Decistion-tree.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
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    "\n",
    "\n",
    "\n",
    "\n",
    "# Importing packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data wrangling \n",
    "import pandas as pd \n",
    "\n",
    "# Array math\n",
    "import numpy as np \n",
    "\n",
    "# Ploting \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# List iteration tracking\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Importing the custom written class \n",
    "from DecisionTree import Node \n",
    "\n",
    "# Importing the custom regression tree \n",
    "from RegressionDecisionTree import NodeRegression\n",
    "\n",
    "# Importing the scikit-learn tree implementation\n",
    "from sklearn.tree import DecisionTreeClassifier, export_text , DecisionTreeRegressor\n",
    "\n",
    "# Inspecting functions\n",
    "import inspect\n",
    "\n",
    "# Time tracking\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Motivating example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets load some data from the famous titanic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading data\n",
    "d = pd.read_csv('data/classification/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>725</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chambers, Mr. Norman Campbell</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113806</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>E8</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>579</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Caram, Mrs. Joseph (Maria Elias)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2689</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Romaine, Mr. Charles Hallace (\"Mr C Rolmane\")</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111428</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>849</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Harper, Rev. John</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>248727</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>702</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Silverthorne, Mr. Spencer Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17475</td>\n",
       "      <td>26.2875</td>\n",
       "      <td>E24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>248</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Mrs. William (Anna)</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>267</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Panula, Mr. Ernesti Arvid</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3101295</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lurette, Miss. Elise</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B80</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>611</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mrs. Anders Johan (Alfrida Konstant...</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "724          725         1       1   \n",
       "578          579         0       3   \n",
       "187          188         1       1   \n",
       "848          849         0       2   \n",
       "888          889         0       3   \n",
       "701          702         1       1   \n",
       "247          248         1       2   \n",
       "266          267         0       3   \n",
       "195          196         1       1   \n",
       "610          611         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "724                      Chambers, Mr. Norman Campbell    male  27.0      1   \n",
       "578                   Caram, Mrs. Joseph (Maria Elias)  female   NaN      1   \n",
       "187      Romaine, Mr. Charles Hallace (\"Mr C Rolmane\")    male  45.0      0   \n",
       "848                                  Harper, Rev. John    male  28.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "701                   Silverthorne, Mr. Spencer Victor    male  35.0      0   \n",
       "247                    Hamalainen, Mrs. William (Anna)  female  24.0      0   \n",
       "266                          Panula, Mr. Ernesti Arvid    male  16.0      4   \n",
       "195                               Lurette, Miss. Elise  female  58.0      0   \n",
       "610  Andersson, Mrs. Anders Johan (Alfrida Konstant...  female  39.0      1   \n",
       "\n",
       "     Parch      Ticket      Fare Cabin Embarked  \n",
       "724      0      113806   53.1000    E8        S  \n",
       "578      0        2689   14.4583   NaN        C  \n",
       "187      0      111428   26.5500   NaN        S  \n",
       "848      1      248727   33.0000   NaN        S  \n",
       "888      2  W./C. 6607   23.4500   NaN        S  \n",
       "701      0    PC 17475   26.2875   E24        S  \n",
       "247      2      250649   14.5000   NaN        S  \n",
       "266      1     3101295   39.6875   NaN        S  \n",
       "195      0    PC 17569  146.5208   B80        C  \n",
       "610      5      347082   31.2750   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets say we want to create a classifier whether a passenger survived or not using two variables: age and fare (price for a ticket). We want to create a simple decision tree to accomplish this task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = d[['Survived', 'Fare', 'Age']].copy()\n",
    "\n",
    "# Droping missing entries\n",
    "dd.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations: 714\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of observations: {dd.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(y='Fare', x='Age', data=dd, hue='Survived')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every decision tree creation starts with the selection of what variable and what value we should choose to split? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the split value \n",
    "age_split = 10\n",
    "\n",
    "# Ploting the original plot\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(y='Fare', x='Age', data=dd, hue='Survived')\n",
    "plt.grid()\n",
    "\n",
    "# Adding the vertical line\n",
    "plt.axvline(age_split, label='Spliting criteria', color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the left and right datasets\n",
    "left = dd[dd['Age']<=age_split]\n",
    "right = dd[dd['Age']>age_split]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a decision to split the data into two parts based on the visual ques from the scatterplot. Some statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations to the left of criteria: 64\n",
      "Observations to the right of criteria: 650\n"
     ]
    }
   ],
   "source": [
    "print(f\"Observations to the left of criteria: {left.shape[0]}\")\n",
    "print(f\"Observations to the right of criteria: {right.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  size\n",
       "0         0    26\n",
       "1         1    38"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.groupby('Survived', as_index=False).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  size\n",
       "0         0   398\n",
       "1         1   252"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.groupby('Survived', as_index=False).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are observations from both of the classes in the nodes. Thus, each of the nodes are are **impure**. But how impure? To measure impurity we use the **GINI** impurity coefficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GINI impurity definition "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GINI impurity index is the default spliting criteria when building decision trees. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formal definition for GINI impurity is as follows: \n",
    "    \n",
    "*Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definition is not very intuitive so lets bring some clarity. \n",
    "\n",
    "Suppose we have two classes in the dataset:\n",
    "\n",
    "$$k_{1}, k_{2}$$. \n",
    "\n",
    "Each of the classes have $n_{1}$ and $n_{2}$ observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of observing something from one of the $k$ classes is: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$p(i) = P(x_{i} \\in k_{i}) = \\dfrac{n_{i}}{n_{1} + n_{2}},  i \\in \\{1, 2\\}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GINI impurity of such a system is calculated with the following formula:\n",
    "    \n",
    "$$G = 1 - \\Sigma_{i=1}^{2}p(i)^{2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function for calculating the impurity is in the **Node** class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    @staticmethod\n",
      "    def GINI_impurity(y1_count: int, y2_count: int) -> float:\n",
      "        \"\"\"\n",
      "        Given the observations of a binary class calculate the GINI impurity\n",
      "        \"\"\"\n",
      "        # Ensuring the correct types\n",
      "        if y1_count is None:\n",
      "            y1_count = 0\n",
      "\n",
      "        if y2_count is None:\n",
      "            y2_count = 0\n",
      "\n",
      "        # Getting the total observations\n",
      "        n = y1_count + y2_count\n",
      "        \n",
      "        # If n is 0 then we return the lowest possible gini impurity\n",
      "        if n == 0:\n",
      "            return 0.0\n",
      "\n",
      "        # Getting the probability to see each of the classes\n",
      "        p1 = y1_count / n\n",
      "        p2 = y2_count / n\n",
      "        \n",
      "        # Calculating GINI \n",
      "        gini = 1 - (p1 ** 2 + p2 ** 2)\n",
      "        \n",
      "        # Returning the gini impurity\n",
      "        return gini\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(inspect.getsource(Node.GINI_impurity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulating some data\n",
    "n = 1000\n",
    "\n",
    "shares_y1 = [\n",
    "    0, \n",
    "    0.1,\n",
    "    0.2,\n",
    "    0.3,\n",
    "    0.4,\n",
    "    0.5, \n",
    "    0.6,\n",
    "    0.7,\n",
    "    0.8,\n",
    "    0.9,\n",
    "    1\n",
    "] \n",
    "\n",
    "y1_counts = [x * n for x in shares_y1]\n",
    "y2_counts = [n - x for x in y1_counts]\n",
    "\n",
    "y = list(zip(y1_counts, y2_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1000),\n",
       " (100.0, 900.0),\n",
       " (200.0, 800.0),\n",
       " (300.0, 700.0),\n",
       " (400.0, 600.0),\n",
       " (500.0, 500.0),\n",
       " (600.0, 400.0),\n",
       " (700.0, 300.0),\n",
       " (800.0, 200.0),\n",
       " (900.0, 100.0),\n",
       " (1000, 0)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the GINI impurities for such data\n",
    "ginis = [Node.GINI_impurity(x[0], x[1]) for x in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(shares_y1, ginis, '-o')\n",
    "plt.xlabel(\"Share of first class in the whole dataset\")\n",
    "plt.ylabel(\"GINI impurity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph shows a very important relationship. The upper bound of the GINI impurity index is 0.5. It occurs when there is an equal number of observations of each class in the dataset. This is the **worst** case in terms of GINI impurity.\n",
    "\n",
    "At the edges of graph, we get that impurity is 0. This occurs when there is only one class in the data. This is the **best** situation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GINI impurity of a split "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going back to the example with the titanic dataset, we have two datasets resulting from the chosen split. Each of the datasets have different GINI impurity indexes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_counts = left.groupby(\"Survived\").size().values.tolist()\n",
    "right_counts = right.groupby(\"Survived\").size().values.tolist()\n",
    "\n",
    "gini_left = Node.GINI_impurity(left_counts[0], left_counts[1])\n",
    "gini_right = Node.GINI_impurity(right_counts[0], right_counts[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gini impurity of the left node: 0.482421875\n",
      "Gini impurity of the right node: 0.47477396449704146\n"
     ]
    }
   ],
   "source": [
    "print(f\"Gini impurity of the left node: {gini_left}\")\n",
    "print(f\"Gini impurity of the right node: {gini_right}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final GINI impurity index is calculated using the weighted average based on the observation sizes in each of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = left.shape[0]/ (left.shape[0] + right.shape[0]) \n",
