decision-tree.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"MsLJHrXGDYG0"},"source":["## Projeto Decision Tree: Sistemas Inteligentes"]},{"cell_type":"markdown","metadata":{"id":"UF_JmjqBDbUU"},"source":["#### 🎯 Objetivos:\n","\n","Neste projeto, usaremos uma árvore de decisão em um conjunto de dados sobre o Titanic com o objetivo de identificar as pessoas sobreviveram ao acidente através de dados sobre sua idade, gênero e tipo do ticket para o navio."]},{"cell_type":"markdown","metadata":{"id":"9dSv_yohEBJ1"},"source":["#### 🔁 Conectando o Google Colab ao Google Drive"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2420,"status":"ok","timestamp":1681222340821,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"rpGHa8kSEBpG","outputId":"67590e74-fc66-4b28-e426-3c148d6701b7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"luAioyKIEDUG"},"source":["#### 📚 Importando as bibliotecas necessárias"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nKWJc1hWEFxv"},"outputs":[],"source":["import pandas as pd\n","import random\n","import matplotlib.pyplot as plt \n","import seaborn as sns\n","import numpy as np\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.tree import plot_tree\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.datasets import make_classification\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"11x6i442EK02"},"outputs":[],"source":["from matplotlib import rcParams\n","\n","rcParams['figure.figsize'] = 8, 5"]},{"cell_type":"markdown","metadata":{"id":"sTwTBia9KEAU"},"source":["#### 🎲 Importando os dados"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BRKyHAWNEMr8"},"outputs":[],"source":["df = pd.read_csv('/content/drive/MyDrive/Projetos Sistemas Inteligentes/train.csv')"]},{"cell_type":"markdown","metadata":{"id":"IwlTPgm2KAuu"},"source":["#### 👀 Dando uma olhada na estrutura geral dos dados"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1681222340823,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"hQfN7WMEENA-","outputId":"58316264-8414-4a41-cd13-887dd1985598"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-55ab6d7a-0551-47c0-8e2c-84fb776dba5d\">\n","    <div 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","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55ab6d7a-0551-47c0-8e2c-84fb776dba5d')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-55ab6d7a-0551-47c0-8e2c-84fb776dba5d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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  "]},"execution_count":167,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1681222340824,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"g79tbsmVVE5m","outputId":"0b286af5-07cc-481e-da43-88b1a5746fde"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  891 non-null    int64  \n"," 1   Survived     891 non-null    int64  \n"," 2   Pclass       891 non-null    int64  \n"," 3   Name         891 non-null    object \n"," 4   Sex          891 non-null    object \n"," 5   Age          714 non-null    float64\n"," 6   SibSp        891 non-null    int64  \n"," 7   Parch        891 non-null    int64  \n"," 8   Ticket       891 non-null    object \n"," 9   Fare         891 non-null    float64\n"," 10  Cabin        204 non-null    object \n"," 11  Embarked     889 non-null    object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","metadata":{"id":"W0PvjX9syLE-"},"source":["Nota-se a presença de dados faltantes nas colunas \"Age\", \"Cabin\" e \"Embarked\"\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1681222340824,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"dlhMOE28npS_","outputId":"26f4ab2d-3c0d-45e6-edf2-fc59a213d2cb"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-2fa2c9c1-cffc-4a6c-8ac9-b46a1169cbc6\">\n","    <div 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","    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     <td>14.526497</td>\n","      <td>1.102743</td>\n","      <td>0.806057</td>\n","      <td>49.693429</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.420000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>223.500000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>20.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.910400</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>446.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>668.500000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>38.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>891.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>6.000000</td>\n","      <td>512.329200</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fa2c9c1-cffc-4a6c-8ac9-b46a1169cbc6')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 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223.500000    0.000000    2.000000   20.125000    0.000000   \n","50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n","75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n","max     891.000000    1.000000    3.000000   80.000000    8.000000   \n","\n","            Parch        Fare  \n","count  891.000000  891.000000  \n","mean     0.381594   32.204208  \n","std      0.806057   49.693429  \n","min      0.000000    0.000000  \n","25%      0.000000    7.910400  \n","50%      0.000000   14.454200  \n","75%      0.000000   31.000000  \n","max      6.000000  512.329200  "]},"execution_count":169,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1681222340825,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"McHK1OOlHRSO","outputId":"30eb7c97-55f4-4f75-9842-6097b8daaa94"},"outputs":[{"name":"stdout","output_type":"stream","text":["Tamanho do Dataset: (891, 12)\n"]}],"source":["print('Tamanho do Dataset:', df.shape)"]},{"cell_type":"markdown","metadata":{"id":"zl00o8WXHARC"},"source":["#### Dicionário dos dados\n","\n","- Survived (Sobreviveu): 0 = Não, 1 = Sim\n","- Pclass (Classe): Classe de ingresso 1 = 1º, 2 = 2º, 3 = 3º\n","- Sex (Sexo): Sexo do passageiro\n","- Age (Idade): Idade em anos\n","- Sibsp: Quantidade de irmãos / cônjuges a bordo do Titanic\n","- Parch: Quantidade de pais / crianças a bordo do Titanic\n","- Ticket (Bilhete): Número do bilhete de embarque\n","- Fare (Tarifa): Tarifa paga pelo Passageiro\n","- Cabin (Cabine): Número de cabine\n","- Embarked (Embarque): Porto de Embarque (C = Cherbourg, Q = Queenstown, S = Southampton)"]},{"cell_type":"markdown","metadata":{"id":"eGY0vFpNNJNa"},"source":["#### 🧹🪣 Data Cleaning"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1681222340825,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"FrlGw80VKXwg","outputId":"3a923d99-f3b8-4830-f0d6-b3f31dfb9d69"},"outputs":[{"name":"stdout","output_type":"stream","text":["(177, 12)\n","(687, 12)\n","(2, 12)\n"]}],"source":["print(df[df['Age'].isnull()].shape)\n","print(df[df['Cabin'].isnull()].shape)\n","print(df[df['Embarked'].isnull()].shape)"]},{"cell_type":"markdown","metadata":{"id":"wvaCAQ4LNQyX"},"source":["##### Retirando a coluna \"Cabin\":"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1681222340825,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"IGdlIgcUNQNL","outputId":"0d0f00d9-8eff-4403-8420-47e4cc635b3e"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-de4dcbd7-020d-4658-b2f4-bb9fb7e892d4\">\n","    <div 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>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>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>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>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>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>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4dcbd7-020d-4658-b2f4-bb9fb7e892d4')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-de4dcbd7-020d-4658-b2f4-bb9fb7e892d4 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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 Embarked  \n","0      0         A/5 21171   7.2500        S  \n","1      0          PC 17599  71.2833        C  \n","2      0  STON/O2. 3101282   7.9250        S  \n","3      0            113803  53.1000        S  \n","4      0            373450   8.0500        S  "]},"execution_count":172,"metadata":{},"output_type":"execute_result"}],"source":["df.drop('Cabin', axis = 1, inplace = True)\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"-RKPbdluyjt5"},"source":["Retira-se a coluna \"Cabin\", pois é a que possui mais de 75% de dados faltantes e preencher esses dados faltantes não seria viável, podendo atrapalhar na classificação"]},{"cell_type":"markdown","metadata":{"id":"BZSnxnABOXaM"},"source":["##### Substituindo valores nulos da coluna \"Age\":"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1681222341400,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"0_MZ0g9kOXzS","outputId":"d2773f18-3826-4a69-cded-3750b6bf0ed4"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Pclass', ylabel='Age'>"]},"execution_count":173,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.boxplot(data = df, x = 'Pclass', y = 'Age')"]},{"cell_type":"markdown","metadata":{"id":"ZvSx5R67zfnR"},"source":["Para a coluna \"Age\" utiliza-se de uma estratégia de preencher os dados faltantes com a mediana de idade de para cada uma das classes de acomodação de cada passageiro."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1681222341401,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"akmn-HyIQjBw","outputId":"e9437492-10b7-4aa0-d334-05619e83a5ae"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mediana da idade das pessoas da primeira classe: 37.0\n","Mediana da idade das pessoas da segunda classe: 29.0\n","Mediana da idade das pessoas da terceira classe: 24.0\n"]}],"source":["print('Mediana da idade das pessoas da primeira classe:', df[df['Pclass'] == 1]['Age'].median())\n","print('Mediana da idade das pessoas da segunda classe:', df[df['Pclass'] == 2]['Age'].median())\n","print('Mediana da idade das pessoas da terceira classe:', df[df['Pclass'] == 3]['Age'].median())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ueNuJKLSPkok"},"outputs":[],"source":["def age_inputs(cols):\n","    Age = cols[0]\n","    Pclass = cols[1]\n","\n","    if pd.isnull(Age):\n","        if Pclass == 1:\n","            return 37\n","        if Pclass == 2:\n","            return 29\n","        else:\n","            return 24  \n","    else:\n","        return Age"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-Ezp9tdiUWnp"},"outputs":[],"source":["df['Age'] = df[['Age','Pclass']].apply(age_inputs, axis = 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":768,"status":"ok","timestamp":1681222342162,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"9It7WDpGUie_","outputId":"73692f5d-6b32-49e9-bead-2f324149e6c0"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 11 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  891 non-null    int64  \n"," 1   Survived     891 non-null    int64  \n"," 2   Pclass       891 non-null    int64  \n"," 3   Name         891 non-null    object \n"," 4   Sex          891 non-null    object \n"," 5   Age          891 non-null    float64\n"," 6   SibSp        891 non-null    int64  \n"," 7   Parch        891 non-null    int64  \n"," 8   Ticket       891 non-null    object \n"," 9   Fare         891 non-null    float64\n"," 10  Embarked     889 non-null    object \n","dtypes: float64(2), int64(5), object(4)\n","memory usage: 76.7+ KB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","metadata":{"id":"X8z7dlevz-2b"},"source":["Com a coluna \"Age\" devidamente preenchida ainda é necessária alguma estratégia para resolver os dados faltantes da coluna \"Embarked\""]},{"cell_type":"markdown","metadata":{"id":"ZwZaLrPaXk8b"},"source":["##### Retirando as linhas que têm valor nulo na coluna \"Embarked\": "]},{"cell_type":"markdown","metadata":{"id":"M09__9Qi0aad"},"source":["Para coluna \"Embarked\" é mais interessante excluir os 2 dados que estão faltantes, pois sua exclusão não influência muito no comportamento do resto do conjunto de dados."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fAQWoGjUWV8I"},"outputs":[],"source":["df.dropna(inplace=True)"]},{"cell_type":"markdown","metadata":{"id":"EiRCDM1fbBOe"},"source":["#### 🔎📊 Análise Exploratória:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1681222342163,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"5TTXfKfZh59e","outputId":"cb4f3e71-199c-4481-b7f5-daedcfce8fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["Sobreviveram: 340\n","Não Sobreviveram: 549\n"]}],"source":["print('Sobreviveram:', df['Survived'].value_counts()[1])\n","print('Não Sobreviveram:', df['Survived'].value_counts()[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gwhtFmRUGpwQ"},"outputs":[],"source":["corr_matrix = df[['Survived', 'Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare',\t'Embarked']].corr()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":473},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1681222342165,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"4bRtklFbHPVH","outputId":"02301a60-ad7b-4567-b33f-67c7918fcba1"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":181,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAoQAAAG3CAYAAAAtsUTRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADBzUlEQVR4nOzdd3hUVbfA4d+k994IBEICgQDSA4QqTUooIihIFRCDoiJgBURBVATBgnwXRAWkCohK77333hN6ep30Nrl/JAwMkzpMSEjWe588V/Y5+8w6+8tk1ux2FNnZ2dkIIYQQQogKy6C0AxBCCCGEEKVLEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCiApOEkIhhBBCCD26c+cOU6ZMoXfv3tSpU4cePXoUqV52dja//vorL774IvXr16d///6cPXu2ZIPNJQmhEEIIIYQe3bhxg3379lGtWjW8vb2LXG/hwoX8/PPPvPHGGyxYsABnZ2dGjBjBvXv3SjDaHAp5lrEQQgghhP6oVCoMDHL63D799FMuXrzIxo0bC6yTlpZGy5YtGTRoEOPHjwcgPT2drl270rZtW7788ssSjVl6CIUQQggh9OhhMlgcp0+fJjExkW7duqnLTExM6Ny5M/v379dneHmShFAIIYQQopQFBwcD4OXlpVHu7e1NSEgIqampJfr6RiV6dSGEEEKI51DHjh0LPL5r1y69vp5SqcTExARTU1ONchsbG7Kzs4mPj8fMzEyvr/m4MpsQZkQFl3YI5UbL+m+UdgjlRoCxR2mHUC4Yl3YA5YhnhqK0Qyg3XjCJL+0QyoX6tzeU2mtL7qC7MpsQCiGEEEIUiypLb5fSdw9gYWxsbEhPTyctLU2jl1CpVKJQKLC1tS3R1y9yQtihQwcUiqJ/E33WDSmEEEII8bx6OHfw1q1b1K5dW10eHByMu7t7iQ4XQzESwo4dO2okhNu2bSMxMZGWLVvi6OhIdHQ0hw8fxtrami5dupRIsEIIIYQQ+cpWlXYEOmvcuDFWVlZs2bJFnRBmZGSwfft22rZtW+KvX+SEcNKkSer//u2336hUqRK//fYbVlZW6vKEhARGjRqFo6OjfqMUQgghhCiMqmwkhCkpKezbtw+ABw8ekJiYyNatWwFo1qwZDg4ODBs2jJCQEHbs2AGAqakpgYGBzJ07FwcHB3x8fFi5ciVxcXGMHDmyxGPWaQ7h0qVL+eKLLzSSQQBra2tGjRrF1KlTeeutt/QSoBBCCCHE8yQ6OpqxY8dqlD38959//knz5s1RqVRkZWnOeRw1ahTZ2dn88ccfxMTE4Ovry++//46HR8kvaNQpIYyPjychISHPYwkJCSiVyqcKSgghhBCiuLLLyJBxlSpVuHbtWoHnLF26VKtMoVAQGBhIYGBgSYWWL502pm7RogXff/89x48f1yg/duwYs2fPpkWLFnoJTgghhBCiyFQq/f1UMDr1EE6bNo23336bYcOGYW1tjb29PbGxsSQkJODr68vUqVP1HacQQgghhCghOiWELi4u/P333+zfv5/z588TGRmJs7Mz9evXfyYrYYQQQgghtJSRIePn0VNtTN22bVtJAIUQQghRNuhxY+qK5qkSwv3793PhwgXCwsJ4++23cXd358SJE1StWhVXV1d9xSiEEEIIIUqQTglhTEwM77zzDufOnaNSpUqEhoYyYMAA3N3d+fvvvzE3N+eLL77Qd6xCCCGEEPmTIWOd6bTK+OuvvyY2NpaNGzeyfft2srOz1cf8/f05cuSI3gIUQgghhCgSWWWsM50Swn379vHBBx/g7e2t9XzjSpUqER4erpfghBBCCCFEydNpyDgrKwsLC4s8jymVSoyNjZ8qKCGEEEKI4iorG1M/j3TqIaxfvz5///13nsc2bdpE48aNnyooIYQQQohikyFjnenUQ/jBBx8wdOhQBg0aRJcuXVAoFOzcuZMFCxawb98+VqxYoe84hRBCCCFECdGph7BRo0b8+eefKBQKvvvuO7Kzs5k/fz6RkZEsXryYunXr6jtOIYQQQoiCZav091PB6LwPYaNGjVi2bBmpqanEx8djY2ODubm5PmMTQgghhCg62ZhaZzr1EK5evRqlUgmAmZkZrq6ukgwKIYQQQjyndEoIp02bRqtWrRg9ejSbNm0iNTVV33EJIYQQQhSPDBnrTKch40OHDrFt2zY2bdrERx99hKmpKR06dKBHjx60adMGI6OneiKeEEIIIUTxVcDVwfqiU+Zma2vLa6+9xmuvvUZkZCSbN29my5YtvP3229ja2tKlSxemTZum71iFEEIIIUQJUGQ//ty5p3Tw4EEmTpxIZGQkV65ceaprZUQF6ykq/UpOTuGPFWu5cPkaFy5fQ5mQyPSJ43k5oHNph5avlvXfeKavZ2VjxfuTR/Nit7aYmZty6cwVfpz2P65duF5o3ZcH9qBb35eoVqMq1jZWRIZHc/rIGRbOXkzo/bB86zVo9gK//TsPgE71ehIfE6+3+3lcgLFHiVzXzMaCzp+9Tu0uTTE2N+HBuWC2T19O6MXbRarvVMOdrp8PpqpfLbIyMrm++yzbvlpGckyC+hy7Kk58cOinPOuvfXcuFzccBUChUNCgbxt8uzbFra4n5naWxN2L5OKGoxz+dROZaRlPfb8luXW9qY0FHT97HZ/ctgw5F8yu6csJK2JbOtZwp/Png/HIbcubu8+y84m2tHKxo8PE13Gv74WVqz3ZWSpiboVx8s8dXPj7QIHXf33Zp3i1eYGTS7azbcqSp7lVADwzFIWfVAwGJkbU/6gv1fu2xsTWkrgrdzk3cy1h+y8WWtfczZ4mXw6mUrt6KAwMCDt8mdNfLCPxbqTGecbW5tQb25sqXZtiUcmBtGgloQcucmHOPyQ/iFafV6VrU2oO7YBdbQ9M7a1Ii04g6vRNzs9eR/y1+3q9b4AXTErm78bjFCZGuI4fhH2f9hjaWpF69TZh3y8j8eDZAuvZdPHHrmcbzOvXxNjZnoyQSJS7TxA+9y9UyiSt8w0szXF5rz92Aa0xcnEgK1ZJ0umr3Bv/A9mpaSV0dznq395QotcvSNrFHXq7lmm9svu5XhKeemw3LCyMTZs2sWnTJq5cuaLuPSyvYuOVzF+0gkquLtSq4cWJM+dLO6QyRaFQ8OPS76hZx5ul/7eK+Jh4+g17mflrf2Jo11Hcu1XwH/Fa9WoScjeU/dsPoYxPoLJHJV4e1IPWnVoysNNwosKjteooFAo+mj6W5KRkLCzzfoJOWaZQKBi46CPcfKtyaMEmkmMT8BvSiTdWTWZBj0nE3C74UZA2bg4MX/05aQnJ7Jq5GhNLU1q+FYBrLQ8W9v6crAzNVXcX/jvMjT1nNcrunb6p/m9jcxNenh3IvdM3OLl8F0lRSjya1ODFcX2p3qouSwZ8rbd71zuFgv6LPsLVtypHc9uyyZBODF41md97TCK2kLa0dnNgSG5b7sltyxZvBeBSy4M/en+OKrctLRyssXFz4OqW48Q/iMbQ2JDqrV+g15zROHpVYu+s1Xlev1bXplRpXFPvt61P/j8GUjXAj6u/bSMhOAyv/m1ov/RDdr76DZHH8/9SZ2RhSqc1EzG2seDS3PWoMrKo/VZXOq2bzObOk0iPTcw5UaGgw6pPsfWpzI0lO1EGh2Ht6YrPsE64t6vPhnYfk5mUMy/dzrcK6fFJXPt9G2kxCZg52+I9oB1dN09lW8+pxF2++yyaRK88vv8A226tiPpjPWm3Q7Dv15Hqi74g6PVJJJ+8nG+9Kt++S0Z4NHH/7CUjJBKz2tVwHNoD6/ZNuRHwAdlp6epzDawt8P7rW4zdnIhZuZW026EYOdpi6VcXAxMjsko4ISxVMmSsM50SwpiYGLZs2cKmTZs4e/YsZmZmdOrUibFjx9KqVatyPYfQ2dGeveuX4+TowMUr1xnw5tjSDqlM6djjRRr4vcAnoz5n96Z9AOxYv5u/D67grQ+H8/mYrwqs/93EH7TK9m49wNJtvxHwaleW/LJc63ifwT1xdXfhvxWbeH3Uq/q5kWeoTvdmVG3qw+q3f+Ly5uMAXNp4lPf2zqb9+H78/f68Auu3ebcXJham/NpjMvEhOQnzg7NBDF0xkYb92nJq5R6N80Mv3uL8P4fyvV5WRia/v/Il907dUJedXrWHuHtRtJ/QD69WdQk+dEnHuy1Zvt2b4dHUh7/f/omruW15ZeNRRu+dTdvx/fivkLZslduWf/SYjDK3LUPOBjFoxUQa9GvLmdy2jLh6j2VPJMYnl+zgtd8n4De8C/tmryFbpTn4YmhqTKfJgzgyfwPtJpTN31PHhl54vuzP6WkruDJ/MwDBaw/SY/cMGk0ewPZe+U8F8nmjEzbeldjSbQox53JGeEL2nCNg9wx8A7tzbkZOkuzUpAZOjbw5MXEx1xfvVNdXBoXi/8NbuLWpx/2tJwG4+MO/Wq8TtGIvfU79jM/Qjhz/dJG+bv2ZMG9QE7te7Qj5+g+iFv4DQOy63fhs+4VKn71BUN+P8617551vSTqq2UubfOEmVeeMx/7lF4n5a7u63O3jYRhXduFGwAdk3H/0JShyft5PGBMCdFxl3KZNG7777jscHR2ZM2cOR44cYebMmbRr165cJ4MAJiYmODk6lHYYZVaHgHZER0SzZ/N+dVlcTDw7N+yhXZfWGJsUf7Dw4VCxtY2V1jEbO2ve/uRNFsz6gwRlou6Bl6I63ZuRGBHHlS0n1GXJMQlc2niUWp0bY2hS8HvKt2szru86o04GAYIPXSIqKIS6PVrkWcfY3BRDY8M8j2VlZGkkgw9d2ZbzIe1Us3Kh91Raaue25dUn2vLKxqP4FKEta3Vtxo1dZ9TJIMDtQ5eIDgrBN5+2fFzc/UiMzU0wNNZ+Hf/AHigUBhxdsKkYd/RsVe3RDFVmFjeWPfoSoUrLIGjlXpyb+mDhnv/fPo+AZkSdCVIngwDKm6GEHbxEtZ7N1WXGVjlblKVEag7PpoTHAZCVmk5BUqOUZKakY2z7/I0G2HZrRXZmFjErt6rLstMyiFm9A8smvhhXcsq37pPJIIByW840D9MaVdRlBjaWOLzakZiVW8m4H47C2AhFIb/35Ul2dpbefioanRLC6dOnc/jwYebOnUvXrl0xNTXVd1ziOVWrng9XL9zgyampl85ewdzCnKpeRZuDZ2tvg72jHb71azHlh88AOH7wlNZ5oz9+k+iIGNYtXf/0wZcSt7qehF66rdVmD84FYWJhhmP1SvnWtXa1x8rZlpDzt7SOPTgXjFvdalrl7ca+wqSrfzD5+mJGrZ+Gd5sXihSnlbMtgMZcurLGra4nYZduwxNtGZLblg5FaMvQPNoy5Fwwrnm0pZGpMeb2VthWceKFvm1o8Go77p++qTXP0sbdkZbv9GT3jJV6mYNZUuzrVSMhOIzMxBSN8qizOUmefR5tAIBCgb2vBzHntNsu+kww1tVdMbI0AyDmfDAZSak0+Lgfrq3qYO5mj0uL2jSaPICoM0GEHdBOfIxtLDB1sMaudhWaz34TExsLwg6UzV7qgpjX9SLt1gNUT7RvytmcoXizOtWLdT0jZzsAMmOV6jLLpnUwMDMl/XYoVf/3KfWurKXe1b/xXvtdsa//XJJtZ3Sm09eGPn366DsOUU44uTpw5tg5rfKHc/+cXR0Julr4gqFNp/7G1Czni0ZcTByzJv/I8f0nNc6p4etFn8E9+WDIJ6ie43kj1i523Dl+Vas8MSIu57irHRHX7uVbFyAh99wn61vYW2NoYkRWeibZqmxu7jvP1W0nUYbFYF/VBf83uzNoycesfHM2N3afLTDOVqN7kKpM5uZe7f99yworFzvuFtKWkfm0pVVuWyYWoS0f8hvRlQ6fDlD/+9bBi2z4cIFW/U6TBxF26Q6XcxfulFXmLnbqnrrHpYTH5hx3tc+znqm9JYZmJqTk0XYPy8zd7EkICiUtJpGDo3+h+ayRdFozUX1eyJ7zHBj1E9lZ2u/lLhu/xLaGOwAZiSlc+OFfglbuK+bdlT5jFwcyImK1yh+WGbs6Fut6LqP7kZ2ZRfzmR1NATKvntJPbx8NIvxvKvfE/YGBjgevY1/Fa8TXXO48hM1I7BiGKnBBOnz6dESNG4O7uzvTp0ws9f/LkyU8VmHg+mZqZkpGmPeSTnltmal603uSxgz/G1NQEz5rV6Nb3JcwttJ+E8+FXYzmy5xjH9p3I4wrPDyMzE7LStXuNHvYkGZuZFFgXyKd+urp+Vnom8SHRLBv6ncY559cdZMzOmXSZNKjAhLDNmF54t3mBjZP+IFWZXOg9lZbC2tLoKdvSKLctH7q0/gih54OxcLShZsdGWDrZav3vVc2/DrW7+bGo9xfFv6FnzDCf9lPltp+hWd5TPgxz71mVR90sdds9qpsWrST24h2uL9pB/PX72NetRp13Amjxw1scDJyrdY2j437F2Mocq2ouePVvi6G5MQpDA7Izn69hPYWZCdl5tNHDBSEGBfx+PsmuVzscBrxExPy1pN8OVZcbWJjlXjSb4IGTUSXnLNBJvRRMjX++x3FoAOGzlz3FXZRxz3HnQGkrckK4e/du+vXrh7u7O7t37y7wXIVCIQlhOWdkbIStnY1GWWx0HGmpaRibav9RM8ktS0sp2uq2U4fPAHB4zzH2bTvIqt1LSE5KYc2idQB07tWB+k3rMaDDsKe5jWfK0NgQczvNeZBJ0UoyU9MxzGNupZFpTllGAXOqMnOP5V3fpND6KfFJnFmznzZjemHj5oAyLEbrnLo9WtDhw1c5vWoPJ5ftyvdaz5JBHm2ZXIS2zHzKtnyyvvJBFMoHUQBcXn+E7t+OZODyz5jf/kMy0zJQGBrw0pdDubDuIKHny+ZWWo/Lyqf9DHLbLys17+Huh/P+DPKoa6huu5y6VlWd6bR2IoffX8C9zTlf5u5vO03ivSha/hRI8Kp9hOzR3L0h6tSjVfB3/jtCj30zATgzbWWx7q+0Zaemo8ijjRS5baQqZP7kQxZ+dagy8z0S9p0ibNZSjWMPr6HcdVydDAIkn7lG2t0wLJvU1jX850MFHOrVl2IlhHn9t6iY6jetx4K/f9Yo69XsNaLCY3By0R72cModConMY9uYwjy4E8L1Szfo2qeTOiF8//O32bVxLxnpmVSq4gY8WnTi6u6CsbFRnlvUlCaPJj688ZfmF6UfW40lISJOPfT7uIdDmAl5DOE99HCoOL/6ybEJGj1aeVGG5rSTuZ2lVkLo1boefeaM5vrus2yc+EeB13mWqjTxYcgTbflLq7EkRsSp2+1xRWnLh0PF+dUvSlte2XycRgM7ULV5bYL3X6B+3zY4elVi88Tfsa2iuWDAxNIM2ypOJEUpC0xUn6WUiDgs3LQXjjwcKn44dPyktNgkslLTMc+j7R6WpYTl1PXq3xYDUxMe7Dyjcd6D7TlzhJ39fLQSwselxycTfugy1fu0eu4SwoyIGIzdtP8+GrvktG9GEf5mmfl64vnb56Reu8udt2fAE0PsmeE57+HMqDitupnRcRjaai/OEwJ0nEN4+/ZtPD099RyKeJ7cuHyTMf3HaZRFR8Zw/dINGjWvj0Kh0FgkUa+RLynJKdwNznv+VmFMzUwwNnnU8+hW2ZWur3Sm6yvaG4cu3/471y/dYFDnkTq9VkkJu3yHPwd+o1GWGBlP2OU7VPOrpdVmVRrWID05lehboU9eSi0hPJakqHjc62tPFq/cwIuwy3cKjcu+qgsASU8sFqnc0JsBv44j5MIt1rzzM6o85naVlojLd1ieR1uGX76Dh18tUCg0FpZUzm3LmCK0ZaU82tK9gRfhRWjLh8PFptY5K2Bt3B0xNDHijXVfap1bv19b6vdry5pRc7i+XXvBVGmIvXQX15Z1MLIy11hY4tTIO/d4Pm2QnU3c1fs4NNBuO6fG3iTcDlfvLWjmZItCAQpDzTWNitwdKhRGea9+f5yhmQnGNtrTSMq61Mu3sPKvj4GVucbCEvOGtdTHC2JS1Y3qS6aSFRXHreFfavQAPpRyMac3Na/5iMYujqQF6X9D7zJF9XxNIyhLdEoIu3btSt26denZsyfdunXD1dVV33GJMi4hPpHjB7Q/xHZv2kennu1p372teh9CWwdbOvZoz4Edh8l4bP5M5Wo5k58f3AkBwNDQEAsrcxLiNbePqdPQF+/aXmz759GeZR+OmMiTXurdkZd6d2TKe9OJCI3UOl7aUpXJee7fd3nzceoGNMe3m596H0ILeyvqBDTn+s4zGr1SD5O32LsRj+pvOUHDfm2wqeSAMjSnd6B6q7o4ebtz9PdH21tYOFhrrRC2drWn0WvtCLt8R2MxhVMNdwYt+oi4+5GsGD6rzK2MTVUmczuPtryy+Ti+Ac2p3c1PvQ+hub0VtQOac+OJtrTLbcu4x9ry6pYT1O/XButKDiTktqVnq7o4ertzvJC2BGjQvx3ZKpX6qSiXNxzJM5F8deF4bu4+w5mVewg5G6RDC5SMuxuPU+ftAGoObq/eh9DAxAiv/m2JOnWT5JCcNrGo7IiRuQnKm48S7LubjtNo0gAc6lcnJneltrV3JVxb1VFfC0AZHIrCwIBqPZsTvPrRU108X/YHIPaxJ8qYOtqQFv1oBS2AZRUn3FrXzXNFc1kXt+UQzoGv4PB6V/U+hAoTIxxe7UTSmatkhOZMPzB2d8bA3FQjeTNytqP60mmgyiZ42BdkxSjzfI204AekXA7GpnNzDO1tyMpdgWzVphEmlZ2JXlJ6TxF5JmTIWGc6JYT/93//x6ZNm/j555+ZOXMmTZo0oWfPnrz00kvY2dnpOcSyZ8Xa9SQkJhERldO9v/fQMcIjc97IA/v1wtrKsjTDK1W7Nu7l/MmLTPnhM7x8PInLfVKJgaEBv36vOeT4v9U5m1D3bt4fAHNLczaeXMuO9XsIvnaLlORUavh60bN/NxKVSfz+45/quvu2HtR6bZ+6OU+AOLznWIk9uq4kXN58jHunu9J71ls416isflKJgYEBe37Q3Eh22IqcRPjH1h+oyw7M+4+6Ac15Y9Ukji7ahomFGa0CAwi/cpczax6txOz82es4VHMl+NAlEsJjsfNwounAjhibm7J16qN5SCaWZgz58xPMbC05tGAjPh0bacQQcyec+4892aQsubr5GPdPd6XHrLdwqlGZlNgEGue25f4n2nJQblvOe6wtD837D9+A5gxeNYkTuW3ZIrctzz3Wlq3e7U2Vpj4E7ztP/INozO0sqd2tGe4NvTmxaBuxd3I2A44OCiU6KO9eybh7kWWmZ/Ch6DNB3Fl/jIafvYaZkw0Jt8Lxeq0NVh5O7JqwUH1ey59G49rSl+Xug9Vl1xfvpMbA9ry49EOuzN+MKiMT38BupEbGaySEwasPUGd0AM2+G4F9PU/ir93H4QVPvAe+SNzVe9zb8mg3gYDd3xJ+8BKxl+6QFpeEjZcb3gPaoTAy5Mw3fz2bRtGjlLPXidt4kEofD8XI0Zb0O6HY9+2ASRUX7n/yaAqOx5xxWLV4gfOePdVl1ZdMxbRaJSLmr8WyaR1oWkd9LDMqTuPRdyFf/YbX0q/wXvsdMSu2YmhtgdPIl0kLuk/0si3P5F7F80enhLB9+/a0b9+e1NRUdu/ezebNm5k+fTrTpk2jdevW9OjRgx49eug71jJj8cq/CQl71Kuwc98hdu7LWfbfo0uHCp0QqlQqPhjyMe9//g79R/bF1MyUy2evMvWDb7kTVPBwcWpKKv+t2ESTlo3oGNAOUzNTIsOj2PbvLv748c8Cn2X8PMtWZbN82ExemjSQ5sO7YGRmTMi5YP6dsIDo4PyHOB9Shsaw6LWv6PL5YDp90p+sjCxu7D7DtunLNXrEgg5cwL6qC82GdsLM1pJUZTJ3jl9l/9x/NZ6ZbGFvhW3lnPlunT97Xev1zq7ZX2YTwmxVNn8Nm0nHSQPxy23L0HPBbJywgJgitGVCaAxLX/uKTp8Ppn1uW97cfYZdT7Tlzd1nsa/mSoPX2mHhYENmWgYRV++yYcICzq/dX8ArlH2Hx86nwYN+uc8ytiD2yj32Dp1NxLFrBdbLTEplR7+vafLlIOqN7Y3CQEH4kSuc+mI5aY/1pqbHJrKl2+fU/6gvlTs3ouaQDqTFJhK0ah/nZqxWPx4Q4Mafu6jcsSGVXqyPsZUZqVFKQvdf5NLP/xF39fkc+rw3YQ4ZDwZj/0rus4yv3ObWyGkkHS94X0XzOl5AzlYzT0o8ekEjIUw6coFbw77AdcJg3D4agiolDeX2o4TOWJTnMHO5IquMdabIfnI3XB0lJiaybds2fvrpJyIjI7ly5cpTXS8jquyvyHtetKz/RmmHUG4EGBdtY21RsOI/r0bkxzNDUdohlBsvmDw/IwtlWf3bpTcsnXpEfwuNzPy1vxCXZ3p5ns2FCxfYvHkzW7ZsISIigurVK8Bu6EIIIYQQ5YTOCeHNmzfZuHEjW7Zs4c6dO1SqVImAgAB69OiBr6+vPmMUQgghhCicDBnrTKeEsGfPnty8eRN7e3u6du3KN998Q5MmTfQdmxBCCCFE0UlCqDOdEsJ69erxySef4O/vj6Fh4XtGCSGEEEKIsqvYCWFaWhqxsbGYmppKMiiEEEKIMiM7Wzam1pVB4adoMjU15cSJE2RlSaMLIYQQogxRqfT3U8EUOyEEaNWqFYcOHdJ3LEIIIYQQ5UJQUBDDhw+nYcOGtGrVipkzZ5KeXvhzy2NjY5kyZQovvvgiDRs2pEePHqxcWfLP7dZpDmHfvn2ZMmUKSUlJtGvXDkdHRxQKzb2w6tatq5cAhRBCCCGKpIw8ui4+Pp5hw4bh6enJ3LlzCQ8PZ8aMGaSmpjJlypQC644dO5bg4GDGjx9PpUqV2L9/P19++SWGhoa89tprJRazTglhYGAgACtWrGDFihUayWB2djYKheKpN6YWQgghhCiWMjLUu2rVKpKSkvjll1/Uj/TNyspi6tSpBAYG4urqmme9yMhIjh07xrfffssrr7wCgL+/PxcuXGDTpk1lLyH8888/Cz9JCCGEEKIC2r9/P/7+/upkEKBbt2588cUXHDp0SJ3sPSkzM+cRmdbW1hrlVlZWJCcnl1i8oGNC2KxZM33HIYQQQgjxdMrIkHFwcDB9+/bVKLOxscHZ2Zng4PwfzVupUiVat27N/PnzqV69Om5ubuzfv59Dhw7x/fffl2jMenl0nRBCCCFEqdPjkHHHjh0LPL5r1658jymVSmxsbLTKbW1tiY8v+JnZc+fOZdy4cQQEBABgaGjI5MmT6dKlSxGi1p1OCWHt2rW1FpE8SeYQCiGEEEIUXXZ2Np999hm3b99m9uzZODs7c/jwYb755htsbW3VSWJJ0Ckh/PTTT7USQqVSyaFDh4iIiGDo0KF6CU4IIYQQosj0OGRcUA9gYWxsbEhISNAqj4+Px9bWNt96e/fuZevWraxfv55atWoB0Lx5c6Kjo5kxY0bZSwjfeOONPMvfe+89Pv7440K7Q4UQQggh9K6MrDL28vLSmiuYkJBAZGQkXl5e+da7efMmhoaG+Pj4aJT7+vqyZs0aUlJSMDc3L5GYddqYuiC9evXir7/+0vdlhRBCCCGeC23btuXw4cMolUp12datWzEwMKBVq1b51qtcuTJZWVlcu3ZNo/zSpUs4OjqWWDIIJZAQ3rp1C1UZydCFEEIIUYGUkUfXDRgwAEtLS8aMGcPBgwf5+++/mTlzJgMGDNDYg3DYsGF07txZ/e+2bdvi7u7O+++/z3///ceRI0eYNWsW//zzD4MHD36qmAqj05DxokWLtMoyMjIICgpi69at9OjR46kDE0IIIYQoljKy7YytrS1Llizhq6++YsyYMVhaWtKvXz/GjRuncZ5KpSIrK0v9bysrKxYvXswPP/zA999/T0JCAlWqVOHTTz8t8YRQkZ2dnV3cSrVr19YqMzExwc3NjS5duvDOO+88dbdmRlT++/SI4mlZ/43SDqHcCDD2KO0QygXj0g6gHPHMKHjHB1F0L5jI/Hd9qH97Q6m9dsrGOXq7lnmP8Xq71vNApx7Cq1ev6jsOIYQQQoinI1PWdCYbUwshhBCifCgjQ8bPoyInhDExMURERGgNF1+9epX//e9/BAUF4eTkxLBhw+jQoYPeAxVCCCGEECWjyKuM58yZw2effaZR9uDBAwYNGsSuXbswNTXlxo0bvPvuu5w4cULvgQohhBBCFKiMrDJ+HhU5ITx9+jQ9e/bUKFu8eDHJycksWLCAdevWsXv3bho0aMDChQv1HqgQQgghRIGyVfr7qWCKnBCGh4dTs2ZNjbI9e/bg6+tL69atATAzM2Pw4MFaGyoKIYQQQoiyq8gJoUKh0Hh+cVRUFPfv38fPz0/jPFdXV2JjY/UXoRBCCCFEUciQsc6KvKikevXqHD58WN0buGfPHhQKhdYjWCIjI3FwcHjqwGTvPP05fH5xaYdQbtSu3a+0QygXppnUKe0Qyo0Yw9KOoPxoHHK6tEMoFzJL88UrYCKnL0VOCIcMGcInn3yCUqnEycmJlStXUrVqVVq2bKlx3sGDB7UeyiyEEEIIIcquIieEvXr1Ijw8nGXLlqFUKqlbty5ffPEFRkaPLhEdHc2ePXt47733SiRYIYQQQoh8Ff/hayJXsTamHjVqFKNGjcr3uKOjI4cPH37qoIQQQgghik2GjHVW5EUlQgghhBCifJJH1wkhhBCifJAeQp1JQiiEEEKI8qECbiitLzJkLIQQQghRwUkPoRBCCCHKBxky1pkkhEIIIYQoH2TbGZ3JkLEQQgghRAUnPYRCCCGEKB9kyFhnOiWEiYmJpKenazyzeP369QQFBeHv70+LFi30FqAQQgghhChZOg0Zf/TRR/z000/qf//yyy98/PHHrFq1ihEjRrB582a9BSiEEEIIUSQqlf5+KhidEsILFy7QunVrALKzs1mxYgWBgYEcO3aMIUOG8Pvvv+s1SCGEEEKIQmWr9PdTweiUEMbHx2Nvbw/AxYsXiY2NpV+/fgB06NCBW7du6S9CIYQQQghRonRKCJ2cnLh58yYA+/bto3Llynh4eACQkpKCkZGsVRFCCCHEs5WtytbbT0WjU+bWtWtXZs2axeHDh9m/fz9vvvmm+tjly5epVq2a3gIUQgghhCiSCjj3T190SggnTJiApaUlFy9eZMSIEQQGBqqPXbp0iW7duuktQCGEEEIIUbJ0SgiNjIx499138zw2b968pwpICCGEEEInFXAxiL7IPoRCCCGEKB8q4Nw/fZF9CIUQQgghKjjZh1AIIYQQ5YNsTK0z2YdQCCGEEOWDJIQ602kO4cN9CJs2bVpu9iG0srHi/cmjebFbW8zMTbl05go/Tvsf1y5cL7TuywN70K3vS1SrURVrGysiw6M5feQMC2cvJvR+WL71GjR7gd/+zVmE06leT+Jj4vV2P8+D5OQU/lixlguXr3Hh8jWUCYlMnzielwM6l3ZoZYK1jRWffvkBnbu3x9zcjPNnLvLNlB+4dP5qsa5jZGTExn2rqFnLi2+/+IHf5i3VOP7OuJE0aFKPho3r4eTiyE8zF/DzzAX6vJUSZ2BiRP2P+lK9b2tMbC2Ju3KXczPXErb/YqF1zd3safLlYCq1q4fCwICww5c5/cUyEu9GapxnbG1OvbG9qdK1KRaVHEiLVhJ64CIX5vxD8oNo9Xke3ZpSrXcLHBp4Ye5iS3JIDA92nOHCj/+SoUzW+73rm4GJES0m9KVW39aY2VoSdeUuR2et5d6BwtvS0s2eNlMGU7VtTlveP3KZA1OXoXyiLesN6UiVlnVwa+SNdWUnrqzZz87xv2pdr8/qSVTx983ztbIyMvmf1xs63WNZYGtrw4xvJ/Fy725YWJhz4sRZPvp4KmfOFt7Ov//2A8OGvqZVfvXaTeq90E7972rVqhB041ie1xg4+G1Wr16v+w2Ickf2IQQUCgU/Lv2OmnW8Wfp/q4iPiaffsJeZv/YnhnYdxb1b9wusX6teTULuhrJ/+yGU8QlU9qjEy4N60LpTSwZ2Gk5UeLRWHYVCwUfTx5KclIyFpUVJ3VqZFhuvZP6iFVRydaFWDS9OnDlf2iGVGQqFgt9X/kztuj4snPcnsdFxDBrxKsv/+5WXOw7idvC9Il9r6Kj+uFd2y/f4hEljiAiP5PKFa7Tt2FIf4T9z/j8GUjXAj6u/bSMhOAyv/m1ov/RDdr76DZHH8/9SZ2RhSqc1EzG2seDS3PWoMrKo/VZXOq2bzObOk0iPTcw5UaGgw6pPsfWpzI0lO1EGh2Ht6YrPsE64t6vPhnYfk5mUCkCzWSNJCYvl9rpDJD2Ixq62Bz7DO+PesQFbukwmKzXjWTSJzjrPCcS7ux/nft9G3K0wfF9tQ88lH/JP/28IPZF/WxpbmNLnr4mYWltw8pf1qDKzaPhmV15ZM5lVXSaRGpeoPrfJ2z0wtjIj4mwwFi52+V7z5Nz/uLxyr0aZkYUpHWaM4F4Rkv2ySqFQsOG/P6lfvw6z5/wfUVExjB49jF0719KsRTdu3ix8lC01NZW3Rn+kUaaMT8jz3JWr/mHL1t0aZUePntL9BsqybFlUoivZhxDo2ONFGvi9wCejPmf3pn0A7Fi/m78PruCtD4fz+ZivCqz/3cQftMr2bj3A0m2/EfBqV5b8slzreJ/BPXF1d+G/FZt4fdSr+rmR54yzoz171y/HydGBi1euM+DNsaUdUpnRrVcnmjRvyJjhH7F1wy4ANv+3nZ3H/mXsJ6MZFzipSNdxdLLnvQ/f4te5ixn32Tt5ntO2UQAP7oVi72DHyeu78zynLHNs6IXny/6cnraCK/NzFrQFrz1Ij90zaDR5ANt7Tcu3rs8bnbDxrsSWblOIORcMQMiecwTsnoFvYHfOzVgNgFOTGjg18ubExMVcX7xTXV8ZFIr/D2/h1qYe97eeBODAqJ+JOHJF43Vizt+i5c+j8XylFUEr9urz9vXKtaEXPr39OTh9BWcW5LTl1b8PMnDnDFpNHMDaPvm35QtDO2HvVYm/ekwhIrct7+w5x8CdM2gU2J0j361Wn7vu1ekk5PaqBl79Ld9r5tUrWatPKwCu/Xuo+DdYRvTt24OWLf14bcBbrFu3CYA1azdw5dIBvpgygSFD897W7XGZmVmsWLGuSK935syFIp/73KuAQ736otMcwof7EM6fP5/3338fY2Nj9bF58+YxYsQIvQX4LHQIaEd0RDR7Nu9Xl8XFxLNzwx7adWmNsYlxAbXz9nCo2NrGSuuYjZ01b3/yJgtm/UGCMlHreEVhYmKCk6ND4SdWQF17diQyPIptGx8laDHRcWz+bwedur6ISRF/Jz/6/H2Cb97m3zX5r/x/cC/0qeMtTVV7NEOVmcWNZXvUZaq0DIJW7sW5qQ8W7vn/jnkENCPqTJA6GQRQ3gwl7OAlqvVsri4ztjIHICVSc1pHSngcAFmp6eqyJ5NBgHtbcpJF2xruxbizZ8+7e05bXlz+qC2z0jK4vGovlZr6YFUp/7asEdCM8LNB6mQQIDYolHuHLlGjR3ONcxMeaI+aFJXPy/6kJ6USvO20ztcobX1fCSAsLIJ//nn0voyKimHN2o306tkFExOTIl3HwMAAa2vtz5i8WFiYa3xWC/EknRLC8qZWPR+uXrhB9hNdzZfOXsHcwpyqXh5Fuo6tvQ32jnb41q/FlB8+A+D4Qe1u+dEfv0l0RAzrlsr8DZG3uvVrc+n8Va3fyXOnL2JhaY6nd+HTMuo3qssrA3owfdJsreuUJ/b1qpEQHEZmYopGedTZnMTEvm4+baVQYO/rQcw57eG56DPBWFd3xcjSDICY88FkJKXS4ON+uLaqg7mbPS4tatNo8gCizgQRVsj8OnMXWwDSYvIe0isrnOtWIy44jIwn2jI8ty2dCmhLx9oehJ/Xbsvws8HYebpinNuWT8PMwRqPNvUI3naKzJS0p75eaWnYoB5nzlzQel+eOHEGS0sLfHy8Cr2GhYU5sdHXiI2+RkTYRX7+6Wss85l+9Pnk8SjjbpKUEMyRw5vo3KmtXu6jTFJl6++ngtF59ce///7LX3/9xe3bt0lL035jnj79/Hx7c3J14Myxc1rlD+f+Obs6EnQ1WOv4kzad+htTM1MA4mLimDX5R47vP6lxTg1fL/oM7skHQz5BJV3bIh/OLk4cP6z9HooMjwLA1c2Z61duFniNL2Z8zKZ/t3Pm5Hkqe1QqkTjLAnMXO3VP3eNSwmNzjrva51nP1N4SQzMTUiLyqJtbZu5mT0JQKGkxiRwc/QvNZ42k05qJ6vNC9pznwKifyM4q+L1cZ0xPVJlZ3N10vGg3VUosXe1IyqM9kiJic4/n3ZZmdpYYmZmQnEfdh2WWrvbEBT9db7RPzxYYGhtx/Z/nd7gYoFIlFw4cPKpVHhYWAYB7JVcuXsx/8VhYWDjfz/4fp89cxMBAQZeX2vPO22/QoH4dOnTqR1ZWFgAqlYrt2/fy739bCQkJo3r1qnww9i02blhGn1eGs3nLrpK5wdJUhp5UEhQUxPTp0zlz5gyWlpb07t2bDz74oEg9wOHh4cyZM4d9+/aRnJxM5cqVefvtt+nVq1eJxatTQvjff//x+eef06dPH86cOUPfvn1RqVTs3r0bGxsbevfure84S5SpmSkZaela5em5ZabmpkW6ztjBH2NqaoJnzWp06/sS5hbmWud8+NVYjuw5xrF9J54uaFGumZmbkp6u/TuZlvs7aWZW8O9k39d7Ucu3Bu8O/7hE4itLDM1MyErXXqihSsvIPZ73MJmhWc4fZVUedbNy29nosbpp0UpiL97h+qIdxF+/j33datR5J4AWP7zFwcC5+cbn2cefGgNf5NK8DSTcCi/6jZUCo3zaMiu3LY3yaUuj3LZ8eN7jMvNoS135vOxPclQ8d4uw4rksMzc3U7+XH5eamtO5YmZecG/qpMkzNP69evV6btwIZvpXn9K3b4B69fC9eyF07zFI49xly//mwrk9zJw5pXwmhGVEfHw8w4YNw9PTk7lz5xIeHs6MGTNITU1lypQpBdaNiIigf//+VK9ena+++gorKytu3LiR52eCPumUEC5atIh33nmHt956i9WrVzNw4EDq1q1LYmIiI0eOxNLSUt9x6oWRsRG2djYaZbHRcaSlpmFsqp2xm+SWpRVxaOLU4TMAHN5zjH3bDrJq9xKSk1JYsyhnMm/nXh2o37QeAzoMe5rbEOWIsbERtva2GmUxUbGkpqTl+S3SNPd38uEHR16srCz5aPK7LJy3lNCQsp2A6ENWajqGecypNDA1zj2e96reh/P+DPKoa5jbzpm5da2qOtNp7UQOv7+Ae5tzvszd33aaxHtRtPwpkOBV+wjZo71K3rlZLZp/P4qQPec5N2ONDnf3bGXm05aGuW2ZmU9bZua25cPzHmf0RFvqyqaqM5Wa+nBu0fZCe2TLCmNjYxwc7DTKIiOjSUlJVb+XH/fwi15qSmqxX+vHnxYy9cuP6NihTYHbycTGxrHkz7/45OP3qFy5Eg8ePN9ziLWUkaHeVatWkZSUxC+//IKdnR0AWVlZTJ06lcDAQFxdXfOtO2vWLNzc3Pjtt98wNDQEwN/fv8Rj1mkO4Z07d2jcuDGGhoYYGhqSmJizMMLKyopRo0axdOnSQq5QOuo3rcfWc/9q/Li6uxAVHoOTi6PW+U6uOWWReWwbU5gHd0K4fukGXft0Upe9//nb7Nq4l4z0TCpVcaNSFTf1ohNXdxf164mKo3GzBhy7vEPjp1JlVyIjonBxddI63zm3LDwsUuvYQ2++OwRjE2M2/bONyh6VqOxRCTf3nD8+trY2VPaohLHx87dXaH5SIuIwd7XTKn84VPxw6PhJabFJZKWmY57HticPy1LCcup69W+LgakJD3ae0TjvwfacOcLOfj5a17CrU5V2i8cTf+1+kYaVy4Kk8Dgs82gPSxf73ON5t2VqXBKZqel5biHzsCy/ukXl83LOlkjXnqPh4pb+TXlw76zGj4eHO6GhEVRyc9E63y23LCS0+F/kUlNTiY6O1UpA83IvdyFZUc593mSrVHr7eRr79+/H399fnQwCdOvWDZVKxaFD+f8OJyYmsmXLFgYOHKhOBp8VnT4VrKys1F2Xrq6u3Lx5k+bNc1aRZWVlERv7dG/8knLj8k3G9B+nURYdGcP1Szdo1Lw+CoVCY5JvvUa+pCSncLcYe749ztTMBOPHenncKrvS9ZXOdH1Fe+Pl5dt/5/qlGwzqPFKn1xLPpysXrzPkldEaZZER0Vy+cA2/Fo20ficbNnmB5KQUbgfdyfea7pXdsLO3Zdvhv7WOvTN+JO+MH0mPFwdw5WLhm64/D2Iv3cW1ZR2MrMw1FpY4NfLOPZ5PW2VnE3f1Pg4NqmsdcmrsTcLtcPXegmZOtigUoDDU/A6tyN2EX2Gk+YfbqpoLHZZ/TFq0kj2DZ5GZ/HwsgIi6fJcqLetgbGWusbDELbctowpoy+hr93Gtr92Wbo28ib8TTkZS8Xu9Hlerd0vibocTfiboqa7zLJ07f5kuXQdolIWFRXLu/CVat2qm9f5u1qwRSUnJXL9e+Jz1J1lZWeLk5EBkZEyh53pVrwrk9FaKkhEcHEzfvn01ymxsbHB2diY4OP//fS9dukRGRgZGRkYMHjyYM2fOYGdnx8svv8wHH3xQoivFdUoI69Wrx7Vr12jTpg0dOnRg3rx5ZGdnY2RkxK+//krDhg31HKZ+JMQncvyA9qrf3Zv20alne9p3b6veh9DWwZaOPdpzYMdhMh6bU1O5Ws62EQ/uhABgaGiIhZU5CfGa28fUaeiLd20vtv3zaM+yD0dM5Ekv9e7IS707MuW96USE5t/rI8onZXwCh/drLzTYumEX3Xt3pkuPDup9CO0d7OjWqxO7t+8n/bHfyaqeVQC4eztnA/UlC1exY/Nejes5Ojvw9ZzJrF2xnp1b9nIv9/e3PLi78Th13g6g5uD26n0IDUyM8OrflqhTN0kOyfmAtKjsiJG5Ccqbj4bI7m46TqNJA3CoX52Y3BWy1t6VcG1VR30tAGVwKAoDA6r1bE7w6gPqcs+Xc4ZxYi/eVpeZOdvSYeUnZKtU7H79uzK/svhxNzcdp/HoAOoNaq/eh9DAxAjf19oSdvomiaE5bWnl7oixuQmxQaEadVtNHIBL/epE5LalnVclqrSso76WrpzqVsPBpzLHf/znqa7zrMXFxbNr9wGt8r/XbaJf3x706dNdvQ+ho6M9/fr2YOOmHRpzxby8clZ2BwfnJOOmpqYYGxuRmJikcc3Jkz7AwMCAbdsfbRnk5ORAVJRmguju7sYbb/Tn3PnL6kUs5Yoeh4w7duxY4PFdu/Kfg6lUKrGxsdEqt7W1JT4+/6eSRUXlLBycPHkyr732Gu+++y7nz5/n559/xsDAgAkTJhQx+uLTKSEMDAwkJCTnA+X999/nwYMHfPPNN6hUKl544QWmTct/89KyaNfGvZw/eZEpP3yGl48ncblPKjEwNODX7//QOPd/q3M2oe7dvD8A5pbmbDy5lh3r9xB87RYpyanU8PWiZ/9uJCqT+P3HP9V19209qPXaPnVrAjnzDivao+sAVqxdT0JiEhFROd9U9x46RnhkzhtiYL9eWFuVzfmoJW3L+p2cPnGe7+Z+SY1aXsRGxzF4xKsYGBrw43fzNc5dui7n3+0a9wDg0vmrWo+3e7jK+Ma1IHZs2atx7OVXA6js4YaZec4iqGb+jRkzPqen+p/Vmwm5X7bnGEWfCeLO+mM0/Ow1zJxsSLgVjtdrbbDycGLXhIXq81r+NBrXlr4sdx+sLru+eCc1BrbnxaUfcmX+ZlQZmfgGdiM1Ml4jIQxefYA6owNo9t0I7Ot5En/tPg4veOI98EXirt5T7zMI0H7Fx1h7unJp3gacm9XCuVkt9bHUqPgiPU6vtISfDeLGhmP4f/Ia5o42xN8Op3a/NlhXcWLXR4/asvOPo6ni78tcj0dteeHPndQd2J6eiz/k9ILNqDIzaTSqG8lR8Zz+VTMh9OzUCKc6Ob1UhkaGONauStP3cxYj3tp+muirmqMytfo8HC4+XCL3/az9/fdGjr73Jr8vnEMd35pERcUyevRQDA0NmTpttsa527f+BUANnxYAuLk5c/L4Nlb99R/XruXsNPBS5xfp3r0jW7fuZv36beq6M76djLdXNXbvOUhISDienh6MenMwlpYWjB9f8MKG51YZWmWsi4e7j7Rs2ZJPP/0UgBYtWpCUlMQff/zBmDFjMDN7+i2c8qJTQtiwYUN1L6CNjQ3/93//R3p6Ounp6VhZFW2TzLJEpVLxwZCPef/zd+g/si+mZqZcPnuVqR98y52ggoeLU1NS+W/FJpq0bETHgHaYmpnmbCj87y7++PHPAp9lLGDxyr8Jeexb6s59h9i5L2d+RY8uHSpsQqhSqRg54D0+nfoBw0YNwMzMjPNnL/Hxe19w62b+w8W6eHVwb1q0aqr+t38bP/zb+AFw8tjZMp8QAhweO58GD/rlPsvYgtgr99g7dDYRx64VWC8zKZUd/b6myZeDqDe2NwoDBeFHrnDqi+UaPXvpsYls6fY59T/qS+XOjag5pANpsYkErdrHuRmrUWVkqc91yN2rr+6YnlqvF374SplOCAF2jJtPiwf9qP1Ka0xtLYi+eo+Nw2cTUkhbZiSlsu7Vr2nzxSD83s9pywdHrnBg2nJSn+glrdHdD99XH+2F5/KCJy4veAKQGBqjmRAqFPj08ifi/K2n3ramrFCpVPToNYTvZkzm3TEjMTc34+TJs4wcOY7r1wseEo+LU7Jp8y46dWzL0CGvYmhowM2g20ya/C2z58zXGILesXMfXqOG8PboN7C3tyUuTsmBg0f55pufivTM5IquoB7AwtjY2JCQoD06EB8fj62tbR41HtWDnCTwcf7+/syfP587d+5Qq1atvKo+NUV2Gd2x1s+9HG+c+YwdPr+4tEMoN2rX7lfaIZQL00zqlHYI5UbMs513Xq6NC99T+EmiUJnpD0rttZOmDSr8pCKynKL92NmiGjRoEHZ2dsybN09dlpCQgJ+fH9988w2vvPJKnvUePHhAhw4d+Pzzzxk8+FEP/NGjRxk2bBhr1qyhfv36OsdVkCL3EE6fPr1YF548eXKxgxFCCCGE0FkZeeBD27ZtmT9/vsZcwq1bt2JgYECrVq3yrVe5cmV8fHw4fPiwRkJ4+PBhzMzMqFGjRonFXOSEcPfuoj/0XqFQSEIohBBCiAppwIABLF26lDFjxhAYGEh4eDgzZ85kwIABGnsQDhs2jJCQEHbs2KEuGzduHO+88w5ff/01L774IhcuXOCPP/5g5MiRWFjk/XhCfSiRhFAIIYQQ4pkrIxtT29rasmTJEr766ivGjBmDpaUl/fr1Y9w4za3vVCqV+lGDD3Xo0IE5c+bwv//9j5UrV+Li4sJ7773HW2+9VaIxl5/daYUQQghRsZWhVcbe3t4sXry4wHPye5BH9+7d6d69ewlElT+dnlSydOlSvv/++zyPff/99yxfrvtETCGEEEII8WzplBCuWLGCqlWr5nnM09OTFStWPFVQQgghhBDFpsrW308Fo9OQcUhICNWqVcvzmIeHBw8elN6ScyGEEEJUTE/7DOKKTKceQisrK+7fv5/nsXv37pXYLtpCCCGEEEL/dEoIW7Vqxbx58wgN1dw1PiwsjP/973+0bSubSgshhBDiGZMhY53pNGQ8YcIE+vfvT9euXWnRogUuLi5ERERw9OhRHBwcSvThy0IIIYQQeaqAiZy+6NRD6Orqyr///ssbb7xBXFwcx48fJy4ujuHDh/PPP/9obLoohBBCCCHKtmL1EN68eZNVq1Zx//59XFxc6NKli9Ymi0IIIYQQpaIM7UP4vClyQnjy5EmGDx9OZmYmDg4OxMXFsWbNGqZMmcLrr79ekjEKIYQQQhROhox1VuQh47lz5+Ll5cXu3bs5dOgQx44do1OnTvz4448lGJ4QQgghhChpRU4Ir1+/zpgxY6hUqRKQs/XMJ598Qnx8vNZqYyGEEEKIZy1bla23n4qmyAlhbGwsbm5uGmUPk8PY2Fj9RiWEEEIIUVyy7YzOdFplLIQQQgghyo9irTIeNmwYCoVCq3zQoEEa5QqFglOnTj19dEIIIYQQRSWPrtNZkRPCd999tyTjEEIIIYR4OhVwqFdfJCEUQgghhKjgdHp0nRBCCCFEmSM9hDqThFAIIYQQ5UJ2tiSEupJVxkIIIYQQFZz0EAohhBCifJAhY51JQiiEEEKI8kESQp3JkLEQQgghRAVXZnsIA4w9SjuEcqN27X6lHUK5cfXq2tIOoVzo0/i90g6h3HA0MCvtEMqNtQ7tSjsE8ZQq4jOI9aXMJoRCCCGEEMUiCaHOZMhYCCGEEKKCkx5CIYQQQpQP8ihjnUlCKIQQQohyQeYQ6k6GjIUQQgghKjjpIRRCCCFE+SA9hDqThFAIIYQQ5YPMIdSZDBkLIYQQQlRw0kMohBBCiHJBFpXoThJCIYQQQpQPMmSsMxkyFkIIIYSo4KSHUAghhBDlggwZ604SQiGEEEKUDzJkrDMZMhZCCCGEqOAkIRRCCCFEuZCt0t/P0woKCmL48OE0bNiQVq1aMXPmTNLT04t1jcWLF1OrVi0CAwOfPqBCyJCxEEIIIcqHMjJkHB8fz7Bhw/D09GTu3LmEh4czY8YMUlNTmTJlSpGuERkZybx583B0dCzhaHNIQiiEEEIIoUerVq0iKSmJX375BTs7OwCysrKYOnUqgYGBuLq6FnqNWbNm0aFDB0JCQko42hwyZCyEEEKIcqGsDBnv378ff39/dTII0K1bN1QqFYcOHSq0/smTJ9m5cycTJkx4ukCKQRJCIYQQQpQPKj3+PIXg4GC8vLw0ymxsbHB2diY4OLjAullZWXz11VeMHj0aFxeXpwukGHQaMs7IyGDt2rVcuHCBsLAwpkyZgqenJ5s3b6ZWrVp4e3vrO04hhBBCiGemY8eOBR7ftWtXvseUSiU2NjZa5ba2tsTHxxd43RUrVpCSksIbb7xRpDj1pdgJ4b1793jjjTeIjY2lTp06nDp1iqSkJABOnDjBgQMH+Pbbb/UeqBBCCCFEQfSxOrg0RUdH8/PPP/Pdd99hYmLyTF+72Anh9OnTcXBwYM2aNdjY2FCvXj31MT8/P+bMmaPXAIUQQgghikKfCWFBPYCFsbGxISEhQas8Pj4eW1vbfOv99NNP1KpVi6ZNm6JUKgHIzMwkMzMTpVKJhYUFRkYlsx642Fc9fvw4s2fPxsHBgaysLI1jzs7OREZG6i04IYQQQojnjZeXl9ZcwYSEBCIjI7XmFj7u1q1bnDhxAj8/P61jfn5+LFy4kLZt2+o9XtAhITQ0NCQ7O+9nBUZFRWFhYfHUQZU0MxsLOn/2OrW7NMXY3IQH54LZPn05oRdvF6m+Uw13un4+mKp+tcjKyOT67rNs+2oZyTGPvg3YVXHig0M/5Vl/7btzubjhKAAKhYIGfdvg27UpbnU9MbezJO5eJBc3HOXwr5vITMt46vt91qxtrPj0yw/o3L095uZmnD9zkW+m/MCl81eLdR0jIyM27ltFzVpefPvFD/w2b6nG8XfGjaRBk3o0bFwPJxdHfpq5gJ9nLtDnrTwXkpNT+GPFWi5cvsaFy9dQJiQyfeJ4Xg7oXNqhlRmWNpYMnzgC/y7+mJqbcv3sdX6f/htBF4MKrKdQKOjQtyMtu7XEq64X1nbWhN8LY//6/az7dR0ZBbw/6/jVYebfswAY2OB1lLFKvd5TabGwsaD/p0Np3KUZpuamBJ+7ycrpi7lz6Vahdb0a1KB1v/Z4N6xJldrVMDI2YphnX63zWvdrz6jv3833OvPH/siR/w481X2UBgMTI2p/3A+Pfm0wsbUk/spdrsxYTeT+iwXWs/KuhOfQjtg3roHdC54Ympmw3e99ku9FaZxnbG9FtddfxK1zI6x9KmNgZEjCzRCCft3Cg/+OluStlRllZci4bdu2zJ8/X2Mu4datWzEwMKBVq1b51ps4caK6Z/Chb775BjMzM8aPH0+tWrVKLOZiJ4R+fn4sWrSItm3bYmCQs0hZoVCQnZ3N6tWr8ff313uQ+qRQKBi46CPcfKtyaMEmkmMT8BvSiTdWTWZBj0nE3A4vsL6NmwPDV39OWkIyu2auxsTSlJZvBeBay4OFvT8nK0Oz1/TCf4e5seesRtm90zfV/21sbsLLswO5d/oGJ5fvIilKiUeTGrw4ri/VW9VlyYCv9Xbvz4JCoeD3lT9Tu64PC+f9SWx0HINGvMry/37l5Y6DuB18r8jXGjqqP+6V3fI9PmHSGCLCI7l84RptO7bUR/jPpdh4JfMXraCSqwu1anhx4sz50g6pTFEoFHyx+Euq+1Zn3YK/UcYo6T40gG//msEHAWMJuZ3/Hl+m5qaMmzOOq6eusGXZFuKj46jduDYDxw+iQauGTBzwWb6vGTh1NClJKZhbmpfUrT1zCoWC8X9MwsO3Glt+/Y+EmAQ6DunKZ6um8UXPjwm/HVpg/frtG9Ouf0fuXb1D5N1wKnlXzvO8a8cus+AD7S/UXUb2wMPXk8uHL+jlfp61xj+Nxr1HM4IWbiUxOIyq/dviv/xjDvb9mpjj1/Kt59C0Jt5vdkV5/T4JN0Kwe8Ez3/PqfPoa4bvOcu2Hf8nOysI9oBl+C97H2qcyV2f9XUJ3VoZkK0o7AgAGDBjA0qVLGTNmDIGBgYSHhzNz5kwGDBigsQfhsGHDCAkJYceOHQD4+vpqXcvGxgYLCwuaN29eojEXOyH88MMPef311wkICKBDhw4oFAqWL1/OjRs3uHPnDmvWrCmJOPWmTvdmVG3qw+q3f+Ly5uMAXNp4lPf2zqb9+H78/f68Auu3ebcXJham/NpjMvEh0QA8OBvE0BUTadivLadW7tE4P/TiLc7/k/+eQ1kZmfz+ypfcO3VDXXZ61R7i7kXRfkI/vFrVJfjQJR3v9tnr1qsTTZo3ZMzwj9i6IWf+xeb/trPz2L+M/WQ04wInFek6jk72vPfhW/w6dzHjPnsnz3PaNgrgwb1Q7B3sOHl9t97u4Xnj7GjP3vXLcXJ04OKV6wx4c2xph1SmtApoRZ2mdfh29Dcc2pzzXjyw8QC/7lvIwPGD+P79WfnWzczI5MM+H3L11BV12baV2wi/H8HgCYNp0Loh5w6e1arXdVBXnNyd2L5qG71HvqzvWyo1ft39qdm0NnPfnsXJLTk9Tsc3Hea7PXPpM64/88f+WGD93cu2sen//iUjLZ0hU9/MNyGMvBdO5D3NL+fGpiYM/WoUVw5fID4yTh+380zZNfKmSp+WXJy6nJv/twmAe2sO0GHvd9T9/HUO9Pwy37qh204R4vMmmUmp1Hg7IN+EMOHafXa0HE/K/Uc9h7cW7aDVmonUHNOTG/M2kpWcps/bEvmwtbVlyZIlfPXVV4wZMwZLS0v69evHuHHjNM5TqVRa0+9KS7H3IfT29ubvv/+mUaNGbNy4EUNDQ/bu3UvVqlVZs2YNVatWLYk49aZO92YkRsRxZcsJdVlyTAKXNh6lVufGGJoUnCP7dm3G9V1n1MkgQPChS0QFhVC3R4s86xibm2JobJjnsayMLI1k8KEr204C4FQz7z+YZVXXnh2JDI9i28ZHCVpMdByb/9tBp64vYmJiXKTrfPT5+wTfvM2/azbne86DewX3RlQUJiYmODk6lHYYZVar7q2JjYjl8JbD6jJljJIDGw/Q4qUWGBXwns/MyNRIBh86sjXnWh41PLSOWdlaMfjDISyfvZwkZZIe7qDs8OvmT1xkLKe2HlOXJcQoOb7pMI07+xXYlgDKqHgy0or3LNeHGnVqirm1BYefw6FigMo9mqHKzOL20kd/G1VpGdxZsRdHPx/M3fN/D2fEJZGZlFroayTfjdRIBh8K3XoSQzMTLKs+uz3tSktZ2ZgacvKlxYsXc+7cOQ4fPswnn3yitXJ46dKl7N5dcIfG0qVLWbCg5KdD6bQxtYeHB9999x0HDx7k4sWLHDp0iO+//77MJ4MAbnU9Cb10W2se5INzQZhYmOFYvVK+da1d7bFytiXkvPZcmQfngnGrW02rvN3YV5h09Q8mX1/MqPXT8G7zQpHitHLOWYX0+LzE50Hd+rW5dP6qVvueO30RC0tzPL212+hJ9RvV5ZUBPZg+aXa+81WFKCrvul4EXbyp9bt0/ex1zCzMqFy9+F+67F3sgZzE8kmDPxxCXGQsW5dv0S3gMqxq3ercuXhLqy2Dz97E1MIMt+ruJfba/i+3IS0ljVNbn8+5cLb1PEkMDiUzMUWjPPZMzjxW27qeJfbaps52AKQ9Z58nushWKfT2U9FUuCeVWLvYkRARp1WemFtm7WpXYF0g3/oW9tbqHsZsVTY3951nxzcrWTHie7ZOW4aloy2DlnxMzQ4NC42z1egepCqTubn3XKHnliXOLk5EhGt/Q43MLXN1cy70Gl/M+JhN/27nzEmZCyeenr2LAzERsVrlsRExADi6Fv/B8X1H9yNJmcSpvSc1yj1re9JtUDd+m/YbKlUZmd2uR3YudsTl0ZYPy+xcS6an2tLWihfaNuLsrpOkFqGnrCwyc7UjLTxOqzwt9/PEzM2uRF7X2M6SaoPaE3Xkivq1hMhLsecQDh06NN9jBgYGWFtb4+vrS9++fYv08OZnzcjMhKx07ZWBD1fzGpvlvxGkUe6xvOunq+tnpWcSHxLNsqHfaZxzft1BxuycSZdJg7ix+2y+r9NmTC+827zAxkl/kKpMLvSeyhIzc1PS07WHhNJy28fMzLTA+n1f70Ut3xq8O/zjEolPVDwmZiZk5PGeTc/9nTQp4D2fl1fHvEajNo2YN3Ge1pBw4NRATu09yZkDZ3QPuAwzMTMhM4+2zNCxLYvKr7s/xqbGHPl3f4lc/1kwzP1seFJWaob6uN4pFDT93xiMbSw4P2mJ/q9fBpWVVcbPo2L3EFpbW3P37l1OnTpFYmIipqamJCYmcurUKW7fvk18fDyLFi2ie/fuXLpUeoshDI0NsXK21fhRGCjITE3HMI95bEamOWUZqfnPb8nMPZZ3fZNC66fEJ3FmzX6carhj45b3N+m6PVrQ4cNXOb1qDyeX6b4pZkkzNjbCycVR48fAwIDUlLQ8d1c3zW2f1NT8JzRbWVny0eR3WThvKaEhBa/2FuJJRsZG2Dnba/wYGBiQnpqOcR7vWZPc38n0At6zT2rTsw1DPhrCtpXb2LJss9ax2k18+e2r357uRsoAQ2MjbJ3tNH4UuW1plEdbGuvQlsXh/3IbEmMTOL/3+U20s1LT85yjbmhmrD6ub/W/GYZrh4acnbAQ5eW7er9+WZSdrdDbT0VT7B7Crl27cvfuXZYvX07lyo/m3ty/f5+3336bPn36MHfuXIYPH86cOXP4/fff9RpwUXk08eGNvyZrlP3YaiwJEXHqod/HWT0cDs6jS/+hh0PF+dVPjk3I8xvg45ShOYtRzO0sUYbFaBzzal2PPnNGc333WTZO/KPA65S2xs0asOK/hRplbRsFEBkRhYurk9b5zrll4WH5b1z+5rtDMDYxZtM/26jskTOX0809p5fZ1taGyh6ViAiLJCOj4DYWFZNvE1++XT1Do2xEy+HERsTgkDvn73H2LjlfyqLDo7WO5aVhm4aMnzOBk7tPMG/iL1rHh08cwaFNB8nMyMSlSs7kfUsbSwCc3J0wMjEiJjxGq15ZVLNJLT5bNU2jbELr0cRFxGGXR1s+LIsrgftzcHfCx8+XvSt3kJVZNlZj6iI1PA6zStptZ5r7eZIaFqfX16s14RW8hr/Epekrubf2oF6vLcqnYieEv/zyCxMmTNBIBgGqVKnCmDFjmD17Nn369GHEiBF88cUXegu0uMIu3+HPgd9olCVGxhN2+Q7V/Gqp9058qErDGqQnpxJ9K/+VqwnhsSRFxeNev7rWscoNvAi7fKfQuOxzV3klPTG5t3JDbwb8Oo6QC7dY887PqLLKdr/3lYvXGfLKaI2yyIhoLl+4hl+LRlrt27DJCyQnpXA7KP82cq/shp29LdsOa++V9c74kbwzfiQ9XhzAlYvX9XcjotwIvnKLSQM1tzWKjYwl+PIt6vrV1fqdrNWoFqnJqTy49aDQa/s0rMWkXydz48INZrw9I8/3p0tlF1z6uPBin/Zax37eMpfgS8G83+09He7s2bt7+TbfDZqqURYfGcfdy7fwaear1ZbeDWuSlpxK2K3893TUVYterTEwMODIv8/n6uKH4i/dwalVHYyszDUWljg0rpF7/LbeXqv68M74ftSPmws2c+OXDXq77vNAhox1V+yEMDQ0FIUi765UhUJBeHjOUJ+Li0up7q2TqkzOc/++y5uPUzegOb7d/NT7EFrYW1EnoDnXd57R6OF7mLzF3o14VH/LCRr2a4NNJQeUoTnfhqu3qouTtztHf9+qPs/CwVprhbC1qz2NXmtH2OU76kUskPPkk0GLPiLufiQrhs96Lp5OooxP4PD+41rlWzfsonvvznTp0UG9D6G9gx3denVi9/b9pD82/6iqZxUA7t6+D8CShavYsXmvxvUcnR34es5k1q5Yz84te7l3R/8fOKJ8SIpPzHNPwEObD9I6oDUtu7VU70NoY29D64DWHN95jMzH3vNu1XI2Qg+7E6Yuq1LDgy8Wf0HE/XCmDp+qnnv4pOlvfqVV1rZXW9r2asfsD74nKrRoPZFlQbIyicuHtBd1ndhyhGYBLWnStbl6H0Ire2v8Avw5s+ukRlu6VM3p3Y+4+3TTP/x7tyHqfiTXT2hv//M8CdlwjJrv9MBzSAf1PoQGJkZUHdCOmFM3SAnJ+Twxr+yIobkpiTd1+1tXuXcL6k8fxr21B7n4xTK9xf+8qIirg/Wl2AnhCy+8wM8//0y9evWoVOnRFi0PHjxg7ty51K9fX/3vsrio5PLmY9w73ZXes97CuUZl9ZNKDAwM2PODZs/UsBUTAfix9QfqsgPz/qNuQHPeWDWJo4u2YWJhRqvAAMKv3OXMmn3q8zp/9joO1VwJPnSJhPBY7DycaDqwI8bmpmyd+ugRbCaWZgz58xPMbC05tGAjPh0bacQQcyec+4892aSs27J+J6dPnOe7uV9So5YXsdFxDB7xKgaGBvz43XyNc5euy/l3u8Y9ALh0/qrW4+0eDh3fuBbEji17NY69/GoAlT3cMDPPeRJEM//GjBk/EoB/Vm8m5H7F2adwxdr1JCQmERGVk3TsPXSM8Micld0D+/XC2sqyNMMrVYc2HeLqiCuM/f4DPGpWRRmjJGBodwwMDFg+Z7nGuV+vyBlVGNlqBADmluZMWzoNK1sr1i1Yh18HzeeLht0J5erpnN/Zo9u1t0PxqpvzzNJTe06Vi0fXndh8lJunr/HmrHepXNODhBglHYd0xcDAgH9++Evj3I9XfAnAh63fVpc5VnamVZ+c57B61vcGoNe7OY+ui3oQxeF/9mlco7KPB1V9Pdn4v3UldUvPTOyZIB6sP0qdif0xdbIh8VY4VV9rg4WHE2fG/6o+r8nct3FqWYd/3Qaqy4yszfEa2QUAx2Y+AFQf8RIZ8clkKJO59cd2IGfz68Y/v016bAKRBy9Rpa/mI9JiTtwg+bEODiEeV+yEcOrUqQwfPpzOnTvj4+ODvb09sbGxXLt2DUdHR376KedxQ1FRUbz22mt6D/hpZauyWT5sJi9NGkjz4V0wMjMm5Fww/05YQHRw4QmEMjSGRa99RZfPB9Ppk/5kZWRxY/cZtk1frtG7GHTgAvZVXWg2tBNmtpakKpO5c/wq++f+q/HMZAt7K2wr58yv6/zZ61qvd3bN/ucqIVSpVIwc8B6fTv2AYaMGYGZmxvmzl/j4vS+4dbPwIfXieHVwb1q0aqr+t38bP/zb5Hxgnzx2tkIlhItX/k1I2KM/9Dv3HWLnvpzesB5dOlTohFClUvHFG18yYuIIeg7viamZKTfOXeeHCT/wILjg4WJre2tcKueMFAz/bLjW8Z1rdqoTwoogW6Vi9htfM2DiUDq/0R0TMxOCz99k4Ye/EBZceI+Ws4cLfT8cqFH28N9Xjl7USghbvpyTPD6Pzy3Oy6n3/g/fT17Fo19rjG0tUV65x9Eh3xN9tODfIRM7S+p8qvl5WvPtnC/Syfci1QmhjU9lDE2NMTS1pfGPgVrXOT12PnfLeUIoW9fqTpGtw86/aWlprF27losXLxIZGYmzszMvvPAC/fr1Izw8HA8P7d37i+vLaoOe+hoix9Kk53uopSy5enVtaYdQLvRp/HzMpXseOBqYlXYI5Uaf1IK3xRJF83LYilJ77TuNO+ntWtVO79TbtZ4Hxe4hBDA1NWXQoEcJW0xMDFu2bGHo0KGcO3eOK1ckARFCCCGEeF7olBACpKSksGPHDjZu3Mjhw4fJysrC19eXzz77TJ/xCSGEEEIUiSwq0V2xEsKsrCwOHDjAhg0b2L17N6mpqTg5OZGVlcXs2bPp3r17ScUphBBCCFEgmUOouyIlhKdOnWLjxo1s3bqV2NhY7Ozs6NWrFz179qRmzZo0b94cZ+fCn1ErhBBCCCHKniIlhIMGDUKhUNC8eXOGDx9Oq1atMDLKqZqQkFBIbSGEEEKIkidDxrorUkLo4+PD9evXOXHiBIaGhsTGxtKpUyesrKxKOj4hhBBCiCKpiM8g1pciJYTr16/n5s2brF+/nk2bNvHpp59iZmZGu3btaN++fb5PLhFCCCGEEGVfkReV1KhRg/HjxzN+/Hj1nMJt27axbds2FAoFf/75JwB+fn6FXEkIIYQQQv/kWca602nbmSZNmtCkSRMmT57MwYMH2bhxI7t27WLnzp24u7uza9cufccphBBCCFEglQwZ60znfQgBDA0NadeuHe3atSM1NZWdO3eyceNGfcUmhBBCCCGegadKCB9nZmZGjx496NGjh74uKYQQQghRZLKoRHd6SwiFEEIIIUqTbDujO4PSDkAIIYQQQpQu6SEUQgghRLkgj67TnSSEQgghhCgXZMhYdzJkLIQQQghRwUkPoRBCCCHKBdmHUHeSEAohhBCiXJBtZ3QnQ8ZCCCGEEBWc9BAKIYQQolyQVca6k4RQCCGEEOWCzCHUnQwZCyGEEEJUcNJDKIQQQohyQRaV6E4SQiGEEEKUCzKHUHcyZCyEEEIIUcFJD6EQQgghygVZVKK7MpsQGpd2AOXINJM6pR1CudGn8XulHUK58M/puaUdQrmhHDa8tEMoN06fdCvtEMRTkjmEuiuzCaEQQgghxPMqKCiI6dOnc+bMGSwtLenduzcffPABJiYm+daJiIhg8eLFHDp0iLt372JtbY2fnx/jx4+ncuXKJRqvJIRCCCGEKBfKypBxfHw8w4YNw9PTk7lz5xIeHs6MGTNITU1lypQp+da7dOkSO3bsoG/fvjRo0IDY2Fj+7//+j1dffZWNGzfi4OBQYjFLQiiEEEKIcqGsLDJetWoVSUlJ/PLLL9jZ2QGQlZXF1KlTCQwMxNXVNc96TZo0YcuWLRgZPUrPGjduzIsvvsi///7LiBEjSixmWWUshBBCCKFH+/fvx9/fX50MAnTr1g2VSsWhQ4fyrWdjY6ORDAK4ubnh4OBARERESYULSEIohBBCiHJCla3Q28/TCA4OxsvLS6PMxsYGZ2dngoODi3WtW7duER0djbe391PFVBgZMhZCCCFEuaDPVcYdO3Ys8PiuXbvyPaZUKrGxsdEqt7W1JT4+vsgxZGdnM336dFxcXAgICChyPV1IQiiEEEIIUQbNnTuXo0eP8ttvv2FhYVGiryUJoRBCCCHKBZUer1VQD2BhbGxsSEhI0CqPj4/H1ta2SNdYvXo18+bN4+uvv8bf31/nWIpKEkIhhBBClAvZlI1tZ7y8vLTmCiYkJBAZGak1tzAvO3bs4Msvv+T999+nX79+JRWmBllUIoQQQgihR23btuXw4cMolUp12datWzEwMKBVq1YF1j127Bjjx4/n1VdfZcyYMSUdqpr0EAohhBCiXFCVkY0IBwwYwNKlSxkzZgyBgYGEh4czc+ZMBgwYoLEH4bBhwwgJCWHHjh1AztNNxowZg6enJ7179+bs2bPqcx0cHKhatWqJxSwJoRBCCCHKBVUZGTK2tbVlyZIlfPXVV4wZMwZLS0v69evHuHHjNM5TqVRkZWWp/33u3DkSEhJISEjg9ddf1zi3T58+zJgxo8RifqqEMCEhgWvXrhEZGYmzszO1atXC2tpaX7EJIYQQQjyXvL29Wbx4cYHnLF26VOPfr7zyCq+88koJRpU/nRJClUrFjz/+yNKlS0lJSVGXm5ubM3jwYD744AMMDQ31FqQQQgghRGHKyqKS55FOCeHMmTNZtmwZb731Fl26dMHJyYmoqCi2bt3KwoULycjI4NNPP9V3rEIIIYQQ+dLntjMVjU4J4T///MP777/PW2+9pS5zdHSkVq1amJmZ8ccff0hCKIQQQgjxnNApIczKyqJu3bp5Hqtbt67GBEkhhBBCiGdBhox1p9M+hF26dGHTpk15Htu0aROdO3d+qqCEEEIIIYpLpcefikanHkI/Pz9++OEHhgwZQqdOnXB0dCQ6OpqdO3dy9+5dxo0bx/bt29Xnv/TSS3oLWAghhBBC6JdOCeHD+YHh4eGcOHEi3+MACoWCK1eu6BieEEIIIUTRVMSePX3RKSF8mgc+CyGEEEKUBJlDqDudEsLKlSvrOw4hhBBCCFFKnvrRdampqaxdu5agoCCcnZ3p06cPlSpV0kdsQgghhBBFppIOQp0VOSH84Ycf2L17Nxs2bFCXpaSk0K9fP4KDg8nOznmi9JIlS1i7di0eHh76j1YIIYQQIh9l5VnGz6MiJ4SHDh2iffv2GmVLliwhKCiId955h5EjR3Lr1i3ef/995s+fz9dff633YPXF1MaCjp+9jk+XphibmxByLphd05cTdvF2keo71nCn8+eD8fCrRVZGJjd3n2XnV8tIjklQn2PlYkeHia/jXt8LK1d7srNUxNwK4+SfO7jw94ECr//6sk/xavMCJ5dsZ9uUJU9zqyXCwMSI+h/1pXrf1pjYWhJ35S7nZq4lbP/FQuuau9nT5MvBVGpXD4WBAWGHL3P6i2Uk3o3UOM/Y2px6Y3tTpWtTLCo5kBatJPTARS7M+YfkB9Hq8zy6NaVa7xY4NPDC3MWW5JAYHuw4w4Uf/yVDmaz3e3+WLG0sGT5xBP5d/DE1N+X62ev8Pv03gi4GFVhPoVDQoW9HWnZriVddL6ztrAm/F8b+9ftZ9+s6MtIy8q1bx68OM/+eBcDABq+jjFXq9Z7KuuTkFP5YsZYLl69x4fI1lAmJTJ84npcDKvhWWkbGWAwZgWn7l1BYWZN5O4iUP38n4+zJYl3GevpsTBo1JXXDOpLm/6RxzLR7b4wbNMbIxxdDF1dSd24h6YcZ+ryLMkFhYoT3x6/h/mobjGytSLxyh5vf/kXM/gsF1rPwrkSVYZ2xbVwD6xeqY2hmwoGm75J6LzLP8w0tzfAa3xfXXi0wdbUnPSaB+JPXufjePFQp6SVxa+I5V+R9CO/du8cLL7ygUbZ9+3bc3d15//33sbS0pF69erz55pt5rjwuMxQK+i/6iLq9W3JqyQ52f7sKS0cbBq+ajL2na6HVrd0cGLL6c+w9XdkzczVHf91EjQ4NGbjsMwyMHz2/2cLBGhs3B65uOc6ur1ewb/YaEiPi6DVnNC9+9Fq+16/VtSlVGtfUy62WFP8fA/F9qxu3/znMqSlLyVapaL/0Q5yb+RRYz8jClE5rJuLiX5tLc9dz/vu/cahXjU7rJmNib/XoRIWCDqs+peawTtzfepKTn//J7X+PUK1Hc7r89wVGlmbqU5vNGolNDXdurzvEyc+XErLnPD7DO9NlwxcYmhmXVBOUOIVCwReLv6Rd73ZsXLKBRd/8ga2TLd/+NQN3T/cC65qamzJuzjhsHWzYsmwLC6f+yvWz1xk4fhBTl0wr8DUDp44mJSkl33PKu9h4JfMXrSD49j1q1fAq7XDKDKvxn2H28muk7d1B0q9zQaXCeup3GNV5ofDKuUxatsG4dp18j5v3ex3j+o3Iunub7MxMfYRdJtX7+R2qjQ4g9O+DXPt8MdlZKhqt+BS7ZrUKrGfb1Ieqb3bD0MqcpBsPCjzXyNocv/VTcX/9RcL+OcSVT37j3m9bMDAzxsDk+f27WBTZevypaIrcQ5iWloaNjY3638nJyVy7do2XX35Z47yaNWsSHh6utwD1zbd7Mzya+vD32z9xdfNxAK5sPMrovbNpO74f/70/r8D6rd7thYmFKX/0mIwyJKenKuRsEINWTKRBv7acWbkHgIir91g2QLOX9OSSHbz2+wT8hndh3+w1ZKs0f+UMTY3pNHkQR+ZvoN2EV/V1y3rl2NALz5f9OT1tBVfmbwYgeO1BeuyeQaPJA9jeK/+Ew+eNTth4V2JLtynEnAsGIGTPOQJ2z8A3sDvnZqwGwKlJDZwaeXNi4mKuL96prq8MCsX/h7dwa1OP+1tzeiYOjPqZiCOa2xrFnL9Fy59H4/lKK4JW7NXn7T8zrQJaUadpHb4d/Q2HNh8C4MDGA/y6byEDxw/i+/dn5Vs3MyOTD/t8yNVTj9pl28pthN+PYPCEwTRo3ZBzB89q1es6qCtO7k5sX7WN3iNf1vctPRecHe3Zu345To4OXLxynQFvji3tkEqdkU9tTNt1JOn3/5G67i8A0nZtw+5/i7AYMRrlh2MKv4ixCRYjx5CydiUWQ0bmeYryk7GoInM+OxzWbtFb/GWJTSNv3Pq04vqXS7nzfxsBCF29H/9931NzyiBO9JiSb93IbSfZU3M4WUmpVHu7BzYvVM/33BqTBmJWxYmjnT8l9fHRl1/W6+1eyirZdkZ3Re4hrFy5ssZ+gsePHycrK4vmzZtrnJecnIylpaX+ItSz2t2bkRgRx9Utj3oxk2MSuLLxKD6dG2NoUnCOXKtrM27sOqNOBgFuH7pEdFAIvj1aFPr6cfcjMTY3wdBY+3X8A3ugUBhwdEHeT4EpC6r2aIYqM4sby/aoy1RpGQSt3ItzUx8s3B3yresR0IyoM0HqZBBAeTOUsIOXqNbz0e+RsZU5ACmR8Rr1U8LjAMhKfTTc8WQyCHBvS06yaFuj4J60sqxV99bERsRyeMthdZkyRsmBjQdo8VILjAr4Pc3MyNRIBh86sjXnWh41tOf3WtlaMfjDISyfvZwkZZIe7uD5ZGJigpNj/r/DFZFJqxfJzsokbcuj+eNkpJO6fTPGvvUwcHIu9Brm/V4HAwUp61ble87DZLA8c+3RAlVmFveXPtq6TZWWwYMVe7Dzq4Wpu2O+dTPjkshKSi30NYxsLHAf8CL3l+4i9W4kCmNDFIV8rgkBxUgIu3Xrxvz581m/fj1Hjhxhzpw5WFlZac0rPHXqFNWqVdN7oPriVteTsEu3IVuzdy7kXBAmFmY4VM9/hbS1qz1WzraEnr+ldSzkXDCudbXv28jUGHN7K2yrOPFC3zY0eLUd90/fJPOJeVw27o60fKcnu2es1DpWltjXq0ZCcBiZiZrDilFnc5I8+zzaAACFAntfD2LOabdd9JlgrKu7qoeCY84Hk5GUSoOP++Haqg7mbva4tKhNo8kDiDoTRNiBgucqmrvYApD22JzO5413XS+CLt5UL9Z66PrZ65hZmFG5evG3frJ3sQdyEssnDf5wCHGRsWxdXj57ZoTuDL1rkvXgPtkpmnNyM6/nfOkw9Cp4iouBswvm/QaSvGgBpFfsuWvWL3iSHBRK1hN/P5VnbuYcr/f0n512zWtjaG5Cyu0w6v82jg63l9LxzlL8NkzDKr+/z+WISqHQ209FU+SvDW+++Sbnzp3j448/BsDCwoKvv/4aa2tr9TlpaWmsW7eOAQMG6D9SPbFysePu8ata5YkRcQBYu9oRee1evnUfP/fJ+hb21hiaGJGV/mj+i9+IrnT49FF73Dp4kQ0fLtCq32nyIMIu3eHyhqPFuJtnz9zFTt1T97iU8Nic4672edYztbfE0MyElDza7mGZuZs9CUGhpMUkcnD0LzSfNZJOayaqzwvZc54Do34iO6vgQYE6Y3qiyszi7qbjRbupMsjexYGLxy5plcdGxADg6OrInWt3inXNvqP7kaRM4tRezYUAnrU96TaoG18O+wKVSgZchCYDeweyY6K1yh+WGTjk36sFYPHmGDKDb5C+f3eJxPc8MXW1Jy0iVqs8Lffvp6nr0/dOW3i5AVBj4uuk3Ann0nvzMLK2wOvDfjT9ewqH204gPY+/w+VFRZz7py9FTgjNzMxYuHAhd+/eJT4+nurVq2NlZaVxTmZmJvPnzy/TPYRGZiZkpWv3wD3slTMyMymwLpBP/fTHrv8oIby0/gih54OxcLShZsdGWDrZYvzEa1Tzr0Ptbn4s6v1F8W/oGTPMp/1Uue2X30IOw9x7VuVRN0vddo/qpkUrib14h+uLdhB//T72datR550AWvzwFgcD5+Ybn2cff2oMfJFL8zaQcOv5HYIyMTMhI4+2Ss9tK5MCfk/z8uqY12jUphHzJs7TGhIOnBrIqb0nOXPgjO4Bi3JLYWqKKkP7dzE7I119PD9G9Rth0rIt8ePfLrH4nicGZsZkp2kvmFH//TQv3vs6L4YWuYvusuFU36/ISk4DIOHibZptno7HiC4EzfjrqV9HlD/FnlhQtWrVfI89XGlcFhgYG2Jup5mwJkcryUxNxzCPVVZGpjllman5D2k8PJZ3fZM86ysfRKF8EAXA5fVH6P7tSAYu/4z57T8kMy0DhaEBL305lAvrDhJ6PljrumVNVj7tZ5DbflmpeQ93P5z3l9cKN0N12+XUtarqTKe1Ezn8/gLubc6Z63l/22kS70XR8qdAglftI2TPea3rODerRfPvRxGy5zznZqzR4e6ePSNjI6zsrDXKlNHxpKemY5xHW5nktlV6Ab+nT2rTsw1DPhrCtpXb2LJss9ax2k18GdP5HR2iFxVBdloaCmPt30WFsYn6eJ4MDLEMfJ+03dvJuqE9KlMRqVIzUJhqf+yq/37qYTsYVe7fhsgdp9TJIED8qRsk3wnHzq/g3SCedzLGoTudZ5rGxMSwZMkSzp07R2RkJM7OzjRo0IBhw4bh4FD6k7KrNPFhyF+TNcp+aTWWxIg49dDv4x6WJeQxHPrQw6Hi/OonxyZo9A7m5crm4zQa2IGqzWsTvP8C9fu2wdGrEpsn/o5tFSeNc00szbCt4kRSlLLARPVZSomIw8JN+3/fh0PFD4eOn5QWm0RWajrmebTdw7KUsJy6Xv3bYmBqwoOdmj1WD7afAsDZz0crIbSrU5V2i8cTf+1+kYaVywrfJr58u1pzr7URLYcTGxGDg4v28Lu9S07bR4drD+HlpWGbhoyfM4GTu08wb+IvWseHTxzBoU0HyczIxKWKC5Cz/yGAk7sTRiZGxITHFOueRPmiio3BwNFJq1yRO1SsymM4GcC0YxcMK3uQ9Mv3GLi4aR40t8DAxQ1VfCzkl1CWQ2nhsZjm8ffTNPfvZ5oe3msPh5/Tn1iUB5AepcTItuwu+tQHeVKJ7nRKCM+dO8ebb76JSqWiZcuWeHp6Eh0dzbJly1i2bBl//PEHDRo00HesxRJx+Q7LB36jUZYYGU/45Tt4+NUChUJjYUnlhjVIT04l5lZovtdMCI8lKSqeSvW1l/u7N/Ai/HLhc7oeDhebWlsAOYtJDE2MeGPdl1rn1u/Xlvr92rJm1Byu5yZDpS320l1cW9bByMpcY2GJUyPv3OP5tEF2NnFX7+PQQLvtnBp7k3A7nMzcFXRmTrYoFKAw1FzzpDAyyv3/hhrlVtVc6LD8Y9KilewZPIvM5OfnAyb4yi0mDZykURYbGUvw5VvU9auLQqHQWFhSq1EtUpNTeXCr4H3IAHwa1mLSr5O5ceEGM96egSqPJNmlsgsufVx4sU97rWM/b5lL8KVg3u/2ng53JsqLrOAbGNdviMLcQmNhiXGtOurjeTFwdkFhbIzt9//TOmbWqStmnbqi/GoSGUcPlkzgZVDCxdvYt6qLoZW5xsISm8Y1co8Xb15wXpS5uzjkl3gm3yz8b4eomHRKCKdOnUqNGjVYuHChxjzChIQERo0axbRp0/j777/1FqQuUpXJ3D6kPSn/yubj+AY0p3Y3P/U+hOb2VtQOaM6NnWc0evjsqub0mMTdjVCXXd1ygvr92mBdyYGE0Jxvc56t6uLo7c7x37eqz7NwsNZ4cslDDfq3I1ulUj8V5fKGI3kmkq8uHM/N3Wc4s3IPIWcLfjLFs3R343HqvB1AzcHt1fsQGpgY4dW/LVGnbpIcktMmFpUdMTI3QXnzUYJ9d9NxGk0agEP96sTkrtS29q6Ea6s66msBKINDURgYUK1nc4JXP3qqi+fL/gDEPvZEGTNnWzqs/IRslYrdr3/33K0sTopPzHNPwEObD9I6oDUtu7VU70NoY29D64DWHN95jMzHfk/dquX0voTdCVOXVanhwReLvyDifjhTh09Vzz180vQ3v9Iqa9urLW17tWP2B98TFVq0nkhRfqUf2od539cx7dZTvQ8hRsaYdupGxtVLqKJy9rkzcHYBUzNU9+8CkLZ/N5nBN7WuZ/P516SfOELq1o1kXrv8zO6jLAjfeAzPMb2oMqSjeh9ChYkRlQe8SNypG6TlbmdmVtkRA3NTkm+GFPs1koNCSbh4G+euTTF2sCYj92+iQ7v6mFdx4t5jn1PlkTy6Tnc6JYQ3b97kp59+0lpUYm1tzahRoxg3bpxegisJVzcf4/7prvSY9RZONSqTEptA4yGdMDAwYP8PmknsoBU5K1zntf5AXXZo3n/4BjRn8KpJnFi0DRMLM1oEBhB+5S7n1uxTn9fq3d5UaepD8L7zxD+IxtzOktrdmuHe0JsTi7YReydnwUN0UCjRQXn3SsbdiywzPYMPRZ8J4s76YzT87DXMnGxIuBWO12ttsPJwYteEherzWv40GteWvix3H6wuu754JzUGtufFpR9yZf5mVBmZ+AZ2IzUyXiMhDF59gDqjA2j23Qjs63kSf+0+Di944j3wReKu3lPvMwjQfsXHWHu6cmneBpyb1cL5sd3+U6Pii/Q4vbLo0KZDXB1xhbHff4BHzaooY5QEDO2OgYEBy+cs1zj36xU5PeEjW40AwNzSnGlLp2Fla8W6Bevw6+CncX7YnVCuns6Z03V0u/aqdq+6OU/oOLXnVIV7dB3AirXrSUhMIiIq58N576FjhEfmzAMe2K8X1lble8jtSZnXrpB2YA8Ww97CwNaerNAHmHbsgoGrG4k/fac+z2r8RIzrNyI6oB0Aqvt31cnhk1RhoVo9g8bNWmJUPWekASMjjDy9Me8/BID0Y4fIul3251gXRnn6JmH/HaHGpNcxcbIl+XYY7q+1w8zDmUvjHu0+UXfuGBxa1WWHa391mZG1OR5vdgNQzwP0GNGFTGUymfFJ3Ptjm/rca1P+pPHqSfitn8r9pTsxsrag2ugAkm6GcH/x9md0t6VDVhnrTqeEsFq1aiiVeX9QJCQk4OGhvfFtWZGtyuavYTPpOGkgfsO7YGRmTOi5YDZOWEBMcP7DxQ8lhMaw9LWv6PT5YNp/0p+sjCxu7j7DrunLNXoXb+4+i301Vxq81g4LBxsy0zKIuHqXDRMWcH7t/pK8xRJ3eOx8Gjzol/ssYwtir9xj79DZRBy7VmC9zKRUdvT7miZfDqLe2N4oDBSEH7nCqS+Wa/TspccmsqXb59T/qC+VOzei5pAOpMUmErRqH+dmrEaVkaU+1yF3X626Y3pqvV744SvPbUKoUqn44o0vGTFxBD2H98TUzJQb567zw4QfeBBc8JCPtb01LpVzereHfzZc6/jONTvVCaHQtnjl34SEPRoV2LnvEDv35fTS9ujSocIlhACJs7/JeZZxh5dQWFmRdSuYhKmfknlJe3GXrkxatcWsUzf1v41q+GBUIyfxUUVHlouEEODSe/NIvf8alV5tg5GtJYlX7nJ28EzijmpvJv84IzsranzaX6PM852cv3spdyM0EsLYQ5c48/q3eH/yGjU+e52slDQitpzkxrRlGgtNhHicIvvJnW+LYP/+/UybNo1vvvmGZs2aqcuPHTvGpEmT+Pzzz2nXrt1TBfZ1tUFPVV884pkhXej6stIo70Uzonj+OZ3/1kGieJTDtJN+oZvTJ90KP0kUqnN46W1r82flwYWfVERDHyzT27WeB0XuIezZU7MHJiEhgWHDhmFtbY29vT2xsbEkJCRgY2PD999//9QJoRBCCCFEcTwf+0uUTUVOCOvWzVnxKIQQQgghypciJ4QzZswo/CQhhBBCiFIii0p0p/PG1EIIIYQQZYlsTK27IieE06dPZ8SIEbi7uzN9+vRCz588eXKh5wghhBBCiNJX5IRw9+7d9OvXD3d3d3bv3l3guQqFQhJCIYQQQjxTsqhEd8VKCPP6byGEEEKIskASQt0ZFH6KEEIIIYQoz4rcQxgTE0NERAS1a9fWKL969Sr/+9//CAoKwsnJiWHDhtGhQwe9ByqEEEIIUZBsWVSisyL3EM6ZM4fPPvtMo+zBgwcMGjSIXbt2YWpqyo0bN3j33Xc5ceKE3gMVQgghhCiISo8/FU2RE8LTp09rPa1k8eLFJCcns2DBAtatW8fu3btp0KABCxcu1HugQgghhBCiZBQ5IQwPD6dmzZoaZXv27MHX15fWrVsDYGZmxuDBg7l27Zp+oxRCCCGEKIT0EOquyAmhQqHQeHRdVFQU9+/fx8/PT+M8V1dXYmNj9RehEEIIIUQRZOvx52kFBQUxfPhwGjZsSKtWrZg5cybp6emF30N2Nr/++isvvvgi9evXp3///pw9e1YPERWsyAlh9erVOXz4sPrfe/bsQaFQ0KpVK43zIiMjcXBw0F+EQgghhBDPkfj4eIYNG0ZGRgZz585l3LhxrF69ukiPAV64cCE///wzb7zxBgsWLMDZ2ZkRI0Zw7969Eo25yKuMhwwZwieffIJSqcTJyYmVK1dStWpVWrZsqXHewYMH8fHx0XugQgghhBAFKSuPrlu1ahVJSUn88ssv2NnZAZCVlcXUqVMJDAzE1dU1z3ppaWksWLCAESNG8MYbbwDQpEkTunbtyu+//86XX35ZYjEXuYewV69ejB8/ngMHDrBkyRJq1qzJL7/8gpHRo5wyOjqaPXv20L59+xIJVgghhBAiP2VlDuH+/fvx9/dXJ4MA3bp1Q6VScejQoXzrnT59msTERLp166YuMzExoXPnzuzfv/8poypYkXsIAUaNGsWoUaPyPe7o6KgxrCyEEEII8Tzq2LFjgcd37dqV77Hg4GD69u2rUWZjY4OzszPBwcEF1gPw8vLSKPf29mbJkiWkpqZiZmZWWOg6KVZCKIQQQghRVpWV1cFKpRIbGxutcltbW+Lj4wusZ2JigqmpqUa5jY0N2dnZxMfHS0IohBBCCFEQfawOfqigHsDySJ5lLIQQQgihRzY2NiQkJGiVx8fHY2trW2C99PR00tLSNMqVSiUKhaLAuk9LEkIhhBBClAsqhf5+noaXl5fWXMGEhAQiIyO15gc+WQ/g1q1bGuXBwcG4u7uX2HAxSEIohBBCiHKirKwybtu2LYcPH0apVKrLtm7dioGBgdb+zY9r3LgxVlZWbNmyRV2WkZHB9u3badu27VNGVTCZQyiEEEIIoUcDBgxg6dKljBkzhsDAQMLDw5k5cyYDBgzQ2INw2LBhhISEsGPHDgBMTU0JDAxk7ty5ODg44OPjw8qVK4mLi2PkyJElGrMkhEIIIYQoF/S5qORp2NrasmTJEr766ivGjBmDpaUl/fr1Y9y4cRrnqVQqsrKyNMpGjRpFdnY2f/zxBzExMfj6+vL777/j4eFRojFLQiiEEEKIckFVZlLCnL0DFy9eXOA5S5cu1SpTKBQEBgYSGBhYQpHlrcwmhJ4ZZeT5M+VAjGFpR1B+OBqU3ITeikQ5bHhph1Bu2CxZVNohlBs29T8s7RCEKDVlNiEUQgghhCiOsrIx9fNIEkIhhBBClAtlZ8D4+SPbzgghhBBCVHDSQyiEEEKIckGGjHUnCaEQQgghyoWnfcJIRSZDxkIIIYQQFZz0EAohhBCiXChL+xA+byQhFEIIIUS5IOmg7mTIWAghhBCigpMeQiGEEEKUC7LKWHfSQyiEEEIIUcFJD6EQQgghygVZVKI7SQiFEEIIUS5IOqg7GTIWQgghhKjgpIdQCCGEEOWCLCrRnc4JoUqlYs2aNWzbto2wsDDS0tI0jisUCnbu3PnUAQohhBBCFIXMIdSdzgnhrFmzWLRoEX5+fjRv3hxjY2N9xiWEEEIIIZ4RnRPCDRs28N577zFmzBh9xiOEEEIIoRPpH9Sdzglheno6jRs31mcsQgghhBA6kzmEutN5lXHPnj3ZvXu3PmMRQgghhBCloFg9hNu3b1f/d8OGDfnhhx+Ijo6mZcuW2NjYaJ3/0ksvPX2EQgghhBBFkC2DxjorVkL4/vvva5WFhISwefNmrXKFQsGVK1d0j0wIIYQQohhkyFh3xUoId+3aVVJxCCGEEEKIUlKshLBy5colFYcQQgghxFORfQh1p/Mq4yNHjhASEkLfvn21jq1btw53d3datGjxVMHpk4GJEfU/6kv1vq0xsbUk7spdzs1cS9j+i4XWNXezp8mXg6nUrh4KAwPCDl/m9BfLSLwbqXGesbU59cb2pkrXplhUciAtWknogYtcmPMPyQ+i1edV6dqUmkM7YFfbA1N7K9KiE4g6fZPzs9cRf+2+3u9d3wxMjGgxoS+1+rbGzNaSqCt3OTprLfcOFN6Wlm72tJkymKptc9ry/pHLHJi6DOUTbVlvSEeqtKyDWyNvrCs7cWXNfnaO/1Xren1WT6KKv2+er5WVkcn/vN7Q6R7LAgsbC/p/OpTGXZpham5K8LmbrJy+mDuXbhVa16tBDVr3a493w5pUqV0NI2Mjhnlqv1db92vPqO/fzfc688f+yJH/DjzVfZQaI2MshozAtP1LKKysybwdRMqfv5Nx9mSxLmM9fTYmjZqSumEdSfN/0jhm2r03xg0aY+Tji6GLK6k7t5D0wwx93sVzKzk5hT9WrOXC5WtcuHwNZUIi0yeO5+WAzqUdWqlRmBhR5aPXcerbDiNbS5Kv3OHezJUo958rsJ59t+Y49m6NZQNvjF3sSQ+JIm7HKR78uJosZbL6PCN7K5wHdMSuc1PMa1ZBYWRE6s0HhC7cQMz6QyV9e2WCpIO60zkh/PHHH+nYsWOex2JiYli9ejWrVq3SOTB98/8xkKoBflz9bRsJwWF49W9D+6UfsvPVb4g8fj3fekYWpnRaMxFjGwsuzV2PKiOL2m91pdO6yWzuPIn02MScExUKOqz6FFufytxYshNlcBjWnq74DOuEe7v6bGj3MZlJqQDY+VYhPT6Ja79vIy0mATNnW7wHtKPr5qls6zmVuMt3n0WT6KzznEC8u/tx7vdtxN0Kw/fVNvRc8iH/9P+G0BP5t6WxhSl9/pqIqbUFJ39Zjyozi4ZvduWVNZNZ1WUSqXGJ6nObvN0DYyszIs4GY+Fil+81T879j8sr92qUGVmY0mHGCO4VIdkvqxQKBeP/mISHbzW2/PofCTEJdBzSlc9WTeOLnh8Tfju0wPr12zemXf+O3Lt6h8i74VTyzrt3/9qxyyz44Cet8i4je+Dh68nlwxf0cj+lwWr8Z5i0akfqf2vICnmAaaeuWE/9DuVnH5B5uWj3ZdKyDca16+R73Lzf6yjMLci8fhUDB0d9hV4uxMYrmb9oBZVcXahVw4sTZ86XdkilzuvH93AI8Cfst42kBofi3L89tZZO4sqrU0g8fjXfetVnvU1GWAxR6/aT/iAKi9pVcR3eDbuOjbnQ5UOyU9MBsGpSiyqfDCRu92ke/LgWsrKw7+5PzfkTuO9ThQff//WsblU8h3ROCG/cuMHYsWPzPFa3bl3mz5+vc1D65tjQC8+X/Tk9bQVX5ucsgAlee5Aeu2fQaPIAtvealm9dnzc6YeNdiS3dphBzLhiAkD3nCNg9A9/A7pybsRoApyY1cGrkzYmJi7m++NEj+5RBofj/8BZubepxf2tOz8TFH/7Vep2gFXvpc+pnfIZ25Pini/R163rn2tALn97+HJy+gjMLctry6t8HGbhzBq0mDmBtn/zb8oWhnbD3qsRfPaYQkduWd/acY+DOGTQK7M6R71arz1336nQScntVA6/+lu818+qVrNWnFQDX/n1+vxH7dfenZtPazH17Fie3HAXg+KbDfLdnLn3G9Wf+2B8LrL972TY2/d+/ZKSlM2Tqm/kmhJH3wom8F65RZmxqwtCvRnHl8AXiI+P0cTvPnJFPbUzbdSTp9/+Rui7nQzBt1zbs/rcIixGjUX5YhA31jU2wGDmGlLUrsRgyMs9TlJ+MRRWZ034Oa7foLf7ywNnRnr3rl+Pk6MDFK9cZ8GbenxcVhWXDGji93IY705YQNv8/AKLW7qX+7h+pOnkol3tNzLfujVGzSDhySaMs6Xww3j+/j9MrbYlckfOZk3LtHudavUv6g0cjLuGLt1L7ry9xf6cPofP+RZWi+ZjZ8kaGjHWn8z6ECoWChISEPI/Fx8eTlZWlc1D6VrVHM1SZWdxYtkddpkrLIGjlXpyb+mDh7pBvXY+AZkSdCVIngwDKm6GEHbxEtZ7N1WXGVuYApETGa9RPCY8DICv3G1x+UqOUZKakY2xrUeT7Kg3e3XPa8uLyR22ZlZbB5VV7qdTUB6tK+bdljYBmhJ8NUieDALFBodw7dIkaPZprnJvw2BB7cfm87E96UirB207rfI3S5tfNn7jIWE5tPaYuS4hRcnzTYRp39sPIpODvcsqoeDLSCv6dy0+jTk0xt7bg8PM6VAyYtHqR7KxM0rZseFSYkU7q9s0Y+9bDwMm50GuY93sdDBSkrMt/pONhMii0mZiY4OSY/9+Disahhz/ZmVlELnu0fVt2WgaRK3dh3bQ2Ju759zA/mQwCxOR+UTSvUUVdlnYvQiMZfCh223EMzEwwreb6NLfwXFDp8aei0TkhbNCgAcuXLyc7WzMbz87OZsWKFTRo0OCpg9MX+3rVSAgOIzMxRaM86mxOYmJft1reFRUK7H09iDmnPWcr+kww1tVdMbI0AyDmfDAZSak0+Lgfrq3qYO5mj0uL2jSaPICoM0GE5dGTZWxjgamDNXa1q9B89puY2FgQdkD7jV+WONetRlxwGBlPtGV4bls6FdCWjrU9CD+v3ZbhZ4Ox83TFOLctn4aZgzUebeoRvO0Umc/xN+Gqdatz5+ItrfdX8NmbmFqY4VbdvcRe2//lNqSlpHFq69ESe42SZuhdk6wH98lOSdYoz7yesxWWoVfNAusbOLtg3m8gyYsWQLpuibUQj7Os50VqcAhZT/ztTDx7AwCLutWLdT1jF3sAMmOUhZ/rbJd7bt6dOELAUwwZv/feewwdOpRevXrRp08fnJ2diYiI4N9//+X27dssXbpUn3E+FXMXO3VP3eNSwmNzjrva51nP1N4SQzMTUiLyqJtbZu5mT0JQKGkxiRwc/QvNZ42k05pHXf8he85zYNRPZGdpf9/osvFLbGvkfLBnJKZw4Yd/CVq5r5h392xZutqRlEd7JEXE5h7Puy3N7CwxMjMhOY+6D8ssXe2JCy54blxhfHq2wNDYiOv/PL/DxQB2LnZcO35Zqzwut53tXB24f03/c00tba14oW0jTu84TmrunNfnkYG9A9kx2r3MD8sKm+9n8eYYMoNvkL5fnsYk9MPYxZ703M+cx2Xklpm4Fq831X1MH7Izs4jedKTA8wztrHAZ2Anl0UtkRGi/fnkjG1PrTueEsFGjRixevJhZs2bx/fffo1KpMDAwoGHDhixevJiGDRvqMcynY2hmQlZ6hla5Ki0j97hxvvUAVHnUzcodjjN6rG5atJLYi3e4vmgH8dfvY1+3GnXeCaDFD29xMHCu1jWOjvsVYytzrKq54NW/LYbmxigMDcjOLDvD7U8yyqcts3Lb0iiftjTKbcuH5z0uM4+21JXPy/4kR8VztwgrnssyEzMTMvNo54fDwCa57alvft39MTY15si/+0vk+s+KwtQUVYZ2+2VnpKuP58eofiNMWrYlfvzbJRafqHgMzEzILuBzyKAY72nHPm1wGdiJkHn/kHargC/RCgU1fvkAQxtL7kz+vdgxP48q4lCvvuiUEKanp7N37158fX1ZtWoVqampxMfHY2Njg7m5ub5jfGpZqekYmmgnGwamxrnHtd+kD+sBGORR19A0582bmVvXqqozndZO5PD7C7i3+QQA97edJvFeFC1/CiR41T5C9miusos6dVP933f+O0KPfTMBODNtZbHu71nKzKctDXPbMjOftszMbcuH5z3O6Im21JVNVWcqNfXh3KLtefbIlkWGxkZY2VlplCmjlaSnpmOURzsb57ZVeiFzUnXl/3IbEmMTOL/3TIlc/1nJTktDYazdfgpjE/XxPBkYYhn4Pmm7t5N1I/9Vn0IUlyo1HUUBn0OqIr6nrZv54vX9O8TtOcO9GcsLPNfz6zex69CYoPd+Ivny7WLHLCoWneYQmpiYMGHCBEJCQgAwMzPD1dW1TCaDkDO8a+5qp1X+cKg4JY9ufIC02CSyUtMxz2Pbk4dlKWE5db36t8XA1IQHOzU/SB9sPwWAs59PgTGmxycTfugy1XNXyJZVSeFxWObRHpa581mS8mnL1LgkMlPT89xC5mFZfnWLyufllgBce46Gi2s2qcXPJ37X+HF0dyQuIg47F+3h94dlceExeo/Fwd0JHz9fjm8+TFYZ7qUuClVsDIo8hoUflqnyGE4GMO3YBcPKHqRtXY+Bi5v6BwBzi5z/LqB3UYj8ZETEYpLHlBrj3LL0IrynLep44rP4M5Kv3ePGqFlQwBffyuNfw/WNbtz9eilRf5ftqUj6lK3H/6todB4y9vLyIjT06eZ7PSuxl+7i2rIORlbmGgtLnBp55x6/k3fF7Gzirt7HoYH2ZF+nxt4k3A5X7y1o5mSLQgH/3959h0VxvHEA/95xd/QqXUQEAQVpVkQUAY3dYO+AmhgLGkVi7yXqLxpR1BiNLRpLotEoIsbYotiCothFQQHp0svBwe3vj5PT86gHAgfvx4cnz83O7s5ObpeXmZ0ZloJkjM3icN7/V6HScioo8cDVaJhBdam0J7EwcbEBV01ZYmCJ4fu6TKugLt89j4eBvXRdGjpZIOtNMgQ1fGfN+ksXZL5ORnLEqxodpy7FPnmNDeNWSqRlpWYi9kkMrDq3BYvFkhhYYuFoicJ8PpJiEmq9LM6DXcFms3HzlPyOLi5VEh0Frr0jWMoqEgNLuNY24u1lYevpg8XlQnPjDqltSr36QqlXX2SvXgzBreufp+Ck0cp7HAMNl3ZQUFOWGFii5iRqLMivZMJ5xZYGsP5tKQTvsvB8/BoI88t/Xhr49oVJwGgk7jqDxO0na+cC5IR89A01TDKPMvb398dPP/2Ehw8b/sS1scF3wOYowHK8uziNzePAfFQPpN19ifwE0V9mKs2bQaO1keS+Z+9A18kCOh8FMuoWRjDoZoPY4DvitOzoRLDYbImpaADAzKsrACDj0WtxmmIzDakyqprowtDVtswRzQ3Jy7Oiumw3TrIu247sgaR7L5GbKKpLNeNm0LYwktrXwNEC+h/VpZa5EUxcbPDyo7qUha5tS+hYNceLUzdqdJy6lp+dhydhkRI/gkIB/jt3E1p62ujQ98P3SU1bHZ0GdEXExXAUFxWL0/VNDaBvWvPpJLp+2R1p8al48d/TGh+rvhWFXQVLgQPFfoM+JHK4UOzVD4JnjyFME03NwdbTB9vEVJyl8N9LyF69WOoHAIr+u4ns1YtR/Fx6sA8hlUkPvgkWRwF6478Qp7F4HOiNckfu3RcoShC1WvOa60KpteS8oVw9LbQ5shwQCvFszKoKRxbrDO6GlqsnI+3EVcSuaLhz2pKGR+YWwo0bNyIzMxMjR46ElpYWdHV1JbazWCycPn26xgWsDe8iXuHN6dtwXDgSSroayIlJhvnI7lBroYuLc3eL87lsmQoDl7b4zXi8OO3F/n/Qeqw7eh4MwNOdIRAKitH2m37gp2aJJ7kGgOjfr8Fm6gB03jAJ2u3MkPU8Hjp2ZrAY2xOZz+IQd+7DclkDLq1D8vXHyHj8BoWZedAwN4TFaDewOAqI+L5hzySffP8Vos7cRtf5I6HcTANZr5PRZnh3qJvo4uJ3H+qyd+BUmHRti6AWH+ry4a//wHasOwbtD8C9n0MgLC6G09f9kJ+WhXu7QiTOY9bLCbo2ol/UChwFNGtjio6zvgQAxPx9D++exUnktx5S2l0sXwFhef4LuYWX957jqx/80NyyBXLSs+E5oS/YbDZObpb8jsw7vAIAEOD6YRBEs+Z66DakBwDAzF7UejvYT7R0XdrbNNw4KdmF1NyqBUzbmiF4x5+f65LqVPHzpyi8dhkqPlPA1tRGSeJbKHr2AdvAELlbNojzqfkvAtfeCe8GuAEAhPGxEMaXPXpbmJQo1TLI7ewCTitR/YLDAcfMAsqjJgAAim6HoeR19KeHaVIOHz+NnNw8pKSJgp0rYbeRnJoGABg7fDDU1VTrs3h1Ki8iCu9Oh6HFwnHg6mqAH5MEvZHu4LXQR/TcDy3SFltmQcOlHW4bDxWnWR9eCiUzQyRsPwn1zm2Bzh+W6xSkZYmXvlN1bA2LLbNQnJGLrOsP0WxoD4ky5IY/R2Fs4547U8jIb1fvpUuXEBgYiJiYGBgbG2PKlCllLg/8scjISBw5cgTh4eFISUmBgYEB+vTpg2nTpkFFpXrzGsscENra2qJdu3ay7l7nbny7Ew5vh79fy1gFGU/jcMV7E1JuP69wv+I8Pi4MX4sOK8ah3bdfgsVmIfnmU9xd/hsKP5rTqSgjF+f6LYX9d8PQvLcTLCd4oDAjF6+OXsWD9b9DKPjwTlbUrxfR3NMRRj3twVVTAj8tG4n/PsLjrX8h81nDX8v4wpydcH47HG2GukJRUwXvnsUheOImJFRSl4I8Pv4csRbdl49Dp1miunx78ymurfoN/E/mx2rdvxPajvjwMNO3M4O+nRkAIDcxXTIgZLFgNbgrUiJjajxtTUPBCIXY5LsWoxd5o7dvf/CUeIiOfIndAduQFF15d7FeC30MCxgrkVb6+emtR1IBoYuXqK7ldt3iMuRu+l60lrHHF2CpqaEkJho5Kxeg+HHtLaHG69YDSr36iT9zWluB01rUBSh8l9rkA8L9R04gISlF/Pmfq2H456roHd+BfTyaVEAIAK++3QqTt2OgO6yneC3jF97fI+d2xa3Oqu/nKDSeMURqW/aNR+KAUNmqBdiKXLAVNWGxWXqN8lezgxp9QCiv4WB4eDj8/PwwfPhwLFq0CLdu3cLixYuhqqqKvn37lrvfuXPn8ObNG3z11VcwMzPDy5cvsXXrVjx48AC//vprtcrAYj6d+baB+LiVjtRMeuWvL5IqClcoqDwTqdSPtmn1XYRGQ+MAdQvWlnv2AfVdhEahS0L99TSMbzm08kxVdOhN3V3H5MmTkZeXh6NHP6yMNHfuXDx9+hQhISHl7peeng4dHck5LM+cOYOAgACcOHGiWg13Mr9DSAghhBDSkAjB1NpPXSkqKsLt27elWgL79++PV69eIT6+/J7DT4NBALCxEQ2eS0lJkdpWEZm7jAEgOzsb58+fR0xMDIrKWN5pyZIlNTk8IYQQQkiVyeN0MbGxsRAIBDA3N5dIt7AQvZ8cHR0NExOTsnYt0927ounuPj1eZWQOCF+/fo3Ro0ejqKgIBQUF0NHRQVZWFoqLi6GpqQk1NTUKCAkhhBAilzw9PSvcfvHixVo5T1ZWFgBAQ0NyBpLSz6XbqyI9PR1BQUHw9PSEmZlZtcohc0C4fv16ODg4YMuWLXB0dMSuXbvQpk0bhISEYPPmzdiyZYushyaEEEIIqbaGMg9hTk5OlbpsW7RoUWvnFAgE8Pf3BwCsWLGi2vvLHBBGRkZi7dq14PF44oIoKChg0KBByMjIwJo1ayRejiSEEEII+Zxq892/mrQAhoaGVqmXNCQkBJqamgBEQeTHsrNF802Wbq8IwzBYtGgRIiMjcfjwYejr61e7zDIHhEVFRVBTUwObzYampqZEJGxpaYlnz2gdUEIIIYQ0PSNGjMCIESOqlLeoqAhcLhfR0dHo3r27OD06WjRtVVXeBdywYQPOnTuH3bt3o02bNjKVWeZRxmZmZnj79i0A0YiWw4cPIzc3F3w+H8eOHZMpOiWEEEIIkZU8rmXM4/HQpUsXnD9/XiI9JCQEFhYWlQ4o2bVrF/bv34/169eja9euMpdD5hbCAQMGiFsBv/32W0yePBmdO3cWr726fv16mQtFCCGEEFJdDeUdwuqaNm0avL29sWLFCvTr1w+3b99GcHAwNm/eLJHPxsYGXl5e+P777wGI5hzctGkTBg8eDBMTE9y/f1+c19TUtMxpacpT7YDw5cuXOHr0KOLj46Gvr48bN27AxcUFwcHBuHbtGvh8PpydnWFlZVXdQxNCCCGENDkdO3ZEUFAQAgMDcfz4cRgbG2PNmjXo16+fRL6SkhIIhR/C3rAw0co/p0+flloueN26dRg6tOoTdVdrpZLw8HBMnDgRxcXF0NHRQWZmJoRCIZYtW4YxY8ZU+aRVQSuV1B5aqaT20EoltYNWKqk9tFJJ7aGVSmpHfa5UMsR0UK0d62TsmVo7ljyo1juEQUFBMDc3x6VLlxAWFobbt2+jV69eCAwM/EzFI4QQQgipGnlcqaShqFZA+OLFC8yYMQNGRkYAADU1NcyfPx9ZWVlITEz8LAUkhBBCCCGfV7UCwoyMDBgaGkqklQaHGRkZtVcqQgghhJBqEtbiT1NTo7WMCSGEEEIaCnlcy7ihqHZA6OPjAxaLJZU+btw4iXQWiyVeYJkQQgghhDRc1QoI/fz8Plc5CCGEEEJqpCkOBqktFBASQgghpFGoxkx65BMyL11HCCGEEEIaBxpUQgghhJBGoSmODq4tFBASQgghpFGgUcayoy5jQgghhJAmjloICSGEENIo0Chj2VFASAghhJBGgUYZy466jAkhhBBCmjhqISSEEEJIo0BdxrKjgJAQQgghjQKNMpZdgw0I7XhZ9V2ERqN9wr36LkKjcVzHrb6L0CjcCzes7yI0Ghr2AfVdhEajfeTG+i4CIfWmwQaEhBBCCCHVIaRBJTKjgJAQQgghjQKFg7KjUcaEEEIIIU0ctRASQgghpFGgUcayo4CQEEIIIY0CBYSyoy5jQgghhJAmjloICSGEENIo0NJ1sqOAkBBCCCGNAnUZy466jAkhhBBCmjhqISSEEEJIo0BL18mOAkJCCCGENAr0DqHsqMuYEEIIIaSJoxZCQgghhDQKNKhEdhQQEkIIIaRRoC5j2VGXMSGEEEJIE0cthIQQQghpFKjLWHYUEBJCCCGkUaBpZ2RHXcaEEEIIIU1cjVsIs7KyEBUVhcTERPTo0QOampooLCwEl8sFm03xJiGEEELqhpAGlchM5oCQYRhs3rwZBw8eREFBAVgsFo4fPw5NTU34+fnBwcEBfn5+tVlWQgghhJByUZex7GRuwgsMDMShQ4cwf/58nD9/XmKot4eHBy5dulQrBSSEEEIIaewuXbqEwYMHw87ODn369MGJEyeqfYzp06fD2toae/bsqfa+MgeEJ0+ehL+/P0aPHg0TExOJbaampoiLi5P10IQQQggh1SZkmFr7qUvh4eHw8/ODo6Mjdu/ejX79+mHx4sUIDQ2t8jGuXr2KBw8eyFwGmQPCzMxMWFhYlLmtpKQExcXFMheKEEIIIaS6mFr8V5d++ukn2NvbY9WqVXB2dsbs2bMxYMAAbN26tUr7FxUVYe3atfD395e5DDK/Q2hmZoawsDB07dpVatudO3dgaWkpc6HqCovHgYH/OGgPcYeCphr4z14jaeMh5F6/X+F+Gn26QmtQdyjbW4Krpw1BQiqyL/2H5KBjEGbnSeVnqypDf+YoaA1wBUdfByUZ2ci79wxx/pvB8As/09XVH01NDaxftxheX/aDiooy/vvvPr6btxIR9x9Vuu+eXzbDx3ukVPqz5y/Rzs5N/LllSxO8irpd5jHGjp+G338/LfsF1CM2j4M284ajxfDu4GmqIutpLJ6u/x2p/1Zcd2oWRjDz9oR2+9bQsjODghIPf3eahfy4NIl8XG01tBzTE4a9naBu1RxsjgJyXibg1a5zePvXrc95aXWOxePAYt5IGI/oDo6mGnKfvsHLdceQ/u/DCvdTsTCCiU9vaLZvDXW7VlBQ4uFaRz/w41LLzK+gqgRz/2EwGOwMRQNtFKXnICv8BR7N3A5hQdHnuLQ6x+JxYPLdGOgOcwNHUxX5T98g7n9HkP1vxa0R2v26oNmXrlB1sABXXxtFCWnIvHAXbwN/R0l2vjgfR1sNeqM9odW7I5QtTcDicMB/+RaJu88g/XTY5768Bi0/vwB7Dx/HwyfP8fDJc2Tn5GLNIn94Dehd30UjtaSoqAi3b99GQECARHr//v0RHByM+Ph4qZ7YT+3ZswcaGhoYOnQoFi1aJFM5ZA4IfX19sXTpUnA4HPTt2xcAkJSUhPv37+PgwYNYt26drIeuMy02zoZmv25I23saha8ToD3cE632LcerMYuRH/6k3P1M1vlBkPwOmSevQJCQCqU2LdHMeyDU3TsiasBsMIUffgmw1VVgcWwduIa6SD8SisLXieA004RqJ1uweRyUNLKAkMVi4cxfv8Le3gabfvwJaWnpmDrVBxf/OY7Ozv3w8mVMpcfg8/mYMvU7ibTsrJwy8x45ehLnQiXfV711667sF1DP2m+ZCuOBnfFqdyhyo5NgOqoHuv42D9eHrUX6nefl7qfT0RIWX/VF9ot45EQlQMvOrNx8NgtGIvnifTzffApMSQmMB3RGp59nQd2qOZ79UP13VhqqdlunQ39gF8TuCkF+TBKMR7nB6fAC3B26CpkV1KVmRyuYftUPuS/ikRf1Fhp2rcrNy1FXRsdTK6BopIO3hy4iPyYJvGYa0HJuAzaP22gCQvPAmdAZ0BVJvwSDH50IvVHusD64GE9HLEPunWfl7tfqh2kQJKUj7c9/UfQ2DSptTGEwsR+0PNvjYZ8AMHxR/ah1sIbJ/LHIvHQPbwOPAyUl0O7fFZY75yLeygRvNx6rq0ttcDKysrFz32EYGejDurU5/ouIrO8iNWjyOMo4NjYWAoEA5ubmEumlvbDR0dEVBoQJCQnYtWsX9u3bBxaLJXM5ZA4Ihw4diqysLAQFBeHnn38GAMyYMQPKysqYPXs2+vfvL3Oh6oKygyW0BrshYe1epO0+CQDI+PMSrM5vg9FCX7waNq/cfd9MX4e8W5ItNvkPX8L0R39oe/VE+rG/xemG83zAba6PqAGzIYhPFqen7mw8v3g/NmzYQLi4dMLI0VPw559nAQB/HD+Dp4+vYfmyuZjgXfnI8+LiEhw+/GeVzhcR8bDKeRs6LScLmAxxwaOVv+HlT6K6i/vjGjyubIDt0jG4NmhFufsmnr+LBKuvUJzHR+tpA8oNCHOex+OCiz8K4j+0HMbsu4BufyyC5YxBiNoejJJ8+f8jRcPJAoZDuuHFioN481MwACDx93/R9epGWC4bh/8GLit339Tz4bhsOREleXy0nDawwoCw9eKxUDLRxa3eC8CP/agFcZt8tlCXRdWxNXS9uuPNqgNI2vkXACDt+BXYXwqE6RJvPBlcfmtE1Nc/IOfmY4m0vMhoWGydBd2hPZB6+B8AQMHzODzo5oeitx/qMHl/KNocWwHj6UOQuP0UhAXy/72UhV4zbVw5/Rt0m+ng0dMXGP3Vt/VdpAatNrt6PT09K9x+8eLFWjlPVlYWAEBDQ0MivfRz6fbyrFu3Dr1794ajo2ONyiFTQMgwDLKysjBu3DiMHDkSERERyMjIgKamJpycnKCurl6jQtUFzX7dwBSXIP3Ihxc2mUIB0n+/AKN5PuAa6UKQmFbmvp8GgwCQfV7U3abY+kMUz9ZQhc4IT6TtPwNBfDJYXA7AApiixvt+5bChA5CUlIKTJ0PEaWlp6fjjeDDGjR0KHo+HoqLKW03YbDZUVVWQk5NbaV4VFWUIBMUQCAQ1Knt9az6wM4TFJXh98EOLp7BQgDeHr8B28WgoG+ugICG9zH0FmdKvKpQlP7bsbs/E0HDodW8HVVN9ZD+T/wFhBgOdISwuQfzBDw9sYaEAbw9fhuXiMVA0bobChHdl7ltcxbrkaKjAeHRPxP5yDvzYVLC4CgCL1ejub52BXcEUlyD10Ic/dJlCAVKPXESLRePBM26GonLq8tNgEADSz92CBWZB+aNnZWFcSpn7Z5y/A83u9lBsaYCCZ7E1vBL5xOPxoNtMp76LQaopJycHKSllf68/1qJFixqd5/r167h+/Xq1Bp+UR6aAUCAQwMXFBTt27EDPnj3h6upa44LUNWVbcxTGvIUwt0AiveD+CwCAkk2rcgPCsnD0tAAAxRnZ4jTVjjZgKymi6HUiTHcsgOYXzgCbhfx7z/B22U7wn1TefSpvHB3aISLiocQ0RADw338RmPL1eFhZmePRo/K7mABRgJfx7jlUVVWQnp6Bo8f+wsJFa5GXly+Vd+kSf/xvwzIIhULcvReJZcs24MI//9bqNdUVzXZmyI1ORPEn38mMiFei7bZm5QaENaX4/vtbmF5217y8UbczQ/6rRJR8UpfZES9F29u1LDcgrCqtLm2goMxDwesk2P8yB3r9OoHFZiErPApPF+xB7uM3NTp+Q6Hazhz86ASpusy9HwUAULFtVW5AWBauvjYAoDg9u5KcALf0udpIvpfk86vNLuOatACGhoZiyZIlleYLCQmBpqYmAFEQ+bHsbNE9Urq9LGvWrIG3tzeUlZXF+QGgsLAQ2dnZUq2OFZEpIOTxeDA0NERJSYksuzcIXH0dCFIypNJL07gGzap1PP2pw8EUlyAr5MML0IqtjAGIuo2LYhMR578ZbA0VGHw7BuaH1+JF7xkoTpUugzwzMtLHtevSgxOSkkR/KRkbGVQYECYlJWPjph24F/EIbDYLfb5wx/RpvnCwt4FHr+Hi75xQKMTff1/Bqb9CkZCQhFatTDH72ykIPnMIQ4ZORMi52mnKr0tKBlooTM6USi9MEaUpGWp9lvNytVTRcpw70m4+FZ9L3ikaaKOwjPu7MDnj/faat7iomBsCAFovGoOCN8l4PHM7OOoqMA8Yjo4nluFGj7koagT1ydXXRlFyGc/K92m8atal8YwhYIpL8O7szQrzKWipQX9sL2Tfelzms5qQsjSUialHjBiBESNGVClvUVERuFwuoqOj0b17d3F6dHQ0AEi9W/ixmJgY7Ny5Ezt37pRI37JlC7Zs2YLIyEgoKipWqRwyv0M4duxY7N+/H66urlU+WUPCUuKBKZLuYiwdEMJW4lX5WFqD3aAz+guk7DyOoteJ4nS2itL7gzKIHrsEwnw+AID/OBqtT25EM+8BSN50qAZX0fAoKyuhsFC6S5j/fvCMkrJShfsvXrJe4vPvv59GVFQ01qxegGHDBohHD8fFJaD/wHESeQ/9dgIPH1zG//63TC4DQgUlHkrK6G4s4QvE22sdi4WOO2aAq6GCyMUHav/49YStxAVTKF2XwsL3dalc87pUEN/fwN1hq8XvXuY8eo3OIWvQYlIfvFov/4Mh2OU8K0vrsjrPymZDukN/bC8kbD+JwpjE8jOyWGi9bTYUNFTxZkn1J9glRJ7weDx06dIF58+fh4+Pjzg9JCQEFhYWFQ4o+fXXX6XSvL29MXr0aPTv3x9cLrfK5ZA5IExMTERMTAx69uyJzp07Q1dXV2p0S1WaS+sLwy8CiyddUSxF0cNNyK/a6ECVTjYw+d9M5Fy9i6QfDkpsKz1G9sU74mAQAPIjnqMwNgmqHdrIWvx6x+VyoaOjJZGWmvoOBQV8KCpK/4JQUhL90cAv4Ettq0zglt1YueI7eHp0r3A6mYyMTBz49Rjmz5uJ5s2N8PZtBb9wGqASfhEUeNK3pIISV7y9ttl/7wMDD0fc9duB7CeN5x0tIV8AlqJ0XbIV39dlLYz+Lb2/Uy/clRiIk3U3CvlvkqHVyarG52gIhOU8K0vrsqrPSvXObWG+cToyL0cgbv1vFeY1W/sVtDza49XMLch/8rraZSZNF8MI67sIMpk2bRq8vb2xYsUK9OvXD7dv30ZwcDA2b94skc/GxgZeXl74/vvvAQBdunQp83impqblbiuPzAHh5cuXweOJfvE/fCg9rxeLxWrQAaEgJR1cQ+lu4dL3WwTJlb8To9TWDGa/LAX/eSzeTFsPlEh+EYuTRe97FadlSu1b/C4TCppqMpS8YXDp2hEX/zkukWZh2QWJiSkwMtSXym/4Pi0hMVlqW2X4fD7evcuQCkDLEhcnCgJ1dLTkLiDkJ2dCyUhbKl1RX0u0PSmzVs9nPXcozCd+gcdrjiDu+PVaPXZ9K0zOgKKhdFemooH2++01fxeztPu5KFV6BGBRWjY4mqo1PkdDIEjJAK+MuuS+r8uiKtSlio0ZrPYvRP7zOER9/YPUs/Jjzf1HwsC3H2LXHkTaiauyF5w0ScIG0mVcXR07dkRQUBACAwNx/PhxGBsbY82aNejXr59EvpKSEgiFnyfolTkglPe1ivlPYqDW1R5sNWWJgSXKjtbi7RXhmRqi1YGVKEnLRMzEFRItgKUKHoleYC/rfUSufjMUvoqvySXUqweRT9Cn72iJtKSkVDyIfAzXbp3BYrEkBpZ07uyEvLx8vHgRXe1zqampQldXB6mplf/iMW9lCkDUWilvsh6/gW43G3DUlCUGlui0b/1+++taO1erib3R9rvhePlzCKK2nam14zYUOY9eQ7ubLRTUlCUGQ2i8r8ucRzUf8JH9QPRdLi/wzH/5tsbnaAjyHsdAw6WdVF2qOYlaQPMfV/ysVGxpAOvflkLwLgvPx68p81lZysC3L0wCRiNx1xkkbj9ZOxdAiJzw9PSsdKqb58/Ln0O1OnnKIvPSdfIu81wYWBwF6IzpK05j8TjQGdELeRHPxCOMucZ6ULSQ7L/n6Gmh1cFVgJBBtM9ylJQzWq4w+i0KnkRDo3cXKGh/GOmj1t0JvOZ6yL0e8RmurG5kZmbh4qVrEj+FhYU48edZGBrqY8iQD/NQNmumjeHDBiL47AWJKWfMzVvC3Lyl+LOioiLU1KRbVZYsng02m43zf18Wp+nqSv8SNjY2hK/vKDyIfCIexCJPEs7cBpujALMJHuI0No8D09FuSL8bJR5hrNy8GdRaG8t8nuZfOsN+jQ/ijl/Ho+WN6x3WUsnBoro0mfDh4cricdB8dE9k3o0SjzBWat4MKjLWZf6rROQ8eg29vh3B1fkw1ZaOmz2UTXTx7mrFK6LIi/Tgm2BxFKA3/gtxGovHgd4od+TefSEeYcxrrgul1s0l9uXqaaHNkeWAUIhnY1ZVOLJYZ3A3tFw9GWknriJ2xb7PczGk0WMYptZ+mhqZWwhLvXnzBq9fv0ZhofSkoV988UUZezQMBfdfIDP4OozmeYPTTBNFbxKhPcwDPBN9xM//sHZgix/nQM3ZDpFmg8RprQ6shGJLI6TsPA7VjjZARxvxtuK0TIml7xJW/wLzg6thcXwD0g+HQkFdBbqTvVD4Kh7vDp2rk2utSydOBOPWzK+wZ/ePsGlribS0DEyd6g0FBQWsXLVJIu/foaIX7ltbOQMADA31EH7nPI4e+wvPn4taV7/o3RP9+3siNPQSTp8+L953/bolsDBviUuXryMhIRlmZi3w9VfjoaqqAn//8icdbsgyIl7h7elbsFk0Coq6GsiNSYbpyO5QaaGLCP9d4nwdgqZB18UGpwzHitM46sown9wHANCss6jlptWkLyDIyocgOx8xe0VzyGk5WaD91mkoyshB6vXHMBnWTaIM6f9FIT9W/oLpT2Xfe4mkv26i9eIx4OlqIv91EoxHukGphR4ez/lZnM82aAZ0utnigsEocRpHXRktvhJ105S+B9hiUh8UZ+ejOCsPcXs/fA+fL/sV7X9fjE6nVyL+4D/gqKug5dQByHuZgPj9H+btk2d5EVF4dzoMLRaOA1dXA/yYJOiNdAevhT6i5+4Q57PYMgsaLu1w23ioOM368FIomRkiYftJqHduC3RuK94mSMsSL32n6tgaFltmoTgjF1nXH6LZ0B4SZcgNf47C2Oq/btJYHD5+Gjm5eUhJEwXfV8JuIzlV1GgxdvhgqJfxh3RTJa9dxg2BzAFhbm4uZsyYgTt37gCAOJr+eGDJ06dPa1i8zytu7o8QvB0P7aHv1zJ++hoxk1ch7470ZKofU7YRDQHXnzpcalvurYcSAWHezYeI8VkOg7njYfjdBAgLCpH99y0krt9XYdeJvBIKhRg4eAI2rF8CvxmToayshPDw+5g8eQ5evHhV4b6Zmdk4G3IRvTx7wHvCCCgosPHy1WssXrIOm37cKfEX24V/rsL86wmYNtUX2tqayMzMxrXrt/D991uqtGZyQ3V35k9oO38EWgx3BVdTFdlP43Brwka8u1Xx3I08LVXYLJBcA9py2kAAQH5cqjgg1LBqDgVFLhQUNdE+8Bup49z7didiG0FACACPZ24HP34kjEZ0B0dTFblPY3F//P+Qeavi5xJHSw2tF4ySSDObLvqDsCA2RSIgzAh7jIgx62AxfyRaLxyDkoJCpJwLR9SqQ41ixZdSr77dCpO3Y6A7rKd4LeMX3t8j53b5S3wCgKqtaJUX4xlDpLZl33gkDgiVrVqArcgFW1ETFpulVzN6NTuoSQeE+4+cQMJHvR7/XA3DP1dFU5wN7ONBASGpFSxGxnbR5cuXIzw8HKtXr8bYsWOxbds2aGpq4vTp07h16xY2bdoEe3t7mQv2cYscqZn2CffquwiNxnEdt/ouQqOgysjvHKYNjYaCfK/Q05C0j9xY30VoFLi65c+b97k117attWO9zai4caixkfkdwmvXrmHq1KlwcHAAAOjr66NTp05YvXo1PD09sW8fvQNCCCGEkLojZJha+2lqZA4I09PTYWRkBAUFBSgrKyMzM1O8zc3NDdeuXauN8hFCCCGEkM9M5oDQ0NAQGRmiebjMzMwkpqGJiIiQy9VLCCGEECK/mFr819TIPKikW7duuHHjBnr37g0fHx8sWLAAkZGR4HK5iIyMxMSJE2uznIQQQgghFWqK08XUlmq1EA4aNAgvXrwAAAQEBGDmzJk4c+YMPDw8EBQUhFatWkFPTw9Lly5FQEDAZykwIYQQQgipXdVqIYyKigKfL5oqRVlZGTweD/PmzcPx48fRu3dv9O7d+7MUkhBCCCGkMjQPoexqPDE1Nc8SQgghpCGgmER2TXbpOkIIIYQQIlLjFkJAcnUSQgghhJD60BTnD6wt1Q4IfXx8pALAcePGSaWxWCzcvXu3ZqUjhBBCCKki6jKWXbUCQj8/6TUmCSGEEEKIfKOAkBBCCCGNAo0yll2tvENICCGEEFLfqMtYdjTKmBBCCCGkiaMWQkIIIYQ0CjTKWHYUEBJCCCGkUWDoHUKZUZcxIYQQQkgTRy2EhBBCCGkUqMtYdhQQEkIIIaRRoFHGsqMuY0IIIYSQJo5aCAkhhBDSKNCgEtlRQEgIIYSQRoG6jGVHXcaEEEIIIU0ctRASQgghpFGgFkLZUUBICCGEkEaBwkHZUZcxIYQQQkgTx2KofZUQQgghpEmjFkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCaOAkJCCCGEkCZO7gPC06dPY/jw4ejQoQPat2+Pfv36YfHixXj37l2dlsPDwwOrVq2qs/OtXbsWHh4edXa+UkFBQbC2thb/ODs7w9vbG+Hh4VU+Rl3XVWMwePBgWFtbV6uem6KqPA8+/f4tWLAAAwcOrPTYBQUF2LZtG/r37w8HBwd06dIFw4YNw+bNmz/LtdS32rjXZRUfHw9ra2uEhoZ+9nPVh0/rtvSnKt9DQj4XTn0XoCZ2796NTZs2wdfXF7NmzQLDMIiKisKZM2eQkpKCZs2a1VlZtm3bBg0NjTo7X31SUlLCgQMHAABJSUnYsWMHfH198eeff8LKyqqeS9f4REVF4fnz5wCAM2fOoGPHjvVcooapqs8DWe/VWbNmITIyEt988w3atm2L7OxsPHz4EP/88w/mzJlT25fTINC9/vl8XLcfpxFSX+Q6IDx48CCGDBmCBQsWiNPc3Nzw1VdfQSgU1vj4fD6/yjeojY1Njc8nL9hsNhwdHcWf7e3t4eHhgaNHj2LZsmX1V7BG6syZM2Cz2ejUqRNCQ0OxZMkScLnc+i5Wg1PV54Es9+qbN2/w77//YsOGDfDy8hKn9+nTB/7+/jUqd0NW2/c6wzAQCATg8Xi1WEr59Gnd1kRRURE4HA7YbLnv9CP1SK6/PdnZ2dDX1y9z28c3hrW1Nfbs2SOxff/+/bC2thZ/vn37NqytrXHlyhXMmjUL7du3x7fffltud9Lly5dhbW2N6OhoAJLdUH/++SdsbGyQlpYmsU9mZibatWuHo0ePitMiIiLg7e0NR0dHdOjQAXPnzpXq7k5OTsbUqVPh4OCA7t27Y/fu3VWpnjpjbGwMHR0dxMfHAxCVd968eXBxcYG9vT369u0r9ZfwxyIiIjB16lS4urrC0dERX375JU6dOiWRRyAQYMOGDejZsyfatWsHV1dXTJ06FTk5OVXaLq8YhkFwcDCcnZ0xceJEZGZm4tq1axJ5oqKiMG7cONjZ2eGLL77A6dOnMX36dEyYMEEi36tXrzBt2jR06NABjo6OmDJlCmJjY+vycj6rqj4Pyntl4erVqxg4cCDs7OwwdOhQ3L9/X7wtKysLAKCnp1fhsUu7Ok+ePIlFixahQ4cO6Ny5M9atW4fi4mJZL63B+Phe37t3L4YNG4YOHTqga9eu+OabbxATEyORv/T5efXqVQwePBh2dna4dOkSANF9P2nSJLRv3x5OTk4YMWIEwsLCJPYvLCzEqlWr0KlTJ7i6umLDhg2Noh7Lk5+fj1WrVqFPnz5wcHCAh4cHli1bJvUcK/0O7969G+7u7rC3t0dmZiYA0e+fQYMGwc7ODt27d8fmzZtRUlJSD1dD5I1ctxDa2tri6NGjMDExQc+ePct8WFfX0qVLMXjwYGzfvh1sNhtFRUU4efIkXrx4IdFFEhwcDFtbW5ibm0sdo3fv3li+fDlCQ0Mxfvx4cfrff/8NAOjbty8A0QNxwoQJcHNzw+bNm1FQUIDAwEBMnz4dx44dE+83ffp0JCcnY8WKFVBXV8fu3buRmJgIDqdh/O/Lzc1FZmYm9PX1kZGRgVGjRgEA5syZAxMTE7x586bCwCMhIQHt27fHmDFjwOPxcO/ePSxZsgQMw2DIkCEAgJ9//hlHjx5FQEAALC0tkZGRgbCwMBQVFVVpu7y6d+8e3r59ixkzZsDV1RVaWloIDg4Wvz/K5/MxadIkaGho4IcffgAAbN++HdnZ2TA1NRUfJy4uDqNHj4alpSXWr18PFouFnTt3wtfXF6GhoY2ixaYmz4PU1FSsXLkSM2fOhIaGBnbv3o3Jkyfj77//RrNmzWBubg4VFRWsX78e/v7+6Ny5M1RVVcs93o8//ghXV1cEBgbiyZMn2Lp1K7hcLgICAmrjUuvNx/d6UlISxo8fD2NjY+Tm5uLo0aMYPXo0zp8/Dy0tLfE+KSkpWLNmDaZNmwYjIyMYGxvj7t278PHxgaOjI9asWQMNDQ08evQICQkJEucLDAyEp6cnAgMDERERgaCgIJiammLMmDF1fOWfx6fBLZ/PR0lJCebMmQMdHR0kJiZi586dmD59Og4ePCiR9++//0bLli2xePFisNlsqKioYN++ffjhhx/g4+ODBQsW4NWrV+KAUN6/e6QOMHLs+fPnTO/evRkrKyvGysqK8fDwYFavXs3ExcVJ5LOysmJ++eUXibR9+/YxVlZW4s+3bt1irKysmGXLlknkEwgEjLOzM/Pjjz+K0/Lz8xlHR0eJY7q7uzMrV64Uf54xYwYzatQoiWNNmDCBmTJlivjzuHHjmFGjRjFCoVCcFhUVxVhbWzNXrlxhGIZhrl69ylhZWTE3btwQ58nOzmacnJwYd3f3yiuplm3dupVxdHRkBAIBIxAImLi4OMbPz4+xsrJi/v33X+bHH39k2rVrJ/X/4GOf1tXHhEIhIxAImKVLl0rU35QpUxg/P79yj1nZdnm1YsUKxs7OjsnOzmYYhmGWLl3KODg4MLm5uQzDMMyhQ4eYtm3bStR3XFwc07ZtW2b8+PHitHnz5jGenp4Mn88Xp717945xdHRkDh06VEdX83lV9Xnw6fdv/vz55d5jGzduFKedPXuWcXJyYqysrJi2bdsyQ4YMYXbs2MHk5eWJ88TFxTFWVlbM2LFjJc4ZGBjIODg4MJmZmbV92Z9NZff6x4qLi5mCggLG0dGROXr0qDi9tG7v378vkX/UqFFM//79meLi4jLPXVqPs2bNkkgfP3484+PjUzsXWI+2bt0q/p5+/HPq1CmJfAKBgAkPD2esrKyY6Ohocbq7uzvTuXNnie9eTk4O4+joyGzatEniGIcPH2bs7e2Z9PT0z3tRRO7JdZexlZUVgoODsWvXLnh7e0NdXR0HDx7E4MGD8fTpU5mO2bNnT4nPHA4Hffv2RUhIiDjt8uXLKCgowIABA8o9zoABA3D//n3xX7wpKSn477//xPsUFBTg3r176Nu3L0pKSlBcXIzi4mKYmZnByMgIDx8+BABERkZCXV0dXbt2FR9bXV0dLi4uMl1fbcjPz4etrS1sbW3h6emJ27dvY9myZejevTtu3rwJZ2dnmJiYVPl4WVlZWLNmDdzd3cXHPXbsmET3k42NDa5evYqgoCBERkZKvSNa2XZ5VFxcjNDQULi5uUFdXR0AMGjQIBQUFODChQsAgEePHsHKykqivk1MTNCmTRuJY4WFhcHDwwMKCgri75qGhgZsbGzw6NGjuruoz6gmz4Py7rEHDx6I0/r374/Lly9jw4YN+PLLL5GRkYHAwEAMGzYM+fn5Esfr3bu3xOc+ffqgoKAAL168qIUrrTsV3ev379/HxIkT0aVLF9jY2MDBwQH5+fl4/fq1xDG0tLTg4OAg/lxQUIAHDx7Ay8sLCgoKFZ7f1dVV4rOFhQWSkpJq7frqk5KSEo4fPy7x4+bmhlOnTsHLywtOTk6wtbXF2LFjAUCqXrt06QIVFRXx54iICOTn56Nv377ie7y4uBguLi7g8/mIioqqy8sjcqhh9DnWAI/Hg5ubG9zc3AAA165dwzfffIPt27dj27Zt1T5eWSOTBwwYgMOHDyMyMhL29vY4e/YsOnbsCENDw3KP4+7uDmVlZZw9exZff/01zp07B0VFRfTq1QuA6H2nkpISrFu3DuvWrZPaPzExEYAokNTR0alSOeuKkpISDh06BBaLBW1tbRgZGYnfo8rMzISlpWW1jrdgwQJERERgxowZaN26NdTU1HDkyBGcO3dOnGfatGlgs9k4efIktm3bBh0dHYwbNw4zZswAi8WqdLs8CgsLQ3p6Otzd3ZGdnQ1AFPTo6ekhODgYXl5e5X4/dHR0UFhYKP6ckZGBAwcOlPkuZ2MaoCLr86C8e+zVq1cSaZqamvDy8oKXlxcYhsHWrVuxY8cOHD9+HN7e3uUeT1dXF4Coa1qelHevJyQkYNKkSWjXrh1WrlwJfX19cLlcfPPNNxLfO+DDtZfKzs6GUCgs933Pj5X+IVSKy+XK/WsgpdhsNuzs7CTSLly4gPnz52PUqFGYM2cOtLS0kJqaihkzZkjV66e/AzIyMgBA/JrNp0p/pxBSHrkPCD/VvXt3tGnTRuJBzuPxIBAIJPKV/oL9VFnBQ4cOHWBkZISzZ8+iVatW+Pfff7Fo0aIKy6GkpIRevXohJCQEX3/9NUJCQuDu7i7+i05dXR0sFgvffPONOEj8mLa2NgBAX18f6enpUtvrep7Fj5X1ICulpaWFlJSUKh+rsLAQV65cwYIFCyQGQRw+fFgiH4/Hw8yZMzFz5ky8efMGJ06cQFBQEExMTODl5VXpdnl05swZAMDChQuxcOFCiW0ZGRl49+4d9PX1y2z9Sk9Pl3jHTVNTE25ubuLWho9V9C6cvCvreVCW8u6xit5DZLFYmDx5Mnbs2CF1/E+PVzrArDbec65L5d3r165dQ35+vsQUPsXFxeLBNx/79Jmqrq4ONptdredEUxEaGoq2bdtKDHq6c+dOmXk/rVdNTU0AommVymqsqE6vDWma5DogTEtLk/rrk8/nIzExEa1btxanGRoaSj2wb9y4UeXzsFgs9O/fH8HBwbC0tIRQKESfPn0q3W/gwIGYMmUKrl27hvv37+Prr78Wb1NRUYGjoyOio6PLDa4AwM7ODjk5Obh586a4SysnJwc3btyQeHG7oejatSv27t2LhIQEGBsbV5q/qKgIQqFQopUqNzdXPBKxLC1btoS/vz+OHTsmHuVdne3yoKCgABcvXkSvXr0kWp4A0ffe398fISEhaNeuHU6dOoW4uDi0aNECgGik67Nnz9ChQwfxPl27dkVUVBRsbGwq7aaTV1V9HpSlvHts3LhxAETfSQ6HIzUNVWk33qeB3oULF+Dr6yv+fP78eSgrKzeaufv4fD5YLJbEwLZz585VaQRw6bPvr7/+wqRJkxrt91EWfD5fqsW+9A/Dyjg5OUFZWRlJSUlSrywQUhVyHRAOGjQI7u7ucHV1hb6+PpKTk3Ho0CFkZGTAx8dHnK9Pnz44cOAA7Ozs0KpVK5w+fRrJycnVOtfAgQOxZ88ebNmyBd26dSuzi+lTLi4u0NLSwqJFi6ChoYEePXpIbJ83bx58fHwwe/ZsDBgwABoaGkhKSsKNGzcwdOhQdOnSBT169ICtrS2+++47BAQEQF1dHbt27YKamlq1yl9XfH198ddff2H8+PGYNm0aWrRogbi4OLx+/RrfffedVH51dXXY2dlh9+7d0NHRAYfDEV/fx60s06dPh62tLWxsbKCsrIzLly8jKysLzs7OVdouby5evIj8/HxMmDABXbp0kdr+yy+/IDg4GAcOHMDOnTsxdepUzJw5E4CohUBXV1eiBWHWrFkYPnw4Jk+ejJEjR0JXVxdpaWm4c+cOOnbs2ChWSKjq86AsWlpaWLx4MWbNmiUeyc8wjHi/mJgYTJs2DUOGDEGHDh2goqKCly9fYvfu3VBXV5fqpouNjcXChQvRv39/PHnyBLt27YKPj4+4FUfeld5XCxcuxOjRoxEVFYV9+/ZVecLvuXPnwtfXF76+vhg7diw0NTXx+PFjaGtrY/jw4Z+z6A2ai4sLVq1ahe3bt8PJyQlXr17FzZs3q7SvhoYGZs2ahR9++AFJSUno3LkzFBQUEBcXh4sXLyIoKAjKysqf+QqIPJPrgNDPzw+XL1/G+vXrkZ6eDm1tbVhbW2P//v0SgcD06dPx7t07bN++HSwWC6NGjYK3tzfWr19f5XPZ2NigVatWiImJqfLwfS6Xiz59+uDYsWMYPny41NQe7du3x+HDhxEUFISFCxdCIBDA0NAQzs7OaNmyJQBR6+SOHTuwfPlyLFu2DBoaGpgwYQLS0tJw8eLFKpe/rmhra+PIkSPYtGkTNm7ciIKCAjRv3rzMrspSmzZtwrJly7BgwQJoaWlhwoQJyM/Px969e8V52rdvj3PnzmHfvn0oKSlBq1atsHHjRvHgmsq2y5vg4GAYGxuXGQwCgJeXF77//nukpKRg7969WL58OQICAmBgYIDp06fj1KlTEu9ftWzZEn/88QcCAwOxcuVK5OfnQ09PD506dZKYj1OeVfV5UBY9PT0EBATgf//7H2JjY2FpaYk9e/aIWxxbtmyJUaNGISwsDH/88Qfy8vJgYGAAZ2dnTJ06Fc2bN5c43pw5c3Dnzh18++23UFBQwNixYxvVaibW1tZYt24dtm3bJl65ZcuWLZg9e3aV9u/YsSN+/fVXBAYGYuHChWCz2bC0tKzy/o3V6NGjER8fj0OHDmHPnj1wdXXFpk2bMHLkyCrtP2nSJBgYGGDfvn04dOgQOBwOTE1N0bNnz0b1rjD5PFgMwzD1XQhCSO3JzMxEr1694OvrCz8/v/ouTpMSHx8PT09PbNmyRTzfKCGEyAO5biEkhAC7du2Crq4umjdvjtTUVOzduxclJSUYNmxYfReNEEKInKCAkBA5x2az8dNPPyE5ORkKCgpwcHDAgQMHYGRkVN9FI4QQIieoy5gQQgghpImT65VKCCGEEEJIzVFASAghhBDSxFFASAghhBDSxFFASAghhBDSxFFASAghhBDSxFFASAghhBDSxFFASAghhBDSxFFASAghhBDSxFFASAghhBDSxP0fCtCwoB61oyIAAAAASUVORK5CYII=","text/plain":["<Figure size 800x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.heatmap(corr_matrix, annot=True)"]},{"cell_type":"markdown","metadata":{"id":"YOIzroTn06su"},"source":["Correlação entre as variáveis do conjunto de dados."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"elapsed":1133,"status":"ok","timestamp":1681224033763,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"req7zdXQwd7y","outputId":"3e788be9-b8c4-420e-f31d-97cbf2fe9884"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Pclass', ylabel='Fare'>"]},"execution_count":194,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAHICAYAAACvVjchAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAyn0lEQVR4nO3deXxU9b3/8fdMQhIkmUCUpsoiSYQAESS3loDEUBBZAoW6XtxolUWwAaOXXpayKm1aCwVlq0RwuWq9oNZaiSwXKRGI/pCCiGAJJMgmcGs0kwTINvP7gwdzHRMIMCHnTL6v5+PRR+ec7zcnn5PMwXfOfM/36/B6vV4BAAAABnFaXQAAAADQ0AjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIwTanUBwcbr9crjYX0RAAAAO3I6HXI4HHX2IwRfIo/Hq6KiMqvLAAAAQC1iYpopJKTuEMxwCAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAahaVLn9Ujj9yvpUuftboUAAAQBAjBCHpff/0vbdv2sSRp27aP9fXX/7K4IgAAYHeEYAS9rKxZ39uebU0hAAAgaBCCEdQ2b96koqIiv31FRV9r8+ZNFlUEAACCASEYQau6ulovv/xCrW0vv/yCqqurG7giAAAQLAjBCFqbNn1w3qBbXV2tTZs+aOCKAABAsCAEI2j17t1XISEhtbaFhISod+++DVwRAAAIFoRgBK2QkBD9/Oejam17+OFHzxuQAQAAbBWC3377bSUmJtb439y5c/36rVq1SgMGDFCXLl00dOhQbdy4scaxSkpKNHXqVHXv3l3JycmaMGGCTp482VCnggaSmtpbMTExfvtiYq7WLbekWlQRAAAIBqFWF1CbF154QVFRUb7t2NhY3+vVq1dr+vTpGjt2rHr06KGcnBxlZGTotddeU7du3Xz9MjMztX//fs2aNUvh4eFasGCBRo8erbfeekuhobY8bVymKVNm6Ve/mvCd7ZkWVgMAAIKBLdNgUlJSjbt75zz33HMaPHiwMjMzJUk9evTQvn37tHjxYmVnZ0uSduzYoc2bN2v58uVKTT17RzAuLk7p6elat26d0tPTG+Q80DCuvvoa/fjHKdq27WP9+Mcpuvrqa6wuCQAA2JythkPU5fDhwzp48KAGDRrktz89PV15eXmqqKiQJOXm5srlcqlXr16+PvHx8erUqZNyc3MbtGY0jHHjHteKFa9r3LjHrS4FAAAEAVveCR4yZIi++eYbXXfddbr33ns1atQohYSEqKCgQNLZu7rflZCQoMrKSh0+fFgJCQkqKChQXFycHA6HX7/4+HjfMQIRGhpUfzsAAADge2wVglu2bKnx48frpptuksPh0AcffKAFCxboxIkTmjFjhoqLiyVJLpfL7+vObZ9rd7vdfmOKz4mOjtbu3bsDqtHpdKhFi2YBHQMAAADWslUIvvXWW3Xrrbf6tlNTUxUeHq6XX35ZY8eOtbCy/+PxeOV2n7K6DAAAANTC5WqqkJC6P7W3VQiuzaBBg7RixQrt3btX0dHRks5Of9ayZUtfH7fbLUm+dpfLpePHj9c4VnFxsa9PIKqqPAEfAwAAANYJqsGt8fHxklRjXG9BQYGaNGmiNm3a+PoVFhbK6/X69SssLPQdAwAAAOayfQjOyclRSEiIOnfurDZt2qhdu3Zas2ZNjT49e/ZUWFiYJCktLU3FxcXKy8vz9SksLNSePXuUlpbWoPUDAADAfmw1HGLkyJFKSUlRYmKiJGnDhg1auXKlRowY4Rv+MH78eE2cOFFt27ZVSkqKcnJytGvXLr366qu+4yQnJys1NVVTp07VpEmTFB4ervnz5ysxMVH9+/e35NwAAABgHw7v98cMWGjOnDn68MMPdfz4cXk8HrVr10733HOPHnroIb/pzlatWqXs7GwdO3ZMcXFxevLJJ9WnTx+/Y5WUlCgrK0vr169XVVWVUlNTNW3aNL/V5y5HdbVHRUVlAR0DAAAAV0ZMTLOLejDOViE4GBCCAQAA7OtiQ7DtxwQDAAAA9Y0QDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIxDCAYAAIBxCMEAAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIxDCAYAAIBxCMEAAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMaxbQguKytTWlqaEhMT9dlnn/m1rVq1SgMGDFCXLl00dOhQbdy4scbXl5SUaOrUqerevbuSk5M1YcIEnTx5sqHKBwAAgI3ZNgQvWbJE1dXVNfavXr1a06dP16BBg5Sdna1u3bopIyNDO3fu9OuXmZmpLVu2aNasWZo7d64KCws1evRoVVVVNdAZAAAAwK5sGYIPHDig119/XePHj6/R9txzz2nw4MHKzMxUjx499NRTT6lLly5avHixr8+OHTu0efNm/eY3v1F6erpuu+02Pfvss/rnP/+pdevWNeSpAAAAwIZsGYLnzJmj4cOHKy4uzm//4cOHdfDgQQ0aNMhvf3p6uvLy8lRRUSFJys3NlcvlUq9evXx94uPj1alTJ+Xm5l75EwAAAICthVpdwPetWbNG+/bt08KFC/X555/7tRUUFEhSjXCckJCgyspKHT58WAkJCSooKFBcXJwcDodfv/j4eN8xAhEaasu/HQAAAHCRbBWCT58+rd/97nd64oknFBkZWaO9uLhYkuRyufz2n9s+1+52uxUVFVXj66Ojo7V79+6AanQ6HWrRollAxwAAAIC1bBWCly5dqquvvlp33XWX1aWcl8fjldt9yuoyAAAAUAuXq6lCQur+1N42Ifjo0aNasWKFFi9erJKSEknSqVOnfP9fVlam6OhoSWenP2vZsqXva91utyT52l0ul44fP17jexQXF/v6BKKqyhPwMQAAAGAd24TgI0eOqLKyUmPGjKnRNmLECN10002aN2+epLNjg+Pj433tBQUFatKkidq0aSPp7NjfvLw8eb1ev3HBhYWF6tChwxU+EwAAANidbUJwp06d9Morr/jt27t3r7KysjR79mx16dJFbdq0Ubt27bRmzRr169fP1y8nJ0c9e/ZUWFiYJCktLU1LlixRXl6ebrnlFklnA/CePXs0atSohjspAAAA2JJtQrDL5VJKSkqtbUlJSUpKSpIkjR8/XhMnTlTbtm2VkpKinJwc7dq1S6+++qqvf3JyslJTUzV16lRNmjRJ4eHhmj9/vhITE9W/f/8GOR8AAADYl21C8MUaMmSITp8+rezsbC1btkxxcXFatGiRkpOT/fotWLBAWVlZmjFjhqqqqpSamqpp06YpNDToThkAAAD1zOH1er1WFxFMqqs9Kioqs7oMAAAA1CImptlFzQ7Bqg8AAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIxDCAYAAIBxCMEAAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIxDCAYAAIBxCMEAAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHFsFYI3bdqkBx98UD169NCNN96o2267TVlZWSopKfHr98EHH2jo0KHq0qWLBgwYoLfeeqvGsSoqKvT73/9evXr1Urdu3fTwww+roKCgoU4FAAAANmarEPztt9+qa9eumj17tpYvX66HH35Y77zzjh5//HFfn08++UQZGRnq1q2bsrOzNWjQIP3617/WmjVr/I41Z84crVq1Sk888YQWLlyoiooK/eIXv6gRqAEAAGAeh9fr9VpdxIWsXLlS06dPV25urmJjYzVy5EiVlZXpjTfe8PX5j//4D+3du1c5OTmSpOPHj6tv376aOXOm/v3f/13S2YDdp08fPfbYYxo9evRl11Nd7VFRUVlgJwUAAIArIiammUJC6r7Pa6s7wbVp3ry5JKmyslIVFRX6+OOPNXDgQL8+6enpOnDggI4cOSJJ2rx5szwej1+/5s2bq1evXsrNzW2w2gEAAGBPoVYXUJvq6mpVVVVp//79Wrx4sfr27avWrVtr//79qqysVHx8vF//hIQESVJBQYFat26tgoICXX311YqOjq7R78033wy4vtBQ2//tAAAAgAuwZQju06ePTpw4IUm69dZbNW/ePElScXGxJMnlcvn1P7d9rt3tdisqKqrGcV0ul6/P5XI6HWrRollAxwAAAIC1bBmCly1bptOnT2v//v1aunSpxo4dqxdffNHqsiRJHo9Xbvcpq8sAAABALVyuphc1JtiWIbhjx46SpOTkZHXp0kXDhg3T+vXrdcMNN0hSjRke3G63JPmGP7hcLpWWltY4rtvtrjFE4nJUVXkCPgYAAACsY/vBrYmJiWrSpIkOHTqktm3bqkmTJjXm+z23fW6scHx8vP71r3/VGPpQUFBQYzwxAAAAzGP7EPzpp5+qsrJSrVu3VlhYmFJSUrR27Vq/Pjk5OUpISFDr1q0lSampqXI6nVq3bp2vT3FxsTZv3qy0tLQGrR8AAAD2Y6vhEBkZGbrxxhuVmJioiIgIffHFF1q+fLkSExPVr18/SdK4ceM0YsQIzZo1S4MGDdLHH3+s9957T/Pnz/cd54c//KHuvvtuPfPMM3I6nYqNjdXzzz+vqKgoDR8+3KrTAwAAgE3YarGMZcuWKScnR4cOHZLX61WrVq10++23a+TIkYqMjPT127BhgxYsWKDCwkJdd911GjNmjO6++26/Y1VUVGj+/Pn661//qrKyMv3bv/2bpk2b5ptO7XKxWAYAAIB9XexiGbYKwcGAEAwAAGBfjWbFOAAAAKC+EYIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMEHIKPHTumGTNmaMCAAerevbu2bdsmSSoqKtKcOXO0Z8+egIsEAAAA6lNAIXj//v2644479P7776t169YqKSlRVVWVJCkmJkbbt2/Xq6++Wi+FAgAAAPUlNJAv/sMf/qCoqCitXLlSknTLLbf4tffu3Vvvv/9+IN8CAAAAqHcB3Qnetm2b7rvvPsXExMjhcNRov+6663TixIlAvgVwUbKyZumRR+5XVtYsq0sBAABBIKAQ7PV6FRERcd72oqIihYWFBfItgDp99dUx5efvkyTl5+/TV18ds7giAABgdwGF4M6dO2vTpk21tlVVVWn16tW66aabAvkWQJ2eeurXF9wGAAD4voBC8JgxY/Thhx9q5syZys/PlyR9/fXX2rp1qx555BEVFBRozJgx9VIoUJucnHdVXl7ut6+8vFw5Oe9aVBEAAAgGDq/X6w3kAO+8845++9vfqqSkRF6vVw6HQ16vV5GRkZo1a5aGDBlSX7XaQnW1R0VFZVaXAZ39tGHMmBHnbV+27BWFhgb07CcAAAgyMTHNFBJS933egEOwJJ06dUpbt27VwYMH5fF41LZtW6WmpioyMjLQQ9sOIdg+3nrrv7V69V/P2z548DDddde/N2BFAADAahcbgi/7Ntnp06f1k5/8RKNHj9aoUaPUr1+/yz0UcFnatYsPqB0AAJjrsscEN23aVCEhIWratGl91gNctOTkH5139pGwsHAlJ/+ogSsCAADBIqAH4/r376+1a9eqHkZUAJfM6XRq/Pj/qLXt8ccnyukMeFVwAADQSAX01NDgwYM1e/ZsjRgxQvfcc49atWpV67zBSUlJgXwb4LySkrro+uvj9OWXhb59118fp06deM8BAIDzC+jBuI4dO/7fgWpZMe7cbBF79+693G9hOzwYZz8lJW49/vhY3/azz/5JUVEuCysCAABWueIPxklSVlZWIF8O1IuoKJfat++g/Px9at++AwEYAADUKaAQfMcdd9RXHcBlKylxa//+s4u17N+fr5ISN0EYAABcEE8OIegtWvRH38OZXq9XixbNt7giAABgdwEvp1VeXq61a9dqz549Kikpkcfj8Wt3OBz67W9/G+i3AWr1+eefKT9/n9++/Px/6vPPP1NSUheLqgIAAHYXUAg+evSoRowYoaNHj8rlcqmkpETR0dEqKSlRdXW1WrRooauuuqq+agX8eDwePf/8wlrbnn9+oRYs+BPTpAEAgFoFlBCeeeYZlZaWauXKlVqzZo28Xq/mz5+vHTt2aOLEiYqIiNDy5cvrq1bAz65dO1VaWlprW2lpqXbt2tmwBQEAgKARUAj+6KOPdN9996lr165+d9zCwsI0atQo9ejRg6EQuGK6du2myMjIWtsiIyPVtWu3hi0IAAAEjYBC8JkzZ9SqVStJZ0OHw+FQSUmJrz05OVnbt28PrELgPJxOpx59dHytbePGPc5QCAAAcF4BpYRrr71WJ06ckCSFhoYqNjZWO3fu9LXv379f4eHhARUIXEhSUhe1b9/Bb1/79omsGAcAAC4ooAfjevTooQ0bNigjI0PS2XmDly1bJrfbLY/Ho3fffVfDhg2rl0KB88nIeFKZmeN8KxRmZDxhdUkAAMDmLjkE//GPf1R6ero6duyoMWPG6LPPPlNFRYXCwsI0duxYnTx5UmvXrpXT6dSQIUM0ZcqUK1E34BMV5dLgwcOUk/Ou0tOHslAGAACok8N7bpWBi9SxY0f94Q9/0E9/+lNJ0jfffKNbbrlFK1asUM+ePa9IkXZSXe1RUVGZ1WUAAACgFjExzRQSUveI33p5cugSczQAAABgqYBXjAPsICtrlvLz96l9+w6aMmWW1eUAAACbYw4pBL2vvjrmWzo5P3+fvvrqmMUVAQAAu7usO8FHjx7V559/Lkm+eYG//PJLuVy1P5CUlMR0Vbhynn562ve2p2vJElYqBAAA53dZD8Y5HA6/feempvq+c/v37t0bWJU2woNx9pKT867efPONGvvvvnu40tOHWlARAACw0sU+GHfJd4KzsrIuqyCgvlVVVdUagCXpzTffUP/+6QoNZdg7AACo6ZITwh133HEl6gAu2d/+9pc62++4454GqgYAAAQTHoxD0PrpTy/8B1ld7QAAwFyEYASt0NBQDRw4uNa29PSfMhQCAACcFyEYQcvr9erIkcO1th069CWLuAAAgPMiBCNoffXVMe3evavWtt27dzFfMAAAOC9CMILWtddepxtv7Cqn0/9t7HQ6deONXXXttddZVBkAALA7QjCClsPh0AMP/EKSo8b+Bx98uNa5qwEAACRCMIJcbOwPlZb2E799aWl99IMfxFpTEAAACAqEYAS9jz7a4redl7fZokoAAECwIAQjqOXkvKszZ8747Ttz5oxyct61qCIAABAMCMEIWnUtm1xVVdXAFQEAgGBBCEbQuphlkwEAAGpDCEbQYtlkAABwuQjBCFqhoaG6++7htbbde+8DLJsMAADOy1Yh+P3339e4ceOUlpambt26adiwYXrzzTdrLH+7atUqDRgwQF26dNHQoUO1cePGGscqKSnR1KlT1b17dyUnJ2vChAk6efJkQ50KGkh6+lBFRET47YuIaKqBAwdbVBEAAAgGtgrBL730kpo2barJkydr6dKlSktL0/Tp07V48WJfn9WrV2v69OkaNGiQsrOz1a1bN2VkZGjnzp1+x8rMzNSWLVs0a9YszZ07V4WFhRo9ejQPSzVC06fP+d720xZVAgAAgoXD+/3brBYqKipSTEyM377p06crJydH27Ztk9Pp1IABA3TjjTdq3rx5vj7Dhw9XVFSUsrOzJUk7duzQ8OHDtXz5cqWmpkqSCgoKlJ6erj/+8Y9KT0+/7Bqrqz0qKiq77K/HlZGVNUv5+fvUvn0HTZkyy+pyAACARWJimikkpO77vLa6E/z9ACxJnTp1UmlpqU6dOqXDhw/r4MGDGjRokF+f9PR05eXlqaKiQpKUm5srl8ulXr16+frEx8erU6dOys3NvbInAUtMmTJLK1a8TgAGAAAXxfZPDm3fvl2xsbGKjIzU9u3bJUlxcXF+fRISElRZWanDhw8rISFBBQUFiouLk8Ph8OsXHx+vgoKCgGsKDbXV3w4AAAC4RLYOwZ988olycnI0adIkSVJxcbEkyeVy+fU7t32u3e12KyoqqsbxoqOjtXv37oBqcjodatGiWUDHAAAAgLVsG4KPHz+uJ554QikpKRoxYoTV5fh4PF653aesLgMAAAC1cLmaXtSYYFuGYLfbrdGjR6t58+ZauHChnM6zJxIdHS3p7PRnLVu29Ov/3XaXy6Xjx4/XOG5xcbGvTyCqqjwBHwMAAADWsd3g1jNnzujRRx9VSUmJXnjhBb9hDfHx8ZJUY1xvQUGBmjRpojZt2vj6FRYW1phfuLCw0HcMAAAAmMtWIbiqqkqZmZkqKCjQCy+8oNjYWL/2Nm3aqF27dlqzZo3f/pycHPXs2VNhYWGSpLS0NBUXFysvL8/Xp7CwUHv27FFaWtqVPxEAAADYmq2GQ8yePVsbN27U5MmTVVpa6rcARufOnRUWFqbx48dr4sSJatu2rVJSUpSTk6Ndu3bp1Vdf9fVNTk5Wamqqpk6dqkmTJik8PFzz589XYmKi+vfvb8GZAQAAwE5stVhG3759dfTo0VrbNmzYoNatW0s6u2xydna2jh07pri4OD355JPq06ePX/+SkhJlZWVp/fr1qqqqUmpqqqZNm1bj7vKlYrEMAAAA+7rYxTJsFYKDASEYAADAvoJyxTgAAACgIRCCAQAAYBxCMAAAAIxDCAYAAIBxCMEAAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGCbW6ANif1+tVRUW51WVckNfrlSQ5HA6LK7mwsLBw29cIAIAJCMG4IK/Xq6ys2dq/f5/VpTQKN9zQQVOmzCQIAwBgMYZDAAAAwDgO77nPkXFRqqs9Kioqs7qMBmX34RDl5eXKzBwnSVqwYKnCw8Mtruj8GA4BAMCVFRPTTCEhdd/nZTgE6uRwOBQeHmF1GRclPDw8aGoFAADWYTgEAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIxDCAYAAIBxCMEAAAAwDiEYAAAAxiEEAwAAwDiEYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMaxVQj+8ssvNWPGDA0bNkydO3fWkCFDau23atUqDRgwQF26dNHQoUO1cePGGn1KSko0depUde/eXcnJyZowYYJOnjx5pU8BAAAAQcBWITg/P1+bNm3S9ddfr4SEhFr7rF69WtOnT9egQYOUnZ2tbt26KSMjQzt37vTrl5mZqS1btmjWrFmaO3euCgsLNXr0aFVVVTXAmQAAAMDOQq0u4Lv69u2rfv36SZImT56s3bt31+jz3HPPafDgwcrMzJQk9ejRQ/v27dPixYuVnZ0tSdqxY4c2b96s5cuXKzU1VZIUFxen9PR0rVu3Tunp6Q1zQgAAALAlW90JdjovXM7hw4d18OBBDRo0yG9/enq68vLyVFFRIUnKzc2Vy+VSr169fH3i4+PVqVMn5ebm1n/hAAAACCq2uhNcl4KCAkln7+p+V0JCgiorK3X48GElJCSooKBAcXFxcjgcfv3i4+N9xwhEaKit/nYwXnX1//0+QkOd/H4AAECdgioEFxcXS5JcLpff/nPb59rdbreioqJqfH10dHStQywuhdPpUIsWzQI6BurXmTMhvtfNmzdTRESEhdUAAIBgEFQh2A48Hq/c7lNWl4HvKC8/43v97bdlCg+vtrAaAABgJZerqUJC6v5UOKhCcHR0tKSz05+1bNnSt9/tdvu1u1wuHT9+vMbXFxcX+/oEoqrKE/AxUH+++/uoqvIoJITfDwAAuLCgCsHx8fGSzo4NPvf63HaTJk3Upk0bX7+8vDx5vV6/ccGFhYXq0KFDwxZ9Hl6vVxUV5VaX0SiUl5fX+hqBCQsLrzGuHgCAxiKoQnCbNm3Url07rVmzxjeVmiTl5OSoZ8+eCgsLkySlpaVpyZIlysvL0y233CLpbADes2ePRo0aZUnt31dRUa5x4x6xuoxGJzNznNUlNBpLl65QeDjjqwEAjZOtQvDp06e1adMmSdLRo0dVWlqqNWvWSJK6d++umJgYjR8/XhMnTlTbtm2VkpKinJwc7dq1S6+++qrvOMnJyUpNTdXUqVM1adIkhYeHa/78+UpMTFT//v0tOTcAAADYh8Pr9XqtLuKcI0eO6Lbbbqu17ZVXXlFKSoqks8smZ2dn69ixY4qLi9OTTz6pPn36+PUvKSlRVlaW1q9fr6qqKqWmpmratGmKjY0NqMbqao+KisoCOoZ09mGuc3eCm7X/mRxOW/09EnTOvY35+D4wXk+VyvLfkcSdYABAcIqJaXZRD8bZKgQHgysRgiMT7yYEwxa8niqV/vNNSYRgAEBwutgQzKoCAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBzm5QIAg/361xP11VfHdO211+k3v5lrdTkA0GC4EwwAhvryy4P66qtjkqSvvjqmL788aG1BANCACMEAYKg5c6Z/b3uGRZUAwWX8+DF65JH7NX78GKtLQQAIwQBgoJUrX1N1dbXfvurqKq1c+ZpFFQHBYe/e3SorK5UklZWVau/e3RZXhMtFCAYAw1RWVmrNmtW1tq1Zs1qVlZUNXBEQPP7wh99ecBvBgxAMAIb5859fCagdMNWSJQsuaT/sjRAMAIa5774RAbUDJiovL9cnn/y/Wts++eT/qby8vIErQqAIwQBgmCZNmmjgwMG1tqWnD1WTJk0auCLA/hYvXhBQO+yHEAwABkpK6lrr/k6dkhq4EiA4/PKXmQG1w34IwQBgGI/Ho+efX1hr2/PPL5TH42ngigD7Cw8P1803d6+1rXv3ngoPD2/gihAoQjAAGGbXrp0qLS2tta20tFS7du1s2IKAIPHYY5m17h87dnzDFoJ6QQgGAMN07dpNkZGRtbZFRkaqa9duDVsQEER+9aupF9xG8CAEA4BhnE6nHn209jtX48Y9LqeT/zQAaPxCrS4AktdTZXUJgCTeiyZJSuqi9u07KD9/n29f+/aJPBgH1KG2xTJWrHjdomoQCEKwRbxer+91Wf471hUCnMd336NonDIynlRm5jh5vV45HA5lZDxhdUmArV1osYzzjReGffGZFwAYKirKpcGDh8npdGrw4GGKinJZXRJgWyyW0fhwJ9giDofD97pZ+5/J4eRXAet5PVW+Tya++x5F43XnnffqzjvvtboMwPYuZrGMJ5+c1DDFoF6QvGzA4QwlBAMAYGO//GWmxo17+ILtCC4MhwAAAKgDi2U0PoRgAACAi8BiGY0LIRgAAADGIQQDAABchBdfXHZJ+2FvhGAAAIA6VFRU6MMP/15r24cf/l0VFRUNVwzqBSEYAACgDsuX/ymgdtgP83IBwBXg9XpVUWH/yfPPrQxo93mhw8LCbV8jGreRI8dq27aPLtiO4EIIBoB65vV6lZU1W/v377O6lEbjhhs6aMqUmQRhWCYsLEy33vqTWodE9O59m8LCwhq6JASI4RAAAAAB8HiqrS4Bl4E7wQBQzxwOh6ZMmWn74RDl5eXKzBwnSVqwYKmtJ/tnOASsVteDcQ888AvuBgcZQjAAXAEOh0Ph4RFWl3HRwsPDg6peoKFdzINx48ZNaKBqUB8YDgEAAFCHuh5848G44EMIBgAAqIPTeeHIVFc77IffGAAAQB3+9re/BNQO+2FMsA14PVVWlxD0gmWuU7vjvQgAtevbt/8Fg27fvv0bsBrUB0KwDZTlv2N1CQAA4AJ+85sZdbY/88yzDVQN6gPDIQAAAOowdersgNphP9wJtkhYWLiWLl1hdRmNQjDNdRpMwsLs+XMMluWIg0F5eXmtr3H5mM84cHa9xleter3O9hEjHmmgai4e78nzIwRbJNjmEA0WzHXa+FVUlGvcOPv9hybYnftDEoFZunQF/wYFIJiXHM/L26y8vM1Wl1EDS46fH8MhAAAAYBzuBAMIWhNTWioshLsbgWBmlcBVVHs19+P/tbqMRsHuS47/13+9qK1bP6yxPzW1tx544OcWVFQ3hkOcHyEYQNAKC3EQggPGzw/2YufhgqNGjas1BD/yyKMWVINAMRwCAADgIv3qV1P9tqdOnWVNIQgYIRgAAOAixcff4HvdvHkL3XBDBwurQSAYDgEAgAHsOvVYsPnudIKzZv1W5eVnLKym8bBi7DIhGEDQqqj2Wl0CEDTvQ6YXrH9MLVh/rJhekBAMIKicm81AEk/kw3a++/4EYG+EYAAADNMi/Xo5QpkZ5HIxtWD98FZ59U3Ol5Z9f0IwgKDCf3RgZ8Hy/nSEOuQI5dn4yxUcv+Vg4LH0uxOCAQSVsLBwLV26wuoyGoXy8nLfmMYFC5YqPDzc4oqCX1hYcPwMPWeq5Qi1NoAEM+4E1w9vlbXDhwjBAIKKnSfSD2bh4eH8XBu5745X/nbdYQsrAWqyYjw9n4UAAADAOI36TvCBAwc0Z84c7dixQ82aNdOwYcOUmZmpsLAwq0sDAKBBhYdHaMmS5aqoqLC6lKBWXl6uSZMyJUm///0ChhHVg7CwMEs+iWq0Ibi4uFg///nP1a5dOy1cuFAnTpzQ7373O505c0YzZsywujwAABqUw+FQRERTRUQ0tbqUoPbdxTFcLhfDiIJYow3Bb7zxhsrKyrRo0SI1b95cklRdXa3Zs2fr0UcfVWxsrLUFAgAAwDKNNgTn5uaqZ8+evgAsSYMGDdLMmTO1ZcsW3XnnndYVF2TsvtTmd5ew/O5rO7JiWUhYw+7XjcS1A3uy+7UTTNeNxLVzIY02BBcUFOiuu+7y2+dyudSyZUsVFBQEdOxQg+ZW9Hq9mjNnlvLz91ldykWx+xKW7dsnatq0WfyD1MgF23Ujce3AHoLt2rH7dSNx7VxIow3BbrdbLperxv7o6GgVFxdf9nGdTodatGgWSGlBxev1KjQ0xOoyGo3QUKdatGjGP0aNHNdN/ePaMQPXTv3j2jm/RhuCrxSPxyu3+5TVZTSoyZNn2PqjKSl4Ji4PCwvXt9+a9f4xVTBcNxLXDuwnGK6dYLluJDOvHZerqUJC6v7UvtGGYJfLpZKSkhr7i4uLFR0dHdCxq6rMW2UnJIRp5epDdbVXkrUr5KDhcN3UH64ds3Dt1B+unfNrtINb4+Pja4z9LSkp0f/+7/8qPj7eoqoAAABgB402BKelpWnr1q1yu92+fWvWrJHT6VSvXr0srAwAAABWa7QhePjw4WrWrJl++ctfavPmzXrrrbf0zDPPaPjw4cwRDAAAYDiH99zo7kbowIEDevrpp/2WTX7iiScCWja5utqjoqKyeqwSAAAA9SUmptlFPRjXqEPwlUAIBgAAsK+LDcGNdjgEAAAAcD6EYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGYbGMS+T1euXx8CMDAACwI6fTIYfDUWc/QjAAAACMw3AIAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAAAAYhxAMAAAA4xCCAQAAYBxCMAAAAIxDCAYAAIBxQq0uAAjEl19+qeXLl+vTTz9Vfn6+4uPj9d5771ldFmBr77//vt599119/vnncrvduv766/XQQw/prrvuksPhsLo8wLY2bdqk7Oxs7d+/X6WlpYqNjVW/fv2UkZGhqKgoq8vDJSIEI6jl5+dr06ZNuummm+TxeOT1eq0uCbC9l156Sa1atdLkyZPVokULbd26VdOnT9fx48eVkZFhdXmAbX377bfq2rWrHnroITVv3lz5+flauHCh8vPztWLFCqvLwyVyeEkNCGIej0dO59lRPZMnT9bu3bu5EwzUoaioSDExMX77pk+frpycHG3bts13TQGo28qVKzV9+nTl5uYqNjbW6nJwCfiXDkGN/1gDl+77AViSOnXqpNLSUp06dcqCioDg1bx5c0lSZWWltYXgkjEcAgCg7du3KzY2VpGRkVaXAthedXW1qqqqtH//fi1evFh9+/ZV69atrS4Ll4gQDACG++STT5STk6NJkyZZXQoQFPr06aMTJ05Ikm699VbNmzfP4opwOfgsGQAMdvz4cT3xxBNKSUnRiBEjrC4HCArLli3TG2+8oTlz5qigoEBjx45VdXW11WXhEnEnGAAM5Xa7NXr0aDVv3lwLFy5kjD1wkTp27ChJSk5OVpcuXTRs2DCtX79eAwcOtLgyXApCMAAY6MyZM3r00UdVUlKi//7v/2aOU+AyJSYmqkmTJjp06JDVpeASEYIBwDBVVVXKzMxUQUGBXnvtNaZ1AgLw6aefqrKykgfjghAhGEHt9OnT2rRpkyTp6NGjKi0t1Zo1ayRJ3bt3r3UqKMB0s2fP1saNGzV58mSVlpZq586dvrbOnTsrLCzMuuIAG8vIyNCNN96oxMRERURE6IsvvtDy5cuVmJiofv36WV0eLhGLZSCoHTlyRLfddlutba+88opSUlIauCLA/vr27aujR4/W2rZhwwbuaAHnsWzZMuXk5OjQoUPyer1q1aqVbr/9do0cOZLpBYMQIRgAAADG4VFgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACMQwgGAACAcQjBAAAAMA4hGAAAAMYhBAMAAMA4hGAAMMSRI0eUmJiot99+2+pSAMByoVYXAAC4sLfffltTpkzxbYeFhem6665Tr1699Nhjj+maa66xsDoACE6EYAAIEhMmTFDr1q1VUVGh7du3689//rM2bdqk9957T02bNrW6PAAIKoRgAAgSaWlp6tKliyTpnnvuUfPmzfXiiy9qw4YNGjJkiMXVAUBwIQQDQJDq0aOHXnzxRR05ckSS5Ha7tWjRIv3P//yPTp48qZiYGPXo0UOTJ09WTExMrcf44osv9NJLL2nbtm06efKkXC6X0tLS9J//+Z9q0aKFr19paameffZZbdiwQSdPnlRUVJQ6duyoiRMnKikpSZJ08OBBzZs3T//4xz/kdrvVokUL/ehHP9JTTz2lqKioK/8DAYBLQAgGgCB16NAhSVLz5s1VVlamBx54QAcOHNBdd92lzp0765tvvtEHH3ygEydOnDcEb926VYcPH9add96pli1bKj8/XytXrtT+/fu1cuVKORwOSdLMmTO1du1aPfjgg0pISNC3336r7du368CBA0pKSlJFRYVGjhypiooKPfjgg7rmmmt04sQJ/f3vf5fb7SYEA7AdQjAABInS0lIVFRWpoqJC//jHP7R48WJFRESoT58+Wr58ufbt26dFixbp9ttv933NY489Jq/Xe95j3n///XrkkUf89nXr1k1PPvmktm/frptvvlmStGnTJt17772aPHmyr9/o0aN9rw8cOKAjR47o2Wef1cCBA337MzIyAj5vALgSCMEAECR+8Ytf+G23atVKc+fOVWxsrNatW6eOHTv6BeBzzt3NrU1ERITvdXl5ucrKynTTTTdJkj7//HNfCHa5XPr000914sQJxcbG1jhOZGSkJGnz5s3q3bs3D+oBsD1CMAAEiRkzZiguLk4hISG65pprFBcXJ6fz7HTvhw4dUv/+/S/5mN9++60WLVqknJwcff31135tJSUlvtcTJ07U5MmT9ZOf/ERJSUnq3bu3fvazn6lNmzaSpDZt2ujhhx/Wiy++qL/97W+6+eab1bdvXw0dOpShEABsiRAMAEGia9euvtkh6ktmZqZ27NihkSNHqlOnTrrqqqvk8Xg0atQov2EU6enpuvnmm7V+/Xpt2bJFy5cvV3Z2thYuXKjevXtLkiZPnqw77rhDGzZs0JYtWzRnzhw9//zzWrlypX74wx/Wa90AEChWjAOARqBt27bKz8+/pK8pLi5WXl6eRo8erQkTJuj2229Xr169fHd3v+8HP/iBHnjgAS1ZskQbNmxQ8+bN9ac//cmvT2Jioh577DG99tpreu2113TixAn9+c9/vuzzAoArhRAMAI1A//799cUXX2j9+vU12s73YFxISEit+19++WW/7erqar+hEZJ09dVX6wc/+IEqKioknX1or6qqyq9Phw4d5HQ6fX0AwE4YDgEAjcDIkSO1du1aPf7447rrrruUlJSk4uJiffDBB5o9e7Y6duxY42siIyP14x//WC+88IIqKysVGxurLVu2+OYdPqesrEy9e/fWgAED1LFjR1111VXaunWrPvvsM99sER999JGeeuopDRw4UO3atVN1dbX++te/KiQkRAMGDGiQnwEAXApCMAA0As2aNdNrr72mhQsXav369frLX/6iq6++Wj179qx1Nodz5s2bp6efflqvv/66vF6vevXqpezsbN16662+PhEREbrvvvu0ZcsWrVu3Tl6vV23bttXMmTN1//33Szo7DCI1NVUbN27UiRMn1LRpUyUmJio7O1vdunW70qcPAJfM4b3QBJIAAABAI8SYYAAAABiHEAwAAADjEIIBAABgHEIwAAAAjEMIBgAAgHEIwQAAADAOIRgAAADGIQQDAADAOIRgAAAAGIcQDAAAAOMQggEAAGAcQjAAAACM8/8B03c0W3KZ9QEAAAAASUVORK5CYII=","text/plain":["<Figure size 800x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.boxplot(data = df, x = 'Pclass', y = 'Fare')"]},{"cell_type":"markdown","metadata":{"id":"SYT_QjJD1DP5"},"source":["Distribuição das tarifas entre as classes de acomodação de passageiros."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":420,"status":"ok","timestamp":1681224174063,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"Yx4gr3IvxI8K","outputId":"5c3afea3-4d33-407b-cb87-330c0b635ffc"},"outputs":[{"data":{"text/plain":["(array([257, 678, 736]),)"]},"execution_count":196,"metadata":{},"output_type":"execute_result"}],"source":["np.where(df['Fare'] > 500)"]},{"cell_type":"markdown","metadata":{"id":"w5abuF2V1lnh"},"source":["Nota-se a presença de outliers muito discrepantes do resto das amostras. Onde apenas 3 passageiros pagaram uma tarifa muito alta. Maior que 500 dolares na época."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1681222342165,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"EVKjHI1EoGSF","outputId":"9a14cfe8-7d04-4578-c6a9-21fac9911c3b"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-daae2435-c850-4ecd-b927-fd0c70ceb8e8\">\n","    <div 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>Pclass</th>\n","      <th>Survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.626168</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>0.472826</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>0.242363</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daae2435-c850-4ecd-b927-fd0c70ceb8e8')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-daae2435-c850-4ecd-b927-fd0c70ceb8e8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   Pclass  Survived\n","0       1  0.626168\n","1       2  0.472826\n","2       3  0.242363"]},"execution_count":182,"metadata":{},"output_type":"execute_result"}],"source":["df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"]},{"cell_type":"markdown","metadata":{"id":"q7C8KRz511TD"},"source":["A taxa de sobrevivência também mostra que passageiros com melhores classes de acomodação tinham mais chances de sobreviverem que os com piores classes de acomodação."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"elapsed":437,"status":"ok","timestamp":1681222342593,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"NxhAsMuZbd5M","outputId":"0bdc668f-54a7-4fc8-e13a-75394d226afb"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"execution_count":183,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(data = df, x = 'Survived', hue= 'Pclass', palette = 'rocket')"]},{"cell_type":"markdown","metadata":{"id":"_Bgg7Cja2hNe"},"source":["Por este gráfico é notável que muito mais pessoas da 3a classe não sobreviveram."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1681222342594,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"qyGiC9cVoLO9","outputId":"336e45ee-265b-46c3-bae2-05ede01dcee5"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-2fe97a8e-db82-4546-a6f9-d05ed45890e9\">\n","    <div 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","        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do sexo feminino é expressivamente maior do que do sexo masculino."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1681222342594,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"EnlGUUJAeCE-","outputId":"30dbab67-ac31-4c1c-f9e8-2dc01f2b3009"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"execution_count":185,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(data = df, x = 'Survived', hue= 'Sex', palette = 'rocket')"]},{"cell_type":"markdown","metadata":{"id":"TkxIgt1f253y"},"source":["O que fica muito mais aparente na análise gráfica."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"elapsed":426,"status":"ok","timestamp":1681222345901,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"orN_xF6kplIt","outputId":"26ecc8d4-7fb3-4265-a7ac-a99b45b24bd6"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"execution_count":186,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(data = df, x = 'Survived', hue= 'Embarked', palette = 'rocket')"]},{"cell_type":"markdown","metadata":{"id":"TpoWUAip3GtO"},"source":["Nesse gráfico analisá-se a chance de sobrevivência de acordo com o porto embarcado. Sendo muito maior se o indivíduo embarcou em Cherbourg, do que em Queenstown ou Southampton."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":358,"status":"ok","timestamp":1681222369270,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"eD0YB-w5qExD","outputId":"9784e5cb-2350-45f8-fab3-7c7083eebe3a"},"outputs":[{"data":{"text/plain":["S    644\n","C    168\n","Q     77\n","Name: Embarked, dtype: int64"]},"execution_count":187,"metadata":{},"output_type":"execute_result"}],"source":["df['Embarked'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":261,"status":"ok","timestamp":1681222434038,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"le4KWkNqqiY9","outputId":"1119e33f-5c74-4e62-88d6-b93e5be4e633"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-21015f56-86ba-4827-b8d7-948aa6fd4f26\">\n","    <div 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>Embarked</th>\n","      <th>Survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>C</td>\n","      <td>0.553571</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Q</td>\n","      <td>0.389610</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>S</td>\n","      <td>0.336957</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21015f56-86ba-4827-b8d7-948aa6fd4f26')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-21015f56-86ba-4827-b8d7-948aa6fd4f26 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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ascending=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":553},"executionInfo":{"elapsed":524,"status":"ok","timestamp":1681222177091,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"c7cQWZuLha49","outputId":"dd0d9310-a7ea-44e3-c109-e89257d1356e"},"outputs":[{"name":"stderr","output_type":"stream","text":["<ipython-input-135-f4bd79f7df04>:1: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n","  sns.displot(data = df, x = 'Age', palette = 'rocket', bins=30)\n"]},{"data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f8d6b897e80>"]},"execution_count":135,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.displot(data = df, x = 'Age', palette = 'rocket', bins=30)"]},{"cell_type":"markdown","metadata":{"id":"vnNnsBJc3jWI"},"source":["Gráfico da distribuição de idade dos passageiros"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":518},"executionInfo":{"elapsed":912,"status":"ok","timestamp":1681222178000,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"FLMLyvXSfJus","outputId":"341e211b-4022-485c-9f07-f80b61d14f02"},"outputs":[{"data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f8d6b7e68b0>"]},"execution_count":136,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 585.222x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.displot(data = df, x = 'Age', palette = 'rocket', bins=30, hue= 'Survived')"]},{"cell_type":"markdown","metadata":{"id":"seGFnjER3oyF"},"source":["Gráfico sobreposto das distribuições dos que morreram e dos que viveram."]},{"cell_type":"markdown","metadata":{"id":"nYvSr8F2aZKj"},"source":["#### ✅ Feature Selection"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FgSSkaYya1p4"},"outputs":[],"source":["features= ['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare',\t'Embarked', 'Survived']\n","df = df[features]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1681222178002,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"x6L0J505c35U","outputId":"3009629f-684a-4a1d-edcd-237f0f4836a3"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-c519e6a5-05dc-41a5-84e2-0a7e3f2f7fd8\">\n","    <div 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>Pclass</th>\n","      <th>Age</th>\n","      <th>Sex</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Embarked</th>\n","      <th>Survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3</td>\n","      <td>22.0</td>\n","      <td>male</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>S</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>38.0</td>\n","      <td>female</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>26.0</td>\n","      <td>female</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>female</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3</td>\n","      <td>35.0</td>\n","      <td>male</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>S</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c519e6a5-05dc-41a5-84e2-0a7e3f2f7fd8')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 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Também foi excluida a coluna \"Cabin\" pela quantidade de dados faltantes."]},{"cell_type":"markdown","metadata":{"id":"BEanSrHCbtHx"},"source":["#### ✅ Tratando variáveis categóricas"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KFzbTormbsx9"},"outputs":[],"source":["df = pd.get_dummies(df, columns = ['Sex', 'Embarked'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1681222178002,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"QXsI4KphUNW3","outputId":"8d95531e-de7e-4b24-d91a-972cf24594a0"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-bf8bbb36-8e4e-4ad8-9830-b6681dc13e34\">\n","    <div 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 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<td>0</td>\n","      <td>71.2833</td>\n","      <td>1</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>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf8bbb36-8e4e-4ad8-9830-b6681dc13e34')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 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Para isso se cria novas colunas para cada uma das classes categóricas e utilizando lógica binária atribui-se o valor de 0 ou 1 do dado a coluna correspondente."]},{"cell_type":"markdown","metadata":{"id":"7w3asR1WSBYg"},"source":["#### 🗂️ Definindo a matriz de atributos e o vetor target:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vB1zupnrSF5w"},"outputs":[],"source":["X = df.drop(columns = 'Survived')\n","y = df['Survived']"]},{"cell_type":"markdown","metadata":{"id":"cnxWvCfwVZIK"},"source":["#### ✂️ Separando nosso dataset em um set de treino e um set de teste"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mFt1dWYvVYx2"},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"]},{"cell_type":"markdown","metadata":{"id":"yZw6vJkFRfaJ"},"source":["#### 🏋️ Treinando..."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nOUPXc4IggS1"},"outputs":[],"source":["clf = DecisionTreeClassifier().fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{"id":"zzH3r7FSSdxZ"},"source":["#### 🤔 Analisando o Classification Report do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZPMSt0UnSdS1"},"outputs":[],"source":["y_pred = clf.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1681222178004,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"27fDy6aNSkqB","outputId":"a50564a9-6c72-4ca3-c35b-83ff817091ff"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.83      0.79      0.81       141\n","           1       0.67      0.72      0.69        82\n","\n","    accuracy                           0.77       223\n","   macro avg       0.75      0.76      0.75       223\n","weighted avg       0.77      0.77      0.77       223\n","\n"]}],"source":["class_report = classification_report(y_test, y_pred)\n","print(class_report)"]},{"cell_type":"markdown","metadata":{"id":"rekW5Swp7-OL"},"source":["- A precisão mede quantas das previsões positivas do modelo são realmente corretas\n","- A acurácia é uma medida geral de avaliação que mede a proporção de previsões corretas\n","- O F1-Score é uma medida de avaliação que considera tanto a precisão quanto o recall.