La data - Chap 6 - Tirez profit de vos données avec l'IA.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["sjoi01Gh5QPr","QLJ6Qzw54iIJ","krgW8FXI6gWQ","Ana8f3Af-Vnb","w4qxV_MDUj2e","fAd5k7IavQR7"],"authorship_tag":"ABX9TyMLB2wzZaCr0DnToiJYG/5E"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Initialisation du Notebook"],"metadata":{"id":"sjoi01Gh5QPr"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"NV740Lf52sFt","executionInfo":{"status":"ok","timestamp":1709562220380,"user_tz":-60,"elapsed":2214,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}}},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from sklearn.preprocessing import MinMaxScaler, StandardScaler\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split, cross_val_score\n","from sklearn import linear_model\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n","from sklearn.dummy import DummyClassifier\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import roc_curve\n","from sklearn import metrics\n","from google.colab import data_table"]},{"cell_type":"markdown","source":["*Récupération* des jeu de données nécessaires"],"metadata":{"id":"s18lHsbf5VOQ"}},{"cell_type":"code","source":["!wget https://raw.githubusercontent.com/datacorner/les-tutos-datacorner.fr/master/datasources/titanic/train.csv\n","!wget https://raw.githubusercontent.com/datacorner/les-tutos-datacorner.fr/master/datasources/univariate_linear_regression_dataset.csv"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cUUi_JSo3nwN","executionInfo":{"status":"ok","timestamp":1709562221139,"user_tz":-60,"elapsed":764,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"5d785bcc-acdb-4390-fc5d-ec5539d50338"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["--2024-03-04 14:23:39--  https://raw.githubusercontent.com/datacorner/les-tutos-datacorner.fr/master/datasources/titanic/train.csv\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 61194 (60K) [text/plain]\n","Saving to: ‘train.csv’\n","\n","\rtrain.csv             0%[                    ]       0  --.-KB/s               \rtrain.csv           100%[===================>]  59.76K  --.-KB/s    in 0.007s  \n","\n","2024-03-04 14:23:39 (8.00 MB/s) - ‘train.csv’ saved [61194/61194]\n","\n","--2024-03-04 14:23:39--  https://raw.githubusercontent.com/datacorner/les-tutos-datacorner.fr/master/datasources/univariate_linear_regression_dataset.csv\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1369 (1.3K) [text/plain]\n","Saving to: ‘univariate_linear_regression_dataset.csv’\n","\n","univariate_linear_r 100%[===================>]   1.34K  --.-KB/s    in 0s      \n","\n","2024-03-04 14:23:40 (49.9 MB/s) - ‘univariate_linear_regression_dataset.csv’ saved [1369/1369]\n","\n"]}]},{"cell_type":"code","source":["titanic = pd.read_csv(\"train.csv\")\n","titanic.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":504},"id":"qfY4srCf3ltd","executionInfo":{"status":"ok","timestamp":1709562221141,"user_tz":-60,"elapsed":12,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"88582a23-0e52-44f4-85d4-abe21e11a178"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","\n","                                                Name     Sex   Age  SibSp  \\\n","0                            Braund, Mr. Owen Harris    male  22.0      1   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n","2                             Heikkinen, Miss. Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  "],"text/html":["\n","  <div id=\"df-a7bfcfa7-6eb5-4989-be56-ce41cc770029\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7bfcfa7-6eb5-4989-be56-ce41cc770029')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-a7bfcfa7-6eb5-4989-be56-ce41cc770029 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","source":["# One-Hot encoding"],"metadata":{"id":"QLJ6Qzw54iIJ"}},{"cell_type":"code","source":["display(titanic.columns)\n","cols = ['Sex', 'Embarked']\n","for col in cols:\n","    display('Column: ' + col)\n","    display(titanic[col].value_counts())\n","data_dummies = pd.get_dummies(titanic,\n","                              prefix=['S', 'E'],\n","                              columns=['Sex', 'Embarked'])\n","display(data_dummies.columns)\n","data_dummies.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"id":"R0XRYIyM4mHE","executionInfo":{"status":"ok","timestamp":1708270973232,"user_tz":-60,"elapsed":263,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"c0ecc1a9-e53a-4173-da6b-e122e8a0c8ea"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n","       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n","      dtype='object')"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["'Column: Sex'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["male      577\n","female    314\n","Name: Sex, dtype: int64"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["'Column: Embarked'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["S    644\n","C    168\n","Q     77\n","Name: Embarked, dtype: int64"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Age', 'SibSp', 'Parch',\n","       'Ticket', 'Fare', 'Cabin', 'S_female', 'S_male', 'E_C', 'E_Q', 'E_S'],\n","      dtype='object')"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","\n","                                                Name   Age  SibSp  Parch  \\\n","0                            Braund, Mr. Owen Harris  22.0      1      0   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n","2                             Heikkinen, Miss. Laina  26.0      0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n","4                           Allen, Mr. William Henry  35.0      0      0   \n","\n","             Ticket     Fare Cabin  S_female  S_male  E_C  E_Q  E_S  \n","0         A/5 21171   7.2500   NaN         0       1    0    0    1  \n","1          PC 17599  71.2833   C85         1       0    1    0    0  \n","2  STON/O2. 3101282   7.9250   NaN         1       0    0    0    1  \n","3            113803  53.1000  C123         1       0    0    0    1  \n","4            373450   8.0500   NaN         0       1    0    0    1  "],"text/html":["\n","  <div id=\"df-785b572f-87f1-4fb6-90cf-fe311355d611\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>S_female</th>\n","      <th>S_male</th>\n","      <th>E_C</th>\n","      <th>E_Q</th>\n","      <th>E_S</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. 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Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"num_unique_values\": 891,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"num_unique_values\": 88,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"num_unique_values\": 681,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"num_unique_values\": 248,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"num_unique_values\": 147,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S_female\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S_male\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"E_C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"E_Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"E_S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["# Feature scaling"],"metadata":{"id":"krgW8FXI6gWQ"}},{"cell_type":"code","source":["np.random.seed(1)\n","NBROWS = 5000\n","def plotGraph(pdf, pscaled_df):\n","    fig, (a, b) = plt.subplots(ncols=2, figsize=(16, 5))\n","    a.set_title(\"Avant mise à l'echelle\")\n","    for col in pdf.columns:\n","        sns.kdeplot(pdf[col], ax=a)\n","    b.set_title(\"Apres mise à l'echelle\")\n","    for col in pdf.columns:\n","        sns.kdeplot(pscaled_df[col], ax=b)\n","    plt.show()\n","df = pd.DataFrame({ 'A': np.random.normal(0, 2, NBROWS),\n","                    'B': np.random.normal(5, 3, NBROWS ),\n","                    'C': np.random.normal(-5, 5, NBROWS) })\n","scaler = MinMaxScaler()\n","keepCols = ['A', 'B', 'C']\n","scaled_df = scaler.fit_transform(df[keepCols] )\n","scaled_df = pd.DataFrame(scaled_df, columns=keepCols)\n","plotGraph(df[keepCols], scaled_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"KlINITUj6jXq","executionInfo":{"status":"ok","timestamp":1708271626089,"user_tz":-60,"elapsed":1066,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"49e9e751-4353-4596-a11a-3cc3222e69ab"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Dataset split"],"metadata":{"id":"Ana8f3Af-Vnb"}},{"cell_type":"code","source":["train = pd.read_csv(\"train.csv\")\n","X = train.drop(['Survived', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked' ], axis=1)\n","X['Pclass'] = X['Pclass'].fillna(5)\n","X['Age'] = X['Age'].fillna(X['Age'].mean())\n","X['Fare'] = X['Fare'].fillna(X['Fare'].mean())\n","y = train['Survived']\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n","print (\"Train=\" + str(X_train.shape) + \", Test=\" + str(X_test.shape))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tqucbHR4-Xba","executionInfo":{"status":"ok","timestamp":1708272663815,"user_tz":-60,"elapsed":7,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"b1793f4d-a224-459e-9cfa-678c144a1024"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train=(596, 6), Test=(295, 6)\n"]}]},{"cell_type":"markdown","source":["# Regression univariée"],"metadata":{"id":"w4qxV_MDUj2e"}},{"cell_type":"code","source":["data = pd.read_csv('univariate_linear_regression_dataset.csv')\n","plt.scatter(data.col2, data.col1)\n","plt.grid()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"aiEJVxGxUx9y","executionInfo":{"status":"ok","timestamp":1709201250669,"user_tz":-60,"elapsed":688,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"88220bc9-8461-4eb2-d91a-9246b451b99e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Entraînement du modèle"],"metadata":{"id":"kwlr_w-JXIAC"}},{"cell_type":"code","source":["X = data.col2.values.reshape(-1, 1)\n","y = data.col1.values.reshape(-1, 1)\n","regr = linear_model.LinearRegression()\n","regr.fit(X, y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"pZrk2BFeVYXs","executionInfo":{"status":"ok","timestamp":1709201485619,"user_tz":-60,"elapsed":13,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"d1d30479-5d60-4143-d691-d8d500484066"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 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center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["Prédiction d'une valeur qui n'existe pas dans le jeu de données ..."],"metadata":{"id":"p-3kkBGJVaBQ"}},{"cell_type":"code","source":["regr.predict(np.array([[30]]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7lrW8ZQtWSgc","executionInfo":{"status":"ok","timestamp":1709201688222,"user_tz":-60,"elapsed":358,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"d3cd2828-0d1a-420b-b49e-31039fa3a4b8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[22.37707681]])"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["predictions = range(30,51)\n","results = []\n","for pr in predictions:\n","    results.append([pr, regr.predict(np.array([[pr]]))[0][0]])\n","myResult = pd.DataFrame(results, columns=['col1', 'col2'])\n","myResult.