChgxYoRd89KoUaPOWbb169eTlJTEqFGjCAoKssuryHDfqijjqT788ENq1apFWFgY7du3Z/ny5YwePZpRo0Zx6NAhNmzYwJAhQwgJCbHt06pVK7p168b3339/zuM3b96ca6+91va8Vq1aNG3a1O59cSal12/u3LkUFhae13mVp7S8pc0ypR599FEAvvvuO7v0mJgYEhMTz+s1LBaL7VrFx8fj7+9f4X1P/bylpaWRkZHBtddey7p168psm5CQQMOGDW3PW7VqRUBAgO26FhcX8+OPP3LzzTcTHR1t2y42Nva8zsnd3Z3333+f48eP06FDB+69994K7yuOp2YsqRbq1KlzUR0sd+3axe+//06tWrXKzU9NTT3r/lu2bGH8+PEsWrSIzMxMu7yMjAy753Xr1i3zYx4cHMzvv/9ue75//34aNWpUZrtzNQ+c7u2336ZJkya4u7sTHh5O06ZNy/SJcHd3p27dunZpu3btIiMjg7CwsHKPW3o99u/fD0Djxo3t8mvVqkVwcPBZy1bapNayZcuKn1AVl/FUffv2Zfjw4VgsFmrUqEGLFi1sHblLX6O8v09sbCw//vjjOTt+n/pDWyo4OLhM/6PydO7cmf79+zNx4kReffVVrr/+em6++Wb+/ve/X9Cor/3792O1Wm1NdKUiIiIICgqynW+pmJiY836N2bNn8+2339KyZUu++OILhg8fbhfsnc3cuXN57rnn2LBhg10fovKC5HNd1yNHjpCbm1vm/QHm37MigWqpq666CoC2bdtqfp5qRsGOVAtnGvlxJqd3HC4pKaFbt2488cQT5W7fpEmTMx4rPT2dzp07ExAQwLPPPkvDhg3x9vZm3bp1jBkzpkwH5zONxDHKmafjYl199dW20Vhn4uXlVSYAKikpISwsjE8//bTcfc4UFFalqi5j3bp1SUhIqNRjnupi3hcWi4Uvv/ySX3/9lW+//ZYff/yRe+65h5dffplff/31vGpNTj9uRZzv5y8rK4uRI0fStm1bFi9eTKtWrXjwwQdZv369rZ/Vmfzyyy/cdNNNXHfddbzzzjvUrl0bDw8Ppk+fzmeffVZm+6r8vEn1pWBHqrXg4GDS09Pt0goKCjh06JBdWsOGDcnOzr6gH7MlS5Zw7NgxZs+ezXXXXWdLP3WUzvmqV68emzdvxjAMux+cHTt2XPAxz0fDhg1ZsGABnTp1OusPWb169QCzlqVBgwa29CNHjpyzRqK0aWHz5s1nve5n+sGtijJWVOlrlPf32b59OzVr1qyU4fznCj46dOhAhw4deP755/nss88YOHAgs2bN4h//+Md5vU69evUoKSlh165ddnNYHT58mPT0dNv5Xqjx48dz6NAh/ve//1GjRg3efPNN+vTpw8svv8zYsWPPuu9XX32Ft7c3P/74o12t1fTp0y+oLLVq1cLHx4ddu3aVyauqz5s4nvrsSLXWsGHDMv1t3n///TI1O7fffjsrV67kxx9/LHOM9PR0ioqKzvgapXeOp94pFhQU8M4771xwuXv16sXBgwfthtPm5OTw/vvvX/Axz8ftt99OcXExkyZNKpNXVFRkCyATEhLw8PDgzTfftDv/ioxCufLKK4mJieG1114rE5CeeqzSIOH0baqijBVVu3ZtrrjiCmbOnGlXzs2bN/PTTz/Rq1evSnmdM12LtLS0MjUVpcs+XMh0BaXlPf0avfLKKwBlRjydj7Vr1/L2228zfPhw2rZtC8CNN97ILbfcwqRJk8o0kZ3Ozc0Ni8Vi9xnet28fX3/99QWVx83NjcTERL7++muSk5Nt6du2bSv3+0Bck2p2pFr7xz/+wQMPPED//v3p1q0bGzdu5Mcff6RmzZp22z3++ON888033HjjjbbhvidOnGDTpk18+eWX7Nu3r8w+pTp27EhwcDCDBw9m5MiRWCwWPvnkk4uqJr/33nt56623uOuuu1i7di21a9fmk08+qbLJyTp37sz999/PlClT2LBhA927d8fDw4Ndu3bxxRdf8Prrr3PrrbdSq1YtHnvsMaZMmcKNN95Ir169WL9+PT/88MMZr1cpq9XKu+++S58+fbjiiiu4++67qV27Ntu3b2fLli22H5rSH8SRI0eSmJiIm5sbd9xxR5WU8Xy8+OKL9OzZk/j4eIYOHWobeh4YGFhpsz9fccUVuLm58a9//YuMjAy8vLzo2rUrn332Ge+88w633HILDRs2JCsriw8++ICAgIALCrRat27N4MGDef/9923NtL/99hszZ87k5ptvpkuXLhdU/uLiYu677z4iIiJ47rnn7PJef/11mjdvzogRI/jmm2/OeIzevXvzyiuv0KNHD/7+97+TmprK22+/TaNGjez6vZ2PiRMnMm/ePK699loeeughioqKePPNN2nRosUFH1OqGUcNAxMpz5mGnrdo0aLc7YuLi40xY8YYNWvWNHx9fY3ExERj9+7dZYaeG4ZhZGVlGePGjTMaNWpkeHp6GjVr1jQ6duxovPTSS0ZBQcFZy7V8+XKjQ4cOho+PjxEZGWk88cQTxo8//lhmmPCZyjp48GCjXr16dmn79+83brrpJsPX19eoWbOm8fDDD9uGVVd06Pm5hiKXN+z4VO+//77Rtm1bw8fHx6hRo4YRFxdnPPHEE8bBgwdt2xQXFxsTJ040ateubfj4+BjXX3+9sXnz5jLX+PSh56WWLVtmdOvWzahRo4bh5+dntGrVym5YcFFRkTFixAijVq1ahsViKfP3r8wynglgDBs27JzbLViwwOjUqZPh4+NjBAQEGH369DG2bt1qt82Zhp737t27zPE6d+5sdO7c2S7tgw8+MBo0aGAbFr148WJj3bp1xoABA4zo6GjDy8vLCAsLM2688UZjzZo15yzzmd4DhYWFxsSJE42YmBjDw8PDiIqKMsaNG2c3lcLZyl6eV1991QCML7/8stz8l156yQCM2bNnn/U4H374odG4cWPDy8vLaNasmTF9+nTjmWeeKfPeONPfrby/+9KlS422bdsanp6eRoMGDYxp06aVe8xzqeh7RZyLxTDUi0tERERcl/rsiIiIiEtTsCMiIiIuTcGOiIiIuDQFOyIiIuLSFOyIiIiIS1OwIyIiIi5NkwpirsFz8OBBatSoocXdREREqgnDMMjKyiIyMrLMGoCnUrADHDx4kKioKEcXQ0RERC7AgQMHqFu37hnzFewANWrUAMyLFRAQ4ODSiIiISEVkZmYSFRVl+x0/EwU7nFxpOCAgQMGOiIhINXOuLijqoCwiIiIuTcGOiIiIuDQFOyIiIuLSFOyIiIiIS1OwIyIiIi5NwY6IiIi4NIcGO/Xr18disZR5DBs2DIC8vDyGDRtGaGgo/v7+9O/fn8OHD9sdIzk5md69e+Pr60tYWBiPP/44RUVFjjgdERERcUIODXZWr17NoUOHbI/58+cDcNtttwHwyCOP8O233/LFF1+wdOlSDh48SL9+/Wz7FxcX07t3bwoKClixYgUzZ85kxowZPP300w45HxEREXE+FsMwDEcXotSoUaOYO3cuu3btIjMzk1q1avHZZ59x6623ArB9+3ZiY2NZuXIlHTp04IcffuDGG2/k4MGDhIeHAzBt2jTGjBnDkSNH8PT0rNDrZmZmEhgYSEZGhiYVFBERqSYq+vvtNH12CgoK+Pe//80999yDxWJh7dq1FBYWkpCQYNumWbNmREdHs3LlSgBWrlxJXFycLdABSExMJDMzky1btlT5OYiIiIjzcZrlIr7++mvS09MZMmQIACkpKXh6ehIUFGS3XXh4OCkpKbZtTg10SvNL884kPz+f/Px82/PMzMxKOAMREZHLTF4GpCdDXiZ4B0BQNHgHOrpUZThNsPPhhx/Ss2dPIiMjL/lrTZkyhYkTJ17y1xERB8tKgWO7ITcdfIIhtCHUiHB0qURcw/EkWPUepCWdTAtuAO3vg5AYx5WrHE7RjLV//34WLFjAP/7xD1taREQEBQUFpKen2217+PBhIiIibNucPjqr9HnpNuUZN24cGRkZtseBAwcq6UxExGmkbIJvRsIXQ2DuKPhiMHzzsJkuIhcnL6NsoAOQthdWvW/mOxGnCHamT59OWFgYvXv3tqW1bdsWDw8PFi5caEvbsWMHycnJxMfHAxAfH8+mTZtITU21bTN//nwCAgJo3rz5GV/Py8vLtsK5VjoXcUFZKbBwEhxcZ59+cC0sfM7MF5ELl55cNtAplbbXzHciDm/GKikpYfr06QwePBh395PFCQwMZOjQoYwePZqQkBACAgIYMWIE8fHxdOjQAYDu3bvTvHlzBg0axAsvvEBKSgrjx49n2LBheHl5OeqURMTRju0uG+iUOrjWzFdzlsiFyztHX9dz5Vcxhwc7CxYsIDk5mXvuuadM3quvvorVaqV///7k5+eTmJjIO++8Y8t3c3Nj7ty5PPjgg8THx+Pn58fgwYN59tlnq/IURMTZ5KZfXL6InJ33OVpEzpVfxZxqnh1H0Tw7Ii5m3zKzr86Z3DYD6l9TVaURcT15GbB4qtlkdbrgBtBlbJWMyqp28+yIiFSa0EYQ2bb8vMi2Zr6IXDjvQHPUVXAD+/TgBtD+fqcbfu7wZiwRkUpXIwJuGG92Rj649mR6ZFszXf11RC5eSIxZg6N5dkREHCQiDm56/ZR5doLMGh0FOiKVxzvQ/Kw5OQU7IuKa8jLgxBHAAsH1nPaOU0QuPQU7IuJ6Tp3Z1eoBka0hoC741YIa4Qp8RC4zCnZExLWcOrOr1QMadoXNX8LhLeDpDzUbQ61Yp5zSXkQuDY3GEhHXcurMrmHNYMO/4cAqKDgB2YfhxFE4vNkpp7QXkUtDNTsi4lpKZ24tOAEefhBUHxolQHEhuHmaAc7uBeDmYQZG1aBzpYhcHAU7IuJavAPMwKYwB0IawJavzEepuldD67/Bvl+cbkp7kWolL6NaDDsHBTsi4mqCos3RVxF9Yds3cGwPePhCSTEYxXBoIxTlQaMbnG5Ke5Fq4/g+2LvY/EwV5YO7N/yxFhpcDyH1HVy4shTsiIhr8Q6E1n+Hg+vNZSOsbpCfBVZ3M+jJz4KUTXDVvWZgJCLnJy/DXGh39wI4vheMErBYzZpU7wDwDXa6Gh51UBYR15KXAes/hsIT5qNGpPnFW1JiNm25eZoTDPrXdLovZJFqIeOgWWuaus1sMi59pG6Dbd+a+U5GNTsi4lrSkyHjT6jZ1AxwjieBbwiE1ATDMO88LVbwUqAjckFyjsKRneaNRWEuYAAW8PCBIzvMfCejYEdEXEtepjnSKi8DajWD1C1m01V+JmABT19zgsHQho4uqUj1VJBlzk6elw4lRSfTi/PNG4mCLIcV7UzUjCUirqW003HqNrji7xDewvwSLi6E4gKzxkeLgYpcOHcfyD1uH+iA+Tz3uJnvZFSzIyKuJSgaghtA2l7YuwQad4e4280RWL6h0KCzOiaLXAyrB4Q1h5Tfy+aFNTfznYxqdkTEtXgHmktBhDYx18Tyq2kOj/WPgHqdFOiIXCyjGJr1hohWgOXkI6KVmW4UO7iAZalmR0RcT0gMXHknrHwLdv4Ebu5m1fqhjVoTS+Ri+dWC5F/NNeaa9oKSQrM253iSmR57k6NLWIaCHRFxPXkZsGY6ZKeaI7FKpe0118TqMlbDzkUuRF4GYIFWf4P1n8CunwADLBYIbwlt74bAOo4uZRkKdkTE9Zy6GOjp0vZqTSyRC3E8CVa9Bxl/QJMe0DgRrr7PnFSwuAA8/SGshVPeSCjYERHXc641r7Qmlsj5ycswA520JLPJymIxl4X4/b9wZLs53QNWiGoPnUY6XVOxgh0RcT3nWvNKa2KJnJ/0A+AXCjUbmetguXuZa2HtXWpO7eDpZy7Jsmeh2Ueu63inquFRsCMirufU4eenC26gEVki56vgBCT9AqlbzQ7KxYXmjMkdh8GKt83ZycFckuXwZqdrKlaw42B7UrP5Mz2XzNxCAn08iAzyoWGYv6OLJVK9eQdC+/sp2rUAS0mhOceOhw+GxR33xt2c6o5TxOnlZcCGf5uBDph9dIwSjJRN5tMmibB7ASUlBu5WCxQXOV1TsYIdB1qfnMbUH7azKum4La19TAhjezajTXSwA0smUv3l5WTAnqVYju7EYpRgWKwYNZtSVOcqvEPqObp4ItVH6Xpznn5mfx13bww3T/DwhaO7oNmNFO+cT6FRjOHmhoebu9M1FWtSQQfZk5pdJtABWJV0nKk/bGdParaDSiZS/WWm/kHRTxPJ2rGEzGMHyTieQuaxg2TtWEzR/GfJTP3D0UUUqT5K15sLiIS8dIyjuyDnOFitUFIAFitGQS7uRTkY7l4UhDZ3uqZiBTsO8md6bplAp9SqpOP8mZ5bxSUScR2emXvJ3b+63Lzcfb/hmVlOXx4RKZ93gNlHJ+NPMAyzVic/05yV3MMXjBIs/jXN/0e1J7fNPU7XVKxmLAfJzC28qHwROTNLbvpF5YvIKYKizYkCU34HiwXDMCjKz8FafBBrzDUQ2gRr4vOcyC/kz6QdBOXk4Fyhjmp2HCbA5+wLpZ0rX0TOzPAJuqh8ETmFdyBccae5yCdgwcAKWGo2xqjTjsLvniDnaDJrvpnGn78vwqvI+bphqGbHQeoE+dA+JqTcpqz2MSHUCfJxQKlEXENBQAN86l9N7r7fyuT51L+agoAGeDugXCLVlqcfxFwLzW+C/GwshbmUHNlNyS+vQVE+npZimoW6kZJrxdM/yNGlLUM1Ow7SMMyfsT2b0T4mxC69fUwI43o10/BzkYsQEFYX925P4VP/art0n/pX497taQLC6jqoZCLVUF6GOWGgXzgUZGN188SS8Qfs/AGK8rFEtCQ/dTf+uQeJjW2Jf3gDR5e4DIthlM4EdPnKzMwkMDCQjIwMAgKqdrjcqfPsBPh4UEfz7IhUmszUP/DM3IslNx3DJ4iCgAYKdETOx/F98OcaWPcJ5ByBzIOQnw1121HSKAF2/kRJ4+6UrPo/rLUa4574LES2qbLiVfT3W81YDtYwzF/BjcglEhBWF04JbtR0JXIe8jJg72LYMgcObwFKwDsIfEMh5zjWIzsh4WmsKZuhy2OQmwaFzjmSWMGOiIiIlJWeDEYxpGwygxijGIoKzKUjLBawWM1FQPcuPrlPkx6OK+9ZKNgREdeWlQLHdkNuOvgEQ2hDqBHh6FKJOL+8TCjMOxnogLkGltUdSoqgpNDML+XE684p2BER15WyCRZOgoPrTqZFtoUbxjvVIoUiTsk7wAxsSgMdMAMcdy8oArCY+WAGOu3vd7rJBEtpNJaIuKaslLKBDsDBtbDwOTNfRM4sKNpc9DO8pX16cYFZSxrZxszrOh66jIWQ+g4pZkU4PNj5888/ufPOOwkNDcXHx4e4uDjWrFljyzcMg6effpratWvj4+NDQkICu3btsjvG8ePHGThwIAEBAQQFBTF06FCys51vUiMRqULHdpcNdEodXGvmi8iZeQdCdDy0udM+4PH0h3qdoP614BNk1pI6aY1OKYc2Y6WlpdGpUye6dOnCDz/8QK1atdi1axfBwSdX/H7hhRd44403mDlzJjExMTz11FMkJiaydetWvL3NsRUDBw7k0KFDzJ8/n8LCQu6++27uu+8+PvvsM0ed2sXLyzA7h+VlmlWJQdFO/2YScSq56eDmCY1ugKB65t2omxek74PdC818ETm70AaQshmue8xcH6so32y6Sk82Vzxv0dfRJawQh86zM3bsWJYvX84vv/xSbr5hGERGRvLoo4/y2GOPAZCRkUF4eDgzZszgjjvuYNu2bTRv3pzVq1fTrl07AObNm0evXr34448/iIyMPGc5qnSenYoEMceTYNV7kJZ0Mi24AbS/D0JiLm35RFzF/uWwfyVs+xYOb/4r0WLehcbeCNEdzLtTETm740mw6n1IO2UB3dI+Og5uuqro77dDm7G++eYb2rVrx2233UZYWBht2rThgw8+sOUnJSWRkpJCQkKCLS0wMJD27duzcuVKAFauXElQUJAt0AFISEjAarWyatWqqjuZs8nLMDtK7loA27+HnfPg13dh0XOweKr5Rjp129MDHTDfZKveN/NF5Nx8asKeRXBow18jR/56pGyCvUshMMrRJRSpHkJizD45XcdDx5HVoo/O6RzajLV3717effddRo8ezZNPPsnq1asZOXIknp6eDB48mJQUswNheHi43X7h4eG2vJSUFMLCwuzy3d3dCQkJsW1zuvz8fPLz823PMzMzK/O07JXW0hzdafYRKMwxF1Nr2c8MfkqDmC5jzRqe9OSygU6ptL1mvkaRiJxdVgokrzA/b97BkJf2V4YBXv5mk5ZuHEQqzjuwWv/2ODTYKSkpoV27dkyePBmANm3asHnzZqZNm8bgwYMv2etOmTKFiRMnXrLj25xaS1OUa37xAqRuh13z4Yq/Q9o+cPeGjD/NN9OJI5Bz3BzeZ/UwF19z9zrlmJcwMBNxFcd2Q9Yhc8KzoPp/jSopBoubGegYxfosiVxGHNqMVbt2bZo3b26XFhsbS3JyMgAREebEX4cPH7bb5vDhw7a8iIgIUlNT7fKLioo4fvy4bZvTjRs3joyMDNvjwIEDlXI+ZZxaS1Nc9FeiFfxrwv4VkLoNlr0KS6aYzVp/roNje81q98Nb/vp3M+Sf8qXsXbVrd4lUS6Wdk0uK4fgeOLLNrF09ss18Xlyoz5LIZcShwU6nTp3YsWOHXdrOnTupV68eADExMURERLBw4UJbfmZmJqtWrSI+Ph6A+Ph40tPTWbt2rW2bRYsWUVJSQvv27ct9XS8vLwICAuwel8Spd45uf1WieQdA5iEoyDbvMEv514IFz5hfyKcO8cvLMHu8F+U79eyUIk7FJwjS95edH6RUrVh9lkQuIw5txnrkkUfo2LEjkydP5vbbb+e3337j/fff5/333wfAYrEwatQonnvuORo3bmwbeh4ZGcnNN98MmDVBPXr04N5772XatGkUFhYyfPhw7rjjjgqNxLqkTr1zdPcBD19w8zADHas7eNYwJ2Zy8wS/cEheZQZIsX3MKbkPbwIs5l1qjdpOPTuliFMJbQQn0sxRV3DKaCzMeUM6jdBnSeQy4tBg56qrrmLOnDmMGzeOZ599lpiYGF577TUGDhxo2+aJJ57gxIkT3HfffaSnp3PNNdcwb9482xw7AJ9++inDhw/nhhtuwGq10r9/f9544w1HnJK9oGizNiZtrxnkBNWDnGNmcBPayJzw7HiS2Scn9xh4eENRHix/Exp3gyaJZt8dN0+o17Fa9XwXcagaEdBlDCz+F0S0hGa9zZpUvzDzsxTW/NzHEBGX4dB5dpzFJZ1n5/T5CXLTzP47TRPh1/fM4KakEK4ZDb+8DOEtIHVr2ePcNhPqd9LdqMj5sFsENMi8ydAioCIuo6K/31oI9FIrnZ8gPdkcZYUFds+HZW9AcT5YLGaT1bHdUKetuQ6JXxjkHDX/DxB1NRRma9i5yPmqEaHgRkQcvzbWZcE70Oyvk5UCRpE5a2vi83Dd4xB7k9l/J20fxD8EgXXN0VdBUWCxmoHONY/Ath80VFZEROQCqGanKhzZAQuehbpXwpqP4MCv4BtqzuAaWBdu/Qj2/QK/vAqJk6FOO7BazSr3vDRY9ykU5WiorIiIyAVQsHOp5WXAyrfALxR2zDMnOfPwg8JcyPzTnEQwfT+ENja3378MrG7w64dw7Whz8kHQsHMREZELpGDnUktPNgOc2Jtgw2fmzK1eAdCkh9mfx80L3D0hrAUUZJlLSBzbYa7UXPTXkhalC66pc7KIiMh5U7BzqeVlmss+5GX+FegEQft7Ycf38Psssy9PcYEZDIW3MGdObppoblerMcRcV/7K6CIiIlIh6qB8qXkHmKOqrG7mfDmNupqBTsqmk9uUFJlBzr7lZm3P1m8gsA5EtjFHXynQERERuWAKdi61oGgIjoH0AxDRCkIbnAx0rG7m2j1eAWbfnvT9ENrQXDPLw0dBjoiISCVQsHOpeQfC1feaw86b9zVXOAdzuHlpfx3/MDiRas63U1JkLiFRlOfYcouIiLgI9dmpCqENzfWu/Gqac+dEtDZnTS4ugIITcHyf2dRlsZjBkH8ts7ZHRERELpqCnargHQgNOsPepeZwcww4vtdswqLk5Dahjc1p7QOizABJRERELpqCnaoSEgO+IXBsr9mHZ91Mcw2swhzw9IH615qPpGVww3hNcS8iIlJJFOxUJe9AqNPG7KQcEQcnDkNBnjliy80NsECLmxXoiIiIVCIFO47gHQi14wAt6ikiInKpaTSWiIiIuDQFOyIiIuLSFOyIiIiIS1OwIyIiIi5NwY6IiIi4NAU7IiIi4tIU7IiIiIhLU7AjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtTsCMiIiIuTcGOiIiIuDQFOyIiIuLSFOyIiIiIS1OwIyIiIi5NwY6IiIi4NAU7IiIi4tIU7IiIiIhLU7AjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtzaLAzYcIELBaL3aNZs2a2/Ly8PIYNG0ZoaCj+/v7079+fw4cP2x0jOTmZ3r174+vrS1hYGI8//jhFRUVVfSoiIiLipNwdXYAWLVqwYMEC23N395NFeuSRR/juu+/44osvCAwMZPjw4fTr14/ly5cDUFxcTO/evYmIiGDFihUcOnSIu+66Cw8PDyZPnlzl5yIiIiLOx+HBjru7OxEREWXSMzIy+PDDD/nss8/o2rUrANOnTyc2NpZff/2VDh068NNPP7F161YWLFhAeHg4V1xxBZMmTWLMmDFMmDABT0/Pqj4dERERcTIO77Oza9cuIiMjadCgAQMHDiQ5ORmAtWvXUlhYSEJCgm3bZs2aER0dzcqVKwFYuXIlcXFxhIeH27ZJTEwkMzOTLVu2VO2JiIiIiFNyaM1O+/btmTFjBk2bNuXQoUNMnDiRa6+9ls2bN5OSkoKnpydBQUF2+4SHh5OSkgJASkqKXaBTml+adyb5+fnk5+fbnmdmZlbSGYmIiIizcWiw07NnT9v/W7VqRfv27alXrx7//e9/8fHxuWSvO2XKFCZOnHjJji8iIiLOw+HNWKcKCgqiSZMm7N69m4iICAoKCkhPT7fb5vDhw7Y+PhEREWVGZ5U+L68fUKlx48aRkZFhexw4cKByT0RERESchlMFO9nZ2ezZs4fatWvTtm1bPDw8WLhwoS1/x44dJCcnEx8fD0B8fDybNm0iNTXVts38+fMJCAigefPmZ3wdLy8vAgIC7B4iIiLimhzajPXYY4/Rp08f6tWrx8GDB3nmmWdwc3NjwIABBAYGMnToUEaPHk1ISAgBAQGMGDGC+Ph4OnToAED37t1p3rw5gwYN4oUXXiAlJYXx48czbNgwvLy8HHlqIiIi4iQcGuz88ccfDBgwgGPHjlGrVi2uueYafv31V2rVqgXAq6++itVqpX///uTn55OYmMg777xj29/NzY25c+fy4IMPEh8fj5+fH4MHD+bZZ5911CmJiIiIk7EYhmE4uhCOlpmZSWBgIBkZGWrSEhERqSYq+vvtVH12RERERCqbgh0RERFxaQp2RERExKUp2BERERGXpmBHREREXJqCHREREXFpCnZERETEpSnYEREREZemYEdERERcmoIdERERcWkKdkRERMSlKdgRERERl6ZgR0RERFyagh0RERFxaQp2RERExKUp2BERERGXpmBHREREXJqCHREREXFpCnZERETEpSnYEREREZemYEdERERcmoIdERERcWkKdkRERMSlKdgRERERl6ZgR0RERFyagh0RERFxaQp2RERExKUp2BERERGXpmBHREREXJqCHREREXFpCnZERETEpSnYEREREZemYEdERERcmoIdERERcWkKdkRERMSluTu6AJe9vAxIT4a8TPAOgKBo8A50dKlERERchoKdqpSXAcf2wIlU8PADDx9I2QxGMeQeh4MbITAK2t8HITGOLq2IiIhLcJpmrKlTp2KxWBg1apQtLS8vj2HDhhEaGoq/vz/9+/fn8OHDdvslJyfTu3dvfH19CQsL4/HHH6eoqKiKS18Bx5Pgp2fgy7th4bPw5T3w3WOQnQJLX4CkX6BxAmQcgFXvm4GRiIiIXDSnCHZWr17Ne++9R6tWrezSH3nkEb799lu++OILli5dysGDB+nXr58tv7i4mN69e1NQUMCKFSuYOXMmM2bM4Omnn67qUzi7vAxY+TbsXQRWD8g8BIU5cGgDbP8OYjrD4S2weTZEtoa0vWbTloiIiFw0hwc72dnZDBw4kA8++IDg4GBbekZGBh9++CGvvPIKXbt2pW3btkyfPp0VK1bw66+/AvDTTz+xdetW/v3vf3PFFVfQs2dPJk2axNtvv01BQYGjTqms9GRI3WIGOG4eUJANGGZeyiazycoohtSt4PPXNcjLdFhxRUREXInDg51hw4bRu3dvEhIS7NLXrl1LYWGhXXqzZs2Ijo5m5cqVAKxcuZK4uDjCw8Nt2yQmJpKZmcmWLVvO+Jr5+flkZmbaPS6pogJodhO0uweu+DvE9gU375P5JYVg/BX8FOWb/3oHXNoyiYiIXCYuuoNyZmYmixYtomnTpsTGxp7XvrNmzWLdunWsXr26TF5KSgqenp4EBQXZpYeHh5OSkmLb5tRApzS/NO9MpkyZwsSJE8+rrBfseBKsmwHbvoWCE+DmCSENoP39sPwNKDxhNm1ZLOb27l4Q3MAclSUiIiIX7bxrdm6//XbeeustAHJzc2nXrh233347rVq14quvvqrwcQ4cOMDDDz/Mp59+ire397l3qETjxo0jIyPD9jhw4MCleaG8DFj1HqQfAA9fM9CxWOHIdtg+F2JvhNpXmAGRxQ3CmoPF3QyENPxcRESkUpx3sPPzzz9z7bXXAjBnzhwMwyA9PZ033niD5557rsLHWbt2LampqVx55ZW4u7vj7u7O0qVLeeONN3B3dyc8PJyCggLS09Pt9jt8+DAREREARERElBmdVfq8dJvyeHl5ERAQYPe4JNKTzY7HaUngE/RXf52/aneO7IDaraHVbZD1JzTuDt0mQstbIKT+pSmPiIjIZei8m7EyMjIICQkBYN68efTv3x9fX1969+7N448/XuHj3HDDDWzatMku7e6776ZZs2aMGTOGqKgoPDw8WLhwIf379wdgx44dJCcnEx8fD0B8fDzPP/88qamphIWFATB//nwCAgJo3rz5+Z5a5cs5Dun7zY7JBbngWxNqREBJiTnHTkh98PSHmOs0maCIiMglct7BTlRUFCtXriQkJIR58+Yxa9YsANLS0s6rOapGjRq0bNnSLs3Pz4/Q0FBb+tChQxk9ejQhISEEBAQwYsQI4uPj6dChAwDdu3enefPmDBo0iBdeeIGUlBTGjx/PsGHD8PLyOt9Tq3xWNzPQAaAE8tJP5uUCXgFQ/xoHFExEROTycd7BzqhRoxg4cCD+/v5ER0dz/fXXA2bzVlxcXKUW7tVXX8VqtdK/f3/y8/NJTEzknXfeseW7ubkxd+5cHnzwQeLj4/Hz82Pw4ME8++yzlVqOC+buY/bDSd16WoYVoq4CNy/YuxSs7uDubXZODopSDY+IiEglshhG6ZjniluzZg0HDhygW7du+Pv7A/Ddd98RFBREp06dKr2Ql1pmZiaBgYFkZGRUbv+dlM3w5xpzskBbwGOFeh0h5lrY+jWk/dXMFdYcWvaDo3vgqnu0XISIiMg5VPT3+4KCHYCCggKSkpJo2LAh7u7Ve4mtSxbs5GXA0pfAJ8CcLLAoH/wj4OgO2PkT5ByDolxzjh2jGGrFQv2O5oSCXcerhkdEROQsKvr7fd6jsXJychg6dCi+vr60aNGC5GRzWYMRI0YwderUCy+xqyldzbxBZwiIgtw02L8S8tJg4yxw9zQDnZJiKC6ChglmR+XgGAioA8f3OvoMREREXMJ5Bzvjxo1j48aNLFmyxK5DckJCAv/5z38qtXDV1vEkWDwFFj0Hqz+ATf81A59rRkGNSPAPA6PErLkxgPgHzNqepf+CX16Bxc/DstfN4ekiIiJyUc472Pn666956623uOaaa7CUzvoLtGjRgj179lRq4aql0okE05JOpnn4gn9tOLgBivOhSU+o2dRcRiLhKfCsAY0SoPMT5r9uXmbws/ItrX4uIiLOJy/DXNtx33LzXyf/rTrvzjZHjhyxzWlzqhMnTtgFP5et9GT7QMfqAQ27woZPIeV3s19OxgGo2RgSn4ff/wM7vjdreqzu5kSD14z6q1PzdvN4EZU7yk1EROSCHU86eVNv9YDI1hBQF/xqQY1wp5w37ryDnXbt2vHdd98xYsQIAFuA83//93+2yf4ua6evVh7ZGjZ/aQY6YNbsBNSG0Maw8p2/5t6xmIGOmxcc+t38f912cGyXVj8XERHncWrrhbsvxPWDTV+aD6sH+NU0b+rb3+dUo4rPO9iZPHkyPXv2ZOvWrRQVFfH666+zdetWVqxYwdKlSy9FGauX01cr9wk2l4woZbFC9lGo1QS2fo1Rswl4+pl5Rgm4eWA5uB6a9oSMP7T6uYiIOI/S1gurB0Wt7sB6cDXUagahDcHNE2vWYbMbxqr3octYp6nhOe9g55prrmHDhg1MnTqVuLg4fvrpJ6688kpWrlxZ6ZMKVktB0eaq5Wl/jaYqyj+5ormnPxQXAiVQcIKSonxzNJaHL4VZRwGwWsDN3R2rxc3s16PVz0VExFn81dqQE9YaL8DY8aPZZwfzp86o3RpLi36wb9lf6Rbzpt3BTVsXNEFOw4YN+eCDDyq7LK7BO9Csvlv1vhnwuHuZK5p7B5qjsLLNoKbE6kFxiYFRVExRQH3cDSsUZFOChUKsePrUxD32JqeJikVERPAO4ER+kXkzvm4GHN2JpcUtULMRlBRhuHlCzjEsYc3h0EZI+tncL7iBQ5u2zjvYKZ1X50yio1UTQUiMWX2XnmyOuGrWy+zQlXEAKAGg5Ph+iI4nJ7+QDQcLCPerS6h/MVaKsNSKJTO8IxFa/VxERJxJUDQFgfXxNYogbR+WTiOx7PgBtswGC4AFIlrC9U+ai2HH9Qc3byg4YQ66sbo5pMXivIOd+vXrn3XUVXFx8UUVyGV4B54cReUbYtb0uP01kWBxEcW4kXXtM+xd+AFFxTv5M7OQPwG/iKb8ETYIv2Oe9Krj0DMQERGx5x1Idut78MvaghHVHuvOn8zmqtJAB+DITtj8FcQPgwUT4Y/VUFJo5sVcBwkTqnyU8XkHO+vXr7d7XlhYyPr163nllVd4/vnnK61gLuXUmp68TPAOYH9+MP2nb+HGJndxXaNcvIuzyXPz55tUH2YvyeKDuzwcXWoREZEy9pWEYXU7RkRQtDlprpsHRkkRABYPH7MvanoypB+AOldCvXg4vg+2z4U/1sDC5+Cm16FGRJWV+byDndatW5dJa9euHZGRkbz44ov069evUgrmck6t6QHcUrNpXjuAz38/zucAeAIFQAHtY0KoE+TjmHKKiIicRWSgD/P21+BOP38sxUVYLe5YPDyxWNygpABCG0B2KhRkg5u7OVCndhw07GKuLHBwLRzb7dzBzpk0bdqU1atXV9bhXF7DMH/G9mzG1B+2syrpuC29fUwI43o1o2GYvwNLJyIiUr6GYf5kNalPZnYmvl6huBedwIKBu8UCNSKx5GVB28FmTc6O70/uGNUBuv4TFj4LuelVWubzDnYyM+0nuTMMg0OHDjFhwgQaN25caQW7HLSJDmbyLXH8mZ5LZm4hAT4e1AnyUaAjIiJO7YroYI6kxmJp2gMjdSsWowiL1cBSlA81r4A9S+D4aUtIHVhlTjrY6AbwCarS8p53sBMUFFSmg7JhGERFRTFr1qxKK9jlomGYv4IbERGpdmqFhcF1I09OtZJzDApzoU5bWPcx5B4vu1PqNujwEIQ2qtKynnews3jxYrvnVquVWrVq0ahRI9zdK61VTERERJzdqQNwMg9CUR5kHjL75Hj6mSsD/NV5GTDnnguqW6X9deACgp3OnTtfinKIiIhIdVQ6ACcoGg781XfXKIb8LPDwNR9griLgEwIBVT+vSoWCnW+++abCB7zpppsuuDAiIiJSTXkHglcghDeHmOvhj9/M9JIi8Aow55wLrue8kwrefPPNFTqYxWLRpIIiIiKXK08f2L0CrnsM1s2EIzsAizmhrm9NuPpehyyDVKFgp6Sk5FKXQ0RERKq7oCjISoFV70Fka4juYM6z4+4FFncIaeCQYqlHsYiIiFSOUxfD/mPNyfTgBtD+foctbn1Bwc6JEydYunQpycnJFBQU2OWNHDmyUgrm8vIy7JaPIChaK5yLiEj1V84SSY7+jbugtbF69epFTk4OJ06cICQkhKNHj+Lr60tYWJiCnYo4nmRW8aUlnUwLbmBGwyExjiuXiIjI+TrTzXsVL/Z5Ntbz3eGRRx6hT58+pKWl4ePjw6+//sr+/ftp27YtL7300qUoo2vJyygb6IA5IdOq9818ERGR6uDIDvhpPMwdDYufg/nPwOKp5k29EznvYGfDhg08+uijWK1W3NzcyM/PJyoqihdeeIEnn3zyUpTRtaQnlw10SqXtNfNFRESc3bE98NNTsOMH83ft6C5zMsHDm53u5v28gx0PDw+sVnO3sLAwkpPNH+fAwEAOHDhQuaVzRXmZF5cvIiLiaHkZcOA3OLjOPr0wB9L3w9EdTnXzft59dtq0acPq1atp3LgxnTt35umnn+bo0aN88skntGzZ8lKU0bV4B1xcvoiIiKOlJ5e/9hWYAU9RrlPdvFe4Zqd0ssDJkydTu3ZtAJ5//nmCg4N58MEHOXLkCO+///6lKaUrCYo2OyOXJ7iBQ2aWFBEROS95mebcOWdSXORUN+8VDnbq1KnD2LFjCQgIoEuXLoDZjDVv3jwyMzNZu3YtrVu3vmQFdRmlcxCcHvA4eA4CERGRCvMOgNw0CGtefn5YM6e6ea9wsDNs2DC+/PJLYmNjufbaa5kxYwY5OTmXsmyuq3QOgq5PwXVPQMcR0LQHFGQ5VYcuERGRcgVFQ24mtOxXNuCJbAvxI5zq5t1iGIZxPjssWbKE6dOn89VXX+Hm5sbtt9/OP/7xD9q3b3+pynjJZWZmEhgYSEZGBgEBVVjtpvl2RESkujqeBKs/Ap8A8Ak2l4XwCYGo9hBaNctCVPT3+7yDnVLZ2dnMmjWLGTNmsGLFCmJjYxk6dCijR4++4EI7ikOCnbwMWDyl/GHowQ3Mmh8niopFRETKcPBqAJc82DnVd999x1133UV6enq1XPXcIcFOyiZY9NyZ87uOd6rZJ0VERJxNRX+/z3uenVI5OTnMmDGDzp07c9NNNxEaGsrzzz9/oYe7/Gi+HRERkSpx3vPsrFixgo8++ogvvviCoqIibr31ViZNmsR11113KcrnujTfjoiISJWocLDzwgsvMH36dHbu3Em7du148cUXGTBgADVq1LiU5XNdpfPtpO0tm6f5dkRERCpNhZuxXnzxRXr06MHGjRtZtWoV991330UHOu+++y6tWrUiICCAgIAA4uPj+eGHH2z5eXl5DBs2jNDQUPz9/enfvz+HDx+2O0ZycjK9e/e2rbr++OOPU1RUdFHlqhKab0dERKRKVLhm5+DBg3h4eFTqi9etW5epU6fSuHFjDMNg5syZ9O3bl/Xr19OiRQseeeQRvvvuO7744gsCAwMZPnw4/fr1Y/ny5YA5q3Pv3r2JiIhgxYoVHDp0iLvuugsPDw8mT55cqWW9JErn23FgT3YRERFXVymjsSpTSEgIL774Irfeeiu1atXis88+49ZbbwVg+/btxMbGsnLlSjp06MAPP/zAjTfeyMGDBwkPDwdg2rRpjBkzhiNHjuDp6Vmh13TYPDsiIiJywS75aKzKVlxczKxZszhx4gTx8fGsXbuWwsJCEhISbNs0a9aM6OhoVq5cCcDKlSuJi4uzBToAiYmJZGZmsmXLljO+Vn5+PpmZmXYPERERcU0VDnYOHjx4SQqwadMm/P398fLy4oEHHmDOnDk0b96clJQUPD09CQoKsts+PDyclJQUAFJSUuwCndL80rwzmTJlCoGBgbZHVFRU5Z6UiIiIOI0KBzstWrTgs88+q/QCNG3alA0bNrBq1SoefPBBBg8ezNatWyv9dU41btw4MjIybI8DBw5c0tcTERERx6lwsPP8889z//33c9ttt3H8+PFKK4CnpyeNGjWibdu2TJkyhdatW/P6668TERFBQUEB6enpdtsfPnyYiIgIACIiIsqMzip9XrpNeby8vGwjwEofDpWXYc6ovG+5+a8WAxUREak0FQ52HnroIX7//XeOHTtG8+bN+fbbby9JgUpKSsjPz6dt27Z4eHiwcOFCW96OHTtITk4mPj4egPj4eDZt2kRqaqptm/nz5xMQEEDz5mdYdt7ZHE8y18ha9ByseMP8d/FUM11EREQu2nnNoBwTE8OiRYt466236NevH7Gxsbi72x9i3bp1FT7euHHj6NmzJ9HR0WRlZfHZZ5+xZMkSfvzxRwIDA20Li4aEhBAQEMCIESOIj4+nQ4cOAHTv3p3mzZszaNAgXnjhBVJSUhg/fjzDhg3Dy8vrfE7NMfIyyq56DuZEg6ve12KgIiIileC8l4vYv38/s2fPJjg4mL59+5YJds5Hamoqd911F4cOHSIwMJBWrVrx448/0q1bNwBeffVVrFYr/fv3Jz8/n8TERN555x3b/m5ubsydO5cHH3yQ+Ph4/Pz8GDx4MM8+++wFl6lKpSeXv+o5mAFPerIWAxUREblI5zXPzgcffMCjjz5KQkIC7733HrVq1bqUZasyDptnZ99ys+nqTDqOhPqdqq48IiIi1UhFf78rXC3To0cPfvvtN9566y3uuuuuSinkZU+LgYqIiFxyFQ52iouL+f3336lbt+6lLM/lRYuBioiIXHIVHo01f/58BTqVTYuBioiIXHIX3rtYKocWAxUREbmkFOw4A+9AjboSERG5RJxmIVARERGRS0HBjoiIiLg0BTsiIiLi0hTsiIiIiEtTsCMiIiIuTcGOiIiIuDQFOyIiIuLSFOyIiIiIS1OwIyIiIi5NwY6IiIi4NAU7IiIi4tIU7IiIiIhLU7AjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtTsCMiIiIuTcGOiIiIuDQFOyIiIuLSFOyIiIiIS1OwIyIiIi5NwY6IiIi4NAU7IiIi4tIU7IiIiIhLU7AjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtTsCMiIiIuTcGOiIiIuDQFOyIiIuLSFOyIiIiIS3NosDNlyhSuuuoqatSoQVhYGDfffDM7duyw2yYvL49hw4YRGhqKv78//fv35/Dhw3bbJCcn07t3b3x9fQkLC+Pxxx+nqKioKk9FREREnJRDg52lS5cybNgwfv31V+bPn09hYSHdu3fnxIkTtm0eeeQRvv32W7744guWLl3KwYMH6devny2/uLiY3r17U1BQwIoVK5g5cyYzZszg6aefdsQpiYiIiJOxGIZhOLoQpY4cOUJYWBhLly7luuuuIyMjg1q1avHZZ59x6623ArB9+3ZiY2NZuXIlHTp04IcffuDGG2/k4MGDhIeHAzBt2jTGjBnDkSNH8PT0POfrZmZmEhgYSEZGBgEBAZf0HEVERKRyVPT326n67GRkZAAQEhICwNq1ayksLCQhIcG2TbNmzYiOjmblypUArFy5kri4OFugA5CYmEhmZiZbtmwp93Xy8/PJzMy0e4iIiIhrcppgp6SkhFGjRtGpUydatmwJQEpKCp6engQFBdltGx4eTkpKim2bUwOd0vzSvPJMmTKFwMBA2yMqKqqSz0ZERESchdMEO8OGDWPz5s3MmjXrkr/WuHHjyMjIsD0OHDhwyV9TREREHMPd0QUAGD58OHPnzuXnn3+mbt26tvSIiAgKCgpIT0+3q905fPgwERERtm1+++03u+OVjtYq3eZ0Xl5eeHl5VfJZiIiIiDNyaM2OYRgMHz6cOXPmsGjRImJiYuzy27Zti4eHBwsXLrSl7dixg+TkZOLj4wGIj49n06ZNpKam2raZP38+AQEBNG/evGpORERERJyWQ2t2hg0bxmeffcb//vc/atSoYetjExgYiI+PD4GBgQwdOpTRo0cTEhJCQEAAI0aMID4+ng4dOgDQvXt3mjdvzqBBg3jhhRdISUlh/PjxDBs2TLU3IiIi4tih5xaLpdz06dOnM2TIEMCcVPDRRx/l888/Jz8/n8TERN555x27Jqr9+/fz4IMPsmTJEvz8/Bg8eDBTp07F3b1isZyGnouIiFQ/Ff39dqp5dhxFwY6IiEj1Uy3n2RERERGpbAp2RERExKUp2BERERGXpmBHREREXJqCHREREXFpCnZERETEpSnYEREREZemYEdERERcmoIdERERcWkKdkRERMSlKdgRERERl6ZgR0RERFyagh0RERFxaQp2RERExKW5O7oAIiKXVF4GpCdDXiZ4B0BQNHgHOrpUIlKFFOyIiOs6ngSr3oO0pJNpwQ2g/X0QEuO4colIlVKwIyKuKS/DDHQy/oC67cAnGIrywd0b9i4F3xDV8IhcJhTsiIhrSk82A53GCbB5NqRuPZkX1hxqt4I6VzqufCJSZdRBWURcU14mRLSE3/8LhzZCSREYJea/hzbCqmmQleLoUopIFVCwIyKuyeoG7l7wx2ooLjQfhXlQUgzFBZCyCfYsNvv1iIhLU7AjIq4nLwNSt4HV3eyjU1IIhTlQlAsF2Waa1QNyjsGq983tRcRlKdgREdeTngx7f4aIOLOGp7jAbMIySsDTH/zDIeuQ2Un56A5zexFxWeqgLCKuJy8TIppD/gmIvBLS94NhgMUCBTnmUPTaraFGJBhF5vYicnbVeM4qBTsi4nq8A8A7GI7sgKir4ESq2UenVERriL3JTK97tbm9iJxZNZ+zSsGOiLieoGizz453DUg/AF2fNpuw8tLNvjqZB2HL19DiZqhzlbm9iJQvLwNWfwh+oVCz0cn5qnKPw+qPoPNjTl/Do2BHRFyPdyAERsEvr5g1O0unQlA9CGsGWMDTDzqPge1zoVZTp/+iFnGo9ANmkFPefFUt+5n5Ec79GVKwIyKuJy8D1n0M/mGwdwk0SoAd38OW2WaHZTcviI6Hq++HogJHl1bEuRUVmLWhMddBdAfz85O+D3YvNAOg8DhHl/CcFOyIiOtJ3QpHtkOLW6AoD3b8AIe3gMUKBuaQ9ORfwScEOjzo6NKKOLeSQtg1Hw5vPpkW3hKuGmo2bxXlOq5sFaRgR0Rcy5Ed8Oc6yDkKBScgtJFZo1PKMMyJBUuK4Oh2oMRhRRVxenkZ8McaaJIIDbuAmycc3Q0758G2udCkp/l5cnIKdpzBmYbzVeNhfiIOkZcBK9+C0CbmUhBeAZB71OycbGMxm7KKcs3qeDVjiZzZsT2w4zs4uBGadDf77oTHQtOeZjNWWKw5X5WTU7DjaOUN5wuKhrjb4PA2cw6Q3OPmGy0wqtoM8xNxiPRkOLLTrLWp1RSO7oJaTczmK8AW6JQUmkPT3Tw17FzkTPIyYM10qNkE2j8AGz4z++gU5JgzktdtB8H1qsVoRs2g7Eh5GScDneJCyM+C3DTY/j3MGwcH18CSKZD0i7lyc8YBTW0vcjZ5mWAUm1/ITXuafQxq1Dbn0jFKzEkF3TzBwxdCG0DNxtXii1rEIdIPmLOQe/rDzy/Azh/g2G4oyAK/mmb+ljmOLmWFKNhxpPRkM9ApyDHfQNmp5p1oXjocXAeBf30Jp241v7wjW0PaXk1tL3Im3gF/9ccphuVvmIFMWhJ0/SfE/c3sVFmrKfiFgYe/2cFSTcMi5SvMhU1fmlM1nDopZ34WZB8207NSqsVvkpqxHCkv06zRSd9vVgl6+pmLFHr6Q+PuZvVgu3vMO9HjSeAZCPmZkHPc0SUXcU5B0VCrGRzdaU58tmU2bPufOfrq6vug4fVmDc+JI1CQC79/CcH1FfCIlKfwBBxaDw2uAyyYQxn/kp9tNg+7eVSL5VYU7DiSdyDUuRIadDEXKvTwNkeSBNWHnd/Dr++a6RarWd1eqzGsOmTOd3A8SX13RE7nHQgdR0Dmn+YIEncvs+mqViz41oQNn5uTDB7cYH5Jg3lXGuH884SIVLnCHPPm280T4m6HRl3N36OifPOmIW2/eWNRDfq9KdhxJKPEnOtj/wrzuYcPNEyApi3AKASrJ6T8bvaET0uCkhLoMcWcBn/V+9BlrO5IRU4X3hx6TDU/VydSzS/rQ7+b/Q1a3mpOMlga6EC1uCsVcQivwL86INcD7yBzpGNpc5ZXADToas6gXA36vSnYcZSsFFj0vDkXiJe/WSUYWBeSV5j9csKaw++zzDvOTqNg+euwdxE07wtYTvbd0R2pSFnhzc3hsMd2Q06aeWea18gMdEoK7betBnelIg4R2hCa9jZbHPYtM2+0S+Vnwf5l4BMEjW5wWBEryqEdlH/++Wf69OlDZGQkFouFr7/+2i7fMAyefvppateujY+PDwkJCezatctum+PHjzNw4EACAgIICgpi6NChZGdnV+FZXKBju+HgWoyswxjeQRi1W2PkHMcoyjNHkAT/FSmnbDKnuW92o/nmKjxhLm4IuiMVOZsaEezxvYKdfm04+sdOju38lRO5p830GtygWtyVijhEjQioFw+evvDnarPvm4cvePiBdwBGYS4lf64l58/NLNt1hD2pzvvb69Bg58SJE7Ru3Zq333673PwXXniBN954g2nTprFq1Sr8/PxITEwkLy/Pts3AgQPZsmUL8+fPZ+7cufz888/cd999VXUKFy43ncJig6KiIjhx1JwX5EQqYGAYJVBcdHLblE1mhG0YZh+EonwzXXekIme0PjmNJ+dsos8Hm1gcdAs7C2uxPSWLjNy/anaCG0D7+9UULHJWFvN3x6uG2fxrdQerlZKCXIoK8ykqyCf/z818t3oHT87ZxPrkNEcXuFwObcbq2bMnPXv2LDfPMAxee+01xo8fT9++fQH4+OOPCQ8P5+uvv+aOO+5g27ZtzJs3j9WrV9OuXTsA3nzzTXr16sVLL71EZGRklZ3L+Sr0CuREQRE+bhZKAKtxynTbVncMN3csp+5QXHiyycrdS3ekImexJzWbqT9sZ1WSOXJx/JJs+jUfzHVNc7F6FdA4OhL/8AYKdC6UZne/fHgHmP1JLVZzKDpQYhgUlxiUGIDFSkFREdeF5fL57wVM/WE7k2+Jo2GYv2PLfRqnnWcnKSmJlJQUEhISbGmBgYG0b9+elStXArBy5UqCgoJsgQ5AQkICVquVVatWnfHY+fn5ZGZm2j2q2jGvKCyRV1JQYsEwAMtfcWdRAUS2hfQ/7HfwDYUrBsLxvea2uiMVOaM/03NtgQ5AflEJn/+ewYMLCrjlO1iXX1efnwt1PAkWT4FFz8GKN8x/F08108X1BEWbvzm1Yk9JtIJ/GO61GmMNj8UnpC4xwR54uVtZlXScP9Odb2FQpw12UlJSAAgPD7dLDw8Pt+WlpKQQFhZml+/u7k5ISIhtm/JMmTKFwMBA2yMqKqqSS39uG457khY/hpLabSmyeGCUFJnT19dqCi1vgeN7IKKVOQla81vM2Sp9gqFxopkfUr/KyyxSXWTmFl5UvpzBqbO+nyptr2Z3d1XegdDgemg72Pw9slghOBq3olzwDqA4rAX53z5KrR2f89z1/ni5W53y83VZjsYaN24co0ePtj3PzMys8oDH18uDPp9lMOmGf9KlYwZuJVm4+QZhObQRfnkNCjLMaDq8hTnBYM0mZr8d3Y2KnFOAj8dF5csZlM76Xh6NEHVdIfXNeeB8a5rLsRzfS3FBPsXpf2DZ+g2BQTXJS9/DlYWfcXvLO53y8+W0wU5ERAQAhw8fpnbt2rb0w4cPc8UVV9i2SU1NtduvqKiI48eP2/Yvj5eXF15eXpVf6PNQJ8iH1nUDKczPYevizzmRsosWHRIJ87Ni6TgcS0mBOc9OxgFYPNkMem56XcGOSAXUCfKhfUyIXVNWqfYxIdQJ8nFAqVzAuUaAaoSo66oRYfbZSf6Vkp9fxvCpiTUrBQpzKHH3oaCghGCvPxjZ9w6ynPDz5bTNWDExMURERLBw4UJbWmZmJqtWrSI+Ph6A+Ph40tPTWbt2rW2bRYsWUVJSQvv27au8zOejYZg/k3tFE7v/M06kmMPpg4OCYO1MLOtmYtn4H9j8BexZZM6ifHCtOVxdRM6pYZg/Y3s2o31MiF16+5gQxvVq5nSdJ6uNc40A1QhR1xYSAwGRuNdqgntBBhgl5Ft9yC4owQCsRbkEndhNg4Dicx6qqjm0Zic7O5vdu0/+gCclJbFhwwZCQkKIjo5m1KhRPPfcczRu3JiYmBieeuopIiMjufnmmwGIjY2lR48e3HvvvUybNo3CwkKGDx/OHXfc4dQjsUpFW49xwppK7aggiizuePoHYynIwpJ10NzA08+cDC2gNmQfhdx0h5ZXpDppEx3M5Fvi+DM9l8zcQgJ8PKgT5KNA52IERZsjQdP2ls3TCNHLg28IYGAtKcTq7oFXiYGnuxsWwGq14G4xnLI506HBzpo1a+jSpYvteWk/msGDBzNjxgyeeOIJTpw4wX333Ud6ejrXXHMN8+bNw9vb27bPp59+yvDhw7nhhhuwWq3079+fN954o8rP5YLkZeLn5Y6flzvUbWcGM8UF0OIWqNnIXE7C6gHpB+DgenOmShGpsIZh/gpuKpN3ILS/z+yMfGrAozmLLh9B0eb6jUd34W61gPWUSVLCmkNumlM2Z1oMwzDOvZlry8zMJDAwkIyMDAICqrAaNmWTOWwToHE3SNlsdkTe+JmZZ3EzA56IOLj6fmicYLabiog4kubZubz9uQ4WTIDUrSfTwpqb62TtWgDXj6mymp2K/n47bQfly8KpVcKlsyLvXw5p++DUKQXT95trZjXv44hSiojY8w50umYKqUKhDaFFP2h+k/nb5e5l1ujsWgCBUU7ZnKlgx5FOrRJ29zIXAt34OfjVgoC65hA/i5u5cGHaPqdsBxURkcuMdyA06FytmjMV7DhaSAx0GQsZf5qrynr4mMP7ik6u/4WHL7h7O2U7qIiIXIZKf7uqSXOmgh1n4B1oPvIzwSsACnNO5nn4QlA9cwE2DesUERFnUY2aMxXsOJOwWGiSCIc3m6ueu7mDu48Z6GhYp4iIyAVx2kkFL0vegdDhIYi4wpzLwCvgZKDjpO2gIiIizk41O86mmrWDioiIODsFO86oGrWDioiIODsFOyJOrri4mMLCQkcXQy5jHh4euLm5OboYIhdMwY6IkzIMg5SUFNLT0x1dFBGCgoKIiIjAYrGce2MRJ6NgR8RJlQY6YWFh+Pr66kdGHMIwDHJyckhNTQWgdu3aDi6RyPlTsCPihIqLi22BTmhoqKOLI5c5Hx8fAFJTUwkLC1OT1uWqGq+JpmBHxAmV9tHx9fV1cElETKXvxcLCQgU7l6PjSbDqPUhLOpkW3MBc8igkxnHlqiDNsyPixNR0Jc5C78XLWF5G2UAHzHWxVr1v5js5BTsiImdQv359XnvtNUcXQ8Sx0pPLBjql0vaa+U5OwY4zycuAlE2wb7n5bzWIlkVOZbFYzvqYMGFClZQjLi6OBx54oNy8Tz75BC8vL44ePVolZRGp9s61CHU1WKRafXacRTVvDxUBOHTokO3///nPf3j66afZsWOHLc3f39/2f8MwKC4uxt298r+Ghg4dyoQJE3j11VdtnWtLTZ8+nZtuuomaNWtW+uuKuKRzLUJdDRapVs2OM3CB9lARgIiICNsjMDAQi8Vie759+3Zq1KjBDz/8QNu2bfHy8mLZsmUMGTKEm2++2e44o0aN4vrrr7c9LykpYcqUKcTExODj40Pr1q358ssvz1iOO++8k9zcXL766iu79KSkJJYsWcLQoUPZs2cPffv2JTw8HH9/f6666ioWLFhwxmPu27cPi8XChg0bbGnp6elYLBaWLFliS9u8eTM9e/bE39+f8PBwBg0aZFeL9OWXXxIXF4ePjw+hoaEkJCRw4sSJs19YEUcKijZvvstTTRapVrDjaKVNV0FR0Lg71G0HVo+T+dWkPVScV2ZuIVsPZrBq7zG2HswgM9exszGPHTuWqVOnsm3bNlq1alWhfaZMmcLHH3/MtGnT2LJlC4888gh33nknS5cuLXf7mjVr0rdvXz766CO79BkzZlC3bl26d+9OdnY2vXr1YuHChaxfv54ePXrQp08fkpMv/POWnp5O165dadOmDWvWrGHevHkcPnyY22+/HTBrvgYMGMA999zDtm3bWLJkCf369cMwjAt+TZFLzjvQbGU4PeCpRotUqxnLkUqbrv5ce7JWJ6w5tOwHuxZAyV8/StWgPVScU/KxE0xfvo/k4zm2tHqhvgzpWJ/oUD+HlOnZZ5+lW7duFd4+Pz+fyZMns2DBAuLj4wFo0KABy5Yt47333qNz587l7jd06FB69uxJUlISMTExGIbBzJkzGTx4MFarldatW9O6dWvb9pMmTWLOnDl88803DB8+/ILO7a233qJNmzZMnjzZlvbRRx8RFRXFzp07yc7OpqioiH79+lGvXj3A7F8k4vSq+SLVqtlxlFObrtxOiTlTt8Lm2RB58ku4OrSHivPJzC0sE+gA7D+Ww4wV+xxWw9OuXbvz2n737t3k5OTQrVs3/P39bY+PP/6YPXv2nHG/bt26UbduXaZPnw7AwoULSU5O5u677wYgOzubxx57jNjYWIKCgvD392fbtm0XVbOzceNGFi9ebFfOZs2aAbBnzx5at27NDTfcQFxcHLfddhsffPABaWlpF/x6IlWqdJHq+p3Mf6tJoAOq2XGcU4fyufuAhy8U/vWjlLoVmt9k/r+atIeK8/kjLadMoFNq/7Ec/kjLoblP1X9Z+fnZ1yhZrdYyzTinLnyanZ0NwHfffUedOnXstvPy8jrj61itVoYMGcLMmTOZMGEC06dPp0uXLjRoYFbFP/bYY8yfP5+XXnqJRo0a4ePjw6233kpBQcEZjwfYlfX0BVqzs7Pp06cP//rXv8rsX7t2bdzc3Jg/fz4rVqzgp59+4s033+Sf//wnq1atIiZGAxFELhXV7DjKqU1Tbh4QVM8MeEoV5Ver9lBxPll5RReVX1Vq1aplN4oLsOsE3Lx5c7y8vEhOTqZRo0Z2j6ioqLMe++677+bAgQPMnj2bOXPmMHToUFve8uXLGTJkCLfccgtxcXFERESwb9++s5YT7EecnVpOgCuvvJItW7ZQv379MmUtDfIsFgudOnVi4sSJrF+/Hk9PT+bMmXPW8xCRi6OaHUc5vWnK0xdCG0FRLhQXQe3W0OZOBTpywWp4n/3jfa78qtK1a1defPFFPv74Y+Lj4/n3v//N5s2badOmDQA1atTgscce45FHHqGkpIRrrrmGjIwMli9fTkBAAIMHDz7jsWNiYujatSv33XcfXl5e9OvXz5bXuHFjZs+eTZ8+fbBYLDz11FOUlJSc8Vg+Pj506NCBqVOnEhMTQ2pqKuPHj7fbZtiwYXzwwQcMGDCAJ554gpCQEHbv3s2sWbP4v//7P9asWcPChQvp3r07YWFhrFq1iiNHjhAbG3uRV1FEzkY1O45S3lA+Nw/wCoA67apde6g4n7rBvtQLLX9trXqhvtQNdo51txITE3nqqad44oknuOqqq8jKyuKuu+6y22bSpEk89dRTTJkyhdjYWHr06MF3331XoaafoUOHkpaWxt///ne8vb1t6a+88grBwcF07NiRPn36kJiYyJVXXnnWY3300UcUFRXRtm1bRo0axXPPPWeXHxkZyfLlyykuLqZ79+7ExcUxatQogoKCsFqtBAQE8PPPP9OrVy+aNGnC+PHjefnll+nZs+d5XDEROV8WQ2MeyczMJDAwkIyMDAICqrAz8PEkcx6dtL0n00qbrkLqV105xOnk5eXZRhGd+gN9vpKPnWDGin3sP+Y8o7Gkeqqs96RIZaro77dz1GNfrqr5UD5xftGhfoxKaMIfaTlk5RVRw9udusG+BPh4nHtnEREXoWDH0UqH8olcIgE+Hg4ZdSUi4izUZ0dERERcmoIdERERcWkKdkRERMSlKdgRERERl6ZgR0RERFyagh0RERFxaQp2RERExKUp2BGRamvIkCHcfPPNtufXX389o0aNqvJyLFmyBIvFQnp6+iV9HYvFwtdff31JX0PEFSnYEZFKNWTIECwWCxaLBU9PTxo1asSzzz5LUdGlX2V99uzZTJo0qULbVlWAUlBQQM2aNZk6dWq5+ZMmTSI8PJzCwsJLWg6Ry5nLBDtvv/029evXx9vbm/bt2/Pbb785ukgil60ePXpw6NAhdu3axaOPPsqECRN48cUXy922oKCg0l43JCSEGjVqVNrxKoOnpyd33nkn06dPL5NnGAYzZszgrrvuwsNDS3iIXCouEez85z//YfTo0TzzzDOsW7eO1q1bk5iYSGpqqqOLJnJZ8vLyIiIignr16vHggw+SkJDAN998A5xsenr++eeJjIykadOmABw4cIDbb7+doKAgQkJC6Nu3L/v27bMds7i4mNGjRxMUFERoaChPPPEEp69jfHozVn5+PmPGjCEqKgovLy8aNWrEhx9+yL59++jSpQsAwcHBWCwWhgwZAkBJSQlTpkwhJiYGHx8fWrduzZdffmn3Ot9//z1NmjTBx8eHLl262JWzPEOHDmXnzp0sW7bMLn3p0qXs3buXoUOHsnr1arp160bNmjUJDAykc+fOrFu37ozHLK9masOGDVgsFrvyLFu2jGuvvRYfHx+ioqIYOXIkJ06csOW/8847NG7cGG9vb8LDw7n11lvPei4i1ZFLBDuvvPIK9957L3fffTfNmzdn2rRp+Pr68tFHHzm6aCKOl5cBKZtg33Lz37yMKi+Cj4+PXQ3OwoUL2bFjB/Pnz2fu3LkUFhaSmJhIjRo1+OWXX1i+fDn+/v706NHDtt/LL7/MjBkz+Oijj1i2bBnHjx9nzpw5Z33du+66i88//5w33niDbdu28d577+Hv709UVBRfffUVADt27ODQoUO8/vrrAEyZMoWPP/6YadOmsWXLFh555BHuvPNOli5dCphBWb9+/ejTpw8bNmzgH//4B2PHjj1rOeLi4rjqqqvKfCdNnz6djh070qxZM7Kyshg8eDDLli3j119/pXHjxvTq1YusrKzzu9in2LNnDz169KB///78/vvv/Oc//2HZsmUMHz4cgDVr1jBy5EieffZZduzYwbx587juuusu+PVEnJZRzeXn5xtubm7GnDlz7NLvuusu46abbip3n7y8PCMjI8P2OHDggAEYGRkZVVBikXPLzc01tm7dauTm5l7cgY7tNYzvxxjGp7effHw/1ky/RAYPHmz07dvXMAzDKCkpMebPn294eXkZjz32mC0/PDzcyM/Pt+3zySefGE2bNjVKSkpsafn5+YaPj4/x448/GoZhGLVr1zZeeOEFW35hYaFRt25d22sZhmF07tzZePjhhw3DMIwdO3YYgDF//vxyy7l48WIDMNLS0mxpeXl5hq+vr7FixQq7bYcOHWoMGDDAMAzDGDdunNG8eXO7/DFjxpQ51ummTZtm+Pv7G1lZWYZhGEZmZqbh6+tr/N///V+52xcXFxs1atQwvv32W1saYPuuK6/869evNwAjKSnJVu777rvP7ri//PKLYbVajdzcXOOrr74yAgICjMzMzDOWu1SlvSdFKlFGRkaFfr+rfc3O0aNHKS4uJjw83C49PDyclJSUcveZMmUKgYGBtkdUVFRVFLXinOBOXFxAXgaseg/SkuzT0/bCqvcv6ftq7ty5+Pv74+3tTc+ePfnb3/7GhAkTbPlxcXF4enranm/cuJHdu3dTo0YN/P398ff3JyQkhLy8PPbs2UNGRgaHDh2iffv2tn3c3d1p167dGcuwYcMG3Nzc6Ny5c4XLvXv3bnJycujWrZutHP7+/nz88cfs2bMHgG3bttmVAyA+Pv6cxx4wYADFxcX897//Bczmd6vVyt/+9jcADh8+zL333kvjxo0JDAwkICCA7OxskpOTK1z+023cuJEZM2bYnUtiYiIlJSUkJSXRrVs36tWrR4MGDRg0aBCffvopOTk5F/x6Is7K3dEFcIRx48YxevRo2/PMzEznCXiOJ5X9gQpuAO3vg5AYx5VLqp/05LKBTqm0vWZ+RNwleekuXbrw7rvv4unpSWRkJO7u9l81fn5+ds+zs7Np27Ytn376aZlj1apV64LK4OPjc977ZGdnA/Ddd99Rp04duzwvL68LKkepgIAAbr31VqZPn84999zD9OnTuf322/H39wdg8ODBHDt2jNdff5169erh5eVFfHz8GTtwW63mvapxSr+l00d0ZWdnc//99zNy5Mgy+0dHR+Pp6cm6detYsmQJP/30E08//TQTJkxg9erVBAUFXdT5ijiTah/s1KxZEzc3Nw4fPmyXfvjwYSIiIsrdx8vL66K/uC6Jc92JdxkL3oGOKZtUP3mZF5d/Efz8/GjUqFGFt7/yyiv5z3/+Q1hYGAEBAeVuU7t2bVatWmXrU1JUVMTatWu58sory90+Li6OkpISli5dSkJCQpn80pql4uJiW1rz5s3x8vIiOTn5jDVCsbGxts7WpX799ddznyRmR+Xrr7+euXPnsmLFCrsRasuXL+edd96hV69egNk36OjRo2c8VmkQeOjQIYKDgwGzNutUV155JVu3bj3r38Ld3Z2EhAQSEhJ45plnCAoKYtGiRfTr169C5yRSHVT7ZixPT0/atm3LwoULbWklJSUsXLiwQlXLTqUid+IiFeVdftBQ4fwqNHDgQGrWrEnfvn355ZdfSEpKYsmSJYwcOZI//vgDgIcffpipU6fy9ddfs337dh566KGzzpFTv359Bg8ezD333MPXX39tO2ZpM1K9evWwWCzMnTuXI0eOkJ2dTY0aNXjsscd45JFHmDlzJnv27GHdunW8+eabzJw5E4AHHniAXbt28fjjj7Njxw4+++wzZsyYUaHzvO6662jUqBF33XUXzZo1o2PHjra8xo0b88knn7Bt2zZWrVrFwIEDz1o71ahRI6KiopgwYQK7du3iu+++4+WXX7bbZsyYMaxYsYLhw4ezYcMGdu3axf/+9z9bB+W5c+fyxhtvsGHDBvbv38/HH39MSUmJbYSciKuo9sEOwOjRo/nggw+YOXMm27Zt48EHH+TEiRPcfffdji7a+XHgnbi4oKBoswm0PMENzHwn4evry88//0x0dDT9+vUjNjaWoUOHkpeXZ6vpefTRRxk0aBCDBw8mPj6eGjVqcMstt5z1uO+++y633norDz30EM2aNePee++1DbuuU6cOEydOZOzYsYSHh9sCgEmTJvHUU08xZcoUYmNj6dGjB9999x0xMWYzcnR0NF999RVff/01rVu3Ztq0aUyePLlC52mxWLjnnntIS0vjnnvuscv78MMPSUtL48orr2TQoEGMHDmSsLCwMx7Lw8ODzz//nO3bt9OqVSv+9a9/8dxzz9lt06pVK5YuXcrOnTu59tpradOmDU8//TSRkZEABAUFMXv2bLp27UpsbCzTpk3j888/p0WLFhU6H5HqwmIYp01UUU299dZbvPjii6SkpHDFFVfwxhtvlOlEeCaZmZkEBgaSkZFxxir0KpGyCRY9d+b8ruMvWR8LcS55eXkkJSURExODt7f3hR/oeJLZBJq292RacANofz+E1L/ocsrlo9LekyKVqKK/39W+z06p4cOH2+7Mqq3SO/FTf5hKOdmduFQTITFmX6/0ZLNm0DvAfB+p75eIXEZcJthxCd6B5qirM92J6wdKLoR3oGoEReSypmDH2ehOXEREpFIp2HFGuhMXERGpNC4xGktERETkTBTsiDgxFxksKS5A70WpzhTsiDghDw8PAK1TJE6j9L1Y+t4UqU7UZ0fECbm5uREUFERqaipgTrpnsVgcXCq5HBmGQU5ODqmpqQQFBeHm5uboIomcNwU7Ik6qdG230oBHxJGCgoLOuN6giLNTsCPipCwWC7Vr1yYsLKzMatYiVcnDw0M1OlKtKdgRcXJubm76oRERuQjqoCwiIiIuTcGOiIiIuDQFOyIiIuLS1GeHk5NlZWZmOrgkIiIiUlGlv9vnmvRSwQ6QlZUFQFRUlINLIiIiIucrKyuLwMAzL5htMTQHOCUlJRw8eJAaNWpclhO3ZWZmEhUVxYEDBwgICHB0cRxC10DX4HI/f9A1AF2D6nb+hmGQlZVFZGQkVuuZe+aoZgewWq3UrVvX0cVwuICAgGrx5r6UdA10DS738wddA9A1qE7nf7YanVLqoCwiIiIuTcGOiIiIuDQFO4KXlxfPPPMMXl5eji6Kw+ga6Bpc7ucPugaga+Cq568OyiIiIuLSVLMjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtTsOOipkyZwlVXXUWNGjUICwvj5ptvZseOHXbb5OXlMWzYMEJDQ/H396d///4cPnzYbpvk5GR69+6Nr68vYWFhPP744xQVFVXlqVSKqVOnYrFYGDVqlC3tcjj/P//8kzvvvJPQ0FB8fHyIi4tjzZo1tnzDMHj66aepXbs2Pj4+JCQksGvXLrtjHD9+nIEDBxIQEEBQUBBDhw4lOzu7qk/lghQXF/PUU08RExODj48PDRs2ZNKkSXbr6LjaNfj555/p06cPkZGRWCwWvv76a7v8yjrf33//nWuvvRZvb2+ioqJ44YUXLvWpVdjZrkFhYSFjxowhLi4OPz8/IiMjueuuuzh48KDdMarzNTjXe+BUDzzwABaLhddee80uvTqff7kMcUmJiYnG9OnTjc2bNxsbNmwwevXqZURHRxvZ2dm2bR544AEjKirKWLhwobFmzRqjQ4cORseOHW35RUVFRsuWLY2EhARj/fr1xvfff2/UrFnTGDdunCNO6YL99ttvRv369Y1WrVoZDz/8sC3d1c//+PHjRr169YwhQ4YYq1atMvbu3Wv8+OOPxu7du23bTJ061QgMDDS+/vprY+PGjcZNN91kxMTEGLm5ubZtevToYbRu3dr49ddfjV9++cVo1KiRMWDAAEec0nl7/vnnjdDQUGPu3LlGUlKS8cUXXxj+/v7G66+/btvG1a7B999/b/zzn/80Zs+ebQDGnDlz7PIr43wzMjKM8PBwY+DAgcbmzZuNzz//3PDx8THee++9qjrNszrbNUhPTzcSEhKM//znP8b27duNlStXGldffbXRtm1bu2NU52twrvdAqdmzZxutW7c2IiMjjVdffdUurzqff3kU7FwmUlNTDcBYunSpYRjmB97Dw8P44osvbNts27bNAIyVK1cahmF+YKxWq5GSkmLb5t133zUCAgKM/Pz8qj2BC5SVlWU0btzYmD9/vtG5c2dbsHM5nP+YMWOMa6655oz5JSUlRkREhPHiiy/a0tLT0w0vLy/j888/NwzDMLZu3WoAxurVq23b/PDDD4bFYjH+/PPPS1f4StK7d2/jnnvusUvr16+fMXDgQMMwXP8anP5DV1nn+8477xjBwcF2n4MxY8YYTZs2vcRndP7O9mNf6rfffjMAY//+/YZhuNY1ONP5//HHH0adOnWMzZs3G/Xq1bMLdlzp/EupGesykZGRAUBISAgAa9eupbCwkISEBNs2zZo1Izo6mpUrVwKwcuVK4uLiCA8Pt22TmJhIZmYmW7ZsqcLSX7hhw4bRu3dvu/OEy+P8v/nmG9q1a8dtt91GWFgYbdq04YMPPrDlJyUlkZKSYncNAgMDad++vd01CAoKol27drZtEhISsFqtrFq1qupO5gJ17NiRhQsXsnPnTgA2btzIsmXL6NmzJ3B5XINTVdb5rly5kuuuuw5PT0/bNomJiezYsYO0tLQqOpvKk5GRgcViISgoCHD9a1BSUsKgQYN4/PHHadGiRZl8Vzx/LQR6GSgpKWHUqFF06tSJli1bApCSkoKnp6ftw10qPDyclJQU2zan/tCX5pfmObtZs2axbt06Vq9eXSbvcjj/vXv38u677zJ69GiefPJJVq9ezciRI/H09GTw4MG2cyjvHE+9BmFhYXb57u7uhISEVItrMHbsWDIzM2nWrBlubm4UFxfz/PPPM3DgQIDL4hqcqrLONyUlhZiYmDLHKM0LDg6+JOW/FPLy8hgzZgwDBgywLXzp6tfgX//6F+7u7owcObLcfFc8fwU7l4Fhw4axefNmli1b5uiiVJkDBw7w8MMPM3/+fLy9vR1dHIcoKSmhXbt2TJ48GYA2bdqwefNmpk2bxuDBgx1cuqrx3//+l08//ZTPPvuMFi1asGHDBkaNGkVkZORlcw3kzAoLC7n99tsxDIN3333X0cWpEmvXruX1119n3bp1WCwWRxenyqgZy8UNHz6cuXPnsnjxYurWrWtLj4iIoKCggPT0dLvtDx8+TEREhG2b00cnlT4v3cZZrV27ltTUVK688krc3d1xd3dn6dKlvPHGG7i7uxMeHu7S5w9Qu3ZtmjdvbpcWGxtLcnIycPIcyjvHU69BamqqXX5RURHHjx+vFtfg8ccfZ+zYsdxxxx3ExcUxaNAgHnnkEaZMmQJcHtfgVJV1vtX9swEnA539+/czf/58W60OuPY1+OWXX0hNTSU6Otr23bh//34effRR6tevD7jm+SvYcVGGYTB8+HDmzJnDokWLylQ3tm3bFg8PDxYuXGhL27FjB8nJycTHxwMQHx/Ppk2b7N70pV8Kp/+IOpsbbriBTZs2sWHDBtujXbt2DBw40PZ/Vz5/gE6dOpWZbmDnzp3Uq1cPgJiYGCIiIuyuQWZmJqtWrbK7Bunp6axdu9a2zaJFiygpKaF9+/ZVcBYXJycnB6vV/mvOzc2NkpIS4PK4BqeqrPONj4/n559/prCw0LbN/Pnzadq0qdM1X5SnNNDZtWsXCxYsIDQ01C7fla/BoEGD+P333+2+GyMjI3n88cf58ccfARc9f0f3kJZL48EHHzQCAwONJUuWGIcOHbI9cnJybNs88MADRnR0tLFo0SJjzZo1Rnx8vBEfH2/LLx163b17d2PDhg3GvHnzjFq1alWbodenO3U0lmG4/vn/9ttvhru7u/H8888bu3btMj799FPD19fX+Pe//23bZurUqUZQUJDxv//9z/j999+Nvn37ljsMuU2bNsaqVauMZcuWGY0bN3baYdenGzx4sFGnTh3b0PPZs2cbNWvWNJ544gnbNq52DbKysoz169cb69evNwDjlVdeMdavX28baVQZ55uenm6Eh4cbgwYNMjZv3mzMmjXL8PX1dZphx2e7BgUFBcZNN91k1K1b19iwYYPd9+OpI4uq8zU413vgdKePxjKM6n3+5VGw46KAch/Tp0+3bZObm2s89NBDRnBwsOHr62vccsstxqFDh+yOs2/fPqNnz56Gj4+PUbNmTePRRx81CgsLq/hsKsfpwc7lcP7ffvut0bJlS8PLy8to1qyZ8f7779vll5SUGE899ZQRHh5ueHl5GTfccIOxY8cOu22OHTtmDBgwwPD39zcCAgKMu+++28jKyqrK07hgmZmZxsMPP2xER0cb3t7eRoMGDYx//vOfdj9qrnYNFi9eXO5nf/DgwYZhVN75bty40bjmmmsMLy8vo06dOsbUqVOr6hTP6WzXICkp6Yzfj4sXL7Ydozpfg3O9B05XXrBTnc+/PBbDOGUqUREREREXoz47IiIi4tIU7IiIiIhLU7AjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtTsCMiIiIuTcGOiFw26tevz2uvveboYohIFVOwIyJVpri4mI4dO9KvXz+79IyMDKKiovjnP/9Z7n5xcXE88MAD5eZ98skneHl5cfTo0Uovr4i4BgU7IlJl3NzcmDFjBvPmzePTTz+1pY8YMYKQkBCeeeaZcvcbOnQos2bNIjc3t0ze9OnTuemmm6hZs+YlK7eIVG8KdkSkSjVp0oSpU6cyYsQIDh06xP/+9z9mzZrFxx9/jKenZ7n73HnnneTm5vLVV1/ZpSclJbFkyRKGDh3Knj176Nu3L+Hh4fj7+3PVVVexYMGCM5Zj3759WCwWNmzYYEtLT0/HYrGwZMkSW9rmzZvp2bMn/v7+hIeHM2jQILtapC+//JK4uDh8fHwIDQ0lISGBEydOXNjFEZFLQsGOiFS5ESNG0Lp1awYNGsR9993H008/TevWrc+4fc2aNenbty8fffSRXfqMGTOoW7cu3bt3Jzs7m169erFw4ULWr19Pjx496NOnD8nJyRdczvT0dLp27UqbNm1Ys2YN8+bN4/Dhw9x+++0AHDp0iAEDBnDPPfewbds2lixZQr9+/dCSgyLOxd3RBRCRy4/FYuHdd98lNjaWuLg4xo4de859hg4dSs+ePUlKSiImJgbDMJg5cyaDBw/GarXSunVru4Bp0qRJzJkzh2+++Ybhw4dfUDnfeust2rRpw+TJk21pH330EVFRUezcuZPs7GyKioro168f9erVA8z+RSLiXFSzIyIO8dFHH+Hr60tSUhJ//PHHObfv1q0bdevWZfr06QAsXLiQ5ORk7r77bgCys7N57LHHiI2NJSgoCH9/f7Zt23ZRNTsbN25k8eLF+Pv72x7NmjUDYM+ePbRu3ZobbriBuLg4brvtNj744APS0tIu+PVE5NJQsCMiVW7FihW8+uqrzJ07l6uvvpqhQ4ees+nHarUyZMgQZs6cSUlJCdOnT6dLly40aNAAgMcee4w5c+YwefJkfvnlFzZs2EBcXBwFBQVnPB5g97qFhYV222RnZ9OnTx82bNhg99i1axfXXXcdbm5uzJ8/nx9++IHmzZvz5ptv0rRpU5KSki7m8ohIJVOwIyJVKicnhyFDhvDggw/SpUsXPvzwQ3777TemTZt2zn3vvvtuDhw4wOzZs5kzZw5Dhw615S1fvpwhQ4Zwyy23EBcXR0REBPv27TvjsWrVqgWY/W5KndpZGeDKK69ky5Yt1K9fn0aNGtk9/Pz8ALNJrlOnTkycOJH169fj6enJnDlzzuOKiMilpmBHRKrUuHHjMAyDqVOnAuZEfy+99BJPPPHEWYMTgJiYGLp27cp9992Hl5eX3Xw9jRs3Zvbs2WzYsIGNGzfy97//nZKSkjMey8fHhw4dOjB16lS2bdvG0qVLGT9+vN02w4YN4/jx4wwYMIDVq1ezZ88efvzxR+6++26Ki4tZtWoVkydPZs2aNSQnJzN79myOHDlCbGzshV8gEal0CnZEpMosXbqUt99+m+nTp+Pr62tLv//+++nYsWOFmrOGDh1KWloaf//73/H29ralv/LKKwQHB9OxY0f69OlDYmIiV1555VmP9dFHH1FUVETbtm0ZNWoUzz33nF1+ZGQky5cvp7i4mO7duxMXF8eoUaMICgrCarUSEBDAzz//TK9evWjSpAnjx4/n5ZdfpmfPnhdwdUTkUrEYGiMpIiIiLkw1OyIiIuLSFOyIiIiIS1OwIyIiIi5NwY6IiIi4NAU7IiIi4tIU7IiIiIhLU7AjIiIiLk3BjoiIiLg0BTsiIiLi0hTsiIiIiEtTsCMiIiIuTcGOiIiIuLT/B2AZ9YB/hvyvAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768776.553878100132.123406(768, 100)
290768766.058482100118.804279(768, 100)
54100117.345599100107.185700(100, 100)
198100115.335757630584.379320(100, 630)
45314361425.04998610055.328944(1436, 100)
..................
164100106.459216365375.683488(100, 365)
165100100.131385365372.642149(100, 365)
199100118.501287630598.732060(100, 630)
13210080.425408365393.936747(100, 365)
50114361422.365785100105.655907(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 776.553878 100 132.123406 (768, 100)\n", + "290 768 766.058482 100 118.804279 (768, 100)\n", + "54 100 117.345599 100 107.185700 (100, 100)\n", + "198 100 115.335757 630 584.379320 (100, 630)\n", + "453 1436 1425.049986 100 55.328944 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 106.459216 365 375.683488 (100, 365)\n", + "165 100 100.131385 365 372.642149 (100, 365)\n", + "199 100 118.501287 630 598.732060 (100, 630)\n", + "132 100 80.425408 365 393.936747 (100, 365)\n", + "501 1436 1422.365785 100 105.655907 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768776.553878100132.123406(768, 100)
290768766.058482100118.804279(768, 100)
54100117.345599100107.185700(100, 100)
198100115.335757630584.379320(100, 630)
45314361425.04998610055.328944(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 776.553878 100 132.123406 (768, 100)\n", + "290 768 766.058482 100 118.804279 (768, 100)\n", + "54 100 117.345599 100 107.185700 (100, 100)\n", + "198 100 115.335757 630 584.379320 (100, 630)\n", + "453 1436 1425.049986 100 55.328944 (1436, 100)" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.210945\n", + "(100, 365) 0.418064\n", + "(100, 630) 0.685232\n", + "(768, 100) 0.904307\n", + "(768, 630) 1.260747\n", + "(1436, 100) 1.202940\n", + "(1436, 365) 1.518347\n", + "(1436, 630) 1.799418\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_11328\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_11328\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Initialize the StandardScaler and BayesianRidge model\n", + " # with 2-degree polynomial features\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge())\n", + "\n", + " # Define the parameter grid for GridSearchCV\n", + " param_grid = {\n", + " \"bayesianridge__alpha_init\": [1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.9],\n", + " \"bayesianridge__lambda_init\": [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-9],\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"R2\": make_scorer(r2_score),\n", + " \"MAE\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"R2\", n_jobs=-1\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X using GridSearchCV\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alpha_init for X: {grid_search.best_params_[\"bayesianridge__alpha_init\"]}, Best lambda_init for X: {grid_search.best_params_[\"bayesianridge__lambda_init\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y using GridSearchCV\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alpha_init for Y: {grid_search.best_params_[\"bayesianridge__alpha_init\"]}, Best lambda_init for Y: {grid_search.best_params_[\"bayesianridge__lambda_init\"]}, R2 score : {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha_init for X: 1, Best lambda_init for X: 0.1, R2 score : 0.9960664324745556\n", + "-------------------------------------------------------\n", + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha_init for Y: 1, Best lambda_init for Y: 1e-09, R2 score : 0.9807764683455873\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "prefix = \"e2e_test3\"\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From dbd442f5a278e58b270391fcc58c58293e1e7c87 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 21:19:44 +0000 Subject: [PATCH 57/78] repeat code removed --- .../bayesian_regression/test_bayesian_ridge_regression.ipynb | 1 - .../test_bayesian_ridge_regression_grid_search.ipynb | 1 - 2 files changed, 2 deletions(-) diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb index 7ff44053..b1b25d76 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb @@ -1575,7 +1575,6 @@ ], "source": [ "# Load the data from the calibrations csv file\n", - "prefix = \"e2e_test3\"\n", "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", "\n", diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb index e58e9909..7a8bd13f 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb @@ -1618,7 +1618,6 @@ ], "source": [ "# Load the data from the calibrations csv file\n", - "prefix = \"e2e_test3\"\n", "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", "\n", From 8460ac625e248964ab5cc1a384a9269d594a6181 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 21:26:45 +0000 Subject: [PATCH 58/78] repeated code removed --- .../test/elasticnet_regression/test_elasticnet_regression.ipynb | 1 - .../test_elasticnet_regression_grid_search.ipynb | 1 - 2 files changed, 2 deletions(-) diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb index 6ac7c844..d7aba9d3 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb @@ -1575,7 +1575,6 @@ ], "source": [ "# Load the data from the calibrations csv file\n", - "prefix = \"e2e_test3\"\n", "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", "\n", diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb index 9253de1a..3c692946 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb @@ -2513,7 +2513,6 @@ ], "source": [ "# Load the data from the calibrations csv file\n", - "prefix = \"e2e_test3\"\n", "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", "\n", From 9200a99503ae88be0bbbbb4cfcc927133874ebf6 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 21:39:57 +0000 Subject: [PATCH 59/78] new metrics file added --- app/services/metrics.py | 53 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 app/services/metrics.py diff --git a/app/services/metrics.py b/app/services/metrics.py new file mode 100644 index 00000000..88ef7ae4 --- /dev/null +++ b/app/services/metrics.py @@ -0,0 +1,53 @@ +import numpy as np + + +def func_precision_x(group): + """ + Calculate the precision for the X axis. + + Args: + group (pandas.DataFrame): A group of data containing the predicted and true values for the X axis. + + Returns: + float: The precision value. + """ + return np.sqrt(np.sum(np.square([group["Predicted X"], group["True X"]]))) + + +def func_presicion_y(group): + """ + Calculate the precision for the Y axis. + + Args: + group (pandas.DataFrame): A group of data containing the predicted and true values for the Y axis. + + Returns: + float: The precision value. + """ + return np.sqrt(np.sum(np.square([group["Predicted Y"], group["True Y"]]))) + + +def func_accuracy_x(group): + """ + Calculate the accuracy for the X axis. + + Args: + group (pandas.DataFrame): A group of data containing the predicted and true values for the X axis. + + Returns: + float: The accuracy value. + """ + return np.sqrt(np.sum(np.square([group["True X"] - group["Predicted X"]]))) + + +def func_accuracy_y(group): + """ + Calculate the accuracy for the Y axis. + + Args: + group (pandas.DataFrame): A group of data containing the predicted and true values for the Y axis. + + Returns: + float: The accuracy value. + """ + return np.sqrt(np.sum(np.square([group["True Y"] - group["Predicted Y"]]))) From 395af410f19cab822903ca36627ec45a2c877c68 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 21:46:53 +0000 Subject: [PATCH 60/78] docs strings updated --- .../sgd_regression/test_sgd_regression.ipynb | 48 ++++++++----------- 1 file changed, 19 insertions(+), 29 deletions(-) diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb index ae556f78..2923630e 100644 --- a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb @@ -1202,11 +1202,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted X and true X values.\n", "\n", - " Parameters:\n", - " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", "\n", " Returns:\n", - " float: The root mean square error between the predicted X and true X values.\n", + " float: The root mean square error between the predicted X and true X values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", "\n", @@ -1215,13 +1215,11 @@ " \"\"\"\n", " Calculate the root mean square error between the predicted Y values and the true Y values.\n", "\n", - " Parameters:\n", - " - group: pandas.DataFrame\n", - " A DataFrame containing the predicted Y values and the true Y values.\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", "\n", " Returns:\n", - " - float\n", - " The root mean square error between the predicted Y values and the true Y values.\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", " \"\"\"\n", " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", "\n", @@ -1395,18 +1393,16 @@ " \"\"\"\n", " Plots the true and predicted points for X and Y coordinates.\n", "\n", - " Parameters:\n", - " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", - " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", - " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", - " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", - " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", - " - title (str, optional): The title of the plot. Defaults to None.\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", "\n", - " Returns:\n", - " - None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Convert the data to numpy arrays\n", " y_test_x = np.array(y_test_x)\n", " y_test_y = np.array(y_test_y)\n", @@ -1511,18 +1507,13 @@ " \"\"\"\n", " Perform analysis on the given DataFrame.\n", "\n", - " Parameters:\n", - " - df: DataFrame\n", - " The input DataFrame containing the data for analysis.\n", - " - ax: AxesSubplot, optional\n", - " The subplot to plot the analysis results on.\n", - " - title: str, optional\n", - " The title of the plot.\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", "\n", - " Returns:\n", - " None\n", + " Returns: None\n", " \"\"\"\n", - "\n", " # Initialize the StandardScaler and SGD regression model\n", " # with 2-degree polynomial features\n", " sc = StandardScaler()\n", @@ -1593,7 +1584,6 @@ ], "source": [ "# Load the data from the calibrations csv file\n", - "prefix = \"e2e_test3\"\n", "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", "\n", From 4b0a6c25d5feb1efe2f106b7d788b07bc53b5503 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 10 Aug 2024 23:20:01 +0000 Subject: [PATCH 61/78] sgd grid search added --- .../test_sgd_regression_grid_search.ipynb | 3186 +++++++++++++++++ 1 file changed, 3186 insertions(+) create mode 100644 app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb new file mode 100644 index 00000000..ab0e4ba7 --- /dev/null +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb @@ -0,0 +1,3186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.997505913985049" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SGD regression model and fit the data\n", + "model_x = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.SGDRegressor(random_state=42, penalty=\"elasticnet\"),\n", + ")\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 779.94150317, 767.73121684, 143.68963566, 72.85602179,\n", + " 1429.93895354, 141.49591683, 139.75017057, 84.38549774,\n", + " 773.05925323, 758.51852956, 1429.46608792, 753.47523395,\n", + " 148.44601855, 94.6708371 , 757.93414329, 782.07870155,\n", + " 134.93549616, 1434.45399846, 75.00434616, 71.55080054,\n", + " 754.49079864, 757.31797299, 151.11235811, 1436.24253279,\n", + " 1428.00814705, 780.62872138, 70.67611648, 74.0053766 ,\n", + " 79.46983054, 85.65043199, 759.72210333, 758.33811849,\n", + " 780.95611876, 760.32308564, 1452.31019321, 1442.79370547,\n", + " 1427.76279144, 1452.05419641, 1448.77361761, 1428.28787937,\n", + " 769.71564457, 86.72521704, 144.7501754 , 1432.88592571,\n", + " 757.71111735, 140.47746462, 783.55987706, 1439.17436619,\n", + " 1439.78876569, 141.25479438, 1424.76527943, 1438.87426038,\n", + " 750.66109946, 1436.86360512, 81.11152212, 769.66536801,\n", + " 73.65601933, 71.59897576, 140.68484057, 780.98573529,\n", + " 1419.25058566, 165.33513618, 141.09378058, 754.34506236,\n", + " 75.6264621 , 94.3479742 , 1439.50277652, 83.81033728,\n", + " 1421.98907679, 1439.40585122, 1439.93376497, 762.19380368,\n", + " 142.08343327, 761.1237607 , 81.82250195, 1419.51995754,\n", + " 1449.22291381, 781.09039245, 1431.92282335, 72.94645957,\n", + " 1420.24067212, 769.88351847, 71.00015447, 766.56602362,\n", + " 154.06300674, 1424.99082915, 1456.70144508, 67.48555571,\n", + " 1440.05337546, 1438.20727976, 70.12066273, 143.08961872,\n", + " 74.436971 , 778.31446392, 95.04313865, 778.84164272,\n", + " 140.38804012, 216.3718145 , 1456.15376195, 80.35024797,\n", + " 770.20536627, 1429.33815027, 776.70940728, 771.30313969,\n", + " 73.58145194, 775.1152214 , 69.16479105, 68.56311367,\n", + " 68.05386191, 1439.02347882, 79.21732083, 1272.78461583,\n", + " 1438.4041329 , 98.89554668, 1423.01619279, 780.33587881,\n", + " 758.84835038, 137.05155418, 1451.12110756, 155.51931207,\n", + " 141.23587337, 755.91845984, 1438.67038275, 142.57908283,\n", + " 777.01143391, 143.21795734, 758.10564932, 1440.16822831,\n", + " 1439.46875483, 1433.17738324, 1438.8749366 , 760.82701589,\n", + " 69.21956503, 1453.15836935, 782.51605758, 1432.16491311,\n", + " 86.0753242 , 164.80094342, 67.90833246, 87.12484877,\n", + " 88.26135074, 74.14699875, 83.46709568, 1423.70945451])" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9726494616458502" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SGD regression model and fit the data\n", + "model_y = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.SGDRegressor(random_state=42, penalty=\"elasticnet\"),\n", + ")\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([158.92473735, 140.8327875 , 93.56914114, 580.88108033,\n", + " 53.39485601, 107.34424052, 103.65373494, 376.84493023,\n", + " 156.13962536, 652.00393796, 623.41964251, 678.43536216,\n", + " 84.13033461, 309.24312429, 651.44805481, 160.76101231,\n", + " 115.08030473, 393.36468221, 570.6579886 , 578.8364957 ,\n", + " 680.38187615, 594.38233666, 72.48800111, 628.92269529,\n", + " 106.08334598, 153.26819822, 577.50988016, 575.47524714,\n", + " 357.5135996 , 361.95354772, 647.60770452, 587.80673378,\n", + " 146.86568172, 606.49092276, 373.98916524, 697.65432179,\n", + " 625.70503867, 374.90218035, 375.32079387, 104.78272484,\n", + " 146.94215167, 361.66851237, 90.84830193, 651.40561631,\n", + " 627.46005753, 96.56913373, 162.6746681 , 385.71276519,\n", + " 669.74658349, 101.89679636, 122.3128838 , 637.82007319,\n", + " 660.09427924, 351.40889859, 364.76782225, 161.77510074,\n", + " 578.05234154, 568.99251358, 107.08905986, 160.03891935,\n", + " 122.05042811, 31.76824487, 101.80824745, 647.62516166,\n", + " 579.15047907, 305.86445272, 665.41745721, 358.96987564,\n", + " 117.6869734 , 389.28297877, 647.97457801, 654.67598974,\n", + " 100.33166238, 605.94583043, 364.65663624, 123.44219045,\n", + " 369.56767767, 161.9258323 , 639.98636861, 571.96927935,\n", + " 121.63364371, 148.02973635, 582.14652841, 151.11073197,\n", + " 76.37322002, 107.84578662, 371.2549907 , 578.89484901,\n", + " 77.01241644, 662.68903232, 567.24630143, 99.6666392 ,\n", + " 574.59875856, 154.74793336, 393.37402133, 159.59104223,\n", + " 101.05189789, -26.09025096, 373.78443893, 347.18020267,\n", + " 156.59942834, 623.82202917, 159.59532748, 155.47047388,\n", + " 579.86984058, 172.14785502, 571.30772076, 572.74665771,\n", + " 579.29487084, 664.68687185, 366.67721615, 41.88513013,\n", + " 384.02931053, 281.95726106, 114.63417123, 162.71657086,\n", + " 600.25349424, 95.22406824, 372.02323887, 75.41594719,\n", + " 94.07593881, 639.2466358 , 379.72794594, 94.87102911,\n", + " 153.58031996, 95.44268531, 630.64933625, 664.09323431,\n", + " 350.86032017, 87.91766242, 649.66997005, 643.86322638,\n", + " 567.46861904, 366.58827096, 155.8042439 , 643.66860065,\n", + " 367.90678268, 14.55888948, 566.60927581, 367.06809405,\n", + " 367.84741984, 585.27399927, 369.97027976, 107.30435295])" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.941503100158.924737(768, 100)
290768767.731217100140.832788(768, 100)
54100143.68963610093.569141(100, 100)
19810072.856022630580.881080(100, 630)
45314361429.93895410053.394856(1436, 100)
..................
16410087.124849365367.068094(100, 365)
16510088.261351365367.847420(100, 365)
19910074.146999630585.273999(100, 630)
13210083.467096365369.970280(100, 365)
50114361423.709455100107.304353(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.941503 100 158.924737 (768, 100)\n", + "290 768 767.731217 100 140.832788 (768, 100)\n", + "54 100 143.689636 100 93.569141 (100, 100)\n", + "198 100 72.856022 630 580.881080 (100, 630)\n", + "453 1436 1429.938954 100 53.394856 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 87.124849 365 367.068094 (100, 365)\n", + "165 100 88.261351 365 367.847420 (100, 365)\n", + "199 100 74.146999 630 585.273999 (100, 630)\n", + "132 100 83.467096 365 369.970280 (100, 365)\n", + "501 1436 1423.709455 100 107.304353 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768779.941503100158.924737(768, 100)
290768767.731217100140.832788(768, 100)
54100143.68963610093.569141(100, 100)
19810072.856022630580.881080(100, 630)
45314361429.93895410053.394856(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 779.941503 100 158.924737 (768, 100)\n", + "290 768 767.731217 100 140.832788 (768, 100)\n", + "54 100 143.689636 100 93.569141 (100, 100)\n", + "198 100 72.856022 630 580.881080 (100, 630)\n", + "453 1436 1429.938954 100 53.394856 (1436, 100)" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 5)" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.224959\n", + "(100, 365) 0.406354\n", + "(100, 630) 0.670418\n", + "(768, 100) 0.920260\n", + "(768, 630) 1.250042\n", + "(1436, 100) 1.201567\n", + "(1436, 365) 1.522869\n", + "(1436, 630) 1.803532\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_3336\\3757417304.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "C:\\Users\\SITAM MEUR\\AppData\\Local\\Temp\\ipykernel_3336\\3757417304.py:41: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " precision_y = df_data.groupby(\"True XY\").apply(func_y)\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Initialize the StandardScaler and SGD regression model\n", + " # with 2-degree polynomial features\n", + " sc = StandardScaler()\n", + " model = make_pipeline(\n", + " PolynomialFeatures(2),\n", + " linear_model.SGDRegressor(random_state=42, penalty=\"elasticnet\"),\n", + " )\n", + "\n", + " # Define the parameter grid for GridSearchCV\n", + " param_grid = {\n", + " \"sgdregressor__alpha\": [0.0001, 0.001, 0.01, 0.1],\n", + " \"sgdregressor__l1_ratio\": [0, 0.2, 0.5, 0.7, 1],\n", + " \"sgdregressor__max_iter\": [500, 1000],\n", + " \"sgdregressor__eta0\": [0.0001, 0.001, 0.01],\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"r2\": make_scorer(r2_score),\n", + " \"mae\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"r2\", return_train_score=True\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X using GridSearchCV\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best alpha for X: {grid_search.best_params_[\"sgdregressor__alpha\"]}, Best l1_ratio for X: {grid_search.best_params_[\"sgdregressor__l1_ratio\"]}, Best max_iter for X: {grid_search.best_params_[\"sgdregressor__max_iter\"]}, Best eta0 for X: {grid_search.best_params_[\"sgdregressor__eta0\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the GridSearchCV to the training data for Y\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best estimator to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best alpha for Y: {grid_search.best_params_[\"sgdregressor__alpha\"]}, Best l1_ratio for Y: {grid_search.best_params_[\"sgdregressor__l1_ratio\"]}, Best max_iter for Y: {grid_search.best_params_[\"sgdregressor__max_iter\"]}, Best eta0 for Y: {grid_search.best_params_[\"sgdregressor__eta0\"]}, R2 score : {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best alpha for X: 0.01, Best l1_ratio for X: 1, Best max_iter for X: 1000, Best eta0 for X: 0.001, R2 score : 0.9975098452778175\n", + "-------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "c:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1616: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR Y------------------\n", + "Best alpha for Y: 0.0001, Best l1_ratio for Y: 1, Best max_iter for Y: 500, Best eta0 for Y: 0.01, R2 score : 