Course_work_1_Indradhar_Paka.ipynb

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    "colab": {
      "name": "Course work 1_Indradhar Paka.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qcf6T4ns7hF3",
        "outputId": "9cab34cd-e3be-416f-d598-2116779213f6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import torch\n",
        "torch.cuda.is_available()\n",
        "device=torch.device(\"cuda:0\")\n",
        "device\n",
        "if torch.cuda.is_available():\n",
        "  device=torch.device(\"cuda:0\")\n",
        "  print(\"running on gpu\")\n",
        "else:\n",
        "  device=torch.device(\"cpu\")\n",
        "  print(\"running on cpu\")\n",
        "\n",
        "torch.cuda.device_count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "running on gpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-7pxE3wXi4N"
      },
      "source": [
        "1. Use Seaborn to investigate the data and present your findings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NM34giuytv6O"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhsNrZxWyATt"
      },
      "source": [
        "data = pd.read_csv(\"/content/Predicting the Price of a Football Player.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dk00Q9vtyJM2",
        "outputId": "87589bf7-154a-4608-80f9-f28d037931e9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "type(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-KIrnXoyOKH",
        "outputId": "a2276124-4c4b-4da1-a564-fd241da8c853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        }
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>name</th>\n",
              "      <th>club</th>\n",
              "      <th>age</th>\n",
              "      <th>position</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>market_value</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>nationality</th>\n",
              "      <th>new_foreign</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>big_club</th>\n",
              "      <th>new_signing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alexis Sanchez</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>LW</td>\n",
              "      <td>1</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4329</td>\n",
              "      <td>12.0</td>\n",
              "      <td>17.10%</td>\n",
              "      <td>264</td>\n",
              "      <td>3</td>\n",
              "      <td>Chile</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mesut Ozil</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>AM</td>\n",
              "      <td>1</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4395</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5.60%</td>\n",
              "      <td>167</td>\n",
              "      <td>2</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Petr Cech</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>35</td>\n",
              "      <td>GK</td>\n",
              "      <td>4</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1529</td>\n",
              "      <td>5.5</td>\n",
              "      <td>5.90%</td>\n",
              "      <td>134</td>\n",
              "      <td>2</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Theo Walcott</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>RW</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2393</td>\n",
              "      <td>7.5</td>\n",
              "      <td>1.50%</td>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>England</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Laurent Koscielny</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>31</td>\n",
              "      <td>CB</td>\n",
              "      <td>3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>912</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.70%</td>\n",
              "      <td>121</td>\n",
              "      <td>2</td>\n",
              "      <td>France</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>Edimilson Fernandes</td>\n",
              "      <td>West+Ham</td>\n",
              "      <td>21</td>\n",
              "      <td>CM</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>288</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.40%</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>457</th>\n",
              "      <td>Arthur Masuaku</td>\n",
              "      <td>West+Ham</td>\n",
              "      <td>23</td>\n",
              "      <td>LB</td>\n",
              "      <td>3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>199</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.20%</td>\n",
              "      <td>34</td>\n",
              "      <td>4</td>\n",
              "      <td>Congo DR</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458</th>\n",
              "      <td>Sam Byram</td>\n",
              "      <td>West+Ham</td>\n",
              "      <td>23</td>\n",
              "      <td>RB</td>\n",
              "      <td>3</td>\n",
              "      <td>4.5</td>\n",
              "      <td>198</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.30%</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>England</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>Ashley Fletcher</td>\n",
              "      <td>West+Ham</td>\n",
              "      <td>21</td>\n",
              "      <td>CF</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>412</td>\n",
              "      <td>4.5</td>\n",
              "      <td>5.90%</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>England</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>Diafra Sakho</td>\n",
              "      <td>West+Ham</td>\n",
              "      <td>27</td>\n",
              "      <td>CF</td>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>214</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.10%</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>461 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    name      club  age  ... club_id  big_club  new_signing\n",
              "0         Alexis Sanchez   Arsenal   28  ...       1         1            0\n",
              "1             Mesut Ozil   Arsenal   28  ...       1         1            0\n",
              "2              Petr Cech   Arsenal   35  ...       1         1            0\n",
              "3           Theo Walcott   Arsenal   28  ...       1         1            0\n",
              "4      Laurent Koscielny   Arsenal   31  ...       1         1            0\n",
              "..                   ...       ...  ...  ...     ...       ...          ...\n",
              "456  Edimilson Fernandes  West+Ham   21  ...      20         0            1\n",
              "457       Arthur Masuaku  West+Ham   23  ...      20         0            1\n",
              "458            Sam Byram  West+Ham   23  ...      20         0            0\n",
              "459      Ashley Fletcher  West+Ham   21  ...      20         0            1\n",
              "460         Diafra Sakho  West+Ham   27  ...      20         0            0\n",
              "\n",
              "[461 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVgjra1WySKu"
      },
      "source": [
        "data['fpl_sel'] = data['fpl_sel'].replace('%','',regex=True).astype('float')/10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOFyV4-3vgId"
      },
      "source": [
        "output_var = 'market_value'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdIhY2tSvp2B"
      },
      "source": [
        "import numpy as np\n",
        "X = data[data.columns[~data.columns.isin([output_var])]]\n",
        "y = data[[output_var]]\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "np.random.seed(37) # Set seed\n",
        "x_train, x_test = train_test_split(X, test_size = 0.25)\n",
        "\n",
        "## dont using iloc as we have projected from data\n",
        "y_train = y.loc[x_train.index.values] \n",
        "y_test = y.loc[x_test.index.values]\n",
        "x_train = X.loc[x_train.index.values, :]\n",
        "x_test = X.loc[x_test.index.values, :]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUrbx4XFvw2o"
      },
      "source": [
        "concat_train_data = pd.concat([x_train,y_train],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bmFIGAa5Zhl",
        "outputId": "51897068-162b-4e16-a067-a9c3236fa805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "concat_train_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>age</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>new_foreign</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>big_club</th>\n",
              "      <th>new_signing</th>\n",
              "      <th>market_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.00000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>345.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.802899</td>\n",
              "      <td>2.191304</td>\n",
              "      <td>758.075362</td>\n",
              "      <td>5.44058</td>\n",
              "      <td>0.312841</td>\n",
              "      <td>56.011594</td>\n",
              "      <td>2.011594</td>\n",
              "      <td>0.040580</td>\n",
              "      <td>3.208696</td>\n",
              "      <td>10.243478</td>\n",
              "      <td>0.313043</td>\n",
              "      <td>0.139130</td>\n",
              "      <td>10.753333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.862686</td>\n",
              "      <td>1.007781</td>\n",
              "      <td>939.588994</td>\n",
              "      <td>1.32894</td>\n",
              "      <td>0.553236</td>\n",
              "      <td>52.883592</td>\n",
              "      <td>0.985290</td>\n",
              "      <td>0.197601</td>\n",
              "      <td>1.244726</td>\n",
              "      <td>5.866625</td>\n",
              "      <td>0.464405</td>\n",
              "      <td>0.346585</td>\n",
              "      <td>11.801618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>4.50000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>456.000000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>860.000000</td>\n",
              "      <td>5.50000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7664.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>264.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age  position_cat  ...  new_signing  market_value\n",
              "count  345.000000    345.000000  ...   345.000000    345.000000\n",
              "mean    26.802899      2.191304  ...     0.139130     10.753333\n",
              "std      3.862686      1.007781  ...     0.346585     11.801618\n",
              "min     17.000000      1.000000  ...     0.000000      0.250000\n",
              "25%     24.000000      1.000000  ...     0.000000      3.000000\n",
              "50%     27.000000      2.000000  ...     0.000000      7.000000\n",
              "75%     29.000000      3.000000  ...     0.000000     15.000000\n",
              "max     37.000000      4.000000  ...     1.000000     75.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PPWJDl20wA_9",
        "outputId": "3d742f3f-a236-4b34-db27-077f5381f487",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "concat_train_data.corr()[output_var].sort_values()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "position_cat   -0.202518\n",
              "age            -0.144592\n",
              "age_cat        -0.116853\n",
              "club_id        -0.052287\n",
              "new_foreign     0.097173\n",
              "region          0.110158\n",
              "new_signing     0.115376\n",
              "fpl_sel         0.484164\n",
              "fpl_points      0.595919\n",
              "big_club        0.624354\n",
              "page_views      0.716096\n",
              "fpl_value       0.771985\n",
              "market_value    1.000000\n",
              "Name: market_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUy-J4AKwKad",
        "outputId": "54e98da5-e8af-403a-c197-6ab5e620d316",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "concat_train_data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name             object\n",
              "club             object\n",
              "age               int64\n",
              "position         object\n",
              "position_cat      int64\n",
              "page_views        int64\n",
              "fpl_value       float64\n",
              "fpl_sel         float64\n",
              "fpl_points        int64\n",
              "region            int64\n",
              "nationality      object\n",
              "new_foreign       int64\n",
              "age_cat           int64\n",
              "club_id           int64\n",
              "big_club          int64\n",
              "new_signing       int64\n",
              "market_value    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxJmd4c-wWzO",
        "outputId": "f3596815-8e13-4735-a413-949d04fdb288",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.boxplot(y='position',x=output_var,data=concat_train_data,orient='h')\n",
        "plt.xlabel(\"Market Value\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHuPkMJ8xX1s",
        "outputId": "9e8171da-7ea6-4532-ccc7-6ddbf010783f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "sns.boxplot(y='position',x='market_value',data=concat_train_data,hue='new_signing',orient='h')\n",
        "plt.xlabel(\"Market Value\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_-yhvimycGR"
      },
      "source": [
        "encoded_data = pd.get_dummies(concat_train_data, columns=[\"new_foreign\",\"big_club\",\"new_signing\"],drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hTZDnUPy1vw",
        "outputId": "0226360e-4b80-4751-d813-8f2f41c5ca25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "encoded_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>name</th>\n",
              "      <th>club</th>\n",
              "      <th>age</th>\n",
              "      <th>position</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>nationality</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>market_value</th>\n",
              "      <th>new_foreign_1</th>\n",
              "      <th>big_club_1</th>\n",
              "      <th>new_signing_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mesut Ozil</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>AM</td>\n",
              "      <td>1</td>\n",
              "      <td>4395</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.56</td>\n",
              "      <td>167</td>\n",
              "      <td>2</td>\n",
              "      <td>Germany</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>Ã€ngel Rangel</td>\n",
              "      <td>Swansea</td>\n",
              "      <td>34</td>\n",
              "      <td>RB</td>\n",
              "      <td>3</td>\n",
              "      <td>137</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.88</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>Spain</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>Romelu Lukaku</td>\n",
              "      <td>Manchester+United</td>\n",
              "      <td>24</td>\n",
              "      <td>CF</td>\n",
              "      <td>1</td>\n",
              "      <td>3727</td>\n",
              "      <td>11.5</td>\n",
              "      <td>4.50</td>\n",
              "      <td>221</td>\n",
              "      <td>2</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Theo Walcott</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>RW</td>\n",
              "      <td>1</td>\n",
              "      <td>2393</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.15</td>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>England</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>Juan Mata</td>\n",
              "      <td>Manchester+United</td>\n",
              "      <td>29</td>\n",
              "      <td>AM</td>\n",
              "      <td>1</td>\n",
              "      <td>2280</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.35</td>\n",
              "      <td>102</td>\n",
              "      <td>2</td>\n",
              "      <td>Spain</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              name               club  ...  big_club_1 new_signing_1\n",
              "1       Mesut Ozil            Arsenal  ...           1             0\n",
              "366  Ã€ngel Rangel            Swansea  ...           0             0\n",
              "260  Romelu Lukaku  Manchester+United  ...           1             0\n",
              "3     Theo Walcott            Arsenal  ...           1             0\n",
              "265      Juan Mata  Manchester+United  ...           1             0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb-Ld1oM0q-d",
        "outputId": "1ee500c0-556b-4ae2-8293-f47c8053c1fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "concat_train_data.corr()[output_var].sort_values()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "position_cat   -0.202518\n",
              "age            -0.144592\n",
              "age_cat        -0.116853\n",
              "club_id        -0.052287\n",
              "new_foreign     0.097173\n",
              "region          0.110158\n",
              "new_signing     0.115376\n",
              "fpl_sel         0.484164\n",
              "fpl_points      0.595919\n",
              "big_club        0.624354\n",
              "page_views      0.716096\n",
              "fpl_value       0.771985\n",
              "market_value    1.000000\n",
              "Name: market_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xqpZ43I0PEd",
        "outputId": "83e9792c-7cdf-494a-f6fa-9027f371f3e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "corr = concat_train_data.corr()\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(240,10,as_cmap=True),\n",
        "            square=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd87ce1d358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJe1f4N51pXF",
        "outputId": "2df3f913-d813-4fac-c525-a0e87b87f323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>new_foreign</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>big_club</th>\n",
              "      <th>new_signing</th>\n",
              "      <th>market_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.209102</td>\n",
              "      <td>-0.049327</td>\n",
              "      <td>-0.099975</td>\n",
              "      <td>0.024667</td>\n",
              "      <td>0.215552</td>\n",
              "      <td>0.077748</td>\n",
              "      <td>-0.164685</td>\n",
              "      <td>0.969917</td>\n",
              "      <td>0.041378</td>\n",
              "      <td>-0.205341</td>\n",
              "      <td>-0.083684</td>\n",
              "      <td>-0.144592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>position_cat</th>\n",
              "      <td>0.209102</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.301136</td>\n",
              "      <td>-0.502653</td>\n",
              "      <td>0.071600</td>\n",
              "      <td>-0.100295</td>\n",
              "      <td>-0.019806</td>\n",
              "      <td>0.019294</td>\n",
              "      <td>0.183599</td>\n",
              "      <td>-0.001018</td>\n",
              "      <td>0.002106</td>\n",
              "      <td>-0.084747</td>\n",
              "      <td>-0.202518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>page_views</th>\n",
              "      <td>-0.049327</td>\n",
              "      <td>-0.301136</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.755074</td>\n",
              "      <td>0.497496</td>\n",
              "      <td>0.547811</td>\n",
              "      <td>0.038063</td>\n",
              "      <td>-0.058465</td>\n",
              "      <td>-0.033223</td>\n",
              "      <td>-0.101979</td>\n",
              "      <td>0.514907</td>\n",
              "      <td>0.107937</td>\n",
              "      <td>0.716096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fpl_value</th>\n",
              "      <td>-0.099975</td>\n",
              "      <td>-0.502653</td>\n",
              "      <td>0.755074</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420925</td>\n",
              "      <td>0.605196</td>\n",
              "      <td>0.067131</td>\n",
              "      <td>0.075629</td>\n",
              "      <td>-0.077714</td>\n",
              "      <td>-0.071966</td>\n",
              "      <td>0.437659</td>\n",
              "      <td>0.093738</td>\n",
              "      <td>0.771985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fpl_sel</th>\n",
              "      <td>0.024667</td>\n",
              "      <td>0.071600</td>\n",
              "      <td>0.497496</td>\n",
              "      <td>0.420925</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.498251</td>\n",
              "      <td>-0.061016</td>\n",
              "      <td>0.049998</td>\n",
              "      <td>0.024549</td>\n",
              "      <td>-0.005185</td>\n",
              "      <td>0.229267</td>\n",
              "      <td>-0.035876</td>\n",
              "      <td>0.484164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fpl_points</th>\n",
              "      <td>0.215552</td>\n",
              "      <td>-0.100295</td>\n",
              "      <td>0.547811</td>\n",
              "      <td>0.605196</td>\n",
              "      <td>0.498251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.011551</td>\n",
              "      <td>-0.218141</td>\n",
              "      <td>0.206685</td>\n",
              "      <td>0.062469</td>\n",
              "      <td>0.258834</td>\n",
              "      <td>0.111885</td>\n",
              "      <td>0.595919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0.077748</td>\n",
              "      <td>-0.019806</td>\n",
              "      <td>0.038063</td>\n",
              "      <td>0.067131</td>\n",
              "      <td>-0.061016</td>\n",
              "      <td>-0.011551</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.057300</td>\n",
              "      <td>0.095204</td>\n",
              "      <td>0.096069</td>\n",
              "      <td>0.049222</td>\n",
              "      <td>0.157004</td>\n",
              "      <td>0.110158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_foreign</th>\n",
              "      <td>-0.164685</td>\n",
              "      <td>0.019294</td>\n",
              "      <td>-0.058465</td>\n",
              "      <td>0.075629</td>\n",
              "      <td>0.049998</td>\n",
              "      <td>-0.218141</td>\n",
              "      <td>0.057300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.152721</td>\n",
              "      <td>0.026559</td>\n",
              "      <td>0.082913</td>\n",
              "      <td>-0.082678</td>\n",
              "      <td>0.097173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_cat</th>\n",
              "      <td>0.969917</td>\n",
              "      <td>0.183599</td>\n",
              "      <td>-0.033223</td>\n",
              "      <td>-0.077714</td>\n",
              "      <td>0.024549</td>\n",
              "      <td>0.206685</td>\n",
              "      <td>0.095204</td>\n",
              "      <td>-0.152721</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.037607</td>\n",
              "      <td>-0.188779</td>\n",
              "      <td>-0.080978</td>\n",
              "      <td>-0.116853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>club_id</th>\n",
              "      <td>0.041378</td>\n",
              "      <td>-0.001018</td>\n",
              "      <td>-0.101979</td>\n",
              "      <td>-0.071966</td>\n",
              "      <td>-0.005185</td>\n",
              "      <td>0.062469</td>\n",
              "      <td>0.096069</td>\n",
              "      <td>0.026559</td>\n",
              "      <td>0.037607</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.137956</td>\n",
              "      <td>0.139128</td>\n",
              "      <td>-0.052287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>big_club</th>\n",
              "      <td>-0.205341</td>\n",
              "      <td>0.002106</td>\n",
              "      <td>0.514907</td>\n",
              "      <td>0.437659</td>\n",
              "      <td>0.229267</td>\n",
              "      <td>0.258834</td>\n",
              "      <td>0.049222</td>\n",
              "      <td>0.082913</td>\n",
              "      <td>-0.188779</td>\n",
              "      <td>-0.137956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.053711</td>\n",
              "      <td>0.624354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_signing</th>\n",
              "      <td>-0.083684</td>\n",
              "      <td>-0.084747</td>\n",
              "      <td>0.107937</td>\n",
              "      <td>0.093738</td>\n",
              "      <td>-0.035876</td>\n",
              "      <td>0.111885</td>\n",
              "      <td>0.157004</td>\n",
              "      <td>-0.082678</td>\n",
              "      <td>-0.080978</td>\n",
              "      <td>0.139128</td>\n",
              "      <td>0.053711</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.115376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>market_value</th>\n",
              "      <td>-0.144592</td>\n",
              "      <td>-0.202518</td>\n",
              "      <td>0.716096</td>\n",
              "      <td>0.771985</td>\n",
              "      <td>0.484164</td>\n",
              "      <td>0.595919</td>\n",
              "      <td>0.110158</td>\n",
              "      <td>0.097173</td>\n",
              "      <td>-0.116853</td>\n",
              "      <td>-0.052287</td>\n",
              "      <td>0.624354</td>\n",
              "      <td>0.115376</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   age  position_cat  ...  new_signing  market_value\n",
              "age           1.000000      0.209102  ...    -0.083684     -0.144592\n",
              "position_cat  0.209102      1.000000  ...    -0.084747     -0.202518\n",
              "page_views   -0.049327     -0.301136  ...     0.107937      0.716096\n",
              "fpl_value    -0.099975     -0.502653  ...     0.093738      0.771985\n",
              "fpl_sel       0.024667      0.071600  ...    -0.035876      0.484164\n",
              "fpl_points    0.215552     -0.100295  ...     0.111885      0.595919\n",
              "region        0.077748     -0.019806  ...     0.157004      0.110158\n",
              "new_foreign  -0.164685      0.019294  ...    -0.082678      0.097173\n",
              "age_cat       0.969917      0.183599  ...    -0.080978     -0.116853\n",
              "club_id       0.041378     -0.001018  ...     0.139128     -0.052287\n",
              "big_club     -0.205341      0.002106  ...     0.053711      0.624354\n",
              "new_signing  -0.083684     -0.084747  ...     1.000000      0.115376\n",
              "market_value -0.144592     -0.202518  ...     0.115376      1.000000\n",
              "\n",
              "[13 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWNr5mLq51tO",