\n","- O recall mede a capacidade do modelo em detectar todos os exemplos positivos"]},{"cell_type":"markdown","metadata":{"id":"4jp8GNETWGrV"},"source":["#### 🤔 Analisando a matriz de confusão do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1681222178004,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"s8LF9aBjWGV7","outputId":"2a62d39c-9c0c-4eb3-9b3c-bf3ddb99783d"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[112  29]\n"," [ 23  59]]\n"]}],"source":["cf_matrix_nb = confusion_matrix(y_test, y_pred)\n","print(cf_matrix_nb)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":480,"status":"ok","timestamp":1681222178470,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"mnZD6rEMWPNX","outputId":"08cca699-b8cf-4ed1-f383-dd0af111f3d7"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":147,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["group_names = ['True Neg','False Pos','False Neg','True Pos']\n","group_counts = [\"{0:0.0f}\".format(value) for value in cf_matrix_nb.flatten()]\n","labels = [f\"{v1}\\n{v2}\" for v1, v2 in zip(group_names, group_counts)]\n","labels = np.asarray(labels).reshape(2, 2)\n","\n","sns.set(font_scale= 1)\n","sns.heatmap(cf_matrix_nb, annot=labels, fmt= '', cmap='Blues')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1681222178471,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"slNXEb5Gfdtz","outputId":"27933c52-29a0-4d91-b854-0a503249adec"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-789fabae-06b9-462b-9537-82c194b9f95c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    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 1           0   \n","830       3  15.0      1      0  14.4542           1         0           1   \n","\n","     Embarked_Q  Embarked_S  \n","376           0           1  \n","458           0           1  \n","732           0           1  \n","507           0           1  \n","830           0           0  "]},"execution_count":148,"metadata":{},"output_type":"execute_result"}],"source":["X_train.head()"]},{"cell_type":"markdown","metadata":{"id":"e7Lz3Byuvn5F"},"source":["#### 🌳 Visualizando a árvore que o modelo construiu"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZhHDOwk8ha25"},"outputs":[],"source":["clf_plot_tree = DecisionTreeClassifier(max_depth = 3).fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"executionInfo":{"elapsed":997,"status":"ok","timestamp":1681222179461,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"RcnNjKcsfDo7","outputId":"da1a86a4-41f2-44db-91cb-236e17b48c4c"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAoAAAAGVCAYAAABuPkCWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADdFklEQVR4nOzdd3gU5RbA4d+29N4TQgiBUELvvaj0oggiCoiIFCkKFrwgJXTEjoAoqCBNEAHpVZEmvUMIAUIJJKT3vuX+EVhYEyBBQkJy3ue5z2VnZmfPjMvH2W9mzlEYDAYDQgghhBCi1FAWdQBCCCGEEOLpkgRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCCGEEKKUkQRQCFGoDAZDUYfwTJLzJoQoTOqiDkAIUbIpFAoyb5xGn5lS1KE8M5TmNpj71CrqMIQQJZgkgEKIQqfPTMGQnlTUYTwz9EUdgBCixJNLwEIIIYQQpYzMAAohnrrk1DTGzl5KRmYW2VodM0e+gZeb81P57CFTvuOHicMK9TNux8QzYe5ysrU6xg58hcq+ZYzrwqNieXPcbMp5ueLh4si0d/sWaixCCJEXSQCFEE/d5r3H6dyyPh2b1yMrW4teX/wveqZnZmFpbpavbRf/8Scf9X8ZV0d7pi/8jS8/GmCyvnndAAKHvlYYYQohRL5IAiiEeOrMzTScvniN5nWqYmttBUBMfBKT568kLSOTRjUrMeDlNoyY/gMzR/Vj7/HzpKZl0LtzK5P9HDt/icXr/0KBAp1eR7M6Vdl+4CTN6lRlSM8OrP3zIFv2HiMjM5txg3tS1a+s8b2hN28z66c1ZGZr6fZ8I7o93zjPWI+dv8zqHQdwc7Lnwze75ev4wm7HGGf9MjKycq0/eDqYARO+5ZV2TenUon6+9imEEE+SJIBCiKeuS8v6RETH8ea42fh6uTHt3b4s+mMXI3p3prJvGcZ+s4TE5DTe7dOF6QtWk56RybdjB+e5L2sLc2Z90J9pP6xCo1azZMb7vDV+NkN6dqBj83p0f6EJNyNj+H7VNqa9d+9y6w+rtzNjZD+c7G0YPv17XmzdEKUy57bobK2OdX8e5M9Dp6nuX46RfboYL1H/tn0/O/45aRLDu727UKtyeeNr/X0lXPT/Kufi4mjPutmfoACGTf+eprWr4mBr/Z/OpxBCFJQkgEKIp06jUTPstU4Me60TSzfuZtPeo1yPiObzRWsBSE5NJyY+kWoVfLgdE8+r7ZuhUuX9zFoFH08AXBztqHjnz2ZmGgD2nwji1617USoUqFUqk/fdiIjmf18vBiAhOZXElDQc7WwASEvPYO2ugzSqUYkebZqY3J/4avvmvNq++UOPT6lQ5PlnADPNvWG3bpUK3LwdIwmgEOKpkwRQCPHUhUfH4eZkj1qlwsHOmvSMLHw8XHmlXVP8vD3Q6fQolQp2HjxFg+r+bNl3nPbN6mJ+J7G7n+K+BEuBabK1fPMeFgYOJyImnukLV5usK+flypi3X8HB1ppsrQ6N+l6CaG9rzcrPR3M86ApzVmwmIyuLXh1a0Lhm5XzNAHp7uBByPRxXRzssLEzvG0xLz8TK0hyDwUBQ6A16dXx4MimEEIVBEkAhxFN38epNxnz9C2YaNVYW5swY2Y+MzCym/rCKtIxMNGoVs97vz+87/2Hu2MEcPHORH9fuZPhrnQr0OY1qVOLtwDnUC6iYa92Qnh34ZPYStFo9DnbWfPZB/1zb1AuoQL2ACsTEJ3EyOBTI3wxg/5deYMLcZWh1esa83QOAn9ftotvzjbl47SZzVmxGrVLSpkltXB3tC3RMQgjxJCgM0m9ICFHI0i8dkELQBaCwtMPSv1lRhyGEKMFkBlAI8cwYPv17MrOyja+//nggttaWRRiREEI8m2QGUAhR6J7kDODFqze5dCOCLq0a5Ln+y1/+yHe5lvt9+tMaQq7dolmdqrzdvW2u9THxSXQZPpX1c8bh7uzAwMA5AMQnpdK4ZiVGv9Wd7qNm4GRvmxPHRwOwf8yHO2QGUAhR2GQGUAjxTKlc3pvK5b0fuP5xkr/zV26gVin5eep7fPTFImITknF2sDXZZuXWfVT39zG+/nHyuwB8u3wjDar7A+Dq5FDoXUaEEOJJkF7AQohiKyE5lUGT5jJixg+MmvUjJy+Ecuz8JRau2QHA6x9/wZhvfqHXR58Rcj0cyGn1VlDnLl03JnH1qlUgKDTMZH1yahoJySl4uTrleu+x85epf+chk7iEZAZM/JZvlm5ALq4IIYozmQEUQhRb6/48SK8OLWjTuBbDp3+fa318UgpLpo/i/JUbbN5zlEr9Xsq1za3IWAK/W2GyrF5ARYb26mh8nZyaToWyOTUErS3MSUlNN9l+1bb9vNKuGSs27zFZfiMiGk9XJzR3avv9NOVd7GysmPXzGvafCKJFvWqPd+BCCFHIJAEUQhRbt6LiaNukNoCxtdr9ynm6otGocXOyJzktPdd6gDLuzsbLtQ9ia21JanoGAKkZmXi737v8m5GZRejNSAb2aJfrfX8fO0frBtWNr+1sctratapXneBrNyUBFEIUW5IACiGKrTJuToRcC8fb3YVL1yNoUfdfCdV9RaAfdMU1PzOA1SuWY+v+47SqX50TQVdo16SOcV14dBw3I2MYPu17Lt2IICk1nW/+NxCAAyeC+OKjAUBO+ziDwYCZRs3pkKv4eXv8l0MXQohCJQmgEKLYevmFJoz+chFrdv6DUqlErVaiy9IVaB/5mQGsVtGHDX8fYcCEb2lSuwrODrYmTxsvmfE+ABPnLmf4652BnMvPapXKWIYmJS2d4dO+x8LCDA9nxzyfJBZCiOJCysAIIQrd45aBudsSTqFQ8N7MBQQOfT3X07klkZSBEUIUNpkBFEIUW0mpaXzw2U/o9Hoa16xcKpI/IYR4GmQGUAhR6KQVXMHIDKAQorBJHUAhhBBCiFJGLgELIUq0IVO+K7TuHH8fPceKzXvQG/RMHdEXT1dHft95gJ3/nEKn1zN/wjA0ahU/rN7GiaAr2FhZ8uXoAYUSixBCFIQkgEII8RjSMzLZfuAEPwQOQ3GnHE1EdDyXr0fwQ+Bw43angkMx06hNlgkhRFGTBFAIUSxcvHqTaQt+w8LcjK6tGtCheT3enfEDWdlaKpT1YPyQXhw7f4nF6/9CgQKdXkezOlXZfuAkzepUZUjPDkycuxyNWs3lsAg6tahHrw4tjPsPvXmbWT+tITNbS7fnG9Ht+cbMWLiay2ERqJRKFk4aUaB4T4dcQ28w8M7U+fh6ufG/Ad05eCaY9MwsBk+aR90AP955tSMHTl4gMSWNQZPm0r5ZHV5pK/f2CSGKntwDKIQoFg6fDWHQK+1ZOGkEXVs3RK1S8u3YwSyaNpKMrGzCbscAOa3a5nwyGC9XJzRqNUtmvM+h0xeN+2letyqLpr7H9gMn0eru1Qz8YfV2Zozsx6Kp77Hjn5Po9Xqu3orkp8nvsuBfs3MZmVkMDJxj8r9pP6wy2SY+MYWE5FTmj38HB1tr9hw7R3xiCgoFLJg0nFtRcVy8epO4xBScHWz5YeIw/jx0hrjElEI8i0IIkT8yAyiEKBa6tGrIgtXb2HHgJK93bomvlxtT5q8kJiGZiJg4YhNyniKu4JPTs9fF0Y6Kd/5sZqYx7se/nBdKpRIvVycSklKNy29ERPO/rxcDkJCcSmJKGq+0bcr4OcvwcnNmWK+Oxku5FuZmjywebWNlQd2qfiiVSuoGVCDoyo2cZQEVAahbtQLXwqOwsbKgXkBFlEol1f3LcSsqBid7mydz0oQQ4jFJAiiEKBasLMwYM/AVYuKTmLFwNR1b1KOKnzdvdWvDuG+XGlu9Ke5r/6ZAkWs/l25E4OXqRHh0HA521sbl5bxcGfP2KzjYWpOt1aFWKXmuYU3aN6vLtAW/EXI93NhvOCMzixEzfjDZr6+XG+OH9DK+rlbRh017jgJw+UY4Xq5O+Hi6snX/CeOyDs3rolaruHwjgvrVKhIadpue7eQSsBCi6EkCKIQoFrbsP86mPUfJyMhiQPe2VK/ow09rd3Iq+GqB9nPo9EUWrdtFxxb1UKtUxuVDenbgk9lL0Gr1ONhZM/29Nxg2bT46nR5ba0t8vdyM2+ZnBtDJ3pYqft68PXEOjnY2zBz5BhqNmk17jjIwcA7lPN2o4e9LtlbH5O9+ZcA/J2lUsxJuTvYFOzFCCFEIpBC0EKLQPa1C0Hd79bo7OxT6ZxUmKQQthChs8hCIEEIIIUQpIzOAQohCJ63gCkZmAIUQhU3uARRCFCuF2bljw+7DLF7/J9PefYOACmUBWL75b/YeDzJ+5qc/rSHk2i2a1anK293bEpuQzP++XoxOp6d+tYoMf71znvv+dvlGjgddQaVUMnbgK/iX82Li3OVcC4/CTKNm8CvtaVijEis272HbgROolEomD++Np4sjbwfOoUJZTwKHvlYoxy2EEP8ml4CFEKXK293bGZO/bK2Oi1dvGdedv3IDtUrJz1Pf40LoTWITktl+4AQ92jRl0bSRnL9yg9T0jDz3+0rbpvwyfRSTh/fm53W7jMs///Atfpz8Lg1rVAJg24ET/DJ9FCPfeJHfd/6DRqPm01H9CvGIhRAiN0kAhRBPxZTvVxIeFQvA7GUbCboSxto/DzIwcA59x3zFhdAwk+0nzl1OZGwCkDMrCDndPIZMnkf/8bP5469D/zmmzXuP0qF5XePrc5eu06C6PwD1qlUgKDSMsp6upKRnoNfrAdCo875w4uXmDIBKqTCWqlEoFPzv61/4+KvFJCbn1CR0d3YgK1tLSlo69jZW//kYhBDiccglYCHEU/Fcw5rsPnqWPp1bc/HaTUb27Up5b3e6v9CEm5ExfL9qG9Pe6/vQfdzt5uFkb8Pw6d/zYuuGKJU5v2NvRcYS+N0Kk+3rBVRkaK+Oee5Lr9dz8FQwsz7ozy8bdgOQnJpOhbI5xaWtLcxJSU2nUc1KfLtsI0s37qZdkzqYaR4+bH63ciuvts+5f+/DN7thZ2PFrkOn+XndLt7v9xJ1qvjRfdRMDBhYPG3UI8+bEEIUBkkAhRBPReMalfh9xwEa16yCn7cHAPtPBPHr1r0oFQqTmn1gWvD5bhXovLp5ONrldNUo4+78yNp99/vz8BlaNahusszW2tJ4iTc1IxNvd1uWbtzNe3260LxuAB99uYjbMfF4uDjmuc+1uw7i5eZE7Sp+ANjdmeFrVb86f/x1iJS0DHYdPs36OeO4fCOCH37bxoR3euW5LyGEKEySAAohngqNRo2tlSVrd/3D841qAbB88x4WBg4nIiae6QtXm2xvY2VBdFwijnY23IiIBnJ389Co7yWNBZ0BvB4exbHzl9m85xgh126x9s+DVK9Yjq37j9OqfnVOBF2hXZM6HDt3CTsbKxQKBTaWFqSmZ5KanoECBVaW5sb9nb54lf0ngvjio7eMy1LTM7C2tOBMyDXKuDmjUICluRlqlQo7GyuS09L/20kVQojHJAmgEOKpadWgOp8tWsuHb3YDoFGNSrwdOId6d/rn3q9LqwYEfvcrlXy8cHawA3J38/jsg/7G7Qs6AziwRzsG9miXs98p39H9hSYAbPj7CAMmfEuT2lVwdrClZ/tmBM77FYPBQPky7lQo68HaPw9iZ21Fm8a1jPv7dvkm0jIyGTx5HhV9vBjzdg8mzFlOfFIKZho1k4f3xtrSgpqVfOk/fjZ6vZ6P3+pe0FMohBBPhNQBFEIUuuJSB3DnwVMsXv8n4wa9anwS+HHMWbGJt19uazID+Liys7UMnjyP2lX8GNm3KyB1AIUQhU8SQCFEoSsuCeCzQhJAIURhkzIwQgghhBCljCSAQohnxt16gIUtPCqWtoMmMmTyPN6eOIdNe44CEBOfxC/r/3rk+zfsPszmvccKO0whhHhs8hCIEELkoXndAAKHvkZWtpbRXy6iQlkPqvqV5c2Xni/q0IQQ4j+TBFAIUSylpWcyYe5yElNSqeFfjpF9XzSuW7hmB4dOX0Sn1zNj5Bt4ODsw+svFJKak4uJox6ej3mTGwtVcDotApVSycNKIx47DTKPmja6t2X/yAvY2Vixcs5PAoa+xats+tv9zMqen77DXcXW05+OvF5OZlY2FmRkv3PeEsBBCFDeSAAohiqU1u/6hdcPqdG3V0NiG7a6+XVozqEc7jpwNYd2fh+jdqSVKpYIfJ7/L3efart6K5Kc8ysJkZGYxYsYPJst8vdwYP+TBBZldHOyITUg2vo5LTOF40BV+nvIeoTdvs2TDbuoGVKBO1Qr06/ocU79f9V8OXQghCp0kgEKIYulGRDSt6ud06rjb7u2u9X8dZsc/J9HqdFT08cTRzob61SryyeylVKtYlj6dW/NK26aMn7MMLzdnhvXqaOwsYmFuVqB6gQAxCUm4ONgaX9+KiuH85RsMDJwDgJerEzcjY6le0QeAqhW8H/u4hRDiaZAEUAhRLPl4unL20nV8PF3R6/UmSeCmPUdZOvN9Dp8NYfuBk2RrdbzSthm9OrRg+LTv6dqqIc81rEn7ZnWZtuA3Qq6HU9m3DFDwGcDsbC3LN+/hnZ4djMvKuDlTu0p5pr/3Rs42Wh1/HT7NxWu3aFijEsFXb1HnTjs4IYQojiQBFEIUS93bNGH8nGWs+/NgrnsAK/p4MjBwLpV8vQCIT0xmzDdL0On1lPVwwdLCnKFTv0On02NrbYmvl5vxvfmdAdx/Ioghk+eh0+vp9nxjKpf3JjwqFgAne1tqV/Hj7YlzUCoVdGpRn84t6zP6y0X8cyoYO2vLJ3w2hBDiyZJC0EKIQnP16lXKly8vhaALSApBCyEKm9QBFEI8campqXz66ad89NFHRR3KM23x4sWkpqYWdRhCiBJIEkAhxBO1a9cuOnfuzK+//krv3r2LOpxn2tatW+nSpQt//fXo4tNCCFEQkgAKIZ6IiIgIhg0bxvDhw/H392fTpk306NGjqMN6ps2ePZsKFSowdOhQRowYQURERFGHJIQoIeQeQCHEf6LValm6dCnffvst1tbWjBs3jg4dOhjLrgBk3jiNPjOlCKN8tijNbTD3ySkkbTAY2Lp1KzNmzCA1NZWRI0fSt29f1Gp5hk8I8fgkARRCPLYzZ84wceJEgoOD6dOnD6NGjcLW1tZkG4PBYJIMivz593lLTk7mm2++Yfny5VStWpXJkydTs2bNIoxQCPEskwRQCFFgycnJfPXVV/z666+SjDxlZ86cITAwkAsXLtC7d2/ef//9XEm3EEI8iiSAQoh8k8uRxYNWq2XZsmXMnj0ba2trPvnkEzp27CgzrUKIfJMEUAiRL2FhYUyaNIn9+/fTrl07xo0bh4eHR1GHVapFREQwffp0du7cSYsWLQgMDKRs2bJFHZYQ4hkgCaAQ4qGysrL4+eef+e6773B2dmbixIk899xzRR2WuM9ff/3F1KlTiY2NZfjw4bz11luYmZkVdVhCiGJMEkAhxAMdO3aMwMBArl69yptvvsm7776LlZVVUYcl8pCamsrcuXP55Zdf8PPzY9KkSdSvX7+owxJCFFOSAAohcomPj+fzzz9nzZo11K5dm8mTJ1OlSpWiDkvkQ3BwMBMnTuT06dO88sorfPTRRzg6OhZ1WEKIYkYSQCGEkcFg4I8//mDWrFnodDo+/PBDXn31VZRKqRn/LNHr9axatYovv/wStVrNmDFjeOmll+QhESGEkSSAQggArly5wqRJkzhy5AhdunRh7NixuLi4FHVY4j+Ijo7m008/ZdOmTTRq1IhJkybh5+dX1GEJIYoBSQCFKOUyMjL44YcfWLhwIZ6enkyaNIlmzZoVdVjiCdq/fz+TJ08mIiKCwYMHM2TIEMzNzYs6LCFEEZIEUIhS7MCBA0yaNImIiAgGDRrEkCFDsLCwKOqwRCHIyMjg+++/58cff6RMmTIEBgbStGnTog5LCFFEJAEUohS6/9Jgw4YNmTRpEhUqVCjqsMRTcP+l/q5duzJmzBi51C9EKSQJoBCliF6v57fffuOLL75ApVIxZswYunXrJg8HlDIGg4F169Yxa9Ys9Ho9H330ET179pSHfYQoRSQBFKKUCA4OJjAwkFOnTtGjRw9Gjx4t5UFKubi4OD7//HPWrl1LnTp1mDx5MpUrVy7qsIQQT4EkgEKUcGlpacyZM4dffvkFX19fJk+eTIMGDYo6LFGMHD16lMDAQK5du0b//v0ZMWKEFPwWooSTBFCIEuz+FmHDhg1jwIAB0iJM5CkrK4uffvqJ+fPn4+LiwoQJE6TlnxAlmCSAQpRAt2/fZtq0aezcuZPmzZsTGBiIj49PUYclngE3btxg8uTJ7N+/n/bt2zNu3Djc3d2LOiwhxBMmCaAQJYhWq2XZsmXMnj0ba2trPvnkEzp27CgPeYgCMRgMbNmyhRkzZpCWlsaoUaPo27cvKpWqqEMTQjwhkgAKUUKcOXOGSZMmERQUxOuvv87777+PnZ1dUYclnmFJSUl8/fXX/PrrrwQEBDB58mRq1KhR1GEJIZ4ASQCFeMYlJyfzzTffsHz5cqpUqcKUKVOoWbNmUYclSpDTp08zceJEQkJC6NOnD6NGjcLGxqaowxJC/AeSAArxjDIYDGzbto3p06eTmprKe++9xxtvvIFarS7q0EQJpNVqWbp0Kd9++y02NjaMGzeO9u3by+0FQjyjJAEU4hkUFhbGlClT2Lt3L23atGHcuHF4eXkVdViiFAgPD2fatGn8+eeftGrViokTJ+Lt7V3UYQkhCkgSQCGeIdnZ2SxatIh58+bh6OjIhAkTeOGFF4o6LFEK7dq1i2nTphEfH8/w4cN566230Gg0RR2WECKfJAEU4hlx/PhxAgMDCQ0N5c0332TEiBFYW1sXdViiFEtNTWXOnDksWbKEChUqMHnyZOrWrVvUYQkh8kESQCGKuYSEBL744gtWr15NzZo1mTJlClWrVi3qsIQwunDhAhMnTuTMmTO8+uqrfPjhhzg4OBR1WEKIh5AEUIhiymAwsH79embNmkV2djYffPABvXr1klpsoljS6XSsXLmSr776CjMzM8aMGcOLL74oD4kIUUxJAihEMRQaGsrkyZM5dOgQnTt3ZuzYsbi6uhZ1WEI8UlRUFDNnzmTLli00adKEwMBAypcvX9RhCSH+RRJAIYqRzMxMFixYwA8//ICHhweBgYG0aNGiqMMSosD27dvH5MmTuX37Nu+88w6DBw+WPtRCFCOSAApRTBw8eJBJkyZx69Yt3n77bYYOHYqFhUVRhyXEY8vIyGD+/Pn89NNPlClThkmTJtGkSZOiDksIgSSAQhS52NhYPv30UzZs2ECDBg2YNGkSFStWLOqwhHhiLl++TGBgIMeOHePFF19kzJgxODs7F3VYQpRqkgAKUUT0ej2///47X3zxBQqFgo8//pju3bvLTfOiRNLr9axdu5bPP/8cg8HA6NGj6dGjB0qlsqhDE6JUkgRQiCJw8eJFAgMDOXnyJN27d2f06NE4OTkVdVhCFLq4uDg+++wz1q1bR926dZkyZQr+/v5FHZYQpY4kgEI8Renp6cybN49Fixbh4+PD5MmTadiwYVGHJcRTd/jwYQIDAwkLC2PAgAEMGzYMS0vLog5LiFJDEkAhnpI9e/YwefJkoqOjGTp0KAMHDpSnIkWplpWVxY8//sj8+fNxdXUlMDCQVq1aFXVYQpQKkgAKUcgiIyOZPn0627dvp1mzZgQGBlKuXLmiDkuIYuP69etMnjyZAwcO0L59e8aNG4e7u3tRhyVEiSYJoBCFRKfTsXz5cr755hssLS0ZO3YsnTt3loc8hMiDwWBg8+bNzJgxg4yMDN5//3169+4tnW+EKCSSAApRCM6dO8fEiRMJCgritdde44MPPsDOzq6owxKi2EtMTOTLL79k1apVVK9enSlTplCtWrWiDkuIEkcSQCGeoJSUFGbPns2yZcvw9/dnypQp1K5du6jDEuKZc/LkSQIDA7l06RJ9+/Zl5MiR2NjYFHVYQpQYkgAK8QQYDAZ27NjB9OnTSUpK4t1336Vfv35oNJqiDk2IZ1Z2djZLlixhzpw52NnZMX78eNq2bSu3UQjxBEgCKMRjMBgMzJs3jzZt2mBjY8PUqVP5+++/ee6555gwYQJlypQp6hCFKDFu3brF1KlT2b17t/HvmLm5OWvXruXtt9+W+wSFeAzqog5AiGfR5s2bmTNnDpGRkWzcuBF7e3vmzZvHCy+8ILMTQjxhZcqUYf78+ezatYupU6fSuXNnXn/9dRYtWoSVlRV9+/Yt6hCFeObIDGAppzcYUErC8kj3n6ekpCTatGmDTqcjNTWVPn368P7778v9SUI8BSkpKXz77bcsXboUW1tbsrKy2LFjB25ubsZtDHo9Cmkx90hynko3SQAFv51PJTpVV9RhFFuu1iperWZtfD1gwAAOHDhg7GFqZWXFkSNH5DKUEE/Jq6++yuXLl8nMzESr1VKtWjXWrl1rsk3y3sVoE28XUYTFn9reA9uW/Ys6DFGE5BKwIDpVR3jKwxPAqJDjHFsyDSsnD6ydPGnQf2Kuba4d3IRCqaJco46FFWq+7P32PXRZmWgsrWn89jTUFlbGddcObuLijqWY2zrhVbMFldr0LvD+69evj1arpVGjRri6uuLv7y/JnxBP0ejRozl16hTR0dGcOHECb2/vXNtoE2+jiwsr8L51egMvzt7LzFdqUbOswxOItmAikzKYuO4sKqWCMo6WfNI5wOS2kmkbz3MjLg0zlYK3mvtRz1d6iIvHIwmgyLfyTbtStdMATq36ktjQszj71Xhqn63NykBtZpGvbev1GYO1sxdXD2zgxtEd+LXoZrK+Sof+/ylJHTZsGMOGDXvs9wsh/psGDRrQoEGDQtn32ZsJNPN3Yf+l6CeWAOoNBrJ1eszVj/6h6GJjzvf96qNQKJi5OYgr0SlUdLM12WbayzVws8vfeCjEg0gCKArMzqsC6QnRRF86ydk/vkOl1lC14wDjel12FvvnfYBem4Wdpx/1+owhISyE4ys+RW1mQbnGnXCv2pBDC8ehVGtwD2hMlfb9cn2OXpvNzRN/cf3QFso17oRPw/b5is/a2QsAhVKJwaDPtf7ijmVc+2cTtV4ZiUPZSo95FoQQJdG+kGj6NSvPvD8vGZdN33SeqKRM3O3M8XKwpH9zP77/+zLnbiZirlES+GJ17Cxzl3xKTMti05lw9ofE8HHHKpR3ffR9wirlvdk+jUqJi425yXqFAib+cRZXG3NGd6ya5+cKkR+SAIoCi716Fv/nenH6929oPvwrzKxsMej1XD+8BQClSk3z4V+i0phzZPFkUqJvEhl8lKqdBuBVozkGg4FbJ3dTtkE7Krbuyb9vQ81IiuPy7lXEXT2PV+1WNB40A41lzj14x5d/SnLkdZPtW4z4GtW/Zge1WRlc3b+e5iO+NllepnZryjXuTFpcBMeWTKfV+/Oe9OkRQjzDbidm4O1oha+LNddjU0nJ0GKpUTG7d12WHbyGVqcnJDKZzGwdc/vW4+CVGDadDqd343v9va9EpfDb0RskpGXRsYYXc/rWRa1Ukpmt48NVJ00+z8fJmo87VTVZdvxaHLN3hlDG0RIbc9N/pt99oRJ2lhp2B0ey5J9rjHjBv/BOhijRJAEU+Xb1n41EBh/FpUItHMpWQqFUYWaVc2ni/ifJtFnpHF82g4ykONJiI8hIisW3cSeCNv/EzWO7qPh8LzyqNyVo40IO/zyRco074RHQ2Pj+5MgbhJ/ei1/L7pRr1NGY/EHO5d38OLFiFgFdBqKxNP3Fffe1tbOXlGsRQpgIjU7hclQyH6w8SWqmFhtzNe52FlTyyBnn/N1tuRCeyI3YVA6HxjJi2TF0egN1fBxN9nPiehyh0Sn0aexL80ouqO+Mj+YaFXP71n9kHPV8nVgyqDGzd17kUGgszf1djevuzvi18Hdl0+nwJ3XoohSSBFDk2917AO8y6HVkp6egsbTBoL93qTUy6DAOZStTpX0/jiwKBIMBtbkldV77iIzEWE78+hmNBkymZo930eu07Pl6uEkC6OpfmzbjlnLrxG4O/TgeK0c3KrXpg61HuXzNAIbsWoGdZ3ncqzbKdQzZGaloLKzJSk1Cr9M+ydMjhHjG7QuJZkLXalT3dsBgMDDm99P0a1qenedznia+HJUMgI+TFc39XRn2fM7sm1ZneqtJzwY+tK/uyeYz4YxcfoLaPo681sgHM5XykTOA2To9GlVOwmhjrsZMbVqmJTVTi7W5mnO3EvGyt3yyJ0CUKpIAiscW0GUQ++aMQqUxM7kH0Mm3GsFbFxN75Yxx2Y0j27l+eCvazHSqdHiTqIvHCd6+BH12JuUad8q1b6VKTdkGbSnboC2Jty6TkRyHrUe5R84A6rXZnFs/H6fy1bl9/iDlGnWkfLMXOf37bGq9MpLLf60i4tw/YDBQ/aWhT+5kCCGeeUevxhov5SoUCtRKJV4OlqRmaRm54gTONmb4udpQycOOPRejGbHsOAoFvN6oHE0rupjsy85Sw+uNyvFaQx8Oh8aSlJ5NGUerR84ABoUn8cPfl1EqoIyjFfV9nYhNyWTbuQj6NPZl2sbzJKRloVEpGde1WqGdC1HySR1AwbwjSY8sA1OaedmoGN7QrqjDEEIUQPzGTx+rDExetHo9aqWSZQev4eVgyfNV3Z/IfouSyqksjl3zd0uNKJlkBlAIIYR4iCnrzxOTkomthZpeDX2KOhwhnghJAIUQQoiHmPLy06t5KsTTIgmgeCISwkJIDL/ywALLd+/BK6iTq74gIewSHtWaULVjf5N1B777kOyMNBRKFY0HTsOg03Hop/EApMdHUbF1TzyqNeHY0mmgUOJYriq1e44qcAxCiNIpJDKZ0KgUOtTwzHP9nF0hvNum4LVEv9oezKXIFBpXcObNZuVN1n382ylSM7WolAqmvFwDByszJq47y5GrsczqWZtad4pTz/3zEuduJWCmUjL15RrYW5kVOA5RukkXaPFEOJSt9NDuGo+T/MVdv4BCqea5j34gIewiGUmxJuubDJlF6w/m49ukMzeObMfC3pnWH8yn9QfzcfINwKN6U8xsHGjx7mye++gHslISSI68UeA4hBClUyV32wcmf8BjJX8XIpJQKRXM71efkNvJxKVkmqyf0aMm896oT6eaXsanj0e2rUTP+mWN26RkaLkclcz3/RrQpXYZdgZFFjgOIWQGUBRYZkoihxaORaUxR6FSU7ltH/R6HbGXT1O10wB2zeiHrXs5km5fo8GbE3Hw9mfvNyNoOWpugT4n7up53CrXA8DVvw7xNy7iWb2pcb1SlfP11WVnYufpa1yu12lJjQnH1t30Xh2FUglS+k8IkYfEtCzGrT2LuUaJWqmgd2NfdHo9Z8IS6N/cj7d+OoyPsxXXYlKZ8GI1KrrZMnLFCWb3rlugzwm6lWjs31vbx4Hg28k0rXiv24f6TgmYLK2eci45NVCd/9UNxEKjxMZcjd5gIDVDK91AxGORBFAU2NUD66nQ6hW86z7Pvjmjcq3PTEng+Y9/Iu56EDcOb8PBO3el+tSYcI4umWqyzNW/LtW6DjK+zk5PRmPhB4DaworstGST7bNSE9n/3UdoM9JMOn5EhxzHtZLpoJwYHkpmSiK2bnIDtxAit42nw+lR35vnqrjz4cqTudYnpGWxoH8DLoQnse3sbUa8YJtrm/CEdGZsOm+yrLaPIwNbVjC+TsnU4meW80+vlbmalEzTeqRJ6dmM/u0UaVlavuxVJ89Y1Solrrbm9P7hICqFgp8HNCzw8QohCaAosLTYCLzrvgCQZy9dGzcflGoNlg6uZKcn51oPYO3iResP5j/0czSWtmRnpAKgzUjD2tXbZL2ZtT3Pj15I+Om9XPrzV+Nl5vAz+/BpcK9vcHZ6KqdWfUHDtybn/yCFEKVKeEI6z1VxA6Cie+7krqyTFZo7iVdKZnae+/BysHxknT8bczWpWTlJX1qmljKOVibr7Sw1/PBmA/aFRLHqyI08LzNfi0klJjmTX4c0YW9INCuP3Mh1L6EQjyIJoCgwK2dPEm9dwsa1DIm3LptclgXTq6wPKjOZnxlAJ98Awo7twKtmC6Ivn8K73gv39qvXYzDoUarUqC2tUWnu3QAddzWI2j0/MH7+saXTCOgyGEuHe+2UhBDifl4OllyOSqGMoxVXopJzFXa+v3Xkg6rn5mcGMMDLjp1BkTT3d+V0WAIv3FdTUG8woDcYUCuVWOfRBeT+7WwtNCgUCuwsNLlmEYXID0kARYGVb/YShxaOJXTfHyiUShQqNegLVkg6PzOATr4BXD+4md1fDMEjoBEWds7Gp429arXkwHcfoVAoUKrNaNBvAgDxYRdx8K5o7E0cHXKCqIvHyUxJAKBWj5E4lqtS8IMWQpRoXWt5MW7tWdafvIVSAWqlgn91eHuk/MwAVvWyZ8uZCIYuOUYjPyecbMyNTxu3qOTKx7+dQqFQYKZWMq5LAJDztPHekGj+uRLLqw3K0r66JwZg2NJjGAwwvmvAYx61KM2kE4gocCcQg14HCiUKhYL98z6k/hufYGHnXIgRFi3pBCLEs6egnUB0egNKRc5M3+jfTjG2U1Wc/vXwRUkinUCEzACKAstKS+af7z/GoNPhfmdmTgghnmXJGdmM/f00Wr2BhuWdS3TyJwRIAigeg7mNA899tKCowxBCiCfGwcqM+f0aFHUYQjw1UghaCCGEEKKUkRlAUSQepzB0fiTfvp6r9VvM5VOcWTMHlEq867SmUps+XDu4iWsHNwOQcOMinaavw8za/onHI4QoPR6nMHRB7L0Yxby/LrFqaDMikzKYuO4sKqWCMo6WfNI5AJ3ewOT154hPy6JJRRf6NPYttFjEs08SQFGi3G39praw4siiSSRH3sDapQytP/wepVrDnq+H4deyB75NuuDbpAvZGakc+O4jSf6EEMXe3xejcLOzAMDFxpzv+9VHoVAwc3MQV6JTuBaTSnVvB3o19GHiurPEp2bhaC09gkXeJAEUD5UQFsLxFZ+iNrOgXONOlK3fjv3zPkCvzcLO0496fcYQFXKckB3LQKHAoNPhUa0JYcd34RHQmIAuAzn6yxSUag2J4aH4NGhPxdavGPefFHGVk6u+RJ+diW+zFynftCsnfv2MpPBQFEolrd7/rkDxmtvcS+Tutn67v/6f4s7Ty3fdPn8Qj2pN/sMZEkI8a0Iik/l86wXM1So61vSkbYAHH606SbZOT3kXGz7uVJUT1+NYfug6CnKeEG5cwYU/L0TS2M+ZAS38mLbxPBqVktDoFNpV86DHfb16r8Wk8tX2YLJ0errU8qJLrTJ8sS2Y0OgUlAoFc/vWK3DMx6/FUdPbgd3BUQColPfGMY1KiYuNOQcvx1LVM6diga+LNRciknLVMxTiLkkAxUNFBh+laqcBeNVonlPU2WCg+fAvUWnMObJ4MinRN4GcVm2NB07n+PJPUao1PP/xj+z+YjABXQYC4FGtKXVf/x9/fzUUvxbdjPsP2vwTjQZMxtzWif1zRuHbuDPJt6/RKo8agbqsDPbNfd9kma17Oer1yV3KIK/Wb5EXDmPt6o1Kc+/pvvDTe6naacB/OkdCiGfLsatx9G9Wnmb+rhgMBgzA571qY65WMXXDOW7GpwFgZaZm6ss1+GzLBcxUSha82YChS44yoEVOi8omFZwZ3bEKw5ce56W6ZYz7/3lfKJNeqo6jtRkfrDxJp5peXItJZV4eiV9mto4PV5m2nvNxsubjTlVNlm06Hc6YzlWNCSDkJIWzd4ZQxtESG3M1Ps5WnLwRTz1fR06HJVDWybTLiBD3kwRQPJRv404Ebf6Jm8d2UfH5Xti6+3B82QwykuJIi40gIykWADuvnEr3FvbOxj+r1PcuPdiXySnObO3sSVZKonF5StQNDv04HoCslESy0pLwa/EyRxdPwsrZi2pdBxtn7FRmFo8sHg15t35Li48ieNsSmg393LhMr9OSGn0LOw/fxzw7QohnUccanvy8P5Q/L0TSs4EPPk5WfLrlAnEpmdxOyiAuJQsAP1drAJxtzCh/589mapVxPxXcbFAqFHg6WJCYdq89XFhcGhP/OAtAQlo2SenZdKtbhikbzuPpYMmgln7Gcc1co3pk8egLEUn4ulhjft9nA9TzdWLJoMbM3nmRQ6GxNPd35UhoLKN+PYmDlQZHK7n8Kx5MEkDxUGpzS+q89hEZibGc+PUzfBq2x6FsZaq078eRRYHGnkj3t39TKHLvJyn8CtbOnqTGRmB232VaW/dy1O71EeY29uh1WhRKFV61WlG2fltOrJhF4q3LOHj7A/mbAcyr9ZsuO4tjv0yh7usfo7a494s45tIpXPxr/8czJIR41liZqfiwfRViUzL5Ylsw7ap5UNnDlr5NajB5/TnudkcwHddyD2yh0Sl4OlgSkZCBvZXGuNzH2YoP2lXG3soMrU6PSqmgZSU32gR48NnWC1yOSsH/Tr/h/MwAhkancPRqHKfDErgUmcwvB67Su3E5NKqcQh42d9rGqZQKRnesisFgYNrG81T3lnubxYNJAige6saR7Vw/vBVtZjpVOryJk281grcuJvbKmQLtJzLoMMHbl+DToB1K1b2vXdXOb3Pk54nodVrMbRxoOGAy++aMxKDXobG0xdb93iXc/MwA5tX6LfHWZZJuX+P4ik8BaDRgMpYOboSf2WvSX1gIUTpsP3+bbWcjyMjW8UZTXwK87Pnln6ucuZlQoP0cCY1j6cHrtA1wR628V1VtQHM/Jq0/h1ZvwMFSQ+BL1flg5Ql0egM2Fhp8nO/9EM3PDGDnml50rukF5Dxp/Gaz8pwOS+CHvy+jVEAZRyvq+zpxOzGDaRvPoVAoeLWBDxYa1UP3K0o3aQUnCtwKrqCO/jKFai++g5WjW6F9RmGSVnBCPHsK2gquoKZtPM/gVhWMT+U+a6QVnJBC0EIIIYQQpYxcAhaFrsGbE4s6BCGEeKLGd61W1CEI8Z9IAigKrLC6eABcO7iJizuW0rD/ZMxs7Nj92SBs3H2wcvKgYf9AAII2/Uj05VNoLK1pOmRWnvsJO7qTs+u/w61yfeq/MQ6AU799TcLNEAw6LdqsDNqOW0rIrhWE7PqVCi1fNpaDOfHrZ8RcOkW7iSsK5RiFEMVPYXbx2HwmnOUHrzHxxeroDQa+3RWCwQD1yjkyuHVFTlyPY8amIDzsLajqZc/w5/1ZffQGO87fRqFQMKRVBer5OuW5778uRLLi0HUUChjZtjLVy9gzYtkxAOLTsmlQ3onhz/szbOkx/FxtGNs5oFCOUTx7JAEUxU6VDv1xLFeF1NhwPKo3NSZwADFXTqPUmNHqEQmoW9WGtCxXheDtS4zLar+a8wTx7aBDRIfkPHXn07ADDt6ViA2991BL3dc/Zu83I57kIQkhSrk3m5WniqcdWp2e7/s1AOC95cdJzdQC0KWWF/2b+xm3b+7vSs8GPqRmavlkzZkHJoBrj4cx7416JKVr+WbHRab3qGl8qGT+7svULeeIRqVkSrcaLD5wtZCPUjxL5B5AYXR82QxSY8MBOLtuHvHXg7m6fz1/fzWUPz8dQPyNYJPtj/4yhbT4nKKkdxOmpIir7PlmBLs/H8TVfzb+55giLxxm9xdDuHFkGwC3zx0kLe42e74eRui+dQ98n7mNPQpV3k/AhZ/ei1etFgBY2DmBMo+6NUKIEuHTLUFEJKQDMH/3JYIjkthw6hYjlh1j4KIjXLydZLL9tI3niUrKAHJmBSGns8d7y4/zzpKjbDp96z/Fo75TukWnN+Bia465Juf1tnO3Gbb0GEev5tRW9XSwzNn+EeOTh70lGVk6UjO12FlqTNadvBFP3XKO/yleUXLJDKAw8qrdmvBTe/F/4TUSwkKo8fJwbD19Kd/8JVKibxG0aSEN35r00H3k1dlDcac8QmpMOEeXTDXZ3tW/LtW6DspzXxZ2LrSf9BsKYN+cUbgHNCEzJR5LBzfq9PqIfXNHUaZ2a8xtCzbAxV8Pps5rowv0HiHEs6llJTf2hkTTq6EPIbeTGfqcP74u1rxYuwy34tP4aV8oE1+s/tB95NXZQ3mnLmB4QjozNp032b62jyMDW1Z44P52nL/Nz/tCaVjeCbVSSRVPO5YNakxalo4PVp6knq+Tcf+LD1ylSy2vB+6rmb8Lb/18GJ0evuhV27j8ZlwaHnYWxlqBQvybJIDCyL1KA0L3rsE9oBG2nuUBuH32AJf/Xg0KhUn9PsCk4vPdWkJ5dfYwt3EAwNrFK1+dPO5Sae5VsXepWIvUmJtoLGxw9a+DQqnEybcaKTG3CpQAxl8PxqFspTyLugohSp4G5Z3448RNGpZ3wtclp5vHP5dj+P1YGEoFqJSmCdL9Q8PdKml5dfZwuNNlw8vB8pF1/P6tXTUP2gS4M37tGa5EpVDBzQYAO0sl3k5WJKZl42htxqErMcSkZDKkdcUH7mv5wessH9yUlEwtn2+7wGc9awOw71I0LSq5PvB9QkgCKIyUag0aS1tC9/1BmTqtAbj010pavf8daXG3ObHC9IELjYUNGYnRmNs4kBKVU2/r35097k8aCzoDqM1IQ21hhcFgIP56MBVa98TZrzqJ4VdwrVSXpIhQKrTsTnZGKgoUJl0+HiT8zF68arUswFkRQjzLNColNhZq1p+6RevKObVIfzt6gzl96hGZmMHn20xvbbE2VxOTkomDlZmxJ/C/O3uo75tVK+gMYJZWj5laiVKhwMosp4NHaqYWa3M12To9t+LTsLPUcDM+jRWHrvP5q7WN701Kz8ZcozRpCWemVmKuUWJARUbWvXquBy/HMKNHrcc7aaJUkARQmPCq2YJTq7+m1isjAXCr0pC/v3wH10q5n44r17gjx5ZMw75MRSzsnIHcnT0aD5pu3L6gM4AxoWc5v34+CpUa77rPYWnvgmfNFhxbMo3dx3bhXqUBlg6uXN2/Ho2VLd51nze+93bQYS5s+YnUmAiOLp5Cg/45pWiiLh6jSof+xu2uH9rCpb9WkZ2RSnZGKjW7v1ug8yWEKP6a+7vyzc6LvNemEgD1fZ0YtvRYnvfHdazhyYxNQVRws8HJ2hzI3dljaveaxu0LOgO4/1I0a4+HoTdA7bIOlHWyYtPpW6w/mXNv4asNfFApFSzaf5XYlCw+XHUSByszpnWvycrD12lZ2Y0qnvcK03eo4cmQX45iMMCAFjkPkSSkZaG+k/gK8SDSCUQUeieQgrh54i8u7lhK3df/h2O5Kvl6z9k/5lO1w5v5mgHMjxO/fkZK5A1jqRvpBCLEs6ewO4EUxO4LkSw7dJ3RHaqYJG8F9c2Oi4xsW/BbWLJ1et5bfpyaZR0Y+lxOb3XpBCIkARTFKgEsjiQBFOLZU5wSwOJIEkAhjwcJIYQQQpQykgAKIYQQQpQykgCKR3paXTH0Oi2HfhzP318N5a9ZbxMbeu6x9nP7/EEiLxx+rPdKBxAhBNwrAl3YIhLSeXH2Xt5bfpzhS4+x7WwEALEpmSw/dO2R7998Jpzt5yIKOUpREskjQqLYuB10CAdvf6p0mIZBr0OblfHI9xj0emOh6bs8qjUprBCFEOKJa1LRhbGdA8jS6hm/7gzlXa2p7GFHn8a+RR2aKMEkARQmtBlpHPllMlmpSTiVr0bNl+/NiF3Y8jORwUcx6LQ0HDAFK0d3Di4cS1ZqEpb2LjR6eyonfv2MpPBQFEolrd7/rkCfrdKYE38jmIykWCzsnNFYWHPt4CYUShXlGnXk/KaFuFdpgF6v4/Jfq9DrtDj71cDSwRXfJl24ff4gSRGhmFnbo1CqSIu7jXOFmrhVqseVvWsxt3HAzrM8J1d9iT47E99mL1K+aVdC9/3BtYObcPKVJulClEZpWVqmbjhPckY2AV72DHve37hu8f5Qjl6NQ2cwMOml6rjZWTBuzRmSM7JxtjFncrcafLEtmNDoFJQKBXP71nvsOMzUSl5r6MPByzHYWWhYfOAqYzsHsOZYGH9eiESpUDCuSwAutuZMWHeWzGwdFhoVrau4PYnTIEoZSQCFidD9f1CmVivKNe6EQa83WeffpjdVOw0gKvgYVw9swP/5XiiUKlp/MN9YMT/59jVa5VHrT5eVwb6575sss3UvR70+955Cc6/SgMRbl9k/5300VjY0HDDlobE2H/4V2ekpHF82E98mXbh1ag9V2vcj+lLOpZsytVsTuu8P3CrVIzLoMA0HTObYkmkmrerKNezAjSPbeO6jBcRePUdQ+ILHOm9CiGfXhpO3aFnZlY41vND/qzDGa43K0b+5H8evxbHhVDivNiiLSqlgbt/6xnHvWkwq8/JI/DKzdXy46qTJMh8naz7uVPWBsTjbmBOXmmV8HZ+axakb8Xz3Rn2uxaSy4vB1apd1pFZZB15vVI5ZWy78l0MXpZgkgMJESlQYXjVbAOS6tHrtn42EHduFQafFvkwFzG0ccPWvy+GfJ+JULgD/F17Dr8XLHF08CStnL6p1HWysV6Uys8hXEehKL7xOpRde59bJ3Vz6ayV2HuXvrbxvXHYoWxkAjaUNep2W7IxU0uJuY+3iZUwA7TzLk3z7GllpySjVatRmFrla1WUkx2Pl7IlCqcTRJ391B4UQJUtYXBrN/csBGHvw3rXpdDh/BkWi1eup4GaDg5UZdXwcmbT+HFU97ejV0IdudcswZcN5PB0sGdTSzzjumWtUBW4TF5uSibONufF1eEI6FyKSGLHsGAAe9paEJ6QT4JVTmqqyh+1jH7co3SQBFCZs3MoSe/UcNm5lc91fd/3QVp7/309EBR8l7NhO9DotFVq+TMXWr7BvzijKNe6EV61WlK3flhMrZpF46zIO3jmXUvIzA5ieEI2ZlS0qMwvMbBzAYEBjaUNK1A0AEsMv4161AYBJIVSPao0J2rQQZ78auY7HwacyF7b8hGf1ZsbPvL9VHUBabAQGvZ6EsJAncAaFEM+ask5WnA9PxNvJCr3BYJIEbjsbwcL+DTh2LY5dQZFodXq61fWmR/2yfLDyJB1reNKykhttAjz4bOsFLkel4O+ek5QVdAYwW6fnt6Nhxo4ekNNppIa3A4EvVQdAq9Pz98UoLkUmU8/XiZDIZGqVdXjCZ0SUBpIAChPlm7/E0cWTuXpgQ657AO3LVGDP18NwKJPTmDwzOY5DP07AYNBj4+qN2sKKvbPfxaDXobG0xdbdx/je/MwApsXd5uCCsSjVGpRKFfX7TUBjZUPIzmXEXD6FQpn319WrVks2f/ISbcb+kmtdmdqt+furoXSZuRHIu1Vd2Qbt2P35IJwrSt9MIUqjrrXLMHXjeTaeupXrHsAKbjaMWHacineSuvi0LCauO4feYMDb0RIrMxWjfj2BTm/AxkKDj/O9jkT5nQE8eDmG95YfR28w0KVWGSq52xKRkA6Ao7UZNcs6MHzpMRQKBe2re9C+uifj153hUGgsthaaJ3w2RGkhnUCEdAJ5BOkEIsSzRzqBPJx0AhFSB1AIIYQQopSRBFAIIYQQopSRewAFrtaqog6hWJPzI8SzR23vUdQhFGtyfoTcA1jK/fuJN5E3OU9CPDvy6hAkcpPzVLrJf/lSrqBJzd69e3n55ZfZsGFDIUVUeDZs2MDLL7/M3r17C/xeSf6EeHYUNKkpreOaJH+lm/zXF/l2/vx5RowYgb+/P127di3qcAqsa9euVKxYkXfffZegoKCiDkcIUQzIuCZKK7kELPIlLi6OHj164OjoyIoVK7CwsCjqkB5LRkYGvXv3Jj4+njVr1uDk5FTUIQkhioiMa6I0kxlA8UjZ2dmMHDmSjIwM5s2b98wOkgAWFhbMnTuXjIwMRo0aRXZ2dlGHJIQoAjKuidJOEkDxSLNmzeLEiRN8++23eHp6FnU4/5mXlxfffvstx48fZ9asWUUdjhCiCMi4Jko7SQDFQ61du5alS5fyySef0KBBg6IO54lp0KABn3zyCUuXLmXdunVFHY4Q4imScU0IuQdQPMSZM2fo06cPL730ElOnTkVRwp6ENRgMjB8/ng0bNrB8+XJq1qxZ1CEJIQqZjGtC5JAEUOQpOjqaHj164OnpydKlSzEzMyvqkApFVlYWb7zxBhEREaxduxYXF5eiDkkIUUhkXBPiHrkELHLJysrivffeQ6/X8+2335bYQRLAzMyMb7/9Fr1ez7vvvktWVlZRhySEKAQyrglhShJAkcu0adM4e/Ysc+fOxd3dvajDKXTu7u7MmTOHs2fPMn369KIORwhRCGRcE8KUJIDCxMqVK1m1ahWBgYHUrl27qMN5aurUqUNgYKDx+IUQJYeMazKuidzURR2AKD6OHz/OtGnT6N27Nz179izqcJ66nj17cv78eaZOnYq/vz9169Yt6pCEEP+RjGsyrom8yUMgAoDIyEi6d++Or68vixYtKtH3xzxMVlYW/fv358aNG6xZs6ZUXCoSoqSScS2HjGsiL5IACjIzM+nbty9RUVGsXbsWZ2fnog6pSMXExNCjRw/c3NxYtmwZ5ubmRR2SEKKAZFwzJeOa+De5B7CUMxgMTJo0iYsXLzJv3rxSP0gCuLi4MHfuXIKDg5k8eTLyG0mIZ4uMa7nJuCb+