0.9726927631930139\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9YAAAHqCAYAAADhztZNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACy6klEQVR4nOzdd3wUdf7H8dembgqbTSgJSAkQQKooKkQU1EMQwYp36lk4j/PUAxSxcqcCNqxn4VT0zrOd/jj1rFgQUUEQFBCQokgLoJAEIZtAIHXn98dsTTZl2U02Ce8nj33M7JTvfCfs7sxnvs1iGIaBiIiIiIiIiByRqEhnQERERERERKQ5U2AtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiLN2pdffonFYuHLL7+MdFbqlJmZyR/+8AfP+6aY96p5bAwvvfQSFouFnJycRj1ubR555BG6detGdHQ0AwcOjHR2mowZM2ZgsVginQ0RkSZHgbWISDNjsVjq9WpKwVpT4A7e3C+r1UrPnj2ZNGkSeXl5kc5eUD766CNmzJgR0Tz4/i2joqLo0KEDI0eObPTP3caNG5kxY0ZYg/JPP/2U2267jaFDh/Liiy/ywAMPhC3tqkpKSsjKyuLYY4+lrKys2vrRo0eTkpLC7t27GywP4eRwOGjfvj1Dhw7FMIxq65cvX05UVBS33nprBHInItJwYiKdARERCc6rr77q9/6VV15hwYIF1Zb37t27MbPVbNxzzz107dqVkpISlixZwrPPPstHH33E+vXrSUxMbNS8DBs2jMOHDxMXFxfUfh999BFPP/10xIPrs846i6uuugrDMNi+fTvPPPMMZ555Jh9++CGjR4+udzpXXnkll156KfHx8UHnYePGjcycOZPTTz+dzMzMoPcP5PPPPycqKooXXngh6P+bYFmtVp599llGjhzJrFmzmD59umfd3Llz+eSTT5g9ezYdOnRo0HyEi91u54knnuDSSy/ln//8J3/+85896yoqKrjuuuvo0qULM2fOjGAuRUTCT4G1iEgzc8UVV/i9X758OQsWLKi2vKpDhw41euDYFI0ePZoTTzwRgD/96U+0bt2av//977z33ntcdtllAfcpLi4mKSkp7HmJiorCarWGPd3G0rNnT7/P3YUXXsiAAQN44oknggqso6OjiY6ObogsHpH8/HwSEhLCFlQbhkFJSQkJCQkB15911ln8/ve/Z9asWVx22WX07NkTh8PBTTfdxEknncRf/vKXsOSjsVxyySW8/PLL3HHHHZx//vmkp6cD8OSTT7J27Vo++ugj/RaJSIujquAiIi3Q6aefTr9+/Vi1ahXDhg0jMTGRv/71r4BZhTdQSWegtrUOh4MpU6bQqVMn4uPjycrK4qGHHsLpdNaZh/fee48xY8bQoUMH4uPj6d69O/feey+VlZUB87px40bOOOMMEhMTOeaYY3j44Yerpfnzzz9zwQUXkJSURLt27bjpppsoLS2t/x8mgDPPPBOA7du3A/CHP/yB5ORktm7dyjnnnEOrVq24/PLLAXA6nTzxxBP07dsXq9VKeno61157LQUFBX5pGobBfffdR8eOHUlMTOSMM85gw4YN1Y5dUxvrb775hnPOOYfU1FSSkpIYMGAATz75pCd/Tz/9NOBfHdst3HkMRv/+/WnTpo3nbwlm6e9pp51GUlISdrud888/nx9++MFvv0BtrDMzMxk7dixLlizh5JNPxmq10q1bN1555RW//X77298CcMYZZ1RrBrFy5UpGjRpFmzZtSEhIoGvXrvzxj3+s9RwsFgsvvvgixcXFnvReeuklwCxxvffee+nevTvx8fFkZmby17/+tdpn0J33+fPnc+KJJ5KQkMBzzz1X63Eff/xxEhMTue666wC444472Lt3L8899xxRUXXfrj366KOccsoptG7dmoSEBAYNGsRbb70V8PwmTZrEu+++S79+/YiPj6dv37588skn1bZdsmQJJ510Elarle7du9d5Dr6eeeYZSktLmTp1KgC7du1ixowZXHLJJUE9dBERaS5UYi0i0kLt27eP0aNHc+mll3LFFVd4So3q69ChQwwfPpxffvmFa6+9ls6dO/P1118zbdo09uzZwxNPPFHr/i+99BLJyclMnTqV5ORkPv/8c+6++26Kiop45JFH/LYtKCjg7LPP5qKLLuJ3v/sdb731Frfffjv9+/f33IQfPnyY3/zmN+zcuZMbbriBDh068Oqrr/L5558HdV5Vbd26FYDWrVt7llVUVDBq1ChOPfVUHn30UU/p2rXXXstLL73E1VdfzQ033MD27dv5xz/+werVq1m6dCmxsbEA3H333dx3332cc845nHPOOXz33XeMHDkyYBvaqhYsWMDYsWNp3749N954IxkZGfzwww/MmzePG2+8kWuvvZbdu3cHrP7fWHmsSUFBAQUFBWRlZQHw2WefMXr0aLp168aMGTM4fPgws2fPZujQoXz33Xd1Vt3esmULF198MRMmTGD8+PH8+9//5g9/+AODBg2ib9++DBs2jBtuuIGnnnqKv/71r57mD7179yY/P5+RI0fStm1b7rjjDux2Ozk5Obz99tu1HvPVV1/l+eef59tvv+Vf//oXAKeccgpg1nB4+eWXufjii7n55pv55ptvmDVrFj/88APvvPOOXzqbNm3isssu49prr+Waa66hV69etR63Xbt2PPjgg1x77bVMnjyZ559/nilTpnD88cfXup/bk08+yXnnncfll19OWVkZc+fO5be//S3z5s1jzJgxftsuWbKEt99+m7/85S+0atWKp556inHjxrFz507P92DdunWev9+MGTOoqKhg+vTp9f4dyczMZObMmdx666384Q9/4JlnniEmJqbO3w0RkWbLEBGRZm3ixIlG1Z/z4cOHG4AxZ86catsDxvTp06st79KlizF+/HjP+3vvvddISkoyfvrpJ7/t7rjjDiM6OtrYuXNnrfk6dOhQtWXXXnutkZiYaJSUlFTL6yuvvOJZVlpaamRkZBjjxo3zLHviiScMwHjjjTc8y4qLi42srCwDML744ota8/Piiy8agPHZZ58Ze/fuNXbt2mXMnTvXaN26tZGQkGD8/PPPhmEYxvjx4w3AuOOOO/z2/+qrrwzAeO211/yWf/LJJ37L8/Pzjbi4OGPMmDGG0+n0bPfXv/7VAPz+xl988YVf3isqKoyuXbsaXbp0MQoKCvyO45tWoP/zhspjTQBjwoQJxt69e438/Hzjm2++MX7zm98YgPHYY48ZhmEYAwcONNq1a2fs27fPs9/atWuNqKgo46qrrvIsc//fbN++3bOsS5cuBmAsXrzYsyw/P9+Ij483br75Zs+yN998M+D//zvvvGMAxooVK+o8l6rGjx9vJCUl+S1bs2aNARh/+tOf/JbfcsstBmB8/vnn1fL+ySefBHVcp9NpDB061ACMTp06GQcOHKj3vlW/b2VlZUa/fv2MM8880285YMTFxRlbtmzxLFu7dq0BGLNnz/Ysu+CCCwyr1Wrs2LHDs2zjxo1GdHR0wM9eIOXl5cbAgQONtLQ0AzCee+65ep+PiEhzo6rgIiItVHx8PFdfffUR7//mm29y2mmnkZqayq+//up5jRgxgsrKShYvXlzr/r7tSQ8cOMCvv/7KaaedxqFDh/jxxx/9tk1OTvZrqxsXF8fJJ5/Mtm3bPMs++ugj2rdvz8UXX+xZlpiY6Nc5Un2MGDGCtm3b0qlTJy699FKSk5N55513OOaYY/y2u/766/3ev/nmm6SkpHDWWWf5/T0GDRpEcnIyX3zxBWCW0paVlTF58mS/KtpTpkypM2+rV69m+/btTJkyBbvd7reuPkMcNUYefb3wwgu0bduWdu3aMXjwYJYuXcrUqVOZMmUKe/bsYc2aNfzhD38gLS3Ns8+AAQM466yz+Oijj+pMv0+fPpx22mme923btqVXr15+n4uauP9+8+bNo7y8PKjzCsSdX3fVZrebb74ZgA8//NBvedeuXRk1alRQx7BYLJ6/VXZ2NsnJyfXe1/f7VlBQQGFhIaeddhrfffddtW1HjBhB9+7dPe8HDBiAzWbz/F0rKyuZP38+F1xwAZ07d/Zs17t376DOKSYmhueff579+/czZMgQrrnmmnrvKyLS3KgquIhIC3XMMceE1PnS5s2b+f7772nbtm3A9fn5+bXuv2HDBu68804+//xzioqK/NYVFhb6ve/YsWO1wDE1NZXvv//e837Hjh1kZWVV266uKrZVPf300/Ts2ZOYmBjS09Pp1atXtTasMTExdOzY0W/Z5s2bKSwspF27dgHTdf89duzYAUCPHj381rdt25bU1NRa8+ault6vX7/6n1Aj59HX+eefz6RJk7BYLLRq1Yq+fft6OnlzHyPQ/0/v3r2ZP39+nZ3C+QZ1bqmpqdXaiwcyfPhwxo0bx8yZM3n88cc5/fTTueCCC/j9739/RL2P79ixg6ioKE81d7eMjAzsdrvnfN26du0a9DHefvttPvjgA/r168ebb77JpEmT/B4s1GbevHncd999rFmzxq/Nd6AHMnX9Xffu3cvhw4erfT7A/P+sz0MRt5NOOgmAQYMGafxrEWnRFFiLiLRQNfVAXJOqnYo5nU7OOussbrvttoDb9+zZs8a0HA4Hw4cPx2azcc8999C9e3esVivfffcdt99+e7XOz2rqEdoIMA5uqE4++WRPr+A1iY+PrxZsO51O2rVrx2uvvRZwn5oeQDSmxs5jx44dGTFiRFjT9BXK58JisfDWW2+xfPlyPvjgA+bPn88f//hHHnvsMZYvXx5UaXDVdOsj2O/fgQMHuOGGGxg0aBBffPEFAwYM4Prrr2f16tWedvE1+eqrrzjvvPMYNmwYzzzzDO3btyc2NpYXX3yR119/vdr2jfl9ExE5WiiwFhE5yqSmpuJwOPyWlZWVsWfPHr9l3bt35+DBg0cUOH355Zfs27ePt99+m2HDhnmW+/YWHawuXbqwfv16DMPwC242bdp0xGkGo3v37nz22WcMHTq01qCpS5cugFl63K1bN8/yvXv31lnS6q6eu379+lr/7jUFd42Rx/pyHyPQ/8+PP/5ImzZtwjKEWV2B7pAhQxgyZAj3338/r7/+Opdffjlz587lT3/6U1DH6dKlC06nk82bN/uNEZ+Xl4fD4fCc75G688472bNnD++99x6tWrVi9uzZnHvuuTz22GPccccdte77v//9D6vVyvz58/1K41988cUjykvbtm1JSEhg8+bN1dY11vdNRKS5URtrEZGjTPfu3au1j37++eerlVj/7ne/Y9myZcyfP79aGg6Hg4qKihqP4S4R8y0BKysr45lnnjnifJ9zzjns3r3bbwihQ4cO8fzzzx9xmsH43e9+R2VlJffee2+1dRUVFZ6HFSNGjCA2NpbZs2f7nX99ekM+4YQT6Nq1K0888US1hx++abkD0qrbNEYe66t9+/YMHDiQl19+2S+f69ev59NPP+Wcc84Jy3Fq+lsUFBRUK4EdOHAgwBEN0ebOb9W/0d///neAaj1vB2PVqlU8/fTTTJo0iUGDBgEwduxYLrzwQu69995q1cyrio6OxmKx+H2Hc3JyePfdd48oP9HR0YwaNYp3332XnTt3epb/8MMPAX8PREREJdYiIkedP/3pT1x33XWMGzeOs846i7Vr1zJ//nzatGnjt92tt97K+++/z9ixYz1DHBUXF7Nu3TreeustcnJyqu3jdsopp5Camsr48eO54YYbsFgsvPrqqyFVNb3mmmv4xz/+wVVXXcWqVato3749r776qmcorIY2fPhwrr32WmbNmsWaNWsYOXIksbGxbN68mTfffJMnn3ySiy++mLZt23LLLbcwa9Ysxo4dyznnnMPq1av5+OOPa/x7uUVFRfHss89y7rnnMnDgQK6++mrat2/Pjz/+yIYNGzxBjTv4uuGGGxg1ahTR0dFceumljZLHYDzyyCOMHj2a7OxsJkyY4BluKyUlJeBY6kdi4MCBREdH89BDD1FYWEh8fDxnnnkmr7/+Os888wwXXngh3bt358CBA/zzn//EZrMdUVB/3HHHMX78eJ5//nlPU4dvv/2Wl19+mQsuuIAzzjjjiPJfWVnJn//8ZzIyMrjvvvv81j355JP06dOHyZMn8/7779eYxpgxY/j73//O2Wefze9//3vy8/N5+umnycrK8uunIBgzZ87kk08+4bTTTuMvf/kLFRUVzJ49m759+x5xmiIiLVqkuiMXEZHwqGm4rb59+wbcvrKy0rj99tuNNm3aGImJicaoUaOMLVu2VBtuyzAM48CBA8a0adOMrKwsIy4uzmjTpo1xyimnGI8++qhRVlZWa76WLl1qDBkyxEhISDA6dOhg3Hbbbcb8+fOrDY1UU17Hjx9vdOnSxW/Zjh07jPPOO89ITEw02rRpY9x4442eoaTqO9xWXcMvBRpqydfzzz9vDBo0yEhISDBatWpl9O/f37jtttuM3bt3e7aprKw0Zs6cabRv395ISEgwTj/9dGP9+vXV/sZVh9tyW7JkiXHWWWcZrVq1MpKSkowBAwb4DYVUUVFhTJ482Wjbtq1hsViq/f+HM481AYyJEyfWud1nn31mDB061EhISDBsNptx7rnnGhs3bvTbpqbhtsaMGVMtveHDhxvDhw/3W/bPf/7T6Natm2coqC+++ML47rvvjMsuu8zo3LmzER8fb7Rr184YO3assXLlyjrzXNNnoLy83Jg5c6bRtWtXIzY21ujUqZMxbdo0v+Hjast7II8//rgBGG+99VbA9Y8++qgBGG+//Xat6bzwwgtGjx49jPj4eOPYY481XnzxRWP69OnVPhs1/b8F+n9ftGiRMWjQICMuLs7o1q2bMWfOnIBp1qW+nxURkebMYhjqqUJERERERETkSKmNtYiIiIiIiEgIFFiLiIiIiIiIhECBtYiIiIiIiEgIFFiLiIiIiIiIhECBtYiIiIiIiEgIFFiLiIiIiIiIhCAm0hloCpxOJ7t376ZVq1ZYLJZIZ0dEREREREQizDAMDhw4QIcOHYiKqr1MWoE1sHv3bjp16hTpbIiIiIiIiEgTs2vXLjp27FjrNgqsgVatWgHmH8xms0U4NyIiIiIiIhJpRUVFdOrUyRMv1kaBNXiqf9tsNgXWIiIiIiIi4lGf5sLqvExEREREREQkBAqsRUREREREREKgwFpEREREREQkBAqsRUREREREREKgwFpEREREREQkBAqsRUREREREREKgwFpEREREREQkBAqsRUREREREREKgwFpEREREREQkBAqsRUREREREREIQ0cA6MzMTi8VS7TVx4kQASkpKmDhxIq1btyY5OZlx48aRl5fnl8bOnTsZM2YMiYmJtGvXjltvvZWKiopInI6IiIiIiIgchSIaWK9YsYI9e/Z4XgsWLADgt7/9LQA33XQTH3zwAW+++SaLFi1i9+7dXHTRRZ79KysrGTNmDGVlZXz99de8/PLLvPTSS9x9990ROR8RERERERE5+lgMwzAinQm3KVOmMG/ePDZv3kxRURFt27bl9ddf5+KLLwbgxx9/pHfv3ixbtowhQ4bw8ccfM3bsWHbv3k16ejoAc+bM4fbbb2fv3r3ExcXV67hFRUWkpKRQWFiIzWZrsPMTERERERFpDhwlDnIcORSVFJFiTaGLvQt2qz3S2WpUwcSJMY2UpzqVlZXxn//8h6lTp2KxWFi1ahXl5eWMGDHCs82xxx5L586dPYH1smXL6N+/vyeoBhg1ahTXX389GzZs4Pjjjw94rNLSUkpLSz3vi4qKGu7EREREREREmpFt+7fx1LdPsbVgq2dZVmoWk0+eTLe0bhHMWdPVZDove/fdd3E4HPzhD38AIDc3l7i4OOx2u9926enp5ObmerbxDard693rajJr1ixSUlI8r06dOoXvRERERERERJopR4mjWlANsKVgC7O/nY2jxBGZjDVxTSawfuGFFxg9ejQdOnRo8GNNmzaNwsJCz2vXrl0NfkwREREREZGmLseRUy2odttSsIUcR07jZqiZaBJVwXfs2MFnn33G22+/7VmWkZFBWVkZDofDr9Q6Ly+PjIwMzzbffvutX1ruXsPd2wQSHx9PfHx8GM9ARERERESk+Ssqqb2ZbF3rj1ZNosT6xRdfpF27dowZM8azbNCgQcTGxrJw4ULPsk2bNrFz506ys7MByM7OZt26deTn53u2WbBgATabjT59+jTeCYiIiIiIiLQANmvtnXTVtf5oFfESa6fTyYsvvsj48eOJifFmJyUlhQkTJjB16lTS0tKw2WxMnjyZ7OxshgwZAsDIkSPp06cPV155JQ8//DC5ubnceeedTJw4USXSIiIiIiIiQcq0Z5KVmsWWgi3V1mWlZpFpz2z8TDUDES+x/uyzz9i5cyd//OMfq617/PHHGTt2LOPGjWPYsGFkZGT4VRePjo5m3rx5REdHk52dzRVXXMFVV13FPffc05inICIiIiIi0iLYrXYmnzyZrNQsv+VZqVlMHjz5qBtyq76a1DjWkaJxrEVE5GigMUlFRKS+fK8ZNquNTHvmUXfNaJbjWIuIiEjD2bZ/G39f/nfW5a2j3FlObFQsA9IHcNOQmzQmqYiIVGO32hmYMTDS2Wg2FFiLiIi0cI4SBw8vfZhPt31KcXmxZ/muol2UV5bz4FkPHnWlECIiIuEU8TbWIiIi0rA279tcLagGKC4v5tNtn7J53+YI5UxERKRlUGAtIiLSwuUV51ULqt2Ky4vJK85r5ByJiIi0LAqsBXgEiAYsNbyOAX6NWO5ERCQ01hhrjetSKeA3XS6j5muADVjVCLkUEZHIOImarwFxwNORy1ozojbWR50fgd5B7rMbaFtlWQfgl7DkSEREGlZ6Ujr92vZj/d71/DQxn1bxYLF411ss5bXsfQA4scqyOKA0/BkVEZEGdhqwJIjtyzGMSVgsk6os/wI4PWy5agkUWB814oDabpwA0jED5m6YN1I5wA4C3zztxnyKBfAn4J9hyaWIiIRft5QrWXDlWiwWM6D2HWjTMMDphOjodkBXoIdrzWbMa0BugBTL8F4DegA/NVjeRUQkVE8DVQPjqmxATxyFbVmRv5PkuN2ktzpMR1sJMa46zr4PZOEM1zQe8zphD2uOmyONY01LHse6ttLpvUCbI0z3DODLAMuTgINHmKaIiIRfFGBe5t1Xe6cTPt18LjkHRhMdFc1Z3c6ia2rXINJ8D7ighnVH/S2FiEgTciywKcDy0zFLnP05Shw8uORBBqQPwBpj5UDpAWzxNg5XHGZd3jpuP/V27FYwa7JWBEh3NTAwbLlvCoKJExVY01IDa+/NlNfDwK1hPo4Ns3Tbl0qwRUQiqx+wocqyU3CUfEiOI4eikiJsVhuZ9swQh9m6ADPQ9nUM8HMIaYqISGj+DUyosiwFcNS617rcdewv2c+clXNok9iGTimdOFx+mNaJrTk+43hscTb6Z/T32eNDYGyVVCyAM8T8Nx3BxInqvKzF6YP5gfYNqg3XK7ig2l1l0PcFEBPju6wIi8Xgttve8tnzX6481Nzh2eTJgdOuTz7S0urOo4jI0ek/mL+/vkH1SsxrwFLsVjsDMwYyLHMYAzMG1hpU1/T7arX6LnsXi8Xg/vv3++z5iysP99eY9p131u+32/9Y1betujw+vpY/jYjIUcGCf1D9W8xrgKPOPSuMCu7/6n56tO7BP8Y8xe2n3sqMM+5m8uCJnNolm5LKEhITfX93x2CxGDz8sIHZETKuY1kwS8sDu+eeI48DMjNrXpeQUOcpNigF1i2KBfjB5/0HhFotLyrKrD6Ynu46gsUMrN3Lv/rKXP7II+Ncx0r02bstcEXAdP/xD3NqGNCqlTl/wgnVt4uLM6cdO3qrMRYUmNNff/XPozudiy8O8iRFRFqEjsCVPu9HYP4uD8JR4mBN7hoW5yxmbe5aHCWOeqUYG2v+vnbrZr6PjjaD17g4c/nKlebyO+9MdR3L92buTsxaTdXd74q5DQPauvrGPOOM6tuVlJjbuF++xroKSRISvOvKyup1WiIiLdDTePu+AEjG/F1+o157O0ocfLfnO1ITUnl/0/vmwqhDMCMK0tYBFk7ufBxWaxlWq/m7u3q1udntt4NZNXy7T4qbquTHa/p0c2oY0L69OT9qVPXtUlLM6YAB3t/5HTvM6UUXmdOkJHOdxWJeMyJJVcFpKVXBfT+4vYGNoadoMYPWykrve6h+cxN4uW9+qlc9qbpPfdM++WRYsQKOPx4+/dR7Q2YY5g2f0wnPPgvXXVe/cxQRaRl8m//E4NtZ5bb923jq26fYWrDVsywrNYvJJ0+mW1q3GlO0WMzA2h2sBncNSAIO+bz338lduuB01p62ryeegJtu8uYpJsa8Pvnu4374W15XX50iIi3KSZi1k9wKCLYzsTW5a5i/ZT6Hyg9xz+J7YIbTDKzvTjY3mOEEnKzes46BGQM9+wX+/b4SswZV4PwcaYwxZgx89BGMHAmffw4VFdWvAb7XrXBQVfCjyq/4B7EfEI6guip3yXHvKn2hXXNN9W3NGyYDi8XpehW43sMSn979O3as+7juan1W1xCsK1aY0zVroI1P32u+N2gKqkXk6OLb/GcwvkG1o8RRLagG2FKwhdnfzsZR4qhXaXay675q8GD/5bfcEiA3FrBYin2uAU7PNWCVz3DYx9ZcSzCgm24yp+4bpnHjzGlUld5qKwL1pyMi0mJ1whtUx2BeD+xBp1JUUkRCbALlzgBPJu/fC0Bi95UUlRR5Fv/1r9U3Na8Br1a5BqRgsRisWePdrl+/uvOUmuo//egjc/rFF3DppeZ8dLT3uBDZB6sabqvZ8x1fOvyVD5xO/3YPG31i9jlz4F//ch25ytAtJgvml93diU0s5hAt9VdSYh6/tLR6u7p58/yP6V7fvz+sWxfUYUREminfB6v3AX/zW5vjyKkWVLvtKNzB5n2beW3dawFLs6Eb5eX+v73Ll3vn//MfeOwxc77ma0DVHsTDd53673/hjTf8f/9FRI4umXjvs9tjDod7ZGxWG47DDtoltcOCxfy1dia6SqoBDE6Zfic26yMAzJ0Ls2a51tR4DfCdQrAl6fv3m7/vDof/73x0NLz6qnkdqhqrRJJKrJs1309Rw9Tod7dfdotxPYp57TW4/npzfu/eKrny60hgl8/TqlKWLJnm2e7nenYa69u+rnNnc9lf/gLnnutd7ztdvz6IExQRabba+cy/S9WgGvArWfBVXllO7za9eWL5E3zz8zccKD1AeaX5mN9dmg2Gp421m7v20HvvwZWu5tz79/un7X8NON+v1GLVqj6e7X78MYgzdZ3q+ef7L/e9PsyZYy6z2+ufrohI8zUXcDU4phehBNWOEgfRlmh2Fe0iMyWTUzufaq6IOoRlRjRmL98WvpjwHpn2TD78EC67zNzE3feRW/UOJ721WNesOd6zXX3v131/5wcONJdNm1Z93QsvmMtatz6Sv0B4KLButnw7Cdtb41bh4r6xqqw0S4qvcPVJtnevf5Vs97b+LwuGEY9hRHHqqQ9Stbdwd3OFmqqEdOpkTpcsgZ07zfnZs72d6birqSe6/iTu6iIiIi3Xc3h/+893vaqzWau3BztUdogf9v5AXHQcq/as4rvc71ifv569xXs5VGa2i95SsMVvH/c1oLQUFi6ECy4w3+/fX/03N/A14AEMI4pBg37AHJLRm6Y7aD7llJrP1v0A9913/Zf38cbpnmZAVW/yRERaJldkSzQQxJPKKrbt38aML2cw/cvpnHzMyfxn7X+4a9hdAERFxfDoyEe56/OZAFSWJ7Bmud3TeWRBQfWHmdWvAe7rQBQDB+YA5hNadxPODh3M6emnB87fcceZ0zVr8FQlv/tu/3UAE1x9Z/5a86BEDU6BdbN0G3DYNT8WaFPLtuFzquvhlbukGMzOw+o3zJVvFfC2/Mm8r8JigQOuYbDd1berpvfzz+b7004z3z/8sDnd6qq56K6qeNj1J6laeiIi0vK4O5OIwSytDizTnklWahYAsVGxnNT+JM7qfhZXHncl3dK6YbfaybRn0iaxDQfKDvDr4V89JddVjRljTkeM8C5LS6vvNeBvgGvoBl7g5pvN6Ndi8QbNS5fiWeab3rZtNaf6ww/+21ct0RYRaZl8f3SPvGMJ3344yp3lfLbtM64YeAUvrXkJAKfhZO76ufxy4Bd+c/Z+wOI3gkNqajDD3bqGBKKUadPMnsotFtizx1z6xRd4lvmm9/335vvjXYXd7pJp33Xu7X/72yBOvgGoV3CaY6/g7k9bFFAZyYwcAXfe44EI94kvItIsBdcMaNv+bTy78ll6tu7Ja+te4+tdX9M6oTU3n3Iz9y2+j/SkdLYWbCU5LplerXthjbFis9q494x7/Xp+jUTeRUSkquOA713zwff+7WtN7hru+uIuz/uhnYayes9qFm5fyIGyAyTHJhMTHUNSbBIju43kwbMexG498uOZTZjcta2axzUgmDhRnZc1O5185ptbUA1wK/AIUBrpjIiINEO+9ZzrVz2nW1o3bsq+ibu/uJvismL6tetHUWkR3+d+z4TjJ5BiTcEwDJw42V20m20F28hKzSLTntkA+TfwBtcLgd80wDFERFoyd1B9IaEE1WD2wxEbFcug9oNIS0gjLSGNSmclMVExfPDTBxiu4Le4vJhPt33KhH0TOOmYk0I4Yj7ea4CdqsPxNncKrJsdd49fIyOaiyP3MGZgDeZTq/wI5kVEpLlJ85mvf4cS+cX55BXnkRibyL7D+/il6Be6p3Xnu9zv+HL7l1QalRSXFzMoYxA3DL6B07qcFmKpRG1iMKsujqC5lFiIiDQNF/nMvx1yainWFEZnjWbu+rms37ueDq06sDZ3LcelH8fU7Kk8v/J5Kl0FecXlxeQV54V8THgBmAAUhiGtpkVtrJuV03zm50csF6FzDT7aCJ2uiYi0TMF1JuHuHdwWb2Nk95E8f+7zrNqzih/3/sgxtmPo2bonPdJ6cKjiEIt2LCI1oSF7gfRtw62exkRE6u8d1/TEsKTWNqkt7296n18O/EK7pHbERMWQGJvIj/t+5INNH3BOz3P8trfGWMNw1D/6zPcIQ3pNhwLrZmWJa9q5UY7m7gzgtdeqL6s6pnSgjguqd7fv3uYtn61OqPP4vq9HHql7XX3ScbNaa14nItK0RPvMBxf42qw2YqNiOa/XeazcvZIt+7fwwU8f8NP+n/il6BeiLdHsPbSX/OJ8tuzfQo4jx/Ob+N573nTCcw3wlVZ1Qa37B+rIzL3O3VN5VTExtf/G6xogIs3Dwz7zK8KSYn5xPkWlRRQcLuCnfT+x79A+oi3RVDor+e66lbxw/j/5de1JGIZBv7b9OKv7GTVeA6KqRJW1XwPcwbX/CBR17Z+TU/N2v/td/dMJ9hj1pcC6WdrRqEdzD611pKp2uW9yPxxYXeN+kyaZA78bhtnzK8Btt9W9rqrTT/ceOz3dXNa/vzktLfXmsXdvcz4jI8gTFBFpFK6xSdga9J6Z9kzOzjqbuevn8uOvP1JhVGBx/TtccZhdRbtol9gOW7yNhNgEv/GvawpY6yvwNaDuEveYGHM8UsOAxx83l3Xv7r9NfQLhTp2q3/BV3f/mm73jYCu4FpGm6XbXNDlsKe47tI9dRbswMEiOTaaotIgOtg4kxCZ4tnH+5z26p3bnj8f/kdpCx5q6ww58DXgh8MY+4uLM+3bDgH+4OhTv2tV/m/r8XkdFmeNfG4Y3pnHvV59jBEOBdbMRYnQbBqNGVV8WH29O3WPQ+Q7FVbu6Hw7Mng2XX27O//e/9V9Xlbv7fvCOb1rmM/qXO+8bN5rTvHA0HxERaTDdgt7DbrXTt21ftju2Y7FYSIhJwGKxEBMVgzXGiqPEQWJsIl3tXYmNjg04/vUll1RPt5VrBK0ertp8V15Z3xzVXeJeXu4dj3TKlOrrBw40p+6xS2uyfTtU1tHX56OPwrXX1pklEZEmYFfYUoqJiqG4vNi8HkTHEGWJIvdgLinxKa4tDCCKkd1HMuev1Tstc49h3aePOa3r9ziwwNXBS0shN9ecnzix+vqTXNm5/vraU6+shNWucrxXXw3uGMFSYN1suOtjxzbqUd1P+Y85Bj79tPp6d4D6yy/mdN686tsEqn5hzjtdr5qraNhs5vsZM8z3zz5bv3U15cGd302bvOt27zanyeF7ACgiEmbPhZxCpbOSnq17cmzrYykuK+bUzqfSKr4VMVHmUCrWGCuJcYl+PYLHui45PXvCG29UT/PgQXP600/m9D//qb5N3dcAo87q2P36mdNon9rwa9ea03/9K7i/g4hI82YPW0qJMYn0a9vP877SWcnh8sPsP2zWKoqJNZ9KPnP1tSz5MKva/oWu/sc2bDCn//539WP4/ra74wr/a8BPdV4D3GNYx/h0u71ypTl95pn6n6/7YXBW9VMJeIxgKbBudr6ve5NQbNpkRtD//S9ggGF2MuMOPt0l1OANouPiak8yUBUQcz7K9bqihmoiUFRkvneXTvs+laptXU15cH+hT3A17XZ/wSwWKC6u/TxERCLH/QMXXetWtbFZbcRGx9I6sTUp8SncPvR2zuhyBsfYjqFtUlviouPIiu/A5I7jsH+wAN9rwObNrjR8CrIXLjSndT2UrPkakO5zHQh8DQCzFNx901ZRYU7dN141VT0UEWlZ1jRIqnExcVza71JPcG0YTgxnJb3TjnW9NwCDX3dkABZapXiHy/3yS3OakkKtfH/bnU7fZZZ6XQMmTIA1a8z5clffl0dyDejRw/sw2H1Nq+0YR8JiGLosBTPwd+S4H9804H/XN9/A7bfDokWuIzqJooKMtHJ27080j27U3Z4hOtq8+antQ2+u813hn2jN+wS/rqHSERFpPO7fyHeB848oBUeJg/sX30/P1j35v/X/x497f2RU1ii6p3UnMTaRk9odT/83F2Gf8SCUlmLBSSylZB5TzuZfzKeQ9bkGxMdDSUl4rgF/+hO88EL1dGrKQ0xMzTdFgfJTdZmuASLSNCUChzF/K511bFt/7uuC3WonLaYVB8qLKXOWsXXfFl767StExZQS3zqHw3m9AFi9Zy3Htx9Ya5oJCXDoUP3ut73XgcDXgL/8xVsjtT7XgLg4b/9Jvvr29Tb5rJqfmo7hFkycqBJrMW3a5BdUexn80v8cavsSV33CVFdbNu8+A2p9UpWY6O2R3N2pjFtt66ryLVFPSjKn7uqN3bvDjz+a8+4v6a231p1/EZHIOLKgGsx21tcOupZ3fnyH5T8vZ1/JPl5f/zpPLH+C19a+ymtL55iP8f3uSgx+yjoXcNZ4I1P1tzvQTU3gfWovrZg0KXBQXfWY7nXnn3/kJQ233ALPhV7bXkSkgZS4pnUUDwfBUeJgh2MHF/a+kO37tvDmd6/y2ooXeGLp31mftw4Ap7OS7Ft+CxhgqfQ0E/JV9bf48OG6j+3dp+ZrwNSpNQe8gbb/7W8DX3+OO67moLq2YxyJEGqRS4uyY0eAoNpl0SKM+Z/ByJHVVrnbI7hFRZnVPHyD3ao3Y94P7unA+hqzdPiw2Xufb6/k7vbUta075hiz6rr7OOXl1fPgbmu9bZu3N3AwS9sffhgRkRapoKSAn4t+pntad5xOJ1FRUZRXlrN7/04+3b2NCRc+wkn/ft1/p1quAaef7v8+Ls78ffVta13zNaB2Tz8dOI361kxy155q1cpb/c83LfcNmcUCjz0WfP5ERBqP+4epTVhS27Z/G8+sfAZbnI20hDSGdD6FMb3OJSnaSsnhA6za9S2uJsxsL9rJpDf+xE0j7sRu9e88s+plwWo1ayzNnetddqTXAPdoEFXTqO81wF2D6fvv/Zf7phPKMQJRYC0md3fZPgzfCg0+62v7sPmWVl93XV0HTah1bW3HqW2duyO1UNMREWlp8orzyCsOMPSBs4Liw0Xk2bxtuI/kGuBbWhCOoRqPdFvf9wcOhO84IiLNnaPEwTMrn6F7andeWfsK3+V+R3xUHAdLD3Jql1OZcsJEEn7J55n3plNilGO13MipvUbQLdUMqmv7zfQtrb700tDyGa5rQGPGAQqsxZRax9Anda0/Ip81QJoiIlITa4w18ApXz441rm+Qa4CIiNSPBbPUel/IKeU4crDF2TxBNYDhKhFfsmMJbRPbcFyrtnw0917PPn2nDgz5uEcDtbEWU5cuMHx44HXDh5vrw67mauAiIhLIqpD2Tk9K9xtaxSMqmn6dTyS9KEAnGQ12DRARkfpxD8tTGHJKRSVF2BPsnqAawILF03/Yurx12Dv6jEfVOg1bSruQj3s0UGDd7PzaMMn26gUPPVQ9uB4+3Gx03KtXAxzU3ctM9XZ7IiISyCkh7d0ppRPXnHBNteC6X/vjuGboDXRavd1/hwa9BnRugDRFRFqit1zT0HsEt1ltHK7w72HMiUFMdCxYIMoSxeFKV7ue1mlknXYBmZ0CPJCValQVvNnpiLdnwDAbPNjsFnXHDrM9XWqqWUrRIDdUvuY3cPoiIs3dfcCdQFlIqditdgYfM5iisiIudl5MSUUJ1hgr0VHRDO56KvZxg2FQdiNdA3a5psc1UPoiIi3FmLCllGnPpG1iW79lFc4KrDFW4uJbcajiMAnWZBgymKyOxzH5jNuxW+1hO77XaQ2QZmRpHGuayzjWZwBfuuZbyn9ZI4zNLSLSYoTvN9NR4iDHkUNRSRE2q41Me2YD3TjVRtcAEZH6c/9mFgD2kFL6Ye8PTPpoEkt2LfEss8WZ14KstCxuHHwj1lhrA18b3OfTEe+D1qYnmDhRgTXNJbCGlnUTcgKw2jXfEs5HRKShua8B+4GW0JlYS7qmiYg0NPdvZmvC0TR0zZ41PLT0Ib7P+94z9GLPtJ7ce+a9HJfRGDWJwvegoCEFEyeqKnizdDLwbaQzESJ3UK02diIiwUmj+Qej0XVvIiIiPq4HniUcPYMDDGw/kMdGPcbmfZspOFxAakIqPVr3oEOrDmFJv3b3+MzbG+F4jUMl1jSnEutewE+u+eb833Yb8Ihrvjmfh4hIYyrADKohXL+duw/s9txUpSWkkdU6q5FuqtwlFSuBQY1wPBGRlsD923kO8GEkMxIi93m0BfIjmZE6qcS6xdqE94N4DfDPCOYlFO6gOjGiuRARaV58q39HEWrvsGty13DnwjtZsWeFZ9lJHU7ivjPvY2DGwJDSrl2Sz7yCahGR+hsCLAc+inRGQuD7QKBpB9XB0nBbzU4r1/RfEc3FkXvNZ744YrkQEWmetrqmoZVY7z6wu1pQDbBi9wru/PxOdh/YHVL6tTvkms5pwGOIiLREy3zm/xqxXIRmrGtqjWguGkLEA+tffvmFK664gtatW5OQkED//v1ZuXKlZ71hGNx99920b9+ehIQERowYwebNm/3S2L9/P5dffjk2mw273c6ECRM4ePBgY59KIynymW+OH8grXFO1rxMRCV43n3lLjVvVZcv+LbRv1Z4bTr6B6wZdx42Db+S8nucRHx3Pit0r2Lxvc92JHBHfPF/bQMcQEWnJ3M11ZkU0F0emr8/84Rq3aq4iGlgXFBQwdOhQYmNj+fjjj9m4cSOPPfYYqane6m4PP/wwTz31FHPmzOGbb74hKSmJUaNGUVLiHcv58ssvZ8OGDSxYsIB58+axePFi/vznP0filBqJ+0lPKWZ75ebC94aqImK5EBFp3vb7zCcfUQpOp5M1eWu4Z/E9PLj0QWYumsmavDVcd+J1xEfHU3C4IDxZ9ePbWeXWGrcSEZHa/OIzf+QPWBvfl8BG1/ypEcxHw4lo52V33HEHS5cu5auvvgq43jAMOnTowM0338wtt9wCQGFhIenp6bz00ktceuml/PDDD/Tp04cVK1Zw4oknAvDJJ59wzjnn8PPPP9OhQ92dsDSfzst8ReNtX9ccOgCzAQdc83uBNhHMi4hIczcJeNo1/zfgvnrv6ShxMOWTKfzf+v+rtm5A+gAGpg/kigFXMDxzeFhyanoPuMA1fzrwRRjTFhE5GrmD6gS8TWyaMnd+o4DKSGYkKMHEiREtsX7//fc58cQT+e1vf0u7du04/vjj+ec/vR1ybd++ndzcXEaMGOFZlpKSwuDBg1m2zGxjsGzZMux2uyeoBhgxYgRRUVF88803AY9bWlpKUVGR36v58f1ANvWnVVfgDapPRUG1iEio/oF5MwVwP7Ct3nvmOHL4uehnUuJTMAwDwzA8z2e/z/ue49sfT4/WPcKY1wK8QbUFBdUiIuEw0zU9DIyJZEbqwTdWaT5BdbAiGlhv27aNZ599lh49ejB//nyuv/56brjhBl5++WUAcnNzAUhPT/fbLz093bMuNzeXdu3a+a2PiYkhLS3Ns01Vs2bNIiUlxfPq1KlTuE+tkez1mW+qwfVp+HdYFrh2goiIBMu3hKI7sLBee+UezCXHkUPX1K7Y4m1UOCuoMCowDAO71U7nlM5hHHJrG94hwiDUnsxFRMTtbswaoWD2En58BPNSG98YZXvEctEYIjrcltPp5MQTT+SBBx4A4Pjjj2f9+vXMmTOH8ePHN9hxp02bxtSpUz3vi4qKmmlw3Qb4Aejtem+haVWztgOFPu+bQ5V1EZHmxMB70zICmEBto0Y4ShzkH8znQNkBDpUfonNKZzLtmVQ4K4iLjsMWb6OjrWOY8vYEcFOVvIqISPgUYo4YdBBYA6Tgf+8dSQ78h4mcB2RGJCeNJaIl1u3bt6dPnz5+y3r37s3OnTsByMjIACAvL89vm7y8PM+6jIwM8vP9x0CrqKhg//79nm2qio+Px2az+b2ar2PxL7luiznGdaRZ8H6xY9ANlYhIQ/H9fX0BaF3jljmOHHYf3M3QTkNpndia0opSSitLKakoYVfhLjLtmWTaM8OQp+4oqBYRaQwHgPau+SKaRi3Wf+MfVG+n6VdXD11EA+uhQ4eyadMmv2U//fQTXbp0AaBr165kZGSwcKG3eltRURHffPMN2dnZAGRnZ+NwOFi1apVnm88//xyn08ngwYMb4Syagjb437T8C/NL9WsE8tIL/y90P6A8AvkQETmaGECia34/5u/w/dW2OlB6gHaJ7Tir21lYY6xsKdjCT/t+4tdDvzKsyzAmHD8Bu9UeQj7+4zq2u823HqyKiDS83cAffd5bgHD2lVFfDtexJ/gsM2jpJdVuEa0KftNNN3HKKafwwAMP8Lvf/Y5vv/2W559/nueffx4Ai8XClClTuO++++jRowddu3blrrvuokOHDlxwwQWAWcJ99tlnc8011zBnzhzKy8uZNGkSl156ab16BG9ZDMCGYRzAYgHDaEtFhYXiyv0h3ijVx2t4x6h2+wDv0GAiItKwivHvLfxO12s/7pIDu9XOa+teY/P+zYzqPooLel1AWWUZcdFxlFWW0SquVQjHr1pKMgaz6p+IiDS8F4C7gK6u91swf5f/AUxshOPb8a+G3gqzBP3oEdHhtgDmzZvHtGnT2Lx5M127dmXq1Klcc423KrNhGEyfPp3nn38eh8PBqaeeyjPPPEPPnj092+zfv59JkybxwQcfEBUVxbhx43jqqadITq7f+J7Nc7it6hwlDjbv28zTK57mhXNfJsqnPkJlJcTE/IBZdTycTgZWVFnWD1gX5uOIiEj9pVD9huY+VvwykkveuoTi8uJqeyTFJvHfi//LScecFMRx/gNcWWVZPFASVG5FRCScrsT8ffZ1ItXv2UPlwGx+VLVjygLMQLv5CyZOjHhg3RS0hMB62/5tfL79c+ZtnseW/VuoNCo5M+MAsy/8BYsFVwm2OYU4oDSEoz0C3BZguQX1+Coi0pT4lyIbhvmgdfDTdn4uj/MsT4pNoqu9K9OHT2dY5rA60izAvJEKdPvgLR0XEZFIC/SQFeAhAt/L11cHYE8DpNv0BBMnRrQquITHDscO1uevp32r9mwr2EZxWTF7Du7h+f2VvLGtDae2bsObV/1IdLR7jzKqV9mLxWyPdzXeXsWXANNc09p0A7aG52RERCSM3MGv9+YqJgZW3uDwbmFAeSV88GM5Nqvv+KIFwGPAw9TeV0YC/kN/iYhI0+Cumj0K+NRn+e2ul5sFGA48Dgx0LXMAn2A2MdpXx3FW++x39FKJNc27xHpN7hqmLZzGhvwNjD9uPPd9dR8p8Sl0aNWBTfs24TScZCRl0DapLQ+f9TBnZ30DzAjDkWNQp2QiIs2Lo2Q7yTHdfB60umsyHYmVwKAw5EpERBpPGuaD01BNAx4IQzpNWzBxYkR7BZfQ7D6wmzsX3snyn5eTEJuAxXV3dKD0ALkHc+mS0oWk2CQOlR/CMAzyD+bjKLkRswTD99Wz5oMA5sfk4Sr7KKgWEWlu7Nau7CzaytT5Uzhv7ljOmzuWt9Z1pKICnDW25LFgdk5Z9dqhoFpEpPnZj/9v+TwgqY59BmAG4777tfygOliqCt6Mbd63mVV7VtE6oTVJsUnsP7yf7I7ZrM5dTUFJAW2T2lJcXkxcdBz92vVj98Hd5DhyGJgxsEpKmwIlLyIiLVC3tG5MP306OY4cikqKsFltHKzIbITRI0REpOkZAxyMdCZaBAXWzZijxEG7pHb8cuAX9h7ayw7HDm4cciMVzgo2/boJDLBgoX/b/lze/3LW5q2lqOTo6vZeRESqs1vtAR6yioiIyJFSYN2MpSaksvvAbkorSymrLKO8spwHvnqA8489n3N7nUvv1r35cd+PFJUUsWDbArqndcdmbV5tyEVERERERJo6BdbNmC3ORv92/elo68ipXU4lOioaa4wVp9PJ+vz1RFmicBx2kBiXyPxt8+mR1oNMe2aksy0iIiIiItKiKLBuxg6WHeSWobfw7+/+za2f3kpRmVnNe1D7QVw36DpiomLo3bY3N35yI51SOtGnXR+1oRMREREREQkz9QrejKVYU3hu5XMs2rmISqOSxNhEkmKTWJ+/nn+s+Afr967HGmMlOiqa4zOOp7W1daSzLCIiIiIi0uKoxLoZK6koIacwh18P/Vpt3Yb8DdAXYqNjObnDyVw76FrPcFwiIiIiIiISPiqxbsYOlx8mPjqe2KhYv+UxUTFYY6yUVZQRExXDvWfcy2fbPqOzvXOEcioiIiIiItJyqcS6GbNZbcRExZAYm0ilUYnTcGLBQqWzktLKUpLikoiLimPD3g1cffzVal8tIiIiIiLSABRYN2OZ9kwGZgxkW8E2DpYdxIJZ1Ts6Kprj2h3HwbKDZNozObXLqQqqRUREREREGogC62bMbrVzS/YtlFWW8cFPH+A47MDA4Lj047h64NUMPmYw/dL7RTqbIiIiIiIiLZrFMAwj0pmItKKiIlJSUigsLMRms0U6O0FzlDj48dcfyT2QS3xMPK0TWtMxpSMdWnWIdNZERERERESapWDiRJVYtwB2q50hHYdEOhsiIiIiIiJHJfUKLiIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIVBgLSIiIiIiIhICBdYiIiIiIiIiIYhoYD1jxgwsFovf69hjj/WsLykpYeLEibRu3Zrk5GTGjRtHXl6eXxo7d+5kzJgxJCYm0q5dO2699VYqKioa+1RERERERETkKBUT6Qz07duXzz77zPM+JsabpZtuuokPP/yQN998k5SUFCZNmsRFF13E0qVLAaisrGTMmDFkZGTw9ddfs2fPHq666ipiY2N54IEHGv1cRERERERE5OgT8cA6JiaGjIyMassLCwt54YUXeP311znzzDMBePHFF+nduzfLly9nyJAhfPrpp2zcuJHPPvuM9PR0Bg4cyL333svtt9/OjBkziIuLa+zTERERERERkaNMxNtYb968mQ4dOtCtWzcuv/xydu7cCcCqVasoLy9nxIgRnm2PPfZYOnfuzLJlywBYtmwZ/fv3Jz093bPNqFGjKCoqYsOGDTUes7S0lKKiIr+XiIiIiIiIyJGIaGA9ePBgXnrpJT755BOeffZZtm/fzmmnncaBAwfIzc0lLi4Ou93ut096ejq5ubkA5Obm+gXV7vXudTWZNWsWKSkpnlenTp3Ce2IiIiIiIiJy1IhoVfDRo0d75gcMGMDgwYPp0qULb7zxBgkJCQ123GnTpjF16lTP+6KiIgXXIiIiIiIickQiXhXcl91up2fPnmzZsoWMjAzKyspwOBx+2+Tl5XnaZGdkZFTrJdz9PlC7bbf4+HhsNpvfS0RERERERORINKnA+uDBg2zdupX27dszaNAgYmNjWbhwoWf9pk2b2LlzJ9nZ2QBkZ2ezbt068vPzPdssWLAAm81Gnz59Gj3/IiIiIiIicvSJaFXwW265hXPPPZcuXbqwe/dupk+fTnR0NJdddhkpKSlMmDCBqVOnkpaWhs1mY/LkyWRnZzNkyBAARo4cSZ8+fbjyyit5+OGHyc3N5c4772TixInEx8dH8tRERERERETkKBHRwPrnn3/msssuY9++fbRt25ZTTz2V5cuX07ZtWwAef/xxoqKiGDduHKWlpYwaNYpnnnnGs390dDTz5s3j+uuvJzs7m6SkJMaPH88999wTqVMSERERERGRo4zFMAwj0pmItKKiIlJSUigsLFR7axEREREREQkqTmxSbaxFREREREREmhsF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1iIiIiIiIiIhUGAtIiIiIiIiEgIF1ke1O4EYwFLPVzpQEJGcioiIiIhIuBQCvah/HBAD3BGRnDYXCqyPKqvw/4LcD1QGsX8+kOazf9dwZ1BERERERBrEULz38XbgpyD2rQQewj+WmB/m/DVvTSawfvDBB7FYLEyZMsWzrKSkhIkTJ9K6dWuSk5MZN24ceXl5fvvt3LmTMWPGkJiYSLt27bj11lupqKho5Nw3dVbMD/+JAdZFATcDRi2v/UCnAPvm4P1i/SncmRYRERERkZA8gfd+/esA6zsDDmqPBR4H4gPse7YrXStmCfjRrUkE1itWrOC5555jwIABfstvuukmPvjgA958800WLVrE7t27ueiiizzrKysrGTNmDGVlZXz99de8/PLLvPTSS9x9992NfQpNVCLmh73UZ5kFM1B2f1EqgUfrSCcV2In/F+x3VbZ5wZX2cyHnWkREREREQjEf8978pirLx+J/T78DSKkjrSlAic8+Dsxg2q0UswQ8UPB99Ih4YH3w4EEuv/xy/vnPf5KamupZXlhYyAsvvMDf//53zjzzTAYNGsSLL77I119/zfLlywH49NNP2bhxI//5z38YOHAgo0eP5t577+Xpp5+mrKwsUqfUBFyA+UU67LPsVcwvghMzUA7Vf/F+uTJ8ll/nOrbaYouIiIiINL4ozNJktzZ479s/CEP6KZhxhgF85bO8DDMOGB6GYzQ/EQ+sJ06cyJgxYxgxYoTf8lWrVlFeXu63/Nhjj6Vz584sW7YMgGXLltG/f3/S09M924waNYqioiI2bNjQOCfQpBRgfpjf81nmDqivCDo1i6X6C8Bqrbp8D/ffb2CWkLulYX6JA7vzzsBp1ycfHTvWvC42NujTFBERERFpAQZixgKG6328a35v0CnVFAckJ1ddfipPPFE1wF7sysfOeqVdm7g4/23796/fukiIaGA9d+5cvvvuO2bNmlVtXW5uLnFxcdjtdr/l6enp5ObmerbxDard693ralJaWkpRUZHfq/l7DjOYdetBfQNqR4mDNblrWJyzmLW5a3GUODzrYmPBMKBbN/N9dDTEx5sfZMOAlSvN5XfeCVCMWc3cbR/ml6q6++83p4YBbdua82ecUX275GRz2ru3uS3AL794159/vrncvU7N60VERETk6BMHrPV578Csvl0/VeMBMEhIMO+x+/VzHSEOEhIgMdFc/v335vKbbgI4FTP2GOmTahdgRrVjJSZClRAvoJ07obzcDJrd9/rr19e9LlJiInXgXbt2ceONN7JgwQKsVmvdO4TRrFmzmDlzZqMes2FdgH8ptVHDdtVt27+Np759iq0FWz3LslKzmHzyZKCbZ9nWreYH1+mEQp++CQYNqppiquv4Y4EPXct8n5x5uZ9Q5eeb819+WT1/xcXmdONGczpiBHz2mRmEf/EFvPtuPU9URERERKRF8i3IOhn4Jqi9A8UD8D5OoxyIZd068169vBz2+hR+By4hdvcU7s7TTGABsNSzhfv+vq7S6h49zKnTaU5vvBGefBKefx4mT6553Z//XHu6DSViJdarVq0iPz+fE044gZiYGGJiYli0aBFPPfUUMTExpKenU1ZWhsPh8NsvLy+PjAyzTW9GRka1XsLd793bBDJt2jQKCws9r127doX35BrViXiD6sABbE0cJY4AXyLYUrCF2d/O9kvLXXI8eLB/GrfcUj1dszrGPCwWp8/LwGKBVau82x17bN15tNn8p599Zk6XLKl6PHM+/ujuM0FEREREjiq+0ekrBBtUV40HYqNiGXLMEAAqnU5W/LKC1FSz76ozz/Tfd8aMALnxVM02fOKAJVgsBuvWBZU1qnaZ9cQT5vSee2pfFykRC6x/85vfsG7dOtasWeN5nXjiiVx++eWe+djYWBYuXOjZZ9OmTezcuZPs7GwAsrOzWbduHfn5+Z5tFixYgM1mo0+fPjUeOz4+HpvN5vdqnu7HHJsazDYUzqD2znHkVAuq3bYUbAG8VSzcT5Zc/cYB8J//wGOPmfOGTzzvrpptGBYMw4FhRLlelgAl3LVzl44fOOD/VCvK55PrWxW81LcDdBERERGRFss3qN4BXBl0Cr7xQGxULKOzRvP59s8BqCiL4+SOJ+JwxAJOfMIy/vc/cFcADhwHuGOBTp5YoH//4Pt8ak4iFli3atWKfv36+b2SkpJo3bo1/fr1IyUlhQkTJjB16lS++OILVq1axdVXX012djZDhphPUUaOHEmfPn248sorWbt2LfPnz+fOO+9k4sSJxB8VRZd3uqbxBNOGwq2opO625e421m7uWvvvvQdXur67+/f77+PfIUGq3xOrVau8xdQ//li/fPp+Qfv2NZfdemvg7SBwW20RERERkZbDt3auA3M86uD5xgOD2g/ivxv+yy8HXB0axRQz5J+nYBbeWUhOLgdg/ny4+GLXkR3+6VXv9OxnTxywbt1XBDPedVyc//spU8zp3XfXvi5SIt4reG0ef/xxxo4dy7hx4xg2bBgZGRm8/fbbnvXR0dHMmzeP6OhosrOzueKKK7jqqqu4J5J1ABqN7xOq4INqAJu1/iX1viXCCxfCBReY7/fvB59R0jzbVn89iWFEMWjQJuASvzTbtTOnp5wS+NjuygerVoG7s/f77jPz0LOndzt3ibbPR0REREREpIV5AnA3h72OusehrplvPNA6sTWFJYUM7TQUgOioGMb2HMtf5t0AQHFxDEuWwNmukbwcDkipcujAcYDFVWK9E3O86/rZvNmcumuqPvmkOf3zn2tfFykWwzDq3yi3hSoqKiIlJYXCwsJmUi08Ee8Y1Uf+3+cocTDzy5meat++slKzeGL034mNtXjaMIwdCx9+WG1Tj7o/Sa1x9xp+yy37eewx/4jcvb87QK763m3OHLj2WmjfHqp2/h4bW73NhYiIiIhIy+G+OU7EHJXnyPnGA7/t/Vt2Fe3irY1vseb67yD6IHHT2zAgYwAl//cv1n8xAGoY8ad+EaW5r8VSSdXyXasVDh+uHgfExEBlpXe7nj1h06a614VLMHGiAmuaW2BdgHdYrb8B94WU2rb925j97Wy/4DorNYvJgyfTLbVbLXseKd8v41H/0RMRERERCUI03n6VwnMv7Y4Hzut1Hnd8dgeZqZkc2/pYyp3lxEfH89O+n8gpzOHxkY9zcseTQzjSfMBV3M0OjrT6emNSYB2k5hVYhz8wdZQ4yHHkUFRShM1qI9Oeid1qD0vagbnPYT/m8FwiIiIiIlI39320g1CqgFflKHHw3Z7vWLZrGW9tfIu1ed4xsY9LP46L+1zMqZ1PZXjm8BCPFAO4i5mbfhgaTJwYsXGsJVT7696kCt8AOsWaQhd7F+xWO3arnYEZA8OfxTql0Ry+UCIiIiIikZfgMx++oBrAbrUTExXDx5s/ZsPeDX7rNuzdQNLmJE7rchprctdUiyWCU4H34UAh4T6PSFJg3az4dn8XXElvoIHfs1KzmHzyZLqlNUSV79rsx1udXURERERE6ubusPirBkm90lnJ6rzVxEXHYY2xYhgGFosFp+Hku9zvOFh2kAe+eoDismIMDC7ufTED2w+k0llJm8Q2RxBopxLscMFNWZPuFVyqKndNPwtqr6oDv7ttKdjC7G9n4yhxBNzP3U3+e+9VX+bboVigZVWX+2/j+1Cg5mHRAu27bZt3fceOgdKubtu2mrcLnD8RERERkaakl8/8qQ1yBKfhpE1CG5yGkwN/LeTg34o4sOEEnIaT1IRUxvQcxfwrP2HJhMVcffzVOHFyRtdhjOj+GwZ26MPML2eybb95s15zHABmNXaoreZqly41359HRVVPd+fOwOkMHdp4cYAC62ajwGf+N0HtucOxg7aJbTkn6xzOzDyTc3qcw5BjhhAbFcuWgi3kOHJq3d89tNaRqtrlvulV17TmLrxjYqB1a3Ofxx83l3Xvbk4LCuCXX/zTP+64wOm491m5EkaMMOcTE2vOo4iIiIhI0/KTa3pBgx2hdWJrstKySE9K9y58bSFtEtvQNrEtZuho3iz/beHfmP7FdMxq3QYYMazfu75aoV3gOMC3+veSgHk54QSIjg6cz/h4OOYYM73nnjOXdekSeNuvv/bmY/p07/6+whUH1DuwLi4OrSt3CVX/I96zuLyYz7d/zj2L7uH5Vc/z79X/ZuPejVx/4vVcNeAqyivL604EuOSS6statTKnPXqY0yuvrG+urqhzi/Jy+PVXc9496Ltbmqsmue8XYM2a2tMbNAgWLDDnDx+ufVsRERERkabnnQZLOdOeyYD0AfRq4y4dN4AouqR0ofCNh/22PVh2kIP3moNJxx1jtsle8sxv+T7v+zoL7UzuMHRkwLXvvAMVFYFLkQ8fhp9/NufrM261O0CfMcOcNtTQvPUOrAcMGMCSJYGfKEhjcBXPMiiovXYf2M3zq54nrziPLvYuJMcns6twF+9teo/pX0xn8Y7FPLfqOU+1japiY81pz57wxhvV1x88aE5/cj1E+89/qm8TqIqFOe90vequhtGvnzmt+uTKd582bWr+O9RVtUNVwUVERETkaGa32pl88mR6t+kNgCXGLIlaf89L5Hwxwm/bm7NvhhKzZPuiJ+4HoGT5n9ju2M6+Q/s82/neY7vv4833Fa44oDik6tiDB5vTuLiat6msNKuK/6aGSr+NXhV83LhxnHnmmdx6662UNVSYL/Wwst5bbtu/jc+2fsaHP31IaUUplc5Ktu7fSn5xPsXlxSz7ZRntktvx3Z7veHz542a1jU2b4NNP4b//xazWYZZmbzYfSOHby/zCheY0Obn2fASqAmLOR7lenWuoJmK68krY4OqcsKLCf11Cgnf7ffsIWt++8O67ZhruL1P79sGnIyIiIiLSMK5vnMNs2kS377YxPXkMALHRcYCBY0dXwAIJuz2b3vOqq+TcmsePv/6I2QmZheLyYmKivP1j+97fV1b6LrP4xAJHVh37+uvh22/N+dLSwNuMdBWId+kCn39efd2sWeYx7XZzWUwIXXvXO7B++OGHWbx4MR9++CEnnHACq1evPvKjSoNzd1iWV5xHUWkRyXHJFJcXU1BSgIGBxfXvcPlhdhbu5Pv878nZthquvRZGjYJLLzUTqqikxzEHPOkWFnqP4W6vfPCg/xMeq7Xu/PmXWO+o8UnVn/7kLQUP9EU7dMic3nyzOfXtaM1XTV/S9evh/PPNeaerU8Lc3LrzLyIiIiLSOF5wTRNq3Sok33xjxgFnnYX9tLMAiC4t47juB3APj/X7V27zbv+iKxYsSWfN9d/hDiv33rmZxJgqnRlV4b3nD1x7tT6mTIE5c8z52oLx+fNrDtrnz4c77jDnC1zdWbmD/yMRVOdlQ4YMYfXq1YwePZpTTjmF8847j4suusjvJU1DjiOHDXs3UFZZRrmzHIvFQlmlt6aB4foXGxVLUWkRZeWlFK1fCYsWVUnJ4KescwFnjR/0qh/Wmp4YVd/nwlqfVE2aBC+84N3elzuQdndM9thj5tQdJAeyahWcZf5OeDot6NrVuz7F1Y+Cu924iIiIiEjkuatsNtBwtZs2we23B4gDnKzpeD5gYImqZEinIT7rXB2ZzYgiZVYqnf5u9h5mlLYiLqaWetn43vMfWYn1HXfAk09606rNueeaATSYNV3B7HcJoL9PF1ZDh5rTUKqDB90reGlpKfn5+VgsFlJSUqq9pGkoKimiuKyYjfkbOS79OJyGkyiL/3/3wPSB/LjvRyqNSiqNSmy/Hgic2KJFGPM/85To+jr9dP/37vYNvm2ta34KVWXnKp5+OnAaAI8+ak7dQ2mBfw9/FovZLhzM3sABTjwRPnONVFbiGgYwJ8ebblGRucw9FRERERGJPHf0WEuHQqHYsSNAUO2yaBEFK5dz88fTSEuoEtj3eJeMpAx6tu6J3Won1mqWrq39wttF95G2n37wQXN7d+Dsu/9DD3m3q2kYrYEDzfl58+Dss81lJSXmUF3u2GD9eu++7t7DA8U79RVULfIFCxbwxz/+kfbt27Nq1Sp69+595EeWBmWz2iirLOPdTe9y+6m3syF/A1/v+hpbnI2isiKOSz+O8449j4eWPES0JZq+bfuS+e4mvzQM3+cu7voR1P5kyLe0+oo6O/5eX+vaup5A1bbed92gQTVvq+G1RERERKR5KKx7kyPhc5/v5hsH2Lf9wvW/uZ6lu5bSa3Zv4qLjiImKodxZTmlFK3YV7sKJk38te4NTu5xKt9RuXHVZaFm64w5vNe1qeQsiRqhvvBAO9Q6sr732Wl5++WX++te/8re//Y3omgYWkyahXVI7erfpzYa9G3hk6SPMOH0GZ3Y9k9joWAoOF7Bi9woeXfoohmFwepfTuf74P2O/7MyaE0xNbYBc1r8jNhERERGRo1M0UAk4Gib5uu7zU1LoltYNa6yVRTsWsXHvRmKjYomLiaOsoow2iW3o07YPI7qPoEOrDg2Tx2ag3oH10qVL+frrrznhhBMaMj8SJnuL9zJ+4HjyDuaxYPsC/vb535h26jTW5q6lW1o3BrUfxID0AbRNbEt6UjrWSmDIkMDVQIYPr3nU9ZB875qG0P2eiIiIiEiL1hXYQoMF1l26mPf7dcQBHVp14K+n/pXZ385mS8EWAKwxVrJSs5g8ePJRHVQDWAyjfoXgZWVlxNU2QFgzVlRUREpKCoWFhdh8x5NqUtwNBx4HptS59eKcxTy/6nkmnjSR5b8sZ++hvVhjrCTEJBAXHYfdamdn4U66pXZj5e6VTD99Ova1ATouGD4cHn4YTj65Ac/pM6CGgeVERERERI5qhYDdNd9A7Ri/+QbuugtH53bsungkebZoSqKcpLfuTI9Ox2G32j2bOkoc5DhyKCopwma1kWnP9FtfPzsBd8Fd022bGUycWO/AuiVrHoF1LGaPgBbMceJqtyZ3DXd9cRfn9TyP9XvXk5mSSUlFCTFRMWzZv4W56+dS7izn6XOeZnjmcLqldjN33LTJ7MCgoMCsFtKlC/Tq1UDn5A6sj/qPoIiIiIhILRr+vnn7jjUs/3Ut//ruX6zfux6ioklKsDGy20huG3ob3dK6hfFoqXhL4JtuLBBMnKg6uM3GJ8AI6vvBy7RncmzrY0mKSyIrNYt9h/aRHJ9MfnE+6/PX06tNL/qn92dU1ij/ahu9ejVgIO2reicJIiIiIiJSm/8B48KeqqPEwae/fsMb699gfeFPnqF+isuL+XTbp8RGx3LvmfceQcl0jUd0Ta8LU3qRp8C62QiuqrTdaufyAZdz1+d3sbNwJ78c+IUDZQc4IeMErjruKrYVbOP6k66PYFuIdhE6roiIiIhIcxMHlAEX0xAlvDmOHJxOp1lSXUVxeTHf531PjiOHgRkDw3zkZ8OcXuQosG6WYoHyWrdwlDh4ac1LlDvLsVvt2OJtVBqVGIbBqj2ruOeMeyLcwYB7oPvPIpgHEREREZHmIB9vO+vwKyopoqSipMb15c5yikqKwnS0jDCl07RE1b1JdV999RVXXHEF2dnZ/PLLLwC8+uqrLFmyJKyZk6rcQWhFrVuB+dRpa8FWYqNjsVlt2BPstE5sTZukNuQV55FfnN+wWa1Va595dVomIiIiIlK7FJ95a9hTt1ltWGNqTjc2yowpwiPPNX08TOk1DUEH1v/73/8YNWoUCQkJrF69mtLSUgAKCwt54IEHwp5B8eUbhNb+wa7riVL4njgdif2u6X0RzIOIiIiISHPylWtaGvaUM+2ZREdF069tv2rrkmKTGJA+gEx7ZhiOdKLP/JQwpNd0BB1Y33fffcyZM4d//vOfxMbGepYPHTqU7777LqyZk0DcT3YO1LpVXU+UwvfEKVhtfOb/FqE8iIiIiIg0N6f6zCeFNWW71c5ZXc/imhOu8Quuk2KTGNl9JDdl3xSmjstWuaZ/DENaTUvQbaw3bdrEsGHDqi1PSUnB4XCEI09SqynATa55CzV1XpBpzyQrNcszeLuvrNSsMD1xCtYTwD7X/BUROL6IiIiISHP2HHAtcAgzLngibCl3TetKamIqfdv1Ja84j5KKEtKT0unRukeYgmqLz/wLYUivaQm6xDojI4MtW6oHa0uWLKFbt3CObSY18w2mA7eFsFvtTD55MlmpWX7Ls1KzmDx4chi7yg+G+4FADPBqBI4vIiIiItKc/Rlvf0VPAoVhTd1utdM/vT8juo1gbM+xnHTMSWGKG7r4zDvCkF7TE3SJ9TXXXMONN97Iv//9bywWC7t372bZsmXccsst3HXXXQ2RRwnob8D9mG0sJgH/qLZFt7RuTD99OjmOHIpKirBZbWTaMyMUVPs+oaq9R3MREREREanJr3jvre00xPBb4fUqsNM1fwn+HbG1HBbDMIL6nzAMgwceeIBZs2Zx6NAhAOLj47nlllu49957GySTDa2oqIiUlBQKCwux2SLV9vhIJAPFrvnHabodAETh/cLvB1IjmBcRERERkZbAt+CqqQbXS4DTXPNxNETHaw0pmDgx6MDaraysjC1btnDw4EH69OlDcnLyEWW2KWi+gTX4f6EmAP+KVEZq4Ju/iQQqWRcRERERkWC9Clzl876pBdfPY7YHd2tq+atbMHFi0FXB3eLi4ujTp8+R7i5hY+ANXl8APgT2RC47fnyD6vtQL+AiIiIiIuFyJdAOONv13oLZfrkpVLUeDHzr8775BdXBCjqwPuOMM7BYLDWu//zzz0PKkBwJA7MTs1IgF/NLFckq12MxA3y3z/Afg1tEREREREI3CtiBt3MwO3AKsDRSGcK/GWgsUBbBvDSeoAPrgQMH+r0vLy9nzZo1rF+/nvHjx4crXxK0EmAo8LXrfRrm+HYHGzEPq/Af9B2OhqdTIiIiIiKR0xn/Wqxfu+Y/wQy8G0sXvJ2UAfQD1jXi8SMr6MD68ccfD7h8xowZHDzYmEGcVLcUKMAMqsHs2MyCOWZ0Qw9v5ftkCqA3sLGBjykiIiIiIiYDOBeY53p/ts/yhvQgMK3KMgdNo0p64wl6HOuaXHHFFfz73/8OV3JyxFIxvzy+D0D+gxlgW4BtYTzWUJ903V9Y97yCahERERGRxvUB5r14gs8y9/364DAepxCzjNaCf1B9u+v4R1dQDWEMrJctW4bVag1XchKyKZgf6swqy7tjfgGiMMeRC8Y2zAHp3V/Or6us3wo4g0xTRERERETC6xBmqbGvb/Hex7ch+GraU/AG03ag0mfdMZixx4NB57SlCLoq+EUXXeT33jAM9uzZw8qVK7nrrrvCljEJl+2uaQFmUOwuWTaAN1yvUAwGloeYhoiIiIiIhFcK3nv/S4H/+qzbBwwIMX0LkIPZxluCLrFOSUnxe6WlpXH66afz0UcfMX369IbIo4RFKmZpsoHZ3jouhLR6uNIxUFAtIiIiItLUzcV7/z4shHTigedc6ThRUO1lMQyj3q3ZKysrWbp0Kf379yc1NVJDOYVfMAN/t0SOEgc5jhyKSnbQxrqeDvbvsVt7ABcCgyKdPRERERERaXDrMIfM3QGMcL2OvrbSvoKJE4OqCh4dHc3IkSP54YcfWlRgfTTbtn8bT337FFsLtnqWZaVmMfnkP9ItrVsEcyYiIiIiIo2nv+slRyLoquD9+vVj27Zw9iwtkeIocbBg+wJ6te7FmZlnck6PcxhyzBB2FO5g9rezcZQ4Ip1FERERERGRJi/ozsvuu+8+brnlFu69914GDRpEUlKS3/qjsSp1c7V532beWP8G6/eu9yzr17Yfl/a7lI+3fEyOI4eBGQMjl0EREREREZFmoN4l1vfccw/FxcWcc845rF27lvPOO4+OHTuSmppKamoqdrs96Orhzz77LAMGDMBms2Gz2cjOzubjjz/2rC8pKWHixIm0bt2a5ORkxo0bR15enl8aO3fuZMyYMSQmJtKuXTtuvfVWKioqgsrH0chR4uDpFU/7BdUA6/euZ+76uQxqP4iikqII5U5ERERERKT5qHeJ9cyZM7nuuuv44osvwnbwjh078uCDD9KjRw8Mw+Dll1/m/PPPZ/Xq1fTt25ebbrqJDz/8kDfffJOUlBQmTZrERRddxNKlSwGzM7UxY8aQkZHB119/zZ49e7jqqquIjY3lgQceCFs+W6IcRw7bC7YHXLd+73ou7nMxNqtqH4iIiIiIiNSl3r2CR0VFkZubS7t27Ro0Q2lpaTzyyCNcfPHFtG3bltdff52LL74YgB9//JHevXuzbNkyhgwZwscff8zYsWPZvXs36enpAMyZM4fbb7+dvXv3EhdXvyGljsZewRfnLOaBJQ/w076fKC4vrrb+ntPv4ZJ+l2C32hs/cyIiIiIiIhEWTJwYVOdlFoslpIzVprKykrlz51JcXEx2djarVq2ivLycESNGeLY59thj6dy5M8uWLQNg2bJl9O/f3xNUA4waNYqioiI2bNjQYHltCWxWG7HRsXS1dyUp1r+dfFJsEv3a9VNQLSIiIiIiUg9BdV7Ws2fPOoPr/fv3B5WBdevWkZ2dTUlJCcnJybzzzjv06dOHNWvWEBcXh91u99s+PT2d3NxcAHJzc/2Cavd697qalJaWUlpa6nlfVHT0tSXOtGeSlZrFloIt9Gzdk8Plhyl3lhMbFcuA9AH0bdc30lkUERERERFpFoIKrGfOnElKSngHCe/Vqxdr1qyhsLCQt956i/Hjx7No0aKwHqOqWbNmMXPmzAY9RlNnt9qZfPJkZn87my0FW4iNjgVcY1gPnqzSahERERERkXoKKrC+9NJLw97GOi4ujqysLAAGDRrEihUrePLJJ7nkkksoKyvD4XD4lVrn5eWRkZEBQEZGBt9++61feu5ew93bBDJt2jSmTp3qeV9UVESnTp3CdUrNRre0bkw/fTo5jhyKSoqwWW1k2jMVVIuIiIiIiASh3m2sG7J9tS+n00lpaSmDBg0iNjaWhQsXetZt2rSJnTt3kp2dDUB2djbr1q0jPz/fs82CBQuw2Wz06dOnxmPEx8d7hvhyv45WdqudgRkDGZY5jIEZAxVUi4iIiIiIBKneJdb17Dw8KNOmTWP06NF07tyZAwcO8Prrr/Pll18yf/58UlJSmDBhAlOnTiUtLQ2bzcbkyZPJzs5myJAhAIwcOZI+ffpw5ZVX8vDDD5Obm8udd97JxIkTiY+PD3t+RURERERERKqqd2DtdDrDfvD8/Hyuuuoq9uzZQ0pKCgMGDGD+/PmcddZZADz++ONERUUxbtw4SktLGTVqFM8884xn/+joaObNm8f1119PdnY2SUlJjB8/nnvuuSfseRUREREREREJpN7jWLdkR+M41oE4Shye9tYp1hS62LuoariIiIiIiByVgokTg+q8TFqubfu38dS3T7G1YKtnWVZqFpNPnky3tG4RzJmIiIiIiEjTVu/Oy6TlcpQ4qgXVAFsKtjD729k4ShyRyZiIiIiIiEgzoMBayHHkVAuq3bYUbCHHkdO4GRIREREREWlGFFgLRSVFIa0XERERERE5mimwFmzW2hvi17VeRERERETkaKbAWsi0Z5KVmhVwXVZqFpn2zMbNkIiIiIiISDOiwFqwW+1MPnlyteA6KzWLyYMna8gtERERERGRWmgcazSOtZvvONY2q41Me6aCahEREREROSppHGs5InarnYEZAyOdDRERERERkWZFVcFFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQqDAWkRERERERCQECqxFREREREREQhDRwHrWrFmcdNJJtGrVinbt2nHBBRewadMmv21KSkqYOHEirVu3Jjk5mXHjxpGXl+e3zc6dOxkzZgyJiYm0a9eOW2+9lYqKisY8FRERERERETlKRTSwXrRoERMnTmT58uUsWLCA8vJyRo4cSXFxsWebm266iQ8++IA333yTRYsWsXv3bi666CLP+srKSsaMGUNZWRlff/01L7/8Mi+99BJ33313JE5JREREREREjjIWwzCMSGfCbe/evbRr145FixYxbNgwCgsLadu2La+//joXX3wxAD/++CO9e/dm2bJlDBkyhI8//pixY8eye/du0tPTAZgzZw633347e/fuJS4urs7jFhUVkZKSQmFhITabrUHPUURERERERJq+YOLEJtXGurCwEIC0tDQAVq1aRXl5OSNGjPBsc+yxx9K5c2eWLVsGwLJly+jfv78nqAYYNWoURUVFbNiwIeBxSktLKSoq8nuJiIiIiIiIHIkmE1g7nU6mTJnC0KFD6devHwC5ubnExcVht9v9tk1PTyc3N9ezjW9Q7V7vXhfIrFmzSElJ8bw6deoU5rMRERERERGRo0WTCawnTpzI+vXrmTt3boMfa9q0aRQWFnpeu3btavBjioiIiIiISMsUE+kMAEyaNIl58+axePFiOnbs6FmekZFBWVkZDofDr9Q6Ly+PjIwMzzbffvutX3ruXsPd21QVHx9PfHx8mM9CREREREREjkYRLbE2DINJkybxzjvv8Pnnn9O1a1e/9YMGDSI2NpaFCxd6lm3atImdO3eSnZ0NQHZ2NuvWrSM/P9+zzYIFC7DZbPTp06dxTkRERERERESOWhEtsZ44cSKvv/467733Hq1atfK0iU5JSSEhIYGUlBQmTJjA1KlTSUtLw2azMXnyZLKzsxkyZAgAI0eOpE+fPlx55ZU8/PDD5ObmcueddzJx4kSVSouIiIiIiEiDi+hwWxaLJeDyF198kT/84Q8AlJSUcPPNN/N///d/lJaWMmrUKJ555hm/at47duzg+uuv58svvyQpKYnx48fz4IMPEhNTv+cGGm5LREREREREfAUTJzapcawjRYG1iIiIiIiI+Gq241iLiIiIiIiINDcKrEVERERERERCoMBaREREREREJAQKrEVERERERERCENHhtkRERKTpcpQ4yHHkUFRSRIo1hS72Ltit9khnS0REpMlRYC0iIiLVbNu/jae+fYqtBVs9y7JSs5h88mS6pXWLYM5ERESaHlUFFxERET+OEke1oBpgS8EWZn87G0eJIzIZExERaaIUWIuIiIifHEdOtaDabUvBFnIcOY2bIRERkSZOVcFFRETET1FJkd/72KhYBrUfRFpCGofKD5F7MJcVP68gPiaezvbOanctIiJHPQXWIiIi4uEocWBgsO/QPmKjYrHF2xidNZq56+eyNm8t5ZXlZLXOopu9G5f1u4z/W/9/XHPCNWp3LSIiRzVVBRcRERHA7LBsxpczWLJzCQkxCfy07yd6tO7Bq9+/yqo9q6hwVpAcn0x5ZTnzt87n6RVPk90pm2dXPqt21yIiclRTYC0iIiJ+HZat2rOKy/pfxrAuw2iT2IalO5dSWFqIYRjYrXachpP2rdqzZOcS9hzYg91qV7trERE5qqkquIiIiJDjyGFn4U6GdhxK77a9iYuO45J+l1BeWU6ftn2wWCxkd8ymfav2VDoraRXfii37tpB7MJceaT2qtcsWERE5miiwFhEREQ6UHmBsj7GUVpZy3+L7KCot4uein5kzdg6X9b+M3m168/Lal/ngpw/IO5hHYmwi/dr1Y1TWKA5XHMZmtUX6FERERCJGVcFFRESEtIQ0fj30K69+/yo//PoDcdFx3HLKLbz6/ats+nUT9yy6hw82fUBxWTHd07qDBXYU7uC5Vc9xTKtjyLRnRvoUREREIkaBtYiIyFFu2/5trNy9ksMVh1n+83KKy4s5o+sZvL/pfRZuW0jfdn1Zv3c9FouForIi8g7mcUyrY+iS0oWVu1eCBQ25JSIiRzVVBRcRETmKuTst65LSBafhJMpiPnNvn9yeDXs34MSJo8RBjCWGuNg4KpwVOA0nreJaUVZZZu7ndEb4LERERCJLJdYiIiJHsRxHDlsLtmKNsRIbHYthGFQalRwoO0B8dDyxUbHERMVQUllCpbOSssoysEC5sxyLxUJSXBKtE1tH+jREREQiSoG1iIjIUczdm3decR5FpUX0T+8PQExUDIcqDhEdFc0Oxw5OPuZkYqNjSYxNpNJZidNwUumspHeb3mpfLSIiRz1VBReXPOA2YAXwKxAPdAcuA66NYL5ERKQhuXvztkf9iyuOc3J7djEHyyw4Dj9JWmwKd3x5iEU7FnHD4Bt4Ze0rrM1bS6o1lZioGKzRVq4ZdI3aV4uINHvLgbnAJuAgcAwwAJgEaNSH+rAYhmFEOhORVlRUREpKCoWFhdhsR8MH5xLgjRD2jwV2AenhyY6IiDSiz4HfeN657wIslsBb+94lGAZUVsJXP/6R7w705kDpAUZ0G8FpXU5ruOyKiEiYZQFbj3DfaOBm4KHwZacJCyZOVGDN0RJYLwBGNkC6SZhPtUREpGmLAgJf8g3D+/JlsfgH3O5593aVlbD+1/kMzGiI64uIiITPacCSBkj3M3wf1rY0wcSJqgre4sUB5TWsuxuYGWR67YFcn/fFgPuuaw6qNi4i0pSMABbWsO40YDEAhaUOvs/7nrW5a8k9mEtsdCyb923mg58+AOCkDicx4YQJHJM4iaFdCoiONlOIiYHj0ke50usNbGzAcxERkeBsBPrWsO4kzKA4mELF94ErgSKfZSNc0zRgX7AZbFFUYk1LLbE+D/ggwPJcwleFexTwaYDlR/1HSkQkwtYD/QMsfxyYUm3pmtw1zPpqFlcffzUPLXmIr3/+2rNuaKehPDjiQd754R3W713vWX5mhxJuOPUzT5Dt9T/gotBPQUREQhCocO0q4OUwHsMGHGjgY0SWqoIHqWUF1nlARpVljfEBr9o4LwE41MDHFBGR6qr+HtuAwlr3WJyzmEeWPUJiTCLnH3s+1hgrB0oP0Cq+FSUVJXSxdaFvel9yHDkUlRRhs9rItGf6dFqWCeyokupRf3shIhIBpwDLfN7HAmUNfMz3gfOrLNsA9Gng4za8YOJEDbfVorTBP6iOx7yxObKg2t22zvdV0/LrrzfwryJ+GPPmLi9g2uPHB067Pvmw2+u3TkTk6PIE1YNqg7qCavD2DH6o4hD/t/7/eHHNi/zh+KsY1+dCLh9wGadmnkJqgp2BGQM5vdswjm8/kNQEu+e3d/LkHKoH0hZgcsDjTZhw5NcA3/uamq5TIiJHJwv+QfUGjjSorun3NSWl+vJnnjkP8xqQ7ZNCX8wRhupOuzZWq/+2gwbVb10kKLBuMSz4t2swgJKwpGwY5pcIzA+tu9qfYcCnrprgc+aAWcXcAM712TsDmF4tzVde8aYRH2/O9+xZ/djff++fD4DCwrrXiYgcXXoAN/m8/x/BlBhn2jPJSs0KsKaCKR9PxW43rydVrwELXc23//EP9/YGsM5n/38AqdVS/fe/vWkkJJjzfQIUbMS4eoJp1877O3/AVevwpJPMaVRU9U7XRESOLsvxf7B6FebvcWglxsnJ5u/rCSeY761WSEyEVq3M5Rs2mMsnTnTv8bXruLGu99sI1KVXq1bQpk3dx//5Zygt9f+d/+67utdFigLrFsH3i9Sahqh+53B45ysqvB/gs84KtPX7VfJwD2ab78BKXPH/5s3V1x13nDl1H2+kq+PZW26pfZ2IyNHDBmzxeW8QbBtnu9XO5JMnBwyuJw+eTEFBgud9ebn3d/fMMwOl1g//a4CD2vpKPeRqNfTDD9XXVVaa0zxX5af+rmbjffrAypX+29Q1bJiISMs0F/+S4kLC3QR01SpzWloKe/ZAkavvskAPRE1lwDjXfCVVa1MVFcHevXUft0cPVwqu3/nbbjOnL75Y+7pIURtrmnsba98P6nXAs+FL2WdYlehocDqhc2fY4dOM7pJL4I03vNv57uda6pfmp59aOOssc5vWreHXX6sfq6Y8+C6Li4OysprXlZYGdaoiIs1UPN5qfvGEWlPJUeLwtKMe3vU0oALDiCUmxrx56doVtm3zbn/55fD66+Z8fa4BCxdaOPNMc5t27bwBc03XgNhY82FuTIwZ0Pumfdpp8NVX3v181+nORkSODs8AE33e1/zj5/v7nmJNoYu9i08/GdVZLGaJ9YED5u/13r1w9tnw8cfebR54AP72N9eR63EN2LDB4heM1/TbX9t6iwUyMyEnp+Z127fXeFpB03BbRw3fT27DDXXl+wXxDaoffbR6UF113syjbz7D2Su5iMjR7HK8QXUqsD/kFO1Wsx21V6zfNcA3qH7iiepBddV58/e/D+Auju4PfE99uYPpiorqJdGLF/v3/SEicvRxB9VRmCXDgW3fv51Ptn5CubOcwxWHSYhJwLLDwkkdTiIxNpHO9s4Bg+yDB/1/X32D6vffrx5UV503rwG+Q37FUPMwwM2fqoI3W6N85q+jIcePrvokCOC55+DWW8353Fz/7at3dmBgsTixWJwsWDDSs92+IxjqbpTrtCcH6A+ntnUiIi1LPuCKaokmHEF1TXyvAVGuu4YXXoCbXE2686r0UVn9GrDRcw34/HMDcygwyM+v//HdrwxX/5x//GP1dffcYy6Liwv+HEVEmh/fJ4o1B9WOEgdLdy3llbWvcOuCW7nr87u4af5NvLL2FTbu3cj8rfOZ+eVMtu3fVm1fdxtr347LwOxf43xXJ+BV+zeqfg3o47kGbNyYAYyu9xlarf7vb7/dnN59d+3rIkVVwWmOVcF9h9SKBioa5ChVq1+437/xBvzud+Z8bi6k17sA2vsDYLEYnrStVrPqdtUqhmB2UObblto3T7WtExFp2XxvqBrmR6+ma8D//gfjXE3n8vLMKoL1TNEnbe81IDERDh+GrKzAfW2kp5vH+fxz+M1v/PPkXhcovyIiLVcaUOCar/1Hb82eNVz/4fWszl1NTJR/ZeX+7fpz7xn38uS3T5KVmsX006d7Sq59q4K73wMsWwbZribdhYX+IzXUbhDg7l1sF9Cxzt/tn3+GTp3Mh7qVlf6/87WtCycNt9Xi+Q6p1TBBdSADB5pTd1ANZulB/Yc4meOZ+93v/geY+7nbQ7uDat/0Bgzw7u1elpxc9zoRkZbL99LdeFHkiSeaU3dQDWZgW/9rgDevv/+92R+IxWIG1eANqquml59vvncH1e6Sad917u0HD67/+YiINE/P4A2qH6tz69ziXFbnriYuOo6yyjKKy4s9r5V7VlLmLCM2KpYtBVvIceTUmM5VV5nTbJ9+0nyH3qrbKtwPWC2WDn77uIN497x7XceOZl8bTqd3Wb9+da+LFJVY09xKrMcDrrGqGvGG6khV7Sihl/0UrFZXF7DNIP8iIk2P+25kEjA7khk5Am/j7SlW1wARkeC5rwFp+A+1G9jbP7zNVe9cRVllGdGWaM479jx6te5FubOcuOg4Tul4Cqv3rGbpz0u5NftWhmUOa9Dce/M/DnirgY8VOnVe1qK5g+r4iOaiPrbt38ZT3z7F1oKtnmVZqX/m76OecD1ZugT4b6SyJyLSDPletptbUA3+w4BZUHAtIhKMhT7z9eusqE1iG2zxNgoOF3Dr0Ft5f9P7vLHB7H04xhLDxX0u5srjruTb3d9iszZGAePZwCfA/xrhWI1LgXWz8pzPfGhDqjQ0R4mjWlANsKVgC4dKo0iMd2KxvIECaxGRYLg7qHk8orkIzTrM3sFFRCQ4I1zT+gfAWWlZjOg2gihLFO9vep+1eWsBM6huk9iGb375hvLKcs7OOptMe2b4s1zNx3hLrR8A/toIx2wcamPdrFzXqEdzt3F47rnqy6q2i6i6LMeRw5OjH2fe79+v8nqPS985x+coj9Z5fN/X9Ok1r/u+hhFcAm1bU/41ZIuINF2+nUhMafCjuX8TX3ih+rK6rgFVl/tv49sIrubbkED7rjc7FCcxsfq6k04KnE63btW3dY8eEegY9e2tXESkcf3sM19Y41ZVdWjVgb+c+BdO7nAyW/ZvITkumcyUTI5tcywZyRnEx8Szq2gX/dr28xtyy/2buNCnkDw6uuZrQEyV4tra77+7u6Z/qzHfvsdyv352/Qm6d6++bvz4wOmccUbt9/oxMf7rQhlZQoF1s9S4VeeuO4J4vqikyDVnMPb187hw7jjuW/QA//jmWYZ1HkaFq8+1yspba0zjqqtgzhyzd7+15sM1v05r4uLMdTffbL539xBele9wLG3bmsvcU/eX63e/83bPr+BaRJqmYtf094161D/9KbT9fX+Dvb26/Mu9ttZ94+PNfdzDqPR3FXRPmwaTJvmnuXJl4DS2b/fmw32D+I9/mNNzzoF168x1j7sqAdR/pAsRkcbUzTUNPnw7tu2x2Kw2erXuxfUnXs81J1zD+ceezwXHXsCg9oPYf3g/+w7vw1HiqLbviBHeeaez5mNU1jDiV+BrwJY685yQAJmZ5j7//re5rFMnc3r//fD00/5pvvJKwGT48ktvPu6/35s2wMknm/lu08Zc/3//B1deWWfWaqTOy2gunZc9h7fEunH+y3wDTMOAU04xu9j3XRYTY34gU1OhoACGDzc/wGty13B8++MAgwvnXszorNG8vu51vtvzHU7DSa82dlZcYz52yinYRte0rrXm5ZZb4LHHvMetKa91fZprGj6mpvciIk2H+0c5MteAYcPgq6/8l8XGQkWFeVPy669mz92ffea/f82/p8GdT03pBRqGK9B+69aZNbDcQXXVbUeM8AbeugaISNPj/s38DPhN0Ht/vfNrvvnlG15f9zqr9qxypWjh+PbHc16v8zgm+RjaJbejX9t+dE3rWu0a8Je/wLPP+i9LTzdr+Zx0EqxYATfeCE884cptva8B12P2dF67mtLbuBH69q35WBaLea0qK6ueTn3u+4OJExVY01wCa99i1Ma/qXIHzr6qfiB95x0lDlITUgKkWsm9ix7kruF/xf+c8Hvv/lS6x7h2e+QRM8j21a4d7N3rv19d56TAWkSal/V42yU3/jXAHTj7qu0aUHX/wPv5noel2ja+2reH3Fz/dU88ATfd5N3mtNNg8eLqxxswwAyqa0q/aj6DG5tbRKSxhPZwdfeB3Ux4fwILty3EcKVhGAaVRiVDOw3lwmMvJC46jraJbTm7x9mkJthJToaDB72Bs68juQZER5sPY4O9Bpx2GixZYsYF7iEa338fzj/fu83118MzAeJzdz527YJrroFPPqmeZ1/33w9/9Wn23WzGsV68eDHnnnsuHTp0wGKx8O677/qtNwyDu+++m/bt25OQkMCIESPY7B7o0mX//v1cfvnl2Gw27HY7EyZM4ODBg414Fo3t7oZNftMm+PRT+O9/MT/w5YA3qI6O9m765pvVl7l522gYPLfinzz+9ZPcufBu/rv+bbLSsnjnh/eprIzC6Yxi6cY/s3rP2gDVRKCkxHx/7rnm+1ur1Bw//fTgg+pPP619OxGRpmdg4xymhmuAO6j2bUP39tvVlwUSqBqgOR/l8wpUVdA0YkT1oBpgyhT/Kty+pem+3EH1woVm1W+oXhpvGNDVVXFKVcFFpOkpqnuTOuQX53Og9AC2eBsWLFQ6K6k0KrHF2dhbvJfi8mJ+Lf6Vf37zDLt2fA8YUGleA9xBdUaGN73ly81pXQ8ifX/b3U1BzfcH6nUNmDLFDKrBG1QDnHeeue1775nvfUvTfY0da047dfIG1VVddZW3ttXfam72XaeIllh//PHHLF26lEGDBnHRRRfxzjvvcMEFF3jWP/TQQ8yaNYuXX36Zrl27ctddd7Fu3To2btyI1WoFYPTo0ezZs4fnnnuO8vJyrr76ak466SRef/31euejeZVYN+B/1zffmA3ZFi1yHdEJVJCaXEHBQbMxQk1Pd6rybudkcc5S1uWvY+nOpXy85WMKSgpgRiX1KbH2W1vlSdioUd4guT6f4kCl0SqxFpHmwf37eCHmWNANoIZrQBtbBb8W1f8aYLGY7fBq+z2tb2nFmDHw0Uc1p+OfXv2uHcFsKyLSNHQHtrnmj+wHanHOYu764i7yivOIjoqmrKKMKEsUB8sPkncwj9uH3kYicTz1yUxeu/A/nDX09yRbDtK7VyUrfrSbR67HNSAlBRyO+v3Wes8l8DXg9tvh4YdrTsctLg7Ky4OPB+pzfWg2JdajR4/mvvvu48ILL6y2zjAMnnjiCe68807OP/98BgwYwCuvvMLu3bs9Jds//PADn3zyCf/6178YPHgwp556KrNnz2bu3Lns3r27kc+mmdu0ye+Gytf+QaOBmnsrqOkJkymKaEs0C7ct9AbVADOi+SnffEpVURHF+ryN1dKJi/P2SP5olc7DzzsvuKD69NPNaaDSdYBLLvH2OC4i0nQ1UFBdyzVg7/HBXQPq85tsbm+ptbTiootqDqqTk2HCBHPe3VN4ffj2cA5mabt7/zFj6p+OiEjj2umatjniFGxWGwmxCZRVlvFL0S9sLdjK5v2b2XNgD07DSZuE1uTk/gBASYVrWF+jkm/TLwCcxMYGTrfqb3dhPTos9+5T8zXg7rtrDqp79oS//92c//lnM6iuycUXe/vPSHYNrpGdbU7d5zR3bt15ro8m2yv49u3byc3NZYRPV3QpKSkMHjyYZa4etJYtW4bdbufEE0/0bDNixAiioqL45ptvaky7tLSUoqIiv9dRb8eOgDdUACxahDH/s4A3Sz16BN7FNxAe2iWbty99i4I79sGMShZetZB3LnkHg+MA8+nQr4d+rZZGebnZI7nF4q0C7u4B/IMPvNvVNPxLWprfKQDeKihu7nN64w1vj+MqqRCRo84RXgN69w68i7vzGqh9mJPavPNO4DQAiovNXmItFm9P4b55sVi8oz+4e3+1WLw9nLt7Ga+sNPe3WLxBfF5e/fMoItI43F1utz3iFDLtmfRu05uu9q5mdXCfH+ShnYaSYW3L/PXvkZRgI73Ip4tv1zXA3fmXr3Hj/N+3amVO33/fu+xIrwH33hs4DYDNm82YwGLx9hTumxeLxezxG+B//zObFFks5rUjOhq+/tpc5z6nyy7zpv3HP9Y/j1XV0TIqcnJdDarSqzR2Sk9P96zLzc2lXZWK/TExMaSlpXm2CWTWrFnMnDkzzDlu5qr2TAYYvs9dfNbXFnj6rrvlFti2fxv3LLqHnq17khyXTKVRyc3zX2Fd/jpuPLGSx85xH6t6ovU9Tn3Wh5KWiEiL1wDXgClTQstSuK4Bhw4deToiIk2D+8cq+YhTsFvtTD55MrO/nU1UVBRpCWkcrjhM37Z9uajn+dz99mRiYuMZ2fl0eryzCINrvTvX8xrgW1YZ6u9ruK4BwcYMoWiygXVDmjZtGlOnTvW8LyoqopP7ccfRKjU1tPUBOEocPPXtU7Rv1R5HiYN9h/excPtC1uatxYKF83qZ2xkGvLr2VQZmDPQbmF5ERBpJA1wDREQkXKIwm+TsDSmVbmndmH76dHIcOeQezCXvYB6b92/mXyueo0NiOgOsmdx06ATsr83y31HXgHppsoF1hqvbuby8PNq3b+9ZnpeXx8CBAz3b5Ofn++1XUVHB/v37PfsHEh8fT3x8fPgz3Zx16WIOQh2oKuDw4eb6IOU4cthasJWzup5FQUkBlUYla/PWAmYJ9QDvfysb925k877NnHTMSUd6BiIiR4F8oAHGgmqAa4CIiIRLLFAKhN5WxW61MzBjIGAWguU4chiVcRr2/cWUOH7l5907OPD32+nyzY/Y//seDBmia0A9Ndk21l27diUjI4OF7tbmmCXL33zzDdmuFufZ2dk4HA5WrVrl2ebzzz/H6XQyePDgRs9zs9arFzz0kHkD5Wv4cLPngF69gk6yqMSsD1JaWUqFs4IKp38DZ5vr2cb+Q1BUWkR+cX7VJERExE9mwyTbANeAug1pgDRFRFqiG13Tw7VuFSx3kH1MRg9e2D2Pe+ZP45EvH+DOJfcws/0mtr34d3jkkQa6BrQ8ES2xPnjwIFu2bPG83759O2vWrCEtLY3OnTszZcoU7rvvPnr06OEZbqtDhw6eIbl69+7N2WefzTXXXMOcOXMoLy9n0qRJXHrppXTo0CFCZ9XQ2mGWWDSAwYPNbrh37DDbUqSmmk+ojvDLZLOaXdInxiZyqOIQUc4oLD7d6UdFGRgGZD1t45hWBrHRsThKHKoOLiJSTRawhXDfVPkJ8zWgbu5ORpts5TkRkSbiIeDhBknZ3XRza+VeGDDAHCy6rJwtcbHMjl/L9P6/x94gR36gQVKNpIhezVauXMkZZ5zhee9u9zx+/HheeuklbrvtNoqLi/nzn/+Mw+Hg1FNP5ZNPPvGMYQ3w2muvMWnSJH7zm98QFRXFuHHjeOqppxr9XBqeDXNw+NDaVtSpV6+w3URl2jPJSs3CUeKgsrKSQ+WHGJA+wNPG2i0+Op5Otk44DjuY+eVMJp88mW5p3cKSBxGRlmEzVcf5bBBhvAbU3y+NfDwREXFzN90EzPGokr0dpG0p3U2OI8dTdTy87nZNWzVA2pFhMQz1iRnMwN+R5b6paj7/Zdv2b+PZlc/Su01vyivLKaks4aU1L3FmlzU8erbZcdm4Ny5gUIdBtElow4dbPiQrNYvpp09XybWIiJ/mdw2oXUs7HxGRhuT+zRwLfFDbhkFZnLOYR5Y9UuP6W7NvZVjmsLAdz8t9PhuAPg2QfngEEyeq/lWz9ChwS6QzUS/d0rrxt2F/Y4djByXlJZQ6SxmQPoDhXc7EYjHHqrZb7SzesZjhXcy2fVsKtjTg0zERkebO3Ttsc9aUH2KLiDRFJwErgHlhTdXddPNI1x+Zn33mm25QHawm23mZ1ObWSGcgKHarneMyjmNwp8F0bNWRxT88i8Villa3fzyNHYU7uLTvpURHRXNO1jmcmXkm+w7tw1HiiHTWRUSakMdd05ZQwnvANb0workQEWk+vvWZX1jjVsFyN92sqryynPSkdMorylmbuzbM9+XuYY6jw5hm5KkqOM2pKjh4q02sBQZEMiNHrLLSQnQ0VFTAP1c/Q2FJIW2S2vDWhrfolNKJTHsmcdFxZCRnkN0xm6zW1b/sIiJHJ/c1IAE4FMmMhKAHZkds0DIeEoiINBY7UOiaD9/v57b925j97Wy2FJi/zYfKDpEQm8D5vc7n4y0fU+4sJys1K0z9IBUBKa75pn8NCCZOVGBNcw2soTl8GKvLAzIwDJg6/zJ2FJZwcZ+L+XDTh5zQ4QTe/uFt1uWvwxpjxWKxcFKHk3jkrEfo3bZ3pDMuItIEXA687ppvjtcA8F7HsjA7ZRMRkfpz/4buAjqGLVX3mNb7Du3jl6Jf2FW0i1V7VlHuLPdsE55+kGKAStd807+OBRMnqip4s+P7ARwVsVwcuQwALBa44eT7+V2f37Hv0D6wwH++/w/Lf1lOubMcp2G2H1yxewWPLXtM1cJFRAB4zWe+EXoJD7tYn3kF1SIiwXP/jnaqdatguce0bp3Ymjd/eNNzT+7L3Q/SkXsRb1BdWNuGzZIC62bJ3R7hU8wS4ObCtx1FLqmJqSzZtYSSihKObXssG/ZuIMYSg9Nwcqj8EO7KFBv3bgzxSywi0pLc4zPfED21NpTJQIVr/pxIZkREpBkr85lPCHvqRSVFIa2v3R9d0wRaYieWCqybpQqf+YyI5SI44/H2YnsukE6OI4cdhTtIjkumorICi8XiKYCpNCqpdJpPtGKjYkP8EouItCR3AfGu+a+A/AjmJRj/8Jn/MGK5EBFp/t5zTUsw77HDp+F6CfetZdVc+wipnQLrZsu3SnhTrw6YB7zimrcA7wPeJ177D++nTWKbansZGCTFJpEQm9BAXf2LiDRXJT7z6RHLRf019/5BRESakvOA7q75V/Afvio0NfUSDmYb60x75hGk6nudanlVwN0UWDdrd/vMN9Xg+nv8S9W9Y6+6g+VVe1bR1d6VUzqe4rdnclwyXe1d6f3/7d17XFRl/gfwz3AbBmEYFAFRrg2bmISIl8U2b6GoZZaZrYmisvWzFEPz1qtVUFN011qV3cQuIm6mlqm1mpliXijFKyamCAhiLWitDhcRuczz+2NkYrgJDjAXPu/Xa14y53nmnO/h6wznO885z3H2f8g3MRGROTOVL1hrxmZKly8RERmzrBo/ewA40SJrVdgqENUvqk5xrXRSIqp/1ENMXOaC38+smg9zPAW8GmcFh6nNCl5bDwCXajw3pnSuhu49t3VjU5WpsOTwEmTdzoKdlR3+3PPP2Jq+FRdvXoSNpQ2cOzijZ+eeiOofBV8nfaf2JyIyR+kAAmo8vwHNQYyxqFlUfwQg0lCBEBGZqZqfs+8CmNMia62eJbyorAhyWzm8Fd4PUVTbArh3/+cQAD+0SGxtibfbaibTLqwBzezg39Z4bgwp9YDuaSn1x1TzvnnWFtYI7hIMD7kHXOxd4Gbv9pBvYiKi9uQmdE+zWwrNddiGtBPACzWeXwDQ00CxEBGZu5rFdU9oPnMNrWZMLwDYYahA9MLCuplMv7AGgM8BjK/xXArda/DaiuY+1boa/y/WMt+IERG1d7VPBzfUn3djiYOIqD2RAyiu8fwiNGe2trV+AE7VeL4RwFQDxNEymlMnWrVRTNTqXoTm4KX6gObe/Z8nA0hqoxhqH0zJ0ZQJCqrvm0dERPqo+TcA9392ANBWd1XoBuCXemIiIqLWVwRgJIBv7j9/DJpSr6LBV7SsbQAm1FpWCHO+pro2Tl5mdgR0R643Q3Nw9VIrbtMS9Y9QmO+sf0RExklA9xTAYmg+nzu34jZ972+jZlGdDBbVRERtbR80n73W959XQvP53HBxqypTIa0gDUdzj+J8wXmoylTN3Gbi/W3ULKqn3Y+j/RTVAE8FB2Aup4LXxwb1f0v1LYBheq679jXU1T6DZvSciIgMKxSaAre2lwFs0XPdCwGsqme5P4Cf9Fw3ERHp72dojtdr6wvgJADNXEfrTq5D9u1sbavSSYmoflHw7djYxME/A/AGUFVruSMA1cOHbISaUydyxNqslUPzbZG01vLh0HyzVP3wesB6Psfvo9LVj9pF9eL722JRTURkHA5C87ncv9byT6H7eW6H32+FUp+b0Bws1XxN7aJaeX9bLKqJiIxDN2g+lw/WWn4KgARCSOCteARLBv0TfvLfzzjKup2F+JPxtUauI6A5rbz6b4AHdItql/vbqvma9ocj1jDnEeva6ptYTB8+AK624PqIiKh12QG420Lrastr94iISH+R0EwmBggBSO5fydlQNSipfaWnDksAudAU8OaLI9bUAFdovk2qfnyG5v0X8Kn1ehbVRESmpRS/f4bfQPOuf5Pdf03161lUExGZlo9R/Rl+7NoRHMh0RXllw4W1LkcAX+L3vwGVMPeiurk4K3i79iJ46jYRUXvlAk4ySUTUPslt5Vh0vC/Wna6/fdmQZbxrTzOxsCYiIiIiIqOhKlMhV5WLorIiONo6wkvhBYWtwtBhmRVvhTeUTkpk3c6q06Z0UsJb4d32QZk4FtZERERERGQUHn6mamoOha0CUf2iEH8yXqe4VjopEdU/il9kPAROXob2NHkZEREREZFxUpWpEHs4VqeorqZ0UiJmcAwLvhZW8+wAua0c3gpv/o5raE6dyBFrIiIyK2q1GuXl5YYOg9o5a2trWFpaGjoMIpOSq8qtt6gGNLeBylXl8rrfFqawVfB32kJYWBMRkdkoLy9HTk4O1Gq1oUMhgkKhgJubGySN37OGiO4rKivSq53IkFhYk9HjBBZE1BRCCOTn58PS0hIeHh6wsOAdJckwhBAoLS3FzZs3AQBdunQxcEREpkFu2/iptg9qJzIkFtZk1DiBBRE1VWVlJUpLS+Hu7g47OztDh0PtnEwmAwDcvHkTLi4uPC2cqAk4UzWZMn6dT0ZLVaaqU1QDmmts4k/GQ1WmMkxgRGSUqqqqAAA2NjYGjoRIo/oLnoqKCgNHQmQaqmeqVjopdZZzpmoyBRyxJqPFCSyI6GHwelYyFvy/SNR8vh19ETM4hjNVk8nhiDUZLU5gQURkWry9vbFmzRpDh0FEJq56puqB3gPRy60Xi2oyCSysyWhxAgsiMncSiaTRR2xsbJvEERAQgOnTp9fb9u9//xtSqRS//fZbm8RCRERkilhYk9GqnsCiPpzAgojMQX5+vvaxZs0ayOVynWVz587V9hVCoLKyslXiiIyMxLZt23D37t06bYmJiXj22Wfh7OzcKtsmIiIyByysyWhxAgsiMndubm7ah6OjIyQSifb55cuX4eDggH379iE4OBhSqRQpKSmYMmUKnnvuOZ31REdHY/DgwdrnarUacXFx8PHxgUwmQ2BgIHbs2NFgHOHh4bh79y6++OILneU5OTk4fPgwIiMjkZ2djTFjxsDV1RX29vbo27cvDh482OA6c3NzIZFIkJaWpl2mUqkgkUhw+PBh7bL09HSMHDkS9vb2cHV1xaRJk3RGx3fs2IGAgADIZDJ06tQJoaGhuHPnTuO/WCIiojbGycvIqHECCyJqcyoVkJsLFBUBjo6AlxegUBgsnIULF2L16tXw9fWFk5NTk14TFxeHTz75BAkJCfDz88PRo0cRHh6Ozp07Y9CgQXX6Ozs7Y8yYMdi4cSPCw8O1yzdt2oRu3bph+PDhuHDhAkaNGoXly5dDKpVi8+bNGD16NDIyMuDp6flQ+6ZSqTB06FD85S9/wT/+8Q/cvXsXCxYswPjx43Ho0CHk5+djwoQJ+Nvf/obnn38excXFOHbsGIQQD7U9IiKi1sLCmkyKBJxhlYha0dWrwLp1QHaNOxIolUBUFODra5CQli5dimHDhjW5/71797BixQocPHgQISEhAABfX1+kpKRgw4YN9RbWgOZ08JEjRyInJwc+Pj4QQiApKQkRERGwsLBAYGAgAgMDtf2XLVuGXbt24auvvsLMmTMfat/++c9/IigoCCtWrNAu27hxIzw8PHDlyhWUlJSgsrISY8eOhZeXFwDN9eBERETGhoU1GbWrt67WuZe10kmJqH5R8O1omINcIjJTKlXdohoAsrKA+HggJsYgI9d9+vRpVv+srCyUlpbWKcbLy8sRFBTU4OuGDRuGbt26ITExEUuXLkVycjLy8vIwdepUAEBJSQliY2Oxd+9e5Ofno7KyEnfv3kVeXl7zd+q+8+fP47vvvoO9vX2dtuzsbAwfPhxPPfUUAgICEBYWhuHDh2PcuHFNHrknIiJqKyysyWipylR1impAcw/r+JPxiBkcw1PCiajl5ObWLaqrZWVp2nv1asOANDp06KDz3MLCos6p0BUVFdqfS0pKAAB79+5F165ddfpJpdIGt2NhYYEpU6YgKSkJsbGxSExMxJAhQ+B7f6R+7ty5OHDgAFavXg2lUgmZTIZx48ahvLy8wfUB0Im1ZpzVsY4ePRqrVq2q8/ouXbrA0tISBw4cwA8//IBvv/0W8fHxePvtt5GamgofH58G94WIiKitcfIyMlq5qtw6RXW1rNtZyFXltm1ARGTeior0a28jnTt3Rn5+vs6ymhOE9ejRA1KpFHl5eVAqlToPDw+PRtc9depUXL9+HTt37sSuXbsQGRmpbfv+++8xZcoUPP/88wgICICbmxtyc3MbjROATqw14wSA3r174+LFi/D29q4Ta/UXChKJBE888QSWLFmCc+fOwcbGBrt27Wp0P4iIiNoaC2syWkVljR/EPqidiKhZ5HL92tvI0KFDcfr0aWzevBmZmZmIiYlBenq6tt3BwQFz587F7NmzkZSUhOzsbJw9exbx8fFISkpqdN0+Pj4YOnQoXn31VUilUowdO1bb5ufnh507dyItLQ3nz5/Hyy+/DLVa3eC6ZDIZ/vjHP2LlypW4dOkSjhw5gr/+9a86fWbMmIFbt25hwoQJOHXqFLKzs7F//35MnToVVVVVSE1NxYoVK3D69Gnk5eVh586d+PXXX+Hv7/+Qvz0iIqLWwcKajJbctvGD2Ae1ExE1i7e3ZqKy+iiVmnYjEBYWhkWLFmH+/Pno27cviouLMXnyZJ0+y5Ytw6JFixAXFwd/f3+MGDECe/fubdLp05GRkbh9+zZefvll2Nraape/9957cHJywoABAzB69GiEhYWhd+/eja5r48aNqKysRHBwMKKjo/HOO+/otLu7u+P7779HVVUVhg8fjoCAAERHR0OhUMDCwgJyuRxHjx7FqFGj8Ic//AF//etf8e6772LkyJHN+I0RERG1PongPStQVFQER0dHFBYWQm4kIxKkucZ6yeElyLqdVadN6aTkNdZEpKOsrEw7o3XNgrBZrl7VTFSWVeNzx8CzgpPpapH/k0REZDDNqRPNZsT6X//6F7y9vWFra4v+/fvj5MmThg6J9KSwVSCqXxSUTrojSEonJaL6R7GoJqKW5+urmf172TJg3jzNvzExLKqJiIioUWYxK/j27dsxZ84cJCQkoH///lizZg3CwsKQkZEBFxcXQ4dHevDt6IuYwTHIVeWiqKwIcls5vBXeLKqJqPUoFAaZ/ZuIiIhMl1mMWL/33nt45ZVXMHXqVPTo0QMJCQmws7PDxo0bDR0atQCFrQK93HphoPdA9HLrxaKaiIiIiIiMiskX1uXl5Thz5gxCQ0O1yywsLBAaGorjx4/X+5p79+6hqKhI50FERERERET0MEy+sP7tt99QVVUFV1dXneWurq4oKCio9zVxcXFwdHTUPh50X08iIiIiIiKihph8Yf0w3nrrLRQWFmof169fN3RIREREREREZKJMfvIyZ2dnWFpa4saNGzrLb9y4ATc3t3pfI5VKIZVK2yI8IiIiIiIiMnMmP2JtY2OD4OBgJCcna5ep1WokJycjJCTEgJERERERERFRe2DyI9YAMGfOHERERKBPnz7o168f1qxZgzt37mDq1KmGDo2IiIiIiIjMnMmPWAPASy+9hNWrV2Px4sXo1asX0tLS8M0339SZ0IyIiKg9mzJlCp577jnt88GDByM6OrrN4zh8+DAkEglUKlWrbkcikWD37t2tug0iIiLATAprAJg5cyauXbuGe/fuITU1Ff379zd0SERERA80ZcoUSCQSSCQS2NjYQKlUYunSpaisrGz1be/cuRPLli1rUt+2KobLy8vh7OyMlStX1tu+bNkyuLq6oqKiolXjICIiag6zKayJiIhM1YgRI5Cfn4/MzEy8+eabiI2Nxd///vd6+5aXl7fYdjt27AgHB4cWW19LsLGxQXh4OBITE+u0CSGwadMmTJ48GdbW1gaIjoiIqH4srImIiAxMKpXCzc0NXl5eeO211xAaGoqvvvoKwO+nby9fvhzu7u549NFHAQDXr1/H+PHjoVAo0LFjR4wZMwa5ubnadVZVVWHOnDlQKBTo1KkT5s+fDyGEznZrnwp+7949LFiwAB4eHpBKpVAqlfj444+Rm5uLIUOGAACcnJwgkUgwZcoUAJoJQ+Pi4uDj4wOZTIbAwEDs2LFDZztff/01/vCHP0Amk2HIkCE6cdYnMjISV65cQUpKis7yI0eO4OrVq4iMjMSpU6cwbNgwODs7w9HREYMGDcLZs2cbXGd9I+5paWmQSCQ68aSkpODJJ5+ETCaDh4cHZs2ahTt37mjb33//ffj5+cHW1haurq4YN25co/tCRETtAwtrogaoylRIK0jD0dyjOF9wHqoylaFDIqI2YAzvfZlMpjMynZycjIyMDBw4cAB79uxBRUUFwsLC4ODggGPHjuH777+Hvb09RowYoX3du+++i02bNmHjxo1ISUnBrVu3sGvXrka3O3nyZGzduhXr1q3DpUuXsGHDBtjb28PDwwNffPEFACAjIwP5+flYu3YtACAuLg6bN29GQkICLl68iNmzZyM8PBxHjhwBoPkCYOzYsRg9ejTS0tLwl7/8BQsXLmw0joCAAPTt2xcbN27UWZ6YmIgBAwage/fuKC4uRkREBFJSUnDixAn4+flh1KhRKC4ubt4vu4bs7GyMGDECL7zwAn788Uds374dKSkpmDlzJgDg9OnTmDVrFpYuXYqMjAx88803GDhw4ENvj4iIzIdZzApO1NKu3rqKdSfXIft2tnaZ0kmJqH5R8O3oa8DIiKg1Gfq9L4RAcnIy9u/fj6ioKO3yDh064KOPPoKNjQ0A4JNPPoFarcZHH30EiUQCQFN0KhQKHD58GMOHD8eaNWvw1ltvYezYsQCAhIQE7N+/v8FtX7lyBZ999hkOHDiA0NBQAICv7+/73LFjRwCAi4sLFAoFAM0I94oVK3Dw4EHtLS59fX2RkpKCDRs2YNCgQVi/fj0eeeQRvPvuuwCARx99FBcuXMCqVasa/V1ERkZi7ty5WLduHezt7VFcXIwdO3Zg3bp1AIChQ4fq9P/ggw+gUChw5MgRPPPMM42uuyFxcXGYOHGidhTfz88P69at0+5HXl4eOnTogGeeeQYODg7w8vJCUFDQQ22LiIjMC0esiWpRlanqHFgDQNbtLMSfjOfINZGZMuR7f8+ePbC3t4etrS1GjhyJl156CbGxsdr2gIAAbVENAOfPn0dWVhYcHBxgb28Pe3t7dOzYEWVlZcjOzkZhYSHy8/N1JvK0srJCnz59GowhLS0NlpaWGDRoUJPjzsrKQmlpKYYNG6aNw97eHps3b0Z2tub3eOnSpToTilYX4Y2ZMGECqqqq8NlnnwEAtm/fDgsLC7z00ksAgBs3buCVV16Bn58fHB0dIZfLUVJSgry8vCbHX9v58+exadMmnX0JCwuDWq1GTk4Ohg0bBi8vL/j6+mLSpEnYsmULSktLH3p7RERkPjhiTVRLriq3zoF1tazbWchV5aKXW6+2DYqIWp0h3/tDhgzB+vXrYWNjA3d3d1hZ6f557tChg87zkpISBAcHY8uWLXXW1blz54eKQSaTNfs1JSUlAIC9e/eia9euOm1SqfSh4qgml8sxbtw4JCYmYtq0aUhMTMT48eNhb28PAIiIiMD//vc/rF27Fl5eXpBKpQgJCWlwcjcLC81YQs3rzGvPLF5SUoL/+7//w6xZs+q83tPTEzY2Njh79iwOHz6Mb7/9FosXL0ZsbCxOnTqlHcUnIqL2iYU1US1FZUV6tRORaTLke79Dhw5QKpVN7t+7d29s374dLi4ukMvl9fbp0qULUlNTtdcAV1ZW4syZM+jdu3e9/QMCAqBWq3HkyBHtqeA1VY+YV1VVaZf16NEDUqkUeXl5DY50+/v7aydiq3bixIkH7yQ0p4MPHjwYe/bswQ8//KAzU/r333+P999/H6NGjQKguZb7t99+a3Bd1V845Ofnw8nJCYBmlL6m3r1746effmo0F1ZWVggNDUVoaChiYmKgUChw6NAh7Sn3RETUPvFUcKJa5Lb1H6Q2tZ2ITJMpvfcnTpwIZ2dnjBkzBseOHUNOTg4OHz6MWbNm4eeffwYAvPHGG1i5ciV2796Ny5cv4/XXX2/0HtTe3t6IiIjAtGnTsHv3bu06q0/F9vLygkQiwZ49e/Drr7+ipKQEDg4OmDt3LmbPno2kpCRkZ2fj7NmziI+PR1JSEgBg+vTpyMzMxLx585CRkYFPP/0UmzZtatJ+Dhw4EEqlEpMnT0b37t0xYMAAbZufnx/+/e9/49KlS0hNTcXEiRMbHXVXKpXw8PBAbGwsMjMzsXfvXu1139UWLFiAH374ATNnzkRaWhoyMzPx5Zdfaicv27NnD9atW4e0tDRcu3YNmzdvhlqt1s7UTkRE7RcLa6JavBXeUDrVP1qhdFLCW+HdtgERUZswpfe+nZ0djh49Ck9PT4wdOxb+/v6IjIxEWVmZdgT7zTffxKRJkxAREYGQkBA4ODjg+eefb3S969evx7hx4/D666+je/fueOWVV7S3muratSuWLFmChQsXwtXVVVtsLlu2DIsWLUJcXBz8/f0xYsQI7N27Fz4+PgA0p1B/8cUX2L17NwIDA5GQkIAVK1Y0aT8lEgmmTZuG27dvY9q0aTptH3/8MW7fvo3evXtj0qRJmDVrFlxcXBpcl7W1NbZu3YrLly/j8ccfx6pVq/DOO+/o9Hn88cdx5MgRXLlyBU8++SSCgoKwePFiuLu7AwAUCgV27tyJoUOHwt/fHwkJCdi6dSsee+yxJu0PERGZL4mofVPLdqioqAiOjo4oLCxs8JQ6al+u3rqK+JPxyLqdpV2mdFIiqn8UfJ04KziRMSorK0NOTg58fHxga2v7UOvge59aUkv8nyQiIsNpTp3Ia6yJ6uHb0Rcxg2OQq8pFUVkR5LZyeCu8obBVGDo0ImpFfO8TERHRw2BhTdQAha2Cs38TtUN87xMREVFz8RprIiIiIiIiIj2wsCYiIiIiIiLSAwtrIiIiIiIiIj2wsCYiIrPCm12QseD/RSKi9oOFNRERmQVLS0sAQHl5uYEjIdIoLS0FoLmHNhERmTfOCk5ERGbBysoKdnZ2+PXXX2FtbQ0LC353TIYhhEBpaSlu3rwJhUKh/dKHiIjMFwtrIiIyCxKJBF26dEFOTg6uXbtm6HCIoFAo4ObmZugwiIioDbCwJiIis2FjYwM/Pz+eDk4GZ21tzZFqIqJ2hIU1ERGZFQsLC9ja2ho6DCIiImpHeAEaERERERERkR5YWBMRERERERHpgYU1ERERERERkR54jTU0t8UAgKKiIgNHQkRERERERMaguj6srhcbw8IaQHFxMQDAw8PDwJEQERERERGRMSkuLoajo2OjfSSiKeW3mVOr1fjvf/8LBwcHSCSSBvsVFRXBw8MD169fh1wub8MIqaUxl+aF+TQfzKX5YC7NC/NpPphL88J8ti4hBIqLi+Hu7g4Li8avouaINTS3ZunWrVuT+8vlcv7HNRPMpXlhPs0Hc2k+mEvzwnyaD+bSvDCfredBI9XVOHkZERERERERkR5YWBMRERERERHpgYV1M0ilUsTExEAqlRo6FNITc2lemE/zwVyaD+bSvDCf5oO5NC/Mp/Hg5GVEREREREREeuCINREREREREZEeWFgTERERERER6YGFNREREREREZEeWFg3w7/+9S94e3vD1tYW/fv3x8mTJw0dEtUQFxeHvn37wsHBAS4uLnjuueeQkZGh06esrAwzZsxAp06dYG9vjxdeeAE3btzQ6ZOXl4enn34adnZ2cHFxwbx581BZWdmWu0K1rFy5EhKJBNHR0dplzKVp+eWXXxAeHo5OnTpBJpMhICAAp0+f1rYLIbB48WJ06dIFMpkMoaGhyMzM1FnHrVu3MHHiRMjlcigUCkRGRqKkpKStd6Vdq6qqwqJFi+Dj4wOZTIZHHnkEy5YtQ83pWphL43X06FGMHj0a7u7ukEgk2L17t057S+Xuxx9/xJNPPglbW1t4eHjgb3/7W2vvWrvTWC4rKiqwYMECBAQEoEOHDnB3d8fkyZPx3//+V2cdzKXxeNB7s6bp06dDIpFgzZo1OsuZTyMgqEm2bdsmbGxsxMaNG8XFixfFK6+8IhQKhbhx44ahQ6P7wsLCRGJiokhPTxdpaWli1KhRwtPTU5SUlGj7TJ8+XXh4eIjk5GRx+vRp8cc//lEMGDBA215ZWSl69uwpQkNDxblz58TXX38tnJ2dxVtvvWWIXSIhxMmTJ4W3t7d4/PHHxRtvvKFdzlyajlu3bgkvLy8xZcoUkZqaKq5evSr2798vsrKytH1WrlwpHB0dxe7du8X58+fFs88+K3x8fMTdu3e1fUaMGCECAwPFiRMnxLFjx4RSqRQTJkwwxC61W8uXLxedOnUSe/bsETk5OeLzzz8X9vb2Yu3atdo+zKXx+vrrr8Xbb78tdu7cKQCIXbt26bS3RO4KCwuFq6urmDhxokhPTxdbt24VMplMbNiwoa12s11oLJcqlUqEhoaK7du3i8uXL4vjx4+Lfv36ieDgYJ11MJfG40HvzWo7d+4UgYGBwt3dXfzjH//QaWM+DY+FdRP169dPzJgxQ/u8qqpKuLu7i7i4OANGRY25efOmACCOHDkihND8obG2thaff/65ts+lS5cEAHH8+HEhhOaDzcLCQhQUFGj7rF+/XsjlcnHv3r223QESxcXFws/PTxw4cEAMGjRIW1gzl6ZlwYIF4k9/+lOD7Wq1Wri5uYm///3v2mUqlUpIpVKxdetWIYQQP/30kwAgTp06pe2zb98+IZFIxC+//NJ6wZOOp59+WkybNk1n2dixY8XEiROFEMylKal98N5SuXv//feFk5OTzufsggULxKOPPtrKe9R+NVaIVTt58qQAIK5duyaEYC6NWUP5/Pnnn0XXrl1Fenq68PLy0imsmU/jwFPBm6C8vBxnzpxBaGiodpmFhQVCQ0Nx/PhxA0ZGjSksLAQAdOzYEQBw5swZVFRU6OSxe/fu8PT01Obx+PHjCAgIgKurq7ZPWFgYioqKcPHixTaMngBgxowZePrpp3VyBjCXpuarr75Cnz598OKLL8LFxQVBQUH48MMPte05OTkoKCjQyaejoyP69++vk0+FQoE+ffpo+4SGhsLCwgKpqalttzPt3IABA5CcnIwrV64AAM6fP4+UlBSMHDkSAHNpyloqd8ePH8fAgQNhY2Oj7RMWFoaMjAzcvn27jfaGaissLIREIoFCoQDAXJoatVqNSZMmYd68eXjsscfqtDOfxoGFdRP89ttvqKqq0jlABwBXV1cUFBQYKCpqjFqtRnR0NJ544gn07NkTAFBQUAAbGxvtH5VqNfNYUFBQb56r26jtbNu2DWfPnkVcXFydNubStFy9ehXr16+Hn58f9u/fj9deew2zZs1CUlISgN/z0dhnbEFBAVxcXHTarays0LFjR+azDS1cuBB//vOf0b17d1hbWyMoKAjR0dGYOHEiAObSlLVU7vjZa3zKysqwYMECTJgwAXK5HABzaWpWrVoFKysrzJo1q9525tM4WBk6AKLWMGPGDKSnpyMlJcXQodBDuH79Ot544w0cOHAAtra2hg6H9KRWq9GnTx+sWLECABAUFIT09HQkJCQgIiLCwNFRc3z22WfYsmULPv30Uzz22GNIS0tDdHQ03N3dmUsiI1RRUYHx48dDCIH169cbOhx6CGfOnMHatWtx9uxZSCQSQ4dDjeCIdRM4OzvD0tKyzozDN27cgJubm4GioobMnDkTe/bswXfffYdu3bppl7u5uaG8vBwqlUqnf808urm51Zvn6jZqG2fOnMHNmzfRu3dvWFlZwcrKCkeOHMG6detgZWUFV1dX5tKEdOnSBT169NBZ5u/vj7y8PAC/56Oxz1g3NzfcvHlTp72yshK3bt1iPtvQvHnztKPWAQEBmDRpEmbPnq09s4S5NF0tlTt+9hqP6qL62rVrOHDggHa0GmAuTcmxY8dw8+ZNeHp6ao+Jrl27hjfffBPe3t4AmE9jwcK6CWxsbBAcHIzk5GTtMrVajeTkZISEhBgwMqpJCIGZM2di165dOHToEHx8fHTag4ODYW1trZPHjIwM5OXlafMYEhKCCxcu6Hw4Vf8xql0YUOt56qmncOHCBaSlpWkfffr0wcSJE7U/M5em44knnqhz67srV67Ay8sLAODj4wM3NzedfBYVFSE1NVUnnyqVCmfOnNH2OXToENRqNfr3798Ge0EAUFpaCgsL3UMHS0tLqNVqAMylKWup3IWEhODo0aOoqKjQ9jlw4AAeffRRODk5tdHeUHVRnZmZiYMHD6JTp0467cyl6Zg0aRJ+/PFHnWMid3d3zJs3D/v37wfAfBoNQ8+eZiq2bdsmpFKp2LRpk/jpp5/Eq6++KhQKhc6Mw2RYr732mnB0dBSHDx8W+fn52kdpaam2z/Tp04Wnp6c4dOiQOH36tAgJCREhISHa9upbNA0fPlykpaWJb775RnTu3Jm3aDICNWcFF4K5NCUnT54UVlZWYvny5SIzM1Ns2bJF2NnZiU8++UTbZ+XKlUKhUIgvv/xS/Pjjj2LMmDH13uYnKChIpKamipSUFOHn58dbNLWxiIgI0bVrV+3ttnbu3CmcnZ3F/PnztX2YS+NVXFwszp07J86dOycAiPfee0+cO3dOO1N0S+ROpVIJV1dXMWnSJJGeni62bdsm7OzseEufFtZYLsvLy8Wzzz4runXrJtLS0nSOiWrOCM1cGo8HvTdrqz0ruBDMpzFgYd0M8fHxwtPTU9jY2Ih+/fqJEydOGDokqgFAvY/ExERtn7t374rXX39dODk5CTs7O/H888+L/Px8nfXk5uaKkSNHCplMJpydncWbb74pKioq2nhvqLbahTVzaVr+85//iJ49ewqpVCq6d+8uPvjgA512tVotFi1aJFxdXYVUKhVPPfWUyMjI0Onzv//9T0yYMEHY29sLuVwupk6dKoqLi9tyN9q9oqIi8cYbbwhPT09ha2srfH19xdtvv61zsM5cGq/vvvuu3r+TERERQoiWy9358+fFn/70JyGVSkXXrl3FypUr22oX243GcpmTk9PgMdF3332nXQdzaTwe9N6srb7Cmvk0PIkQQrTFyDgRERERERGROeI11kRERERERER6YGFNREREREREpAcW1kRERERERER6YGFNREREREREpAcW1kRERERERER6YGFNREREREREpAcW1kRERERERER6YGFNREREREREpAcW1kRERERERER6YGFNRERkoiQSSaOP2NjYNonjzp07eOSRRzBnzhyd5bm5uZDL5fjwww/bJA4iIiJDkQghhKGDICIiouYrKCjQ/rx9+3YsXrwYGRkZ2mX29vawt7cHAAghUFVVBSsrq1aJ5ejRo3jqqadw6NAhPPnkkxBCYOjQobC1tcW+fftaZZtERETGgiPWREREJsrNzU37cHR0hEQi0T6/fPkyHBwcsG/fPgQHB0MqlSIlJQVTpkzBc889p7Oe6OhoDB48WPtcrVYjLi4OPj4+kMlkCAwMxI4dOxqNZeDAgYiKisLUqVNx584drF27Fmlpafjoo49aYc+JiIiMS+t8bU1ERERGYeHChVi9ejV8fX3h5OTUpNfExcXhk08+QUJCAvz8/HD06FGEh4ejc+fOGDRoUIOvW758Ob7++muEh4dj//79+OCDD9C1a9eW2hUiIiKjxcKaiIjIjC1duhTDhg1rcv979+5hxYoVOHjwIEJCQgAAvr6+SElJwYYNGxotrGUyGdauXYsRI0Zg5MiRCA8P1zt+IiIiU8DCmoiIyIz16dOnWf2zsrJQWlpapxgvLy9HUFDQA1//8ccfw87ODhcuXEBhYSEcHR2btX0iIiJTxMKaiIjIjHXo0EHnuYWFBWrPW1pRUaH9uaSkBACwd+/eOqdxS6XSRre1fft27NmzB8ePH8eECRMwe/ZsbNy4UZ/wiYiITAILayIionakc+fOSE9P11mWlpYGa2trAECPHj0glUqRl5fX6Gnftd24cQMzZszAO++8g8DAQGzatAkDBgzAiy++iJEjR7boPhARERkbzgpORETUjgwdOhSnT5/G5s2bkZmZiZiYGJ1C28HBAXPnzsXs2bORlJSE7OxsnD17FvHx8UhKSmpwva+++ir8/f0RHR0NAOjXrx/mzZuHV199FYWFha29W0RERAbFwpqIiKgdCQsLw6JFizB//nz07dsXxcXFmDx5sk6fZcuWYdGiRYiLi4O/vz9GjBiBvXv3wsfHp951bt68GQcPHkRiYiIsLH4/tFiyZAkUCgVmz57dqvtERERkaBJR+0IrIiIiIiIiImoyjlgTERERERER6YGFNREREREREZEeWFgTERERERER6YGFNREREREREZEeWFgTERERERER6YGFNREREREREZEeWFgTERERERER6YGFNREREREREZEeWFgTERERERER6YGFNREREREREZEeWFgTERERERER6YGFNREREREREZEe/h/Aus7Z/XGzwQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5eafcabfe6ddc66102ea49bba92d2af8627a3438 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 11 Aug 2024 13:45:44 +0000 Subject: [PATCH 62/78] SVR model added --- .../test_support_vector_regression.ipynb | 1637 +++++++++++++++++ 1 file changed, 1637 insertions(+) create mode 100644 app/services/calib_validation/test/others/test_support_vector_regression.ipynb diff --git a/app/services/calib_validation/test/others/test_support_vector_regression.ipynb b/app/services/calib_validation/test/others/test_support_vector_regression.ipynb new file mode 100644 index 00000000..69e670d2 --- /dev/null +++ b/app/services/calib_validation/test/others/test_support_vector_regression.ipynb @@ -0,0 +1,1637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.svm import SVR" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9961613725956199" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SVR model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), SVR(kernel=\"linear\"))\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 786.06817646, 774.16112732, 169.97543307, 100.89124975,\n", + " 1423.89137523, 167.92634901, 166.02161111, 112.45139996,\n", + " 779.34904981, 764.74057119, 1423.50780012, 759.70612299,\n", + " 174.62120954, 122.38562533, 764.06525372, 788.16916383,\n", + " 161.36933805, 1428.41975816, 103.13801231, 99.63506207,\n", + " 760.68326135, 763.60871109, 177.33607007, 1430.1852322 ,\n", + " 1421.98908793, 786.76917174, 99.09670387, 102.13959107,\n", + " 107.74679153, 113.51843804, 765.96773821, 764.59866928,\n", + " 787.03137631, 766.4524878 , 1446.02493565, 1436.67715617,\n", + " 1421.82373044, 1445.77717367, 1442.54420182, 1422.28241681,\n", + " 776.17737048, 114.57451133, 171.12398447, 1426.87564021,\n", + " 763.93598843, 166.93101567, 789.57658774, 1433.07580019,\n", + " 1433.696823 , 167.75862327, 1418.81230066, 1432.79231492,\n", + " 756.96248244, 1430.78491926, 109.28163314, 776.03060028,\n", + " 101.90368919, 99.89096906, 167.04919653, 787.09657094,\n", + " 1413.37639964, 191.16678106, 167.48295527, 760.65407598,\n", + " 103.89477225, 122.03101928, 1433.42245163, 111.81119227,\n", + " 1416.0712344 , 1433.29441884, 1433.84309126, 768.2707198 ,\n", + " 168.36465857, 767.25929896, 110.12371189, 1413.64286489,\n", + " 1442.99494097, 787.15769045, 1425.92767291, 101.1499266 ,\n", + " 1414.34581531, 776.33835113, 99.3240292 , 773.09016685,\n", + " 180.13223027, 1419.03425187, 1450.3530087 , 95.87425145,\n", + " 1433.86791747, 1432.13659254, 98.56522234, 169.38009215,\n", + " 102.60133276, 784.53115873, 122.70041959, 785.08390781,\n", + " 166.89140842, 240.88111696, 1449.82506188, 108.56071336,\n", + " 776.51964396, 1423.37638438, 782.89937547, 777.64203979,\n", + " 101.62134053, 781.39209615, 97.57369452, 97.07062968,\n", + " 96.59802601, 1432.94318354, 107.34396499, 1269.52845407,\n", + " 1432.2997475 , 126.41023556, 1417.0923705 , 786.4788734 ,\n", + " 765.03051268, 163.5215889 , 1444.8509884 , 181.5226558 ,\n", + " 167.54808799, 762.14252596, 1432.56272421, 168.93219589,\n", + " 783.1772012 , 169.68478027, 764.32277305, 1434.07494872,\n", + " 1433.35631165, 1427.09854866, 1432.78102547, 766.986891 ,\n", + " 97.63885695, 1446.8629281 , 788.6356597 , 1426.17452062,\n", + " 114.10164258, 190.72124042, 96.30508092, 114.91215112,\n", + " 116.08955388, 102.11466932, 111.6232333 , 1417.77562652])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9456979902446316" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SVR model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), SVR(kernel=\"linear\"))\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([167.24704403, 147.57189819, 96.43602343, 542.75354681,\n", + " 50.63898134, 110.68899627, 106.98656955, 358.34588604,\n", + " 163.39465024, 594.96665535, 624.53630295, 618.98636251,\n", + " 86.94829389, 299.86834513, 595.46795559, 168.10352024,\n", + " 118.67347325, 431.52192945, 537.38300556, 535.6244927 ,\n", + " 621.07478963, 554.29994981, 74.93587593, 635.2230296 ,\n", + " 124.27884373, 162.07453039, 541.23069001, 538.93990105,\n", + " 339.83045969, 348.20521822, 588.3756925 , 544.74820729,\n", + " 156.92353993, 566.77476522, 410.11203721, 683.12824096,\n", + " 631.35054651, 421.6438783 , 416.1310996 , 119.63038418,\n", + " 154.23903683, 346.97858209, 93.64383376, 640.71732205,\n", + " 582.36577712, 99.57021438, 170.15476265, 427.13960383,\n", + " 662.03897297, 105.18201667, 143.51769748, 639.99738366,\n", + " 603.11715806, 389.77640643, 352.8206183 , 169.04518056,\n", + " 538.71511892, 533.88775644, 110.31668333, 168.47878744,\n", + " 141.30325282, 32.57508055, 105.16473031, 593.85895671,\n", + " 533.19237558, 300.1892727 , 659.38637758, 343.67620852,\n", + " 137.55271905, 429.6418013 , 646.52589448, 596.22555029,\n", + " 103.51697055, 565.39427129, 352.32717805, 143.41078667,\n", + " 415.39896191, 171.2800319 , 635.73183918, 535.62152859,\n", + " 140.98390414, 156.23138868, 544.84704822, 160.15060369,\n", + " 78.96341239, 124.94589752, 418.45994587, 535.6410221 ,\n", + " 83.35537538, 649.44984764, 530.55473909, 102.83666981,\n", + " 543.12677866, 163.1070225 , 377.37382271, 168.05582622,\n", + " 103.88075762, -29.50094072, 417.8858622 , 332.83038364,\n", + " 163.39679933, 632.73749667, 166.80345497, 163.73063147,\n", + " 537.4425775 , 179.87647713, 538.12727975, 536.0505489 ,\n", + " 534.47182702, 655.94570271, 347.13044782, 31.85424472,\n", + " 425.13641163, 278.85029093, 131.66024006, 169.26102716,\n", + " 563.37777408, 98.64646084, 415.20221784, 77.99328961,\n", + " 96.94780331, 590.3178895 , 421.46462058, 97.87870389,\n", + " 161.63930867, 98.39272484, 588.22144553, 663.39624996,\n", + " 392.21952928, 97.98572011, 640.2000581 , 584.72292022,\n", + " 527.6615485 , 412.9880516 , 164.75429106, 641.19814588,\n", + " 348.66943067, 14.96823934, 530.73058264, 355.18010497,\n", + " 357.14498777, 540.35508543, 353.21847186, 123.0124774 ])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768786.068176100167.247044(768, 100)
290768774.161127100147.571898(768, 100)
54100169.97543310096.436023(100, 100)
198100100.891250630542.753547(100, 630)
45314361423.89137510050.638981(1436, 100)
..................