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"5rghxwLOXT1n","executionInfo":{"status":"ok","timestamp":1709201834249,"user_tz":-60,"elapsed":12,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"09810974-c4a6-420d-e37f-fa933c70f87e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   col1       col2\n","0    30  22.377077\n","1    31  22.965519\n","2    32  23.553962\n","3    33  24.142404\n","4    34  24.730846"],"text/html":["\n","  <div id=\"df-0c4a42a2-29eb-467a-91b7-5dc35505332f\" class=\"colab-df-container\">\n","    <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\" 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["final = pd.concat([data, myResult])\n","plt.scatter (final.col2, final.col1)\n","plt.grid()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"mILbJB_jX0q1","executionInfo":{"status":"ok","timestamp":1709202385827,"user_tz":-60,"elapsed":547,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"a9027676-5b3e-4859-f74c-2e51bf72eff3"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6t0lEQVR4nO3dfXiU9Zn3/88EkwwPyYSAMIM8GAGBmAKGNZJbSxUTHtpFLOxdn3Kvq/50pehPYWuVvddC2u4B2Psubbc0rdZq98dGXPqrIrVEASUcuIlgMGoK5YZsClaSsBKY8DRhmlz3H3Eik3lOJtc8vV/HwXE4Mxcz3+91cTjnfL/neV4WwzAMAQAAmCQt1gMAAACpheADAACYiuADAACYiuADAACYiuADAACYiuADAACYiuADAACYiuADAACY6opYD6C3rq4unThxQllZWbJYLLEeDgAACINhGDp79qzGjBmjtLTgaxtxF3ycOHFC48aNi/UwAABAH3zyyScaO3Zs0GPiLvjIysqS1D347OzsGI8m+txut9566y3NmzdP6enpsR6OKZgzc05mqThv5syc/Wlvb9e4ceN6vseDibvgw7PVkp2dnbTBx5AhQ5SdnZ1S/4CZc/JLxTlLqTlv5sycgwknZYKEUwAAYCqCDwAAYCqCDwAAYCqCDwAAYCqCDwAAYCqCDwAAYCqCDwAAYCqCDwAAYKq4azIGAAAGRmeXoX1NbTp51qVRWVYV5eVqUJr591GLaOVjzZo1slgsXn+mTp3a87rL5dLy5cs1YsQIDRs2TEuXLlVra2vUBw0AACJT1dCsm9e/rbufr9Xjm+t19/O1unn926pqaDZ9LBFvu1x33XVqbm7u+bN3796e11asWKFt27Zpy5Ytqq6u1okTJ7RkyZKoDhgAAESmqqFZyzYdULPT5fV8i9OlZZsOmB6ARLztcsUVV8hut/s873Q69cILL6iyslJz586VJL344ouaNm2aamtrNXv27P6PFgAARKSzy1D5toMy/LxmSLJIKt92UKX5dtO2YCIOPo4cOaIxY8bIarWquLhYa9eu1fjx41VXVye3262SkpKeY6dOnarx48erpqYmYPDR0dGhjo6Onsft7e2Sum9o43a7Ix1e3PPMKRnnFghzTg2pOGcpNefNnBPLvqY2tZ27qMxBgY9pO3dRtUdPqigvt+e5SOccybmxGIbhLxjya/v27Tp37pymTJmi5uZmlZeX69NPP1VDQ4O2bdum+++/3yuQkKSioiLdeuutWr9+vd/3XLNmjcrLy32er6ys1JAhQ8KeCAAAiJ0LFy7onnvukdPpDHlX+oiCj97OnDmjCRMm6Ic//KEGDx7cp+DD38rHuHHj9Nlnn4UcfCJyu93asWOHSktLU+q2zMw5+aXinKXUnDdzTqw572tq0wO/3h/yuF/dd4PPykckc25vb9fIkSPDCj76VWqbk5Oja6+9VkePHlVpaakuXbqkM2fOKCcnp+eY1tZWvzkiHpmZmcrMzPR5Pj09PeEucCSSfX7+MOfUkIpzllJz3sw5McyeNEq5wwarxenym/dhkWS3WTV70ii/OR/hzjmS89KvJmPnzp1TY2OjHA6HZs2apfT0dO3atavn9cOHD+v48eMqLi7uz8cAAIAwdXYZqmk8pa31n6qm8ZQkafWifEndgcblPI9XL8o3td9HRCsf3/rWt7Ro0SJNmDBBJ06c0OrVqzVo0CDdfffdstlsevDBB7Vy5Url5uYqOztbjz32mIqLi6l0AQDABFUNzSrfdtCrpNZhs2r1onxVlBX6vGb//LUFBQ5TxxlR8PHnP/9Zd999t06dOqUrr7xSN998s2pra3XllVdKkjZs2KC0tDQtXbpUHR0dmj9/vn72s58NyMABAMAXPL08em+teHp5VJQVau9Tc+Oiw2lEwcfmzZuDvm61WrVx40Zt3LixX4MCAADhi6SXR/HEESaPzhc3lgMAIMHta2rz6V56OUNSs9OlfU1t5g0qCIIPAAAS3MmzgQOPvhw30Ag+AABIcKOyrFE9bqD1q88HAAAwV2eX4ZM0WpSXK4fNGrKXx+VNxGKJ4AMAgAQRrJR29aJ8Ldt0QBbJKwCJVS+PYNh2AQAgAXhKaXsnlnpKaSWpoqxQdpv31ordZlVFWaHpvTyCYeUDAIA4F24p7d6n5qo03x4XvTyCIfgAACDORVJKWzxxRFz08giGbRcAAOJcopXShkLwAQBAnEu0UtpQCD4AAIhznlLaQJkbFnVXvcRLKW0oBB8AAMSZzi5DNY2ntLX+U9U0npLUXSoryScAicdS2lBIOAUAII4E6+VRUVbo85r989fiqZQ2FIIPAADihKeXR++SWk8vj4qyQu19am7cl9KGQvABAEAcCLeXR2m+Pe5LaUMh5wMAgDgQSS+PREfwAQBAHEi2Xh7BEHwAABAHkq2XRzDkfAAAEAOdXYZX4uisCcPlsFnV4nT5zfuwqLuyJVF6eQRD8AEAgMkCldPePsOh5/Y0ySJ5BSCJ2MsjGLZdAAAwkaectndyaYvTpef2NOnhOXmy27y3Vuw2qyrKChOql0cwrHwAAGCScMppX/+wWdVP3qq6Y6cTupdHMAQfAACYJNxy2rpjpxO+l0cwbLsAAGCSVCqnDYbgAwAAk6RSOW0wBB8AAJikKC9XDpvV5860HhZ1V70kQzltMAQfAAAMkM4uQzWNp7S1/lPVNJ6S1F0uK8knAEm2ctpgSDgFAGAABOrlsXpRvirKCn1es3/+WrKU0wZD8AEAQJR5enn0Lqltcbq0bNMBVZQVau9Tc706nCZbOW0wBB8AAERROL08yrcdVGm+PanLaYMh5wMAgCgKt5fHvqY28wYVZwg+AACIInp5hEbwAQBAFNHLIzRyPgAA6IfOLsMrcXTWhOFy2Kxqcbr85n1Y1F3Zkuy9PIIh+AAAoI8CldPePsOh5/Y0ySJ5BSCp1MsjGLZdAADoA085be/k0hanS8/tadLDc/Jkt3lvrdhtVlWUFaZEL49gWPkAACBC4ZTTvv5hs6qfvFV1x06nZC+PYAg+AACIULjltHXHTqdsL49g2HYBACBClNP2D8EHAAARopy2fwg+AACIUFFerhw2q8+daT0s6q56SeVy2mAIPgAACMLTx0PqzvXo7DI0KM2i1YvyJcknAKGcNjSCDwAAAqhqaNbN69/WA7/eL0l64Nf7dfP6t1XV0KwFBQ5VlBVSTtsHVLsAAOCHp4+HISlz0BfPtzhdWrbpQE+AUZpv9+pwSjltaAQfAAD0Ek4fj/JtB1Wab9egNAvltBFi2wUAgF7C7ePhyQVBZAg+AADohT4eA4vgAwCAXujjMbDI+QAApDxPOa0naXTWhOFy2Kxqcbr85n1Y1F3VQh+PviH4AACktKqGZpVvO+iV4+GwWXX7DIee29NEH48BwLYLACBlecppeyeXtjhdem5Pkx6ek0cfjwHAygcAICWFU077+ofNqn7yVu3/z//SZ4dq9av7btDsSaNY8egnVj4AACkp3HLaumOne3I7aCAWHQQfAICURDlt7BB8AABSEuW0sUPwAQBISUV5uXLYrD7VLB4WdVe9UE4bfQQfAICU0NllqKbxlLbWf6qaxlOSustlJVFOazKqXQAASS9QL4/Vi/JVUVbo85r989copx0YBB8AgKTm6eXRu6S2xenSsk0HVFFWqL1PzfXqcEpVy8Ai+AAAJK1wenmUbzuo0ny7iieOMHl0qYucDwBA0gq3l8e+pjbzBgWCDwBA8qKXR3wi+AAAJC16ecQncj4AAEmjs8vwShydNWG4HDarWpwuv3kfFnVXttDLw1z9WvlYt26dLBaLnnjiiZ7nXC6Xli9frhEjRmjYsGFaunSpWltb+ztOAACCqmpo1s3r39bdz9fq8c31uvv5Wn3lB+/o9hnd5bL08ogffQ4+9u/fr1/84heaPn261/MrVqzQtm3btGXLFlVXV+vEiRNasmRJvwcKAEAgnnLa3smlLU6XntvTpIfn5Mlu895asdusqigrpJdHDPRp2+XcuXO699579fzzz+v73/9+z/NOp1MvvPCCKisrNXfuXEnSiy++qGnTpqm2tlazZ8+OzqgBAPhcOOW0r3/YrOonb1XdsdP08ogDfQo+li9frq997WsqKSnxCj7q6urkdrtVUlLS89zUqVM1fvx41dTU+A0+Ojo61NHR0fO4vb1dkuR2u+V2u/syvLjmmVMyzi0Q5pwaUnHOUmrOO97mvK+pTW3nLipzUOBj2s5d1P7//K/PczuyJUldnX9RV2d4nxFvczZDpHOO5NxEHHxs3rxZBw4c0P79+31ea2lpUUZGhnJycryeHz16tFpaWvy+39q1a1VeXu7z/FtvvaUhQ4ZEOryEsWPHjlgPwXTMOTWk4pyl1Jx3PM352aLQx3x2qFa/P9S/z4mnOZsl3DlfuHAh7PeMKPj45JNP9Pjjj2vHjh2yWqNTlrRq1SqtXLmy53F7e7vGjRunefPmKTs7OyqfEU/cbrd27Nih0tJSpaenx3o4pmDOzDmZpeK8423O+5ra9MCvfX8Q9/ar+27oc1VLvM3ZDJHO2bNzEY6Igo+6ujqdPHlShYWFPc91dnZqz549+ulPf6o333xTly5d0pkzZ7xWP1pbW2W32/2+Z2ZmpjIzM32eT09PT+oLnOzz84c5p4ZUnLOUmvOOlznPnjRKucMGhyynnT1pVL9zPOJlzmYKd86RnJeIql1uu+02ffzxx6qvr+/581d/9Ve69957e/47PT1du3bt6vk7hw8f1vHjx1VcXBzJRwEA4Fdnl6GaxlPaWv+pahpPSeoul5Uop00UEa18ZGVlqaCgwOu5oUOHasSIET3PP/jgg1q5cqVyc3OVnZ2txx57TMXFxVS6AAD6raqhWeXbDnqV1DpsVq1elK+KskKf1+yfv0Y5bXyJeofTDRs2KC0tTUuXLlVHR4fmz5+vn/3sZ9H+GABAivH08ui9tdLidGnZpgOqKCvU3qfmenU4pZw2PvU7+Ni9e7fXY6vVqo0bN2rjxo39fWsAACSF18ujfNtBlebbVTxxhMmjQ6S4sRwAIO7ta2rz6V56OUNSs9OlfU1t5g0KfUbwAQCIe+He8j7c4xBbBB8AgLgX7i3vwz0OsRX1hFMAAPqjs8vwSRotysuVw2YN2cujr03EYC6CDwBA3AhWSrt6Ub6WbTogi+QVgNDLI/Gw7QIAiAueUtreiaWeUlpJqigrlN3mvbVit1lVUVZIL48EwsoHACDmwi2l3fvUXJXm2+nlkeAIPgAAMRdJKW3xxBH08khwbLsAAGKOUtrUQvABAIg5SmlTC8EHACDmPKW0gTI3LOqueqGUNjkQfAAATNfZZaim8ZS21n+qmsZTkrpLZSX5BCCU0iYfEk4BAKYK1sujoqzQ5zX7569RSps8CD4AAKbx9PLoXVLr6eVRUVaovU/NpZQ2yRF8AABMEW4vj9J8O6W0SY6cDwCAKSLp5YHkRvABADAFvTzgQfABADAFvTzgQc4HAGBAdHYZXomjsyYMl8NmVYvT5Tfvw6LuyhZ6eSQ/gg8AQNQFKqe9fYZDz+1pkkXyCkDo5ZFa2HYBAESVp5y2d3Jpi9Ol5/Y06eE5ebLbvLdW7DarKsoK6eWRIlj5AABETTjltK9/2KzqJ29V3bHT9PJIUQQfAICoCbectu7YaXp5pDC2XQAAUUM5LcJB8AEAiBrKaREOgg8AQNQU5eXKYbP63JnWw6LuqhfKaVMbwQcAoM86uwzVNJ7S1vpPVdN4SlJ3uawknwCEclp4kHAKAOiTnYda9d03Dvv08li9KF8VZYU+fT7sn79GOS0IPgAAfbLilXq5Or1XMFqcLi3bdEAVZYXa+9Rcrw6nlNPCg+ADABCRzq7uLh7BenmUbzuo0nw75bTwi5wPAEBE6o6dDvq6p5fHvqY2cwaEhEPwAQCIyGfnOsI6jl4eCITgAwAQkZHDMsM6jl4eCIScDwBAUJ1dhlfi6MxxOdp5yLeU1sOi7soWenkgEIIPAEBAVQ3NPiWzE4ZnauXU7v+2yDvxlF4eCAfbLgAAv6oamrVs0wGfG8W1tnc/vv+/TZDd5r21YrdZVVFWSC8PBMXKBwDAR2eXofJtBwOW00rS7xtaVf3kXNUdO00vD0SE4AMA4GNfU5vPikdvLe0u1R07TS8PRIxtFwCAj3DLZCmnRV8QfAAAfIRbJks5LfqCbRcASHG9S2mL8nJVlJcrh82qFqfLb96HJNmzKadF3xB8AEAK81dK67kz7epF+Vq26UDActqnF04luRR9wrYLAKSoQKW0njvTSlJFWaFPOe3o7O7HJdNGmzNQJB1WPgAgBYUqpfXcmXbvU3NVmm/32pa5fmyW3qzabvKIkUwIPgAgBYUqpb38zrTFE0d4ldO63W4TRohkxrYLAKQgSmkRSwQfAJCCKKVFLBF8AEAK8pTSBrszrYM702KAEHwAQAro7DJU03hKW+s/VU3jKUndd56V5BOAcGdaDDQSTgEgyQXr5VFRVujzmv3z17gzLQYKwQcAJDFPL4/eJbWeXh4VZYXa+9Rcnw6nrHhgIBF8AECSCreXR2m+nTvTwlTkfABAkoqklwdgJoIPAEhS9PJAvCL4AIAkRS8PxCtyPgAgSXR2GV6Jo7MmDJfDZlWL0+U378Oi7soWennAbAQfAJAEApXT3j7Doef2NMkieQUg9PJALLHtAgAJzlNO2zu5tMXp0nN7mvTwnDzZbd5bK3abVRVlhfTyQEyw8gEACSycctrXP2xW9ZO3qu7YaXp5IC4QfABAAgu3nLbu2Gl6eSBusO0CAAmMclokIoIPAEhglNMiERF8AEACK8rLlcNm9bkzrYdF3VUvlNMinhB8AEAC6ewyVNN4SlvrP1VN4ylJ3eWyknwCEMppEa8iCj4qKio0ffp0ZWdnKzs7W8XFxdq+fXvP6y6XS8uXL9eIESM0bNgwLV26VK2trVEfNACkoqqGZt28/m3d/XytHt9cr7ufr9XN69+WJFWUFVJOi4QRUbXL2LFjtW7dOk2ePFmGYejXv/61Fi9erA8++EDXXXedVqxYoTfeeENbtmyRzWbTo48+qiVLlujdd98dqPEDQErw9PLoXVLb4nRp2aYDqigr1N6n5np1OKWcFvEqouBj0aJFXo//+Z//WRUVFaqtrdXYsWP1wgsvqLKyUnPnzpUkvfjii5o2bZpqa2s1e/bs6I0aAFJIOL08yrcdVGm+nXJaJIQ+9/no7OzUli1bdP78eRUXF6uurk5ut1slJSU9x0ydOlXjx49XTU1NwOCjo6NDHR0dPY/b29slSW63W263u6/Di1ueOSXj3AJhzqkhFecsmTPvfU1tajt3UZmDAh/Tdu6iao+eNCWxNBWvNXMO//hwWAzD8BdMB/Txxx+ruLhYLpdLw4YNU2Vlpb761a+qsrJS999/v1cgIUlFRUW69dZbtX79er/vt2bNGpWXl/s8X1lZqSFDhkQyNAAAECMXLlzQPffcI6fTqezs7KDHRrzyMWXKFNXX18vpdOo3v/mN7rvvPlVXV/d5sKtWrdLKlSt7Hre3t2vcuHGaN29eyMEnIrfbrR07dqi0tFTp6emxHo4pmDNzTmZmzHtfU5se+PX+kMf96r4bTFv5SLVrzZxDz9mzcxGOiIOPjIwMTZo0SZI0a9Ys7d+/Xz/+8Y9155136tKlSzpz5oxycnJ6jm9tbZXdbg/4fpmZmcrMzPR5Pj09PakvcLLPzx/mnBpScc7SwM579qRRyh02WC1Ol9+8D4u6K1tmTxplaoJpKl5r5hz8uHD1u89HV1eXOjo6NGvWLKWnp2vXrl09rx0+fFjHjx9XcXFxfz8GAFLWoDQLvTyQVCJa+Vi1apUWLlyo8ePH6+zZs6qsrNTu3bv15ptvymaz6cEHH9TKlSuVm5ur7OxsPfbYYyouLqbSBQD6aUGBQxVlhSrfdtDrRnJ2m1WrF+XTywMJJaLg4+TJk/rbv/1bNTc3y2azafr06XrzzTdVWloqSdqwYYPS0tK0dOlSdXR0aP78+frZz342IAMHgFSzoMCh0nw7vTyQ8CIKPl544YWgr1utVm3cuFEbN27s16AAAP4NSrPQywMJj3u7AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAUxF8AAAAU10R6wEAAGKrs8vQvqY2nTzr0qgsq4rycjUozRLrYSGJEXwAQAqramhW+baDana6ep5z2KxavShfCwocMRwZkhnbLgCQoqoamrVs0wGvwEOSWpwuLdt0QFUNzTEaGZIdwQcApKDOLkPl2w7K8POa57nybQfV2eXvCKB/CD4AIAXta2rzWfG4nCGp2enSvqY28waFlEHwAQAp6OTZwIFHX44DIkHwAQApaFSWNarHAZGg2gVA0qF0NLSivFw5bFa1OF1+8z4skuy27nMHRBvBB4CkQuloeAalWbR6Ub6WbTogi+QVgHjCtNWL8gnaMCDYdgGQNCgdjcyCAocqygplt3lvrdhtVlWUFRKsYcCw8gEgKYQqHbWou3S0NN/Or/nLLChwqDTfzjYVTEXwASApRFI6WjxxhHkDSwCD0iycE5iKbRcASYHSUSBxEHwASAqUjgKJg20XAEkhEUpHKQEGuhF8AEgK8V46Gm4JMAEKUgHBB4Ck4Skd7f0lb49xnw9PCXDvFRlPCbCnrJUeJUgVEeV8rF27VjfccIOysrI0atQo3XHHHTp8+LDXMS6XS8uXL9eIESM0bNgwLV26VK2trVEdNAAEsqDAob1PzdXLD83Wj++aqZcfmq29T82N2Zd3uHeP/f1HJ+hRgpQRUfBRXV2t5cuXq7a2Vjt27JDb7da8efN0/vz5nmNWrFihbdu2acuWLaqurtaJEye0ZMmSqA8cAALxlI4unnmViieOiOm2RbglwP+0tYHb2yNlRLTtUlVV5fX4pZde0qhRo1RXV6c5c+bI6XTqhRdeUGVlpebOnStJevHFFzVt2jTV1tZq9uzZ0Rs5ACSAcEt72867A75GjxIkm36V2jqdTklSbm539nhdXZ3cbrdKSkp6jpk6darGjx+vmpqa/nwUACSkaJb20qMEyaLPCaddXV164okndNNNN6mgoECS1NLSooyMDOXk5HgdO3r0aLW0tPh9n46ODnV0dPQ8bm9vlyS53W653YF/CSQqz5yScW6BMOfUkIpzlkLP+/qxWZowPFOt7YFLgIcPyVDbhUshP2vkkCvi4vym4rVmzuEfHw6LYRh92kRctmyZtm/frr1792rs2LGSpMrKSt1///1ewYQkFRUV6dZbb9X69et93mfNmjUqLy/3eb6yslJDhgzpy9AAAIDJLly4oHvuuUdOp1PZ2dlBj+3Tysejjz6q3/3ud9qzZ09P4CFJdrtdly5d0pkzZ7xWP1pbW2W32/2+16pVq7Ry5cqex+3t7Ro3bpzmzZsXcvCJyO12a8eOHSotLVV6enqsh2MK5syck1m48955qFXrtv9RLe2XlQBnW/X0wqkqmTZaOw+1asUr9ZL89yjZcOdMlUwbHf0J9EEqXmvmHHrOnp2LcEQUfBiGoccee0yvvvqqdu/erby8PK/XZ82apfT0dO3atUtLly6VJB0+fFjHjx9XcXGx3/fMzMxUZmamz/Pp6elJfYGTfX7+MOfUMJBzjucGXKHmvXD6WM0ruCrg+BdOHytL2qCI+nzE+nzw7zs1hDvnSM5LRMHH8uXLVVlZqa1btyorK6snj8Nms2nw4MGy2Wx68MEHtXLlSuXm5io7O1uPPfaYiouLqXQB0C/J0IAr1N1jI7m9fTKcD6SuiKpdKioq5HQ6dcstt8jhcPT8eeWVV3qO2bBhg/76r/9aS5cu1Zw5c2S32/Xb3/426gMHkDo8HUJToQFXOD1KUul8IDlFvO0SitVq1caNG7Vx48Y+DwoAPEJ1CLWouwFXab49brZgBhLnA8mgX30+AGCghdshdF9Tm3mDiiHOB5IBwQeAuBZuY61UacDF+UAyIPgAENfC7RAazU6i8YzzgWRA8AEgrhXl5cphsypQ9oJF3VUeRXm5Zg4rZjgfSAYEHwDi2qA0i1Yvypckny9cz+PVi/JTJrmS84FkQPABIO4tKHCooqxQdpv3VoLdZlVFWWHK9bXgfCDR9fnGcgBgpkgacKUCzgcSGcEHgIQRqkNoquF8IFGx7QIAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAExF8AEAAEx1RawHACBynV2G9jW16eRZl0ZlWVWUl6tBaZZYDwsAwkLwASSYqoZmlW87qGanq+c5h82q1YvytaDAEcORAUB42HYBEkhVQ7OWbTrgFXhIUovTpWWbDqiqoTlGIwOA8BF8AAmis8tQ+baDMvy85nmufNtBdXb5OwIA4gfBB5Ag9jW1+ax4XM6Q1Ox0aV9Tm3mDAoA+IOcDSBAnzwYOPPpynFlIjgXQG8EHkCBGZVmjepwZSI4F4A/bLkCCKMrLlcNmVaA1A4u6v9iL8nLNHFZAJMcCCITgA0gQg9IsWr0oX5J8AhDP49WL8uNiS4PkWADBEHwACWRBgUMVZYWy27y3Vuw2qyrKCuNmK4PkWADBkPMBJJgFBQ6V5tvjOokzUZNjAZiD4AMIQ7xVbAxKs6h44oiYfX4oiZgcC8A8BB9ACFRsRM6THNvidPnN+7Coe6soXpJjAZiLnA8gCCo2AuvsMlTTeEpb6z9VTeMpr+TRREqOBWA+Vj6AAEJVbFjUXbFRmm9PuS9Rf6tBE4ZnauXUL47xJMf2Ps7OqhGQ8gg+gAAiqdgIlH+xr6lNn134S1zkifjTl1wWz2pQ76Cstb37XO081KqF08dKSozkWADmI/gAAuhPxcbOQ62SpAd+vV8dnd1ftPGWJ9KXXJZw+nes2/5HzSu4qifAiPfkWADmI+cDCKCvFRtVDc1a8Uq9z3HxlCfS11yWUKtBktTSTv8OAMERfAAB9KWdeSJ09uzPGOnfASAaCD6AAPpSsZEInT37M0b6dwCIBoIPIIhI25knwspAf8YYajVIkuzZ9O8AEBwJp0AIkVRsJMLKQH/G6FkNWrbpgCyS19aN52w8vXAq1SwAgmLlAwiDp2Jj8cyrVDxxRMAv10S47X1/xxhoNWh0dvfjkmmjozhaAMmIlQ8gijwrA0+8XOfzWrx09gxn9SLUGP2tBl0/NktvVm0fyKEDSBIEHzBdrG/SNtCfv6DAoQ13ztSlJu8AJJ46e0aj+2jv/h1ut3tAxgog+RB8wFSxvkmbWZ9fMm20ft8k/eq+G+K2wyndRwHECsEHTLPzUKu+WfmhT38JT2Mrf9Uj0RSoLfhAfn5RXq7S09Oj+p7RRPdRALFAwilMs277H2PWfCsRmn8BQKog+IBpWtpj13wrEZp/AUCqYNsFcaUvzbfCSSBNhOZfAJAqCD4QVyJtvhVuAmkiNP8CgFTBtgtMY8+ObvOtSO7MmgjNvwAgVRB8wDRPL5wqKfybtAUTaQJpX24SBwAYGAQfME3JtNER3aQtmL4kkEZ6kzgAwMAg5wOmikZjq84uQ+8e/a+wju2dQEpjLQCIPYIPmK4/ja2qGpq15vWDQct2L+dJII11S3cAwBcIPpAwqhqa9cimA2Eda1H3dkpRXm7MW7oDALxFnPOxZ88eLVq0SGPGjJHFYtFrr73m9bphGPrOd74jh8OhwYMHq6SkREeOHInWeJGiOrsMPf3bj8M69vIE0h0HW8KuiAEAmCPi4OP8+fOaMWOGNm7c6Pf1Z599Vj/5yU/085//XO+9956GDh2q+fPny+WieRP6rvY/T+nMhfDumupJIJ07dbT+8dWPaakOAHEm4m2XhQsXauHChX5fMwxDP/rRj/RP//RPWrx4sSTpX//1XzV69Gi99tpruuuuu/o3WqSsmsZTYR339Zlj9L++MVM7DrZo9tpdajsfOGC5vCKGm6sBgHmimvPR1NSklpYWlZSU9Dxns9l04403qqamxm/w0dHRoY6Ojp7H7e3tkiS32y23O7xfuonEM6dknFsgvefc2WWo7thpfXauQyOHZWrWhOEhkz/TjL8oc1DoFYpxORl6q+FTrXilXoakzEGhx3fSeV5ud3boAyPAdU4dqThv5pwaIp1zJOfGYhhGn9ecLRaLXn31Vd1xxx2SpP/4j//QTTfdpBMnTsjh+CKR7xvf+IYsFoteeeUVn/dYs2aNysvLfZ6vrKzUkCFD+jo0AABgogsXLuiee+6R0+lUdnbwH3Qxr3ZZtWqVVq5c2fO4vb1d48aN07x580IOPhG53W7t2LFDpaWlSk9Pj/VwwrbzUKvWbf+jV4mrPduqpxdOVcm00UH/rmfO6RNmauUW3xwMz5rHhjtnBnyvzi5DX/nBOzpzMXBknTM4