TRLAUm7ZsmWsXbuWKVOmUL169aIOp9ioUaMGU6dOZc2aNSxfvryowxFCFICMa3mTcU3cTx4CKcUOHz7MzJkz6d+/P926dSvqcIqdbt26ERQUxIwZM/D396dRo0ZFHZIQ4hFkXHs4GdfEXXIPYCl169YtevToQeXKlfnpp59Qq+W3QF60Wi1vv/02Fy9eZO3atXh5eRV1SEKIB5BxLX9kXBMgCWCplJ6eTu/evUlMTOT333/HycmpqEMq1uLi4njllVewt7dnxYoVWFpaFnVIQoh/kXGtYGRcE3IPYClzt1F4aGgo3333nQyS+eDk5MS8efMIDQ1lwoQJcvO0EMWMjGsFJ+OakASwlFm0aBGbNm1i5syZVKlSpajDeWZUrVqVmTNnsnHjRhYvXlzU4Qgh7iPj2uORca10kxskSpEDBw7w+eefM3jwYDp16lTU4TxzOnXqRFBQEJ999hmVK1emadOmRR2SEKWejGv/jYxrpZfcA1hKhIWF0aNHD2rWrMkPP/yASqUq6pCeSTqdjsGDB3Pu3Dl+//13ypYtW9QhCVFqybj2ZMi4VjpJAlgKpKam8tprr5GZmcnq1auxt7cv6pCeaYmJibzyyitYWFiwcuVKrK2tizokIUodGdeeLBnXSh+5B7CEMxgMjB07lps3bzJv3jwZJJ8Ae3t7vvvuO27evMnYsWPl5mkhnjIZ1548GddKH0kAS7gffviB7du389lnn+Hv71/U4ZQY/v7+zJo1i+3bt7NgwYKiDkeIUkXGtcIh41rpIglgCfb333/zzTffMHz4cNq2bVvU4ZQ47dq1Y9iwYXz99dfs2bOnqMMRolSQca1wybhWesg9gCVUaGgoPXv2pGHDhsybNw+lUnL9wqDX6xk2bBjHjh1j9erVlC9fvqhDEqLEknHt6ZBxrXSQBLAESklJoWfPngCsXr0aGxubIo6oZLt7vhUKBb/99pucbyEKgYxrT5eMayWf/HwqYfR6PaNHjyYqKorvvvtO/tI+BTY2NsybN4/IyEg+/vhj9Hp9UYckRIki49rTJ+NayScJYAkzd+5cdu/ezZdffinT9k+Rn58fX375JX/99Rfz5s0r6nCEKFFkXCsaMq6VbJIAlgBpaWkYDAZ27tzJvHnzGDVqFK1bty7qsEqd1q1bM3LkSObOncuuXbswGAykpaUVdVhCPJNkXCseZFwrueQewGecwWDghRdeoFevXnz//fe0aNGC2bNno1Aoijq0UslgMDBy5Ej27dvH0KFDWblyJX/++af89xCiAGRcK15kXCuZJAF8xl29epUOHTrg5uaGnZ0dCxYsoEyZMkUdVql269YtBg8eTFJSElFRUWzfvh1fX9+iDkuIZ4aMa8WPjGslj1wCfsadPHkSgISEBCIiIpgwYUIRRyQmTJhAREQECQkJwL3/RkKI/JFxrfiRca3kkQTwGff7778DoFQq6dWrFzNnziziiMTMmTPp1auXsUbZ6tWrizgiIZ4tMq4VPzKulTxyCfgZt3z5coKCghgzZgy2trZFHY64T3JyMp9++ikBAQH06dOnqMMR4pkh41rxJeNaySEJoBBCCCFEKSOXgIUQQgghShlJAIUQQgghSpkSlwDq9HJF+0Hk3DxZcvfEg8m5ebL0cj4fSM7Nk2OQdm+PVJLOUYm8B3D6vnhuJGqLOoxixcdezbgWjkUdRomToTVQ8v4G/TcKBViopUDsk7YxJJXYtJLzj8+T4GylpGsl66IOo0RJ/vtHtIm3izqMYklt74Ft64FFHcYToy7qAArDjUQtl+LuJYCZ108Sv3EWKnt3FCoNLr2/KLLY4jbMRBd/C4XaDMeuY1HZuZJy5HfSzv+JQqnCsesY1E7exu2zo6+RsPlzDAY91jU7YF3vJZL2LiIj5AAKc2us63TFqnobdKkJJGz5An1mClYBL2Bdt2uRHWNpYjBAXv8kHztymEkTPsHT0wuNRsN3C39+6rHdNfGT/3EzLAyNmRlTZ8zCzd2dkcPfITU1BZVKzadffI2j470fB6tX/sq2rZvJysxkwKAhPPdCG76fN4c9u//CxsaG7j1fpWPnB3+/lJIQF4rYND2RqboHrs9MS2bPDxPQZmWi12bTasgUEiOuo1AqsXMvy+mNi2j+1riHbm/r4lVo8V8/uYezW5eh12nxb96Fqs/1MFl/af8mLu5dj42zBy3fDkSpLpH/PBV72sTb6GJvmCw7cSOBGVsu4mFnjlql5JteNYsoOpi6OZjw+HQ0aiUTOlfB1dacm/HpfLXzElk6Pb3qe9PC38W4/erjt9hxPhKFAoa0LE+9co5kZOv4fPslopIzqeNjz4BmvhwKjePnA9ewMlMzrlNlXG3Ni+wYn5ZS8zfMqnYn7Jr3e+R2BoMehaJgV8b12RkoNRb52tauZX/UDp5kXD1OyvE/sH9uEGnn/8S1/3dk3TpP6okN2LcZZtw+9dQm7NoMxaxMNWKWvIt1vZcAsG//HuZl7/0lTN7/C/btRqC29yhQ7KLwdOv+CgOHDH3kdnq93lhbK7/S09OxtLTM17bvDH8XrzLeHD50kFW/LufdUR/wxTdz0Gg0bNqwnm2bN/J633t/N7r1eIWer71Oeloa7w4bzHMvtAHg40/GU6duvQLFKZ6eKwe3UaFJRyo0bo8uOwuDQW9M6JJjwvO1fUFpMzNQm+dv7Ctboxnl6rTCoNezcfpAkwRQr9Vy+eA2Oo9dQNCuVVw/tYfy9V8ocDyi8HSp6UH/puUeuZ3eYEBZwBZxGdk6LDSqfG07sLkvnvYWHLsWz5oTt3inlR8/H7jG5BcDsLXIndI0r+hMz3plSM3U8sm689Qr58iqozd5uY4X1cvYGbdbdvgGc16vza34dJYfDmNUm4oFOoZnUalJAO+XenITaed2YsjOxKHjB5h5ViJm+Qdo3CtiyM7EsvoLJP39E+i02Lboh0WFRrn2YTDoybh0iLRTmzHzqYFt49fy9dlqB08AFEolkPOXRGXnBrosDBmpKC3tTLd39MaQkQq6LBSae79IEnfMRWnthEOHkagdPNHG3STprwXo05Owb/8eGmefxzw7ojCs+301WzZtICMjg08mTqJqQDXeGfgWlStXISMjgw6duzDv26/RZmsZNHQ4zZq3yLUPvV7Pvr1/88ea36lTtx793no7X5/tVSZnRlmlVBp7d2o0GgCysjIp71fBZPu76zIyM/HzuzcIfvHpDJydXRgzfoJxn6L4UGvMibpyFu+aTTG3yqmdd2n/JhQqFe7+tUiMvMHObz4gOyON50fMeuD2N07tIzszDUs7Z1oNmpTnZyXevkHw7jXEhoXQ6eP5+Yrv7oyeTpeNvYfp+JQYeQOnsv4oFAo8q9bnyj9bJQEsxjacjmDH+UgytHpGt/OnsoctI1eext/NhgytnrYBbizYexWtzsBbzcrR2M8p1z70BgP/XIll4+nb1PK2p3ejsvn6bE/7nB8cSqUChUJBtk5PVFImUzcHowD+16ESTtZmubZXK+8lpRduJxOZnMn3e0N5p2V5qpexR6VQYK5WUt7FiqCIpP9wdp4dpSYBTDu1hcyrxzHzqopty/5Y1+mCNj6cpL2LcHppHAa9DsvqbTHz8Cdm5RicX5uFQqki9rdxJgmgPjON1ON/kBF6FIvy9XHo/BEq65zLZ0l7FpF545TJ5zp2HWNM+u4yGPQkH1iOQ8cPADAvW4PI798EDLi+Oc9kW/Py9Yhd+T8w6LBt9gYANg1ewa7lW2SFB5P01wKcugeSFXYWt0E/gcFA4u4FOPeY8oTPoCioP9b+zuGD/1CtRk2GDBvBy6/05GZYGN/Pm8O0Tz9Dp9XSqeuLVK5SlfeGDmHO/IWo1Sref9c0AUxNTWH1yl/558B+GjdpyoRJU3FydgZg/txvOXHsqMnnTpo2kzLepgmaXq/n54U/8MnEyQAkJiYwctg7pKWlMmf+wlyxf/Pl52zZtIEPR48BoHfffrwz/F3Onz3L7K++ZNaXXz/RcyX+uwpNO5KyMYLN0wdi51EuV/KmzUin08fzuX7ib0L2rqdau9fz3N7ayZ3GvT/g4PIviLpyDrcK1Y37CDu9n4t7/sDcxp7KrbrR6PX3AYi6fJZja74z+bzyDdvkusx7bvsKzu9cSUCbV02WZ6UlY2aZcy+fxsKKzLTkJ3FKxBO06cxtjl2Lp6qnLW839+XFWp7cSkjnp/3XmNilKjq9gXbV3KnkbsNHq8/yZc8aqJQKxqw5Z5IApmZqWXcynMNX42ng68iYDpVwvJOw/bjvGqfCEkw+95NOlfFyML3aoTcYWHLwBqPb+5OQlk1oTCorBzfkUmQKSw7eyHP2bvHBG3SpmfNv8bWYNF5r4E15F2vG/XGeua/XRqc3kJiezdWYVJIySsczBKUmAbz/EnD6hb9JOboWFAoUqpxToFCq0LjnfGmyIy/lJF2APiPFZD+6lBjSzu3AKuAFrGp3MiZ/AHat3spXLEl//4xlwPOoHb3QZ6aSHrwX92HLyI66StLexTh2/si4bfKen3F+dTpqp7LE/DoaqxptUVrm/Fo386qCPj0RALWLL2rHnGbphszUAp8f8eTdfwl45/ZtrFyxDKVCgVqdM8OmVKmoVLkKAMHBQbw3dDAASUmmvz6jo6LYvHED7Tt2plv3V4zJH8DQEe/lK5bv5symXcfOeJfN+ZVtb+/A4uUr+fuvP1m+dDEf3En07hr14WgGvTOUQf370b5TZ+zs7QGoVqMGiYkJBTwT4mlQqTXUfXkIdV8ewrntK7h8cCvq+64aOJTxQ6FU4uRTiVtBRx64vVPZnHHQuWwlUmLCTRLAywe3gUJJ5VbdcKtQw7jcrWINOv3v0TOB1dv3puoLPdk6ayiVWr5knHk0s7IlKz1n3MrOSDMuF8XH/ZeA/wqO5vfjt1AqQHVnZk2pVODvlpPEh0Sl8NHqswAk/yuZiknJYtv5SNpWdaNrTQ9j8gcwsIVvvmJZuO8abaq6UsbBkvQsHX6u1thZaKhV1oEVh8NybX8oNI6Y5EyGtCwPgK2Fmlre9igUClR3rooMalmecX+cx9fZmvLOVgU4M8+uUpMA3i/lyO+49P0aXWIkCVu/Mi6/e3nMzKMSTj0moVCbY9CZfnk1zj64DfqZzMuHSdjyBQqNBTYNe2LmVSVfM4DpwXswZCRhXfvuk0QKFBoLFEo1SktbDJmmCScYUFrYolCpUSgUGPQ6DJlpKM2t0MaHo9Dk/DJSO3igS0sAvR6FOn/35IinZ8XSX1iw6BciIiKYMWUSkPN9u/udqxpQjc++mo25uTnZ2dkm7/Ut78eqtes5sG8v0yZPxMLCgr79+hNQvUa+ZgD/3LmdpMREuo3MmY3R6/Xo9XrUajU2NjaYmZne7JyVlYWZmRnm5hZYW+cM6KmpKVhb23AzLCzf9x6KpyslJgIrR1eUKjXmNg5oM9NM1ieEh2IwGIgLu4yNs+cDt4+/eQWAuJuX8GvUzmQfz70zjaTIMC7sXsPxNfMp36ANVZ7rnq8ZQF12FiqNGSq1BrW5Jao7P4QA7NzLEn/zMgaDgYgLx3HxC3ii50Y8Wb8du8mc12sRmZTJ59tDgJwbmu6OZ1XcbZjarRrmaiVanem9peWcrVgyoD6HQuOYtT0EC7WKXg28qeppm68ZwN0Xo0lKzzYmc5ZmKsxVSrK0eq7FpuFhb/rv3834dFYcCePzHvd+yAR42XE1Jo0yjpbo7pRxqOVtz9zXaxMUnsTxG6YxlFSlMgE0961H9JL3MC9XO8/1Ns36EHNnBlDj6odDe9NZFoVCiYV/Eyz8m6CND0d754mp/MwAJu78DpWtC9FLR2JRvj62zd/AzLsa0b+MwKDX4dAu57MSd32HfZth2DR+jbg1gRgwYOHXEKWZFQlbvyY76gooFDi0HwWAbbM3iPt9Iuh12Ld79zHPjCgsDRs34e0336B+g4Z5rh8wcDDvvjMIgIr+lfj4k/Em65VKJS1ataZFq9bcDAvj2tVQIH8zgF9+Ngs3NzcG9X+DRk2a8Xrfvowc/g5KhRKNmRmTp88E4KvPP+WD0WP44bu5nDl1Eq1WS983c77T3371JSEXg1EolYwZN/Gxz4MoPLE3Qtj9/ThUGnM05pa0GjKF68f/Nq43s7Jl1+wPycpI5flhnxJ1+Uye26clxLDti3extHMymeW7y869LI1eG4U2K4PQQzuA/M0ABv+9jusn/kav01KhcXvUZhbcPHsQhVJJmWqN8GvUls0zB2Pt5EbLgZOe5KkRT1j9co4MW36Kuj4Oea5/o7EPH64+A0AFVxve/9clWaVCQdMKzjSt4MythHSux+b8+MjPDOCcv67gamPOiBWnaODryJtNy/Faw7KMXHkapVLBuE6Vjdu9+3wFFv1zndiULD5cfRYHKw3TulWjT8OyTN8STFqWjrea5cxq/nzgGidvJOJqa8bH7Ss95pl5tpTIOoBDNkWblIER4O+k5ocurkUdRomTnm3IswxMaaYELDVSB/BJW3wq+aFlYJ6Euw+NVGzSsVA/50lxt1bRv7ZcLn6S4tdPy1UGRuRQOfvg+NL4R2/4jChxnUCEEEIIIcTDlcpLwEIIIXLzb96lqEMQQjwlMgMohBBCCFHKyAxgHrJuX0IbfRWrGu3yXH/3AY2CStg+m+zIy1hUaIRts74m6zKuHCF53y8oLGxx6jYegzaTuHU5tfx0SdHYNHgZm4Y9Sdj6FdmxN9C4++PQdnjBD04UaxeDL3ApJIQuL76U5/q7D2oU1KwZU7kYHEyz5i14e/A7JusGv9WPu7cCj50QiF+Fkl8Bv7SJvRFC/M3LVGzaKc/1h1fOptFrIwu834PLvyDuRgjeNZpSq0v/XOvTEmNZ/fHLvDJzNdZO7tw8d4hT63/EzNqW1oOnYmZlw5HfviXq8llUGjOeGzoDCxv7Aschio+QyBRCo1PpUN09z/V3H84oqK92XuJSVAqN/Zx4s4lpR5JRq86QqdVhbaZmyksBWJmpWHTgOkevxePpYMHYjpUJjU7l2z8vAxAWn86Hbf1pWcklr48qNWQGMA9mHv4PTP6Ax0r+ssKDQanCtd8csm6HoEuJM1mfcvg3XPp8hXWdLqSe2oTKxhnXN2bj+sZszLyqYF6hMVm3gkClwbXvNygUCrJuXypwHKJ4q1yl6gOTP+Cxkr+gc2dRqdT8vGQ5wReCiI2JMVmvVKlYuHgpCxcvleSvhHL2qfTA5A94rOQv5uoFlEoVnccuIPZ6MOmJsbm2Cdr1G65+1Yyvz21fTofRc6ncqhsX9/5BVloKcWGX6PLJQiq1eJHQwzsKHIcoXiq52zww+QMeK/m7EJGMSqlgfp86hNxOIS41y2T9/zpUYn6fOrSq5MLOoEhiUjIJiUrhuz61qephy4HLsVRyt2Fu79rM7V2bso6W1CvnUOA4SppSnwDq0hKJXjaKmJX/I/a3cWSGnSHz+kmS9i8BIOrHQcStm0LUwrfJjsypjxWz/IMCf05W+AXMy9UFwNynNtm3Q4zr9FnpKMysUGjMMfetS3Z4sHGdQadFmxCBxrlszv+7+QGgdvMj6+b5xz5uUfQSEuIZ/FY/3h06mPffHcbJE8c5duQwP/6QU06jd8/ujB39Aa/16EbIxYsAvDMwf8XG73f27BkaNMrpZlOvfgMuBJl+bwwGA4P6v8GUiePJzMz8j0clilpGSiJbPhvKjq9Hsevbj7gdcoqI4OOc2vgzAOsn9WP39+P5I7AvcWE5PyK3fj6iwJ8TFXoOz6r1AfCoXJeY68Em6zPTkslMScDGJacOanZmOhoLK9RmFnhVqU90aBBqMwvMLG0w6PVkpadgbm2X63NE8ZWYns2IX0/x4eqz/G/NOU7fTOTEjQQW/3MdgLcWHydwQxBvLjrG5aicGrcjV54u8OcERSQZE7baPg4E3zbtFGPSHg4FtxMzjcWcK7hac+5WonHb2JQsLDQqrM3lAmipPwNppzZjU/9lLKu0IubXj3Ot16cl4PjWd2SFB5N2bgf27kNzbaNNiCB+46cmy8x9apvUBTRkpKB0zSlcqTCzNOkwYshIQWludW/dfZ08Mq+fMtYrVDv7kHJkNda1O5N144yx84d4Nv2x5nd69e7DC23bM+JODcD7xcfF8cuKlZw/e47NG9dTqXLu7+etmzeZNH6sybK69RuY1AdMTkqmYkV/AKysrUlONu008vlXs7Gzt2fp4p/5Y+3v9Hq9z5M4PFFEQvauJ+D5nvjWf57tX43KtT4jOZ4u438i5moQlw9upWFZ/1zbJEeHs+/nqSbLPCrXoW63wcbXWWkpaLzvtW/LSjMtYn/hz9+p0ro753eturN9MmYWOdurLazITk9BqVZj7ejGmnGvolSqeDHwl/907OLp2ng6gh51y/BcZVc+/O1MrvUJadkseKMOFyKS2XY+khFuNrm2CU9IZ8aWiybLapd1MKkJmJKhxc8l57tjZaYiJTN3mbeMbB0bT0fwRc8aZGv1nA9PQm8wcCoskdTMe+WTDlyOpVlF51zvL41KfQKoTYjAsmprADQeuS9/qZ3LolBpUNm65GoLZ9zGwRPXN2Y/9HMUFjbo71TaN2Slo3J0fOA6pbm1cV3GpQNYVmsD5FyaVtt7EL3sfdQOniitHfJ9nKL4uXXzJm3adQCg8p2WcPfz8fVFozHDzd2NlOS8e6OW8fZm4eKlD/0cWztbUlJzvrtpqal4l/UxWX+3zVur555n+RL5B/hZlxwTjm/9FwBw9smd3Nl5+KBSa7BydM2VtN1l6+r1yMLOZlY2ZN/Xvs3W7d4PUm1WBgkRV6nd9a37trclKyNne21GGhpLGxLCr5IaH02PGau5fmIP53eszPNeQlE8hSdm8FyVnPqyFd1zJ3dlnSzRqJS42pqT8oD+ul4OlsztXfuhn2NjoSY1K+f9aVk6yjjk7nb1+fZLDGhWDhtzNZhDq0ouvPfracq7WuNgda/rzP4rsYxul/vvRWlU6hNAtYMn2ZFXUDt6kR0ZikXFxg/e+AE1s/MzA2jmVZW0839iWakpmTdOG5NOAKWZJYasNAzaTDKvn0TjdS8ZyLp1waSzh12rAQAk7JiDuV+DghyqKGbKeHsTcjEY77JluRQSQvNWrU3W322rBPCgeu35mQGsXr0m27ZuolXr5zlx/Bht25sW+b3b5u3MqVN4lfFGPNtsXbyIC7uEnVsZ4m5exrtms39tcV+R7gd8r/IzA+jqV43Qwzvwqd2C2yEnKd/gBeO6lJgIkqNusv2rkcTfvEJWahJt3vuC7Iw0tNmZRAQfx9UvAIPBgLm1LQqFAnMbO7LS8v6hI4onL3sLLkelUsbBkitRqTStYDqzdn85+Ad1nMjPDGCApy07g6JoXtGF02EJvFDFNIFbeSQMXxcrGpZ3Mi7rXrcM3euWYfnhMGp559xakJ6lIzVTi6utafvL0qrUJ4BWtTsTtzaQ1JMbQKFCoVRj0Bes2n5+ZgDNvKqQdmYb0UvexdyvASobJ5OnjW0a9SR62fso7zwFDDlPI2vcK6BQ5NyqadBpiVnxIQqlCsuqrVHbuT3eQYtioVuPV/j4/ZGsWb0KlVKFWq1Gpy1YB5v8zABWq1GDDevXMaBfH5o2a46zi4vJ08ZDBw5ArdFga2vLtE8//y+HJIqBSi1f4q/vxnBxzzoUSiVKlRpdAce0/MwAupYP4NL+TWyeOZgy1Rtjae9s8rRx1/E59xzu/WkK9V4eAkD1dr3Z+tkwzK1saTVkKuZWthgMBjZ/OgQMBlq8Hfh4By2KRNdanoz74zzrT4WjVChQKxXo9AVrLpafGcCqnnZsORvJ0OUnaVTeCSdrM+PTxi9UdeX7vVep7mXH4dA42ldzp2stT8auO09appZa3vZUL5NzlePItXga+jo+9LNKk1LfCs6g14FCiUKhIGbVGBw7f4zKxunRb3zGSCu4wvFfWsHpdDqUypzv3nvDhhA4ZTrOLs9+WQJpBVc48tsKTq/Xobgzpu385gOavzUOS/uSec+TtIJ78grSCk6nN6BU5FytGP37WcZ2rIyTtVkhR1h0SloruFI/A6jPSCZu9XgMeh0Wd2bmhHgakpKS+PC94Wh1Opo0bVYikj9R9LJSk9k1ZzQGvY4y1RqV2ORPFL3kDC1j155DqzfQsLxjiU7+SqJSnwCqrBxwfXNuUYchSiFHR0d+XrqiqMMQJYyFrQNdPllY1GGIUsDBSsP8vnWKOgzxmEp9HcD/6nFqAuZX9JJ3Cf+sA7qkKACyY28Q/csIope8S8LOnKQ1M+wMUYuGEr14OMmHVhVaLKLoPU4NwPwa0K8PzRrUJfL2beOynxZ8z4B+fZg1I+dhgPBbN2nXugWD+r/BhLH/K7RYRPHxOPUB82vzzMEsGdqa1LhIk+XXT+zh9zE9Cu1zRfHyOHUBC2JvSAy9FhwB4HpsGu8sO8nQ5SeZfacrSERiBi/OPciIFaeYuin4YbsqcSQBLMacuk/Csmor42ulpT3Or3+Ga7856NMSyY4NQ+3ghWu/b3HtP4+MywfRZ2cUXcDimfXZV9/Qpt297jexMTEEXwji5yXLUanUBJ07C0Dzlq1YuHgpU2fOKqpQRQnx3LCZ+NZ/Ptfya8f/wtrpwZ0khCiIv0Oicbvz1K+DlYavXq3J/D51SEzP5kZcTvm1JhWcmNu7NhO65C7HVZKVmkvAWbcvkbD1KxQac6xqtMeq2gvErhqDQZuF2rU8jp0+JPP6SZIPrgQUYNBhUaEhaUG7sfBriF3L/sRvnAlKDdroq1hWb4NN/ZeN+8+OuU7i9tkYtFlY1eqEde1OJGz9muzoq6BU4tr3mwLHrLIxvXdHZXWvR6bizsMDKtt7943dvfFbFK2LwReYPjkQCwsLurzUjQ6duvDu0MFkZ2XhV7Ei4wOncOzIYX5Z9BMKFGh1Wpq1aMmObVto2qwFQ4aNIHDcGNQaDVcuXaJjl64mxZlDr1xh1oypZGVm8lL3V+jWvQczp07m8uVLqJRKFixaUuCYXVxMHxAKOn+OevVzygw1aNSIc+fO0rxFSw4e2M+Afn145dXX6NSl6387UeKJib0RwoFfPkVtbo5/0874NWrHjm/eR5edhWMZP5q9OZaI4OOc3boUFAoMOh3eNZoQenQX3tWbUOelgez9aQpKlZqEW6H4NW5PwAs9jftPCL/KweVfoMvOolKLrlRq8SL/LP2M+FtXUCiVdPr44U8M58Uqj3sTwy8cw92/FleP/vWfzod48kIiU/h8ewjmGiUdq3vQtqobH/1+lmytnvIu1nzcoRInbiSw/HAYCnIeEGns58SfwVE0Lu/EgOa+TNscjEalJDQ6lXbV3OhR917tyGsxqXy16zJZWj1danrQpaYnX+y4RGh0KkolzH29doFjPn49npre9uwOjgbA3vJePUClQmEsU3PkajxDl5+kW20v2lcrPT8+Sk0CmHntOLbN38DSv+mdmmoGnHvNRKE2J27DDLTxtwBQmlnh1D2Q+C1fgsoMt/7fEf3LCGjZHwCLio2w6PQBMUtHYl333j+AyfsW4/jSeJTWjsSu/BirWh3Ijr2OSx7lYQzZmcSsNO3qoHb2wbHTh/k6luzoq+jSElE73avZlhF6DJVjGRRqqW9U1A4f/IeBQ4bSsvVzGAwGDAYD3373A+bm5kwY+z/CbuQ8YWdlZc2sL79m2uSJmGnM+GX5Kga80Zshw3IuuzVv0YpxEyczsP8b9OjZy7j/BfPnMWPWFzg5OzN8yEBe7PYyV0Ov8GMe5WAyMjJ4919dRsqVL8/4wCkPPYbk5GSsbXIKu1pbWZOclISLqxtrN21FoVAwfMhAmjZvjoODlFQoDsKDjlC76wB8ajfPGd8MBtqO+gq1xpw9CyeRFHUTAI2FNc8Nnc6BX2ai1JjRddxPbJoxiDovDQSgbM1mNOs3hi2z3qFK63s/cE9u+JHWg6dgYefEjq9G4t+sCwkRV+n0v+9zxaLNymDH1++bLLP38KHZm2Nzbftvl/ZtpNlbn0gCWAwduxZP/6blaFbROWdcAz5/pQbmaiVTNwVzMz4dyOnUMfWlAD7bFoKZWsmCN+oydNlJBjT3BaCJnxOj2/szfMUpXqrtZdz/zweuM6lrVRytNHzw21k61fDgWmwq83rXyhVLZraOD1efNVnm42TFxx0qmSzbdOY2YzpWNiaAd4VGp5KYnk1ZJyuytHp+HdQABfD+b2dp7OdkkiiWZKUmAbSq0YHkfYtJD9qNTYMeqJ19SNj8ObrUOHSJt9GlxAGgvtOuTWXjhOZu6zb1vSebNG45dflU9h7o0+71F9TG3iRu3WQA9OmJ6NOTsK77IvEbpqO298S21QDj7JxCY/7IuoEPos9MJWH7bJzuexRdlxRF8j/LcX51+mPtUzxZXV96mR/mz2XHtq307vsG5cqXZ8rECcTGRhMRHk5sTAwAFf1zipm6uLhS4c6fzczvJfD+lSqjVCrx8ipDQkK8cfmN69cY81HOP7AJCQkkJibS49XXGD/mY8p4ezN0xHvG75qFhcUj6wTmxdbWlls3wwBITUvF1s4OM7N7fw/q1K3HzbAwSQCLiYpNO3Nqw49cPbqTam1ew97Dh/2LZ5CeGEtK7G3SE2MBcCyT00vc0t7F+GeV5t53ztG7IgqlEhsXTzJS7o1vSbfD2P19zpiTkZJAZmoSVVp3Z+/CSdi4eFL35SHG75zazOKRNQTzEnP1Ag5e5VFr5EdscdSxhjs/H7jOn8FR9KznjY+TJZ9uCyEuNYvbiRnEpeb0fL7bss3ZxozyLjktTs3U9+42q+BmjVKhwNPegsS0bOPysPh0Jq4PAiAhPZukdC3dansxZVMwnvYWDGrha/yOmWtUj6wdeCEiGV8XK8zVpne6pWZq+WrXJQK7VM0VW62y9tyKT5cEsKRRmlni0GEUupRYErZ+jVX1Nmg8/HFq2pu49dMw1im//wpqHldTs6NCUTl4oEu8jfK+S7Jq57LYtx+Jysoeg04LShWWlZpjFfA88Vu+RBt1BY17Tqu5x50BNBgMxG+ahV3LAcZLvwZtFvEbP8Wh4/sozawKfF7Ek2dpZcWYcROJiYlm5pTJdOzShaoBAfR/exDj/jcaw53v2v2X6/O6dH/5UgheZcoQHn7LJNEq5+vL/8ZNwMHBkezsbNRqNc+90Ib2HTsxfXIgly5epFKVnHtZHncGsGq1amxcv47X+rzBsSOH6dipC2lpqVhZWWMwGAg6f45evaVncHGhsbCkSd/RpCXG8s+SWVRo0h7ncpWp2akfexYE3usko3j4ABd/6zK2Lp6kxERgYXNvfLPz8KFJ39FY2Nij12pRqFSUq9MKv4ZtObDkU+LCLuHskzP78rgzgPG3rnAr6DC3Q04SFxbCqY2LTFrJiaJlpVHxYVt/YlOy+GJHCO0C3KnsbkPfxj5M3njB2Onj/q9YXuNaaHQqnvYWRCRmYH9fizYfJ0s+aOuPvaUGrU6PSqmgpb8Lbaq68dn2EC5Hp+J/p5dwfmYAQ2NSOXotntNhiVyKSuGXf67Tr4kPM7ZcZGBzX2M3kLQsHVZmKgwGA8ERyfSo60VpUWoSwLRzO0k7uwNDdga2zfqg8apK8oFlZN08V6D9ZF49RvLBFVhVa4NCee/02bZ4k/j10zDotKis7HF8aTwxv44GvQ6FhQ1q57LGbfM7Axi3fhqZ106ijQ/HtkV/FAolmddOGmce7dsMIzsqlOyY6yRs+RIAp5fGo7KTgs9FaeumjWzauJ709DTeHjSEatVr8tOCHzh18kSB9nPwnwMs+mkBHTt1Qa2+910bPHQEn3z8EVqtFkdHR6Z9+jnDB7+NTqfDxtaWcuXLG7fN7wzguP+N5uiRQ4SFhTFk2AgaNW6Cf6XKDOjXB/9KlQmoXoODB/Yzd/bXqDVq2rRtj6urdKIpLq4c2s7lf7agzUynVuf+uPhV4/SmxURePlOg/dw6d5gzW5ZQoVF7lKp737k6Lw1kzw8T0Ou0mNs40HrwFLZ/NTLntZUt9p7ljNvmdwZwz4JAwi8cIznqJnVeGoR/8y74N+8C5Dx9LMlf8bI9KIpt5yLJyNbxRhMfAjxt+eXgDc7cSirQfo5cjWfpoRu0DXBHrbyXIA5o5sukDRfQ6g04WGoI7FqFD347g05vwMZCjY/TvQmO/MwAdq7hQecaHkDOk8ZvNi3H8evxHL+RQGJ6zszjiOcrkJiezQ97r6JWKniusisuNqVnBrrUdwIpiPiNM7Fr9TaqZ7AFm3QCKRz/pRPIwwSOG8Owd0fh7uFRCHsvXNIJpHDktxPI47rbsu1ZegJXOoE8eQXpBFJQ0zYHM7hFedzsns0kq6R1ApEyMEIIIYQQpUypuQT8JDh2ffRTbEI8CZOnf1rUIYhSpuXbE4s6BFHCje9cuursFXcyAyiEEEIIUcpIAnhHYbZ0Sz29lcjv+5EVcRFtQgQR33QneulI4jbMeOB7Mq+fJPL7fiZx5bUs+Z/lRC8dSfTSkYR/9SIASft+IeLOn0XxU5gt3Tb8sZbuXTsRdP5cnq3bQq9c4dWXX6R714752s+g/m+w7JdFJutef+VlNm/cAOTUJHy+eePCORjxxBRmS7dL+zexZlwvYq5dMC47v+PXh35mQvhV1k3sw5pPXjUu02u17P5+PJtmDOLS/k0ABP+9jl9HdczVLk4UD4XZxm3z2dv0XniU4NvJBIUn8c6ykwxZepIFe6+abPfNrsvM3HoRgLjULEasOMXQZbm3+/d7Rqw4xTvLTvLmomMATFwfxIgVpxiw+Dhj1uY8HDp23flCb1VXlOQS8FNi26wvZp6V0SZEYFGxMY5dPn7o9hp3f9wGLiR21diHLrNt2gfbpn3Ijr1B8r5fALBr8SZZN0rul1Y83NuD3yGgWnXCb92kectWTJwyzbjO09OTxct/5YP3Hp0QvD34HTp3Nf0hcWDfXuzs75UHGTx0OCeOH3tywYtnUq0u/XHxzamrptdqiQ279NDtrZ096PLJQnbNuTcOXj+1B7cK1QkYMpXtX75HhcYdqNL6ZaKunH3InkRJ9mZTH6p42KLV6fm+bx0A3lt5mtRMLdbmahLSsglPzMDxTjmZXReieLG2J+0C3Hl/1Rnjdv82qk1OSbbDoXGcDEsAYMpLAQD8cSrcWCBp5svVSnQCWOJnAOM3f442IQKAxL9+ICviIqknNxG9dCRRP79DVkSI6fYbZ6JLigLuzQpmx1wnZvkHRP8ygtRTW/5zTJmhR4le8i5p53Y+cBulhU2urh55LbsrI+QAFpWa/efYxOObGjiB8Fs5HRe+/fpLgs6fY93vqxnU/w3eeK0nF4LOm2wfOG4MkbdvA/dmBUOvXGHI2/15q+/r/LF2zX+O6W7rti2bNgI5NQqtrKzz9d5ffl7I4AFvcjH43szOlk0b6dCx83+OSzwZ+xfPIDkmHICjq+cRc+0CF/euZ8usoWyY+hYx102b2+/9aYpxNu3uDF1C+FW2fj6cTTMGEbJvw3+O6fLBLfg1avfQbTTmlmgsTOuWRoeex7NqfRQKBY7eFUmMLJwnUcWjfbr1IhGJOX3l5/8dSvDtZDacjmDEilMMXHKCi7eTTbaftjmYqKRM4N6s4LWYVN5beZp3lp1k05mI/xSPWpWTquj0BlxszDDXqABYc+IW3evcq9vn7WhJaqYO/Z3iJhrVw1OcfZdjaeHvYrLswOVYmlXM3aawJCrxM4CWlVuQEbIfm4Y9yY68jP3zQ1C7lMO6The08eEk7V2E00vjHrqPvNq8KRQ5XyxtQgTxG01v2Df3qY1dq7wv86lsnHEbuhQFCmJ+HY25X0OTHr+PK+PKEZx7Tnv0hqLQPPdCG3b/9Sd93niT4AtBvPf+h5T3q8DLr/TkZlgY38+bw7RPP3voPvJq86ZU5nzXbt28yaTxpg8i1a3fgKEj3stzX/+lddtzL7Sl60svExF+i0kTxrHg5184deIEAdWro1Kr8rUPUfjK1W3FjRN7qNbudeJuXKRBz+E4eJancsuXSIq6xcn1C2g1aPJD95FXmzfFne9ccnQ4+36earK9R+U61O02OM99GfR6bp07zHNDp3N267ICHUtWWgpmFjk/TjQWVmSlpxTo/eLJaVnJhb0hMfRq4E1IZApDW/vh62zFi7U8uZWQzk/7rzHxTieNB8mrtZvyTmHo8IR0Zmy5aLJ97bIODGzh+8D97QiK5Of912lY3hG1UkFalo7biRl0qnGvVFZVD1vm/32VlUdv8nwVV5MuH3kJvp3Mh20rGl+nZelIy9KVmlqAJT4BNC9fj9Tj6zEv3wC1S06x0szLh0g5uhYUChSqf5+C3PXL8mrzprJyAEDt4Fmgtm4KtZnxE8zK1kQXH/6fE0BdajwKtRlK8/zN7IjC0ahJE1av+pUmTZvhVyFnUNm/dw8rVyxDqVCgVv+rvdB9VfLvluPMq82bo2NO0lbG27tAbd3+S+s2W9uc2mpeZbyNCeia1asYO2Eiu3Zsz3cMonB5BTQkePdavKo1wsErpwB42JkDBP35GwqF0qSYM/x7dMv5zuXV5s3C1gEAW1evArV1u3ZiNz61WzzWsZhZ2ZCVkQpAdkYaZpY2j7Uf8d818HXkj5PhNCzviO+ddm7/XInj9+O3UCpApTT9Jt3/6m5h4bxauzncuVTr5WD5yELO/9YuwJ02Vd0Y/0cQV6JTOBwaT9danibb/Hr0JkNbl6eJnxPj/ggiMikDdzuLPPcXfDuZSm42Jt1KDl+No1F5pwLF9Swr8QmgQqVBYWFD6smNWFZpCUDKkd9x6fs1usRIErZ+Zbq9uTW65FiUVg5o43Iu5/27zdv9SWNBZwD1WWkozawwGAxk376ITf2XMWRnYtBmobR8vIKmGZcOYlGxyWO9Vzw5Go0ZtrZ2rFn9G8+3aQvAiqW/sGDRL0RERDBjyiST7W1sbImOjsLRyYmw69eB3G3eNJp7SWNBZwDz27otIyODrMxMk3v7UlNTsLa2ISkxkezs7DufH8bo90cSHRWJQqGkTr16eHmVKdA5Ek+WSq3BzMqGi3v+wLfecwCc37mSjh9/R2rsbQ4smWWyvcbShrSEGCxsHUmKzOn1/O82b8r7us4UdAYwMeIGty+e4PLBrcSFhXBx73r8m3UmMzUZS7uH//hwLV+NiAvHcSxTgfibl7FzL/vQ7UXh0aiU2FioWX8qgtaVcy6R/nbsJnNer0VkUiafbze9dcraXE1MSiYOVhpuxqUDuVu7qe+7HFvQGcAsrR4ztRKlQoGVmQozlZLwxAyOXY8nU6snPCGD/Zdj0BsM2FmoUSgUWJupSMvSkZqpRXHnfffbdymG5v6ml3r3X4qlb+PS870r8QkggGWlZiTsmIN92+EAmPvWI3rJe5iXq51rW6sa7YnfNAuNmx9K65xfAv9u8+bUfZJx+4LOAGbdPE/S7oWgUmNZpRUqW2cyrh4jO/IKto17GbfLjrxM4q7vyIq8TMyvo3F5/fM8lwFkXDqAQ/uRBT8x4olr9dzzfP7pdD78eAwADRs34e0336B+g4a5tu3y4ktMGv8J/pUq4+ySM8j+u83brC+/MW5f0BnA0ydP5mrdFhsTw7gxo7l4IYh3Br7FzM+/IiT4AiEXg3mj/wDje39dtpR9e//GYIARI3NmJH9eugLIeUJYpVJL8ldM+NRuyaEVX9HotVEAeAU0YMunQ/CoXC/XthWbdWLfT1NxKlsRS/ucf/z+3ebt+WH3qhMUdAawdte34E4Lt62fj8i5FB0Zxvldq2jS5yPjdumJsfy9IJC4GxfZ+vkInntnGj51WrL3x0lcPbqLSi26ovr3jLl4qppXdOGbPy/z3gsVAKhfzpFhy09R18ch17Ydq7szY8tFKrhZ42STc+Xh363dpnYLMG5f0BnA/ZdjWXviFnoD1C5rT1knKz5q5w9ARGIGi/+5TvOKLpR3sWbGlovoDQZ8na0p72LNhtMR2Fqoea6yaSesE9cTeLPJvRaGOr2BG3FplHcpPVfSpBXcU5B+4W+SD/6KQ8cPMPOsnGt9yuHVWFRujtrBM493F1zSvl9Iv7Ab98GLjcukFVzhKKxWcI9r145tLP7pRz6ZOImAatXz9Z5lSxbz3PNtKOPtXeDPWzB/Hju3b2P1HxuNy6QVXOEo7FZwj+vqsT85u2UpTfv9z/gk8P2uHfsLc1sHPCvXLfC+g/9eR9Cfv9Hho7lY2ee+MV9awT15hdkKriB2B0ez7HAYo9v7U8Xj8f8bf78nlH5NyuWaAcyPsevOY6FWEtg153td0lrBSQJYSkgCWDiKWwJYHEgCWDiKawJYlCQBfPKKSwJYHJW0BLDEl4ERQgghhBCmSl0CWJgdP+5n0GmJWzc5p97goqFk3Tr/6DflIePKYTJCH6/Q7tM6VpG3wuz4cb/s7GzGfPQBg/q/Qb/evThz+tRj7efA/n0c+ufAY733aR2ryFthdvq4X063jnFsmTWUjdMGPHaR5ptnD3Lr/OHHeu/TOlbxeJ5W4eSIxAxenHuQ91aeZviKU2w7l1PfMjYli+WHwx75/s1nb7P9fOnuMFMqHgIpChmhR9C4VcTp5T4Y9DoM2ZmPfI/BoDfWF7zLokKjwgpRlBAHD+ynUuXKDPjiK3Q6HRkZ6Y98j16vN5Z3uatZ88cr3yFKj5vnDuFU1p9a7/RHr9ehy8x45HsMer2xruBd3jWkaoH475pUcGJsx8pkafWMXx9EeRcrKnvY0qdR6XmS978o0QmgPiuN+A0z0acnYVYmAPvnhxjXJe1fQubV46DX4dhtPCo7N+LWBObU+LNxxunliSRs/Zrs6KugVOLa95sCfbZCbU7W7YvoUuJQ2TihMLci9fRWFEoVVjXakbR3Eebl64FeR8qR3zHodJh7V0Np64J1rY5kXDlMdvQ1lJZ2KJQqdImRmJWtjnm5OqQeX4/S2gG1iy+J22dj0GZhVasT1rU7kXpiI6lntmLm9fAineLJSktLZcLYMSQlJlC9Zi1GfnDviccff5jPoYMH0Gl1TP/sCzw8PBn9/kiSEhNwcXVl5udfMXPqZC5fvoRKqWTBoiUF+mxzCwsuBJ0nNiYGZxcXrK1tjE/qdu76It/Pm0OjJk3RabWsWLYEbbaWmrXr4ObuxovdunNg/z5CL1/C3sEBlUpNRHg4tevUoX7DRvy+aiWOTo6U96vIrBlTycrM5KXur9Ctew/WrF7Fhj/WUr16zSd9OsVDZGeksffHyWSmJuHqV40GPe/NiJ3a+DPh54+g1+toPXgK1k7u/PXdWDJTk7C0d+a5d6bxz9LPiL91BYVSSaeP8/+EL4DazJzYa8GkJ8Ziae+M0tKaS/s3oVCpqNikIyf+WIhXQAMMeh3nd65Er9PiVqEm1o6u+Dfvws2zB4m/FYqFjT0KlYqUmNu4+9fEs0o9gnevxcLWAQev8hxc/gW67CwqtehKpRYvEvz3Oi4d2IRr+YBHBymemrQsHVM3B5Ocnk2Alx3DWvsZ1y3+5zpHr8Wj0xuY1LUqbnbmjPsjiOT0bJxtzJj8YgBf7LhEaHQqSiXMfb32Y8dhplbyWgNvDobGYWepYfE/1xnbsTJrTtzizwvRKJUwrlMVXGzMmLA+iMxsPRYaJa0rl+774kt0Aph6chOWlZpjVbM9BoPprfo2jV7Frnk/Mq+dIO3kZqwb9gCFEtc3ZhuL8mbHXscljxIvhuxMYlaa9vJVO/vg2OlD42uL8vXQRoUSu/JjFBY2OL004aGxurz2KfqMFBK2fIF1rY6kX9yPbdPeZF4/lbO/yi1IPbkR83J1yAg9imO38SRsmmXaoaRGW9LO7cT1zblk3Qoiec+Dm2GLJ2vt6t947vkX6PJSN/R60+9an379GThkKEcOH+KPNb/zet9+qFRKFi5eavyuXQ29wo95lHjJyMjg3XcGmSwrV7484wOnGF83atyESyEXGfHOIGxtbZn+2RcPjfXb+T+QnJzMtEkTeLFbd3b/uZO33h7M8WNHAHi+TRvWrP6N+g0bcfDAfqbN+pzJE8aZdCjp3LUrWzZtZNHSXzl7+hTz5815rPMmCu7inj/wqdMK/2adMPzru1a9XW9qdx1A+IVjXNy7nmptX8tJ9P433/hdS4i4Sqf/fZ9rv9qsDHZ8/b7JMnsPH5q9ea/2pFdAA+JuXmb716Mws7Kh9eCp/96NiXajviYrLYX9i2fg37wL10/8Tc1Ob3L74gkgp5PJxT1/4FmlHjfPHaL14CnsWzTVpDNJhSYduXJoG13GLiQq9Bzx6xY81nkTT96GU+G09HemY3UPY/u1u15r4E3/puU4fj2eDacjeLW+NyoFzO1d2/hdvBabyrzetXLtNzNbx4erTW8v8HGy4uMOlR4Yi7O1GXGpWcbX8WlZnLqRyHd9anMtJpUVR8Ko7W1PLW97Xm9YllnbQh64r9KiRCeAuribWDZqCpDr0mraqS2kX9iNQadF4+aHysoB83K1iftjGmZelbFp2BPrui8Sv2E6antPbFsNMFYMV2jM81X7z6ZRT2wa9SQ9eC8pR383diIB7pVLBzQeOV9qpYUNBp0WfWYausRI1A6exgRQ4+qLNvYG+oxkFCoNSo1F7g4lqfGoHDxQKJSYeTz4L4p48m5cv07L554HyHVpdf26NezYthWtVou/fyUcHR2p16Ah4/73EQHVa9DnjTfp8eprjB/zMWW8vRk64j3jd83CwiJftf/69utP3379+XPnDlYsXYJfhQrGdfc/6F+las4Miq2tLdpsLampKdyOiKCMt7cxAfSrUJGroVdITkpCrdFgaWmZq0NJXGwcXl5lUCqVVM1nuRnxZCRFhlH9TreNf19aDdm/katHdqLXaXH0roiFrQMelevy94KJuPpWpVq716nSujt7F07CxsWTui8PMX7X1GYW+ar5V73d61Rv9zrXju/m/M6Vxg4kOe5915zL5ZS8MrOyQa/Tkp2eSkpsJLauXsYE0LGMH4kR18hMS0al1qA2t8jVmSQjKQ4bZ08USiUu5ao89nkTT15YfDrN7/TSVSpMn/zfdOY2fwZHodUZqOBqjYOVhjo+DkzaeIGqHrb0auBNt9peTNkUjKe9BYNa+Bq/i+YaVYE7hcSmZuFsfa/7UXhCBhduJzNixSkAPOwtCE/MIMAz56nxyh7SaaZEJ4AqJ2+ybgWhdvLOdX9d2tkduL41n8xrx0k//xcGnRbrui9iU/9lYn4djVWNDjmzhwHPE7/lS7RRV9C457T3ys8MoC45BqWFLQqNOUorezAYUJpbG7uLZEeHYu53p1DrfX9xLCo0JGnvIsy8q+U6Ho2HP0n7lmBRsfGdzzTtUIICdAm3MRj0ZEdeejInUeSLT7lynD19Gh+fcrnur9u8YT1Lfv2NI4cOsn3rFrKzs3nl1dfo9Xofhg8ZSNcXu/HcC21o37ET0ycHcuniRSpVyfmHLj8zgNFRkdja2WNhYYGDoyMGgwEbG1uuX78GwOVLl2jctBlgmpw2ad6c7+fNoWat2rmOp2rVABZ8P48WLVvlfOa/OpQoFArCw2+h1+sJvhD0RM6hyB8797JEhZ7Dzr1srvvrLv+zha7jfyY86GhOIqjVUuW57gS80JPtX42kYrPOlKvTCr+GbTmw5FPiwi7h7JPzYzE/M4Bp8dGYWduiNrPAwsYBDAbMLG1IjMwpGxJ/8wpeATlFz+8fb71rNObE+oW4Vcz9Y8G5XGVObfgJ75o539F/dyZBASmxERj0emJvXMz1flF0yjpacj48CW9HS/QGg0kSuO18JAvfqMOx6wn8v73zDIyi+vrws7M92WTTKyEhgdB7BxGRXhUsoGJB7AJWFP+KXcSuoK+9UqR3pFcpShEE6QkQ0klPNsnuZsv7YSGFFJKQbAi5zycyM3vnzuHcmTNn7j2/zScuYrHauL1DEHd0Cub5RUcY2tafm5v5MKClHx9uOE1USi7N/BxBWVUzgAVWG4sOxPFw77DCbUF6DW2D3Qtr+FmsNrafTuVMsoHOoZ6cTjbQvtG1ybDWd27oANC1wwgyVr1H7uG1peYAKv3CSZ37LEo/R6bElptB+oq3wWZD7hWMTKUldf4LYLMi0+hQeBdNKq1MBtCSlUzW0tcdUnSSHI+R05DUOgx/LcR84QhIZRel1ETeRObsu/GbWPozh7b5zaTMmYL7lKVA2QolLq37k/LL06hD2lbVXIJrYPSddzH9lZdZsXRxqTmATZtF8uiE+4mMdAR1GenpTJv6PDarlZDGoWhdXHjq0YexWq3o3NwIbVKUUalMBjAhIYFPn5uCUqlEoVDy5jszcHN347eff+TQwQMoSuldO+h36