164100114.912151365355.180105(100, 365)
165100116.089554365357.144988(100, 365)
199100102.114669630540.355085(100, 630)
132100111.623233365353.218472(100, 365)
50114361417.775627100123.012477(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 786.068176 100 167.247044 (768, 100)\n", + "290 768 774.161127 100 147.571898 (768, 100)\n", + "54 100 169.975433 100 96.436023 (100, 100)\n", + "198 100 100.891250 630 542.753547 (100, 630)\n", + "453 1436 1423.891375 100 50.638981 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 114.912151 365 355.180105 (100, 365)\n", + "165 100 116.089554 365 357.144988 (100, 365)\n", + "199 100 102.114669 630 540.355085 (100, 630)\n", + "132 100 111.623233 365 353.218472 (100, 365)\n", + "501 1436 1417.775627 100 123.012477 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768786.068176100167.247044(768, 100)
290768774.161127100147.571898(768, 100)
54100169.97543310096.436023(100, 100)
198100100.891250630542.753547(100, 630)
45314361423.89137510050.638981(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 786.068176 100 167.247044 (768, 100)\n", + "290 768 774.161127 100 147.571898 (768, 100)\n", + "54 100 169.975433 100 96.436023 (100, 100)\n", + "198 100 100.891250 630 542.753547 (100, 630)\n", + "453 1436 1423.891375 100 50.638981 (1436, 100)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(141, 5)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.242163\n", + "(100, 365) 0.412466\n", + "(100, 630) 0.648346\n", + "(768, 100) 0.927538\n", + "(768, 630) 1.229444\n", + "(1436, 100) 1.204913\n", + "(1436, 365) 1.537352\n", + "(1436, 630) 1.797777\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Initialize the StandardScaler and SVR model\n", + " # with 2-degree polynomial features\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), SVR(kernel=\"linear\"))\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X\n", + " model.fit(X_train_x, y_train_x)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_x = model.predict(X_test_x)\n", + " r2_score(y_test_x, y_pred_x)\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y\n", + " model.fit(X_train_y, y_train_y)\n", + "\n", + " # Predict the values and calculate the R2 score\n", + " y_pred_y = model.predict(X_test_y)\n", + " r2_score(y_test_y, y_pred_y)\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9YAAAHqCAYAAADhztZNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACz4UlEQVR4nOzdd3wUdf7H8dcm2XSSTYAkICVA6EUUEbCABUEFT088y1nw5DwLoKhYTwVs3KGehRNRf556ep53elYsCIoNaYL0Ii2AQBIg2SxtU+f3x2SS3WQ32dRNeT8fj33M7JTv9zuQndnPfpvNMAwDEREREREREamRkGAXQERERERERKQpU2AtIiIiIiIiUgsKrEVERERERERqQYG1iIiIiIiISC0osBYRERERERGpBQXWIiIiIiIiIrWgwFpERERERESkFhRYi4iIiIiIiNSCAmsRERERERGRWlBgLSIiTdo333yDzWbjm2++CXZRqpSamsr1119f+r4xlr18GRvC66+/js1mIz09vUHzrcyTTz5J165dCQ0NZeDAgcEuTqMxffp0bDZbsIshItLoKLAWEWlibDZbQK/GFKw1BlbwZr0iIyPp0aMHkyZNIjMzM9jFq5bPPvuM6dOnB7UMnv+WISEhtG/fnlGjRjX4393GjRuZPn16nQblX375Jffccw+nn346r732Gk888USdpV2e2+0mLS2NXr16kZ+fX2H/BRdcQHx8PPv27au3MtQlp9NJu3btOP300zEMo8L+pUuXEhISwt133x2E0omI1J+wYBdARESq58033/R6/89//pMFCxZU2N67d++GLFaT8cgjj9ClSxfcbjc//PADL774Ip999hnr168nOjq6QcsyfPhwjh8/Tnh4eLXO++yzz3jhhReCHlyfd955XHfddRiGwc6dO5kzZw7nnHMOn376KRdccEHA6Vx77bVceeWVREREVLsMGzduZMaMGZx11lmkpqZW+3xfvv76a0JCQnj11Ver/X9TXZGRkbz44ouMGjWKmTNnMm3atNJ977zzDl988QWzZ8+mffv29VqOuuJwOHj22We58soreeWVV/jTn/5Uuq+wsJCbb76Zzp07M2PGjCCWUkSk7imwFhFpYq655hqv90uXLmXBggUVtpd37NixBg8cG6MLLriAU045BYA//vGPtG7dmr/97W989NFHXHXVVT7POXr0KDExMXVelpCQECIjI+s83YbSo0cPr7+73/72twwYMIBnn322WoF1aGgooaGh9VHEGsnKyiIqKqrOgmrDMHC73URFRfncf9555/H73/+emTNnctVVV9GjRw+cTid33HEHgwcP5tZbb62TcjSUK664gjfeeIP77ruPiy++mOTkZACee+451qxZw2effaZ7kYg0O2oKLiLSDJ111ln069ePlStXMnz4cKKjo3nggQcAswmvr5pOX31rnU4nU6ZMoWPHjkRERJCWlsZf//pXiouLqyzDRx99xJgxY2jfvj0RERF069aNRx99lKKiIp9l3bhxI2effTbR0dGccMIJzJo1q0Kav/76K5dccgkxMTEkJSVxxx13kJeXF/g/jA/nnHMOADt37gTg+uuvJzY2lu3bt3PhhRfSqlUrrr76agCKi4t59tln6du3L5GRkSQnJ3PTTTeRk5PjlaZhGDz22GN06NCB6Ohozj77bDZs2FAhb399rJctW8aFF15IQkICMTExDBgwgOeee660fC+88ALg3RzbUtdlrI7+/fvTpk2b0n9LMGt/zzzzTGJiYnA4HFx88cVs2rTJ6zxffaxTU1MZO3YsP/zwA6eeeiqRkZF07dqVf/7zn17n/e53vwPg7LPPrtAN4qeffmL06NG0adOGqKgounTpwg033FDpNdhsNl577TWOHj1amt7rr78OmDWujz76KN26dSMiIoLU1FQeeOCBCn+DVtnnz5/PKaecQlRUFC+99FKl+T7zzDNER0dz8803A3Dfffdx4MABXnrpJUJCqv669tRTT3HaaafRunVroqKiGDRoEO+9957P65s0aRIffvgh/fr1IyIigr59+/LFF19UOPaHH35g8ODBREZG0q1btyqvwdOcOXPIy8vjzjvvBGDPnj1Mnz6dK664olo/uoiINBWqsRYRaaYOHTrEBRdcwJVXXsk111xTWmsUqGPHjjFixAj27t3LTTfdRKdOnfjxxx+5//772b9/P88++2yl57/++uvExsZy5513Ehsby9dff83DDz+My+XiySef9Do2JyeH888/n0svvZTLL7+c9957j3vvvZf+/fuXfgk/fvw45557Lrt37+a2226jffv2vPnmm3z99dfVuq7ytm/fDkDr1q1LtxUWFjJ69GjOOOMMnnrqqdLatZtuuonXX3+dP/zhD9x2223s3LmTv//97/z8888sXrwYu90OwMMPP8xjjz3GhRdeyIUXXsiqVasYNWqUzz605S1YsICxY8fSrl07br/9dlJSUti0aRPz5s3j9ttv56abbmLfvn0+m/83VBn9ycnJIScnh7S0NAAWLlzIBRdcQNeuXZk+fTrHjx9n9uzZnH766axatarKptvbtm3jsssuY8KECYwfP55//OMfXH/99QwaNIi+ffsyfPhwbrvtNp5//nkeeOCB0u4PvXv3Jisri1GjRtG2bVvuu+8+HA4H6enpvP/++5Xm+eabb/Lyyy+zfPly/u///g+A0047DTBbOLzxxhtcdtll3HXXXSxbtoyZM2eyadMmPvjgA690tmzZwlVXXcVNN93EjTfeSM+ePSvNNykpib/85S/cdNNNTJ48mZdffpkpU6Zw0kknVXqe5bnnnuM3v/kNV199Nfn5+bzzzjv87ne/Y968eYwZM8br2B9++IH333+fW2+9lVatWvH8888zbtw4du/eXfo5WLduXem/3/Tp0yksLGTatGkB30dSU1OZMWMGd999N9dffz1z5swhLCysyvuGiEiTZYiISJM2ceJEo/ztfMSIEQZgzJ07t8LxgDFt2rQK2zt37myMHz++9P2jjz5qxMTEGL/88ovXcffdd58RGhpq7N69u9JyHTt2rMK2m266yYiOjjbcbneFsv7zn/8s3ZaXl2ekpKQY48aNK9327LPPGoDx3//+t3Tb0aNHjbS0NAMwFi1aVGl5XnvtNQMwFi5caBw4cMDYs2eP8c477xitW7c2oqKijF9//dUwDMMYP368ARj33Xef1/nff/+9ARj/+te/vLZ/8cUXXtuzsrKM8PBwY8yYMUZxcXHpcQ888IABeP0bL1q0yKvshYWFRpcuXYzOnTsbOTk5Xvl4puXr/7y+yugPYEyYMME4cOCAkZWVZSxbtsw499xzDcB4+umnDcMwjIEDBxpJSUnGoUOHSs9bs2aNERISYlx33XWl26z/m507d5Zu69y5swEY3333Xem2rKwsIyIiwrjrrrtKt7377rs+//8/+OADAzBWrFhR5bWUN378eCMmJsZr2+rVqw3A+OMf/+i1ferUqQZgfP311xXK/sUXX1Qr3+LiYuP00083AKNjx47G4cOHAz63/OctPz/f6Nevn3HOOed4bQeM8PBwY9u2baXb1qxZYwDG7NmzS7ddcsklRmRkpLFr167SbRs3bjRCQ0N9/u35UlBQYAwcONBITEw0AOOll14K+HpERJoaNQUXEWmmIiIi+MMf/lDj8999913OPPNMEhISOHjwYOlr5MiRFBUV8d1331V6vmd/0sOHD3Pw4EHOPPNMjh07xubNm72OjY2N9eqrGx4ezqmnnsqOHTtKt3322We0a9eOyy67rHRbdHS01+BIgRg5ciRt27alY8eOXHnllcTGxvLBBx9wwgkneB13yy23eL1/9913iY+P57zzzvP69xg0aBCxsbEsWrQIMGtp8/PzmTx5slcT7SlTplRZtp9//pmdO3cyZcoUHA6H175ApjhqiDJ6evXVV2nbti1JSUkMGTKExYsXc+eddzJlyhT279/P6tWruf7660lMTCw9Z8CAAZx33nl89tlnVabfp08fzjzzzNL3bdu2pWfPnl5/F/5Y/37z5s2joKCgWtfli1Veq2mz5a677gLg008/9drepUsXRo8eXa08bDZb6b/VsGHDiI2NDfhcz89bTk4Oubm5nHnmmaxatarCsSNHjqRbt26l7wcMGEBcXFzpv2tRURHz58/nkksuoVOnTqXH9e7du1rXFBYWxssvv0x2djZDhw7lxhtvDPhcEZGmRk3BRUSaqRNOOKFWgy9t3bqVtWvX0rZtW5/7s7KyKj1/w4YNPPjgg3z99de4XC6vfbm5uV7vO3ToUCFwTEhIYO3ataXvd+3aRVpaWoXjqmpiW94LL7xAjx49CAsLIzk5mZ49e1bowxoWFkaHDh28tm3dupXc3FySkpJ8pmv9e+zatQuA7t27e+1v27YtCQkJlZbNapber1+/wC+ogcvo6eKLL2bSpEnYbDZatWpF3759Swd5s/Lw9f/Tu3dv5s+fX+WgcJ5BnSUhIaFCf3FfRowYwbhx45gxYwbPPPMMZ511Fpdccgm///3vazT6+K5duwgJCSlt5m5JSUnB4XCUXq+lS5cu1c7j/fff55NPPqFfv368++67TJo0yeuHhcrMmzePxx57jNWrV3v1+fb1g0xV/64HDhzg+PHjFf4+wPz/DORHEcvgwYMBGDRokOa/FpFmTYG1iEgz5W8EYn/KDypWXFzMeeedxz333OPz+B49evhNy+l0MmLECOLi4njkkUfo1q0bkZGRrFq1invvvbfC4Gf+RoQ2fMyDW1unnnpq6ajg/kRERFQItouLi0lKSuJf//qXz3P8/QDRkBq6jB06dGDkyJF1mqan2vxd2Gw23nvvPZYuXconn3zC/PnzueGGG3j66adZunRptWqDy6cbiOp+/g4fPsxtt93GoEGDWLRoEQMGDOCWW27h559/Lu0X78/333/Pb37zG4YPH86cOXNo164ddrud1157jbfffrvC8Q35eRMRaSkUWIuItDAJCQk4nU6vbfn5+ezfv99rW7du3Thy5EiNAqdvvvmGQ4cO8f777zN8+PDS7Z6jRVdX586dWb9+PYZheAU3W7ZsqXGa1dGtWzcWLlzI6aefXmnQ1LlzZ8CsPe7atWvp9gMHDlRZ02o1z12/fn2l/+7+gruGKGOgrDx8/f9s3ryZNm3a1MkUZlUFukOHDmXo0KE8/vjjvP3221x99dW88847/PGPf6xWPp07d6a4uJitW7d6zRGfmZmJ0+ksvd6aevDBB9m/fz8fffQRrVq1Yvbs2Vx00UU8/fTT3HfffZWe+7///Y/IyEjmz5/vVRv/2muv1agsbdu2JSoqiq1bt1bY11CfNxGRpkZ9rEVEWphu3bpV6B/98ssvV6ixvvzyy1myZAnz58+vkIbT6aSwsNBvHlaNmGcNWH5+PnPmzKlxuS+88EL27dvnNYXQsWPHePnll2ucZnVcfvnlFBUV8eijj1bYV1hYWPpjxciRI7Hb7cyePdvr+gMZDfnkk0+mS5cuPPvssxV+/PBMywpIyx/TEGUMVLt27Rg4cCBvvPGGVznXr1/Pl19+yYUXXlgn+fj7t8jJyalQAztw4ECAGk3RZpW3/L/R3/72N4AKI29Xx8qVK3nhhReYNGkSgwYNAmDs2LH89re/5dFHH63QzLy80NBQbDab12c4PT2dDz/8sEblCQ0NZfTo0Xz44Yfs3r27dPumTZt83g9EREQ11iIiLc4f//hHbr75ZsaNG8d5553HmjVrmD9/Pm3atPE67u677+bjjz9m7NixpVMcHT16lHXr1vHee++Rnp5e4RzLaaedRkJCAuPHj+e2227DZrPx5ptv1qqp6Y033sjf//53rrvuOlauXEm7du148803S6fCqm8jRozgpptuYubMmaxevZpRo0Zht9vZunUr7777Ls899xyXXXYZbdu2ZerUqcycOZOxY8dy4YUX8vPPP/P555/7/feyhISE8OKLL3LRRRcxcOBA/vCHP9CuXTs2b97Mhg0bSoMaK/i67bbbGD16NKGhoVx55ZUNUsbqePLJJ7ngggsYNmwYEyZMKJ1uKz4+3udc6jUxcOBAQkND+etf/0pubi4RERGcc845vP3228yZM4ff/va3dOvWjcOHD/PKK68QFxdXo6D+xBNPZPz48bz88sulXR2WL1/OG2+8wSWXXMLZZ59do/IXFRXxpz/9iZSUFB577DGvfc899xx9+vRh8uTJfPzxx37TGDNmDH/72984//zz+f3vf09WVhYvvPACaWlpXuMUVMeMGTP44osvOPPMM7n11lspLCxk9uzZ9O3bt8Zpiog0a8EajlxEROqGv+m2+vbt6/P4oqIi49577zXatGljREdHG6NHjza2bdtWYbotwzCMw4cPG/fff7+RlpZmhIeHG23atDFOO+0046mnnjLy8/MrLdfixYuNoUOHGlFRUUb79u2Ne+65x5g/f36FqZH8lXX8+PFG586dvbbt2rXL+M1vfmNER0cbbdq0MW6//fbSqaQCnW6rqumXfE215Onll182Bg0aZERFRRmtWrUy+vfvb9xzzz3Gvn37So8pKioyZsyYYbRr186IiooyzjrrLGP9+vUV/o3LT7dl+eGHH4zzzjvPaNWqlRETE2MMGDDAayqkwsJCY/LkyUbbtm0Nm81W4f+/LsvoD2BMnDixyuMWLlxonH766UZUVJQRFxdnXHTRRcbGjRu9jvE33daYMWMqpDdixAhjxIgRXtteeeUVo2vXrqVTQS1atMhYtWqVcdVVVxmdOnUyIiIijKSkJGPs2LHGTz/9VGWZ/f0NFBQUGDNmzDC6dOli2O12o2PHjsb999/vNX1cZWX35ZlnnjEA47333vO5/6mnnjIA4/333680nVdffdXo3r27ERERYfTq1ct47bXXjGnTplX42/D3/+br//3bb781Bg0aZISHhxtdu3Y15s6d6zPNqgT6tyIi0pTZDEMjVYiIiIiIiIjUlPpYi4iIiIiIiNSCAmsRERERERGRWlBgLSIiIiIiIlILCqxFREREREREakGBtYiIiIiIiEgtKLAWERERERERqYWwYBegMSguLmbfvn20atUKm80W7OKIiIiIiIhII2AYBocPH6Z9+/aEhPivl1ZgDezbt4+OHTsGuxgiIiIiIiLSCO3Zs4cOHTr43a/AGmjVqhVg/mPFxcUFuTQiIiIiIiLSGLhcLjp27FgaM/qjwBpKm3/HxcUpsBYREREREREvVXUZ1uBlIiIiIiIiIrWgwFpERERERESkFhRYi4iIiIiIiNRCUAPr1NRUbDZbhdfEiRMBcLvdTJw4kdatWxMbG8u4cePIzMz0SmP37t2MGTOG6OhokpKSuPvuuyksLAzG5YiIiIiIiEgLFNTAesWKFezfv7/0tWDBAgB+97vfAXDHHXfwySef8O677/Ltt9+yb98+Lr300tLzi4qKGDNmDPn5+fz444+88cYbvP766zz88MNBuR4RERERERFpeWyGYRjBLoRlypQpzJs3j61bt+JyuWjbti1vv/02l112GQCbN2+md+/eLFmyhKFDh/L5558zduxY9u3bR3JyMgBz587l3nvv5cCBA4SHhweUr8vlIj4+ntzcXI0KLiIiIiIiIkDgsWKj6WOdn5/PW2+9xQ033IDNZmPlypUUFBQwcuTI0mN69epFp06dWLJkCQBLliyhf//+pUE1wOjRo3G5XGzYsMFvXnl5ebhcLq+XiIiIiIiISE00msD6ww8/xOl0cv311wOQkZFBeHg4DofD67jk5GQyMjJKj/EMqq391j5/Zs6cSXx8fOmrY8eOdXchIiIiIiIi0qI0msD61Vdf5YILLqB9+/b1ntf9999Pbm5u6WvPnj31nqeIiIiIiIg0T2HBLgDArl27WLhwIe+//37ptpSUFPLz83E6nV611pmZmaSkpJQes3z5cq+0rFHDrWN8iYiIICIiog6vQERERERERFqqRlFj/dprr5GUlMSYMWNKtw0aNAi73c5XX31Vum3Lli3s3r2bYcOGATBs2DDWrVtHVlZW6TELFiwgLi6OPn36NNwFiIiIiIiISIsV9Brr4uJiXnvtNcaPH09YWFlx4uPjmTBhAnfeeSeJiYnExcUxefJkhg0bxtChQwEYNWoUffr04dprr2XWrFlkZGTw4IMPMnHiRNVIi4iIiIiISIMIemC9cOFCdu/ezQ033FBh3zPPPENISAjjxo0jLy+P0aNHM2fOnNL9oaGhzJs3j1tuuYVhw4YRExPD+PHjeeSRRxryEkRERERERJo8p9tJujMdl9tFfGQ8nR2dcUQ6gl2sJqFRzWMdLJrHWkREREREWrId2Tt4fvnzbM/ZXrotLSGNyadOpmti1yCWLLia3DzWIiIiIiIi0vCcbmeFoBpgW842Zi+fjdPtDE7BmhAF1iIiIiIiIi1YujO9QlBt2ZazjXRnesMWqAlSYC0iIiIiItKCudyuWu0XBdYiIiIiIiItWlxk5eNMVbVfGsGo4BJMB4F+QGaAx08CZtdfcUREpIFNAuYAgYxj2htYDCTUa4lERKShrAbOAnI5MRk+vtL3UYYBhYWLgHklx4svqrFucSIBW8mrLYEH1QB/9zjXBtxY56UTEZH69Ee87+MvEFhQDbAJSPQ4t019FFBEROpNOmb4Z93HTwJyAbDZ/J8VEgLh4UeBs/F+hqyux7I2PQqsWwTPD0Gej/2tML8wGX5ek/yk+38e6W6u2yKLiEgdWUnZvfpVH/vDgMfw/wzYDjh8nHfII91r67rQIiJSZ1Iw79Vd8P1j6lDAwGYzyM3LYU3mz3y/61vWZP5Mbl4OMMpPuieVpOuoj0I3OZrHmuY8j/XJwM8+tt8NzKpl2rHAUR/bD6BaDBGRxmAH0M3H9hOAX2uZ9lTgaR/bLwY+rGXaIiJSN6KB4+W22TCfD6m1SNcJtAaKy20PAwpqkW7jpHmsW7QfMD80nkF1B8pqH2obVAMcKUnrQLntbVHXfRGRYLPjHVTbgGzM+3Ztg2qApyh7pqR6bP+Isi9tIiISHNdi3os9g+oZmPfsYnwF1U63k9UZq/ku/TvWZKypYt5qB1BUkt7fPbYXluQ7uhZlb7oUWDc7NuBMj/ddMf/o91Q/JVvFF0BYmOe2NthsBvfc49nwoaikHBf5TXvyZN9pB1KOxMSqyygi0jKNxbz/Fnpss75IVW/QMX/318jI8tt38vjjBubzxtINM7j37cEHq3/vto4LDzffn3227zKed161LlNEpBlxYj4D3vLY9m/M58DDfs/akb2DB79+kNs/v537vrqP2z6/jYe+fgibzfB5r46O9tw2EZvNYNasdR4pfllSjnS/eT7ySM1jgdRU//uCGQsosG5Wyv8lWX3jai4kxBwJMDm5JAebGVhb27//3tz+5JNWfi96nD0Pc7C0iv5e8uOWYUCrVub6ySdXPM76AtWhg3ksQE6O7zJaLxGRlikS+NTj/YcEPjCZb3a7eV/tWhIzh4ZCRIR5bzYM+Oknc/uDD4L5vPHMz6q5qOjxx82lYUDbtub62Wf7L0doaMVtixb5vvcvWFD1dYmIND8v4P0DahrmPdnPUN8lnG4nsxbP4rtd35HtzibXnUu2O5tvd30LQHh4AYYBPXuax4eFmT+wRkaa996fSxrI3ntvv5L8TvFIvQtwp898p00zl4YB7dqZ66N9VHTHx5vLAQPK7vW7dpWU3WkurbK0b2++v/XWSi+53iiwbhaspt+WT6jtl6nyMjLK1t1uKCoy1884o/yRN5fkHVHyPg9/X6wsrpL55q0PpqeCkm4ae0oq3AcPNpe+gnARkZbLc3DKFMz78MV1lvr2kt9oi4shNxfySrIaNMjX0Qbe/axtQE6Fo6xahawsc/nNN77zfvBBM9+OHatbahGRluJsvAcbNoCtAZ259dBWlu9bzr7D+/jl0C9sy9nGL4d+Yd/hfUBZL+rNJeMUFxVBdjYcL2llPnBg+RRX4B2HPAP09Zl3SEkkus/Mii+/rHiMFSesWWMuL7zQXHoG4dYzKTvbXA4f7jO7eqfAusmbh3fTbwOzKWDdsmqOe/f23n6jjxm3zGYYbmy2Yo+X2ZTkhx/KjuvQoep8I0ri88iSiu8VK8zl6tVlxxQXlzX96NMnoMsREWlGPH+8nADsr/McYmPN5ZAh3tunTvVRGhvYbBeXewY4sNkMVq4sO65Xr8Dytmq3d+/2f4z15WzhwsDSFBFpPk4CvilZD6W6lWtZR7P41fUr0fZouji60Dm+M10cXYi2R5sHlCRn1RyXr1R74IGKaZrPAcPjGbC+NBbw/A7fr1/V5UtI8F5+9pm5XLQIHI6SIhpmnm63+f7Kyivp641GmWrSDuLdj7nu20FbQatl48ay9blz4f/+ryRnj6zL1m2YAyVMr3EZ3W4z/7w873JY6wkJ5q9T//oXXHMNbNpUreRFRJo4z6D6Q+qylhrMVkOe996lS8vW33oLni4ZGNz/M6B8Gav3DLDyrqqbj7X/3HOrlbyISBN3OWVzScdhzUldHeGh4SRGJZJxJINfXWWDW8ZFmKNfFxaEeT0HrG6gAO+8AzNnmuv+nwMnACVV0pyCWaMduOxs81ngdHo/j0JDy1o62WxmzGK3Q2Gh2bVo0aJqZVMnVGPdpLX1WK+fzsVW/2VLWMlPMf/6F9xyi7l+oNzA4N4DCEzzqrX44Yeydhu/BjgwrWcfuk6dzG1W3wmrycfVVwdWAy4i0ny081j/iboOqqGsj7XFaj300UdwbcnU1dZ92FJxIJmyWouVK8tGKreaFQbCc0Ca8sH+pJLWjzExgacnItL0OYF3S9ZbU5OgGsAR6aBtTFtceS6v7db7sPBCr+dAdElF9qefwlVXmevlxz/yfgbs9YgFlrPao8p6/frAyugZC1hNz++/v2ywyuKS9upWF1J/XYvqm2qsmyzP30Tqf8Quq4lFURHMm2fWDoMZVLdpU/FYbzbMEWKt6Vd+AMrakVjTwflrDtKxo9nH+ocfypoCzp5tNkPftq3sF6lAA3URkabvI8Aa/GIk4LOzc52xngF5efDVV3DJJeb27Oyy5nmex1Zko6zmOgY4WnpcUpK5PO003/l6pWIzg/38/LJtL7xgLo8cCfRqRESaA8+b78Eap3K84Dhje4ylqKiItVlrASgyiujdprc5cW/Jfdh6Dhw/bgauY0t6nubklDXJtviOBSq2XrICYmvQsbPO8l3GE080+1ivXl3WlPzhh+GTT8xBNOPizL7YVkzSuXNAl17nbIahcZQDnfS78bgIs281mHNS310vudhsZo21NVDZmWd695Eur+q/pLIP1I03GqXNyMufX77pX/lh82fNgrvvNvv8HT3qva9Vq7JBDkREmi/PZtbFlR1Y8xzKBbFjx5o1FP5U/QyYCphtx6dOXcDTT4/0eX5lzb99BdaBNhcXEWk+woGS6tlaVrB9tvUzbvrkJk7vdDo9WvegoLiA6LBoDh0/xHMXPEOYvZCCfHP6xEsvhQ8+8J9W4LGAjQceKC5tRl7+/KpigVdfhRtu8L0vsHJUT6CxogJrmmJgbf0FRQHHglmQGqh5XzsREYGmfR+NBkqGkm1yZRcRaQw+pWyg4leBG2qcktPt5OPNH3Pnl3eSX5SPgYFhGNhsNlqFt6JXm178bfTfGJgysA7KDWZ/8JNK1mdQ2dzajUmgsaL6WDc5nu2um1pQDfB91YeIiEgAtge7ADXg+dwaGrRSiIg0XVZQHUltgmqAdGc6e1x76J/Un8LiQvIK88gvyievMA+n20mqI5VUR2ptC+xhIGVjRE2rw3QbBwXWTc6hkuXVQS1FzXmO0R/h9ygREfHF7rHeNWilqB2rCfiyoJZCRKTpcXqsH/d3UGApuZ0cPHaQaHs0tw6+lUmnTiIyLBKbzYbNZqNvUl9+1+d3tcrDtyyP9W/qIf3g0eBlTYrnfNVvBa0UtTe95JVf+WEiIlJOYcmyKU/YvICy5uyTgL8HsSwiIk1JQtWHBGBH9g7+tvRvrNq/ik0HN5FXmMeY7mP44IoP2JO7hyh7FFlHs9jn2ke6M70Om4Jb7Jh9xM+mOXULUo11k2KNHNajQXKzhsn/178qbis/p3T5beW3ex/j2fSjY5XlOHiwYvpxcRXTPfNM3+f7LoO3nj397xMRaRySPdbrf8Jm65740UcVt9XuGQDmvKYAL/jNPySk4rkvveQ/7ccfr/parNezz5rb4+Mr7tuxw386IiKNQ07Vh/jhdDuZtXgWn239jOzj2USHRdMxviNfbv+Su768i8+3f85V/7uKdze+yx9PuYGT2p3oNXBlZc+BkHKRpf/ngGflmtNvWUNDK577j3/4T3vWrMqv3TN+qLqM1afAukna0qC5WVNr1ZTn3HNlQ+VZTcKrniOrbduK2x56yBwZ3DNNfyOWjxpVdpw1NUD5D/4vv1RZDBGRILOaz93VoLlaU2vVlO9nQNX3/gcegA8/NM/ZXtKd/Oab/af95z/7Tsf6gnT55eZxQ4aU/RBrzSJhGPDMM+Z6t24V0xARCb5LPdYdNU5l66GtLNi5gPDQcMJDw+mS0IXsY9kcyT/Cmsw19Gzdk7iIOLKOljXZtr4/V8XfkNi+nwOW/n7TmzbNnObXMGDnTnPbhAn+077nHv9l+/RTOHy48rJZc2Rby+pSYN1k3BjsAjB6dMVtESXdpK355y66KNDUAhvEzF7SndCajN5y991lv0pVNgUYwPz5Zesec9KXUi21iDQtTwUl1yuuqLitVStz2b27ubz22pqk/JHPrY89BhdfbK5//HFN0vX2n/+Yy6VLYZCPab8XlrSuD1MnORFplKx5rmrXHDzzaCax9lgOHjvI5oObyXHncCT/CNH2aFqFtyI8NJy4iDh25e4qOaMsEvZ1j7fmsO7Tx1yWD3z9swaw9P9D68MPw5gx5npl0z0GIpAfB6w44eefa5aHHh9NhjXpc8P+FhISYk7efsIJsG9fxf3WXKJ795oB6rx5FY8pH7haE8xXNveqYZhN0AsLzeA6NLTiMXPnwi23lL0fNcr/dZQvgzUhvTVi/t13w5NP+j9fRKQlstuhoAB69ICtWyvuP3LEXP7yi3mffestePNN72Nq+gwASE6GLI9xbipLu6rJQ30dm5ICGRne+woKEBFpxGreX8XpdhJjj6HIKCIxKpHW0a0JtYVSUFxAXlEeobZQQm2h7D28l4TIBNxAmL2IwoIQ+vaFjRsrppmbay43bChrqv3qq97HlG96XVwMNtsSKutfbd2ny8cg//63/7T9PQesH0xzciChbrqp+6Qa6ybn2/pNfssW+PLLkp/2DTDMbxjWH3SEx0DeVhAdHl55kr6af5jrISWv0T6PsZqg5/sZ4+zmm81jX3zRfP/ll1WXwapdiYgw+25bTUKq6pMhIhJcfRomGz/PACuo9py+86uvzGVsbOVJ+n8GPOfxHPDdTDAz03w/caL53qotueaasqbdqanmtqpaH/30E5x2mvexGRll5bFqsdWKSUQaN0eNztqZvZPPt37Oqv2rCAsJY2v2Vn459AvFRnHpaOADkgeQfTyb7olpJER452MF1Z6B6TffmMv4+Mrz9ry/W5Vb3rGA/+bie/ea7++4w3x/1VXm8oYbzGeBYUBamrnN1/37kUegqAjatSurXffF6iZa09pqAJthVPUbb/MX6KTfwWX9pdTjf9eyZXDvvfDttyU5FhNCISmJBezLNttil9U0+BcaatY0W8f5+gsz93nu8E60snx69674i1llefnO26zh9heM61MhIo2LdUNMBXbWTxY+ngF28kg9oYCte81fJQN5BkREgNsdyDMAyp4DFZ8B/s6p7T7P9zab2YzdGmejOs8SEZGGVfNYwOl28p8N/+G/6//L4fzDXNj9Qj7c/CFrMtdwQqsTOFZwjLTENM5PO58PN33Alv3ribFHkzPtCOFhBaSd4GbjLjNGCuQ5EBUFx47VPhbwfU719kVHw3E/M5N5HltZ2oHGiqqxFtOWLV5fqMoY7O1/IVU12fP8damoqOrszON/V2lthed7q6bZMMyg2uGAyZPNbZs3V56XZ416H49Kn/nzff8ypi9UItJ4fVU/yVbyDPgl7SKg2O8XqfL30by8qrMrO8f/MyAhoWxE8rfKzTDZp0/Z6N1//GMgF2jyNXK4VRs/dWrg6YiINKzadTBOd6ZTXFzM+gPrcRe6eWH5C/Rp04eHhj/E+BPHM+fCOfy29295+senyS80b+JDOg4DwFZYwIbU3wDFFQb/tZS/f/sLZCueYz0DzvH5HGjTpqxv9TvveJ9/4omQnm6u33qr/3yOHav6+77VL7y2zcTVx1pMu3b5+EJV4ttvMeYv9NmJ+aSTvN9bfbLnzi3b5qt/nel64H81Km5uLvz97+bLV1lsNnNAtb17zf5y/ssgItKUdK2fZGv4DDjrLO/34eFm9x3PQLim91+ns+KI5NbI3Zs2VRy9OzvbXFr9wa18nnnGbELoqx9eWJh3CyvPdEREGo/3a3W2y+3CXegGIK8wDwODj375CFtJLXGMPYZnz3+WZ857GkdEHJE2OyfuN0i1EqjkOVB+U2Sk2WrJMxCu+jnguyXWoUMVBx2zvvuvXQtdunjvy8kxl1Z/8ECfN9YUXrW9/yuwFpP1l+jB8GzQ4LG/sj9Sz9rq8tOiVJQWWNkomxIlkDKU3x/oh0rBtoi0WHX0DPCsra6LqRprsq/89IlTppgvXzRQmYg0DT7miaqGuMg4IsMiKSouYu/hvXSI60DGkQyO5B+h2CjGZrNx8MgBIjMPsuKnf3BL6/PpfP8sDDymgwjwOeBZW33llYGW0PfNuKbPgQ0bqndeXcUAagoupqraPtTLEHpP10OaIiLNWcUAuDacbierM1bzXTc7a154GOd1l3uPUmmpz2FURUSkCufX6uxURyo2bHRP7E5BcQG7cncRFxFHWmIavdv25tJel3LGCUM5NTOMP+/pQtf7Z1Xs11Ovz4FO9Zh2w1GNtZg6d4YRI3w3BRwxwtxf5z6ohzRFRJqzawEf8xrWwKYDm3h6ydNsPLARe7GNqAMH6H1CLJNn3uP9parengEiIhKYS4GAJ4iuwBHp4JT2p2BgUGgUsjZzLZlHM2kV3opzu5zL8M7DCckv4MR/f93AsYDlgnpMu+FoVHCa2qjgB4A29ZNFuRFhAfODNGsWnHpqPWRoXVMykFEP6YuINBetgJJJo+tgdohth7Yx5YsprNi/onRbjC2CLkY8A7LtTNvbHcc//1vPz4AdgNVRusV/FRERqULtZgj6ftf3LN69mN5te1NQVIC7yE1YSBjpznSW713OlCFTGJ4ZEaRYoHE/AwKNFVVj3eR0BVxVHlUjQ4bASy+Zg9hYM6h37gw9e9ZPfqUUVIuIVG43kFgnKTndTn7c86NXUA1w1MhjZ4gLe8eOpF96LQOvnlDPz4AGmptbRERoFdGKxb8uZvGvi33uj4uMgyEDgxQLNA8KrJuMfsB6ajt4QZV69tSHR0Sk0am7vm3pznQOHT/kc9/RYjfHI0NxtYmDU4bXWZ6+Wf33JtZzPiIizclgYEWVR5WX6kglLSGNbTnbKuxLS0gj1ZFqvmmwWOCBBsijYWnwsiZjXbALUMfmVn2IiIj4sKNWZ7vcLiLDIv3uLyguMGsuGszfqz5ERKTFs+aW+qlGZzsiHUw+dTJpCd6z8qQlpDF5yGQckY7aFa/aZpYsoxo43/qjGusmaQYwLdiFqKVbSpb6ExQRCUwIUIzZL7nm/dHiIuPIPp5Nv7b9WH9gfYX9fdr2Kau5qDd/rOf0RUSamx2U9Umuma6JXZl21jTSnem43C7iIuNIdaQGIaj29GMQ865bqrFuUqxfdKYHsxB14KDHuiYRFREJTJHHes2n3Up1pOJ0O7my35X0a9vPa9/g9oOZetrUBviS9apVmnrOR0SkOXLU/MxIBwNTBjI8dTgDUwYGKaju7rE+MAj51w+NCk5TGRXcYv1S9T1wRjALUguev7a1+D8/EZFqsHksi2ucyo7sHbz404s4Ih0kRiXiLnTTOqo1p3U6jbTEtKoTqJW3MKcNAz0DRESq4wVgUsl6U75/Ws+yGyj7obXxCjRWVGBNUw2soel+oBpg6jARkWapbqaocrqdbD20lcyjmUSFRZEUk0TH+I4NVHNhPQOigaMNkJ+ISHNi3UMHAGuCWZAauhx4t2S9acQygcaKQW8KvnfvXq655hpat25NVFQU/fv356efyjrlG4bBww8/TLt27YiKimLkyJFs3brVK43s7Gyuvvpq4uLicDgcTJgwgSNHjpTPqpnw/AO8LGilqDnPHwYUVIuIVE9Xj/Wa9bXbkb2D6d9M55HvHuGllS/x7LJn+cfP/yD7WHbdFLFS53msK6gWEam+a0qWawFnEMtRU1ZQPSCopagPQQ2sc3JyOP3007Hb7Xz++eds3LiRp59+moSEsmlFZs2axfPPP8/cuXNZtmwZMTExjB49GrfbXXrM1VdfzYYNG1iwYAHz5s3ju+++409/+lMwLqmBRJQs/4d3f+XGLtZj/UDQSiEi0rRt91jv5vcoX5xuJ88vf57tOdu9tm/L2cbs5bNxup21L55fOcDCkvUT6jEfEZHm7E2P9bqbirFheP4g3BRr2ysX1Kbg9913H4sXL+b777/3ud8wDNq3b89dd93F1KlTAcjNzSU5OZnXX3+dK6+8kk2bNtGnTx9WrFjBKaecAsAXX3zBhRdeyK+//kr79u2rLEfTagpuaWpNwudSNhL4KGB+EMsiItLUDQWWlawvBM4N6KzVGat5aNFDfvc/evajDEwZWNvC+dHUnlsiIo2ZdU+NAo4FsyABSgEyS9ZzqM0AbA2tSTQF//jjjznllFP43e9+R1JSEieddBKvvPJK6f6dO3eSkZHByJEjS7fFx8czZMgQlixZAsCSJUtwOBylQTXAyJEjCQkJYdmyZTRfnl9KmsKUVbd4rCuoFhGpnaUe6yP9HlWey+3yud1d6OZw3mEOHTvEwh0LWbB9AWsy1tRhDXaox7qCahGR2ptRsjwOPBDMggTgH5QF1dfQlILq6ghqRLZjxw5efPFF7rzzTh544AFWrFjBbbfdRnh4OOPHjycjIwOA5ORkr/OSk5NL92VkZJCUlOS1PywsjMTExNJjysvLyyMvL6/0vcvl+4tG43c38CTmFCzhQH5wi+OXailEROqegfco4VXfX+Miy35pt4fYGdRuEK0iWpHrzqVv277838//x4ebPyQuIo5erXtxcruTmXzqZLomdq0k1aqEUzaC+ZuVHSgiIgF7GHgKOAzMxOxvPSeYBfLDcyTzMJrzcyCoNdbFxcWcfPLJPPHEE5x00kn86U9/4sYbb2Tu3Ln1mu/MmTOJj48vfXXs2LFe86s/s4BOJesFNIKx6Mo5iHdQrX7VIiJ1y3PAMRtVzW+d6kglLSENe4idC9IuYOHOhdz95d2sP7CeJ354gq92fEXn+M648lxsPrSZ9QfW17LvdQjm8wngLMoG3RERkdpzUfb9/0VgdBDL4ssEyoJqKHseNE9BjcTatWtHnz59vLb17t2b3bt3A5CSkgJAZmam1zGZmZml+1JSUsjKyvLaX1hYSHZ2dukx5d1///3k5uaWvvbs2VMn1xMcu4CTStY9ay+C7R6grcd7A40CLiJS1xLwDq4TgZf8Hu2IdDD51Mmcn3Y+76x/h80HNnNJ70v4bc/fcn7a+dw6+FaGdhhKh1YdcLqdHM0/yracbaQ702tQNs9a9MuBRTVIQ0REKldEWSPkL4H4IJbFU3vMJuAQaKuqpi6ogfXpp5/Oli1bvLb98ssvdO7cGYAuXbqQkpLCV199Vbrf5XKxbNkyhg0bBsCwYcNwOp2sXLmy9Jivv/6a4uJihgwZ4jPfiIgI4uLivF5N2yrMZuEWG3B2kMpi5f+kx/vm/0ESEQmeBLzvszdTWU+vroldOSnlJEJsIdx3xn1kHs7kx19/JNQWSn5RPicmn8hfR/6VtIQ0CosLAf99s307He8feecC/6nG+SIiUj0FQLuSdRfmPXh1kMqSXpL//pL3rSjrDtS8BTWwvuOOO1i6dClPPPEE27Zt4+233+bll19m4sSJANhsNqZMmcJjjz3Gxx9/zLp167juuuto3749l1xyCWDWcJ9//vnceOONLF++nMWLFzNp0iSuvPLKgEYEbz5m4d3U+hvMP+rNDViGbnh/mQpBQbWISEMxKBskrAjzfny6zyOPFxznrNSz+OSXTxiROoKf9v3EXxf/le0528lx57A9Zzt/GfkXzk87H3uI3atvtn8rS/L80WNbNnBTzS9JREQCtA+4weP9SXgPHNkQwoEuHu9HYQb6LUNQp9sCmDdvHvfffz9bt26lS5cu3Hnnndx4442l+w3DYNq0abz88ss4nU7OOOMM5syZQ48ePUqPyc7OZtKkSXzyySeEhIQwbtw4nn/+eWJjY31lWUHTnG6rMidgfrg8HaD+mmLfCPxfuW3fA2fUU34iIuLfV1QcKfwx4M+l71ZnrGbJniX8tP8nVu9fzfac7dw+5HY+2vIRazLNuUWTY5I5ud3JXDPgGi7sfiGOSIef/HIwm6B7GgT8VBcXIyIi1RaG+QOrpQNQn11f+wIbPd7baE611IHGikEPrBuD5hdYW3z1t24LZPnYXl2bgd4+tg8GltdB+iIiUjunYNYie7IBh3C6bfxn/X84eOwgM76dwaW9L2Xroa38nPEzRklLoxh7DKmOVIZ2GMpTo57yEVi3A8rPvtG8vkyJiDRdqykbh8nTHcDf6iD9WcC9PrYvwhyssvloEvNYS30zMGuOPR3A/OJjvU4NMK0fgEiP88oH1a1K8lNQLSLSOPyEeV92eGwzgEQckQn88aSbue3UR0iggB6te7AqYxU2m42wkDBCbaHYQ+yEhYSx6cCmksHLrL7T1qt8UL0dBdUiIo3FQMx7/h3ltj9D2X08HDNADsQLQITHueWD6t+V5HdWjUrbHKjGmuZcY13eRcC8Ok6zPpuYi4hI3dmBORaGt0C/Bdh8TjoxEfh7LcokIiINpyPwax2mV1ctYRs31ViLD59g/pJkAG8B9hqk0dUjDU2hJSLSdHjev7OB3hQVBR5Ym2KAhR7pKKgWEWk69lB2/76f6g9uFgLc4pFG8w+qq0M11rSkGmsREZEyP+z6gQPHDvDLoV/4cPOHrMtaR5FRRGRYJO1i2xEXEcewDsOYdta0SgYvExERab5UYy0iIiKVio2I5c21b2IYBg8Nf4jR3UbTLaEbbaLbcOj4Ifol9WPykMkKqkVERKoQFuwCiIiISHCkOlLpHN+Zxb8uZvm+5QxqN4iRXUfiLnTTOqo1I7uNpH2r9sEupoiISKOnGmsREZEWyhHpYPKpk0lLSKOguICle5fy2bbP2J27mzM6n6GgWkREJECqsRYREWnBuiZ2ZdpZ09h2aBvHCo/hLnRTUFTAoWOHSIxKVDNwERGRACiwFhERaeEOHj3I/iP72XJoC/lF+ezM2cn3u7/nrM5ncc/p99A1sWuwiygiItKoKbAWERFpwTYd2MS0b6bx9c6vOZx/GIAByQO47sTr+Oeaf2IPtfPoOY+q5lpERKQSCqxFRERaKKfbydNLnmbTwU2lQTXA2sy1AJzZ6UzWZq4l3ZnOwJSBQSqliIhI46fBy0RERFqodGc6Gw9spLi4uMK+tZlrSXWkUlBcgMvtCkLpREREmg4F1iIiIi2Uy+3CHmInJMT314H8onzsIXbiIuMauGQiIiJNiwJrERGRFiouMo4oexQ2bMSGx2IYBoZhgGHuj7JHMSB5AKmO1KCWU0REpLFTYC0iItJCpTpSSY1PxR5ip32r9kTboyksLqTQKOSklJNoFd6KO4bdoYHLREREqqDBy0RERFqwEakj2J6znV9dv9K9dXcMw6B76+7cdPJN9GjTg/at2ge7iCIiIo2eAmsREZEWKt2Zzrsb3+Ws1LNIjErEXegmMiyS7OPZPL/8eR4e8bACaxERkQAosBYREWmhXG4XBcUFLN271O9+ERERqZoCaynldDtJd6bjcruIj4yns6Oz+tWJiDRjcZFx2EPsDGo3qKzG2h5J9rFsVu5fqdHARUREAqTAWgDYkb2D55c/z/bs7ZyYciJto9sSFhLGickn0i+5nwJsEZFmKNWRyuV9LueVVa+w/sD60u392vbjxpNv1GjgIiIiAdKo4ILT7eT55c+z6cAmRnUdRVFxEbl5uew/sp/vdn/Hv9b8i53ZO4NdTBERqQffpH/DTqf3PX6ncyff7PomOAUSERFpglRjLaQ701mTsYZebXpRTDFfbv+StVlrzXlMbTC0w1ASohNIiE5QzbWISDOS7kxn75G99Gjdg+MFxykoLsAeYifKHsXew3tJd6YzMGVgsIspIiLS6CmwFnKO59CrTS8u7X0pf1/xdwqLC+nXth9HC44CsNe1l1dWvkL/pP4KrEVEmhFrcDJ7qB17qN3vfhEREamcmoILsfZYhnceTn5RPrucuwgLCaPYKCbEFsL27O043U72H9nPvsP7gl1UERGpQ1UNTqbBy0RERAKjwLqF23FoBwePH+SFFS+weM9i1mSuYVXGKva49hAXEccJcSdwJP8ImUczcRe6cbqdwS6yiIjUkVRHKmkJaT73pSWkafAyERGRACmwbsGsmuinfnyKE+JOoH9Sf7oldKN7YnciwyLZkbOD9q3aU1hcyLGCY0Tbo9nl3BXsYouISB1xRDqYfOrkCsF1WkIak4dMVvcfERGRAKmPdQuW7kzHle/i9E6n8/GWj/mQD4kNj2VN5hriwuNIjk2m2CgmNCSUU9ufypH8IzgiHMEutoiI1KGuiV2ZdtY085ngdhEXGUeqI1VBtYiISDUosG7BXG4XYSFhfPrLp2QdzaKwuJDxJ44nKiyKnzN+JvNIJolRiQxuP5hLel3C7tzdJEYlBrvYIiJSxxyRDo3+LSIiUgsKrFuwuMg4Mo9kciT/CEfzj7L/yH4eWvQQF/e6mAu6X0AIIQw+YTDzfpnHT3t/onVMa/ol9Qt2sUVERERERBoVBdYtWKojlXRnOoeOH8KVb06pkleUx383/BeA1lGtmXPhHPYe3suV/a7kH6v+Qevo1sEssoiIiIiISKOjwctaMEekg7jwOIqNYuwh3vOX2kPsFBlFRNujufWUW/nX2n/Ru21vjRArIiIiIiJSjmqsW7hW4a0YmDKQVftXAWAYBjabjWKjmG4J3XAXulm1fxVdE7pqhFgREREREREfFFi3cOFh4VzV7yoMw2DZ3mUUFRdhYHBi8omM6zOO1tGtubjXxXR2dFZQLSIiIiIi4oMC6xaus6Mzb619i5FdR3JZn8s4VnCM8NBwDhw7QOaRTE5qd5ICahERERERkUoosG7hHJEObjnlFmYvn83SvUtLt6clpKnpt4iIiIiISABshmEYwS5EsLlcLuLj48nNzSUuLi7YxQkKp9tJujMdl9tFXGQcqY5UBdUiIiIiItKiBRorqsZaALPmemDKwGAXQ0REREREpMnRdFsiIiIiIiIitaDAWkRERERERKQWFFiLiIiIiIiI1IICaxEREREREZFaCGpgPX36dGw2m9erV69epfvdbjcTJ06kdevWxMbGMm7cODIzM73S2L17N2PGjCE6OpqkpCTuvvtuCgsLG/pSREREREREpIUK+qjgffv2ZeHChaXvw8LKinTHHXfw6aef8u677xIfH8+kSZO49NJLWbx4MQBFRUWMGTOGlJQUfvzxR/bv3891112H3W7niSeeaPBrERERERERkZYn6IF1WFgYKSkpFbbn5uby6quv8vbbb3POOecA8Nprr9G7d2+WLl3K0KFD+fLLL9m4cSMLFy4kOTmZgQMH8uijj3Lvvfcyffp0wsPDG/pyREREREREpIUJeh/rrVu30r59e7p27crVV1/N7t27AVi5ciUFBQWMHDmy9NhevXrRqVMnlixZAsCSJUvo378/ycnJpceMHj0al8vFhg0b/OaZl5eHy+XyeomIiIiIiIjURFAD6yFDhvD666/zxRdf8OKLL7Jz507OPPNMDh8+TEZGBuHh4TgcDq9zkpOTycjIACAjI8MrqLb2W/v8mTlzJvHx8aWvjh071u2FiYiIiIiISIsR1KbgF1xwQen6gAEDGDJkCJ07d+a///0vUVFR9Zbv/fffz5133ln63uVyKbgWERERERGRGgl6U3BPDoeDHj16sG3bNlJSUsjPz8fpdHodk5mZWdonOyUlpcIo4dZ7X/22LREREcTFxXm9RERERERERGqiUQXWR44cYfv27bRr145BgwZht9v56quvSvdv2bKF3bt3M2zYMACGDRvGunXryMrKKj1mwYIFxMXF0adPnwYvv4iIiIiIiLQ8QW0KPnXqVC666CI6d+7Mvn37mDZtGqGhoVx11VXEx8czYcIE7rzzThITE4mLi2Py5MkMGzaMoUOHAjBq1Cj69OnDtddey6xZs8jIyODBBx9k4sSJREREBPPSREREREREpIUIamD966+/ctVVV3Ho0CHatm3LGWecwdKlS2nbti0AzzzzDCEhIYwbN468vDxGjx7NnDlzSs8PDQ1l3rx53HLLLQwbNoyYmBjGjx/PI488EqxLEhERERERkRbGZhiGEexCBJvL5SI+Pp7c3Fz1txYREREREREg8FixUfWxFhEREREREWlqFFiLiIiIiIiI1IICaxEREREREZFaUGAtIiIiIiIiUgsKrEVERERERERqQYG1iIiIiIiISC0osBYRERERERGpBQXWIiIiIiIiIrWgwFpERERERESkFhRYi4iIiIiIiNSCAmsRERERERGRWlBgLSIiIiIiIlILCqxFREREREREakGBtYiIiIiIiEgtKLAWERERERERqQUF1iIiIiIiIiK1oMBaREREREREpBYUWIuIiIiIiIjUggJrERERERERkVpQYC0iIiIiIiJSCwqsRURERERERGpBgbWIiIiIiIhILSiwFhEREREREakFBdYiIiIiIiIitaDAWkRERERERKQWwoJdABEREREREWlYTreTdGc6h/MOkxCZwPHC4xwvOE58ZDydHZ1xRDqCXcQmRYG1iIiIiIhIC7IjewfPL3+e3bm7uSDtAt5Z/w47nTvp4uhCdHg0aQlpTD51Ml0Tuwa7qE2GmoKLiIiIiIi0EE63k+eXP8/2nO0MajeI/2z4D3sP7yUmPIYDxw7gPO5k08FNzF4+G6fbGeziNhmqsRYREREREWkh0p3pbM/ZDkBSTBIDkgdwVupZHC84TnhoOLucu/jx1x8pKCog3ZnOwJSBwS1wE6HAWkREREREpIVwuV0A2EPsdIrvxMsrX2bl/pWl+we1G8R1J17HP9f8k0PHDgWrmE2OAmsREREREZEWIi4yDjAD6H+u+Sfrs9bzu76/o2frnhQUFxAZGonT7eTs1LMJCwkrHeTM5XZpYLNKKLAWERERERFpIVIdqaQlpJEYlchO507+PPzPvLfxPf674b8A2LBxYvKJPHrOo9hD7Ez/Zjrbc7ZjD7EzqN0gOsZ1JCk2iXax7RRke7AZhmEEuxDB5nK5iI+PJzc3l7i4uGAXR0REREREpN7syN7Bgh0L2HJoC0v3LGXF/hUUFhdiwwZAWEgY4/qM45ZTbmHlvpXEhMeQHJPMqoxVhIeEU2QU0Sa6DWEhYZzX5Ty6JHYJ8hXVn0BjRdVYi4iIiIiItCBdE7ty8vGTySvK48WfXiQ8NJyosCgMw8Bms1FsFLMhawMHjx4kOTaZVuGtcOW5aB3VmmW/LuOTXz7BHmJnYMpA4sLjSIhOaPE115puS0REREREpIXp3ro7EaERxNhjMAyDvKI88ovzySvMIyosilbhrThScITnlj7Ht+nfsj1nO/uP7OeU9qcw58I5tApvxfoD63ll1Svsyd0T7MsJOtVYi4iIiIiItDCOSAf9kvoRFxFHiC2EouIiDAxiwmPo0KoDB48dJPtYNqPTRrNo5yJ+2PND6blndDyDR85+hAcXPcj6A+vJPJpJf/oH8WqCT4G1iIiIiIhIC9Q3qS9juo9hbeZaCooLsIfYKTaKyc3LpW9SXwqKC/h4y8fsyNnhdd7iPYtpG9OW0d1G8/EvH+MudAfpChoPNQUXERERERFpgRyRDu4YegcntzuZ1tGtiYs0a697JPbg6v5Xc7zwOGsy1wCUDmxmWZ+1nlRHKjF2c2Czlk411i3WJGAOUN1B4eOAr4FBdV4iERERERFpCCOA7wDomgh/G+3rmP9xaW94aDgYxmEMA5b/Cqe9bu4NsYVQZBQxqtsourfu3kDlbrwUWLcoA4E1tUzDBZzi8X4hcG4t0xQRERERkfoVj/ldviKbd2U0xcXe22w2CAmBYZ2h+GEAg2P5W3hvk4NrBlzT4kcEh0bUFPwvf/kLNpuNKVOmlG5zu91MnDiR1q1bExsby7hx48jMzPQ6b/fu3YwZM4bo6GiSkpK4++67KSwsbODSN2YvAbaSV/mgOgx4DLPWOpDXiT7SH1mSdmQ9lF1ERERERGruIspigfJBdWtgLb6+94eEGKzYu4zPts7jrNeH0+ZR2JUDRUVlZ8dEwHUn3k7XhG7AkAa4lsatUdRYr1ixgpdeeokBAwZ4bb/jjjv49NNPeffdd4mPj2fSpElceumlLF68GICioiLGjBlDSkoKP/74I/v37+e6667DbrfzxBNPBONSGhmbj21nAYtqmN5qj/UcIAmwfsTIK8mvO/BLDdMXEREREZHamw+c72P7P4FrA0ohIiyCjVkbmXH2DF5Z+QqX/HcjIYRwrPAYozqE8bex6wkNtY5ejhkLvAT8qQ7K3/TYDMOobifbOnXkyBFOPvlk5syZw2OPPcbAgQN59tlnyc3NpW3btrz99ttcdtllAGzevJnevXuzZMkShg4dyueff87YsWPZt28fyclmh/m5c+dy7733cuDAAcLDwwMqg8vlIj4+ntzcXOLi4urtWhtOG+CQx/sIoD5H6nuLih/QD4GL6zFPERERERGpKATvcZTOAb6qdipOt5MZ38xgV+4uBrUbRGJUIu5CN5FhkYSGhHJ538tLmoBfCfyn3NlBDTHrVKCxYtCbgk+cOJExY8YwcuRIr+0rV66koKDAa3uvXr3o1KkTS5YsAWDJkiX079+/NKgGGD16NC6Xiw0bNjTMBTQqKzF/KfIMqrOpaVBts1V8AURGlt9+DY8/bgBjPM6+hMr+vB580Hfa1SkHwEsvBZ6OiIiIiEjzZTX7toLa6JL16gfVAAlRDp694G98cOX/eHDEA9x66k18nf41t515HTedciMJUY6S79/v8OyzBpDgcbYN73GZvFXn+3t4uPex/fsHtq+hBTWwfuedd1i1ahUzZ86ssC8jI4Pw8HAcDofX9uTkZDIyMkqP8Qyqrf3WPn/y8vJwuVxer6bvWrz/eO/C/CAl+D48QHY7GAZ07Wq+Dw2FiAjzj9gw4KefzO0PPggwryRPq6WAge/m6PD44yVHGNC2rbl+9tkVjzvvPHPZt695bPkP3s03m8vsbHN/377VvkQRERERkSYuAfO7uMUJHK11qlFRNnKO59Kt1xHAxufXfYCjlZ3oaBuGAWvXmsfdcQeYFXpOj7NXAlE+042OhnJhnk+7d0NBgRkDWO2s16+vel8wBC2w3rNnD7fffjv/+te/iIxs2IGvZs6cSXx8fOmrY8eODZp/3euH2RzbYgBP1WkO27eby+JiyM2FvDzz/SCfs27lAW96vLdh9sn2ZgXJWVnm8ptvKqZ07Ji53LPHXHp2XJg61Vxecw0klPx+EMwPk4iIiIhIwwulLKDtiBkLxFcrBafbyeqM1XyX/h1rMtbgdDtL9zkiHWzb1AqwUVQYxoED4Rwtidkr1hDH4z3osRtfFW1Hj0JOxfCggu4ls3gVF5vL2283ly+/XPm+YAhaYL1y5UqysrI4+eSTCQsLIywsjG+//Zbnn3+esLAwkpOTyc/Px+l0ep2XmZlJSkoKACkpKRVGCbfeW8f4cv/995Obm1v62mNFbU1SJ8Bq9m4196h7sbHmcki5Af+s4NaT1TzcZiv2eDmw2QxWriw7rlevqvMtGacOl6ssELe6zs+ebS7fektNwUVERESkJbIBJZElNwO7q53CpgObmPrlVG799FYeWvQQ9y68lxnfzMAzrkhMNJfnnON97vTpPkpkA5ttdblYwMBmg3Xrqle2/Hzv