Xf/rv8/Q//Ov74czJVkkjc626s0n5kS97NaM69yXFaSBlKj/tvsrFefNnJmzP56di3BENfiw2+2SpNbWVq+Vj9bWVs2cOdPv38nMzFRmZqbP8+np6Ul9gRNpflUNzfpm5YefBw1ffLkdP92hb1Z+GHZ30PVvHpGr0/+Xo0XSd984rHkFV/n9Ak2XVH7H9KCltncUjlPbxU51BPgMf5+56mvXyZqZEdbxfTFQ1zmey4cT6d92NKXivJlzagh3zpGcl6i2V8/Ly5PdbteuXbt6nmtvb9d7772n4uLiaH4UTBLpPVSCCdYYzF879N4WFDj093PyAr7+3J4m/emzCyHHIUm5Q9MTtqV6JDfUA4B4FPHKx7lz53T06NGex01NTaqvr1dubq7Gjx+vJ554Qt///vc1efJk5eXl6ZlnntGYMWN68kKQWCK5h0o0KkZ6t0O/XGeXodc/DP7Funn/cdmzM9Xa3uE3YJKkEUMzVLPqNmVckXi3NgoVDFrUHQyW5tvp4AogbkX8f9/3339f119/va6//npJ0sqVK3X99dfrO9/5jiTp29/+th577DE9/PDDuuGGG3Tu3DlVVVXJarUGe1vEqWDBQLjH7TzUGvbnedqh+xNuIHR30XhJ/u9ea5H0z18vSMjAQ+rbDfUAIN5E/H/gW265RYZh+Px56aWXJHVXwHz3u99VS0uLXC6Xdu7cqWuvvTba44ZJggUDl/vsbIffrZeqhmateKU+5N+3qDtnoSgvN+Ax4QZCV48cmrR3r41GMOhPZ5ehmsZT2lr/qWoaT9F4DcCAinm1C+JbUV6uHDarWpyugNsYkvS9Nw7pl3ubvBIeg20R+LN6UX7QrYJwA6EjrWd106QrVf3krao7djqpbiY3cphvcrY/4Z4rKb6TVwEkp8Rce05gnl+Uv/+4OSF+YQ5Ks2j1onxJvtsYvfVOeAy1ReAxfMgVeqJksjr+0hX0nHgCoVDj+Ok7jbr7+Vp95QfvyHnxkhbPvErFE0ckfOBR1dCsb/5bXcjjQq0g9X5PklcBmI3gw0RVDc2a/6M9kqRv//8f6e7na3Xz+rfj/n/wCwocfrcxeutd/RLu0n+XYdGGnUf0+Ob6kOfkrhvGh72SkkxfoJ47+jov/iXksc98LfgKkkc0K5kAIBIEHybx/MLsXW6aKF+QCwoc2vvUXD3ztWlBj7s84THcpX9nr8Zh/s5JVUOzbl7/tjbs/D9hjzlZvkA9QUK4hg8Nr28JyasAYoXgwwTJ8gtzUJpFJ85cDOvYd49+plkThoe1TdJb73MSaGsg3PdK9C/QcLevPKKdlBpp8ioAhELwYYJk+YVZ1dCsF979U1jH/vSdo/rKD97R7TP6lrDoOSe1/3kqoqTVQBL5CzTSsYe74hTt4wAgXAQfJkiGX5iRLv1L3dsnv9jTpPnX2X1eyxkcXhvemsZTfVrx6C2Rv0AjGXskyaahEnjDKX8GgL4g+DBBMvzCrO1DEOBZraj6Q0vPczmD07WiZLI23lsY4bv0TTJ8gYZb5WNR6HLlywWrZPI8juT9ACBcBB8mSPRfmFUNzVpeGfiGbpFwXnTrRzuPyHnhUljnpPiakX3+rGT5Ag2n3Hn4kL7dqyZQJVMyNGQDEL9oMmYCz5fHsk0H4v4XZmeXoX1NbT2NuU6fv6TllQf6nXPh4bn/yPfeOKRnvpav5ZXd5+Ty97/8nMyeOEIOm7VPWy/2JGqU5QkSejcDyxmcrvtvulqPzp3c538/CwocKs23e133ZGjIBiB+EXyYxPPlsfaNP0g63/N8qC/I3sHAQH4p+Ot0mWbp78aHL08y6cFmp54ouVYv7zvuVYLc+5ysXpSvRzaFt/LyzVsmaoo9Kym/QAcySBiUZonKjQEBIBwEHyZaUODQLZNH6M2q7Xp26XSNsg0N+uVhZttrTzlr70BjIKt/f/pOoyTJnp2pFSWTdfXIoX6/UBcUOPQ3hVfpNwc+DfmeI4ZmaPHMqwZszACA/iP4MJnnS/WrX3IoPT1wxUegYMDTgCua+/GR3oMl2lrbO/SjnUdUUVYY8Nf3TZOvDCv4yA3z3ieJiHuwAEgWJJzGIbObkkXaxCrawpmTPTu8SqBwj0s03IMFQDIh+IiRYLcwN7spWTz0F+lpKtZ4yu958VQMBRPPFUP9kSwdcgHAg22XGJn/oz06drqj5/Hly+dmNyWLp/4iyysP6Mxl93q5/Lx4KoZ6f8XGW8VQtEUSjJI0CiAREHyEIZoVJzsPtUrS59UdX7xHi9OlRzYd0IqSyXJ3hvcL9rOzHersMkKOJdT4PasKLU5XVPI+hmYM0oVLnX16rzMBbjJXUdbdlMw2JF1nLngfkzMkXWuXfClp8x6SoUMuAFyO4COEaCb5dXYZWrf9j1o51fc1zxf1hp1Hwn6/771xSL/c2xR0LOGO/64bxkd0x1h/RgzN0PcWFygtTT09TfobzHj6gqz67cc63Svo8Aj0fLJIhg65A83MknQA/UfwEUS0K072NbV59bOIhmBjCWf8knyCk7545mvT9Hc35fX8D99fQ6y+MhQ8wLCoew6l+fak/MIJtTJlUXdvlGTMdwkHVUBA4iHhNICBSPIbiGVxz6f/z1cb9OqBP/ckaYYz/lW//ViP9PFW9R6eNuiXBx5Sd2+O6idvVe7Q8G4g1x+hklUTHfdgCYwqICAxsfIRwEAk+Q3Usrgh6dT5S1rx7x9KkkZnZeieGyeEHH9/tytCffHVHTuttvNuZQ7q18eE7eH/732dv9TZ8ziZfv0Gaq+eTC3kIxUqwE72FTEgkRF8BDAQSX5Febmf96E4H/LY/mg9eymi3JG+6v3F13vfvcV5ccDHcLnLAw9pYBqyxRL3YPFGFRCQuAg+AhiIJL9BaRY9vXCqLjXV9XVYcSFncLo23luoG67OVd2x09pa/6n+9NkFn3u05A7N6Pdneb5WbUPS5bzgjiiBNdq/fuMhqZF7sHyBKiAgcRF8BECSny/P1+y6pV/SWZdbX/nBO0F/eZ4+fyniz0izeN9PxrO6Iinsm8tdLlq/fklqjD9UAQGJi+AjAE+SX6CSUUPd5ake4fwqrmpo1opX6rW+aMCHPyBsg6/QuqXTJclvFU1vkaxSeM7UT+8u1PChGX7P4wM3Xa1fvfunSIctqX+/fs28zw7Cxw8EIHGlTPDRlyXzQEl+Hht2/h9t3n9ct89w6PUPm4P+Ko71zdui4czFv+hQ81m9sv+TqM9j+NB0fX3mVRo+NCPgtSnNt/c5+Ojrr1+SGuNXsB8IqV4FBMS7lAg++rNk7kny++nbR/wmcTY7XfrFniaf53v/KvYkx5lV+TFQfrwrOoms9myr7i4aL+fFS3qt/oTazl/SC+/+SS+8+6eA16YvnVj7++uXpMb4RhUQkJiSPviI1pL55v2fRPS5vX8VR7u5WMYgiy6F2YY9HrW2uwJ2VA10bUJthfUWjV+/JDXGP6qAgMST1E3GotUorK+3nL/8V3HbuY6Qx0diYYE9qu9ntmBnPNi18fzStfe6w23OkHTlDPFuaGa3Wfudj0FSY2LwVAEtnnmViieOIPAA4lxSr3xEa8m8v79qT551RaXs1CPNIv3NrHHa+mHydm8Mdm0C/dKVFPVfvyQ1AkD0JXXwEa0l8/7+qo32r+KHvpyn/zZppBw2a1TunTJQcvzcgTZSga5NoH4X0c67IKkRAKIvqbddorVk7vn1G+nXi+e+J0V5uT3v0R9pFunv5+Rp1Vfze74Uo/2V9//eOrHPf9eenakVJZP147tm6uWHZmvj3YX9Hk88bGcE2uqJxrYOAKSipF75iNaSeaSJjp73lrx/Fa9elK8nXo6su2mWdZDumDlWV48Yov9RfLUyrvgiXvR8Ka55/Q9qaQ+cU2KRNDo7U5JFre3Bz8XjpVOUf5VNa14/GDRJ1vOe//sbM/XZuQ6/2xydXYYcNqtOn4u8zXq8bWeQ1AgA0ZPUKx/RvBtooF+/DptVfz8nz2dVw9+v4gUFDm24c2bY47dI+sHfzND37ijQg1++xivwuPw93336Nq0ouTbge0jSmtuv05rbwzsX3e85VytKJod8z5smjQyY5Hf5+Q/0HuGMJ16Q1AgA0ZHUKx9SdPsABPv1++0F08L6VVwybbR+3yT96r4b9NmFv2hUllWnz3foe28c6nPr7kFpFj1eMllT7MNCzjPcc9H9ntdqij2rX+fOE3D1vp/N5W3T6dEAAKkl6YMPKbpL5oESHSO94VdRXq7S078oDZ3/eSOy/owvnHlGei6ice78BVyXvwfbGQCQWlIi+JDi/26g0RpfOO8T6WdFa2y9A65ovz8AIDEkdc4HAACIPwQfAADAVAQfAADAVAQfAADAVAQfAADAVAQfAADAVAQfAADAVAQfAADAVAQfAADAVHHX4dQwuu+52t7eHuORDAy3260LFy6ovb3db7fPZMScmXMyS8V5M2fm7I/ne9vzPR5M3AUfZ8+elSSNGzcuxiMBAACROnv2rGw2W9BjLEY4IYqJurq6dOLECWVlZcliSb6bi7W3t2vcuHH65JNPlJ2dHevhmII5M+dklorzZs7M2R/DMHT27FmNGTNGaWnBszribuUjLS1NY8eOjfUwBlx2dnbK/AP2YM6pIRXnLKXmvJlzaohkzqFWPDxIOAUAAKYi+AAAAKYi+DBZZmamVq9erczMzFgPxTTMOTWk4pyl1Jw3c04NAznnuEs4BQAAyY2VDwAAYCqCDwAAYCqCDwAAYCqCDwAAYCqCDxNt3LhRV199taxWq2688Ubt27cv1kMaUGvWrJHFYvH6M3Xq1FgPK6r27NmjRYsWacyYMbJYLHrttde8XjcMQ9/5znfkcDg0ePBglZSU6MiRI7EZbJSEmvPf/d3f+Vz3BQsWxGawUbJ27VrdcMMNysrK0qhRo3THHXfo8OHDXse4XC4tX75cI0aM0LBhw7R06VK1trbGaMT9F86cb7nlFp9r/cgjj8RoxP1XUVGh6dOn9zTVKi4u1vbt23teT7Zr7BFq3gNxnQk+TPLKK69o5cqVWr16tQ4cOKAZM2Zo/vz5OnnyZKyHNqCuu+46NTc39/zZu3dvrIcUVefPn9eMGTO0ceNGv68/++yz+slPfqKf//zneu+99zR06FDNnz9fLpfL5JFGT6g5S9KCBQu8rvvLL79s4gijr7q6WsuXL1dtba127Nght9utefPm6fz58z3HrFixQtu2bdOWLVtUXV2tEydOaMmSJTEcdf+EM2dJeuihh7yu9bPPPhujEfff2LFjtW7dOtXV1en999/X3LlztXjxYv3hD3+QlHzX2CPUvKUBuM4GTFFUVGQsX76853FnZ6cxZswYY+3atTEc1cBavXq1MWPGjFgPwzSSjFdffbXncVdXl2G3240f/OAHPc+dOXPGyMzMNF5++eUYjDD6es/ZMAzjvvvuMxYvXhyT8Zjl5MmThiSjurraMIzu65qenm5s2bKl55hDhw4ZkoyamppYDTOqes/ZMAzjK1/5ivH444/HblAmGD58uPHLX/4yJa7x5TzzNoyBuc6sfJjg0qVLqqurU0lJSc9zaWlpKikpUU1NTQxHNvCOHDmiMWPG6JprrtG9996r48ePx3pIpmlqalJLS4vXdbfZbLrxxhuT/rrv3r1bo0aN0pQpU7Rs2TKdOnUq1kOKKqfTKUnKzc2VJNXV1cntdntd66lTp2r8+PFJc617z9nj3/7t3zRy5EgVFBRo1apVunDhQiyGF3WdnZ3avHmzzp8/r+Li4pS4xpLvvD2ifZ3j7sZyyeizzz5TZ2enRo8e7fX86NGj9cc//jFGoxp4N954o1566SVNmTJFzc3NKi8v15e//GU1NDQoKysr1sMbcC0tLZLk97p7XktGCxYs0JIlS5SXl6fGxkb94z/+oxYuXKiamhoNGjQo1sPrt66uLj3xxBO66aabVFBQIKn7WmdkZCgnJ8fr2GS51v7mLEn33HOPJkyYoDFjxuijjz7SU089pcOHD+u3v/1tDEfbPx9//LGKi4vlcrk0bNgwvfrqq8rPz1d9fX1SX+NA85YG5joTfGDALFy4sOe/p0+frhtvvFETJkzQv//7v+vBBx+M4cgwkO66666e//7Sl76k6dOna+LEidq9e7duu+22GI4sOpYvX66Ghoaky18KJtCcH3744Z7//tKXviSHw6HbbrtNjY2NmjhxotnDjIopU6aovr5eTqdTv/nNb3Tfffepuro61sMacIHmnZ+fPyDXmW0XE4wcOVKDBg3yyYpubW2V3W6P0ajMl5OTo2uvvVZHjx6N9VBM4bm2qX7dr7nmGo0cOTIprvujjz6q3/3ud3rnnXc0duzYnuftdrsuXbqkM2fOeB2fDNc60Jz9ufHGGyUpoa91RkaGJk2apFmzZmnt2rWaMWOGfvzjHyf1NZYCz9ufaFxngg8TZGRkaNasWdq1a1fPc11dXdq1a5fXnlqyO3funBobG+VwOGI9FFPk5eXJbrd7Xff29na99957KXXd//znP+vUqVMJfd0Nw9Cjjz6qV199VW+//bby8vK8Xp81a5bS09O9rvXhw4d1/PjxhL3WoebsT319vSQl9LXuraurSx0dHUl5jYPxzNufqFznqKavIqDNmzcbmZmZxksvvWQcPHjQePjhh42cnByjpaUl1kMbMP/wD/9g7N6922hqajLeffddo6SkxBg5cqRx8uTJWA8tas6ePWt88MEHxgcffGBIMn74wx8aH3zwgXHs2DHDMAxj3bp1Rk5OjrF161bjo48+MhYvXmzk5eUZFy9ejPHI+y7YnM+ePWt861vfMmpqaoympiZj586dRmFhoTF58mTD5XLFeuh9tmzZMsNmsxm7d+82mpube/5cuHCh55hHHnnEGD9+vPH2228b77//vlFcXGwUFxfHcNT9E2rOR48eNb773e8a77//vtHU1GRs3brVuOaaa4w5c+bEeOR99/TTTxvV1dVGU1OT8dFHHxlPP/20YbFYjLfeesswjOS7xh7B5j1Q15ngw0T/8i//YowfP97IyMgwioqKjNra2lgPaUDdeeedhsPhMDIyMoyrrrrKuPPOO42jR4/GelhR9c477xiSfP7cd999hmF0l9s+88wzxujRo43MzEzjtttuMw4fPhzbQfdTsDlfuHDBmDdvnnHllVca6enpxoQJE4yHHnoo4YNsf/OVZLz44os9x1y8eNH45je/aQwfPtwYMmSI8fWvf91obm6O3aD7KdScjx8/bsyZM8fIzc01MjMzjUmTJhlPPvmk4XQ6YzvwfnjggQeMCRMmGBkZGcaVV15p3HbbbT2Bh2Ek3zX2CDbvgbrOFsMwjL6vmwAAAESGnA8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGAqgg8AAGCq/wuPuvhw2X/D7wAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Classification"],"metadata":{"id":"vBUnuxFUtca8"}},{"cell_type":"markdown","source":["## Confusion matrix"],"metadata":{"id":"fAd5k7IavQR7"}},{"cell_type":"code","source":["def Prepare_Modele(X, cabin):\n","    target = X.Survived\n","    sexe = pd.get_dummies(X['Sex'], prefix='sex')\n","    cabin = pd.get_dummies(cabin.str[0], prefix='Cabin')\n","    age = X['Age'].fillna(X['Age'].mean())\n","    X = X[['Pclass', 'SibSp']].join(cabin).join(sexe).join(age)\n","    return X, target\n","cabin = titanic['Cabin'].fillna('X')\n","X, y = Prepare_Modele(titanic, cabin)"],"metadata":{"id":"lVSBi7fYtffy"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Entrainement de l'algorithme Random Forest"],"metadata":{"id":"QzjwA5wyt16m"}},{"cell_type":"code","source":["tree = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n","tree.fit(X, y)\n","p_tr = tree.predict(X)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BJh5eSlht0u7","executionInfo":{"status":"ok","timestamp":1709392249671,"user_tz":-60,"elapsed":658,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"64cb9823-55f7-40d2-f3d1-57339aef83a4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9214365881032548"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["Matrice de confusion"],"metadata":{"id":"GfxfE8uit-HH"}},{"cell_type":"code","source":["c_tr = confusion_matrix (y, p_tr)\n","print (\"Matrice de confusion / Random Foret\\n\", c_tr)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vTfxtIkft_p2","executionInfo":{"status":"ok","timestamp":1709392276803,"user_tz":-60,"elapsed":274,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"9703715d-b044-4567-d4cd-f464eec6b658"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Matrice de confusion / Random Foret\n"," [[525  24]\n"," [ 46 296]]\n"]}]},{"cell_type":"markdown","source":["## ROC and AUC curves"],"metadata":{"id":"MEAy7Ca4vLgE"}},{"cell_type":"markdown","source":["Entraine deux autres modèles sur les mêmes données"],"metadata":{"id":"5EE21w6NwXEC"}},{"cell_type":"code","source":["lr1 = LogisticRegression()\n","lr1.fit(X, y)\n","dummy = DummyClassifier()\n","dummy.fit(X, y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":218},"id":"3bWF2ItlwcLZ","executionInfo":{"status":"ok","timestamp":1709392984150,"user_tz":-60,"elapsed":262,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"b4ed2fd8-eace-4dbd-9d73-e5e50541de01"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"output_type":"execute_result","data":{"text/plain":["DummyClassifier()"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["FP_tr, TP_tr, Thres_tr = roc_curve(y, tree.predict_proba(X)[:,1])\n","plt.plot(FP_tr, TP_tr, label=\"Random Forest\")\n","FP_rl, TP_rl, Thres_tr = roc_curve(y, lr1.decision_function(X))\n","plt.plot(FP_rl, TP_rl, label=\"Régression Linéaire\")\n","FP_dm, TP_dm, Thres_tr = roc_curve(y, dummy.predict_proba(X)[:,1])\n","plt.plot(FP_dm, TP_dm, label=\"Dummy\")\n","\n","plt.xlabel (\"False Positives\")\n","plt.ylabel (\"True Positives\")\n","plt.legend ()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"q5cm3AyvvVtv","executionInfo":{"status":"ok","timestamp":1709393075000,"user_tz":-60,"elapsed":715,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"5ec4c65c-518c-4fc0-b70d-6a00f2821069"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7e2823367b20>"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Calcul de la surface AUC"],"metadata":{"id":"0EaIVJtmyitn"}},{"cell_type":"code","source":["print (\"AUC Dummy: \",metrics.auc(FP_dm, TP_dm))\n","print (\"AUC Reg. linéaire: \",metrics.auc(FP_rl, TP_rl))\n","print (\"AUC Random Forest: \",metrics.auc(FP_tr, TP_tr))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ptb3M-_tyTdO","executionInfo":{"status":"ok","timestamp":1709393472761,"user_tz":-60,"elapsed":270,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"9cdc402a-a0e5-4f4e-a5b1-75ab59d71726"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["AUC Dummy:  0.5\n","AUC Reg. linéaire:  0.860615792669287\n","AUC Random Forest:  0.9708374609870152\n"]}]},{"cell_type":"markdown","source":["# eXplainable AI (XAI)"],"metadata":{"id":"iBfzu4Cn2WnD"}},{"cell_type":"code","source":["train = pd.read_csv('/content/sample_data/california_housing_train.csv')\n","test = pd.read_csv('/content/sample_data/california_housing_test.csv')\n","all_data = pd.concat([train, test], axis=0)"],"metadata":{"id":"yQf0cSdN2aSD","executionInfo":{"status":"ok","timestamp":1709562303878,"user_tz":-60,"elapsed":293,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}}},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":["Regardons la distribution et correlations potentielle entre median_income et