        "outputId": "8923c5a1-408d-498a-c34f-5d565748a3c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.pairplot(encoded_data,height=2.5);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EH24AAoA0gGiPzDLM9HxWSeoBQKiKOHEhhen5rOl6NxYLShIvlx1/7T3cWCyYtnNyLqssJMjnPPl6CpNz+u2ZraeHXj2z/elkUum84uzAwz6+mEIqne+xZZ2xQqt28EG6oNrnH6TZthtgs88n57JN/XlofJuyaCHbePrSNPq8HghVEc89sV35Aq7xGiZns03HXn57BofGt637GECww3R6STU2TKeXAABT81nV8qkGfRrx0W6MdUbaYTFeEKujMUdQi53nvnkdzz2xvSlmaung0Pg2ZWFarq9VhyCsQi8uqZWdvjyNQ+PblPcnX081vT9xIaXkD2o+8tvfuo67hYqpeKtmjxmfubFYUDb4yOc5f2UGMwtLlN8QClPpnHqOkc7ZbgtL81OW1gf01kbsxO4+sfu67WzPbr9jRUMEQRhDax4lr1O1zss+vLPUlse++ORuJX+8s1RuWys7cWE5BrTmnJOz7cdozkbocV1jrfu6A9a6iY0JaZZwGt/X0Oz3SbNEC1bMM5nc5MNxXD+ArwH4V5IkNV2dJEkSAEmj3r/gOO4qx3FX79y5Y4Ol2sxnBeVGyQhVEemsYLpeJqdetpDXP6ceaY1zZnL67Zmtp4dePbP9yTp6ms3kyhp9XLbTRFNYoVU7oD43htFY2xonOA6qNhYqNd1ysSXiC1VR+azTYwBhD2vNDzrFhrSB2GHER7sx1hlpx6kxeiPRSbONmtSKnRzX/FpLB3r1SReEUdYeZ7XjklYZx+m/l/OH1eQXRuKtnj2r8Rmt84gSKL+xCZbWD7RgaX7Cki0srQ/orY10Gz3N2t0ndl633e3ZrXW7+9JOjMbZbTt2guO4Vf/btmOnjVdDbASMaFZrHrWanLNUqSnnECX1emqxQeuzNGfb2KzX7xeI9QtplnAapFmiG1ihFab+XBcAcBznwfIGn/8mSdLXHx7OcBwXlyRpnuO4OIAFtbqSJP0+gN8HgP3796tuBLKLeCQAv4dvumF+D49YRP/xXHr1Qn6PatlQyPwjv+Jhv+o5o2E/tgS9mu15eN5UPT2iGrYMhfwIeNym+pN19DQbDfs0+thnr5Em0LuXLEN9bgyjsVYrvrS+D3rduuWtTxz2e3hI0vqIAYQ9rDU/6BQbYgZihxEfNZs7NNvauR2nxuiNRCfNtmpS7X5KUvNrLR3o1SddEEZZe5zVj0t6Gtd635g/GM0vjMRbLVtX6zNa5+E5UH5jEyytH2jB0vyEJVu6kTN1zRadNZVuo6dZu/vEzuu2uz27tW53X9qJ0Tg7N/tDvPDld1Z9/lc//6R54whCBSOa7TSPMpJzBrxuJX908+r1YiqxwcWpf5bmbBub9fr9ArF+Ic0SToM0S3QDK7TC1JN8OI7jAPxnANckSTrXUHQRwD99+PqfAnjdbttWSyIextmJZFPSf3YiiUQ8YrreyEAQ557f11R27vl9GBkImrZzbDiCM0eb2ztzNInxYf32zNbTQ6+e2f50MslYCGeOtPTxkSSSsVCPLeuMFVq1g8diQdU+fyzGtt0Am30+Nhxp6s9L79/G6SOJJhtPHU6gWKnC7+Hxte/O4tjB0bZrGN8eaTr20oFRXJ68ve5jAMEOiVi/amxIxPoBAGPxiGr5WIM+jfhoN8Y6I+2wGC+I1dGYI6jFzuNP7cHX351tiplaOrj0/m28dGBUNc6SLgi70ItLamWnDiVwefK28v7M0WTT+7MTSSV/UPORX/6JPdj68IcJjXWMxFs1e8z4zMhAEL/1M83nOXZwFKND/ZTfEApjsbB6jhEL224LS/NTltYH9NZG7MTuPrH7uu1sz26/Y0VDBEEYQ2seden9221x+PhTe/CJwf62PPaVdz5S8set/b62tbKzE8sxoDXnHNvefozmbIQeezTWuvc4YK2b2JiQZgmn8UkNzX6SNEu0YMU8k5Mkdn6wxnHcjwP4KwBTAOStTF8E8LcAXgOwE8BNAM9LknRP71z79++Xrl69aqG1nanVREzPZ5HOCohF/EjEI3C7O++r0qsnihJuLBawkBcwFPJjZCAIvvUnAaukUqljci6LTE5ANOzH+HAEXq+rY3tm6+mhV89sf1rI2jq+BTXNFksVpNJ5ZHJlRMM+JGMh9AW83WzWMqzQqh0slQR8kC4off5YLIj+gDN+EWOwzy3XbSNynEjnBAz1+wBOBAced5bKGOz3we3isLnPi7oI3FkSEAv7ldfyNQDLf/M8kxPgcfHICxVsCfpYiAGEPdiqWS0KpTKm00tKbEjE+hEMrOyyFoQapuazSvlYPAK/v/mBiUZ8tBtjnZF2nBqjHYItmpVzhDv5MuJhP0q1OgpCHbFNPoR8HqRzAvq8LlTrIrYEfbo6uFcow+PiUazUO9Yh1iVMxFm9uCSXZR7qWpIkcByHYqWOaNiP7ZEArmVyTbGT5znlfHJ+sZBf8YuBoA95oYp5E/G21R6zPiOKEj66s4SPFwvwe1zY3OfBp6Jhym+MwYRu7aBUqmIqnVvJMWJhBAKentjC0vyUpfUBvbWRBizXrN19YvC6Hdme3X5nd18axLY4y3Gc6Sf5sLSuTfQc2zSrNo+Khv3YubkPN+8VceteAX1eN6JhH2L9fqTSOWRyAgZDPlTrdfjcboiSBJ7jkBcqiEX8KFclZHLN8VstPwZAc/n1heW6fVAScL1hrXtPLIhNDlnrJpiENEs4Dcs1my0J+H6DZj8ZCyJCmiVUMDjPNKxZpv5clyRJfw1t4w9a2XbjQm00rL2o3Fqmh9vN4/Edm/H4jtXZoleP5znsHuzH7sH+VZ1TXuyZzwqIRwJIxMNNG4ckSYIoYfn/hj8WLIoS8kIVD4pVBDxuiKKkXDvPc/C4OLh4Dh4Xr9onanNtI/2pVk+rX/SuzSxm73m3kSDpvmeZSqWOxaUy7uQrcHEchkP+ti+8WcQFl+57ltHzV7vt+OH9AjLZMu4ulTEU9mHbJj9u3xcQDfuwqc8FwIe7SxWE/G5wqGBTnxf7d27BrftFLOTb/c5M3CMIoxiJ+XzLww9b34uSCAlQ/olS899Xldvp5KOiKKFal1AVJdTqzWOyYVsN5Apm8wmCDWo1ER8tFiGKEjb1ebBYqCAc8MDnESFUReRKRYT8XuSECrwuV9sj4uVzTM8vf5k0EPShUhfXlPPYmTuxkqcRxlHL1+VNOMsbZdyo1OsYCPrwmZEBU3GtcZ5Qq4mYmn2A29kStvb7wHHAI1uDeHSovf5j8WXbvvW9DIY3BRDyuZHOCYhHVjYGteqM5zmMDAQhScDNewUEvW7w3Io2FwtleHke94oVBH1u9HldyAu1pvPIn72zVMajg/2kY8IR1CHqvu8Fy5lSb33HyNqIndjVJ16vC/tHtljejkytXof4cO1KlCTU6nV4LZqv270W43bz2BL0Ptw06rV8w1rjj3HiYT/G2NhURBDMI+dv2VIFHJY3nAvVKgb6vZh7UMSdpTKypSo2BzzwuIBssYq5BwKCPjeGwj4MBH3YubkPt+4XsVgow+Pi4Pe44XO78cmo+o8yWtcQ3G6e5vIEQVgCrbUQhDlaZwrO+RaXsBuO45SZOvfw/Vpg/xt3GxBFCW9Op3H8tfcgVEXlUZfPJGIAoFnmlAGuVhNx4f3bOHEhpVzD2YkkJh7fhlpNxMWpeZy8uFJ25kgSR8bicLt5zXoANMt4njPVn3plWn2td21mF0X09GDnPS+UyvhGaqHt3vxkcqjp6REsIgg1TV2xvNGnVKriUirdZvfhZKxnv5Y1ihW+YAZRlPBXHy5g7kEZpy9NK7acOpzAH/3tTXjdHF749C786usrdh47OIpY2IurN+832e+0WEs4EyMxv1NsKJYquJzKtJUfSkaVX7cb8dFKpY4Lk3M42eAfZ44mMTE+DK/Xxcz4RPSWWk3EG9PzuF+oQKiJOPfN64oeXjowilev3sIL+3c2/f+zn96J0Wg/DnwyCp7nVPUo1/23z+xdtabs1Cb5gfPQin+DIS8+//+82xUNdmrv1OEEhjcV8A8+MdR0XrXPHjs4ijem5vHsWBznr8yo6kxNh8cOjmL75gC+8s7HOPCpGF5+e6ap7JXv3MT9YgXnnt+Hp/dG8da1DOmY6AhL85N8ScAbqTtttjybHETI5l8psjL3YckWVuywCiP5thPbAuzPbTrNOQiCUEf21T/86x/gn/y9EaRzQlOu2DoX+8XPjeK1qzdx9Wa2qfyXDozij//nzbZ8sdXv13tcJ+zhQUnAWyr529PJQXoyCtEEK2stpFnCaZBmCaNY8b05ZYRYfsSlPHgBgFAVcfy193BjsaBb5hSm57PKhABYvoYTF1KYns9iaj6rCEouO3kxhan5rG49vTKz/Wmmr/XsMAsr93w6vaR6b6bTS7baYQY9XbHMVDqnbnc612PLOmOFL5jhxmIB+VJd2eAj23L60jR+/rOP4sUndysbfOSy81dm4HG52+x3WqwlnImRmN8pNqTSedXyVDqvnMOIj07OZZXFduU8r6cwOZc1bCux/pmez2JmYQl3CxVlgw+wrIeX357BofFtbf+fvzKDydmsohU1PcqfNaMpO7VJfuA8tOJfvlTvmgY7tXf60jTypXrbedU+e/7KDH7+s48qX9q2qrC6AAAgAElEQVTIxxvtUtPh+SszmFlYwotP7la+sGkse+6J7cp5puezpGPCECzNT66lC6q2XEvbr1tW5j4s2cKKHVZhJN92YluA/blNpzkHQRDqyL764pO78fFioS1XbJ2DnbyYwotP7m4rP3EhpZovtvr9eo/rhD1c18jfrvcgfyPYhpW1FtIs4TRIs4RRrPjenDb5AMjkBKVTZYSqiIW8oFvmFOaz6teQzgpI58qqZZlcWbeeXpnZ/jTT13p2mIWVe57RuTeso6crlnFyn1vhC2bI5AQUyjVVW0qVGkoaZVp1nBRrCWdiJOZ3ig1GYocRH01r2JLJCYZtJdY/81nh4Z+pgKoeOE79f1GCohUtPcqfXa2m7NQm+YHz0NJboVJrO2ZWg0bbaz2v1me18hW5vpYORQmadeUn8ApVUbNd0jHRCkvzE5ZsYWXuw5ItrNhhFXbqz26t253bdJpzEAShjuyrpXLN8Fys1JDvth7v5PfrPa4T9sBS/kawDStrLaRZwmmQZgmjWPG9OW3yARAN++H3NHeF38NjKOTXLXMK8UhA9RpiET9iYZ9qWTTs062nV2a2P830tZ4dZmHlnkd17g3r6OmKZZzc51b4ghmiYT+CfreqLQGvG30+9TKtOk6KtYQzMRLzO8UGI7HDiI/GNWyJhv2GbSXWP/FIAC4OcHFQ1YMkqf/Pc1C0oqVH+bOr1ZSd2iQ/cB5aegt63W3HzGrQaHut59X6rFa+ItfX0iHPQbOuJK28jkdIx4QxWJqfsGQLK3MflmxhxQ6rsFN/dmvd7tym05yDIAh1ZF/t87kNz8UCDflu4/E+b+c1sPUe1wl7YCl/I9iGlbUW0izhNEizhFGs+N6cNvkAGBkI4tzz+5TOlf/e5MhAULfMKSTiYZydSDZdw9mJJBLxCMbiEZw50lx25kgSY/GIbj29MrP9aaav9ewwCyv3PBHrV703iVi/rXaYQU9XLDMWC6vbHQv32LLOWOELZhgZCCLkd+HU4USTLacOJ/Cfvv0DfOWdj/DrR5vtPHZwFNVarc1+p8VawpkYifmdYkMyFlItT8ZCyjmM+OjYcARnWvzjzNEkxocjhm0l1j+JeBifGOrHQNCL40/tadLDSwdGcXnydtv/xw6OYnx7RNGKmh7lz5rRlJ3aJD9wHlrxLxRwdU2Dndo7dTiBUMDVdl61zx47OIo/+PYPcOzgqKbO1HR47OAoRof68ZV3PsJLB0bbyr7+7qxynkQ8QjomDMHS/GRvLKhqy96Y/bplZe7Dki2s2GEVRvJtJ7YF2J/bdJpzEAShjuyrX3nnI4wMBNtyxdY52JkjSbzyzkdt5Wcnkqr5Yqvfr/e4TtjDHo38bU8P8jeCbVhZayHNEk6DNEsYxYrvzTlJ/knhOmP//v3S1atXDX9eFCXcWCxgIS9gKOTHyEAQPM91LHMKtZqI6fks0lkBsYgfiXgEbveykAShhqn5LDK5MqJhH8biEfj97o719MrM9qeZvtazwywG7eiqCNQ0WyiVMZ1eUu5NItaPYMAZO0D1dMUypVIVU+ncit2xMAIBT6/NMoRBX7Bct6Io4Yf3C8hky7hbKGMo5IOL53D7gYBoyIfNQRfuF+q4u1RByO9Gv8+FTX1ebN/Uh1v3i46OtYQl2KLZTjG/U2wolipIpfNKeTIWQl/A23QOIz5aqdQxOZdFJicgGvZjfDgCr9e1KluJnmO5Zms1Ed9L57BUqaEuSiiW6wgHPBCqNQS8blRqdfT7PFgqV+FxuRAN+7BzS7NWZD1mcmVsCXohQcJA0GdaU3Zqk/yg69ii2db4x/McbiwWkMkJ6PO6UK2L2LIGDba1N5fF7WwJW4M+RCM+7Nisft5G2+IRP0J+DzJ5AbGwH3URuLOkrjNRlPDx3QJu3Sugz+tGNOxTcpl7hTI8PI97xQr6fW4EvC7khRqi4ZXzkI7XjOW6ZQWW5if5koBr6YJiy95YEKFAb54oYMU6gMW29CTW9qpPrMBIvu3EtgD7c5tOc46H2BZnOY7DC19+Z9XnfPXzT2K9rmsTprBt7SBXqgDgkBOqKFdFbAl6UarW4HG5kBWq2BTwwOcCAB7Far0p1925uSFfdPEoVupNOWIj6z2uEwBs0O2DkoDrDfnbnlgQm3qUvxFsw8p3YqRZosuQZglmMPi9uWHN0iYfgjDPhllYJtYVpFvCaZBmCadBmiWcBmmWcCKkW8JpkGYJp8H+Jp9/+VlArK263vD2Hbj9w1urrkcwD8VZwomQbgmnQZolnAZplnAahjXL/mM1HIy881X+ZUzjzld5J/58VkA8EkAiHja0E1/vnGbb06tn1hb5V0HpnIB42I+xhl8F2W2nGftZQd7Vl86VEXPQ03AA+3/51i1Y+qXsajEbV6xAFCV8dGcJtx8UsbnPi0KljrxQw6Y+D5aEKoJ+D3KlKoZCfuyNhjCbLTX5IgDm/ZPYWCyVBHzQsCP/sVgQ/Q078lfzJB89H3XC2ET0nmKpgg/SeWSFKvq8buSFKgaCPgyGvYj1B3Atk1vzWEBaJLpJpVLH1FwW8zkBW/u9iIX9ytOlOmlNrRxYyRPikeUn7izk21/X6hJu3S8i+PBJOzu3LNf9+G4BN+8V0O9zo0/lKTt6ba91nrTWcxMbD725td2w9CQflvqFlXmY3XHF7ntg5/qI3esCdvfluhmDxJrpJwARxFqQfWixUIaX53FnqYyAzwWvi8f9QhXhgBsensf9YgXhgAdDIR+GwyvztMF+H3geiAS8uv63bnyV6Cn0hAnCKKzktKRZwmmQZgmjdHve54zdAg5EFCW8OZ3G8dfeg1AVlb9h+UwiBlGUcOH92zhxIaWUnZ1IYuLxbbqDpt45AZhqj+c5zXp6kwY9W2o1ERcm53Dy9ZX2zhxNYmJ8GG43b6udZuxnZbIkCDVcnJrHyYsN/XgkiSNjceY3+hRLFVxOZdpsP5SMMr3Rp1Sq4lIq3Wb34WSM+Y0+tZpoKq5YgShKeCOVxr//82s4dvATmMuW8Xt/+SFe2L8TL789o9j30oFRnP3GB/jFz43itas3cfVmVvFFr5vDF776d8z6J7GxWCoJ+LPUnbbY8I+Sg+gP+A3FPCM+6oSxieg9xVIF35jO4Et/MdMWV89OJOF1Z/Fv/nRyTWMBaZHoJpVKvW1ucOpwAjfvFfBjuwfx1rWMpta0tCjnCZv7vHjxR3fh/JWZttf/24+N4Nw3ryv1jh0cxWi0HwCacoxjB0fxyndu4n6xYqjttcyTGn+cQT5GGEHNf+S5td0bWvIlAW+o5EPPJgdt3+jDUr+wMg+zO67YfQ/sXB+xe13A7r6kMYgg1obsQ7/55rW2+VhjXtn4+uxEAqLENefDhxL42ru38M9//FFV/yNfJbrBg5KAt1Tyt6eTg/QFNNEEKzktaZZwGqRZwihWzPvoj7haxI3FgpKEA4BQFXH8tfdwY7GA6fmsMljKZScupDA9nzV9TrPt6dUza8vkXFYRqVx28vUUJuf027PCTjP2s8LUfFYZGICH/XgxhakOOmGBVDqvansqne+xZfpMpXPqfZ7O9diyzpiNK1ZwY7GAX/mT93BofBu8LjdOX5rGofFtysKHbN/Lb8/g0Pg2nLyYwotP7laOH3/tPUzOZpn2T2Jj8UG6oBobPkgva9JIzDPio04Ym4jek0rn8auvp1Tj6okLKXy4sLTmsYC0SHQTtbnB6UvTyJfqmJ7P6mpNS4tynvDcE9tx/sqM6mt5g49c7/yVGUzOZttyjPNXZvDcE9sNt72WedJqPkMQgLr/yHNru7mmkQ9dS9uvW5b6hZV5mN1xxe57YOf6iN3rAnb3JY1BBLE2ZB9Sm4815pWNr/1ud3s+fHkaLz65W9P/yFeJbnBdI3+73oP8jWAbVnJa0izhNEizhFGsmPfRJh+LyOQE5UbJCFURC3kB81n1snRWMH1Os+3p1TNrS1qjLJOz304z9rNCOlfW6MdyjywyTsahtjvVbgCm44oVyP7FcUChXFNeq9knHy9Vak3HRQltn2XJP4mNRafYYCR2GPFRJ4xNRO+R9aYVV9Xi52rHAtIi0U205gaFSk0zNspa09KirPNGP9B63VpPzUc4znjba5knreYzBAFo+08m14scn525Ekv9wso8zO64Yvc9sHN9xG6t292XNAaZZ9uOneA4btX/3F6/qXrbduzs9SUTKjSueWmtc7W+ltfGWj9bqtQ0/Y98legGLOVvBNuwk9OSZglnQZoljGLFvI/tv/njYKJhP/wevumG+T08hkJ+BDxu1bJYRP/RXXrnlF+vtr2Q36N7TjO2eHhetSwa9mNL0GurnWbsZ4VY2KfRj74eWmWMqENtd6rdABCPBEzFFSuQ/QsAgn638lrNPkla/j/gdTcdb33yMGv+SWwsOsUGI7HDiI86YWwieo+sN0A9rqrFz9WOBaRFopvENfQU9LoRj+hrTUuLjTpvLNd6rVav8bgkGW97LfOk1XyGIABt/4mGe5HjszNXYqlfWJmH2R1X7L4Hdq6P2K11u/uSxiDzzM3+EC98+Z1V13v180+arkewR+Oal9Y6V+treW2s9bMBr1vT/8hXiW7AUv5GsA07OS1plnAWpFnCKFbM++hJPhYxMhDEuef3NSX9557fh5GBIBLxMM5OJJvKzk4kkYhHTJ/TbHt69czaMjYcwZmjze2dOZrE+LB+e1bYacZ+VhiLR3DmSEs/HklirINOWCAZC6nanoyFemyZPmOxsHqfx8I9tqwzZuOKFYwMBPFbP7MPl96/jUqthlOHE7j0/m28dGC0yb6XDozi8uRtnDmSxCvvfKQcP/f8PoxvjzDtn8TG4rFYUDU2PBZb1qSRmGfER50wNhG9JxkL4dePJlXj6tmJJD4x1L/msYC0SHQTtbnBqcMJhAIuJOIRXa1paVHOE7723VkcOziq+vr4U3ua6h07OIrx7ZG2HOPYwVF8/d1Zw22vZZ60ms8QBKDuP/Lc2m72auRDe2P265alfmFlHmZ3XLH7Hti5PmL3uoDdfUljEEGsDdmH1OZjjXll42uhWmvPhw8l8Mo7H2n6H/kq0Q32aORve3qQvxFsw0pOS5olnAZpljCKFfM+TpKkzp9yIPv375euXr3aUxtEUcKNxQIW8gKGQn6MDATBP/z5aK0mYno+i3RWQCziRyIegdvdec+V3jnNtqdXz6wtlUodk3NZZHIComE/xocj8HpdPbHTjP0GMd+4CmqaFYQapuazyOTKiIZ9GItH4Pc74wFcxVIFqXResT0ZC6Ev4O21WR0plaqYSudW+jwWRiDg6bVZhjAYVyzXLbDsXx/dWcLtB0Vs7vOiUKkjX65hU8CDQrmKPq8HOaGKwX4fHouFMZstNfkigK76O+FobNFsJ5ZKAj5IF5TY8FgsiP7Ayi5rIzHPiI92e6wjeoLlmi2WKvggnUdWqKLP60ZeqGJL0IehsBex/gCuZXKrzjFbIS1uKCzXbKVSx9RcFumcgIF+L2JhP3ZuWdZUJ62plQMreUIs7EddBO4stb+u1SX88H4RfV43omEfdm5Zrvvx3QJu3Sug3+dGwOtCXqghGjbW9lrnSWs9N6HARH5gB3pza7vJlwRca8iH9saCCAV680QBlvqFlXmY3XHF7ntg5/qI3esCdvelQa3YFmc5jrP9CTlm1sOdYucGxjbNyj50r1CGm+dxd6mMgNcFr5vHg0IVoYAbHp7H/WIFkYAHgyEfhsPL87T5rICt/T64eSAc8OrGasoXNwSW6/ZBScD1hvxtTyyITT3K3wi2YSWnJc0SXYY0SzCDwXmfYc3SJh+CMM+GWVgm1hWkW8JpkGYJp0GaJZwGaZZwIqRbwmmQZgmnQZt8WnCKnRsYirOEEyHdEk6DNEs4DdIs4TQMa9YZjwRxKPLO1/msgHgkgEQ83PZUGnm3ltrTbFZbT94Bls4JiIf9GGvYAaZ3TrPtmb12s5i1xck4+akyTn2Sj/zrwHSujJjDnp5khd91A9l3FwtleF08ipU6hvp9KNXqmL1fwvCmAPq9LtxYLKI/4AIncbi7VMHwpgA29blxr1ABBw55oYZitYZIwINoyI9dJmKAE+OIE21ej3S6D0bitZH40qmdbvm5EV3ZpT1WYxfLlEpVXFvIQ6jVUReBcrWOXQNBeN0c0tkyCpUadm0JYteWPsw+KCKTWzn2yFZr7mOjXvq8boiSCJ7jUKzUDeuH4t0y67UfWq9r5+Y+3Lpf1HwvX7coSvj4bgE37xUQbHgiT+vTTFvP05h3dOpHtT4HoGtvo33djqdr1YCVGup0H9eLXlmCpTkhS0/yYSlWspLL2G2H3XPnQqmM6fSSor9ErB/BgM+StvTW16yAJT0TBGEOrRisddxI/mkkr+yUi4mihFv3CkjnBNxdqiAe8WMsvhzTzOR13cx9KfbZAz1hgiCI9Qor8Y0VOwj26fYc2hnfXDuQWk3Ehfdv48SFFISqqPwNy4nHt4HnObw5ncbx195Tys49vw/PJGIQRclUvVpNxIXJOZx8faXemaNJTIwPg+c5zXMCMNWeXsKtd+1mF5hEUTJli5Mplaq4lErj5MWGe3okicPJGPMbfYqlCi6nMm22H0pGmd7oIwg1XJyab7P7yFic+Y0+VvhdN5B99zffvIYX9u/Ey2/PKPYdOziKV75zE/eLFRw7OIo3pubx7Fgc56/MNPR/Av1+N24uFpuOH39qD3YPBnHgk1HDMcCJccSJNq9HOt0HI/HaSHzp1E63/NyIruzSHquxi2VKpSq++f0FPChWUKjUldi4ayCAX/hfPoHTl6aVvvx3PzWGglDFb7zxPUvvo5peTh1K4Pe+/SFuLpYMtUvxbpn12g9q13V2IonfeXtG0Ujr+3PP78PTe6N461qmqd6xg6MYjfbjwCejANB03l0DAfzSgVH8ztszbXmHVj9q9bnXzeELX/07TXu17FtrPF2rBqzUUOu55f5ujOHrQa8swdKcMF8S8EbqTpstzyYHbd/ow1KsZCWXsdsOu+fOhVIZ30gttLX3k8mhrm/0qVTqmutrVmz0YUnPBEGYQysGH0rEcXl6vu34kbFhfOv7C01+/6Wf+xFUatKq80q1HFWuI4oS/urDBcw9KDfNE88cTeJIMo63Z+4YPpdW+2ZzX4p99vCgJOAtlfzt6eQgfQFNEISjYSW+sWIHwT5WzKHp2xOLmJ7PKgk8AAhVEScupDA9n8WNxYKSwMplx197DzcWC6brTc5llQUIuezk6ylMzmV1z2m2PbPXbhaztjiZqXROcXbg4T29mMJUOtdjyzqTSudVbU+l8z22TJ+p+ax6n69Bu3Zhhd91A9l3D41vU75ok+07f2UGzz2xXXn98599VPmyWv7MyYvTEEW0HT/3zeuYnM2uKgY4MY440eb1SKf7YCReG4kvndrplp8b0ZVd2mM1drHMVDqHD+8s4W6h0hQbD41vUxZugeW+/D//+xTuFiqW30c1vZy+PI1D49sMt0vxbpn12g9q13XiQqpJI63vj7/2Hqbns231zl+ZUXKA1vMeGt+mnKc179DqR60+n5zN6tqrZd9a4+laNWClhrT6e73plSVYmhNeSxdUbbmWtv9+sxQrWcll7LbD7rnzdHpJtb3p9FLX29JbX7MClvRMEIQ5tGLwlMbxybn2HHJy1lxeqZajynVuLBaQL9Xb5oknX1+2bTXn0mrfbO5Lsc8ermvkb9d7kL8RBEF0E1biGyt2EOxjxRyaNvlYxHxWUG6UjFAVkc4KyOTUyxbygul6aY2yTE7/nGbbM3vtZjFri5PJ5Moa97TcI4uM41Tb0w61G7DG77qB7LscB1X7OG7ldalcU/1MQeO4KGFVMcCJccSJNq9HOt0HIzHPSHzp1E63/NyIruzSHquxi2UyuTJECRCl5riqFWdFCW3Hun0ftfTCcc3v9dqleLfMeu0Hoxppfa8VI+QcoPW8sh9o+YNaP2rZpuY7Ru1bSzxdqwas1JBWf1vRFrEMS/MqtmxhJ1ayksvYbYfdc2c79ae3vmYFLOmZIMywbcdOcBy3qn9ur3/VdTiOw7YdO3t9uapoxmCN2KUWZ1rnd/JnjeSVWvOuTE7QXFPTsk1vDtfNtQSKffbAUv5GEATRTViJb6zYQbCPFXNotv/+jIOJRwLwe/imG+b38IhF/Aj5PaplQyE/Ah63qXoenlcti4b98LjUy2IRPwDOVHtmr90s0bDflC1OJhr2adxTa/7mfDdxqu0xh9oNWON33UD2XdmeVvskaeV1n089/gX96sd5DquKAU6MI060eT3S6T4YiXlG4kundrrl50Z0ZZf2WI1dLBMN+/DhwvKT8dT6Ti1WNmLFfdTSi9SwSaJTuxTvllmv/WBUI63vtWJEYw6gVq51XK0ftWxT8512+7ofT9eqASs1pHXu9aZXlmBpXsWWLezESlZyGbvtsHvubKf+4hr6ioat6UuW9EwQZpib/SFe+PI7q6rz6uefXHUduR6LaMZgjdgVU/F7F9c5r1rtvCsa9mOxUFmVbXpzuG6uJVDssweW8jeCIIhuwkp8Y8UOgn2smEPTk3wsIhEP4+xEsmmR+exEEol4BCMDQZx7fl9T2bnn92FkIGi63thwBGeONtc7czSJ8eGI7jnNtmf22s1i1hYnMxYL48yRlnt6JImxWLjHlnUmGQup2p6MhXpsmT5j8Yh6n69Bu3Zhhd91A9l3L71/Gy8dGG2y79jBUXz93Vnl9R98+wc4dnC0pf8T4Dm0HT/+1B6Mb4+sKgY4MY440eb1SKf7YCReG4kvndrplp8b0ZVd2mM1drHMWCyMRwf7MRD0NsXGS+/fxqnDiaa+/Hc/NYatQa/l91FNL6cOJXB58rbhdineLbNe+0Htus5OJJs00vr+3PP7kIiH2+odOziq5ACt5730/m2cnUiq5h1a/ajV5+PbI7r2LtsX6Xo8XasGrNSQVn+vN72yBEtzwr2xoKote2P232+WYiUruYzddtg9d07E+lXbS8T6u96W3vqaFbCkZ4IgzKEVg8fiEdXj48PtOeTYdnN5pVqOKtcZGQgi5He1zRPPHF22bTXn0mrfbO5Lsc8e9mjkb3t6kL8RBEF0E1biGyt2EOxjxRyakySp86ccyP79+6WrV6/21IZaTcT0fBbprIBYxI9EPAK3e/nmiaKEG4sFLOQFDIX8GBkIgn/4c1Gz9SqVOibnssjkBETDfowPR+D1ujqe02x7Zq/dLGZtsZCuNq6m2VKpiql0DplcGdGwD2OxMAIBTzebtYxiqYJUOq/YnoyF0Bfw9tqsjghCDVPz2ZU+j0fg9zvjoWcG/c5y3bYi++69QhkeF49ipY7Bfh+EWh2375cQj/jR73Pj5r0i+nwu8OCwWKggFvZjc9CD+4UKAA75cg2lch2hgBuxsB+7TMQABuNIR5xoc5exXbNqdLoPRuK1kfjSqZ1uja9GdGWX9qzIGXqMLfnBtYU8hFodoggI1Tp2DQThdXNIZ8soVmrYuSWIXVv6MPugiExu5dgjW625j7JeMjkBfV4XJEkCx3EoVuqIho3ph+LdMj3oB1vibOt17dzch1v3i5rv5esWRQkf3y3g1r0C+rxuRMM+7Nyy0ida523MOzppUK3PAeja22hft+PpWjVgpYY63Ucb/ZaJ/MAOWJoT5ksCrqULii17Y0GEAr35xT1LYwYr8zC7cyq7586FUhnT6SWlvUSsH8GANb+Q1VtfswKW9NyAbXGW4zjTT3UxW8/MerhT7LQbM/1iUZ/0NDfQisFax43kn0byyk65mChKuHWvgHROwOLS8lrb2MOYZiav62buy2jssxvLdfugJOB6Q/62JxbEph7lb8S6YMPMwwj2MRjfKM4SzGBwDm1Ys7TJhyDMQwkN4URIt4TTIM0SToM0SzgN0izhREi3hNMgzRJOgzb5tGC7nf/ys4BYW3W94e07cPuHt1Zdzyy0yYcg1gTplnAapFnCaZBmCadhWLPOeDxFj2n8ZbLRXyKv5Zx6ZfLu//msgHgkgEQ83PbUnW6eU+9Xg1b0i54tZvrS6dj5S7VuI//yLZ0TEG/4lQjrOLnPWfIFUZTwg4UlLCwV4ebduLu03J/RiA+VioRMXkBOqCHkW34qj8vFYT6rb3fj9cUjftRFYCHf+2slnI+RsafTr+iNxA6W4iJL8YJYPcVSBR+k87izVEG/3w2vi4PPzSPk57G4VMdCvox4xI9ELIy5vODY+0w6XT/IcXaxUEbY70WlJiIWaZ+vfHy3gJv3Cgg+fGLPcDiAa5mcUk+UJPjcy0/nGQr5wXPAx4srn5ef8KOlHbXjAAwda3yqUKONjU8V6iakf0JG/qVXOldGrMdPGmXpST5m1g6sgpUcz+64Ybc27WzP7icT261nGmMcglgzvRGGsI7Gp0wGfW6UKjUEvC5w4LBUrmFrvw/Vmoi7hQo29XmQK1WxJegFzy2XFyo1DPb7wHFAJODVzT0JohvQEyYIo7CSH5BmCadBmiWM0u05LW3y6YAoSnhzOo3jr70HoSoqfx/2mUTM9ACnd04AmmWiKOHC+7dx4kJKKTs7kcTE49vA81zXz1mt1nEplcbJiytlZ44kcTgZg8/n7nq/1Gqipi1aixtW3B9WKJTK+EZqoa3/fzI5xPymk0qljguTczj5eoPtR5OYGB9meqOPk/ucJV8QRQlvpObxX/7mI/z0Eztx+vK7ik3//qfHcWepjP/w599Xjh07OIqg14Xf/cuPcL9YUbW78fo293nx4o/uwvkrMz2/VsL5GBl7SqWq5ngYCHgMxQ6W4iJL8YJYPcVSBd+YzuBXG7R07OAoBoIecBzfdPzMkSS+dW0Ob31w13H3mXS6fpDj7O+8PYMX9u/Ey2+3j99A+3zli89+CgGvG1/6i+V6r1691Vb/2MFRvPKdm7hfrODYwVGMRvvxudEhvHUt06adp/dGVY973Ry+8NW/U4596ed+BJWaZBb+TK4AACAASURBVKi+3OaBT0a7qkvSPyEjCDVcnJpvyzGOjMVt3+iTLwl4I3WnzZZnk4O2b/Qxs3ZgFazkeHbHDbu1aWd7xVIFl1OZtrYOJaOWbPSxW880xhCEedT85/945lMo10Wc++Z15djxp/bA5+Lx0h+v5JinDifwe3/5IW4uluD38Pi1wwn86Xdv4Rc+9wnV3JN8kugGD0oC3lLJ355ODtIX0EQTrOQHpFnCaZBmCaNYMaftzc+cHMSNxYIysAGAUBVx/LX3cGOxYMk59cqm57PKpF8uO3Ehhen5rCXnnErnFLHJZScvpjCVzlnSL3q2mOlLpzOdXlLt/+n0Uo8t68zkXFZZ5AQe2v56CpNz2veSBZzc5yz5wo3FAn7lT97Hi0/uxunL0002fXhnSdngIx87f2UGdwsVPPfEdk27G6/vuSe2Kxt85HOsF78n7MfI2KM3HgLGYgdLcZGleEGsnlQ6r2zkAVbiaJ/X03b85MUU/snff0R576T7TDpdP8hx9tD4NmWDDtB5vnK3UMGvvr5ST63++SszSv5w/soMJmezmJ7PqmpH6/jkbLbp2OSs8fpym93WJemfkJmaz6rnIDpzZKu4li6o2nItbb8uzawdWAUrOZ7dccNubdrZXiqdV20rlc53vS3Afj3TGEMQ5lHzn8ViRdngIx87983rWCxWmo6dvjSNQ+PblPe/dmkaLz65WzP3JJ8kusF1jfzteg/yN4JtWMkPSLOE0yDNEkaxYk5Lm3w6kMkJSofLCFURC3nBknPqlc1n1cvSWf165s9ZVi3L5MqW9IueLVpYYQcr6PU/66Q17ksmx/Z9cXKfs+QLsi2lcq3NJlGCqp2iBHDcyvtWuxuvj+PUz7Ee/J6wHyNjT6fYYCR2sBQXWYoXxOrR0luh0h5zhaqIB8Vq03un3GfS6fpBjrN647fa/ZZzBrmeVv3G/EGUtOO61nFRarZXK1fRq99tXZL+CZk0Q/MTluZKZtYOrIKVHM/uuGG3Nu1sz26t261nGmMA8G5wHLfqf+udbTt2Ur90QC9nbUQtx2zMW+X3pUpNs/6G8knCMljK3wi2YSU/IM0SToM0SxjFijkt/bmuDkTDfvg9fFPH+z08hkLmH7PV6ZxaZQGPW7UsFvEj5Pd0/ZzlmqhaFg37sLXf1/V+iUcCmrZoYcX9YYVoWL2Po2G2/2wUAMQ17ks0zPZ9cXKfs+QLsi19vvb44uLU4xHPAXVx5X2r3a3Xx8q1Es7HyNjTKTYYiR0sxUWW4gWxerT0FvSq53Sb+jxN751yn0mn6wc5zgL647dWziCXadWXpJXXPAfEI+ra0Tre+uRxrVxFa7zgOXRdl6R/QibG0PyEpbmSmbUDy2xhJMezO27YrU0727Nb63brmcYYAGINL3z5nVVXe/XzT1pgDDvMzf6Q+qUDav6jt87VSGPeKr8PeN2a9TeUTxKWwVL+RrANK/kBaZZwGqRZwihWzGnpST4dGBkI4tzz+5oWls89vw8jA0FLzqlXloiHcXYi2VR2diKJRDxiyTnHYmGcOdJcduZIEmOxsCX9omeLmb50OolYv2r/J2L9PbasM2PDEZw52mL70STGh7XvJQs4uc9Z8oWRgSB+62cex1fe+QinDiWabHp0sB//+h9+sunYsYOj2Br04uvvzmra3Xh9X/vuLI4dHGXiWgnnY2Ts0RsPAWOxg6W4yFK8IFZPMhbCr7do6djBURQr1bbjZ44k8d/+34+V9066z6TT9YMcZy+9fxsvHVAfv9Xu90DQi18/ulJPrf6xg6NK/nDs4CjGt0eQiEdUtaN1fHx7pOnY2Hat+uG243Kb3dYl6Z+QGYtH1HMQnTmyVeyNBVVt2RuzX5dm1g6sgpUcz+64Ybc27WwvGQuptpWMhbreFmC/nmmMIQjzqPnPlj4vjj+1p+nY8af2YKDP23Ts1OEELk/eVt7/2uEEXnnnI83ck3yS6AZ7NPK3PT3I3wi2YSU/IM0SToM0SxjFijktJ0lS5085kP3790tXr17tyrlEUcKNxQIW8gKGQn6MDATBt27H7+I59cpqNRHT81mkswJiET8S8Qjcbt6yc5ZKVUylc8jkyoiGfRiLhREIeCzrFz1bzPSlxXS1ETXNFkplTKeXlP5PxPoRDDhjB2ilUsfkXBaZnIBo2I/x4Qi8XlevzeqIk/vcoC9YrlvZlh8sLGFhqQQ378LdpTKGQj7ENvlQqUjI5AXkhBr6fW7Ew364XBzSOX0fbry+WNiPugjcWbLd7wn7sVyzRsYevfEQMBY7WIqLPRw7NwKWa7ZYquCDdB53CxUEfW54XRy8Lh7hAI/FpToWlsqIhf1IxsKYywuOvc+kU9uwLc7eK5QR8ntRrYuIhtvnKx/fLeDWvQL6vG5Ewz4MhwO4lskp9SRI8Lp4FCt1DIX84DngxuLK53duWT6flnbUjgMwdEyu32qj3Ga3If13xJaclgUEoYap+exKDhKPwO/vzUOZ8yUB19IFxZa9sSBCgd48acDM2oFVGMzxLNes3XHDbm3a2V6xVEEqnVfaSsZC6At4LWkLsF/PLK0dAADHcaafIEP11OuZWe+38z5YdG22rXfJ+WDQ50apUoPf6wIPDoVyDVv6vajVJNwtVrAp4EFeqGJTnxdujkO+XEOxUsdAvxcuDggHvLq5J7EhsFy3D0oCrjfkb3tiQWzqUf5GsA0r+QFplugypFmCGQzOaQ1rlrk/18Vx3B8COARgQZKk5MNjvwbgfwdw5+HHvihJ0p/ZZRPPc9g92I/dg917mofeOY20tzydab7PhuqpzIN4nkPI70GxUkfI72kauH0+N7b2+1AXJWzt98Hna5eM2jnlhEBe5FLbVDSfFRCPBJCIh9sWL9SuT++cWnbo0el8LODiXEovcA/fO4V6XYQoSRAlQJQk1OsiAPbt97jc4DkOHAe4OA4eF3NhUhNRlJAXqnhQrCLgcUMUpZ5pmuc5PDrUD57ncOteAYMhH0RIuDa3hH6/C36PCzzPIVuq4kGpiq0hLz4zMqBrrxzjRgaCyoTDCt91QmwguovbzePxHZvx+A7z5+BaxqzW941I6PLswgRW5DaEfbhdy3E0EvBgsVDBcMSPcl3Eh3fKiEcCeOaxGAA05Vs7N/fpbqBkMeaRTtcPcpzVonExceeWIFw8MJ8VkC3VIEoSBkN+FMp1FCo17NoSxNi2TYpOH2nRR6umd27ua/oiplyrYzjS16R1WWetdVtzEzm/eXRopc1aTcTU7APczpawtd+HaNiHHZvbf2yxWh8j/RMyPM+B4ziAA3iOYyZG95pu5G/dguc5eFwcXDwHj4vv6RzMzrjhdvPwunm4XRy8bpflm6zsbG81uX03EEUJ1bqEqiihVpcgitb+IJOltQPCAnj38rhBWALPc3hkaxBuF5DJllF4uJ7u4jiky3VE+iSE/B6U6yK29vuwb9smXMvklHnZ339koC1+yT7furZtZA2dIAiiW9AclCAIwlq6Padl8dvr/wrgSwBeaTn+25Ik/V/2m8MOtZqIC+/fxokLKQhVUXmE78Tj23SFIIoS3pxO4/hr7yn1zj2/D88klr8AsrNMFCXNawCgWcbznOo5n94bxVvXMqpt6S1Q6PUJKwsbglDDxal5nLy40h9njiRxZCzes19uGqVUquJSKt1m++FkrOnJF6xRqdRxYXIOJ19vsPtoEhPjw8w/hchsfLAKNR87dnAUr3znJu4XKzh2cBRBrwu/+5cf4X6xgjNHErgRKeAffGKop77rhNhA2E+nmFYsVXA5lWkrP5SMKr/4dXJ8IdiiUqnjL2YWcK9QxelL09jc58WLP7oL56/MKNr6jZ8ag8fF4V//6aTumEAxj2ABvZzB6+bwrw7uQTonNGlcS6dq5zo7kcTvvD2Dm4sl+D08XjowihMXUvi3z+xtOocZf1DLv04dTmB403JOA2jPi8jHCCOwlD/kSwLeSN1py3eeTQ727Gk+LMDaPMwu7L5uO9uzez3Dbj/fqJrdUIg100/JITojihL+6sMFzD0o4/Sl6aYc8K3UPD6ze0DJW/fviuD5T+9q8u9Wf9PKQX/ik0O4ODVHvkqsiQclAW+p5G9PJwfpKRMEk5BmCadBmiWMYsU8jLmMUJKkbwO412s7WGR6PqvcfAAQqiJOXEhhej6rW+/GYkGZKMj1jr/2Hm4sFmwv07sGvTKtc07PZzXbMtsnrDA1n1UGBmDZxpMXU5jqcL9ZYCqdU7c9neuxZfpMzmWViTfw0O7XU5icY7/PzcYHq1DzsfNXZvDcE9uV13cLFeX9yYvTyJfqPfddJ8QGwn46xbRUOq9ankrnlXM4Ob4QbDE5l0W1JikLys89sV1ZRAaWtfXF/z6FmYWljmMCxTyCBfRyhkPj2/DxYqFN41o6VTvXiQspHBrfprx/+e0ZHBrf1nYOM/6gln+dvrSS05CPEWuFpfzhWrqgmu9cS29sPbM2D7MLu6/bzvbsXs+w2883qmYJolvcWCwgX6or8zFgJQf8Zz/+SFPe+uKTu9v8u9XftPLFyTnyVWLtXNfI365v8PyNYBfSLOE0SLOEUayYhzG3yUeHL3AcN8lx3B9yHKf6rHeO4/4Fx3FXOY67eufOHbWPOJr5rKDcfBmhKiKdFXTrZXLq9Rbygu1letegV6Z1Tq06C3nzfWIneppN58qqNmZyZTtNNEXGobanNXSRydmrCzOYjQ9mMBJrtXxMfmK0UBUhSmh6X6jUeu67rMQGorusNT/oFNOMxDwnxxfCfvTzAwGFck3RE8dBVVutf+VBbUygmEd0i7XEWb2cgeMAUVLXuJpOO+Ufje9bz2HGH7TyLzmnIR9jGyesH7CUPzh1jmc1rM3D7MLO67a7Pbu1brefb1TNEoQRjK53Nc7HZISqiPuFatPxksbnGv1NK1/Uig1WxVnCuejplvI3gkVIs4TTIM0S3cCKeZhTNvn8LoBHAewDMA/gt9Q+JEnS70uStF+SpP2Dg4N22mcL8UgAfk/zLfN7eMQi+o/8iob9qvWGQn7by/SuQa9M65zxiHZbZvvETvQ0Gwv7VG2Mhn12mmiKqENtj2voIhpm/7F6ZuODGYzEWi0fk/++uN/Dg+fQ9D7odffcd1mJDUR3WWt+0CmmGYl5To4vhP3oaTYe9iPodzfpSU1brX8JSG1MoJhHdIu1xNlOOYOLU9e4mk47navxfes5zPiDVv4l5zTkY2zjhPUDlvIHp87xrIa1eZhd2Hnddrdnt9bt9vONqlmCMILR9a7W+Riw7Eebg56m430+9c81+pvmmrfGcaviLOFc9HRL+RvBIqRZwmmQZoluYMU8zBGbfCRJykiSVJckSQTwBwA+02ubekEiHsbZiaQiAvnvtSXiEd16IwNBnHt+X1O9c8/vw8hA0PYyvWvQK9M6ZyIe0WzLbJ+wwlg8gjNHmvvjzJEkxjrcbxYYi4XVbY+Fe2yZPmPDEZw52mL30STGh9nvc7PxwSrUfOzYwVF8/d1Z5fXWoFd5f+ZIAqGAq+e+64TYQNhPp5iWjIVUy5Ox0Mo5HBxfCLYYG47A4+Jw6nACfg+Pr313FscOjjZp6zd+agyjQ/0dxwSKeQQL6OUMl96/jZGBYJvGtXSqdq6zE0lcnrytvH/pwCguT95uO4cZf1DLv04dXslpyMeItcJS/rA3FlTNd/bGNraeWZuH2YXd121ne3avZ9jt5xtVswTRLUYGggj5Xcp8DFjJAf/rX3/clLd+5Z2P2vy71d+08sWx4Qj5KrFm9mjkb3s2eP5GsAtplnAapFnCKFbMwzhJkjp/ymY4jhsBcFmSpOTD93FJkuYfvv5lAH9PkqSf1TvH/v37patXr1ptqu3UaiKm57NIZwXEIn4k4hG43Z33aomihBuLBSzkBQyF/BgZCIJ/+BNvu8v0rkGvTOucem2Z7RODrOrDnVDTrCDUMDWfRSZXRjTsw1g8Ar/f3c1mLaNUqmIqnVuxPRZGIODptVkdqVTqmJzLIpMTEA37MT4cgdfr6rVZhjAYHyzXrYwoSvj4bgG37hUQ9LkhQUK2WEPQ50LA40KlLiJXqmFL0IvBkBc7Ntvmuz09P7FqbNOsHp1iWrFUQSqdV8qTsRD6At6mczg5vhCrwnLNVip1XF/II1+u4V6hgnjEr8TUWNiHxx5ODozkjBTzCDAQZxt1ONjvh4tffoxtn9cFSZLgdvEolOsoVmrYuSWIR7Zq67RV0zs39+HmveJyPuJ1o1KvIx7pUz2HGX+o1URMz2VxO1vC1qAP0YivKachH7OMnuvWLljKH/IlAdfSBSXf2RsLIhSgJwqwNg+zC7PrU05oz+71DLv9nDXNchyHF778zqrP+ernn6R6Dq23lrZ0vsuwdb3rh/cLyGTLuFsoIxryw81z+OH9ErZt8qPf58FisYyhkB/bIwFcy+R0/U0rX7Q7zhI9wXLdPigJuN6Qv+2JBbGJ8jfCPKRZwmmQZglm6PY8jLndAhzH/RGAzwHYynHcLIBTAD7Hcdw+ABKAGwA+3zMDe4zbzePxHZvx+I7V1eN5DrsH+7F7sL/nZXrXoFemdU69tvQwW89O/H43Pv3IQK/NMEUg4MFnHGi71+vC/pEtvTbDFGbjg1XwPIdHh/rx6FB3fcxq33VCbCDsp1NM6wt4O8Y8J8cXgi28XheS2zd1/JyRMYFiHsECajoc2WpOk2rnMpqPmPEHt5vH4zs343Fs7to5CaIRlvKHUMCPzzxCi5WtsDYPswu7r9vO9uxez7DbzzeqZgmiW/A8h10D/dg10JzfPb5zJR8cxcqTfTv5m1a+SL5KdINNlL8RDoM0SzgN0ixhlG7ndsxt8pEk6R+rHP7PthvSBeRf4qRzAuJhP8Yafokj79CXf6Wj9qSb+ayAeCSARDxsaJe+Xj299vTKzF6DXhlhHPlJPulcGTGHPcnHqbYbeRoHq7Did7Idi4UyAh4XhKoIobr86/tNfV7U6nV4XC48KFWxKeCBUKvB73bjQbGKobAfe6MhzGZLPb8OgmikU2wwEvOM+Ginz+iNy8TGQRQlzN5fQjpbQSa/rMn4Jh9m7wmIR/yoi8BCvllDjbHZ6+JRrNRVNbbascRs3mq2PcKZmJk3dGO+1JqTFMp1FCo17NJ4GpAoSrh1b/nXV3qfU7suLd9Ts6XRB3du7sOt+0XyAUKTQqmM6fSSkoMkYv0IBnwb3haWYGUssztPtLs9O9cYnPpkYoIgrEctDwXQdGxTnxtzD7Rz2z6vGxwnoVKTcHeprJyH57mm8WQ45EcqnVPibCIWxlxeaBpvADAxBhHsQk+YIIzCSk5LmiWcBmmWMMpa19FbYf8bd4dSqdRxYXIOJ19PQaiKyt/UnhgfhtvN483pNI6/9p5Sdu75fXgmEYMoSrjw/m2cuLBS7+xEEhOPb9O90bWaqFmP5znN9gBoltVqoqlr0DsnTTKMIwg1XJyax8mLDf1/JIkjY3HmN8s41fZiqYLLqUyb3YeSUeY3+oiixITfyXb85pvX8M+ffAQcBxQqdZy/MgOhKmLXQAC/8NlP4PTl6Zb37zbFmf/4FzO4uVii+EEwQafYYCTmGfHRTp/Ryy1oo8/GQRQlXL15FzfulnDy4nSD5hKYvbeEgM+rxFxZQ0/vjeKtaxn85pvX8ML+nXj57RlVja12LNHLP43+OVkWxi7CWvTuM6A+b5A1u5b5UmtOUqzW23yjNQa//f0MZjJLup9Tu67NfV68+KO7NOs12tLqg2cnkvidtynvIdQplMr4RmqhLcf4yeSQ7ZtrWLKFJVgZy+zOE+1uz841hlKpikupdFtbh5Mx2uhDEBsctfnP+Z/dh1yphl9tiIenDifwR397E9cXljRz29NHEvi//8eHSg74H/7XcfAcj1/5k/eU9bJf/NxoWyz6j/+jOW/0ujl84at/R/MpQpUHJQFvpe606ejp5CB9AU00wUpOS5olnAZpljDKWtfR1aA/4moRk3NZZbEDAISqiJOvpzA5l8WNxYIyWMplx197DzcWC5iezyo3WC47cSGF6fmsbnt69fTa0yszew16ZYRxpuazysAAPOz/iylMddACCzjV9lQ6r2p3Kp3vsWWdYcXvZDsOjW/DYrGCu4WK8oUXABwa36Zs8FF7L8eZQ+PbenodBNFIp9hgJOYZ8dFOn9Ebl4mNw43FAuoip2zwAWTNTePHRqNNMVfW0PR8VonN8uaCxnJZY6sdS8zmrY3XwsLYRViLmXmDrNm1zJdacxI132iNwZOz2Y6fU7uu557Yrluv0ZZWHzxxgfIeQpvp9JJqjjGdXtrQtrAEK2OZ3Xmi3e3ZucYwlc6pt5XOdb0tgiCchVoeWq1JygYf+djpS9P4+c8+qpvbnro43ZQDziwsKRt8gOX1MrVY1Jo3Ts5mez4GEexyPV1Q1dH1NGmEaIaVnJY0SzgN0ixhlLWuo6tBm3wsIp0TlBslI1RFZHLLj9RUK1vIC5jPqpels4Jue3r19NrTKzN7DXplhHHSubJG/5d7ZJFxnGp7xqF2A2DG72Q7OA4QpeV/jXZxnP57AEr9xvcUP4he0ik2GIl5Rny002f0xmVi45DJCbiTV9fcQl5dI3KeqBVzZY2tdiwxm7c2XgsLYxdhLWbmDVraWs18qTUnMRKDjXxO7bqM+hblPcRqYWl+wpItLMHKWGZ3nmh/e/bpj7ROEIQWanlooVxTjRmlSk15rZW/NuaAndbP1OoIVRGihLbPUC5JyNCYRhiFlZyWNEs4DdIsYZS1rqOrQZt8LCIe9sPvae5ev4dHNOxHVKNsKORHPBJQLYtF9B/rpVdPrz1dW0xeg14ZYZxY2KfR/+w/Ct2ptkcdajcAZvyu0Q4Xt/xPza5O7yWp+T3FD6KXdIoNRmKeER/t9Bm9cZnYOETDfgyG1DU3pKGReGTluJ7GVjuWmM1bG6+FhbGLsBYz84ZGzbbWMaq71pzESAw28jmt6zLqW5T3EKuBpfkJS7awBCtjmd15ot3t2bnGQFonCEILtTw06HerxoyA16281spfG3NAo+tnrXlj61/RoVySaITGNMIorOS0pFnCaZBmCaOsdR1dDdrkYxFjwxGcOZpsWsw9czSJ8eEIRgaCOPf8vqayc8/vw8hAEIl4GGcnmuudnUgiEY/otqdXT689vTKz16BXRhhnLB7BmSMt/X8kibEOWmABp9qejIVU7U7GQj22rDOs+J1sx6X3b2NLnxcDQS+OHRxV7Lr0/m2cOpTQfC/HmcuTt3t6HQTRSKfYYCTmGfHRTp/RG5eJjcPIQBAuXsKZIy2x80gCf3M90xRzZQ0l4hElNr90oL1c1thqxxKzeWvjtbAwdhHWYmbeIGt2LfOl1pxEzTdaY/DY9kjHz6ld19e+O6tbr9GWVh88O0F5D6FNItavmmMkYv0b2haWYGUssztPtL09G9cYxmJh9bZi4a63RRCEs1DLQz0uDr/eEg9PHU7gP337Bw25bbhtrDh9JNGUA35iqB+/9TMrn7n0/m3VWNSaN45vj/R8DCLYZU8sqKqjPTHSCNEMKzktaZZwGqRZwihrXUdXg5MkqfOnHMj+/fulq1ev9tSGSqWOybksMjkB0bAf48MReL0uAIAoSrixWMBCXsBQyI+RgSD4h1vvazUR0/NZpLMCYhE/EvEI3O7O+7H06um1p1dm9hr0ytYRXb0gNc0KQg1T81lkcmVEwz6MxSPw+93dbNYynGp7sVRBKp1X7E7GQugLeHttliEM+p3lupXtuFcow+9xoVwVUarWUazUsanPg1q9Do/LhWypikjAA6FWg9/txoNiFUNhH/ZGw5jNltZ7/CCMY7lmjdApNhiJeUZ8tNNn9MZlghlsibOz95eQzlaQyS9rLr7Jh9v3BcTCftRF4M5Ss4YaY7PHxaNYqSMabtfYanM4s3mr2fYIS7AtN1jNvKEb86XWnKRQrqNYqWHnliAe2aoeg2/dKyCTK+t+Tu26tHxPzZZGH9y5uQ+37hfJB1YPE/mBHRRKZUynl5QcIxHrRzDQm18FsmQLS7AyD7M7T7S7PTvXGEqlKqbSuZW2YmEEAh5L2mIY2+Isx3F44cvvrPqcr37+Sarn0HpraUvnuwxbNKuWhwJYORb2Y1PQg/msem6byQno87rAc0C5JuHuUhnxh+fhea5pPBkO+ZFK55Q4m4yFMZcXmsYbADSfcjaW6/ZBScD1dEEZ0/bEgtgUoKc9Ee2wktOSZokuQ5olmMHgeqZhzbL/jbuD8Xpd2D+yRbWM5znsHuzH7sH2X7253Twe37EZj+9YXXt69fTa0yszew16ZYRx/H43Pv3IQK/NMIVTbe8LePEZB9oNsON33bCDhesgiEY6xQYjMc+Ib3T6jN64TGwceJ7DzoEQdrZIbsfmFd08OtTfVsdIbF1tDDebt5ptj3AmZuYN3ZgvrVZfPM9hZGs/RrYa/3zr+Vt9z4gt5AOEHsGAD595hI2NNCzZwhKsjGV254l2t2fnGkMg4HHsugBBENailYe2Hts1YG4+1vqZ1ji7299+DhbGIIJdNgX8+Mwj9GUz0RlWclrSLOE0SLOEUda6jt52vu6chlgtjbv3W39FbbZM3gE2nxUQjwSQiIeVHWB6ZfKvodK5MmItv4bSa8/u6zPTltNx8lNl5F/1pXMC4mE/xhzytAkn9zlLviCKEj6+W8CdfAkcx2OxUMa2SACVuoi7SxWE/W5E+jyo1yUsVWooV0Xs2NKHcq2O2fultjjVem5WrpNYH+iNkTKdfrmuN5bKGNFup8+Q/gkALb8CdUOo1lCo1BD0ulGti/B73LiTL2Og3wuek+DhXShU6gj63KjU69jS54OLB+aza9dRt/M6Yn1Cv2oiCPOwNK/KlQR8r8GXPxULItwjX2ZpjDGSS65H7NbmUknABw36eywWRL9F+rP72uzWM0v+QxBOpPHpj5V6HWGfB/eKFfR53dixxYfZ+2VkcmUMKEhZ3QAAIABJREFUhX0Iel3o97mxfdPy0xvlOVy5VoeL45AVKhgI+jfM2EHYD83FCKdBmiWcBmmW6BW0yacHiKKEN6fTOP7aexCqovL3LZ9JxADAVJkoSrjw/m2cuJBSys5OJDHx+DYA0Cyr1URcnJrHyYsrZWeOJHFkLA6v16XZnt7k34rr03tEvhkbnUCxVMHlVKbt3hxKRpnfdFKp1HFhcg4nX2+w/WgSE+PDTG/0cXKfs+QLsi1/+Nc/wE8/sROnL09jc58XL/7oLpy/MqPY98VnPwWfx4VTF6eVY8ef2oP/8jc3cL9YUeJU4yIHS9dJrA9qNVFzjJS1VyiV8Y3UQlts+MnkEIIBHwShpjmWNm6a7aTdTp8h/ROAuk6++OynUKqK+Or/vIkX9u/Ey2+vxNrTRxL4k6u3cPVmFn4Pj5cOjOLVq7fws5/eiVe+cxP3ixXTOjKb85FeNxYPSgLeSt1pi5FPJwdp0YMgOsDSvCpXEvCmii8/kxy0faMPSzmRkVxyPWK3NpdKAv5MRX//KDnY9Y0+dl+b3XpmyX8IwomIooS3v5/BTGYJf/z/3Wqaf+0aCOAXPzfaFKtOH0lgU58bV2/ebxor5HnZC/t34tWrH+CXDoyu+7GDsB+aixFOgzRLOA3SLNFLKGvsATcWC8pkGgCEqojjr72HG4sF02XT81lloiCXnbiQwvR8Vrdsaj6rBB+57OTFFKbms7rt2X19ZtpyOql0XvXepNL5HlvWmcm5rLIgBjy0/fUUJueyPbZMHyf3OUu+INvy4pO7cfry8gae557Yrmzwke27W6goG3zkY+e+eR3PPbG9KU6pnZuF6yTWB3pjpPKZ9JJqbJhOLwGA7lgqY0S7nT5D+icAdR3cLVTw29+6jkPj25QFZrns1MVpvPjkbuX9y2/P4ND4Npy/MqPEW7M66nZeR6xPrqcLqjHyepq0QBCdYGle9T0NX/5eD3yZpTHGSC65HrFbmx9o6O8DC/Rn97XZrWeW/IcgnMiNxQImZ7M4f2Wmbf51aHxbW6w6dXEaYb+3bayQ52Xy/xth7CDsh+ZihNMgzRJOgzRL9BLa5NMDMjlBcXgZoSpiIS+YLpvPqpels/pl6VxZtSyTK+u2Z/f1mWnL6WR07g3rpDXuSybH9n1xcp+z5AuyLaVyTbGJ49Bmnyi1HxOqIjhu5XU622w/S9dJrA/0xkiZTrFBbyxdOUdn7Xb6DOmfANR1IMdTtVgrVEWUKrWm9/LnGuOtGR11O68j1idOzq8IotewNK9iyZdZGmOM5JLrEbu1aaf+7L82e/XMkv8QhBPJ5ATN+ZfWfOzOknoM+//Ze//gNs70zvPb+NkgQEAiRQKgZImShxrLBCnF0XgzvtxkIsVeT0aWWN4aeZNcuZLb3M7dXsaqaGs3uaxXXmu0u9m6G+XkZKt2Nlc7O97b3FrJzOnXZLyeyNmdSzzZijyxSdDySB5b0lAkSImiABJEA2h03x8UWvjR3Wg0uhtvk8+nSiWi337f93nf93me93lfvN2oXZdthLmDcB6W4jeCMALpLOE2SGeJbkKHfLpAPMqD99d3Pe/3YLCXN52WjIVU0xIx/bRENKiaFo8Gdetzun1m6nI7cZ2xYZ2kxrjEo2yPi5v7nCVbqMrSE/TVydQon5drvsb7PZDlh38nYvXys9ROYn2gN0dWaeUb9ObSh2W01t1W95D+E4C6HtT6UzUdCQV8dZ9ludnfmtEjq+M6Yn3i5viKILoNS+sqlmyZpTnGSCy5HnFaN53UP+fb5qw+s2Q/BOFG4lG+5fqr8fNARN2H1a7LNsLcQTgPS/EbQRiBdJZwG6SzRDehQz5dYLg/jNNH99UtBk4f3Yfh/rDptNFkFKcmUnVppyZSGE3GdNPGkjGcPFyfdvJwCmPJmG59TrfPTF1uJ5XoVR2bVKK3y5K1ZmwohpNHGmQ/ksL4UKzLkunj5j5nyRaqsnzznY/xyqFR8H4PvvXuDI4dHKmTrz8cwKuHR+uuHX96N779w5k6P6VWNgvtJNYHenOkck8iouobRhMRANCdS6sY0d1W95D+E4C6HvSHA/jNX9iNi+/fxksH6n3tq4dH8fo7HyufXzowgkuTt3Hs4Ijib83qkdVxHbE+2Z0Iq/rI3QnSBYJoBUvrqsc0bPmxLtgyS3OMkVhyPeK0bj6uoX+P26B/TrfNaX1myX4Iwo0M94cxti2GYwdHmtZfF9+/3eSrXj08ipxQaporquuy6v8bYe4gnIfWYoTbIJ0l3AbpLNFNOLn6CO86Y//+/fKVK1e6LYYmkiTjxmIeC8sCBnt5DPeH4fFwHaWJooTpuSwyWQGJGI/RZAw+n6dlmiCImJrLYj5XRDwaxFgyBp73tazP6faZqctmLK1ETWdXCyWkM8vK2KQSvegJBays1jZKpQomZ7OYzwmIR3mMD8UQCHi7LVZL3NznBm3Bdr2tyvLJ3TzurhQAeLCYL2IoFkK5IuFuvoTeoA+bQn5UJBkrJRElUcK2zT0oihXcXio0+SkT7STWD7brrN4cWSVfKGI6s6L4htFEBOHQwxP5enNpFSO62+oe0n9XYLvOVvVgPiegx++FIFaQL4noCfggViTwfh/urhTR1xOA1yPD5/FitVxBT8CLckXC5p4gvJ61n6PoVI+sjuuIrmC7zt4vCLiWySs+cncijE0helKZ6AhHYloWYGldlSsI+LDGlh9LhBHtki2zNMcYiSWxDnXWad1cKQj4oEb/Hk+EEbFJ/5xum9P6zNLeAQBwHIcXvv5O22W+8eWnKJ9L83VSl853GY7prCTJuHVvzR+VKxVEgn4srZbQE/Dhkb4gZpaKmM8VMdgbRDjoRSTow7ZNPbi1tLq2hgt4UapI4MBhWSihLxzU3AMj1j20FiPcBuks4TZIZwm3YVhnfa1vIezA4+GwayCCXQMRy9J8Pg/2PrIZex9prk8vjed9+MzO/rbl1MOO9pmpy+30hAJ4UmNsWCcQ8GL/cF+3xWgbN/c5S7bg8XB4dDCCRwfblyW1dVPLsllpJ7E+0Jsjq4RDQTy5U/s1m3pzaRUjutvqHtJ/ArBOD4a3dK5HVsd1xPpkU4jHkztpg4MgzMDSuirKkC2zNMcYiSXXI07rZsRB/XO6bU7rM0v2QxBuxOPhMLwlormeGtqkfp3sjugGtBYj3AbpLOE2SGeJbmH5IR+O457XS5dl+dtW12k31Sd4MjkBySiPMYNP8NQ+ZR2Pqj/ZrJZWfQpsLisgGQthNBlteiOPWprZ+qpvH8jkikhovMmn3TL10gjjrIe3yrhNB5YLAq7WnLrdkwij1yWnbvX8Q7cxog9rb/9ZwVxWwEpRRH84gJViGVE+AAky+sNBnXx53LyXRzjgQzwaxPa+tftq6x3s5eH1AHPZ+r+TMR4VCVhYNqerWm3Tk4tgC6P6qXePkVjBiE+3wo6taI9VsrgJN81bkiTjJ/dWcHeljJWSiEK5gkQ0iKIoIVcQsas/jJ0DkSY/mIzxECsybi2tduSX7Ogrp/vfTePdCay0U00OAKpztFp6K9kby9++uQcz91cxnysiXxKxsz8MSa6f66vlL+aLCHo9uLtSAh/wIOj1oj8S0LSNxvk9HFx7Q5aH47BaqlgaS7CMG2V2KyytCZ18k0orWIpTWJFFb2/HDgqFMqYyuYdvskxEEQr5bavPSVswuw9IEMTGoHGNpban1E6s1DiP7In3YiZbUM3b6J9GE1HMLgvKvdtiIVydz5mak4z4Pqv2G4xCMWfn0BsmCKOQvRFugxX/xoocBPtYvXdgx2r/uQf/DwJ4CsDbDz7/PIB3ALjqkE+pVMG5yVmcOJ+GUJaU3+KeGB/SXeBLkow3pzM4fvY9Jd/po/vw7GgCADTTJEnGufdv4+VzD+s7NZHCxN6tAKCZ5vFwpuorlSq4MDWHExdq2nc4hcNjSQQCXlNl6qVRUGCc1UIJl9LzTWNzKBVn/qCPnv6zrAPLBQHfTd9p6vMvpAaYP+gjipKmf+j2l/JG9EGSZFz+cB4fLazgzOXryn0vHRjBG1du4YX92/HGlVv4rWf3NOVrLPvYwRGMxCP4/Mgg3ro635T2+g9uYmm1hGMHR/DdqTl8YSxZV2c7uqrVtmf2xFXrHolHcODTcabtYKNhVD/17jESKxjx6VbYsRXtsUoWN+GmeUuSZPzVx3dwZ6WE2ftCnf86/vRufOMvb2BptYSvfWkf/vbjD33R5p4Afu2/G8bp713ryC/Z0VdO97+bxrsTWGmnmhx/8Ms/hZIoa87Rp4/uQ8DH4Tf+6G9ayq5W/qmJFFaLIv7Fdz/E5p4AXvzsjqa5PuDj8NVLH+CXn9yB3/uzersIB7wY2rzSZBtqdf3OFx5DwOfFP7s4bWkswbI+ulFmt8LSmnClIOBPVdZKv5gacPygD0txCiuyCIKoubdjx0GfQqGMi+lMU33PpRK2HPRx0hbM7gMSBLExqI2DtOJMtT0hrVhJbR45eSSFf/3n13FzsVCXVxSlOv+0oz+E//XzI/W+sSGv0TnJiO+zar/BTF9TzGmO+wUBb6nEb8+kBugLaKIOsjfCbbDi31iRg2AfO/YOLN9xkGX512RZ/jUAfgCPy7L8d2RZ/jsARh9ccxWTs1kluAUAoSzhxPk0JmezuvluLOaVCbGa7/jZ93BjMa+bNj2XVQa4mvbyuTSm57K6aWbrm5rLKs5Had+FNKY6KFMvjTBOOrOsOjbpzHKXJWuNW3Xgaiav2udXM2zLDUDXP3QbI/pwYzGPqdtZZWOket9rb1/HofGtyv9q+RrLPnP5OiZn1nymWtrzT2xT/v71zz3aVGc7uqrVNq26J2eyzNvBRsOofurdYyRWMOLTrbBjK9pjlSxuwk3z1o3FPEQJ+PGdfJP/Ov29a4qP+4d/XO+Lnn9im3LAp3q/Gb9kR1853f9uGu9OYKWdanJMzujP0cfPvofJmawh2dXKf/lcGnfzJUX31eb6yZksDo1vVQ741MpxN19StQ21uu7mS8oBn1ayGu0f1vXRjTK7FZbWhB9orJU+6MJaiaU4hRVZ9PZ2bKkvk1OvL5OzpT4nbcHsPiBBEBuD2jhIK85U2xPSipXU5pET59M4NL61KW+jfzo0vrXZNzbkNTonGfF9Vu03GIVizs65phG/XXPBXjfhLGRvhNtgxb+xIgfBPnbsHdj5WNEjsizP1XyeB7DdxvpsIZMTlA6vIpQlzOcE3XzzGvkWHrw+UyttLquelsnqp5mtL5MrarSvaLpMvTTCOPM6Y8M6btUBN/e5nn/oNkb0YT4nQJKheh/H1f/fmE8tjyRr9wnHPfy7UBQ70lWt+rXqrv5UCMEORvVT7x4jsYIR/2KFHVvRHqtkcRNumrfmcwLu5cu6PrP6d+04Vn1o4/3t+iU7+srp/nfTeHcCK+1Uk8OI/koymtLVZNeLBQB93ddLU7ONdtrSaSzBsj66UWa3wtL6hCVZWIpTWJFFb2/HDpzWByfrM7sPSBDExqA2DtKKJbXmBrVYqdXeVW3eRv+kVX9jXiNzkrF9DWv2G4xCMWfnsBS/EWxD9ka4DVb8GytyEOxjx96BnYd8LnMc9585jvtVjuN+FcB3APyZjfXZQjLKg/fXdxPv9yAe1X/NVlwj32Avr5uWjIVU0xIx/TSz9SWiQY32BU2XqZdGGCeuMzas41YdcHOf6/mHbmNEH+JRHl4OqvfJcv3/jfnU8ng47T6R5Yd/9wR9HemqVv3JmLZcrNvBRsOofurdYyRWMOJfrLBjK9pjlSxuwk3zVjzKoy/s1/WZ1b8bfZEVfsmOvnK6/9003p3ASjvV5DCiv41vBNeSXS8WqP2sla6VpmYb7bSl01iCZX10o8xuhaX1CUuysBSnsCKL3t6OHTitD07WZ3YfkCCIjUFjHNTOnpBarNRq76o2r5Z/apXXyJxkbF/Dmv0Go1DM2TksxW8E25C9EW6DFf/GihwE+9ixd2DbIR9Zln8DwNcB7H3w79/KsvwVu+qzi7GhGE4eSSkdz/vXfo92fCimm2+4P4zTR/fV5Tt9dB+G+8O6aaPJKE5N1Nd3aiKF0WRMN81sfWPJGE4ebmjf4RTGOihTL40wTirRqzo2qURvlyVrjVt1YE8irNrnexJsyw1A1z90GyP6MNwfRmprDMcOjtTd99KBEVyavK38r5avsexjB0cwvm3NZ6qlffuHM8rff/j9HzfV2Y6uarVtNBnTlIt1O9hoGNVPvXuMxApGfLoVdmxFe6ySxU24ad4a7g/D5wF2DYSb/Nfxp3crPu5rX6r3Rd96dwbHn97dsV+yo6+c7n83jXcnsNJONTnGtqnPk1X9PX10H8a3xQzJrlb+qYkUtoQDiu6rzfXj22K4+P5t/OYvNNvFlnBA1TbU6uoPB/DPnhu1PJZgWR/dKLNbYWlN+LjGWunxLqyVWIpTWJFFb2/HlvoSUfX6ElFb6nPSFszuAxIEsTGojYO04ky1PSGtWEltHjl5JIVLk7eb8jb6p4vv3272jQ15jc5JRnyfVfsNRqGYs3N2a8Rvu12w1004C9kb4TZY8W+syEGwjx17B5wsy63vciH79++Xr1y5YklZpVIFk7NZzOcExKM8xodiCAS8LfNJkowbi3ksLAsY7OUx3B+G58Ejo3ppoihhei6LTFZAIsZjNBmDz+dpmWa2PkEQMTWXxXyuiHg0iLFkDDzv66hMvbR1hKUNUtPZ1UIJ6cyyMjapRC96QgErq7UNt+rAckHA1Uxe6fM9iTB6Q+44sa7nH2qwXW/VMKIPkiTjk7srmMsKyBdF9IUDyJdERIJ+yJDRHw7q5Mvj1r08egI+xKNBbO9bu6+23oEID69n7RXEtX8nojwqEnBnxZyuarVNTy6iLWzXWaP6qXePkVjBiE83aMe6WNEeq2RxExbOW47o7E/ureDuShkrJRFCScJgNIBSRUKuIGJnfxi7BiJNfjAR5SFWZPxkabUjv2THHO903ODWOKVdDLazK34WgOocrZbeaoway9++uQcz91cxnytitSRiuD8MSa6f66vl38sXEfB6cHelhKDfA97nRX8koGkbjfN7OOiFWJHAcRxWSxXEo9bFEizDgMxdiWm7AUtrwpWCgA9q1kqPJ8KIdGmtxFKcwso6TG9vxw4KhTKmMrmH9SWiCIX8ttXnpC2Y3QdcZzjmZzmOwwtff6ftMt/48lOUz6X5OqlL57sMx3S2cY2ltqfUTqzUOI/siUcxky2o5m30T6lEFLPLgnLvtlgIV+dzpuZHI77Pqv0GozAQc9qN7Xp7vyDgWk38tjsRxiaX7HUTzsLK/gFBGMWgfyM/SzCD1XsHlq/2OY77C1mWf5bjuGUAtVE3B0CWZdmex3psJBDwYv9wn2padUDmsgKSsRBGk1FlQDweDrsGItg1EGnKJ0kyloUy7q+WEfL7IEly04S51nn113w+D/Y+shl7H2mWRa8+pUyVdRDP+/CZnf2q95ttg14+vT7TS9PCTB7CfkqlChZXirizXIKX4zDUy9u6wWgVQa8fHo4DxwFejkPQa98mpV2o+Q4WkGVgtVTBUr6EFaGMlWIFkaAPBVHEUr6MoU0hPJ6I4tHBh09jVr9Iu3kvD0la++mMxgM88zkBPQEfNvWsjd1cVoAkQ1mI7BqIYLg/jBuLecxlBeXLNwCQZCjXnhzuN7VRoOXvPB4Ojw5G8Oiguv+syh6Prn0ZeWtpVfm8DjctmMbI/FlFaz/R4+Hg93Lwejj4vR7V8eMa7LLxM6A/zwPNuqOnK3rnuI20WZJklCsyypIMsSJDktg9GN5Ov7TCDeffPR4Oj/RFUJHz8HiATSEOK0URy4KIaMiHoljB39xaQlmqwOfx4u5KEQORIO7lS5BlQIYM3u/Ftk09pg4iVPvZjN/UGqt27NAKnK6vW7DSTrV1g8/naZJte9/afP3fPllU9KOVn1LTSUmSIcmAJMvY0R/GIw/m2ap9S5KszLuDvWsHjPKlivJ3NZZQm58BgOOACO9DwOvBsiB2dLCntmytNZcZ/2alX1SDFd3aCAT8PgR9Hvi9HII+LwL+7q2pfJxPiV64B5+7RbFcRlGUUKrIKIoSiuUyfL7uvhK92+swn8+DgM8Dn5dDwOe1fV+E47g6feA4e9vOB/3YEgmiIsnYEgmCD9q3VpckGbK8NpfIsv1xsNP7WrSPRhCdoRYHNe79NN4jihKmbt9X7G5PvBcz2YISq41t3YS9jzz0o7V7WbWxcePeQ2NMLUkyenk/VksV9PL+tuNTo77PyLq51T1G4lWnY067Y2iCYBkj31kSBNFMo5WQ1RBaiKKE0oN9jJIoQRSljtZhlu/IyLL8sw/+131nLsdxm2VZXrK6ficRRQnn3r+Nl8+lIZQl5dVKE3u36g6KXj4ApsrUQ5JkvDmdwfGz7yllnj66D8+OJkxP0qy03awcbmC1UMKl9DxOXHjYtpOHUziUijP/Nh9BEHFhaq5J9sNjSaYP+pRKFZybnMWJ8zVyH0lhYnyI+af2WLaFWh+0uSeAFz+7A2cuX1fkPHZwBK//4CaWVkt1Mqv5rmMHRzASj+DzI4N46+p8Xdorh0bxb77/EW4uFur8HABVHxjwcfiNP/oby/yimf4QyhJ29IfwlQMjdWPnlCyEMVrNo0bsr1Ao42I60+QXn0slDD/xbGQ+t2rOd5M/tKLNdsRKdlKV99/9xY/xP/zMMBaWizj9vWt1vnLrpiDyJRmvXpyu85Pf+uEtHHgsgTeufICvHBgxPE9sxH4mrMFojNKufmjd/8yeeFOMcGoihd9/+zpuLhZU593aWKT6d8DHqc7PAR+Hr176AC/s347X3r5uSpeNttWszZCtrR9YivFZWuPlC0V8J73QJMsXU4MIh5w96MPKGDkth9P64KRfc7ptTo8dKzpLEBsJNbs7eSSFs399E1duZg2v7b/2pX2QZAn/6E8mVe23E19pxPdZuSfBYrzKokydcr8g4K30naZxfSY1QG+ZIOqg+IBwG6z4t2xBwH9WkeNvpwYQIz9L1GDHOrOb3vlyF+u2hOm5rDLpAYBQlvDyuTSm57Km85ktU48bi3klOK2Wefzse7ixmDddJittt6O/WCGdWVaMHVhr24kLaaQzy12WrDVTc1lV2acYH5fJ2azyhTbwQO7zaUzOsi03wLYt1Pqg55/YphzwAdbkPHP5Op5/YluTzGq+68zl65icWfMXjWmvXprGofGtyueqn9PygZMzWUv9opn+AIBD41ubxs4pWQhjtJpHjdjfVCan7hczOcvkMHqPEdzkD61osx2xkp1U5X3xqV34+G5eOeADPPSVm3qCygGf6vVXL03jxad24bW3ryu+x+g8sRH7mbAGozFKu/qhdb9ajPDyubQSI6jNu7WxSPVvrfl5ciaLQ+NblQM+RmQ1KntjfrM2Q7a2fmApxmdpjTedWVGVZTqz4rwsjIyR03I4rQ9O+jWn2+b02LGiswSxkVCzuxPn03jxqV3KZyNr+3/4x+/h+sKKpv124iuN+D4r9yRYjFdZlKlTrmXyquN6LePeNhH2QPEB4TZY8W8/0pDjR+RniQbsWGd285CPO48/1zCXFZTBqCKUJWSygul8ZsvUYz6nXubCsvkyWWm7Hf3FCvO5omrb5nPFLklknIxLZc9o2Mp8jn19YtkWan0Qx0FVzuqb3Wtl1vJd1Z/Y0iun+nlhWdAtp/FaJ37RKI3yaPWJE7IQxmg1jxqxPyt8upH53Ko5303+0Io22xEr2UlV3kJRhCSr+5B7+bLq9UJJVPxlO/PERuxnwhqMxijt6ofW/a1iBCOxCMdp3yfJnc/dRttq1mbI1tYPLMX4LK3xWForszJGTsvhtD446decbpvTY8eKzhLERkLL7golse6zkbW92l5Wq300I77SiO+zck+CxXiVRZk6haWYiWAbig8It8GKf2NFDoJ97FhndvOQj70/aO0AyVgIvL++C3m/B4mY/iu49PKZLVOPeJRXLXOw13yZrLTdjv5ihXg0qNq2eNTZ14+bIeFS2ZMathKPsq9PLNtCow9Sk7P6O921Mmv5Lg+n3d7a3/uu+jm9chqvdeIXjaIlTzdkIYzRah41Yn9W+HQj87lVc76b/KEVbbYjVrKTqrw9QR+8nLoP6Qv7Va+HAj7FX7YzT2zEfiaswWiM0q5+aN1vNEbQSm/8u/G+avzQiS4bbatZmyFbWz+wFOOztMZjaa3Myhg5LYfT+uCkX3O6bU6PHSs6SxAbCS27CwV8dZ+NrO3V9rJa7aMZ8ZVGfJ+VexIsxqssytQpLMVMBNtQfEC4DVb8GytyEOxjxzqTfkyxA0aTUZyaSCmDwvvXfqdyNBkznc9smXoM94dx+ui+ujJPH92H4f6w6TJZabsd/cUKqUQvTh6ub9vJwymkEr1dlqw1Y8mYquxjjI/L2FAMJ480yH0khfEhtuUG2LaFWh/0rXdncOzgSJ2cxw6O4Ns/nGmSWc13HTs4gvFta/6iMe2VQ6O4NHlb+Vz1c1o+cHxbzFK/aKY/AODi+7ebxs4pWQhjtJpHjdjfWCKq7hcTUcvkMHqPEdzkD61osx2xkp1U5f3mOx9j55Ywjj+9u8lX3l8t4pXnRpv85OvvfIyXDozg0uTttuaJjdjPhDUYjVHa1Q+t+9VihFMTKSVGUJt3a2OR6t9a8/P4thguvn8bLx0YMa3LRttq1mbI1tYPLMX4LK3xRhMRVVlGExHnZWFkjJyWw2l9cNKvOd02p8eOFZ0liI2Emt2dPJLC6+98rHw2srb/2pf2YWQwomm/nfhKI77Pyj0JFuNVFmXqlN2JsOq47k64t02EPVB8QLgNVvzbpzXk+DT5WaIBO9aZnCx354U6HMf9jSzLP2VX+fv375evXLliV/EKoihhei6LTFZAIsZjNBmDz9f67JRePrNl6iFJMm4s5rGwLGCwl8dwfxiexqP/bcJK2+3oL4NY+pNzajq7WijIoJpWAAAgAElEQVQhnVnGfK6IeDSIVKIXPaGAldXahiCImJrLKrKPJWPgeV/rjF2mVKpgcjaL+ZyAeJTH+FAMgYC322IZwqAt2K63akiSjE/u5nHrXh5R3g+/l0O+VEFPwAtBrOB+XkQyFsTjDTLX5usJ+BCPBrG9b81/Vf3afE5AT8ALWZbBcRxWSxXEo/V+Ts0HArDcLxqlUZ7tm3twa2m1K7K4gK7obCOt5lEj9lcolDGVyT30i4koQiG/pXIYvccIbvKHVrTZwljJEZ2typsrlMBxHFaKIlYEERHeh1jIj2JZgihV4PV4cXeliC2RILweoCIBy0IJfeFg2zETY/1MWIftOms0Xm9XP7TubzXP1n4eiPDwetZ+prD2b635GViLH+7li/B7PapxhxGMttWszWwAW2MiPnCCLq53m2BpjZcvFDGdWVFkGU1EEA5152lJVtZhTuuK0/rgpF9zum1Ojx0rOqtUxHF44evvtF3mG19+ivK5NF8ndel8l8F0bNBod3viUcxkC22v7SVJ1rXfTnylEd9n5Z4Ei/FqF2SyXW/vFwRcy+SVcd2dCGNTiN7OQjTDWnxAEK0w6N9s19lsQcCPauT4dCKMGPlZQgWD60zDOmvrIR+O454A8LNY+2muv5Rl+Yc1aX2yLN+zq26aHAgHoICGcCOkt4TbIJ0l3AbpLOE2SGcJN0J6S7gN0lnCbdAhH8pnW76NeMiHIDQgvSXcBuks4TZIZwm3YVhnbXsMheO4EwC+BODbDy59g+O4P5Zl+RQAaB3w4Tju3wE4BGBBluXUg2t9AN4AMAzgBoCjsiwvtStT7Vsf2nnaUi9f9XTrXFZAMhbCaDKqnG7Vy2dWFr36qifAMrkiEm08aVR9Uj+TE5CM8hireVLfjrbr5SPqyRUEfFhzAvSxRBhRl5wANauP3caKN210CzfYlihKmJ7N4na2gC2RIBKxIIplGTfu5hH0exAKeBHlvfBwXswsrSIS9EGCjFxBxGBvEOWKrOqraqm+/efmvTzCD97+s23T2pP47frc2jIbfRoAU36ccCet3qxmxHfozbdGMRs/NGLEX1hRl1P1bDQEQcTN+yvIrlYwv1xEMhbEppAPmVwRAZ8XiyslDG3Sj82qbyiZf/DGEq8HmMsaGwM7Yly3sN7bZxe1PnLbJh5eD4dMTkB/OAgJMvrDwbb0rifgQ6lSQX84WKfLWtf1xqrdMW0VE9TK0K5+WOEzSUfXH8sFAVdr1oR7EmH0dmlNuFIQ8EGNLI8nwoh0SRaW1j6syOL0WtaK2LYdnHx7k5vfqkwQhDNIkoyfLOUxny1iMV/CYG8QhbKIkN+nvL26dt2VLZRQkYC7K0UkYyHsifdiJlvAfE5AMsZjcaWEuQZ/qreGa2dvSis+NBM3GsljtFyKW52B3uRDGIVsknAbrPg3VuQg2MfqvQM7v3H/FQB7ZVkWAIDjuN8F8B6AUy3y/XsAfwDg9Zprvw3gsizLv8tx3G8/+Pxb7QgjSTLenM7g+Nn3IJQl5TdVnx1NtNzE1conSTLOvX8bL59LK2mnJlKY2LsVHg+nmQ+AKVlEUdKsTxQlXJiaw4kLD9NOHk7h8FhS92BFqVTBuclZnDhfk+9IChPjQ/D5PJa3XS8fa4cRuk2uIODN9J2mMX02NcD8QR9BEE3pY7cpFMq4mM40yf1cKsH8QR89/8CKbTXKuKM/hK8cGKmT+djBEQxt4vEffnADV25mlWvfnZrDF8aSOHP5epOvqt1IVvPZxw6OYNvmEP6Pt36Em4sFwz5Xr8w/+OWfQkmU2/bjhDtZLZRwKT3f5BsOpeLoCQUM+Q69+dbolyFmY5lGjPgLK+pyqp6NhiCIuDKziNmlIk5cmFb67fjTuxH0evAv3/ywZWym5X9f/8FNLK2WdMdAb8wAczGuWyB9NUetj9zcE8CLn91RN5+/dGAEb1y5hd96dk9bevfSgRG8/WEGf/fJHXW6XC3vKwdG8PtvX9ed+9sdU6MxgZE2NWKFzyQdXX8sFwR8V2VN+IXUgOMHfVYKAv5URZZfTA04ftCHpbUPK7I4vZa1IrZth3yhiO+kF5ra98XUoOUHfVrF/gRBEJIk4//7aAGz94t49eLDNdn/9uxjKFYknP7etbo54a3pWfz8p5N49dJ0nc88+9c3URJl/NLf2lFXzskjKRxOJfH29Tu6a7jTR/ch4OPwG3/0N5qxn1Z8+MyeON66Ot9W3Ggk1jQaj1Lc6gz3CwLeUonfnkkN0BfQRB1kk4TbYMW/sSIHwT527B3YueMwC6BWg4MAbrfKJMvy9wE0vuXnCIBvPvj7mwAm2hXmxmJemaAAQChLOH72PdxYzJvONz2XVQajmvbyuTSm57K6+czKolff1FxWcSLVtBMX0piay+qWOTmbVTZllHzn05ic1W+D2bbr5SPq+TCTVx3TDzP6esICZvWx20xlcupyZ3Jdlqw1brCtRhkPjW9tkvnM5ev48Z08XnxqV921X//co8oXgtXrVV9Vi5r/OXP5Oq4vrODQ+FblmhGfq1fm5EzWlB8n3Ek6s6zqG9KZZQDGfIfefGsUs/FDI0b8hRV1OVXPRmNqLgsv51UO+ABr/Xb6e9ewuFoyFJtp+d/nn9jWcgzsiHHdwnpvn13U+sjnn9jWNJ+/9vZ1HBrf2rbevfb2dbz41K4mXa6W9/K5dMu5v90xNRoTGGlTI1b4TNLR9cdVjTXh1S6sCT/QkOWDLsjC0tqHFVmcXstaEdu2w3RmRbV905kVy+tqFfsTBEHcWMxjuVBRDuYAa75icbWkHPCpXnv5XBq/8jM7lQM+1esnzqfx4lO78Oufe7SpnBPn1/ZQW63hjp99D5MzWd3YTys+nJ5rf1/LSKxpNB6luNUZrmnEb9dc8P0C4Sxkk4TbYMW/sSIHwT527B1YfsiH47jf5zjuNQBZANMcx/17juO+ASAN4L7JYuOyLM89+DsDIK5R99/nOO4Kx3FX7ty5U5c2nxOUjqsilCUsLAu6Fevlm8uqp2Wygm4+s7Lo1ZfJFVXT5nNF3TIzGrLM5/TlNNt2vXwbEX2dNTemLGBWH7uNm/vcSdvS01s9GmXkOKjKLMlAoSTWXSsURU1fVYuW/5Hktfpqr7XyuXplSrK67EbLJJzFrM5WaeUbjPgOvfnWuBzm4odGjPgLK+pyqp71iJ7OZnJF3FlW1zlJRtM1tdhMy/9W/aTeGNgR47qF9d6+TjAa0+rpnhm904oPquW1mvvbHdN2YoJWbWrECp9JOtoencYHTsDS+oQlWVjaV2BlHeb0+FgR27aDk+1jSdfdjhv8LEHUYlRn53MC8ipxqFZceD9fVo9lS6JmPNu4t6q3h9Z4rTb204oPteYvvbjRSKxpNB6luNU6WIoPCPfipE1SfEBYgZP+jfwsYQV27B3Y8SafKwDeBfD/AvgdAH8O4L8A+CcAzndauCzLMgBZI+3fyrK8X5bl/QMDA3Vp8SgP3l/fXN7vwWCv/uuy9PIlYyHVtESM181nVha9+hLRoGpaPKr/2uKkhizxqL6cZtuul28joq+z5saUBczqY7dxc587aVt6equHloyNnz0cEAr46q71BH2avqoWLf/j4QBZrr/Wyufqlenl1GU3WibhLGZ1tkor32DEd+jNt8blMBc/NGLEX1hRl1P1rEf0dDYRDWKgV13nGt+g3Co2a/xc9ZN6Y2BHjOsW1nv7OqGdmFZL98zonVZ8UC2v1dzf7pi2ExO0alMjVvhM0tH26DQ+cAKW1icsycLSvgIr6zCnx8eK2LYdnGwfS7rudtzgZwmiFqM6G4/yCPPNcahWXLgp7Fe9Hgr4NONZrb3Vxs9qa8Da2E8rPtSav/TiRiOxptF4lOJW62ApPiDci5M2SfEBYQVO+jfys4QV2LF3YPkhH1mWv6n3z2Sx8xzHJQHgwf8L7RYw3B/G6aP7lA7k/Wu/KTncHzadbzQZxamJVF3aqYkURpMx3XxmZdGrbywZw8nD9WknD6cwlozpljk2FMPJIw35jqQwPqTfBrNt18tH1PNYIqw6po8l9PWEBczqY7cZS0TV5U5EuyxZa9xgW40yXnz/dpPMxw6O4NGBMF5/5+O6a3/4/R/j2MERVV9Vi5r/OXZwBCODEVyavK1cM+Jz9coc2xYz5ccJd5JK9Kr6hlSiF4Ax36E33xrFbPzQiBF/YUVdTtWz0RhLxlCRKzh5eLSu344/vRv9PQFDsZmW//32D2dajoEdMa5bWO/ts4taH/mtd2ea5vOXDozg0uTttvXupQMj+OY7HzfpcrW8UxOplnN/u2NqNCYw0qZGrPCZpKPrjz0aa8I9XVgTPq4hy+NdkIWltQ8rsji9lrUitm2H0UREtX2jiYjldbWK/QmCIIb7w+jlvXjlufo1WV9PAMef3t00J/zHv/oErxwabfKZr7/zMf7w+z9uKufkkbU91FZruNNH92F8W0w39tOKD0eT0bbjRiOxptF4lOJWZ9itEb/tdsH3C4SzkE0SboMV/8aKHAT72LF3wMmy6ktxOobjuE+g8sYdWZZ3Gcg7DOCSLMupB5//dwCLsiz/Lsdxvw2gT5blf6xXxv79++UrV67UXZMkGTcW81hYFjDYy2O4PwxP43F3FfTyiaKE6bksMlkBiRiP0WQMPp+nZT6zsujVJwgipuaymM8VEY8GMZaMged9LUoESqUKJmezmM8JiEd5jA/FEAh4bWu7Xj6X0XrA2kBNZ3MFAR9m8sqYPpYIIxpyxxMNZvWx2xQKZUxlcg/lTkQRCvm7LZYhDNqW7XrbUsbZLG5nC9gSDiKxKYhiWcaNu3kE/R6EAl5EeS88nBczS6uIBH2QIGO5IGIgEkRZkpHJCUg0+KpaJEnGJ3fzuHUvj56AD/FoENs29eDW0mrbPre2zEafBsCUHyfapqs6W2W1UEI6s6z4hlSiFz2hgJJuxHfozbdGMRs/NGLEX1hRl1P1MIbtOisIIm7eX0F2tYL55SIS0SA29/iQyRUR8Hpxb7WEZJTH6JB2bLZ980O/OBDh4fWs/fSGkTGwI8Z1C+u0fbbrbK2P3LqJh8/DIZMroi8cgAwZ/eGgYb2bzwnoCXhRrkjoCwcVXda63mqs2h1TvZigUYZ29cMKn7lOdVQNJuIDJ1guCLhasybckwijt0trwpWCgA9qZHk8EUakS7KwtK/AyjrM6bWsFbFtO+QLRUxnVpT2jSYiCIfseUK2Vey/QXDMz3Ichxe+/k7bZb7x5acon0vzdVKXzncZjsYGkiTjJ0t5zGeLuLdawpZIEEJZRMjvg9/LIV+qIB59uO7KFUoQJeDuShHJGI898ShmsgUsLK/tcS2ulJBp8Kd6a7h29qa04kMzcaORPEbL3UBxqx626+39goBrNfHb7kQYm1zy/QLhLAZtcsOswwj2MejfyM8SzGD13oGdh3z6az7yAL6EtcM5J1rk+38AfB7AFgDzAF4BcA7AWQDbAdwEcFSW5Xt65dDkQDgABTSEGyG9JdwG6SzhNkhnCbdBOku4EdJbwm2QzhJugw75UD7b8q2HQz4EYRGkt4TbIJ0l3AbpLOE2DOusba/VkGV5seHS/8lx3LsAdA/5yLL8SxpJBy0RzGJqnyiNR9WfZG43TY/qKa+5rIBkLITRZNTQ02p6+exoQ/WJrkxOQDLKY0zl7UDttt1sPjdTfRtOJrf2lL5b3oYDuFd2Nz+xx5qNVN+oc/NeHpGgDz1+LxbzRfB+HxbzRSSiPHxeDneXS4jwPvQEvMgVRISDPpQqFUNP9avVaVcfsNa/hLO0enJYb96rYnYOr4UlPbSiPYQ5SqUKpmazmMsJ2BIJIOTzQAKwsFxEyO9Fb9CHPfEoAgEvbt1be5IkXxKxoy+MnVvqn9pkQZeI9U8n/kJLV2vjjPCDN/dt72vW72SMh1iRcevBGwJ7Al4sC2Ldk9XtrHUar+uV0aoNnUA2vHFgaX3C0tzPkg2wMkZOj4+R+Net9Tk9pizpM0EQ+igx6GIefMCDoNeL/kgAuUIZs1kBWzeFwAFYWBHQG/Tj/moZg1Eee+K9mMkWsJgvIuD1YPXBm370YsfGe43EnZ22jXzR+oPeMEG4DdJZwm2QzhJGsXrNbts37hzHPVHz0QNgv531dQNJkvHmdAbHz74HoSwpv1P57GgCAEyl6QXOoijh3Pu38fK5tJLv1EQKE3u36iqBXj6Ph7O8DaIo4dzkLE6cf1jfySMpTIwPwefzmGq7Xl+v18WGIIi4MDWHExdq+vFwCofHkswflnGr7KuFEi6l55vkPpSKM3/QhzUbUZPnd77wGAplCb/3Z9ewuSeAFz+7A2cuX1fSjx0cwes/uIml1RJeOjCCN67cwm89u8dwG+zsA9b6l3CWfKGI76QXmnzDF1ODCIeCKJUqmvNe9YsHs3N4LSzpoRXtIczRqG87+kP4yoGRurE4dnAEP7lfQIz34YO55Tpfe/roPjyzJ463rs4zoUvE+qcTf6Hl99R0+NjBEYzEI/j8yKCS1ireODWRwu+/fR03FwuG1jqN9arZX6Mt2eG7WZoPCHthaX3C0tzPkg2wMkZOj4+R+Net9Tk9pizpM0EQ+mjtdQX9XrxyYVp1/6vWZ53965s48FgCr719vWXs+K/evIoX9m9X7jUSd1rdNvJF7ud+QcBb6TtNc9ozqQH6AppgEtJZwm2QzhJGsWPNbudOzNdq/v1LAD8N4KiN9TnOjcW8EvgCgFCWcPzse7ixmDedpsf0XFYZ/Gq+l8+lMT2XNZ3PjjZMzmaVjZdq2onzaUzO6tdntq/XK1NzWWViAB7044U0plqMNwu4VfZ0ZllV7nRmucuStYY1G1GT526+pGxwPP/ENuULt2r6mcvX8fwT2yCUJbz29nUcGt/aVhvs7APW+pdwlunMiqpvmM6sAIDuvKeUYXIOr4UlPbSiPYQ5GvXt0PjWprE4c/k6PlpYwbJQafK1x8++h+m5LDO6RKx/OvEXWn5PTYfPXL6OyZlsXVqreOPlc2kcGt9aV7beWqexXjX7a7QlO3w3S/MBYS8srU9YmvtZsgFWxsjp8TES/7q1PqfHlCV9JghCH629ruoBn9pr1f2v6rUT59N48aldyqGd6nWt2PHQ+Na6e43EnVa3jXyR+7mWyavOadcyNK4Em5DOEm6DdJYwih1rdtsO+ciy/PM1/56WZfl/kmX5R3bV1w3mc4IyGFWEsoSFZcF0mh5zWfV8maz5fHa0IaORNp8z33az+dxMJlfU6MdilyQyjltln3ep3AB7NqImjyRDucZxUJWX4+r/bqcNdvYBa/1LOEsr36A371UxO4fXy8GOHlrRHsIcjfqm5U8lGciXRNU0rfEjn0bYQSf+QsvvaZUpyfX1GY03atP01jqN9WqVX2tLdvhuluYDwl5YWp+wNPezZAOsjJHT42Mk/nVrfU6PKUv6TBCEPq32uvSuCWUJhaL6+kwtdmyMM43EnZ1Avmh9wkqcQhBGIZ0l3AbpLGEUO9bslv9uDsdxx/XSZVk+bXWd3SIe5cH7PXWDwvs9GOzllb/NpGmRjIVU8yVi5vP18n7L2+D3eFTT4lEefeGAqba36uv1SCIa1OjHYBelMoZbZY+7VG6APRtRk8fL1fsNNXlluf7vdtpgZx+w1r+Es7TyDUkN/YhHH+qH2Tm8Xg529NCK9hDm0NK3xs8eDggHfKppyRg7ukSsfzrxF1p+T6tMD9dcn5F4ozZNb62jZTt6tmSH72ZpPiDshaX1CUtzP0s2wMoYOT0+RuJft9bn9JiypM8EQehjZK9L6xrv96AnqL4+U4sdq2ntxJ1Wt418kfthJU4hCKOQzhJug3SWMIoda3Y73uTTq/MvYkN9XWO4P4zTR/fVBd6nj+7DcH/YdJoeo8koTk2k6vKdmkhhNBkznc+ONowNxXDySH19J4+kMD6kX5/Zvl6vjCVjOHm4oR8PpzDWYrxZwK2ypxK9qnKnEr1dlqw1rNmImjz94QB+8xd2g/d78K13Z3Ds4Ehd+rGDI/j2D2fA+z146cAILk3ebqsNdvYBa/1LOMtoIqLqG0YTa2GN3rynlGFyDq+FJT20oj2EORr17eL7t5vG4tjBEXxqMIJe3tvka08f3YfRZIwZXSLWP534Cy2/N5qMNl0/dnAE49tidWmt4o1TEylcmrxdV7beWqfRdtTsr9GW7PDdLM0HhL2wtD5hae5nyQZYGSOnx8dI/OvW+pweU5b0mSAIfbT2ul49PKq5/1W9dvJICt9852O8dKB5faYWO158/3bdvUbiTqvbRr7I/exOhFXntN0JGleCTUhnCbdBOksYxY41OyfXPrpoIRzHfRPAMVmW7z/4vBnA12RZ/h9tqbCB/fv3y1euXLG9HkmScWMxj4VlAYO9PIb7w/B4uI7S9BBFCdNzWWSyAhIxHqPJGHy+1me19PLZ0YZSqYLJ2SzmcwLiUR7jQzEEAt6O2m42n41YWrmazgqCiKm5LOZzRcSjQYwlY+B5y1/AZQtulX21UEI6s6zInUr0oicU6LZYhjBoI7brba08n9zN49a9PCJBH0J+LxbzRfB+HxbzRSSiPHxeDosrpbX0gBfLgoiegBflioS+cLBtO7fTTzDogzYKjumsHvlCEdOZFcU3jCYiCIcensjXm/eqmJ3Da2FJD61ozzrFdp0tlSqYms0ikxPQFw6gx++BBODOcglBvwfRoA974lEEAl7cupfHfK6I1ZKI7X1h7NyypjMs6RLRdWzX2U78hZau1sYZPQEf4tEgtvc163ciykOsyPjJ0mpdvBGP8ti+uQe3llbbWus0Xtcro1UbOoFsmI34wAlYWp+wNPezZAMGx4hpX2sGI/GvW+tz2u5Y0ucaHPOzHMfhha+/03aZb3z5Kcrn0nyd1KXzXYYjOlsbgwZ8HvA+L/ojAeQKZcxlBSQ38fCAw8KKgN6gH/dXyxiMBrEnHsVMtoB7+SL8Xg9WSxXEo/qxY+O9RuLOTmDUF613bNfb+wUB1zJ5ZU7bnQhjU4je0ESYhnSWcBukswQzGFyzG9ZZO79xH68e8AEAWZaXOI77KRvr6woeD4ddAxHsGmh+SZHZND18Pg/2PrIZex9pT069fHa0IRDwYv9wn6osZttuNp+b4XkfPrOzv9timMKtsveEAnjShXID7NmIx8Ph0cEIHh10Th47+4C1/iWcJRwK4smd2q/Z1Jv3qpidw2thSQ+taA9hjkDAi59uoW9VhrdEMLzFuliUIMzQib/Q0lW9OEMtz6fi6m9gaHeto3a9lS3ZYW9kwxsHltYnLM39LNkAK2Pk9PgYiX/dWp/TY8qSPhMEoY9eDDrewv8atXM9n2CnryBftD7ZFOLx5E76splwD6SzhNsgnSWMYvWa3c5DPh6O4zbLsrwEABzH9dlcH3NUT79XnzJSezJULU2P6imvuayAZCyE0WRUOeVVfXNKJldEouHNKYVCGVOZ3MO3qiSiCIX8AB4+DZXJCUhGeYzVPA2lV5/ZtjsJK3KYZaUg4IOaE6CPJ8KIuOQEqJ5esUyrt3WwjFl7tUOOj+7kcGeljBVBxJZIAAEfh0JZQi/vw6cHo/B4OOXtEvmSiB01b5cgCKcxYju5goAPa/zxY4kwojX+WG8OrmJkTnKr7yScZbVQwvSDp8sTsbU5amm1jF7eh9WSiIEID0mS8ZP7BdIjggnoqSbCjbCyllwuCLhaYz97EmH0dsl+WHyrULfXPizJ4rTOOh23OjmXrPeYnBX/RhBuQxBEXLuzjHypgsV8Cds28RAlGXeWS4jyPoSDXvRFAljKr73VJxryYSgWQkUCFpbJ3ojuQGsxwm2QzhJug3SW6BZ2Hrr5GoAfcBz3xw8+fwnAP7exPqaQJBlvTmdw/Ox7EMqS8ju2z44mAEAzTS/IF0UJ596/jZfPpZV8pyZSmNi7FaIo4cLUHE5ceJh28nAKh8eSkGUZF9OZprTnUgl4vR6cm5zFifM1aUdSmBgfgsfDadant2Gl13YnFzGsyGGWlYKAP03faRq3X0wNMH/Qp1SqaOoVyxtj+UIR30kvNPX5F1ODzB/00fMPTm4wi6KE712dx43FPM5cvq7I8tUjKQR9wLWMiJmlAjwch48WVurucZN9EusHI7aTKwh4U8UfP5saQDTEQxBEzTm4etDHyJzkVt9JOMtqoYRL6XmcuJDG5p4AXvzsjjpf+tKBEbx65QP83c9sx+s/uIml1RLpEdFV7hcEvKXiQ59JDdCmB8EsrKwllwsCvqtiP19IDTh+0Kd2/qmV5VAq7vhBH1bWPizJ4rTOOh23OjmXrPeYnBX/RhBuQxBE/NeP7uDeahmvXpxWXYsdf3o3EtEg/vG3ppRrrzw3in/zXz/CzcUC2RvhOLQWI9wG6SzhNkhniW5i246DLMuvA3gewPyDf8/Lsvwf7KqPNW4s5pUFMwAIZQnHz76HG4t53TQ9pueyysZRNd/L59KYnstiai6rOJFq2okLaUzNZTGVyamnZXKYnM0qGxdK2vk0JmezuvWZbbuTsCKHWT7I5FXH7YMM+/Lr6RXLTGdWVPt8OrPSZclaY9Ze7ZDjaianbHJUZfmn59PY1BPE3XwJZVHG1O1s0z1usk9i/WDEdj7U8McfPvDHenNwFSNzklt9J+Es6cyyom/PP7GtyZe+9vZ1HBrfijOXr+P5J7aRHhFd55qGD73mgpiW2Liwspa8qmE/V7tgP7XzT60s6cyy47KwsvZhSRanddbpuNXJuWS9x+Ss+DeCcBtTc1mUKzJevTituRY7/b1r+OhOvu7aqxencWh8q/KZ7I1wElqLEW6DdJZwG6SzRDex9bEiWZY/kGX5Dx78+8DOulhjPicoRl1FKEtYWBZ00/SYy6rny2QFZHJF1bT5XBHzOmkZDVnmc4JufXqYbZ/VsCKHWfTGjXX09Ipl3NznZu3VDjkkGaqyLK2WIclAviRq3uMW+yTWD0Zsp5Vv0JuDH4yzY9wAACAASURBVJbRek5yq+8knKVWHzlO3ZdWr3Pcw2ukR0S3cHN8RWxcWFlLsmQ/LMnCytqHJVmc1lmn41Yn9W+9x+Ss+DeCcBuZXBH5othyLSbJaLrGcfWfyd4Ip2ApfiMII5DOEm6DdJboJt35wfINQDzKg/fXdy/v92Cwl9dN0yMZC6nmS8R4JKJB1bR4NIi4TlpSQ5Z4lNetTw+z7bMaVuQwi964sY6eXrGMq/vcpL3aIYeXg6osm3v88HBAOODTvMct9kmsH4zYTivfoDcHPyyj9ZzkVt9JOEujPqrpjCw//L96jfSI6BZujq+IjQsra0mW7IclWVhZ+7Aki9M663Tc6qT+rfeYnBX/RhBuIxENIsz7Wq7FGn+Fq3ZdVv1M9kY4BUvxG0EYgXSWcBuks0Q3oUM+NjHcH8bpo/sU467+5u5wf1g3TY/RZBSnJlJ1+U5NpDCajGEsGcPJw/VpJw+nMJaMYSwRVU9LRDE2FMPJIw1pR1IYH4rp1me27U7CihxmeTwRVh23xxPsy6+nVywzmoio9vloItJlyVpj1l7tkOOxRBTHDo7UyfLVIyncXy1iSzgAv49Damus6R432SexfjBiO49p+OPHHvhjvTm4ipE5ya2+k3CWVKJX0bdvvTvT5EtfOjCCS5O3cezgCL79wxnSI6Lr7NbwobtdENMSGxdW1pJ7NOxnTxfsp3b+qZUlleh1XBZW1j4syeK0zjodtzo5l6z3mJwV/0YQbmMsGYPfw+GV50Y112LHn96NTw2E66698twoLk3eVj6TvRFOQmsxwm2QzhJug3SW6Ca+bguwXvF4ODw7msBjL/33WFgWMNjLY7g/DM+D4/x6aVr4fB5M7N2KkcEIMlkBiRiP0WQMPp8HPp8Hh8eS2LmlB/O5IuLRIMaSMfD82hA/l0pguDYtEUUo5AcATIwPYdeWMOZzAuJRHuNDMQQC3rU0jfo6abtTsCKHWSIhHr+YGsDwlieVcXs8EUYkxP7THoGAV1evWCUcCuKLqcG6Ph9NRBAOsX/qVs8/OC3H03vi+OhODmNbY1gWRPRHAgj6OBTKEh4d8OHT8Sg8Hg4jgxE8sX0zVksitveFsXOLe+yTWD8YsZ1oiMezDf74sUQY0Qf+mOd9unMwYGxOcqvvJJylJxTAoVR8Td+W1/Tt//57T2JptYxe3odCqYLf/6WfgiwB2/t6SI+IrrMpxOOZBh+6OxHGJhfEtMTGhZW1ZG+Ixxca7GdPIozeLthPdf6p3VdIJXrREwo4Lgsrax+WZHFaZ52OW52cS9Z7TM6KfyMIt8HzPvzcpwZw7c4yvvGrn8FivoStMR6f+XtP4u5KCZGgD5GgF32RAN74+z+DTLaICO/F1k0hPDn8JO6skL0RzkNrMcJtkM4SboN0lugmdMjHRjweDrsGItg10PwWEL00PXw+D/Y+shl7H2lO43kfPrOzXzVfKOTHkxppgYAX+4f72q5PD7PtsxpW5DBLJMTjyZ3unAz09IplwqEgntzJ/qEeNczaqx1yPJbc1PK+4S0RDG9xp20S6wsjthNt4Y/15uAqRuYkt/pOwll6QoGW+gYAe7dvdkAagmjNJhfHtMTGhZW1ZC9D9tMTCmjuKzgNK2sflmRxWmedjludnEvWe0zOin8jCLfB8z6MP9J6jbW9D01zwqODZG9Ed6C1GOE2SGcJt0E6S3QLOuTTIZIk48ZiXnm6x+hpfFGUMD2XxVxWQDIWwmgyqjzppVemXj476mMpzUzb3M5qoYR0ZrnrT0qaoVSqYHI2i0xOQDLKY8wlT74VCmVMZXKqb71iHZZsodaWE1EeqyURS6tlCOUKoiE/soUyYiE/ZLkCjvOiUBIR8vuQL4nYvrkHXi+HuWx7fpVwH2bn0G7I0cqnGfEdRuppdY9Vdu5WH02sUasHPQEvorwPMd6PT+6tIhzwIRz0olCuYCAShCwDC8tF5Esidmi8Na0dWzRrt1bbOyv+gzBGqVTB1GwWczkBWyIBJKI8tm3qwa2lVc01wa17eczn9HW3Srv6IEkyPrmbx817eYQDPgz2BsFxeOBbeVQkYGHZmD3UlhOPBrG9r/5+K3w/sbHJF4qYzqww8aZRltanLNkNK+swp/vE6bWzk/GrIIiYmssikysiofKWTqtxeuxY0VmCcBuNMerOvh4sF0XM1tgSAEP21bhvtiyUcTdfRJQPoCRKSMR4DPXySGdydX7P4+Hqyt8T78VMtsDEfEiwyf2CgGuZPL1hgmgJK/EB6SzhNkhnCaNYvaalQz4dIEky3pzO4PjZ9yCUJeV3dZ8dTegG06Io4dz7t/HyubSS79REChN7t8Lj4TTLlCRZM5/eZGu2PgDMpGn1p17b3L5BsVoo4VJ6HicuPGzbycMpHErFmT/oUypVcG5yFifO18h+JIWJ8SGmv0QuFMq4mM409flzqQTzB31YsoVa37i5J4D/5ed2IV+q4Mzl64psLx0YwRtXbuEffP5TuHx1Dvse6cdrbz9MP3ZwBK//4CaWVkuG/CrhPszOod2Qo5VPM+I7jNTT6h6r7NytPppYQxQlnH9/Fv/k3FSdz0zEeHzjLz7BtYUVHDs4gv4eP36UWcayINb533b1rhazdmu1vbPiPwhjqPmcVw+PYiqQxT/6k0nVNcHbP5rH9fkVXd2t0q4+qN1/7OAIwgEv/uTdGXxhLGm63mMHRzASj+DAp+PweDhLfD+xsckXivhOeqEpxvhiatDxgz4srU9ZshtW1mFO94nTa2cn41dBEHFhaq6pbYfHkrYc9HF67FjRWYJwG5Ik18Wom3sCePGzO+rixq8eSSEa8uHYf3pP174a981e/OwO/Ke/voUX9m9X9sb274jh6P4d9b7oSAqxhvJPHknhX//5ddxcLFAcSTRxvyDgrfSdpjntmdQAfQFN1MFKfEA6S7gN0lnCKHasaWn11gE3FvPKIhwAhLKE42ffw43FvG6+6bmsMllW8718Lo3puaxumXr57KiPpTQzbXM76cyyMjEAa207cSGNdGa5y5K1ZnI2qzgq4IHs59OYnGV7XKYyOdU+n8rkuixZa1iyhVpbfv6JbbibLymbHlXZXnv7Og6Nb8UrF6bxKz+zU9nEqKafuXwdzz+xzbBfJdyH2Tm0G3K08mlGfIeRelrdY5Wdu9VHE2tMz2WVAz7AQ5/5yd08fv1zjyqfe4J+LCwXm/xvu3pXi1m7tdreWfEfhDHUfM4rF6ZxfWFFc00wOZNtqbtV2tUHtfvPXL6Ou/kSfv1zj3ZU75nL1zE5k1Xut8L3Exub6cyKaowxnVlxXBaW1qcs2Q0r6zCn+8TptbOT8evUXFa9bTaNqdNjx4rOEoTbaIxRn39iW1Pc+E/Pp1EW5Zb21bhvduby2h5Z7d7Yi0/tavZFKuWfOJ/GofGtymeKI4larmXyqnPatQzpCFEPK/EB6SzhNkhnCaPYsaalQz4dMJ8TlMGoIpQlLCwLuvnmsur5MllBt0y9fHbUx1Kamba5nflcUbVt87lilyQyTkZjLOdzbI+Lm/ucJVuotWWOAyQZqrJx3Nr/S/myZnr171Z+lXAfZufQbsjRyqcZ8R1G6ml1j1V27lYfTayhpQeSDBRKovI5XxQ1/W87eleLWbu12t5Z8R+EMbR8jiSj6Vp1TWBEd6u0qw9a90syUCiKHdcryVDut8L3ExsbltYnbMnCjt2wsg5zuk+c1gcn49eMw21zeuxY0VmCcBuNMWp1T6sWoSwh/2BNVnut0b4a981q98iqaMWlauVzXP1niiOJKizFbwTbsBIfkM4SboN0ljCKHWtaOuTTAfEoD95f34W834PBXv1XcCVjIdV8iRivW6ZePjvqYynNTNvcTjwaVG1bPOrsa9nNkNQYy3iU7XFxdZ8zZAuNtuzloCqbLK/9vzns10yv/t3KrxLuw+wc2g05Wvk0I77DSD2t7rHKzt3qo4k1tPTAwwGhgE/5HOZ9mv63Hb2rxazdWm3vrPgPwhhaPqfxFwRq1wRGdLdKu/qgdb+HA3qCvo7r9XBQ7rfC9xMbG5bWJ2zJwo7dsLIOc7pPnNYHJ+PXhMNtc3rsWNFZgnAbajGqmi2FA76ma4321Wj31b9rr2nFpWrly3L9Z4ojiSosxW8E27ASH5DOEm6DdJYwih1rWjrk0wHD/WGcPrqvLhA/fXQfhvvDuvlGk1GcmkjV5Ts1kcJoMqZbpl4+O+pjKc1M29xOKtGLk4fr23bycAqpRG+XJWvN2FAMJ480yH4khfEhtsdlLBFV7fOxRLTLkrWGJVuoteVvvTuD/nAAxw6O1Mn20oERXJq8jVcPj+I//tUneOlAffqxgyP49g9nDPtVwn2YnUO7IUcrn2bEdxipp9U9Vtm5W300scZoMop/PjHW5DN3bgnj//r+j5XPq0IZA73BJv/brt7VYtZurbZ3VvwHYQw1n/Pq4VGMDEY01wRj22ItdbdKu/qgdv+xgyPYEg7gD7//447qPXZwBOPbYsr9Vvh+YmMzmoioxhijiYjjsrC0PmXJblhZhzndJ06vnZ2MX8eSMfW22TSmTo8dKzpLEG6jMUb91rszTXHjV4+k4PdxLe2rcd/s2MERXHz/dt3e2Dff+bjZF6mUf/JICpcmbyufKY4katmdCKvOabsTpCNEPazEB6SzhNsgnSWMYsealpNlufVdLmT//v3ylStXbK9HkmTcWMxjYVnAYC+P4f4wPI2PpaogihKm57LIZAUkYjxGkzH4fJ6WZerls6M+ltLMtM1mWg90G6jp7GqhhHRmGfO5IuLRIFKJXvSEAlZWaxulUgWTs1nM5wTEozzGh2IIBLzdFqslhUIZU5mc0udjiShCIX+3xTKEQVuwXW+BeluO9/JYLYlYWi1DKFcQDfmRLZQR4/2QIYHjPCiURIT8PuRLIh7Z3AOfl0Mm155fJdyHQZ9vu84akaOVTzPiO4zU0+oeq+Y8t/pol2C7zlb1YC4roCfgRW/Qh00hP27cW0VPwIdw0AuhXMGWSBCyDCwsF7FaErG9L4ydW9rXO7P3WpHPqfI2OLbrbKlUwdRsFpmcgP5IAIkoj22benBraVVzTXDrXh7zOX3drdKuPkiSjE/u5nHrXh49AR8Ge4PguLVX5yaiPCoScGfFmD3UlhOPBrG9r/5+K3w/oYojMS0L5AtFTGdWlBhjNBFBONSdpwJZWp+yZDesrMOc7hOn185Oxq+CIGJqLvuwbckYeN7XOqNJnB47VnRWqYjj8MLX32m7zDe+/BTlc2m+TurS+S7DET9bG6Pu6OvBSlHEXI0tATC0Zm/cN1sWyljMF9HLB1CuSIhHeQz18khncnV+z+Ph6srfE49iJltgYj4kTGG73t4vCLiWyStz2u5EGJtC9LYnohlW4gPSWcJiSGcJZjC4pjWss/atUDcIHg+HXQMR7Bpo70k6n8+DvY9sxt5HzJW5tpwx7pskSUa5IqMsyRArMiTp4YJIFCXcy5dwd6UEv8eDbbFQk1KprZ/08kmSjGWhjPurZYT8PkiSXLdpr5Vmpj/1+rJVn9xYzCvGxOIiiGsY48bPbkCGxbOozVRQUfnsjkM+Hg+HXt6P1VIFvby/q/rs8XAY7g/D6wFWSyLyxQqWVstIxoLwez2oSDIkyCiLMoZiAXh7g5jLCtjx4Al+j4fD8Bbnn1AmnMXsHNoNOcoVEZIsQ5IBSZZRrogI4OFcKaN+omz8DBibb5X8GvuWZue8RgIBL/YP93VWCNE1RFGCJMnoCXixWqogyvuwVCijKEoIeCWURQ6iJCG7WsZquYJ4lMf2zZtxa2kV/+2Txaa4px1brL23nVjKantnxX8QxggEvPjpGp9Te1AtGQth++YeeDxcnU6t/XRXEHNZGZxOSFPNk8kKCPo8uHVvBUv5EiTI6A8HFb1s1NedW8J4dDBSV4YsA5IMJa16fTFfRE/Ai6V8GcuCiEQsiBjvx/xyEfEoj5/bPaiq+0ZthPSZINqHJbsRRQklUUKpIqMkShBFyakHj+pwuk/8fi+CPg/8Xg5Bnxd+v70HxsVKpS4eFyuVunjc0rpkETKg/BNlEXZuoTo9diztHRCEm6juVW3vC+OTu3ncuJdHOODDY8lelEUZ3//oDmK8Hxy3dq9QlvBfri8gyvuRL5URDQbg93GQZaBUkRDwepT4MxryI1+qoC8cqItf+8Jrh376wgH4fB54PFzTngAr8yFBEO7Gqj1HgiAIQh2Ph4Pfy8Hr4eD3ejpeh9EhHxchihLOvX8bL59LQyhLyivzJvZu1d1AKpUqODc5ixPnH+Y7eSSFifEhANBM8/k8eHM6g+Nn31PSTh/dh2dHE2uyaOTzeDhNOQGYaoPVSJKs2TZWNjcKhTIupjM4caGmjw+n8FwqwfybZfR0juU3RawUBPxp+k5Tn/9iagARxk/esqbTkiTjrz65A0mSMXu/iBMXphW5jh0cwes/uIml1RJeOjCCl8+n8St/awe+8Zc3sLRaYs4WCSJfKOI76YUm3/DF1CDCoSBWCyVcSs83pR9KxZWn2434RdbsmGATQRDx1ofzuH2/gDOXr2NzTwAvfnYHzly+XudnkzEev/dnU7i5WMCO/hC+cmCkLv7qVLdIXwmzaK1pDo8N4c9+tFCnU7Uxg5p+qenhSwdG8MaVW3hh/3a8ceUWfuvZPXhmTxxvXZ1X1VcAqrpczfOv3ryKL39uF7IFscnO2pWNbIRol1YxiJMYiXc2IoIg4sLUXFO/HB5L2vrml25jdn/KLE7qn5v3BYxA8xNBdEajDe3oD+F//rlP4dWL06prs8bY9B98/lP44yu3cOCxBF57Wzu21ItfyVYJo9wvCHhLZU57JjVAb5kgmIR0lnAbpLOEUexYQzv/aBFhmum5rDL4ACCUJbx8Lo3puaxuvsnZrPKlYjXfifNpTM5mddNuLOaVhUQ17fjZ93BjMa+bT09Os22wGr22scJUJqdMDMCDPr6QxlQm12XJWqOnHyzzQSav2ucfZNjRCy1Y0+kbi3mIFcDr8SoHfKpynbl8Hc8/sQ1CWcJrb1/HofGtOP29a8o11myRIKYzK6q+YTqzAgBIZ5ZV09OZZaUMI36RNTsm2GRqLouP7qwoG8fPP7FN+Rt46Gc/vpvHofG1A9aHxrc2xV+d6hbpK2EWrfXA5Gy2SadqYwY1/VLTw2psUf3/+Nn3MD3XXHa1PC1druY5NL4VmVxR1c7alY1shGiXVjGIkxiJdzYiU3NZ9XW7w3scTuP03o6T+ufmfQEj0PxEEJ3RaEOHxrfi1YvTmmuzxtj0lQvTePGpXcoBn+p9jbGlXvxKEEa5pjGnXVsncxqx/iCdJdwG6SxhFDvW0HTIx0XMZQVl8KsIZQmZrKCbL5NTzzefE3TT5jXSFpb18+nJabYNVqPXNlaYzxU1+rjYJYmMo6cfLOPmPmdNp+dzAu7ly7izrN6n1Z/dqP7deI0lWySIVr7BiO8w4hdZs2OCTTK5IiQZiq5UfWgtQlmCJEPxq1r3dKJbpK+EWTTXAxo6pRcfaOlhbWwhlCXNOheWtdc81Twchzqb61Q2shGiHVhan7AkC0tkNmi/OL2346T+rXddp/mJIDqj0YZq11pa667G2LRQFFvGlnrxK0EYZb3PacT6g3SWcBuks4RR7FhD0yEfF5GMhcD764eM93uQiOm/8isZ5VXzxaO8blpcI22wVz+fnpxm22A1em1jhXg0qNHHzr6W3Qx6+sEybu5z1nQ6HuXRF/ZjoFe9T2W5/u/GayzZIkG08g1GfIcRv8iaHRNskogG4eVQpytqeuPhoPhVrXs60S3SV8IsmusBDZ3Siw+09LA2tuD9Hs06B3u11zzJ2MPrjTbXiWxkI0Q7sLQ+YUkWlkhs0H5xem/HSf1b77pO8xNBdIaWDan9Xf3cGJv2BH0tY0u9+JUgjLLe5zRi/UE6S7gN0lnCKHasoemQj4sYTUZxaiKlKEH199pGkzHdfGNDMZw8Up/v5JEUxodiumnD/WGcPrqvLu300X0Y7g/r5tOT02wbrEavbawwloji5OGGPj6cwlgi2mXJWqOnHyzzeCKs2uePJ9jRCy1Y0+nh/jB8XqAiVXDy8GidXMcOjuDbP5zB/8/e3Qe3ceZ3gv82XhsECEiiKAKSLMvyUH4BSSk+jWvj27dYGcfJyDJvspFT2Tun7nYrs7XJ2hUld9m6c8lrlfZ2c5Uo58lddie1t1czVTsVazI5WVImPs/Ylb3b8uwlmhmJIi1bcmxZI4kgJUoCSBCNt+77g2oQILobTbDReBr4fqpYEtFA96+f/j2/5+lGA5SDK3+b/NzUTRz90t7aY6L1RaJ0MmZYG9LJGABgLDlouHwsOVhbh526KFo/JjGNpxJ4dDiGVw+OQg768J0f3qj9H1its3u2RnFu6iYA4OzFm03zr43mFvOV2mV2PjCxPdGUU/VzBqP8MspDfW6h/3vyyH6kU3HTfDXL5XRqJZ6zF29iJB427GfrjY19hNar1RzETXbmO/1oPJUwPm93+RqH29y+tuNm/nn5uoAdHJ+INmZtHzp78SZefyFtem62dm76xuE0vvHBp3jlWeu5pdX8lciuvSZj2t4eGdOo9zBnyWuYs2RXJ86hJa3+I7495MCBA9r58+e7HYbjKhUVM7NZZLIKkgkZ6VQCgUDre7VKpSqmbmUxl1MwEpcxsT2BUMjfcpmqari2kMf8ooJtgzJ2D0Xh80ktX2cVZ7v74DSrfbNpXU9uxShnC4UyLmVymMsVMRIPYzwZRyQSdHKzHWOVHyJbKij4MJOvtfmTyShiEW98SsZmTnc8b+vj+cm9PJZLFSwWqphbLCIVDyMY8CGTVRCPBFGpqkglIgj4JWRybfdF6m2u5ayVfKGImcxSrTakkzFEI6t35C8XSpjOLNaWjyUHMRAJNazDTl10YGyi7ut4zipKBZfnclgsVrBcqmJkMAwNwO3FEqJhP6IhP0rVKuRAAMvlKkbiMnZtHsD1e8uO5hbztWe4XmfNzgfqc2o4JsPvQ8v5gf6auZyCoN+HpWIZsXAQGjQMRcO111nlq9ky/fG7+SIiIT/u5ctYLFYwMhjGpkgQ80tFW7Gxj3SEEPMDN7Sag7jJznynHylKBZdms6vn7akEZDmw9mk9l7NuX9txM/+8fF3ADtGuHUiShJe+/sG61/nWV5/h6zz6uo1sy+K9DFevd312J4/rd/MYCAWQTIRRrmj4yb1lxOUgJAlYyJcQl4NYLJYRl4NYLlUQCwcRDkhQNaBUVRHy+7BcqmLboPG8l3PJvtDxvL1fUHClbkzbm4xiUw+NaeQ65ix5DXOWhGHzHNp2zjZddSCxBQI+7HtoM/Y9tL7XhUJ+HNi9Zd3LfD4Je4Zj2DPc/ElBq9dZxdnuPjjNat9EEYkE8fQjQ90Ooy1W+SGyWETG0494cwAWLad9PgkPDxnHYtT/d28VI24iI9FIGE8/Yv6G2kAk1LJe26mLovVjEpMsB/BTD69/jHU6t5iv1C6z8wGjnGo1P7Cbh1bPM1vWat17tll/gwT7CDmh1RzETXbmO/1IlgP4Yh+2i9vXdtzMPy9fF7CD4xPRxvh8Eh7dFsOj2xr70BdGNvbtYmvnveyr5IRNPT6mUe9hzpLXMGfJLqfPoT11k48kSdcALAKoAqhomnaguxFZq/9U6Ui88W57/W6t2ayCVCKCdCpeu1vL6nXtLtM/WZbJFZFc88myduNsd9+tllEj/VsfMjkFqbiMcY98Gw5gnXMiE+mTsuvllb6lx7mQL9Y+tZRKyKiqwPyigmg4gHJFxXK5gng4iOVy4/KBUAClahUh/8o3VdR/St+JuOZySsP26tvSqTauX4/+ia3Z7Oo6AXjiWNIqOzXPzpja6jl2xgW3aoFI2/FK/XOK/snRz+/mkZCDCPolLClVhIM+ZJUSEnIIwYAETcPKN/20mIvp3/KzkfbTY7qVXUbY70e+VMGuLVE8vKW9dZsd03471r1Cr20L+SLicgiliopkorPjq5vrcfKcCWh/DrB2fTsTEVyey7WMayPtxj7ZeSK1cbag4OO6Tyg+lowi0aVPKLbb7zpBlGPkdhxun/O7+U0+Xr2eQUTicWKelckqCAdWzvW2xmQMhgPIrGN9dq8v2xlb3T4/F2m8Xy9R5gf8hgmyy8v9jfqTKPVNlDhIfE6/5+/FM9Sf0TTtTreDaEVVNbwzk8HRUxeglNXa3819Pp2Eqmo4ffEmXjs9XVt2YnIMk/t2wOeTTF8HoK1lpVIVZy7N4tiZ1e0dPzyGw+MphEL+tuK0Gtyt9t0qTl4MblQqVXF66haOvV133F4cw+TEduFv9FGUimnOiXxhLF8o4s+n55vi/vLYNuFv9LHqdyL1LT3O333nMl46sAtfe/8qNg+E8PJPP4w337tai/3ol/Yi7PfhX73zkeHyV54dxVvnr+OlA7vw1vnr+J3nn9jQvta3n9H2Th7Zj+eeGMG7l+c23MZGx+rVg6P45g8+x73lEv63X/kplCqa8MeSVtmpeZWK2nJMbfUcO+OCW7VApO14pf45pVW90uvjP/37X8Af/eUn+HyhYDkXe3gogn/27GhD3q23/Yxq+0bWbXZMnarD5C69tv3h+1cb8sON8dWN9dip7+vZTigg4Te+9eN178Pa9R14OIEjX3y4Ycwwimsj7dZv9bcbRGrjbEHB/z19u2m+83Njw67f6NNuv+sEUY6R23G4fc6/XCjh3PRc0/YOjY04fqOPV69nEJF4nJ5nrZzrfYhf/uKu2jWkVuuze33Zztjq9vm5SOP9eokyP7hfUPCuwfztubFhvgFNDbzc36g/iVLfRImDxNeJ9/xZnTvk2kK+NokDAKWs4uipC7i2kMfMbLY2WOrLXjs9jZnZrOXr2l12aTZbKzD6smNnpnGpxfas4mx3362WUaOpW9laZwceHLe3pzF1y7r9RWCVcyKbySwZxj2TWepyZK15rkcNQQAAIABJREFUpW/pcR6a2FF7k+8rT+2svUENrMR+8ntXsLBcMl3+tfev1tZxaGLHhve1vv2Mtnf01AXMzGYdaWOjY/Xme1fxlad2QimrmLrhzHbIPXZqnp0xtdVz7IwLbtUCkbbjlfrnlFb1Sq+Lr5+ZwaGJHbXHzeZihyZ2NOXdetvPqLZvZN1mx9SpOkzu0mvb2vxwY3x1Yz1OnzNN3ci2tQ9r1/fyM3uaxgyjuDbSbv1Wf7tBpDb+OJM3nO98nHE/lnb7XSeIcozcjsPtc/7pzKLh9qYzi45vy6vXM4hIPE7Ps/RzvfprSK3WZ/f6sp2x1e3zc5HG+/USZX5wxWT+dqUL8zcSm5f7G/UnUeqbKHGQ+Drxnr/XbvLRALwrSdIPJUn6tbULJUn6NUmSzkuSdP727dtdCG/VXE6pHSidUlYxv6hgNmu8LJNVLF/X7rJMrmi4bC5XbDvOdvfdalk/ssrZjElbzeXEbyurnBPZnEfjBqz7ndM2Umv1OCUJtXjr/69TyipUDZbL9cf1fzeyr/XtZ7Y9s5q43u2aHSvpwQd5VM14+/1aJ53Q6fmBnZpnZ0xt9Rw744JbtUCk7fTi3MIqZ+3Uq/r6WP+40VzMbB3raT+j2r6RdZsdU6fqMDnPKmf149at8bXT63H6nEmf/9jZttX6CsWKrbg20m5er78iXT8wI1Ibi3Su1G6/6wRRzsPczhW3z/ndzD+vXs8QkRfqLFE9p3O2E/Osted6rdZn9/qynbHV7fNzkcb79RJnfsAxjexxs79xfkBOcLO+sc6SEzrxnr/XbvL525qmPQXg5wH8uiRJf7d+oaZpf6xp2gFN0w4MDw93J8IHRuIy5GBj88pBH7YNykglIobLkgnZ8nXtLkvGw4bLRuLhtuNsd9+tlvUjq5xNmbTVSFz8trLKOZGNeDRuwLrfOW0jtbY+zvp4jWKv//Zao+Wa1vjvRvZ1bfsZbS+VcKaNzY6V9uBNPb9kvP1+rZNO6PT8wE7NszOmtnqOnXHBrVog0nZ6cW5hlbN26lV9fax/3Goutvb39bSfWW1vd91mMZr1ES8f615hOaetO26dPK5O1YL1rsfpc6a1395vdx/Wrm8gHLAV10bazev1V6TrB2ZEamORzpXa7XedIMp5mNu54vY5v5v559XrGSLyQp0lqud0znZinrX2XK/V+uxeX7Yztrp9fi7SeL9e4swPOKaRPW72N84PyAlu1jfWWXJCJ97z99RNPpqm3Xzw7zyA/wvA092NyNzuoShOHtnfcEH75JH92D0URToVx4nJsYZlJybHkE4lLF/X7rLxVALHDzdu7/jhMYy32J5VnO3uu9UyajS+PYHjL645bi+OYWK7dfuLwCrnRJZOxgzjTidjXY6sNa/0LT3Osxdv4pVnRyEHffjOD2/g1YOjDbEf/dJeDA2ETJe/8uwozk3drP270X2tbz+j7Z08sh/pVMKRNjY6Vq8eHMWf/egG5KAP4zud2Q65x07NszOmtnqOnXHBrVog0na8Uv+c0qpe6XXxjcNpnJu6WXvcbC529uLNprxbb/sZ1faNrNvsmKZT8b461r1Cr21r88Pp4+pULVjvepw+Z5rYmWhrH9au7xsffNo0ZhjFtZF267f62w0itfFjyajhfOexpPuxtNvvOkGUY+R2HG6f848lBw23N5YcdHxbXr2eQUTicXqepZ/r1V9DarU+u9eX7Yytbp+fizTer5co84O9JvO3vV2Yv5HYvNzfqD+JUt9EiYPE14n3/CVN01o/SwCSJEUB+DRNW3zw/+8BOK5p2jtGzz9w4IB2/vx5V2NcS1U1XFvIY35RwbZBGbuHovA9+GhmpaJiZjaLTFZBMiEjnUogEPC1fF27yxSlgkuzWczlihiJhzGeSkCWAxuKs919t1rmMY4GbZSzpVIVU7eymMspGInLmNieQCjkd3KzHWOVcyLLF4qYySzV4k4nY4hGvHHXrc2+1fG8bUWP826+iKDfh+VSFamEjKoKzC8qiIb8KFc1FMoVDIaDWC43Lh8I+VGqqgj5fShXVWyJhh2pI/Xtl4yvbO/2UmNbOlW/6tczHJPh9618XZ++TgC9Uied0PWctcNOzbMzprZ6jp1xwa1xVqTtCDa36HjOqqqGz+7kcf1uHnE5iKBfwlKxilDAh5xSQlwOIRyQoGrAcqmKkbj1XGzX5gFcv7e8ofbTY5rNLiPk9yNfqmDXlige3tLeus2OqWDHuld0PGf12nY3X8SgHEK5qjbkZSfGVzfX4+Q5E9D+HGDt+nYmIrg8l2sZ10barYt90hPzAyeIVPeyBQUfZ/K1+c5jySgSke58mr7dftcJopyHuZ0rbp/zLxdKmM4s1rY3lhzEQCTUkW159XqGw1yrs5Ik4aWvf7Dudb711Wf4Oo++biPbsngvQ8i5gRPzrLmcgqDfh0WlhKFoGINyEHPrWJ/d68t2xla3z89FGu/XS5T5wf2Cgit187e9ySg2dWn+RmKz2d+ErLXUn2zWN9ZZEobNOZntnPXSTT57sPLtPQAQAPAtTdP+pdnzOTiQCzihIS9i3pLXMGfJa5iz5DXMWfIi5i15DXOWvIY3+fB1HXtdP93kQ9QC85a8hjlLXsOcJa+xnbOe+RiKpmmfAtjX7Ti6rf4O/rWfytbvtJ3NKkglIkin4rU7bfW7wzI5Bam4jPG6u8P0TyhlckUk13xCyWqdVrFYsVpnu/vu5GtEYnVsROfVb8QpFMq4lMmtfmIvGUckEux2WLaIlO+qquFvbi/h2p08IiE//D4gHPBDQxVKGUhEgnh8JA6fT8Jnd/L4fCEPOeRD2O/HUCyEXVu81VfJ2+z0naWCgg/r7sh/MhlFrO6OfDtjm52a3ioWkfo5dY+iVPDRfA45pYLlUhWbBoKIhQK4t1zGcrmC7YkIBuUA5nJF5EsVPLwlike2up8rqqrh+t181+Og7tNr10K+iNCDb/HTaxgAw7q23nqofyNVtlBCVQXuLBUxHAvD5wMSkZBlvaxf17bBlW/Ym82ubldVNczMrnzSZigahgoNQ2u+SdAq3o3UbtZ9yhUUfFQ3B3k8GUW8S58K9Oo5HnWG2/VpsaDgcl1feCIZxWCH+oLb1wXcbkuOLUTt0c9v5heLyC6XEZX9iAT9qFQ1zC+u1IugX8KiUkWpomIwsnKONjQQargWD7R/fZxovfgNE+Q1zFnyGuYs2WV1r0Y7vHG3AAFYOZF4ZyaDo6cuQCmrtb/l+nw6CVXVcPriTbx2erq27MTkGCb37VhZNnULx95eXXb8xTFMTmyHqmo4c2kWx87ULTs8hsPjKQQCPtN1+nySaSytvsrebJ1WJzJW+251sb6dGEWhKBXTYyP6jT75QhF/Pj3fFPuXx7YJfRG4UCjj7HSmKe4XxpLC3+gjUr6rqoa/mM7gt769GsurB0cRDfmxaSCI/3R1Hl8YSeDm/QIqVRg+b/vmJTz72Ign+ip5m52+s1RQ8N3p20214RfGhhGLyLbGNjs1vVUsIvVz6h5FqeDdj+dw814Bb753FUpZxcNDEfyTv/cFvHF2ppYbbxxO44/+8hN8vlDoSq6oqob3P57D1bmlWpzM2f6k167ffecyXjqwC197vzEfQgEJv/GtHzc89twTI3j38ty66uGJyTG8O3MLP/NYCm+cW+0Lrx9K4zs/uo7/7m8/aph7Rut69eAovvmDz3FvuYSv/zdP4fZiqaHGv/LsKN46fx2/8/wTeD6dBADT+my1zM4HFlj3+1uuoOAdgznI82PDrt/o49VzPOoMt+vTYkHBXxj0hZ8fG3b8Rh+3rwu43ZYcW4jao5/ffHo7j5Pfu7I613whjX/7H1fPu9aeh73y7Cj+5fnr+PWfGcXkxHaEQv62r48Trdf9goJ3DcbP58aG+QY0CYk5S17DnCW7SqWq6b0a7d7ow1mjh1xbyNdOwgFAKas4euoCri3kMTObrZ0Y6MteOz2Nmdkspm5la0mjLzv29jSmbmVxaTZbKz61ZWemcWk2a7lOq1isWK2z3X138jUisTo2opvJLBnGPpNZ6nJk1i5lcsZtnsl1ObLWRMr3awv52o07eixvvncVd/IlfHI7j8mnduHk966gXNFMnzd1I+uZvkreZqfvfJjJG9aGDzMrz7Ezttmp6a1iEamfU/dcms3ik/nVG2cA4NDEjtoNPsBKbrx+ZgaHJnbUfnc7V64t5DF1I9sQJ3O2P+m169DEjtoNPsBqPkzdyDY9NjObXXc9fO30NP7h33qkdoOP/vgb52bw8jN7THPPaF1vvncVX3lqJ5SyisVCtanGf+39qzg0saO2Tqv6vJHazbpPH5nMQT7KuJ8DXj3Ho85wuz5dNukLlzvQF9y+LuB2W3JsIWqPfn6j3+ADPJhrnm0871p7HqbPG/Vr8UD718eJ1uuKyfh5pQtzSSI7mLPkNcxZssvqXo128SYfD5nLKbWDr1PKKuYXFcxmjZdlsgoyJq+byynI5Iomy4qW67SKxYrVOq20s712YxSF1bER3ZxHY/dq3IBY+W4Wi6oBqgYsLK20c75YsXyeV/oqeZudvtOqNtgZ2+zU9FaxiNTPqXsyuSJUDQ25IEkwzA1JavzdzVyZyylNcXYjDuo+vXaZ5amqoekxs7raqh7ez5cNHy+UKqa5Z7Yuvf+YzVf0/ZlftD432kjtZt0nkc5PRIqFus/t+uRm/rmd6+63JccWonZYnd+sPe8y+l2/Fg+0f32caL04fyOvYc6S1zBnyS6rezXaxZt8PGQkLkMONh4yOejDtkEZqUTEcFkyISNl8rqRuIxkPGyyLGy5TqtYrFit00o722s3RlFYHRvRjXg0dq/GDYiV72ax+CTAJwFDsZV2jsoBy+d5pa+St9npO61qg52xzU5NbxWLSP2cuicZD8MvwTAX1v6uaY2/u5krI3HZNE7mbH+pr11m4/7ax8zqaqt6uCkaNHw8EgqY5p7ZuvT+YzZf0bTVmKzq80ZqN+s+iXR+IlIs1H1u1yc388/tXHe/LTm2ELXD6vxm7XmX0e/6tXig/evjROvF+Rt5DXOWvIY5S3ZZ3avRLt7k4yG7h6I4eWR/w0Xyk0f2Y/dQFOlUHCcmxxqWnZgcQzqVwPj2BI6/2Ljs+ItjmNiewHgqgeOH1yw7PIbxVMJynVaxWLFaZ7v77uRrRGJ1bESXTsYMY08nY12OzNp4Mm7c5sl4lyNrTaR83z0Uxe//UmMsrx4cxdZoCF8YjuL0j67j6Jf2IuiXTJ83sTPhmb5K3man7zyZjBrWhieTK8+xM7bZqemtYhGpn1P3jKcSeHRbDK8eHK3lwtmLN/H6C+mG3HjjcBrnpm7Wfnc7V3YPRTG+M9EQJ3O2P+m16+zFm3jl2eZ8mNiZaHosnYqvux6emBzDf/jPn+H1Q4194fVDaXzzg09Nc89oXa8eHMWf/egG5KAPg7K/qca/8uwozk3drK3Tqj5vpHaz7tPjJnOQx5Pu54BXz/GoM9yuT0+Y9IUnOtAX3L4u4HZbcmwhao9+fnP0S3sb55ovNJ53rT0P0+eN+rV4oP3r40Trtddk/NzbhbkkkR3MWfIa5izZZXWvRrskTdNaP8uDDhw4oJ0/f77bYThOVTVcW8hjflHBtkEZu4ei8D34+GulomJmNotMVkEyISOdSiAQWEmWUqmKqVtZzOUUjMRlTGxPIBTyAwAUpYJLs1nM5YoYiYcxnkpAlgMt12kVixWrdba7706+Zh0cWxFgnLNWx0Z0+UIRM5mlWuzpZAzRiPh3rxYKZVzK5FbbPBlHJBLsdli22Mz3juetHsvf3F7CtTt5REJ++HyA7PdDg4pCRcMmOYjHk3H4fBI+u5PH9bt5hAI+yAE/hmIh7NriaF8lb+t4ztrpO0sFBR9m8rXa8GQyilhk9S5rO2ObnZreKpYOj2vkDFfmBx/N55BTKiiUVCQGAoiFArhXKKNQrCK5KYy4HMRcrojlUgW7tkTxyFb3c0VVNVy/m+96HNSSa3X2br6IoN+H5VIVI3G59oaiUV1bbz3ctXkA1+8tI1cooaICd5aK2BoLI+AD4pGQZb2sX9dwTIbft/I1uvp2VVXDzIP6vSUaggYNQ9Fwwzqt4t1I7WbdN+XKnFYEuYKCj+rmII8no4hHuvNJf6+e4wmi53LW7fq0WFBwua4vPJGMYrBDfcHt6wJut6VI1w4AQJIkvPT1D9a9zre++gxf59HXbWRbFu9luDKnvX43j9uLRdxfLmMg7MdAyI9KVcP8YhHbBsMIBSQsKlWUKypicgD3C2VsHgg1XIsH2r8+Tj2n43l7v6DgSt34uTcZxaYuzSWpJzBnyWuYsyQMq3s16tjOWW/cLUA1Pp+EPcMx7Blu/rRcIODDvoc2Y99Dza8Lhfw4sHuL4TplOYAvPjJkuMxqnVaxWLFap5V2ttdujKKwOjaii0bCePoR713wjUSCeNqjbS5Svvt8EkZHBjE6MtjyuY9ui+HRbd2PmfqXnb4Ti8h4+hHzybmdsc1OTW8Vi0j9nLpHlgPYv8t4Xldv99bu5onPJ2H31ljX46Dua1W7jJa1Uw/brY1G66rPW59Pwr6HNq97HXaWtRMb9Zd4izmIm7x6jked4XZ9GnSxL7h9XcDttuTYQtQeJ89v2r0+TrRemwSaSxLZwZwlr2HOkl1W92q0gzf5CEj/RI1+J5fRp0CNlll90kj/dMBsVkEqEUE6Fa99OsBqmdX22n2d1TIrZttrd33tvs5Ny4USpjOLtWM6lhzEQCTU7bBs8WrsXv50qig5Xamo+Gwhh3xRw6JSwVKxgq2xEErVKiLBACqqhnDAj5xSQsjvx0g8jJ2bVj593+3YqT/pd1BncgpScRnjBndQt/oUvf4tPZlcEckW39JjledWYyuRTq+z9/JV3CuUsXkgiEWljEE5iFKlilRiAI9sXf2GlLmcglRCRlUF7i4XEVrzTSpG9VaUMYV6Az/VRNS+Vt8m2K+xiDRO2ZlL9iK35629/E0+brclzzmI2lcqVTEzm8VSqYyQP4A7Syt1Iujz4Sf3C9i1OYJSRUVmsdgwJog0blF/4bkYeQ1zlryGOUvdwpt8BKOqGt6ZyeDoqQtQymrtb2M/n04CgOmyYrGCs9MZHDszXVt2/PAYXhhLIhj04/TFm3jt9OqyE5NjmNy3AwBMl/l8kun2VFVr63VW+2B1YlOpqIbbOzy+Hd//eH7d67NqZ1FOsJYLJZybnms6pofGRoS/WcarsecLRfz59HxT3F8e2yb8jT6i5HSlouI/X7uNu/kKbt4r4M33rtbieeNwGt8+fx1fejKFb/3V53jpwC68df46/tF/+QgGwoGG/i1af6TeVSpVcXrqFo69XdfvXxzD5MT22pszuYKCd6ZvN9WG58eGEY/IUJQKzlyabVp+eDxVu9HHTh81G+sm9+3gRXeq0evsrXtF/O9/+QleOrALX3t/tda+8uwo/qfT0/gffu4JhIMSfuNbP8bmgRBe/umH8Sd/fb3p+Ub1VpQxhXrD/YKCdw1q6HNjw7zoQdTCUkHBdw36zy+MDbt+c41IsYg0TtmZS/Yit+etiwUFf2GQfz8/Nuz4jT6FQtn0+lonbvRxuy15zkHUvlKpijOXbuFP/upz/OJTu/DGuR/V+tHrL6Tx15/ewRf3bMUbZ2caxoTDYym8f/W2EOMW9Reei5HXMGfJa5iz1E08exPMtYV8bcIPAEpZxdFTF3BtIW+57FImVysi+rJjZ6ZxKZPDzGy2dvKuL3vt9DRmZrOWy6y21+7rrJZZMdve1K1sW+trNw43TWcWDY/pdGaxy5G15tXYZzJLhnHPZJa6HFlrouT0zGwWfsmPT+aXajf46PG8fmYGLz+zB3/w/Ss4NLEDX3v/Kg5N7MCdfKmpf4vWH6l3Td3K1t6UAR70+7dXxhfdR5m8YW34KLOSo5dms8Zj8OzqOuz0UauxlUin19ljZ2ZqtbQ+Z/Ta+lvfvoCpG1koZRVfeWon3nzvquHzjeqtKGMK9YYrJjX0Sob5RNTKhyb958Mu9B+RYhFpnLIzl+xFbs9bL5vk3+UO5J/V9bVOcLstec5B1L6pWyv95+Vn9uCNczMN/eiNszP4B1/cVbvBR3/82Nsr1wZEGbeov/BcjLyGOUtew5ylbuJNPoKZyym1YqBTyirmF5UWy4qGy+ZyRcxmjV+XySqWy6y21+7rrJZZMd1em+trNw43WR1T0Xk1dq/GDYiT07NZBbcXi1A1GMZTKFWglFVIEmr/mj1XpP5IvctsHJnLreZfq9qQsVE77PRRq7GVSKfX2fpaWq/+cVVbeay+5tqpt6KMKdQbvDy/Iuo2kfqPWLGIM07ZmUv2IrfnrW7mn9u57nZb8pyDqH16zS8UK4b96M6icf0wu2bA8yvqNJHmb0R2MGfJa5iz1E28yUcwI3EZcrDxsMhBH7YNyi2WhQ2XjcTDSCUihsuSCdlymdX22n2d1TIrpttrc33txuEmq2MqOq/G7tW4AXFyOpWIYHgwDL8Ew3gioQDkoA+ahtq/Zs8VqT9S70qZ9J2R+Gr+taoNSRu1w04ftRpbiXR6ndVzxShn9Bpb/83vVs9fW29FGVOoN3h5fkXUbSL1H7FiEWecsjOX7EVuz1vdzD+3c93ttuQ5B1H79Jo/EA4Y9qP687T6x82uGfD8ijpNpPkbkR3MWfIa5ix1E2/yEczuoShOHtnf8EbMySP7sXsoarlsPBnH8cNjDcuOHx7DeDKOdCqOE5ONy05MjiGdSlgus9peu6+zWmbFbHsT2xNtra/dONw0lhw0PKZjycEuR9aaV2NPJ2OGcaeTsS5H1pooOZ1OxVHVqnh0WwyvHhxtiOeNw2l884NP8Zs/uxfnpm7ilWdHcW7qJoaioab+LVp/pN41vj2B4y+u6fcvrowvuseTUcPa8HhyJUfHUwnjMTi1ug47fdRqbCXS6XX2+OE0zl5cqaX1OaPX1t//pf2Y2JmAHPThOz+8gVcPjho+36jeijKmUG/Ya1JD9yaZT0StPGnSf57sQv8RKRaRxik7c8le5Pa89QmT/HuiA/lndX2tE9xuS55zELVvfHsCJybH8I0PPsXrh9IN/ej1F9L49l9fx+svpJvGhPFUe9eviTaK52LkNcxZ8hrmLHWTpGlat2PoiAMHDmjnz5/vdhhtUVUN1xbymF9UsG1Qxu6hKHwPPopttaxQKONSJoe5XBEj8TDGk3FEIkEAQKWiYmY2i0xWQTIhI51KIBDwtVxmtb12X2e1zIrZ9tpdX7uvq7OuJ7dilLPLhRKmM4u1YzqWHMRAJOTkZjvGq7HnC0XMZJZqcaeTMUQj3rjr1mZOdzxvKxUVny3kkC9qWCxWkC9WMBQNoVStIhIMoKppCPl8WCyWEfT7MRIPY+emAVy/t7yR/ki9q+M5WypVMXUri7mcgpG4jIntCYRC/obn5AoKPsrka7Xh8WQU8cjqp+4UpYJLs9nVMTiVgCwHGtZhp49aja3kGa7V2Xv5Ku4Vytg8EMSiUkZcDqJYqSKVGMAjW1dOJvWcS8ZlVFXg3nIRQb8Py6UqRuLm9daBeRJ5R8dz9n5BwZW6Gro3GcWmCD+5TBvS8bwVxVJBwYd1/efJZBSxLvUfkWIRaZyyM5dED+as2/PWxYKCy3X590QyisEO5Z/V9bVOcLstbW7PtZyVJAkvff2Dda/zra8+w9d59HUb2ZbFexmu5GypVMXMbBb5UgVBvx93lorYNhhGyO/DT+4XsGtzBKWKisxiEcm6MUGkcYuEwnMx8hrmLHkNc5a8xnbOBlo/hazoE3T9Yo7RTTCzWQWpRATpVLzpJhij16mqhkWljPvLZUSCAaiq1jTpNzqfCQb9CAd8CPolhAN+BIOrF5V8PgmDchDLpSoG5WDD+lRVQ7mqoaxqqFQ1qKrW8Lo9wzHsGW7+JpNAwId9D23GvoeM28VsH6zWadUuVtszaxMr7cbhJhWq5e8iC/j98EkSJAnwSxIC/qaLnELyS/5aBZUe/O4VlYqKu/kS7iyVEPT5sDMRMbq43HGBgA+jI5tQKlVxeS4HALi3XMLmgRDuLpcQl4NYLpcRCvixsFRCJOjHx8UcfnKvgFQigl2bB+DzSYb9EIAQfRMQp07Qxvl8EoJ+CX6fhKDfZ3gcQ1KgoTaEpMYpVFWrQgNqP1WtCrNplp3xSqttqTPs5C9zXFyqqiFXUHF7qYgtsRD8EhCPBCBpQKGsYqlYaZh7aRqgasAjW6N41Gf+7XRGx1yfJ6mqhk9vL2EupyCVWLlhaH5x5Xm7Nq/cqGknV5hX/WlTRMaBh8O4tpBHtlDCJ3PLuLN0D8OxMHw+YPNACJWqhuv3lhENBTASD2PXFusb0MzOo/Rl2wZl+H3AbHb1eaqqmZ6frWc7a5cPhAJQNRU+STK8ga7TeW+2fva33rB22tDNj2lJa+Yma393k9X5fDdiaTWXdIN+s1EmpyAVlzFufLORYyoVFaWKilJVQ6miolJRO3pjin/Nl5Gv/d1JVtfXOsHqml0ndfqcg6gXhUJ+jO/YhJnZLBbyKzf45EsVaKEAwgEflkoVyAE/gn4fNE3DR/M5xMJBpGJhLCwVcXuxBL8kIRULY3apyHkaEREREbWFN/lsgKpqeGcmg6OnLkApq7Wv2nw+nYSqajh98SZeOz1dW3ZicgyT+3bA55OEeV2louL01C0ce3v1dcdfHMPkxPa2LwZVKqppLFYXfKza0+qNovW+ppVOrLMdSwUF352+jWNn6o7N4TH8wthw1z4taVepVHU8r9ygKBWcuTTb1OaHx1NN38ghGtHavFSq4p3LGdy8V8Cb712txfSbP7sX3/twFr/41C68cW6m9virB0fxzR98jnvLJZyYHMPh8e34/sfzTf0wFJDwG9/6cVf7JiBOnaCNszNmtaoN+UIRfz4937T8y2Pbat8EZidn2h0/18tOLMxxcRnV+9dfSCMRCeDoqYtbbgX2AAAgAElEQVQNubNpIGi7ZlodcwC1ZZsHQnj5px9uqO0nJsfwh+9fxecLBcvtMK/6l37s//1/+pumOcC/+q/GcbGQxb9+56OGecHoSAzPPjbSdGONnTw1ml/8/i/th6qp+O//dKpljW2Vq0bLXz+Uxr/9fz5p6gdGcTmZ92axPvfECN69PMf+5nGLBQV/YXBO+PNjwx37BhMzduY7/cit+Vsrbp8Pun3uvFwo4dz0XNP2Do2NOP6NwW4fU7fnR6LkLJFX6X3oD9+/ipcO7MJb56/jV55+GH/w/SsNc9CBoB//83c/xD/9+19A5t4SUpsHm2rY+Wu3ceqHs5ynUcfcLyh412Au+dzYML9lgoTEnCWvYc5SN/HsbQOuLeRrJ+EAoJRVHD11AdcW8piZzdZOmPVlr52exsxsVqjXTd3K1i4C6cuOvT2NqVvZttvFKpZ229PJ17TSiXW248NMvjYw6HEcOzONDzPuxtGOTuSVGy7NZg3b/FKL3BWBaG0+dSuLT+aXam8C6zH9wfev4OVn9tTe3NMff/O9q/jKUztr9WLqVtawH07dyHa9bwLi1AnaODtjVqvaMJNZMlw+k1mqrcNOzrQ7fq6XnViY4+IyqvdvnJ2BqqIpd9ZTM62Oef2yrzy1s6m2v3Z6GocmdrTcDvOqf+nH3mgO8NlCvnaDj/7Ym+9dxdSNbFNu2M3T+vXo84vf+vYFXJ1fslVjW+Wq0fI3zs0Y9oNO573Z+mdmjedS7G/ectnknPByF84J7cx3+pFb87dW3D4fdPvceTqzaLi96cyi49ty+5i6PT8SJWeJvErvQ4cmduBr71/FoYkdtRt8gNU56MJyCYcmduD1MzP4W18YMaxhk0/tqv3OeRp1whWTueQVD7y/QP2JOUtew5ylbuJNPhswl1NqHVenlFXMLyqYzRovy2QVoV6XMVk2l1PsN8QaVrFYsYrTyde00ol1thdH0eTYFF2Nox2dyCs3ZNjmjsajajCMqVCqGD4uSav/N9sfdc3fJ+hG3wTEqRO0cXbGrFa1wU69tpMz7Y6f62UnFua4uMzqY75UaXpsPTXT6pjXL5Mk49ouSc2vW882qLfpx75QbJ4DmM0XVA1NuWE3T+uX1c8vjPqEUY1tlauttmUnLqfy3mz9ZmMK+5u3iHROKFIsInFr/taK2+eDbp87u5l/bh9Tt+dHouQskVfpfUg/LzI7P1K11XOn+UXjfrewVGz4nfM0chrnb+Q1zFnyGuYsdRNv8tmAkbgMOdjYhHLQh22DMlKJiOGyZEIW63Umy0bi7X+NmFUsVqzidPI1rXRine3FETY5NuJ/FXon8soNSba5o/H4JRjGNBAKGD6uaav/N9uftd9a3I2+CYhTJ2jj7IxZrWqDnXptJ2faHT/Xy04szHFxmdXHaCjQ9Nh6aqbVMV+7zKqGW22HedW/9GM/EG6eA5jNF3wSmnJjPXmqL6ufXxj1CaMa2ypXW23LTlxO5b3Z+s3GFPY3bxHpnFCkWETi1vytZRwunw+6fe7sZv65fUzdnh+JkrNEXlXfh9b+q9PnnZr2oFYNGvfzoVi44XfO08hpnL+R1zBnyWuYs9RNvMlnA3YPRXHyyP6GCf3JI/uxeyiKdCqOE5NjDctOTI4hnUoI9brx7Qkcf7HxdcdfHMPE9kTb7WIVS7vt6eRrWunEOtvxZDKK44fXHJvDY3gy6W4c7ehEXrlhPJUwbPPxFrkrAtHafHx7Ao9ui+HVg6MNMf3mz+7FNz74FK8fSjc8/urBUfzZj27U6sX49oRhP5zYmeh63wTEqRO0cXbGrFa1IZ2MGS5PJ2O1ddjJmXbHz/WyEwtzXFxG9f71F9Lw+dCUO+upmVbHvH7Zd354o6m2n5gcw7mpmy23w7zqX/qxN5oD7B6K4p8//3jTvGBiZ6IpN+zmaf169PnF7//Sfoxui9mqsa1y1Wj564fShv2g03lvtv50Ks7+1gOeMDknfKIL54R25jv9yK35Wytunw+6fe48lhw03N5YctDxbbl9TN2eH4mSs0Repfehsxdv4pVnR3H24k385s/ubZqDDg2EcG7qJt44nMYPPpkzrGGnf3S99jvnadQJe03mkns98P4C9SfmLHkNc5a6SdI0rfWzPOjAgQPa+fPnO74dVdVwbSGP+UUF2wZl7B6KwvfgI6KVioqZ2SwyWQXJhIx0KoFAwCfc60qlKqZuZTGXUzASlzGxPYFQyL+hdrGKpd32dPI1DsWxsY2sYZSzSwUFH2bymMsVMRIP48lkFLGINz7V0Ym8coOiVHBpNltr8/FUArIcaP1CAdhs847nbX08l+dyyCkVFEpVbB4IIqeUMSgHUVWr8Et+3F0uIRWXEfBLuHGv0FAvjPohAMf7e7s6UXvIUMdz1s6Y1ao25AtFzGSWasvTyRiikcY79u3kTLvj53rZiYU53raO52ypVMWlW1lkcgq2REMI+SWo0CBpEu4XyhgeDCOdSsDnk9Z1DK2Oef2yZFxGVQVuL608b9fmAVy/t2xrO8wrIbkyN9CPfa5QQkUF7iwVsTUWRsAHbBoIoVLV8JN7yxgIBTASD2PXFuPcsJunwzEZft/Kn7DRn6eqmu0a2ypX9eVzOQUDIT80TYMkSVguVTESN4+rE3lvtv4e72+uzWm7bbGg4HLdOeETySgGu3ROaGe+049szt9cmR+4eQ7u9rnzcqGE6cxibXtjyUEMREId2ZZbc3Kd2/ValJytbUiS8NLXP1j3Ot/66jN8nUdft5FtWbyX4VrO6n1oIV/EYDiI5VIFkVAAuUIZAyE/5IAfd/IlbBoIQg76EAsHkYqFMZ3J1dWwOGaXir06TyP7Op639wsKrtTNJfcmo9jkkfcXSEjMWfIa5ix5je2c9cY71wLz+STsGY5hz3Dzp9cCAR/2PbQZ+x4S+3WhkB8Hdm8x3sE2WcVixSpOJ1/TjXW2IxaR8fQj3hwMOpFXbpDlAL74yFC3w2iLaG0eCvmx76HNtp+f3rGp4XezfihC3wTEqRO0cXbGrFa1IRoJ4+lHrN/kspMz7Y6f62UnFua4uEIhP/4Lm/V+PcfQ6pgbLXt02+r/7W6HedW/9GNv5Qsjrb+RYb15untrrGG53RrbKlfXk8udznuz9bO/9YZBgc4J7cx3+pFb87dW3D4fdPvceSASwtMubc/tY+p2vRYlZ4m8Su9D67W2Zu6JBDlPo47bJNBcksgO5ix5DXOWuoU3+XSQflf/bFZBKhFBOhVv+mYd/RNWRp/0NFpmtU79U1SZXBHJNZ+i0j/RlckpSMVljNd9ostqe4VCGZfqPmUwnowjEgm23Pd2989qWT/y8icl282dbvPytydZ1QcR1PfvbYMywiHg1t0i7hXK2DwQxGKhjOG4jCeT8dq3Tyzkiwj5fYafigca93n7pggGwwHcyRcRCfqxVKxiUSkjEQliOBaGJAGz2ZVt+33N/+92zVFVDZ/dyePzu3lEW3yLATnLztjTqn9ZjcHreY7VeG2XnVrgxHacGrO9NPaLEquiVDCTyeH2UhGD4QDkoB+LxRJi4RDu5ouIyQHE5SAK5SqGouGGbzCZyykYioaxXKrA75cQ9vsxFAth15bVb0tzcv/stpkobUudoaoafnIvj7lsEXfyRezYNIAnRgZxI1toOub1uZBKrHxr1Pxi4/+3xcIoVKq4ca+AVCKCx4Zj+Pj2YlPdWzv3sDvmr32dTwI+W1jf+KyP69fv5hENB1AsV5EYCKFYWe2XTuV4r/WfXtufjRLp/ESkWOzMq9wiynmY23G4fQzczD+3r8Ww7hF5y3qube9MRHB5LtdUm9eekw/HQ1hYKqH64BsvU4kI9m6NYmZuseG8PRDwNaxf/1bVVtuzw8444na96vXtuYHfMEF29WL+U28Tpb6JEgeJz4n3ZOrxJp8OqVRUnL54E6+dnoZSVmt/43py3w74fBLemcng6KkLtWUnj+zH8+kkAJguU1XNdJ2Vioozl2Zx7MzqsuOHx3B4PAWfT8LpqVs49nbdshfHMDmxHYGAz3R7xWIFZ6czTet8YSxpebOGqmpt7Z/Vsn6cTOQLRfz59HxT+395bJvwN/oUCuW2cqfblgoKvjt9uynuXxgbFv5GH6uaI8KNPkZ14fjhNE6dv45nH0/ia+9fbYh7eDCEY2/P4KUDuxqW1dcEo30++qW92CQHsLBcxpvvXW14XA748G/+46e4t1zCqwdH8c0ffN70/27VHKP2efXgKEZHYnj2sZG+rIFusRqz6m+wtepfilIxHYP1NzrsPKdUqpqO13Yne3ZqgRPbsdNubq7HDaLEqigVnJuZbTjG/+PPP45SVcPvvfvjhhoyEPTj33/wGY6/mMbtxVLDa155dhRvnb+OX/7iLkRDfjy8NY98UXV0/+y2mShtS52hqhr+30/mcet+EW+cnYFSVvHwUAS//jOjDXXo5JH9eO6JEbx7eQ5HT13A5oEQXv7ph/Hme1cb/l+f49/8wefYsSmMIwcebqivJybHcHh8O77/8XzT2NpqzDcbk+vnDa3GZ6N16H3uV55+GN/6q8/xO88/4UiO91r/6bX92SiRzk9EisXOvMotopyHuR2H28fAzfxz+1oM6x6Rt6znurfRnPfE5Bi+/GQSZ2cyDY//ry/tw/3lCv7Fg/myXntOnf8c5z/PQg768L/8gwn4JR9+69sXGtb3h+9fxecLBRx4OIEjX3y4aXt2xgI744jb9arXt+eG+wUF7xqMn8+NDfMNaGrQi/lPvU2U+iZKHCQ+J96TWav77/z2qJnZbG1SDABKWcVrp6cxM5vFtYV8bbDUlx09dQHXFvKWy6zWeWk2Wysi+rJjZ6ZxaTaLqVvZWtLUlr09jalb1rFcyuSM15nJWe57u/tntawfzWSWDNt/JrPU5chaazd3uu3DTN4w7g8z4uegVX0QgVH/PnZmBi8/s6d2E4/++Gunp7FYqOLQxI6mZfU1wWifT37vCgbCwdqbgPWP38mX8JWndkIpq3jzvauG/+9WzTFqnzffu4qpG9m+rYFusTP2tOpfVmOwzs5zrMZru+zUAie249SY7aWxX5RYLxkc4zv5En7v3Y+basjCcgmHJnZgsVBtes3X3r+KQxM78OZ7V3EnX8Jioer4/tltM1Haljrj2kIei4Vq7QYfADg0saOpDh09dQEzs9laLnzlqZ218bz+//rz9fH75Wf2NNXX106v1DSjsbXVmG82JtfPG1qNz0br0PvcH3z/Cg5N7HAsx3ut//Ta/myUSOcnIsViZ17lFlHOw9yOw+1j4Gb+uX0thnWPyFvWc23baM772umV66NrH69UUbvBR3/s2JlpvPzMntrvn8wv1W7wqV/foYkdALAyLzbYnp2xwM444na96vXtueGKyfh5xQPXusldvZj/1NtEqW+ixEHic+I9mbV4k0+HzGaV2oHSKWUVmayCuZzxsvlF62VW68zkiobL5nJFZEzWOZdrFYv5Oq20u39Wy/pRu+0vAq/G7tW4AeuaIwKz/l0oVQwfz5cqkCRY1gSzfc6brFPVAEla/d3s/92oOWbto2ro2xroFjtjT6v+ZTUG6+w9x3y8tstOLXBiO06N2V4a+0WJ1SiXVM24Xup1L180rot6nVU1mNbOjeyf3TYTpW2pM+ZySlMOmo3x9TWs/jlmz5ckoGCS32a1rtWYb5aP9a9rNT5braP+XydyvNf6T6/tz0aJdH4iUix25lVuEeU8zO043D4Gbuaf27nOukfkLeu5tm02hzWqM2bnbIVSpfa72XmfPk81nRfbGAvsjCNu16te354bRJq/kdh6Mf+pt4lS30SJg8TnxHsya/Emnw5JJSKQg43NKwd9SCZkjMRlw2XbBq2XWa0zGQ8bLhuJh5EyWedIvFUs5uu00u7+WS3rR+22vwi8GrtX4wasa44IzPr3QChg+Hg0FKj9f+0yvSaY7XPUZJ0+CdC01d/N/t+NmmPWPj4JfVsD3WJn7GnVv6zGYJ2d51iN13bZqQVObMepMdtLY78osRrlkl8yrpd63YvKxnVR01afZ1Y7N7J/dttMlLalzhiJy6Y5uPb3VKIxF8z+r/+uacBA2HjdSZO8ajXmm+Vj/etajc9W66j/14kc77X+02v7s1EinZ+IFIudeZVbRDkPczsOt4+Bm/nndq6z7hF5SzvXttf+blRnzObLkdDqn0A0O+/T56mm82IbY4GdccTtetXr23ODSPM3Elsv5j/1NlHqmyhxkPiceE9mLd7k0yHpVBwnJsdqB0wOrvwd23Qqgd1DUZw8sr9h2ckj+7F7KGq5zGqd46kEjh9uXHb88BjGUwmMb0/g+Itrlr04hont1rGMJ+PG60zGLfe93f2zWtaP0smYYfunk7EuR9Zau7nTbU8mo4ZxP5kUPwet6oMIjPr38cNpfOODT/HKs6NNcQ9G/Dh78WbTsvqaYLTPR7+0F8vFMl49ONr0+NZoCH/2oxuQgz68enDU8P/dqjlG7fPqwVFM7Ez0bQ10i52xp1X/shqDdbaeYzFe22WnFjixHafGbC+N/aLEOp5KNB3joWgIv/3cY001ZGgghHNTNzEo+5te88qzozg3dROvHhzF1mgIgxG/4/tnt81EaVvqjN1DUQzKfrz+Qrp2jM9evNlUh04e2Y90KlHLhe/88EZtPK//v/58ffz+xgefNtXXE5MrNc1obG015puNyfXzhlbjs9E69D73mz+7F+embjqW473Wf3ptfzZKpPMTkWKxM69yiyjnYW7H4fYxcDP/3L4Ww7pH5C3rubZtNOc9MblyfXTt4wEf8C/q5st67fnmB5/Wfn90Wwy//0v7m9Z3buomAKzMiw22Z2cssDOOuF2ven17bthrMn7u9cC1bnJXL+Y/9TZR6psocZD4nHhPZi1J02/17jEHDhzQzp8/39UYKhUVM7NZZLIKkgkZ6VQCgcDKwVNVDdcW8phfVLBtUMbuoSh8PqnlMqt1KkoFl2azmMsVMRIPYzyVgCyv3O1fKlUxdSuLuZyCkbiMie0JhEL+ltsrFMq4lMmtrjMZRyQSbLnv7e6f1TIBORqYUc7mC0XMZJZq7Z9OxhCNeOMO0HZzp9uWCgo+zORrcT+ZjCIW8cYd61b1oU7H89ZMff8ejskIh4Bbd4u4VyhjcySIxWIZw7Ewnkwl4PNJuLaQx918EUG/D8ulKkbizTWhfp9TCRmDchAL+SLkoB/5YhU5pYxNkSC2xsKQpJWvxBuOyfD7mv/f7Zqjqho+u5PH9bt5DIQCGImHsWuL0DXQLR3PWTtjT6v+ZTUGr+c5VuO1XXZqgRPbcWrM9tLYbzPWjuesolQwk8nh9lIRsXAAkaAfi8UyYuEg7uZLiIb9SMhBKJUqtkTD2D0UhapqmHmQf0PREJZLFfh8EuSAH0OxEHZtWTn5dPpY2D2+XsqDHuRKnf3JvTzmskXcyRexIxHBE8k4bmQLTce8PheScRlVFbi91Pj/rdEwlEoVN+8VkEzIeGx4EB/fXmyqe2vnHnbH/LWv80krfWM943P9uB4NBVCqVhGXgyhV1Vq/dCrHe63/iFJrRSHS+YlIsdiZV7lFlPMwm3E4xu1j4Gb+uX0tRtA67lqdlSQJL339g3Wv862vPsPXefR1G9mWxXsZruXseq5t70xEcHku11Sb156Tb4uHcHephIoK3FkqIpWQsXdrDDNziw3n7YGAr2H9uzYP4Pq95Zbbs8POOOJ2ver17cGFvL1fUHClbvzcm4xik0eudZO7eB5GXmOzvrHOkjBsvidjO2d5kw9R+zihIS9i3pLXMGfJa5iz5DXMWfIi5i15DXOWvIY3+fB1HXud12/yIXIQ85a8hjlLXsOcJa+xnbP8c11ERERERERERERERERERERERILjTT5ERERERERERERERERERERERILjTT5ERERERERERERERERERERERIKTLP6OradJknQbwOcubnIrgDsubs8KY2nWiTjuaJr2vFMra5GzorRjO7wau1fjBqxj7+e8ZTytiRbTVgAf9WnOMpZmosQBsM5y27217X7J2U7hPnVHP+atKHEAjMWMKPMDt4l0DJzWy/sG9G/OAmIeW8bUmig5K1q7AIxpPUSKqx/zVpQ4AMZihrW2mSixiBIH4J1YmLPdx1iaOZKzPXuTj9skSTqvadqBbscBMBaR42iXl+P3auxejRsQJ3ZR4tAxntZEi8nteETaf8YibhyAOLF0Mw5uu7+27ZRe2Ie1uE+9T5T2ECUOgLGYESkWN/XyfvfyvgG9v39WRNx3xtSaKPGIEkc9xmSfqHF1mij7LUocAGMxI0ososQBiBOLKHEAjEXkOADGYkaUWJyKg3+ui4iIiIiIiIiIiIiIiIiIiIhIcLzJh4iIiIiIiIiIiIiIiIiIiIhIcLzJxzl/3O0A6jCWZqLE0S4vx+/V2L0aNyBO7KLEoWM8rYkWk9vxiLT/jKWZKHEA4sTSzTi47f7atlN6YR/W4j71PlHaQ5Q4AMZiRqRY3NTL+93L+wb0/v5ZEXHfGVNrosQjShz1GJN9osbVaaLstyhxAIzFjCixiBIHIE4sosQBMBYjosQBMBYzosTiSBySpmlOrIeIiIiIiIiIiIiIiIiIiIiIiDqE3+RDRERERERERERERERERERERCQ43uRDRERERERERERERERERERERCQ43uRDRERERERERERERERERERERCQ43uRDRERERERERERERERERERERCQ43uRDRERERERERERERERERERERCS4nr3J5/nnn9cA8Ic/nfxxFHOWPy79OIp5yx8XfhzFnOWPCz+OYs7yx4UfRzFn+ePSj6OYt/xx4cdRzFn+uPDjKOYsf1z4cRRzlj8u/TiKecsfF34cxZzljws/jmLO8seFH9t69iafO3fudDsEonVhzpIXMW/Ja5iz5DXMWfIa5ix5EfOWvIY5S17DnCWvYc6SFzFvyWuYs+Q1zFkSSc/e5ENERERERERERERERERERERE1Ct4kw8RERERERERERERERERERERkeB4kw8RERERERERERERERERERERkeAC3Q6A3KGqGq4t5DGXUzASl7F7KAqfT+p2WNQCjxtR72M/J9o49qNVbAsichNrTn/h8aZuYv6RE5hHRNRvWPeIqFeJUt9EiYP6D2/y6QOqquGdmQyOnroApaxCDvpw8sh+PJ9OstAIjMeNqPexnxNtHPvRKrYFEbmJNae/8HhTNzH/yAnMIyLqN6x7RNSrRKlvosRB/Yl/rqsPXFvI1woMAChlFUdPXcC1hXyXIyMrPG5EvY/9nGjj2I9WsS2IyE2sOf2Fx5u6iflHTmAeEVG/Yd0jol4lSn0TJQ7qT/wmnz4wl1NqBUanlFXMLyrYMxzrUlTUCo8bUe9jPyfaOPajVWwLInITa05/4fGmbmL+kROYR+SUX/7Vf4zZO/cMl6W2bsaffOPfuRwRkTHWPSLqVaLUN1HioP7Em3z6wEhchhz0NRQaOejDtkG5i1FRKzxuRL2P/Zxo49iPVrEtiMhNrDn9hcebuon5R05gHpFTZu/cQ+rF3zZe9vbvuRwNkTnWPSLqVaLUN1HioP7EP9fVB3YPRXHyyH7IwZXDrf9NwN1D0S5HRlZ43Ih6H/s50caxH61iWxCRm1hz+guPN3UT84+cwDwion7DukdEvUqU+iZKHNSf+E0+fcDnk/B8OonHX/k7mF9UsG1Qxu6hKHw+qduhkQUeN6Lex35OtHHsR6vYFkTkJtac/sLjTd3E/CMnMI+IqN+w7hFRrxKlvokSB/Un3uTTJ3w+CXuGY/wbgB7D40bU+9jPiTaO/WgV24KI3MSa0194vKmbmH/kBOYREfUb1j0i6lWi1DdR4qD+wz/XRUREREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkON7kQ0REREREREREREREREREREQkuEC3AyCxqaqGawt5zOUUjMRl7B6KwueTWi4jZ1QqKmZms5jNKkglIkin4ggEeG8ekdcZ1U8ArKlEXWRnXtPvc59O7n+/ty2R07zcp7wcO7mH58rUScwvd7Det8Y2IqKN4phGRL1KlPomShzUf3iTD5lSVQ3vzGRw9NQFKGUVctCHk0f24/l0EgBMl/Fk0xmViorTF2/itdPTtTY+MTmGyX07OEAQeZhZbQ0FJPzGt37MmkrUBVZznvqbm/t57tPJ/e/3tiVympf7lJdjJ/fwXJk6ifnlDtb71thGRLRRHNOIqFeJUt9EiYP6EzOMTF1byNdOJAFAKas4euoCri3kLZeRM2Zms7WBAVhp49dOT2NmNtvlyIhoI8zq59SNLGsqUZfYmdf0+9ynk/vf721L5DQv9ykvx07u4bkydRLzyx2s962xjYhoozimEVGvEqW+iRIH9Sfe5EOm5nJKrTDplLKK+UXFchk5YzZr3MaZLNuYyMvM6qeqoekx1lQid9iZ1/T73KeT+9/vbUvkNC/3KS/HTu7huTJ1EvPLHaz3rbGNiGijOKYRUa8Spb6JEgf1J97kQ6ZG4jLkYGOKyEEftg3KlsvIGalExLCNkwm2MZGXmdXPtd+2zZpK5B4785p+n/t0cv/7vW2JnOblPuXl2Mk9PFemTmJ+uYP1vjW2ERFtFMc0IupVotQ3UeKg/sSbfMjU7qEoTh7ZXytQ+t9+3j0UtVxGzkin4jgxOdbQxicmx5BOJbocGRFthFn9nNiZYE0l6hI785p+n/t0cjTdlb0AACAASURBVP/7vW2JnOblPuXl2Mk9PFemTmJ+uYP1vjW2kTd9fPky/t6Xf7Hp55d/9R93OzTqQxzTiKhXiVLfRImD+lOg2wGQuHw+Cc+nk3j8lb+D+UUF2wZl7B6Kwvfg6yasltHGBQI+TO7bgdFtMWSyCpIJGelUAoEA780j8jKz2goA32VNJeqKVnMeu8/pZZ3c/35vWyKneblPeTl2cg/PlamTmF/uYL1vjW3kTWXNh9SLv930+Ozbv9eFaKjfcUwjol4lSn0TJQ7qT7zJhyz5fBL2DMewZzi2rmXkjEDAh30Pbca+h7odCRE5yax+sqYSdY+deU2/z306uf/93rZETvNyn/Jy7OQenitTJzG/3MF63xrbiIg2imMaEfUqUeqbKHFQ/+GtZEREREREREREREREREREREREguNNPkREREREREREREREREREREREghPuJh9JkjZJkvSnkiR9JEnSZUmSflqSpC2SJH1PkqSrD/7d3O04iYiIiIiIiIiIiIiIiIiIiIjcItxNPgDeBPCOpmmPA9gH4DKAfw7gPU3TRgG89+B3IiIiIiIiIiIiIiIiIiIiIqK+INRNPpIkJQD8XQD/BwBomlbSNO0+gBcBfOPB074BYLI7ERIRERERERERERERERERERERuU+om3wAPALgNoD/U5KkH0uS9O8kSYoCGNE0bfbBczIARoxeLEnSr0mSdF6SpPO3b992KWSi9jFnyYuYt+Q1zFnyGuYseQ1zlryIeUtew5wlr2HOktcwZ8mLmLfkNcxZ8hrmLIlKtJt8AgCeAvBvNE37KQB5rPnTXJqmaQA0oxdrmvbHmqYd0DTtwPDwcMeDJdoo5ix5EfOWvIY5S17DnCWvYc6SFzFvyWuYs+Q1zFnyGuYseRHzlryGOUtew5wlUYl2k88NADc0Tfv/Hvz+p1i56WdOkqQUADz4d75L8RERERERERERERERERERERERuU6om3w0TcsA+IkkSY89eOgggA8BnAHwqw8e+1UAb3chPCIiIiIiIiIiIiIiIiIiIiKirgh0OwAD/wzAf5AkKQTgUwD/LVZuRjolSdI/AvA5gCNdjI+IiIiIiIiIiIiIiIiIiIiIyFXC3eSjadoFAAcMFh10OxYiIiIiIiIiIiIiIiIiIiIiIhEI9ee6iIiIiIiIiIiIiIiIiIiIiIioGW/yISIiIiIiIiIiIiIiIiIiIiISHG/yISIiIiIiIiIiIiIiIiIiIiISHG/yISIiIiIiIiIiIiIiIiIiIiISHG/yISIiIiIiIiIiIiIiIiIiIiISHG/yISIiIiIiIiIiIiIiIiIiIiISHG/yISIiIiIiIiIiIiIiIiIiIiISXMdv8pEkaaDT2yAiIiIiIiIiIiIiIiIiIiIi6mUdu8lHkqRnJEn6EMBHD37fJ0nSH3Vqe0REREREREREREREREREREREvaqT3+TzBwB+DsACAGiadhHA3+3g9oiIiIiIiIiIiIiIiIiIiIiIelJH/1yXpmk/WfNQtZPbIyIiIiIiIiIiIiIiIiIiIiLqRYEOrvsnkiQ9A0CTJCkI4FUAlzu4vb6gqhquLeQxl1MwEpexeygKn0/q2OsqFRUzs1nMZhWkEhGkU3EEAh29N4zqlEpVTN3KIpNTkIrLGN+eQCjk73ZYtrSbc0S9xKwftHp8IV9EyO/DcqnK/uMRrHnta9V2brVtrx1DtisBbP92ea3dNhpvO6/3WhuRMxSlgkuzWWRyRSTjYYynEpDlTl5WIpG5XQfc3B6vhbmjG2NJrx1bjsdE1Arnb0TUq0Spb15+H5fc5fTcvZPZ/k8AvAlgB4CbAN4F8Osd3F7PU1UN78xkcPTUBShlFXLQh5NH9uP5dNIyCdp9XaWi4vTFm3jt9HTtdScmxzC5b4enT4C9olSq4vTULRx7e7X9j784hsmJ7cIPEO3mHFEvMesHzz0xgncvz5k+/rvvXMZLB3bha+9fZf/xCNa89rVqO7fatteOIduVALZ/u7zWbhuNt53Xe62NyBmKUsGZS7M4dqbu/PTwGA6Pp/hGUR9yuw64uT1eC3NHN8aSXju2HI+JqBXO34ioV4lS37z8Pi65qxNz946dwWiadkfTtH+oadqIpmnbNE37rzVNW+jU9vrBtYV87eADgFJWcfTUBVxbyHfkdTOz2dqJr/66105PY2Y268DeUCtTt7K1gQFYaf9jb09j6pb47d9uzhH1ErN+MDObtXz80MSO2g0+9cvZf8TFmte+Vm3nVtv22jFkuxLA9m+X19pto/H+/+y9e3hb1Z3u/25ZliXZlmI7tiQncZwQBxLJTmDMpaelLQlwUgiJD9D0MtMLhTLzDG3SoUxvDxMOmcwwMJQ5UJgzU9oyQPsrpKWTGy1TmpTSOQXaUBLHJoVASEJiyXYcR/JNkmXt3x+2FF323tra2pe1pe/nefwQtC/ru9d617suWtpLyfVmyyNCHQ4Hw+kJVGBufLq7D4dpfqAi0dsH9EyP5sL0wYi2pNzKltpjgiAKQf03giDKFVb8zczf4xL6okXfXbNFPhzHPcFx3A9y/7RKrxIYjETThZ8iOp3E0FhUk+uCYeHrQmHp6wh1CImU22CE/fxXqjmCKCfE6oGYt6Y+5zhQ/TEZ5HnKKZR3euVtuZUh5SsBUP4rxWz5Vmq8Sq43Wx4R6hCKxETGpzGDIiKMRG8f0DM9mgvTByPaknIrW2qPCYIoBPXfCIIoV1jxNzN/j0voixZ9dy3fRboXwPNzf/sAuACMa5he2eNx2WGvzi4ye7UFLfV2Ta7zuR2C13nd0tcR6uATKTePi/38V6o5gignxOqBmLf63OfPp/pjLsjzlFMo7/TK23IrQ8pXAqD8V4rZ8q3UeJVcb7Y8ItTB66oRGZ/WGBQRYSR6+4Ce6dFcmD4Y0ZaUW9lSe0wQRCGo/0YQRLnCir+Z+XtcQl+06LtruV3Xcxl/PwKwCUC3VulVAu1NtXho0+qsL4Ef2rQa7U21mlzn97mwvSeQdd32ngD8PrcKT0MUorPVjW0bs/N/28YAulrZz3+lmiOIckKsHvh9LpHP3Xho02rsOXQam9d0UP0xEeR5yimUd3rlbbmVIeUrAVD+K8Vs+VZqvEquN1seEerQ6XNj24ac8emGADppfqAi0dsH9EyP5sL0wYi2pNzKltpjgiAKQf03giDKFVb8zczf4xL6okXfneN5Xq34pBPiuAsBPM/z/DI90uvu7uYPHDigR1K6kkzyOD4ygaGxKFrq7WhvqoXFwml2XSKRRH8wjFA4Cq/bDr/PDatVyxdAmYrCGVgEQpqNx2fQOxDGYCQKj8uOrlY3bLYqNZPVDKWaIzRHc90S5xGrB4U+PzsRQ3WVBZPxGXhcFV9/TKFZ8jzlFMo7vfJWxXSY0GwZ5iuhAJn5z4RmWcJsui01XiXXM5BHpFsDiEYTOBwMYzASg8dVg06fG3a71eiwzELZaVZvH9AzPZoLA6CDZo1oS8qtbBloj1lCN5/9yPU3wbfxLsFjL973RVzzzcfzPg/uehC/ef45NUMkygPNdUv9N0Jlyq5PS5gXmf5G3+MSzKD2PK1mrTnHcWMA+LlgeAAhAF/XKr1KwWLhsLS5Dkub63S5zmq1YNWiBqxaVNRlhErYbFXobm80OgxFKNUcQZQTYvWg2M8J9qGyU06hvNMrb8utDClfCYDyXylmy7dS41VyvdnyiFAHu92KS5c0GR0GwQh6+4Ce6dFcmD4Y0ZaUW9lSe0wQRCGo/0YQRLnCir+Z+XtcQl/U7rtrtsiH5/l6re5NEARBEARBEARBEARBEARBEARBEARBEARBEJWE6ot8OI67ROo4z/N/VDtNgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgihntHiTz7cljvEA1miQJpOk9lZL7cOXubea1DEt0iPMSWqv8GA4Cp/bAb/PZZq9wkmPBEuUqkcj9Ux1iTAKNbQn5x6kce2hPFYPPfKSyovIpFg96K0f0qv2mHlMqCWkvXzKvf5TmRPlit7zx1pitngJohKIx2fQOxBGKBKFz2VHZ6sbNluV0WERBEGUDCv+xkocROWh+iIfnuevUvueZiSZ5PFCfwh37jiI6HQS9moLHtq0Guv8XgAQPaZ04COVHg2mzEkikcTOQ6dx986+dJlu7wmgZ9UC5id1SY8ES5SqRyP1THWJMAo1tCfnHqRx7aE8Vg898pLKi8ikWD3orR/Sq/aYeUyoJaS9fMq9/lOZE+WK3vPHWkL1lCDYIx6fwc7eAWzddb4vuW1jAD1drfQFNEEQpoYVf2MlDqIy0XRWiOO4AMdxmziO+2zqT8v0WOL4yER6UAMA0ekk7txxEMdHJiSPaZEeYU76g+H0ZC4wW6Z37+xDfzBscGSFIT0SLFGqHo3UM9UlwijU0J6ce5DGtYfyWD30yEsqLyKTYvWgt35Ir9pj5jGhlpD28in3+k9lTpQres8fa4nZ4iWISqB3IJz+4hmYrZdbd/Whd6Cy+5IEQZgfVvyNlTiIykSzRT4cx90D4Dtzf1cBeADABq3SY43BSDRdqVNEp5MYGotKHtMiPcKcBMPCZRoKs1+mpEeCJUrVo5F6prpEGIUa2pNzD9K49lAeq4ceeUnlRWRSrB701g/pVXvMPCbUEtJePuVe/6nMiXJF7/ljLTFbvARRCYRE6uVghOolQRDmhhV/YyUOojLR8k0+NwNYCyDE8/wtAFYBcGuYHlN4XHbYq7Oz115tQUu9XfKYFukR5sTndgiWqdfNfpmSHgmWKFWPRuqZ6hJhFGpoT849SOPaQ3msHnrkJZUXkUmxetBbP6RX7THzmFBLSHv5lHv9pzInyhW954+1xGzxEkQl4BOplx4X1UuCIMwNK/7GShxEZaLlIp8pnueTABIcx7kADAFYpGF6TNHeVIuHNq1OV+7UPsTtTbWSx7RIjzAnfp8L23sCWWW6vScAv4/9tXKkR4IlStWjkXqmukQYhRrak3MP0rj2UB6rhx55SeVFZFKsHvTWD+lVe8w8JtQS0l4+5V7/qcyJckXv+WMtMVu8BFEJdLa6sW1jdl9y28YAuloruy9JEIT5YcXfWImDqEw4nue1uTHH/SuAbwH4JICvAhgHcHDurT6a093dzR84cECPpERJJnkcH5nA0FgULfV2tDfVwmLhCh7TIj1CE1TNXCHNJhJJ9AfDCIWj8Lrt8PvcsFq1XJunHqRHZtFctyxSqh6N1DPVpcrULAuooT059yhDjTOn2TLMY8PQIy8NKC/mNEucp1g96K0fA/2lYnRr5jGhlpiwbdNcs+Ve/01Y5manYnzWaPSeP9YSg+PVTbMfuf4m+DbeJXjsxfu+iGu++Xje58FdD+I3zz+nZohEeaC5buPxGfQOhDEYicLjsqOr1Q2brUrNZInKgvoHBDPI9DfyWcJsyNasVasIeJ7/67l//hvHcS8AcPE836tVeixisXBY2lyHpc11RR3TIj3CnFitFqxa1IBVJnwHFumRYIlS9WiknqkuEUahhvbk3IM0rj2Ux+qhR15SeRGZFKsHvfVDetUeM48JtYS0l0+5138qc6Jc0Xv+WEvMFi9BVAI2WxW62xuNDoMgCEJ1WPE3VuIgKg/Nfv7FcdxujuM+zXFcLc/zxyttgQ9BEARBEARBEARBEARBEARBEARBEARBEARBqIWW73j+NoAPAXiT47ifchx3M8dxdjkXchxXxXHcGxzH7Z37/yUcx73Gcdw7HMc9y3GcTcO4CYIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIptNyu6zcAfsNxXBWANQC+COAHAFwyLt8C4EjGufcD+Bee55/hOO7fANwK4P+qH7V+pPYoTu3Rp8Yexal9/0KRKHwuOzoz9v3TIj1Ce6TKlCBySSSS6A+GEQxH4XM74Pe5YLVquZZTHzKfa2GDA3ZrFYbHY0V7mdo+SL5KFIvZ6qheGjdbvpiZzDL1ue2YSQJDY2x6otb6Iw/XF8pvaYqpm0bnpdHpE8YSjSZwOBhGKBKD11WDTp8bdrtm00qmoVLrhd7PTfoj1IDF+ioVU6F4WXse1uIhCILaT0I+5OGE2ZiamsbhUASDkRg8rhp0el1wOKp1j4N8lpCL2j6rqco4jnMAuAHAJwBcAuBJGdcsBHA9gH8AcCfHcRxmFwl9eu6UJwH8b5h4kU8yyeOF/hDu3HEQ0ekk7NUWPLRpNdb5vYoLMx6fwc7eAWzd1Ze+57aNAfR0tcJqtaieHqE9UmVKC32IXBKJJHYeOo27d57Xy/aeAHpWLTD1l+WZz9XgtOGzH1iMh/cdLdrL1PZdLXycKG/MVkf10rjZ8sXMZJZpKX5a6N4s3k/v+xPZUH5LU0zdNDovjU6fMJZoNIHdh4PYujtjfLohgA2dvoqewKzUeqH3c5P+CDVgsb5KxQRAMl7Wnoe1eAiCoPaTkA95OGE2pqamsacvlOdvNwS8ui70IZ8l5KKFz2r27QnHcTsw+zaeNQAeBXABz/NflnHp/wHwNQDJuf9vAnCO5/nE3P+fArBA5XB15fjIRLoQASA6ncSdOw7i+MiE4nv2DoTTi0FS99y6qw+9A2FN0iO0R6pMCSKX/mA4/SU5MKuXu3f2oT9obr1kPteNlyxMf+kFFOdlavsg+SpRLGaro3pp3Gz5YmYyy7QUPy10bxbvp/f9iWwov6Uppm4anZdGp08Yy+FgOD1xCcyNT3f34XCFt9mVWi/0fm7SH6EGLNZXqZgKxcva87AWD0EQ1H4S8iEPJ8zG4VBE2N9CEX3jIJ8lZKKFz2r5E+nvY3Zhz1/xPP9rnueTmQc5jrsm9wKO49YDGOJ5/nUlCXIcdzvHcQc4jjswPDysLGodGIxE04WYIjqdxNBYVPE9QyL3HIxENUmPUAcpzUqVKUHkEgwL6yUUVl8venpt5nNxHBR7mdo+SL5qLljoH+hZR9VAL42bLV/0QgvNZpZpKX5a6N4s3k/v+1ciUpql/JammLppdF4anb7asNA/MBOhSExkfBozKCI20LNesKRZvf2A9GdOWNIswGY7JhVToXhZex7W4lECa5olCDlIf79A7Schj0rt0xLmZVBHfyOfJdRAC5/VbJEPz/P/xfP8jMQp9wt89kEAGziOOw7gGcy+BehhAPM4jku912ohgNMiaX6X5/lunue7m5ublQevMR6XHfbq7Ky3V1vQUm9XfE+fyD09Lrsm6RHqIKVZqTIliFx8boegXrxu9fWip9fmPpdSL1PbB8lXzQUL/QM966ga6KVxs+WLXmih2dwyZdkTtdYfebj6SGmW8luaYuqm0XlpdPpqw0L/wEx4XTUi49MagyJiAz3rBUua1dsPSH/mhCXNAmy2Y1IxFYqXtedhLR4lsKZZgpCDlG6p/STkUql9WsK8eHT0N/JZQg208Fkt3+RTiLwNxnie/ybP8wt5nm8H8EkA+3me/3MAvwZw89xpnwOwS7coNaC9qRYPbVqdLszUvmvtTbWK79nZ6sa2jYGse27bGEBXq1uT9AjtkSpTgsjF73Nhe0+2Xrb3BOD3mVsvmc/13OunsGVthyIvU9sHyVeJYjFbHdVL42bLFzOTWaal+Gmhe7N4P73vT2RD+S1NMXXT6Lw0On3CWDp9bmzbkDM+3RBAZ4W32ZVaL/R+btIfoQYs1lepmArFy9rzsBYPQRDUfhLyIQ8nzEan1yXsb16XvnGQzxIy0cJnOZ7n1YqvuIQ57o88z18icfyjAO7ieX49x3FLMftmn0YAbwD4C57nJd911d3dzR84cEDNkFUlmeRxfGQCQ2NRtNTb0d5UC4slb91TUcTjM+gdCGMwEoXHZUdXqxs2W5Vm6RH5C9VKQUizUmVKELkkEkn0B8MIhaPwuu3w+9ywWvPWcmquW7XJfK4FDQ7YrVU4MxEr2svU9kHyVd0wnWbFkFlHmUEvjZstX2TArGYzy9TrsmMmCQyPs+mJWuuPPDwLzTVL+S1NMXXT6Lw0Ov0MmPXaciYaTeBwMIzBSAweVw06fW7Y7dbCF5Y5MutF2WlWbz8g/elO2WkWYKodkxVToXhZex6D49FNsx+5/ib4Nt4leOzF+76Ia775eN7nwV0P4jfPP6dmiER5oLluqf0k5FKpfVrCvExNTeNwKHLe37wuOBzVuaeRzxLMoLbPMqsynudfAvDS3L+PAbjMyHjUxmLhsLS5Dkub61S7p81Whe72Rt3SI7RHqkwJIher1YJVixqwapHRkaiL0HMtQ33R91HbB8lXiWIxWx3VS+NmyxczI1SmF7Sw6Yla6488XF8ov6Uppm4anZdGp08Yi91uxaVLmowOgzkqtV7o/dykP0INWKyvUjEVipe152EtHoIgqP0k5EMeTpgNh6MalzHgb+SzhFxUnz9X5S4CcByXt+FczmfHtUqbIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIMoJLfdCeEXqM57nb9QwbYIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIoG1TfrovjOC+ABQAcHMddjPN7h7kAONVOrxxJ7ck2GInC45K/f7HUdUrvGY/PoHcgjFAkCp/Ljs5WN2y2qpKfkZBHIpFEfzCMYDgKn9sBv88Fq1XLtXnqYdbYzRq32cn0qJZ6O6osQDAcRes8O0YnpjEQnsL8uhp4XDVY1GDsHvO5KPVXoryQowO1zlEjFr3uQ/VDf+T0B0PhKGqsFoSjcTTV2lVt61JpjEzEYKuyYDI+Q2VPGOYFmek6bVbEZ2bQVFujyjhJLB2he5AXEqXA0viEpVhYolLzRW9v0zOfaS5MH4xoH1lsk0uZz2XteViLhyAIYGpqGodDEQxGYvC4atDpdcHhqDY6LIJByMMJszE5FUdfaCztbwFvPZwOm+5xRKMJHA6GEYrE4HXVoNPnht2u+vILogxQe0yrhcr+J4DPA1gI4KGMzyMAvqVBemVFMsnjhf4Q7txxENHpJOzVFjy0aTXW+b2SDarUdQAU3TMen8HO3gFs3dWXvm7bxgB6ulppckMHEokkdh46jbt3ns//7T0B9KxawPyEoVljN2vcZkfIv7as7cDvj43g2oAP9+7pT39+zw1+tM6bwJXLWpgYZCj1bKK8kKMDtc5RIxa97kP1Q3+K7Q9uXtOBZw+8iS+v6VClrUulf/8LR/CJ7jY8sv8olT1hmBcIpTur+ZP4+roVJY2Tink+8kKiFFgan7AUC0tUar7o7W165jPNhemDEe0ji21yKfO5rD0Pa/EQBDG7wGdPXwhbd2e0aRsCuCHgpYU+RBbk4YTZmJyKY2/fYJ6/rQ94dF3oE40msPtwMC+ODZ0+WuhDZKHFmFb1GQee55/kef4qAJ/nef6qjL+NPM//TO30yo3jIxPphhQAotNJ3LnjII6PTCi+Tuk9ewfC6UmN1HVbd/WhdyBc6mMSMugPhtOVHZjN/7t39qE/yH7+mzV2s8ZtdoQ86uF9R/H5Dy1JL/BJfX7vnn6MTc0U9C+9UOqvRHkhRwdqnaNGLHrdh+qH/hTbH3xk/1Gs71qgWluXSmN914L0Ap/cOIjKwygvkNJ8qeOkQulk3oO8kCgFlsYnLMXCEpWaL3p7m575THNh+mBE+8him1zKfC5rz8NaPARBAIdDkfQXz8Bcm7a7D4dDEYMjI1iDPJwwG32hMUF/6wuN6RrH4WBY2GfLfDxIFI8WY1otf1b0/ziO+z7Hcb8AAI7jVnIcd6uG6ZUFg5FouoBTRKeTGBqLKr5O6T1DItcNRqSvI9QhGBbO/1CY/fw3a+xmjdvsiHnU6MS04OcT8URB/9ILpf5KlBdydKDWOWrEotd9qH7oj5L+IMep19al0kjdUygOovIwygsKab6UcZKcdFL3IC8kSoGl8QlLsbBEpeaL3t6mZz7TXJg+GNE+stgmlzKfy9rzsBYPQRDAYCQm0qbFDIqIYBXycMJssOJvIUbiINhHizGtlot8ngDwXwBa5/7/bQBf0TC9ssDjssNenV0s9moLWurtiq9Tek+fyHUel/R1hDr43A7B/Pe62c9/s8Zu1rjNjphHNdZWC35ea7MW9C+9UOqvRHkhRwdqnaNGLHrdh+qH/ijpD/K8em1dZhpU9kQKo7ygkOZLGSfJSSd1D/JCohRYGp+wFAtLVGq+6O1teuYzzYXpgxHtI4ttcinzuaw9D2vxEAQBeFw1Im1ajUEREaxCHk6YDVb8zctIHAT7aDGm1XKRz3ye53cASAIAz/MJADMaplcWtDfV4qFNq7O+IHlo02q0N9Uqvk7pPTtb3di2MZB13baNAXS1ukt9TEIGfp8L23uy8397TwB+H/v5b9bYzRq32RHyqC1rO/DEf7+He27wZ31+zw1+1DuqCvqXXij1V6K8kKMDtc5RIxa97kP1Q3+K7Q9uXtOBvb2nVWvrUmnsOXQam9d0UNkTAIzzAinNlzpOKpRO5j3IC4lSYGl8wlIsLFGp+aK3t+mZzzQXpg9GtI8stsmlzOey9jysxUMQBNDpdWHbhpw2bUMAnV6XwZERrEEeTpiNgLde0N8C3npd4+j0uYV9tszHg0TxaDGm5XieVyu+7Btz3EsAbgLwIs/zl3AcdwWA+3me/4gmCebQ3d3NHzhwQI+kVCeZ5HF8ZAJDY1G01NvR3lQLi4Ur6Tql94zHZ9A7EMZgJAqPy46uVjdstqqSn7FMKJyBRSCk2UQiif5gGKFwFF63HX6fG1arlmvz1MOssZs17iLQXLdKyPSo5jo7qiyzr0n3ue0YnZjGQHgK82tr4HHXYFGDPP/SC6X+SsiGSc3mIkcHap2jRix63adC64ehmpXTHxyMRFFdZcFYNI7G2hpV27pUGmcnYqiusmAyPgOPq2LK3qxorlmjvCBT805bFaZnkmisrVFlnCSUjtg9KtQLtcYU/QM1YGl8wlIsLCEzX8pOs3p7m576o7kwAGXaP2CxTS5lPpe15zE4Ht189iPX3wTfxrsE6mHTuwAAIABJREFUj7143xdxzTcfz/s8uOtB/Ob559QMkSgPNNft1NQ0DociGIzE4HHVoNPrgsNRrWayRJkg08PLrk9LmJfJqTj6QmNpfwt46+F02HJP01yz0WgCh4Ph8z7rc8Nut6qZLFEmqD13oOUin0sAfAdAAEAfgGYAN/M836tJgjlQ40DoAHVoCDNCuiXMBmmWMBukWcJskGYJM0K6JcwGaZYwG6RZwmzQIh/CjJDXEmaDNEuYDdIsYTZka1bLpWSjAD4C4ELMBvQWgNUapkcQBEEQBEEQBEEQBEEQBEEQBMEMbx05go9cf1Pe5775DXjmye8ZEBFBEARBEARhZrRc5PNTABt4nu8HAI7jPgzgMQCdGqZJEARBEARBEARBEARBEARBEATBBNO8RfDtP8FdDxoQDUEQBEEQBGF2tFzk81cAdnIcdwOASwDcB+A6DdMzhNQ+lal9uoX2TVbzmBSpvdyC4Sh8bgf8Pld6LzepY1qkp8XzVSIy95RkErOWs5nz3Izk6qStwYmTo5OydJO6dmQiBg4cRifiaKqzgeeBcHQaixucqKriEAyfvxeAdHpOmxXxmRk019VgJgkMjQmfp4d+zVpfiOKIx2fQOxBGKBKFz2VHZ6sbNltVUefI0YpaelIjFr1gKRY9SSZ5vHdmAifOTqDJaUMiyWN4PJbulwFA/0AYp8NTmF9Xg3p7Fc5NJkrOI7H8FvocgGKfLyZtojJRqodir8s83+e2YyYJnJ2MwWax4OxkHC57NaqrOEzEZwTHPifPTmAwEsNEPIHFjbVYMn+2bqTqb63NCo+rBm2Npek5s29kq7JgUiAegiiFiakY+kPj6bGS31uHWkeNIbHI6VdVWixSczTlkN7U1DQOhyJp/XV6XXA4qjVJKxpN4HAwjFAkBq+rBp0+N+x2LadQKxO9NWRUmoDyPkshLbLiPymor04Q7HFuKoq3QxPp9nO5txbzHHajwyIIUagtIeTCir+NTUVxJCOOFd5a1JPPEgKoPc7UbITK8/wfOI7bDOCXAKIAruZ5flir9IwgmeTxQn8Id+44iOh0EvZqCx7atBrr/F4AUP2YVEOWSCSx89Bp3L2zL33d9p4AelYtAADRYxYLp3p6UvdU+nyVyORUHHv7BrF19/k83rYhgPUBD/OLTqTqBsvlbOY8NyNCOtneE8B39h/FiZEpSd2krr3/hSP4RHcbHtl/NH2PLWs78NQrJzA6Gced1yzHE//vOEYn43j00xcjnuCz0vubq5fDUW3BP/7iT+nPhM7TUr9mrS9EccTjM9jZO4CtuzL8ZWMAPV2t6UnYQufI0YpaelIjFr1gKRY9yXzu5S11+NTli3Hvnv50Hjz8ydWITCXwdxlleM8Nfvz4tRN4e2hccR6J5fe1Kzz45ZHBrM9z/XRxkwNfXtOR1X8sJo5KLWtCGKV6KPa6zPMbnDZ89gOL8cwfTqb7H6nPHt53VHDss/+tQRwdHM86/uinL0ZsmsdXf3Iwq//S4anDmgs9ivQs1TeiekKowcRUDM/3DeWNla4PtOi+0EdOv6rSYpGao9FiAYPe6U1NTWNPXyhPfzcEvKov9IlGE9h9OJiX1oZOHy30URG9NWRUmoDyPkshLbLiPymor04Q7HFuKopf9g3n+ci1gWZa6EMwCbUlhFxY8bexqSh+IRDHxwLNtNCHyEKLcabqIxiO4/ZwHLeb47jdAL4JwAkgBuD7c5+VDcdHJtKNDQBEp5O4c8dBHB+Z0OSYFP3BcHqQmrru7p196A+GJY9pkZ4Wz1eJ9IXG0pUdmM2rrbv70BcaMziywpi1nM2c52ZESCd37+zD+q4F6f8X003q2vVdC9JfYqWueXjfUdx4yUJEp5N46MW30//uPRXOS+9ffvU2zkzEsz4TOk9L/Zq1vhDF0TsQTk++AnP+sqsPvQNh2efI0YpaelIjFr1gKRY9yXzu2z58QXqBDzCbB9MJPr3AJ/XZvXv6cduHLygpj8Tyuz+Y7525frq+a0Fe/7GYOCq1rAlhlOqh2Osyz7/xkoV4eN/RrP5H6jOxsU/vqXDe8d5T4fQCn9RnD+87it5TYcV6luobUT0h1KA/NC44VuoPjesei5x+VaXFIjVHUw7pHQ5FBPV3OBRRP61gWDgtjZ6tUtFbQ0alCSjvsxTSIiv+k4L66gTBHm+HJgR95O0Q1UuCTagtIeTCir8dEYnjCPkskYMW40wtfoJSMRvJDkai6cJIEZ1OYmgsCp6H6seWNteJxhIMC8cSCkfBQ/ieoXAUk/EZLG+pw20fvgBTsQScNVY8/vK7JaU3GZ9R/fkqkcFITDCvBiMxgyKSz2AkiganDTdeshDc3ALr514/xXw5mznPzYiQhzY4bbjIW48vrVkGIFs3ma/qnJqe9RmOE/aUlO4y/50U8Z8knx2X2Hla6VeqLWG5vhDFERLxxcFINOscYQ+aPUeOt6qlJzmxsKJblmLRk0w9JJM8brtyKV5+awhXLm8Bx0G0/zcVT6T/rSSPxPJbqG+Y66dini03jkota0IYpXoQu+7twdlF3bnbz709OIbbrlyK514/ldZwppaldM3zwv0KqT6JUj2nnqvUekawh1Fby+TC0lipUD+lEmORmqNZtcj86Q1GYoJzV1roL8SQ1ssZvTVkVJpA4T6L2LYkhbTIiv+kMOtcIEGUMyz13whCDjTvQ8iFFX9jJQ6CfbQYZ6q+yIfn+d9wHFcF4Fc8z1+l9v1ZwuOyw15tySoUe7UFLfX29L/VPiaGz+0QvM7rtgPgRI/Nc1bjU5cvxtd+egjR6fNbOfjcytOrt1er/nyViMdVI5hXHpe+r0JXgs9tz9u2YMvaDnhdbJezVyTPvSbIczOS66Ep3fxthh+ldJP7qs4ta5fBXj375YZQmfF8/r+rOOFzc9/0KXaeVj5VqC0hyoNF8xyCvrhgniN9jk9EC54575TjrWrpqVAsLOmWpVj0REgP96z3499efgcnRqbSPpmbLw6bNf1vJXkklt8+d/7nYn6qtKwqtawJYZTqQey6w6cj+MqzB0W3n9u8pgMWCwT7H1JxCNUDqT6JUj2nnqtQPIS5MGprGSFYGp8W6qdUYizSc0LmT6+t0SE4d9XW6Ch8cZGIzQuYYS7GTOitIaPSBKT7LFLbkhTSIiv+k8Ksc4EEUc6w1H8jCDnQvA8hF1b8jb5TJOSixThTk1khnudnACQ5jnNrcX9WaG+qxUObVmdNpj60aTXam2o1OSaF3+fC9p5A1nXbewLw+9ySx85NTOdt73Dvnn6cm5hWnJ4Wz1eJBLz12LYhO4+3bQgg4K03OLLCzCSRty3Bw/uOYiZZ4EKDmV9rwz03+LPy/J4b/JhfazM4svIk1w8+3p2/3UVKN7mv6txx4BS2rO3AnkOnsXlNR1aZbVnbgZ/98RTs1Rbcec3y9L87F7rz/Odvrl6O+bW2rM+EztPSp8gXKwNnjVVQ37U159dbd7a6sW1jju9vDKCrdbY7Jcdb1dJToVhY0i1LseiJkB7u3duf3vIw5ZO5bdr3Xn63pDwSy2+/L987c/10z6HTef3HYuKo1LImhFGqB6HrNq+Z7TtEp8W3n3tk/1EkZvi8/sdzr+fXtcyxT+dCd97xzoVufPvj2TFsWduBroVuxXpOPZdQ34jqiXkxamsZIfzeOsHxqd+r/y9qC/VTKjEWqTmackiP5yE4d8XzBS5UQKfPLaj1To2erVLRW0NGpQlI91mktiUppEVW/CeFWecCCaKcmeeswrYN/hwf8WOes8rgyAhCGJr3IeTCir/V1lQJfqdYW0M+S2SjxThTi+26UowDOMxx3IsA0pvP8Ty/WcM0dcVi4bDO78VFm6/E0FgULfXnX6kKQJNjYlitFvSsWoCOljqEwlF43Xb4fe70r/vEjgVFXn8XikSxqoT01H6+SsTpsGF9wIP2+U4MRmLwuGoQ8NbD6WB/wcnQmLCuhsejuKCF3dcqhsZi+PFrJ/DAzaswFU/AYbPiey+/iyXznVjSwv7iKrOR66FiW/0Nj+dv9RcMR/HUKyfwfz6xGhYL8MNbL8foRByNdTaABy7y1mNRgxPWKg5dC91prwGAizZficFIFE5bFaZnkphfV4MPLmvG8Hg07zw9fKpQW0KUB2fGhV/HODIRQwdm/cVmq0JPVyuWzq9Nv6q9q9UNm212UCDHW9XSU6FYWNItS7HoiZgeUq/GT/nk9z/XjfDUNObX1aDeXoX2phVZ2wAUi1R+C30OZPtpW4MTl7Q1KCqrSi1rQhilesi87u3BMRw+HcHTr55AMDy7tUV0Wnj7ueh0Eh0tdbjQW49rV3oxOhnDs1+8Amcn43DZq7Hj9iswEZ/Jq19rLvRgWXMdLmlrwGQ8gbbGWiyZP1c3vFfi5NkJOG1WeFw1aGtUruf0c3nrcXYihmdvvwKTAvEQ5sKorWWEqHXU4PpAC9rnX5Yen/q9dah16P8LxUL9lEqMpdAcjdnT03NbIrvdig2dPizJmIvp9Llht2s5hVp56K0ho9IEpPsshbYlkdIiK/6TwqxzgQRRzhwbnsKr757BDz5/KUbGY2iqq8FP/3ASbkc1lnvmGR0eQeRB8z6EXFjxt5NnpwS/U2x0LsPKVvJZ4jxajDO1HKH+bO6vrLFYOCxtrhPcD1KLY1JYrRasWtQgONkndqyUV9VKpafF81UiTocNly1pMjqMojHraxU9LjveHhrH5h+/kf7MDHGbmUw/ODY8Lqmb3GOjk3E019cU9JL2+dnHxfwnd9JJT58iXyx/5PqizVaF7vbGku6hlp6kYlEzHTVgKRa9ENND5i/aRyfj8Lkd+OCyZlXTFstvsc9zPyulrCqxrAlxlOohdR0AfOXZg3n1SGyM1OGpz+hXyEvTYuHQPr8urz8CzPY91PzSi+pH+WHU1jJi1DpqcNkSNl47XqifoiesxCI1R2P29PSuC3a7FZeacC7GbOitWaPSBMTb6EJjvEJaZMV/APPOBRJEOeNzO/Dz/kH87GAw/Zm92oLPfXCJgVERhDQ0riXkwIq/+dwOwe8UjRqzE2yj9jhTs58q8Dz/pNCfVukRyjDqVbVEeWPW1yqaNe5ygbb6I8oZNTRM9YDIREgP23sC2Nt7Ov3/pA+CkEZ8+zkX+S3BBDReJ4hZqC4Q5Uo5jfHK6VkIolyg9pMgiHKFFX9jJQ6iMtHsTT4cx3UAuA/ASgDpJWs8zy/VKs1yIZFIoj8YRjAchc/tgN/nSr86NpnkcXxkIv0a1sxX1UkdE7tnKa+qlUqPUId4fAa9A2GEIlH4XHZ0Gvja3WKwWDhcfWELfnjr5QhFovDOvTKYdX2YNW6zkesdbQ1OnBydxGAkipW+ejz/5SsxPB5Fc50dVRbgtfdG4HPbcZGnDt//XDfGozPwuWuw0idcNlp6E/keIYZU2w3If92slMYsFg7XrvDg2duvmEtnts0u5h7FnFMIqg/Gkcr75npbekuelno7rFXAAzetSm8JtLjRiZNnJzAYiWEinsDiuW2CcsupkH6VxEa6IDIpRRfFXFtsOsVsP9fW4BS8t5qaV7v+UH00P1arBev9PixudCIUicE79zpnrbeWEYOl8SlL+mYpFj3RUw9WqwXXr/SirTHj1eZe5f2VQlRqmVYCLPkYIH+MZwYsFg5rOprx9Bcuy2qzzPgsBFEuWK0WrFvZjLbG89utrvTWGtaXJNiG+j+EmbBaLfifOf62wgB/s1otuG6lJ2ucEvDWk88Sgqg9FtFyu64nANwD4F8AXAXgFmj45qByIZFIYueh07h7Zx+i08n0qr+eVQtgsXB4oT+EO3ccTB97aNNqrPN7AUD0WDLJi94ztdCn2FfVJpO8aHrU8KtDPD6Dnb0D2LrrfLlt2xhAT1cr8wt9Eokkdh8eENUcq5g1bjOR6x2Lmxz48pqOrDx/aNNqXLvCg18eGcSdOw6iwWnDLR9sx0Mvvp0+Z8vaDgyNx7DmQk+W52jpTeR7hBhSbXfuQh+p180W0lgyyafrhZgG5ehUDS1TfTAOobx/9NMX463BsbzyOHVuAm8OjOHhfUdFy0mufpXGRrogStFFMdcqTUfO9nNi987sr5SqebXrD9XH8iCRSGJvf5CJ8QlL41OW9M1SLHqitx7i8Rns6Q/pkl6llmklwJKPpZAzxjML8fgMdvcFmcpfgqh0Jqfi+HnfMLbuzqiXGwJYH/DA6bAZHR7BENT/IczGxFQMvxDwt+sDLah16LfFdDSawN6+wbw4NnT6YLdruQSDMBtajEW0nBVy8Dy/DwDH8/wJnuf/N4DrNUyvLOgPhtMTeAAQnU7i7p196A+GcXxkIt3Ipo7dueMgjo9MSB6TuqdSpNIDZjsFx4bH8cq7Z3BseBzJJK84rUqldyCcruzAbB5v3dWH3gHl5aYXWmhOD8wadwoz1Ltc71jftSAvz+/ccRCvHR/BW6EIGpw23HjJwvQCn9Q5D+87it5T4bTniN0/15vUjF3NexPmRq53FKqjhTQmR4NqnVMIqg/GIZT3vafCguUxNjWTXuCT+XlmOanZ9pEuCCFK0UUx1xrRB+gPCtc9JWmKpXH49DlFfTqqj+UBS+MTlsanLOmbpVj0RG896JlepZZpJcCSj6UoJ72xmL8EUen0hcbSXzwDc/Vydx/6QmMGR0awRjm1R0Rl0B8aF/S3/tC4rnEcDoYF4zhsku8UCf3Qoq+s5SKfGMdxFgBHOY77Esdx/wuA8E/XiTTBcDRdwCmi00mEwlEMRoSPDY1JH5O6p1Kk0kut+r3ukd/iU4+/huse+S1e6A8xueCAZUIieTwYUV5ueqGF5vTArHEDME29y/UOjoNgnr9y7Cz+/eVj+MwVi1FjtQiek+SBobHsspHyJrVjV/PehLmR4x1y6mghjcnRoFrnFILqg3EI5X2SF/bSiXiiYDmp2faJ6cIMfRdCO0rxi2KuNaIPIFZ/lKQplsa+Pw0p6tORT5cHLI1PxManIQM8niV9sxSLnug9X6FnetSfKV9YnGcrJw+hukMQ7DEYiYnUy5hBERGsUk7tEVEZsOJvIZE4QuSzRA5ajEW0XOSzBYATwGYAfwbgLwB8VsP0ygKf2wF7dXax2Kst8Lrt8Ljsgsda6qWPSd1TKVLp0apfdfCJ5LHHpbzc9EILzemBWeMGzLPaXsw7cv+fn/vC+pH9R7Fkfq3gORYOaKnPLhspb9IqdjXuTZgbOd4hp44W0pgcDap1TiGoPhiHUN5XccJeWmuzFiwnNds+p0h6Tno9f0VTil8Uc60RfQCfW700xdKYSUJRn458ujxgaXziqa8RHp/W6/cq9HQsDOmbpVj0RO/5Cj3To/5M+cLiPFs5eUiLSDvRYkA7QRDELB6XSP/NRfWSyKac2iOiMmDF31iJg2AfLcYiWi7y4QE8DWA3gG4AywE8rmF6ZYHf58L2nkC6oO3VFmzvCcDvc6O9qRYPbVqddeyhTavR3lQreUzqnkqRSo9W/apDZ6sb2zZml9u2jQF0tSovN73QQnN6YNa4AfOsts/1jj2HTufl+eY1HfjZH08BmH2G0+cmcec1y7PO2bK2A10LZ31R6v6Z3qR27GremzA3crxDTh0tpDE5GlTrnEJQfTAOobzvXOgWLI96RxW2rO2QLCc12774zAw2r+nI8/TpmWSBK4lyphS/KOZaI/oAfp9w3VOSplAaqT6Rkj4d+XR5wNL4pKbagns3+LNiyfx/PWFJ3yzFoid6z1fomR71Z8oXFufZyslDrFUc7lmf3U7cs94PaxVncGQEUbm4HFXYltN/27bBD5eDFq4S2ZRTe0RUBvOcwv42z6mvv9XWCMdRV0M+S2SjxVjEqlZwAvwIwN8COAyARsIysVotuH6lF22NTgxGYvC4atDpdcFqnS30dX4vLtp8JYbGomipt6O9qRYWCyd5zGLh0LNqATpa6hAKR+F12+H3udP3TCZ5HB+ZwGAkCo8r+55iWCycaHqpVb+ZX2bSqt/isdmqsCHgw5ImJ0KRGLyuGnT63LCZ4NdjVqsFGzpb0d5Ui1AkCp/Ljs7W85pjFavVgvV+HxY3Zuc563EDs6vtFzc5sL5rAbi56rvn0Glm6l2mz6z01eP5L1+J4fFZ72hrcOKStgacGJnAG++fw9OvnkBwbgsCe7UFnQvcaHU7sObCFrw/OgmnzQqPqwZtjbODjGPD41n+JeWTubHI9TxA2veIysZqtUi2s4C8OmqxcLh2hQfP3n4FguEofG4H/D5XWmNyNKjWOYXIj3X2mak+aI9Q+bU1OHHq3CR+dOvliCZmEJtOYn59DWaSSVzZMR+XtDVgMp7AogYnrFUcXntvJO1/cvQrl6baGjx74CRu/dBScBzA88CzB05iXcBb8Fql3kywj8XC4eoLW/DDWy9HKBKF12VHV6s8vyjGr9Rsp4X0mHnv5jo7qizAH06czevXZKZZjK5T8S+4/Qrs+9MQZpJI94mUjKWo31IesDSuqqupxr4jQfz7Z/4M5yanMc9ZjR+9+h4ubW/UPRaW+iGVWtdstiqs93vQ3nR+7irgrddsvkLP+ZFS+jNEcSQSSfQHw1ljHy39zWarQk9XK5bOr023zV2txs6zFfKQQn0JvfNQCrfDhuf+eBIP3LwKU/EEHDYrnvrdMTxw82pD4iEIArBVWREcHceTt1w26zEuO149OohuA/pvBNuUMm4nCCOwWqxIJOJ46pbLMDg22096JzQKq0XLZQ/5OKrJZwl5aDEW0VLtwzzP79bw/mVJPD6DPf0hbN3Vh+h0Mr2Sq6erFTZbFSwWDkub67C0uS7vWqljVqsFqxY1YNWi7M+TSR4v9IfSW4ikVuiu83tlLfQRSq+twYntPQHcvfP8M2zvCaCtwVl8hlQwySSP/UeHFZWN0SSTPH711pDpYk8kktjbH8zTbs+qBcwv9GlrcOLLazqYrHdyfGZpcx3aGpwYmYhjdDIO4PwvJr/1n4fx9XUrsM7vxTJPvaz7inlhKZ4HSPssUdmItbMpWuvtuOOjHdi6O6N93xBAa8aXtskkj18eGZTUpxwNqnWOFHJiJbQjs/xSvnb/C0fwie42PLL/aLpMNq/pwLMHTuLr61bg2hUe0TIrpF+5tDfV4uvrVuSlUeiXX6V6M8E2iUQSuw8PKO5fFeNXarTThfoX7U21svSqRNcWC4fOBfNw+ly06HqkVX4QxsLSuKq13o6rV7TiL59+XbQvoxes9UMqsa7F4zPY2z8oOnelNnrOj7A8ti4nEokkdh46rfv8i81WxdyXLmIeUqgvYVQeitHeVIsvfOgCVfowBEGoQ2u9Hb6Genzuid8b3n8j2KbUcTtB6I23zgartQafzfE3b51N9ziEfFbvOAhzoPZYREt3vofjuO9xHPcpjuNuTP1pmF5Z0DsQTk+SALNbeWzd1YfegbAm6R0fmUgPvlLp3bnjII6PTCi+58nRSXxn/1Hc+qGl+NKaZbj1Q0vxnf1HcXJ0Uq2wKwItykYvzBp7fzCc7sgCs3HfvbMP/UFt6p+anBydFIydhXonVw8p73jg5lXYvHbWO55+9QROjEwJnq9EZ2bVJmF++kKR9AIfYK59392HvlAkfY6Z9GmmWMudVFms71qQXuADzJbJI/uPYn3XAty54yD6g2HNyyz1S+Sfb74Sz9x+OX6++UpZX76Rnsobs/WvCulRrl6V6lppPSLKE5b8sS8UwWMvZY/zH3vpaFZfRi9YypdKpRzmrsSgOS19MFv/wAgK6Z61PKQ+DEGwh5y5KIIA2GtTCKIQfaExEX8bq8g4iMpEyzf53ALgIgDVOL9dFw/gZxqmaXpCkWjWNlfArCkMRqKapDcokt7QWFTxr9AGI1GcGJnCY79+J+vzUu5ZiWhRNnph1tiDYeG4Q+FoyW830BqW81xubCnveHtwDI/uf0fW+cU+M8v5RJQ3ctp3M+nTTLGWO6my4DgIlknqc7E2Tu0yU/I2A9JTeWO2/lUhPRbTr1Gq60p8KwghDEv+GBIZ52s1VyEFS/lSqZTD3JVUWjSnpT1m6x8YQSHds5iH1IchCLbQu70mzAuLbQpBSDEYiYn4W6wi4yAqEy0X+VzK8/yFxVzAcdwiAE8B8GB2QdB3eZ5/mOO4RgDPAmgHcBzAJp7nR9UNV1/E9lT2ueywV1uyTMFebYHHpc0rFD0uO7oXu/HZ/7EUU7EEnDVWPPm7Y2iZe2Wjkr2dPSLP0EKvgSyKQmXDMmbVgM/tEMxzr5vtuAG281wsNq/LjmPD42kf9Llnz6urqcLX112IRqcNzhorTp+bxI9/fxKO6iq88u6ZtGcW88wpz52ansGWtcuw48ApBMPR9DUcOBwbHs/b354g5CLWrqfwuexY3OTA+q4F4OY+3nPodFb7LkfThdJRK95C57DsOZVCPD6D3oEwRsbjeOoLl+L06KRgmfD87H9b5zk0KTM1NEl6Km98bmHtqdW/SiZ5vD86gcFwDGcmYlgwzwm/zwWLhVOkzUJ6lKvX3PN8bjs+3r0Q5yancej9c7LGVQThEek/GOGPes9VSMFau6FW/8xMsDZ3pXZarNS7ckbr/oEWaFXXxe5byOt8boegVuXkod7PQhCEMfhE2k8j+m8E27DULiv5TpKoPDyuGpHxSI3ucQjP+esbB2EO1PY3LRf5/I7juJU8z79ZxDUJAF/lef6PHMfVA3id47gXAXwewD6e5/+J47hvAPgGgK+rH7I+SO2p3NnqxraNgbx9zbta3ZrE0lpvx6buxfjaTw+dT29uX1alezu3N9XioU2raQ/mEpEqG9Zpa3Bie0/AdHvYX9hcJ5jnFzbXGx1aQViud0KxPfrpi/FmcCwv3n//zCV4Z2gC//xfb6U/37K2A1+99kL8/d5+HDgRTp977QqPrGcW8twtazvw1CsnMDoZx5a1HfjKswcxOhnP2t+eIOQi1a6ntLTSU487PtqRfn1nyl/8nvP+Uqgey0lHrXgLncMYLk/gAAAgAElEQVSy51QC8fgMdvYOZPUX/35jAFvXr8C2vUfSn21e04FnD5zEo5++GENjUWxZ24GH9x1VrczU0iTpqbzx+1yC/UK/r/TxTTLJ47fvDGHgXAz37unPqg8tLhv+8uk/Fq3NQnqUq9fM8xqcNnz2A4uz6p+ccRVB+OpqBPsPvjr9Jwz9Xhe2bQjkxRLwunSPhaV2Q6220GzorQc950cWuh2446qOvHm5hW6H6mlVMis89YLznys8+nuKHLSq61L3LeR1Kzz1glotlIdGPEs5+yFBsMxyj1N4rtvD9hw9oT9ajtuLQel3kkTlscJbKzgeWeHVd0y4wlsrOGbXOw6CfbTwN47neZXDnLsxxx0BcAGA9wDEAHAAeJ7nu4q4xy4Aj879fZTn+SDHcT4ALxV6S1B3dzd/4MABxfFrybHhcVz3yG/zVhj+fPOVWNpcl/5ldupXD12tbthsVZrEcuD4WfzF91/Li+WHt16O6ioOn/juq3nHnr39Cqxa1CB539QvN4bGomipL9tfbqj6QLmalSqb7vZGNZNWnWPD47jlP36fXr3K88De3tN44vOXMf3K3kPvjyrWPAvIrHea6lZubDwPXP+dfB989otX4BOP55fB7R9eipkk0q9MT3lme1NtwWcW89zvf64br713Fj/JeatPyosJZjBEs8VQqF0H5PuLVD2Wk45a8co5p0LaeiVorlmxPsJ/3HIphsbiGDg3hYXzHJjntKJ1njPtuQ1OG268ZCE4DrBwwI0XL0D7fOV+p5YmAdKTwWiu2dQvVULhKLxuO/w+tyoTdMeGx/HmQAR3zU1ap7BXW/DgzavwpR+/kfWZXG0W0qNcvabOC4ancOuTB0zbx2QU5vsHavCH90bwmR/8Pk87T3/hMly6pEnXWFgb47HSbhTRFpaVZvXWg57zI2afF1ARTTV7bHgcX/vpwdm3S8QTcNiseOp3x/DAzauZHI+r2e8t5r5ajA+NehYd0M1nP3L9TfBtvEvw2Iv3fRHXfPNx2Z8Hdz2I3zz/XGnBEmZGU93+/r0RfFagL/nUFy7DZTr3JQn2kTlu11Sz1A8j5PKH90Zw108P5Y1HHrx5Ve5YWVPNsjRmJ9imCH+TrVkt3+SzrpSLOY5rB3AxgNcAeHieD84dCmF2Oy+ha24HcDsAtLW1lZK8phTaU9lmq9JtEYfUvqxVFk7xPpy0B7M8pDRr5j1zzbqHvdn3ntWr3inx2tzYXnn3jGBeB0V0n+SRft1h6rOUngo9s5jnjkUTeGTfO3mfs65Toni07h8UatcB+f4iVY/lpKNWvHLOobZeOwppVryPEMOWZw6mP3vm9suxtLku7bnBcDSrbf4fFzSVtMhHLU0CpCezU0izVqsFqxY1qN6fGoxEMRFLCOpwIp7I+0yuNgvpUa5eU+e9PThm6j5muWKG+YNQJCbq93rD2hiPlXZDzbawECxpVm896Dk/YvZ5AZaQ0uxgJIoDJ8I4cOKNrM9ZHY9rVdcL3VeL8aFRz2IGWPJZgpCLtNey05ck2EercXsuUpqlfhghl1AkJjge0cLfpL/HJZ8l5KGFv2m2yIfn+RNKr+U4rg7AcwC+wvN8hMv4ZpfneZ7jOMHXD/E8/10A3wVmV9MpTT8XtfcTLmX/eKlYlByT2ke9usqieB9OpXlWafttSmnWJ7IPvBn2zPW47Ljtg4twtX8Bhud+bfRiP/t72LO09yzLKPXaZJLHybMTGIzEMB5LYMvaZdiR8xad5jrhvVRdNVXwL3BjcWMXhsai2HHgfVE95fqPzy3sc7mf+9x2fLx7ISbjMzg2PF6y19Ne9OygRv9Aqjw9In6dqVE1/KWU/kPufa5dOR9/fsUSjE5Mo7G2Gj989b2s+6iVFqGMQpptFfG11nl2PH3rZeg/HcHUdAJelx3JJA+nrQqb1y5DkgdefmsIVy5vQZUFcFRbkUzyir2JdEKkKKRZuW1isW2nx2XHyERctJ3/0pplAIDnXj8Fm5WDo7oKr7x7Ju/eiUQS/QNhnA5PYX5dDaosQHgygcVNtVgyv3D7XShusTbA57bj2PA49RUMQqv5AzXxuoT7xh6X/tt1eVx2dC92z751I5aAs8aKJ393zDDPT70BORSJwueyo1PDNyBLoWdbqJbXqoGc/q+aSM1dqZ6W2yH4bFrOC5TrXJiUZvXWUAql9USreAt5iJQ2lPqPVr6lZZnq5W9m6BsQRC7SXlsj8v2C/n1Jgn1Y8Foj+mGEOfHq6G9SmvW6agTHyeSzRC5afP+s5Zt8FMFxXDVmF/j8iOf5n819PMhxnC9ju64hveLRYj9hpfvHS8UCQNGxizwi+xZ6alFTXa1oH06leUb7bWazwlMnuJfjSg/7v37x1FVjubcBn3/i91mxe+qqjQ5Nkgub6wTrw4XN9UaHZnqSSR773xrE0cFxPLzvaDp/t6ztwFOvnMDoZByb13Tgvl+8iXs3+HHP7v70Od/62EWoqa5Kb3OR8oa2hvz9o8X859FPX4wv/X9vZH3m97nTXtzgtOGzH1icFVspXk970ZcXhcqztd4u6NetGZOZy+cLt7fLi3iLSluDU7BdFqoLUrTW23H1ilb85dOvi8artK9CaE8ikcTweAz33ODHvXvOe+V9N3bi0PthPPjLt9KfLfe48M7weJb/3bPej397+R2cGJnCd18+VpI3kU4IOchtE5W0ne1NtXh/dCKvPmzbGMAj+97GgRPhdF/CWWNNvxY3897JJJ83BrnnBj9+/NoJvD00XjAGOXH7fS5B/x4aj2GTQEzUVyBSrPCKjNe9+vvsQrcDmy5djK/NbY+XqmsL3Q7dY4nHZ7CzdwBbd/VlxdLT1ar7Qp+Fbge2bQzkxaJ3vug9/mhrcOLLazpK7pfKZbnHKTxW96if3gpPPe64qiOvTFd4XKqnBVTuXJjeGgJKqycL3Q5BXZRa16X604W00VpvF6wXrQUW1WjVh9eqTGl+hSCUs9xbKzhftdyAviTBNqx47fL5Ipot4S3URHnSIeJvHTr7W4e3Fpu6c8bJBsRBsM8KT73g3EEp40yO59lZlM7NvrLnSQBneZ7/Ssbn/wxghOf5f+I47hsAGnme/5rUvdTan1yr/YSV7B8vFQsARcdGxmOS+xZGowkcDoYRisTgddWg0+eG3S69Nkxpnplwv01N93LUc895tTHrfr+H3h/FY79+G39+xRKcm5jGvNpq/OjV93DHVctZ1aASdNujPJNjw+PYefA0vvvysTxdPPKJi8Fzs74YmZrG5HQCbY1OTMaTqLVVIZ5I4itzA4zM64Q8Rcx/nv/yleA45HluyouHx2L43BP5mlXq9QzsRV9OGKLZTAqVpxy/PnD8LP7pF2/OruqPJ+C0za7q/8bHVsr29GPD4/inX/TnvYHnGx/zF6Urue2Lkr4KAUCn/cmXt9Thtg9fAD7Jo8Zqgc3K4a/nFvOksFdbcPuHl2ZtTWivtuCBm1fh7cExALO/cnni85cp9ibSSVmgqWbltonFtp2ZvzKsq7FiOpFEaCwKT70d9/3iTRw4EU6fu3ntMsE+yM83X4mx6LTgGOSBm1dh84/fKNh+Hxsexy3/8Xt88tI2LGxwYjKWwOhkHOsC3qzt8FK/xA+Fo/C67XDZq/Ex6iuUguH9Az34w3sjuP+FI+n+g8NmxVO/O4avr1uBS3UeV7E0XmdprHzo/VH8/d7+vF9u/t16f26+MOG1aqF3er9/bwR/KzB39c83r1J9jkHvZ2OpbuVQVpotNc1Sy0nq7Qhib+splOaB42fx1Z8czKsX3/746oJeqMXbo7Qq0yLuq1vf4CPX3wTfxrsEj7143xdxzTcfl/15cNeD+M3zz5UWLGFmNNWtWefoCf1hxWtZ6ucTbFOEv5HPEkxwbHgcX/vpwby5gwduXq3YZ1l7k88HAXwGwGGO4w7OffYtAP8EYAfHcbcCOAFgk14BabWfsJL948ViSe1BLhYnz4sfGx6Li+5bmEgksbc/WPSviZTmGe23mc3wmHB+nBlXf895tTHrfr8jEzGsXtSU9XaLzWs6cHaC7bjNwGAkiqSIF01OzyAyFcc//uJPWb+gD0/FMR6bSZ+Xe52Qp4j5z/B4FFcsnZ93fsqL1fb6ctiLnjhPofIMFWifAeDsZBxrLvJmrerfvKYDo5Nx2XGEp+K46kJflkfds96PyJT8ewCQFS+grK9CaE+qv9R7OoLNP34D3/jYhXBYq1BrtwqWazJnPX90Ool3hsbw6P53sto5peVMOiEKIbdNLKbtFP+VoQ+vvTeStcAHgGgfZGgsinOT04LHpuIJyRhSjEzE8IX/sQST0zP42wyPb3HZ0dZ4/gs8q9WCVYsa0uOaV949Q30FoiChSAwHToRx4MQbWZ8bMa4SG68HDRivy+3L6MHIREywj6f3GFLv8YfU/JQ26cVE567UJiSida2erVLnwowYM5eSZinlVOjN7L88Mih4rFCaoUhUpF5Ie2EyyYumWcpifa3KlOZXCEI5IxNxwfpzdqK4eSSi/GHFa838nRihL0Njwt9BDo/pOw4bEvkudEjnOAj2kZo7UDwvr3KMJcHz/H/zPM/xPN/F8/zqub+f8zw/wvP8Wp7nO3iev5rn+bN6xZTapzgTNfYpTiZ5HBsexyvvnsGx4XEkc7+BEcBpswrG4rRVScYpdczrqhE85nHVoD8YTi/wAWaN6e6dfegPZk+a51Ioz8SePbUfXe51lbrfZlOdcNk01rK/l6NHQlcs47Lb8Mj+o1maf2T/UdTbbQZHZn48LjuqOAjq4tiZcZzJGHRGp5O4d08/FjfW4iJPPS7y1gte56iuyvMRpZ6tttdr1XYQxlCoPH0ixz2u8+Xd4KgW9Jd5DvnbGM4kgXv39mfXlb39SCQLXJiDnHgJdsntLy2Y58TIZBzvn50ULNfceXp7tQUzc5pJ6bC6qvQhgZK+LVEZyG0Ti2k7j49MpL+YAma1fOeOgzg+MiF4H7E+SEu9XXQM4rBZJWNIYauyYGQynt7yMxXPt/7zMI6PTIhep0dfgeql+ZEar+tNU61NMJamWv3HSiz1ZVgZQ+o9/pCan9ICPecYaqwWwbTU6C8JUalzYUaMmT0uOxY3OXDHVcvwpTWzf4ubHLLSLKWcpPotUscKpanUC6XSLAWtyrSS51c++bnb8JHrb8r7++TnbjM6NMIkiPXfGg3ovxFsw4rXmvk7MUJfWuqFtdJcr69WWkTGKS06x0Gwj63KIjh3UMo4k6lFPiyS2qc4VUlTv24oZZ/i1C84rnvkt/jU46/hukd+ixf6QwUnXeMzM9i8piMrls1rOjA9k5SMU+pYp8+NbRsCWce2bQig0+eW/MWIFFLpST273+fC9p7sWLb3BOD3uYvM4fJgPDYtWN7jsWmDIyuM31snqCu/l+1f2MSnk4Kajxf7DTqRR3tTLToXuvF361fmafonB04JvmliMp7AXT89hH94/gi2rO3I84bNz7yR5yNKPVttr9ei7SCMo1B5+r0uQc8LeM/vpzqd4AX9ZXpG/heuZ8aFfxlwZry4XwZ0trqxbWNOvBsD6GqtzPbWbOT2l06MTCDJAzsOnMrrN/zj/+pE10J3nu/+7I+n0veb9duZkmJS2rclKoO2BqdgH7+twZl1XjFtp9SvDIXu07nQLXpvoTHIPTf48b2X35XVfk/GZyTfFCSG1n0FqpflQUBGH0MvookE7lnvz64r6/2IJhK6x8JSXyaeEB5DTs/oO4bUe/whNj8V1+i5A956kbpQr3pa4Whc8NkiUW3eelCpc2FGjJkXuh2446oOfP+/j+HR/e/ge789hjuu6sBCt6PgtaWUk1S/RepYoTSVeqFUmqWgVZlW8vxK8MwofBvvyvsLnhk1OjTCJJybFP5+4dwU+98vEPoid9yuNWb+TozQF55PCmqF5/Udh0WnhcfJMQPGyQTbTMZnhHc6KWFenrXtupjDYuGwzu/FRZuvxNBYFC312fsmS+2pLMbxkQnc/8IR3PqhpeDmTr3/hSO4yFuPpc11ovsiN9XW4NkDJ9PX8Tzw7IGTWBeYfZ3qtSs8ePb2K+aus8Pvc6djWdPRjKe/cBlCkRi8rhp0zh2z2624LtCM9vmXYTASg8dVg5XeWtjt1vQvRjJFl/mLEbFnl4rl2PA4fvDf7+KBm1el95z7wX+/m372nlUL0NFSh1A4Cu/cdaXuCW1Wmmrt2P+nd7Ly6snfHcMHl/mNDq0gg+PTeDs0iv+45TIMz9WbF/tPo7u9AUsd7K5grbVbBTVfW6PNrxHVJh6fQe9AGKFIFD6XHZ2tbtg0+iWlEtoba8HzwIMfX4VTo5MYi87g6VdPwGbl0LXAje986mJMxhI4MxHDjgPv48TZyfQWAE+9cgK3f3gpOlvd8Lhq8OVn3sCJkSkA53911vZXH4Cj2ooGZzWevf0DmJ6ZQWNtTXri59jwuKhXF/J6oDi/l3M/QlkbagSFynNgLCroeQPtDVhqn13c6J03+6vR9V0L0m3/nkOn8375KVWPm+tqBO8xvy7bV8X6ESlstiqs93vQ3uRMt/0Bb33RfmGW8is3rFYLrlvpQVvj+fI7eHIUo5NxPP3qiXQ/0cIBCxvsqOIs2HH7FZiIz8Bpq8LmZ95AMGPBdupX96+8ewat8+wYm0pgdGoa0ekZLG2qxZLmOslyTSZ5HD59Dn8KRXDblUvx3OunEAxHceeOg7goY+92Mb0o1RHpzzycHJ3Ed/YfzRrDfGf/UVzS1pCnjwZnNX7ylx/AdCKJs1NxzHPY8OqxEXjd2ZqprRHuszmqZ30seywy64MWCyfo4xYLhw2drWhvqkUoEkVLfQ2qLMDfXLMcbY21WDJfvB+RSCRRXWVBW4MDW9Yuw44Dp9L1y15tQXOdPc+TV3jqcSo8hcFIFBd66vHClitx+twUxqIJtM5zIJnkVakDYr/Sz6yXBPsEx2Owcgk8dctlGBybLetTIxEEx2NYWsTbANXAWV2NX791HP/+mT/DuYlpzKutxo9efQ+3f7hD1zgA9foyauB12wX9SO+3CuXPw5z3Pi2wVVXh4Psj+PfP/BlGJ6bRWFuNH776Hj64rEmT9JwOG67Nmbta7q2F06H+mwjcdpvg3Nu3P75a9bSA2b5dJc6FWSyc6FylVhwZjOCxXx/Nmo997NdHcaGnDqsWNQAQb1+tVguuX+nN6oN3el2yyin1doRcn0i9HUHsmNVqyeqjpMaHqTRttir0dLVi6fzadLxdMuaBUm80yh1XZr6todCYUohS51XE0iw0500QhDgNzmocPxPBDz5/Kc6Mx9BcV4P//ONJXNreYHRoBGPIGbfrQVOtHcfPDMxqdiyG5vpZzWrVxyTMi9VSJdhnv2yJNn12MRzVVrxx8nSezy5rWaxrHAT7iPXBS5k7oEU+MrBYOCxtrstrzKT2VJYaaIxMxPCJ7rb0a5lSKwzPTsTQ1uDEzkOn09tkpVbM9qxagNZ6O+74aAe27j5/bNuGAFrr7ZL7KScSSezuC2LrrozrNgbQ09WKZJLHz/uG8+65odOHFZ56bNsYyLtuhceleD/p8FQcN13SlrXn3D3r/YhMzf4iymq1YNWihrLed1wui+fbsal7cVZebdsQwOL5hX9hZDThqTguaJmHzz/x+6xyDk+xvt8vj3s3+HHP7v503Pdu8KcNl2Xi8Rns7B0QrOdGL/QR8osta2ffJGGzcvjqNRfi3eHx9DYX9moL/n5jAD/vHUjfIxiO4pF97+CZ2y/HRHwmvcAnRYPThkOnwvj7vW9m+c4lbY0AIMurxbxe7BkK+b3U/QjlbahRSJXn2NQ0OrzZnnfvBj/Goud/ZSLVhqcoVI/tNgvu+OgybM3wqG0b/HDazk+2JhJJ0X5EalI2Hp/B3v7BkvzCbOVXTkSjCeztG8zRkh/33diJb/7sMB779TvpN5GcGp3Ew/vewScvbUOHpw6XLm7E19etyCq31JvR3PZq3PKhJQiFo1l+/O2Pr8bHAsLlKqSDzWs68PSrJxAMR9N7t4vp5doVHtE+Y6GFRaQ/8zAYieLEyBQe+/U7eZ+L6eNbH7sIU9NJ/Muv3sjTzEtHhzAwOoUtazuytLp5TQc2P/MG/m79SsQTvKA+xMZ0v3prSFRPYnq7+sIW7D48kOW3W9Z24KlXTmB0Mo47r1mOU+cm8Mb7o+lzZrcI6cjz38d+fRQnRqYEPVsIOXVA6lf61DcxD4lkAgm+Cp/N6GNs2+BHIqn/rwLr7BZcvcKHv3z69axY6u36L0RQoy+jFqlfPef2vfT+1bPUnJAWbWMVB0E9VGnUDo9PRfFLgbmr6wLNqHOou6Cq3iHc53Y5tNN6Jc6FxeMzonOVWtVjqflYQLp9TSSS2NMfUhRvIZ8QO1aojwLMLvTpbm8sKh8KjU3ljCnFUDqvkkzyomlaLJyu/kYQ5cTChip0t8/HF/7jD1lt2sIGdn4USrBBoXG7Xiyeb0d3e3OOZs3xnRihL4lkAn/90WV53+XN8KW9rbxYGmuFfbaxlnyWyMZbZxPsg3vrlP9wpbx/FqIxSvcwltp3rT8YTg9oUsfu3tmH/mAYfaEIHntpdjXtl9Ysw60fWorHXjqKvlAk6+1AX1qzDLdduRT3v3AEx0cm0DsQTg9CU/fcuqsPvQNhHA6G04JKH9vdh8PBME6Fp7DjDyfwwM2rcP9NnXjg5lXY8YcTOBWeUryf9EwSuHdvf9axe/f2g3ZDyuft0IRg2bwdKm2PbD1IzPDC5VzEtjRGEEsk8a8vvZNVx/71pXfyviBhEal6bjRCnvDwvqP41nUr8NVrL8I7GQt8Usf/blcfvvChpfBlvOUk9Ws2oT2CP969ML3AJ3UPOZ5UyjOosWd9JVNOeRqbSaYHFMDss9yzux+xjMatLxQR9PS+UCR9TqF6PBVPpr9sOH+PfkzGz6cj1Y+Qm44cyqn8zIZw360foXA0q/36t9+8g+MjU1jftQAP7zuK3lNhnBydxDq/Fz/ffCWeuf1yPHv7FfjO/tnFBbd9+AK8d2Yiz4+/+hPxchXSwSP7j+LGSxZm/TpZTC/9wbAiHZH+zIXTZs1rt1NvkAKEy/PMRBz/8qu3BTXTeyqMf/zFn/DUK7PjlM1rZzX/9KsncGJkCr2nZnXV4LThjqtmx0VvhSJ4MxjGK++ewbHh8awtqwrpSex470C+3z687yj+oSeAL131/7P35uFxVFfa+NvV+67W1t2WLMlCm92yZWxhDAkGbCAkY8xmG5J8JiwZTSaAmTAkzGQCHpv5yLAmGJwfYQlhyQwmIWxOhg+wAYcJTrAhxpZX2VjCtlq7eu+u7q76/dGqUlf3rep2S91q2fU+D89jVF1Vt+4999xzzr3nPQ147n+PYVf3qOA3y+dVEfXv8nlV/P+n6mwSspkDJHspeV7KmB4YDcaJa/9osLCBSwDwhMht8YQK35Zi8n2Ss565NfjxbYfRMxIsaDsKvTYGo2R5mGgJUDHsE4mP7MtDfGQ0GMemlLjApg+6pmTenc6Yinls0WmI8VizLhFYl5pHE2mvlJ6QupbNvGYYFkcH/EQbRwyZfNNsfMpcIPU9Uu+UbX8ZMnLHlyPk9frLEXlNkyFEJr+9UJjOe2IyCguFQkncy1MU+NjDUICsZ4cCsp6VIcRet0/EBvfl/EyZyWcCEMuOzHS6VarumicUJV5ze8KIMqzoaVoWrGg2itsbEW0nw0LkWgQxhsXObg92dn8muN7vC4MVuS/TtdEg+fsG/RHR/jpT0Sc6bsXfV/0+ctv7fcXd9gFfhDjHBqaBfLol9NFUQ0xXHuzzCf4/9fpwkMY/XliP/+/DoxgJ0rj/6rl86a1HV88XZHHNtBly0knZZiLI2fCTj9OpT7PR19nM0Uy/GfST35O8hvZ6yM9we8J8ZvBk6IvTafymG8TsugAdT1u/GBZQKBLXGRb8+HD/fXxkkGdGC0Vionah2LiKyYGSSuhpTmeL/U5MXjPJkSx/0wt0PI61SxvT/JRoPDGGpPEUk8Vez7j/0usJ41CfD09sS5d7m0GDNYtrBe+stOjwxLYujARpQfZ3JnkSuy6mSz/9cpRvU+p3cPMx9Z5k1shUnU1CNnOgrsyYZi8lz0sZ0wPF5BMWU1uKyfcplqznXONTub+vsPIg9r7+PLyvz0uOC0yHWMx0wlTMYzrGEN8pZZNw6+ugn865vVJ6AgDxWjaxjFzZLTP1fTY+ZS6Q6l+xeLHbExaNo8u2vwwZmVFM9puM4kYmv71QkGVWRrYQ3csr8B6kLLMyskU+ZEU+5DMBcKdbkwclm9OtYrWY7Rad6DMdVh1icVb0PrVSQcxG2dyxGE6J97Gs2DO1KDNpJWs051JPWq9WEZ+ZzNYhIwG7RSs6NsWOSjO57ZXm4m57hVmL9lorbji/HqFIDAatCs//+SgqTMXdbgCS83yqIabzSvQqNNrN+LR7hHjdrFVBSSlw/zWtCITjMGqVODYUQF2ZEZe7HKjqWIytB/oRZxIBsFx00kS/Qc6Gzx2nU59mo6+dWdRczTSPnVY98XryGir2G4dV+J6J1n89ncZvusEhIm9cHN9p1eGaBdVQUkCT3Qy1ksLaZQ1ospvhsCRKvB4bCqDPG4ZBk7DLuodCMGhVUCpOTV+KycGylkrMrSrhNxfEfue05iZHsvxNL5QZtcQ66Ze3Jsr8ksZTTBadVh3293oF13RqCjaDBtcsqIZCAbQ4zLjp/Fr85q/j7wSA1z79Ev/6jdk41OfDQbcXc5xm1JWbMsqTqPyK/J0dS6B3WnVosZuxdlkDGBZ4dddx/jdi93D/78jgG2UzByhKgctdDrSsvQD9vjAqzTrUlRnlshbTDMXkExZTWybDlpks5BoXmu7tKLQ8iL2vMg/vs1vIcYHpEIuZTnBadCL9nL957BCxPbl3Sq2vaorKOeYiNT/NOnXOsQwxhpuWtRdIHn7J1e/MZJ8AEPgadovQ9mekyRQAACAASURBVJDqX71aPB6eqY9kyJAhjmKy32QUNzL57YWCLLMysoXoXl6B9yBlmZWRLfIhK3K5rgmAO93K0dhle7qVy6pMvo/LqpxtN2PDla2CaxuubMVsuwVzZ1iJ1+bNsEqyA7kcFmxYkXLfila0OiyY67QSr811WlFjM+D2pY149qOjeGJbF57501HcvrQRNTYDX086+T6uZnSm77v1YuEzb724EbPtlskYktMKTQ4jcWyaHMWffatSKrBuuUvQ9nXLXVApi3tTQauksKq9Bj/63W7c/fs9+OHvdmNVew20GWqOFwOk9MNUg6QTfvi1ZpQZtbj3jb0oNWhwxzKhLr3z0ib82+t7cddvd6PfG8FD7xzATb/eiW9s/BPe7nQDAOZWlaDFYcGzHx3FSzt60p7B6R0pnTSRb5Cz4SeG06lPKQWDDStcKfraBUoxvi67HBbcelHK+ndRI1od4+tfpnncXGEirgvNFebx9zgtxPXZ5RzXBdm0JRNOp/GbbnA5zGlysO4KF2ba9Kgt02PN4lo8+9FRbNzahbt+uxvdQwH8dudx3PXb3ega8OPtTje+sfFP+ObTf8F1T32M25c2orZMj6e3H0FduTFNlz6ySnxcxeQg+YCP1O9cTmtOciTL3/RCXZkRd18+m9c5z350FHdfPpsfL9J4lhk1+MElTUSZmVtt5eX01V3H8eOvt+CG82r559/1290oNWlx8/mz+L+9tfsErl2QsPGe2NaFX24/ik97RsEwbEZ5EpXzGdY0fbvhylZs+TyRwHDDebW463e7sXFrQs+uWVyLHUcG0vX42D3c/6fqbLE+zWYOUJQC9RUmLK4vR32FST7gMw1h1iqJNoZZV9gDJEBx+aeTYctMFnKNC033drgcZLvU5cgPq4ZWqcD6lLmwfoUL2jzEGOY4jFjdXiuIC6xur8WcaRCLmU6YYzcT+9llN2e+OUfkuubXlRklY6uZIDU/pd6Zqb1SzDhSyPQt2fiUJHDMQpyvwcVvuBJikv0r8U7Z9pchI3fYLWRb0m4pvC0po7iRyW8vFGSZlZEtDGoKq1P28la318CgLuxeniyzMrKFQUOWFaM2d1mRmXwmgFxPt1KUApfNtmNzx2L0esJwWvVwOS2gKAWOe0J45ZNuPLiyDSE6Br1GhRf+fBTttTbUV5hwhcuBujID+rwR2C1azHVYoNEoJdmBTvrC2PTBYUE7N31wGO11iWf+XWsl6soX8c90OUzQ6VQ4OuAn1kNeUGMDAL5mNPfMx7cdxoKaxDPFskaPe0LEGtbc98ViDDp7PYJ+UU2DAxb5QFdfEK/sHJOFpJOo9RVGtNcVd7aKVa/Bq5/2pMnxgyvnT3XTJBFlWL6OJ5cR+osPuvDIquJuNwBoNEqsaHViVpkBbm8EDosWc51WaAqcQZqM5AyumlI9Xrh5Ef7WM4JqmwEUpcD+Xi/oGIsntx/FDefV4uGVbVArKRwd9OO5/z2GXk8iMLXuzU48tLINB/t8eHXXcUFmGqdrhgMR6NRKLKixIUjHMNNmgEqpwK6eYWiUFEr0amzuOA/ReBylRu0pZ7LL2fCTj+nWp9LrkxLv7e/FL9csxEggCptRjd/s+AIdFY38/Sd9YWLN1fY6G+p1ic0QjUaJ5S67YJ1vdZj5eXxwwEdcz5sdJrTNTKzNKhWFFXNnoK7MCLc3DIdFh3kzrIK1NNUuAJLsAl12GzPTbfxOJxweDPDjZ9YpMaPEgCFfGHNmWPFv35iDzpMe2Awanmb/0XcP4Zav1mPT+134/LgHT20/mmbbbe5YjFA0DqdVB18ohtYqK8LROGaVGdMOBjAMiy8GA+geDsCoUcE1w4w/3H4BBvziciAlL7nIkSx/0wtSvg93PXk8y41ahGNxDPojePHmRRgJRhM+ijOhy5Y229FYacLCGhsGAxHYLTp8+5m/pMl1x5J6/m+3fGUWzDoV1l/hgkGrwtPbj+DHr+1BTakBg/4Iqm16/M/aC9BHkKfU9lWYdFBSwK4vR7CwxoZXxr7LYdWhucKMZrsJ3nAMf//CzjSW1QdXtuGFPx/l51ylWYdqqx4tdhN6PRGYdEqUGNT47PgIyiTsFXkOnDmg4yx2HhvEr248B4O+CCrMWrz2aQ+aHfnbBBdDV1+QaIdMhX+ajV1VKBRL1nOh29Hnj+KQewS/vmkRBsb00LudJxJjoM9D1qpCgd/u7EmLj9yz3DXprzrQFyTKV33FuXmT9TMxFnZo0E/s52TfZrKRaf1Mt1l0cDmtoCgFTvrC4zG65HhtFnpHan5SlAJLGyvw4s2LBLEcrk1S7bWLsJplYriRjBPrTBl9SjG2nkzMQlL9T1EKXNVWhcZKE9xjdhVn+wHAJc2VeOmWc+H2huG06DB3hlW2e2TIyAJ93jgOuUf59brCrMN7nSdQV25EbdlUt05GMSGT314oDPiYcf/HH0GFKeH/1FeYZJmVIUAoymBTyl7epg+68PDKtoK2o88bJ/rssp6VkYo4wwpkNmGDd2Hj9Wfn/Ez5kM8EwJ1uTa19nOl0K8OweGd/H7Fmcp83jJ3dHuzs/kxwT78vjBqbAX/Y5+YP3nBZDVe1VfFZDaS27Dg6JFr7udqqxx86+/mDN1w26VXzZkjWdAfINaO5eu9c1mgqPazUM2tsBry++wTx+0734AYJoyEaS1sc+NHvdgvqoI6G6KluWkY4TVo+I4uXqxWtcBZ52StPiMZ17TVptWe94eLvc4Zhse3wwCnXYs9ne1Jrwz+0ch5iDHDnb4Uy/eKObjzw9kEAwGPXz+f/zSEcZXCwz4dn/nSU/z1Xe52iFKgrM+KA2yd4139c1YqX/9qNpS0OwXg+uno+FtSU5tQnYnpNRu6YLn0aizGS61OcYTB/Zhn+4cVdAtlmmPH1zu0RX/+476fpOLZ09hHXZI1GiV5PmLj29nrCaJuZ+DfDsHjvYL+kLhgKRIi6bjgQOaWxmC7jd7qBk4Pff3ocaxbX4pF3DuC69hp857m/pulW7qAP5+wyLIhyGIrGsbi+POO7Sbr9jmWNaLSbsLTZLqlbxeQlVzmS5W/6QMr3Sd5USx5PTta+99Jf0+4BgM6T4+v+2mUNRLkeSxrHvCoLtGol7kqyS9ctd+G//9qN7YcHsHFrV0a/g2tfXZkxbQ4kt4u79t0L6oltOtTnw85uj2DOMQyL46NhwTPXLm3E5p09uPvy2aK2nDwHzgzE4gzOrinHzb/+RCC/8QKzxAAJX55kh2RijMhXWzLZVYUCx4CcaifW2AwFbUeu8alc4QnROKuyBDcm2R/rlrvgyVO8IhyNE+Mj4Wh80t8lZre7vfmR9Uy+xumKXpF+TvZt8gGp9VPKZnF7yPHabPSO1Pyk6Tje3Nsr6gNKtbfaqsetFzem3Vtt1Uu2R0qf11eYJH1KAKK2kBSzENd+qe9RqSi0zbSljX8sxuDNPSfPuDkiQ8ZkIEDHiOt1gI5NddNkFBmy8dsLAU+IRl25ReD/TJf9GRmFxUgoSoxvj4aiBW1HgI4RffagrGdlpCAQjhNt8EAkd59WtoQnAC4D4Y9rL8DLHefij2svyGrRE8tsODYU4Bl5kqFTJ+oMd/Z6iMw6nb0eybZoVRTxmWolhc9PeojMOp+f9PA1o1PvM2iUktekIHWf1PedibDo1PwCBYxnAFt06iluWWbsdXv5rKDbljYkGAQ+OIy9bu9UN00SZpE+N2mLv8+l9EqxtEcBBbF/r1lQDSChCyrMWqKOYNnx369qrxZkppHe9ZPX9+KG8+vT3jeVfSJj+iLT+kQpyLKtUIzbA1JrMQepNRkAKkzk+VGedIAyG12gUVLE9ia3RUbxosyogU5N4ZoF1di47TCWz6vKqFvZscMOSgVE7cxsQJKvx7YexufHPbJulSGKXGwUqXtSrzEsWa45l+y7S87Cujc7Bc9av6UTHUvOAndOIlu/41TaJWbPpM450jO5uS3bLTLiLLB+S7r8xtjCt0XMTi+fgkSOXOMR+UDPSJBnOeZ838e3HUbPSLCg7cg1PpUrYnGWLJvx/AinIgt7e7JQaSHLeqU5P7J+psbCsvFtCg2pdT4bf04MUvMzkw8ohf19XuK9+/ukY29SsedM/ZBrTHsiOFPniAwZkwGjRkVcr40aOfdfhhDFsrcwnfdnZBQWJXqyrFj1hZUVMT1rkPWsjBRo1WR/QjOBQ+vybs4EwWUgLK4vTytnIAapzAapOsNiWS7usZI2Ym3xhGli7WdfmIZbIgNOqmZ0rvXepe7L9H1nGgZ8EWJ/DPojU9Si7OH2RvgTiU9s68Km97vQPRRCn7e42z7oJ/f50DTo81xrsReyPUcHA8Q2KsY2ndctd+HpD48QdcTvPz3O/77JbhZkpIp9eygSK6o+kTF9kWl9GhDRHQNJukNqLeYgtSYDAEUB65YL67auW+5Csh2YjS4I0nHib4L05GdCy5h8hGMx3HdlK5TUuA6V0q13LEvoUJ2aQpPdLGpnZgMx+WLYqWFykDE9kIuNInVP6rVXdx1P06/3Xz0XZYbEgTgxeyDOsrx9wf0tk9+RbbtIbVq7tBFbPj+B+6+em5Udw81teW6d2ejzkm2M/inwq7zhKFGuveHCZkoC0nGFQiOZESPZ952KuZtLfCpX9IvEK/p9+ZFNsfcN5OF90Xgc61cIbe71K1yIxfNjK5+psbBsfJtCQ2qdz8afk4LY/MzkA0ohV9mRij0Dp2aHJV/L9NxccabOERkyJgOFXq9lTF8Uy96C2P7MdNgTk1FYFMtenqxnZWSLifoTJMhHyfIIsRrFXGZD8sTnMhuk6hM7rXrifQ5rIiMiFIpij9uLPm8EdosWcx0W6PVqlBl1GPb3p9VKLzWWwaJjic+0W3QoNWoka7rnUu+9zKjFtgNuvoa1QZOoo355qwNqpVLy+840VI5lSqb2R0WRl7wCAIdFi9ULnbhqQY2gDqXdUtxt5zLJUvt8KjPJsoWUXpnK9tgMGlyzoBoKBdBkNxHbuKjOhkV1C3FsKIADfX4c6POjY0k9muxmHOrz8SVnuN87LFrsOTGKIB2H3aKD05r+7bVlejhKiqtPZExfOK16tNdaccP59QhFYjBoE2sXtz7ZzZllrcyow7HBk2k1pb/SMF6c1ykyj+2WxHOseg0+60nU+B3yR1Bm0uL1T3twbn0p//tsdIHYb7j3yChuWHRaROMsbAarwClIHc8Wuxk/Wz0fx4YCuHZhNYwaJWrLDAhF49jcsZjXoZydmQ1IslNbpkeL3YwgHcfRAf8pPS8VYrazjOkNu0VH1KEVJh2ODviJ402yI5QKwGHRgWETcnf9OTWothnAMAzMOhU2Xn829BolSg2JrK3/7RrEQyvbYNWriHOk1KABADitOlyzoBpKCig3aRGLMegZCfLtqrEZ+P/n2ENSn6WAAgbtuC/T6wnjxR3d6FhSj5pSAzQqJU6OBnHl/CosqCkRyLWYTiax/sg482C3kP2Tyinwqyw6Nf725RB+uWYhRgJRlBrVeGnHF2ivsxW8LWVGbU7xiHxATMdNxdwt5DrqKLA9KRofyQO7jk6lwtb9vfjlmoUYDUZRYlDjNzu+QMeSxkl/F4CMsb7TFVa9Bkf6R9NilZxvMxF5jsUYdPZ60OsJw2nVw+W0ZFXWyW7RobZMj+Xzqvhyt2/tPoFKsw6+cAybd+5L0zsbrz875z4AMvuAUv2QSXbE+oGiFLikuRIv3XIu3N4wHBYd5s2wptlhYj6lVEz7stl2bO5YPPZOHVxOa9bjJvatZ+ockSFjMiC2fuaLnU7G9IXUGlhIlE/j/RkZhYWYrJQVWFZkPSsjW5QZdZPuT8iHfPIEhmFFaxRXW/XYcGWraM1ksfrELqcF/3FVa1oNYpfTilAoirf2unHvm0nPXNGKK1odaCjXo8lhE9Re3bCiFQ3lBlBI/Dv1PpfdDLVaKVlbPpe68zU2A65fVCuoo558n9j3nYlodBiJY9PomFgGTCHQ4jCiva5CUIdyw4pWtBR521tE+rzY2w2MZ2KRarxPVXue+NbZONznx2NbE7SJ7bVWrF/h4ktmcBmRG7cews5uD39ydfPOHpi0KgQjURg1KowEEydZdWoKd17ahM4TXvz07QOC73ziW2fjtv/6DOEog9qy8br0a5c2CuqyTmWfyJi+aCw3YHW7cO3asKIVjeUJWWqxk3XHbLtR8AySXuSeAQCz7Sbic+bYE/ZAtVWP9lkpz0iyH4DsdEGNzUBcbzOt4TKKAy6nBZ29MXQPRXDHska8/ElPmq6789ImROMMHnrnELqHQrxeXPnkxwK5OHdW2Slt/qXKV22ZHt+7sAF3Jc2NXGu2S9nO8kGf6Y0ZZh1Rh/b5grj517uI402yI3RqCs0OCy5prsRdlzXj+EgID79zIK0G+7orXHjywwSThk5NYcMVc7DuChfWvzVuf6xb7sJjWw/iHy+sRyjK4GfvHUI4yuCNv50Q+Di1Zfq0/0/149Zd4cKTH3Th4pZK3LGskW/vSJBGTakBj287zLfl0dXzUVMqtENIepuzh2S7RUaTiH/SNAX+SZPDiEtmz8A/vLhryttSV2bE3ZfPLgrfR0zHzSjwhkih19E5drNoLCkfKORcmG03EWWds8knG1KxvtMZM8w6Yqxyhlk3IXmOxRi8vvtEWn9e1VaV8aBPjc0gGutkGJaPMyT7YrPtlgn1g8thIcp2q8OSsR+kZEeqHwDgzT0nRfsok08pdo1hWLyzvy+ncZP61uYKsp/cXJEffSNDxumEYrIlZRQ3pNbAQkKWWRnZolhkpVjaIaP44XJaiHp2In6ffMgnTzg2FMADb+/nT2QBwANv70eLwwxfOIpN7x8WnNba9P5hNNtNaJspngWnUlG4qq0KjZUmuD1hOMYyIlQqCp9+OcIrEWCsDvObe1FXnliEydcWQaui8MrO7jRmnWaHCWadGi//Nf3agppEG9/pPJnIbApEUWJMZDYtqLGhvsIkmn3RMxIk1lHm7hP7vjMRh9wBvLc/vY/ryg1YNKu4s1X2uwOiMlfMbd/vDhDnw3TocykWsKlqT12pkT94AwDn1lfgFx908bpPp6LgCdL4zvn1uLglgJd29GDjtsN4dFUb7vvDfowEafzmlnOxuWMxhgM0PvtyFHGGxU/fPSCQrTtf+Rv+cPsF+OPYt+vVSlz31A6Eowxe3NGNW75aDyUFLGupxNyqEnmzWMYpo9PtI+q0WeUGnDOrDPvcfmz6IGVd/+Dw2HUt/wxevyRleXPPAIB9fX7sPDbAs/2UjzH11FcY0V5Xiv19Xj6ozLfjjb0C+yEbXdAzEsTj24TtfXzbYX4tllHcUKkoRGIK/OjVz3mWE62KwlNrFsIXiYFlAKNOiZ+9cwjL51VBSQELa214fOshgfz96qMjqCrRiTL6iNlyl7scaL79AvQMB2DUqHDD2MYMMK6TW9ZecMqyJFb/PZdnySgu7HV7BToSSOjIH17WIjreFKXArDKTwI7gfrO5YzEO9/vx1PajuOWr9Wk12Ne/1YlbvlqPTe93IRxlsOnDI9h43dl47sZz0O+LQE0p8OSHR/D5CS/Ob6jAU9uP8vcvn1cl8FVS/z9RkucwHrv+bNAxBsFIDCNBGtefW4s7Xv4MNoOG/05KAbhmmPHgtW0I0jHUlBoxq3x8niXPsTlOM/5we0JvGzRKROMMLm91yGxWMoQ+YRKjyFT4J4eKyMebKFvEZGKv20vsF85+KxSkYlD5WEcPDfoFvrNeo8ILY7EkqbhWzu9zB4j2dj7mwr4+P3pHfHj+pkXo94VhN+vwcVcf9vXlZ0ylYn2nM6TmTqlRk7Nd2NnrIcYdGyszy6ZUzBIAMZbbXpudDyVmW5/0hYlziWNJk+oHKdnZ/eWIwOcDEj5fY2WirVJ9lMmnFNO/Rwf8GcdNrB+kfAFfOEqMj+ZL3xQaB/fvx4V/dy352uEjcBa4PTJOLxSTLSmjuJFp365QODSN92dkFBapssLZUIWWFVnPysgWKhWFFXNnoK7MKGDTnIjfJx/yyROGApG0rNK1SxsxHIhg0E/zNduT4faE0TZT+rkqFYW2mba03/V5yXX/+rwRKBQgXhvwRaDXUFja4hBknXHtpOOM6DUlpcDFzU5BZtO65S54Q7Rk9oVUbU/OQSV935mIkWAU82eWCfp47dJGjASjU920jBCTx35vcdehHAlGiTI/Gir+PgfEWcCmAgzD4oDbJ5ADhQK87nNadVizuBaPvjeuI39wSRN+/edj2Of28eW5ogyDhXXl+PjIIDZu7cJtSxvI+swf5mvbf3xkkP9NryfM69rzzzo1xgoZMjiIrrFjtXXd3ghxXe9L0nl+OoZrF9QI9Mu65S4E6Bj/m5Egjbpyi4CpJ6H3E2xWvR7yGppqP2TSBX3eMLG93Foso/jByWSvJ4zff3ocaxbXoiPJXrhn+RwM+Gl+jJ/89tlE+dtzfBQ/eWNfWpZtpqzhsypNOKtSqG85JNt1p/ZN0jaijOmL4SBN9IvirPR49/vIMtHrCYNhE/8W83O4zSynVYfr2mtw3dM7BO8e8Cf0KvccDqnPIz2fjrEY9Edw35Z9SVlaLtgMGoHdwWHj1i5+Ds0aY2+TmatkZIti8gkHfGR7aMBXeB9vImwRkw23yPrV5w0XtB1SMah8rKO9njB2dnuws/szwd+ziWvlggEf2d7Oh/zF2TicNhO+I2CYcYFh45P+Lg5nYixMau5E40zOdmG2PhMJYvYoN59z9aEyxUlJc6nfFwbLku2c5HeKyY6UTojEWFEbi3uOmE8ppX8z2fO5xIv7vGGEojHiWjgcKO4YY7aIshScV95FvLb3p39f4NbION3gCZFtSc80iXXLKByKJSbjCZH3Z2SZlZGKPm+EaEP1FXgPUtazMrIFw7B472D/pMYx5EM+WSCXOtAaJZWWVbpx22Fs7lgMp1VPrNnO1RKWel84HMOeXg/c3ggcFi3mOq3Q6VSwW7TEZ9otCQYBsdrllEKBbQfcacwCi2bNgVqpEP2GaJzF+i2dwqzZLZ146ZZzcWwogF99dCQtW7zFYc5Y1zkX5Fpru9hhM6ixeWePIOtm884ePLSybWoblgXsFpE6lJbirkNpM6iJMv/CzYumuGXZQUw/TAWODQVwuN8HnZrimSbmV1t5ubhmQbWgr20GDQJ0DD/+xmyUGtXYftACTzgKvVqJj48MwqBRYfVCJ5Y0lWPBzBIE6Dj6vWE889EXGAnSAh2SrZ7JRbfLODNRKbLGcrV1HSI6z56k84waFdZv+TRt3UzWL1a9tN53WvXE+tic/ZAtiqXOtozckbzOpurTcJTBfVv24Wer56N7OIDqEgNKjBr80yuJAInTqsM1C6rR6w3hvPoyOMfk54DbC506Qc9PKdKzhh94ez/P/OO06hBngFA0jjuWNeD9A/24oKkSCgWgVAAOy6nLUj5sRBnFAZtejb99OYSnxrKaKsxanBgJpNUnTx3vVJlwWnVY1V4Ni14Fi1aJtcsa0FhpJsoNywLzqiz4568184EWYNy2e2hlGw72+WDRKgX6sNluRm2ZHt1DIcHzkp9/w3m1/AEf7pn3vtmJjiX12Li1S3BffOy21Ez25Gx1bk4ecHtRVaLH3KrJYyORbZ3pj2LyTyrMWqL9UGEuvI9XaNYaKThF1i97DmvhRCAVg8oHnFY9vvuVmbjEVYWBMaaPdztP3S7NFoWUPwUovLKzJy1O9qPLZ0/6u6YKxRBHk5o7pUYNVi904qoFNRj0RVBh1uK1T3sEdoLYGue06onPzUY2DRoV8V6DRgmzTp2zrSrFUmO36ER8zcRzpa5JwaLTEH3LR1bNhwUgzqdyU+b5NJGYr9S9Un2vVlKi33KmQoz9x1luw8vPPzMFLZJRrMgUZ5Ihg0OxxAplmZWRLewWsn9gL/AepCyzMrLFsaEAXv+sB79csxAjgShKjWq8tOOLCcUx5EM+GZBrlmUgHCeefA1E4lhQXUKs2d5cYZZ8H03H8eae3rTafivmOuFymIjPdDlM2NkzirVLG9OyN8LRGExaNTGzW0kBQZr8DUE6Dl84Srw2HKChpEB8pjdEY161TbKu86liIrW2ix10LEbMuqFjscw3TzEMGgU2rHDh3jc7BZlvRm1xbyoMB2iiXI8E6ClqUfYIh2Oi+mEqDvr0ecN4Zedx/PSauXB7wti63w2X04x1V7iw/q1OQWY8x+qTLOv3XdkKnZriy25xfztw0ov7/+cA/7cffq0ZtWUGgQ7JVD8ekDPoZZwatCpgdXtNyhrrgnZsajWK1N5tTKq9OyiS/T7oT2b7iRL1foBOnPyfbTfj1osb+ZJdOjWFDVe2YrbdckrfUyx1tmXkBoZhoaJYXubEmEzoOINSvRp3/W43NlzZyh8mSNa3T20/ih9c0gSdisJP3x7XrfdfPZdnJQGS2FCe2gGbQYMbzqvFY1vH5XTdFS48+WEXuodC0KkpNDssqCk9tcME2ehuGdMTGqUCy2Y7BWxT61e4EI7F+QM1pPFOlolkufvgQD++vbgWj77XCZtBgzuWNabJ4/sHevHNRbXY1T1CnB8H+3x45k9H8Z/XzMVtFzfinhS9uun9w+geCuGt3Sd42yUcZVBbpseMEj3xmTWlBn5zitPfL+7oFvyGy4LksiRJc3Ky7BHZ1jk9EImRffJILH+MIuKI4/sXNWBdko+3foULAJPxzslGoVlrpDDHbsaD185D14AfDJs47HpWhQkuu7mg7fCHY+QYVDg/8YP6ch2aHDbcKGC7aUV9uT4v76MUDG69qCEtxqBUTL78hUQYOEN08cdiskGxxNHmzrBiw5Wtab7NvBlWxOMM2usqBAynG1a0wjl2CEVqjXM5LfiPq1rTvs/ltGZsEx2PE+OndJyZkK0qxVKzcKYNq89Jiede2Ypqa2IuSV2TAsOyRD3JgoVGRRH1uVaV2T7whGisXCicH/9+RXYxX0+IFo0Xk4MWrQAAIABJREFURxmW2PfROAOGAfFbGJbN2N7TFWLsP71vPDwFrZFRzAiKxJmCtMwwIUOIYokVyjIrI1u0OIy49aLGtHh8i6OwccSALLMysoQ/EsWy2cIKSetXuBCI5C4r8iGfDJDKtpAKHmnVFPEUoUZF4eCAj1h3utlhglmnFn3fkD8iqEEOJGqQzyo3QKOkyM+0L0aJQYvNOzsFdaM37+zBVxrOlmTksVt0xMwZu0UHjZIiZpOUGjWIMxB9Zqa6zlLZpqRrE6m1XezQqFTEE6DtdcV/AjRIs9j0QZewVvkHXUV/elUsW8xm1Ex10zJiT6+HqANmlRtwzqyygrfHbtFBo1LAadXhxEgId32tBXGGBQvgVze2Ix5n8czYRti3z61Jyzq954296FhSn/FvD/2/g/jD7RcINqsy6Rkgd90u48xEJAbJbN7k2rvJp7CTa++Wi2QfJ2dLmjTS2frHPSE+CM5dv/eNvWivPbX62MVSZ1tGbjg2FECQZvm60woAP/56MyhKAbNWDYNWhd7RIPRqJUoNGjRVmqBXK6FTU0TWn5+9dyhNt/74tT0CVpLk+65ZUM0fqOB+v/6tTjy0sg33/3E/ej3hnPRpNrpbxvREJM7ym0hAQmbWvdmJZ7/TjkdXzceAP8KzCIit5wO+CH706m7cdnEDasuM6Or38QfRXvi4Gx1L6tHiMEOtpDASiOCmr5yFj48OobGSzMzDlb84OhjAU9uPpunVF25aBE84ij0nPPjvv3Tjlq/Ww6xTYo7TArcnjB9/vRl0nEU4xsCkVYJlx7K3Os5DNB6HWklh7cuf8QfluPdyWZBcpjtpTk6WPSLbOqcHtCol0X7QqpRT0BolfpHk4wHAL6bIxys0a40UjgwH0g5/hOgYjgwH0FpVUrB2aFTkGJQ6T4c29rsDxPhUsv07mWBYij/gA4yzqOWD1UqvUeGznhP41Y3nCOICZ1XWTvq7OBSSWadY4mgajRJXuByoKzOgzxuB3aLFXIcFGo0Sn3wxKhnfyLTGXdVWhcZKE9yeMBxWHVxOK9+fUnFHjVI5znZOx2DQJPy+rzSUTSieKcVSs7/PS/Txmu2JtVrsWqax0qrE9STLgmibJetQMZlkWODf3xLe++9vdeI3303EfC9prsRLt5wLtzcMp0WHuTPGGQql4sVlRi2Raf7yVgeGA7QIC33pKUqdDBlnHgwaNXH+tNedPux0MiYHxRIrlGVWRrY46A7wsdFkWUnYi4VjoDLKMisjS0SiDH5L2GO6ewKMsfIhnwzItRZlOBbF95Y08M4Ll50QicUwHCBn4/V5I6LsOX3eMLxhMrPLSDCKmEi96hOeEC6b7SCewnU5rXjvQB/xvkF/BC67WTRzJhSNEVkN9BoKXw6HRJ8JSNd1FsvEAUC8plMriO/KVx34QsITIp8AnQ61HPt9EWKt8gFfcdfL1qqA9rryFJl3Qaee6pZlhttLZgkpdA1SDjU2A9Yua8KNz4335R3LGvHCx90YCdK489Im/PSaufj5e4dgt+iIbWdSkrLE/jbgD+OsSqE+EdMzHIqlzrCM6YFghmzeEB3Dxc3CU9ip2b7RODn7PRofz8QfDZJZ8jzBhN6fLLmV5X96wxOiEaTjfI1yjuHk0XcOCfRtnzeMZz46io4lZ+H3u77EuuUu9HrJNhpJtzbZx8sgKalxtiAx5qDD/T6sWVyLF3d0o9cTzkmeMuluGdMTfSI2yqCfxqPvHhQw+aSyzHAyMRSI4FuLavGz9w4J7GJO3jZu7cLaZQ2YV23FaCiGe5NsuXXLXXhy+zjTVDLDDsOS5Xl71yAoBfiDbgN+GmsW16LjxV38nHvi/S4is9Wjq+fjstl23H35bNFMdo4N4IDbmzd9LOv60wMBOkaMKwSmgFHEEyb7p95w4f1TMdYaf55Ya6QQisYwHIzyBwa5dTgULWxbglGyrATz1I6RIFkeRoP5kQextSQv/q6CIcYFFHlgDQIKz6zT6yGvD4WOo8ViDP6wz0387kzxjUxrnEpFoW2mLe17MrHcKRQsVqXEOtevcIEzTXKJZ1KUQpQhKBpP2EOkb+EOCuc6VlLs7GLP5a5JyeSgnzw2Q34asRiDN/ecFJVlsXsH/RHMry7B9YuErEUcg0SvJ0D0xyPTgOl8OuD673wXvYMjaX+XS3+dHghFyfGscIHtFBnFj2LxH093RkUZkwexeH2+/B8xiMqsrGdlpCAgIisTie9M73pGBQCXZZmM5CxMhmFxdMCPj48M4uiAH8zYLolGqSJmJ2iUSpQZtcRnlho1MGhUqC3T49aLG3Db0sR/tWV6GDRK2AzkDP8SgxqlJo3oM1UqCle1VWFzx2L88v8swOaOxbyDxdWqTr3PadVhr9tLzJzZ6/YiGIkTs6iCkTgqTOTvK0+i1SX12bGhAB54ez9u+Wo9blvagO9eUI8H3t6PY0MB0Swdi4783fmqA19IWPXk8bZOgxMnlWayDFSYC1sP81QRiYEo11MQtz5l2C3kPq8scA1SDj0jQfzba3sEffnY1sO4ZkE1wlEGj757CHqVEv95zTwcHwkS255K3iD2N71ayeuRbJFJt8uQkQyDhrym6zWJs9J6sevq8bPUaqWSmC2pVo5n4pcZyWsaxyY2WXIry//0RizOCmwEErPOY1sPY8AfwaqFM3Hfln24euFMBOkYzqsvI459i93M251Oa0I+Zjss+OPaC/Byx7lY1mIX3Ed6RpwBNm5L6PlsbGUZZw7EbBSlQoHvX5iQO5tBgztf+Rv29Xrwl6ND2HagD0f6fDg2mJAdBRT8AR9g3C6+ZkE1/7w4A4TphI2Rqo//+bIWrF3WgKfWLOQPBgGJsjqktunGMtW5axzjjs2gwb9+YzZC0Ti+e0E9vn1uTdr8u/OVv6FnJIjLXQ5+Dv1x7QWCA0wcG0Dq3OLeX2nWTXjuyLr+9IBRxMYwagqfr2XVkf1TyxT4p2oVRZTvfLHWSCESZYnrcCRa2PVOr85sj04mbAY1zzzMxW827+xBiSE/8iC2ltjzEWMQYQ1i2cIy63T2evLyPrE4YKHjaFLf7RAb77H4RqY1jqbj2HlsGFs+P4ldx4ZBjx1eEYstHhsKJO6LsTxjGSfXv/igC5GY9HzO9NxklpoHrp2Lh1a2YdsBN0qNWskYajZjJWYviPWR3aKTvAZIj41JIm6dSZalYtA9I0E8vu2woO8f33YYPSNBqEVi7Mm+tIzc0Ts4AueVd6X9Rzr4I2P6Qcw+0Knk3H8ZQhSL/5hNjFWGDKB4ZEW0HbKelZECsT0mwwTiO/Ihnwzgsiy5BS45C5PL1PjGxj/hm0//Bd/Y+Ce83ekGw7AYCZIzMUaCUTBI1BlOfiZXF5lhGXxvSQOe/egontjWhWf+dBTfW9IAlmURjZHZeqIxBnqVEuuucAmeue4KF/RjNN5cFsvXWp1om2njs4Eayw3YsKJVcN+GFa1oLDdKZs4MiGRfDPgjUKsUWL9C2Jb1K1zQqBSSfTYUiOC69hrBt1/XXoPhQET0JHE0zuA/rhK2P9ta28UOX4jM6OCbQH2+QiFIR4kyXux1KMWyA/uniA3nVKBVUcR5l2qcFwpukexAjso9HGWwz+3F/x4Zwis7j6fJy7orXCgzaAR/u+/KVpQbNWlytfblz3g9ki2kdLsMGano94nohjF2MrHrA/5x3eHJwNIDQNI+ACZPbmtsBuLaWeg62zJyQ4COwZ1kF4kx6zAs4LDoYDNo0NXvw31/2I9/+f3nxLF/6J0DvO11w3m1eOJbZ2NWuRH1FSYsri/H3CorL3uv7jqOO5aly+nvPz2OcDTB+pONrSzjzEGJQYn7rhTK3brlLjz0zgHc/fs9eOZPR7FmcS2aKk3YfdyD7zz3V9z86534uyc+wjv7+nDnK7ux4+iQqF3ByeCWz08gECGze3T1+1BtM0ClBEaCNN+OhkpTmv10x7JGmLUqbD/Yz+tkhQKwGTRYsziRYb5xa2K+iLER9nnDfLb/4vpy1FeY0krPUZRCMLe49z+6ej5qbIYJzx3Z1pk4iuGQoj9Mth8mUrM9VwyI2TtTwNY6EiT7myN5YpGRgl9E7wQihc3czMQIOdnwR6LE+E2+ZNOqU46x7CbHrlyw6Cd/k180LpAnWReLd+WLldfltBRFHE2KUajVYSHGKlsdFgDSaxxNx/H65yfxf579C277r8/w7Wf/gtc/PwmajkuyFACAL0IT5dqfQa4zPbfaqsfqcxI2xN2v7sEPf7cbq8+pRbVVD4oC1i13pdlJKirzWEnZ2lI+XyYbQWpsjFoVvndhStz6wgaYtSrJ+wBgtt2MDSk24YYrWzHbbpGMCRfT+iNDxnRDv9ia5pfnjwwhisV/FNX5sszKSEGhbXYxiO4byDIrIwVSrJa5Qj5KlgFSNZePDvhFa0DbLYnshOQB47JOTNrxjCeFAmBZYPPOHnzN5YA/EiWe5NrcsRg6tZL4TK1aCY1agRklWjx34zkY9EdQbtKCjsehUSeCyaFQFHvcXkGda71ejU63DzuPDSRqjfsjqDAlao3PKjfAadWKfgM79u/Ua+UmLegYS6wr9y9fn4MvBsnZLc23XwCNUrxedKlRLdIWHRbOtKGuzAi3NwyHRYd5M6x5q11eSJj15G82TwMmH4NGjb99OYRfrlmIkUAUpUY1XtrxBdrrClffPRdw2YGpfT5VbDinBgW27u/FL9csxGgwihKDGr/Z8QVaHOaCt4RhWETjDLEvm+1mOK06jARpxJnE5vRIkMaLO7p5nUgpgNEgjRc+7kbHkno0VprRPRTAJ18M4JuL6vDiLYvQ54kgEIlhMBABHWN53ZstfaiUbj+V7zw2FECfNwy75dTvlzF9UGnWor3WihvOrxesa5VjmcMce1mqvCezl1kNZJ1uTcp2LjVoRe0DYHLkFoAgS5J7z+PbDqfV2ZZlvDhh0KjwWc+oQJ5IskUpEmO9qr0acQZwWnVYPq8Kw/4IXrrlXPjCUaiVFPad9IAey07m2Af+cPsFaSWTkmXPYdHh/PoybD3YjzgDnhlFp6awrKUSc6tKRG3lB97ej6oSHYJ0XJarMwTxONBYacDDK9sQiMQwo0SHx7YeQvdQCMC4zf/wyjbcNUZZy/390XcP4cGVbdCqKKxd1sCXlnt113GMBGksmFmCh1a24fk/JzaCRoI0cT6019rw0o4vcOtFTfjFtxfAqFXBbtaiusSAvSc96FhSn2DuGfMhIrE47v56C0aDUTy9pp2fU6m+CsdGmPo+gya7TWcxvS7GCCBm64jp68lYM85UZCq9UiiYdGqiDWLUFt4nrMjC3ikUbAY1z4oRomMwaBL90l6Xey37XMExMab2S+kYE+NUt8OWp3aYtGRmpxduXpSX94VjLGY7jXjhpkXo8yV0nV6NjAwruUA0