988+ay4feaTyfcEQtFHBzz33XNaV+5f9wx/+QK9evbj33nvp2LEjdrudr776inHjxgGwZcsWdu/ezbBhwwAYNmwYjz/+OFlZWSQlJQGwYMEC4uLi6NOnj9+8IyIiiIiIqKcra0iTAOtHgdbAwTrPwWpeYVm6tGz9rbfg6afNdc+a5LJ1W7klVDfwv+IKcxkdbf6yZbOZH6K33ir7dcrK026HwkIYOBBWr65WNiIiIiIiTUyMx/ozwJRqp7Dt0Dbu/vJuVuxfUZaqPYaCogIAjh83sHkEA195dNf+3/9gxgxz3X8s4Dk6eShlMwo1P0GrsW7VqhX9+vXzesXExNC6dWv69etHfHw8EyZM4M4772TRokWsXLmSP/zhDwwbNoyhQ4cCMGrUKPr06cO1117LmjVrmD9/Pg8++CATJ05sJoFzZXKAF0rWY6mPoBrK+lhbrFb7H30E15YMBJ6d7X1OxcHGjNJfq1auTCs9bvPmqvP/73/NpdXcxCrLxIlw6qnmutXPu8D8/LOm/HTdIiIiIiLNyhSgpM8kL1GToNrpdvLjnh+9gmqAowVH2encCUBEZKFXLGC1Yp0/H0ombqJcA+NyccBoj1rrAtat+0O1ylh+kqcpU8zlww9Xvi8Ygj4qeGWeeeYZxo4dy7hx4xg+fDgpKSm8//77pftDQ0OZN28eoaGhDBs2jGuuuYbrrruOR4JV/9+gEj3WD9d7btYHKi/P/KXqkkvM99nZZf2bPY+t+Poawwhh0KDtwNleaZY0NuC00yrmG1bSpqJfP3MZUvIXO2NGWTPxU07xTscaaE1EREREpPnJBZ4rWW9DTeeNTnemc+j4IZ/7jhaYtVrW93VrefQo/PADnF9SCe10Qny57twV4wAbhtELwwihf//XS8ofmK1bzaUVAzxXctl/+lPl+4Ih6PNYNwZNbx7raOB4yXr9/ffZbGaNtdV/YexY+PRT/8dX/ZfUBUgHYOrUDJ5+2ntEd+t8q7VJ+fe+jm3TBg4d8r1PRERERKT5qXk3S0/fpX/HhgMbmP7tdJ/7s+7OICKyEPdxOwBXXgn/KT9dtWdJqixKWblttooHR0bC8eMVY4GwMCgqKjuuRw/YsqXqfXWlycxjLTVhBdV/rtdcDMN7UIB58/zVRgcazO4sXXvqqRS/5/t77+vYgwdrUg4RERERkaboIo/12n3xjYuMI/t4Nv3a9vO5f8JHN5KRUzZl1zvv1DYWKDvIMHpWOP/4cWufd3qFhd7HeQbOle1raAqsmxzP/7LHglYKT5UNz1+R56cugDH2RURERESkhDVX9TmVHhWIVEcqTreTK/tdWSG4Htx+MFNPm4oj0lHrfLyVzInFL3WcbvCpKThNrSm41YRiOxD8zsQ7snfw/PLn2Z6zvXRbWkIak0+dTNdEf+Wrm+YrIiIiIiItx1+A+0vW6+Y79I7sHbz404s4Ih0kRiXiLnTTOqo1p3U6jbTEtKoTqBErFrgCeKee8qg7gcaKCqxpSoF1DGWj/wX/v83pdjL9m+leQbUlLSGNaWdN8/MrVw5lg68F/zpERERERBo/KyA9Afi1zlJ1up2kO9NxuV3ERcaR6kith5pqT6cDP5asN/5YQH2smyUrqH6zQXKzhsn/6KOK22w2cyTB7Tnbmff7j5j3+0+Y9/sPSo/blrONhKh4H1NvAXgOI+5/WrSQkIrnvvSS9zE5OZ7p+jZwoK8y+L6mytIREREREQkOz5G06y6oBnBEOhiYMpDhqcMZmDKwNKi2vhvPn192rOf3c4v1PjTUO11fcYB53mKPo37wWy5fscDu3ea+Tp0q7rvySt/p+C6D/325gQ9a7l3emp0mwXVNg+ZmTa1VnsvtKlmzYf7aFOrzON8DG3xYssz3eQ7AAw/Ahx+a52wvqRS/+WbvYxITK5xWgTWvtWHAXXeZ69Y0XtYH6667YO5c720iIiIiIo3DgKDlbE2tBZUPUlZc7Hu771jACkNH+U0vIgJOOME8x6pc69zZXD7+ODzzjHea/kYst9nghhvM43r0MLe1b28ub78ddu0y9733nrnN4fB/jZVRYN1knBLsAnDFFd7v4yLj+OwP/wIgMjkdgJ/+Xi7y9eviKo947DG4uOSwjz+uuD862lwGElxbwfJTT5lLz2H5re033VR1OiIiIiIiDa+kqrYOBi2rqQkTKm5r3dpcnniiubzllkBTe7xkedzvEcePw68llfPl56a+9lqYMsVcX7eu8pyKi+HVV811a9TwjAxz+eyzZu03wMKFgZTbPwXWTcbKkmXHBs3Vbk5bR48e8N//eu9LdaRSnNcKgJHP3AZAxo8XAGYfa4uvphfmenHJy/DbPCM52Xx/xx3m+zdLWsF/9JH5YYuOLqt9roxhwI4dMHRo9a5fRERERKTx+KpBc4uKMpcDB8I//lFxf3a2uVy92lxaLUA9eX7Ht5qL22z3ecQCVXfNHDLEXIaHl2373//MYweUVObfcEPV12Ol/fDDZdvCwsztVtmdzqrT8UWBdZOzu+pDamPLFvjyy5K2FAYYBQBs3Wru9uyvv3KxA4CwSGt+u2LAZo4KPmQy1gALvpp/mOshJa+OfufBy8w030+caL6/9lpzaTVPP1o2tZ5fp51mLrt1g2XLAvx3EBERERFpacrHAsVmLGB1rWzTpuzQH0q6R1fVetTzO77VatQ7Fqh8TuxbboHly831vLyy7ePGeTfh9hX4e4qNNZehoTB9etl2ay7sc84J7Hr80ajgNJVRwa2fburxv2vZMrj3Xvj225Ici7GTR+oJBWzda9ZMG4avX5G8yxQRUYjbbS89ztdfmLnPc4d3ov7P8VcG05AhsHSp732VpWPlV1mZRUREREQaXi7gKFlv2FggimP06FbImu3xZu6VfA+3xMbC4cOVf68ONBaYMgWee85/OpawMDNo93eMw1E2KFll6fgqs0YFl+rZssXrg1TG4Je0i4Bivx8iw7CVvsBGXp69yuzMX6Me8vtLFUBCQtmI5G+95et885WUVLbNV1B99tll6VhN23v39j5m6tSKI46LiIiIiASfj8GG6prfWKCY1R0uBoorjPptKf8d/siRqrPzrrE+w2cscN99/oPqLl3gL38x13fvrjh+kqeEBP9BdWJi2UjjVp/tmgq4xvro0aPExMTULrdGSjXWmE0+Ro8ul2MxdtzkEw0LFuA84xTSnemc1O7E0mPOOsvGokVl50REQH6+2RfaarZdXtlf3A6gm7W1wnG+Avlnnqn4R5+cDFlZ3h8Umw26d4dffvGdTvlj/e0TEREREQmuKwFryOuGjQWiOMIx4sw5t0aZI3h7fnceOxY++aTsfUwMHDtmNs++7DLfWZV917YS6oiv7q7+K/V87/Msi81mDqi2enX103E6IT6+7H2gsWIAwz6ZBgwYwBtvvMEZZ5wR6CnSlOTkVNhkWA0aIiLYkWjj+W+mszt3N499O4jEqERCbCH0T+qP092ndL47z34P11Q5K1jlQ/gFGuBmZlZ+blXpKJAWERERkcYrof6zqCwWKLe/su/OnuMfBf4dO8rn1srOr873+9qkUx0BB9bjxo3jnHPO4fbbb+fxxx8n3HNINmn6Evx/YJ1XXMzzm99gvz2Pawdcy7/X/ZsNBzYQEhKCDRsjOo/gjqF30DWxazUzfa92ZRYRERERafYuAXwMt12XKokFAtpfKz3qMe2GE3Af61mzZvHdd9/x6aefcvLJJ/Pzzz/XZ7mkoXXuDCNGVNweEcHem67m0mE3cNMpN7EjZwf9U/ozqN0gdjt3s+/wPlZnrGb28tk43c5qZvp+XZRcRERERKQZG131IbXlLxYAc3vnzvWY+fX1mHbDCbjGGmDo0KH8/PPPPPjgg5x22mmcd955hJWbRPj99xUs1a+vgHPrPtmePeGvf/UetCAigvQ3/87epEj+vuRvbMvZxqaDmwA4Kfkkpp42lad+fIr03HRiwmNId6YzMGVgNTI9VrKsss24iIiIiIjUF1+xAJhB9axZ5v56M64e02441QqsAfLy8sjKysJmsxEfH18hsJb6YsMcrOB8oKB+shgyxBwae9cuyMnBmdaRtZEZ/O2HJ9h/eD/FRjEhhGCz2VibtZYQWwiX9r6Uj7d8zPGC47jcrhpm/GadXoaIiIiISPO0G+hUP0mXiwVISDBrqus1qG4+qhUVL1iwgBtuuIF27dqxcuVKepefs0jq0aPAg0Bh/WbTs2fph2dXxhrsh6PIOJJBpD2SwqJCDAyKjWIAVmasZGzPsQAUFBcQF9lYR1QXEREREWkOBgLZ9Ze8RyxQv4Y0QB4NK+A+1jfddBMXXXQRN954I0uWLFFQ3eD+3OA5Hi04ypZDW/jl0C9ggCvPRavwVl7H5Bflc7TgKL3b9CbVkVqN1IfWaVlFRERERJqvm0uWFUfvbpqWlyybT0wZcGC9ePFifvzxRx5++GFC/c0OLg2k/kfOc7qd/OPnf1BYbNaQh9hCOJx/mOTYZOLCy2qm7SF2BrcfzE2n3FQ65VZglpUsm9+vVSIiIiIidetFj/XcoJWi7m0MdgHqTMBNwVetWqUptoLuGuAtYGu955TuTOdX16/ERcQxqN0gXHkukmKSyDqaRUx4DCmtUujVuhcnJp9I98TuZBzJqEbqD3qsL63roouIiIiINEPWmEuOkmVT1TzH6Aq4xlpBdWPgOcjXFfWak8vtwh5qZ33Weq498Vr6JPVhe/Z2ou3RxIbH0qdtH2446QY+3/Y5M76dQXhodf4+Hi9Ztq2PoouIiIiINEPNpRl4UclybVBLUdea588FzVpvYBPwX+A/9ZZLXGQcx/KPkZ6bzr/X/5vrBlzHWZ3P4sCxA9hD7eS6c/n9/37P8cLj/LbXb0mJSQkw5Y881rPqo+giIiIiIs1QvMd6OJAfrILUQqLHev+glaI+BFxjLY2FZz+EmHrLJSkmiSh7FEfyj+Byu3jw6wdxup3M+2Uef/vxb/xn/X84XnicgSkDmXDyBFITUgNM+ZKSpVpAiIiIiIhUj1XLWwD8JZgFqYH/UVbr/lIwC1IvVGPdJN0FPA0cw+yv/Fid53Dg6AEu7nkxeYV5OPOctI1py/82/Y/f9/89vdv0ZnXGakJDQtnv2o8jwhHgwGU2j/W8Oi+ziIiIiEjz1h+IBY4A9wP3Bbc41XJZydIO/CmYBakXNaqx/v7777nmmmsYNmwYe/fuBeDNN9/khx9+qNPCiT9PAVEl649XdmCN5bpzWbhjIbcPuZ0JJ03gir5XcGnvS9mYtZFnlz5LQmQC36Z/S0hICH2S+gSQYheP9e31UmYRERERkebvsMe6ze9RjYtnOZtiE/aqVbvG+n//+x/XXnstV199NT///DN5eWbNY25uLk888QSfffZZnRdSfDlG2R+oNUJg3YmLjGNA8gCeWfoMGw9sJCkmib2H93I43/wgGxj8vv/vGdl1ZAC11c8C6SXrZwFd67SsIiIiIiItixNzdHAw+1435im4OnisO4NViHpX7Rrrxx57jLlz5/LKK69gt9tLt59++umsWrWqTgsnVcn2WK/bX6tSHal0jOvI+gPrKaaYrKNZtIluQ4/WPejZuidFxUWclHISXROqCpInAXd4lHFRnZZTRERERKTliQduL1l3AdFBLEtlWgN7S9bH4j0AW/NS7cB6y5YtDB8+vML2+Ph4nE5nXZRJApaAd7NqG3U1DL8j0kFSbBIxdnOAtGKKyXHncDT/KG2j2+KIcnC84HgVqZwCvODxvrhOyiYiIiIiIs9iBqsAx2l841KHU1YROAj4JIhlqX/VbgqekpLCtm3bSE1N9dr+ww8/0LWrmvg2vK6Yf7DW0PWJwFzgplqnnBKbQo/WPThecJyC4gLsIXai7FHYQ82WCnGRcZWcHUnZAGWhQGGtyyMiIiIiIp4+ASYA/8DsGmrDbG4d7Jphz9a05wBfBasgDabaP2vceOON3H777Sxbtgybzca+ffv417/+xdSpU7nlllvqo4xSpQS8+1jfTF00DU91pNK7TW/iIuNoHd2auMi40qA6LSGNVEeqj7OmluRtBdUnoKBaRERERKS+vAp84fHeAaQEpyh0xzsOeYmWEFRDDWqs77vvPoqLizn33HM5duwYw4cPJyIigqlTpzJ58uT6KKMEzMC7ptiGGdj+WqPUHJEOJp86mdnLZ7MtZ1vp9rSENCYPmVxu0LIcvCd8B/gz9TEVmIiIiIiIeBqN94BmmZixwEwaZkquN4Hrym1zEvya84ZjMwyjRsNJ5+fns23bNo4cOUKfPn2IjY2t67I1GJfLRXx8PLm5ucTFVda8uanwFeQmAodqlJrT7STdmY7L7SIuMo5UR6pHUP0VMLLO8hIRERERkdr4C+Yc156uAN6ph7xuweyG6ul2zP7fzUOgsWKNA+vmpPkF1pZrgbd8bL8c+E8t0/asGffU4v+cREREREQagfbAfh/bvwfOqEW664ABPrbH0xyn06q3wPrss8/GZvPff/frr7+uTnKNQvMNrC0PAo9Xsj8ceB7/A54NBZZVcn4YkIXZ11tERERERBqPIcDySva3A5YCnXzsy8UMondXcn4PYEuNS9fYBRorVruP9cCBA73eFxQUsHr1atavX8/48eOrXVBpCI9R1te5G7Cj3P58zAHPbq5mum8C19SuaCIiIiIiUo+sCrJczCC6/JS5+4HO1UzTDhygJfWhrkq1A+tnnnnG5/bp06dz5MiRWhdI6tv2cu+vBf5F1U24Y4FvMOegExERERGRpiUeOObxPhc4F1gZwLm9gSUokPavzvpYb9u2jVNPPZXs7OyqD25kmn9TcBEREREREamuQGPFas9j7c+SJUuIjIysq+REREREREREmoRqNwW/9NJLvd4bhsH+/fv56aefeOihh+qsYCIiIiIiIiJNQbUD6/h473b1ISEh9OzZk0ceeYRRo0bVWcFEREREREREmoJqBdZFRUX84Q9/oH///iQk1H5qpRdffJEXX3yR9PR0APr27cvDDz/MBRdcAIDb7eauu+7inXfeIS8vj9GjRzNnzhySk5NL09i9eze33HILixYtIjY2lvHjxzNz5kzCwqr9m4HU0L7D+9h6aCs5x3NIjEokrXUa7Vu1D3axREREREREGkS1os/Q0FBGjRrFpk2b6iSw7tChA3/5y1/o3r07hmHwxhtvcPHFF/Pzzz/Tt29f7rjjDj799FPeffdd4uPjmTRpEpdeeimLFy8GzEB/zJgxpKSk8OOPP7J//36uu+467HY7TzzxRK3LJ1VbnbGaB796kBX7V5RuG9x+MI+d8xgDUwYGr2AiIiIiIiINpNqjgp9yyin89a9/5dxzz62XAiUmJvLkk09y2WWX0bZtW95++20uu+wyADZv3kzv3r1ZsmQJQ4cO5fPPP2fs2LHs27evtBZ77ty53HvvvRw4cIDw8PCA8tSo4DWz7/A+/vTxn7yCasvg9oN5+aKXVXMtIiIiIiJNVr2NCv7YY48xdepU5s2bx/79+3G5XF6vmioqKuKdd97h6NGjDBs2jJUrV1JQUMDIkSNLj+nVqxedOnViyZIlgDkSef/+/b2aho8ePRqXy8WGDRv85pWXl1dn5W7Jth7a6jOoBlixbwVbD21t4BKJiIiIiIg0vICbgj/yyCPcddddXHjhhQD85je/wWazle43DAObzUZRUVG1CrBu3TqGDRuG2+0mNjaWDz74gD59+rB69WrCw8NxOBxexycnJ5ORkQFARkaGV1Bt7bf2+TNz5kxmzJhRrXJKRTnHc2q1X0REREREpDkIOLCeMWMGN998M4sWLarTAvTs2ZPVq1eTm5vLe++9x/jx4/n222/rNI/y7r//fu68887S9y6Xi44dO9Zrns1RQlTl/eyr2i8iIiIiItIcBBxYW12xR4wYUacFCA8PJy0tDYBBgwaxYsUKnnvuOa644gry8/NxOp1etdaZmZmkpKQAkJKSwvLly73Sy8zMLN3nT0REBBEREXV6HS1R99bdGdx+MCv2+e5j3b119yCUSkREREREpGFVq4+1Z9Pv+lJcXExeXh6DBg3Cbrfz1Vdfle7bsmULu3fvZtiwYQAMGzaMdevWkZWVVXrMggULiIuLo0+fPvVe1paufav2PHbOYwxuP9hr++D2g3n8nMc1cJmIiIiIiLQI1Zpuq0ePHlUG19nZ2QGnd//993PBBRfQqVMnDh8+zNtvv80333zD/PnziY+PZ8KECdx5550kJiYSFxfH5MmTGTZsGEOHDgVg1KhR9OnTh2uvvZZZs2aRkZHBgw8+yMSJE1Uj3UAGpgzk5YteLp3HOiEqge6tuyuoFhERERGRFqNagfWMGTOIj4+vs8yzsrK47rrr2L9/P/Hx8QwYMID58+dz3nnnAfDMM88QEhLCuHHjyMvLY/To0cyZM6f0/NDQUObNm8ctt9zCsGHDiImJYfz48TzyyCN1VkapWvtW7RVIi4iIiIhIixXwPNYhISFkZGSQlJRU32VqcJrHWkRERERERMoLNFYMuMa6IfpXS9PmdDtJd6bjcruIj4yns6MzjkhHsIslIiIiIiJSr6o9KriILzuyd/D88ufZnrO9dFtaQhqTT51M18SuQSyZiIiIiIhI/Qp4VPDi4uJm2Qxcas/pdlYIqgG25Wxj9vLZON3O4BRMRERERESkAVRrui0RX9Kd6RWCasu2nG2kO9MbtkAiIiIiIiINSIG11JrL7arVfhERERERkaZMgbXUWlxk5SOpV7VfRERERESkKVNgLbWW6kglLSHN5760hDRSHakNWyAREREREZEGpMBaas0R6WDyqZMrBNdpCWlMHjJZU26JiIiIiEizZjM0j1bAk35L5TznsY6LjCPVkaqgWkREREREmqxAY8WA57EWqYoj0sHAlIHBLoaIiIiIiEiDUlNwERERERERkVpQYC0iIiIiIiJSCwqsRURERERERGpBgbWIiIiIiIhILSiwFhEREREREakFBdYiIiIiIiIitaDAWkRERERERKQWFFiLiIiIiIiI1IICaxEREREREZFaUGAtIiIiIiIiUgsKrEVERERERERqQYG1iIiIiIiISC0osBYRERERERGpBQXWIiIiIiIiIrWgwFpERERERESkFhRYi4iIiIiIiNSCAmsRERERERGRWlBgLSIiIiIiIlILCqxFREREREREakGBtYiIiIiIiEgtKLAWERERERERqQUF1iIiIiIiIiK1oMBaREREREREpBYUWIuIiIiIiIjUggJrERERERERkVpQYC0iIiIiIiJSCwqsRURERERERGpBgbWIiIiIiIhILSiwFhEREREREakFBdYiIiIiIiIitRDUwHrmzJkMHjyYVq1akZSUxCWXXMKWLVu8jnG73UycOJHWrVsTGxvLuHHjyMzM9Dpm9+7djBkzhujoaJKSkrj77rspLCxsyEsRERERERGRFiqogfW3337LxIkTWbp0KQsWLKCgoIBRo0Zx9OjR0mPuuOMOPvnkE959912+/fZb9u3bx6WXXlq6v6ioiDFjxpCfn8+PP/7IG2+8weuvv87DDz8cjEsSERERERGRFsZmGIYR7EJYDhw4QFJSEt9++y3Dhw8nNzeXtm3b8vbbb3PZZZcBsHnzZnr37s2SJUsYOnQon3/+OWPHjmXfvn0kJycDMHfuXO69914OHDhAeHh4lfm6XC7i4+PJzc0lLi6uXq9RREREREREmoZAY8VG1cc6NzcXgMTERABWrlxJQUEBI0eOLD2mV69edOrUiSVLlgCwZMkS+vfvXxpUA4wePRqXy8WGDRsasPQiIiIiIiLSEoUFuwCW4uJipkyZwumnn06/fv0AyMjIIDw8HIfD4XVscnIyGRkZpcd4BtXWfmufL3l5eeTl5ZW+d7lcdXUZIiIiIiIi0sI0mhrriRMnsn79et555516z2vmzJnEx8eXvjp27FjveYqIiIiIiEjz1CgC60mTJjFv3jwWLVpEhw4dSrenpKSQn5+P0+n0Oj4zM5OUlJTSY8qPEm69t44p7/777yc3N7f0tWfPnjq8GhEREREREWlJghpYG4bBpEmT+OCDD/j666/p0qWL1/5BgwZht9v56quvSrdt2bKF3bt3M2zYMACGDRvGunXryMrKKj1mwYIFxMXF0adPH5/5RkREEBcX5/USERERERERqYmg9rGeOHEib7/9Nh999BGtWrUq7RMdHx9PVFQU8fHxTJgwgTvvvJPExETi4uKYPHkyw4YNY+jQoQCMGjWKPn36cO211zJr1iwyMjJ48MEHmThxIhEREcG8PBEREREREWkBgjrdls1m87n9tdde4/rrrwfA7XZz11138e9//5u8vDxGjx7NnDlzvJp579q1i1tuuYVvvvmGmJgYxo8fz1/+8hfCwgL73UDTbYmIiIiIiEh5gcaKjWoe62BRYC0iIiIiIiLlBRorNprptkRERKRxc7qdpDvTcbldxEfG09nRGUekI9jFEhERCToF1iIiIlKlHdk7eH7582zP2V66LS0hjcmnTqZrYtcglkxERCT4GsV0WyIiItJ4Od3OCkE1wLacbcxePhun2xmcgomIiDQSCqxFRESkUunO9ApBtWVbzjbSnekNWyAREZFGRoG1iIiIVMrldtVqv4iISHOnPtYiIiJSqbjIiqOgFhQVcLzgOPlF+eQX57Pi1xVEhEXQydFJA5qJiEiLo8BaRERE/HK6nYTaQmkT1YYdOTuIskdhGAb9kvqREpuCPdTOkfwjrNy3krbRbXlz7ZvccsotGtBMRERaFAXWIiIi4pM1Evju3N1ckHYBew/vJa8oj4t6XMS7G95lp3MnKbEp7M7dzYjUEfym+29wRDqYvXw2086appprERFpMRRYi4iISAXlRwJfuGMhD5zxADnuHF5b/RqH8w/zmx6/oWN8R2zYwAZ7XHs4tcOpPLv0WdKd6QxMGRjcixAREWkgCqxFRESkgnRnOrtzd3N6h9Pp3bY3YSFhrMtaR3JsMhuyNnDtidfy4eYPeeXnVwgLCSPUFspJKScxPHU4RcVFGtBMRERaFAXWIiIiUsHhvMOM7T6WvKI8HvvuMVx5Ln51/coLF77AI+c8wnsb3uN4wXHaxbYj80gmkfZI1mat5ZWVr3BK+1N8DngmIiLSXGm6LREREakgMSqRg8cO8ubaN9l0cBPhoeFMPW0qH2z5gC0Ht/DRlo/Ylr2No/lH6ZbYDWwQZgtja/ZWurfuTqojNdiXICIi0mBUYy0iIiJedmTv4Kd9P3G88DhLf12KgcHZXc7m4y0fk3k0kwFJAzAwCLGF4Mp3wRFoG92WguICioqLSIlN0cBlIiLSoiiwFhERkVLWoGWd4ztTbBQTYjMbt7WLbceGAxsoMopoFdGKsJAwio1iAA7nHybVkUpMeAxhIWG0b9U+mJcgIiLS4NQUXEREREqlO9PZnrOdyLBI7KF2DAyKjWIO5x8mIjSCEFsIq/av4uR2JxMbHktseCxxEXEYGBzOP0z3RDUDFxGRlkeBtYiIiJSyRvM+cPQAR/KP0D+pPwYGYSFhHCs8RlhIGAu3L+SSnpfQP6k/BUUF5OblUlRcRMe4jkw4eYKagYuISIujpuAiIiJSyhrN+9td33J2l7O5/sTreX3N62w5tIX+Sf3ZfHAz9jA77258l56te3JOl3OIDY+ld5veLNixgLzCvCBfgYiISMNTjbWIiIiUSnWkkpaQxuH8w8z6YRY/7/+Zu4bdxSU9L2HWebMY13sciZGJhIWEsXL/StZnrad9bHv+/PWf2ZC1gYSohGBfgoiISINTjbWIiIiUckQ6mHzqZLKPZ7Ny/0reXPcmb657kxOTT2Rcn3HE2mO5vO/l9G7Tm4yjGew/vJ831rxB+1bt6d2mt/pXi4hIi6TAWkRERLwkRifyp5P/xMCUgRw8dhB7qJ2th7Yya/EsiowiBrcfzJAThvDmujcBiLRHkpaQxuQhk9W/WkSkkXC6naQ703G5XUTZo4gMi8TpdhIXEUdnR2fdr+uYAmspcRbwPVBcbns4MA84r6ELJCIiDeZ94CogH4DYMDi1PQxuB19sj+V3/8svPXJA8gAmnDSBASkDePTsR3G5XcRFxpHqSNWXNBGRRmLzgc0s2LGAYqOYvKI8Qm2hHM4/TKe4TszbOo/O8Z2ZfOpkuiZ2BVzAxcAawA0UYoaJ4UAPYCEQF6xLaTJshmEYwS5EsLlcLuLj48nNzSUuriX80TiA3Fqm8SQwtfZFERGRBnYf8NdqneH5TaG4GPLyHERH59RtsUREpE5sP7Sdr3Z+xSs/v8LajLUUGoWE2kI5tf2pXDPgN1w74GGi7HnYbObx1jJwMcA+WkqwHWisqBrrFuMK4L91mN7dJS+ADCC5DtMWEZG6lUV17tMFBWVftEJCvL90hYZCVJQTsDZOAmbXTTFFRKRWnG4nW7O38srPr7Aucx1hoWHYsfPlNUcZ2mExNttibDbvH0zL2CmrqS7EbMVU4OO4o0B8yXp34Jd6uJKmR4F1s+fvJ6hOwK4apLcAGFVuW0rJ8iLg4xqkKSIi9WM4Zjef8myYTfvO8XnW0SInM76ZwbacbRX2vTZ2IYkxbo9g++8lLxsVuxOJiEhDSnemcyT/COsy1xEemkfOPeYPpJ6KiuD1VT35cFt37h52N8NTh1cjhznAZMru91spizf2AB1qeQVNl6bbarb6UDGojgCMkldNgmow+1pbaXxZbt8nJXlm1jBtERGpG1mY9+PyQfU6zPt3Mf6CaigbGTwtIc1re1pCGq7CDYSEWM+BcI+9Rkme1fmCJiIidcnlduEucrPnjjxy7zNbGYFZQ/23H8H+aChvr/8nGcevBSAusrrNuW8FijDv+Q+V29cRiK5V+Zsy9bGmufWxXgucWG7bXOCmes43EsjzeB+C+aETEZGGVf5HVQdQs/7QniPKVj5A2bPAHeW2ZQJJNcpXRERq5tfs33NCwr9L3xcVw9UfXE7P1j0pKC4gxh7DSSknsde1l00HNzHtrGl1NPBkKN6tlq4A3qmDdIMv0FhRNdbNSjzeQXVbzF+TahZU22wVX/6233KLuyQvSzGV1V6PH+877UDK4XAEtk9EpGX5mopBtUFNg2qbDRKiHJzUbiAjugznpHYDcUQ6Svtce74mT55SkpfDI4Vk/DUJnDCh5s8Az+80/p5TIiItU1hpUG0YcO27kTzy3UNsObiFR797lKd+fIoXlr/ACyteoGN8R24bclulQbW/e2x8fMXtc+YUYXYxsvwH/11Sq3fvjoz0PnbQoMD2NTQF1s2GDXOofIuB2RSw9gzD/ACB+Qfr2aTky5LW4HPneub7sMfZKcAtFdL85z/L0oiIMNd79KiY99q13uUAyM2tep+ISMtyKXCux/v/w/vHzpozDEhIMNfLPwO++spc//vfraNzyuW7F19frP7xj7I0oqLM9T59KuYdVjISTFJS2X3+8GFzOXiwuQwJ8TcIj4hIS2IDirDZwJ3Xla92fknP5AdYuH0h27K3EWOPoXVUa9pEt2HF3hW8t+k9EqISqkw1Nta8x558svk+MhKio6FVK3P7hg3m9okTwXwOGcAZ5crlGaOYWrWCNm2qvqpff4W8PO97/apVVe8LBgXWzYLnlxarlrpuOZ1l64WFZX+85/mc3npGuTLMxZwn2ze321xu3Vpx34klFfBWfqNKxk2bOrXyfSIiLccA4AOP9wYwoU5zyM4uWy8oKLvvnuO3m3b52mv/1RHHjpnLTZsq7isq6VGUWdL4qX9/c9mnD/z0k/cxVplUay0iLYsL73vsQqIit9M3qS992vbBle+iU3wnuji64Ih0cPDYQVpFtGKvay/pzvSAc1m50lzm5cH+/eAqiZV9/Shqju+xweN9POWDa5cLDhyoOt/u3c2lda+/5x5z+dprle8LBgXWTZ7nB+lm6qqWujyrhqJTJ+/tV1zho0SlzTEMbLbiktcibDaDBQvKjmvduvrlmD/fXM72MbNLZftERJqnrpgDklnqp+rWqjnu0sV7+9VXVzy27BmQ4/EMKMZmM/j667LjkgLofm3la7eby3Ull7ppE5x5Zll+nksRkZYl3mPdwGq91L5Ve2LCYzhWcIyc4zkUFBdgD7HTJroNybHJuAvdHDp2KOBcrHv2+ed7b3/iiYrHms+APuVigVbYbAYbN1bv6qwKOMtf/2ouH3mk8n3BoOm2mrRQj/UngfqpqvX8srLLYzDxp56C/5ZMje3ZDM+7SZ4N7+Bfc16LiNSNr4GdJeuhmHOO1j3PZ8COHWXrzz4Lb79trlf+DJiMOR0XQBRwLOC8rfm0CwsrBs7ffaegWkRaOs+bX8UfVlNiU+jbti8DkgcQGx7LsYJj2EPtbDm4hW/Sv+F4wXF2Zu+kS2KXCudajhzxvsd+/nnZ+scfw5//XJJ7wLFA8+27oxrrJusWykbeu4j6CqrB+8NhfbBeegnuvttcz8jwPr7iQAdlv1YtWFA2B/ahwH8kKzV6tLmcPLl6+0REmh/PPtX1E1SD9zPAmgv11VfhjpJBwDPLjVFZ8Rkwu/QZ8PXX3TBHEIesABtYGUbZKyXF3HbDDRX3WTUU4eG+0xERaV48m376DlZTHalc1f8qvtr5FQ8uepAnvn+CR759hC0Ht3D/Gfez5dAW1h9Yj9Pt9JuL1cfac+AyMMfYuPhic738GEeVxQIbN3av1lVGRnq/v/dec/nww5XvCwZNt0VTnW6r/n/5sT5A5fut/fe/cPnl5npGBiQHXAFdVmabzShNOzLS7K/RpYt3bYjT7eTj79IZP/pEoJic44dJiHKUnrd2rXc/6/LlFRFpvoL3DPjf/2DcOHM9MzOwJt0lKXikXfYMiI6G48chLc33WBvJyWY+X38N557rXSZrn6/yiog0X38D7ipZfxx4wOdRTreTu+bfxYdbPsR53InNZqPYKKZVeCvO7nI28RHxnNL+FIZ2GMrgEwZXON9mMwNra9BI6z67ZAkMG2au5+Z6z9bg38dASSTOLcAcrzT93bt//RU6djR/2C0q8j6+sn11SdNtNWvBaU4xcKC5tIJqMGsPAp/i5MnStcsv/xAwz8srmf7aCqqtX7amfzOddw89hDkfdggJUfGAQWysedyAAWUpW/lb+0REmi/PX/u/arBcTznFXFpBNZiBbeDPgLLn1e9//yJgnnf8uLnNCqrLp5eVZb63gmrPvnPWPuv4IUMCvx4RkabLCqoT8BdUA6Q709l8cDMJkQn0bNOTrgld6Z7YndiIWOb9Mo8uCV04eOwgc1bMqbTW2nLddebSCqrBe+qtyv0GSC1Zf7HCOVYQb61b+zp0MMfZKC4u29avX9X7gkE11jS1GuuXMAcpg6bZXzkSKImk/fwo4HQ7mf7NdLbnbK+wLy0hrQ4nshcRaYqsbyJtgACGVG1Usih7bq0DgvgNSESkSUrEnNoQqqpg+y79Ox5a9BDrs9ZTUFzAkfwjXvvvP+N+ou3RfLn9S5674DkGpgyslxJ7s55hdiC/AfKrPdVYN1s3e6w3taAawHP4vorzW4P565qvoBpgW862ak0NICLSvAz3WG9qQTWAZ7vx/kErhYhI02UF1R9VeWRcZBxR9ijiInwHg22i27Dv8D6i7FG43BXnmq4fe0qWBQ2UX8PRqOBNiucIMU25oYEd88M0F3ixwt7yH2x7iJ1B7QbhiHRwrOAY+w/vJ8wWRof4Dqq5FpEW5vuSZUJQS1E7BmU1Fll4B9siIuLfQI/131R5dKojld5telNQVECRUcTRgqNYjZVP73Q67Vu1Z/62+djD7MRFNlSr3Q4e60nU11TBwaDAuklJadDcrL4Kc+fCTTd5b4OKA9p4biu/3ZNh5FP2pepd4Hde+60P9rzff4A1pdgH1s4z76HfVfeQFJ3E19d/gfcXtMoHKwgNNftg+Ct7IGmIiATPox7r2fWem3V//L//gwkTvLdBbZ4Bnu+S8fdDsa/z160z+88FlnbV5fC1b9IkmD3b9zkiIsG1pmR5T0BHOyIdTD51MrOXzyYkJITEqETyi/Lp07YPV/a7krfXvo09zE5aQhqpjtQK51v3yIULy8a5qOz7dGioOT1i+fPLM4x/A1dRVcsrz7wse/aYfat9pR8RUXFua39lscoeFeV9Tm3iADUFb5Iyqj6kDt18c9XHVMZzOpSKf6yXVzg+1ZFKWkIayad/xtg73+Hs18+FiT3Mnd//lciwSJY8fz3m70JF5Bx38uWX0KmT/zK89FLFDyaYgy9Y5Roxwtym+VBFpHGy5g8JbdBc//jH2p3v+xmQWdkppSIizHOsKVT6968szarz9xz4zN8xCqpFpHFa6rH+14DP6prYlWlnTePJ857kLyP/wn2n38dJKSfx9rq3KTAKSEtIY/KQyZW2Ah05smzd1/dpS1GR7+0V79lXeuz1PzdWVBSkpprn/OMf5raOHc1lq1bm8qGHyp4D1oDI5YWVVCU//jj8+9/muvV9v2dP81lTFzR4GU1l8LJMymqsG+a/rHwtxGmnmcPre24LCzM/RAkJkJNjBqfffON9vu+/sMqvZ0f2DmYvn02P1j3489d/JufDO2HpnwGDR795gofOegCw8X8//YNBJwyqcrAFf79SeWrTpmxubX0qRKTxsW5kwXkGDB8O33/vvc1uN2sn2rSBgwfNGo2FC73P938/rd71+EuvbVsz786dIT29emloii4RaTpigaOYP64WVnGsf063k3RnOi63i7jIOFIdqX6D6vLPgVtvhRdf9N6WnGzO0DB4MKxYAbffDs8+632+73tsPODCrOf1E5H7KY9hmFN8HT4MZ50FixZVnlcg9/7Kzg80VlRgTVMJrM3aWVPDf6myAmdP5eeO9vdHW17ZsdZ1VDzQMMwPftv4KArzw0u3X3Hnt1zzpyNc1GtM+dLSqRPs2uX/OjIyzOnBPMvoq5z6RIhI4xS8wNoKnD3V3TMAyj8Hyt+H27Uz7+G+9lUnOA6kjHoGiEjjZN2wXgBubZgcS6bAOnKkLHD2VJPngNVc3GYrpuyafMcCns48E374ASIjy6ZpLJ/+ySfDypW+r8NXueo6sA5qU/DvvvuOiy66iPbt22Oz2fjwww+99huGwcMPP0y7du2Iiopi5MiRbLUmuiyRnZ3N1VdfTVxcHA6HgwkTJnDkiPdQ8s2DFVQPq/So+mIF1aEeLRDffbfiNl98Ndkz10NKXgt8HuOIdPDVL8t4e+07tDnpOwD+87fhXPTORaVph0Y6+WzVzwDs3l0x7/HjzWVEhPmLWmXls5qBhGnkARFpdO4Lau5WUO15f3z//YrbfPH/DEjxeA74b9o9cqT/oDqrGmPeWPOj9vcYjPzcc80va4YBDoe5Td2BRKRxa5igujwrqE7xGPJpaUnr9KQqxqD0vL9bfbDL7v8VnwHl7/VTpphBNZQF1d26mcvBg8uOX7WqRpdWZ4IaWB89epQTTzyRF154wef+WbNm8fzzzzN37lyWLVtGTEwMo0ePxu3Rw/zqq69mw4YNLFiwgHnz5vHdd9/xpz/9qaEuIQh+rN/kt2yBL7+E//wHszahgASPwWc9ByS4vKR7dFFRxQneq2JO/F5c8hpZOhF8+cniEyISOJJ/hNue+Q5roLJpI6Zh1XQ8/fU/+fDXl/A3ZP8//2ku8/KqLqP1Z+Wvf4iISPAE3p+uVnw8A9q0Kdtd4HGrHTfOXJo1D2XbQwL4ZmHe6zM9ngO+nwFjxsBXX5nrvmoRrB9MrWMqc/SouVy7tmzbwoXwnfm7bYVWWSIiLVL550BRAYMHl+3ev79sfVhJfV9Wlve92/qhsjLesYDh9zlw773w3HPmuudzYMcOc7l8ubl8/HFz+fHHgVxkPTEaCcD44IMPSt8XFxcbKSkpxpNPPlm6zel0GhEREca///1vwzAMY+PGjQZgrFixovSYzz//3LDZbMbevXsDzjs3N9cAjNzc3NpfSL2h5FWPli41jBEjSn8sgmID8g1j6dLS348Mo+JvSl6l9HGcf92Nyq4rLMxtnPzHZw3HTIcRcsGUkvIUG6e8dIqBPceAYiOh1wrj78v+bkBRFXn5LlNISNl6p06BlFlEJBise+Uj9ZdFgz8DDKOyZ8Bvf1t1GoHes9et831sUpK5zzAM48IL9QwQkcZqj1HXsUDO8Rzj5/0/G9/u/NZYvX+1kXM8xzCWLavwHIi1uUqfA3a7eW7dPQesa7rO596HHvKfhrX9oYeqziskxNz3+OOG8e9/V13u8gKNFRtto9edO3eSkZHBSI9h6OLj4xkyZAhLlizhyiuvZMmSJTgcDk455ZTSY0aOHElISAjLli3jt7/9bTCK3jRt2WL+JPTttxX33XsvxuaXzGHzyune3XdyTz1Vtu6/D/NvgKd9nu90OyksjGPV/90G3Fa2Y+jjrNy/kpA/t6Z4egE5mwcxacggoKyZX2KiWfMQSD+54mL1sRaRpuSh+km2hs+A3r19J2cNXAM1v8d+8IHvNKzzhw83l75Gcy3fV85q/p1ZbjDyrCzvpuG+jhERCb7/1mlqO7J38Pzy59mes710W1pEeybnnUjXpUu9DzaKKn0OWK2XLK1amYOKedYcV/0c2OSznI96zDJZ/jmQmwvx8eYx1nFRUd7HW/2xrda1f/5zxTJYg6+Vz6cm8UCjDawzSjpUJZfrGJucnFy6LyMjg6RyjfrDwsJITEwsPcaXvLw88jzGY3e5XHVV7KZr164KX6gMq6fAtyX7Sz5Mlf2hee6bOrWqTO/GX2Cd7kxn7NsPcejYIXq07sG5Xc5l0meTcOW7MIAQWwhMDyU8NJyEyAT+c9l/GJE6AoDsSqZ3LV92BdEiItTLM2DKlNoVqar7s9WEO5Bz/aWlZ4CINA1rqj4kQE63s0JQDbDt1zXM3reMaVdcjOOfZiBfk+eAZ1hV9T02BCjG33zWlZ0fFxf486iytOryx9QWOY/1zJkziY+PL311tCZEa8mq6lxWL53P5vjd43Kbn8pQWyg9EnuQX5SPK7/sk2pgYMNGYXEhxwqOkXNcneNERGosKM8AEREJTMWa4ppKd6ZXCKoByC9g2+41pA/xk1e9PAesSbHb1kPaDa/RBtYpJUPOZZb7GSEzM7N0X0pKClnlhgQtLCwkOzu79Bhf7r//fnJzc0tfe/bsqePSN0GeI5TVZH+N+B9dIC7SHMo+xBYCNogMi/Tab8Nm7itZLzaKcbqd9VBGEZHG5Nn6STYozwAREQnMdXWWklV5VUG43dwf4ntA4Pp9DvjpV9TENNrAukuXLqSkpPCVx1CfLpeLZcuWMaxkCLphw4bhdDpZ6TFh2ddff01xcTFDhgzxm3ZERARxcXFerxavc2cYMcL3vhEjzP11brXfPamOVNIS0oiyRxFqC8XAYGDyQDOgJoQQWwg2mw0bNvom9QVg66GtftMTEWke7qifZIPyDBARkcB0qLOUrMqrCqKioHUiccX2ivvq/TnweD2m3XCCGlgfOXKE1atXs3r1asAcsGz16tXs3r0bm83GlClTeOyxx/j4449Zt24d1113He3bt+eSSy4BoHfv3px//vnceOONLF++nMWLFzNp0iSuvPJK2rdvH7wLq1e/qZ9ke/aEv/614herESNg1iyfgxXUnbkVtjgiHUw+dTK92/TmWMExNh/czHUnXseJySdis5lNwA3DYGDKQK478Tq+3PElq/avYkf2jnosp4hIsPy+fpMPyjOgaz2kKSIilbEqryqIjSXtzEtIzS7XGblBYoG6++EgmGzm8OLB8c0333D22WdX2D5+/Hhef/11DMNg2rRpvPzyyzidTs444wzmzJlDjx49So/Nzs5m0qRJfPLJJ4SEhDBu3Dief/55Yq0hogPgcrmIj48nNze3Eddeh2DN3Vy2rAdbtpiDE+TkmE0+Oneuxw+SNbyf/+txup3scu7iSP4RduTsYPPBzUSERZBXlEdkaCShIaHYbXZmr5jN/Wfczy+HfmHaWdNwRDrqqcwiIsFS9T2z1oLyDIgBjtRTHiIizYV1z/w3cGWtUtqRvYPZy2ezLWdb6bbU+FTGDxzPcech4gptdHaF4HADHTvW03PgV8Aa56pxjyQZaKwY1MC6sWgagfVa4MSS9ebwX5YJWP3gA7uefYf3sSZjDYfzD3Mk/wiGYbBi7wo+2/YZ3RO7c06Xc1i6dymPnv0oA1MG1lfBRUSCpAEC6wZlXU8mkFTZgSIiQhTgBuxAfq1Tc7qdpDvTcbldhIaEsv7AeuZvm09BsdnHOi0hjcmnTqZrYn21LmoLHMR8FhRXcWxwKbCuhqYRWEPz+lLlOaFd4NezI3sHj373KJ9t+6x020nJJzHp1En8evhXjhccZ0DyAAa1H6RaaxFpZqz7ZgTml6umLAuwptNsDs80EZH69hUwsmS97u6bTreT6d9M9zlSeFpCWj22BLWeabdTbwNz1pFAY8VGO4+1VMZG8/ki8mS1ju6a2JU7h93JqSecirvQTYw9hrYxbXlhxQt0iOtAqiOVVftXse/wPoZ1GEZaax99SEREmqQbgH8AecEuSB1IrvoQERHxcK7H+hzg1jpJ1e/0W8C2nG2kO9ProSXoVx7rz9Zx2sHTaEcFF1++DHYB6kgrj/Wp1T67Y3xHfjn0C9/v/p7YiFje2/AeI7uOZOOBjfx18V958scnmbpgKlPmT2HTgU11V2wRkaB61WO9uQz8lVn1ISIiUiK1ZDmxzlL0O/1WgPtrxqp5D3xMrKZAgXWTcp7HelNubGANUjOqRmc7Ih3cduptXN7ncg4dOwQ2eGvtWyzdu5SC4gKKDbOfxop9K3h6ydOa31pEmhFrrs+dQS1F7Xh2BVLfahGRwHne+5fWSYp+p98KcH/1eQbqh+s47eBSYN3k3FWyLApqKWou0WN9fo1TSYhO4Ic9P+AudNOrbS82HNhAmC2MYqOYYwXHsIYO2HhgI+nO9NoVWUSk0djosd49aKWouSyP9f8FrRQiIk1XVMlyWJ2k5nf6Lcw+1qmO1DrJp0x8ybL5haHN74qavac81m1+j2qcFgA5JetdapVSujOdXbm7iA2PpbCoEJvNVvrPUWQUUVRs/vBgD7HXUxMWEZFgaVOy3AasD2ZBasCzb/WlQSuFiEjTdcxjvVOtU3NEOph86uQKwXVaQhqTh0yu44HLBnms5/g9qqlqyu2JW7AMyqaqCqcuhtxvGJ5Nv3fUKiUrWM4+nk2b6DYV9hsYxNhjiLJH1UMTFhGRYDpA2Q+r/Wk6g1l6/pbfVMosItIY3Q48B//f3p3HRVXufwD/DNswOMCgCIgiS3ATCxFxuVi5oqJllln9vC6o3LqWS1ia9iqFJMVut5vLvaktIraoZWpdzQxRMMoVBcUUAUGsC6hXhkVClnl+f4wcGTZRlln4vF+vecmc55lzvsfvbN95znkOrgDYhpZe19qrsxcihkVIl9+ys7aDh8qjlYvqowBO3f47BIDpfT/niLVRcgYw9PbflQAi9BhLc93f5bUaU1MsJ+clw1PlicE9Buu0K62U8FR5wtfRtw0OYSEi0rfa76PGcPTSFNyJeZY+AyEiMgGrceeQ8MmtskaVtQp9XfpiiMcQ9HXp2waX2Ko5dN0cwL5WXrdh4Ii10UrAnS9TywE8Ct3JzQxJ7adZ68xsXnM+SGZhJj478xnmD5qPrp264tzVc7Ayt4JjJ0c83PXhNjiEhYjIUHwC4K+3/7aB7uGBhuRTAF/e/tscurObExHR/SnDnVrA0C/FW/sH4Cq9RdHWZKJmlqcOrLkX/TZMtZ+oGwD8TV+BNKJ2fD2gPWSldVy6cQnrjq9DZmEmLM0sEdgtEG52bnBSOsFF6dIGh7AQERmazrhznpoFtEcxGZIoAMtq3e/wXzmIiFpRMe5MBgYY5nts7VrgCrT1gHFpbq3IwhrGXlgDuk/YQAAn9RVIHbXj8kRLz6tuiLpc3cbngxARGTpn6M62bSgf6z7QTrBWw1DiIiIyJXWLa0MpXuvGdQ5Abz3F0jLNrRV5KLhJELhTxCZD/4eDxEF3orIgAL+0yZZqzgchIuq4CqD9snL+9n0ZgLMAHtZbRPXP+2ZRTUTUNuwAFOFOEesG4AkA/9FbRMDLANbXum8oxX7b4uRlJkNAe+5aDRmAwY30bUsy6BbVX6GtimoiIqrxK4APat33g34mNetTZ7sKsKgmImprdtB9r90D7Xvxr+0cx2+3t1u7qBboCEU1wMLaxFQBeK/W/SPQPrnPtMO23dDwCMWz7bBtIiICwlG/iJVBW+y2tYO4M1Je4xsY7oRqRESmSAAIrnX/IWgvzdselNDWAzX6oaP9sMrC2uQshPZJXPv4f39ov/B4tfK2Cm6vVwbtL1Q1fkRHeyERERkOAW1RW+Ms7rxXX23wEfevx+31jqy1zOV2DBNbeVtERHR3cdC+B1vevl+JO58B21p5W/G11n3z9jKz29tPbuVtGT4W1iarCPWL22zcefIvbMG6lbfX4VJnueftbRrqZb+IiDqKidC+H3evs9wZ2vfvzi1YdxjufJb8XqdNAMhrwbqJiKh1VEA7YVhtkwHIUFVlhZT8rTiccxip+alQl6vvYb2/QXuJRxl0R8cB4ACA6vsN2OhxVnCYwqzgzeEOIPcufWTQzmdnA+1h5eVo3ovjR7CYJiIyZDsBPNOMfhbQnhdtDu1h3JW4+xFIfmifU46IiOj+dQVwHUIAMhlQuwIUAhDCDObmXQDYAlBBO0hXDOAGmq4HbG/3M13NrRU5Yt1hXIb2y1HtGcTrEtB+iSqC9nCOpl5EQ2utj0U1EZFhqxnBFgD6N9GvCkAJADW0ox2NFdUWtdbHopqIyPBdg7q8EBH7J6GiSrewlskAc3MNgGvQXh73FICs2/cbqgfMoJ3LScDUi+p7wcttdUiaOvcLAHgDKG2k/2gA+9s0IiIiai8nGlgWDO25cg1xAHABgFObRURERG0vR52D04XleObrJ3SW97C7hhXDk2FnJWBhocGdgTgzaOdt+hq6c2lQQzhiTdCec1eCO6MPdW8sqomITNsBNP4ZcAMsqomIjF9xecOjy78Vd0XotyH45beD0A7Aidv/VkH7GcCiujlYWBMREREREZk4O+um55K6Wzs1jYU1ERERERGRifNQecDbwbvBNm8Hb3ioPNo3IBPDwpqIiIiIiMjEqaxVmDdwXr3i2tvBG/MGzYPKWqWfwEwEL7eFjnK5LSIiIiIi6ujU5WrkqHNQXF4MO2s7eKg8WFQ3obm1ImcFJ5NW+43D3toe7ip3vnEQERERUYelslahr0tffYdhclhYk8m6dOMS1h5fi6zCLGmZt4M35g2cB6/OXnqMjIjakkajQUVFhb7DoA7O0tIS5ubm+g6DyKhwQISMGQtrMknqcnW9ohoAMgszse74OkQMi+AbNZEJqqioQHZ2NjQajb5DIYJKpYKLiwtkMpm+QyEyeBwQIWPHwppMUo46p15RXSOzMBM56hweAkNkYoQQyMvLg7m5Odzc3GBmxvk5ST+EECgrK8PVq1cBAN26ddNzRESGjQMiZApYWJNJKi4vblE7ERmfqqoqlJWVwdXVFTY2NvoOhzo4hUIBALh69SqcnJx4WDhREzggQqaAP+eTSbrbBe7v1k5Exqe6uhoAYGVlpedIiLRqfuCprKzUcyREho0DImQKWFiTSfJQedS7Rl8NbwdveKg82jcgImo3PJ+VDAWfi0TNwwERMgUsrMkkqaxVmDdwXr3i2tvBG/MGzeN5OkREbcDDwwOrV6/WdxhEZGQ4IEKmgIU1mSyvzl6IGBaBqOFRWBS0CFHDoxAxLAJeDpxZkogMg0wma/IWGRnZLnH4+flh9uzZDbZ99tlnkMvluH79ervEQkQdDwdEyBRw8jLqMGTgIXlEZFjy8vKkv7dv345ly5YhPT1dWqZUKqW/hRCorq6GhUXrf3SHhYUhMjISH3zwgTTpVo2YmBg8+eSTcHR0bPXtEhHVqBkQqbmOtZ21HTxUHiyqyWhwxJpM1qUblxCZEImlh5bivSPv4a1Db+HthLdx6cYlfYdGRAQAcHFxkW729vaQyWTS/QsXLsDW1hb79u1DYGAg5HI5kpKSMGPGDDz11FM66wkPD8ewYcOk+xqNBtHR0fD09IRCoYC/vz927NjRaBxTp07FH3/8gW+++UZneXZ2NhISEhAWFoasrCxMmDABzs7OUCqVGDBgAA4cONDoOnNyciCTyZCSkiItU6vVkMlkSEhIkJalpaVh7NixUCqVcHZ2xrRp03RGx3fs2AE/Pz8oFAp06dIFwcHBuHnzZtP/sURklFTWKvR16YshHkPQ16Uvi2oyKiysySTd7XqI6nK1fgIjIsOnVgMpKcDhw0Bqqva+Hi1ZsgSrVq3C+fPn0adPn2Y9Jjo6Glu2bMGGDRtw7tw5LFiwAFOnTkViYmKD/R0dHTFhwgRs2rRJZ/nmzZvRo0cPjB49GqWlpRg3bhzi4+Nx+vRphISEYPz48cjNzb3vfVOr1RgxYgQCAgJw8uRJ/PDDDygoKMBzzz0HQDuiP3nyZMyaNQvnz59HQkICJk6cCCHEfW+TiIioLfBQcDJJvB4iEd2XS5eAtWuBrFrvH97ewLx5gJd+5mdYvnw5Ro0a1ez+t27dwsqVK3HgwAEEBQUBALy8vJCUlISNGzdi6NChDT4uLCwMY8eORXZ2Njw9PSGEQGxsLEJDQ2FmZgZ/f3/4+/tL/aOiorBr1y589913mDt37n3t27/+9S8EBARg5cqV0rJNmzbBzc0NFy9eRGlpKaqqqjBx4kS4u7sD0J4PTkREZGg4Yk0middDJKJ7plbXL6oBIDMTWLdObyPX/fv3v6f+mZmZKCsrw6hRo6BUKqXbli1bkFV332oZNWoUevTogZiYGABAfHw8cnNzMXPmTABAaWkpFi5cCF9fX6hUKiiVSpw/f75FI9apqak4dOiQTpy9evUCAGRlZcHf3x8jR46En58fnn32WXz88ccoLCy87+0RERG1FY5Yk0ni9RCJ6J7l5NQvqmtkZmrb+/Ztx4C0OnXqpHPfzMys3qHQlZWV0t+lpaUAgL1796J79+46/eRyeaPbMTMzw4wZMxAbG4vIyEjExMRg+PDh8Lo9Ur9w4ULExcXhH//4B7y9vaFQKDBp0iRUVFQ0uj4AOrHWjrMm1vHjx+Pdd9+t9/hu3brB3NwccXFx+OWXX/Djjz9i3bp1ePPNN3Hs2DF4eno2ui9ERETtjSPWZJJ4PUQiumfFdzmS5W7t7aRr1646s4kD0JkgrHfv3pDL5cjNzYW3t7fOzc3Nrcl1z5w5E1euXMHOnTuxa9cuhIWFSW0///wzZsyYgaeffhp+fn5wcXFBTk5Ok3ECujOf144TAPr164dz587Bw8OjXqw1PyjIZDI88sgjePvtt3H69GlYWVlh165dTe4HERFRe2NhTSaJ10Mkontmd5cjWe7W3k5GjBiBkydPYsuWLcjIyEBERATS0tKkdltbWyxcuBALFixAbGwssrKycOrUKaxbtw6xsbFNrtvT0xMjRozAiy++CLlcjokTJ0ptPj4+2LlzJ1JSUpCamoq//OUv0Gg0ja5LoVDgz3/+szTxWmJiIt566y2dPnPmzMGNGzcwefJknDhxAllZWdi/fz9mzpyJ6upqHDt2DCtXrsTJkyeRm5uLnTt34tq1a/D19b3P/z0iIqK2YTKF9b///W94eHjA2toagwYNwvHjx/UdEulZzfUQo4ZHYVHQIkQNj0LEsAh4OehnAiIiMnAeHtqJyhri7a1tNwBjxozB0qVL8frrr2PAgAEoKSnB9OnTdfpERUVh6dKliI6Ohq+vL0JCQrB3795mHT4dFhaGwsJC/OUvf4G1tbW0/J///CccHBwwePBgjB8/HmPGjEG/fv2aXNemTZtQVVWFwMBAhIeH45133tFpd3V1xc8//4zq6mqMHj0afn5+CA8Ph0qlgpmZGezs7HD48GGMGzcOf/rTn/DWW2/h/fffx9ixY+/hf4yIiKjtyYQJXLNi+/btmD59OjZs2IBBgwZh9erV+Prrr5Geng4nJ6e7Pr64uBj29vYoKiqCnYGMSBAR0b0pLy+XZrSuXRDek0uXtBOVZWbeWabnWcHJeLXKc5KIiPSqubWiSRTWgwYNwoABA/Cvf/0LAKDRaODm5oZ58+ZhyZIld308C2siIuPXakWMWq2dqKy4WHv4t4cHoFK1TpDUobCwJiIyfs2tFY1+VvCKigokJyfjjTfekJaZmZkhODgYR44cafAxt27dwq1bt6T7xQYyIQ0RERkAlUovs38TERGR8TL6c6yvX7+O6upqODs76yx3dnZGfn5+g4+Jjo6Gvb29dLvbLKlEREREREREjTH6wvp+vPHGGygqKpJuV65c0XdIREREREREZKSM/lBwR0dHmJubo6CgQGd5QUEBXFxcGnyMXC6HXC5vj/CIiIiIiIjIxBn9iLWVlRUCAwMRHx8vLdNoNIiPj0dQUJAeIyMiIiIiIqKOwOhHrAHg1VdfRWhoKPr374+BAwdi9erVuHnzJmbOnKnv0IiIiIiIiMjEmURh/fzzz+PatWtYtmwZ8vPz0bdvX/zwww/1JjQjIiIiIiIiam0mUVgDwNy5czF37lx9h0FEREREREQdjNGfY01ERETNM2PGDDz11FPS/WHDhiE8PLzd40hISIBMJoNarW7T7chkMuzevbtNt0FERASwsCYiItKrGTNmQCaTQSaTwcrKCt7e3li+fDmqqqrafNs7d+5EVFRUs/q2VzFcUVEBR0dHrFq1qsH2qKgoODs7o7Kysk3jICIiuhcsrImIiPQsJCQEeXl5yMjIwGuvvYbIyEi89957DfatqKhote127twZtra2rba+1mBlZYWpU6ciJiamXpsQAps3b8b06dNhaWmph+iIiIgaxsKaiIhIz+RyOVxcXODu7o6XXnoJwcHB+O677wDcOXx7xYoVcHV1xYMPPggAuHLlCp577jmoVCp07twZEyZMQE5OjrTO6upqvPrqq1CpVOjSpQtef/11CCF0tlv3UPBbt25h8eLFcHNzg1wuh7e3Nz799FPk5ORg+PDhAAAHBwfIZDLMmDEDgPYSl9HR0fD09IRCoYC/vz927Nihs53vv/8ef/rTn6BQKDB8+HCdOBsSFhaGixcvIikpSWd5YmIiLl26hLCwMJw4cQKjRo2Co6Mj7O3tMXToUJw6darRdTY04p6SkgKZTKYTT1JSEh577DEoFAq4ublh/vz5uHnzptT+4YcfwsfHB9bW1nB2dsakSZOa3BciIuoYWFgTtTJ1uRop+Sk4nHMYqfmpUJer9R0SEd0DQ3gNKxQKnZHp+Ph4pKenIy4uDnv27EFlZSXGjBkDW1tb/PTTT/j555+hVCoREhIiPe7999/H5s2bsWnTJiQlJeHGjRvYtWtXk9udPn06tm7dirVr1+L8+fPYuHEjlEol3Nzc8M033wAA0tPTkZeXhzVr1gAAoqOjsWXLFmzYsAHnzp3DggULMHXqVCQmJgLQ/gAwceJEjB8/HikpKfjrX/+KJUuWNBmHn58fBgwYgE2bNuksj4mJweDBg9GrVy+UlJQgNDQUSUlJOHr0KHx8fDBu3DiUlJTc2392LVlZWQgJCcEzzzyDM2fOYPv27UhKSpImRz158iTmz5+P5cuXIz09HT/88AOGDBly39sjIiLTYTKzghMZgks3LmHt8bXIKsySlnk7eGPewHnw6uylx8iIqDn0/RoWQiA+Ph779+/HvHnzpOWdOnXCJ598AisrKwDA559/Do1Gg08++QQymQyAtuhUqVRISEjA6NGjsXr1arzxxhuYOHEiAGDDhg3Yv39/o9u+ePEivvrqK8TFxSE4OBgA4OV1Z587d+4MAHBycoJKpQKgHeFeuXIlDhw4gKCgIOkxSUlJ2LhxI4YOHYr169fjgQcewPvvvw8AePDBB3H27Fm8++67Tf5fhIWFYeHChVi7di2USiVKSkqwY8cOrF27FgAwYsQInf4fffQRVCoVEhMT8cQTTzS57sZER0djypQp0ii+j48P1q5dK+1Hbm4uOnXqhCeeeAK2trZwd3dHQEDAfW2LiIhMC0esiVqJulxd7ws5AGQWZmLd8XUcuSYycPp8De/ZswdKpRLW1tYYO3Ysnn/+eURGRkrtfn5+UlENAKmpqcjMzIStrS2USiWUSiU6d+6M8vJyZGVloaioCHl5eRg0aJD0GAsLC/Tv37/RGFJSUmBubo6hQ4c2O+7MzEyUlZVh1KhRUhxKpRJbtmxBVpb2//H8+fM6cQCQivCmTJ48GdXV1fjqq68AANu3b4eZmRmef/55AEBBQQFeeOEF+Pj4wN7eHnZ2digtLUVubm6z468rNTUVmzdv1tmXMWPGQKPRIDs7G6NGjYK7uzu8vLwwbdo0fPHFFygrK7vv7RERkengiDVRK8lR59T7Ql4jszATOeoc9HXp275BEVGz6fM1PHz4cKxfvx5WVlZwdXWFhYXux3OnTp107peWliIwMBBffPFFvXV17dr1vmJQKBT3/JjS0lIAwN69e9G9e3edNrlcfl9x1LCzs8OkSZMQExODWbNmISYmBs899xyUSiUAIDQ0FP/73/+wZs0auLu7Qy6XIygoqNHJ3czMtGMJtc8zrzuzeGlpKf72t79h/vz59R7fs2dPWFlZ4dSpU0hISMCPP/6IZcuWITIyEidOnJBG8YmIqGNiYU3USorLi1vUTkT6pc/XcKdOneDt7d3s/v369cP27dvh5OQEOzu7Bvt069YNx44dk84BrqqqQnJyMvr169dgfz8/P2g0GiQmJkqHgtdWM2JeXV0tLevduzfkcjlyc3MbHen29fWVJmKrcfTo0bvvJLSHgw8bNgx79uzBL7/8ojNT+s8//4wPP/wQ48aNA6A9l/v69euNrqvmB4e8vDw4ODgA0I7S19avXz/8+uuvTebCwsICwcHBCA4ORkREBFQqFQ4ePCgdck9ERB0TDwUnaiV21g1/uW1uOxHplzG9hqdMmQJHR0dMmDABP/30E7Kzs5GQkID58+fjt99+AwC88sorWLVqFXbv3o0LFy7g5ZdfbvIa1B4eHggNDcWsWbOwe/duaZ01h2K7u7tDJpNhz549uHbtGkpLS2Fra4uFCxdiwYIFiI2NRVZWFk6dOoV169YhNjYWADB79mxkZGRg0aJFSE9Px5dffonNmzc3az+HDBkCb29vTJ8+Hb169cLgwYOlNh8fH3z22Wc4f/48jh07hilTpjQ56u7t7Q03NzdERkYiIyMDe/fulc77rrF48WL88ssvmDt3LlJSUpCRkYFvv/1Wmrxsz549WLt2LVJSUnD58mVs2bIFGo1GmqmdiIg6LhbWRK3EQ+UBb4eGRzm8HbzhofJo34CI6J4Y02vYxsYGhw8fRs+ePTFx4kT4+voiLCwM5eXl0gj2a6+9hmnTpiE0NBRBQUGwtbXF008/3eR6169fj0mTJuHll19Gr1698MILL0iXmurevTvefvttLFmyBM7OzlKxGRUVhaVLlyI6Ohq+vr4ICQnB3r174enpCUB7CPU333yD3bt3w9/fHxs2bMDKlSubtZ8ymQyzZs1CYWEhZs2apdP26aeforCwEP369cO0adMwf/58ODk5NbouS0tLbN26FRcuXECfPn3w7rvv4p133tHp06dPHyQmJuLixYt47LHHEBAQgGXLlsHV1RUAoFKpsHPnTowYMQK+vr7YsGEDtm7dioceeqhZ+0NERKZLJupe1LIDKi4uhr29PYqKiho9pI6oOS7duIR1x9chszBTWubt4I15g+bBy4GzghO1pfLycmRnZ8PT0xPW1tb3tQ6+hqk1tcZzkoiI9Ku5tSLPsSZqRV6dvRAxLAI56hwUlxfDztoOHioPqKxV+g6NiJqBr2EiIiK6HyysiVqZylrF2b+JjBhfw0RERHSveI41ERERERERUQuwsCYiIiIiIiJqARbWRERERERERC3AwpqIiEwKL3ZBhoLPRSKijoOFNRERmQRzc3MAQEVFhZ4jIdIqKysDoL2GNhERmTbOCk5ERCbBwsICNjY2uHbtGiwtLWFmxt+OST+EECgrK8PVq1ehUqmkH32IiMh0sbAmIiKTIJPJ0K1bN2RnZ+Py5cv6DocIKpUKLi4u+g6DiIjaAQtrIiIyGVZWVvDx8eHh4KR3lpaWHKkmIupAWFgTEZFJMTMzg7W1tb7DICIiog6EJ6ARERERERERtQALayIiIiIiIqIWYGFNRERERERE1AI8xxray2IAQHFxsZ4jISIiIiIiIkNRUyPW1IyNYWENoKSkBADg5uam50iIiIiIiIjI0JSUlMDe3r7Rdpm4W+ndAWg0Gvz3v/+Fra0tZDJZo/2Ki4vh5uaGK1euwM7Orh0jpNbGXJoW5tN0MJemhfk0Hcyl6WAuTQvz2faEECgpKYGrqyvMzBo/k5oj1tBemqVHjx7N7m9nZ8cnrolgLk0L82k6mEvTwnyaDubSdDCXpoX5bFtNjVTX4ORlRERERERERC3AwpqIiIiIiIioBVhY3wO5XI6IiAjI5XJ9h0ItxFyaFubTdDCXpoX5NB3MpelgLk0L82k4OHkZERERERERUQtwxJqIiIiIiIioBVhYExEREREREbUAC2siIiIiIiKiFmBhfQ/+/e9/w8PDA9bW1hg0aBCOHz+u75ColujoaAwYMAC2trZwcnLCU089hfT0dJ0+5eXlmDNnDrp06QKlUolnnnkGBQUFOn1yc3Px+OOPw8bGBk5OTli0aBGqqqrac1eojlWrVkEmkyE8PFxaxlwal99//x1Tp05Fly5doFAo4Ofnh5MnT0rtQggsW7YM3bp1g0KhQHBwMDIyMnTWcePGDUyZMgV2dnZQqVQICwtDaWlpe+9Kh1ZdXY2lS5fC09MTCoUCDzzwAKKiolB7uhbm0nAdPnwY48ePh6urK2QyGXbv3q3T3lq5O3PmDB577DFYW1vDzc0Nf//739t61zqcpnJZWVmJxYsXw8/PD506dYKrqyumT5+O//73vzrrYC4Nx91em7XNnj0bMpkMq1ev1lnOfBoAQc2ybds2YWVlJTZt2iTOnTsnXnjhBaFSqURBQYG+Q6PbxowZI2JiYkRaWppISUkR48aNEz179hSlpaVSn9mzZws3NzcRHx8vTp48Kf785z+LwYMHS+1VVVXi4YcfFsHBweL06dPi+++/F46OjuKNN97Qxy6REOL48ePCw8ND9OnTR7zyyivScubSeNy4cUO4u7uLGTNmiGPHjolLly6J/fv3i8zMTKnPqlWrhL29vdi9e7dITU0VTz75pPD09BR//PGH1CckJET4+/uLo0ePip9++kl4e3uLyZMn62OXOqwVK1aILl26iD179ojs7Gzx9ddfC6VSKdasWSP1YS4N1/fffy/efPNNsXPnTgFA7Nq1S6e9NXJXVFQknJ2dxZQpU0RaWprYunWrUCgUYuPGje21mx1CU7lUq9UiODhYbN++XVy4cEEcOXJEDBw4UAQGBuqsg7k0HHd7bdbYuXOn8Pf3F66uruKDDz7QaWM+9Y+FdTMNHDhQzJkzR7pfXV0tXF1dRXR0tB6joqZcvXpVABCJiYlCCO0HjaWlpfj666+lPufPnxcAxJEjR4QQ2jc2MzMzkZ+fL/VZv369sLOzE7du3WrfHSBRUlIifHx8RFxcnBg6dKhUWDOXxmXx4sXi0UcfbbRdo9EIFxcX8d5770nL1Gq1kMvlYuvWrUIIIX799VcBQJw4cULqs2/fPiGTycTvv//edsGTjscff1zMmjVLZ9nEiRPFlClThBDMpTGp++W9tXL34YcfCgcHB5332cWLF4sHH3ywjfeo42qqEKtx/PhxAUBcvnxZCMFcGrLG8vnbb7+J7t27i7S0NOHu7q5TWDOfhoGHgjdDRUUFkpOTERwcLC0zMzNDcHAwjhw5osfIqClFRUUAgM6dOwMAkpOTUVlZqZPHXr16oWfPnlIejxw5Aj8/Pzg7O0t9xowZg+LiYpw7d64doycAmDNnDh5//HGdnAHMpbH57rvv0L9/fzz77LNwcnJCQEAAPv74Y6k9Ozsb+fn5Ovm0t7fHoEGDdPKpUqnQv39/qU9wcDDMzMxw7Nix9tuZDm7w4MGIj4/HxYsXAQCpqalISkrC2LFjATCXxqy1cnfkyBEMGTIEVlZWUp8xY8YgPT0dhYWF7bQ3VFdRURFkMhlUKhUA5tLYaDQaTJs2DYsWLcJDDz1Ur535NAwsrJvh+vXrqK6u1vmCDgDOzs7Iz8/XU1TUFI1Gg/DwcDzyyCN4+OGHAQD5+fmwsrKSPlRq1M5jfn5+g3muaaP2s23bNpw6dQrR0dH12phL43Lp0iWsX78ePj4+2L9/P1566SXMnz8fsbGxAO7ko6n32Pz8fDg5Oem0W1hYoHPnzsxnO1qyZAn+7//+D7169YKlpSUCAgIQHh6OKVOmAGAujVlr5Y7vvYanvLwcixcvxuTJk2FnZweAuTQ27777LiwsLDB//vwG25lPw2Ch7wCI2sKcOXOQlpaGpKQkfYdC9+HKlSt45ZVXEBcXB2tra32HQy2k0WjQv39/rFy5EgAQEBCAtLQ0bNiwAaGhoXqOju7FV199hS+++AJffvklHnroIaSkpCA8PByurq7MJZEBqqysxHPPPQchBNavX6/vcOg+JCcnY82aNTh16hRkMpm+w6EmcMS6GRwdHWFubl5vxuGCggK4uLjoKSpqzNy5c7Fnzx4cOnQIPXr0kJa7uLigoqICarVap3/tPLq4uDSY55o2ah/Jycm4evUq+vXrBwsLC1hYWCAxMRFr166FhYUFnJ2dmUsj0q1bN/Tu3Vtnma+vL3JzcwHcyUdT77EuLi64evWqTntVVRVu3LjBfLajRYsWSaPWfn5+mDZtGhYsWCAdWcJcGq/Wyh3few1HTVF9+fJlxMXFSaPVAHNpTH766SdcvXoVPXv2lL4TXb58Ga+99ho8PDwAMJ+GgoV1M1hZWSEwMBDx8fHSMo1Gg/j4eAQFBekxMqpNCIG5c+di165dOHjwIDw9PXXaAwMDYWlpqZPH9PR05ObmSnkMCgrC2bNndd6caj6M6hYG1HZGjhyJs2fPIiUlRbr1798fU6ZMkf5mLo3HI488Uu/SdxcvXoS7uzsAwNPTEy4uLjr5LC4uxrFjx3TyqVarkZycLPU5ePAgNBoNBg0a1A57QQBQVlYGMzPdrw7m5ubQaDQAmEtj1lq5CwoKwuHDh1FZWSn1iYuLw4MPPggHB4d22huqKaozMjJw4MABdOnSRaeduTQe06ZNw5kzZ3S+E7m6umLRokXYv38/AObTYOh79jRjsW3bNiGXy8XmzZvFr7/+Kl588UWhUql0Zhwm/XrppZeEvb29SEhIEHl5edKtrKxM6jN79mzRs2dPcfDgQXHy5EkRFBQkgoKCpPaaSzSNHj1apKSkiB9++EF07dqVl2gyALVnBReCuTQmx48fFxYWFmLFihUiIyNDfPHFF8LGxkZ8/vnnUp9Vq1YJlUolvv32W3HmzBkxYcKEBi/zExAQII4dOyaSkpKEj48PL9HUzkJDQ0X37t2ly23t3LlTODo6itdff13qw1warpKSEnH69Glx+vRpAUD885//FKdPn5Zmim6N3KnVauHs7CymTZsm0tLSxLZt24SNjQ0v6dPKmsplRUWFePLJJ0WPHj1ESkqKznei2jNCM5eG426vzbrqzgouBPNpCFhY34N169aJnj17CisrKzFw4EBx9OhRfYdEtQBo8BYTEyP1+eOPP8TLL78sHBwchI2NjXj66adFXl6eznpycnLE2LFjhUKhEI6OjuK1114TlZWV7bw3VFfdwpq5NC7/+c9/xMMPPyzkcrno1auX+Oijj3TaNRqNWLp0qXB2dhZyuVyMHDlSpKen6/T53//+JyZPniyUSqWws7MTM2fOFCUlJe25Gx1ecXGxeOWVV0TPnj2FtbW18PLyEm+++abOl3Xm0nAdOnSowc/J0NBQIUTr5S41NVU8+uijQi6Xi+7du4tVq1a11y52GE3lMjs7u9HvRIcOHZLWwVwajru9NutqqLBmPvVPJoQQ7TEyTkRERERERGSKeI41ERERERERUQuwsCYiIiIiIiJqARbWRERERERERC3AwpqIiIiIiIioBVhYExEREREREbUAC2siIiIiIiKiFmBhTURERERERNQCLKyJiIiIiIiIWoCFNREREREREVELsLAmIiIyUjKZrMlbZGRku8Rx8+ZNPPDAA3j11Vd1lufk5MDOzg4ff/xxu8RBRESkLzIhhNB3EERERHTv8vPzpb+3b9+OZcuWIT09XVqmVCqhVCoBAEIIVFdXw8LCok1iOXz4MEaOHImDBw/isccegxACI0aMgLW1Nfbt29cm2yQiIjIUHLEmIiIyUi4uLtLN3t4eMplMun/hwgXY2tpi3759CAwMhFwuR1JSEmbMmIGnnnpKZz3h4eEYNmyYdF+j0SA6Ohqenp5QKBTw9/fHjh07moxlyJAhmDdvHmbOnImbN29izZo1SElJwSeffNIGe05ERGRY2uZnayIiIjIIS5YswT/+8Q94eXnBwcGhWY+Jjo7G559/jg0bNsDHxweHDx/G1KlT0bVrVwwdOrTRx61YsQLff/89pk6div379+Ojjz5C9+7dW2tXiIiIDBYLayIiIhO2fPlyjBo1qtn9b926hZUrV+LAgQMICgoCAHh5eSEpKQkbN25ssrBWKBRYs2YNQkJCMHbsWEydOrXF8RMRERkDFtZEREQmrH///vfUPzMzE2VlZfWK8YqKCgQEBNz18Z9++ilsbGxw9uxZFBUVwd7e/p62T0REZIxYWBMREZmwTp066dw3MzND3XlLKysrpb9LS0sBAHv37q13GLdcLm9yW9u3b8eePXtw5MgRTJ48GQsWLMCmTZtaEj4REZFRYGFNRETUgXTt2hVpaWk6y1JSUmBpaQkA6N27N+RyOXJzc5s87LuugoICzJkzB++88w78/f2xefNmDB48GM8++yzGjh3bqvtARERkaDgrOBERUQcyYsQInDx5Elu2bEFGRgYiIiJ0Cm1bW1ssXLgQCxYsQGxsLLKysnDq1CmsW7cOsbGxja73xRdfhK+vL8LDwwEAAwcOxKJFi/Diiy+iqKiorXeLiIhIr1hYExERdSBjxozB0qVL8frrr2PAgAEoKSnB9OnTdfpERUVh6dKliI6Ohq+vL0JCQrB37154eno2uM4tW7bgwIEDiImJgZnZna8Wb7/9NlQqFRYsWNCm+0RERKRvMlH3RCsiIiIiIiIiajaOWBMRERERERG1AAtrIiIiIiIiohZgYU1ERERERETUAiysiYiIiIiIiFqAhTURERERERFRC7CwJiIiIiIiImoBFtZERERERERELcDCmoiIiIiIiKgFWFgTERERERERtQALayIiIiIiIqIWYGFNRERERERE1AIsrImIiIiIiIha4P8BTAKhrXDpmssAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b49049c59dea6f348641a33bf30b9ca243aec4d1 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 11 Aug 2024 14:08:23 +0000 Subject: [PATCH 63/78] warning ignore added to some notebooks --- .../test_bayesian_ridge_regression.ipynb | 11 +++++++++++ .../test_bayesian_ridge_regression_grid_search.ipynb | 11 +++++++++++ .../test/sgd_regression/test_sgd_regression.ipynb | 11 +++++++++++ .../test_sgd_regression_grid_search.ipynb | 11 +++++++++++ 4 files changed, 44 insertions(+) diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb index b1b25d76..cf893460 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb index 7a8bd13f..7a796de8 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb index 2923630e..ffb0cedd 100644 --- a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb index ab0e4ba7..f439bba0 100644 --- a/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, From dc1203bb44b40279eca07d42ff2680839c6bc96f Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 11 Aug 2024 14:13:55 +0000 Subject: [PATCH 64/78] warnings removed --- .../linear_regression/test_linear_regression.ipynb | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb index 1e8cf50e..2e3bae3a 100644 --- a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb +++ b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, From 6c9935662a1c188bce4863401a1b9393ccb29b06 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 11 Aug 2024 15:09:23 +0000 Subject: [PATCH 65/78] warnings handling completed --- .../test_elasticnetCV_regression.ipynb | 11 +++++++++++ .../test_elasticnetCV_regression_grid_search.ipynb | 11 +++++++++++ .../test_elasticnet_regression.ipynb | 11 +++++++++++ .../test_elasticnet_regression_grid_search.ipynb | 11 +++++++++++ .../lasso_regression/test_lassoCV_regression.ipynb | 11 +++++++++++ .../test_lassoCV_regression_grid_search.ipynb | 11 +++++++++++ .../test/lasso_regression/test_lasso_regression.ipynb | 11 +++++++++++ .../test_lasso_regression_grid_search.ipynb | 11 +++++++++++ .../ridge_regression/test_ridgeCV_regression.ipynb | 11 +++++++++++ .../test_ridgeCV_regression_grid_search.ipynb | 11 +++++++++++ .../test/ridge_regression/test_ridge_regression.ipynb | 11 +++++++++++ .../test_ridge_regression_grid_search.ipynb | 11 +++++++++++ 12 files changed, 132 insertions(+) diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb index 7fff896a..9d1bcb1d 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb index 97eaa675..edf9dabf 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb index d7aba9d3..b9e61811 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb index 3c692946..0011e0ae 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb index a53764cb..b7ba3863 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb index c1866bd0..cf54c4e7 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb index 119d1216..fb114353 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb index b9cc534b..9103462c 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb index 434c39c8..758dc214 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb index 8bac9000..af90462e 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb index e80ce590..019f342a 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb @@ -35,6 +35,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb index 39ab9355..8d683abb 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb @@ -37,6 +37,17 @@ "from sklearn import linear_model" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, { "cell_type": "markdown", "metadata": {}, From 384d31f3e00f241c68290322fddd7f86f43fc50f Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 11 Aug 2024 19:06:49 +0000 Subject: [PATCH 66/78] legend box fixed --- .../bayesian_regression/test_bayesian_ridge_regression.ipynb | 2 +- .../test_bayesian_ridge_regression_grid_search.ipynb | 2 +- .../elasticnet_regression/test_elasticnetCV_regression.ipynb | 2 +- .../test_elasticnetCV_regression_grid_search.ipynb | 2 +- .../test/elasticnet_regression/test_elasticnet_regression.ipynb | 2 +- .../test_elasticnet_regression_grid_search.ipynb | 2 +- .../test/lasso_regression/test_lassoCV_regression.ipynb | 2 +- .../lasso_regression/test_lassoCV_regression_grid_search.ipynb | 2 +- .../test/lasso_regression/test_lasso_regression.ipynb | 2 +- .../lasso_regression/test_lasso_regression_grid_search.ipynb | 2 +- .../test/linear_regression/test_linear_regression.ipynb | 2 +- .../test/others/test_support_vector_regression.ipynb | 2 +- .../test/ridge_regression/test_ridgeCV_regression.ipynb | 2 +- .../ridge_regression/test_ridgeCV_regression_grid_search.ipynb | 2 +- .../test/ridge_regression/test_ridge_regression.ipynb | 2 +- .../ridge_regression/test_ridge_regression_grid_search.ipynb | 2 +- .../test/sgd_regression/test_sgd_regression.ipynb | 2 +- .../test/sgd_regression/test_sgd_regression_grid_search.ipynb | 2 +- 18 files changed, 18 insertions(+), 18 deletions(-) diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb index cf893460..51fbd3b0 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb @@ -1375,7 +1375,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb index 7a796de8..4d77bdf0 100644 --- a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression_grid_search.ipynb @@ -1377,7 +1377,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb index 9d1bcb1d..72725b6e 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression.ipynb @@ -1381,7 +1381,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb index edf9dabf..f05a85ed 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnetCV_regression_grid_search.ipynb @@ -1383,7 +1383,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb index b9e61811..591912bd 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression.ipynb @@ -1373,7 +1373,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb index 0011e0ae..5a819bda 100644 --- a/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/elasticnet_regression/test_elasticnet_regression_grid_search.ipynb @@ -1375,7 +1375,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb index b7ba3863..c2e2e677 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression.ipynb @@ -1501,7 +1501,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb index cf54c4e7..8ed96777 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lassoCV_regression_grid_search.ipynb @@ -1503,7 +1503,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb index fb114353..2271eb4f 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression.ipynb @@ -1383,7 +1383,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb index 9103462c..0d959c4d 100644 --- a/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/lasso_regression/test_lasso_regression_grid_search.ipynb @@ -1385,7 +1385,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb index 2e3bae3a..8af00eaf 100644 --- a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb +++ b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb @@ -1375,7 +1375,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/others/test_support_vector_regression.ipynb b/app/services/calib_validation/test/others/test_support_vector_regression.ipynb index 69e670d2..477eba6e 100644 --- a/app/services/calib_validation/test/others/test_support_vector_regression.ipynb +++ b/app/services/calib_validation/test/others/test_support_vector_regression.ipynb @@ -1365,7 +1365,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb index 758dc214..7d12edff 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression.ipynb @@ -1379,7 +1379,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb index af90462e..09015177 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridgeCV_regression_grid_search.ipynb @@ -1381,7 +1381,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb index 019f342a..7ee47760 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression.ipynb @@ -1375,7 +1375,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb index 8d683abb..84211402 100644 --- a/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/ridge_regression/test_ridge_regression_grid_search.ipynb @@ -1377,7 +1377,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb index ffb0cedd..c04d51ba 100644 --- a/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression.ipynb @@ -1381,7 +1381,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" diff --git a/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb b/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb index f439bba0..7cc8b622 100644 --- a/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb +++ b/app/services/calib_validation/test/sgd_regression/test_sgd_regression_grid_search.ipynb @@ -1383,7 +1383,7 @@ "plt.xlabel(\"F1\")\n", "plt.ylabel(\"F2\")\n", "\n", - "plt.legend()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Show the plot\n", "plt.show()" From 45215db6bdc41bffc5775eed1b9c10d330158fa2 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 11 Aug 2024 23:20:43 +0000 Subject: [PATCH 67/78] SVR model grid search added --- ...upport_vector_regression_grid_search.ipynb | 1681 +++++++++++++++++ 1 file changed, 1681 insertions(+) create mode 100644 app/services/calib_validation/test/others/test_support_vector_regression_grid_search.ipynb diff --git a/app/services/calib_validation/test/others/test_support_vector_regression_grid_search.ipynb b/app/services/calib_validation/test/others/test_support_vector_regression_grid_search.ipynb new file mode 100644 index 00000000..de3c3e12 --- /dev/null +++ b/app/services/calib_validation/test/others/test_support_vector_regression_grid_search.ipynb @@ -0,0 +1,1681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Needed Libraries\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit Learn imports\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.svm import SVR" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings filter\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prefix for the calibration data to identify the correct file\n", + "prefix = \"e2e_test3\"\n", + "\n", + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "\n", + "# Drop the columns that are not needed\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(720, 6)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
0506.971497282.207611406.131836278.658783100100
1518.564636280.534271412.582733279.688538100100
2524.403320282.937195417.401550282.717865100100
3530.841187287.072388422.359680283.891907100100
4534.370300287.437531426.682861285.813660100100
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x point_y\n", + "0 506.971497 282.207611 406.131836 278.658783 100 100\n", + "1 518.564636 280.534271 412.582733 279.688538 100 100\n", + "2 524.403320 282.937195 417.401550 282.717865 100 100\n", + "3 530.841187 287.072388 422.359680 283.891907 100 100\n", + "4 534.370300 287.437531 426.682861 285.813660 100 100" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
left_iris_xleft_iris_yright_iris_xright_iris_ypoint_xpoint_y
count720.000000720.000000720.000000720.000000720.000000720.000000
mean365.496955328.690510245.821749332.613119768.000000365.000000
std171.62534826.492830175.05515926.174230578.907128229.656271
min150.721909279.04608232.873291278.658783100.000000100.000000
25%162.563515300.02177440.361696307.497986100.000000100.000000
50%390.666397331.088699259.016006335.506729768.000000365.000000
75%549.767090352.795532437.989388353.5658651436.000000630.000000
max564.934509370.810760451.664612377.2171331436.000000630.000000
\n", + "
" + ], + "text/plain": [ + " left_iris_x left_iris_y right_iris_x right_iris_y point_x \\\n", + "count 720.000000 720.000000 720.000000 720.000000 720.000000 \n", + "mean 365.496955 328.690510 245.821749 332.613119 768.000000 \n", + "std 171.625348 26.492830 175.055159 26.174230 578.907128 \n", + "min 150.721909 279.046082 32.873291 278.658783 100.000000 \n", + "25% 162.563515 300.021774 40.361696 307.497986 100.000000 \n", + "50% 390.666397 331.088699 259.016006 335.506729 768.000000 \n", + "75% 549.767090 352.795532 437.989388 353.565865 1436.000000 \n", + "max 564.934509 370.810760 451.664612 377.217133 1436.000000 \n", + "\n", + " point_y \n", + "count 720.000000 \n", + "mean 365.000000 \n", + "std 229.656271 \n", + "min 100.000000 \n", + "25% 100.000000 \n", + "50% 365.000000 \n", + "75% 630.000000 \n", + "max 630.000000 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe the data to see the statistics\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Regression plot for the data\n", + "sns.pairplot(\n", + " df,\n", + " x_vars=[\"left_iris_y\", \"right_iris_y\", \"left_iris_x\", \"right_iris_x\"],\n", + " y_vars=[\"point_x\", \"point_y\"],\n", + " kind=\"reg\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap for the correlation of the data\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\", linewidths=0.2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data for the left and right iris and the point\n", + "plt.scatter(df[\"left_iris_x\"], df[\"left_iris_y\"], color=\"blue\")\n", + "plt.scatter(df[\"right_iris_x\"], df[\"right_iris_y\"], color=\"red\")\n", + "plt.scatter(df[\"point_x\"], df[\"point_y\"], color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris X and right iris X\n", + "X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + "X_y = df[\"point_x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_x = sc.fit_transform(X_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82489499, 0.91640561],\n", + " [0.89249106, 0.95328188],\n", + " [0.92653465, 0.98082844],\n", + " [0.96407189, 1.00917137],\n", + " [0.98464908, 1.03388465]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_x[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_x, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9961613725956199" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SVR model and fit the data\n", + "model_x = make_pipeline(PolynomialFeatures(2), SVR(kernel=\"linear\"))\n", + "model_x.fit(X_train_x, y_train_x)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_x = model_x.predict(X_test_x)\n", + "r2_score(y_test_x, y_pred_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 786.06817646, 774.16112732, 169.97543307, 100.89124975,\n", + " 1423.89137523, 167.92634901, 166.02161111, 112.45139996,\n", + " 779.34904981, 764.74057119, 1423.50780012, 759.70612299,\n", + " 174.62120954, 122.38562533, 764.06525372, 788.16916383,\n", + " 161.36933805, 1428.41975816, 103.13801231, 99.63506207,\n", + " 760.68326135, 763.60871109, 177.33607007, 1430.1852322 ,\n", + " 1421.98908793, 786.76917174, 99.09670387, 102.13959107,\n", + " 107.74679153, 113.51843804, 765.96773821, 764.59866928,\n", + " 787.03137631, 766.4524878 , 1446.02493565, 1436.67715617,\n", + " 1421.82373044, 1445.77717367, 1442.54420182, 1422.28241681,\n", + " 776.17737048, 114.57451133, 171.12398447, 1426.87564021,\n", + " 763.93598843, 166.93101567, 789.57658774, 1433.07580019,\n", + " 1433.696823 , 167.75862327, 1418.81230066, 1432.79231492,\n", + " 756.96248244, 1430.78491926, 109.28163314, 776.03060028,\n", + " 101.90368919, 99.89096906, 167.04919653, 787.09657094,\n", + " 1413.37639964, 191.16678106, 167.48295527, 760.65407598,\n", + " 103.89477225, 122.03101928, 1433.42245163, 111.81119227,\n", + " 1416.0712344 , 1433.29441884, 1433.84309126, 768.2707198 ,\n", + " 168.36465857, 767.25929896, 110.12371189, 1413.64286489,\n", + " 1442.99494097, 787.15769045, 1425.92767291, 101.1499266 ,\n", + " 1414.34581531, 776.33835113, 99.3240292 , 773.09016685,\n", + " 180.13223027, 1419.03425187, 1450.3530087 , 95.87425145,\n", + " 1433.86791747, 1432.13659254, 98.56522234, 169.38009215,\n", + " 102.60133276, 784.53115873, 122.70041959, 785.08390781,\n", + " 166.89140842, 240.88111696, 1449.82506188, 108.56071336,\n", + " 776.51964396, 1423.37638438, 782.89937547, 777.64203979,\n", + " 101.62134053, 781.39209615, 97.57369452, 97.07062968,\n", + " 96.59802601, 1432.94318354, 107.34396499, 1269.52845407,\n", + " 1432.2997475 , 126.41023556, 1417.0923705 , 786.4788734 ,\n", + " 765.03051268, 163.5215889 , 1444.8509884 , 181.5226558 ,\n", + " 167.54808799, 762.14252596, 1432.56272421, 168.93219589,\n", + " 783.1772012 , 169.68478027, 764.32277305, 1434.07494872,\n", + " 1433.35631165, 1427.09854866, 1432.78102547, 766.986891 ,\n", + " 97.63885695, 1446.8629281 , 788.6356597 , 1426.17452062,\n", + " 114.10164258, 190.72124042, 96.30508092, 114.91215112,\n", + " 116.08955388, 102.11466932, 111.6232333 , 1417.77562652])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_x" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_x, y=y_pred_x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Left iris Y and right iris Y\n", + "X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + "y_y = df[\"point_y\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data by standardizing it\n", + "sc = StandardScaler()\n", + "X_y = sc.fit_transform(X_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.75576606, -2.06278627],\n", + " [-1.81897195, -2.02341661],\n", + " [-1.728208 , -1.90759914],\n", + " [-1.57201225, -1.86271308],\n", + " [-1.55821997, -1.78924048]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data for the first 5 rows after standardizing\n", + "X_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100\n", + "1 100\n", + "2 100\n", + "3 100\n", + "4 100\n", + "Name: point_y, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display first 5 rows\n", + "y_y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9456979902446316" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a SVR model and fit the data\n", + "model_y = make_pipeline(PolynomialFeatures(2), SVR(kernel=\"linear\"))\n", + "model_y.fit(X_train_y, y_train_y)\n", + "\n", + "# Predict the data and calculate the r2 score\n", + "y_pred_y = model_y.predict(X_test_y)\n", + "r2_score(y_test_y, y_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([167.24704403, 147.57189819, 96.43602343, 542.75354681,\n", + " 50.63898134, 110.68899627, 106.98656955, 358.34588604,\n", + " 163.39465024, 594.96665535, 624.53630295, 618.98636251,\n", + " 86.94829389, 299.86834513, 595.46795559, 168.10352024,\n", + " 118.67347325, 431.52192945, 537.38300556, 535.6244927 ,\n", + " 621.07478963, 554.29994981, 74.93587593, 635.2230296 ,\n", + " 124.27884373, 162.07453039, 541.23069001, 538.93990105,\n", + " 339.83045969, 348.20521822, 588.3756925 , 544.74820729,\n", + " 156.92353993, 566.77476522, 410.11203721, 683.12824096,\n", + " 631.35054651, 421.6438783 , 416.1310996 , 119.63038418,\n", + " 154.23903683, 346.97858209, 93.64383376, 640.71732205,\n", + " 582.36577712, 99.57021438, 170.15476265, 427.13960383,\n", + " 662.03897297, 105.18201667, 143.51769748, 639.99738366,\n", + " 603.11715806, 389.77640643, 352.8206183 , 169.04518056,\n", + " 538.71511892, 533.88775644, 110.31668333, 168.47878744,\n", + " 141.30325282, 32.57508055, 105.16473031, 593.85895671,\n", + " 533.19237558, 300.1892727 , 659.38637758, 343.67620852,\n", + " 137.55271905, 429.6418013 , 646.52589448, 596.22555029,\n", + " 103.51697055, 565.39427129, 352.32717805, 143.41078667,\n", + " 415.39896191, 171.2800319 , 635.73183918, 535.62152859,\n", + " 140.98390414, 156.23138868, 544.84704822, 160.15060369,\n", + " 78.96341239, 124.94589752, 418.45994587, 535.6410221 ,\n", + " 83.35537538, 649.44984764, 530.55473909, 102.83666981,\n", + " 543.12677866, 163.1070225 , 377.37382271, 168.05582622,\n", + " 103.88075762, -29.50094072, 417.8858622 , 332.83038364,\n", + " 163.39679933, 632.73749667, 166.80345497, 163.73063147,\n", + " 537.4425775 , 179.87647713, 538.12727975, 536.0505489 ,\n", + " 534.47182702, 655.94570271, 347.13044782, 31.85424472,\n", + " 425.13641163, 278.85029093, 131.66024006, 169.26102716,\n", + " 563.37777408, 98.64646084, 415.20221784, 77.99328961,\n", + " 96.94780331, 590.3178895 , 421.46462058, 97.87870389,\n", + " 161.63930867, 98.39272484, 588.22144553, 663.39624996,\n", + " 392.21952928, 97.98572011, 640.2000581 , 584.72292022,\n", + " 527.6615485 , 412.9880516 , 164.75429106, 641.19814588,\n", + " 348.66943067, 14.96823934, 530.73058264, 355.18010497,\n", + " 357.14498777, 540.35508543, 353.21847186, 123.0124774 ])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the predicted data\n", + "y_pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "sns.regplot(x=y_test_y, y=y_pred_y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data dictionary for the true and predicted values\n", + "data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + "}\n", + "\n", + "# Scatter plot for the true and predicted values\n", + "sns.scatterplot(x=\"True X\", y=\"True Y\", data=data, label=\"True Values\", alpha=0.7)\n", + "sns.scatterplot(\n", + " x=\"Predicted X\", y=\"Predicted Y\", data=data, label=\"Predicted Values\", alpha=0.7\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"True and Predicted Points for X and Y\")\n", + "plt.xlabel(\"X Values\")\n", + "plt.ylabel(\"Y Values\")\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new dataframe with the True X and Y values\n", + "df_data = pd.DataFrame(data)\n", + "df_data[\"True XY\"] = list(zip(df_data[\"True X\"], df_data[\"True Y\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768786.068176100167.247044(768, 100)
290768774.161127100147.571898(768, 100)
54100169.97543310096.436023(100, 100)
198100100.891250630542.753547(100, 630)
45314361423.89137510050.638981(1436, 100)
..................