median_house_value"],"metadata":{"id":"fN3xAeYd2yIK"}},{"cell_type":"code","source":["all_data.plot.scatter(\"median_income\", \"median_house_value\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":468},"id":"ZAMaK-tk2w5a","executionInfo":{"status":"ok","timestamp":1709562338010,"user_tz":-60,"elapsed":1148,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"483e8c80-0f25-4310-c45e-88e8f07b57ec"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='median_income', ylabel='median_house_value'>"]},"metadata":{},"execution_count":8},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlYAAAGxCAYAAACgDPi4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACmdklEQVR4nOzdeXxTZfY/8E+SpnvTphulULrQytYiBdpaNkEZGAcV3EWlDFb9oYAK4jYq6oyKgzIiiDJ+YVgcxG1cUEaEwQKySAsFpCwF7MJaWrqlbbrn/v6oN2a5uUtys7Xn/Xrx0iY3N0+X5J48z3nOUTAMw4AQQgghhDhM6e4BEEIIIYR0FxRYEUIIIYTIhAIrQgghhBCZUGBFCCGEECITCqwIIYQQQmRCgRUhhBBCiEwosCKEEEIIkQkFVoQQQgghMvFx9wB6EoPBgEuXLiEkJAQKhcLdwyGEEEKICAzDoKGhAbGxsVAq+eekKLByoUuXLiEuLs7dwyCEEEKIHc6fP4++ffvyHkOBlQuFhIQA6PrFaDQaN4+GEEIIIWLodDrExcUZr+N8KLByIXb5T6PRUGBFCCGEeBkxaTyUvE4IIYQQIhMKrAghhBBCZEKBFSGEEEKITCiwIoQQQgiRCQVWhBBCCCEyocCKEEIIIUQmFFgRQgghhMiEAitCCCGEEJlQYEUIIYQQIhO3BlavvPIKFAqF2b+BAwca729pacGcOXMQERGB4OBg3HHHHbhy5YrZOc6dO4cpU6YgMDAQ0dHRePrpp9HR0WF2zM6dOzF8+HD4+fkhOTkZ69atsxrLypUrkZCQAH9/f2RlZSE/P9/sfjFjIYQQQkjP5vaWNkOGDMH//vc/49c+Pr8Paf78+diyZQs+//xzhIaGYu7cubj99tuxd+9eAEBnZyemTJmCmJgY7Nu3D5cvX0ZOTg7UajXeeOMNAEBpaSmmTJmC2bNnY+PGjdixYwceeugh9O7dG5MnTwYAfPrpp1iwYAFWrVqFrKwsLFu2DJMnT0ZxcTGio6NFjcXddhVX4siFOgzvp8XYlCjj7SVVjSiv0SMhIgiJkUHG2w6UVgNQ4LqkCOPtts7XJyzA6hx8TJ+TYRiz/z9QWoOrDa2ICvGDAsDPpdVQQIHbhvcxG7fpuQ6UVuP0lQZcqGlGU1snYjR+uG14X7Nx5ZdU4+sjF6FUKDAgJgTX9AqBQgGcvKxDZLAfokL8UKFrQW9NADoZBlcbWgEAxy7U4USFDgoA/cIDEaMJAAMgLFCNI+W1OFenR7w2EPGRwSi92ogAXxUGxmigDfJFWVUTLuuaUVHfgiu6FqgUCmgCfdHU2o6WNgP8fZUI8vOBr0oFbYAa+rYOKBQK9NUG4MTlBlTqWsCAgY9SiSA/FRQA6prbYWAAhQJQKxUIDVQDUCBI7YPQQDUu1Tejtb0Tfmol1Col2jsNaG1n0GkwINBPBbVKiRp9G1QKBfqGBcBPrUJ9cwf8fZTI7h+B6wdEI+/UFfzvRGXXcUoFfFUK+KiUUKsUaGzpQENLB5RKBfx9lOg0MPD1USLYzwchfmqEBfki2M8Hwf4qVNS3oLm9EwBwtbEVvioV/FRKVDa1AAwQoPaBvr0D7Z0MQgN80C88CAwDtLR34FJdC6Bg0D8yBJd1zajUtcBfrUKgrw8YMBjcOxSVDS04X6MHAPiolFAAaGnvRLC/D9RKJa42tqK5vRMGBlCrAF+Vquv7UXf9LDs6DWAAqH/73prbOtHS0Yn2TgYqBRDo6wNNgBoKAI1tHfBVdX3OrGlqg69KiSGxofD3VUGpAKoaWnG+Rg8flRLhgb4wgEGQ2gcGBmjt6ES1vhXNbQYE+ioR6OuDSl0LGtsMAAC1AggNVKOprRMdBgN8lUr4+ijhp1aioaUD+jYDlAB81UooAaiUCgT6+QAMA18fFdo6O9HQ3PVhkQGDtk4Gfj4qDIsLQ1ZSBGr1bdhVXIlafRtC/NQI8lMBUKC2qRVQKNAn1B8GAAooUK9vQ1N7B/qEBSI8yA/VDa2obGpBRyeD2NAABPiqcLaqAW3tBsSE+qNfeCCuNLRCCcBPrcLg3hpcPyAKF+tajK8hACi92oT/nahAY1snQv190DvUHwyAW67tgz+mxmDxf09i9+lKtHd2/W0rfvs+/XyU8FWr0CvYF5pAX5RdbUKFrhUKABFBakSG+MFf3XVNqNe3IzTQB/2jglFYXotafRs6DQyaOzrBGAB/tRK9NP7Q+PuitaMD1fo2tLUbkBgZjBsH9cLZqgZc0bUiyFcFTYAal+uaoW/vRK8QfzS0tKO5vRPD+2l/+/6aASjQJ6zr/aJS14KTlxugBIO6lg6UVjUiKtgfE4dEo6K+BWVX9QgLUOOamBBkJUWAYRh898sllF1tQlJUMIb2Des6T30LKhpaoNO348RlHUL8fTAjOwF9wgLw0f5yFFc0INhfhdiwQNw4KNr4HqdSKHDkfC1qmtotbgc6GRjHFxnsi6F9Q3HkfD1qm9owqHcIojT+xvdu0/d1g4HB10cuQQGgf1QwqhpbEBHsh+gQP+P75tC+oehkYPL+/ft1g31vZ8dg+X7Pd62wvAax1wYFgCyTaxLX9YvvejPh7Z3G28venGLz+Z1NwTAM464nf+WVV/D111/jyJEjVvfV19cjKioKH3/8Me68804AwKlTpzBo0CDs378f1113Hb7//nvcfPPNuHTpEnr16gUAWLVqFZ599llUVVXB19cXzz77LLZs2YKioiLjue+9917U1dVh69atAICsrCxkZGTgvffeAwAYDAbExcVh3rx5eO6550SNRQydTofQ0FDU19fL1iuwvLoJ01buRa2+3XibNlCNf+dm4e9bi7H7TJXx9uykCHQaGOSX1ZidIzspAqseGIHQQDXn+UyNS4nCiunpv13wzdXp2/D4piNmzymFxt8HW+aNRVxEIOr0bXhsYyH2/Vpt17kIIYT8zkepQIfBdZd7rmuF2Pf1jAQt1Cql2XGW5xNzvfEBcFamAEvK9dvtOVZnzpxBbGwskpKScP/99+PcuXMAgEOHDqG9vR0TJ040Hjtw4ED069cP+/fvBwDs378faWlpxqAKACZPngydTofjx48bjzE9B3sMe462tjYcOnTI7BilUomJEycajxEzFnfhCoJq9e2YunIv9p69anb7/pJqq6CKvX3epsM2z2dq79mrxmMtPb7piNVzSqFr6cCtK/cYz0VBFSGEyMOVQRXAfa0Q+75eUFZrdZzl+cRcbzp473UetwZWWVlZWLduHbZu3YoPPvgApaWlGDt2LBoaGlBRUQFfX1+EhYWZPaZXr16oqKgAAFRUVJgFVez97H18x+h0OjQ3N+Pq1avo7OzkPMb0HEJj4dLa2gqdTmf2T05d0//cQVCHgUGnhMnI3Weq8Gn+Od6gCgA6GQa7z1Sh9GqT2e0lVY3YfaZK0nNyqdW347OD5+ye9SKEEOJ+ltcK9hohx/mkXG8Sntti93Pay605VjfddJPx/4cOHYqsrCzEx8fjs88+Q0BAgBtHJo/Fixfj1Vdfddr5j1yok/V8+0vFzxCVVTeZrXmX/5YLIweaqSKEkO6BvVbIdY0oq24SPsjN3L4UaCosLAzXXHMNzp49i5iYGLS1taGurs7smCtXriAmJgYAEBMTY7Uzj/1a6BiNRoOAgABERkZCpVJxHmN6DqGxcHn++edRX19v/Hf+/HlxPwiRhvUNk/V82YkRoo9NiDBPJIwPD5RtHKP6ix8HIYQQz8VeK+S6RiREBMl6vXEGjwqsGhsb8euvv6J3794YMWIE1Go1duzYYby/uLgY586dQ3Z2NgAgOzsbx44dQ2VlpfGY7du3Q6PRYPDgwcZjTM/BHsOew9fXFyNGjDA7xmAwYMeOHcZjxIyFi5+fHzQajdk/OV0/IBpajiRyoCtRUaVQiD7XuJQo3JPZz+b5WCqFAuNSoqx2aCRFBWNcSpSk5+SiDVTj7pH9MI5jhyAhhBDvYHmtYK8RcpxPyvXGHbsD3RpYLVy4ELt27UJZWRn27duH2267DSqVCtOnT0doaChyc3OxYMEC5OXl4dChQ5g1axays7ONu/AmTZqEwYMHY8aMGTh69Ch++OEHvPjii5gzZw78/PwAALNnz0ZJSQmeeeYZnDp1Cu+//z4+++wzzJ8/3ziOBQsW4P/+7/+wfv16nDx5Eo8++iiampowa9YsABA1FnfZPGeMVTCkDVRj85zRGJ0caXZ7dlIEMhPCrc6RnRSBFdPTbZ7P1OjkSOOxllZMT7d6Tik0/j7YPGeM8Vw0c0UIIfLwUTr2oVcqrmuF2Pf1zASt1XGW5xNzvXFXrpNbyy3ce++92L17N6qrqxEVFYUxY8bg9ddfR//+/QF0FeV86qmnsGnTJrS2tmLy5Ml4//33zZbfysvL8eijj2Lnzp0ICgrCzJkz8eabb5rVw9q5cyfmz5+PEydOoG/fvnjppZfw5z//2Wws7733Ht566y1UVFRg2LBhWL58ObKysoz3ixmLEGeUW2D9dKYKhedqrepYlV5tQll1k1kdkNKrTThQUg0GsFnHyvR8fbWBVufgY/qcAMz+/+eSalQ3tiIy2A8qpQL7f8unslXH6uj5Wiz8/BecqWw03hYdrMZfp6ZhQG+N8dwHy2rw2cHzOHOlAXXNv+8FiQ3zw+3pfREfEYRL9c2IDQ1Ah4FB9W81kD7JP4fqpt8T9iODfQGGwdUm/iR+AAgPUqNGxHGsOG0AQvx8EBceiBOXdbhSb17HSgmgtrkdnQbAIPqs5npr/FCjb0drh+0zTBrUCycrdKhpsqxjpURjS3tXHSuFAp0GBp0Snz9IrYKPSoH6FvH7cWI0fmhsaYe/WoUAtRKXda3gGb4s1EogNECNYH8fXKxtRruDzxcZpEZbJ4Pmtg6HzwV01eUKD1DDT+1js45VelwYsvpHoK6pDTuLq1Cjb4XGT41APxWUCgWqm1oBKNAnNAAGMF11rJrb0NTWVccqIsgPVxtbUdXYivZOA2JDAxD4Wx2r1t/qWMVHBKFC1wIVFPDzVWJQjAbjB0bjQm0zqht/r2NVdrUJ246b1rEKAAMGt1zbBzel9cab/z2JncVXeOpY+SE0UG2sYwUASgXQYXJ18vdR4JpeIRgQo8Gh8hrUNrXB8FsdK4OxjlUANAFqtLZ3orKhRdTreExyBBgG0Ld1YHg/LcYPjMbF2mYwAPpqu94vrja04vglHVSKrlpzJVVNiAr2wx+G9MIVXQtKqpqgDVQjpVdXHSsA2PLLJZRUNSIpKhjXxoUZz3OpvhkNzR04cbkewX5ddaz6agPx0f4ynLrcgBB/H/QOC8CNg6KN770+SgWOnq/D1cY2q9tNxxcZ7Itr48Jw5HwdahrbMDhWg8gQP+N7t+n7OgB8VXgRAJAcHYyqhlaEB/siRuOP45fqERnsZxw3+/5tet0AYDYGy/d7vmuF5TUI6Lo2WNax4rp+cZ2LPcaZdaykXL/dGlj1NM4MrLqrnDX52Hv2qtnuD5VCgdHJkdiQm2n3sbaOl2Ll9HR8evCC6J0uGfFarJ6ZwVkDjMUWu/vHtmIcv6SD2B3Spt9nSVUjbli6y+axeQvHCxbve/yTwzgh4fktx9Hc3oGCslrRjzOtUSM0fjmwP4OcNfnYc6bK7kCWtXZWBuLDAyWNWwlgTEqU8W9TzEWkp3H0ZyLm97t2VgYmDIi2f5BeQkyxTWKblOu32yuvE2KLre25pttuTSv0ij2W73gp1u0vw+qcDJRXN+Gpz4/gTCX/bpXCc3WYt+kwZ5DnaHFV02lyNv/AMmhUKoDBsbbfEOr0bXho/UEcLBcfEFlif96Lbh4sKbBia9RsyM00jn/P2SrewO6j3Ex0GBhEBPni7R9Oi/rZscEMwzDYlF8uW1mPhIggybuV+kcHmS1tJEZ2XfBKqhqRV1xJF0D8/jOx12vTUnHzip+g45lBtdyI091wvbfwFXomjvOo5HVCTAltzzW9kEk5VszxYhSU1eLRjYcwNC4M2xeMR0aCFnxpDGzQsft0FfKKK81qgdlbXHXx7WnIWzgeG3Izzd4kufIPDAxQdFGHCW/vRM6afNSb1Cyr07dhwts7RQVV8/+QgjdvTxM4StpUl2XNmxXT0zEmmTvRVYmu2b+xKVGYMCAaQ/uGYUNuJvIWjsd796VD42/782JWUgQ6DAbcsHQXnv+yyOZxZs+nAFL7aJCRoLVKljVNqJW6U+lMZRPuX/0zfjlfB6Drd5CzJh83LN2FWWsLOH9PptgAzLKmnDPI/VxSz2fP85dUNeKxjw+hsZU7qLK1Eae74Xpv4Sv0TBxHM1bEYwldqEw/aUo5VszxYu37tdo4G7Y6JwPzNh0WnAXJ+dfvDb7HpUThqUkpkmdO2NknWzlyoYFqbMjNROnVJsz7uLBrWc/kftMZIgB4eMNBweKwrFuv7QOhDILvj9kunMuHrXnD8ARmBgAF5bXIWZNv9qmbnd0YmxyFh9YXoMAkSEzto8Ebt6Xh7R9OSw5g2YAU6NoYYvpzEjNTyKfokg63rtyLcSlRaO80IL/UvDOC5e8JcO0MhNzPJfV89jy/2Nnf4fFhNjfidBdSZ/KJPGjGingsW1tquT5pSjmW73h7HCjpSsBng5kND2aIfuzes1fxwtfiZk5M8c0+mWIYBkUWQRVgXcVY7LJdRoJW1BtxgZ3LiWwA/NjGQsEL496zV5G7vsBqJoMBgwBf88+M4YF+YBiIrtacFqtBaqzG6g1S19yBjHgt1s7KQN7C8Xjl1sEoPF9rNtNmOVMoVMIEAPacrcL+kmqrsXF1OnDlDITczyX1fPY8v9jZ38cmJHf7pTCpM/lEHhRYEY/GdaGyVfJByrG2js9OipBc5sHyMj3ummjRQVsnwxhnQ4R8lJvJebE3vdBYLpmIeWM9IKHi/t0j+oo6ryOOnrfuE8alk2FwsLzWatnM1sX4ha+OiR7DbcP72AxIC8prER6oxsvfHLdatgNgXJZkg6/DiyYJLp0KbRJgL4C2WnnYajXlCLmfS+r57Hl+Sa1OunluFSB9Jp/Ig5YCiUczXdIS2h0k5Vih4z/JP4fnvhR3IWa3C5taMT0duesLRCeCp8ZqcPJyA+cFgd1p1ycsAEWXrIMw9kJz16p9ZjNP7DIjn/fzzkpKMv/s4AXcldHPaZWPT1ysx7IdZ+x67N6zV/HQhgLO76fzt5k7sSxnvCy98FURTl5usHp+dtnOMhE9NsyxFl3sBVAooP255KpsOwvFBOVSnkPq2O15fjEBP/t66glLYLaWp3vSz8AdKLAiXkHK7iCpO4m4js9MtC6kyiU9LtQ4m8Ceg83xMA2q0vpocIxnZuqN29Lw9jbunW3srFvhef4AyDKgYGdtOHcIAgj0U+GQhKAK6FriK73aJPiGzT6/1FIWq3b/alazTIpOhhEMElP7aHDyEncAC/yeu9YnzJ/3PHwBbunVJuia2/DCV0Vmx2kD1ajXt/Nu/VfCvIaZ5QVQKKA1Tch3NO9K7tkOvY0kcpbl2J+adI3k5xcT8PPNYndHK6anW+V+9rSfgatRHSsX6ul1rOSso2J5LinnFnusUJ0ry0Rm9kL20IYCHCyrNVsiVCkUCPH3QV2zdS7UqP4R+Pjhrgr+7OyZadE905IS9tR32jxntM2gzR5s3Z96fbvVGzb7MwBgM5FfpQQ6nVwA1JbNc0fjr9+eEDWTyBUIqRQKDIoN4V2+jQ8PtDlz4qMEZ/FTlUKBrKRw+CiVgonaYuuv8dVwE0tqbTg+N6/4SfSyN1+ALvT8XGNWoitgXnHf8B47S0N10hxDBUI9VE8NrOTcWcR1LlsBjuW5pY6DK3DISNDiptQYbMo/h18rm8wuukoAQX4qNLRKq1eenRSBTY/wt0ZypM5Vah8NNuZehxp9G+ZtKrSr8KcpywKjlm/YpoHrM18cxaHyWrPnU0BqMQZpMuK1KDxXZ3UxzkwMh1qltPp9vjRlMP7y1TGrnZMqBaAJsP7bempSCqau3CfrmE3/Dtmfp0qhQCfDWF0Iuf4u+QgVhOXDFzxLee3a/aFg7mirGmVCz3+uWo+pK/eY/d662nyNQVyEZzfvJZ6LAisP1VMDKzk/9Yr5tC5XZXYWe6ELD1Rj6bYzss38mBK6+DlSJV6pAMYkR+GVWwc7VNFc6GflaJFTVkp0kFXQysXWshnX0se4lCh0GAw4UFJj9fsfHh/Gu4TIFiI1DXDkqtjOMv39c/0c2XIRQ/uGGW9j/y4r6lvwPE8+IFdlcamzx47OduQVV2LW2gLJj2PHLuX55Xy/IYRFldeJx5CzjorYaulyVGY3xeZg3fnBPhQ6UJWcT1l1ExiG4bzYOVol3vBbmYEDFjWSpBLKy7C3yKkloQr2SgAjErQ4W9loNiuhCfDB69NSOTclMAzDGVSKycvqMDBWgcmK6ekY/3ae6NpfQm5Z8RO+f3wc4iIC8djGQqtdkUUXdbj1vb1mszWmCfJ8THOR7J09drQCur2bHdixi31+qttEPAGVWyBOJWcdFalb/B2pzG6qTt+Gu1btw8HyWtlmKCy9seWkzYrbcpU2aG6T2lIZCPJV4a07hnJWdzclZZu7WEoFMKh3CNL6mH86HJMSBbVKCV2zeTK0rrnDrCZYYmQQJgyIFrXDjI9KAataWdVNrbIFVQDQ2NqJW1fuQUlVI2+piT1nqqxqOEmp4eZoXSp7K7AnRQVjZLxW9PH2VkWnuk3EE9CMFXEqOXcWSf3U60hldlOPbzqCQzLMVA2LC8OxC/WcwYflTjj2Yrd8+jC8n3dW1PlHxmt5k7K/P3ZZcmXwprZOvPH9SRxeNMnmMXX6Njz+ifzFKQ0McPJyA/IWjgcAUbNPXDOV5TV6wZpiGn8fNLZ0cOZY5fzr9yUsdnbHGXW8avXt+Pf+ct5jDADnzIuYnV+OzObIkSe5ZmYG5yyfUgGEBtiuaC8F1W3iRg2YXYsCK+JUctZREdsyhOvcUsZh+ibE/HbREcMyid5URoIWq3MyMPvfh7C/RFzxy91nqvDwhoMoLK+z+X0Ojw/DYxOSjW+YlrWsTBWU12Lz3NEAIGlpsVbfjp/OVGFsCnfvvsc3HcEJCTWipHp3x2ncMbyvcTkur7iS9/iy6iZoA9VWgYDG3wdNrR3o5PjTaWrtQKjF708ToLaqaM8GvLljEuz/hnicrBD3c7Ss4SSmhpsjdan4ZrrE5i2FBqqxc+EEq1pjY5K7ArQafZvDu9aobpM5asDsHpS87kI9NXldrp1Fts4ldleg0Dg4k4ZjNbyFJRUAhvTRYMX04QgP9OUNnNgE6p9Lqh3alcfKiNdi9cwMs+/zu18uYe7HtmeP1s7KQHx4oOQk9vl/SMETN1rXFRLa7aVE19Ldq1OH4OeSat4kayHszq7235oo25K3cDxe/uY4Z3K5UKmHj3IzcaG2GVcbWrB0u32FSh3RJ8wPF+taBY+zZ6ef0O/K8py/z/bBbNZOjrE4c+u/nO833o4S+eVDyevEo0itiG7PueSozP7Q+oNWyelCMzEjfwtuqptaUXi+Fm/81rrkkQ0FOFvZZFZWQM5dZADw2A3Wvc4G9+Z/wSdEBNmVZzK8H3d+jNAsyOBYjfGC5mh+S62+Hbeu3IPDiybxzkrwzTIK1c/6+9ZTomst2YutUcblsoigiu3XKHV5R+xsjtTdnVIrsAOOJ8PzkfP9xptRIr/7UGBFXEbON1PLczlSmb1O34aHNxzkzE9ir8NKhXk/N6UCGBGvxf/ljLT6dDwyXsu5s01qUJUWq8ExnsDu/R/PYnic1iy4EnPxtGeS+v92l2JonzCrQE4op2XFfcONj5GjDQ67LMmXUyRUoZ6Ps4Oqj3IzkRAehCkrfoKuxboSuZi/kTtH9EXOmnybMzJ8AZeYXCypuzsdyVtyZu6PM4M3byB3SyIiHgVWpMexfDMXk5zePzoIZ678HiyxeSHzNh22ugjJVZJh9vX98enBCzZzyg6V13LmuAhdPJOigpHaRyMpiODLp0nto7EqOmorz40vD00o+Z5VeK4WY1OibM5KOKuPoSPYnwebp/bu9HS76joBwBeHLljl3e09exWPbjwkWLndVimKwvO1knMKHclb4poVGxmvxaxRCRjcJ9TmOSkJWzxK5HcfCqxIj8H1Zp6RoBXVhPjMlSZkJGgxc1QChsSGGpdiuC5CgoUtLWa/bBncJxQrUqJsNhZmd4j9cqEOQ/uGmV10bAUd7M9A6syM5fKB0HIR166ukqpG3hIFd4zoKyqwMl2WtJyVYH8GQr0ZXc3y5yF00cuI13ZVrDe5TaVQIL0fdzHTTobBvl+rrern2AqIEyODOBP8U2PF53460m+Oa1bsYHmt8fdvGRBSErZ0lMjvPhRYEY/h7E+jXG/mUsooFJbXIUB9Aa/cqsF3Ry/h3R9P2zUOhUBPF8s3vscmJPPObjzzn18Q4udjdsFlW69YcrSI54GSauMsn+V5bPVjY3+vV+pbeM9d1cB/P9CVwM7O+pj+vXAFCVyJ6mKDWntlxGvx599mXQDYzPERuui9Ni3Vqi2LJsAHtwztzRt8Wgb1fPk0XL9DoZxCrir0UokpeGsZEMqxK7EnogbM7kGBFXE7V3watTm7JOEiy16kHGkLAwgnULMVxFlCsxunLjdY3bb7TJVVS5Q545Mdbjfz3JfH8J/CCzZn0Ex3UEpNgv6HiF14K+8bbrNfpM6iwTXXzzk0QI3kqGCrXoKOSIkOxpM3pmBwn1Bj9XxAOMeH76I3b9NhzgKoS7fbF8xb5tMIzbZaBqCWS5lC+D4kiakBZhoQ2lqepCRsYZTI7x4UWBG3c8WnUWcUdLTF0SbDbAVx9ntPigoWLPsgpOiiDo9uLHRgVL8TmuU7fqne5qyWo1o7DZznFVsFvV7fDrWPEqOTI0UHfEFqJZrabUfDH+aMtLms9sZtaRgaF2Z2vNCSLd9uLq6EdzEs82nE7OY0XS4WO8sh5kOSlHYfYnaSUhK2sJ6eyO9qFFgRt3LVlmBXJjQPidUgxF+N/FLzhr+WTYNt4freX78tDVNX7nXOgCUSmuVbv68Mg3trnNKsWqWQVtzUkgHAvl+rraq526p75aNUoNlGUGW6ZMvWCzJVdEmHW1fuNS7LHiyvwSf5F8yq7LNBh1kOnJ1V7Af1DsHpikZR+TSCuzmnDwdgeynTFr4PScunD5PcpLuivgV9wgJ4j6EkbOJpqFcgcStX9fay1U/NGQJ8VVhyx1CMTo40u31IH2lFYU2/92vjwjCqf4Qs43O2grJa5As0fH5qUgrm/yEFc8YniTqnAkB8RACOXqiXYYS/z3KwvQRXTE/HGItlrtRYDToMjM1geHh8GFZMTxfsk7j7TBWmrtyHv313ymbrItZD6w/iuJ1J93/n+Jvjmmmq07fhlc0nOM9h2qPP9Ocjhq2fg7GLwPqDkmcwn//yGHL+lQ9toBoqi5euvf0ECXE2mrHqgUqqGnGgtBqAAtclRTj0xiR0rq77a6AAkGVxf0lVIyoEEpordS14ZXMRIoP9MGVorKjCiOz9lfUtqGhoQe9Qf0SF+GPhpGugb+sQtfPMEQfLapG7oQDzbkxBrb7VuDvt2EUdAn2V0LeJq2h1/GK92ffoTT0ShIa6dJu0quYMgPLqZizdZl+OkSXLWQ42F2X36Ur8eKoSkcF+iAj2460U/9iErgKtjtTNYoOOo+dr8crmEzh8vs7msUoAwZw9DbtmpYb2Dfvte6jCj6euICLYDzcPjTXLUyypasTjnxy2maTuSGKz0IekAp7XndAuznp9u1XLIUrCJp6KWtq4kLtb2tTp2/DYxkLs+9W85Up2UgRWPTBCUqK40LkYMHj034VW7V1G9Y/Am7en4cWvj9u1pKPx9zHLMxFqSdMdjOofgWf/OABTV+5z91CM0uNCcfi87dmjb+aMwp/XFojOfXKljHgtPn90lNltdfo2zr9XPnkLx0MbqMYDqw84lP8GCLdOAmwvJbOvAQaMzdfk3+8Yihe/LhJ8bdjTnoYl1DKHz9pZGUiICMLPJVfx/JdFNo+TY1ciIfaQcv2mpcAe5PFNR6zedAFgf0m12XKEHOd6fNMRzovUvl+rMXXlXruTmi2Td02XUpyRLG0qxM89E7z7fq3GC1/bvti4h+0l1XEpUVi67YxVA2NPoA1UY/XMDKvbbf29cjFdgpKrAbWYwMwyqFIquuqwbcjNROhvyfO2XpNTV+4R9dpwZOnd1nK7SqFARgJ3SyQWGyjFhPLnU3UYGEnLk4S4AwVWPYRQ7Rg2WVquc/HdX6tvl22rO7uUsvt0FW+eixwaWjsELxDO4uxWK1LxLVlNz+yL3TL3ReSzcJJ1c2guGQla7Fw4wTgzW1LViE355Vjx42lJs5zsEhT7OnDV92nJwHTls5VebRJ8TYp9zTmaCL5iejpnntfqnAybQZdpnhRVCyfdAeVY9RBiyg2I3bbsytIFYh12IM/lzdvT0CvUH+//eBYHy2t584Mem5CMhIggbD56Ee+IqLskp9RYDU5ebnBq8CiH0xYJ2s4WEewreEx6XBhW52QYl4y5lszESI8LMy49O5JbJSc5NnjIVY2br26SmGKVVC2cdAc0Y9VDiCk3IPbToCf2YkuPs38mKSspAhMGRGP1zAyE+PN/1mAvFLcMjbX7+ez1xm1pVrMBriZmWSfdom6Ts12XFCm44/Pw+TqzJWN7girL84h9HaT3C7PrucRKiAhy+DUpdyI4145CNujKWzgea2dlIG/heOMypilbs16UqE68BQVWPQT7SdAWKduWxZyL7/6urdPWSwJctwthlxLGXRMluZyC5TJEdVMrbwHG9Lgw47G65nYE+6kkjdURo/pHYGhcGF65dbDkxyrRlfQvB75lHdbqn8oQp+XPlZEL+/t7bdoQBAn8PkyXjB2x+0wVPsk/B4VCgewk4RIYj9+YgryF4zFnfH+HnpcL+5qxVT7B8jhTSnTNgtoKcJxFqIyD2ABMrJKqRuQVV4pOdSDEURRY9SArpqdz1kLKToqQ/GlQ6FwrpqdzXnRG9Y/A5jljOD+Rct1uyTJAMP0ku2J6OobHh4n+Hiw/BQstcTa3d6JO34acNfmY9v4+NLZ2in6uBAdnFNhVEXuWYUMCfLBl3ljjhWrx7WmSzxHq74O5E/rj1alDUN3UintG9sVwGzMxe89eRbCIRH8/H+u3n/hwaQHZ9Mw45BVX4qnPjqJRRFVyR5aMTT335TFMeHsnTl4Wzn1jZzlHJobL8tym6vTt+NPy3djDEyxmJ3G/5sakRGHjQ9d57PKa1DpaltjX6g1Ld2HW2gJMeHsnctbke+SmCtK9ULkFF3J3uQVW6dUmHCipBgM4XMdK6FylV5vwc0k1Zx0rW/2rTG+/UKvHjpNXzOpY8fW9yiuu5G1YbHasydZytt4WX90ioCsB+lBZreiEZaUCGJMcZcw7+bmkGqcrGrB2X5nIM5iPl2EYu7a0j0uJMrbIcWRbvBRiGh6/dedQNLR0YFNBOc5ccf6Mwt/vSMOz/+H/HcvF9HcPuO7nbuqj3Eyz/n49qWccWw2fK1eLGjcTqaRcvyl5vQeSs2+U0Ln47rd1H3t7SVUjOgwMZo5KNDuO75xSck3Kqps4e7zx4Wo+zGdMcpRxVsz0+7InsCqrbkKlQEFVW0xb5CRFBUNrUWzRGcR8ZHv6i1+gDVS7bBbho/3lLnkeoKvfnpjEbGfqsIhs7X3tCxXl9TSuapVFCBcKrIjHEdPI1RYpF6+ECOc0Cjb1p7QY1OjbEBqoNrs4jUuJ4uxNx+f9vLOSAztTbHPkkqpGlxTuFBs68I3F0YbWlhwt5CnFiunDOROzLXfGOZOU8gRcwZMjr0V3EtMqiwIr4iwUWBGPw9fIVcwUvtDFi10OYH779CpWWp9QHLsorVfdc78tLVrOEI3qHwF/tRJ6Gw1+uRxysBXP+n1luHlorEeWy7DFW/MUtIFqhAdal4H4vXVOFXL+le+WMVjiC54cfS26C9XDIu5EyevEowg1chWzs8d0V9F796VblQdgk9alBhjnaprsbuRsOSuz79dqSUEVIJyvJPRiZotJhnvwTEN3oWtu5+xmwO5QiwsPtFkwc1T/CM5dnEpFV7AkVp2eewyWbAVPuesLHH4tugtfFXhq3EycjWasiEeRcwqfzSe5eWgsZ9Ku1No/9c0duHVYbxy7WOeRPfDEhGll1U1Yu6fM2UPxeqmxGrxxWxqe+vwIzlRKDyA6GfO8Nq5ZoVH9I5CZGG7WSifzt52DXGU/2Hy9oxfqRM12Mfi9NITlxhEWXy6SULNyT19OE1OQ1Ft4W45bT0eBFfEozprC50raZT/V/nSmSvSS04ofz0LXLLyt31OpFOh2TaqdYcV9w5EYGYSnJg3A7H8X2n0eNvjgmhU6UFKD0cmRyFs43rhz9j+HLqDwXJ3ZcUoFMCJea1x6G3dNFLKTIkT3NmSXo7lyoxxZFvb05TS+KvDewltz3Ho6WgokHsXVU/grpqdjSB/xpS/Kq/Ue31KGC/vz6/S+obuU5d/Zws+POnS+hIggweXtZ744iue/PIbnvjyGgvJaq+NMewKyVj0wAvER0mp+mTYsZwl9kMlI0Mr+WnSkYKc9j3W0HpY78eW4Ec9FgRXxOK5saREaqMbye8Wd15WV1uU2PL6rx50ntiMSQ0puEUvFkZMk9LXp39mu4kpJRWBNKfF7VXihWSGxOz1NewKGBqrx11tTJY2JDeR+kjBj+dKUwbK9Fh0p2NkTi33KkW9K3IOWAonHcfUUvpgSDSoF8OqtQ/DU5784bRzO9NiEZIQGqhEaqO4q9XC2SjAZ3pOsn5WJuuY2HD5fh+H9tGho6cBjG/mX6IbHa7E6JwM1+jazvyPLvytbf2dHLtTZPd7QQLUx+NAGyLNkY7n0dv2AaLvqkc1Yk29cThIK+qr1bbK9FrlmX/acqcJD6wvw+aOjjLdx5RN56+5ER1DJCO9FgRVxiDOTKi3zohx9LtPHMwxjdq4V09ORu77AZsJuJwM89fkvCPZT2T2L4U6mF+UV09MxdsmPvH0RPc1fvjpmVoNKzAxWQVkt5m06jBXT020WmOX7mxrWN8zu8dbq2/HfossY3DsEs9aJ6wRgC1sexHJ8dfo2pEQHI9+O2mZsUCLUe5L9u7FVWFTsa9JWkrwBQEF5Le5atQ9L7xqGF78ussonempSSo8s9kklI7wXBVbELq5MqnT0ubgeb4o915wbkgXb4cgZVAX5qhDkp0JlQ5ts57TEdVEWajbtiU5YFPYUO0tja1ZD6G+qpKoRBnT1prT3ZyXUHkksW0tvj286gkPldXadkw1KFL/lS9lq/WIrYJH6mhSafTlUXoupK/dYbQzZe/YqavStvI/trjM3tmbShX43xP0ox4rYxZVJlY4+l1B1dfZcrsw/GhYXhmvjwsyCqkBf+V+OXBdlVxcIHdQ7BB/nZnHWZhKi/C1vWlrFr9/Zykex9Tf16MZDZrk8upYOqFz8LqlSKJCRoMXaWRnIWzgeG3IzrYIVW/k3UpVVN9mV0yj1NSn02jIwXcEyVz5R0UX+avndeebGlfmmRD40Y0Ukc0UfLnaJwVZ5ALHPZWusXOe6UKtHah8NTlzSOTX/6KPcTLyf9yv2/Wq+XV7fZm/4II2UAFIbqEZyVDAOlYtvPG3p5OUGvPH9Sbtmfq6JCcGpyw12PvPv2FkNttm2rb+pfb9WW33a7HTNr8WIvXDyzcaKDY7T+mhwjCcwSYgIkpzTaM/rn519kdrGiZUaq8HJyw09buamO5SM6IkosCKSOTOpUmjZjuu5LPOlpIzVVM6/HMuFEevwuVrRNYgctfdsldVSmNiL3Ku3DMbM0Yk4V63H1JV7HCqKak+PvqTIIKiV0qvccwkP9EXOmnxRf1cujqOMnvpDCiJD/HFdUoRZb0mVQoFOhpFU3Pblmwdj/MCuEgM5a/I5l5OGx4cZdxqyOVRiXrf2vv6F8hj5vHFbGt7edrpbFPu0h73Ns4l7UGBFJHNmUuXDGw5K6on3/o9nUWByvGWehyPLe8F+KuhbO2W/0P5j+xmZz2ibZQVwllA/RR8lMC29LwDgxa+LrLa1KxVA/6ggu6qSi1UicTt5oFqJlnaD2e+LndV4bcsJuy7orrTU5O/C1m4/9u+bDY5t/f7yiqswa0wiAO7ftSbABwVltcacQik5i0KvKR8bwXBooBpfPDoKd63a1zUDajIrrFIooAnwga65g3NWamhcWLeduaGq6t0P5VgRyZxRxLNO34Y7P9iHgrJaUctwKoUC2kC1VZVqyzwPW2MVo7G1EwNjQiQ/zlHyzNGYM62BBHRd5J6alGLz+A4D8MCan7Fmz6/YzTGzZWDg1KDKHvp2A4It8rgyE8NRq29DQVkthNKRZJock4Wt2UHTv2++39/uM1XYfboSecWVqPmtZELewvFYOysDGfFaziRxsTmLQq+pGWvyeWtMrc7JwJjkKLPbRidHYvOcMYL5RN5c7NNST6zN1VMoGMYLy0h7KZ1Oh9DQUNTX10OjEV/t2xPV/9bgVa5dgTlr8iXlX2QkaHkLK+YtHG98863Xt+OhDQWiCzGaio8IRHm1a5O9HdmJZstHuZkYm2J+MfvDP3Z6XHAkp7Q+GgT7qUUvu2oD1YjR+ONkheM5Xc6Wt3A8jl+sx1yRwRD72qxuasUNS3fxnlcoaCmpasSJyzqs31dm8zXFzjTx1ZiyNfvUHWeluNhaohX6uRH3kHL9pqVAYhcxSZWO1rix9FFuJjoMDFQKBQ6fr+UNlH4uuYqy6iaEB6qxdNsZs2Mz4rUYPyAKb207Lficrg6qQgPUaGgW/sSqAET3NwSADotpwJKqxm4dVAHgTdrmUqtvxxu3peFRgcKjgO2lOlcpq27Cun1loo9nZ6RmjUkQPK+UEgu2kuPFbC6xlTfUE/KJXLEBiLgPBVbEIVxvgnLXuFEqgDHJUUjrEyo6sf35L4ts3ld4rg7FV8TPSnDtSDKlUsqzcyxIrUK9iKAKAIbEaiQlhFvmvbm65IK38PdVcVamVykUGNw7BBOH9EKovw82HjjvUGClVACDYzV4ZvIAuzZNVNa3SMoZYy/YD41N5D0uItDX5n1c+Y/HBf4Gu2uNKUdRVfXujQIrIjup7SeEkmFHxGuNCbh89ajE6mQYSUttc29IxuqfSm1XZTfIs3zX1C6++OiK+4bjfI0eh8/XYng/LZRQIOdfB6yaLCsAjP0t7810BpGSK7lV1Ldg4eRrAMAq2fvYJR2O2bG7kcuIeC1mjkpAXHiQYDslU0oFEOirwrN2Fh/tZBjepPe3t53mLKb60PqDnH//QvmQYjay9MTkbaqq3r1RYEVkJXWKu07fhlc2n+A8lxJdF6DPZ48SvVzoDH5qlWBV9sW3pWH1nhIcPl/v1LGoFApkJobj5W+Om/08tIFqzuTssEA1XpuWalVqILWPd+f4OQtbLX1cShQ2zxmNan0b3s87i0I7K5xz0fh37chjl6dH9Y9AZmK4WS6YraVGA+NY9f+EiCCbLWIA7h2kj286gkKB2TElwLkbky9QcmX3Bk9DVdW7N/rgSmQlZorbFF9V9DEpUVg9M0PUeePDA5yymw4AwgPVWPnjWd5jNIFqfDVnDBbdzN97zVGjkyOhUMDqZ1arb+dM/K/Vt+Opz61/xpYtYoi5vWev4u1tpxEfHoiCslqHK5ybspzZPFBSA7VKady1l7dwPA4vmmT8+qPcTKREBzv8vOyO3RqBJUzT1yj7gUZopXtEvNbsazE1plzZvcETUVX17otmrIispExxC81CvTp1iPGTq9AngPKaZtFjBMTlRbGfHpduOyP4iX3GmvzfGsZeI2kcUlXU63FaYtI5V5K/MyvLdwfsDGt+aY3LnutCrR4TBkSjpKoRecWVSIgIQnpcGB5afxBnKhsdfp6Fv/1tSnmNis1/lFpjipK3qap6d0aBFZGVlCluKQmcchfpFJNsPjo5Eo+MTcQD/8oXdc49Z6rQ3Nbx2/dfZZXvJAepQRXhp1TwB5mVDfwNgOU0Y02+1RKgNlCNOpl2H1bru/pSSnmNCgVh1/YNNc6wSNnNR8nbv+sJuyB7GloKJLITO8UtpYKzqxokZ8RrsXJ6urH57Zs/nBL9WAOAgvJaNLS0ISspwnmDFEmlUNjV+LgnsVzCsvSP7cIlOeRkmVdVq2+XVFaDj+lMlNjXKBuE2RLi72tXPhQlb5PujN51iezETnHb+uTMYpfXTFt42PqUDcDqPoUCgtW2LS2561rjWEuqGlEksRYSABy9UI8xyVHIWzgeZdVNeGPLSVmWcoRY7kwcHh9mV1FUZ4zF06gUwEcPZmFUSqTk4rTehmsmSsoy1FOTrhGd7C52hx8lb5PuzKNmrN58800oFAo8+eSTxttaWlowZ84cREREIDg4GHfccQeuXLli9rhz585hypQpCAwMRHR0NJ5++ml0dJi/qe/cuRPDhw+Hn58fkpOTsW7dOqvnX7lyJRISEuDv74+srCzk55svAYkZS3fB5nmUSuzXZkpM+wmuT86mTJNZ+T5lc903NjkK2UkRktrZHCipRklVIzbll2PD/nLRjzNlYH7fqh8fHohJg6LtOo9Ub9yeZpYAffvwPi55Xi6eHFQBXT0UZ288hDp9GzoMBq8MqpKigjAsLszstuykCIzqbz5bOjw+DHdn9OV8LYt5jdb8toRoS1l1k13tWbhes4N6hxhzwQiRSo7rlhw8pqVNQUEB7r77bmg0GkyYMAHLli0DADz66KPYsmUL1q1bh9DQUMydOxdKpRJ79+4FAHR2dmLYsGGIiYnBW2+9hcuXLyMnJwcPP/ww3njjDQBAaWkpUlNTMXv2bDz00EPYsWMHnnzySWzZsgWTJ08GAHz66afIycnBqlWrkJWVhWXLluHzzz9HcXExoqOjRY1FiDe0tHHXFuj/HruMx3gqXpu22uD7lG15H1frHT79I4Pwq0wvypToYJfMVLEyErR4ccpgnK/Rd7Ub8fCmw55gUEwwTla47nckBwW6itZ++/hYANyvh9KrTTh+sd7q78Ce13JJVaNgG5yXvzlud3uWo+fr8MLXx8xmh3tK2QUiD1dct6Rcvz0isGpsbMTw4cPx/vvv47XXXsOwYcOwbNky1NfXIyoqCh9//DHuvPNOAMCpU6cwaNAg7N+/H9dddx2+//573Hzzzbh06RJ69eoFAFi1ahWeffZZVFVVwdfXF88++yy2bNmCoqLfq3Hfe++9qKurw9atWwEAWVlZyMjIwHvvvQcAMBgMiIuLw7x58/Dcc8+JGosQbwis3NW/6uYVP/Euu62dlYEJA+yf+blr1T4cKhfX4JkQb5CRoMXqnAybFw45X8t853rl1sGi+w9yLRVSzzziKFf8DUm5fnvEUuCcOXMwZcoUTJw40ez2Q4cOob293ez2gQMHol+/fti/fz8AYP/+/UhLSzMGVQAwefJk6HQ6HD9+3HiM5bknT55sPEdbWxsOHTpkdoxSqcTEiRONx4gZi6XW1lbodDqzf56M3QJtme9kugXaWc8rlMvkSDJrSVUjCsooqCLSOKsumj0G9w6B0mJABWW1GP92Hudym9yvZb5leDE7/GwtFR49X+uW9xzSfbjrusXH7cnrn3zyCQoLC1FQYF3VuqKiAr6+vggLCzO7vVevXqioqDAeYxpUsfez9/Edo9Pp0NzcjNraWnR2dnIec+rUKdFjsbR48WK8+uqrPN+9Z3HXFmih502N1Tj0vGL64gWolWhu98ZMG+IsyS5eyuVL+D9xmbu3Za2+HQ+tL8Dnj44yu13u1zJfsruYHX62ioHW6PnLWfSksgvEPp5YusOtM1bnz5/HE088gY0bN8Lf39+dQ3GK559/HvX19cZ/58+fd/eQeLlrC7TQ875xW5pTzw+Agipi5cOckchbOB7z/5DitOd4atI1WDsrA9/MGYWkSPuqqxeU11p9KnfWa5kr2Z3d4We5SUSlUGBcShSY32YOuGYUnDlTLTdPSYwm5jyxdIdbA6tDhw6hsrISw4cPh4+PD3x8fLBr1y4sX74cPj4+6NWrF9ra2lBXV2f2uCtXriAmJgYAEBMTY7Uzj/1a6BiNRoOAgABERkZCpVJxHmN6DqGxWPLz84NGozH758mE3iCdFfXbel6loisBcajFzie5zk8In2e+OIr8smr874Tzdv4OiwvDhAHRWLrtDH65UGf3eTYfvWh2wXfma5krwHBkqTA1VuPy9xwp7NnxSFzHXdctPm4NrG688UYcO3YMR44cMf4bOXIk7r//fuP/q9Vq7Nixw/iY4uJinDt3DtnZ2QCA7OxsHDt2DJWVlcZjtm/fDo1Gg8GDBxuPMT0Hewx7Dl9fX4wYMcLsGIPBgB07dhiPGTFihOBYugN39a/iet4xyVGyPa9QWQdPZ5lfQ5yvoKwWz35xDMfsqGUm1uFzddiUXy6qHx+fd7afwYS3d+KuVfvw3S+XUHq1SfbXMl+AwS4Vmpb72JCbidBAtagZaU/umdfTexp6A0/ru+gRuwJNjR8/3rgrEOgqcfDf//4X69atg0ajwbx58wAA+/btA/B7uYXY2FgsWbIEFRUVmDFjBh566CGrcgtz5szBgw8+iB9//BGPP/64VbmFmTNn4p///CcyMzOxbNkyfPbZZzh16pQx90poLEK8YVcgy139q5z9vKVXm7D56EW8s/2M7OcmxJNkxGuxemYGavRtsrymHNl5JeaxntgzT0ypCU8ZK3Hu35CU67fbk9eFvPPOO1AqlbjjjjvQ2tqKyZMn4/333zfer1Kp8N133+HRRx9FdnY2goKCMHPmTPz1r381HpOYmIgtW7Zg/vz5ePfdd9G3b1+sXr3aGFQBwD333IOqqiosWrQIFRUVGDZsGLZu3WqW0C40lu7EXf2r7HlesdWe2fPfMjSWAisRFIBs7VSI6xWUd+0a3LlwgsOvZUebJq+Ynm5VT85yRsETe+Z5YmI0sc1T/oY8bsaqO/OmGStv4EhROK5P0D2FEvI3tfYGnt5mx1kyErT4fPYo4QN55BVXYtZa653bLLF15qTMKEj5wOQsNGNFWF5Xx4oQeziS++DtOVf2iA72xYOjE3pkUAXYbrOjABCoVqK77m0oKLPeNSiVHDuvSqoaRQVVnpQs7omJ0cTzUWBFvJKjReHYZNs3b3eslIM3qWxsw7/2lrl7GB6HAaBvN0hu2O1NyqodC6wcCTDEBEqmOw09LVnc0xKjiefz+BwrQrgI5T7M+7gQGx+6TnBJMDMxXM5hEWIlPjwAr9+Whtn/PoTG1k63jEGOWj5i8qS48AVKy6cPs1rO5yI2l8sZy4d8xVEJ4UKBFXEre98IhZYmTlzSYd6mw7y7ldjnzojXovBcXY/Mt7LHpEHR2HayUvjAbi4+PADlNc2Cx62YPhwz1+Y7PagK9lNZPYdKAYxOlmfJyp4AQyjp/eH1B1F4rk70GGwli7uiCa+nJEYTz0eBFXELR98I2aWJPWerOHsAGgCbn3C5nttHqaAtcCL1xKAq2E+FptZOsz8RMUFVWIAaF+qaUetgftCsUQnIGZWAl785brNswYrp6XhoQwEKymqN942WsRYcS0qAITSzXFBey3u/JVszb3yzYtTImbgaBVbELeR4I1wxPR33r/mZty0G1ydcrufuoA7NHi00QI0BvYKRXybtQiwXe2eb6prb8ffvTzr8/Gv3leHXqia8Pi0VL3xdxLkcFxqoxuezR3nUkpWYdlJisMEj1/fjaCkIQuRGgVU34urtybaeT2gcu4ored8IN+Wfw3VJEYLfQ2igGsvvTefdDt3a3ml8ziMX6tBbEyCYz0E8S3q/MKz7cyaqm1p5f9eeSszMFgD0CfPD5bpWm7s295ypwgtfF3Eux5VUNaLwfK3xa8vXjrtKFyRFBUMbqOacsZNS/oIvl4tqTRFPQ4FVN+CK/AIxz/fatCF48evjNsfB9Tguz395TNT3UKdvwyubT/Cea/a/C+GjVHjUjBRXLgzhtnDSNZgyNBahgWoUnnfPbJWrXKxr5b3fcnk7MTLIuONOymvOme8NlkqqGm0ug+paOjjzG9nZqVenDhE18+aJTXhJz0blFroBV29PtvV8U1fu5R0H1+P4CH0PYs/nSUFVgFpJQZUEb287bdyeHx7o6+7heATT0glCr313ly4Qmk3686gEm6UMEiODMGFAtOBsE9WaIp6GZqy8nKvzC/iej+uTKTuO3aerJC/B8X0PtsbhLvERAXj+pkE4faUR/9h+2uZxze09tTynY9jgYFDvEJy83ODm0bgXOwMj9Nq39ZpzZe6R0GzS4D6h2HBtrMN5YfaWgiDEGSiw8nKuzi8Qej5bDjuwjMP1Pdg7Dmcpr27G7H8XIj48wN1D6ZbYYKCny4jXGl8LQq8BodecK3KP2NkkWzsZ2ed3tJQB1ZoinoSWAr2cq/ML7N3lkx6ntfs5ub4HuXYbyU1sojIhUmkD1Vg9M8P4tdBrQOg156rcI1dWLhe7fEiIM9GMlZcT+4nQFc+nCfCBrrmDcxzjronifJxSAQyO1SDAR2UziZXre0iKCsbIeC0OSqyDQ4gpb2nMnJGgxeqcDLOEc1uvRSWAMSlRtl9zAEaYzHw5G80mkZ6GZqy6AVf3srL1fJvnjOEdB9fjDAxQdFGHgvJaaAJ8bD6Wy6xRCQ58F4TYbszsSd66cyg+nz2KcxffiunpVm2ZDAA6DAbU69u5X3PoKszp6sbGNJtEegoFw1AfD1fR6XQIDQ1FfX09NBqN7Od39SdCW88nNI7Sq02Y93EhTlzSmdXsUQIYkaDFYxOSOR9rWYunpKrR7rpGSgU4K7YT4on4SiTkrMm36kDAzvayxXbvWrUPh8preY8hhNgm5fpNS4HdiKt7Wdl6PqFxMAyDokvW1dINAArKahER5Gv2eL5aPFxLHWKMiNeatf4gxJPZ6kogZlcwwzCcf+tUmZwQ56ClQOJyQruZ/vLVMbOv+WrxrJiejhHxYaKfe+KgaOPSClftG0I8kWkQZOoExwcUU2XVTaJ2DhNC5EOBFXE5od1MRRd1xgsI+4ncckaKvdDU6NtwprJR9HP/72Qlnv7iF6S+vBW3DYu1yusixJOt31dqFlyt21fGe3xEkC9VJifExSiwIi6XFBWM1Fj+NWr2U7TQp+1vj16y2TKDT2NrJ+Z/ftSuxxLiLuv2lWPC2zsx/cOfcfR8neCu2Ld/OO3UyuQlVY3IK660mkkjpCejwIq4xeu3pfHez36KFvq0/Un+OdnGRIi32F9SjRcslsy5sMuHcu8cZnsU3rB0F2atLTC2HXLlLkNCPBWtgxC3CPEX96dnq1YP61J9i9xDIy6gAtCTOib6qxVoa2cgZ0Mjrg0gXNgK63LWkuLLe6RdhqSnoxkr4hZSEmpfmzYEQX4qZw+JyChG48d7f08KqgCgpZ2BUin/Ron4iEAIndU0h0qOWlJCeY+0LEh6OocDq5YWmjEg0klJqH3x6+NobPX8Qo7kd/dlxbt7CB6nw8Dgml7yJoqXV+thq9CIHDlUnM9JuwwJ4WVXYGUwGPC3v/0Nffr0QXBwMEpKSgAAL730EtasWSPrAEn3JDahlv10LFTM0wmTAcQBVMWC2+krTQhUyz/7qgAQbDGr66zuC7TLkBB+duVYvfbaa1i/fj2WLFmChx9+2Hh7amoqli1bhtzcXNkGSMTjqkzOfs0wDMpr9FApFOhkGKgUQCcD49diK52bfs0ec6C0GoACfcICjOdiGAbv/XgWJy/XY0hsKObckGJ1/hXT0zFv02GzAofD+4XhqUkpyCuuREJEEA6U1vB+zwoAA3uHoKm1A+c8uAFyWl8Njl0QlxPTHSzddtrdQ3C5vtoAXKgV/hvUt8u/EMqga6frW3cORWSIn9O6L7DvARnxWkm9PYXOR/0DiRw85e/JrpY2ycnJ+Oc//4kbb7wRISEhOHr0KJKSknDq1ClkZ2