LpAnWefYvgslQyoVhRVzZ0x5HI1jdUn9bodVh15/BL0jPjx/0yL0j433x4f70Ou3oV6vBkUpsLSxAi/evAhubwQOixZznVZQlAKfn/Rg0/vjvg4AbHr/MOrLjTxLQfrYJlgKjBphvBZI+GMPrWwDkChj1dnrQa8nDKdVD5fTApUqcX9tmR7L51Xx9721+wQqTInn7u/z4t43UpjS39iLZrsJVr0G7x/s5eNnNmMilnNuvYtnZm+sNMHtCcNh1cHlHB8rKXuBUgBmnRLP3XgOBnwRVJi18IZpHB8Noq7chMtm27G5Y/HYtySey61vUmMTY1isfyslbv1WJ37z3XMl7wOAk94QKAWbsAnpGIwaFcKxGE56Q5IxYYfIuHHMQzJkyBBHZYHXNBnTF8XiPxaTzyGjuFFom10MYvsCsp6VkYpyE1lWuCpIuUA+5JMFxGouS2VqzJ9Rgg0rWvlyV1zWSXOFGf3BML5/UQNftoNj3FCrgIA3DptBg2sWVPOO4au7jiNIx0HHyDWcA5EotCoNTo5GeEeLY8IoNWgQCkXx1l53WluuaHUgSMdwdq2w1vi6K1wI0jHUlRuwYYWLpynmMqUqLRocGwwS28LVxrx0jlNQV+4HlzQhEInCK8JO0zMcgE6tJF4L0nHMrSrBo6vnpwVZa2wGvLO/b8qDr/lAJBbDuuUu/tAXl80zHWpOGzQKXDLbiX94cZdAdgya4h4TFnGsX+FKm5ss4lPdtIyg43HMn1km6HOuxnuh8cVgAL/5yxdpOnDt0kY8/M4B3HBeLQxqJZ7cfhQAeF2y6f0u6NQU7ry0Cc/97zGMBGkYNSo8/M4BXH9ODS5rnYG5VSV48/OT+PFYKTDuuS/u6D7lGsFiuj0bFMvGj4zCoNKsxHVjmZfceN93ZSsqzYlN3GicrDui8XHdEWPIv4kz479RUsCtF50Fg0aNQCQGo06F2tKzoEyKt09Ebjn0ecPoHgph0/tdgr8nzyFZxosXA74I3j/Qj3VXuLD+rU5sPzj+72RbTgkWP9vahbsvb8Ebnx3HmsW1ArvtjmWNeOHjbowEaV6Pctm3A/4wzqoUyliq7DEMixOecJqMcAd8gHRb2WnV4br2Glz31A5Zrs4wHO4P4p43hDbBidEIXzYrHGUQZ1ly9vdoEBRF4antR/n777y0CToVhX97fS9GgjTuWT4Hr336Ja6cX52ma9dd4cJoIIKFteW47ulx2bv/6rkAABYs4kyCFfGsChOe/egILm+dgVue38n/9v9e3Yr6clNa+17ZeRz3LJ+D+7bsE3ybLxwFw7C8XEsdmiTpdSk/M1X/Z9LXE10zzlSc6kGrfCHGxLGqXVizff0KF2JM4f0Tq15JjA9Y88CkkgklBiVWp/TLhhUu2AyFb0tgjMU2NTZSaBZbjlE51dbUqPKzvg4HaGLsaiRA5+V9GpUCe44H0uRvbrVl0t/FsGS7nWHzM+84Ns9UGeLYPCf9fQyL9w72T7mdz7G63JtkH3CsLgf6vXDYTPjOc38VjAHHqEPTcby5tzft3qvmzcBwMMHGk9qfI0EaC2psxNgix1LgDUeJ93rDUcRiDF7ffYIvR8Ux41zVVgUlBXzvwoZ0e3zMjxNl0vBG4HJacZlrhiCWk8yyyjGzt81M70Mpe8GkVWE0GMMdL+9O6cMYGIaVjKVKjc32rgHiO0eDNC5srMR/XNWa1kcc81C/L4KfvN4puF+npvDizYsQjZPtwCAdx9lVJXjw2nnoGvCDYRPlVs+qMPHMTjJkyBCHVgWi/TYF58VlTAMUg/+ooliizKoomRFahhDlJrJ/WmEqrE+oVZFlViufvpCRAr9I7CAwgdiBLGYTgFQGyMEBH97bfzKNyaTZYUIkyvA1nrkM+l980IVZZW2w6FW44bxavqY7twlj1qkQpBXEDP+HVrbBH4kTMymeu/Ec7HF7+Y127tq9b+5FXbkhUQPurU/T7nvh5kXo9USwKaWdmz7oQk3ZPFE2okdWzQdYFj9775DgmT977xBeunkRaIbM7qFRJTIwSJkvdosOFKUgZpn0jATxwNv7BVk2D7y9Hy0O87QPZmtVKjy5vVPQx09u78JD17ZNddMyIkizxBr2+crqmyxQUBLn5sMri7/Py4xaHBv0Jli5xrK0Xvu0B5e3Ogrelu6hAK6aPxMP/L8DeHBlG7r6fQKmh8e2HsbPV8/HtQurAQBv7+1Fx5J6VFn1OOEJobXKgmsXVoNSAAzLonsoxDNL9IwE+QM+wHh2V8eS+oLWCC6WjR8ZhcGAL85vTgOJ8b7njb148eZFqCsH1Eqy7kjW1ypK5DdJ+mUkSCPOKngmC35zOji5myWZMlgBWcaLGeUmLa47Zyae/LALD65sAwXgoXcOCO2FD7uwauFMjARp9HpCuHrhTH4jFBhn7Lnlq/XY9H4XNm4b/3eqLIghmyyvSrNQ1q5ZUJ2WoZuLXMksU9ML3nC6Dk2WOQCoLdOj1KDh2Xpe3XWcZ4eaVW7C7S9/Jrj/0XcP4a7LmvhDQvdt2YcXb14EBix+9LvP0+bDDy9rSfNPfvzaHnQsqcdMmwFbPj+B7qEQv8mVqv/+7bW9eOaG9jTdORKk4Q1FcctX66GkgIZKMx555wAUihowrAJqpQKVZh2+GPLjtv/6LOvNVDE9rVcrBYeHAFlf5wunctAqn1BRSv6gAdeGdVPkV3mCcWJ8YCr8U1+IIfqbv7nl3IK3xaApDlYhOsbyzK7JMaiz8iSvlWYtMXZVMYEsRCn4QnFyjOGmyZ8LlCKzbT+ZKDNqiTKUL1/+2FCgKOJoxz0hAeMOyyYYd9prbQinxE0Boe/0+UkPkRmnvtwIm57MMvXizYsy2q9mEYaq529ahM5eD394hbv2k9f3orHSBH8kRozJPvuddtSVm1AiwupaYlCjZyRIfG4qyyoJUn7dgC9CXD+ev2lRRtvhuCeEVz7p5mVSr1HhhT8fRXutDRYJ1nGVisJylxO1pQYBwxLHPOQNRYmHAz2hKCrMOiJznUGthNsfhtsbTjvw7faHUaeTbR0ZMqQQiYFsv02DWLeMwqMYYi0xRiHLrIysMBxgiLLyyKr5mFVRuHZEYrLMysgOpjzEDuRDPhMAV984NUOhxmbAx18M4OJmIZPJuuUueMM0fOE4MYN+MBBBuULLB0mA8U2Y9lobPCFyRoknFEUkyhCDkEMBGiwL4rU+bwQKBfnagC8CFiC2c8hPw6pXEdsSjsbgj8SJzxwM0KgwaXHnpU149N1DAsdMp1KixmbA7Usbif0plmVSYdYQ2zEciEz7oPZQgCb2//Akb/bmA2L1MPNVU36yMOgn9/lQnrIRJxNOkxbnzKoQsHLdd2UrnHkKsoqBYVjoNRQCdAzdQyEc6vPhiW3C/gxHGexze/HEti4+GGxQK/HzrYfR6wljpm0eWBZgAFTbDHBadej1hDHgD4vqsya7uaA1gotl40dGYTDgp8nrmj+hG4az0NcDvgh57U+quRpnQAwOvzTJG1ZcnW2xDFZAlvFihkrJwGbQ8DoWINtrdJzBPcvn4IltXfinSxqJ48kF17l/k2RBClJZXgzD4oshP+5Y1sjbtkqKrMNPRa5klqnpB7H65Fx2e22ZHrde1Ii/f3GnwJ7fvLMHN58/C5EY2c8xJ6Wfcn6PzaABHWPT5kM4RvZPGBa45429/IGjcJTBAbeX+NvDfb40ls21SxOMWNxho7XLGvCtRbXQqSh0JH3PHcsaYTNoeLasTIdwSHp67dJGrH35M9x9+WyBvMv6Oj/I5kBsISDK/uArvF/V7yfbMgMTqB+fc1tE9MpUtCVIx3DtAiGr0LrlCXbkQiJER4kxqFA0P4xCQTpOjF09c0N7Xt7XJzLmfXmYC2J2e77kq9qqx+r22hRmqlZUW/V5ed9QIFIUcTQpdlFPKEZs42gwIc9ukbXP7Q3DIMIS7g8n5qSU/eoNk9nHveEoRoIijIOeMFiQbVx/OMH+5IuQs3Z9kShiDPm5fd7M67iUX9d5kmzP9PsiYFiWeNiGsx36vGHs7PZgZ/dngvv7fWF4QzHit4yGEmxHWzp7iWxHKhUFm0FDPBxoM2jAsnEiQxuQiCVycWTuOx599xDaqktQVy7bOjJkSKHPS17T+os8Ri+j8CiWWIssszKyRTax9kJATGaLfS9URuHBsgwxdsCyuVdjkQ/5TAA9I0E8vk2YdfL4tsNYUGODVqXC+i0pDDlbEhl3lhJy3bUqqx5DAfJGoj8SQ6lBM37KKymrYdGsOQBY4jPtZi3YsX+nXbNoEWdYIntOqVEDjYoi3uewJGowk5h8Fta2waRVEO8zaVUw6hL1MzuW1INhAUqRqLNp1FLoHiZnr5w90wYAxEyjR1bNF63ZPN1RZtSQx8aQn7rskwm7RSvCylTcdSjNOpWo7BY7Ovt8RKaRWeVGtNeVFqwdx4YC8ARjsI5ld+nV6XqktkyPhkozblvaAAB4+ZMerFo4k8/Yt1t0aSU9Nu/s4TdVSGM022EpqMNRLBs/MgoDi4huMOsSuqFUTF8bx/W1WE3p5JqrgyKbF5PtnGTDwCLLePEizlAwaJV44ptnI8awKDWqUVumR/dQiP+NTk2hqdKML0eCGAnSqCk1EMeTZcf/3WI3Y3PHYkG5rYng2FAAt/3XZ7AZNLz91mQ3T1iuZNaS6QexOumcLdBiN/MMZsC4Pf/0mnbsPelBmWJ87XdadbhmQTWUFOAs0fEHgXVqCntOeLHl8xO4d/kcdA8FUWXT48RoEBQAm0FDbINORQkOvAEAw5JtDYdVj2c/OoIHV7ZBTSmw3+3lWQq53yyuL0NXnw/3/8+BtI3vZOaiTIdwOD1d1bEYWw/0CxgRU+Vd1tf5QTYHYguBcjNZdsuMhfcJK0VsmQpz4X28MlNm26tQMGiEDLzA1DDwatXiMah8IEjHREvN5wOiMYY8yJ+Y3Z4vWd/n9mLTB4cFMrTpg8Nospswv8Y26e/TKKmiiKNJrV+UIiKIOQJjTOZj88ohYlvYzVrQMTKDuFqVVANZBGVGss4tNWqgokRipFYdFCDHQR1W7dhzddh2oCsta/crDS4oFOR7jZrMpSak/Dox+8tu0aLSrMVNX6lLS8B0WBK2QyobJ3dvhSkxNqTYdHvtbEm2o7aZNkRi5MOBv/pOO1QUJcrWFaBjRD+50IcpZciYjqgUWT+nwn6TUdwolliLLLMyskWFiK9cXmCfUExmK2WZlZEChYLiEweByfHZi3/nugggRlMnlXUyJJL1P+CL4GtzHOQaxTOs2Of2igbxmirMxOyeVocFXUN+rLvClVb/Wa9RIhKLYsOKVr5kF3efkmJh0qrw/Ysa0mqNW/Uq1NkMeHDlPHT1J9U8rjRhtt2E/gCN68+pScu+sFu08ARpYv1ys1YJf5jBz987xCs8hgV+/t4hPLJqPnzhADFI1DMcgEqpEGExIvdzvgJLhUSIjhFreodixe/EqigGt17UmCZzxV471axVCRgHOLmeDod8Bv2FORyQCX3eMA71+1GiV+P/Xt2KQDgm6NPaMj2+f1GDQI/+HpYJAAAgAElEQVStXdoIikoc/tmwohVDgQgeWtmGE6NBPP/nbmzcdhhPrWnnN1VIGy6zygu74VIsGz8yJgeZ6GgterJu4A75AAxxLU3wUSXgDYtkb4bHs6vLTIXbyMtUZ1uW8eJFkI6hzxtJk7dffNDFlxu689ImDPkjePmTHtx5aRN++sf9RBl+4eNu/t86DTVpB3yAcXaRXk+Yt5WdVh3uv3ouX3YxF7mSWUumH6x6ZZovcseyRjyz/QguaqkEw5Iz2D/7chQzS/U4PhLEHcsa8fInPUR/YPPOxN/f3tuL69pr8IMkvXXv8jmYVW7AI+8cTNPB9yyfg1gsYZuwSSbqW7tPpPkyd17aBItehW8uqoUCQIxhoFMpMTLG2Ma15V9//zk6lpzFs/Ykf0/yQaJsDuFQlAJBOo6NW9MZEZPlXdbX+UE2B2ILAZNWRfatdYX3T0J0DD+4pIkvfadTU/jBJU0IRwvvn8bicXxvSYOAWWvdchdi8cLHAUI0mXEkVOB+EWU3yhPr04wSPZGRw2nNzwHDcpMSt17UwB8C4Fg+yk2ZD0KcKkxaJXHembST/y4gwRD0rUW1aXMrX8xBQZrMblfoOJrU+nVyNEicV9GxOa5Xk8fIoFEizrL4yd/NHmOsScQyK8xaqJSZ9bdCAWJslVIALqeFHMt1WnHCE8SGFa40+SwxJFgHmytMxHhuc4UZnW4v0U6Pxk8tfsam/LzKpsV9V7YKEqjuu7IVVTYtQhGWyIyztLkSAKBRg/g9GjXgLNESGXecNi06T/iIsuX2hNE2E/BFyId1fJEYojERdiF/BLMdFqKfXFMq2zoyZGSCWUsR10+TLvPBRxlnFool1mKSZVZGlogzLH789RYMBmje5iszahBPNYryDIuO7KdY9PnxHWRMX4iVrvWGcmffLf6d6ymGFE2d3aITOaGng1IkE6PSrIVKReGqtio0Vprg9oThsOrgGqtRHInFcddlzXj4nYP8++66rBl0nMFJX5gPjgNcVsNetNfZcGwoiP/+i7Be8jPbj8By4VmoKzdArQzh4ZVtCNAxGDUqROMx6FQq0DFynevHrpuPI0NBhOi4oObx+hUuHBkKwjXDika7ScDI02g3oabUiF2+CLF++cMr2+CNkEuOecM0bHoybWuJXo0ow5IzRepmi2SmTP/MVYNGhfVvpWTivdWJF/OUiTeZYFiKKKsvFXnbh0MRzCrT46k1CzEciKLUqEYwEsVoqPip9cRqvFv1aom7Jh92iw4apQKzyg0I0gy6h4IwaJS47eIGqJQKzHFasLN7BN+9oB6v7jqOXk8YG7cdxq9vOgeVZh3+4aVdgvn/vSX1eHL7UaiVCn5TpRg2XIpl40fGxMEwLLYd7MPnxz28QzC32oqlzXZ+PBmGRX2FMbGORmIw6lRQKxVgOaeBpYhraXIGd6lBQ2TAe3TVfP434VgsrRTMuuUuRKbgcKcs48ULg0aFdW8m7AOO1eTEaAgPXtuGAB2FWasCA8AfjuEfl9RjMBDFkubEQYo7L2lEo92MmlIjKAXQ4jDDoFGh0qyFQgH85YuhSau7TsrOHgnSWFBTgj9OQK5k1pLph9FgDNWlWjx/0yL0+8L8GJabtLjnjb347gX1xDGNMQyqSvT4+XuHcPP5s/DDy1qIjD8PrmzDT/+4H9csqE5jJtiwZR86ltRjaYsDb+/tFejgOMNgKEjjX74+G25PCE6rDiNBGmuXNiEWi+FXN7bDH4nDpFFhKEDjr18M45WdxzESpPGz1fOxeWcPHlzZhq5+n4Bp576xdyYfztGpKXBifiqHcLKRd1lf5w+ZDsQWAt5QlOhb//TquQVvi1mngtOqEfhKo8HIlCREqCglOQPupsL7mxqVksiK8tyN5xS0HVLMJvmAWLmu9trJZ54BALcnik0p9vamsblQXzm57xoORqBVUYJ4l1ZF8Qc7JxslejXe3debFu+6+/LZeXmf2NpS6Dia1Ppl1qqJ8+rlv0+wDQ0Hovjt2DrMxUBf+PNRzLikGQ6LGl+qlIJY5n1XtqLMmFlXUQoFnvxQqHOf/LALG68/GyoVheUuJ2pLDXB7I3BYtJg7Fsvt90WgVSkE/mI8HseAL4LaMhMODviIMbJmhwmecBT/s6dXwPLz9PYjaHGYM7ZXKmZ9cjQCBViBHCvAonc0Am+YfNjmy5EgGuxm9I5GBPONZRPzra68DaUGDZFxZ8ttX4XTqhdlOwLEGcvLDBooKQUxJlxl1WFWuRGPrJqPf/7t+Hc+sqrwyV4yZExHBCIMcf18eGVhGQdlFD+KJdYSlGVWRpYIReOg46zA5rvrsmaEooU9uO4Lx4ky+5AsszJSUGokn4GYCCPxGXXIRypTX+zaF4Nkmrrm2y9AbakBty9tTMviqLEZ4AnRxI06LnNEpaLQNtOGtpnCNlaYtNAoFQInTKNUoNykxclR8mnaPm8YM6w6HOr3Y+1/j9dL5hypeJzFv77WmbZAv9KxGEMBmlyLO0hDr1bxWTHcu9a92YnnbjwHFKXAV2eVw6pTC5xbilJg0E8TGY6GAjRKDGoi5e4D185DNM7i5U+E117+pAcLa2yIMXFirTqGiRPZhrKpXZ6JuWGqIVa6bTiQn8DSZEKMyWo4kPuJxEKg0qxD5wkf/umV8YMmG1a0YuY0yISOxhj86+UtGAqOn1wuNWgQi+dezzEX1NgMaHaa4IvEcMDtA8MCITqOKqsOI6EYOl7cJdA13IbY8ZFQGq3zY1sPo2NJPVa1V8Nu0aXN2UV1ZXmZs9nqhmLY+JExcfQMB+BOYlsAALcnjJ7hAOrKE2PLAhgNRtMyRTnazdEw+QCrJ4mlR6+miOxsBs14JopFp8Wrnx5OC1bfs9yV/44gQJbx4sTAWKa+06rDmsW1vNw9tf0o7ljWiGqbHk9vPwpPOIrvX9SAJ97vEsilWafCWZUmMAwLFolsreMjIfzkjT08E9Cjq+fjstl29IwEc7aTxLKza0qNvGzlApm1ZPpBRSngHqVxZCAAhgW6+v2oKzfiifcTsvvqruNpLDt3LGtElU2PniE/vndhA578sAvfv7CBaF929fvQ6wlDoQDxOsMCG7cJy2XVlunRseQsQTBo3RUuxONxGDVKeOIM9h73gIVCwKzA2S7/+fZ+rF3WhK5+H5Fpp7HSxAdIORmd4zTj/LPKTukQTrbyLuvr0xf+SIzoW/sjhWesUSuVCEVZ/CjJnl+/wgW1svAZimKlWwJTULrFF46SmSnChW2LUqEgsoEo8xTjCIgcEvBH8vPd4VicaG9HYpPv76opFf7l958Kvk+npvB8ng6R0fEYrl2YEu+6wgU6T8xU1VY9Hrt+PqIxVpDAkE0cbbIhtn4NB8kxJe6gVSgaBx3jEi4ABQA6xiJEx+EJKYmlzF+65dyM7QnScaLODdJxxGIMtnT2psWAr2qrgkGjxElPJG3+NdgTB3V6PeR4rtsTRlOlCV+f6xSM/x3LGlFbasjYXqnSKholhZ+8kR4L3tyxGEYNuRy1QZPYJujzRmDVqdHsMPMHz/50SI1+bwQBETaeL0eCuKChQpTtCEiME8knViiAOEuOCZ9Tlzg4qFULY+VadfHEb2XIKGZ4sohXyZABFE+sRUxmvbLMykiBTa/Bw+8ISRIefucgNv99YUvQekVk1ifLrIwUMCyIiTLZ+CliOGMO+UhlNwAQvdY9HCA6Lz3DASgUINYaXlBjg1WvwaufpmeVPLgykbEfizHo7PWg1xOG06qHy2mBSkUhzgD3/8+BNEfrKw0V0KrI9Z/VygRbBymgU6JX48jA/8/evce3cdf5/n9/JVmWLFt27MSXXJxbkzSNk5YSClsol5aWFLpNuZWyZ4Flu3TP7we0j+3CWTgHCpT+FpZLd7ktbLmc3fJboLDslgClQCnQ5UCXhkvTpG2a0jZpEtu5Ob5KlmV9zx++xLZmJMceSTP26/l46JFYI818NPrM6Dvf+cx3nG+D1dU3rLpYxPWqs1MFikwymVHt2tupW6YM+3rrzg5ds225lha43cjwiHPHTHY0p6GRnGujsz5W5Xil3lf//CKlMmmH0Yb6tbmtoWA+FBq5we37KafaaueD7kQAbh0Vj4YdY49F/T2s4uDwqOPVVfO5J2K5LElEZY8PTtsW3rPjXC2pKe89SA/1DKmmKqzHOwemxfL3116gW777aN6+5voXrdOXf/GUqiNh1xNz57bWqX1Jjet+2stCn0K/FX4qAoR3Tg1mNDRj1LqbL9+oU4MZrVk69pqJIlenoldJqo85X2k69UTAwVMpxxH3GmvO0eblY79XW9qSuu6i6cO4T+0UBaQzt3VzGrVkojjyXa/YpM7TaR09nZq8bdCSmqjS2VH1DI3omRMDerSzf9q+7sZLN+jevZ26ZGOzDp8a1Hf3dOq9/7Fn1vtCpwLJKza36K4bXjCtPXU2+1K3oktGLQkWY4yOnp7eXr/psg26/oVr1d2fGbtCXHaySK0+XqWctTp8akjNybgGM1ntvGCFalzaxueO38Jh4u+Z060d2z7CoTPP/c2OzXknxT703X36xz+5UP/v18Y6iW687JzJmCdeM7VYqL0xrjWNNdNeMzH/tUsT03J/c0udDvempt1KYzZFxeQ76n0yWqc0dpLbqT1UqsKHQtobE9q+ul5vvnjdtJFPKnHrlqW1ziPoNNWW9zjs+EBGd/7q4LSRN+781UGtL1HxX23ceZ9cqpGd4lXufVdeOzXkfOuzUo3kUxVyHsW5VCNTPX2qX32pbN5tnJ4+1a9Nre79aKXg9lvo1qcUj44VFbbVV+uNz1+dVxjVVl+tQ6dSjt9fd9/0CzucNNe5j2Kwr7PXsQ94Q3Ot+tNZxw77rSvGjuOWNziPcNNWH1PfsPN7L17fVDTeQrdWyeWci5+HMqNqq4859iG3JMcuYmlvjDuu31VL4sqM5ly3/UIj10vS0Hhuz8z1L755u2StY59wemRUz5wc1Du+9ru8Zd5z4yUUOANFJF36q4LQ143yCoXMvPtwvEDOYraODQy73uqznMhZzFaXW+H/LI5T3Pi/WmAKY8wOSZ+SFJb0JWvtR2f73kJXN0hynVbo6oZCB1Pb2xsdT9S1L6lRNpvT3Q8fcbz641i/8zyPD6Q1MjrqeG/o1MiI+k5NGd51vJNrYnjXpkRUb3/Z+mn3o377y9arKRHVyUHnzov+9IhaXIZ7bq6r1p6jvZMFPhPvueU7e7VuaUKjdlR/99qtk1frho20bllCOTuqeNS5Y+b/v/75qo5Yx2lf/fOLdHzAOc6Tg5mCJ17dinUOnhzUge6BvJMN65fWatWSGtfvp5yFPlVh5yvxqmZxH/FKS8YijrEnfV6g1N3nnGfH+vx/u66hTFYfvffxadvCR+99XHf+eXmHie/uS2s0l1+R+lhXn+O6DYekD1y1RScH0o77m5CRNrcmdahnyHU/7WWnTqHfCjqPFqb0yKhu//ET077z23/8hL7ylu2Tryk2slpvynl675T7qbbVx11H3JtQrFMUkKR4VVgfunqLjpx2PoGRs1JvKqt/+MmB8VsPjRXv7Ohom2xnuRUvTFy1df2L1un2+/bMel/oViAZjZjJDvmzLZosVnTJqCXBMehyS5d//JMLJy9umGgrHu5J6d3/tke3Xr1FVkZ/OD4w+d62+ljeiD+3XdOhr/ziD7r92vN1+NSQbrnqPN36vUennRz66oMHFasK6ZzmOt142Tna2FynJ48NOG4/vz98evL5nHU+OWbM2P47Gg4pM5rLi+m9O87V4139eccSn7n/wORoWZ/9k+cok7WzKiom3xe3VCabl2M3XrpBqZHyj1jjehFQiQofClnVENe1z5ve33Lrzg6taij/SCTZXM7x2Hc0V94RVePRsHqGMtNGIJlaFOG1VMb5NrOpEt1mdsBt5KASjJjUlHDuC5vPMOqFuPV3HS/RCYq+1KjjSDflvjV8obZeemTUcd83PH77hZRLscidf36RmuuqHW8J1TyLW9dFwnLsd42EC4/G49ZmmBjZKhmL6ENXb8kbGTYZr9Kjnc59JUdOp3T+qsK3v3MrSlpWG9PAcNa5P7sqrJUNNVq5JD5tZJyVS+Ja2TA2elB21Dqu33+9/vkKhZzXUWj8kNVt5HpJGh4ZdfyswyOjWlITdewT/tfrn+/a/97dl6Z9dBaue8tfqPNET97z+w/8QW0ViAflUWxkNGBCLmf1o8e6K37hq9sxR08A7m6B8qqrjjje+qguVt5zkOxnMVvLXGoultXO/Rbb/j7jPoUxJizpc5Iul3RY0kPGmF3W2kdn8/5CBTnW5WDsWH9atdXOxQqJaFhxlwKgZbUxPdszpKHh7LQDpqHhrJ7tGVJfasT56o9ltQUP0HqGMvrCz/fl3Rv6k6+/QDVVchzeNRmrkpVVfzqbV9BiZdWadL6apKUupp5URh/84y364JQDtw/+8RYNZbIaGHY+MOvuS2tFQ1wnBqYX0Lz7FZu0ujHhelB8YmDsnuduB8XLXK6OS1RHXE+8FiqmOtqbcjzZsG1lvfrSLt9Pc23RA2wvWVklouFpOZSIhmWLv7XiRq1Va31sWuyt9TGN+jz6QoVtftcz6DxM/OnB8p4AWNkY094j/Xmx5Kzz1fUvWNek9/77Hl33vHb91cs3Trslxk2XbdCGllqtXZrQfz190nU/7WWnTqHfCjqPFqZ+l+HGp94Gw22EuomO/oYa5+kNNWeust/Sliw4dPmEQp2igCSd6B/WvY906i9fut5xBJGQkQ4c69ebXrBaX33woD59/9gt4CbaiJJ78cLj4wWZbrc9ctsXuhVI3vDidbMuFJrtPCm6DB63E7NTC2om2uLveNnYLblu2bVPN7x4nVbUxydf09mb1lcfHBulYnVjXJta67SlrV4Xti/RqcFhZUetunpTuumyDVpWV61Dp4b01QcPTha7ffJHj+u/v+QcxaIhrVuWcNxvz7zLqVsB8o2XbtDIaE5NiepptyK2VhoaGdVHZhRev+/uvZMjAKVHctpzuDev0I78hpOqUDgvx+7afUj/3zVbyx5LXcy5/6OuAhdyPNrV53jR0cbmWl3QXr5jdkk6NTjiOILO2qXlHVUoEQ279l2VQqwqoi88MKN/6oEn9fHXnV+S5TUmnNvbS0pQeNObGnEsMCnVbSKaihxreO2Ey+3VT5T55Fmhtl6iOuK47/vEeH4dc7tAq39YqxpijreEis7igrmu3mF94edP5vW7rlt6vpbXO/efttbHlLO2YF/SyYGMhkdGp/WRDY+M6uRAxnU0sKWJ4v1Q4ZBcbtMn9aUzjnncPzyig6eG9Ikf7Z8shBrNSZ/40X6d11av9c216na5ALW7P61krMpxHc3md2mZS5/30tpqHet3/k5PDWbUnHR+X02J9m9Btv+xx/SSV73WedqBP+il7/p83vN7P/K2s16OW8FQ29Il+sa/fOms54fSqXU5f5WIBua0IMrk6RPOv8ub3nmJ1jeX7xg1yHe3QHmFQ8bxHG+5C9fZz2K2aqNhx8L/2uq5t2mDlGUXSXrSWvuUJBljviFpp6RZFfm0uBwQNNfFJv/vNO3I6bFbz0w9EKupCut0KqNal9FKwiHpyOmU4223vtxcq96U88n4I70pnVfjfNutcEg6NTjseG/oU4MZtSarHXdo37zhBepNO189+8U3b9cFK5c4n3RcXq+HD5/WZ39zIO+WY++58jzVuOy4muuqlc7m9PEf7p+2vI//cL/+5a0XuXde1ERlXb6HmmhE6azzVWLRiHF8T1NttOBQugMuJ3UH06PqdzkR0dWbLutJ12jYecOOhv0/osNQJqd/uO+JaR0G/3DfExXpjD4b1ZGQ4xVJ1VX+X+d+uUVa1+lhNTrcVuC7Dx/R+686Tx+ecXX98b60Dp5MKTUyqm/tPqybLtugNU0JNSaiaklWq71xbLjuYvtwr5RrOfAP9xH7zuyDi+0bomHj2ECb2pHMKD3wSm2sSr96+pRODmZ069VbdMuUvLvpsg2qqQrrCw88pZ6hzGRRQcqh3VOswOFs9oVuBZK5GbW9Z1M0SdHlwuHW/p9ZUJMeySmdPdNuz1nl3aKrszetL//iKV3/onVqb6pRJBKaHOUml7P68WNd+suv/lZt9TG95sKVeuNF7VqzNKF4VUhffvPzdHxgWI8cPq2muuq8Y66/ffVWfeonT0zG8+3fHM57zfuvOk99qRF946FD2tHRqjVNCd102Ub9z/94ZPI1H97Z4Zi7Zsq5RbdCO/IbM0WrQrruee15/QOVOD4pdxFJIW6j2R05nSp7kU+iOuI4gk65T0T0pjOOfVelKkw53u/cP3W8vzSjz5RzxKT6eJVjgcnHX1uaAqawy8gokRJtWrUuBXulutWam0JtvaFM1vHWTf3DY/nc7HaBVm21+odHHUeh+dKbt6uYwUzWMa+HMlnVVjvvAyMho+GsdZw2+bmyo859xG/ZrpVL4o7ff0t98SKfzt60Y5Hhc9obFA07F4k+d/X5Onhq0PFzHjo1qPXNtQULj3pSGZd1NKpiMqPOIzRlc2P/d1pmdVXI9X0jMxuT0IgNqW3nuxynzaWYx03niR7H5XR+5xOeLQPeCPKdAlBeB08NOv4uT/w2lIvb720ljjngb26F6yfLXLjOfhazdWpoWE2JqO5403PVMzSiJTVVymRz6hma+zF0kIp8Vkh6dsrfhyU9f7ZvXtOUmLwFwcSGdvu1F2hN09jVVW7TjvcP6yu/fHpascJXfvm0Pvba8wseTLkVigykRwseLBWaZ7zK5SR+VUiDGeeRdQYzo65Xzw4OZwuedNy6vN5xCOxty+u1r6vXsegmEjbqH3IuYhrKZLVuWcL14NVa5ytQWpLV6k2F9e3f5hccve+V5zmONpSIRlw7/rp600o6FCHEqkKqi0dcC5im3lKlHE4OZmRnnByzVjoZgGHeBtLOHSP9w+UfVv5sHOsb1tf/6+C0PPvSA3/QOy/dUPzNFVbj0ulequHZ3XT1DUuyeaPy/PcXr9e3f/tsXgfT65+7SrGqkC5e36TnrGpQe2NCa5cm8oYBLbYP90q5lgP/KHT144SQjOrjEX3idedrMJNVIhpRKCSFNZanx/oz+tcHi+87GKUHXuhNjUy2pf7+vgP6H6/YpLb6uLI5q8M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\n",
            "text/plain": [
              "<Figure size 2340x2340 with 182 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts20Ndcz3KkS",
        "outputId": "1f7b77a6-4264-4c6f-a4fc-94230733e3e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "encoded_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>name</th>\n",
              "      <th>club</th>\n",
              "      <th>age</th>\n",
              "      <th>position</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>nationality</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>market_value</th>\n",
              "      <th>new_foreign_1</th>\n",
              "      <th>big_club_1</th>\n",
              "      <th>new_signing_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mesut Ozil</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>AM</td>\n",
              "      <td>1</td>\n",
              "      <td>4395</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.56</td>\n",
              "      <td>167</td>\n",
              "      <td>2</td>\n",
              "      <td>Germany</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>Ã€ngel Rangel</td>\n",
              "      <td>Swansea</td>\n",
              "      <td>34</td>\n",
              "      <td>RB</td>\n",
              "      <td>3</td>\n",
              "      <td>137</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.88</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>Spain</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>Romelu Lukaku</td>\n",
              "      <td>Manchester+United</td>\n",
              "      <td>24</td>\n",
              "      <td>CF</td>\n",
              "      <td>1</td>\n",
              "      <td>3727</td>\n",
              "      <td>11.5</td>\n",
              "      <td>4.50</td>\n",
              "      <td>221</td>\n",
              "      <td>2</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Theo Walcott</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>28</td>\n",
              "      <td>RW</td>\n",
              "      <td>1</td>\n",
              "      <td>2393</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.15</td>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>England</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>Juan Mata</td>\n",
              "      <td>Manchester+United</td>\n",
              "      <td>29</td>\n",
              "      <td>AM</td>\n",
              "      <td>1</td>\n",
              "      <td>2280</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.35</td>\n",
              "      <td>102</td>\n",
              "      <td>2</td>\n",
              "      <td>Spain</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              name               club  ...  big_club_1 new_signing_1\n",
              "1       Mesut Ozil            Arsenal  ...           1             0\n",
              "366  Ã€ngel Rangel            Swansea  ...           0             0\n",
              "260  Romelu Lukaku  Manchester+United  ...           1             0\n",
              "3     Theo Walcott            Arsenal  ...           1             0\n",
              "265      Juan Mata  Manchester+United  ...           1             0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrSO9sWNX0S7"
      },
      "source": [
        "2. Build models using all the algorithms above to predict market_value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTLrzDhf3boi"
      },
      "source": [
        "#data=data.drop(columns=['name'])\n the same code has been added below",
        "#make note of this..... cuz im removing from main data not from encoded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0i_apdtd3-60"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "label_encoder=preprocessing.LabelEncoder()\n",
        "encoded_data['club']=label_encoder.fit_transform(encoded_data['club'])\n",
        "encoded_data['position']=label_encoder.fit_transform(encoded_data['position'])\n",
        "encoded_data['nationality']=label_encoder.fit_transform(encoded_data['nationality'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEg-v0Oz4rbZ"
      },
      "source": [
        "encoded_data=encoded_data.drop(columns=['name'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBP1NuX65L5N",
        "outputId": "8f2af7fd-86f6-4f14-cc1b-ed857ca65654",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "encoded_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>club</th>\n",
              "      <th>age</th>\n",
              "      <th>position</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>nationality</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>market_value</th>\n",
              "      <th>new_foreign_1</th>\n",
              "      <th>big_club_1</th>\n",
              "      <th>new_signing_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4395</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.56</td>\n",
              "      <td>167</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>15</td>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>137</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.88</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>47</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>11</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3727</td>\n",
              "      <td>11.5</td>\n",
              "      <td>4.50</td>\n",
              "      <td>221</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>2393</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.15</td>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>11</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2280</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.35</td>\n",
              "      <td>102</td>\n",
              "      <td>2</td>\n",
              "      <td>47</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     club  age  position  ...  new_foreign_1  big_club_1  new_signing_1\n",
              "1       0   28         0  ...              0           1              0\n",
              "366    15   34         9  ...              0           0              0\n",
              "260    11   24         2  ...              0           1              0\n",
              "3       0   28        11  ...              0           1              0\n",
              "265    11   29         0  ...              0           1              0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dlk7uJup24rv"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X = scaler.fit_transform(encoded_data[encoded_data.columns[~encoded_data.columns.isin([output_var])]])\n",
        "Y = encoded_data[[output_var]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iu-l6Mh-VcG",
        "outputId": "3e974c6a-75e4-402e-d806-191db22bdd74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        }
      },
      "source": [
        "encoded_data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>club</th>\n",
              "      <th>age</th>\n",
              "      <th>position</th>\n",
              "      <th>position_cat</th>\n",
              "      <th>page_views</th>\n",
              "      <th>fpl_value</th>\n",
              "      <th>fpl_sel</th>\n",
              "      <th>fpl_points</th>\n",
              "      <th>region</th>\n",
              "      <th>nationality</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>club_id</th>\n",
              "      <th>market_value</th>\n",
              "      <th>new_foreign_1</th>\n",
              "      <th>big_club_1</th>\n",
              "      <th>new_signing_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>club</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.041378</td>\n",
              "      <td>0.003524</td>\n",
              "      <td>-0.001018</td>\n",
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              "      <td>0.037607</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.052287</td>\n",
              "      <td>0.026559</td>\n",
              "      <td>-0.137956</td>\n",
              "      <td>0.139128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.041378</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054261</td>\n",
              "      <td>0.209102</td>\n",
              "      <td>-0.049327</td>\n",
              "      <td>-0.099975</td>\n",
              "      <td>0.024667</td>\n",
              "      <td>0.215552</td>\n",
              "      <td>0.077748</td>\n",
              "      <td>0.051207</td>\n",
              "      <td>0.969917</td>\n",
              "      <td>0.041378</td>\n",
              "      <td>-0.144592</td>\n",
              "      <td>-0.164685</td>\n",
              "      <td>-0.205341</td>\n",
              "      <td>-0.083684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>position</th>\n",
              "      <td>0.003524</td>\n",
              "      <td>-0.054261</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.148666</td>\n",
              "      <td>-0.028122</td>\n",
              "      <td>0.000701</td>\n",
              "      <td>-0.020999</td>\n",
              "      <td>-0.011167</td>\n",
              "      <td>0.023718</td>\n",
              "      <td>0.062404</td>\n",
              "      <td>-0.030554</td>\n",
              "      <td>0.003524</td>\n",
              "      <td>-0.061075</td>\n",
              "      <td>-0.027684</td>\n",
              "      <td>-0.118118</td>\n",
              "      <td>-0.046163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>position_cat</th>\n",
              "      <td>-0.001018</td>\n",
              "      <td>0.209102</td>\n",
              "      <td>-0.148666</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.301136</td>\n",
              "      <td>-0.502653</td>\n",
              "      <td>0.071600</td>\n",
              "      <td>-0.100295</td>\n",
              "      <td>-0.019806</td>\n",
              "      <td>-0.034447</td>\n",
              "      <td>0.183599</td>\n",
              "      <td>-0.001018</td>\n",
              "      <td>-0.202518</td>\n",
              "      <td>0.019294</td>\n",
              "      <td>0.002106</td>\n",
              "      <td>-0.084747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>page_views</th>\n",
              "      <td>-0.101979</td>\n",
              "      <td>-0.049327</td>\n",
              "      <td>-0.028122</td>\n",
              "      <td>-0.301136</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.755074</td>\n",
              "      <td>0.497496</td>\n",
              "      <td>0.547811</td>\n",
              "      <td>0.038063</td>\n",
              "      <td>-0.081125</td>\n",
              "      <td>-0.033223</td>\n",
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              "      <td>-0.058465</td>\n",
              "      <td>0.514907</td>\n",
              "      <td>0.107937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fpl_value</th>\n",
              "      <td>-0.071966</td>\n",
              "      <td>-0.099975</td>\n",
              "      <td>0.000701</td>\n",
              "      <td>-0.502653</td>\n",
              "      <td>0.755074</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420925</td>\n",
              "      <td>0.605196</td>\n",
              "      <td>0.067131</td>\n",
              "      <td>-0.098131</td>\n",
              "      <td>-0.077714</td>\n",
              "      <td>-0.071966</td>\n",
              "      <td>0.771985</td>\n",
              "      <td>0.075629</td>\n",
              "      <td>0.437659</td>\n",
              "      <td>0.093738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fpl_sel</th>\n",
              "      <td>-0.005185</td>\n",
              "      <td>0.024667</td>\n",
              "      <td>-0.020999</td>\n",
              "      <td>0.071600</td>\n",
              "      <td>0.497496</td>\n",
              "      <td>0.420925</td>\n",
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              "      <td>-0.061016</td>\n",
              "      <td>-0.057530</td>\n",
              "      <td>0.024549</td>\n",
              "      <td>-0.005185</td>\n",
              "      <td>0.484164</td>\n",
              "      <td>0.049998</td>\n",
              "      <td>0.229267</td>\n",
              "      <td>-0.035876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fpl_points</th>\n",
              "      <td>0.062469</td>\n",
              "      <td>0.215552</td>\n",
              "      <td>-0.011167</td>\n",
              "      <td>-0.100295</td>\n",
              "      <td>0.547811</td>\n",
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              "      <td>0.498251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.011551</td>\n",
              "      <td>-0.009951</td>\n",
              "      <td>0.206685</td>\n",
              "      <td>0.062469</td>\n",
              "      <td>0.595919</td>\n",
              "      <td>-0.218141</td>\n",
              "      <td>0.258834</td>\n",
              "      <td>0.111885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0.096069</td>\n",
              "      <td>0.077748</td>\n",
              "      <td>0.023718</td>\n",
              "      <td>-0.019806</td>\n",
              "      <td>0.038063</td>\n",
              "      <td>0.067131</td>\n",
              "      <td>-0.061016</td>\n",
              "      <td>-0.011551</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.064884</td>\n",
              "      <td>0.095204</td>\n",
              "      <td>0.096069</td>\n",
              "      <td>0.110158</td>\n",
              "      <td>0.057300</td>\n",
              "      <td>0.049222</td>\n",
              "      <td>0.157004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nationality</th>\n",
              "      <td>0.141633</td>\n",
              "      <td>0.051207</td>\n",
              "      <td>0.062404</td>\n",
              "      <td>-0.034447</td>\n",
              "      <td>-0.081125</td>\n",
              "      <td>-0.098131</td>\n",
              "      <td>-0.057530</td>\n",
              "      <td>-0.009951</td>\n",
              "      <td>0.064884</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.061459</td>\n",
              "      <td>0.141633</td>\n",
              "      <td>-0.123843</td>\n",
              "      <td>0.070364</td>\n",
              "      <td>-0.139468</td>\n",
              "      <td>0.002776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_cat</th>\n",
              "      <td>0.037607</td>\n",
              "      <td>0.969917</td>\n",
              "      <td>-0.030554</td>\n",
              "      <td>0.183599</td>\n",
              "      <td>-0.033223</td>\n",
              "      <td>-0.077714</td>\n",
              "      <td>0.024549</td>\n",
              "      <td>0.206685</td>\n",
              "      <td>0.095204</td>\n",
              "      <td>0.061459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.037607</td>\n",
              "      <td>-0.116853</td>\n",
              "      <td>-0.152721</td>\n",
              "      <td>-0.188779</td>\n",
              "      <td>-0.080978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>club_id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.041378</td>\n",
              "      <td>0.003524</td>\n",
              "      <td>-0.001018</td>\n",
              "      <td>-0.101979</td>\n",
              "      <td>-0.071966</td>\n",
              "      <td>-0.005185</td>\n",
              "      <td>0.062469</td>\n",
              "      <td>0.096069</td>\n",
              "      <td>0.141633</td>\n",
              "      <td>0.037607</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.052287</td>\n",
              "      <td>0.026559</td>\n",
              "      <td>-0.137956</td>\n",
              "      <td>0.139128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>market_value</th>\n",
              "      <td>-0.052287</td>\n",
              "      <td>-0.144592</td>\n",
              "      <td>-0.061075</td>\n",
              "      <td>-0.202518</td>\n",
              "      <td>0.716096</td>\n",
              "      <td>0.771985</td>\n",
              "      <td>0.484164</td>\n",
              "      <td>0.595919</td>\n",
              "      <td>0.110158</td>\n",
              "      <td>-0.123843</td>\n",
              "      <td>-0.116853</td>\n",
              "      <td>-0.052287</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.097173</td>\n",
              "      <td>0.624354</td>\n",
              "      <td>0.115376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_foreign_1</th>\n",