164100114.912151365355.180105(100, 365)
165100116.089554365357.144988(100, 365)
199100102.114669630540.355085(100, 630)
132100111.623233365353.218472(100, 365)
50114361417.775627100123.012477(1436, 100)
\n", + "

144 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 786.068176 100 167.247044 (768, 100)\n", + "290 768 774.161127 100 147.571898 (768, 100)\n", + "54 100 169.975433 100 96.436023 (100, 100)\n", + "198 100 100.891250 630 542.753547 (100, 630)\n", + "453 1436 1423.891375 100 50.638981 (1436, 100)\n", + ".. ... ... ... ... ...\n", + "164 100 114.912151 365 355.180105 (100, 365)\n", + "165 100 116.089554 365 357.144988 (100, 365)\n", + "199 100 102.114669 630 540.355085 (100, 630)\n", + "132 100 111.623233 365 353.218472 (100, 365)\n", + "501 1436 1417.775627 100 123.012477 (1436, 100)\n", + "\n", + "[144 rows x 5 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the data\n", + "df_data" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True XPredicted XTrue YPredicted YTrue XY
340768786.068176100167.247044(768, 100)
290768774.161127100147.571898(768, 100)
54100169.97543310096.436023(100, 100)
198100100.891250630542.753547(100, 630)
45314361423.89137510050.638981(1436, 100)
\n", + "
" + ], + "text/plain": [ + " True X Predicted X True Y Predicted Y True XY\n", + "340 768 786.068176 100 167.247044 (768, 100)\n", + "290 768 774.161127 100 147.571898 (768, 100)\n", + "54 100 169.975433 100 96.436023 (100, 100)\n", + "198 100 100.891250 630 542.753547 (100, 630)\n", + "453 1436 1423.891375 100 50.638981 (1436, 100)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only 5 rows\n", + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 5)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's filter the data to remove the unwanted values\n", + "df_data = df_data[(df_data[\"Predicted X\"] >= 0) & (df_data[\"Predicted Y\"] >= 0)]\n", + "df_data = df_data[\n", + " (abs(df_data[\"Predicted X\"] - df_data[\"True X\"]) <= 100)\n", + " & (abs(df_data[\"Predicted Y\"] - df_data[\"True Y\"]) <= 100)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(141, 5)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "df_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean True XY\n", + "(100, 100) 0.242163\n", + "(100, 365) 0.412466\n", + "(100, 630) 0.648346\n", + "(768, 100) 0.927538\n", + "(768, 630) 1.229444\n", + "(1436, 100) 1.204913\n", + "(1436, 365) 1.537352\n", + "(1436, 630) 1.797777\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Precision is calculated via the Root Mean Square from the\n", + "# successive data points [in degrees of visual angle θi between\n", + "# successive (x1,y1) to (xi+1, yi+1) samples], both for each eye\n", + "# individually and as a mean from the two\n", + "\n", + "# Another option to describe the variation in the data is to\n", + "# measure the standard deviation of the data set, equivalent\n", + "# to the RMS normalized by the mean\n", + "\n", + "\n", + "def func_x(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted X and true X values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted X and true X values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n", + "\n", + "\n", + "def func_y(group):\n", + " \"\"\"\n", + " Calculate the root mean square error between the predicted Y values and the true Y values.\n", + "\n", + " Args:\n", + " group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n", + "\n", + " Returns:\n", + " float: The root mean square error between the predicted Y values and the true Y values.\n", + " \"\"\"\n", + " return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n", + "\n", + "\n", + "# Calculate the precision for the X and Y values\n", + "precision_x = df_data.groupby(\"True XY\").apply(func_x)\n", + "precision_y = df_data.groupby(\"True XY\").apply(func_y)\n", + "\n", + "# Calculate the mean precision for the X and Y values\n", + "precision_xy = (precision_x + precision_y) / 2\n", + "precision_xy = precision_xy / np.mean(precision_xy)\n", + "\n", + "print(\"mean\", precision_xy)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dictionary to store the data\n", + "data = {}\n", + "\n", + "# Iterate over df_data to get the data\n", + "for index, row in df_data.iterrows():\n", + "\n", + " # Get the outer and inner keys\n", + " outer_key = str(row[\"True X\"])\n", + " inner_key = str(row[\"True Y\"])\n", + "\n", + " # If the outer key is not in the data, add it\n", + " if outer_key not in data:\n", + " data[outer_key] = {}\n", + "\n", + " # Add the data to the dictionary\n", + " data[outer_key][inner_key] = {\n", + " \"predicted_x\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted X\"].values.tolist(),\n", + " \"predicted_y\": df_data[\n", + " (df_data[\"True X\"] == row[\"True X\"]) & (df_data[\"True Y\"] == row[\"True Y\"])\n", + " ][\"Predicted Y\"].values.tolist(),\n", + " \"PrecisionSD\": precision_xy[(row[\"True X\"], row[\"True Y\"])],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an numpy array to store the transposed data\n", + "data = np.array([y_pred_x, y_pred_y]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(144, 2)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shape of the data\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a KMeans model with 8 clusters\n", + "model = KMeans(n_clusters=8, n_init=\"auto\", init=\"k-means++\")\n", + "\n", + "# Fit the data to the model\n", + "y_kmeans = model.fit_predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot figure size\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot for the data\n", + "plt.scatter(\n", + " data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s=90, c=\"blue\", label=\"Cluster 1\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s=90, c=\"red\", label=\"Cluster 2\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s=90, c=\"green\", label=\"Cluster 3\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 3, 0], data[y_kmeans == 3, 1], s=90, c=\"yellow\", label=\"Cluster 4\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 4, 0], data[y_kmeans == 4, 1], s=90, c=\"lightgreen\", label=\"Cluster 5\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 5, 0], data[y_kmeans == 5, 1], s=90, c=\"purple\", label=\"Cluster 6\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 6, 0], data[y_kmeans == 6, 1], s=90, c=\"orange\", label=\"Cluster 7\"\n", + ")\n", + "plt.scatter(\n", + " data[y_kmeans == 7, 0], data[y_kmeans == 7, 1], s=90, c=\"pink\", label=\"Cluster 8\"\n", + ")\n", + "plt.scatter(\n", + " model.cluster_centers_[:, 0],\n", + " model.cluster_centers_[:, 1],\n", + " s=120,\n", + " c=\"black\",\n", + " label=\"Centroids\",\n", + ")\n", + "\n", + "# Plot title and labels\n", + "plt.title(\"Clusters\")\n", + "\n", + "plt.xlabel(\"F1\")\n", + "plt.ylabel(\"F2\")\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title=None):\n", + " \"\"\"\n", + " Plots the true and predicted points for X and Y coordinates.\n", + "\n", + " Args:\n", + " - ax (matplotlib.axes.Axes): The axes object to plot on.\n", + " - y_test_x (list or numpy.ndarray): The true X coordinates.\n", + " - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n", + " - y_test_y (list or numpy.ndarray): The true Y coordinates.\n", + " - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n", + " - title (str, optional): The title of the plot. Defaults to None.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Convert the data to numpy arrays\n", + " y_test_x = np.array(y_test_x)\n", + " y_test_y = np.array(y_test_y)\n", + "\n", + " # True points as a list of tuples\n", + " true_points = [(y_test_x[i], y_test_y[i]) for i in range(len(y_test_x))]\n", + "\n", + " # Define the error range\n", + " error_range = 0.05\n", + "\n", + " # Create a DataFrame with the true and predicted values\n", + " data = {\n", + " \"True X\": y_test_x,\n", + " \"Predicted X\": y_pred_x,\n", + " \"True Y\": y_test_y,\n", + " \"Predicted Y\": y_pred_y,\n", + " }\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " sns.scatterplot(\n", + " x=\"True X\",\n", + " y=\"True Y\",\n", + " data=data,\n", + " label=\"True Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"red\",\n", + " )\n", + " sns.scatterplot(\n", + " x=\"Predicted X\",\n", + " y=\"Predicted Y\",\n", + " data=data,\n", + " label=\"Predicted Values\",\n", + " alpha=0.7,\n", + " ax=ax,\n", + " color=\"green\",\n", + " )\n", + "\n", + " # Calculate the circle radius\n", + " circle_radius = (\n", + " error_range\n", + " * (max(y_test_x) - min(y_test_x) + max(y_test_y) - min(y_test_y))\n", + " / 2\n", + " )\n", + "\n", + " # Iterate over the true points\n", + " for true_x, true_y in true_points:\n", + "\n", + " # Get the predicted values within the error range\n", + " x_within_range = [\n", + " y_pred_x[j]\n", + " for j in range(len(y_test_x))\n", + " if abs(y_test_x[j] - true_x) <= error_range\n", + " ]\n", + " y_within_range = [\n", + " y_pred_y[j]\n", + " for j in range(len(y_test_y))\n", + " if abs(y_test_y[j] - true_y) <= error_range\n", + " ]\n", + "\n", + " # If there are more than one predicted values within the error range\n", + " if len(x_within_range) > 1 and len(y_within_range) > 1:\n", + "\n", + " # Calculate the combined predictions and true values\n", + " combined_predictions = x_within_range + y_within_range\n", + " combined_true = [true_x] * len(x_within_range) + [true_y] * len(\n", + " y_within_range\n", + " )\n", + "\n", + " # Calculate the R2 score and MAE for the combined values\n", + " r2_combined = r2_score(combined_true, combined_predictions)\n", + " mae_combined = mean_absolute_error(combined_true, combined_predictions)\n", + "\n", + " # Create a circle patch\n", + " circle = plt.Circle(\n", + " (true_x, true_y), circle_radius, color=\"yellow\", fill=False\n", + " )\n", + "\n", + " # Add the circle to the plot\n", + " ax.add_patch(circle)\n", + " ax.text(\n", + " true_x + 0.1,\n", + " true_y + 0.1,\n", + " f\"R2={r2_combined:.2f}\\nMAE={mae_combined:.2f}\",\n", + " fontsize=8,\n", + " color=\"blue\",\n", + " )\n", + "\n", + " # Set the title and legend\n", + " title = title if title else \"True and Predicted Points for X and Y\"\n", + " ax.set_title(title)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def analysis(df, ax=None, title=None):\n", + " \"\"\"\n", + " Perform analysis on the given DataFrame.\n", + "\n", + " Args:\n", + " - df (DataFrame): The input DataFrame containing the data for analysis.\n", + " - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n", + " - title (str, optional): The title of the plot.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + " # Initialize the StandardScaler and SVR model\n", + " # with 2-degree polynomial features\n", + " sc = StandardScaler()\n", + " model = make_pipeline(PolynomialFeatures(2), SVR())\n", + "\n", + " # Define the parameter grid for GridSearchCV\n", + " param_grid = {\n", + " \"svr__C\": [0.1, 1, 10, 100, 1000],\n", + " \"svr__gamma\": [0.0001, 0.001, 0.01, 0.1, 1],\n", + " \"svr__kernel\": [\"linear\", \"rbf\", \"poly\"],\n", + " }\n", + "\n", + " # Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error\n", + " scoring = {\n", + " \"R2\": make_scorer(r2_score),\n", + " \"MAE\": make_scorer(mean_absolute_error),\n", + " }\n", + "\n", + " # Initialize GridSearchCV with the model and parameter grid\n", + " grid_search = GridSearchCV(\n", + " model, param_grid, cv=5, scoring=scoring, refit=\"R2\", n_jobs=-1\n", + " )\n", + "\n", + " \"\"\"For Left Iris X and Right Iris X model training and testing\"\"\"\n", + " # Left iris X and right iris X\n", + " X_x = df[[\"left_iris_x\", \"right_iris_x\"]]\n", + " X_y = df[\"point_x\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_x = sc.fit_transform(X_x)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(\n", + " X_x, X_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for X using GridSearchCV\n", + " grid_search.fit(X_train_x, y_train_x)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_x = grid_search.best_estimator_\n", + " y_pred_x = best_model_x.predict(X_test_x)\n", + " r2_score_x = r2_score(y_test_x, y_pred_x)\n", + " print(\"-------------------MODEL RESULT FOR X------------------\")\n", + " print(\n", + " f'Best C for X: {grid_search.best_params_[\"svr__C\"]}, Best gamma for X: {grid_search.best_params_[\"svr__gamma\"]}, Best kernel for X: {grid_search.best_params_[\"svr__kernel\"]}, R2 score : {r2_score_x}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " \"\"\"For Left Iris Y and Right Iris Y model training and testing\"\"\"\n", + " # Left iris Y and right iris Y\n", + " X_y = df[[\"left_iris_y\", \"right_iris_y\"]]\n", + " y_y = df[\"point_y\"]\n", + "\n", + " # Transform the data by standardizing it\n", + " X_y = sc.fit_transform(X_y)\n", + "\n", + " # Split the data into training and testing sets\n", + " X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(\n", + " X_y, y_y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Fit the model to the training data for Y using GridSearchCV\n", + " grid_search.fit(X_train_y, y_train_y)\n", + "\n", + " # Use the best model to predict the values and calculate the R2 score\n", + " best_model_y = grid_search.best_estimator_\n", + " y_pred_y = best_model_y.predict(X_test_y)\n", + " r2_score_y = r2_score(y_test_y, y_pred_y)\n", + " print(\"-------------------MODEL RESULT FOR Y------------------\")\n", + " print(\n", + " f'Best C for Y: {grid_search.best_params_[\"svr__C\"]}, Best gamma for Y: {grid_search.best_params_[\"svr__gamma\"]}, Best kernel for Y: {grid_search.best_params_[\"svr__kernel\"]}, R2 score : {r2_score_y}'\n", + " )\n", + " print(\"-------------------------------------------------------\")\n", + "\n", + " # Plot the true and predicted points for X and Y\n", + " plot(ax, y_test_x, y_pred_x, y_test_y, y_pred_y, title)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------MODEL RESULT FOR X------------------\n", + "Best C for X: 1000, Best gamma for X: 1, Best kernel for X: poly, R2 score : 0.9960671583695893\n", + "-------------------------------------------------------\n", + "-------------------MODEL RESULT FOR Y------------------\n", + "Best C for Y: 1000, Best gamma for Y: 1, Best kernel for Y: rbf, R2 score : 0.9998572969740602\n", + "-------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the calibrations csv file\n", + "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", + "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", + "\n", + "# Create a list of DataFrames\n", + "df_list = [df]\n", + "\n", + "# num_rows = len(df_list) // 2 + len(df_list) % 2\n", + "# num_cols = min(2, len(df_list))\n", + "\n", + "# Calculate the number of rows and columns\n", + "num_rows = len(df_list)\n", + "num_cols = 1\n", + "\n", + "# Create a figure and axes\n", + "fig_height = 5 * num_rows\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, fig_height), squeeze=False)\n", + "\n", + "# Iterate over the DataFrames\n", + "for i, df in enumerate(df_list):\n", + "\n", + " # row_idx = i // num_cols\n", + " # col_idx = i % num_cols\n", + "\n", + " row_idx = i\n", + " col_idx = 0\n", + "\n", + " ax = axes[row_idx, col_idx]\n", + "\n", + " # Perform the analysis\n", + " analysis(df, ax)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fe108320f14189d8a98b8a528823baf067779803 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Tue, 13 Aug 2024 22:14:30 +0000 Subject: [PATCH 68/78] minor fix in linear regression notebook --- .../test/linear_regression/test_linear_regression.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb index 8af00eaf..5d5c8386 100644 --- a/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb +++ b/app/services/calib_validation/test/linear_regression/test_linear_regression.ipynb @@ -1586,7 +1586,6 @@ ], "source": [ "# Load the data from the calibrations csv file\n", - "prefix = \"e2e_test3\"\n", "df = pd.read_csv(rf\"C:\\Users\\SITAM MEUR\\Desktop\\web-eye-tracker-main\\web-eye-tracker-main\\app\\services\\calib_validation\\csv\\data\\{prefix}_fixed_train_data.csv\")\n", "df = df.drop([\"screen_height\", \"screen_width\"], axis=1)\n", "\n", From 2cdce47266b162188c52dd465591499e977de4f5 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 24 Aug 2024 22:45:34 +0000 Subject: [PATCH 69/78] config file grid search updated --- app/services/config.py | 77 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 app/services/config.py diff --git a/app/services/config.py b/app/services/config.py new file mode 100644 index 00000000..a7c1821b --- /dev/null +++ b/app/services/config.py @@ -0,0 +1,77 @@ +hyperparameters = { + "Lasso Regression": { + "param_grid": { + "lasso__alpha": [ + 1e-15, + 1e-10, + 1e-8, + 1e-3, + 1e-2, + 1e-1, + 0.5, + 1, + 5, + 10, + 20, + 30, + 35, + 40, + 45, + 50, + 55, + 100, + ] + } + }, + "Ridge Regression": { + "param_grid": { + "ridge__alpha": [ + 1e-15, + 1e-10, + 1e-8, + 1e-3, + 1e-2, + 1e-1, + 0.5, + 1, + 5, + 10, + 20, + 30, + 35, + 40, + 45, + 50, + 55, + 100, + ] + } + }, + "Elastic Net": { + "param_grid": { + "elasticnet__alpha": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0], + "elasticnet__l1_ratio": [0, 0.01, 0.2, 0.5, 0.8, 1], + } + }, + "Bayesian Ridge": { + "param_grid": { + "bayesianridge__alpha_init": [1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.9], + "bayesianridge__lambda_init": [1e-9, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1], + } + }, + "SGD Regressor": { + "param_grid": { + "sgdregressor__alpha": [0.0001, 0.001, 0.01, 0.1], + "sgdregressor__l1_ratio": [0, 0.2, 0.5, 0.7, 1], + "sgdregressor__max_iter": [500, 1000], + "sgdregressor__eta0": [0.0001, 0.001, 0.01], + } + }, + "Support Vector Regressor": { + "param_grid": { + "svr__C": [0.1, 1, 10, 100, 1000], + "svr__gamma": [0.0001, 0.001, 0.01, 0.1, 1], + "svr__kernel": ["linear", "rbf", "poly"], + } + }, +} From 1811310f603dc88da573a38c876e7a0e15a90f55 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 24 Aug 2024 23:05:30 +0000 Subject: [PATCH 70/78] session file updated --- app/routes/session.py | 102 ++++++++++++++++++++++++++++++------------ 1 file changed, 73 insertions(+), 29 deletions(-) diff --git a/app/routes/session.py b/app/routes/session.py index e6c69076..0872b1b8 100644 --- a/app/routes/session.py +++ b/app/routes/session.py @@ -1,18 +1,26 @@ -from flask import Flask, request, Response, send_file -from app.services.storage import save_file_locally -from app.models.session import Session -#from app.services import database as db -from app.services import gaze_tracker +# Necesary imports +import os +import re import time import json import csv + from pathlib import Path -import os -import re +from flask import Flask, request, Response, send_file + +# Local imports from app +from app.services.storage import save_file_locally +from app.models.session import Session + +# from app.services import database as db +from app.services import gaze_tracker -ALLOWED_EXTENSIONS = {'txt', 'webm'} -COLLECTION_NAME = u'session' +# Constants +ALLOWED_EXTENSIONS = {"txt", "webm"} +COLLECTION_NAME = "session" + +# Initialize Flask app app = Flask(__name__) @@ -139,49 +147,85 @@ def calib_results(): - file_name = json.loads(request.form['file_name']) - fixed_points = json.loads(request.form['fixed_circle_iris_points']) - calib_points = json.loads(request.form['calib_circle_iris_points']) - screen_height = json.loads(request.form['screen_height']) - screen_width = json.loads(request.form['screen_width']) - k = json.loads(request.form['k']) + """ + Generate calibration results. + + This function generates calibration results based on the provided form data. + It saves the calibration points to a CSV file. Then, it uses the gaze_tracker module to predict the calibration results. + + Returns: + Response: A JSON response containing the calibration results. + + Raises: + IOError: If there is an error while writing to the CSV files. + """ + # Get form data from request + file_name = json.loads(request.form["file_name"]) + fixed_points = json.loads(request.form["fixed_circle_iris_points"]) + calib_points = json.loads(request.form["calib_circle_iris_points"]) + screen_height = json.loads(request.form["screen_height"]) + screen_width = json.loads(request.form["screen_width"]) + k = json.loads(request.form["k"]) + model = json.loads(request.form["model"]) # Generate csv dataset of calibration points os.makedirs( - f'{Path().absolute()}/app/services/calib_validation/csv/data/', exist_ok=True) - calib_csv_file = f'{Path().absolute()}/app/services/calib_validation/csv/data/{file_name}_fixed_train_data.csv' - csv_columns = ['left_iris_x', 'left_iris_y', - 'right_iris_x', 'right_iris_y', 'point_x', 'point_y', 'screen_height', 'screen_width'] + f"{Path().absolute()}/app/services/calib_validation/csv/data/", exist_ok=True + ) + + # Generate csv of calibration points with following columns + calib_csv_file = f"{Path().absolute()}/app/services/calib_validation/csv/data/{file_name}_fixed_train_data.csv" + csv_columns = [ + "left_iris_x", + "left_iris_y", + "right_iris_x", + "right_iris_y", + "point_x", + "point_y", + "screen_height", + "screen_width", + ] + + # Save calibration points to CSV file try: - with open(calib_csv_file, 'w') as csvfile: + # Open CSV file + with open(calib_csv_file, "w") as csvfile: writer = csv.DictWriter(csvfile, fieldnames=csv_columns) writer.writeheader() + + # Write calibration points to CSV file for data in fixed_points: - data['screen_height'] = screen_height - data['screen_width'] = screen_width + data["screen_height"] = screen_height + data["screen_width"] = screen_width writer.writerow(data) + + # Handle I/O error except IOError: print("I/O error") # Generate csv of iris points of session os.makedirs( - f'{Path().absolute()}/app/services/calib_validation/csv/data/', exist_ok=True) - predict_csv_file = f'{Path().absolute()}/app/services/calib_validation/csv/data/{file_name}_predict_train_data.csv' - csv_columns = ['left_iris_x', 'left_iris_y', - 'right_iris_x', 'right_iris_y'] + f"{Path().absolute()}/app/services/calib_validation/csv/data/", exist_ok=True + ) + predict_csv_file = f"{Path().absolute()}/app/services/calib_validation/csv/data/{file_name}_predict_train_data.csv" + csv_columns = ["left_iris_x", "left_iris_y", "right_iris_x", "right_iris_y"] try: - with open(predict_csv_file, 'w') as csvfile: + with open(predict_csv_file, "w") as csvfile: writer = csv.DictWriter(csvfile, fieldnames=csv_columns) writer.writeheader() for data in calib_points: + # print(data) writer.writerow(data) except IOError: print("I/O error") # data = gaze_tracker.train_to_validate_calib(calib_csv_file, predict_csv_file) - data = gaze_tracker.predict(calib_csv_file, calib_csv_file, k) - return Response(json.dumps(data), status=200, mimetype='application/json') + # Predict calibration results + data = gaze_tracker.predict(calib_csv_file, k, model_X=model, model_Y=model) + + # Return calibration results + return Response(json.dumps(data), status=200, mimetype="application/json") # def session_results(): From 47773ec3de761cfdc4101a890701e7284dd8ee80 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 24 Aug 2024 23:18:40 +0000 Subject: [PATCH 71/78] minor update --- app/services/config.py | 1 + 1 file changed, 1 insertion(+) diff --git a/app/services/config.py b/app/services/config.py index a7c1821b..941c23fb 100644 --- a/app/services/config.py +++ b/app/services/config.py @@ -1,3 +1,4 @@ +# Hyperparameters for the models hyperparameters = { "Lasso Regression": { "param_grid": { From 53d9bcc3d2839f51a2f6b26abc3c455e57d1d39c Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 24 Aug 2024 23:23:08 +0000 Subject: [PATCH 72/78] requirements are updated --- requirements.txt | 27 ++++++++++++++++----------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/requirements.txt b/requirements.txt index de955826..c4d5d87f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,18 +1,23 @@ -blinker==1.7.0 +blinker==1.8.2 click==8.1.7 -Flask==3.0.2 -Flask-Cors==4.0.0 -itsdangerous==2.1.2 -Jinja2==3.1.3 -joblib==1.3.2 +Flask==3.0.3 +Flask-Cors==4.0.1 +itsdangerous==2.2.0 +Jinja2==3.1.4 +joblib==1.4.2 MarkupSafe==2.1.5 numpy==1.26.4 -pandas==2.2.1 +pandas==2.2.2 +matplotlib==3.9.0 +seaborn==0.13.2 +plotly==5.22.0 +nbformat==5.10.4 python-dateutil==2.9.0.post0 pytz==2024.1 -scikit-learn==1.4.1.post1 -scipy==1.12.0 +scikit-learn==1.5.0 +scipy==1.14.0 six==1.16.0 -threadpoolctl==3.3.0 +threadpoolctl==3.5.0 tzdata==2024.1 -Werkzeug==3.0.1 +Werkzeug==3.0.3 +streamlit==1.37.0 \ No newline at end of file From 05a2875189e1b29365b38ab5f1d055aa08c6e3cc Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sat, 24 Aug 2024 23:28:49 +0000 Subject: [PATCH 73/78] streamlit dashboard code added --- app/services/dash.py | 539 ++++++++++++++++++++++++++++++------------- 1 file changed, 378 insertions(+), 161 deletions(-) diff --git a/app/services/dash.py b/app/services/dash.py index 6c271f08..662ca8f4 100644 --- a/app/services/dash.py +++ b/app/services/dash.py @@ -1,187 +1,404 @@ -import plotly.express as px -import streamlit as st +# Necessary imports +import warnings + +warnings.filterwarnings("ignore") + +import os +import numpy as np import pandas as pd -import matplotlib.pyplot as plt import plotly.express as px -import pandas as pd -import numpy as np +import plotly.graph_objects as go +import streamlit as st + +# Sklearn imports from sklearn import linear_model +from sklearn.svm import SVR from sklearn.model_selection import train_test_split -from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_squared_log_error, r2_score -import matplotlib.pyplot as plt - -dataset_train_path = '/home/nata-brain/Documents/tcc/web-eye-tracker/public/training/1685126241.2630084natanael/train_data.csv' -dataset_session_path = '/home/nata-brain/Documents/tcc/web-eye-tracker/public/sessions/1685126241.2630084natanael/session_data.csv' - -raw_dataset = pd.read_csv(dataset_train_path) -session_dataset = pd.read_csv(dataset_session_path) -dataset_t = raw_dataset -dataset_s = session_dataset.drop(['timestamp'], axis = 1) - -def model_for_mouse_x(X, Y1, model): - print('-----------------MODEL FOR X------------------') - # split dataset into train and test sets (80/20 where 20 is for test) - X_train, X_test, Y1_train, Y1_test = train_test_split(X, Y1, test_size=0.2) - - model = model - model.fit(X_train, Y1_train) - - Y1_pred_train = model.predict(X_train) - Y1_pred_test = model.predict(X_test) - - Y1_test = normalizeData(Y1_test) - Y1_pred_test = normalizeData(Y1_pred_test) - - print(f'Mean absolute error MAE = {mean_absolute_error(Y1_train, Y1_pred_train)}') - print(f'Mean squared error MSE = {mean_squared_error(Y1_train, Y1_pred_train)}') - print(f'Mean squared log error MSLE = {mean_squared_log_error(Y1_train, Y1_pred_train)}') - print(f'MODEL X SCORE R2 = {model.score(X, Y1)}') - - col1, col2, col3, col4 = st.columns(4) - col1.metric("Mean absolute error (MAE)", f" {mean_absolute_error(Y1_train, Y1_pred_train)}") - col2.metric("Mean squared error (MSE)", f" {mean_squared_error(Y1_train, Y1_pred_train)}") - col3.metric("Mean squared log error (MSLE)", f" {mean_squared_log_error(Y1_train, Y1_pred_train)}") - col4.metric("MODEL X SCORE R2 ", f" {model.score(X, Y1)}") - #print(f'TRAIN{Y1_pred_train}') - #print(f'TEST{Y1_pred_test}') - return model - -def model_for_mouse_y(X, Y2, model): - print('-----------------MODEL FOR Y------------------') - # split dataset into train and test sets (80/20 where 20 is for test) - X_train, X_test, Y2_train, Y2_test = train_test_split(X, Y2, test_size=0.2) - - model = model - model.fit(X_train, Y2_train) - - Y2_pred_train = model.predict(X_train) - Y2_pred_test = model.predict(X_test) - - - Y2_test = normalizeData(Y2_test) - Y2_pred_test = normalizeData(Y2_pred_test) - - print(f'Mean absolute error MAE = {mean_absolute_error(Y2_train, Y2_pred_train)}') - print(f'Mean squared error MSE = {mean_squared_error(Y2_train, Y2_pred_train)}') - print(f'Mean squared log error MSLE = {mean_squared_log_error(Y2_train, Y2_pred_train)}') - print(f'MODEL Y SCORE R2 = {model.score(X, Y2)}') - - #print(f'TRAIN{Y2_pred_train}') - #print(f'TEST{Y2_pred_test}') - - col1, col2, col3, col4 = st.columns(4) - col1.metric("Mean absolute error (MAE)", f"{mean_absolute_error(Y2_train, Y2_pred_train)}") - col2.metric("Mean squared error (MSE)", f" {mean_squared_error(Y2_train, Y2_pred_train)}") - col3.metric("Mean squared log error (MSLE)", f" {mean_squared_log_error(Y2_train, Y2_pred_train)}") - col4.metric("MODEL Y SCORE R2 ", f" {model.score(X, Y2)}") - return model - -def normalizeData(data): - return (data - np.min(data)) / (np.max(data) - np.min(data)) - -def train(model): - # Drop the columns that will be predicted - X = dataset_t.drop(['timestamp', 'screen_x', 'screen_y'], axis=1) - - Y1 = dataset_t.screen_x - Y2 = dataset_t.screen_y - # print('Y1 is the mouse_x column ->', Y1) - # print('Y2 is the mouse_y column ->', Y2) - - MODEL_X = model_for_mouse_x(X, Y1, model) - MODEL_Y = model_for_mouse_y(X, Y2, model) - - GAZE_X = MODEL_X.predict(dataset_s) - GAZE_Y = MODEL_Y.predict(dataset_s) - - GAZE_X = np.abs(GAZE_X) - GAZE_Y = np.abs(GAZE_Y) - - return GAZE_X, GAZE_Y - -def showSaccades(model): - - x, y = train(model) - datetime = session_dataset.timestamp - - fig_plt, ax = plt.subplots(figsize = (30, 20)) - - ax.plot(x, y, 'r*') - - i = 0 +from sklearn.preprocessing import StandardScaler, PolynomialFeatures +from sklearn.pipeline import make_pipeline +from sklearn.metrics import ( + mean_squared_error, + mean_absolute_error, + mean_squared_log_error, + r2_score, + median_absolute_error, + explained_variance_score, + max_error, +) + + +# Get all the files in the data directory +data_dir = rf"C:\Users\SITAM MEUR\Desktop\web-eye-tracker-main\web-eye-tracker-main\app\services\calib_validation\csv\data" +files = os.listdir(data_dir) + +# Extract the prefixes from the file names +prefixes = [ + file.split("_fixed_train_data.csv")[0] + for file in files + if file.endswith("_fixed_train_data.csv") +] + +# Set the page configuration for the Streamlit app and set the title +st.set_page_config(page_title="Streamlit Dashboard📊", layout="wide") +st.title("Streamlit Dashboard📊") + +# Prefix for the calibration data to identify the correct file +st.subheader("Select from your collected data") +prefix = st.selectbox("Select the prefix for the calibration data", prefixes) + +# Load the dataset +dataset_train_path = rf"C:\Users\SITAM MEUR\Desktop\web-eye-tracker-main\web-eye-tracker-main\app\services\calib_validation\csv\data\{prefix}_fixed_train_data.csv" +try: + raw_dataset = pd.read_csv(dataset_train_path) +# File not found error handling +except FileNotFoundError: + st.error("File not found. Please make sure the file path is correct.") + + +def model_for_mouse_x(X1, Y1, models, model_names): + """ + Trains multiple models to predict the X coordinate based on the given features and compares their performance. + + Args: + - X1 (array-like): The input features. + - Y1 (array-like): The target variable (X coordinate). + - models (list): A list of machine learning models to be trained. + - model_names (list): A list of model names corresponding to the models. + + Returns: None + """ + # Split dataset into train and test sets (80/20 where 20 is for test) + X1_train, X1_test, Y1_train, Y1_test = train_test_split(X1, Y1, test_size=0.2) + + metrics_list = [] + + for model, model_name in zip(models, model_names): + # Train the model + model.fit(X1_train, Y1_train) + + # Predict the target variable for the test set + Y1_pred_test = model.predict(X1_test) + + # Filter out the negative predicted values + non_negative_indices = Y1_pred_test >= 0 + Y1_pred_test_filtered = Y1_pred_test[non_negative_indices] + Y1_test_filtered = Y1_test[non_negative_indices] + + # Compute the metrics for the test set with filtered predictions + metrics_data_test = { + "Model": model_name, + "Mean Absolute Error (MAE)": mean_absolute_error( + Y1_test_filtered, Y1_pred_test_filtered + ), + "Median Absolute Error": median_absolute_error( + Y1_test_filtered, Y1_pred_test_filtered + ), + "Mean Squared Error (MSE)": mean_squared_error( + Y1_test_filtered, Y1_pred_test_filtered + ), + "Mean Log Squared Error (MSLE)": mean_squared_log_error( + Y1_test_filtered, Y1_pred_test_filtered + ), + "Root Mean Squared Error (RMSE)": np.sqrt( + mean_squared_error(Y1_test_filtered, Y1_pred_test_filtered) + ), + "Explained Variance Score": explained_variance_score( + Y1_test_filtered, Y1_pred_test_filtered + ), + "Max Error": max_error(Y1_test_filtered, Y1_pred_test_filtered), + "MODEL X SCORE R2": r2_score(Y1_test_filtered, Y1_pred_test_filtered), + } + + metrics_list.append(metrics_data_test) + + # Convert metrics data to DataFrame + metrics_df_test = pd.DataFrame(metrics_list) + + # Display metrics using Streamlit + st.subheader("Metrics for the test set - X") + st.dataframe(metrics_df_test, use_container_width=True) + + # Bar charts for visualization + for metric in metrics_df_test.columns[1:]: + st.subheader(f"Comparison of {metric}") + fig = px.bar(metrics_df_test.set_index("Model"), y=metric) + st.plotly_chart(fig) + + # Line chart for visualizing the metrics + st.subheader("Line Chart Comparison") + fig = px.line(metrics_df_test.set_index("Model")) + st.plotly_chart(fig) + + # Box plot for distribution of errors + st.subheader("Box Plot of Model Errors") + errors_df = pd.DataFrame( + { + "Model": np.repeat(model_names, len(Y1_test)), + "Actual": np.tile(Y1_test, len(models)), + "Predicted": np.concatenate([model.predict(X1_test) for model in models]), + } + ) + errors_df["Error"] = errors_df["Actual"] - errors_df["Predicted"] + + # Create the box plot + st.dataframe(errors_df, use_container_width=True) + fig = px.box(errors_df, x="Model", y="Error") + st.plotly_chart(fig) + + # Radar chart for model comparison + st.subheader("Radar Chart Comparison") + + # Normalize the metric values for better comparison + metrics_normalized = metrics_df_test.copy() + for col in metrics_normalized.columns[1:]: + metrics_normalized[col] = ( + metrics_normalized[col] - metrics_normalized[col].min() + ) / (metrics_normalized[col].max() - metrics_normalized[col].min()) + + # Create the radar chart + fig = go.Figure() + for i in range(len(models)): + fig.add_trace( + go.Scatterpolar( + r=metrics_normalized.iloc[i, 1:].values, + theta=metrics_normalized.columns[1:], + fill="toself", + name=metrics_normalized.iloc[i, 0], + ) + ) + + # Update the layout + fig.update_layout( + polar=dict(radialaxis=dict(visible=True, range=[0, 1])), showlegend=True + ) + + # Display the radar chart + st.plotly_chart(fig) + + +def model_for_mouse_y(X2, Y2, models, model_names): + """ + Trains multiple models to predict the Y coordinate based on the given features and compares their performance. + + Args: + - X2 (array-like): The input features. + - Y2 (array-like): The target variable (Y coordinate). + - models (list): A list of machine learning models to be trained. + - model_names (list): A list of model names corresponding to the models. + + Returns: None + """ + # Split dataset into train and test sets (80/20 where 20 is for test) + X2_train, X2_test, Y2_train, Y2_test = train_test_split(X2, Y2, test_size=0.2) + + # Initialize empty lists to store the metrics data + metrics_list = [] + + for model, model_name in zip(models, model_names): + # Train the model + model.fit(X2_train, Y2_train) + + # Predict the target variable for the test set + Y2_pred_test = model.predict(X2_test) + + # Filter out the negative predicted values + non_negative_indices = Y2_pred_test >= 0 + Y2_pred_test_filtered = Y2_pred_test[non_negative_indices] + Y2_test_filtered = Y2_test[non_negative_indices] + + # Compute the metrics for the test set with filtered predictions + metrics_data_test = { + "Model": model_name, + "Mean Absolute Error (MAE)": mean_absolute_error( + Y2_test_filtered, Y2_pred_test_filtered + ), + "Median Absolute Error": median_absolute_error( + Y2_test_filtered, Y2_pred_test_filtered + ), + "Mean Squared Error (MSE)": mean_squared_error( + Y2_test_filtered, Y2_pred_test_filtered + ), + "Mean Log Squared Error (MSLE)": mean_squared_log_error( + Y2_test_filtered, Y2_pred_test_filtered + ), + "Root Mean Squared Error (RMSE)": np.sqrt( + mean_squared_error(Y2_test_filtered, Y2_pred_test_filtered) + ), + "Explained Variance Score": explained_variance_score( + Y2_test_filtered, Y2_pred_test_filtered + ), + "Max Error": max_error(Y2_test_filtered, Y2_pred_test_filtered), + "MODEL Y SCORE R2": r2_score(Y2_test_filtered, Y2_pred_test_filtered), + } + + metrics_list.append(metrics_data_test) + + # Convert metrics data to DataFrame + metrics_df_test = pd.DataFrame(metrics_list) + + # Display metrics using Streamlit + st.subheader("Metrics for the test set - Y") + st.dataframe(metrics_df_test, use_container_width=True) + + # Bar charts for visualization + for metric in metrics_df_test.columns[1:]: + st.subheader(f"Comparison of {metric}") + fig = px.bar(metrics_df_test.set_index("Model"), y=metric) + st.plotly_chart(fig) + + # Line chart for visualizing the metrics + st.subheader("Line Chart Comparison") + fig = px.line(metrics_df_test.set_index("Model")) + st.plotly_chart(fig) + + # Box plot for distribution of errors + st.subheader("Box Plot of Model Errors") + errors_df = pd.DataFrame( + { + "Model": np.repeat(model_names, len(Y2_test)), + "Actual": np.tile(Y2_test, len(models)), + "Predicted": np.concatenate([model.predict(X2_test) for model in models]), + } + ) + errors_df["Error"] = errors_df["Actual"] - errors_df["Predicted"] + + # Create the box plot + st.dataframe(errors_df, use_container_width=True) + fig = px.box(errors_df, x="Model", y="Error") + st.plotly_chart(fig) + + # Radar chart for model comparison + st.subheader("Radar Chart Comparison") + + # Normalize the metric values for better comparison + metrics_normalized = metrics_df_test.copy() + for col in metrics_normalized.columns[1:]: + metrics_normalized[col] = ( + metrics_normalized[col] - metrics_normalized[col].min() + ) / (metrics_normalized[col].max() - metrics_normalized[col].min()) + + # Create the radar chart + fig = go.Figure() + for i in range(len(models)): + fig.add_trace( + go.Scatterpolar( + r=metrics_normalized.iloc[i, 1:].values, + theta=metrics_normalized.columns[1:], + fill="toself", + name=metrics_normalized.iloc[i, 0], + ) + ) - st.pyplot(fig_plt) - - - -tab1, tab2 = st.tabs(["Dados Brutos", "Dados Processados"]) + # Update the layout + fig.update_layout( + polar=dict(radialaxis=dict(visible=True, range=[0, 1])), showlegend=True + ) + # Display the radar chart + st.plotly_chart(fig) + + +# Set the title of the app and the tabs +st.subheader("Eye Tracker Calibration Data Analysis and Prediction") +st.write(f"Select the tab to view the data and metrics for [{prefix}] data") +tab1, tab2 = st.tabs(["Raw Data", "Metrics"]) + +# With the first tab with tab1: - st.title("Dados obtidos pela calibração") - st.dataframe(raw_dataset) + # Display the raw dataset + st.subheader("Data Obtained from Calibration") + st.dataframe(raw_dataset, use_container_width=True) + # Two columns for the plots col1, col2 = st.columns(2) with col1: - st.subheader("Olho esquerdo") + # Subheader + st.subheader("Left Eye") df = raw_dataset - + # Create the scatter plot fig_left = px.scatter( df, - x = "left_iris_x", - y = "left_iris_y", - color = "left_iris_y", - color_continuous_scale = "reds", + x="left_iris_x", + y="left_iris_y", + color="left_iris_y", + color_continuous_scale="reds", ) - + + # Display the plot st.plotly_chart(fig_left, theme="streamlit", use_container_width=True) - with col2: - st.subheader("Olho direito") - + with col2: + # Subheader + st.subheader("Right Eye") + + # Create the scatter plot fig_right = px.scatter( df, - x = "right_iris_x", - y = "right_iris_y", - color = "right_iris_y", - color_continuous_scale = "reds", + x="right_iris_x", + y="right_iris_y", + color="right_iris_y", + color_continuous_scale="reds", ) + # Display the plot st.plotly_chart(fig_right, theme="streamlit", use_container_width=True) - - fig3 = px.line(raw_dataset, y=["left_iris_x", "left_iris_y", "right_iris_x", "right_iris_y"], title="Left and Right Iris Position") - st.plotly_chart(fig3, theme="streamlit", use_container_width=True) -with tab2: - st.subheader("Sacadas") - fig_plt, ax = plt.subplots(figsize = (30, 20)) - - x = raw_dataset.left_iris_x - y = raw_dataset.left_iris_y - datetime = raw_dataset.timestamp - - ax.plot(x, y, 'r*', linestyle = '-') + # Create the line plot + fig3 = px.line( + raw_dataset, + y=["left_iris_x", "left_iris_y", "right_iris_x", "right_iris_y"], + title="Left and Right Iris Position", + ) + # Display the plot + st.plotly_chart(fig3, theme="streamlit", use_container_width=True) - i = 0 - for xy in zip(x, y): - i = i+1 - ax.annotate(f'{i}', xy) - - st.pyplot(fig_plt) - - col1, col2 = st.columns(2) - - - st.subheader("Predição Regressão Linear") - showSaccades(linear_model.LinearRegression()) - - st.subheader("Predição Regressão Linear Ridge") - showSaccades(linear_model.Ridge(alpha=.5)) - - st.subheader("Predição Regressão Linear Ridge com Cross Validation") - showSaccades(linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))) - - st.subheader("Predição Regressão Linear Lasso") - showSaccades(linear_model.Lasso(alpha=0.1)) \ No newline at end of file +# With the second tab +with tab2: + st.subheader("Model Performance Comparison") + # Create a list of models to be trained + models = [ + make_pipeline(PolynomialFeatures(2), linear_model.LinearRegression()), + make_pipeline(PolynomialFeatures(2), linear_model.Lasso(alpha=0.1)), + make_pipeline(PolynomialFeatures(2), linear_model.Ridge(alpha=0.5)), + make_pipeline( + PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5) + ), + make_pipeline(PolynomialFeatures(2), linear_model.BayesianRidge()), + make_pipeline( + PolynomialFeatures(2), + linear_model.SGDRegressor(random_state=42, penalty="elasticnet"), + ), + make_pipeline(PolynomialFeatures(2), SVR(kernel="linear")), + ] + model_names = [ + "Linear Regression", + "Lasso Regression", + "Ridge Regression", + "Elastic Net", + "Bayesian Ridge", + "SGD Regressor", + "Support Vector Regressor", + ] + + # Drop the columns that are not needed + X = raw_dataset.drop(["screen_height", "screen_width"], axis=1) + + # Split the dataset into input features and target variables + X1 = X[["left_iris_x", "right_iris_x"]] + X2 = X[["left_iris_y", "right_iris_y"]] + + # Standardize the input features + sc = StandardScaler() + X1 = sc.fit_transform(X1) + X2 = sc.fit_transform(X2) + + # Target variables + Y1 = raw_dataset.point_x + Y2 = raw_dataset.point_y + + # Train the models + model_for_mouse_x(X1, Y1, models, model_names) + model_for_mouse_y(X2, Y2, models, model_names) From 9bf5c6d54622285a427570b2ca6d6e35fb945da4 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 25 Aug 2024 00:38:01 +0000 Subject: [PATCH 74/78] readme update --- README.md | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 00916ac2..5f0d34d5 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,26 @@ # 👁️ Eye Lab: Gaze Tracker API -Eye Lab is an open source tool to create eye tracking usability tests. It started as a final undergraduation work for Computer Engineering of student [Karine Pistili](https://www.linkedin.com/in/karine-pistili/) that created the first prototype. The idea is to evolve it to a more complete and useful tool with the help of the community. +Eye Lab is an open-source tool to create eye-tracking usability tests. It started as a final undergraduate work for the Computer Engineering student [Karine Pistili](https://www.linkedin.com/in/karine-pistili/) who made the prototype. The idea is to evolve it into a more complete and useful tool with the community's help. -The current version of the software allows users to create their usability sessions of an website, recording the webcam, screen and mouse movements and use this information to find out where the user has been looking into the screen by using heatmaps. +The current version of the software allows users to create their usability sessions of a website, recording the webcam, screen, and mouse movements and use this information to find out where the user has been looking into the screen by using heatmaps. ## 👩‍💻 Setting up project locally -The project consists of two parts, this repository contains the backend of the application and the frontend can be found [here](https://github.com/uramakilab/web-eye-tracker-front). Install it as well. +The project consists of two parts, this repository contains the backend of the application, and the frontend can be found [here](https://github.com/uramakilab/web-eye-tracker-front). Install it as well to have the full application running. ### Prerequisites -* [Python 3x](https://www.python.org/downloads/) +- [Python 3x](https://www.python.org/downloads/) ### 1. Create virtual environment -Before installing all dependencies and starting your Flask Server, it is better to create a python virtual environment. You can use the [venv package](https://docs.python.org/3/library/venv.html) +Before installing all dependencies and starting your Flask Server, it is better to create a Python virtual environment. You can use the [venv package](https://docs.python.org/3/library/venv.html) ``` python -m venv /path/to/new/virtual/environment ``` -Then activate your env. On windows for example you can activate with the script: +Then activate your env. On Windows for example you can activate with the script: ``` name-of-event/Scripts/activate @@ -40,12 +40,18 @@ pip install -r requirements.txt flask run ``` +## Contributors ✨ + +The project is selected to be part of the [Google Summer of Code 2024](https://summerofcode.withgoogle.com/programs/2024/organizations/uramaki-lab) program, and [Vinícius Cavalcanti](https://github.com/hvini) is the main mentor of the project along with Marc Gonzalez Capdevila, Karine Pistili Rodrigues. The active development of the project is being done by [Sitam Meur](https://www.linkedin.com/in/sitammeur/), selected as a GSoC'24 student for the project. Here is the project details in the [GSoC'24 website](https://summerofcode.withgoogle.com/programs/2024/projects/lEPzZg7S). + +To see the full list of contributions, check out the ahead commits of the develop branch with respect to the main branch. Full logs of the project development can be found in the [Daily Work Progress](https://docs.google.com/document/d/1RjCnGjYYgPKvFUrN8hSjPX29aayWr6eEopeCN3QZwEQ/edit?usp=sharing) file. Hoping to see your name in the list of contributors soon! 🚀 + ## 🧑‍🤝‍🧑 Contributing -Anyone is free to contribute to this project. Just do a pull request with your code and if it is all good we will accept it. You can also help us look for bugs, if you find anything create and issue. +Anyone is free to contribute to this project. Just do a pull request with your code and if it is all good we will accept it. You can also help us look for bugs if you find anything that creates an issue. ## 📃 License -This software is under the [MIT License](https://opensource.org/licenses/MIT). +This software is under the [MIT License](https://opensource.org/licenses/MIT). Copyright 2021 Uramaki Lab From 1d12e49aec67fa801e976a79c2a660eeb924a925 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 25 Aug 2024 06:11:53 +0530 Subject: [PATCH 75/78] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5f0d34d5..e996539b 100644 --- a/README.md +++ b/README.md @@ -42,9 +42,9 @@ flask run ## Contributors ✨ -The project is selected to be part of the [Google Summer of Code 2024](https://summerofcode.withgoogle.com/programs/2024/organizations/uramaki-lab) program, and [Vinícius Cavalcanti](https://github.com/hvini) is the main mentor of the project along with Marc Gonzalez Capdevila, Karine Pistili Rodrigues. The active development of the project is being done by [Sitam Meur](https://www.linkedin.com/in/sitammeur/), selected as a GSoC'24 student for the project. Here is the project details in the [GSoC'24 website](https://summerofcode.withgoogle.com/programs/2024/projects/lEPzZg7S). +The project is selected to be part of the [Google Summer of Code 2024](https://summerofcode.withgoogle.com/programs/2024/organizations/uramaki-lab) program, and [Vinícius Cavalcanti](https://github.com/hvini) is the main mentor of the project along with Marc Gonzalez Capdevila, Karine Pistili Rodrigues. The active development of the project is being done by [Sitam Meur](https://www.linkedin.com/in/sitammeur/), selected as a GSoC'24 student for the project. Here are the project details in the [GSoC'24 website](https://summerofcode.withgoogle.com/programs/2024/projects/lEPzZg7S). -To see the full list of contributions, check out the ahead commits of the develop branch with respect to the main branch. Full logs of the project development can be found in the [Daily Work Progress](https://docs.google.com/document/d/1RjCnGjYYgPKvFUrN8hSjPX29aayWr6eEopeCN3QZwEQ/edit?usp=sharing) file. Hoping to see your name in the list of contributors soon! 🚀 +To see the full list of contributions, check out the ahead commits of the "develop" branch concerning the "main" branch. Full logs of the project development can be found in the [Daily Work Progress](https://docs.google.com/document/d/1RjCnGjYYgPKvFUrN8hSjPX29aayWr6eEopeCN3QZwEQ/edit?usp=sharing) file. Hoping to see your name in the list of contributors soon! 🚀 ## 🧑‍🤝‍🧑 Contributing From b818e5adca7ce2d62106e37edadd481f040b7bc2 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 25 Aug 2024 00:50:14 +0000 Subject: [PATCH 76/78] readme minor change --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e996539b..57806925 100644 --- a/README.md +++ b/README.md @@ -14,13 +14,13 @@ The project consists of two parts, this repository contains the backend of the a ### 1. Create virtual environment -Before installing all dependencies and starting your Flask Server, it is better to create a Python virtual environment. You can use the [venv package](https://docs.python.org/3/library/venv.html) +Before installing all dependencies and starting your Flask Server, it is better to create a Python virtual environment. You can use the [venv package](https://docs.python.org/3/library/venv.html) to create a virtual environment. To create a new virtual environment, run the following command: ``` python -m venv /path/to/new/virtual/environment ``` -Then activate your env. On Windows for example you can activate with the script: +Then activate your environment. On Windows for example you can activate with the script: ``` name-of-event/Scripts/activate From 5b9f36151526844b2f95b7f5d220199751473196 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 25 Aug 2024 01:15:56 +0000 Subject: [PATCH 77/78] minor change --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 57806925..6d73f053 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ flask run ## Contributors ✨ -The project is selected to be part of the [Google Summer of Code 2024](https://summerofcode.withgoogle.com/programs/2024/organizations/uramaki-lab) program, and [Vinícius Cavalcanti](https://github.com/hvini) is the main mentor of the project along with Marc Gonzalez Capdevila, Karine Pistili Rodrigues. The active development of the project is being done by [Sitam Meur](https://www.linkedin.com/in/sitammeur/), selected as a GSoC'24 student for the project. Here are the project details in the [GSoC'24 website](https://summerofcode.withgoogle.com/programs/2024/projects/lEPzZg7S). +The project is selected to be part of the [Google Summer of Code 2024](https://summerofcode.withgoogle.com/programs/2024/organizations/uramaki-lab) program, and [Vinícius Cavalcanti](https://github.com/hvini) is the main mentor of the project along with [Marc Gonzalez Capdevila](https://github.com/marcgc21), [Karine Pistili Rodrigues](https://github.com/KarinePistili). The active development of the project is being done by [Sitam Meur](https://github.com/sitamgithub-MSIT), selected as a GSoC'24 student for the project. Here are the project details in the [GSoC'24 website](https://summerofcode.withgoogle.com/programs/2024/projects/lEPzZg7S). To see the full list of contributions, check out the ahead commits of the "develop" branch concerning the "main" branch. Full logs of the project development can be found in the [Daily Work Progress](https://docs.google.com/document/d/1RjCnGjYYgPKvFUrN8hSjPX29aayWr6eEopeCN3QZwEQ/edit?usp=sharing) file. Hoping to see your name in the list of contributors soon! 🚀 From 9e13df93ecde0ce9f2aa74aa87f1e9fc7e84a495 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitamgithub-MSIT@users.noreply.github.com> Date: Sun, 25 Aug 2024 01:21:24 +0000 Subject: [PATCH 78/78] gaze_tracker file predict function updated --- app/services/gaze_tracker.py | 326 ++++++++++++++++++++++++----------- 1 file changed, 226 insertions(+), 100 deletions(-) diff --git a/app/services/gaze_tracker.py b/app/services/gaze_tracker.py index 5cbffe21..b2506c9f 100644 --- a/app/services/gaze_tracker.py +++ b/app/services/gaze_tracker.py @@ -1,114 +1,238 @@ -from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_squared_log_error, r2_score -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler, PolynomialFeatures -from sklearn.pipeline import make_pipeline -from sklearn.cluster import KMeans -from sklearn import linear_model -from pathlib import Path -import pandas as pd -import numpy as np - +# Necessary imports +import warnings -def predict(data, test_data, k): - - df = pd.read_csv(data) - df = df.drop(['screen_height', 'screen_width'], axis=1) +warnings.filterwarnings("ignore") - df_test = pd.read_csv(test_data) - df_test = df_test.drop(['screen_height', 'screen_width'], axis=1) - - X_train_x = df[['left_iris_x', 'right_iris_x']] - y_train_x = df['point_x'] - - sc = StandardScaler() - X_train_x = sc.fit_transform(X_train_x) - - X_test_x = df_test[['left_iris_x', 'right_iris_x']] - y_test_x = df_test['point_x'] - - sc = StandardScaler() - X_test_x = sc.fit_transform(X_test_x) - - model = make_pipeline(PolynomialFeatures( - 2), linear_model.LinearRegression()) - model.fit(X_train_x, y_train_x) - y_pred_x = model.predict(X_test_x) - - X_train_y = df[['left_iris_y', 'right_iris_y']] - y_train_y = df['point_y'] - - sc = StandardScaler() - X_train_y = sc.fit_transform(X_train_y) +import numpy as np +import pandas as pd +from pathlib import Path - X_test_y = df_test[['left_iris_y', 'right_iris_y']] - y_test_y = df_test['point_y'] +# Scikit-learn imports +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler, PolynomialFeatures +from sklearn.pipeline import make_pipeline +# Model imports +from sklearn import linear_model +from sklearn.svm import SVR +from sklearn.cluster import KMeans +from sklearn.model_selection import GridSearchCV + +# Metrics imports +from sklearn.metrics import make_scorer +from sklearn.metrics import ( + mean_squared_error, + mean_absolute_error, + mean_squared_log_error, + r2_score, +) + +# Local imports +from app.services.metrics import ( + func_precision_x, + func_presicion_y, + func_accuracy_x, + func_accuracy_y, +) +from app.services.config import hyperparameters + + +# Machine learning models to use +models = { + "Linear Regression": make_pipeline( + PolynomialFeatures(2), linear_model.LinearRegression() + ), + "Ridge Regression": make_pipeline(PolynomialFeatures(2), linear_model.Ridge()), + "Lasso Regression": make_pipeline(PolynomialFeatures(2), linear_model.Lasso()), + "Elastic Net": make_pipeline( + PolynomialFeatures(2), linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5) + ), + "Bayesian Ridge": make_pipeline( + PolynomialFeatures(2), linear_model.BayesianRidge() + ), + "SGD Regressor": make_pipeline(PolynomialFeatures(2), linear_model.SGDRegressor()), + "Support Vector Regressor": make_pipeline( + PolynomialFeatures(2), SVR(kernel="linear") + ), +} + +# Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error +scoring = { + "r2": make_scorer(r2_score), + "mae": make_scorer(mean_absolute_error), +} + + +def predict(data, k, model_X, model_Y): + """ + Predicts the gaze coordinates using machine learning models. + + Args: + - data (str): The path to the CSV file containing the training data. + - k (int): The number of clusters for KMeans clustering. + - model_X: The machine learning model to use for prediction on the X coordinate. + - model_Y: The machine learning model to use for prediction on the Y coordinate. + + Returns: + dict: A dictionary containing the predicted gaze coordinates, precision, accuracy, and cluster centroids. + """ + # Inicialize standard scaler sc = StandardScaler() - X_test_y = sc.fit_transform(X_test_y) - - model = make_pipeline(PolynomialFeatures( - 2), linear_model.LinearRegression()) - model.fit(X_train_y, y_train_y) - y_pred_y = model.predict(X_test_y) + # Load data from csv file and drop unnecessary columns + df = pd.read_csv(data) + df = df.drop(["screen_height", "screen_width"], axis=1) + + # Data for X axis + X_x = df[["left_iris_x", "right_iris_x"]] + X_y = df["point_x"] + + # Normalize data using standard scaler and split data into training and testing sets + X_x = sc.fit_transform(X_x) + X_train_x, X_test_x, y_train_x, y_test_x = train_test_split( + X_x, X_y, test_size=0.2, random_state=42 + ) + + if ( + model_X == "Linear Regression" + or model_X == "Elastic Net" + or model_X == "Support Vector Regressor" + ): + model = models[model_X] + + # Fit the model and make predictions + model.fit(X_train_x, y_train_x) + y_pred_x = model.predict(X_test_x) + + else: + pipeline = models[model_X] + param_grid = hyperparameters[model_X]["param_grid"] + + # Initialize GridSearchCV with the pipeline and parameter grid + grid_search = GridSearchCV( + pipeline, + param_grid, + cv=5, + scoring=scoring, + refit="r2", + return_train_score=True, + ) + + # Fit the GridSearchCV to the training data for X + grid_search.fit(X_train_x, y_train_x) + + # Use the best estimator to predict the values and calculate the R2 score + best_model_x = grid_search.best_estimator_ + y_pred_x = best_model_x.predict(X_test_x) + + # Data for Y axis + X_y = df[["left_iris_y", "right_iris_y"]] + y_y = df["point_y"] + + # Normalize data using standard scaler and split data into training and testing sets + X_y = sc.fit_transform(X_y) + X_train_y, X_test_y, y_train_y, y_test_y = train_test_split( + X_y, y_y, test_size=0.2, random_state=42 + ) + + if ( + model_Y == "Linear Regression" + or model_Y == "Elastic Net" + or model_Y == "Support Vector Regressor" + ): + model = models[model_Y] + + # Fit the model and make predictions + model.fit(X_train_y, y_train_y) + y_pred_y = model.predict(X_test_y) + + else: + pipeline = models[model_Y] + param_grid = hyperparameters[model_Y]["param_grid"] + + # Initialize GridSearchCV with the pipeline and parameter grid + grid_search = GridSearchCV( + pipeline, + param_grid, + cv=5, + scoring=scoring, + refit="r2", + return_train_score=True, + ) + + # Fit the GridSearchCV to the training data for X + grid_search.fit(X_train_y, y_train_y) + + # Use the best estimator to predict the values and calculate the R2 score + best_model_y = grid_search.best_estimator_ + y_pred_y = best_model_y.predict(X_test_y) + + # Convert the predictions to a numpy array and apply KMeans clustering data = np.array([y_pred_x, y_pred_y]).T - model = KMeans(n_clusters=k, n_init='auto', init='k-means++') + model = KMeans(n_clusters=k, n_init="auto", init="k-means++") y_kmeans = model.fit_predict(data) - data = {'True X': y_test_x, 'Predicted X': y_pred_x, - 'True Y': y_test_y, 'Predicted Y': y_pred_y} - + # Create a dataframe with the truth and predicted values + data = { + "True X": y_test_x, + "Predicted X": y_pred_x, + "True Y": y_test_y, + "Predicted Y": y_pred_y, + } df_data = pd.DataFrame(data) - df_data['True XY'] = list(zip(df_data['True X'], df_data['True Y'])) - - # remove unwanted data - df_data = df_data[(df_data['Predicted X'] >= 0) & - (df_data['Predicted Y'] >= 0)] - + df_data["True XY"] = list(zip(df_data["True X"], df_data["True Y"])) - def func_precision_x(group): return np.sqrt( - np.sum(np.square([group['Predicted X'], group['True X']]))) + # Filter out negative values + df_data = df_data[(df_data["Predicted X"] >= 0) & (df_data["Predicted Y"] >= 0)] - def func_presicion_y(group): return np.sqrt( - np.sum(np.square([group['Predicted Y'], group['True Y']]))) - - precision_x = df_data.groupby('True XY').apply(func_precision_x) - precision_y = df_data.groupby('True XY').apply(func_presicion_y) + # Calculate the precision and accuracy for each + precision_x = df_data.groupby("True XY").apply(func_precision_x) + precision_y = df_data.groupby("True XY").apply(func_presicion_y) + # Calculate the average precision and accuracy precision_xy = (precision_x + precision_y) / 2 precision_xy = precision_xy / np.mean(precision_xy) - def func_accuracy_x(group): return np.sqrt( - np.sum(np.square([group['True X'] - group['Predicted X']]))) - - def func_accuracy_y(group): return np.sqrt( - np.sum(np.square([group['True Y'] - group['Predicted Y']]))) - - accuracy_x = df_data.groupby('True XY').apply(func_accuracy_x) - accuracy_y = df_data.groupby('True XY').apply(func_accuracy_y) + # Calculate the accuracy for each axis + accuracy_x = df_data.groupby("True XY").apply(func_accuracy_x) + accuracy_y = df_data.groupby("True XY").apply(func_accuracy_y) + # Calculate the average accuracy accuracy_xy = (accuracy_x + accuracy_y) / 2 accuracy_xy = accuracy_xy / np.mean(accuracy_xy) + # Create a dictionary to store the data data = {} + # Iterate over the dataframe and store the data for index, row in df_data.iterrows(): - outer_key = str(row['True X']).split('.')[0] - inner_key = str(row['True Y']).split('.')[0] + # Get the outer and inner keys + outer_key = str(row["True X"]).split(".")[0] + inner_key = str(row["True Y"]).split(".")[0] + # If the outer key is not in the dictionary, add it if outer_key not in data: data[outer_key] = {} + # Add the data to the dictionary data[outer_key][inner_key] = { - 'predicted_x': df_data[(df_data['True X'] == row['True X']) & (df_data['True Y'] == row['True Y'])]['Predicted X'].values.tolist(), - 'predicted_y': df_data[(df_data['True X'] == row['True X']) & (df_data['True Y'] == row['True Y'])]['Predicted Y'].values.tolist(), - 'PrecisionSD': precision_xy[(row['True X'], row['True Y'])], - 'Accuracy': accuracy_xy[(row['True X'], row['True Y'])] + "predicted_x": df_data[ + (df_data["True X"] == row["True X"]) + & (df_data["True Y"] == row["True Y"]) + ]["Predicted X"].values.tolist(), + "predicted_y": df_data[ + (df_data["True X"] == row["True X"]) + & (df_data["True Y"] == row["True Y"]) + ]["Predicted Y"].values.tolist(), + "PrecisionSD": precision_xy[(row["True X"], row["True Y"])], + "Accuracy": accuracy_xy[(row["True X"], row["True Y"])], } - data['centroids'] = model.cluster_centers_.tolist() + # Centroids of the clusters + data["centroids"] = model.cluster_centers_.tolist() + # Return the data return data @@ -124,8 +248,8 @@ def train_to_validate_calib(calib_csv_file, predict_csv_file): # data['point_y'] = np.log(data['point_y']) # Separe os recursos (X) e os rótulos (y) - X = data[['left_iris_x', 'left_iris_y', 'right_iris_x', 'right_iris_y']] - y = data[['point_x', 'point_y']] + X = data[["left_iris_x", "left_iris_y", "right_iris_x", "right_iris_y"]] + y = data[["point_x", "point_y"]] # Crie e ajuste um modelo de regressão linear model = linear_model.LinearRegression() @@ -148,8 +272,12 @@ def train_to_validate_calib(calib_csv_file, predict_csv_file): def train_model(session_id): # Download dataset - dataset_train_path = f'{Path().absolute()}/public/training/{session_id}/train_data.csv' - dataset_session_path = f'{Path().absolute()}/public/sessions/{session_id}/session_data.csv' + dataset_train_path = ( + f"{Path().absolute()}/public/training/{session_id}/train_data.csv" + ) + dataset_session_path = ( + f"{Path().absolute()}/public/sessions/{session_id}/session_data.csv" + ) # Importing data from csv raw_dataset = pd.read_csv(dataset_train_path) @@ -159,10 +287,10 @@ def train_model(session_id): train_stats = train_stats.transpose() dataset_t = raw_dataset - dataset_s = session_dataset.drop(['timestamp'], axis=1) + dataset_s = session_dataset.drop(["timestamp"], axis=1) # Drop the columns that will be predicted - X = dataset_t.drop(['timestamp', 'mouse_x', 'mouse_y'], axis=1) + X = dataset_t.drop(["timestamp", "mouse_x", "mouse_y"], axis=1) Y1 = dataset_t.mouse_x Y2 = dataset_t.mouse_y @@ -182,7 +310,7 @@ def train_model(session_id): def model_for_mouse_x(X, Y1): - print('-----------------MODEL FOR X------------------') + print("-----------------MODEL FOR X------------------") # split dataset into train and test sets (80/20 where 20 is for test) X_train, X_test, Y1_train, Y1_test = train_test_split(X, Y1, test_size=0.2) @@ -195,13 +323,12 @@ def model_for_mouse_x(X, Y1): Y1_test = normalizeData(Y1_test) Y1_pred_test = normalizeData(Y1_pred_test) + print(f"Mean absolute error MAE = {mean_absolute_error(Y1_test, Y1_pred_test)}") + print(f"Mean squared error MSE = {mean_squared_error(Y1_test, Y1_pred_test)}") print( - f'Mean absolute error MAE = {mean_absolute_error(Y1_test, Y1_pred_test)}') - print( - f'Mean squared error MSE = {mean_squared_error(Y1_test, Y1_pred_test)}') - print( - f'Mean squared log error MSLE = {mean_squared_log_error(Y1_test, Y1_pred_test)}') - print(f'MODEL X SCORE R2 = {model.score(X, Y1)}') + f"Mean squared log error MSLE = {mean_squared_log_error(Y1_test, Y1_pred_test)}" + ) + print(f"MODEL X SCORE R2 = {model.score(X, Y1)}") # print(f'TRAIN{Y1_pred_train}') # print(f'TEST{Y1_pred_test}') @@ -209,7 +336,7 @@ def model_for_mouse_x(X, Y1): def model_for_mouse_y(X, Y2): - print('-----------------MODEL FOR Y------------------') + print("-----------------MODEL FOR Y------------------") # split dataset into train and test sets (80/20 where 20 is for test) X_train, X_test, Y2_train, Y2_test = train_test_split(X, Y2, test_size=0.2) @@ -222,16 +349,15 @@ def model_for_mouse_y(X, Y2): Y2_test = normalizeData(Y2_test) Y2_pred_test = normalizeData(Y2_pred_test) + print(f"Mean absolute error MAE = {mean_absolute_error(Y2_test, Y2_pred_test)}") + print(f"Mean squared error MSE = {mean_squared_error(Y2_test, Y2_pred_test)}") print( - f'Mean absolute error MAE = {mean_absolute_error(Y2_test, Y2_pred_test)}') - print( - f'Mean squared error MSE = {mean_squared_error(Y2_test, Y2_pred_test)}') - print( - f'Mean squared log error MSLE = {mean_squared_log_error(Y2_test, Y2_pred_test)}') - print(f'MODEL X SCORE R2 = {model.score(X, Y2)}') + f"Mean squared log error MSLE = {mean_squared_log_error(Y2_test, Y2_pred_test)}" + ) + print(f"MODEL X SCORE R2 = {model.score(X, Y2)}") # print(f'TRAIN{Y2_pred_train}') - print(f'TEST{Y2_pred_test}') + print(f"TEST{Y2_pred_test}") return model