ejtpYqWnNxVksbrsrk2kC15FICbDVzBozg+bKTItBpMCBfQvXyoX00+Cj3OmNbjjp9Gx7bWIh9v1ZLGichnkapAIL9PKOhM1/7G3uJeY+R8rx83RTkHDfpGVzx9yTl+m1XYBUQEIBTp04hPj7eLLA6ceIEMjMz0dgovmBjT+KswCpnTb5dbV0ssZ84AchyPi7jUqKMu4Zy1uRztuMghNjPGT0Aud5jVAoFhseH2eztac/5qHchsYcr/p6kXL/tyrEaPHgwfvrpJ6vbv/jiC6Sny7+mT2yztUPHHuyuHrnOx4XdNWSrxxkhxDFy787j2wVYUFYrOaiSuquQipASPp64S9WuHKtFixZh5syZuHjxIgwGA7788ksUFxdjw4YN+O677+QeI+EhtEPHE9GuIUKcj61f5SgxuwClPI/Y89FyIRFD7r9POdg1YzV16lR8++23+N///oegoCAsWrQIJ0+exLfffos//OEPco+R8BDaoeOJEiKCvHLchHgTuXbnyb0LUOz5+IqQEsLyxF2qdtexGjt2LLZv347Kykro9Xrs2bMHkyZNknNsRARbZQvswZY6kOt8XNhSCuy4CSHyUgKy1q+Su9egmPN54vIO8UzO7IVpL6q83g1w9QHT2jFVzta9EXO+7KQIZCaESzr/0D4aY12dkqpG3J3RF+n9wiSPk5DubPHtadjwoP0JtyPitbLXr5K716DQ+agIKZFC7r9PR9mVY6VUKqHgmdHo7OxpDSvcKzRQzdkHzPRroOvNyEepQIeBsfqvZQKq0PnYY0uvNuFASTUYdNXxYc8FACt/PIPjl8zrWLHNW03zJjLitfhTWm/4+6pwXVKEcazsef78r3zeN9rUPhr8KbU3Tlc2YHT/SNw1Mg6lV5vwc0k1qhtbERnsh9gwf+T8q0DWnzshzhAT6o+LdfbnTgb4yt8C1tZ7jLPO54nLO8Rzyf336Si7yi188803Zl+3t7fj8OHDWL9+PV599VUqEGqDs8oteDrTom0vf3Nc9LbYOn0bHt5wEAUCtbI2zx2NoX3DBMdww9Jddn8PhLjKN3NGYeHnv+BMpe2yNXMm9MexCzrOnbXdpWwBlWQgnkTK9duujzZTp061uu3OO+/EkCFD8Omnn1JgRQBwF23jYpo3Yfop4/FNR3CoXLgAaXVTm+AxXdXhCfFcSgBjUqKwdNsZ/FrFXwvwzhFxuGM4w/lhwdbrydtwdWZw5/IOIWLJOmd83XXX4ZFHHpHzlMSDSG0XwLWrh4/ptlgpda74lgXEBneEuJsBQENLOw6fr+M9LjspAomRQcgrruQ9zh3bzOXkacs7hIglW2DV3NyM5cuXo0+fPnKdkngIe+rJ2FMAVKVQGHsEnrgkrq+e0K4PqcEdIe509EId7/2psRqsemAEgJ6Th5QYSQEV8S52BVZardYseZ1hGDQ0NCAwMBD//ve/ZRsc8Qx89WRs5TpIKVyqRNen05x/5Rtv0/gL/2lmJ0XgqUnXGIMxribSNFNFvIlBION1xX3DERqoRp2+Da9sPmHzuFH9IygYIcRN7Aqs3nnnHbPASqlUIioqCllZWdBqtbINjrifreBEKI9DSgHQYH8f6JrNG0bzNbONCw/Ae9OHY+m205i6cq/xdnYWrbqpFeU1elTUt4geAyGeRImupUEWm7TNvtaEZmKd1JGKECKCXYHVn//8Z5mHQTyVlHYBpjlYbNE2Mc2c+YIoLsF+Pli67bTVhWXPmSqMfzsPtfp2G48kxDsE+/uYvS5Mk7bFzMTuL6n2+uR1QryV6MDql19+EX3SoUOH2jUY4nnE5HHYysF6fVoqXvi6SPbluJOXG3ASDVa3GwDJQZWPEugwCB9HiFz8fZRoEfija2rtREa8Fo/dkGy1zC12md3bk9cJ8VaiA6thw4ZBoVBAqOyVQqGgAqEeTOrOvqSoYIyM16KwvNbm0kTOmnzssQie9p69ihe+LsKG3EwMfeUHm7NSg2KCcbKCf2u5M1FQRVytrdOAjHgt7hzZF3/77gQaW63fLzsZBgXltZyvU7HL7N0leZ0QbyM6sCotLXXmOIiT2bOzj33MQY5aUpoAH7w+LRVHztXy5mCt3VvCu9TnzqCKEHcwMEBBeS2gAPRt/B9CuWadkqKCkRqrQRHPztnUWA3NVhHiJnZVXif2cWfldXuqGHM9xvKxNfpWFF20/QYfHeyLykbhAp6EEGt5C8dzBkhHz9eZbdywtHnOaAyNC3PiyAjpWZxeeZ114sQJnDt3Dm1t5hfOW2+91ZHTEpnZs7NPKEGWfawQCqoIkY6twm5r1unauDBkJGhxsLzWbAegUgGMSY6ioIoQN7IrsCopKcFtt92GY8eOmeVdsSUYKMfKs0jZ2Sf2MWLERwSgvLrZoXOk9tHwzogR4kk0/j5Y9cAItHYakBARhGe+OIpD5bWC9aksjYjX2mzdwi7Rc/XQHJMcRS1fCHEzpT0PeuKJJ5CYmIjKykoEBgbi+PHj2L17N0aOHImdO3fKPETiKHsqNEupQ2XLrOwEux6nQFdAlbdwPJbfSxcJ4n4qke+UBoYx2+CzOicDY5KjRD+PUgFkJGjx+aOjbOY+ctWwUgLIiNdiQ26mzccRQlzDrhmr/fv348cff0RkZCSUSiWUSiXGjBmDxYsX4/HHH8fhw4flHidxgK2aUpZFB8U8xvKxQFf9KNPNdUpF1yfu+lZp9alYQ2I1eGNamnFcfGMvr2lCebXjs2uE8OkUuXu0sbUT96/5vYMAu0Hk6IU6s84CtgjNONlaojegKyGealcR4n52zVh1dnYiJCQEABAZGYlLly4BAOLj41FcXCzf6IhsVkxPNwZCLKFO8VyPMX3sa9NS0WEwwPKaE+Lvg4KyWryz/YykMfqru/4ciy7pcOvKvchZk496fTvnODITw9FhMFBQRTwa2/pJqEju/D+kIG/heN4Zp5KqRnz7yyXe85RVN9k9VkKIPOyasUpNTcXRo0eRmJiIrKwsLFmyBL6+vvjwww+RlJQk9xiJDOzpFG/5GB+lAh0GxvjYnDX5OFBSY/W4+mb+mSolgMGxGqy4bzh0zW144asiFF3SoaXdPEQz7UdoOfaXvzlOzZWJx2M3eTw0NpH3uFuv7WPz9chVKsUWdllfar06Qoh87AqsXnzxRTQ1dX0y+utf/4qbb74ZY8eORUREBD799FNZB0jkZU+neMvHlFQ1YlN+ud0V1UfEa7F6ZkZX4+U1x3HCRj0ey12L7D9qrky8TSfDSF6OZwn1BTQ9jzZQjZw1+ZLq1XVnFGASd7ArsJo8ebLx/5OTk3Hq1CnU1NRAq9WaNWcm3Uudvg0PrT/IWTBUioLyWszbdBhPTUoRFSBZ7lqUY8ciIa6UEBGEFdPTMW/TYbO/eaHleLEfItjzzNt02CoIM5357SnsKYhMiFzsCqz+/e9/47bbbkNQ0O8Xu/DwcNkGRTxPnb4NE97eKVuD471nr6JG3yrqWMtdi3LsWCTEFSzrUbFL2j+XXAWgwHVJEbwXeqEPEfP/kGJcRrSnXl13xTXL1xMDTOIediWvz58/H7169cJ9992H//73v1S3qgd4eMNB2YIqoOvNXqg+lVLR9SmTq6XHuJQoKGlylHi40EC12YxUnb4NL39zHM9/WYTnvzyGCW/vxM3Lf8Iv5+s4Hy/0IWJ4P63x9SGmXl1PwAaYlhsGTANMQpzJrsDq8uXL+OSTT6BQKHD33Xejd+/emDNnDvbt2yf3+IgHKKlq5CxGaMswCVWfg/1UNu8bEa/F+AFR+CT/nNWb4Yrp6Rgc69q2QIRIVatvR43+9+4DXDMplrtgTbEfIlQ2UixmrMk3Ps6eenXdEQWYxN3sCqx8fHxw8803Y+PGjaisrMQ777yDsrIyTJgwAf3795d7jMTNpOY0PTExBRseFDfd3thqe7azoKwWf/3uBJ777ZP9ff/3s/HCExqoxqPX098a8XzshdzWTAprz9kqzNtkXQOQr+wJ8PsSl60gTKVQcM78dlcUYBJ3syuwMhUYGIjJkyfjpptuQkpKCsrKymQYFvEkUnOaEiKCMO6aKN5P2iqFAql9pM047fu12uzC88GuXyU9nhAxFOiqfs739ytFRJAvAOEPKAYGnEtVbNkTWx9WTJe47KlX191QgEncze7ASq/XY+PGjfjTn/6EPn36YNmyZbjttttw/PhxOcdHPMDvb1T8x1m+cQkVGH19WqrksbAXkJKqRuohSJyCAVDf3I6pw3pjUO8Qs/t87Ejse/uH0wDEf0DZfPQiZx6QUJHRsuomYxCWt3A81s7KECw62l1RgEncya7A6t5770V0dDTmz5+PpKQk7Ny5E2fPnsXf/vY3DBw4UPR5PvjgAwwdOhQajQYajQbZ2dn4/vvvjfe3tLRgzpw5iIiIQHBwMO644w5cuXLF7Bznzp3DlClTEBgYiOjoaDz99NPo6DAvULlz504MHz4cfn5+SE5Oxrp166zGsnLlSiQkJMDf3x9ZWVnIzzdvPyFmLN6MrU21iSOfCWDfqMx7nmn8zTeVsm9cJVWNyCuuRI2+zexN/qPcTKydlYG/356GpKhA7D5zFRkJWsmzAmXVTVRygTjV6SuNeOrzX1D0W4211D4abHwoE8P7aSWfi/0wYNx0IXD8O9vPYMLbO61yrqQscSVGBmHCgOgeOztDASZxJ7vKLahUKnz22WeYPHkyVCrbycdC+vbtizfffBMpKSlgGAbr16/H1KlTcfjwYQwZMgTz58/Hli1b8PnnnyM0NBRz587F7bffjr179wLoaq0zZcoUxMTEYN++fbh8+TJycnKgVqvxxhtvAABKS0sxZcoUzJ49Gxs3bsSOHTvw0EMPoXfv3sZ6XJ9++ikWLFiAVatWISsrC8uWLcPkyZNRXFyM6OhoABAci7eq07fhsY2F2Pdrtdnt2UkRWPXACOMbka3K7aZfawPVVnV62NoxiZFBKK9uwtT39qKu2TxBV6VE1zSBSAkRQWaNbglxtpOXGjD348PQCXQVsIWtxcZVy8oWy/IA9vT87OnsKYhMiKMUjBOvUGlpafjvf/+LuLg40Y8JDw/HW2+9hTvvvBNRUVH4+OOPceeddwIATp06hUGDBmH//v247rrr8P333+Pmm2/GpUuX0KtXLwDAqlWr8Oyzz6Kqqgq+vr549tlnsWXLFhQVFRmf495770VdXR22bt0KAMjKykJGRgbee+89AIDBYEBcXBzmzZuH5557DvX19YJjEUOn0yE0NBT19fXQaDxjR5tllWZT41KiJNV8yVmTb/NNf0NuJtL/us3hkg2pv7XCYVvq2GoSTYgnyVs43uwC/8uFOvzlq2OilrNNH1uvb7f54YVmYwhxHinXb4eT1/mUlZWhvV3chbSzsxOffPIJmpqakJ2djUOHDqG9vR0TJ040HjNw4ED069cP+/fvBwDs378faWlpxqAK6KoKr9PpjLle+/fvNzsHewx7jra2Nhw6dMjsGKVSiYkTJxqPETMWLq2trdDpdGb/PIlQVWcpNV+Easd8WnBOljpYRZd0xmWS16el8u6WIsTdlApwbtIY2jcM380bi7yF4zH/Dym85zAtD0BLXIR4PqcGVmIcO3YMwcHB8PPzw+zZs/HVV19h8ODBqKiogK+vL8LCwsyO79WrFyoqKgAAFRUVZkEVez97H98xOp0Ozc3NuHr1Kjo7OzmPMT2H0Fi4LF68GKGhocZ/UmbuXEFMnpLYmi9C59pfUs17P5/YUH+rYqB7z17F/M+OYNaYBHyUm4n4iAC7z0+IsxgYoOiijjNnCuhaqrplaCzvObjKA/T0HCpCPJnbA6sBAwbgyJEjOHDgAB599FHMnDkTJ06ccPewZPH888+jvr7e+O/8+fPuHpIZMbuUxNZ8ETpXdlKEqPNwuVTfAoPFal8nw+BgeS1mrS3AjDX5du3WIkQOo/pHmM0g5S0cj9RYjdWbK5szZcmTygOwG0+oOjkh9nN7YOXr64vk5GSMGDECixcvxrXXXot3330XMTExaGtrQ11dndnxV65cQUxMDAAgJibGamce+7XQMRqNBgEBAYiMjIRKpeI8xvQcQmPh4ufnZ9zxyP7zJOwbui1S3tSFLg73ZPSDVuJyhZRaV79W0S5B4h4MYz6DxDAMii7pYLA4jq+lirvLA9Tp25CzJh83LN2FWWsLbM6wEUKEuT2wsmQwGNDa2ooRI0ZArVZjx44dxvuKi4tx7tw5ZGdnAwCys7Nx7NgxVFZWGo/Zvn07NBoNBg8ebDzG9BzsMew5fH19MWLECLNjDAYDduzYYTxGzFi81Yrp6RjV33o2KTspQvKbutDFYfOcMQgLsA6uLMs2mD7WnlpXhLjS/pJqs2DJnpYq7s6d4mtaTAiRxq5yC3J5/vnncdNNN6Ffv35oaGjAxx9/jJ07d+KHH35AaGgocnNzsWDBAoSHh0Oj0WDevHnIzs427sKbNGkSBg8ejBkzZmDJkiWoqKjAiy++iDlz5sDPzw8AMHv2bLz33nt45pln8OCDD+LHH3/EZ599hi1bthjHsWDBAsycORMjR45EZmYmli1bhqamJsyaNQsARI3FW4UGqvHxw9eh9GoTDpRUgwFwXVKEXcsPoYFqvHLrYOSX1nCeJy4iEEdenoSfzlThx5OVCA/2xc1DY83KNvgoFegwMMZyDnX6Nmj8faBrsW+bOyGuMO/jQmx86DpUN7Wior6F91i+5XV3lAewtYnFdIaNcrkIEc/hwKqlpQX+/v6c9/3zn/+0Sgo3VVlZiZycHFy+fBmhoaEYOnQofvjhB/zhD38AALzzzjtQKpW444470NraismTJ+P99983Pl6lUuG7777Do48+iuzsbAQFBWHmzJn461//ajwmMTERW7Zswfz58/Huu++ib9++WL16tbGGFQDcc889qKqqwqJFi1BRUYFhw4Zh69atZmMXGou3c/QNvU7fhsc3HbHaBv7UpGtw4nI9AIUx0BqbEoWxFkuQtp7/8U1H0EBBFfFwxy/pMP7tPN6dr55ab0rMDJunjZkQT2ZXHSuDwYDXX38dq1atwpUrV3D69GkkJSXhpZdeQkJCAnJzc50xVq/niXWs5CK2ppRl4VE+JVWNuGHpLrmGSIhbZSRoMXNUAobEhnpUoCL0OrOswUVIT+T0OlavvfYa1q1bhyVLlsDX19d4e2pqKlavXm3PKYkXs1XDisv+kmrReRvUtoZ0B4tuHoyMeC0Kymox9+PDHpcY7km7EgnpDuwKrDZs2IAPP/wQ999/v1lLm2uvvRanTp2SbXDEO0gNgLh2Ru0qrsS7O07jJ5OlRLFNawnxZF8evoDCc3Vmt3laYri7dyUS0p3YlWN18eJFJCcnW91uMBhEV1on3Yc9ARCbt1Fe3YRpK/ea5aZoA9XYPGcMkqKCMTJei8LyWqut64R4C662NZ6WGG6rFyghRDq7AqvBgwfjp59+Qnx8vNntX3zxBdLT6RNOT2OrOSwfdmeUZVAFALX6dtz83k+4tq8WB8trZR8vIc6gBCR/APC0xHBqWuw5SqoaUV6jpyDXC9kVWC1atAgzZ87ExYsXYTAY8OWXX6K4uBgbNmzAd999J/cYiRdYMT3dqjmsLWzexq7iSpu7qOqbO8yWBQnxZOlxYQjxV4v6+zcltrMB6Tls7bCmRtvew64cq6lTp+Lbb7/F//73PwQFBWHRokU4efIkvv32W2OpBNKzWBY43Dx3tGDh0SMX6njPKXm7KiFucl9mHF65dbDo4ykxnNhCxVq9n911rMaOHYvt27fLORbSDZguJQgVHh3WN8w9gyREZp8duoBIDXc9Py7D+4VJSgynZaGegYq1dg92BVbnz5+HQqFA3759AQD5+fn4+OOPMXjwYDzyyCOyDpCIJ/Tmu6u4Ekcu1GF4P62xQCffY2zdx96uUgAX61qgAJD1W9BUUtWIA6XVMC0Iyj6WbfDKnu/6AdHQBqp5iyoS4ikC1Uro27mzqArKalF0sV70uR67IVnUsg4tC/UsVKy1e7ArsLrvvvvwyCOPYMaMGaioqMDEiRORmpqKjRs3oqKiAosWLZJ7nISH0Jsv18670AAfDOilQX5ZjdVjGDCc53ttWipe/LrIZh4JV+uZ7KQI/P2OoVaPY5/r3w9mYer7e9FhoIU/4tlsBVWspdtOiz7X+3lnMTxOKxgc8S0LbcjNFP18xDsI7bCmnDzvYFflda1Wi59//hkDBgzA8uXL8emnn2Lv3r3Ytm0bZs+ejZKSEmeM1es5q/I6V9Vztn3GhtxMpP91m6hZIfYxADjPpwnwga65Q/TOP5Y2UG31OL7nIsSTqBQKBPgq0djaKes52denLVQRvWcSej8n7uH0yuvt7e3GJsf/+9//cOuttwIABg4ciMuXL9tzSmInW1XP2TX5T/PPiV5qYx9j63y1+na7AiCux/E9FyGeJL1fmKxBFWCeM2OLmGUh0v1QsVbvZ9dS4JAhQ7Bq1SpMmTIF27dvx9/+9jcAwKVLlxARYb0TjDiP0Jvv/tJqF42EkO5pSlqM0+qp8eXM0LJQz0TFWr2fXTNWf//73/HPf/4T48ePx/Tp03HttdcCADZv3ozMTJqqdCWhN9/sRAp0CXHE//3kvNQGvuCIevj1bImRQZgwIJp+z17IrsBq/PjxuHr1Kq5evYp//etfxtsfeeQRrFq1SrbBEWFCb773ZPaDVuTuIfYxts6nDVRb3U5Id3epvpXzdqUCgq+JjHgtspMi7A6OaFmIEO9jV/I6sY+zktfr9e1WVc9NdwWer9bj1pV7RO8KBMB5vtenpeIFnl2BhPQkGQla/OOuYbyvCZVCgaykcPgolQ6VTKBlIULcS8r1267AKjExEQqeT2m0K5CbswIrltCb709nqlB4rtasjhXfY2zdx95+/GI93pawxZyQ7oYNkI5eqEPOv/JtHpe3cDwAUHBEiJeScv22K3n9ySefNPu6vb0dhw8fxtatW/H000/bc0oiA6EGqmNToowBlZjH2LqPvT0+PNDuwCohIhBl1fyJ94R4Oram1KwxCbzHlVU3Ub4MIT2EXYHVE088wXn7ypUrcfDgQYcGRLwHm9+152wVpNb3pKCKeKIgXyWa2vgLgZpiyyY8NDaR9zjawUdIz2FX8rotN910E/7zn//IeUriImy7Gb66OlzHrpiejsGx8i9rEuIOeglBlaklP5ziTFJXKoDUPvT6IKQnsbsJM5cvvvgC4eHhcp6SOJmUXmS2jh2bEomiizqXjZkQZ7F3J8/xizpk94/A6ORIs9eHgQGKLuow4e2d1OOPkB7CrsAqPT3dLHmdYRhUVFSgqqoK77//vmyDI84npReZrWNr9Nzb0QnpKRgA+36tNiapz/u4ECcu6WA6/0U9/gjpGewKrKZNm2b2tVKpRFRUFMaPH4+BAwfKMS7iAmw7HEum7TYYhkF5jR6VuhabxxZd1HE2YCbEWykVkJw3CHQlqceHB6LokvUMrunripLYCem+7AqsXn75ZbnHQdxAqB3OvI8LOS8QXF6aMhiv/fcE6pspuCLezUcJDO+nRX6Z9DY2CRFBgj38+NrYEEK8n905Vp2dnfj6669x8uRJAF39A2+99VaoVCrZBkecS6gdzgmRQRUAjEwMx7V9tXbtECTEk3QYgDOVjchbOB4/l1Tj+S+PiXpcRrwWiZFBECoNSDsECene7Aqszp49iz/96U+4ePEiBgwYAABYvHgx4uLisGXLFvTv31/WQRLnYMsl7D17FZ0mFwN2GUTM/iiVQoHRyZE4V91EFdlJt1Grb0dxhQ4xof6ijtcGqrF6ZgZKqhpRXqNHRrwWhefqzF5X7GvFdLaKPZ6KhhLSfdhVef1Pf/oTGIbBxo0bjbsAq6ur8cADD0CpVGLLli2yD7Q7cHbldXtwtcMJ9lOhsbVT1ONH9Y8AwwD7S6qdNURCnEIB/l2A8REBWPvnTNywdBfveTIStFh617V48evjZq8jbaDarI2U6a5AKbtxCSHu5/SWNkFBQfj555+RlpZmdvvRo0cxevRoNDY2Sj1lj+CJgRWLbVPzft5ZHCqvFbWclxargTbIz2rGixBvkBqrEcwhzFs4Hi9/c9x6VhfA4FgNVtw3HImRQchZk291jEqhwPD4MDw2IdlqRsrW8aOTI2nXICEeyOktbfz8/NDQ0GB1e2NjI3x9fe05JXEzNjekQELC7jEJOViEeIK0PhosmDTAGOikvryVd3a2rLqrCK7lrO4Yk9klvt21BWW1VkGVmN24tCxojZZNibewK7C6+eab8cgjj2DNmjXIzOz6dHXgwAHMnj0bt956q6wDJK4jtEuQEG/3/67vjwkDoo1f//OBkbh/zQGbxydEBCE0UI0NuZk2m5ILvW4sdwFKPb6no2VT4m3sammzfPly9O/fH9nZ2fD394e/vz9Gjx6N5ORkvPvuu3KPkbiI0C5BQrzdkNhQs69Hp0RiXEoUFBbHqRQKjEuJMgtwEiODOBspC71uIgLNZ/GFjqddg+b4ihgT4onsCqzCwsLwzTffoLi4GF988QW++OILFBcX46uvvkJoaKjwCYhHYncJWvY748JeeLiO57uPEHcwDZRKqhqxKf8cPsk/Z+x3OTYlyuz40cmRWDE9XdS52deNLW9vO815vK3XTXebrZLSh5TrsbvPVFnlcJoumxLiaRzqFZiSkoKUlBS5xkI8wIrp6Xh04yHs+9V8l19ogI9Z8U/TC49l/onpfbnrC3CwXHqhRULklJkYjtemDcH0D3+22sE6qn8EPrh/BGr0bZxLfWI8NSnFZrkRrrwprrwtKcGcN5BjCY+WTYk3siuw6uzsxLp167Bjxw5UVlbCYDCvePTjjz/KMjjieqGBavgolVDCvI5VY0snMhK0nDuc+PJP5tyQjFlrC1z3DRDC4VSFDk98cgSHz9dZ3bfv12pjDz97L9I1JmUVuFgGAEJ5W87g6uRvKX1IbaFlU+KN7AqsnnjiCaxbtw5TpkxBamqqWUNm4t2k7nDaVVyJvOIqRAb7YsrQWONSC/sGTnlbxBPU6ttRq6+zeT/fbjwxAYm9AUBipPODHHckf8u189FWEWOuYquEeAq7AqtPPvkEn332Gf70pz/JPR7iZmKn3surm3Dre3tR3/z7J/W3t522asY8LiUKw/uFofBcnbOGTIgsLGeVxAYkdfo2vLL5BOc5xQYAzpxNkmPmSCo5l/B6wrIp6V7sCqx8fX2RnJws91iIBxD7yXvaSvOgimUaVAH47Q2diocSz2c5qyQ2IOE6jiUUADh7NsldNbPkXMJzx7IpIY6wa1fgU089hXfffVew2SjxPklRwRgZr7X6wzDdsbSruNKsVQefToZBJ/2ZEA9nuRtP7G40W8exXp06hDdAcnYpATEzR87gjJ2PtspdEOJpRM9Y3X777WZf//jjj/j+++8xZMgQqNXmbxxffvmlPKMjLsV+eubaxWf6yfvIhToXj4wQ5xnVP8JqVkn0krgDS16umE1yZ/I3LeGRnkp0YGVZn+q2226TfTDEvbg+PSsVwIh4rdnSx7C+YS4eGSHyS4kOxoc5IzmDF7EBiSOBi9ylBLjytNyZ/E1LeKSnEh1YrV27VvLJ9+7di5EjR8LPz0/yY4lr2fr0bGCAgrJas0/P1w+IhjZQLWo5UKVQQKEAOsR0dSbERbSBarx917XGpTDLC35SVDCykyKsal4BQHZSBBIjgxxOWpdrNkkoT8vdM0eu2PlIiCdxqECokJtuuglHjhxBUlKSM5+GyEDqp+fNc8bg5vf2WCWwW+4KHJ0cief+OAD3rzkgOi+LEGer1bdj6sq9xq+5EsZtVZFhb3ckaR2QbzZJKMmeZo4IcS2nBlaU3O752OUDlUApsqKL9bhS34Krja04W9WIlKgQLLt3GK42tOL4JR2UCqC1vQPbT1RBh98Dq8v1ehwoq8G796bj3z+XYduJSid/R4RIZ7nbr6Sq0ar7AGvfr9XYfbrKZqV1QDhpneXobJKUPC1PnDlyddFSQlzBqYEV8VxcywdhAWrUcZRQAIClFv3OTFnOUpk6U9mEV7/lXi4hxFNYBiJCM7iHz/O3aTp+qd5qlyFXACFlNonrHN7a8sUdRUsJcRUKrHooruUDW0GVEFtBFSHuoPH3wRu3p2FIbChuf3+vpCVoNhARyn9Kj9Py3r9+XxluHhorOoDgm03iO4e3tnxxR9FSQlzFrjpWxLsJ1d4hxBuM6h8BLcfsRlNrBz4ruACGYSTn9bGBiFAdpnHXRGFkvO3git3wIUedKr5zOKNelLOJrRFGiLdyamBFPQQ9k9DyASGeLjspAnPGJ3MGTp1MV++/A6U1os/HFYismJ6O0cmRZseZ5j/NGpXAe86fS646HECICUKExulp3FW0lBBXoeT1HogaIxNvp1Yp8cJXxwSOEv/+wxWICOU/DYrVCJyV/4OlmPwnsTlU3rTrz1uXLwkRy6mBVUNDgzNPT+xka5s3Id6Cb0ce67qkSN5yBq9OHWIViHAliNvKfxIql5CVGM47PjEBhJQgxBN3/XFxZ9FSQlzBrqXAK1euYMaMGYiNjYWPjw9UKpXZP+L5uJYPRvWPQHZShJtGRIh8UqKDkRgZxLtMZtp7rk7fhpw1+bhh6S7MWluACW/vRM6afNQL5GjxnV+O/CdvzKESw9uWLwmRQsHYsV5300034dy5c5g7dy569+5tlUs1depU2QbYneh0OoSGhqK+vh4ajdAygmtwLR/899hlPLax0M0jI8R+i24ejAfHJBq/Flomy1mTb3MGRcwuNVvnr9e3W9WpklpWwNFzeHKtKG9ZviREyvXbrsAqJCQEP/30E4YNG2bvGHskTwysuNy84icUXdS5exiE2C1v4XjRF+qSqkbcsHSXzfs/ys3E2JQoh8YjRwAh9RxUK4oQ+Ui5ftu1FBgXF0eJ6d1USVUjBVXEq7G9/MQSShCfsSZf1LIgH9NlR1edQ45SD4QQ6ewKrJYtW4bnnnsOZWVlMg+HuBuVYiDebFxKFFY9MELSY8TskvW2gIRqRRHiPnbtCrznnnug1+vRv39/BAYGQq02n1auqRFfP4Z4FirFQLxRah8N3rgtDcF+Pig8XytpyU3MLlmu3nuezFtb3RDSHdgVWC1btkzmYRBPkRQVjIwELQ6W1UqoAkSIe6TGdgVU/SICHcon4mqGzMVbAhKqFUWI+9gVWM2cOVPucRAPwCa7FpTxN5glxNmUAAL9VNC3dcJgEuErFcCQWA0WTBpgNivF7uozJaX3HFsMdPfpKuT8K9/mcZYBiafuuKNaUYS4j8MFQltaWtDW1mZ2myfveCO2cSW7EuIOwf4+nM29DQxw7KIOPkqFWVFPrpkme5bvxl0TJSog8YYdd1yzcFQrihDnsyuwampqwrPPPovPPvsM1dXVVvd3dnY6PDDiWrYuToS4QyNHUGVqxpp8YyAjdz6RmICEb8edmBkyVxBqyUMIcQ67dgU+88wz+PHHH/HBBx/Az88Pq1evxquvvorY2Fhs2LBB9HkWL16MjIwMhISEIDo6GtOmTUNxcbHZMS0tLZgzZw4iIiIQHByMO+64A1euXDE75ty5c5gyZQoCAwMRHR2Np59+Gh0d5m/MO3fuxPDhw+Hn54fk5GSsW7fOajwrV65EQkIC/P39kZWVhfx88yUBMWPxVrQbkHgSg4hj2EBG7nwiNiDJWzgea2dlIG/heGzIzTTORHnbjjs5Sj0QQsSzK7D69ttv8f777+OOO+6Aj48Pxo4dixdffBFvvPEGNm7cKPo8u3btwpw5c/Dzzz9j+/btaG9vx6RJk9DU9Psb0/z58/Htt9/i888/x65du3Dp0iXcfvvtxvs7OzsxZcoUtLW1Yd++fVi/fj3WrVuHRYsWGY8pLS3FlClTMGHCBBw5cgRPPvkkHnroIfzwww/GYz799FMsWLAAL7/8MgoLC3Httddi8uTJqKysFD0WT1RS1Yi84krBN3sxuwFVCgUGxYTINTRCBCl5+hizgcyFWud8KLAVkIiZISPmxL4PEdId2FV5PTg4GCdOnEC/fv3Qt29ffPnll8jMzERpaSnS0tLQ2Nho12CqqqoQHR2NXbt2Ydy4caivr0dUVBQ+/vhj3HnnnQCAU6dOYdCgQdi/fz+uu+46fP/997j55ptx6dIl9OrVCwCwatUqPPvss6iqqoKvry+effZZbNmyBUVFRcbnuvfee1FXV4etW7cCALKyspCRkYH33nsPAGAwGBAXF4d58+bhueeeEzUWIa6svG5PDghXWw9T41KicH9WHP7fv6ndDXFMqL8P6gWW+wAgrY8GxwQK1s7/Qwre2X7G5v1rZ2VgwoBoyWO0RahSu5Sq792dN+SiESKG0yuvJyUlobS0FAAwcOBAfPbZZwC6ZrLCwsLsOSUAoL6+HgAQHt7VFf7QoUNob2/HxIkTjccMHDgQ/fr1w/79+wEA+/fvR1pamjGoAoDJkydDp9Ph+PHjxmNMz8Eew56jra0Nhw4dMjtGqVRi4sSJxmPEjMWT2FN1masxakaCFu/dl25cDnnuy2NOGS/pWcQEVQCwYNIAbHgwg/eY3hp/3vuv1LfIOlPSXRsjOwNVfyc9kV3J67NmzcLRo0dx/fXX47nnnsMtt9yC9957D+3t7fjHP/5h10AMBgOefPJJjB49GqmpqQCAiooK+Pr6WgVrvXr1QkVFhfEY06CKvZ+9j+8YnU6H5uZm1NbWorOzk/OYU6dOiR6LpdbWVrS2thq/1ulc0yrG3l1SQsmuu4orUetAWw9CpEqICBJsn/XMf45BG6hGvb6dMzeL/TAg50wJ7bgTJuduTUK8iV2B1fz5843/P3HiRJw6dQqHDh1CcnIyhg4datdA5syZg6KiIuzZs8eux3uixYsX49VXX3X58zq6Syoxknv30JELdY4OjRBRTMsbbMo/J3i8rrkdoYFq3sBfzl17lh9CVAoFOhkGRy/UoZNh7NqB56k1sexF1d9JT+VwHSsAiI+PR3x8vN2Pnzt3Lr777jvs3r0bffv2Nd4eExODtrY21NXVmc0UXblyBTExMcZjLHfvsTv1TI+x3L135coVaDQaBAQEQKVSQaVScR5jeg6hsVh6/vnnsWDBAuPXOp0OcXFxYn4kDnFW1WWhJRdC5GI++yOcBtrJALX6dnyUm4kLtc14nmPJ2hkzJdpANV7+poxzZkbsDFl3zUOi6u+kpxIdWC1fvhyPPPII/P39sXz5ct5jH3/8cVHnZBgG8+bNw1dffYWdO3ciMTHR7P4RI0ZArVZjx44duOOOOwAAxcXFOHfuHLKzswEA2dnZeP3111FZWYno6K4E1e3bt0Oj0WDw4MHGY/773/+anXv79u3Gc/j6+mLEiBHYsWMHpk2bBqBraXLHjh2YO3eu6LFY8vPzg5+fn6ifhZycVXV58fen5BoiIZxmjUpAzqgEs7/RrMQI0Y/X6dsRE8r/AUDOmRK+orpiZ8i8oSaWPaj6O+mpRAdW77zzDu6//374+/vjnXfesXmcQqEQHVjNmTMHH3/8Mb755huEhIQYc5VCQ0MREBCA0NBQ5ObmYsGCBQgPD4dGo8G8efOQnZ1t3IU3adIkDB48GDNmzMCSJUtQUVGBF198EXPmzDEGNbNnz8Z7772HZ555Bg8++CB+/PFHfPbZZ9iyZYtxLAsWLMDMmTMxcuRIZGZmYtmyZWhqasKsWbOMYxIaiyeROweE8quIK9wwyLq8QVJUMEb1j8C+X62LEVtat68Mf7+TPx1BrpkSoaK6YmbIunseEuWikZ5IdGDF7gK0/H9HfPDBBwCA8ePHm92+du1a/PnPfwbQFdAplUrccccdaG1txeTJk/H+++8bj1WpVPjuu+/w6KOPIjs7G0FBQZg5cyb++te/Go9JTEzEli1bMH/+fLz77rvo27cvVq9ejcmTJxuPueeee1BVVYVFixahoqICw4YNw9atW80S2oXG4klCA9V45dbBOFBaAwWA2DB/XKxrwfdFl5GVFIHEyCDsKq5EXnEllAoFUnqF4LqkCDAMgwOl1QAUuO634wDKryKuYVpR3XQZ7IP7R4hqklxQXgvFb7vzHJ0pEcp5EltUl2+GTOgcP5dUe3VgRdXfSU9kVx0rYh9X1bHiytmwpFICnSLKW2cnRWDVAyOw99cqPLaRtkgT52MDIK5lsLs+2IeCcv4m4WtnZWB4nNYqEJM750monhWLr66VmHN0h3wrQrydlOu36Bkr0yRsIfaWXCDyENNMWUxQBQD7S6qp5gxxKVvLYCVVjYJBFdC11OfITInYnKekqGBoeXYiipkhs5WHJPTchBDPJTqwOnzY/OJaWFiIjo4ODBgwAABw+vRpqFQqjBgxQt4REkmc0UyZmjMTd7BcQhO79PbyN8eNMzy2SofYIiXnqaSqkTfvcHi/MFG5RFx5SELPTQjxXKIrr+fl5Rn/3XLLLbj++utx4cIFFBYWorCwEOfPn8eECRMwZcoUZ46XCKBmyqS7sEwyF9PPErBd2VtMvzopfQCFjn3shmRRy3fs7Nqbt6eJfm5PQT0ACbFmVx2rpUuXYtu2bdBqtcbbtFotXnvtNUyaNAlPPfWUbAMk0oi9+BDiqZQKYHCseQ5Dnb4Nr2w+IerxljM8UupESam9JHedpszEcFnP50zdtfYWIXKwq1egTqdDVZX1tHVVVRUaGhocHhSxn60+ZoR4CwMDFF3UYcLbO5GzJh/1+nZReYOW2BkeKf3qpPQBlLtnoDf1IKQegITYZldgddttt2HWrFn48ssvceHCBVy4cAH/+c9/kJubi9tvv13uMRKJuJopE+KN9p69itz1Bdh9pspmcrctCRFBxpwpy8eazmpZ4nr92Kq9JOVYMeQ+nzPY8zMlpCexaylw1apVWLhwIe677z60t3clb/r4+CA3NxdvvfWWrAMk0ln3MQNy/lVg8/hbh8bgZEUDzlTSGyLxLJ0Mg4MCOwGVgFnzZdPdeHnFlbyPLatuAsMwZvWqpOwolLtOkzfUfaIegITwsyuwCgwMxPvvv4+33noLv/76KwCgf//+CAqiF5MnYXdECV1cSq7qUVJFSe/EO42I15qVYTCd4RHKg3r/x7NmjzXNE5Kyo1Dq7kNXn09O1AOQEH4ONWG+fPkyLl++jHHjxiEgIAAMw0BBuT0eR+iNsOiSzkUjIcQ+GQlaFJbXcVZS55vh4etXpwnwQeG5OrPnsbdmlFCV9u6EegASws+uHKvq6mrceOONuOaaa/CnP/0Jly9fBgDk5ubSjkAPxBYy5OLvY9efACEuoUTXLNLqnAze3KPEyCBMGGDdZxDgzlsaHh+GWn27YJ6QUDmBOn0bctbk44aluzBrbYFZwn135g25YIS4i10tbXJyclBZWYnVq1dj0KBBOHr0KJKSkvDDDz9gwYIFOH78uDPG6vVc1dLGktjWG4R4mox4LVbPzDBu4RfKPeKbOTJ9bFl1E2attZ13ODAmBOFBvmaNn7nKCeSsybc5c9MTKqV7ci4YIXJySksbU9u2bcMPP/yAvn37mt2ekpKC8vJye05JnOhAaY27h0CIXe4Y0Rc1+jZjMGMr90hMXSXTxwp9njxVYV02xnKZUEqV9u7Kk3PBCHEXu9aBmpqaEBhonbdTU1MDPz8/hwdF5MEuUzz/5TF3D4X0cCPjtcIHcXjuy2Oiltek1lVi84SUElJCLZcJpVRpJ4T0HHYFVmPHjsWGDRuMXysUChgMBixZsgQTJkyQbXDEMfYUVSREbpkJ4VgzM0NyIGNqz5kq5K4v4Mx3sreu0orp6VYV3sVgAybaHUcI4WLXUuCSJUtw44034uDBg2hra8MzzzyD48ePo6amBnv37pV7jMQO9jZjDvRVQt9mED6QEBEUAPzVKoQGqvHatCG45b29qG+WnthtAHCwvNaYF2W6zCdm5siyVhXQVTNq+b3pkvMP2YCJdscRQrjYNWOVmpqK4uJijBkzBlOnTkVTUxNuv/12HD58GP3795d7jMQO9jZjbmmnoIrIhwGMs0Yvfn3crqCKi+kyn2CtqryzNnftSWkBxdVahnbHEUIs2bUrEABaWlrwyy+/oLKyEgaD+cX41ltvlWVw3Y0zdwXuKq7E10cuQgEFbhveB33CAmgnIPEYi29PxfNfFsl+3ryF45EYGWRzd54mwAe65g7eXXv1+nbM23TYbIY3OykCCgUEdwWyaHccId2b03cFbt26FTNmzEBNTY3V7hqFQoHOzk57TkvsUF7dhFtW7IGupcN425eHL0Lj74O02BAcu0RNsYkncE7hYLZ9yorp6chdX2DW/ia9XxhnOxzLXXt8bWTEBky0O44QwrJrKXDevHm4++67cenSJRgMBrN/FFS51rSVe82CKpaupYOCKuIRAtRKxIb6O+XcCRFBqNO3Yd6mw2ZBVEaCFn8encD7WMtde1xFRvkKjxJCCBe7ZqyuXLmCBQsWoFevXnKPh0iwq7gStd28wjPxfs3tBlyqb5H1nKYJ4uwyoKnC8joAZbznoF17hBBnsGvG6s4778TOnTtlHgqR6siFOncPgRCR7ErltIlNEOcrtVBQVouMeK1VYjpXEropoTY2hBDCx64Zq/feew933XUXfvrpJ6SlpUGtNk/mfPzxx2UZHOE3rG+Yu4dAiCh9tYEYlxJlVwkQS4tvT8P0zH4AgMLz1jlUpv48KgEBvhfMntfWrj0x1dsJIUSIXYHVpk2bsG3bNvj7+2Pnzp1QmHwiVCgUFFi5yPUDoqENVNNyIPF4M9bky/ZB4LqkCOP/C5VaGNwnFBuujRWVhM5Xvb0n9P0jhMjDrqXAF154Aa+++irq6+tRVlaG0tJS47+SkhK5x0h4bJ4zBhp/6/hY4++DTx++jvM+QtzB0aVrriU8W3WoLI8VSkK3t3o7IYRYsuuq29bWhnvuuQdKpV1xGZFRXEQgfnllMn46U4WvCi8CAG4b3gdjU6IAAMPitPjpTJXMGS6EuJ6tJbwV09Ot6lBJLdIppno77QwkhIhhV2A1c+ZMfPrpp/jLX/4i93iIncamRBmDKZa9bW0I8QTjUqKwcPI1qG5q413C46tDJRb1/SOEyMWuwKqzsxNLlizBDz/8gKFDh1olr//jH/+QZXDEMfa2tSHE3T7KzbT6oCDEkSKd1PePECIXuwKrY8eOIT29a5q9qMi8TYVCRM8t4hpCn8IJ8TRKBTAiXosOA2OsjO4qciwpEkKI3b0CiXTO7BVoS86afOw5WwUD/ZaJFwj2U6Gx9ffuDe4od0B9/wghlqRcvyn7vJtbMT0dI+K17h4GIaI0tZq3xNpzpgr3r/7ZpbvyqI0NIcQRFFh1c6GBanw+exQy4rX0yyZOl+FgEG85sWoAUHRJhwlv70TOmnzUO1CzjSqqE0Jcga61PcTqmRkYIzEZmBApwgLU+PzRUdg8dzRS+5hPlWckaPHWHWkOBV5ssU6p6vRtyFmTjxuW7sKstQWyBGmEEGIL5Vi5kDtyrCyZ5o+8/M1xyr8isnrz9jRkJUUgMTLIKleJq2WMPfIWjpe0TMc2aeba7UcV1QkhYki5flNZ7m6upKoR5TV6JEQEgWEYlNfooVIoUFbdhOmZfXG2qgGX6lrcPUzSTTz35TEAvyedmwZAXC1j7CGlWKetWm6mFdUpl4oQIicKrLopuWYHCLGHZY89OYvVSinWSRXVCSGuRjlW3ZRcswOE2MOyx56YYrXaQDWUAmXwMuK1kgIhqqhOCHE1Cqy6IVsNZQlxtbLqrsBKKMD5KDcTOxdOwOBY/tyFP49KkPT8Yps0E0KIXCiw6oaolQ3xFOyMkFCAMzYlCqGBaiy/l7/K+eA+oZLHsGJ6OkYnR5rdRhXVCel+PKWkCuVYdUMULRNPkNpHYzYjJKZljDN69snRpJkQ4rm4cord0bWBReUWXMhV5Rbyiisxa22B085PiBib547G0L5hVrcLBTj1+narAMydb5KEEM/mipIqVG6hh6Pmy8TdtIFqzqAK6GoZwzdjJMcMk2mZEZqdIqT78sSSKhRYdUO2llMUAIIsmtwS4gy1+naH39CEAjAunrYkQAhxLk8sqULpON0UV8Lu2JQobHzoOjeNiPQ07I5AV+IqM2JvKxxCiOfzxJIqNGPVTdlaTtmUX+7uoZEegusNzZlLdJ64JEAIcS5nbHhxFAVW3Zz1copABUZCZGBZI8oVS3SeuCRACHE+MTuOXYkCqx4mKzHc3UMg3cT0zDjkl9bg1yrzJb/spAirNzRbS3QPbSjAYxOSZZnB8sQlAUKI83laSRUKrHqYpKhgZCdFYH9JtbuHQrzctX3D8Mi4/gCAAyXVYABclxRh9YbGt0RXUFZrLA3i6AyWJy4JEEJcx54NL85Ayes90KoHRiA9TnoFa0JMPfflMUx4eyde/uY4bkrtjemZ/Tjf1MR2ApAjyZyqrBNC3I1mrHqg0EA1lVwgAAA/HyVaOwwOnYMNiGwV4hNbV41NMv/pTBXGpkTZNRZPWxIghPQ8NGPVw9Tp2zDtvb04U9no7qEQD+BoUAWY77rjYqtPoC0z1uQjZ00+6vXtdo8pMTIIEwZEU1BFCHE5Cqy6iZKqRmzKP4dP8s9ZXeDYxpS7T1fhgdUHcORCnXsGSbo1vrpVXEt0fKj2VM/gKU1zPRX9fLwTLQV6uTp9Gx79d6FVMnp6XBjuzYrDFwcvoKCs1k2jIz0J3647yyW69388i8JzdWZJ5qao9lT3RhXy+dHPx7vRjJWXe3zTEc4dfofP1+HZL45RUEWcTqVQWNWtMp0lNf3EzS7RvXjzYAzqHSJ4bndUbyfORxXy+dHPx7vRjJUXs7WNnRBXGh4fZtx1x/VJmzUuJQqvTUvFi18Xif67pdpT3Q9VyOdHPx/vRzNWXkzsNnZCnKmgrBbzNh1Gvb6d85M2a+/Zq5i6co/N+01xzYKR7kFMhfyejH4+3o9mrLyY2G3shDgbW0Wdb+m5k2FQK3Kn3/B+YVR7qpuiCvn86Ofj/WjGyoux29gJcTe2irpcCsp/nwUj3Yut8hs0S9mFfj7ejwIrL7diejqykyLcPQxCZEfJut0XVcjnRz8f7+bWwGr37t245ZZbEBsbC4VCga+//trsfoZhsGjRIvTu3RsBAQGYOHEizpw5Y3ZMTU0N7r//fmg0GoSFhSE3NxeNjebFL3/55ReMHTsW/v7+iIuLw5IlS6zG8vnnn2PgwIHw9/dHWloa/vvf/0oeizuEBqqx6ZHrkLdwPBbfnoaXbx6MjHitu4dFPIxSXG1OTuNSorB5zmisnZWBDQ9m8B6bEa+1WQhUpVBAG6gWXShUqPAo8V5s+Y28heOxdlYG8haOx4bcTCol8Bv6+Xg3twZWTU1NuPbaa7Fy5UrO+5csWYLly5dj1apVOHDgAIKCgjB58mS0tLQYj7n//vtx/PhxbN++Hd999x12796NRx55xHi/TqfDpEmTEB8fj0OHDuGtt97CK6+8gg8//NB4zL59+zB9+nTk5ubi8OHDmDZtGqZNm4aioiJJY3EnhmEQE+qP8QOj8fc7h7p7OMTDhAbY94b8UW4mNuRmYmhcGCYMiMa4a6J5lylWz8ywWQh0dHIkNs8ZI6lQKEDJut0ZVcjnRz8f76RgGBsV+lxMoVDgq6++wrRp0wB0BQqxsbF46qmnsHDhQgBAfX09evXqhXXr1uHee+/FyZMnMXjwYBQUFGDkyJEAgK1bt+JPf/oTLly4gNjYWHzwwQd44YUXUFFRAV9fXwDAc889h6+//hqnTp0CANxzzz1oamrCd999ZxzPddddh2HDhmHVqlWixiKGTqdDaGgo6uvrodFoZPm5cW1vT43VoOiSTpbzk+5BpVCgf1QQTktoZaQNVOPwoklWt9fr2zFv02He4oVsIVAfpQIdBsaqZ9/u01XI+Ve+qHHkLRxPFxZCiFtJuX57bI5VaWkpKioqMHHiRONtoaGhyMrKwv79+wEA+/fvR1hYmDGoAoCJEydCqVTiwIEDxmPGjRtnDKoAYPLkySguLkZtba3xGNPnYY9hn0fMWNyFa3v7CQqqiIVOhsHpykZo/MVvBK7Vt3Muw4lZpmA/aY9NieL8xD3umijB/oFKBShZlxDidTw2sKqoqAAA9OrVy+z2Xr16Ge+rqKhAdHS02f0+Pj4IDw83O4brHKbPYesY0/uFxsKltbUVOp3O7J+c2EJylm1B2La6juTVkO7pqT9cAx8JfxjHL9XbvM/RZQqh/oEGBmjvNNDOQEKIV/HYwKo7WLx4MUJDQ43/4uLiZD2/UCG5ATHCLUNIz/LdscswSFj9X/NTqdPGYjnzlRGvtXpDyi+toZ2BhBCv4rGBVUxMDADgypUrZrdfuXLFeF9MTAwqKyvN7u/o6EBNTY3ZMVznMH0OW8eY3i80Fi7PP/886uvrjf/Onz8v8F1LI1RILtjPx3N/wcRp0vpowDUnpfH3QUFZLQwSsioPn69z+q68xMggxIcHoqC81jjbyqKdgYQQb+Ox193ExETExMRgx44dxtt0Oh0OHDiA7OxsAEB2djbq6upw6NAh4zE//vgjDAYDsrKyjMfs3r0b7e2/Lyds374dAwYMgFarNR5j+jzsMezziBkLFz8/P2g0GrN/crJVSE4JYGS8tusiKuszEm9wrkYPrtipoaXDrvMd4GjyLTdq40EI6S7cGlg1NjbiyJEjOHLkCICuJPEjR47g3LlzUCgUePLJJ/Haa69h8+bNOHbsGHJychAbG2vcOTho0CD88Y9/xMMPP4z8/Hzs3bsXc+fOxb333ovY2FgAwH333QdfX1/k5ubi+PHj+PTTT/Huu+9iwYIFxnE88cQT2Lp1K5YuXYpTp07hlVdewcGDBzF37lwAEDUWd3lt2hAE+6vMbjMA0LVQXkpPVd/MHUDZu/3XFduGqY0HIaS7cGuvwIMHD2LChAnGr9lgZ+bMmVi3bh2eeeYZNDU14ZFHHkFdXR3GjBmDrVu3wt/f3/iYjRs3Yu7cubjxxhuhVCpxxx13YPny5cb7Q0NDsW3bNsyZMwcjRoxAZGQkFi1aZFbratSoUfj444/x4osv4i9/+QtSUlLw9ddfIzU11XiMmLG4w4tfH+e8kJ6+In5bPSF8ruOp7F9S1YjyGr1VOQWp2NnXvWevmm3GUCkUGJ0cSTsDCSFew2PqWPUEctexKqlqxA1Ld8kwMtKTpfbRQMEAxzjKdGQnRWDTI9dZ3c5VP82ylpVUYupjEUKIO0i5frt1xoo4RigvhRAxwgP98Pq0VLzwdRFnUMOFq34a29tvQ26mXeNgdwmyxUUdnQUjhBB3oMDKiwnlpRAixt6zV/HC10Wigxq2fpol0x18jgREiZEUUBFCvJfH7gokwpKigpEaK+9OQ9LzWAZEQkU/aQcfIYTYRoGVl8tMDHf3EEg3ITYgoh18hBBiGwVWXi6/tMbdQyDdhNiAyFb9NJVCQb39CCE9HgVWXqykqhFF1HCZWFACyIjXIjVWI+oFbk9AxNXnb3RypM1kd0II6Skoed2L0a5AwmVEvBarZ2YAgFX5Ai72BES0g48QQrhRYOXFaFcgMaVUdAVVn88eZbzNMvgBunKpfJQKdBgYhwMiR3fwyVVglBBCPAUFVl7MVrVqpQKSGu2S7mFMMnfdKcvgxxMCGGcUGCWEEE9AldddSO7K6wBwrlqPqSv3oFb/e2/AYD8VGls7ZTk/8Wwf5WbKMvMkRO6ZpZw1+Tbb19hbYJQQQpyFKq/3IC9+XQSdRa9AfRsFVd0dG4SMTYly6vM4Y2bJ2QVGCSHEnWhXoBdjL1CdFpOOtAzYvSgA+CjNSxtoAnzw+rRU7gfIiK91jb2owCghpDujwMqL0a7AniFArUSHRbSsa+7AC18XoaSqEXnFlSi9Kn8wYitwN51Zskd3KDDqzJ87IcS70VKgF6NdgT2Dvt1gdRsb3NywdJfxNrmTv8XMLNmzZGdr0wW7vOnJy4CUdE8IEUIzVl4sKSoYWnozJ79xdInOkjNnlry1wKgzlkYJId0LzVh5sZKqRrPdgMS73JbeB18dvijb+eRO/nbmzJI3FhilpHtCiBg0Y+XFKMfKu2U5qYG2nMnfzp5ZSowMwoQB0V4RkFDSPSFEDJqx8mKUY+WdlADGpEQh00mBlZzJ3944s+Qs3SHpnhDifDRj5cXYpRqVwnwrPttUd1xKFCx26RMPMOa3ZGf29yeVxt+H9/fujMDHm2aWnEXo9daTfzaEkN9RYOXl+JZqVkxPx5hk5xaQJNK8eXsaNuRmGneQrZiejlH9I0Q/XhuoxpZ5Y702+dvb0c+dECKEWtq4kDNa2rB2n67C4fO1GN5Pa1WN+7aVe3H4fJ2sz9fTRYf4YUZ2POr17Th+qR4afzV+OHFF8HF5C8dzzmyUXm3Cd0cvYun2MzYf+9adQ3HXyDirx/X0JTp3oJ87IT0LtbTpQYTq6pRUNVJQJbOMBC3uHNkXXxy8gIKyWlGPUSq6miTbuggnRgZh3o3XoKCszuYuPMugin0cXdhdj37uhBBbaCnQyz22sdBqC/juM1V4dOMhAMDO4kp3DKvbUQAYGBOCjHgtCspq8ewXx0QHVUBXUCVmuYiWmgghxLvRjJUXK6lqxL5fqznv2/drNUqvNmFT/jkXj6p7GpsShQ6DAQdKaiQ9LjVWgzduS8PQuDCr+0qqGlFeozdbTqJdeIQQ4t0osPJiB0q5gyrWd0cv4Uwl1dZx1Ft3DkV0iB9mri2Q/NgV9w23CozEtEWhpSZCCPFOtBTo1fhrKVQ3tbpoHN3b+v1ldgVVAHfRSGqLQggh3RcFVl5MqHL3DQN7uWgk3duJSzq7H2tZNJJti9JpsRnXtC2KJympakRecaXHjYsQQjwVLQV6saSoYGQnRWB/ifWSYHZSBMZdE4XUWA2KHAgMCGCwoyCJrX56YtqieMISoJjlSkIIIdZoxsrLrXpghFX17nEpUVj1wAgAwOu3pbljWD2erZ183tIWhZYrCSHEPjRj5eWEdpFdGxeGcSlR+OlsFagULDe1SoH2Tuk/nI9yM9FXG2j8uQMQ3MnHtkWxVavKE2ar2OVKS6bLlZ4wTkII8UQUWHUTiZFBYBjGmCzNXvhKqhpxz8i+qNW34thFWhLkIhRUKQEYTL5mgyC2wr1pkCEm4FgxPR3zNh02C148qVaVtyxXEkKIJ6LAqhvgyofJToqAQgGbda6IeCPitSgo/70Y6KDYECycfI3N47nqU5ny9FpV3rJcSQghnogCq26AKx+GK6Gd2JYSHYySqibO5bkNuZk4er4WL3xVhKJLOhRd1OHW9/ZaJXNLTfj21FpV3rBcSQghnoqS172cre373Z3G3wfBfirZzrf07msxPD7M7DbT5bml287g5OUGs/stk7m7U8I3tdYhhBD70IyVlxPKh+luspPC8diEZPQJC8ANS3fZPO7N29MQG+aPnH8JF/bMiNfi7R9Om/X+y4jXmjWyFkrmZn77f75jvGmmx9OXKwkhxFPRjJWXE8qH6W5uS++LDgODA6X8Pft6hfpj3DXRSI3V8B6XGquB2kdpNdNUeK7OONMkJplbzDHeKDEyCBMGRFNQRQghItGMlZezlQ/TXT3zn19EHccmWL9+Wxqmrtxr+3x/HMA5q2U60yQmmZsR+NlTwjchhPQMNGPVDXDlw2QnRWBU/wiz27Q9oGK2SqHAuJQo4wwLW8dLqeA+Tqh8VVl1kzF4VSnMT2L6XGKOsUTtYgghpPuhGatugC8fxvK2Tfnn8PyXx2R53mA/FVKiQ3D0fJ1ZnSd3Su8XhntG9jXLaeKrGyXUqJqdaRJTe0psfSpqF0MIId2XghFawyCy0el0CA0NRX19PTQa/twfZympauRN+hYrI0GL1TkZAGAVTEgVGuCD/ze2P5ZsK7br8W/enoYgfx9s2FdmloBuGazYSsTOWZNvs7TAhtxMs+cSk8wtdIyU5yOEEOJ+Uq7ftBTYw9hashIrNVaDvIXj8fnsUQgNVBtnyzY8aF9A8NadQ3H05cn4Y1qMXY8HgKykCHxecAGF5XVmt+85U4X7V/9sXGqzlYgtpbSAmGRuvmNslccwzekihBDivWgpsAfiWrISq+iSDg3N7Va325s4HxniB8C+JHx2lsdWqQPDb+Od8PZO3qU2V5YWoHYxhBDSvdGMVQ9U3dSKWWMS8FFuJtbOysDmuaMx7re+d2L85SvrHC17yz6Y7pZbMT0dWUnhoh/LziqJqeUlplCnK0oLULsYQgjp3mjGqgfhS5pmZ2xOXKzHqt2/8jZsLrqksyp4ydcGRRPgA11zh2B7lNBANXyUSqumx5aUiq7+fWw+kpigzlMKdVK7GEII6d5oxqoHeWj9QeyxWDIznclJjAzCoFgNFkwagPiIAN5zcRW8tJWrtHnOGFE5TGz+kdAOQwMDFJTVGvORpOSNeUKhTmoXQwgh3RfNWPUAdfo2PLzhIA6W11rdx87kHD1fh6XbTovOu+JasuLLVRKTwyS1PY9pPtKK6emY/e9Dgs2nPWGpjdrFEEJI90WBVQ/w+KYjOMQRVJl64etjOHmpgfcYQNySVWIkd6Bg63aW1Dwt0yApNFANtYp/AtZWoU53Efp5EEII8T60FNjNGZfXBDbaFV3UidqN58wlK7FLekpFVx0t06DEVqNkUwsnXyPLOAkhhBBbKLDq5oSW15SAYKPiN29Pw9pZGchbOB4bcjOdWh2cK//IshUPm2OVsyYf9fqu0g9ilhGrm9rkGyghhBDCgZYCu5GSqkaU1+jNcnaEltdGxGvx4s2DeRsVZyVFSF6y4hqL2Me8OnUIAJjlH931wT4cKq81S2xnE+835GaKWkb0hPwqIfb83AghhHgOCqy6Ab4yCra297MlCz6fPcp4vBwlAOzpgyf0mJKqRhTwJN6XXv29UbKt5UBPy6+yRP0DCSGke6ClwG7g8U1HsPfsVbPbTMsocC2vjUmOMvb6s3WMPflUQmOx5zFiqpUDXd/DqP4RVvdnJ0V4fCkDe35uhBBCPA/NWHk5W0nblgUxhbb3y1ECQOxYpD5GbLXy0EA1Pn74OpRebcKBkmowAK6zYxnT1ez5uRFCCPFMFFh5OSm958Rs73ekBIA9ffDEPGbCgGhJS5XeVsaA+gcSQkj3QUuBXs6Tes/ZMxaxj+nO1co96XdICCHEMTRj5eU8qfecPWMR+5juXK3ck36HhBBCHEMzVt2AJ83m2DMWKY9JjAzChAHR3S7Y8KTfISGEEPspGEZEuW0iC51Oh9DQUNTX10Oj4S/KaQ9Pms2xZyyeNH53oZ8BIYR4HinXbwqsXMjZgRUhhBBC5Cfl+k1LgRKtXLkSCQkJ8Pf3R1ZWFvLz8909JEIIIYR4CAqsJPj000+xYMECvPzyyygsLMS1116LyZMno7Ky0t1DI4QQQogHoMBKgn/84x94+OGHMWvWLAwePBirVq1CYGAg/vWvf7l7aIQQQgjxABRYidTW1oZDhw5h4sSJxtuUSiUmTpyI/fv3cz6mtbUVOp3O7B8hhBBCui8KrES6evUqOjs70atXL7Pbe/XqhYqKCs7HLF68GKGhocZ/cXFxrhgqIYQQQtyEAisnev7551FfX2/8d/78eXcPiRBCCCFORJXXRYqMjIRKpcKVK1fMbr9y5QpiYmI4H+Pn5wc/Pz9XDI8QQgghHoBmrETy9fXFiBEjsGPHDuNtBoMBO3bsQHZ2thtHRgghhBBPQTNWEixYsAAzZ87EyJEjkZmZiWXLlqGpqQmzZs1y99AIIYQQ4gEosJLgnnvuQVVVFRYtWoSKigoMGzYMW7dutUpoJ4QQQkjPRC1tXKi+vh5hYWE4f/48tbQhhBBCvIROp0NcXBzq6uoQGhrKeyzNWLlQQ0MDAFDZBUIIIcQLNTQ0CAZWNGPlQgaDAZcuXUJISAgUCoXZfWw03NNms+j77lnfN9Bzv3f6vun77gm66/fNMAwaGhoQGxsLpZJ/3x/NWLmQUqlE3759eY/RaDTd6o9RLPq+e56e+r3T992z0PfdfQjNVLGo3AIhhBBCiEwosCKEEEIIkQkFVh7Cz88PL7/8co+r1E7fd8/6voGe+73T903fd0/QU79vU5S8TgghhBAiE5qxIoQQQgiRCQVWhBBCCCEyocCKEEIIIUQmFFh5iJUrVyIhIQH+/v7IyspCfn6+u4fkVIsXL0ZGRgZCQkIQHR2NadOmobi42N3Dcrk333wTCoUCTz75pLuH4nQXL17EAw88gIiICAQEBCAtLQ0HDx5097CcqrOzEy+99BISExMREBCA/v37429/+xu6Y2rr7t27ccsttyA2NhYKhQJff/212f0Mw2DRokXo3bs3AgICMHHiRJw5c8Y9g5UR3/fd3t6OZ599FmlpaQgKCkJsbCxycnJw6dIl9w1YJkK/b1OzZ8+GQqHAsmXLXDY+d6LAygN8+umnWLBgAV5++WUUFhbi2muvxeTJk1FZWenuoTnNrl27MGfOHPz888/Yvn072tvbMWnSJDQ1Nbl7aC5TUFCAf/7znxg6dKi7h+J0tbW1GD16NNRqNb7//nucOHECS5cuhVardffQnOrvf/87PvjgA7z33ns4efIk/v73v2PJkiVYsWKFu4cmu6amJlx77bVYuXIl5/1LlizB8uXLsWrVKhw4cABBQUGYPHkyWlpaXDxSefF933q9HoWFhXjppZdQWFiIL7/8EsXFxbj11lvdMFJ5Cf2+WV999RV+/vlnxMbGumhkHoAhbpeZmcnMmTPH+HVnZycTGxvLLF682I2jcq3KykoGALNr1y53D8UlGhoamJSUFGb79u3M9ddfzzzxxBPuHpJTPfvss8yYMWPcPQyXmzJlCvPggw+a3Xb77bcz999/v5tG5BoAmK+++sr4tcFgYGJiYpi33nrLeFtdXR3j5+fHbNq0yQ0jdA7L75tLfn4+A4ApLy93zaBcwNb3feHCBaZPnz5MUVEREx8fz7zzzjsuH5s70IyVm7W1teHQoUOYOHGi8TalUomJEydi//79bhyZa9XX1wMAwsPD3TwS15gzZw6mTJli9nvvzjZv3oyRI0firrvuQnR0NNLT0/F///d/7h6W040aNQo7duzA6dOnAQBHjx7Fnj17cNNNN7l5ZK5VWlqKiooKs7/30NBQZGVl9aj3OaDrvU6hUCAsLMzdQ3Eqg8GAGTNm4Omnn8aQIUPcPRyXol6Bbnb16lV0dnaiV69eZrf36tULp06dctOoXMtgMODJJ5/E6NGjkZqa6u7hON0nn3yCwsJCFBQUuHsoLlNSUoIPPvgACxYswF/+8hcUFBTg8ccfh6+vL2bOnOnu4TnNc889B51Oh4EDB0KlUqGzsxOvv/467r//fncPzaUqKioAgPN9jr2vJ2hpacGzzz6L6dOnd7s+epb+/ve/w8fHB48//ri7h+JyFFgRt5szZw6KioqwZ88edw/F6c6fP48nnngC27dvh7+/v7uH4zIGgwEjR47EG2+8AQBIT09HUVERVq1a1a0Dq88++wwbN27Exx9/jCFDhuDIkSN48sknERsb262/b2Ktvb0dd999NxiGwQcffODu4TjVoUOH8O6776KwsBAKhcLdw3E5Wgp0s8jISKhUKly5csXs9itXriAmJsZNo3KduXPn4rvvvkNeXh769u3r7uE43aFDh1BZWYnhw4fDx8cHPj4+2LVrF5YvXw4fHx90dna6e4hO0bt3bwwePNjstkGDBuHcuXNuGpFrPP3003juuedw7733Ii0tDTNmzMD8+fOxePFidw/Npdj3sp76PscGVeXl5di+fXu3n6366aefUFlZiX79+hnf58rLy/HUU08hISHB3cNzOgqs3MzX1xcjRozAjh07jLcZDAbs2LED2dnZbhyZczEMg7lz5+Krr77Cjz/+iMTERHcPySVuvPFGHDt2DEeOHDH+GzlyJO6//34cOXIEKpXK3UN0itGjR1uV0zh9+jTi4+PdNCLX0Ov1UCrN32ZVKhUMBoObRuQeiYmJiImJMXuf0+l0OHDgQLd+nwN+D6rOnDmD//3vf4iIiHD3kJxuxowZ+OWXX8ze52JjY/H000/jhx9+cPfwnI6WAj3AggULMHPmTIwcORKZmZlYtmwZmpqaMGvWLHcPzWnmzJmDjz/+GN988w1CQkKMeRahoaEICAhw8+icJyQkxCqPLCgoCBEREd06v2z+/PkYNWoU3njjDdx9993Iz8/Hhx9+iA8//NDdQ3OqW265Ba+//jr69euHIUOG4PDhw/jHP/6BBx980N1Dk11jYyPOnj1r/Lq0tBRHjhxBeHg4+vXrhyeffBKvvfYaUlJSkJiYiJdeegmxsbGYNm2a+wYtA77vu3fv3rjzzjtRWFiI7777Dp2dncb3uvDwcPj6+rpr2A4T+n1bBpBqtRoxMTEYMGCAq4fqeu7elki6rFixgunXrx/j6+vLZGZmMj///LO7h+RUADj/rV271t1Dc7meUG6BYRjm22+/ZVJTUxk/Pz9m4MCBzIcffujuITmdTqdjnnjiCaZfv36Mv78/k5SUxLzwwgtMa2uru4cmu7y8PM7X9MyZMxmG6Sq58NJLLzG9evVi/Pz8mBtvvJEpLi5276BlwPd9l5aW2nyvy8vLc/fQHSL0+7bUk8otKBimG5YAJoQQQghxA8qxIoQQQgiRCQVWhBBCCCEyocCKEEIIIUQmFFgRQgghhMiEAitCCCGEEJlQYEUIIYQQIhMKrAghhBBCZEKBFSGEEEKITCiwIoR0W+PHj8eTTz5p/DohIQHLli1z2fP/+c9/9vqWLYQQaahXICGkxygoKEBQUJDLnu/dd98FNbcgpGehwIoQ0mNERUW59PlCQ0Nd+nyEEPejpUBCiMuNHz8e8+bNw5NPPgmtVotevXrh//7v/9DU1IRZs2YhJCQEycnJ+P77742PKSoqwk033YTg4GD06tULM2bMwNWrV433NzU1IScnB8HBwejduzeWLl1q9byWS4H/+Mc/kJaWhqCgIMTFxeGxxx5DY2Oj8f5169YhLCwMP/zwAwYNGoTg4GD88Y9/xOXLl0V9n5ZLgePHj8fjjz+OZ555BuHh4YiJicErr7xi9pi6ujr8v//3/9CrVy/4+/sjNTUV3333nfH+//znPxgyZAj8/PyQkJBg9X0mJCTgtddeM/4s4uPjsXnzZlRVVWHq1KkIDg7G0KFDcfDgQbPH7dmzB2PHjkVAQADi4uLw+OOPo6mpSdT3SQj5HQVWhBC3WL9+PSIjI5Gfn4958+bh0UcfxV133YVRo0ahsLAQkyZNwowZM6DX61FXV4cbbrgB6enpOHjwILZu3YorV67g7rvvNp7v6aefxq5du/DNN99g27Zt2LlzJwoLC3nHoFQqsXz5chw/fhzr16/Hjz/+iGeeecbsGL1ej7fffhsfffQRdu/ejXPnzmHhwoUOfd9BQUE4cOAAlixZgr/+9a/Yvn07AMBgMOCmm27C3r178e9//xsnTpzAm2++CZVKBQA4dOgQ7r77btx77704duwYXnnlFbz00ktYt26d2XO88847GD16NA4fPowpU6ZgxowZyMnJwQMPPIDCwkL0798fOTk5xmXKX3/9FX/84x9xxx134JdffsGnn36KPXv2YO7cuXZ/n4T0WAwhhLjY9ddfz4wZM8b4dUdHBxMUFMTMmDHDeNvly5cZAMz+/fuZv/3tb8ykSZPMznH+/HkGAFNcXMw0NDQwvr6+zGeffWa8v7q6mgkICGCeeOIJ423x8fHMO++8Y3Ncn3/+ORMREWH8eu3atQwA5uzZs8bbVq5cyfTq1UvU9zlz5kxm6tSpNr9vhmGYjIwM5tlnn2UYhmF++OEHRqlUMsXFxZznu++++5g//OEPZrc9/fTTzODBg41fx8fHMw888IDxa/bn+NJLLxlv279/PwOAuXz5MsMwDJObm8s88sgjZuf96aefGKVSyTQ3N4v6XgkhXSjHihDiFkOHDjX+v0qlQkREBNLS0oy39erVCwBQWVmJo0ePIi8vD8HBwVbn+fXXX9Hc3Iy2tjZkZWUZbw8PD8eAAQN4x/C///0Pixf//3buHySZMI4D+Pcih8QhmtIiAkUwzMhoKnOJpqShyc1wiCgiiGpzKEIkiqCG1gghBIemiiiIuiAisIiK648NTQ0FIRHVne8gHa+v+vK+573VS98POHh4v+d+zyBfnnvuwjg/P8fj4yPe3t7w/PyMp6cnGI1GAIDRaITValXPMZvNuLu709Y0svv+tV4ikUB1dTXsdnvec8/OztDV1ZV1rKWlBbOzs5BlWV3Z+nmM93ksNLeVlZU4OjrC8fExotGo+pt0Og1FUZBMJuFwOLS2S/TtMFgR0acwGAxZ3wVByDomCAKAzO2xVCoFn8+HSCSSU8dsNuPy8vKvx7+5uUFnZyf6+vowOTmJiooK7O7uIhgM4uXlRQ1W+a4zXcSTfvnqKYoCACgrK9Nct9AY7/NYaG4BIJVKobe3F4ODgzm1ampqdLkmou+CwYqIvjy32414PI7a2lqUlub+bVmtVhgMBuzv76tB4OHhAZIkwev15q15eHgIRVEwPT2NkpLMdtNYLPbvmvgDLpcLt7e3kCQp76qVw+GAKIpZx0RRhN1uV1ertHC73Tg9PYXNZtNcg4gyuHmdiL68/v5+3N/fw+/34+DgAFdXV1hfX0dPTw9kWYbJZEIwGMTIyAi2trZwcnKCQCCgBqZ8bDYbXl9fMTc3h+vraywtLWFhYeEDu8rl9XrR1taG7u5ubGxsIJlMYnV1FWtrawCA4eFhbG5uYmJiApIkYXFxEfPz80VtpgeAsbEx7O3tYWBgAIlEAhcXF1hZWeHmdSINGKyI6MuzWCwQRRGyLKOjowP19fUYGhpCeXm5Gp6mpqbg8Xjg8/nQ3t6O1tZWNDU1FazZ0NCAmZkZRCIROJ1ORKNRhMPhj2qpoHg8jubmZvj9ftTV1WF0dBSyLAPIrCzFYjEsLy/D6XQiFAphfHwcgUCgqDFdLhe2t7chSRI8Hg8aGxsRCoVgsVh06IjoexHSxWwWICIiIiIVV6yIiIiIdMJgRUSkkclkKvjZ2dn57Msjok/AW4FERBr97jUPVVVVur0+gYj+HwxWRERERDrhrUAiIiIinTBYEREREemEwYqIiIhIJwxWRERERDphsCIiIiLSCYMVERERkU4YrIiIiIh0wmBFREREpJMfUCH/ujlCvdUAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["scaler = StandardScaler()\n","def prepare_data(data, scale=True):\n","  names = data.columns\n","  if (scale == True):\n","    scaled_data = scaler.fit_transform(data)\n","    data = pd.DataFrame(scaled_data, columns=names)\n","  y_label = [\"median_house_value\"]\n","  X = data[names]\n","  del X[\"median_house_value\"]\n","  y = data[y_label]\n","  x_train,x_test,y_train,y_test=train_test_split(X, y,\n","                         test_size=0.2,\n","                         random_state=1)\n","  return x_train,x_test,y_train,y_test\n","x_train, x_test, y_train, y_test = prepare_data(all_data)\n","model_2_rforest = RandomForestRegressor()\n","model_2_rforest.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":130},"id":"om4QP_1Y3Gx3","executionInfo":{"status":"ok","timestamp":1709562458126,"user_tz":-60,"elapsed":15940,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"e87385df-b6ea-4dc7-9a4a-eec906e934e2"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-9-7f9ff1077312>:17: 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","  model_2_rforest.fit(x_train, y_train)\n"]},{"output_type":"execute_result","data":{"text/plain":["RandomForestRegressor()"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["Scoring du modèle"],"metadata":{"id":"PZTrUZl_4kIb"}},{"cell_type":"code","source":["print(f\"Score / training data: {round(model_2_rforest.score(x_train, y_train)*100, 1)} %\")\n","print(f\"Score / test data: {round(model_2_rforest.score(x_test, y_test)*100, 1)} %\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oIQ5DxjA3NFC","executionInfo":{"status":"ok","timestamp":1709562458504,"user_tz":-60,"elapsed":382,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"d0c87957-0fae-4c95-e783-ff392499da7c"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Score / training data: 97.5 %\n","Score / test data: 81.0 %\n"]}]},{"cell_type":"markdown","source":["Influence des variables sur le modèle:"],"metadata":{"id":"WhRcT-oO3ZPN"}},{"cell_type":"code","source":["def display_feat_imp_rforest(rforest):\n","  feat_imp = rforest.feature_importances_\n","  df_featimp = pd.DataFrame(feat_imp, columns = [\"Feature Importance\"])\n","  df_featimp[\"Feature Name\"] = x_train.columns\n","  df_featimp = df_featimp.sort_values(by=\"Feature Importance\",\n","                                      ascending=False)\n","  print(df_featimp)\n","  df_featimp.plot.barh(y=\"Feature Importance\",\n","                       x=\"Feature Name\",\n","                       title=\"Feature importance\",\n","                       color=\"red\")\n","display_feat_imp_rforest(model_2_rforest)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":613},"id":"SyujJQ3Z3Vkv","executionInfo":{"status":"ok","timestamp":1709562765341,"user_tz":-60,"elapsed":955,"user":{"displayName":"Benoît Cayla","userId":"16127805405482571098"}},"outputId":"dcdc9b74-c576-4850-9c65-200b21857d34"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["   Feature Importance        Feature Name\n","7            0.519986       median_income\n","0            0.161654           longitude\n","1            0.150812            latitude\n","2            0.062461  housing_median_age\n","5            0.035512          population\n","4            0.026236      total_bedrooms\n","3            0.024656         total_rooms\n","6            0.018683          households\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}