              "      <td>0.026559</td>\n",
              "      <td>-0.164685</td>\n",
              "      <td>-0.027684</td>\n",
              "      <td>0.019294</td>\n",
              "      <td>-0.058465</td>\n",
              "      <td>0.075629</td>\n",
              "      <td>0.049998</td>\n",
              "      <td>-0.218141</td>\n",
              "      <td>0.057300</td>\n",
              "      <td>0.070364</td>\n",
              "      <td>-0.152721</td>\n",
              "      <td>0.026559</td>\n",
              "      <td>0.097173</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.082913</td>\n",
              "      <td>-0.082678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>big_club_1</th>\n",
              "      <td>-0.137956</td>\n",
              "      <td>-0.205341</td>\n",
              "      <td>-0.118118</td>\n",
              "      <td>0.002106</td>\n",
              "      <td>0.514907</td>\n",
              "      <td>0.437659</td>\n",
              "      <td>0.229267</td>\n",
              "      <td>0.258834</td>\n",
              "      <td>0.049222</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.053711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_signing_1</th>\n",
              "      <td>0.139128</td>\n",
              "      <td>-0.083684</td>\n",
              "      <td>-0.046163</td>\n",
              "      <td>-0.084747</td>\n",
              "      <td>0.107937</td>\n",
              "      <td>0.093738</td>\n",
              "      <td>-0.035876</td>\n",
              "      <td>0.111885</td>\n",
              "      <td>0.157004</td>\n",
              "      <td>0.002776</td>\n",
              "      <td>-0.080978</td>\n",
              "      <td>0.139128</td>\n",
              "      <td>0.115376</td>\n",
              "      <td>-0.082678</td>\n",
              "      <td>0.053711</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   club       age  ...  big_club_1  new_signing_1\n",
              "club           1.000000  0.041378  ...   -0.137956       0.139128\n",
              "age            0.041378  1.000000  ...   -0.205341      -0.083684\n",
              "position       0.003524 -0.054261  ...   -0.118118      -0.046163\n",
              "position_cat  -0.001018  0.209102  ...    0.002106      -0.084747\n",
              "page_views    -0.101979 -0.049327  ...    0.514907       0.107937\n",
              "fpl_value     -0.071966 -0.099975  ...    0.437659       0.093738\n",
              "fpl_sel       -0.005185  0.024667  ...    0.229267      -0.035876\n",
              "fpl_points     0.062469  0.215552  ...    0.258834       0.111885\n",
              "region         0.096069  0.077748  ...    0.049222       0.157004\n",
              "nationality    0.141633  0.051207  ...   -0.139468       0.002776\n",
              "age_cat        0.037607  0.969917  ...   -0.188779      -0.080978\n",
              "club_id        1.000000  0.041378  ...   -0.137956       0.139128\n",
              "market_value  -0.052287 -0.144592  ...    0.624354       0.115376\n",
              "new_foreign_1  0.026559 -0.164685  ...    0.082913      -0.082678\n",
              "big_club_1    -0.137956 -0.205341  ...    1.000000       0.053711\n",
              "new_signing_1  0.139128 -0.083684  ...    0.053711       1.000000\n",
              "\n",
              "[16 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Uf2ialV-SdF"
      },
      "source": [
        "x_test['club']=label_encoder.fit_transform(x_test['club'])\n",
        "x_test['position']=label_encoder.fit_transform(x_test['position'])\n",
        "x_test['nationality']=label_encoder.fit_transform(x_test['nationality'])\n",
        "x_test=x_test.drop(columns=['name'])\n",
        "x_train=encoded_data.drop(columns=['market_value'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yqit9e-HJCQF"
      },
      "source": [
        "Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-JUDSIa5ndy",
        "outputId": "d3c3d1aa-ffab-41c8-874f-07a5546bf476",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "Regression_Linear = LinearRegression(normalize=True)\n",
        "print(Regression_Linear.get_params())\n",
        "print(Regression_Linear.fit(x_train,y_train))\n",
        "pred_linear = Regression_Linear.predict(x_test)\n",
        "print(\"MSE score:\",mean_squared_error(y_test,pred_linear))\n",
        "print(\"R2 score:\",r2_score(y_test,pred_linear))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'normalize': True}\n",
            "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)\n",
            "MSE score: 2253.1826765864375\n",
            "R2 score: -11.382032075377651\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBtvaez5G490",
        "outputId": "f59f6e7c-6c99-4b9f-8eae-5cef40697f96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_linear"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 73.12537239],\n",
              "       [ 68.14267011],\n",
              "       [ 70.41664979],\n",
              "       [ 71.89918818],\n",
              "       [ 89.41323628],\n",
              "       [ 38.99397245],\n",
              "       [  4.96890692],\n",
              "       [ 56.45567605],\n",
              "       [ 12.38012772],\n",
              "       [ 80.31377304],\n",
              "       [ 66.81711412],\n",
              "       [ 38.78692983],\n",
              "       [ 13.40458301],\n",
              "       [ 71.25849892],\n",
              "       [ 46.88400985],\n",
              "       [ 52.49250857],\n",
              "       [ 55.21153362],\n",
              "       [ 63.52825238],\n",
              "       [ 37.58320781],\n",
              "       [ 51.43370009],\n",
              "       [ 40.45156502],\n",
              "       [ 41.11822136],\n",
              "       [100.66050843],\n",
              "       [ 22.74572104],\n",
              "       [ 39.7530292 ],\n",
              "       [  2.43032904],\n",
              "       [ 87.82668467],\n",
              "       [ 54.31485398],\n",
              "       [ 32.20614711],\n",
              "       [ 60.86910207],\n",
              "       [ 12.271169  ],\n",
              "       [ 77.63582677],\n",
              "       [ 28.5377136 ],\n",
              "       [ 69.11834428],\n",
              "       [ 58.67717185],\n",
              "       [ 36.06984864],\n",
              "       [ 13.96390841],\n",
              "       [ 47.98727429],\n",
              "       [ 60.38858314],\n",
              "       [ 40.43814005],\n",
              "       [ 18.00494856],\n",
              "       [ 85.48394133],\n",
              "       [ 61.30833476],\n",
              "       [131.58782016],\n",
              "       [ 90.22545779],\n",
              "       [ 50.68140783],\n",
              "       [ 58.35751051],\n",
              "       [ 32.45599056],\n",
              "       [ 44.08484616],\n",
              "       [ 19.1110305 ],\n",
              "       [ 69.69451135],\n",
              "       [ 68.63852788],\n",
              "       [ 17.71302106],\n",
              "       [  8.16982675],\n",
              "       [ 70.06065283],\n",
              "       [ 42.1039904 ],\n",
              "       [ 73.03813623],\n",
              "       [ 29.90638175],\n",
              "       [ 22.05339107],\n",
              "       [  3.88014347],\n",
              "       [ 54.29944775],\n",
              "       [ 75.16729041],\n",
              "       [ 18.69495508],\n",
              "       [ 74.66751644],\n",
              "       [ 59.56201332],\n",
              "       [ 91.68803061],\n",
              "       [ 71.01750015],\n",
              "       [ 42.48988432],\n",
              "       [ 35.90189611],\n",
              "       [ 44.11845076],\n",
              "       [ 15.9723239 ],\n",
              "       [ 61.12041179],\n",
              "       [  3.85508535],\n",
              "       [ 62.17606843],\n",
              "       [ 38.03156691],\n",
              "       [ 63.00375175],\n",
              "       [ 35.50998131],\n",
              "       [ 80.91979176],\n",
              "       [ 60.7239552 ],\n",
              "       [ 59.05024001],\n",
              "       [ 34.73191995],\n",
              "       [ 88.83385586],\n",
              "       [ 76.9148335 ],\n",
              "       [ 77.29176248],\n",
              "       [ 29.88861262],\n",
              "       [ 89.34713874],\n",
              "       [ 70.60795374],\n",
              "       [ 73.74705464],\n",
              "       [ 25.36791032],\n",
              "       [ 88.6588616 ],\n",
              "       [ 45.91998141],\n",
              "       [ 54.71654876],\n",
              "       [ 40.4204733 ],\n",
              "       [  5.39203227],\n",
              "       [ 70.58276117],\n",
              "       [ 40.33870446],\n",
              "       [ 33.64631818],\n",
              "       [ 32.98627263],\n",
              "       [ 79.98916392],\n",
              "       [ 31.90135988],\n",
              "       [ 81.83349848],\n",
              "       [ 35.3457274 ],\n",
              "       [ 91.82023052],\n",
              "       [ 70.37070928],\n",
              "       [ 87.45459934],\n",
              "       [ 40.36938225],\n",
              "       [ 33.98714154],\n",
              "       [ 91.46427384],\n",
              "       [ 66.13155582],\n",
              "       [ 56.07543971],\n",
              "       [ 10.16942613],\n",
              "       [ 87.07987742],\n",
              "       [ 28.84426771],\n",
              "       [ 66.82464179],\n",
              "       [ 57.18244945],\n",
              "       [ 59.28697464]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fnYr6MUNOugS"
      },
      "source": [
        "Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_A3fK0sOch_",
        "outputId": "b02f1505-d87c-4e3f-8995-2985ba14d359",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "model_lasso = Lasso(alpha=0.01)\n",
        "model_lasso.fit(x_train, y_train) \n",
        "pred_lasso= model_lasso.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_lasso)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_lasso))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE score: 44.635748023994644\n",
            "R2 score: -9.948664696519803\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_pLUhIXO71R",
        "outputId": "c9fc5f8d-0ec9-4362-ce11-b30416c2d3a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_lasso"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 69.44203588,  64.61460507,  66.39410946,  68.12412721,\n",
              "        85.05507233,  37.00225692,   5.05254466,  53.11637642,\n",
              "        12.40384909,  75.71142705,  64.38593412,  37.07370991,\n",
              "        12.76664097,  67.26542013,  45.56956223,  50.06284604,\n",
              "        52.1043572 ,  59.7371816 ,  35.85502347,  48.88083416,\n",
              "        39.59543366,  39.3792177 ,  95.56679436,  21.77692069,\n",
              "        37.98903061,   2.54045004,  83.48681665,  51.73612414,\n",
              "        30.72267749,  57.48927675,  12.19046353,  75.2380979 ,\n",
              "        27.1307373 ,  66.49798364,  55.57621614,  34.11189035,\n",
              "        14.54520278,  46.43443744,  57.67840658,  39.10493691,\n",
              "        17.48405633,  80.76563297,  57.74750722, 127.16746682,\n",
              "        86.10647007,  48.75350125,  56.01497764,  30.73421952,\n",
              "        42.28459104,  18.4554149 ,  67.04797233,  65.26654321,\n",
              "        17.55907594,   8.02165115,  66.49079578,  40.53084107,\n",
              "        69.65714441,  28.4473953 ,  20.73873059,   4.0695873 ,\n",
              "        51.14796865,  70.73162505,  18.80043165,  71.11691622,\n",
              "        57.09058368,  86.69250462,  66.84981377,  40.21148728,\n",
              "        34.16457613,  43.32713713,  16.37161103,  58.00868986,\n",
              "         3.78265111,  58.51550575,  35.8898621 ,  59.38317204,\n",
              "        33.69862589,  76.36986346,  58.26177815,  55.91585279,\n",
              "        32.77087293,  83.75326069,  72.52432012,  72.99780543,\n",
              "        28.16913576,  84.36202013,  66.53642397,  69.70835923,\n",
              "        24.84420357,  83.80369332,  44.64290894,  51.51009239,\n",
              "        39.69402688,   5.84949739,  66.97570708,  38.3527497 ,\n",
              "        31.82269495,  31.36657806,  77.07786984,  30.49566634,\n",
              "        77.29974211,  35.32251458,  88.85770974,  66.68193552,\n",
              "        82.59456781,  38.42229686,  32.70302918,  87.55834858,\n",
              "        62.43354256,  53.3896723 ,  10.33206872,  84.46602574,\n",
              "        27.21747953,  63.99509772,  53.7923523 ,  56.90014559])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_LGSoC1I_jH"
      },
      "source": [
        "Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTLACNXMCsmv",
        "outputId": "5ecda1a4-38ed-4820-86af-cb7f2887a4ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "Rigression_Ridge = Ridge(alpha=0.01)\n",
        "Rigression_Ridge.fit(x_train, y_train) \n",
        "pred_Ridge= Rigression_Ridge.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_Ridge)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_Ridge))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE score: 47.42696824958404\n",
            "R2 score: -11.360790565498204\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YF9YWF1kOBPH",
        "outputId": "83a9c968-c171-4387-9da8-bd04c517b123",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_Ridge"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 73.07300803],\n",
              "       [ 68.09096068],\n",
              "       [ 70.36345854],\n",
              "       [ 71.84680593],\n",
              "       [ 89.35239859],\n",
              "       [ 38.96641984],\n",
              "       [  4.96427368],\n",
              "       [ 56.40971814],\n",
              "       [ 12.37510158],\n",
              "       [ 80.25046516],\n",
              "       [ 66.78167792],\n",
              "       [ 38.75758676],\n",
              "       [ 13.38709359],\n",
              "       [ 71.20232382],\n",
              "       [ 46.8641008 ],\n",
              "       [ 52.45139874],\n",
              "       [ 55.16702752],\n",
              "       [ 63.47247039],\n",
              "       [ 37.55926949],\n",
              "       [ 51.39489406],\n",
              "       [ 40.43464329],\n",
              "       [ 41.09437768],\n",
              "       [100.59020205],\n",
              "       [ 22.72395642],\n",
              "       [ 39.72338897],\n",
              "       [  2.42565624],\n",
              "       [ 87.76398359],\n",
              "       [ 54.27324803],\n",
              "       [ 32.18306662],\n",
              "       [ 60.81772336],\n",
              "       [ 12.26602784],\n",
              "       [ 77.60054416],\n",
              "       [ 28.51473939],\n",
              "       [ 69.07948229],\n",
              "       [ 58.62937649],\n",
              "       [ 36.04239837],\n",
              "       [ 13.96292392],\n",
              "       [ 47.96398774],\n",
              "       [ 60.34671732],\n",
              "       [ 40.41557605],\n",
              "       [ 17.9928427 ],\n",
              "       [ 85.41726045],\n",
              "       [ 61.25351128],\n",
              "       [131.52809885],\n",
              "       [ 90.16535597],\n",
              "       [ 50.65162941],\n",
              "       [ 58.32203332],\n",
              "       [ 32.42951223],\n",
              "       [ 44.0554942 ],\n",
              "       [ 19.09456254],\n",
              "       [ 69.65313329],\n",
              "       [ 68.59045799],\n",
              "       [ 17.70775247],\n",
              "       [  8.16186552],\n",
              "       [ 70.01141896],\n",
              "       [ 42.08071991],\n",
              "       [ 72.99021636],\n",
              "       [ 29.88601976],\n",
              "       [ 22.0279516 ],\n",
              "       [  3.87564547],\n",
              "       [ 54.254606  ],\n",
              "       [ 75.10454957],\n",
              "       [ 18.69239914],\n",
              "       [ 74.61551798],\n",
              "       [ 59.52617593],\n",
              "       [ 91.61786075],\n",
              "       [ 70.96082859],\n",
              "       [ 42.45873846],\n",
              "       [ 35.87792718],\n",
              "       [ 44.10184868],\n",
              "       [ 15.97071406],\n",
              "       [ 61.07752362],\n",
              "       [  3.84759047],\n",
              "       [ 62.12622566],\n",
              "       [ 38.00069767],\n",
              "       [ 62.95471521],\n",
              "       [ 35.48003871],\n",
              "       [ 80.85662808],\n",
              "       [ 60.68851397],\n",
              "       [ 59.00554401],\n",
              "       [ 34.70463208],\n",
              "       [ 88.76328455],\n",
              "       [ 76.8522523 ],\n",
              "       [ 77.22924895],\n",
              "       [ 29.86149997],\n",
              "       [ 89.27716765],\n",
              "       [ 70.55171289],\n",
              "       [ 73.69354329],\n",
              "       [ 25.35242774],\n",
              "       [ 88.58910436],\n",
              "       [ 45.90045275],\n",
              "       [ 54.6714266 ],\n",
              "       [ 40.40456252],\n",
              "       [  5.3907685 ],\n",
              "       [ 70.53375499],\n",
              "       [ 40.30856872],\n",
              "       [ 33.62164882],\n",
              "       [ 32.95983474],\n",
              "       [ 79.9498896 ],\n",
              "       [ 31.8785231 ],\n",
              "       [ 81.77027371],\n",
              "       [ 35.34366604],\n",
              "       [ 91.77920828],\n",
              "       [ 70.31569656],\n",
              "       [ 87.38510853],\n",
              "       [ 40.33908086],\n",
              "       [ 33.96458715],\n",
              "       [ 91.40878074],\n",
              "       [ 66.07945209],\n",
              "       [ 56.03912402],\n",
              "       [ 10.16663319],\n",
              "       [ 87.04160473],\n",
              "       [ 28.81772347],\n",
              "       [ 66.78241107],\n",
              "       [ 57.13370558],\n",
              "       [ 59.24923918]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8zI0FZnPDrR"
      },
      "source": [
        "Nearest Neighbour Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVYjK2T7PJcG",
        "outputId": "bc332451-8c5f-4c3e-e2c9-b83e14291ea4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn import neighbors\n",
        "from math import sqrt\n",
        "rmse_val = []\n",
        "for k in range(20):\n",
        "    k = k+1\n",
        "    model = neighbors.KNeighborsRegressor(n_neighbors = k)\n",
        "    model.fit(x_train, y_train) \n",
        "    pred_neighbor=model.predict(x_test)\n",
        "    error = sqrt(mean_squared_error(y_test,pred_neighbor))\n",
        "    rmse_val.append(error) \n",
        "    print('RMSE value for k= ' , k , 'is:', error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE value for k=  1 is: 10.764039549063034\n",
            "RMSE value for k=  2 is: 9.06017954834648\n",
            "RMSE value for k=  3 is: 8.674522577872686\n",
            "RMSE value for k=  4 is: 8.727643082273227\n",
            "RMSE value for k=  5 is: 8.3945589931607\n",
            "RMSE value for k=  6 is: 8.399096831577005\n",
            "RMSE value for k=  7 is: 8.016849778182168\n",
            "RMSE value for k=  8 is: 7.975249525708023\n",
            "RMSE value for k=  9 is: 7.953968967709978\n",
            "RMSE value for k=  10 is: 7.86157585983879\n",
            "RMSE value for k=  11 is: 7.941886591023533\n",
            "RMSE value for k=  12 is: 8.095267215670118\n",
            "RMSE value for k=  13 is: 8.132441697464586\n",
            "RMSE value for k=  14 is: 8.323159136316256\n",
            "RMSE value for k=  15 is: 8.3956963923118\n",
            "RMSE value for k=  16 is: 8.517144730533625\n",
            "RMSE value for k=  17 is: 8.640876296364445\n",
            "RMSE value for k=  18 is: 8.68649013612236\n",
            "RMSE value for k=  19 is: 8.778032310906209\n",
            "RMSE value for k=  20 is: 8.774911812480529\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54QyS5WXQTjq",
        "outputId": "7fcc7579-2f93-4254-fa2e-9ba467917a10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_neighbor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18.525 ],\n",
              "       [ 5.75  ],\n",
              "       [ 2.6875],\n",
              "       [13.225 ],\n",
              "       [ 7.725 ],\n",
              "       [ 4.625 ],\n",
              "       [ 6.5125],\n",
              "       [ 2.5875],\n",
              "       [ 4.3   ],\n",
              "       [ 3.3375],\n",
              "       [16.875 ],\n",
              "       [11.95  ],\n",
              "       [ 3.2375],\n",
              "       [ 8.025 ],\n",
              "       [21.15  ],\n",
              "       [22.35  ],\n",
              "       [ 4.4   ],\n",
              "       [ 6.4875],\n",
              "       [17.275 ],\n",
              "       [ 7.25  ],\n",
              "       [20.525 ],\n",
              "       [15.825 ],\n",
              "       [16.175 ],\n",
              "       [15.825 ],\n",
              "       [ 9.475 ],\n",
              "       [ 2.8   ],\n",
              "       [23.05  ],\n",
              "       [16.3   ],\n",
              "       [17.825 ],\n",
              "       [ 7.1   ],\n",
              "       [ 4.075 ],\n",
              "       [22.325 ],\n",
              "       [ 4.575 ],\n",
              "       [19.65  ],\n",
              "       [ 6.025 ],\n",
              "       [ 2.45  ],\n",
              "       [19.25  ],\n",
              "       [16.525 ],\n",
              "       [18.975 ],\n",
              "       [15.475 ],\n",
              "       [ 6.9   ],\n",
              "       [10.675 ],\n",
              "       [ 4.7625],\n",
              "       [37.125 ],\n",
              "       [10.05  ],\n",
              "       [19.7   ],\n",
              "       [10.35  ],\n",
              "       [ 2.4625],\n",
              "       [18.3   ],\n",
              "       [ 6.475 ],\n",
              "       [19.7   ],\n",
              "       [20.05  ],\n",
              "       [ 8.875 ],\n",
              "       [ 4.925 ],\n",
              "       [ 8.825 ],\n",
              "       [16.075 ],\n",
              "       [22.7   ],\n",
              "       [ 9.5   ],\n",
              "       [ 1.475 ],\n",
              "       [ 3.95  ],\n",
              "       [ 5.95  ],\n",
              "       [ 3.9875],\n",
              "       [23.55  ],\n",
              "       [11.175 ],\n",
              "       [16.925 ],\n",
              "       [ 5.7   ],\n",
              "       [ 2.2625],\n",
              "       [ 6.4625],\n",
              "       [ 6.625 ],\n",
              "       [19.3   ],\n",
              "       [16.2   ],\n",
              "       [ 7.075 ],\n",
              "       [ 1.6625],\n",
              "       [ 2.3   ],\n",
              "       [ 6.35  ],\n",
              "       [ 1.575 ],\n",
              "       [ 7.525 ],\n",
              "       [ 5.2625],\n",
              "       [15.975 ],\n",
              "       [ 9.95  ],\n",
              "       [ 1.4625],\n",
              "       [ 4.825 ],\n",
              "       [ 2.8875],\n",
              "       [ 6.825 ],\n",
              "       [ 1.65  ],\n",
              "       [ 4.7375],\n",
              "       [ 2.4375],\n",
              "       [ 7.375 ],\n",
              "       [ 7.9   ],\n",
              "       [ 8.575 ],\n",
              "       [18.9   ],\n",
              "       [ 1.9   ],\n",
              "       [22.925 ],\n",
              "       [ 9.5   ],\n",
              "       [ 8.5   ],\n",
              "       [ 5.275 ],\n",
              "       [ 7.15  ],\n",
              "       [ 4.275 ],\n",
              "       [22.325 ],\n",
              "       [ 5.6   ],\n",
              "       [ 9.975 ],\n",
              "       [18.525 ],\n",
              "       [36.375 ],\n",
              "       [ 5.2   ],\n",
              "       [ 7.125 ],\n",
              "       [12.65  ],\n",
              "       [10.35  ],\n",
              "       [17.125 ],\n",
              "       [ 3.525 ],\n",
              "       [ 6.6   ],\n",
              "       [ 7.5   ],\n",
              "       [22.325 ],\n",
              "       [ 1.6625],\n",
              "       [18.3   ],\n",
              "       [ 2.2625],\n",
              "       [16.3   ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PA2gmrVaPrOr"
      },
      "source": [
        "Support Vector Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUTXKb0sPkj8",
        "outputId": "39ba4b6d-454e-42d4-bc19-3bf25d372b27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "SupportVectorReg=SVR()\n",
        "SupportVectorReg.fit(x_train,y_train)\n",
        "pred_SVR=SupportVectorReg.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_SVR)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_SVR))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE score: 10.47592874913882\n",
            "R2 score: 0.39691213061159725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGqiWv2mQLWr",
        "outputId": "621ca584-f7bd-48e4-b89b-96ec13609e83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_SVR"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14.53281406,  4.77086358,  2.88536553, 12.20794391,  6.13326102,\n",
              "        3.71821802,  4.4489073 ,  3.25738372,  4.00498143,  3.46055645,\n",
              "       17.63266242, 11.14774777,  3.42010457,  6.43108812, 19.68998933,\n",
              "       19.61529994,  3.86764761,  4.76239334, 17.97400419,  7.47606327,\n",
              "       16.50296479, 17.90636461, 12.5396061 , 17.75714944,  8.95435769,\n",
              "        3.28269683, 16.32215791, 13.50037028, 17.19110831,  7.55345741,\n",
              "        3.874396  , 20.1213742 ,  3.95606975, 14.89571491,  5.0078406 ,\n",
              "        3.14603626, 19.08544012, 18.5303699 , 18.71074992, 12.71028764,\n",
              "        4.90661501,  8.40363551,  3.82225918, 24.62874288,  7.90312163,\n",
              "       19.16520679,  9.21814185,  2.81780348, 19.30034903,  6.67084458,\n",
              "       19.18118646, 15.69785339,  7.93803664,  4.04493451,  6.41307083,\n",
              "       18.11963416, 19.85140662,  9.85812803,  2.50382445,  3.60096196,\n",
              "        4.17346259,  3.58956793, 19.74930116,  8.82213743, 17.26599724,\n",
              "        4.72457037,  3.09288959,  4.50333029,  5.30864173, 19.02157028,\n",
              "       13.58960863,  5.73843453,  2.72177629,  2.94012536,  4.87590959,\n",
              "        2.40614167,  6.85498774,  4.13674699, 17.72416341, 10.74704278,\n",
              "        2.58235369,  4.63276634,  3.02228531,  6.80630754,  2.59797594,\n",
              "        3.70165491,  2.79622466,  6.75310633,  8.02344392,  6.42940631,\n",
              "       19.54721359,  2.80589278, 20.13221371, 10.08418961,  8.60259715,\n",
              "        4.04255907,  5.1360845 ,  3.64586978, 20.07689209,  4.99364326,\n",
              "       10.35663549, 16.25141713, 15.13469404,  4.92623574,  6.87593545,\n",
              "       10.5635815 ,  9.15347378, 13.18829731,  3.39539133,  4.93796769,\n",
              "        8.05977928, 20.14886394,  2.53581222, 19.27235908,  2.99060968,\n",
              "       13.62223236])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xInINrLLP5nf"
      },
      "source": [
        "Tree Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8hycXy0QDqT",
        "outputId": "d42cb738-4d35-4d19-e506-1750b1db0d2f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "DecisionTreeReg=DecisionTreeRegressor()\n",
        "DecisionTreeReg.fit(x_train,y_train)\n",
        "pred_tree=DecisionTreeReg.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_tree)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_tree))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE score: 9.841880295889636\n",
            "R2 score: 0.46770586839901285\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfADc5gaRlRe",
        "outputId": "e5ab6e40-ef34-4582-e845-0518401a270c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_tree"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8.  ,  3.5 ,  0.75, 20.  ,  8.  , 18.  ,  4.  ,  0.75,  6.  ,\n",
              "        3.  , 12.  , 10.  ,  1.  ,  8.  , 20.  , 10.  ,  5.  ,  8.  ,\n",
              "       20.  ,  8.  , 30.  , 20.  ,  8.  , 20.  , 15.  ,  7.  , 20.  ,\n",
              "       15.  , 20.  , 13.  ,  5.  , 40.  ,  5.  , 40.  , 10.  ,  0.75,\n",
              "       35.  , 20.  , 20.  ,  8.  ,  3.  , 13.  ,  1.  , 45.  , 30.  ,\n",
              "       20.  , 10.  ,  3.  , 20.  ,  8.  , 24.  ,  8.  , 12.  ,  7.  ,\n",
              "        6.  , 20.  ,  8.  ,  8.  ,  0.5 ,  1.5 ,  2.  ,  2.  , 10.  ,\n",
              "       25.  , 35.  ,  0.5 , 10.  ,  5.  ,  8.  , 40.  , 18.  ,  7.  ,\n",
              "        2.5 ,  0.75,  6.  ,  0.75,  8.  ,  3.  , 35.  ,  5.  ,  0.5 ,\n",
              "        2.  ,  1.5 ,  5.  ,  4.  ,  5.5 ,  0.5 ,  8.  ,  8.  ,  6.  ,\n",
              "       20.  ,  2.  , 30.  ,  8.  , 18.  ,  6.  ,  8.  ,  5.5 , 40.  ,\n",
              "        5.  , 12.  , 75.  , 30.  ,  1.5 , 10.  , 13.  , 30.  ,  8.  ,\n",
              "        2.  ,  7.  ,  2.5 , 75.  ,  0.75, 30.  ,  0.5 , 35.  ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRBUYqFcRrVb"
      },
      "source": [
        "Random Forest Regression "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JW66RDVNRsUd",
        "outputId": "de33085a-5e0a-4eaf-eea9-a46105e95848",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "RandomForestReg=RandomForestRegressor()\n",
        "RandomForestReg.fit(x_train,y_train)\n",
        "pred_forest=RandomForestReg.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_forest)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_forest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MSE score: 6.158698043109998\n",
            "R2 score: 0.7915637053996277\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3iF8rr6SA06",
        "outputId": "dc17cd1b-6047-4d88-82b1-21b635d73a2f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_forest"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11.085 ,  6.635 ,  4.0725, 10.6   , 13.64  ,  5.805 ,  5.39  ,\n",
              "        3.8725,  6.725 ,  6.405 , 28.97  ,  7.95  ,  3.47  ,  8.59  ,\n",
              "       20.43  , 11.6   ,  6.275 ,  5.94  , 10.365 ,  7.89  , 28.06  ,\n",
              "       12.91  , 14.755 ,  8.81  , 10.055 ,  5.2075, 12.985 , 12.81  ,\n",