    "w2 = right.shape[0] / (left.shape[0] + right.shape[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_final = w1 * gini_left + w2 * gini_right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final gini impurity: 0.4754594914889033\n"
     ]
    }
   ],
   "source": [
    "print(f\"Final gini impurity: {gini_final}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GINI gain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last metric used in deciding where to split the data is the GINI gain metric. Its calculated by the difference of non splited GINI of the dataset and the GINI of the selected split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$GINI gain = \\Delta Gini = Gini_{parent} - (Gini_{left} \\dfrac{n_{left}}{n_{right} + n_{left}} + Gini_{right} \\dfrac{n_{right}}{n_{right} + n_{left}} )$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GINI_impurity_df(df:pd.DataFrame, target):\n",
    "    \"\"\"\n",
    "    Function to calculate the resulting GINI impurity of a split\n",
    "    \"\"\"\n",
    "    # Getting the counts and ginis\n",
    "    counts = df.groupby(target).size().values.tolist()\n",
    "\n",
    "    gini = Node.GINI_impurity(counts[0], counts[1])\n",
    "    \n",
    "    return gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_orig = GINI_impurity_df(dd, 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gini non-splited: 0.48238903404499056\n",
      "Gini with the proposed split: 0.4754594914889033\n",
      "Gini gain: 0.006929542556087243\n"
     ]
    }
   ],
   "source": [
    "print(f\"Gini non-splited: {gini_orig}\")\n",
    "print(f\"Gini with the proposed split: {gini_final}\")\n",
    "print(f\"Gini gain: {gini_orig - gini_final}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At each split decision, we chose that split that has the highest GINI gain. If the GINI gain is non positive, we do not perform the split."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spliting on numeric variables \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When deciding where to split a numeric feature, we first sort all the values, get the means between neighbouring values and calculate the GINI gains with each of the means.\n",
    "\n",
    "The full code to get the best split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    @staticmethod\n",
      "    def ma(x: np.array, window: int) -> np.array:\n",
      "        \"\"\"\n",
      "        Calculates the moving average of the given list. \n",
      "        \"\"\"\n",
      "        return np.convolve(x, np.ones(window), 'valid') / window\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(inspect.getsource(Node.ma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    def best_split(self) -> tuple:\n",
      "        \"\"\"\n",
      "        Given the X features and Y targets calculates the best split \n",
      "        for a decision tree\n",
      "        \"\"\"\n",
      "        # Creating a dataset for spliting\n",
      "        df = self.X.copy()\n",
      "        df['Y'] = self.Y\n",
      "\n",
      "        # Getting the GINI impurity for the base input \n",
      "        GINI_base = self.get_GINI()\n",
      "\n",
      "        # Finding which split yields the best GINI gain \n",
      "        max_gain = 0\n",
      "\n",
      "        # Default best feature and split\n",
      "        best_feature = None\n",
      "        best_value = None\n",
      "\n",
      "        for feature in self.features:\n",
      "            # Droping missing values\n",
      "            Xdf = df.dropna().sort_values(feature)\n",
      "\n",
      "            # Sorting the values and getting the rolling average\n",
      "            xmeans = self.ma(Xdf[feature].unique(), 2)\n",
      "\n",
      "            for value in xmeans:\n",
      "                # Spliting the dataset \n",
      "                left_counts = Counter(Xdf[Xdf[feature]<value]['Y'])\n",
      "                right_counts = Counter(Xdf[Xdf[feature]>=value]['Y'])\n",
      "\n",
      "                # Getting the Y distribution from the dicts\n",
      "                y0_left, y1_left, y0_right, y1_right = left_counts.get(0, 0), left_counts.get(1, 0), right_counts.get(0, 0), right_counts.get(1, 0)\n",
      "\n",
      "                # Getting the left and right gini impurities\n",
      "                gini_left = self.GINI_impurity(y0_left, y1_left)\n",
      "                gini_right = self.GINI_impurity(y0_right, y1_right)\n",
      "\n",
      "                # Getting the obs count from the left and the right data splits\n",
      "                n_left = y0_left + y1_left\n",
      "                n_right = y0_right + y1_right\n",
      "\n",
      "                # Calculating the weights for each of the nodes\n",
      "                w_left = n_left / (n_left + n_right)\n",
      "                w_right = n_right / (n_left + n_right)\n",
      "\n",
      "                # Calculating the weighted GINI impurity\n",
      "                wGINI = w_left * gini_left + w_right * gini_right\n",
      "\n",
      "                # Calculating the GINI gain \n",
      "                GINIgain = GINI_base - wGINI\n",
      "\n",
      "                # Checking if this is the best split so far \n",
      "                if GINIgain > max_gain:\n",
      "                    best_feature = feature\n",
      "                    best_value = value \n",
      "\n",
      "                    # Setting the best gain to the current one \n",
      "                    max_gain = GINIgain\n",
      "\n",
      "        return (best_feature, best_value)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(inspect.getsource(Node.best_split))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Growing the full tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping missing values\n",
    "dtree = d[['Survived', 'Age', 'Fare']].dropna().copy()\n",
    "\n",
    "# Defining the X and Y matrices\n",
    "Y = dtree['Survived'].values\n",
    "X = dtree[['Age', 'Fare']]\n",
    "\n",
    "# Saving the feature list \n",
    "features = list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the available hyper parameters of a tree\n",
    "hp = {\n",
    "    'max_depth': 3,\n",
    "    'min_samples_split': 50\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root\n",
      "   | GINI impurity of the node: 0.48\n",
      "   | Class distribution in the node: {0: 424, 1: 290}\n",
      "   | Predicted class: 0\n",
      "|-------- Split rule: Fare <= 52.277\n",
      "           | GINI impurity of the node: 0.44\n",
      "           | Class distribution in the node: {0: 389, 1: 195}\n",
      "           | Predicted class: 0\n",
      "|---------------- Split rule: Fare <= 10.481\n",
      "                   | GINI impurity of the node: 0.32\n",
      "                   | Class distribution in the node: {0: 192, 1: 47}\n",
      "                   | Predicted class: 0\n",
      "|------------------------ Split rule: Age <= 32.5\n",
      "                           | GINI impurity of the node: 0.37\n",
      "                           | Class distribution in the node: {0: 134, 1: 43}\n",
      "                           | Predicted class: 0\n",
      "|------------------------ Split rule: Age > 32.5\n",
      "                           | GINI impurity of the node: 0.12\n",
      "                           | Class distribution in the node: {0: 58, 1: 4}\n",
      "                           | Predicted class: 0\n",
      "|---------------- Split rule: Fare > 10.481\n",
      "                   | GINI impurity of the node: 0.49\n",
      "                   | Class distribution in the node: {0: 197, 1: 148}\n",
      "                   | Predicted class: 0\n",
      "|------------------------ Split rule: Age <= 6.5\n",
      "                           | GINI impurity of the node: 0.41\n",
      "                           | Class distribution in the node: {0: 12, 1: 30}\n",
      "                           | Predicted class: 1\n",
      "|------------------------ Split rule: Age > 6.5\n",
      "                           | GINI impurity of the node: 0.48\n",
      "                           | Class distribution in the node: {0: 185, 1: 118}\n",
      "                           | Predicted class: 0\n",
      "|-------- Split rule: Fare > 52.277\n",
      "           | GINI impurity of the node: 0.39\n",
      "           | Class distribution in the node: {1: 95, 0: 35}\n",
      "           | Predicted class: 1\n",
      "|---------------- Split rule: Age <= 63.5\n",
      "                   | GINI impurity of the node: 0.38\n",
      "                   | Class distribution in the node: {1: 95, 0: 32}\n",
      "                   | Predicted class: 1\n",
      "|------------------------ Split rule: Age <= 29.5\n",
      "                           | GINI impurity of the node: 0.44\n",
      "                           | Class distribution in the node: {0: 17, 1: 34}\n",
      "                           | Predicted class: 1\n",
      "|------------------------ Split rule: Age > 29.5\n",
      "                           | GINI impurity of the node: 0.32\n",
      "                           | Class distribution in the node: {1: 61, 0: 15}\n",
      "                           | Predicted class: 1\n",
      "|---------------- Split rule: Age > 63.5\n",
      "                   | GINI impurity of the node: 0.0\n",
      "                   | Class distribution in the node: {0: 3}\n",
      "                   | Predicted class: 0\n"
     ]
    }
   ],
   "source": [
    "# Initiating the Node\n",
    "root = Node(Y, X, **hp)\n",
    "\n",
    "# Getting teh best split\n",
    "root.grow_tree()\n",
    "\n",
    "# Printing the tree information \n",
    "root.print_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it turns out, the best first split is the fare feature at value 52.227.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg7r8UBmWZbbsqw1lmW9UX95mGVZyy3L2mlZ1rOWZfnWl/vVX95Zf31qT9dNREREpC/ojR6y7wNbmlz+M3C3bdtpQAHwzfrybwIF9eV3199OREREZMDr0UBmWVYycAbwYP1lCzgReKH+Jo8C59b/fk79ZeqvX1R/exEREZEBrad7yP4O/BSoq78cBRTatu2tv5wBJNX/ngTsB6i/vqj+9iIiIiIDmmXbds8c2LLOBE63bfs7lmUtAH4MXA18WT8siWVZKcDbtm1PsCxrI3CqbdsZ9dftAmbbtp17xHGvB64HiIuLm/7MM8/0SP1LS0sJDg7ukWP3B4O5/YO57aD2D+b2D+a2g9qv9vd8+xcuXLjKtu0ZrV3n04OPexxwtmVZpwP+QChwDxBuWZZPfS9YMnCg/vYHgBQgw7IsHyAMyDvyoLZtPwA8ADBjxgx7wYIFPVL5JUuW0FPH7g8Gc/sHc9tB7R/M7R/MbQe1X+13tv09NmRp2/attm0n27adClwMfGTb9mXAx8AF9Te7Cni1/vfX6i9Tf/1Hdk9134mIiIj0IU6sQ/Yz4EeWZe3EzBF7qL78ISCqvvxHwM8dqJuIiIhIr+vJIcsGtm0vAZbU/74bmNXKbSqBb/RGfURERET6Eq3ULyIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBymQCYiIiLiMAUyEREREYcpkImIiIg4TIFMRERExGEKZCIiIiIOUyATERERcZgCmYiIiIjDFMhEREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBymQCYiIiLiMAUyEREREYcpkImIiIg4TIFMRERExGEKZCIiIiIOUyATERERcZgCmYiIiIjDFMhEREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBymQCYiIiLiMAUyEREREYcpkImIiIg4TIFMRERExGEKZCIiIiIOUyATERERcZgCmYiIiIjDFMhEREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBzWY4HMsix/y7JWWJa1zrKsTZZl3V5fPsyyrOWWZe20LOtZy7J868v96i/vrL8+tafqJiIiItKX9GQPWRVwom3bk4EpwKmWZc0B/gzcbdt2GlAAfLP+9t8ECurL766/nYiIiMiA12OBzDZK6y966n9s4ETghfryR4Fz638/p/4y9dcvsizL6qn6iYiIiPQVPTqHzLIst2VZa4FDwPvALqDQtm1v/U0ygKT635OA/QD11xcBUT1ZPxEREZG+wLJtu+cfxLLCgZeBXwOP1A9LYllWCvC2bdsTLMvaCJxq23ZG/XW7gNm2bececazrgesB4uLipj/zzDM9UufS0lKCg4N75Nj9wWBu/2BuO6j9g7n9g7ntoPar/T3f/oULF66ybXtGa9f59Ogj17Ntu9CyrI+BuUC4ZVk+9b1gycCB+psdAFKADMuyfIAwIK+VYz0APAAwY8YMe8GCBT1S5yVLltBTx+4PBnP7B3PbQe0fzO0fzG0HtV/td7b9PXmWZUx9zxiWZQUAJwFbgI+BC+pvdhXwav3vr9Vfpv76j+ze6L4TERERcVhP9pAlAI9aluXGBL/nbNt+w7KszcAzlmX9AVgDPFR/+4eAxy3L2gnkAxf3YN1ERERE+oweC2S2ba8HprZSvhuY1Up5JfCNnqqPiIiISF+llfpFREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBymQCYiIiLiMAUyEREREYcpkImIiIg4TIFMRERExGEKZCIiIiIOUyATERERcZgCmYiIiIjDFMhEREREHKZAJiIiIuIwBTIRERERh/k4XQGR3pZRUM7OQ6X4+bgZFRdMVLBf9z9IVRnkbIWyHIgYCtGjwVX//Sd3F+TvAv9QiBkLAWHd//gi0ufsziklPa+csAAfRsWFEOLvcbpK0ocokMnAlJ8O29+GnR9A2mIYdRpEprIps4irH/6KnNIqAOaNiOLOCyaRFBHY9rGKM2Hnh7D5VRgyB8adDdGj2r59VQl8fi98+mdz2e0LFz4Go0+DfcvgiQugutRcN+UyOOl2CIrpnnaLSJ+0fHce1zzyFeXVtQBcMiuFn5wymsigHvhCKP1Sh4csLcuab1nWNfW/x1iWNaznqiVyDCoK4PUfwDs/N4HsnZ/D6z/AW5rPg5/ubghjAF/syuOr9IK2j+Wtgk//Cq99D3a+Dx/9Hp66GIoOtH2fQ1sawxhAbbW5f/4eeOunjWEMYO2TcHB919sqIn1eQVk1v3xlQ0MYA3h6xX62HCxxsFbS13QokFmWdRvwM+DW+iIP8ERPVUrkmOTthD0fNy/b8zF23g5W7msZvrZnt/OmWJAOqx5uXpa/ywxHtqU0u2VZWS5UFkH2hpbXlWS1fSwR6fdKKmvYeaisRXl2caUDtZG+qqM9ZOcBZwNlALZtZwIhPVUpkWNit17s47I4fUJCi/KpQyK670EAwoeCdcRLKyoNguMg7aSWt49UZ7PIQBYV5Mfc4ZEtyodEtjNVQgadjgayatu2beo/hSzLCuq5Kokco6gRkHp887LU47GiRnLJrCGcMDIaMAHt2wtGMG1oeNvHikiFqVc2LwsfCjFj2r5PzBg4/0HwC62/fSqc9wCEJsDi2yF+kin38YfT/tx4WUQGpCB/H35z1njGxJt+DH+Piz+cO4FxiaEO10z6ko5O6n/Osqz/AOGWZX0LuBb4b89VS+QYBEbC2f+ALW/Ajvdg5Mkw9kwIjCA1EP512TT25Zfj8XGRGhmEx6ed7yU+fvC1n0LiFNj4IgydB+PPh7Dkdu7jCxO/DsnTzXy2kCQIiTXXxY2DK1+Fwv3gFwQRwxvPvhSRAWtsQihPfWsOmQUVBPm7SY0KwrIsp6slfchRA5ll/mKeBcYAxcBo4De2bb/fw3UT6brI4XDczebnCMH+HsYldmKpibAkmHGN+emMiFTzc6TASPMjIoNKZJAvkUG+TldD+qijBjLbtm3Lst6ybXsioBAmIiIi0s06Olay2rKsmT1aExEREZFBqqNzyGYDl1mWtRdzpqWF6TzTbGQRERGRY9TRQHZKj9ZCREREZBDrUCCzbXsvgGVZsYB/j9ZIREREZJDp6Er9Z1uWtQPYA3wCpANv92C9RERERAaNjk7q/z0wB9hu2/YwYBHwZY/VSkRERGQQ6Wggq7FtOw9wWZblsm37Y2BGD9ZLZECybZttWcW8teEgS3fmkNdko3MRx1SVwv4VUFkIGSuhutzpGokMOh2d1F9oWVYw8CnwpGVZh6jf11JEOu6LXXlc87+vqK6tA2Dx2Fj+dP4kYkL8HK6ZDFo1VfDVg/DBbTD6dnjwKrOl14xvgtvjdO1EBo12e8gsyxpS/+s5QDnwQ+AdYBdwVs9WTWRgKSyv5rbXNjaEMYAPthxi44EiB2slg17edvjw9uZl7/0K8nY6Ux+RQepoPWSvANNs2y6zLOtF27a/Djza89USGXhKq7zszmnZsZxXpmFLcVB5Pth1zctqa8w+rCLSa442h6zpzqfDe7IiIgNdTLAfp06Ib1E+PDrYgdqI1AsfAv7hzcuCYky5iPSaowUyu43fRaST/DxubjlpNMenRQMQGuDD3y6czLjEUIdrJoNa5DC4+CmISDWXo0bCxU9CWLKj1RIZbI42ZDnZsqxiTE9ZQP3v0Lh1kj5JRDphRGww/75iOgeLKgjw+JAUEeB0lUQg9Tj45vvw5Wo4+x0Iina6RiKDTruBzLZtd29VRGSwCPLzIS02xOlqiDQXHAs+/gpjIg7p6DpkIiIiItJDFMhEREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4rCjbS4u0n+VHITSXAiOgZB4p2sjvclbBQXp5veIVPDx65nHKT4IZfobE5Fjp0AmA9Oez+Dl66E4E0IT4bwHYNjxTtdKekPRAfjkL7DmMbAsmH4tHH8LhCZ07+Ps/gRevsEE/9AkOP+/kHpc9z6GiAwaGrKUgSc/HZ693IQxMP8+e3ljj4kMbDvehdWPgF0HdbXw1X9h54fd+xh5u83fVMlBc7n4ADx7GRTu697HEZFBQ4FMBp7i/VBZ2LysshCK9jtRG+ltG19qWbblte59jOIMqCpuXlZRAIX6GxORrlEgk4EnIApcR4zGu3wgMNqZ+kjvGjKnZVnKrO59jMAocLmbl7l9ISiqex9HRAYNBTIZeKJHwql3NC879Q6ISnOmPtK7JlwA4UMbL0eOgLFnd+9jRI2Ek//YeNmy4NQ/Q6T+xkSkazSpXwYetwemXg5JM8zcntAkiB1jymXgix0D17wFh7aCBcSMhbCk7n0MH1+YfjWkzDF/Y2HJEDMG3HpLFZGu0buHDEyeAEiaan5k8AlLNj89SX9jItKNNGQpIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBymhWGl7yjJhoProDQbooZD/BTwC3K6Vn1WVU0tq/cV8OGWQ0QE+rJgTAzjE8OOcqdSyFoHebshOBYSp0BwXK/Ud9CwbchaD9mbweMPCVMgcpjTtRKRPk6BTPqG8nx4+6ew+ZXGstP/CjO/afYJlBY+35nLtY+ubLh8/ye7eO7GuYxLCG39DrYN656Gt37cWDbuXDjz7xAY0aN1HVT2LYPHzoHaanM5fChc8ZL2UhWRdmnIUvqGQ5ubhzGAD34DBelO1KbPK6vycs+HO5qVlVZ5+XJ3Xtt3yt8D7/+mednmV8xzL92juhyW3NEYxgAK98LeZc7VSUT6BQUy6Rsqi1uWVZdBTXnv16UfqKuzKauubVFe2UpZg5ry1p/PqqJurNkg5600AexIJQd7vy4i0q8okEnfEJUGnsDmZSlzICzFmfr0cSEBHm782vBmZW6XxZwRUW3fKSwFkmc1L/MNgkgNpXWbwEiYdnXL8iFzer0qItK/KJBJ3xAzCi5/0UyAdvnA2LPhrHvAv435UMLisXHcc9EUJqeEceLoWJ68bjaTktqZ1B8QBmffa55bl495ri9/0Tz30n0mXQjzf2TCbmgifP0hSJrhdK1EpI/TpH7pO4bOgytfg6piCIoxZ6hJm8IDfTlnahKnTIjHZYGvj/vod4odA+f/F8pywC/UhDTpXmFJcOKvYeZ14PaYs1lFRI5CgUz6loAwhYRO8vd0IIg15fGHcA0F9yiXywQzEZEO0pCliIiIiMMUyEREREQc1mOBzLKsFMuyPrYsa7NlWZssy/p+fXmkZVnvW5a1o/7fiPpyy7Ksf1iWtdOyrPWWZU3rqbqJiIiI9CU92UPmBW6xbXscMAf4rmVZ44CfAx/atj0S+LD+MsBpwMj6n+uB+3uwbiIiIiJ9Ro8FMtu2D9q2vbr+9xJgC5AEnAM8Wn+zR4Fz638/B3jMNr4Ewi3LSuip+omIiIj0Fb0yh8yyrFRgKrAciLNt+/Cy1VnA4Z2Nk4D9Te6WUV8mIiIiMqBZtm337ANYVjDwCfBH27Zfsiyr0Lbt8CbXF9i2HWFZ1hvAHbZtL60v/xD4mW3bK4843vWYIU3i4uKmP/PMMz1S79LSUoKDg3vk2P3BYG7/YG47qP2Duf2Due2g9qv9Pd/+hQsXrrJtu9WVont0HTLLsjzAi8CTtm2/VF+cbVlWgm3bB+uHJA/Vlx8Ami6OlFxf1oxt2w8ADwDMmDHDXrBgQY/UfcmSJfTUsfuDwdz+wdx2UPsHc/sHc9tB7Vf7nW1/T55laQEPAVts2/5bk6teA66q//0q4NUm5VfWn205ByhqMrQpIiIiMmD1ZA/ZccAVwAbLstbWl/0CuAN4zrKsbwJ7gQvrr3sLOB3YCZQD1/Rg3URERET6jB4LZPVzwaw2rl7Uyu1t4Ls9VR8RERGRvkor9YuIiIg4TIFMRERExGEKZCIiIiIOUyATERERcZgCmYiIiIjDFMhEREREHKZAJiIiIuIwBTIRERERhymQiYiIiDhMgUxERETEYQpkIiIiIg5TIBMRERFxmAKZiIiIiMMUyEREREQcpkAmIiIi4jAFMhERERGHKZCJiIiIOEyBTERERMRhCmQiIiIiDlMgExEREXGYApmIiIiIwxTIRERERBymQCYiIiLiMAUyEREREYcpkImIiIg4TIFMRERExGE+TldARPq4vF2w8wM4uA7SFkPqfAiOdbpWA0tJNqQvhV0fQuJUGLEIooY7XSsR6UUKZCLStuJMeOYyyNliLq99Eo77IZz4S3B7nK3bQOGthqV/h+X/MpfXPgkJk+HS5yEkztGqiUjv0ZCliLTt0JbGMHbYl/dBQboj1RmQCvbAin83Lzu4Dg5tdqY+IuIIBTIRaVudt/Uyu7b36zJQ1XnBrmulXM+xyGCiQCYibYsZA8HxzcsmXQLhqY5UZ0CKGAbjz29eFpoMsaOdqY+IOEJzyESkbRFD4YoXYeUjkPEVTLwAxp0DHn+nazZw+AbC4t+aeWObXoIh82DalRCW4nTNRKQXKZCJSPviJsDpd4K3SkGsp0QMhfk/gNk3go8fWJbTNRKRXqZAJiJHZ1kKY71Bz7HIoKU5ZCIiIiIOUyATERERcZgCmYiIiIjDNIdMRAYUb20da/cX8u6mLCzL4pTx8UxJCcftameifEUh7FsGW98C9/FwaCvEjum1Oks3KtgHuz+GfV+abb6Gfw3Ckp2ulchRKZB1RlUJ+ASAu/nTZts2ZVVeAn19cLX3pi8DU02l+bcbJmRXe2vx1tkE+uql2VWr9xVwyX+XU1tnA/Dw0j08e/0cpqdGtn2nDc/DWz82v48eAY//Bq5+S/tJ9jflBfDGD2HXB+byuqfMGm9n/wP8Qpytm8hRaMiyIwrS4ZM74cHF5sWetaHhql05pfzp7S2c+68v+ONbW9h5qNS5ekrvqi6DbW/DY+eYn21vQ3V5lw5VU1vHFztz+eajK7nwP8t4ec0BCsuru7nCg8NTy/c1hDEAb53Ni6sz2r5DcSZ89IfmZSUHIWt9D9VQekzejsYwdtimlyBvpzP1EekEfQ0/mupy+OB286IGyNkK29+G6z4k3zeeHzyzhg0HigHYeaiUL3bm8vh1s4kO9nOw0tIr9n4BT1/cePnpi+HyFyFtcacPtT6jkMsfWs7hHPHDZ9fytwsnc/40DbV0VkV1yy2Hylopa1BXB7VVrZRr66J+p7WtvkD/l9IvqIfsaAr3Noaxw8pyIGcre3LLGsLYYVuyStiTW9aLFRTHrH6slbLHu3Soz3fm0aRTB4D/fLKbksqaLh1vMLt0ztAWZd+Y3s6q96GJMO/m5mV+IRA3vptrJj0uKs0sZNzUkHkQOcKZ+oh0gnrIjsblA24P1B7xwej2xeNuPc96NI9scPAPbaUsrEuHCvR1tygL8Xe3PxFdWjUrNYKHr57BQ5+l43LBdfOHMSM1ou07uFww41oISTAhOyASrnxNk/r7o+BY+Mb/YO1TsOsjGHUqTLoIAtv5/xfpIxTIjiYiFeZ9Hz67q7EsYQrEjmO4J4izJifw+rqDDVedNiGe4THBvV5NccDUK2HdM43DJG4PTLmsS4c6bkQ0wX4+lFY1Drl878SRmtzfBQG+Ppw4Jo7jR8YAtPnFqZmQeJhxDUy+BD7/EpKm9XAtpcdEj4LjfwwzrzPh2jfQ6RqJdIje7Y/G7YE5N5qNf/d8aoYxhi+AkDiCgV+cPpZFY+JYvbeAqUPCmT08itAAj9O1lt6QPBOufRe2vwtYMOpkSOzaB/nYxFCevWEOn2zLoaiyhhNHxzIlJbxbqzvYdCiItbiTti7q9zLXwLu/hP1fQuoJcNLvIWGi07USOSoFso4IioFxZ5ufIySEBXDu1CTOnZrkQMXEUS4XJM8wP91gfGIY4xO7NuQpIkDhfnjyG2aeL5j1yJ69HL75rukFFenDNKlfREQGhoI9jWHssMJ0s3SRSB+nQCYiIgNDa4u/Wi7w1aKw0vcpkImIyMAQNQrmfKd52fE/NsthiPRxmkMmIiIDg18QnPATGHkyFGVA+BBzQpZHC3VL36dAJiIiA0dgJIxY6HQtRDpNQ5YiIiIiDlMgExEREXGYApmIiIiIwzSHTOQY7MopZUum2WB+bGIoI7RtVtfYNmRvgkObwcffTMSOqN8kvOgAZK2HyiKIGQ1xE8Gtty4RGVj0ribSRZszi7n0wS8pLDcbz4cHenjqutmM02r7nbd/OTx2DngrzeWI4XD5C+DjB89fAxkrTLnLDZc8CyNP6t7Ht23I2Qp5O6HagrI8CIoy1xVnwaFN4K0yG45HDu/exxYRQYFMpMteWXugIYwBFJbX8Oq6TAWyzqqugCV/bgxjAAW7If1zc8bc4TAGUFcL7/zc7Bl6ODB1hz2fwVMXmNA1+nZ483k4/U6oLoPnr4aDa83tAiLgilcgcUr3PXZfUJoNB1ZBWSlsfweSppst40Sk12gOmUgXbcsqblG2PbvUgZr0c94KE8COVJxphimPVLQfaiq67/HL8+HNH5kwdtjmVyBrowlqh8MYQEUBLLsPvDVHHqX/qiqBD38PT19invOnLoJP74LqcqdrJjKoKJCJdNH505JblJ03JdGBmvRzgZEw9cqW5UPnQPQosKzm5ZMugZC47nv8qhLI39lKeTHkbmtZnrkGvAMorORuhzWPNy9b8R8zfCsivUaBTKSL5o+M5tbTxhDs50Ownw+3nj6G+SOjna5W/zT5Iph7k5kzFhQN594PSTPN5P6LnoCwZHD5wJTLYf4PwO3pvscOioWRp7YsD02Aoce1LJ/wDfAfQMPSrfU22nb39kKKyFFpDplIF0UF+XHD10Zw9uREsCAhLMDpKvVfYclw0u0w+wYTtkLiG68bcyYkzzIBISQBfHy797F9A2Dxb6GyGPZ9DpbbBML4SeYxT/w1fPoXqK2GCRfAlIu79/GdFjkCwoZA0b7GsuhREDnMuTqJDEIKZCLHKCFcQaxbuNwQntL6dcGxPfvYsWPg0meg+ABs2A9TTjblngCY/0MYfx7Uec3eiJ4B9v8dmgCXPA2f3gm2D0y8EOb/qOefcxFpRoFMRATAP9T8uLObl7vcEDXCmTr1lvgJcP4D8OlncMK3zNCxiPQqzSETERETwlw+CmMiDlEgExEREXGYApmIiIiIwxTIRERERBymQCYiIiLiMAUyEREREYdp2QvpmNxdkLMFfPwhbhyEHsMWQYX7IHuz+T12HEQM6Z46ysCUtwey15uFW6NHQfJMcOm7pIgMLApkcnSZa+Cxcxo3eo6fBBc9DhGpnT/WoS3wxNfNApwAoUlw+YsQO7bbqisDSO4OePMW2POJuezjB994HEaf4my9RES6mb5mSvu8VbD0741hDCBrPaR/3rXjbXy5MYyB+X3ji8dURRnAMtc0hjEwf48f/haKMx2rkohIT1Agk/ZVl5sAdqTc7V073oGVLcsyWikTASjPa1mWtwMqilqWi4j0Ywpk0r7ACLO33ZGGzuva8caf37JswgVdO5YMfJHDW5aNPKXtPS9FRPopBTI5uimXwqSLwLLMxsqLb4eU2V071sjFMPcmcHvMz9zvQdri7q2vDBwps+G0v4B/mLmcejwc/2PwC3a2XiIi3UyT+uXoIobCWf+AE35i9roLH9r1s9xC4mHxbTDjGsA2x3J7urW6fUZ1OVSVQGAUuPv5S82ptgSEw+wbTBCrLoPIYRAU3XuPL/1SlbeWovIawgM9+Pq4na6OSIf0808J6TUef4ge2T3HcnsgakT3HKuvOrAKPvo/yFoH486FOd/uv23OWAUf/9HMJRx3Hsy5sffbEjeudx9P+q0tB4u596MdLN+dzwmjYvj2ghGMigtxuloiR6VAJtLd8nbCY+dCVbG5/NV/oSAdvvFI/xtqy90Fj5/bpC0P1Lflf/2vLTLgZRVVcN2jX3GgsBKAl9ccYGNmEU9/aw7RwX4O106kfQpk0r9lb4b0pWY4LfU4SJru/BBozo7GAHPYzvfNgrj9racnd1srbXkPivaZRX37m4J9sO8LE5qTZ0LyLHPiyiCx9WAxX+7Jo6yqljnDIpmcEo6Pe+BMJU7PK28IY4ftyC5lb16ZApn0eQpk0n9lb4ZHToeKAnPZsuDyl2HEQmfr5RvQsszH3yxq2t/4BrYs8wSA27/363KsSrLhxW9CxorGskW3wXE/GBQr/285WMxF/1lGcaUXAJcFT3xzNvPSBs6cPH9Py/lilgUBHn3USd838N+FZODa82ljGAOwbfjkL1BTTkZBOU8v38dNT6/m6eX7yCgo7716xY6HYQualy38JUQM6706dJfYcTDsa83LFvyya7s0OO3QpuZhDOCTP0PBHmfq08s+25HTEMYA6my4b8lOqmpqHaxV90qLCeKC6cnNyq6em8rwmFa+WIj0MfraIP1XeW6rZRUVldz26i4+3HoIgNfXHWTRmFjuvmgKoQG9MJwZHAPn/sssgluQbraaSpzeP3thgmNNWzJWQuHe/t2WmsqWZd5KqK3u/bo4oKiipkVZQVk13jqbfth326pgfw8/O3U0p4yPZ3dOKSNjg5mcEo6/esikH9BfqfRfsePNeIRtN5ZNuICdhXZDGDvsw62H2J1TypQhvTRfKCzJ/PQnJdlmB4bqUig9ZMIYQFiy+envYkaDfzhUFjaWjT3HLL0yCHxtVAz//HhXs7Jvzh9GkJ8P1d46duWUUlrlZWtWMSNigvH007llMSH+nDTOH4hzuioindI/X3EiABX5cNLvIWkaRKXB8bdAeS7VtXWt3ry2aXCT5nJ3wBPnw6NnmgnvT15g/h1IokbAla80hrD5P4STftv6PLkBaHJyOP+7eiZTUsIZERPEn8+fyKIxcdTU1vHy6gzOvHcpe3LLOPMfS3lz/UHq6vR6EelN6iGT/ithMjxyJgw/wczPWvkwnH4Xw2LDmDoknDX7ChtuOnVIOMOjtUxDmza/BtkbGy8fXAdb34Tjvu9cnXpC4lT4+n+hqhQCI00P6yDh53GzcEwss4ZF4q2rIyzAF4DtWSX86tWN1NYHMG+dzc9fWs/E5DBGxOg1I9Jb1EMm/VfiNLjiZfAEQkkWnPl3SDuZyCBf/nbhFH6weCSTk8P4weKR/O3CKUQE+Tpd475rz5JWyj7t9Wr0Ch8/CIoaVGGsqSA/n4YwBpBTWkVNbfPesMqaOvJKq3q7aiKDmnrIpP9yuWDoXLPfoV3XbEufYdFB/GDxKL6zcAS+bm2dclRjzmoZwEaf4UxdpFclhPkT4HFT0eRsy1B/H+JD++HSJiL9mHrIpP9zudrcX1FhrINGnQrjzmm8PP58GHmSc/WRXjMsOoh7L51KaIB5DUUEerjv0mkMiQpyuGYig0uPBTLLsh62LOuQZVkbm5RFWpb1vmVZO+r/jagvtyzL+odlWTsty1pvWda0nqqXiLQiYgic8y+44TOIHg3n3AvhKU7XSnqBZVksHhvHmzcdT1pMMK/fNJ8TRsU4XS2RQacne8geAU49ouznwIe2bY8EPqy/DHAaMLL+53rg/h6sl4i0xi8YEiaZlfh9NZl7sEmJDCTA101yxOA461Skr+mxQGbb9qdA/hHF5wCP1v/+KHBuk/LHbONLINyyrISeqpuIiIhIX9Lbc8jibNs+WP97Fo0r9yUB+5vcLqO+TERERGTAs+weXCzTsqxU4A3btifUXy60bTu8yfUFtm1HWJb1BnCHbdtL68s/BH5m2/bKVo55PWZYk7i4uOnPPPNMj9S9tLSU4ODBO2wzmNs/mNsOav9gbv9gbjuo/Wp/z7d/4cKFq2zbntHadb297EW2ZVkJtm0frB+SPLy/zQGg6Qzi5PqyFmzbfgB4AGDGjBn2ggULeqSiS5YsoaeO3R8M5vYP5raD2j+Y2z+Y2w5qv9rvbPt7e8jyNeCq+t+vAl5tUn5l/dmWc4CiJkObItJbasqb7w0q0h5vNVSVOF0LkQGhJ5e9eBpYBoy2LCvDsqxvAncAJ1mWtQNYXH8Z4C1gN7AT+C/wnZ6ql4i0oqrMbJX06DlmD8ttb0F1mdO1kr7KtmH/cnj+anjoZFjxX7M5vYh0WY8NWdq2fUkbVy1q5bY28N2eqouIHMXez+GZS83vQWfA05fA5S9C2mJn6yV9U9ZGePQs8NZvr/TWj6GyCI6/ZdBuSSVyrLRSvwwutg0H18Pap2Hjy5C/2+kaNcrbbeq09mnIWt+7Q4erHmlZtvqJ3nt86V+yNzaGscO++AeUaKaJSFdpL0sZXPZ9CY+dDbXV5nLYELjiJYge6Wy9cnfAY+dCcYa57OMHV7xq9ursDQHhHSvrqIK9kLUBvJUQOw7ixh39Ptmb4dBm8PGHhMnHtlNA7g4TGrAgfgJEpXX9WN0oq6iSTZlFlFTWkBYbwtiEUNyuftij5OPbssw3CFz6SBHpKr16ZPCoqYBP72wMYwBF+yD9c+cD2Z7PGsMYmN6Hz/4KiU+Apxc2eZ52Jax/Fuq85rLbA1Mu7dqx8nbCE9+AgvreR08gXPUaJM9s+z77l8Nj55j/I4DoUXDJMxA1ovOPn7XBDKdVFJjLQdFw5WsQN77zx+pGBwsruOnp1azcWwiAj8vif9fM5PiRvbhNUfZmMzxdXQZD5kLS9Db3gW1XwmQIjofSrMayRbdBcGz31VVkkFEgk8HDWwWF6S3LmwYhpxS3sspLwR7Tw9QbgSx5Flz7Lux4H8ri4Zp3IXFq146157PGMAbmzM3P7oZv/M/0/B2ppgI++UtjGAPI3Q57l3UtkK17tjGMAZTlwsaXHA9kGzKLGsIYgLfO5vbXN/H8jfOICGylx6m7ZW2CR06Hyvo6WC7TCzv8hM4fKyrNhOxdH0PRfkhbZP6GRKTLFMikf6gsgZID4BNoNsLuioBwmH4NvPer5uWpxx9z9Y7ZsOPhs7ual02/5tiGDTvD5YL4yRAQBWt3mD0tXV2cYlqQboLU2LPNEFb6UsjdZgJXq4Gs3PSqHaloX9ceP2t9y7LsTV07VjcqLKtpUbY/v4Ly6lp6ZfvI3R83hjEAuw6W/g1SZnUt9MeMNj8i0i00qV/6vtzt8Ozl8M/Z8J/5sObJ5r0pnTHh63DCT83m2aGJcP5/IbnVRZN7V9JMOO8BCEkAvxD42s9gwvm99/jFB+H9X8M/Z8ChLfDBb6Ek66h3a9XIxZB2Enx5vwmZARFw4m/aDpeBUTDl8pblQ7o4f661odZJ3+jasbpRWmzLFcDPm5pEbHArIbUnVBy5tTBQltM4TC0ijlIgk76tphI++iPsWWIuVxbBq9+Bg+u6drzQRFhwK3x3BVz/CUy60ExGdppfEEy+CK7/FL6z3NQxNLH3Hn/n+7D8/voPZxuW3Qe7PurasSqLYfm/zXCrbZs1zQ4dpYdq8sUw60Yzdy0gAs66t+tBecSJsPCXZu6abxAs/i0M+1rXjtWNxieG8q/LphET4ofLgvOmJnLj10bg8emlt+ERLVYcgjnfBr/Bu1WOSF+iIUvp28oOwdbXW5bn7YQhc7p2TJcLwhzeu76i0JxNeORQUYhDk6I3vNCybONLXZvYv395K8d6AeZ8p+1esvAUE0InXwhuXzOpv7XhzY4IjoUTfgKTLwEs83/ddG0sb7WZ1B4Q3qtrZvl53Jw+MYEZQyOorKkjLswPPx93w/VZxRVkFVUREehhSGQgVnfXLWk6XPocfPwnqC6GuTfDqNO69zF6SkUheAK6/jch0g8okEnf5hsCUaMgZ0vz8qBePDOtOxVnwobnYfWjEDECTril68HyKMqqvFRU1xIV7Hv0D/fkmbDnk5ZlXRHVyhmr8ZNNj1VbcnfCazfBvi/MZPO534Pjbu76/7Nltb5sRuZa+PweOLjWDF9PuRwiU7v2GF0UG9pyvtbK9Hy++9RqsourCPJ186evT+K0CfF43N3Ye+bxh1GnwNB5UFfbe/MTj0VRhnm9rHncvA8c/yMz501kANKQpRPydsLqx+Dj/4PdS6Cq1Oka9V2BEXD6nc2/GY8+HRKmOFalLqurM1vMvP8byNsFO98zSz1kbezmh7FZtiuXKx5azun/+Iy/f7CDzIKjzLmbeAGENuk1DBsC487pWgVS55sAdph/OBz3/dbXrgITDr76rwljYCabf/EP2L+ia4/flvzd8Pi5sOkl8/und5q5ctVdnI/YTQ4VV3Lz02vILjYLrZZV1/KDZ9awM7uH3hf8QvpHGKv1wrL7zf9R3i7Y/rZZQzB7s9M1E+kR6iHrbQXp8MQFZkmDw869v+trPg0GqfPh+iWmF8U/zCw0GtwPe8iKM808raa8lWYx1PgJ3fYwmw8Wc8VDK/DWmZX+7/lwB+XVtfz8tDFtL0IaOxauedtM6E+vhlPf7PrZrJHD4NJnzZmNtVUQM6b95SsqCs08syNlroExZ3StDmCGJqExCB7a1nw5DIAtr8CCn0PsmK4/zjE6VFJFZlFls7I6GzIKyxmbGOpQrfqAkkz46oHmZTUV5m+0IwsN9wZvFWC1/WVDpBMUyHrbwfXNwxiYs9tGnAgh8c7Uqa+zLBPCYjvxJlxRaJY/KNoPYSlmIUv/sB6rYoe4fczZnUeeIdrN82K2ZZc0hLHDHv8ynWuOSyUxPKDtO0YMNT8Hl3Q9jB0WmmB+OsIvBFJmQ+ERy1zEju3aY1eXw55PTS+b5YJ5N5tlRVp7nt2+5kQCB0UE+hIR6KGgvPmyGLEhZmizts7mQEE5lmWRFB6Aq4+s7J9ZWFG/64CXkbHBjE0Ixac7h1hdPuakjIrq5uU9NY+s6ID5AlFTYeartaeqzAzxL7sXXB7zN5Y6v3fWDJQBS0OWva215RqqSkz3vHSPmkr44j6zWvsr3zH/fn6vKT8WFUVQ23ItqQ4LiafmxNubFdnhQ/HGTWr/fgX7YMWD8OwV8NXDLYPLEQI97hZlEYG++Hbnh2V38vE1Q5pNv5CknQwpZm7dloPF/O39bdz09Gre2ZhFUflR/g/2fg5PX2T+Tf8MnvqG2TIrIhXij3iu537PlDsoKSKAO78xueH/x7LgZ6eOZlRcMIdKKrn7/W2cdPennHT3J9z78Q5yS6uOcsQeUFXS7PWTWVjBH1/fQOH+rQTmb+LZpRv5fFfe0Y9TXW56TrM3H/31GJoIi5u/XogY3vL/8FjVVMD65+A/x5vet1e+bYZI27N3KTxzCez9wgSzJ7/e/GQWb415vxDpBPWQ9ba48ebsOm+TN6NZN3S8N0GOLm8HLD1ikdWld8H4cyB+YuePl59O7dqncW9+kdrEGbjmfgcrofMfClXeWh7Km8D8Ex8loWAlFQEJfMV4hpeFMzWqjTtVFMIbP4BdH5rLW16DkafA+Q+0OQ9oQlIYI2KC2JVT1lD2qzPGEh3Sh89Qi58I131g9qD08Yfo0RAUxc5DpVz63y8beo9eX3eQ3587nivmpLZ9rJX/a1m25kmYfpXpxRh5MpRmm97ArE1m4njE0J5pVwedODqWN2+ez4GCCqKC/RgZF4S/x4fX1x/kvo8bw8Hd7+9gWFQQZ0/ppbOEy3Jh29tmqD0oBo6/BYbM4+ChQ/wq5jMSvvozeKtYFDeFlfl/pLA8jPC2dh0o3Efdh7/HteE5sCzqplyBa+FRlneZcL45OSN9KYQPMf9/x9p7e6SD6+ClbzVe3vSyOZnozL+13ntq2+YL0pHWPwvDv2ZOHFl2nzlxZOKFMOkix/++pH9QIOttcePhylfNhOL83TD1SrMWlqtlr4Z0UWWxedNsyrZNeWdVl1H77q9wbzNLb7hzd8Cu9/Fe+wE+UcM6daickir+uuQAf6nzEBOygNJKLxU1ZdwVVcrUIRGt3ylvR2MYO2zHu+bEkLjxkLEStr4FgZHmDLqESaREBvLQ1TNZs7eA3NIqJqeEMyk5vPNt74iiA2ZepH+oObvyWIZswlLMTxObM4tbDOXd/f4OTh4XT1wrZysCZgi0tbLKIvjyX2bIKyDChDLbhq/9tOt17iYul8XIuBBGxjWv+0urW27r9fr6zPYDWWWR2XZqx3tm7l7a4sYV9TPXwvZ3oarInByTNBM87QT1La+bLwSHpX8G17zL0Koior/8XUOxJ3stk3b8i8oxE6GNQObd8hY+G54zF2wb15rHqEmagWfGVW0/vl+Imc4x4sS2b3Oscre3LNv4gplb2NbyOK39jfkGm97sx89tnKv48R9Nb9tZ92g4U45Kgay3WZZZ5uCiJ0yXfWAbH8TSdRGpZvir6UrzIfFdGpqqzNmD/7Yj1kEry6X64JZOB7JAXx+GRAawJ7ecnJLGYafIoHYmBB8ZLJuW7/kEnrqoseyLe8zE/PiJpEYFkRrVwwveZq6Bpy82z7NlwfxbYN5N3XoGX10r7ffW1bX5tAAw42rY+Lw5exPMXKQpl4BfqAlj3qrGv42UuRCW3G317W4Tk8L4cnfzFfbHJRxlLuT65+GtWxovL/snXP0mVBWbvSyryxrLL3/J7EPZmvICMw+vqbpa2PsFIQHRLW4etPcD/O1ioJX61dXhs+WVFsWu7W9De4GsNwREtiyLHN72gtGWBbO+CZtfNmcEg+lJm/gNM2/1yBNHNjxrehZjRnVvvWXA6aOTSgYBT4DCWE8JS4KLn2mYg0TKHHO5C4vBVta6Wh22qLI7/10mMsiX28+egI/LwtftwrLg+LRoxrd3Jl1Umpnw3tSQeWb4Zsmfj6hUiRna6Q1VJfD2rSbYuOsD5Wd3tb6P5DEYmxBKsF/z5/p7C0cSH9ZOb0PyLLj6bZj/Q5j/I7jmHUiaYXqJLnvBnPVpWTD6DDj7HtO710edPy25WWCPDfXj9IntnPxTlAkf/b55WfEByNpgNgKvLmt+3Wd/bXvZD7ePCbFH8g3CExrXotiOn4w7oPWwWIdFYVzL9fYKY7q41l13ihnTfGN0twcW3db+F4vk2ebLz7zvm7B19dtm4V2fVv4u3X7mS4HIUeivRAakvPBxZJ/4EO6qQmr9wokLj6GtaVrt8YYNpXj69whdcXdDWU38NCojuvZtd2ZqBA9fPYMtB0uICPJlSko4caHtnNEVGGmWRdn0shmqHHkqjD/XBPqaspa37+oen51Vnk9VdRXrFz7F5vJQony9TK5eQ0rR/m59mNHxITz1rdk8s2IfO3NKuXjmEL426ihLnrjcMGS2+TnSsBPMB2lVCQTFgu9RzqZz2NiEUF769jy2ZhVjWRZj40MY0l7Pp+1tPj/1sNqalmEMzBqIdm3rx/ILgYW/gKcubCzzD4eh83AFRcGEC8zQHoBfKNYpf2wz3LpcFjlDzyJ4x6v4FO4GoDpqLPlJi7r0uuxWhftMWE9bBCVxZpeHDc9D6nFt95K5fcxIx5GLOseNM/MhszY0lp3wE8dPHJH+QYFMBpzyai//+HAHj36xt75kL1fNHcrPThtDoG/n/uTDgwL4NO5CEhaMIS5/JUWho9juP5nR/i2HbDri4205fOfJ1Q2Xx8QH89BVM0mKaGcV+6gRcMKP4bgfgrvJXMN53zf7eh7mckPq8V2qV6cFRPDBpLv47utZgJmbNzZ2Mg+OSaG7p5tPSjZz4Grr7LbXUeuMwEjz00+kRgeRGt3B4efQZHPm6GdNTmrxDTZBISzZlB8eZgOzG0J7e1kOWwBXv2XmMQZEwoiFjWuAnf5XmHmdCbdRI9pfaw5wx43mpUn/ZpidAZbFHiuF2XGt7OrQ24r2mZ0AAEbfDqv+z/R0Lb6t8/vchibChY/Dns8gd5s5CSFlttmuTeQoFMhkwNmdU9YkjBmPLtvLN2akMCGpc2uRbckq5tsv78VbF0pcyGnkl1dTWZPLf8JLSI3u3KbMuSVV/O715quMb80qZVNmcfuB7LCmYQzMpOzzHzQT1YNizIdr0rRO1amrcmp8+d0nzefKbDlUycbSoG4PZNTVQcFu3NXlZqi2I3PUCtLNgrvUr2E3WM5yc7lMSAqOgzWPmeG4Od82a7rV1sAVr5h5YRWFZm/RkYvbP57Hz/QUpR7X8rrAcBg6t8NVGx4TTOisKew6lIbLZbE4JojIoD5w5u8RJ5IAZlpAQBenlEQOMz/9SEV1Lfvyy6jy1lHlrW22x6r0HgUyGXDKq1tf062t8vZUemup8poehaarqZdVtzHMc5Rj5ZdVtygvreriGnSBETDpGzD2LLDc4NN7C5xWeetabUtZTXuz7bvyQCWw9in44DYzHJs4Hc79V/sr62dvhifOh5KD5nJYipk75uBq/L0qNAFmXw9TL69f+Lb+bd7tMcsyDJlreskcOOsvOtiP6OA+EMKaSpwCc74LX/7TXA5NhJNu73zvWD91oKCcu97dxivrMrllgpfPXt/MTSemERfWt4fzByL1o8qAkxoVREpk8zeTlMiALp11OCwqiFmpzb8pB/v5MDK2c71jAHGh/lw8s/kZfT4ui1FxrZxC3xke/14NY3C4Lc17FrqlLUc6uA7e/mnj3LjMVfDRH9qfK7fh+cYwBma3hs2vdm+9+gPfwMYw1pSPr5ZgaCowCk78FdzwqTmJ5roPoQvrDPZX727K5uW1mdg22MATy/fx2c5cp6s1KCmQyYATG+rPA1fM4JTxcYQG+HDK+DgeuGIGsW2tW9WO6BB/fnnGOM6enEhogA8zUyP4z+XTmNiFdb08bhffGm9zw4wwIgI9TEwI5JFzohjnm9PpYznN43Zx/Qkj+Nbxw4kI9DApKYxHr53FuIRuPmMxf0/Lsh3vmAVL25LxVcuyzNUty0QO8w0026v5Bre/UO0AU1tn88b6gy3KP9xyyIHaiIYsZUAamxDKPRdPobCihvAAD/6erv+pT04J58/nTySzuIKIAF8ijxxyqazfIuVoe2Xmp5Py5mX8zOXLtVPPIbBsPyHvvQxn/xNi0rpcv86qrbMpLK8m2M8Hv1a2WeqolMhAbj1tDNcdP4xAj5uQgB7opQtuubwCcRNbX47hsEkXmQVMmxp/XvfWS2QAcLssZg+PZPW+5vNBpw/VkkxOUCCTAcvf40P8MQSxpgL8fBgRc8RwXGWxWfX8078AllnxfdQpra/iDVBTDsWZuOq8xOX/tbG8qgs7CHTRntwyHl+2l7c2HGRSchg3nTiSicld33Td5bLaXjG/OyROab68gm8wnPonaGO9KwDSToK5N8OK+wEL5n4Xhi/suTqK9GPnT03ijXWZ7C8w0wDGxAdz4phYh2s1OCmQiXRV+lJ46brGyy9+Ey55Bkaf1vrtI4bB+PPh8PYxAJYLonvn1P+ySi+3v76JJdvMEGnW5kpWpOfz6nePY2hPr+rfVcGxcMZdMPObJgBHpUH0UXoTQ+Nh8W/Miv1Y5szM1uZSDWS5282WPQHhEDO2W3dPkIFlZFwIz90wlx2HSijes57HzprdpekdcuwG2buUSDda/VjLsjVPNA9kdXWNaxD5BsD0a6CuBra+CaFJcNwPzEbavWB/YXlDGDussLyGXYdK+24gA7P8wNB5nbuP23PUdbEGrPTP4ckLTI8swJTL4KTfQVDX1s6TgS8hPICE8ACWZPoojDlIgUykq0ITWpYF129rc2gLrH8O9i2DiRfAqNPMafTv/NQsUTHnO1B2CN77BYSntH6sbubrduHrdlFdW9es3P8Y5pFJH1OeD2/e0hjGANY+aYZ903pwg24ROWYKZCJdNeUys0bW4a1qfPzNJtaF++HJC80K4GBC2cH1Zm/F7E1mDaiDaxuP03QT9B40NCqImxelcdd72xvK5g6P7P6lKsQ5lcWQs6VleWn2MR22pLIGj9ul8N5DDhRUsOVgMdW1dYyOC2FEF5bVkf5PgUykq5JnwDffhb3LAMusWp4wGba/1xjGDlvzOMy7CUaeDNvfaX5d5PBeqa7bZXH5nKFMSApjfUYhw6KDmD40kuiQPrZQpzSorbNZu7+QD7dk43ZZLBoTx6TkMFxtbSEVHAMjFpmtjprq4srxeWVVvL8pm4c/30NUsB/fW5jG7GGR+Li1YlJ32ZNbxrWPfMWeXLPXaIifD09+azaTurC0jvRvCmQixyJhivlpymrlw8qywLZh8W9Nj9jBtWaD8JN+D/G9twhleKAvC0bHsmC0zqLqD1bvK+DiB76kts7sgPDvT3bx7PVzmdbWsgS+QXDyH+DlHMhaD55AOPmPXf4be2dDFr98ZaO5kF3K8t15vHDjvLYfvyNKD5nXQ9BRNokfJJbuyG0IYwAlVV4eWrqHv35jsoLvIKNAJtLdYsdCZBrk72wsm/ktiEg1q6Rf8YpZPd43yPSOWd2wYbYMSE98ubchjAHU1Nq8vCaj/UAUNw6ufO2Y/8aKyqt54LPdzcrqbFiRnt+1QFaeDxtfgs/uNPMoF9wK484++vp9A9zu3NIWZVsOFlNZU0uwAtmgokAmfV9NBRxYDQdWmoVCU2b12jBfl4QlwaXPmDMpM76CMWeadbB8fM31gRHmRwafvF2wfzmU5UDSDLMZvKftPQNLK1vuc1rcSlkL3fA35nZbhPq3XOw32LeLHxu7l8BbtzRefu17EBgJY87o2vEGiPlp0fzv8/RmZV+flkxwK8+9DGwKZNL3bXsbXrim8XLUSLj8RYgYClkbYffHUFFkziJLmtEYfJwUPRLm/6D7jleUYT7Eg2JN4DuKujqbvfnllFV5SY4IIDywA89J1nrIWAnlYZC1AeInmvL8dLPmWs4WGHocpMyGoKjGeuXtBv8Qs4L+YFvvqzMK0uGJC6CgSa/ThY/BuHPavMvlc4by4dbm29hcMD25jVt3wKGt5kuCtwKSpkPC1MZlWY4Q7OfhRyeN4ppHGreiCgvwMHNYZOcf17Zh1SMty9c/N+gD2Yyhkfz6jLH87f3tVHnruGz2EM6aPHi2b5JGeveUvq30ELz7i+ZleTvMptNVJfDI6Y1bFy29Cy57EdIW9X49u0Ntjdm7sa4GwoeCX/2ZVrs+gpe+ZfZvDIqBrz8Iwxe0eZjSKi/PfbWfP7+zlSpvHRMSQ/nbhVMYFd/O2ZQHVsMjZ5jlEkbfDg/fBFe/CSGJJgwf3gty2X1mqOmEn8CBVfD69+HQZnOG6dd+BtOuagxrfVFRhgkkhfvMvKrE6RDQzftvtiVzbfMwBvDuL2HIPDMZvxWzhkXy4JUz+M+nu/BxWVx/wghmpHax5+vQFvN6Kc83l10+Zmgz9ThySipZu7+IwtIqPtySzZSUcKKC/Zg3Iopnr5/D0p25hAd6mJ8Wzej2/o7aYlnmxII9nzQvj0jtWlsGkLBAD9fOH8YpE+KprbNJDA/Ao6HKQUmBTPo2bzVUFLQsr62GPZ82hjEw38I/+QsMmWs2C+5ptTWmR2lD/bY+Ey+A5Jld6yUqy4Pl98PnfzfHHXUanPJ/5rrnrjThE0wv2XNXwvWftHnm3KYDRfzujc0NlzdmFvOXd7dy7yXTCPBtY9mCNU80X7uqugzWPQcjT2q5Mfdnf4UJ58NHfzBhDMzSHx/ebuYvjTq18+3vDaWH4KUbYW+TfS5P/qNZE66NXqJu1fT5PayiAGqr2rxLkJ8Pi8fFcfzIaCwLfH2OYdmJXR83hjGAOi8s/RsVsVP5yzs7eH5VBrdM9PKTR1dy1byh/PzUMQT4+jB7eBSzh3dDyJ52FWx43vxtgdmPdMLXj/24A4BlWSRH9MJ7lvRpiuHSt4UmwIzrmpe5PRA7BqpbToalstB80BxFcUUN5dUdmIvTnv0rTI/DygfNzyOnQ8byLh5rOXx6pwljANvfhrVPQPGBxjB2WGWRKW96ubrxw35vfssP/o+35ZBX1vYHP0X7WynbZ4LvkepqTJ3SP215XcHext+ry8xQcmcc0ZajqauzKSivptpbe/QbZ29qHsYAPv6DGUrsDbHjTK9UUzO/ZXohj8LP4z62MAZmIeIjFWdyqKiU51dlNCt+bNledjc5869bJE2Da9+Dc/4F5/7bLBmT0HtnGIv0deohk77N5YY5N4JfkNmqKHwoLPo1xIyDqjJY8iez0Ophc28C/7aHoPLLqnh3UzYPfraHiEAfbl40ijnDo/D16cJ3k9WPNn9suw5WP27mWXXW/laC3KaXYdy5JoAeDmoAbl+zDU5ZrjlxYNl9EBBphgxT5xPXyrpiExJDW52g3WDalbDjveZlUy6DmNFm4nXTnpVx55q5bDFjG3vIDguOM72a6Z/BkjtMQJ53E4w+o/2hzLJc2PaWCbmWG8afA0PntzsfMCO3hNIDm6ktSMcVHIt/4niGJbaznEdNRetl7fRQtaumEjtnG3VFB3CHJ0PMmPbnL8ZPgitehg9/bwLw9Gtg6mVd752rKsE+tJW6shzckcPMFlxNj1VTaf52XPVBbsSJsPTu5seYfQOHqlp+DNg2VNZ0IOR2VvwE8yMiLSiQSd8XnmLmLc38ljkj7fDcqqRp5gPu079CRR7M+S6Mbn+47N1N2dz60oaGy1f9bwXP3zCXGaldmKjsbaX3qLWyjogd27IsaaY5geG0u+DNH5hPScsFp//VLKux5gl44/uNt3/yfLjmHcYnTeX8qUm8tMb0ogX7+fDrs8YRGtBOIBt2gum5+PQvJvCd928TiAJCKbvoRdzL/4V/9hrKRp9H3cSLCAlPMWuoPX9VY0/l+PMgcao5G/bJr5v6Arx2E5ztgmmXt/346Uuh5KA5ccA/zMx38gs1i++2oqzKi3vXu4x594aGHtHcGT/ikO/3iI1uI/hFjwK/kOY9jqNONZuPd5a3mppVj+N59ye4bRssi5rT78Ez/fLGAHQkl8s8z1e8bIJgG/PGOqSyGO8nd+Gz7B7cAG5fai54DM/Y08w6d1vfNIsRx4yBWTdA0lQznH7REyYQVpfWB+XTGVIXSHJEABkFjYE1LTaY1K7ub1pXZ+Z5Fu4zm8NHjwJPAN7aOtbuL+TdTVm4XBanjItnSkp424vcigwylm3bR79VHxUyLMSeftv0Hjl2YWEh4eHhPXLs/qBftd+uMx/+bX0Q1quts9mYWURFdfNv/ikRgSRFNC490OG2VxRC9sbmZXETIKAD9z2Stwpyt5mtb8D0bMRNMOtI2XVm/pG3Gnz8zGKfdq05seHIXp/woRCegrfOpqK6lto6G39PJ7a8qa2hsKSU8HAzcbzKW8fevLL63jWbOhtqautIiQzEZVlQVQrecjMU5wk2PUSF+8xPU76BED+59f+jOq8JY02HOy3L9Pi0sSF2TVUFnux1LYanvbGT8AlsZ5J+VYmpW3WpOUEiJKHFshMd+f+vqyrDlbW2MXQCWC7qEqbg6oX5i3UVRbiyNzQvdHuwE6ZglWaZ7bsOc7nNDhKe+nrVeU293Y0Bvby6lgOFFQRRRTl+JIYHENjWfMOjKc+HnK3m79YCwodBaALFVbVsOVjc8JRZlsW4hFBC/PtOv0C/et/rAWp/z7f/k2s+WWXbdqvfNPvOK0GkqyyXeeM/2s0s8HG5gOaBzN3Vb+j+oWYYqroEsMA3uLH3rj3V5U1CTJD5YPTxM0OANWXmw9ITYM5cBNM+32BoNhpmtZyPBA2Bx8dlde2Dzu2h6ZNZVVOL22WRnmfmE1mWRUpEAFXeOgI8btPeI9vcWuhyedpenNS2oTSnZVlNGdB6IHPZta3OFbTqjtJD6RdieiPrvC3a2hl2bXXzMAZg11Hnre6VQGa3NrevtsaE9qIDzcvras18vsOBrJW/m0BfNyNigigq8pIQFoTV1cWKvVWQt71xKN8GCvaAfxjZxc2fMtu2yS2t6lOBTJrz1tmUV3upqTVf7AI9PlrHugf161fC6KjRLLl6SY8ce8mSJSxYsKBHjt0fDNT2f7Y9hyv/t6LhgyEswMNzF81hdHxjr0qH235oi1lXqrh+snSYL5z/n9aHHw/b/Qk8eUHjZPnRp8GZ90BIXOcbs/PD5kODARFw0SvmTMdj0LT9H27J5puPriS+yfUhJb48ctEsxia0scJ61sbmy5FYLvjGCzBiYeu3ryoxS24cXNe8fOYP4bibW72Lt+QQ7odPxirY01joclN13tP4JU08eiPb0ZH//9y9W4h+cnHzE0sCIsg96zmiU0Yd0+N3RN6Or4h66qRmCccbNYbqc58g8JHFZu5eUwv/r931zg475td95lp44GtA83mM9td+z3VfRvNhfvMTC84fksTfLpzS9cfrZgP1fa+jmra/sLyaO97eyjNfmd7WMgv+cNl0TpkQ384R+rfe+P+3rmk70eosy04oraqhoLyLc4T6mK60pbC8mtKqmqPfsAOKK2oorujcsfLLqo75zMjZw6N4/oa53HLyKH579jieu6F5GOuUjS9CcZOz04r2m4n4bSnPh7d+0vzMxW1vQ9a6tu/TntTj4Zp3YOGv4LQ74eq3moWxjIJydhwqoayq68/ZkcO7AHll1dS1N9MhfgJc8zac+mdTt2vfgdT5bd/eL8SckNCUb3DbAQ7wCYml9rz/YoenmgL/cGrOewi/+HbCcDfyxI4g4+QHGvdjDIkn46R/4x/TuBRJQTe+Xo5UGz2GzEX3NWw7VBcxnKwT/4ZvzAhYeMS6fSGJjYv89rSQBAg9YuFilxsrPIUr5gxtcfOvTzuGRW6lR23NKmkIY2C2zfrlKxs4WNTKyTHSLfp1D1lvqfbW8sWuPO5+fzuFFTV8c/4wTp+YQHRwy7PZ+roj23Ld/GGcdpS25JVW8fbGLP772W7CAjz8cPEo5o2Iwq+j85KaKK308tHWbO792OzzeNPCNE4cE9vuNiGZhRW8tDqDZ77aT0pEAD9YPIpZwyK7NKzi6+NiRmpk1ybxH+nAqpZlGSvbvn1VqZnsfKQjh+s6yscXhswxP01U1nh5Y30Wv3tjE8UVXk4YGc1tZ41nRGwHhlOPMDwmCJdFswCWFhtEQljb2/0AEDfe/HTUiBPNIqVbXjchZ/SpRw0RPkNmwnXvQ3EmBETgiWj5gd9TwgJ8KUtbyNaQN6gpycYTGkdY7FCC/T3kllbx5vqDPLR0DxGBZrX7uSOiu3YmbxtiI0LYN+58tkZNxluWj09ECtGxSWYz6kkXmRMVtr0NUWkw6uTe22osJA4ueAieuwpKs02wPuvvEDOaWZEWD181gweX7sbtcvGt44cz41g2KZcelV/W8gt7bmk1JZVe2uocl2OjQNYB6/YXcc0jXzWMDvzm1U1YwBVzU52sVpes21/YrC2/fnUTlmVxeSvfXg97b1M2v3qlcfL6tY9+xbPXz2VWF7ZQ+XJ3Hjc/s7bh8s3PrOXBK6ezeFzr3eC1dTaPfJHOA5+aFc4zCipY9dAKXv7uPMYnOvyuMP58s4p+UxPOb/v2wbEw5izY8mrz8qi0bq3WxgPF/Pj5xl63T3fkcvcH2/nrhZPx6+RaViPjQrjv0mnc+tIGiipqGBETxN0XTiEyqJu3p/IEwPCvmZ/OCI41Pw5IDA8kMXwMMKZZ+VsbDnLba5sA2JcPVz/yVdfP5G3HkMhAiGwltAaEm6Hw0ad16+N12JC5cP2ShqB8eHPzQDecODaO+aOisWwLTzcGVOl+Q6MCW3wZm5ISTlyov3OVGuD0iuiAFXvyW8zfffjzdIr64fDlst2ttGXpnjaHD4sranjo8z3NymwbPt+Z26XHf25lywVIm3aLHymruJJHv0hvVlZdW8e2rJLW79Cb0k6Cud8zk6TdHvN72uK2b+/xh0W/MhuNA/iHmwUyu3lxzD2tLOj5zsYscko6v96Wx+3i9IkJvHnzfN68eT7P3TCXicnh3VDLgamovJqHl7Z8vazYk9/GPQao0ESzZEnUiBYnc/i63Qpj/cCouBD+ffl0ooPNl6+pKeHccf5EwtpbPkeOiXrIOiA0oOXTFBXk6ZdvKq29mKKC/fBxtz7853G7iA7yZecR5eGBXXtRxoW2HBpt7xuXr9siLMDDoSPCREAXhku7XWg8LP6tWeDTwiw54T7K8xI9Ci563PQeeALNGmvdLCq4Ze9VWmwwIX5df7knRwSSrNGlo/Jxu4gM8iU9r/luA+2uASfSB3ncLk4eH8/E5DBKKr3Eh/rr77iH9b9E4YDZw6OIaBJAXBZ8f/EoAn37X56d01pbFqW12ZYAXzc3LRpJ05UhwgM9zBvRtb3tvj49Gb8mQdbPx8U3prc9sTcmxJ9fntF8onZqVCDjk/rIJAa3B6LTzLDj0cLYYX4hZgX8HghjABMSwzhxTOMwnp+Pi9vOGkdYYDcPM0oLQX4+/HDxqGadQpFBvszuwvC+SF+QEBbAqLgQhbFe0P8ShQNGxYXw3A1zWZGeT0mll5mpEUw62rBNVYlZfXzl/8zWMzOuhaQZvbOJcTtGx4fw7A1z+So9n9JKLzOHRTLxKOFm9rBIXrhxHl+l5xPs58PMYZGMigvp0uNPSYngxW+bY9k2zBoWyYSjPP7J4+J4+luzWbW3gLhQf2amRpr5M06rqYL9X8LKh83lGdea+TPtbZ/Thro6mzX7C3l6xT7yyqq5bFYKc4ZHE9yFNZpiQ/2584JJbMkqpqTCy/CYYEbFdX5Cv3TN7OFRvHDDXL7aW0CIvw+zUiMZ2cXXi4gMHgpkHTQyLoTYED+qa+uICWk+xFZVU0tBRTXhAR78PfVP6a6P4LkrG2+08QWzREEbW8Eci7JqL6UVXiKDfDs0jDoqLqRTgcrH7WLa0AimddMZUROSwkgMM89hZAfOVA3w9WH2sChGxobg5+MipK98U9u/HB47u/Hyllfhitdg+AmdPtTGA0Vc/MAyamrNBL+Ptx7in5dO44xJCV2qWlSwH/PTjmFrnj6ksLyabVkllFR5GR4dxPCY3g+XBeXVeFt57bfG18fF9NRIpnfzJP5Oqyw2iwt34QuCiPQ+BbIOqKqpZdveA1RlbQVvJXuj0xgyZDixof5szSrmng928PmuXOYOi+L7i0cxLsoFn/21+UFqa8yioMkzzErtudvNG2Zkatf20qu3Zl8Bd767jU2ZxZw6Pp7rTxjepeUNektRRTXvbMziHx+aWWk3L0rj1AnxhAW0/aGRk5dD7d7l+Kd/SEXwECpHLiJy6MSur7DfXVY90vyybcOax7oUyJbuzG0IY4fdv2QnC0bHEHQMc7/6u5ySSm5/fTNvrD8ImBXlH712FjN7IuwU7of8PWY7qupy8A2kssbLx9tyuPPdbZRWevnW8cM5d2pih4JZb6murcXX3WROZXEmbHzJ/C1Gj4Z5N0PKTOcq2A+UVXvZdaiUsiovmYUVJIYfZVkXkR4weN/pO+HQgXSGfflbQna+BkBteCqZpz7EISZw/WMr2ZdvFsp7d3M2mw4W8+L1M4izWpl0brmgvACW/hW+uNeUBUXDpc9BUuf35NyTW8aVD62gpH7hz2dX7md/QTkPXDG93XW9nLRsVx4/e7FxD76fvbiB0AAPp01ovSfIW1uHZ8urxHxwS0NZ3fr/UHDhK0QNGdPqfXpNa8PPrf2/d0Bra6o5nTf7gg0HihvCGJg9F//wxmYe++bs7j3bK2MlPHUhlOfB6Nthyecw/xZWZ9by7SdWN9zsj29twd/j5oq5vbfmWVu2ZhXz3Ff7Wb2vkHOnJnLS2HiSwnxh+b/h83vMjXK2wc4P4LoPj3kHh4Eqv6yKez7YwaPL9nLLRC+//efnPHzVTCYm95F5qjJoaFJ/BwQeWklIoD8s+Dl87ae4x51N+MZH2Z1T0hDGDssoqCC9sAaOv6X5QXz8zBpLWesawxhAWS689dPGTaU7YXdOaUMYO+yLXXnsL6ivU1WJ2d6n6abNDnt+ZUaHyg4rzz9A+LI/NStzlR7Ek7OhjXscXUV1LTuyS0jPLaOu3SXnj2L61c1P6bcsmHZFlw41Py2q2ckOAN9ZmNYnesfKqrys2VfAB5uz2ZZVQu2xPGedlNvKUh2bDxZTWtmNK+BXFJnXYHleY9kX90LWej5rZXmXx5alU9Kdj98F+/PLufLhFTz8eTpr9xfy29c2c9/HO/EWZphA1lRNORza7ExF+4ENGUU8uqzxPTKnpIo73t5yTDtciHSF8+/2/UCoxzZDGeufMwVB0YQsuJWgNp69QI8PJC6Ey1+EtU+bSf2TLoLEqbD2qZZ3OLASKgrMZtWdEOTbsjfGz8eFv4/LfDN+80fmxAK/EDj5/2DiBdALGx+3Z0hUy8dPiWh7eMBDnRlCOoKrrmsfiPvyyrjjna28tSELPx8XN52YxuVzhhLelTMQk2fBVW/C2idNGJt8KaTM6lK9JiSF8ewNc3hldSb5ZVV8fXrysS8kWl1mnrug1jfo7ojSSi/3L9nJP5fsAsDjtrj/8uksHtuFvTe7YGgrfy+LxsZ17y4ZlQWQ2cquC8UHiA1pOZ0gIcy