              "       13.03  ,  6.0675,  6.515 , 36.27  ,  5.625 , 30.58  ,  7.095 ,\n",
              "        4.2575, 21.105 , 14.04  , 14.42  , 15.24  ,  8.5675,  9.485 ,\n",
              "        3.83  , 54.4   , 22.725 , 18.97  , 17.185 ,  4.295 , 14.385 ,\n",
              "        7.3075, 20.52  , 12.97  , 10.095 ,  5.1075, 10.46  , 16.57  ,\n",
              "       12.195 ,  8.35  ,  2.1175,  4.5925,  4.6   ,  4.7   , 21.35  ,\n",
              "       14.2   , 28.26  ,  6.7425,  4.965 ,  5.6   ,  7.68  , 30.98  ,\n",
              "       17.16  ,  6.77  ,  3.06  ,  4.32  ,  7.6925,  1.6465,  8.155 ,\n",
              "        5.8425, 27.09  , 10.115 ,  1.9365,  7.13  ,  5.6425,  9.29  ,\n",
              "        2.3425,  5.584 ,  4.1725,  7.8525,  9.26  ,  9.895 , 21.3   ,\n",
              "        4.0475, 25.91  ,  9.68  , 11.9   ,  5.455 ,  7.355 ,  6.44  ,\n",
              "       32.09  ,  8.0425, 10.39  , 40.99  , 39.3   ,  5.34  ,  8.055 ,\n",
              "       10.215 , 14.04  , 19.25  ,  6.3125,  7.73  , 10.41  , 46.46  ,\n",
              "        2.6965, 27.53  ,  5.1025, 21.735 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYQ3sideSInb"
      },
      "source": [
        "Gradient Boosted Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIod2FRFSL4H",
        "outputId": "17a6fd9b-87ef-4211-8fff-f323152004cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "regressor = GradientBoostingRegressor(\n",
        "    n_estimators= 500,\n",
        "    max_depth= 4,\n",
        "    min_samples_split= 5,\n",
        "    learning_rate= 0.01,\n",
        ")\n",
        "regressor.fit(x_train,y_train)\n",
        "pred_gradient = regressor.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_gradient)))\n",
        "print(\"R2 score:\",r2_score(y_test,pred_gradient))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MSE score: 6.180443705944269\n",
            "R2 score: 0.790089177057778\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpgaNfkYSpZf",
        "outputId": "840fe05f-925d-4fc1-ddd4-81f83ebaccee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pred_gradient"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10.3818251 ,  6.99254079,  3.45510829, 11.16539339, 15.74263033,\n",
              "        9.08928724,  4.84370186,  3.20559398,  6.10623523,  5.4674084 ,\n",
              "       27.37021636,  5.60066466,  2.72172661,  8.58877528, 16.71083431,\n",
              "        9.88300648,  4.91517262,  5.30727826, 10.2120249 ,  7.99615795,\n",
              "       26.74875144, 13.25192867, 14.00432588,  4.58545422,  6.4618961 ,\n",
              "        3.89155912, 13.05250368,  7.958597  , 13.13934646,  5.83435203,\n",
              "        6.70935409, 41.03980812,  5.21396444, 33.63287206,  5.46850459,\n",
              "        3.61797898, 17.50035557, 13.68477617, 11.83337985, 15.35333777,\n",
              "        6.37531114,  9.09006337,  3.10422382, 51.56919255, 21.68988084,\n",
              "       14.83977273, 21.36820902,  3.46402425, 11.7954571 ,  5.62462546,\n",
              "       16.60808211, 12.48553192,  7.78365191,  5.00863522, 10.33587096,\n",
              "       15.25883101, 12.14740603,  8.14731491,  1.16067274,  2.7833553 ,\n",
              "        4.31062053,  4.03537426, 20.06241374, 12.8134112 , 27.005685  ,\n",
              "        6.99315618,  3.32329555,  6.49298787,  7.81755168, 33.24596122,\n",
              "       13.9198868 ,  3.61013714,  2.49904667,  3.39432271,  5.37259679,\n",
              "        2.94147521,  7.58819131,  5.97880678, 26.0812038 ,  9.63827202,\n",
              "        1.3727455 ,  6.42552776,  5.02433688,  9.58600401,  1.84618014,\n",
              "        5.92139411,  4.56089604,  8.0138974 ,  8.88677386,  7.29308103,\n",
              "       23.8819829 ,  3.42544901, 21.81725504,  5.84689044, 12.71734396,\n",
              "        7.80579713,  7.56019329,  7.45834116, 38.31334207,  7.13597508,\n",
              "        9.17909973, 38.34298146, 34.96969865,  5.42993851,  6.62111218,\n",
              "        9.18360692, 13.56580453, 29.81241464,  4.97861762,  3.77792251,\n",
              "       10.25725456, 49.43525325,  3.22467572, 27.36306513,  3.35824189,\n",
              "       18.64791537])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lk3Vp3vpVTXi"
      },
      "source": [
        "3. Tune the hyperparameters and build the most accurate model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7MFIA1CaHqB"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV,RepeatedKFold,RandomizedSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QotsdttAaJ8L"
      },
      "source": [
        "For Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dNCKc1ZVRbZ",
        "outputId": "1cbffdce-f15f-4c91-e8ad-c6a1dfc5bfc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = LinearRegression()\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "space = dict()\n",
        "space['fit_intercept'] = [True, False]\n",
        "space['normalize'] = [True, False]\n",
        "space['copy_X'] = [True, False]\n",
        "search = GridSearchCV(model, space, scoring='neg_mean_absolute_error', n_jobs=1, cv=cv)\n",
        "result = search.fit(x_train, y_train)\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "print('LinearRegression GridSearch Accuracy: %s' % result.best_estimator_.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: -4.319281419264125\n",
            "Best Hyperparameters: {'copy_X': True, 'fit_intercept': True, 'normalize': True}\n",
            "LinearRegression GridSearch Accuracy: -11.382032075377651\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VONtZDzhN14A"
      },
      "source": [
        "Accurate model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byyzOQ59N4gM",
        "outputId": "17058929-5e80-4f70-8de7-4199cd74fe2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "linearRegressor = LinearRegression(normalize=True,fit_intercept=False,copy_X=True)\n",
        "linearRegressor.fit(x_train, y_train)\n",
        "yPrediction = linearRegressor.predict(x_test)\n",
        "print(\"RMSE score:\",np.sqrt(mean_squared_error(y_test,yPrediction)))\n",
        "print(\"R2 score:\",r2_score(y_test,yPrediction)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE score: 54.74359131021873\n",
            "R2 score: -15.468805129349892\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UOAzOqZaOae"
      },
      "source": [
        "For Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlgrelJFaWrm",
        "outputId": "3caa087e-9a5a-4a60-981e-a6d2a6517a17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = Lasso()\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "space = dict()\n",
        "space['alpha'] = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 100]\n",
        "space['fit_intercept'] = [True, False]\n",
        "space['normalize'] = [True, False]\n",
        "space['max_iter'] = [1,10,50,100]\n",
        "space['copy_X'] = [True, False]\n",
        "space['warm_start'] = [True, False]\n",
        "space['selection'] = ['cyclic', 'random']\n",
        "space['precompute'] = [True, False,]\n",
        "search = GridSearchCV(model, space, scoring='neg_mean_absolute_error', n_jobs=-1, cv=cv)\n",
        "result = search.fit(x_train, y_train)\n",
        "\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "print('Lasso GridSearch Accuracy: %s' % result.best_estimator_.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: -4.240566475479193\n",
            "Best Hyperparameters: {'alpha': 0.01, 'copy_X': False, 'fit_intercept': True, 'max_iter': 10, 'normalize': True, 'precompute': True, 'selection': 'random', 'warm_start': True}\n",
            "Lasso GridSearch Accuracy: -10.808741351789669\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1172.3352495614417, tolerance: 4.791169366666667\n",
            "  tol, rng, random, positive)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXw_n7L7OfQm"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBDPEzzvOiKo",
        "outputId": "f991c836-496a-414a-b2d2-463e8aed7e06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_lasso = Lasso(alpha=0.01,selection='random')\n",
        "model_lasso.fit(x_train, y_train) \n",
        "pred_lasso= model_lasso.predict(x_test)\n",
        "print(\"RMSE score:\",np.sqrt(mean_squared_error(y_test,pred_lasso)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_lasso))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE score: 44.991174613855385\n",
            "R2 score: -10.123723485983499\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5vmYxRFbDKI"
      },
      "source": [
        "For Ridge Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8q7EtzqBhua5"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkF_uSq1bGzb",
        "outputId": "aab7a1c6-927a-4075-c418-e6614adf6b37",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = Ridge()\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "space = dict()\n",
        "space['solver'] =  ['svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']\n",
        "space['alpha'] = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 100]\n",
        "space['fit_intercept'] = [True, False]\n",
        "space['normalize'] = [True, False]\n",
        "space['max_iter'] = [1,10,50,100,500,1000]\n",
        "space['copy_X'] = [True, False]\n",
        "search = GridSearchCV(model, space, scoring='neg_mean_absolute_error', n_jobs=-1, cv=cv)\n",
        "result = search.fit(x_train, y_train)\n",
        "\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "print('Ridge GridSearch Accuracy: %s' % result.best_estimator_.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: -4.227049326675834\n",
            "Best Hyperparameters: {'alpha': 0.1, 'copy_X': True, 'fit_intercept': True, 'max_iter': 10, 'normalize': True, 'solver': 'sag'}\n",
            "Ridge GridSearch Accuracy: -16.53391516942623\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS1Q_JcAO2EU"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "veg_m_GaO4DF",
        "outputId": "3ce71e80-770d-4bbd-ae67-78824196c35a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rr = Ridge(alpha=1,solver = 'lsqr')\n",
        "rr.fit(x_train, y_train) \n",
        "pred_rr= rr.predict(x_test)\n",
        "print(\"RMSE score:\",np.sqrt(mean_squared_error(y_test,pred_rr)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_rr))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE score: 7.3203682790345175\n",
            "R2 score: 0.7055162179805238\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnK5intObrgi"
      },
      "source": [
        "For Nearest Neighbour Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6P8aDAbbic-",
        "outputId": "e7144d75-a992-449f-efc1-0255f6bea8b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model=neighbors.KNeighborsRegressor()\n",
        "\n",
        "leaf_size = list(range(1,50))\n",
        "n_neighbors = list(range(1,30))\n",
        "p=[1,2]\n",
        "hyperparameters = dict(leaf_size=leaf_size, n_neighbors=n_neighbors, p=p)\n",
        "\n",
        "search = GridSearchCV(model, hyperparameters, cv=10)\n",
        "result = search.fit(x_train, y_train)\n",
        "\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "print('Knn GridSearch Score: %s' % result.best_estimator_.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: 0.5200726289993008\n",
            "Best Hyperparameters: {'leaf_size': 1, 'n_neighbors': 24, 'p': 1}\n",
            "Knn GridSearch Score: 0.5680141582840241\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czRREZI6PJaE"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7-v5IiMPLxh",
        "outputId": "93d4db3c-1a6b-4e23-d5ab-413670cdb894",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = neighbors.KNeighborsRegressor(n_neighbors = 16,algorithm='kd_tree',p=1,weights='distance')\n",
        "model.fit(x_train, y_train)  #fit the model\n",
        "pred_knn=model.predict(x_test) #make prediction on test set\n",
        "error = sqrt(mean_squared_error(y_test,pred_knn)) #calculate rmse\n",
        "\n",
        "print('RMSE value :', error)\n",
        "print(\"R2 score:\",r2_score(y_test, pred_knn))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE value : 8.622021473671152\n",
            "R2 score: 0.5914796253878369\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fe3EWwFLc5IH"
      },
      "source": [
        "For Support Vector Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3h3DIjJMeLZr",
        "outputId": "8cfb7ea3-9b37-4999-9d9d-f471f6b0dfa3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = SVR()\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "space = dict()\n",
        "space['max_iter'] = [1,10,50,100]\n",
        "space['kernel']= ['linear','poly','rbf','sigmoid']\n",
        "space['gamma'] = [1, 0.1, 0.01, 0.001, 0.0001]\n",
        "space['C']: [0.1, 1, 10, 100, 1000]\n",
        "\n",
        "search = GridSearchCV(model, space, scoring='neg_mean_absolute_error', n_jobs=-1, cv=cv)\n",
        "\n",
        "result = search.fit(x_train, y_train)\n",
        "\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "print('SVR GridSearch Accuracy: %s' % result.best_estimator_.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: -6.099489733412793\n",
            "Best Hyperparameters: {'gamma': 0.0001, 'kernel': 'rbf', 'max_iter': 100}\n",
            "SVR GridSearch Accuracy: 0.2185178234939857\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xmv-nihOPsFE"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWvAwtY6Pt4e",
        "outputId": "6d4d7a6a-84af-4960-eeb2-684e7a1d8059",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "'''\n",
        "SVR()\n",
        "SupportVectorReg=SVR(kernel='poly',gamma=1)\n",
        "SupportVectorReg.fit(x_train,y_train)\n",
        "pred_svr=SupportVectorReg.predict(x_test)\n",
        "\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_svr)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_svr))\n",
        "'''\n",
        "SupportVectorReg=SVR()\n",
        "SupportVectorReg.fit(x_train,y_train)\n",
        "pred_SVR=SupportVectorReg.predict(x_test)\n",
        "print(\"MSE score:\",np.sqrt(mean_squared_error(y_test,pred_SVR)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_SVR))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE score: 10.47592874913882\n",
            "R2 score: 0.39691213061159725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTsvRUypfVu7"
      },
      "source": [
        "For Tree Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHdBqSAifijf",
        "outputId": "ac426f9e-c0e0-4419-9afc-d53d6e3fbebc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = DecisionTreeRegressor()\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "space = dict()\n",
        "space['max_depth']= [None,1,2,3,4,5,6,7]\n",
        "space['max_features']= [None, 'sqrt', 'auto', 'log2', 0.3,0.5,0.7 ]\n",
        "space['min_samples_split']= [2,0.3,0.5]\n",
        "space['min_samples_leaf']=[1, 0.3,0.5]\n",
        "space['criterion'] = ['mse','friedman_mse','mae']\n",
        "space['splitter'] = ['best','random']\n",
        "search = GridSearchCV(model, space, scoring='neg_mean_absolute_error', n_jobs=-1, cv=cv)\n",
        "result = search.fit(x_train, y_train)\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: -4.537185920582436\n",
            "Best Hyperparameters: {'criterion': 'friedman_mse', 'max_depth': 5, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'best'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BuP_M3XxQxJM"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n49wVXFAQzvY",
        "outputId": "e31f8d19-4361-4398-ec07-5d7eddcda5bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "DecisionTreeReg=DecisionTreeRegressor(criterion= 'friedman_mse', max_depth= 6, max_features= 'auto', min_samples_leaf= 1, min_samples_split= 2, splitter= 'random')\n",
        "DecisionTreeReg.fit(x_train,y_train)\n",
        "pred_tree=DecisionTreeReg.predict(x_test)\n",
        "print(\"RMSE score:\",np.sqrt(mean_squared_error(y_test,pred_tree)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_tree))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE score: 7.11986429441369\n",
            "R2 score: 0.7214270436370289\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lm3htPXjfnj4"
      },
      "source": [
        "For Random Forest Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnv4aAwNfplz",
        "outputId": "a6491fff-9f93-4260-c30b-12b334e5d5c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = RandomForestRegressor()\n",
        "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
        "max_features = ['auto', 'sqrt','log2']\n",
        "max_depth = [int(x) for x in np.linspace(10, 1000,10)]\n",
        "\n",
        "min_samples_split = [2, 5, 10,14]\n",
        "min_samples_leaf = [1, 2, 4,6,8]\n",
        "\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf\n",
        "              }\n",
        "              \n",
        "RF_RandomCv = RandomizedSearchCV(estimator=model,param_distributions=random_grid,n_iter=100,cv=3,verbose=2,\n",
        "                               random_state=100,n_jobs=-1)\n",
        "result=RF_RandomCv.fit(x_train,y_train)\n",
        "\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "print('Random Forest GridSearch Accuracy: %s' % result.best_estimator_.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:   43.9s\n",
            "[Parallel(n_jobs=-1)]: Done 158 tasks      | elapsed:  2.9min\n",
            "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  5.7min finished\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:739: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  self.best_estimator_.fit(X, y, **fit_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best Score: 0.7774042844927579\n",
            "Best Hyperparameters: {'n_estimators': 1600, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 1000}\n",
            "Random Forest GridSearch Accuracy: 0.7597747041311681\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_Yoi7WERoze"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYyBn0-hRqnf",
        "outputId": "60e62b60-391e-481e-8eff-c2e8ade1676c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "RandomForestReg=RandomForestRegressor(max_depth=8, max_features='sqrt',n_estimators=200)\n",
        "RandomForestReg.fit(x_train,y_train)\n",
        "pred_forest=RandomForestReg.predict(x_test)\n",
        "print(\"RMSE score:\",np.sqrt(mean_squared_error(y_test,pred_forest)))\n",
        "print(\"R2 score:\",r2_score(y_test, pred_forest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "RMSE score: 6.679054568133203\n",
            "R2 score: 0.75485360871305\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fgezxt8HfqJ4"
      },
      "source": [
        "For Gradient Boosted Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDWYVt0-fuIM",
        "outputId": "e665909a-6ca6-472a-fb20-c96552b63daf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model =GradientBoostingRegressor()\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "space = dict()\n",
        "space['n_estimators']= [200, 500]\n",
        "space['max_features']= ['auto', 'sqrt', 'log2']\n",
        "space['max_depth'] = [4,5,6,7,8]\n",
        "space['alpha'] = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 100,0.99]\n",
        "space['loss'] = ['ls', 'lad', 'huber', 'quantile']\n",
        "space['subsample'] = [1]\n",
        "search = RandomizedSearchCV(model, space, scoring='neg_mean_absolute_error', n_jobs=-1, cv=cv)\n",
        "result = search.fit(x_train, y_train)\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best Score: -3.45074930135845\n",
            "Best Hyperparameters: {'subsample': 1, 'n_estimators': 500, 'max_features': 'log2', 'max_depth': 4, 'loss': 'lad', 'alpha': 0.1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkHoM22CTLF4"
      },
      "source": [
        "Accurate Model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNsTZOriH_1M",
        "outputId": "3662a0b2-7ece-41b8-bfaf-3ecea267dec7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regressor = GradientBoostingRegressor(\n",
        "    subsample =  1,\n",
        "    n_estimators= 500, \n",
        "    max_features= 'log2', \n",
        "    max_depth= 5, \n",
        "    loss= 'lad', \n",
        "    alpha= 0.01\n",
        ")\n",
        "regressor.fit(x_train,y_train)\n",
        "pred_gboost = regressor.predict(x_test)\n",
        "print(\"RMSE score:\",np.sqrt(mean_squared_error(y_test,pred_gboost)))\n",
        "print(\"R2 score:\",r2_score(y_test,pred_gboost))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "RMSE score: 6.906251422901736\n",
            "R2 score: 0.7378919913863964\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBTbP2lbg3M2"
      },
      "source": [
        "4. Use model selection approaches discussed in class to choose the best model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJHgv-mUgkpT"
      },
      "source": [
        "From all the above models we can observe that the SUPPORT VECTOR REGRESSION, reports us the best results as the R2 value of the SVR is lowest among all other regressions. The Best Score of SVR is  -6.099489733412793, Best Hyperparameters: {'gamma': 0.0001, 'kernel': 'rbf', 'max_iter': 100}, Accuracy: 0.2185178234939857, MSE score: 10.47592874913882 and R2 score: 0.39691213061159725\n",
        "So, Support vector Machine is the best algorithm among all others in this scenario. So we deployed Support Vector Machine Regression in Restful API.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DK45-Ggi5mCY"
      },
      "source": [
        "5. Genetic Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaNeb6mV556g",
        "outputId": "1a6e44af-aef7-4231-c48f-75e1a83cb631",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import csv\n",
        "import random as rand\n",
        "import math\n",
        "import operator\n",
        "\n",
        "\n",
        "def euclideanDistance(instance1, instance2, length):\n",
        "    distance = 0\n",
        "    for x in range(length):\n",
        "        distance += pow((instance1[x] - instance2[x]), 2)\n",
        "    return math.sqrt(distance)\n",
        "\n",
        "\n",
        "def getNeighbors(trainingSet, testInstance, k):\n",
        "    distances = []\n",
        "    length = len(testInstance) - 1\n",
        "    for x in range(len(trainingSet)):\n",
        "        dist = euclideanDistance(testInstance, trainingSet[x], length)\n",
        "        distances.append((trainingSet[x], dist))\n",
        "    # print('Distances: ')\n",
        "    # print(*distances, sep=\",\")\n",
        "    distances.sort(key=operator.itemgetter(1))\n",
        "    neighbors = []\n",
        "    for x in range(min(k, len(distances))):\n",
        "        neighbors.append(distances[x][0])\n",
        "    return neighbors\n",
        "\n",
        "\n",
        "def getResponse(neighbors):\n",
        "    classVotes = {}\n",
        "    for x in range(len(neighbors)):\n",
        "        response = neighbors[x][-1]\n",
        "        #print('Neighbors: ' + neighbors[x][-1])\n",
        "        #print(classVotes)\n",
        "        if response in classVotes:\n",
        "            classVotes[response] += 1\n",
        "        else:\n",
        "            classVotes[response] = 1\n",
        "    # print(classVotes)#coba1\n",
        "    sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)\n",
        "    return sortedVotes[0][0]\n",
        "\n",
        "\n",
        "def getAccuracy(testSet, predictions):\n",
        "    correct = 0\n",
        "    for x in range(len(testSet)):\n",
        "        if testSet[x][-1] == predictions[x]:\n",
        "            correct = correct+1\n",
        "    return (correct / float(len(testSet))) * 100.0\n",
        "\n",
        "\n",
        "def generateChromosome(chromosome):\n",
        "    return [rand.randint(1, 100) for x in range(chromosome)]\n",
        "\n",
        "\n",
        "def desimal(biner):\n",
        "    return int(biner, 2)\n",
        "\n",
        "\n",
        "def kNN(k, testSet, trainingSet):\n",
        "    # generate predictions\n",
        "    predictions = []\n",
        "    for x in range(len(testSet)):\n",
        "        neighbors = getNeighbors(trainingSet, testSet[x], k)\n",
        "        result = getResponse(neighbors)\n",
        "        predictions.append(result)\n",
        "        #print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))\n",
        "    accuracy = getAccuracy(testSet, predictions)\n",
        "    #print('Accuracy: ' + repr(accuracy) + '%')\n",
        "    return accuracy\n",
        "\n",
        "\n",
        "def crossover(one, two):\n",
        "    parent = [one, two]\n",
        "    zero = '0'\n",
        "    male = \"{0:b}\".format(parent[0])\n",
        "    female = \"{0:b}\".format(parent[1])\n",
        "    length = max(len(male), len(female))\n",
        "    if length % 2 == 1:\n",
        "        length = length + 1\n",
        "    while len(male) < length:\n",
        "        male = zero + male\n",
        "\n",
        "    while len(female) < length:\n",
        "        female = zero + female\n",
        "\n",
        "    child = []\n",
        "    half = int(length / 2)\n",
        "    male1 = male[:half]\n",
        "    male2 = male[half:]\n",
        "\n",
        "    female1 = female[:half]\n",
        "    female2 = female[half:]\n",
        "\n",
        "    child.append(desimal(male1 + female2))\n",
        "    child.append(desimal(female1 + male2))\n",
        "    return child\n",
        "\n",
        "\n",
        "\n",
        "# prepare data\n",
        "trainingSet = x_train\n",
        "testSet = x_test\n",
        "split = 0.67\n",
        "\n",
        "print('Train set: ' + repr(len(trainingSet)))\n",
        "print('Test set: ' + repr(len(testSet)))\n",
        "\n",
        "accResult = [[]]\n",
        "chromosome = 10\n",
        "population = generateChromosome(chromosome)\n",
        "\n",
        "for x in range(len(population)):\n",
        "  accResult.append([population[x], kNN(population[x], testSet, trainingSet)])\n",
        "\n",
        "del accResult[0]\n",
        "\n",
        "for x in range(200):\n",
        "        status_one = True\n",
        "        status_zero = True\n",
        "        accResult = sorted(accResult, key=lambda l: l[1], reverse=True)\n",
        "        newChromosome = crossover(accResult[0][0], accResult[1][0])\n",
        "        for i in accResult:\n",
        "            if newChromosome[0] == i[0]:\n",
        "                status_zero = False;\n",
        "            if newChromosome[1] == i[0]:\n",
        "                status_one = False\n",
        "        if status_zero:\n",
        "            accResult.append([newChromosome[0], kNN(newChromosome[0], testSet, trainingSet)])\n",
        "        if status_one:\n",
        "            accResult.append([newChromosome[1], kNN(newChromosome[1], testSet, trainingSet)])\n",
        "\n",
        "accResult = sorted(accResult, key=lambda l: l[1],reverse=True)\n",
        "print(\"accuracy: \\n\")\n",
        "print(accResult[0][1])   "
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train set: 345\n",
            "Test set: 116\n",
            "accuracy: \n",
            "\n",
            "86.20689655172413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rl86ihjOz2CX",
        "outputId": "776c5686-c7ec-4be3-d85c-12daa1ed3133",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install flask-ngrok"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting flask-ngrok\n",
            "  Downloading https://files.pythonhosted.org/packages/af/6c/f54cb686ad1129e27d125d182f90f52b32f284e6c8df58c1bae54fa1adbc/flask_ngrok-0.0.25-py3-none-any.whl\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from flask-ngrok) (2.23.0)\n",
            "Requirement already satisfied: Flask>=0.8 in /usr/local/lib/python3.6/dist-packages (from flask-ngrok) (1.1.2)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (1.24.3)\n",
            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (1.1.0)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (1.0.1)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (2.11.2)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=0.8->flask-ngrok) (1.1.1)\n",
            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhSo3QhU5Tr5"
      },
      "source": [
        "6. RESTFUL API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJf9JAe0JY2x"
      },
      "source": [
        "from flask_ngrok import run_with_ngrok\n",
        "from flask import Flask"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13Suc6Jj1G98",
        "outputId": "9c75e13d-0fac-4df3-ea65-94dbbb2a797a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from flask import Flask, render_template, request, Response\n",
        "app =Flask(__name__)\n",
        "\n",
        "df = pd.read_csv('/content/Predicting the Price of a Football Player.csv')\n",
        "\n",
        "df1=df\n",
        "df1.drop([\"name\",\"club\",\"position\",\"nationality\",\"fpl_sel\"],axis='columns',inplace=True)\n",
        "\n",
        "x=df1[df1.columns[~df1.columns.isin([\"market_value\"])]].to_numpy()\n",
        "y=df1.market_value.to_numpy()\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.25)\n",
        "\n",
        "run_with_ngrok(app)\n",
        "\n",
        "@app.route(\"/\" , methods=['GET','POST'])\n",
        "def index():\n",
        "    return render_template(\"ml_assignment.html\")\n",
        "@app.route(\"/data\", methods=['GET','POST'])\n",
        "def data():     \n",
        "    if request.method == \"POST\":\n",
        "         opt= request.form['excellent']\n",
        "         m = int(opt)*1\n",
        "         a= request.form['age']\n",
        "         b= request.form['position_cat']\n",
        "         c= request.form['page_views']\n",
        "         d= request.form['fpl_value']\n",
        "         e= request.form['fpl_points']\n",
        "         f= request.form['region']\n",
        "         g= request.form['new_foreign']\n",
        "         h= request.form['age_cat']\n",
        "         i= request.form['club_id']\n",
        "         j= request.form['big_club']\n",
        "         k= request.form['new_signing']\n",
        "         li=[a,b,c,d,e,f,g,h,i,j,k]\n",
        "         model=SVR()\n",
        "         model.fit(x_train,y_train)\n",
        "         y_pred_svr=model.predict(x_test)\n",
        "         market_value=model.predict([li])\n",
        "    return render_template(\"ml_data.html\",market_value=market_value )\n",
        "\n",
        "app.run()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://e18f32e10f59.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsxXeWPA8wrR",
        "outputId": "d1ec6fce-6868-423e-beeb-42c5164644d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ngrok authtoken 1k2FFfPooTPMAKtuJFQ0iCleB2o_39Y3J6rSwnpkESb46Z7h3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Authtoken saved to configuration file: /root/.ngrok2/ngrok.yml\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}