/RXjuDrV1NoXl1QT7+Rx1F4zt2SUcKm4eVp/9ah8/nTeeCE+g2eS7KXf/21WkgW1Dea5ZJsY3qNsPf6Cw5fvLyr0FFFXU9IkvRDJ4qIesA9zecsj4qrGgLBd710eMjAngmnmpzW57+ZyhDI8NAr8gs0joBQ/B6XeauWOW1fp8saHzIaDzH76j4kNZOLr5xO1bTh7N0DA3fPRHE8bA9JS9flOzPRO9tXVUe1vuU9jTzpuaRGCTIBno6+br7Sx7ERA9hMqZ32le6AnElTCh049t2zbPr8rgrQ1ZAFR567jrve2s3lfQoftX1tQ271Hz8YXU4+Dcf8E5/zS/d3TpiyNYlsWUlAh+e854/nHpNL42OrbrHwZ1dZD+OTx5ITywAD69E4ra7oVsz7bskoYwBlBTa/PzF9eT2cqHWE+YkBTG784Z37Du3LQh4dxy0qgubdvVpsAoGDKvZXn4EI5Liyaqya4EHrfFdxem4dvJHQ+OZk9uKb9/YzNn/GMpNz+9hg0Hitq9fWvzJ90ui2KfGLM2nn84lSNOozZuslkfL6GX9rLsboX7YMkd5u/46Utg3zJarGx9jFo7Y/u4tGgitEwMAFlFFWzPLun03sPSeYr/HeAqbvlhZmVtwN+u4qZFaZwwOoY9OWUMiw5iUnIYQe2tTxY/CRb+Ej65A+pqISwFTv0T+Hf+tPjIIF/+dP4k1mUUkllYwai4ECYlheEqz4Jtb7S8Q95OapPnsGpvPg9+toe8siqunpfK8SNjutZD1AWTksN56dvzWJ9RRB02k5PDGZvQTs+gZVE95SrqAmMIXP8YNRFpVM38NsHJnV/dvqiihlfXZrYoX5VewIlj2u7xySgo5831B3ltXSYzhkZwyawhjGmvzk7L3gCPn2NOJAH46A8mlC/6baeXXckpqWxRlltaTVFFTa9MfA7y8+GKOUM5YWQM5dVekiICu3+lcL8QOO0OeOYys0E8mE3R4ycxyt8sebM2o5DK6lomJocxoZu37CqtquE3r2xqGB7N2lzJivR8Xv3ucQyJar1HaFRcCFfPHUpksDnzO8DjxtdtkRgRQLbrXN4/bg7Prs1lVLQfV81JZlJE187WdVRtDXxxH6z4j7lclGHObP7WR53bJ/UoJiSF8b2FI/hX/RePoVGB/OSU0QTUf3Gsqqklr6yaUH+fPjs3tyd4a+v4eNshfvHSRnJKq5g2JJz/O29i337v6+cUyDoiuZUzlMadAwERRLpcLBwdy8LRHTxWQBgc9wMYc4b5kIxIhZDW93FsJme7+aAFiJsIMaMAiA/zJz7syPuHQPRYOLSxeXFQLOszCrnkv8sb5gGt2ruWv104mfOntd1L1d3GJIR2+EVt2zaPrK/koaXDWDj8TjLyvWx+tJCXvlPMmPjOvTEE+rqZmBTKvvzmq6gPb+es1IpqL3e+u60hyG3KLOadTVm8+O15JEf0gbXQWpO9mYOTb2ZT8FyKa31J881j3Ma/4jPregjr3P/zkMiW+9mNiAlqdceFDqssNkP0gZEmDB2FZVmkRnf/UFUzCZPhug+gIB22F8JxJzYsFzEiNrhHz1zen1/RYq5aQXkNu3LK2gxkAR43OaXVPNJk7tNfLpiE27J4YmU2935kwsWGA/Du1nxe/k5w/1sLrTgTVj3cvMxbCdmbuzWQhQf6ctOikZw5KZHdG77ihbPnNpxFuyO7hEe+2ENBeQ2+Losr5w3rtuV/+rrt2SXc8Piqhtf+6n2F3PrSBh69dpYWie0hGrLsiJTZcMJPGoejRiwy2+XU9zbYtk15tRe7ta700kPmzMqmfHzxRo+lImFmyzBWWwMlWWb+z2FZG+GR06le+QTVq56ER86Ag+vbrm9AOJz+Z7MG0WGjz4CEKSzblYefj4ubF6bxq9PHMDQqgPuX7KKkA93RlTW11Hjrjnq77pRVVMl/PtlFUUUNr2wqYOW+Esqra9l2sPN7Wfr6uLlxQVqzHpbJyWHMSm37DTajoILX1jXvVcsurmJHdmmnH7+3ZAaP47vp87nuvWp+9GEp577jx2dT7wJ353tBR8aFcM/FUwmtX6B2aFQgf7twCpFBXQxkB1bDk9+Af0yGpy6CzLVdO05HlOdBWV6L4hpvHQXlrezrGRIPQ+b0+tpdvj4uPK1sXebvafvteVtWMW9uONis7A9vbGbHoRIe+HR3s/LSKi9bszp/0pDj3L7g18qXLk/3fxHy83EzJiGUQF93QxgrKq/mo62HsLDYdaiMWhtW7s0nvZW9Ygei9Lxyjjx/Z83+QrKKW/aaS/dQD1lHBEbC134GEy+E2irTq1X/zX53TikvrMrgo62HWDA6hm9MTzHfpkuzYf3zsOxe8A2BxbeZOWWeANbuL+ThpXvYlVPKxTNTOHl8vNnPMW+n6aLf/IrpBVt8GyTPoGLPMpbNfoj/bnZh2/CtWXXM2/UFAe1tSj30OLj+E8jbAf5hEDsOgqJJifTylwsm8eyK/RRX1nDVvGH4uSxc7ayxUFhezafbc3j48z1EBvrxrROGMTM1Eh93z+d5l8vC18dFWXXz+W7uNvbePJqJSWG88t15pOeW4XG7GBUXQmw7e2m6LAu3ZeE9Imy3tfdnr/NWmXk2bl8zP9Gy2FgeyerMxmH2Ohtu/7yCKZPDafe7fdZGOLAKyoPN7/ET8LhdnDU5kckp4WaYMsyfqKYT6qtKTU+Gb+DRe9+KMuDpi8yXFIC9n8Mzl5glGUITu/wUtFBRCFteM3PnsMzZ0aPPhIBQvkrP56nle9maVcKiMXGcMTGesYlh5uSHzDWQtQEq48yZyRG9s7TF0MhAvrcwjbs/2NFQNj8tqt3Fm4ta+QJVXOnFW2fj5+Oi6ogvTj49tUNI4X7z3IUlHfOE++yiSoora4g7vGdiaAKc/Ad45dsNt7GjR2O1977XjTKLKlm6M5fPdpjey23ZJXyVXsDk5PCe77HtAyKDWn4piQryJaQLu4dIx+iZ7Si3p2GY8LD8smp++Ow61mUUArA1q4TPduTy+LWziNzyOrz3y/pbZsGzl8NVb7AtYAqX/vdLyusDxq9f3UReaTXfPyER662fwa4PzF3SP4XHz4VvL+Mr91SufTW74XG/3AuPnjeDr7VXX8uC2DHmp4kgPx++9diqhiHLdRlF3HbWuHYnkH+09RA/eq7xhIBPduTw/I1zmTakC133dXXmjLZt75rLo0+BxOltzm2KC/XnJ6eM5hcvNw6/xgT7Mb6r83hqaxhWsZlhectMqA6bC6Fj27z5kMhArjluGP/9rLHXYWx8aJe3jmrXoS1m0nJVqempSZwGbh8oyYaMFY1DNSkzIbg+NCy5A9Y/A54AMzdx6uUUV7c8dGZhFeXVXiwL1u4vZENGEanRgUwfGmnmgmWuMT2v1WVmHa6Hb4ar34TEKQ3PQws52+GdW83fbEAElSfdwbNlUyiscnHimFgmHrm9WH56Yxg7rDjTDBN2IZDV1dnsyS0jo6Cc6GA/RsQG4+9xw55P4LWbGm/4yrfh4nA2hx7Ht59YhY/LRWJ4AP/7fA97csu44/yJhOx6G164xtx+9O3w9F/g0md7bL/RpnzcLq6al8qklHA2ZhQxLCaIaUMimgffI6RGB+PrdlFd2xi8ZqRGMCQikFtOHsVtrzUucxEf5se4xG6e91Ndbr44vnMrVBbCqNNMeIpO6/Shauvs+rlKGzhUUsX0IRH84bwJjE0IZUf0iVQtfoLowvWUB8RRHDWNYX5xtPfqr/bWsTunlOziShLCAxgeHdSlL49lVd6GMHZYVnEluaWt9K4OQGPiQ7hwRjLP1S9L5LLg/86bQELYAFs0t6rUfBHNWAkVw8yKCpHDHKmKAllHHFwHu5eAtxqGL4CkaeBysye3tCGMBfq6Ka+uZVNmMUVF+USueKDlcfZ8wtbI4Q1h7LD/fLqbC8cHkXg4jB1WVQKF+3h+R8tvKk9vq+Nrs2HnoVLeXJ/JV+kFnD4xnoVjYkkICyC/rIrPd+bx4qoMRsYFc+7UJMYnhrEqvYDaOhsfl4XbZVHlreOp5fs4fWI8caEtX2illTX855PmQyC1dTaf78ztWiDLWGE++Ovq1/j5/G7zwT9kTpt3OXNSInGh/nyw5RCpUYEsHBPLsK5+Q937hQm6dv0HWWAkXP0WxLYeyjw+Lq4/YRhTUsL4ZHsOE5LCOGFkjOnRtG0TZHYvMTcevsAsbdLKIq9HdWgL/O80M7cKzCLCV7xiFgz+8Hew9onG2067Ck75P1jzBKyrX0alugze/QVEjyItdgaW1fxktPOnJREV7MeDn+3mrve2N5TPGxHFPy+dRsSaJ5sPk1eXmmVe6gNZCzWV5mSBw3+zFQX4v3YDIxc8w6Uf1PGvJbt4/sa5zfd89Q+hRcUsV+vDUh3wyfYcbnxiFVXeOiwLfn7qGK6cO5SAVY+2vPG6p9k1ZjJXzE0lu6iSPXllXDUvlZzSKvYdymf8Oz83t/PUvwYObYKs9b0SyMDMYzJzUc2m8EUVNWw6UESAr5uhUUEtzqocFRfM/66Zya9f2cDu3HJOHB3DraePJSTAw7lTk0gKD+SDLdkMjwli4ehYhrYxF+2wqppath8qoaTSy6bMItJigts/k/XgumY9V2x/20yVOOsfnR7uPTxXqWFe674Cfv7Seh64fDo/fmUn6zJchAbMoKK6lpragzz9rSHMHdH6Ui41tXW8vOYAv3h5Q8P73N8unMyZkxLbHQVoTai/B7fLarHuXuggmdgfHujLL04fy/nTkskrrSI1Oqhnvog6bdPL8Nr3zO+jb4dn7oTLnu/0fNvuoEB2NAfWwCOnNa6F9ckdcOVrZokD4LQJ8YxPDCO/rIqIIF+2ZZVQY/tASIJZC6ypoJhWz9j287hwu93mw+DINbfcvgT7t3yDC/b35VBxJdc9+hXpeWaS+tKduVw6awi3nT2Ol1cf4PdvbgFgyfYcnl25nxdvnEegnw83L0rDts03yYhAX75Kz8fP7Sa3pIr1GYXszCklodJLbmkVgb5ugvxavjEHdHXZgTVPNoYxML+vebzdQBYa4GHR2DgWHevaV9Xl8MmfG8MYQHk+7PmszUAGEBPizxmTEjlj0hG9OBkr4ZHToba+S2rJn0y468o2Nbs/aQxjYOr46V1m2LppGANY/ShMvRw2PNfyOPuXM+6ERTxwxQx++9omsoor+fq0JL69YASZhRUNW1YdZhYSLieisJXFg1srO6w0G7a+3qI4wZuBy0rk+7OCiM36BIot89xGj4ToUeaElqV3N97h+B9DVPu9KjklVWw5WExJZQ0jYoIZHR9CVlElP35+XcPQnG3Dn97eytzhUUxqbagxfAgRgb4899W2hnWnlu3K49wpSfj5WFSGDWf9rHvYWBJMVF0R++fcTkpVD8wVKs0xi7RWFUPUyIYe7KKKatbvL2JrdjHjEsL463vbWL2vEF+3ix+dPIpLZw1pNpHasiyOS4vmhW/Po7TSS0yIf8NZgWEBviweF8ficR17vdR463h25X5ue20TP5rg5eZ7l3LH+ZO4YHpy29uT5e1oWbbpZTjxV53+INuTW9Yi9KzbX0RmUSXrMszyH8UVje8ZB4vansO0O6eUX9aHMQBvnc1PX1zPhKSwTu+BmhodyNXzhvLQ0vSGsqkpRzkrfIAJD/RlzvAop6vRc4oOwPu/bl52aLOZo61A1gdtea15SKrzwvL/wJA5BHhcBPq6ueu9xuB19uQEaixfcxJA+tLG8BEUDcNOIKLQQ1yoH9lNFnW8fPYQ3AGh5sPpo983PtbQ4yAkngumB/H8qgN4699k3C6Li2cNYUtWCRkFFVwwPoTxURbv763jma/2ccXcIdzzUfM3zOIKL1sOFjNjaATffWo1uaXVDcf656VT8fVx8de3t/L4l+ZD+JaJXr58fzu/OGMsN504kmseaVyHLdDXzbwRXXyRVrUyubiy/TWXuk1dDZQdalneNAh1xprHG8MYmN/XPtl+IKsoNHOntrxmPpBHn262tCnPN9ePWGR67Ta9DGXZZpX1ttoSO94M9zUVORxfHxcnpYUw7crhVNTUEhcWhCc8iC0Hi5sNcR12qLiSqomX4bfjvWblVRMu4vCgWUVxPt7KIvzC4vD1CwTfYIgc0eKDudQdzh9OCOKCnT/Bd6/XzCsq2AtXvGx6lud937Sx5CCEJJr1sTz1c/hKsqEwHVy+JqT5h5BdVMHfP9jO5WmVjPR4eXmtL/mjhhHu7yGvrJogXzdpscFkFlaSU1pFVnElk6ZeAeueaXzufINh4jcoyfO2WAT09fWZfGfBMN4d9xe+//p+oJhbJtZyf9ZE/jNuBO3NIsvIL+fgoWz8vcVU+oSTGBdLUv0ix3V1NtnFlfj6uBqHHkuy4PXvmyG+iGFm2P7iJ6gdMo+31h8kNaSOkWFQVlFBef0q8dW1ddzx9lZmD4tkais90pFBfq2fYFG43zzHnkCIHN7ugtC7cku5/fXNhAV4CPSFEH8ffvPqRqYPjSAtNtjUO28n+ASYUO0fAoGt9FBFjzTPdSe1NlcpMsiXiEBfjhsR1TDZ3sdlsWRbDkntLLeSW1rd8D55WGVNHbmlVe0GsvIqLztzSimrquVgYQUJ4QH4+rj59tfSmDY0ki935TIuMYzj0qKJDjHP9/78MrZnl+KyLEbHB5MY3vWTDYoratiVU0pNbR3Do4MbHkN6WG21GYk6UiuLkfcGBbKjOfxB2VRZDth1FJZ7eXH1gWZXvbbuIBfOSIGUyXDJs2YVfk+gWcE9ZgzZe/dzyawhFFfUkFtazai4ENbuL6C4rILojK9g0W/MY/qFmDfUov1MGTKX526cy4ebs6kDFo+NY0pKOF/uzuOlM92MXf8bPHs3c2HqSXx+6vUAWLT+zXb7oZKGMAZm+PGRL9JJCAtoCGOHPbl8HxfPHMK8EVE8/a3ZfLDlEGEBPpw4Jo5xXZ3DNe0KM/ekqelXt3uXgrJqvtiVyytrD5AWE8JZkxO69vj+YTDzenj7J41llgVDW1kUtCPKWm6r02pZU+ufhbd/2nh5+b/h2newR51KWfwMXNvfwl2RR/XZD+ITEEpA9GjsyBFY+Y2Ls9pRo7AiR1I7/0e40z9teEOpi5uENWQuVnkBfPJnopbfb+4QHAcXPUFQ4HjmDI/ky92Nf9NhAR6igv14N3MUU4//Cynr7wW3L/uOv4t15SM5Cyjb8Rm+H/6KgNwtVI44jdLjf0Zw8jg49c/wzEUN6515R5zEh4XxfD18F77TL4eDa838jAlfh02vQuw4ara/j+eN75mTEXz8qTn7X3gmnEftwXW4Vj6Etf5Z8AvBPu4HWOPOJb/Q4o9Jy3C9+XuoLuXbQ+ZSlPZbPs8bwnXzh+Hr42LDgSImp4QTHughOSLAzL279j0zL8SyzLBv/AT8i1sJ40Cl1+YPHze/bmtOJatzfRjayjrOYM7AC85ewYzPbsfKXI2dNJPC42+jJGAOpVVenlq+j/99nk6ovw+/OH0si8bGEnBoMysm/57Xt1eSUVjJaQt+zPzMlfgF55AYZHPdM9soq67F7bK49aThvL/dQ15ZDRfNTOF/n6fz1PJ9fGNGMlOHROBxuyir8rLzUCl5pVWkRAYyIibYDMsdWAN7lpgTFILjIGkGpC0yQ4qZa2H7u+aL0ahTIXkWeaXV/HZ+ICdaq9heHs6bc0p5v2Yy+WVVkL0Pnr0M8uunLUy6CE76nRnKHn4i7P7IlPv4wSl3mMdoQ3lVDV/syuflNRm4XS7Om5rInOFRjIkP4eKZKTzzlVkDzrLgD+dOIDU6iO8uTOOmp9eQV1aNZcHVc1Nb3b3hsIQQP/w9LiprGr94hPr7EN/OiTv5ZVXc++FO/vdFOrdM9HL7Pz/n4atnMiEpjOgQP86YmMAZE5uv47Yho5Cfv7SBTZnmC+aMoRH87pzx7b4v5dVPI3lt7QHGxIdy5qQExiSEkllYzm9f28x7m8084dFxwdx32TRGxg7A4cG+JjQZpl7ZfHkVH/92R0x6kgLZ0Uw4H1Y/0rxs9g3g9uBjtzJ7GnDX1cCe5eaNzMfPLADrcsM17xAbmsz/vb0V24aIQA9vrM/k2uOGEWBVw/Z3zI8nwKy3Y9uQthi3y2LakIgWc7amBuYSuORaM98HCNr5BidW5mJPf5qb58Xw+w8bl2sIDfBhXEQdb+5oWefsYjPhuzUVNV78PG7mjohuc95GpwyZZ4LqF/eY9h33Axgyt927vLwmg9+9YYZf3+cQT3+1jxdunGe+vXdWWLJ5zM2vmLlLky6ky6u/TL8Ktr3ZvKy9vSyLD5phzabKciBjJaUhwwl56fKGLW/8tr1G6bmPUOiOYM+8+xix42FCM5dSlHg8u0ZewwifcJYX11E28wlGWRnUWH4sK41nfnkkEw99AcvvN4sQB8WYXSbe+zU+pz/KzNRIEsIC+GJXLiNjQ1g4Joba6iqe21jC3YWj+fH8p/HmHeDyVb6MiC1lQeImQp67sKG3yX/bK9SU51Jx0ZMEFO41vbp1XvN6KMpgdryHeL8geP2njdv3bH8HTv4jNXn7GsMYgLcSz+vfpSZpOj6bXsJa87gpL8/Dev/X1IUlM8oThOuD31A06gIqApOI3f0yYavuJWbanymqqOH5VWbC8Wc7chkeHchFM+rnfCVMbLE6fVJEAEnhAc16yS6YlkSwnw+FFS1fF6Xt7GVo5e8k7I3rzP8fYB34ioi3rqf4ktd4ZZvFvR/tbDjG955ewzPXz8HPHs11L2xsGH77eFsOPzt5KufU1HLLKzsaziSurbP543u7eOCisaSXwP+9taVhqsOLqzN4+vo5jE8I4/5PdvHPj83j+Pm4+Pfl01k41N/M5/rkz42VDU00Zyz6Bpkh9sNzBZfdB5e/xJjQcUzP+BP+mV+yY/TtpCy7jUtSF1Mddj8s+UdjGAPzhWLMmTDubDjv32auXWWx6Tk7yofY0p153PDEqoa2vLE+k4eumsnCMbH8/LQxnDMlkbyyalKjzFyloooa7nhnC3ll5v/GtuF/X6Rz8vg44tuYWJ7qyuLek0K45aMyiiu9hAd6uGdRIEOsQ0DrE7U3ZBTxvy/SGy4fKqnijre38p8rp7e5yPdbGw42hDEwWy0t2ZbTZiCzbZtnv9rPX94xoykfbDnE0yv28cK357F2X0FDGAPYll3K08v386szxnZ63pt0ko8H5v/Q7Nax7imzIsKVr5lRCyeq48ij9icps0yA+PRO80Ey/wfmmyGQ6iliWJQ/e/Ia5zQkR/gx3K8Q3rvLFBz+8Knzwu5PSBz5TW48YTh1tTVQW43bbwj+Pm5iQgPNB2jW+sbuUrfHDAu1IbBkT0MYO8wn40so2Mn5eQ8Td9LFvJjuYWRYHefE5pBWuIw5wxcBzYeZrpqXyoiYYFKjAhvmo4HZYzL1KJOBO803EEafaibAQ+NwVRuyiir5+4fN61tYXsOWg8VHD2R1debDxzfInMVZeMAEouIDpseguswMEc/7Pgw7rvNtGXocXPg4fPZXc/n4W0xZW6pKGlfPb8K2fLD2fNpi/8GA5X8nM2E+571QwNSkK5g19CqWH6xl7boC3kws5+8fbGfzwTJoWMyiGHd4LhOD8+GUP8K+L01bZ10PhXuJqcvm7Q352Lg4YWQMe/PL+dNbW3nrmhFcPmcIGw4U8/uPMrh6eA1nTxnG5KQw6nK+bDFs6tm/lIqc3QR8cFuLIejZX1+Albuz5V6KG56nJmkeniPLayqwy/OxtrzW8vnKWEldyly+WPgC/7eilgPF1Vw8/gSuCF5JkE8dL65uvoPG7txyduWUkdTGgr25JZX89qxxLN2Zy86cUuYOjyIpIoBqbx3nTEnihVWNx/O4rXYXgw0o2tUQxhoUZxJQtJOnVrQcbvpqTz5RwZ5mc6EA/vv5PuYPn9is1xpM+KisqmTZrrJm807rbHhpVQbuGVZDGAOzDdhPX1zPx9cOJXj5f1rUi7xdZl226iPmxX32V6JO+RNkftm8fekfEFCZCemftGx89mYTyELizE8H1NWZeWq+bhcLhodQa8Mnu4t5de0BFo6JJTzQt8UXvqyiCtZntJzi0N4cMlfJAU5a8S3emHszee5oYrwHSf78Xkh+ss0z5zKLWg5PfZWeT1F5TauBrKa2jhXpLac5rNrb9tSHzKJK7vuo+fzNvLJqthwsZu3+wha3/3RHDmXVIwkZJCcQOCpiCJz4S5hzI6xYD0NmO1YVBbKj8QSYADHseDPRusnK4rHeg9w/p4h/7orm8/1VzEn246aR+cRVZ7S+35pdR1psCPGlmwj46p94CnZRMv4y6tLOwicwDM69H974genRCE2EM++BmHa2AGhr0UTfYCKylnHmjhc5M2ESHMiFjelw0ZNMSQnnwStncOe7Wymq8HLd8cM4Y2I8dTZcc9wwlmzLYV1GISH+FtcdP6y7t41rUs/2g9hhNja09lQerWI5283k953vQ9pJ5sxEv1Dzf1ie17hRPDSf5N8ZfsHmg2nEiY2X21HjF0HljO8R8sUdTY4RQknYSFyH9rS8g203/B2tOVDKmqaj47bV2tNibh4aD8/9pPHD98BqmHczHh9f/j63kp8s9+P5VRnEBPvxn1ODGeFNZ0PVuIYP+JraOu77aCd3XzQZq7UN732DsTwBtPYf4wJwtf624nUHtDxxxTeISp9QPGFDsI6YD1cXlsIW9yiufnNXwyTtB1YW450+h/MsT6vtb4+NxfefXUuIvw9J4WZB5AlJYfzpvAlcNW8o/h4X727MJsDX5r5LpjH1yGU7mnC3ccaoyy+ElAgf9uc3/5CPDfWjppX5e7ZtE+BxERvix6GSxrBqWZAS7o+Pq+UcwjrbnOhwpJySKqrrbNO7fiTLahnGwAwpt/VMutwwYjGseax5eXzn95EFGB/l5tZhRaRu/StYbnaf+i3eLm27dzoi0JdpQ8JZva+wWXl7c8gISYDKIoYs+xUNo81+IRDc9m4oya3M/Zo3IqrNvSw9bhdzh0e1CGCzhrWzH7Ftt/6RYNtMGxrBo8uaTxc5cUwMwdrYvPdYlpnnbTm7Vr5W6u8o36CW27zEjGbM9v9wl/dPvDN5KXfX/ZmxW+6FuLEw/4fYEcM5OOfX5Ez7PgTHwogFuHI2E/rs+Xi2vQ6HNhPy8S8J2/CI6c2JnwCXvQjfXQHf+hhGnWzeFNsSOxbGntO8bPFvTflpd5iJ3xkrzcTvhCmQMBk/j5vF4+J47sa5vHnzfK47fjgxIf7szSvnttc2UVRRw6nj46mts7nttc2k5zu7KnVCWADfO3Fks7JQf5/2z3Qqy4UXrjVDMjnbzL8vftOs6TX7hua3dftC6vxjq6Rf8FHDGEB2bRD3FMwlff6dkDyT4nGXsuz4R/gkLxx72AktNiavmHUzwaHhzBrWfKj6cM/Odxc0PzvRt/6Dwluc1fLDd9X/wBPAhKxXeGLyJt64LIGXFpeyaMtvcCdN4pVW9vh8fW0mPgkTqBhxarPyshNuIzhhlBmubCogwvwNj1jYcpP1439EXVAsmQv/TtXQBeRM+R7VQ79G5on3gH8UNcf9qNnOEnbEMP6/vfMOj6M6+/Z9tldpteq9S5Zt2XKXC2Abg00zvXdMCyWBQBJSviS8IQm8SeClBEIJJfQOBtOLTXA37l3FstV71/ad749ZS1qrAKYIrHNfly9rz87uztnZmfPMU36PJ3kme9t1AyrwntvaxrYGL4vGhS+y6dGWYb2mrT1eerwB6js8bDzQRrc3wNp9LXgCCoXJDm5blM9/lkwnzWlh4fgEzMMsiJrYApTJl4WNKdOvRhs3hp8dmxumvJ/iMDEtw8nYxEjsh7znkjmZ5CTGcO8pKb0dJIw6DXcuTKQgIYJLZoaXFQgBZ05JIdVpGaCukhNnReNIG/gbN9jU1lA5CwYuOLN/qobxU6aHj2fNg8g0mHW9WjxykCmXH1YVsUaj4aq0GnI+uQZdzXp01WvI+3gJV6TVD/kau1nP7YvH9eZ/aTWCW47LY+xw5350Dpz+SJ+av8EGZzwK0VlDvqQwJZKfzM3mYHQw1WnmF4vG9FatDsai8fFM7ddCaU5ODEfnxQ65fZLDzE/mhkc7HBY9BYkRzMh0ctqk5N7xiSmRnDstDXE48jmSHzXSBP+qtOxTtXf8blX1PqEQIpNxHX8nhpJ3iT2wkmDGLFy5J2COTKU+EMFz+dk8tqoaq1HHr4+/lIXRqVh2vjiwcm7tQzD1clVp3Ryp/vsqWJxw4t+g6AJVcDM6W73wCkFNzCxci9/A0LIXnyECXfIkEu3J6AHaKoms36ZqScUVQFwB1tDFZ+OBVjYeaOWWQj+gG75R+vfEWVOSibUZeG1TNVmxNs6anDx8X77m0r6+nwep26qO5y5SL9BbX1QNiInnQeawErvfGiadlmXlAZ7fkcbUtP+hrtrHno1dPHKxEVvmNLrOX4p289Po3c24J1yCJusooiwG7ji1kPd31LGyrIk5ObEsHBePI+Q9+PvZE1i6uYZIi54zJqWQF2/D06gfeGJrDbjRUz7ldu5dXsEnH9cxJj6K3y58huKIRFIcAxPek6PMWBxxdB7/Vzpqz0fT3UTQmYU+uQih1arSGxHJamVp3Fj1u4zNh2CQqkvXs6u6Ga8/SF5iFLmpCTjqdlAadzR/Ls9h7fZWZmWdziUxSUztKsUdO5bgBS+haS5F0ZkIRudgSRyDtX1gGCjGZqS5y8OpRckUZzl5b3sd0zOjOWVi4rANzwfzeFgMWiyh377NZKAg0UD9nnCjxesPsL+5B18gSJrTojaYjohHFF8HaTNR2isRjnREUhFYY5iWofD6dbPZXduByaBlfHJkb+j/0Uum8vrmaqpaejh5YhJzcqJBo2FmfjLLLhHUtLtxWo1kJjjR2mOYZgnw7JUzeG7tfrRCcMGM9FAuqcI95xTxuze20+Xxk+Y084+zi3BYTTDjJ6qnaPOzEJ0HM69Tr1cBn6ptt+o+tdq3+Lq+ZP/TH1Yrf+ttarg7/+S+a9Elb0JruWowR+cOW7E5JIqCbeuTA4Ztu1+GcccP+bLCFAdv3DCLymYXdrOOrBgrBt0wN6kardpnOGG8ek20JQxrjIEq7XDTglwWT0xi37b1vHrKrGG7dwCMT3bwwAWT2FvfqVZZxtuJHeY1QggumJFGapSZVzdWU5Bo5/RJyb2Vn38+bTxL5mTi8wfJjLESNUjlqeTIZ+RX2x8DlevV3KPuRnWh1+jhkjepi5rE6c+0YDXMojjrFNZtbaFtTQtLb3Dx9o5m7l9xgHFJkfR4A9z86m6ecUQwZ7DwmN6sGkegtsFprwRztHq3p/2SQ2SPV0Oq/fAFgjzw2QGeW9sNqHdeGlHKa9fFU2RpgefPg6aQVIfeApcsJSt+ElfMzuTxlX2hs8tmpZMV25dDFgwqCMFXu3PzudT2Oy3lqncwoVB1CR8G9R0eHvlvORaDjpWljSREGMmMsaoL42Ac6p3pP26PUxP5J5xzWPvyTYixG/nj4rGUlpUy3tJEj7DwUX0M45IiEBoNtpxZkKNWfB7087h9fl7bVMWnuxuYle1k6ZZqerx+fnpsLh/uauBPb++kMDmSug43lz+5npeuKSbWmk+mNTYsx6l68q34g5H8+u3NbKlUZUa21XRxxbPbeeFqG+eOs7J0i7Y3sdxm1HFWgQV6WrGv+l/Y8nzfRE5/WDW+rDEw4Wz1Xz/2tfRw5Uv7KQv1/LMYWnjuyhgitRnc/OLO3nDe0q31bKvp4skLCkiv36gmotdsQggN2gnnwJQrKEwpZEyCjd11aq6kEPD7k8dyfD/v2KWzvpqq9pgEO8fkxrCin/r6rxblD96FIERTp4eKsl1YW3eiV3zssuUSnzORNKdV7dwRmzegnlmjEYxPjmR88sAbq+LsaIoHk4wx2knJzOdQ5SOjXsvsnBhm5ww8d06blMyktIMtrcx9UgkRiVD8EzVMrzX0XUO0esg6Ri2iUYLhaQPRWWp+7PLlMHNu+AfZYtV/3wQhECbHwGHzl4tLJ0SYSRhEtHq4zyI650v17fpj1GkpSIygfo/2S42x3v2KNA9ZXDAYMTYjp09O4fTJA/WtrEYdhYP8XiSjC2mQfRm73wN/lxqS0hnUhXzri7Dib7TPf7g3wbS0sS9EVNfhYW9lHe+c4CbjwLN4jVHsnXEq6yrbmJOZpXoVOvolBE27SvXWVKxUKzNdrerFc9FdqvdL/zUuRqi6Uq9sCE94DipQ3tBFkVjZZ4yB6q377z8wn/0kN8zP5pi8GPa39ODsKOXCOblYDDq63D5WlTXzn9X7ibYZuLhYvUsftgJox+vhSt4TzoNFd4Ll66n7d3v9/PWdXezq10z8rvf2MCk1avCFDWg0pmHOWYyttC9RvCtnMS5jGt9wWfnGHBdZzcLSa9TjLzQcP/vnaM197a28/gBBBbUFEFDW2E2Wpp7Lcj8noX4FdbnzWM4s9tQl8OSqCvxBhU39koLXlLcwOzuVPVMepci1BkvXfqoT5vO5N4+JnZ5eY+wgPd4AzV1e5m77M6/OP55tvhR07nZendtE3vZXwH5DuDEGqmxH2swhez2uLmvuNcYOfsa/VpRz6cy0AblV+5q6KW0Lkr77dbXrAajGwpYXILWYlLQZPHrJVLZWtdPa46MgMYLC5MMT5oy2GbnrrIlsrWqjvsNNTpyNwhTHsDcY7vq9FC2/DF1b6EbFYKXa/AJK1FE/iJDSsAr8Q3myvsfG6WFMXwI7X+/TZtQaoPDs4V8jkYwipEE2HLXboKMS3vuVKl0BsOddtWfbhieI0PoH5vUKsOg13JxVTeJ7VwJgAqbufonkM98AR4YaUuiuVz0YMflquEcJwuvX9ImUBnyw7OeqplLypK+122a9lvhI44DFz2kzQPUg6uuNu8HnwmmN4phQ65blyyt6BSeX72nkhuc39W7+zrZaXr52JkWpQxhXrfvDtbZA7bc4+ZLeDgdfldZuL5+XDtT22t/SPaRBVtYhWG1ewuKj5uNs3UJz1ATeastiZocg9ltQ7jhsPJ1oPvhtnzGuBNF+/ndIm4E/ewHrK1p55LMyWnu8XDE7k2Py4rAGOji14g6Mtaowb0LlOk5Pmk712MeJtRnZ18/wAYiy6ilIimBvfTbnbAC9dhZx7Qb+dFo2Lm8As16LyxfeusugVb0XY5ZfyxihYXneH8j/4g8w41q1AvLQebjbB1T39udAy8BE9L31nUO24rEYtGoPz0NpVosMUvVdpJp3gqYDjLmgGTdw269IQqSJhMihE7wPxVqzss8YA/B249z8ED3ZU7FaDl8IdFSSMh0ufxdKPlQLP3IXqNc3iUQCSINseFwtqrBrMHwBo+RDOPqXRMXE85NjsnlweZ9o55VzMkmPAMPWf4a/JuAlpnENjJ0N6cVQ9QUYHWopdsJE1Thrrxy4Dx2VX9sgc9qM/OGUcVz1nw29xuKElEi1D5l2puqNG3e6Wqiw511V7HEIz1W3x8eDy8PLtX0BhZWlzUMbZJ7OwdWP3V9fEd9q0DIuKYJt1eHl7/3byByKUaflwY09/EskEGtPp7HTg6K4mDftK7R78vsGrbqsa3dR2+7GYdGTEW09PO9IWxVUrh043lrB1qo2LnxsDQfz13/6wmbuPmciJ0bVYOzYR2Xx7TTp4onx15G69X6SAtX8bEEuF/97be9roq0GijOjsRh0nD8jjWmZTro9frJirDT3ePEF/Pzj7Inc9tpWOtyql2J+fizpMVaYfBFsfb6vAlJvgQnn0hXQY9cawjoS+BIn4zcnYEYNYzd1ezDptL3HpDgrmn8d0v/0rCkpZMRYWTwxkaVbanvHz5ycTEG8HVKmqeH6/sSPU7XbXr9Gzd80RapiyRe+3Ceb8m1zSNWhuWvgOWluL8GPF/j6BpkvEKSiqZtub4C0KDPOYRqIH3FotKqMUOr0wZ9vr1ZlVOyJwwrMSiRHKtIgGw5b/BAV4QLSZ2LSa7nm6Gxm50RT1eoi2WFmfHIkRq13UFkGvVCgqRSeOUsNFeotahuVBberOTmHhjJBVRI+DI7Ji+X1n8yipKGLSLOe8cmRasKzbgKNpzxNSWM3bj/kzJlPWnLiMO8k0A0SmhxsrJfIFEiaAjVf9HuBcVhNtaHo9gY4Y3IKB1pKaHepGl4nT0hEN0x9cLfHzzXHZPOv5aV0e/wEgkGunZszrNAnoMqNrLwPNLNhbSkULIaIBL7Y38JPntlIQ6cHs17LHaeN55SJicMmFyuKwoHmHtpcPhIjTWpeitFCMHkamqp1YdsGbAl8XtrMIcWEPLyinOPPS+KT4ie46RMXHS4/keZo7p3/FHONJmbEO3n1J7PYUd2ByaBhYoqD3Hg7gaDC6rIm/vrubrQCzpmWxl/f2UW3N0C01cDdZ0+gtaMTh1lHboKDlCgLNaKAlkUv42jehM+dwLZFLxNjLWBvXQfx8x8jb/3v0bRV4Ek9ijV5vyC+W4/Z2836/S1sqWwj2mZkZlY00zKcTEm28KcTs/jbJwdw+QJcOCWe08Y6cFqN/Py4PObmx1HR2ENmrIXJaVE47GZVnPHAWugIhdrzFqnFFnVbKU09ixUxt1LdBUcXeZi66XVsCRO/dvh7OLpb69m8v5kNB9pI07io2F9BRnoGmqxjYN0DYdv6JlyE3uL42p/R4fLx9JoK7vmwBH9QISfOygMXTGZMwuH3Rqxq7aHD5SMh0jR4C6UfAwE/lLyvtpXqblS9ZovvU/NOJZJRhDTIhiM2X/UkbXsp3Gsy/gzY9zlEpRNp0TM759DMJANi6hXw1k/7hrQGtd1I7RbVCNMa6K1b/+xvUHiOqnz9woXqXaJGC8fdcdgtHPRaDUVpURQdou5f1djKzz/xsa5K9fo5rQH+c2aQ8XGDv4/VqOOG+blc/XSfcWXUaQZNMu7F7IBTH4APfgdlH6vJtSfdA7FjBmxa1tDF+ooWWnu8nJapEN++BU1LmeoVTJ6GxWDh5Q2VnDctFYNOg16rYU15M4QCaXvrOlm3r4VOj4/pGU4mpDoQCNzeALcvHk9FczcZ0Vb2NXUjhMDrD7DpQBuflTQSadYzJzdWLaOv2wFPnaJ6iPKnwLt/gJ5mmqbezM9e2NyrEeXyBbj1lS2MSbQzbghVbq8/wLvb6li1q4LsiCBfNOq49tgx5NnNVE3+HfltV6oVYELQNOFaWo35vZV+/bEZtdQRzQ0fltITSrZvd/m44SOFt/PGkwyYNEEmRXaARo9Wox7rnTXtLN/TyOKJiaRHW7nx+U34Aqq119zt5bbXt7N00kaS1v9FFbONupkPdzbxh6UdmPS53Fjg46qX27njtAbyE+yc8b6Riyc8QO64IJ9WCf77sYv3x+tYtqOOO0IdFABedVZx/3mTKAps4+IN13DsjCX4dVYSS+9Ev/9iiL8Ku1lPlMVAh91PlNWA3Ry6BCUUwpIP1N6YOpN67pmjqKhr5sLVgvpO1eP6OPD3Bedwls9FnyDuNyQY5L0dDdzytuqhu6XQz30vlnLf6UGqujMoPvMVKpq78AYUMhx64nMOL8y2vaadv72/t/dxaUM3f3tvDw9cMBmPP8DmylYONPcQazcyMTVq2IpRXyDIO9tq+f2bqlRNdpyVv581cdB+lz8kerx+9jf3IFBlSswGHTTugpcu7otE1GyEN2+ES96QnjLJqEIaZMNR/YXaCf74P6mq5wGvajituk9duMeerIZRDsXdDuXLVU2wis9V/bKUqVCzRc0Xm3MzCK1qmEWmQOmnqn2ReTRc85kqXKq3qjIW33IC7vpqF+uq+nLLWrq9/GtdC3ent2OwDm5gHJUbw3+umM6bm6txWg2cPCFp0AqyMOLHwrlPQ1e9Ksg6SIVleWMXF/57DXXtHq6abMex9y9oavrlEs3/fzjn3MwvFo3hyqc29OpRTcuIYnxSJHvqOjj3kTW09aieM42A/1wxg9w4Ky9ucPPY5325P4snJpITa2V1WTOXPtHXKN1uLOWla2dS0LB9YEPZ1ffTkHU5Va3h44oCVa2uIQ2y8sZuxhrrSZ6WTqsryOwCha7Gcpq1iZz7tps7T3wbgyaIXqfjzR0tzKgPMis7mllZTqZlRqOgsK+xmzMmp1DX5e81xg7S5fFT0xlA21NCZO0mGtwCk1Zg7NxDVWAmLd0wLdVKhqijzB3sNcYO0tTlpd6QShKoXQbyTuD9HQF+OTeJhYk9lNa0ccvcJD7aWc+pRUmcNyWZXFMTTroZHx3Jgonj6PYF+Fe/UD1AZYuLPfUdFJnqwZlN0to/9T253k1PwTk8v76elCgLNqOWth4fL6yv5PJZGerCHJms/uvHti4r9Z3h3Q3uWuvimGmRwxZodLp97G/uRiDIiLZiNamXOr8/yPbadlq7faQ5zWTH2alvauLvnzVw/fQIJkd00OXSYKrXsK/FzcoqPy92RLJ8jxqyzYmz8VBKBLmhQ++t2ozorEOJSMKQPGGYPRo8t+6/JU209ngoKS8nue0LJjWuot0xnl0d0zBOnNTXmPwQtle3c/OLm3u9qmUN3fzq1a08f3Ux0VYjjZ1uKltc2E06MmKs6LVfLjm5v7mbHm+AypYeUoepPD1IaX0Hexu6VYMw1srYxIihK59RvXl3vbubt7bWIgScNTmFnx+fR2JL+cC0kNpNaoeBb9Egc3n9bDzQxvvb64i2GVgwNn7Ic1giGQmkQTYc21+Dpr3w/m/VfBatAba/qj5nsKlGlatNbdjbXqkaV4lFanXTwbY8iROhvUqtOjzpbnXB+eDN8P5wJ/5dVebvalDvDks+VL1pOr3qXQoG1Fy2nUsBRRWDTZ7y5ZIYg1DaNjA/anO9j+6gjqFMP7NBx9F5scMKHw6KwQrOoTWANle2Udeuep6Oj2nGvPOQxO7P/hfGncZROVm8cd0sShu6iDDrGZcUSUKkifdX1TEl0cD52aALeljZ6uCBT0v41aIxLN1SS5RFT1asjfLGLpZuqeXy2Znce0gbpk6Pn5WlTRQ4B5m93kyUWUeMzYDbFyQv3k51Ww/1HR7i7UOXxlt8bXzc6uCu97bh9gWJtRv525njsekVbls0hr9+Ws7+5h50GsFFxemkxdjQaQRp0Vbu+6QERYFJqQ4iLXoCQQWDVoO3n8q7Uachyqqnu7GdX38Rz6bqHoSASydGcHlENUmmaKLKniFmw934j/onQtjDCk/sRh3Rwea+ge4G7pibQdKaP2Fa8y6V+bdzVfA5Tjrqd9i1QX6bshntu7eC3818ewKBiU9RQSSd7oEhYL/PB+/9hP0Ln2BH7u14XF2M0dVS0PgBlZ0BEiLN/P7NHXS4/USYdfzxlHFUt7nIGaKRsjc40HPo8gbwK0MbGJWtPfxp6U4+2KWKjp44PoHfnlRApEnPyxuruPPd3Xj8QeLsRv5+9kRyIjXcM1fL9HU3omnbx/L8/+GJAhclxsUkRJh4cX1fHllpQxdPr97P708ag9j5GoZ3blE92iYHgZPuQVt4xpD7lTiInMKE1Eh8Xg+FFU/i3PooAJFAQuwE6lKeIto2+PlT2tA1IMS9t76LiqZu6trdXPfsRvY396DXCm49Pp8Li9OHVH5XFIWPdjXw8xc3c3Wem1/f/zn3nlfE3Pw4utx+1u5r5uUvqkiwmzhtcjJFqQ5K6jt4Zs0Bnll7gEBQISvGyu9PGcvc/CFc7cCHO+t5a2tt6DPh5S+qmJrh5Ny4QdovWWNgsC4R/Sip72RTZSsub5CiVAeFyZHDVn5/treJa57p8/Q/+t99vHztzOFFpiWS7xGp1D8crpa+v+t3qGX53i4192vubWql0Mp74elTYekN8PRp8Pk/VAPqqJ+remXVG9UqxshUyDhKVc1PmADzfgNH/wLm/ho2PA6d9aqi/CtXqDID7/4KXrhIvUusWg9PnKA+v/qf8OQJcEge0ldlcvrAysSTx8XisB1mxVh7FexeBpueUwsV/IM3XA+jcQ9sf43urr5Efb0ysBUMfg/4Pei0GgpTHCyemMSxBfEkRKoLW7api7vtz7Pgs7OZ+/lF/LLhNq4bF8Td08VVR2Vx+qRkDFoNp09K5qqjsujpcQ1qRLT1+NTCCntS+BPH/pHEuBjuP38SF89Mx6jXsKAgnn9dNJm8hD5FeH8giL+fwbTfZeR/3t6F26eONXZ6+N2bO2kNGFm6uYb9oX6h/qDCk6sq8PkDbK5s54X1lb2G06bKNl5eX0mURcd187JJcpiYnRNNUqSJ6+ZmE6118/wuH5uq1fdSFHhycwe7WjXE9OwjZt3/QnQOWe1ruWOes1eF3KDV8Lf5FtK23a8OaLTgzCW25hNMZe/2zsFU8jaxdSugaS/at2/sa8fTWYf2jWvINLRz/rRwb5ZRp6EgWkv5zDu58GMz171Vx80fdXHaR5F8MfGPtLuD/L83dvQWFHS4/Pz+zR20dvUd+0BQCcu/zIsIYNKHX6aunuYkQjOw/+BBPtxR32uMAbyzvY5P9zSytbqd29/aicevHpeGTg+/e2M7Po2eqbv+hqa3mlIhYe1fKTQ3U9k6iFertAlf3Xa0b/+sr5enuw3t2z/DW7V5yP0anxLJedNSex87LHp+d1IBDnc1zu2Ph21raNxKRGfZoW/RS+QgRS0xNgMWvZb/eWtn72/MF1D467u72VnTPmD7g+xr6ubG5zfSGcqxbHf5uPG5TRxo7mb5ngaWPLWB97bX8eTqCs59eDXbq9vZU9fFU6v393qty5u6efSzcuoH6QsJavHHO9tqB4x/tLNevdmdckXfoNCoKQ6RQ+fP7qnr4JyHV/P06gMs21bLeY+sZn1Fy5Dbd7p8/N9He8PGujx+Ngzzmq9EMKj++47xD9J269vm0I4Yku8f6SEbjgnnDtRgGnuaaijYEtR8l5X3hD+/+gFVWyd1Blz5sWrI6Yyqp8yZCa371Iv4p39Rt9cZ1aT+zjrV2OpP4y5o2KV61/q79IMB+OIpSJ/1tac0OSuBm+Z188/P9uELKMzPj+G84qzDqxpsr4aXLoPqUAhQCDjvecg/YejX1G5Vc7XcbRTO+w9ajeoF2u1PpMgc1Sf7AWpityOdpi4Py/c08OL6SnLibJw/PY0JKQ6maPZi2f1C7+b6xu3MaHqV2tSb2Li/lR217SQ7zGyqbGVcYiQXFFq5bFYaTV0+AoqCVgg63H7GJ9khOonAZW+zr74NV0UdzZf9l+jETDz+AG9tqea5dWqy+eqyZj4vaWRiigONEKwpb+bfodDokjmZFGdFU9XmGdC3rqrVRatbYfW+gQtATZuLbQ0DDcVV5c1ceVQmURY9iycksXZfC4snJhFl0RPQ6Pm4YqARu60xwFGWdrYueJbnDzgorRWcmy9YenYUDfpkkqwK2Wt+gze+CL29Ec/Mn2KKzsK674MB72Xe9wGkjBvYl7W1Ak1HNUvs24icW8xrWxtIizJz/fRIJjUt41lfIdUdXUzLiMKs17KuooUHV9Vz5qTEAZIbXR4/zV1uOlw+VpY18UxI6+6SmRlMTosiq2Mtzy2K4dE9NvZ1KFyQL1jofo1A52UQ4SQQVGjt8WI36nplNT7YWTdgLh/vbsAwfqDcxYGWHura3aRXfj7guY6aEtKcA9tqzcuPQ9dRNrDjhqcD0V4JKUUDXgMQbTXy25MKOGdaKl1uPxkxVtKcFny11QNDdoBJO/QinB1r5czJyby6US0C0mkENy3Iw2rUsnaQ31hlq4vpQ+jn1ra7e28eDtLp8VPZ6uK+T8I9yh5/kHX7WtQes4ewYX8rnR4/g7Ub12gEM7OjWX9IU+5pmU4w2eHY30PhWWpSvzPrS3NnNx5o5fp5Oawqa8blDXDD/Fw+3FlHUZoD4yDFNkFFwe0f+H16Bhn7Svhcqm7k2odVzcgZV0ParL4UE68LvJ1giQHN4fs9VO9+DSv2NLJgbDwnT0gcXnvuMHD7/Kwpb+HJVRXMi+hhZWkT0zKihu+IIPlOkAbZcKRMg8vegXWPQluFWnVX9insfgsKFuObdg36QxcrRcHX045eCLV9x6GNeP1uKPuk32OP6iHLmgvKwIsyQf/A3CYYuBh8RaKsBm5YkM/iyal4A0FSoyxYD7eJbd2WPmMM1IX73dvU720oVf5tL6u5c8D4TX/kPyf9H/ds0fDEriDHLH6e+M0PoKndpBZTTL0CxWDlxVVl/O19Vcx2fUUrb22p5fXrZpFTvwli8ymfeSc+nZXMrfdgrPiYxoKbmJfo5R95VUS3fEHzxMm83Wmm0SVIjjTzp2W7exeg9GgLJ46LobnLwyvb4e4PG7ihwM/9b7bzp9N8RBi6eGF9uMju/hYXe2paCGr0XPFk3/z/W9LEE5dNJT5iYN5PrN2IxahlQpKdLdXhkiDxEQY8moEJ3EUpDuxmHW9squkVf91U2cbU9ChOLYigOM3OK4fkt42Jt3DAPJYLXi2jy6N6Rdbvh9uOieWK6RpeKQ3ytvN3rChpoTDJRkG3gwXtbuzJs7FVfBb2Xu7kWeg0Fkx6i3o8IhLV8Hz9DvzWOJJKnuFm130smXo5+pbdmN94CRb+hS7M/OGUND7Z1UCn289Nx+ZS2thNvF0/aPg1KULPJ7sbuOnFzb3j726v45VrZ5FhjGTysrO5L7kYb3wS1q2f4nPm4TbdxL6mLp5atZ93t9cyIdnBT4/NoTDFwVE5MVS1ulg0PgFFgfe21zErK3rQMHN8hJEYmxElsQhRuznsuW5DDPTAqUVJvBnq9Tk+KYILZqRBT4e6EAf65bfpzSjDNLEGsJv0odZH/V4WnUVg7Glod77RO6ZEJGNKHDvk+2TH2VkyJ5PjxsbT3O0lNcpCdqwVm1HHuKQIdtSEy8QkDKM+H2v0o9eKsFxDk15DnCk4eAN7lEEbfE9IjiRxGEX9xROTWbaltlc0uCAhguPGhsw3S9TX0ii0GnT87o0dvV6d1eXN/PbEAvwBhcEuZ5EWA9cek8WvXu1rqabTCKZlDNMQfDj2r4Jnz+x7vPcduPRttS9u1QZYfifUb4fxZ8G0K4ZN3RiKpi4PP3t+M9tC3s1NlW2sLG3ioYumDOohPVzWlrdyWSivdkqhn4v+vZbnryqmOGtwnUfJd4c0yIbC74Z3fqEKVmYfq7Ycee/X4A8tgLVbaNYlkBCZoobtDmJPoNmQzJCX5cEENZtLQGuECefDluf6vVcixBbA5Ith+yvhr5ly2WFPTafV9PZQ+0a4BgmDdFQNbyw29nUJ0LWVM/vjMyia9Su8M28iSuuGiFvVMniTFSJTqW13D0ge7/L42XSgFUfGiSyzXMp9yyro9rRw9pTfcMnkAE59gKt6HsG4ZRkAdp7nqtyTqDL+lQc/PsA1R2eT5DCj1cDnJU18sb+Ddo8a2jnIztoO7v+klNsWhjc2P4hRr+WJ1QcGjL+0oYrr52bxzwuKsBr1tLt8OK0GUBR0QsM1x+Twmze29xYinDoxCaM5gqigj+evnE6nJ4A3EMSk0+Aw66lsdpEfb+XWhfk0dnqItRt5Y1Mlbd0elkyysWZ/B1VtajjxuPwopji9rG01DpD4eHBtK0dPyGXFnjLe36mG87ZVt5MQ0UxmtIXxuYsIlL2Dtv0ACC01Cx5ia8TRdDcpjDv3v1jr19PqCpCQloR1xs1UuJ0Yjn2Mar+NqjY3EVE6cqbdzti1v2LMuJN4ZkM9U9KjCCgKB1pcTEl3MN7cxv87eQy3v7ULf1BBrxX88ZQC0swefvVGBRfNSMNpM6AVgk2VbawpbyYuayz6CZchumrxGhxY7fF45v2Rdo2dP7y2jc9CbZA+7Khn/f4W3rx+NgvGxtPm8vHc2gMIIbioOI25+bFEWw1cPzebB1eUoSiqgPLti8eTFR9Jw9F/wVe9hVpDBu4OD7UnPYU3tpgTTWbiI4xcdVQWXn+QjBgLTqsRv2cinkX/wPjuz9WbJq0ez6K70ScXAVDa0ElJfRcmvZYxiXYSQy12Kurb2F7bSZcnQE6clcnp0WgMFrTH/p5g3Fg0u98mmDIdzZRLwJHKUHS5/SzbVsc/P1U1Ao06Df+6aArzxsRxx2njeXd7HUadBq1GYNAJxiUNnSeV5dnDX+ZF8OtPVCNOrxX873w72b4Sbl6QS6fbj1mvRasRlDZ2MSMzmkSHiXOmpvBSqCOI02rgthPG9BZPDEZOnI3nriqmpLELTejxYG2KXF4/u+s6qWp1kRhpYkxCBLZD3nd3XeeAENvbW2u4sDhtyM9fOC4Bk07Lk6sriLMbuXJO1uG1K1IUWPfIwLEtL6qpKU+f1qfDuPp+VV/v9IfBMHzV7IHmHvzBIKlOCxaDjvLGrl5j7CCrypqpaOpmYqrj6+/3IASDCk+u2hc2pijw+sYqYu1GEuymYY/p16WqtYeWbi+xdmPvOSHpQxpkg9FeDc1lsCtkHG14DJInQ95COHgXW3gW9SKafdMfYNLuuzFVr8KTVMzmglswKs6hDTLnIP3V8k9SvQ/zfgMJ42Dbq6p44uRLICpN7SN30auw+kFVfmPm9YcVrvzWic1X8z36S4JMvABsidDTApVr1OrUmHz1zjEqHd/4s9GXvN+3fdCPITYTq9ZN5/4t0FyCpqWUQGIRmq5uOi35aLWDh1M3eNP547LNvY+fWVtJtC2H67JrMZYsC9vWWLKM5Jk3cu70DP767u7eirfjCuJIibZQ3TrQC/nZ3kZ+uTCPUyb2eUgAUp1mHDbzoFIVZr0Go0HLgRYXd3+4BV9AwWHR87ezJuANBPnjWzs5d6oq4aHTalhT1szqfa2MT4xgT30X7S41nGo36ZiWFoVRp2FCahSXP7EebyCIQavh9sXjMJgMpNX8l1dOzqe8zYxRpyHb1kGkpwEvA2UZtBqBN0BYbhVAXYebLreXbSTSPfMZdtd2kKnUct0WM5srtwCqt+TXJxRz+8c7SXYYuf9YQVKEi61eG/d8VMKOmg4sBi1L5mQiZv6d5gY3Xn+Qez5Sw10Z0RbGJNjpQQ+eTm6Yn4MvoBpk5mAPLqK4+ugs7v5ob293ibn5sSRFmtjWoUE35lc89nkFlXVuzp18DfoqPTOMnl5j7CBtPT5K6rto6/Hy6H/7Fpl/rSgnP97OwjwHY2IN3HfeJDrdPiLNeqL1Puo73KzzZlBnSKCssZtJ+hpWacbxzqf7Wb2vmb+eUUgwqOAPKmpBhcVAZWeAOsdCtAvfRNNVS9CWRMCeTXK7l5aeLq57ZiOT06Po8vhp6fbyzwsm4/W4+fWbu1kXCtsZtBoeurCIY8cm4qlYh3HDvyFxIpqKFXgUwYpkM3UeAzOzosmNDy962F3X0WuMgRp6++WrW3nrxtnoNIKlm2uo63CjEfDTY3PRDpOSoFH8nLbzF0xY8FNKum0sO7aZrC2/hfR7SYo0s7Wrneo2FzqNINlhxqzXEG018vuTx3L+9DQ63X6yYq2kRH15Hmp8pIn4yKG9df5AkJc2VPKHpTt7x25akMu1x2Rh0vctV+ZBuj7otRo0A/tK9GLSa0l0mDhrSjJmvQ6bSTd8+7fh0A5S/ao1qMVah4pi714K7b9Vr5eD0NTp4d8ry3n0s334gwqLxifwmxPGoBnimA01fjgIAYZBRB3d/iBLnlxPmtPC7aeOIzPmm93AK4rCp3sauOWlLbT2+Ii1G7nvvCJmZo9k65QfHtIgG4zmEgiGl9pTvRFyF8KuN9U8skkXk2G2csP7ZuyaXzBjmmBdnULLDj0PTRomxp84AU57CN67TZXHyDhKzZ/Qm9U74pk3wNQr1dyygyee3gw5CyBzrnr7ovv23NXfiIRCOP8FtU1SR7Xq4TvqZnW/1z2iNmQ/SPJUOO85VjOB3Jm/J3HTfYBCbdHPKGUCk5qqMC7/I4baDb0v6S7+OZFTc7l0ZkZYdaTTaiDNaeHTPY0cypuba7giTc9gYgE9ip4PdtaHyQ98uKuBYwvicVgGngpjEiJQFAWTXst1c7PZXNlGVqyNWJuBHTUdzB8Ty1tba3vv1LUawdz8GKpbXdz1Xp8nsK3Hxx/e3MHDF0/GFwjy8GfhKvazc5xotYInVlX0JmNbDFr+cnohyQ4Tt7+1szfM5w0Euf3tHYxdMoXE1Q+S0FlLQlqx6nmt2kBw9s0kZ87GaTXQ0t1XYHHprAwidAE0QhA4JMxu1gZp6wlwy8s78AeD3DlLx+Z+PS/dviBvbq7l6NxYlu9t5JblGv5zgZ/HV1b1hsZ6vAHu/6SUsYmT6HT7w4yliuYevjjQypTUdP7w/q7e6sDLZ2ewoTPI23srMel1Ya2+lu9p5MTxiUTbDFz2xIbe+f/9ozKWzMnEpNcMCH+qxwBe33SIuDKwbFstGRFw48u7wsbjI4y8fKWV/c09/OMDNek7pdDPix+VcNsJYxifEskdy3bRGNKhM2g1PHvVDDTALa/soKbdjarY30Z69C7uO6+IFXsaOW1SMu9tryPCrOeUiUlsr27H7fX1GmMHj+Vd7+9ljFNL8ge/UBfyEjWXz9hUwpi0k7n6zW5i7UZevLo4zKtdE+qh25/GTg+NHR7+uHQndR3q80EF/u+jEoqzoocMQdVZ8oiyxJH/2fXU5t9O3hd/oDtzEW3mHCrqevjLO30FKgerKbPj7NhM+m9d92xfczd/XrY7bOzej0s4riCecf28Wcfkx/LAp6VhOWDXzcvBNMhN0kE+L23iyqf6ri8RZh0vXzOT/GGEeRs73awpb2FlaROFyZHMyY1Rc7imX6UaWgfPJY0WJpwN3kGiAzqzaqwNwYb9LTy0vO+a8N72OgoS7Fw6O4OZWdGsLu+riF40Lp7MmG+vZZcQgstmZfDBzvreqWg1gvHJkby5uYaK5h6eXr2f3540Fu3hGq+ohSPXP7upN4e0sdPDdc9u5K0b53wlQ360IA2ywRjs7kdoIHu+mkvjSAO9mUjgz2dM4MOd9Xy0p4G5ebEsGBuPwzKMdpjerDYMT58Dvm61ksh4SMm/fog7yMOQufhO0epVr2HyFDXPzZagGotNpaq+VX+qN0DDTh5Zb6G0oYhLC59FAE9u9JAT18Y/Z7SEGWMA1vUP4Ck4k61VPdxyfB5bq9qJC7m6yxu71VDgIaREmWk1xWOOL8JQv7l33BtfRKMuiQ37dw94za7aDubmODhubDwfhsJ5NqOOs6emIIIB3t5SgxCCvHgbH+yoo6HTwz8vmMTHO2r51aIx7KrtQAjVgFuxu56pmQPlQWra3bT3+LhwRjoP9PNsxNqNpEVZ2V3X2WuMgWrgvL6piiWzMwckHrt9Qeq7/CiONETrPlXzLoRijeVAcw9XzM6ktt1FQ6eHCSmRbDrQyuI8M5cX2XlsY19+UUGciQKn4I7ldXgDQcx67aDVVvubu1k4TvX7ljW5aPQawxaKg9R3eAb01wTYUtlGhye11xiblOqgrt3Nu9vrOH1SMusGSUTfXdeBEMoAo+v5dQc4fVIy501P5T+r+3qzTk6LIj7CRFq0hZVl4fuWEmUZtAKwvsNDu0fh8c/DwzZVrS66PD56vIFeYwxUI+rBT0s5b1rqAKNof3MP9R1u/AElrJ3alqo27jtvEm2ugYUb+5t76PQpg7Ya07tbACONnR62VrWHGWQOs35AH93sWCtajeCLAwNblFW3DV2Vus9l5r2EW1mQupBgt439s/7Cu65xTPUYeX7d3rCE//Kmbkrqu4aVt/gmtPf4BhxvRYHWnvDq7cLkSF68ZibLttbQ7vJx6sQkJmcMbRx2un3c/cGesLEOl591FS1DGmQef4AHl5fxxMoKAF5YX8nElEgeu3Qasakz1Jyxba+q1faFZ6q5s91NkDIDqvq1SJv/O4jKGHLf1gxyHr21tZYlczK566wJrNjTwJryFubkxnB0buywWm+Hw5R0Jy9cVcybW2pwiipuOyGfp1ZV9D6/bFst183LIeYbtPmqaXMNKOhp7fFR1+6WBlk/fmAr/A+E2Hwwht9JM/1q1bulCzeW0pwWlszJZMmcIUqYhiJq6FyHHx2HJvAHfWG9D3vxe5ibn87npU3ctbJvMVsyJxMCAyvjCHjR46e+w829H5WQG29jS2UbDZ0eHrpgImaDlpQoc69wq1GnYdH4RHqEkc8L/0Jh4jvE1q2gMeEYtsWcSIpOz7QMJ5Ut4R6UvAQ7Bn8n/oDCz4/LI667nEtnZbCupIbFuRncMD+Hu97bw8YDbQAsKIgj2WFiZrKWX7yzizSnBQWF1zZW8/cTk4lzDDSoEyJMRJk1lDd2cevx+eyp7yDdaUGv1fDfPY3YrAMvsgdaXERZDJj0mrBF0WLQEmMzIMYuVntjHpSkiMpAscUTrTXwm9e3E2c3Em0z8OnuBgoS7Rg1Qa42fkDhgvmsqNUxwRlkrn4nRl8sXSEpCpcvgE4z8LJwdF4s60ISATE2AxajjtxYG3sbwnMiY+0GzIN4KebkxGA363BY9LT1+JidG8ODIcN0Z00Hk9McA4yGvAQ7WjEwnBJh0mM1aul0+bj1+HzqO9w4rQaaujzYjDompjh4Z1tdb6utKIuegkQ7yU7tACMm3WnCqNMOEN8F0CB636M/la0u7IMsikJApNnA24fIOygKlDV2MSVloNbasWPicBoFgZg8tE39ZBl0RrqsqUADwIDFDODmBXk88lk5XR4/qU4zF85Ix6jTUJTqYHOoCOQgicOECVOdFq77wss9wTx+Ns7HT1fqMev9HD/LMEAUGaCl5ytI2xwmKVFqzl59R58RbDfqSD1k0RZCUJTqoOgr5lIFggrdgxxj1yBjB9nf3BNmmABsqWqnpL6T2JwYNQ0j45AqXHs8nPUYVK5XJY6SiiB5Wl+0YxAGa501JS0Kk15LmlPPxTMzuHhmxjCz+2YYdBpmZEUzIyuat96r57fLwte+ickO7Idb+BUi2mZEqxFhN3smvWbQm+rRjNQhGwyLU03OPPNxVVX/3GfUFjO6oS9qkn440tSK1P5YnBCbz3Fj45me2XcnOy0jiuPGxaOJK1C36Ycn50T0sVn89oQ8THotu2o7aej0sHhCIpPitCEDLIGbF+Tys2NzuW5eDu0uHw6TFhxpPKq/kDvi7+VR/YXgSMOsg3OmpDAmoW9hPLkwgbEJdvKiYFqMl/s+LqGh0807W6u5caqVd8s8FKU6uPfcIn65MJ87zyjk4uJ0LHoNR6UauHa6k7p2N/XtHn4yw8mcVD3j4wzcenxer4vfbtTxP6eO5YFPyynOjubuD/ewvboDly9IRXM3r22tGbTaa/HEJHJijNxxWiHWkJFjNai9NAvirfi7mtXf5TG/VPXsxpyMojWT5rRwalESDZ0edtV2YjFq+cXCMby514UvtpBT117A3R23ctm2S7C6qmjVxXLG5D7Np063n+vmZmM36hACjh8bT2KkidKGLjWH7eQC8hKj+O1JY8JyeU6akEhhnBG7SceifhITBYl2tcerTsOdZxSSEW3B6w9iCr12T30n+QkR5PfLkzpjUjKZ0VYKUyJJiQpP/r11YR6ZMTYuKE7nqVUVvLmlmn9/vo8p6VEkRJrRawWXzEznpgW53LQglwuL0zHpteQlOLnz5CyMoZyZOLuRe07PIzs+kouK08M+w6TX4A2ogqeHclFxOgWJEZw5OVyH7fzpaeTE2gatgHNaDUxItPI/iwt6nz8qJ5prj0pDZ42kfsED+OInqhtHJFN34hP8/Qt18dJrBeMPScpPj7awbGsNF85I44b5OczOjmFXbTspURZuXzyWGJu60AkB18/NHjapP81p4bFLphJjN+DyBUiINPLwxVPIjLFxalHSgO2npn937ZkSIs3866Ip5IfO0fRoC49eOpX0QY7D18FhMXD1UeGVjtovqbIMBJUB4rsA/i/THXOkqR6zo2+BnGPBPLzw7KzsGCam9IVjY21GLp2Vge4rdFf4tjHptczO7gttOyx6bjw2p1dS5nDJjrXy+5PH9tqlGgF/Ob2QjG9ZwuPHjhisCfaPhalTpyobNmz48g0Pg+XLlzN37tzv5L1/DHzj+bfsg03Pwo5X1ZDmrBtVLTbU8EN5YzegkBVrIyoU4u2q2IhuzQOYGjbRnbuYYNGF2BPzACg5UEtpq5dIo5ZxcQYinXE0drhYuqWWez8pweUNcNaUFC6ZmU6Kw0xtbRWBQJB2D0QaQavVkJiYQl27h26vn6o2FyadlvgIPdE2M7vrOsgN7MPV1UpZo5dJaXY+qbdQGXAwNy8aEHR6Aph0GqItOgqSo2hpqMbSU0Vdt0AIQbxFwWVJISouiZ72JrY2Bmju9pHqMFLb3M5fPq7k3Ckp5MTb2XSglVi7kcmpkdjNBhQFlu9t5L6PS3D5Apw9JZXzZ6RSmOygx+ViR10PDZ0e4u1GxiXaMJuM0LKP4Ae/R7N7KehMBI+6Fc20K8ESRU1LN7sbuuh0+UmPtrBiTz1Lt9Zxx+IxaJv3YuyqwmuKxpQ0jvGZydS1u1hd3sLjK/dxWnw7KWOnEGMz4vEHiI8wUtPSQ2OXhzSnhaK0aPQho2b7/gb2tbiJMOkoSLASFxXJXe/uYnNlOzOynCiKqvW1raqN564u5qFPSkmPsZISZWZnbSd3f6h6hTQCrjwqk8lpUfgCQawGLUWpUThtRkoaOllT1kx9h4dpGVFMz3SqrZaA2nYXtW1uHBY9mTFWhBA0drp5es1+HvmsHIHgurnZXDAjjWibkaA/QEVtPW09HpKcNhJi1fByTZuLNzZV8/IXVVyW2UN+0fRQJaiGmnYX//hgL10eP1fOyeTsqanER5iobu1h44E2Klt6SI+2MinNQZLDzIq9jVz2xLpeT5zDouelq2eSl2AHv4+9dW24fEEyooxEOhyA2n+0uamBaNoJGiPY223hn5+WkuQwc8O8HKZlOAckoO+oaeeJlRVsqWzj1KIkTi1K7m15VN3q4kBLDxEmHdlx1rCE+KFo7vKwYc1Kps2c0+u5qGrt4aHlZby4vhKbSccvF+azuCh5SNX/b4vWbi/N3V4cFv03CpX1p6Xbwye7G3j88wpi7QZ+ckwO0zKdYblR/a973V4/P39hc29VMkCSw8Qr184ats/o4dDY6WZPfRc+f5DceNuIhfGWL19O4dSZ7K3vxOUNkB1n+9Z0zzy+ACUNXTR0uEl0mMmOtQ1aUDCSfB/rvhDiC0VRpg763A/JIBNCLALuBbTAY4qi3Dnc9tIg++74VuavKGrhgt76lQsR/F4P3p5OzBFOxFcUVCxv7MLrD5Ada0MfEjNs6HCzq66D5i4v0TYDBQkRvSX21a0uqtp60AtBRqwNp9XAK19U8otXtjI7w84JsR3cs9NIh8vP+zcfRUa0utB3un2Y9doBd64dLWpYKcI5dF5NMKjQ6fZhMeoI+gM0tLVj1OuJc4aX3ZfUdaiemTgr5q+wiOLtUcvqtXqIyhxShNIXCNLj8WM36enx+mnq8hJh0uE8ZLFr7HSzZf1qFsyf9+WfPQSf7W3kksfDO0n8+oQxXHNMNk2dHjZXtVHW0MX45Ah6vAE27m8lI8bK9AwnvoCCQSfI+IZVXcGgEkpsV0iMNH9l4eN2l5eNa1cx75DffkuXB28wSMIwOlsH8foDbKlqZ01ZM5EWPTMyo3s9PsPR0u2hvsNDpFlPksNMh8uHQafp9SQOhj8QxOULDBpCPRwGO+99gSC17W70WnFESBV0e1TdtcGETw+d/4GWHt7aUsOyrbVMz3Ry/vTUYYsAfuzIdW9kDbIfTA6ZEEIL/BM4DqgC1gshliqKsnP4V0p+sAjxtZsD6wxGdIavd0c8mKZaXIRpUI0jj09tnvzJngYcZj0mgxan1UBevB2NEHy+r5MZtgBNXV4uLk4jJcrSu5gPtegNZ4gdRKMRRB4s9tBqSI0fpNy7diu5JR+qwrm+hWruif5LvguDBeLGfOnn67Wa3s+3mfRDJgbH2k3ovkE1FajJ9XedUcid7+2mxxvg0lkZLJ6ohr5i7EYWFMSzoKBPz/24scOLqZbUd/JZSSPVrS6OyYtlSrpzgC5VfwJBhS1VbXy6uwEhYP6YOCYkO9BoBBXN3awsaWJPXSezcqKZlukk2mqkw+Vj44FWVuxpZLzWS2lDZ1h/zUMN1+Ew6LRMy3B+bdFRp9WI09r3ORFfQfxTp9Vg/xqhre3V7azY00iHx8f8/DgmpTm+VJFdr9WQ9hWajf9Y+DpC2GlOC9fPy+Hy2RmYdNrDl8mQSL4CPxiDDJgOlCqKUg4ghHgBOBWQBpnkW2NVeTOXP9Gnrv/g8jJeunYmYxMjePzSqdyxbBca0c7lszO4YnYm+u8rj6NuGzx5Yl+13ar74MJXIPe47+fzv0VsJh3nTk9j7phYfAHVQ3W4JfMVTd1c9O+1vUnej6+s4O9nTeCsqUOLpm460Mp5j6zBH0oAemh5GS9ePZMkh5nrntnIzlq1yvQ/a/bz02Nz+en8HN7eWsNvXt8OwC2Ffv7x+Dqev6r4W29TM5LsqGnnnIdX9xYwPPJZOU9dPp2j8wZWBUvCsRh+SEul5EjlhxTATQYq+z2uCo1JJN8KPV4/9/fTMwNV9X91WRM6rYZj8uN45dqZ5MXb+d1JY3vzcb4XylcMlD747G+D6xr9SIiPMJMSZflG+kXbqtvDKu4A7np/D42dA3W4DvL8ugO9xhioDbZf3VjFgZaeXmPsIP9aXsbe+s4w3TiAmjb3gPZDP3Y+29sUVk2qKPDgp6W4B6nglEgk3z8/mBwyIcRZwCJFUa4MPb4YmKEoyg2HbHc1cDVAfHz8lBdeeGHAe30bdHV1YbN9C+2FfqQcifMPKgplDd24/eELUHyEiTh7X6hoRObeVQ+d4XIJ6EwQk6dq4H2fu/IDOvZtPT4qW8ONUq0Q5Mbb0Q/RwWF/cw8d7nC5CodZj9NmpLxxYOuynDgb5Y3dBEPXwngz1LvUcNW32TNwpKnv8NBwiCFr1mvJirVx0Gb+IR37kUDOX87/u57/vHnzfvg5ZEA10D8OkRIaC0NRlEeAR0BN6v+uEvBkcuOROf+WLyq59eWtvY81Al65dhqT0/vyfUZk7pXr4PFrw9tQnfYvKJr//e4HP6xjv6u2g9seXBmmw/bz4/JYMD9nyET9T3c3cHm/pu8A/1kyjXSnhf/34KqwDganFSWx5OhCti8v5Z+fqmKutxT6eWiPkdevK/5Kyfg/FjZUtPDrh1eHSTn837lFzJ/UF4j4IR37kUDOX85/JOf/QzLI1gO5QohMVEPsPOCCkd0lyZHGgoJ47jlnIv9euY9oq4Frj8lmQopjpHcLkibBxW/Ayv9T+4DOvB5yfnz5Y982BYkRPHdVMY+uKGdfczcXzEhj0biEYasmp2c6eeTiKTz8WTkaAdcek8209CjMBh3PLJnOU6sq2FTZxqkTk1hclIzFqOOS4nRi7UZeWFdJpLmL566ccUQZYwATUx08vWQGDy0vo93l5cqjspibL/PHJJIfCj8Yg0xRFL8Q4gbgfVTZi8cVRdkxwrslOcJwWAycPjmFheMT0GkGL30fEbR6yDoG0mZC0K9WT0oAtWrzvvMn4Q0Ev1KFnNWo4/hxCRydF4uAMFHLsUmR/Pn0Qjz+AFZjXzgyPtLMZbMyOXtKKutXf07Rt9yj8YeAXqthdk4M0zKiCCiDN+iWSCQjxw/GIANQFOUd4J2R3g/Jkc8PtmpKZwBkO5FD0es0vWK0X5Wh9Lt0Ws2QKuhfRxLhx8oP5iZEIpGE8UOqspRIJBKJRCIZlUiDTCKRSCQSiWSEkQaZRCKRSCQSyQgjDTKJRCKRSCSSEUYaZBKJRCKRSCQjjDTIJBKJRCKRSEYYaZBJJBKJRCKRjDDSIJNIJBKJRCIZYaRBJpFIJBKJRDLCSINMIpFIJBKJZISRBplEIpFIJBLJCCMNMolEIpFIJJIRRhpkEolEIpFIJCOMNMgkEolEIpFIRhhpkEkkEolEIpGMMNIgk0gkEolEIhlhpEEmkUgkEolEMsJIg0wikUgkEolkhJEGmUQikUgkEskIIw0yiUQikUgkkhFGKIoy0vtw2AghGoH939HbxwBN39F7/xgYzfMfzXMHOf/RPP/RPHeQ85fz/+7nn64oSuxgT/yoDbLvEiHEBkVRpo70fowUo3n+o3nuIOc/muc/mucOcv5y/iM7fxmylEgkEolEIhlhpEEmkUgkEolEMsJIg2xoHhnpHRhhRvP8R/PcQc5/NM9/NM8d5Pzl/EcQmUMmkUgkEolEMsJID5lEIpFIJBLJCCMNskMQQiwSQuwRQpQKIW4b6f35rhFCPC6EaBBCbO835hRCfCiEKAn9HzWS+/hdIoRIFUJ8KoTYKYTYIYT4WWj8iP8OhBAmIcQ6IcSW0NxvD41nCiHWhs6BF4UQhpHe1+8SIYRWCLFJCPF26PGomb8QokIIsU0IsVkIsSE0dsT/9g8ihHAIIV4RQuwWQuwSQswcDfMXQuSHjvnBfx1CiJtGw9wPIoS4OXTd2y6EeD50PRzRc18aZP0QQmiBfwInAGOB84UQY0d2r75zngQWHTJ2G/Cxoii5wMehx0cqfuAWRVHGAsXA9aFjPhq+Aw8wX1GUiUARsEgIUQzcBdyjKEoO0AosGbld/F74GbCr3+PRNv95iqIU9Sv3Hw2//YPcC7ynKMoYYCLq7+CIn7+iKHtCx7wImAL0AK8zCuYOIIRIBn4KTFUUZTygBc5jhM99aZCFMx0oVRSlXFEUL/ACcOoI79N3iqIonwEthwyfCjwV+vsp4LTvc5++TxRFqVUUZWPo707UC3Iyo+A7UFS6Qg/1oX8KMB94JTR+RM79IEKIFOAk4LHQY8Eomv8QHPG/fQAhRCRwNPBvAEVRvIqitDFK5t+PY4EyRVH2M7rmrgPMQggdYAFqGeFzXxpk4SQDlf0eV4XGRhvxiqLUhv6uA+JHcme+L4QQGcAkYC2j5DsIhes2Aw3Ah0AZ0KYoij+0yZF+Dvwf8EsgGHoczeiavwJ8IIT4QghxdWhsVPz2gUygEXgiFLJ+TAhhZfTM/yDnAc+H/h4Vc1cUpRr4O3AA1RBrB75ghM99aZBJhkVRy3CP+FJcIYQNeBW4SVGUjv7PHcnfgaIogVDYIgXVQzxmZPfo+0MIcTLQoCjKFyO9LyPIHEVRJqOmaVwvhDi6/5NH8m8f1UMyGXhIUZRJQDeHhOiO8PkTypFaDLx86HNH8txDuXGnohrlSYCVgak73zvSIAunGkjt9zglNDbaqBdCJAKE/m8Y4f35ThFC6FGNsWcVRXktNDyqvoNQqOZTYCbgCLnx4cg+B2YDi4UQFajpCfNRc4pGy/wPegpQFKUBNYdoOqPnt18FVCmKsjb0+BVUA220zB9UQ3yjoij1ocejZe4LgH2KojQqiuIDXkO9HozouS8NsnDWA7mhSgsDqit36Qjv00iwFLg09PelwJsjuC/fKaGcoX8DuxRFubvfU0f8dyCEiBVCOEJ/m4HjUHPoPgXOCm12RM4dQFGUXyuKkqIoSgbquf6JoigXMkrmL4SwCiHsB/8Gjge2Mwp++wCKotQBlUKI/NDQscBORsn8Q5xPX7gSRs/cDwDFQghLaA04eOxH9NyXwrCHIIQ4ETWvRAs8rijKn0d2j75bhBDPA3NRu9zXA38A3gBeAtKA/cA5iqIcmvh/RCCEmAP8F9hGXx7Rb1DzyI7o70AIMQE1cVWLenP2kqIo/yOEyEL1GDmBTcBFiqJ4Rm5Pv3uEEHOBWxVFOXm0zD80z9dDD3XAc4qi/FkIEc0R/ts/iBCiCLWgwwCUA5cTOhc4wucfMsIPAFmKorSHxkbTsb8dOBe10n4TcCVqztiInfvSIJNIJBKJRCIZYWTIUiKRSCQSiWSEkQaZRCKRSCQSyQgjDTKJRCKRSCSSEUYaZBKJRCKRSCQjjDTIJBKJRCKRSEYYaZBJJJJRhxDiNCGEIoQYNZ0JJBLJDxtpkEkkktHI+cDnof8lEolkxJEGmUQiGVWE+pbOAZagKvQjhNAIIR4UQuwWQnwohHhHCHFW6LkpQogVoQbc7x9sLSORSCTfJtIgk0gko41TgfcURdkLNAshpgBnABnAWOBi1J6eB/uc3g+cpSjKFOBx4Iju3iGRSEYG3ZdvIpFIJEcU56M2EQe1Tcr5qNfClxVFCQJ1QohPQ8/nA+OBD9WWd2iB2u93dyUSyWhAGmQSiWTUIIRwAvOBQiGEgmpgKfT1dBzwEmCHoigzv6ddlEgkoxQZspRIJKOJs4CnFUVJVxQlQ1GUVGAf0AKcGcoliwfmhrbfA8QKIXpDmEKIcSOx4xKJ5MhGGmQSiWQ0cT4DvWGvAglAFbATeAbYCLQriuJFNeLuEkJsATYDs763vZVIJKMGoSjKSO+DRCKRjDhCCJuiKF1CiGhgHTBbUZS6kd4viUQyOpA5ZBKJRKLythDCARiAP0ljTCKRfJ9ID5lEIpFIJBLJCCNzyCQSiUQikUhGGGmQSSQSiUQikYww0iCTSCQSiUQiGWGkQSaRSCQSiUQywkiDTCKRSCQSiWSEkQaZRCKRSCQSyQjz/wE/3Ga3qntEqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the split value \n",
    "age_split = 8 \n",
    "\n",
    "# Ploting the original plot\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(y='Fare', x='Age', data=dd, hue='Survived')\n",
    "plt.grid()\n",
    "\n",
    "# Adding the split line\n",
    "plt.axhline(52.77, label='Best fare split', color='green')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-learn comparison "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The go-to machine learning framework with python is the scikit-learn package https://scikit-learn.org/stable/index.html. \n",
    "    \n",
    "The package has a decision tree creation implementation: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html?highlight=decisiontree#sklearn.tree.DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see how it compares to the custom tree creation that I have written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_tree_start = time.time()\n",
    "\n",
    "# Initiating the Node\n",
    "root = Node(Y, X, **hp)\n",
    "\n",
    "# Getting teh best split\n",
    "root.grow_tree()\n",
    "\n",
    "custom_tree_time = time.time() - custom_tree_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scikit_tree_start = time.time()\n",
    "\n",
    "# Using the ML package \n",
    "clf = DecisionTreeClassifier(**hp)\n",
    "clf.fit(X, Y)\n",
    "\n",
    "scikit_tree_time = time.time() - scikit_tree_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom tree growing time: 0.4791076183319092s\n",
      "Scikit tree growing time: 0.002000570297241211s\n"
     ]
    }
   ],
   "source": [
    "print(f\"Custom tree growing time: {custom_tree_time}s\")\n",
    "print(f\"Scikit tree growing time: {scikit_tree_time}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be expected, the highly optimized scikit-learn solution works faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both methods have a visualization: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root\n",
      "   | GINI impurity of the node: 0.48\n",
      "   | Class distribution in the node: {0: 424, 1: 290}\n",
      "   | Predicted class: 0\n",
      "|-------- Split rule: Fare <= 52.277\n",
      "           | GINI impurity of the node: 0.44\n",
      "           | Class distribution in the node: {0: 389, 1: 195}\n",
      "           | Predicted class: 0\n",
      "|---------------- Split rule: Fare <= 10.481\n",
      "                   | GINI impurity of the node: 0.32\n",
      "                   | Class distribution in the node: {0: 192, 1: 47}\n",
      "                   | Predicted class: 0\n",
      "|------------------------ Split rule: Age <= 32.5\n",
      "                           | GINI impurity of the node: 0.37\n",
      "                           | Class distribution in the node: {0: 134, 1: 43}\n",
      "                           | Predicted class: 0\n",
      "|------------------------ Split rule: Age > 32.5\n",
      "                           | GINI impurity of the node: 0.12\n",
      "                           | Class distribution in the node: {0: 58, 1: 4}\n",
      "                           | Predicted class: 0\n",
      "|---------------- Split rule: Fare > 10.481\n",
      "                   | GINI impurity of the node: 0.49\n",
      "                   | Class distribution in the node: {0: 197, 1: 148}\n",
      "                   | Predicted class: 0\n",
      "|------------------------ Split rule: Age <= 6.5\n",
      "                           | GINI impurity of the node: 0.41\n",
      "                           | Class distribution in the node: {0: 12, 1: 30}\n",
      "                           | Predicted class: 1\n",
      "|------------------------ Split rule: Age > 6.5\n",
      "                           | GINI impurity of the node: 0.48\n",
      "                           | Class distribution in the node: {0: 185, 1: 118}\n",
      "                           | Predicted class: 0\n",
      "|-------- Split rule: Fare > 52.277\n",
      "           | GINI impurity of the node: 0.39\n",
      "           | Class distribution in the node: {1: 95, 0: 35}\n",
      "           | Predicted class: 1\n",
      "|---------------- Split rule: Age <= 63.5\n",
      "                   | GINI impurity of the node: 0.38\n",
      "                   | Class distribution in the node: {1: 95, 0: 32}\n",
      "                   | Predicted class: 1\n",
      "|------------------------ Split rule: Age <= 29.5\n",
      "                           | GINI impurity of the node: 0.44\n",
      "                           | Class distribution in the node: {0: 17, 1: 34}\n",
      "                           | Predicted class: 1\n",
      "|------------------------ Split rule: Age > 29.5\n",
      "                           | GINI impurity of the node: 0.32\n",
      "                           | Class distribution in the node: {1: 61, 0: 15}\n",
      "                           | Predicted class: 1\n",
      "|---------------- Split rule: Age > 63.5\n",
      "                   | GINI impurity of the node: 0.0\n",
      "                   | Class distribution in the node: {0: 3}\n",
      "                   | Predicted class: 0\n"
     ]
    }
   ],
   "source": [
    "root.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Fare <= 52.28\n",
      "|   |--- Fare <= 10.48\n",
      "|   |   |--- Age <= 32.50\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- Age >  32.50\n",
      "|   |   |   |--- class: 0\n",
      "|   |--- Fare >  10.48\n",
      "|   |   |--- Age <= 6.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- Age >  6.50\n",
      "|   |   |   |--- class: 0\n",
      "|--- Fare >  52.28\n",
      "|   |--- Age <= 63.50\n",
      "|   |   |--- Age <= 29.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- Age >  29.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |--- Age >  63.50\n",
      "|   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(export_text(clf, feature_names=['Age', 'Fare']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Result frame\n",
    "results = X.copy()\n",
    "\n",
    "# Predictions\n",
    "results['scikit_learn'] = clf.predict(X[features])\n",
    "results['custom_yhat'] = root.predict(X[features])\n",
    "\n",
    "# Asserting that every prediction is the same \n",
    "np.all(results['scikit_learn'] == results['custom_yhat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the predictions are the same. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression trees "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the response variable $Y$ is not binary but continuous, the spliting criteria of Gini impurity looses its sense. We then use what are called regression decision trees. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main difference between the classification decision tree is that each node saves the mean of the $Y$ variable in itself and the spliting logic aims to minimize the sum of squared **residuals** in each node. \n",
    "\n",
    "The residuals are calculated by the formula:\n",
    "\n",
    "$$r_{i} = y_{i} - \\widehat{y}$$ \n",
    "\n",
    "Where $y_{i}$ - is the true i-th observation in the node and $\\widehat{y}$ is the average of the response variables in the node."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try to create a decision tree where the dependent variable $Y$ is the miles per gallon that a car can go and the explanatory variables $X$ are horsepower, accelaration and the weight of the car.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"data/regression/auto-mpg.csv\")\n",
    "\n",
    "# Subsetting\n",
    "d = d[d['horsepower']!='?']\n",
    "\n",
    "# Constructing the X and Y matrices\n",
    "features = ['horsepower', 'weight', 'acceleration']\n",
    "\n",
    "for ft in features:\n",
    "    d[ft] = pd.to_numeric(d[ft])\n",
    "\n",
    "X = d[features]\n",
    "Y = d['mpg'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in features:\n",
    "    plt.scatter(X[feature], Y)\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('mpg')\n",
    "    plt.title(f\"{feature} relationship with mpg\")\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The spliting criterion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main difference between a classification tree and a regression tree is the spliting criterion. Each node has the y mean saved and we can get the residuals by subtracting that mean from all the y values that fall into that node.\n",
    "\n",
    "Then we search for the feature and the feature value which minimizes the **mean squared error (mse)** statistic:\n",
    "\n",
    "$$mse = \\dfrac{1}{n} \\Sigma_{i=1}^{n} \\left( y_{i} - \\overline{y} \\right) ^ {2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_tree_start = time.time()\n",
    "\n",
    "# Initiating the Node\n",
    "root = NodeRegression(Y, X, **hp)\n",
    "\n",
    "# Getting teh best split\n",
    "root.grow_tree()\n",
    "\n",
    "custom_tree_time = time.time() - custom_tree_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "scikit_tree_start = time.time()\n",
    "\n",
    "# Using the ML package \n",
    "clf = DecisionTreeRegressor(**hp)\n",
    "clf.fit(X, Y)\n",
    "\n",
    "scikit_tree_time = time.time() - scikit_tree_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom tree growing time: 0.7820971012115479s\n",
      "Scikit tree growing time: 0.014009714126586914s\n"
     ]
    }
   ],
   "source": [
    "print(f\"Custom tree growing time: {custom_tree_time}s\")\n",
    "print(f\"Scikit tree growing time: {scikit_tree_time}s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root\n",
      "   | MSE of the node: 60.76\n",
      "   | Count of observations in node: 392\n",
      "   | Prediction of node: 23.446\n",
      "|-------- Split rule: weight <= 2764.5\n",
      "           | MSE of the node: 32.65\n",
      "           | Count of observations in node: 191\n",
      "           | Prediction of node: 29.42\n",
      "|---------------- Split rule: horsepower <= 70.5\n",
      "                   | MSE of the node: 25.84\n",
      "                   | Count of observations in node: 69\n",
      "                   | Prediction of node: 33.68\n",
      "|------------------------ Split rule: acceleration <= 23.6\n",
      "                           | MSE of the node: 23.52\n",
      "                           | Count of observations in node: 67\n",
      "                           | Prediction of node: 33.381\n",
      "|------------------------ Split rule: acceleration > 23.6\n",
      "                           | MSE of the node: 0.09\n",
      "                           | Count of observations in node: 2\n",
      "                           | Prediction of node: 43.7\n",
      "|---------------- Split rule: horsepower > 70.5\n",
      "                   | MSE of the node: 20.43\n",
      "                   | Count of observations in node: 122\n",
      "                   | Prediction of node: 27.011\n",
      "|------------------------ Split rule: horsepower <= 84.5\n",
      "                           | MSE of the node: 17.65\n",
      "                           | Count of observations in node: 53\n",
      "                           | Prediction of node: 29.03\n",
      "|------------------------ Split rule: horsepower > 84.5\n",
      "                           | MSE of the node: 17.03\n",
      "                           | Count of observations in node: 69\n",
      "                           | Prediction of node: 25.459\n",
      "|-------- Split rule: weight > 2764.5\n",
      "           | MSE of the node: 21.34\n",
      "           | Count of observations in node: 201\n",
      "           | Prediction of node: 17.769\n",
      "|---------------- Split rule: horsepower <= 127.0\n",
      "                   | MSE of the node: 16.76\n",
      "                   | Count of observations in node: 103\n",
      "                   | Prediction of node: 20.669\n",
      "|------------------------ Split rule: horsepower <= 71.5\n",
      "                           | MSE of the node: 14.83\n",
      "                           | Count of observations in node: 3\n",
      "                           | Prediction of node: 31.2\n",
      "|------------------------ Split rule: horsepower > 71.5\n",
      "                           | MSE of the node: 13.39\n",
      "                           | Count of observations in node: 100\n",
      "                           | Prediction of node: 20.353\n",
      "|---------------- Split rule: horsepower > 127.0\n",
      "                   | MSE of the node: 8.02\n",
      "                   | Count of observations in node: 98\n",
      "                   | Prediction of node: 14.721\n",
      "|------------------------ Split rule: weight <= 2998.0\n",
      "                           | MSE of the node: 0.0\n",
      "                           | Count of observations in node: 1\n",
      "                           | Prediction of node: 32.7\n",
      "|------------------------ Split rule: weight > 2998.0\n",
      "                           | MSE of the node: 4.74\n",
      "                           | Count of observations in node: 97\n",
      "                           | Prediction of node: 14.536\n"
     ]
    }
   ],
   "source": [
    "root.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- weight <= 2764.50\n",
      "|   |--- horsepower <= 70.50\n",
      "|   |   |--- acceleration <= 23.60\n",
      "|   |   |   |--- value: [33.38]\n",
      "|   |   |--- acceleration >  23.60\n",
      "|   |   |   |--- value: [43.70]\n",
      "|   |--- horsepower >  70.50\n",
      "|   |   |--- horsepower <= 84.50\n",
      "|   |   |   |--- value: [29.03]\n",
      "|   |   |--- horsepower >  84.50\n",
      "|   |   |   |--- value: [25.46]\n",
      "|--- weight >  2764.50\n",
      "|   |--- horsepower <= 127.00\n",
      "|   |   |--- horsepower <= 71.50\n",
      "|   |   |   |--- value: [31.20]\n",
      "|   |   |--- horsepower >  71.50\n",
      "|   |   |   |--- value: [20.35]\n",
      "|   |--- horsepower >  127.00\n",
      "|   |   |--- weight <= 2998.00\n",
      "|   |   |   |--- value: [32.70]\n",
      "|   |   |--- weight >  2998.00\n",
      "|   |   |   |--- value: [14.54]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(export_text(clf, feature_names=features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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