wCGDujH/MVLS+27dcAgHnloPBu27gTKVigZOmwED42/h44dq17gV1B9mt98Gzt+eJPTO1eVmgPoGRzBug+exCvEoZKQn5PO9m9ew26z4ebXCKXahfWfTHaofri4oQ8s+ipRmQygIS2Jv/9vGnK5EplcQZ+J01FrdRxdP5fk04eRlXNfC+3Yl4UvjuK2N0rPbw3t3I8/Zj7BuE/XAGUrk0R0H8yaGY/g36z050JB3TGyfSDvrD3J6n8TS80BjPBxZdLv/9L0UlCXkVfA66uOY7NBI08tLko5zy48gtVmR6dR0NjLpfC3lc0A7o1OZ8qCf7HZ7IxoF0ikv47ELMfCJE9XFe0a6Xl6/mFkMhjcyp/Brf15beVx/jqXgZvmhg5/KoUoBN1AEIWgawdRCLo0ohB07SAKQZdGFIKueUQh6PIRhaAFAoFAIBAIBPUaEQAKBAKBQCAQNDBuyI/gjfU35GVdE8ImtYNMBtINN4ni2pCJr7+1greLeF+/EmGTmkehD6jrLly33Gi2ueHmAFptduSSeAKVhbBNzWK32wvLFghKImxTs1y5wlJQhLBNzVGWaougJDeSjW6MqyhGbQU4NpuNJ554gtmza6fg7axZs3jiiSdKFRGuSUTwV7PUVoBzI/iaCP5qltoKcG4EXxPBX81Rm4HNjeBrULs2cjY3zpXUMrt372bbtm306VM7eql9+vRh27Zt7Nmzp1baF9QfhK8JnIXwNYGzEL52/SECwEqycOFCmjdvTvv2tVOHqkOHDkRGRrJw4cJaaV9QfxC+JnAWwtcEzkL42vWHCAArQXJyMlu3bmXs2LG19mlLJpMxbtw4tmzZwsWLF2vlHILrH+FrAmchfE3gLISvXZ+IALASLF26FJVKxahRo2r1PKNGjUKlUrF0aWllBkHDQPiawFkIXxM4C+Fr1yciALwKVquVxYsXM3z4cNzcarfivJubG8OGDWPx4sVYraLif0ND+JrAWQhfEzgL4WvXLyIAvAq7du0iISGBsWPHOuV848aNIz4+nl27djnlfILrB+FrAmchfE3gLISvXb+IAPAq/P7777Rq1Yq2bds65Xxt27alZcuWLFiwwCnnE1w/CF8TOAvhawJnIXzt+kUEgBWQmJjIjh07anXi6pXIZDLGjh3L9u3bSUpKcso5BXWP8DWBsxC+JnAWwteub0QAWAFLlixBo9EwYsQIp5535MiRaDQalixZ4tTzCuoO4WsCZyF8TeAshK9d34gAsBwsFguLFy9m5MiR6HQ6p55bp9MxcuRIFi9ejMViceq5Bc5H+JrAWQhfEzgL4WvXPyIALIcdO3aQnJzMuHHj6uT8Y8eOJSkpiZ07d9bJ+QXOQ/iawFkIXxM4C+Fr1z8iACyHhQsX0rZtW1q1alUn52/dujVt2rQRVc0bAMLXBM5C+JrAWQhfu/4RAWAZxMfHs3PnTqctWy+PcePGsXPnThISEuq0H4LaQ/iawFkIXxM4C+Fr9QMRAJbB4sWLcXV1Zfjw4XXaj2HDhqHValm8eHGd9kNQewhfEzgL4WsCZyF8rX4gAsArKCgoYMmSJYwaNQoXF5c67YurqyujRo1iyZIlYiLrDYjwNYGzEL4mcBbC1+oPIgC8gu3bt5OSklLnqevLjBs3josXL7J9+/a67oqghhG+JnAWwtcEzkL4Wv1BBIBXsGDBAjp06ECLFi3quisAtGjRgvbt24uq5jcgwtcEzkL4msBZCF+rP4gAsBixsbHs3r37unlzuczYsWPZtWsXsbGxdd0VQQ0hfE3gLISvCZyF8LX6hQgAi7Fo0SJ0Oh1Dhw6t666UYNiwYeh0OjGR9QZC+JrAWQhfEzgL4Wv1CxEAXsJsNrN06VJuv/12tFptXXenBFqtlttuu42lS5dSUFBQ190RXCPC1wTOQviawFkIX6t/iADwElu2bCEtLe26S11fZuzYsaSmprJly5a67orgGhG+JnAWwtcEzkL4Wv1DBICXWLhwIZ06daJZs2Z13ZUyiYyMpGPHjqKq+Q2A8DWBsxC+JnAWwtfqHyIABGJiYti7d+91++ZymXHjxrFnzx5iYmLquiuCaiJ8TeAshK8JnIXwtfqJCABxvLno9XqGDBlS112pkCFDhqDX61m0aFFdd0VQTYSvCZyF8DWBsxC+Vj9p8AGg2Wxm2bJl3H777Wg0mrruToVoNBpuu+02li1bhtlsruvuCKqI8DWBsxC+JnAWwtfqLw0+ANy0aRMZGRnXfer6MuPGjSM9PZ3NmzfXdVcEVUT4msBZCF8TOAvha/WXBh8ALliwgK5duxIREVHXXakUERERdOnSRVQ1r4cIXxM4C+FrAmchfK3+0qADwLNnz7Jv37568+ZymbFjx/L3339z7ty5uu6KoJIIXxM4C+FrAmchfK1+06ADwEWLFuHh4cHgwYPruitVYvDgwXh4eIiJrPUI4WsCZyF8TeAshK/VbxpsAGgymVi+fDljxoxBpVLVdXeqhFqtZvTo0SxbtgyTyVTX3RFcBeFrAmchfE3gLISv1X8abAC4fv16MjMzufvuu+u6K9Xi7rvvJjMzkw0bNtR1VwRXQfiawFkIXxM4C+Fr9Z8GGwAuXLiQHj160KRJk7ruSrUIDw+ne/fuoqp5PUD4msBZCF8TOAvha/WfBhkAnjlzhoMHD9a7iatXMm7cOA4cOEBUVFRdd0VQDsLXBM5C+JrAWQhfuzFokAHgwoUL8fb2ZsCAAXXdlWtiwIABeHl5Neg3mOsd4WsCZyF8TeAshK/dGDS4ANBoNLJy5cp6OXH1SlQqFWPGjGHFihUYjca67o7gCoSvCZyF8DWBsxC+duPQYALAgoICdu/ezbp168jOzq63E1ev5O677yY7O5v169eze/duCgoK6rpLDR7hawJnIXxN4CyEr914KOq6A87i8OHDPPzww7Ru3ZqePXvW+zeXy6hUKnr06MFvv/3GsWPHmDt3Ll27dq3rbjVohK8JnIXwNYGzEL5249FgMoAuLi4AHDt2jISEBB599NE67lHN8Oijj5KYmMixY8cAcHV1reMeCYSvCZyF8DWBsxC+duPRYAJALy+vwn/n5OQwY8aMOuxNzfH++++Tk5NT+Hfx6xTUDcLXBM5C+JrAWQhfu/FocAGgXq9n0aJFtG3bto57VDO0bduWRYsWodfrAfD09KzjHgmErwmchfA1gbMQvnbjIbPb7fa67oSzmDNnDoMGDcLf37+uu1LjJCUlsXnzZsaPH1/XXREgfE3gPISvCZyF8LUbiwYVAAoEAoFAIBAIGtAnYIFAIBAIBAKBAxEACgQCgUAgEDQwqhUA2sRX43KpyDZWm7BbeZRnGzFDoXwqaxu7zVbLPbm+qez1i/FZPtWxjd1mrYWeXJ9U5Vob+nisDBXZyG4X9ruS6tqk2nMAP92bRWy2pVonvVEJcVfwfE99hce8uT2D81nCbsUJ0yt485byV179m2Qmt0AM+uK4KiXaB1S+EGvWuk+xpMfVYo+uTxRejdAPfb7Sx4vxWZqrjc+KyFj2JpbU8zXboesMhU8YnmPerNJvsjZ8gTUjvlb6U9+RewajH/xMhcfkn9yJLS/TOR26zpFcPNC2uLlav622EkhstoWzGeJGWVXOZ1k4ndZwpGZqgtwCG9kmkZ0pSdUCYkt6HJaUs7XUlxsHMT5rFkvqeQqSTtd1N647rBnxWFLO1XU36i22vExsuel13Y16T53NATQmniHr3w3l7k9e/2W12k1a+zkxP04ideecUvsubviKMx+MJOvfjYXbYn6eQszPUzj75QMkr5sFQPziNzn9/jDyYo5Uqw/OxJIchem/jeXuz936f9VqN3fTLLLmTSFv79xS+8xn95E1ZxLZi6dhMxmq1b4ziYs+yf6ta8rdv/yHT6rV7pKvZ/LFSxPYuPCHMvdnp6fy4ujuZKYmAzB72kRmT5vI+0+OYdl3HwIw4/HRhdtzc7Kq1Y/a5kyqkQ2nyu/bl3uSq9Xu538mMWn5eeYcTC2176s9yYz8+TQbTxedd/GRdJ5Yeo6nl58nLstcrXPWBWKMVp2odAsbz5rK3f9/B3Or1e6s/blM2ZjF3P/ySu3bl2Bm0oYspm3LxmC+/r84nEnJZ/3JjHL3f7kroVrtfrYjnqeXRvHbgYul9j2/8ixPL41i6upz5Jkdn73f2BDD0O+PcSTB8X+SbbQweVk0k5ZF89aGC9fV1IpTF5JZu/doufs/W7ilWu1+OH8jj3wwh5/W7ilzf2qWgd5PfkRyena5237fvJ+HZvzKxJlziE12TnBbZwGgJrAZ+vaDy93vP2RSldvMjz+JTJITOvFLTImnsRhKGtGr9z34DnqqxLbQCbMInTALXfPe6Jr1uHTuyXj2uLPK568LFP5NUbcZVO5+11ufKndfeVgST4EkR3/fLKxJZ0q9aRn3L8Z93CdoOozAdHhtldt3No0iWtD11hHl7h/9yAtVbvPCmWNIcjnPfPgzcdEnyc5IK3XMztW/E9q8TeHfk2f+yOSZP9Km+y206nITAHpvv8Ltrm4VTx+oK5r5aBjcvPy+TepV9ZpgJy/mI5dkfDk6jNOpRtLzSn5NuKeDN0/19CuxbcuZLL4eE8YTPf1Yeaz8B9/1hhijVaepl4JB4epy9z/VuepyXafSLMglmDVIz5l0K+n5JYO8xSeMfNLfnRFNNayNKj/4vF5o5qtlSIvyP81Puimoym2eTM5DLsn46o6mnE7JJz2vZDZ8ar9GfHVHU/pG6Nl8OhOAKTcFcVc778JjVHKJt4eG8uWYCIL1Kg7GXT8vIM0b+zO8Z/kFrJ8b27/KbR4/n4hckvjh5fs5eSGJtKzSLycLtxykdZOgCrdt2Hecn195gCl39mPpjkNV7kd1qPYn4KpgzcsifuHryJQqZJICr973YLdZyb9wBJ++D3Lum4movBtjTo0hcPSraAIiuPDrczR+8LMqnccYdxyX8E4AaEM7YEw4hS6yZ+F+ha58iZe884fx7few4zg373KPq0ts+VnkrHgTmUINkhxt93Fgs1IQdxSXXveT+ctjyL1CsKbFoBv+Cgq/CLIXvID7uKpluCyJx1GGdgRA0bg9lqTTqCIcwbHdnI9MpUWmVKNs3AnTsc01fp3XQm52Jj/NeBGlWo1cruTWOx7EZrVw9vhhBo97lI+mjMMvOIzk2LPc98K7BDeJ5Kv/Pc7TM76t0nliTh0lsn03AJq26Uxs1HFad+1TuD/PkE1udiZefqVvwlFHDzD0vicByMlMY9ZLDxPaoi2jJjyLTCa7hqu/drKMFl7fEI9KIUMhybingzdWm50jSfk82NmHiYvP0thDTUyGiVf7BxHhreG5VTF8Niq0Suc5fjGfTsEObdEOQS6cSjHSM1RXuN/LpfStyU+nxGy1k2uy4a6WX9uF1hJijFadLJONN//MQS2XIZdgXEstVjscvVjA/W1deOyPTELc5cRkW3mlp44ITwUvbMnmk/7uVTrP8VQLHf2VALT3V3A63UKPYMc82nyLHa1Shloho1OAks3nr68AMCvfwvT1MagVkmNcdvR1jMvEXB7s6s/DC08XjcsBITT10fLsirN8fnt4lc5zPDmPzo0c47BDkCunLubTM0xZuD/Q3WEvSQa2S7cqb1dliTY0SgmNUio8rq7uaJmGPF7+ejlqpQKFQs4Dg7tjsdo4HBXHIyN6c9/bPxEa4MX5xDTeengkzUL8eOqT3/m/F+6p0nn+O5tA1xaO+1+nyMaciEnkpnZNC/fn5BnJNOQR5KOvcJu/pztmixVDvgl3V+01Xn3lcEoAmHlwDR7dR+Pe6hZi50wttd+am0nQI9+Qn3CS7H83oAko/UZszkggccXMEttcQjvge+vDRe0YDajVDoeX1C5YjZV78zCnxaH08EemUF794DrE9O8faDrdjrp5X7IXvVxqvz0vE939X2FJPInp2CYUfhGljrFmJmL444MS25Qh7XHpM6Hwb5sxF7mP4+EsU7lgL/YJyWYyIFNdevtWabGbqvcpprbYu2E5fUaOo0PvAXzz+tOl9huyMnjukzlcOPMfB7auIXhi6QUCaUnxzP/89RLbItp0Ztj4Ir/MN+QQGOoY5GqtC/m5OSWO37VmIb2H3cWOlfNKbE9JuICnXyAKpcPXpnzwEy5u7iz95gOO7/+T1t2qN5m3plhzIpPRbTy5JcKdqWsulNqfmW/lmzFBnLyYz4ZTWTzVS1PqmIRsMzO3JZbY1iHQhYe7+Rb+bTDZCPdyPCRclBIG09VXUbYNdOH+BY55jP83umoBp7MQY7Tq/BFl4vZIDX0bq3l5a3ap/ZkmO1/11HEyzcKmcyYiPEs/thINVj7YW/J+395fyYR2LoV/5xbYcFE6XhxcFDIM5qJPkwazDVelI1TRKiC34Pr5bAmw5ng6Y9r6cEtTPS+uKj13MCvfyqt3hnDiYj4bTmXQ1Kd0AJGQbeb9zbEltnUIdmVi94DCvw1mK01Ul8alquxxaSywsfpYOh+NbFJhn9PzCjgYZ+DBrnWjGrLiz3+5+9bO9O/cgimfLyy1PzMnj1/+9yDHziXyx1//8UzIraWOiU/J5K2fS2bQOzcP4fHbiu7TOXlGIoId9zZXjQpDfsmXh0VbD3LHLR1ZsPlAhds6NGvEXdO/w26Hn165v3oXXUWcEgAWZCbi1rofAOqApqX2q7xDkCmUKNx8yg3aVJ5BhE6YVeF55Bod1ks3O5spD5VX5VLgOSd34dbipkodW5dYsxJRtegLgNy/tB0lr0bI5EokN1/s5dhR7hGI/t7PKzyPpHHFbnbMkbGb85A8iuwoqXXYzZceKOZ8ZOqqf4qpTdKS4+lw00AAgsObl9rvGxyKQqlE7+1XKmi7jHdAMJNn/ljhebQ6N4x5DjuY8vPw9mhUuM9sMpIUe5ZB4x4t9bujf22nbfdbCv92cXNkMVp3v5n46JN1HgAmZhfQL8LRp6Y+pYO7EA8VSrkMH1dFufOkgtxVzLqt4gBNp5bIvfT7vAIbQe4Vr2jONVvZcTab+fdGcDbNxM/7U5l6S2BlLsmpiDFadRINVvo2dvz/N/Usndlt5CahlMvwdZFKBG3FCdTJ+XxgxVMoXJUSeZcCuzyLnSBd0QwonUoqDPryLRQGg9cLidlm+jV1XF9Z47KRhxqlXMLXVYnBVP64nD2m9AtHcXQqOXmXx6XZRrC+9Lj8eHscD3fzR1dBFt5is/P+ljie7xuMXKobWyakZjGgSwsAIkNKB6GN/b1QKuT4eerIyTOW2UawrwffvXRfhedxc9GQeynoyzWaaeTrUbjPaC7gXGIqE0f0rnCbId/E1oOnWPbeE0TFX+S7Vbt49YGhlb7W6uKUAFDpEYgpOQqVVxCm5OgSn2UBKPHZq+wBXpkMoKZRS7KPbsGteW/yY/7FvU2/SvUv98xfBI97t1LH1iVyfSCWi9HIPYKwXoyGS598LiPj6nasTHZBEdgS0/EtqJr2whJ7BHWLW4rOodJiN+djt5gpuHAIRWDLa76umsTbP5j4c6fxCWxEwrnTtCr2WRZK2qi8AkiVyQA2jmzDPzvW06Z7X6L/O0iHPkVzvNKTE0hNjOPr6U+RGHOGPEMOj77+OQDHD+xi4quOz31WSwF2ux2FUsX5E0fwD6n4jdoZBLoriUo1EuSuIjqt5GdZKPk5p7wKUpXJALb007LlTDa9w9z4NyGvMOgsDxmgvfT5y10jx2C+PmvMiTFadQJ1cqIzLQS5yYnOtNIjuOT+4tMiysvLVSYD2NJHwZbzJno1UnHkooVbGhfNMdQqZOQX2DFb7RxKLqClt1MejZUm0F3lGJd6NdFpRnqFlRwvJb2q/HF5tQxgS38XNp/OpHcTd/5NyOXWZiWD6gWHUgjz0tC1sVuF/f1yVwKDm3sSUUYm0lkE+eg5E3uRRr6enIm7yE3tSga/JfyqHMeqTAawdZNANuw7zs0dmnHodCwDuxSNt8TULOJSMpn82QKi4lPIzjMyecwtpba988hItGolCrmE3lVbbkBa0zjFyz06jyB+4etkHlgFMgmZXFHlIqGVyQBqg1uSdWgdMT9OwrVpVxQ6L4yJZzBdPIu+/WDSdv1O1uF1IJNhzc3Aq9dYLLmZyOQK5JqiB13y+i8xnPyT3NN/4dnzTvTtyp/A7UzU7YeRs+JNTIdXg0wCSQ5VtGNlsguKwBYYj64na94UlE26Irl6YUmOwppyFnWbQWi63kX2788h07ihG/naNVxRzdNz8Gh+mvEie9YtQZIk5HIFNmvVyhVVJgMYGtmGfZtX8cVLE2jRqRfunt7ERZ8kMSaKrreO4PlPHavQ5306neEPOBY0GbIykMsVaF0dN8/8XANfT38KtUaLh68/A8dOrMYV1ywjWnrw+oZ4Vh3PRJKBQpJVeRVfZTKALf20rDuZxaTl5+naWIeXi4IzqUbOppkY3FzP74fSWHcqExkyMvItjG3vTesALU8tP4/NZmfKTQEVtl9XiDFadYY1VfPmnzmsPmNCkoFcAmsV4/vKZABbeCtYH21kysYsugYq8dJKRKVbOJtpZVC4mrtaanhuczZuKhmv9dZV2JazGdHKi+nrY1h5LA1JJkNezXF5tQxgS38X1p3M4OmlUXRr7IaXi5IzKflEpxnp30zPd3uTaB3gwt8xOQxu4cmIVl58uSuBnWez2RuTw13tfWgT4MKa4+lEpxpZ+V8a93TyLRWwOoPb+7Tn5a+Xs2zHISRJQiGXsFirtrq7MhnA1k2CWLPnKI98MIcercPx1rty6kIyUfEXGd6zLb/870EA3vxpDU/efjP+Xu6ltrlq1LSNCObh93/DZrfz4riB1bvoKlLtQtDPbUirdB1Au83qCPxkMmLnvUzgbS9XuCCjvhLuqeCzwRUvIHloZUq164wVt2P2klfQDZ2K5Fr/7RjpreSX23zL3b8n1ljpOoA2qxWZ5LDRt29O5p5n3sTd8/pc1HMtuKtl9Aop/SmoPNLmPV+pOoBWm90xcVsm4+U/Ynn5lsAyF2TUFxS+4Xjf92mlj7+W8Qk35hi92visiJTvHrpqHcDiPvfKtmym9tDhpa0/KqXKgEh8H/ulSr9JX/BSleoAFrfRS6vPMa1/I7xcru8569VF4dsEr3EfVnhM7j+rrloH0GqzIclkyGQynp21iOkPDsdbf31Ph6gOkqsXrp1GVeu3zlkFnJ9D/IJXsdusuEZ0vSGDP2dgNxrIWfYa2Kwom3Sp9w+W2iDPkM0P7z6HzWqlRceeN2TwV5vkmKy8uj4Oq81O1xBdvQ7+6gIxRquOwWzntZ05WG3Q5VJmTlCSHJOV//1xHquNSxnzGzP4q0lyco288NVSrDYbPVo1uSGDv2vFKXd3hasHoRO/csapbmgkFz368bPruhvXNTq9J89+9Etdd6Pe4qFV8NXosLruRr1FjNGqo9dIzB50fdbAvF7w0Cr4vztKLyoSlI+Hmws/TnPOatr6Sr171brw63O11vbp94cR8/MULvzmKA2SH3ecmB+eIuaHJ0nZ8n2tnbe2yV5Q9ULHlcV8ZjdZvz9P1vxnsWYnY7dayFnxJlnznyX/7wW1dl5n8NX/Hq+Vdu12O7999D9mvTyR799+FktBAWnJ8UwfP4DZ0yYy95Pre85WRTy3KqZW2r2QYeKp5eeZtPw8s3cXKY9UpCZSn6jNMZo1bwppnw7Fmp1SuC1v71yy5k0hd1PF86rrCy9sKV0+pibZFWtm/Kr6U3z8Sp5dUTsykMk5Zp5cEsWkZdHM2BxbuDDsSjWRP06kM3lZNJOXRTPo2//INtYPGdmnPvm91tp+5IM59Hn64xLqIFdus9vtTP9+FY99OJfnv1xCgaVmF7/VuwCwNtEENSd0wiwaP+CYM6QJjCT0kf8j9JGvyY89htV4fdfTcjZ2cz6mE1txH/cJ+ns/R+7uj/n0ThTBbdDf+zmWpNNCsLsMEs6dRqXWMOWDHwlt3oaTh/YC0KprHybP/JHxL1z/K9KdjV4r5+MRjflydBhZRguxmaarqokIHLjd9ibq5n0L/7blpmNNOoP+vlkgyR2qIoIK2XnBhJ+LeFxeiY+rkv+7I4Ivx0QgySA6zVimmsiwll7MHhPBByPCaO6rxV0jppbMfGI0/Tu3qHDbmbiLaNRKvntpPG2aBPHXsZoN5Gvtf8GYeIak1R8jKTW4dxiCe9v+xM19Cbu1ALVvGAGjppJ77hDpu+cDMrBZcW3Wnez/tqFr2g2ffhNIWD4DmVyJ+eJZ3NsOxLP7mML2TSnnSV77OXaLGX2n4Xh0Gk7Smk8xXTyLTCan8YQvqtxnU1IUMT9OQtfiJrx7j0Mmd5jHbrOicPNBUpYvTVQTWJKjyN3wCSg1qNsMRt3yVrKXTANLAXKfMHRDXqDgwuGizJrNijK8O+aT21A26YbLTQ9iWDsT5AqsKedRt+6PptPoovZTY8jd/AVYzKjbDUPTbhiGjZ9hTTkPkoT+nqoprxQkHAe7nZyFLyJ5heA6cAq2rCQUAY76e3LvUCyJJwsVCmqLuOiTLPryXZRqDd36j6TzLUP55o1JWArMBDaOYOzk6Zw5sp8tS39BhgybzUrLzr059OdGWnTuxdB7n2Dep9ORK5QkxkTRpd8w+owYV9h+0oWzLPlmJhaziR6DRtNj0O0s+uo9kmKikUkSk2eWrQVcHnpvX2w2x2o0Y64BV51jhdzJf/bwxUsT6D30Trr0G15zBiqDM6lGPt6RiEYhMaS5nv7N3HlpbSwFVjthXmqm9g3kUHwu8w+nIQOsdugeomNbdDbdQlyZ0NWXGVsTUEoyzqabGBjpzpg2RfPdzmeY+PzPJMxWO8NbeDC8pQef7kzkbLoJuUzGF1dZJXwl+mIPDEkmA2RXVROpDerbGAWQrphzbUk6haJxewCUoR2xJJ5AEVi6ZmZtEJVu4ZN9uWgUMLiJmlvD1Ezblk2BDcL0cl7oruNwcgELjucDl/wuSMm2GDPdApU82M6FmXsNKCQ4n2mlf5ia0c2LFkPFZFn4Yn8uZqtjdfGwCA2f7TNwPtOKJMFnA6r+qfmfpALa+CnZcaH29afPpOTz0fY4x7hs4cmASA+mrj5PgdVGEy8NU/s14lCcgfmHHNlcq81Oj1A3tp7JonuoGxO6+fPe5liUkoxz6UYGRnowpp1PYfvn0418tjMBs8XGiFZeDG/lxSfb4zmXbkSSwazRFa8SvpLiNf6UcgkfVyVbz2SWqybyV0wOPUIrLiFTHU5dSGbGnHVoVEpG9GrL4G6teOaLRZgtVsKDfHj1gaEcOBnDb+v/QiaTYbXZ6NUmnE37T9CzTTiPjerDmz+tQamQEx2fwpDurbn71s6F7Z9LSOXD+RsxFVi4rU97brupPTPnric6PhVJkvHt1IpXCpeFj770verKbb4eblgvPStqQyGk1gLA3LMH8On7ILrmvRxpYbudRvd9gKRUk7DsPczp8QBIKheC736LpFUfIZOrCHv0a2J+fBqffo6aV7pmPdCNeIELP0/Bo0vRSpfU7b8QdOfryF09iZvzIvoOQzGnxNB4Qun5N7YCE7FzSyqQqL1DCBhVclv4lPlIahfiF76OqWk31P7hZB3ZROr2n3GN6FYYENYWBecPou31AKqmPS+l0u243zkTmUJFzpoZWDMcNpOptLjd9gaG9Z8gUyjR3/8VWXMnw02OpeWq8B4oBz9P9vxnUXcosln+7l9xG/kaMhdPcha9hLrtEKxpF3Avo+SEvcBE9uKSSgZyrxB0Q4o+VdnzMrDnZ+E29iPyd/1KwZk9SF4hFMT+iyK0E5a4I8i9Gl3ZdI1z+vDfDLrnMdp0uxm73Y7dbufxN2ejVKmZ+8lrpCY6al9ptK48NO1DFs5+B4VSxXOf/MbnUx9i6L1PANCq603cPek1Zk+bSK+hRVrQ63//lgemvo+bhxffvP4U3QaMIjn2HJM/KF0qxmwy8u0bJRVI/ILDGDt5euHfLjo9xjwDMx4fjc7DixEPTcFmtfDqtytAJuOb6U/RsnNvXN09at5YlzgQl8uDnX3oFebmsBnwwbAQ1AqJ97YkEJ/leNi5KCXeGtSIj3YkopLL+HpMGE8vP8+Ero5VoT1CdbzQN4ApK2IY1apIl/SXA6m8PiAYT62cF9fGMrSFnpgMM7PLCPxMFhtT15asTxbioWJq39KFns+lm8gyWgnxULEtuupqItdKfRujZWE3GpCpilREbKayC1LXBgeTCnigjZaejVSFfjeznzsquYwZe3KIz3H8H2oVMt7o48YnfxtQSjK+Gqxn8sYsHmznaKdHkIrnuyl5dlM2o5oVvZj/ejSf13q74amR8dK2HIaEq7mQbeXzgaXLkJgsdl7eVvLTcYi7IwgtzrpoI1N76JwSAB6INfBgF396N3EvtM+HI8JQKyTe3XSB+CxHwWEXpcRbQ0L5aFscSrnEN3c15amlUUzo5ih43CPMjRf7BTN5WTSj2hQthPt5fzJvDArBU6vghVXnGNrSk5gMI7NHl5aMM1lspVRHGnuqmdqv5D39YJyBWX8m0EivQqeSV6gmsutcNg92LanxXRPsO3GeR0b0pk/7ZpeeAfD5M3ejVip4/cfVxF10fL531ah5/4nbee+3dagUCn7+34NMnDmHx0Y56sX2bhvBK+OH8NhHcxnTt2Nh+9+t3sW7j96Gl7sLkz9fyMhe7TiXmFZmiRijuYApny8qsS00wKtahZ3dXTUY8k3c+dp3eLm7MGnMLVVuoyJqLaLRdxhK6vafyf5vK5497kTl05iklR9gMaRTkJWMxeBYwq32czie3M0btb+jEK5MUVR9XO0fgUySUHoEYM3LKtxuToslfvGbgENr2JqfjUfXUSQuexelRyA+t04sLPQoKdVXrSEIINc63kx0zXpgSjmP2j8cfbuBuLfpT/yi1zEmR6Pxr9obUlVQtx1M3u5fMZ3chrbzHci9QjCs/whbbjq2rOTCZe9yH4edJJ134b+L20zuF45MJiHpA7AXs5k1I46clW8DYM/Pwp6fg6bDSAxrZiDXB6LtM6HQZjKl+qq1yGRqVxSN2iGTSShD2mFJOo2m293knjtAzsIXkbl4ILl41JR5yqXbgJGsn/8th3ZuoO9t9+IXHMaCWW+RnZFGxsUEsjMc88MuS7e5e/kU/ltZLKsbFBaJJEl4+QWRm5VZuD0lPoZfP3gJAEN2Jnk5WfQedhdzPn4V74Bgho1/qtBuKrXmqjUETx7ai7d/EA//72O2LZ/DPzvX0+WWYXBpYV94646kJsbWagA4tLmenw+ksjU6mzvbetHYQ8UH2xNJz7OSbCgo/Jwa7uWwj7eLgiaX/q2SF731R3irkWQyAtyVZBmLbvSxmSbe3OQIhrLyrWQbrYxq7cm7WxIIdFcysatvoc3UCumqdQPBoQby+Z9JTB/gUL2oqppITVDfxmhZyDQ6bJmOQt12cx6S2nk17waHq/n1aB7bYkzc0UJLiLucj/4ykG60kZxrIz3f8f/ZxMOhMuGtlQr/Xdzvwj3kDr9zlcgqViIqLtvK27scCj9ZJjs5Jjsjm2qYscdAoE7OhHbaYn4nu2rtwFNpFkL18hLnrk2GtPTkl33JbI3K5K52PjT2VPPB1jjS8iwk55hJuzQum3g7sp7eLgrCL/1bJS/6RN3UW3NpXKrIyi+aGhGbaeKN9Q6JxyyjhWyjldvaePPOpliC3FVM7O5fYlxerW4gQOdGOn69J5JZfybw94WcctVELDY78VlmQj0rX76qsozo1ZbvVv3Jxv0nuGdAV0IDvHj3l3WkZRtITMsmNcsxfSsi2JEN9dHrCuXb1MqiMKhpI18kSUagt55MQ17h9gvJ6bzy7QrAoTGcnZvPHX078voPqwny1fPEbTcX2k2jUl61dmBl+evYOYJ9PPjwyTHM27iPjfuPM6R76xppG2oxAJRUWgKGP4clJ42kNZ/i3m4gmsBIvPvcR8LSd4pKb5cYV6UHmeliNEqPAAoyk5C7FA1WlXdjAoY/i9xFj91qAUmOrkUf3Nv0J2n1x5iSo9Fckp2rTAbQZjYiUyiRSXLy447h0fU2bBYzkkKFTJKQ1C5Iitp9wMiUWnQDn8FmSMew8TPUrQag8I9E2+Mecla/V+xAWdn/voQ15SySPgBbVhKyYjaTX/pMK2mLbKZqdhPqlrdi2PAp1pRoFH4Om1Umu6AIaIHpv40AWFLOIen9kUlydIOfw263Y1j7PorgmnPW8lBpXLjzyVfITk9l0f+9R5dbhtEooiUD7prAbx/9r6jK+1Xslnj+DF7+QaRfTMBV71G43S84jDufnIaruwdWSwGSXEG7nv3odPNgFn75LgnnThfKzlUmA2i32XBxc/y/uLrpyTfkYMrPQ611wW63Ext1nD4jx1GbaJUSz/UJIC3Pwqc7ExnYTE+kr4b7Ovrwzub4Ii2BYnYqw2REp5kIcFOSlF2AXlMkDdXYQ82zffzRaxRYrHbkEvRpoqN/U3c+3pFIdJqpUNKqMhlAu93OzG2JPNzVF59L4vNVVROpCerbGC0LhX9zTEc3QOcxFMQcQt1qQDUsUT20ChnPdNWRnm/js30GBjRRE+ml4J7WWt7bXSTNWNxiZYVeZzOtBOgkknJt6NVFR4S4y5nS1RW9WsJisyOXwU0hKm4NU/Pp3waiM600vaQlXJkM4LlMCweTCjhyMZuoDAtz/8tjfBsXagsXpcRzfYNJyy3gkx3xDIz0INJXy32d/Xh744VCKZSr2Sc6zUiAu4qkbDN6bdFjvrGHmuduDkavLTYuw93p38yDj7fFEZ1mLNQRrkwGsMBqQ3kp8NSpJFRyWblqIv8m5NI+qHZKsWjVSl6+bzCpWQZmzt3AkO6taR7qz0NDb2f696soMlzF97OouBSCvD1ITMvCQ1f0/xwa4MVL9w7CQ+eCxWJFLpe4pWMkg7q1Ysac9ZyJu1goN1eTGUC73Y67q+M+qXfV1LhCSK0FgNlHNpH173psZiPeN49HG9yStJ1zyI89WqV2cqP2k/bnfNzbDijxCdbnlodIWPI2dpsFuYsHQXdMJ3bOi2CzIml0qLxDCo+tTAbQnBFP4vL3kZRqNMEt0Qa3JPvYNjL2LQe7DZfQ9iXarA1Mxzdj+m8j9oJ8tD3vQxHYkpy9cymI/69K7ZjPHSD/r99RteqPTCp6KLv0fhDDqnex26xIWnd0I18je9FLYLMi0+iQezUuPLYy2QXJ1ROFfyRZ859B0nqgG/Ua1uxkDGveB5mEtssdyJQ1/7Z3JQe3/8H+LWswGfMZOHYioZFt2LjwB84dP1yldk4e2svmJT/Tue9Q5MV8bci9j/PbR69gtVhwdffggakz+Hr6k1itVrSubvg1Cis8tjIZwBadevLXxhXMnjYRSVIw4ZWPOHfiMGt+/RK5QkH73gPQe1Wv8G5l2XQ6i/WnszAW2BnfyZuW/lrm/JPK0cT8KrWzPzaX+YfSGNDMHUWx+UAPdfHh7c0JWKx2PLRypvcP5sU1sVhtdnRqOSEexbL8lcgAHkrI45/43MIs49O9/MpUE6lt6tsYBchZ/R4FFw5hzUzApfeDKMM6I/cNJ2veFOS+4U6b/wew+byJjWdN5Fvt3NdaS0tvBXP/y+G/lKoV3z6QaOb34/n0D1OVmIf2YFsX3t1twGqz466WeK23jpe2ZmO1g04po7F7ka0rkwEcEqFhSITjHvbCluxaDf4ANp7KYMOpTPILbNzf2c8xLg9c5Ghi1RYg7o81MO9gCgMiPUqMywnd/Hlr4wWsNjt6rYLXBzbmhZXnsNrt6FRyQjyKvohUJgN4Ijmf7/5KQgYEe6joHKJDkslKqYkA7DqbRb9mHlW6jsqy/q9jrN37H/mmAiYM70nrJkH8tHYP/0bFVamdv4+f49d1exncvTWKYhnVx0b24bXvV2Gx2vDQaXn3kVFM+mwhVpsNNxc1oQFFn9krmwGc/v0q9p+MIS4lg8dG9aFby7BS23q0asLKP//lsQ/noZBLzHzi9ipdz9VwihJIdUlYPgPfWx9Bqa/5OQO1QW0rgVQGw9qZaPtMRO5euwFETVKTSiDV5bJkm4dPadHw65HaUgKpCjO2JvBIN1/8dNdvUVpnK4FUhvo2RmtbCaSqzNxrYGJ7Lb4u8qsf7AScoQRSFd7bHMujPfzx09X+lIjaoKaUQKpKcam2+sS1KIGIde0CgUAgEAgEDYzruhhP0Oj/1XUX6h264dPqugv1kvuef6euu1Dv+N+tQXXdhXqJGKPXxrSezlu0Uh95dUDtTlW6UXnz4RF13QWnIzKAAoFAIBAIBA2MOgkAa1POLfPQOs7OHo8x4RTmjETOfHQ7MT9PIWHZe1f9bdzv/yN1x68AmC6eJ+aHJzn//RNk7F8JOGoPnvlgZK31/WrUplyU8eh6Mr9/EEuSQxUgd9MssuZNIW/v3HJ/Y7dZyd08i6zfn8ew0VGg1pJ0mqy5k8ia/0zhHBfDxs/I/HFCrfW9LGpLxg3g700rmfH4aGLPHCfm1FE+f/FBPnvhAdb+9iUAR/du4/OpD/HJc/exa+2icttZN+9rPpw8ltnTJnJg+x8AzP30NT6f+hBf/u8xMlMdkmc/vvt8rV5PWdSWpBvAupOZjP89mlMp+WQbrTyy+ByDvz9ZuN+xKvgCk1fE8P7WhHLbWXo0nSeWnuPJZec5GOeYJP/LgRRG/lyzc84qy/U2PgsuHCbz+wdL9MtmMpC9eBpZcyZhPrsPgLzdv5I+67Za63tlqU05t/XRRh5cncmptKJ560tO5ld4TovNzju7cpi0IYv10Y7Vl6vPGBmzNJ2UvNqvOVmc2pJyA4dM2/h5pzh1MY8jCbk8vjiKJ5dEseBSsemyZNwMJitTV5/jySVR/B2TU27bH2yNY9KyaJ5aGsWFDEcNw8tt3T//FLP+TKDAauPxxVHM3BpbbjvXSm1Kuq3efYQ7X/uOE+cT2fNfNI99OI/HPpxH/2c+49SFZNKycnnsw3lMnDmH/1u+46rt3fvWT/yx17GoLCk9m8c/msfD7//G6VjH82Dm3PXc/XrNSNPekBlA75sfQBPkWNmmi+xJ6IRZBI15tcLfmC6ex2YuWmKdeXA1voOeIvSRr8n+dwPgWHmsDrhxBbm1vcajCGjukIaS5Ojvm4U16Uy5k23NJ7Y6ZN/u+RTdIEdQn7fnN9xGv43b7W+Tv+c3AHSDnkNy9SqzjfrKwLGPENKsFY0iWvDsx7/y3Ce/ce7kEYx5Blp1vYlnP/qF5z6Zw77Nqyps544npjF55o+OOoDA0Pue5NmPfmHg3RP5c+1CACa+VvlFDPWFBzr70NxXi4tS4tORjWnlX1Thfl9sLu0CXJh9eygKScbZtLJLH/QK1fHNHU34eEQI8w6lAfBQF1+aeteuYk9dUdXxKfdvin5CyQeF6fBaNB1G4D7uE4z7FwOOlceXS8vcyIxvo6W5t2PWk8VmJyqj4iBud5yZlj4KZg9yZ/N5MxabnZHNNHQLqp+LKyri/i5+NPdzIchdxZdjwvn6zqbsOZ+NscBWpozb6mPpjGrtxee3h7PwcEq57b7QN5gvx0TweM9AVv7nGKOzx0Qwe0wEvcPc6RHqhlIu8ebgxuW2UR94eHgvWoYF0qtNBN+9dB/fTr2XYF8PIkP82Lj/OGP6duDHafdz/HwiuUZTue3sPhqN3rVocd+v6/bywrgBfDLpTr5fvRuAaeOHlKkiUh1qNABMXPkh5gxHgdGLm77BmHCKzINriPl5Cue/ewxjQknNyYTlMyjIcohFX84KmlLOc+GXZ4n54Sky/1l7zX3KjdpHzI+TyDqyscLjMv5eimfXordglXcjbKZc7BZzrZcyMaz7GGtWkqO/27/DknQK479ryZr/LJm/PYnlilV0hrUzC4XdL7/dW1JjyFrwPFlzJ2E88sc19ceSeBxlqKMKuqJx+1Lnv4z57D4siSfJmv8s5tO7Lm00Irl6IbnosV26pppkway3SEt2FBhe9fMXxJ45zt4Ny5g9bSKfPHcfsVEnShw/79PphZm0y1m0pAtn+fJ/j/H5iw/y18YV19QfucKxAtZmtaL38kWp1hRus1oKSpSIKYvl333Et29MKrwmb/9gACRJQlZmha9r48PtiSRmOxQNvvnrIqdS8llzPIMpK2N4bOk5TqWULAMzY2sCFw2OVbGXs4LnM0w8uyqGp5afZ+2JzGvqj0Iuw11TcjVnkLsSo8VRSDa/wIaruuzVnoGXCj8rJBnVLGZQKerr+JTUuhLFpx2/PYGycSdkSjUylRZ7Qc3WFSuLj/8ykGRwBFvfHcrlVJqFtVFGnt2UxZPrMzmdXrKaxMy9hsIM2+UMXUyWhec3ZzFpQxZ/RF97nzeeM3FraMWB3IlUC538lchkMsI95MTl1HzW74OtcUXjcU8ipy7msfpYGpOXRfPYojOcuphX4vj3Nsdy0eA4/nJW8Hy6kWdWnOXJJVGsPX5tK2N9dMrCun6STFaiVl5xGbfjyXl0bqRDrZBwUUoYC2xltqe4VEA7v8BaWLD6Mofjc+kYfG11Ad/99Q8SUjMBmL1kGyfOJ7Ji52Ee+3AeD7z7CydjSj6D3vxpDcnpDp+6nBU8l5DKkx/P5+H3f2Plrn+vqT+XOXYukZahgchkMkL8PDHkm7DZHPcopbz81evr/zrG4G6tCv+Ou5hBZIg/nm4uGE01r0RTo4tA3Fr2wXDyT7x63o0p8QyagU+g8gnFo/MIzOkJpG77iaA7XquwjbIk3mSSwyHNGQkkrphZ4niX0A743vpwmW0p3LwJnzwPZDJi57yArmn3EsWkL1OQmYSk0iBpi5Z/u4Z3IXbuVOx2Gz597q+qKaqEKvImzKd3oe16J9bkMyhueQy5dyia9sOxZiaQt+sX3EZUvCCmLAkpmcxhN2tmIoY/PihxvDKkPS59yv4sazPmIvcpkoqylyMVZc/LRNGkCy43P0L278+jbNoTmdoFa3ocSBKW1PNVtMTVadvzVo7u3cYtt48n/uxJRk14Bv+QJvQcPIbUxDjWzfua+1+s+HN/WdJu0iUfS0uKZ/7nr5c4PqJNZ4aNf6rc9g5s/4P1876hRadehfUDty2fw/aV8+g78p5yf9d31H0Mve9JYk7/x+qfv+ChaY7SBzabjU2LfuTupyseK9WhTxM3/jxn4O72XpxJNfJEDz9CPdSMaOVJQraZn/an8Fr/4ArbKEvmTbr0pEjINjNzW2KJ4zsEuvBwt8qXEQl2V/FvYh7jf48m0leD/1XKzPx2MJXhLT0q3X5Vqa/jsyzsplxk6mK/NRpq/QX3phAVu+LM3NlCy5kMK491VBCqlzO8qYaEHCu/HMnjf70r1octS+Ltss8lGqx8sLekDdr7K5nQruyafTa7nf0JBbzRx42FJ8oPJg1mOy5KxzlclDIM5pp/ybg53J0/z2ZxdwdfTqfk80SvQEI9NYxs7U1Clokf9yUzfWDF2bGy5N2Kj8f3N5f8tNoh2JWJ3QMqbHP/hRyC9SrUiqIcUXEZt1yzFReVI5DRquQYTFY0yrLzSc+sOEtStpkZw4pqfcZlmvB3Lwo2q8stHSPZfug09w7sxqnYZCbf2Y8mgT7cfnMH4lIy+Hbln7zzSMUlUsqSeZMu1U6MT8nkrZ9LJqI6Nw/h8dturrDNHYfP0LdjMwBahQUye+l25m3cx4AuLVEpyw67Dp+Jo2VYAPJiNrEVe7G11cJLbo0GgK7hXcjYvxLXiG6ofMMAMJzZS8bfS0EmIZNKnq5EhuPSxZUl8aZw9QBA5RlUKUm3yxRX7nBp3B5zejzaMgLA9L2L8ep5V2H2EiBl6w80uvd9VN4hxM55Aff2g5CUtfNpSRnWGeOhVSibdEHuEwaAOfovjAeXg0xWym7Fa7/bL1U4L0tC6rLCgNwjsEqSUZLGFbvZ8eZpN+cheZS92lOmdkUR0h6ZQoXkGYQ9Pwttn4nkbvwcmasHykbtKn3OytK8Qw92/7GYFp164h/ikBE8tv9P/ly9AJkkQ7pSr1lW2lZlSbvp9A4dW++A4KsWcr6SLrcMo9PNQ/j5/akknD9DUFgz+o2+nz4jxjF72kR6DB6Ni650bSkXN8e20Mg25GYXyYH9MecrOt08BJ/AmtdR7tLIlZXHMugW4kqYp8Of914wsPRoOpJMVqJoLJRUGbh8+ylL5s3jktpAkLuqUrJuFbH+VBa3NnVnTBsvvtiVxNGkPNoGlP0w//uCgdRcC492r71aofV1fJaFTO2K3ZSHTO2C3ZyHTFP7K2o7ByhZdcZIl0AlYXpH0PBXvJnlp4zIZFTsc5eeC2VJvOk1jiMDdfKrFnQuzp+xZno1uvpnXJ1KRl6B4/x5BXZ0qprPyHcJ0bHivzS6NnYjzMsRiO+NyWbpkTRkFGXQLlPeeLxS3q34eKyMnFtxLhrMzD14kfeHhxVuu1LGzVUlJ+9SEJhvtqIrJ0sP8MXt4USl5vPzvmTeHeZoc9e5bPo0ufZ6e91bNWHp9kP0aB1Ok0CHzNuuo1Es3HIQmUxWopgzlO1bZcm8ebg57jfBvh7VknU7eCqGx0bdBMDcjfuYfMct9G4bwUtfLycpPZuAMmoNLtt5iGn3DWbLwaL50FKx55dUlnTJNVKjAaBMoUSu0ZF5cBVuLR0RcsbexTR+6AsKspJJWvNJieMljSuWnFTkrh6Y0x0Vu6+UeCuu/lHVDKDNlIekdshrGRNO4tl9NLYCE3aLuVD3F3D0bfXHWPOzsRoNuEZ0vdQ/t0vnl4Gt9ib9yuRKZBodpsNrUDV3iFIbDyzB/Z7PsGVfJHdDyTlgMrUrNkOq4zNruuNBfKWEVHG7VTXDoAhsien4FlRNe2GJPYK6xS3YC0zYrWYkTZHdFEGtsKacQ3L3w5adjEyrR+Hqhfu4j7FmJ2Pct7hG7FOib0olWlc39qxfSrte/QHYsXIek97/gYyURBZ9WTL7p3XVkZV2EZ3ek5QEx03ySmm3y59soeoZQEuBGYVShSRJaLQuKJSqwm0KpRKVRotCqcKYl4tMJkOtLQpkjHm5aFxcSU2MQ6VxzIE7vHszeYZsegy6/ZptVRZKuQydWmLV8QxuDnfchBYfSeeLUaEkGwr4ZEfJ7J2rSiI114KHVk5cluMTxJUyb8UfUjWRAbRhx/3SA8VdLcdgsmGy2DBb7bgVe9DEZ5n5/XAaHwyr3bIX9XV8lv3bFhRcOISySVfs5nynKPUo5TJ0ShlrzpjoE+IIvJacNPLZAHcu5tn4dF9JlQtXpYzUPBt6tUR8juPT4pUSb8WDxqpmAGOzrRxOtrDpnImoDMfn6CHhanLMdjw0RQFDC28Fh5ILaOIh52ymlWBdzReeVsoldGo5q4+lF47HRYdTmTU6nOScAj7eHl/ieJ1afmk8KojLdMwlu1Le7crxWJUMoNlqY8bmOF64JbgwwwelZdxa+mv5J95A1xA38gpsaJQS2UYLaoVUImtottpQySVcVXJUxbbvjcnhvaHX9qIIoFTI0bmoWb7zEP06Oeb9z9+0n2+n3kdSWhbvz91Q4nidi5qUTAOebi5cuJgBlJZ5UyiK3WOqkQGMS8nAz9MN5aV2Lsu5yWQydBoVeUYzuUYTMmS4aIpeROJTMnn562VczDQgyWR0jAyhkZ8nZ2Iv4uuhQ6Ou+bmnNV4HUNeiNxfXzcJv8CQAXMK7EPPzZFzCOpQ6Vt9+MIkrZqL2j0ChcywSuFLiLfjutwqPr2oGMD/2P1K2fA+SArdWfVG4+ZAbfQBjUhTevYu0VhuNexeA3HOHyL9wBG2jVnj1HkfCounY7aBr2g1JXbsSQKqmvcjd8iUu/R2BhjK0M9nznkHRuEOpY9VtBmH44wPkfhHILtntSgkpt9vfLDy+qhkGRWALjEfXkzVvCsomXZFcvTCfP4j1YjTabncX9aP9cAxrZpC/dy7qdsOQSXKMh1djOrEVmVp3VU3S6tKme1+WffcRtz/yIgCRHboz6+WHadq2S6lju946kvmfv0FQWDPcPR0qLVdKu0145aPC46uaATz613Z2rV2I3WYnok0n/IJD2bFqPkf2bMFqsdD5lmGo1Br2bliGVudOh95Fuqurfv6chHOnkUkSdz75CgArvv8Evbcvs6dNpHmHHgwa92i1bFQRvcPcmLUrmUm9HaonXYJdmbziPB3KmI8zuLmemdsSifBWF0qtXSnz9tagokxldTKAz62K4UyqiedWxTC5tz8Dm+l5Y2M8q45n4qaWuL+TD4cT84hKNTKuQ5HSzi8HUknLszB1bSweGjlvD675jOll6uP4tFyMIm/r11iSo8heOBX3sR+hbj8Cw6p3yP/rd7S9H6iWLapDr0YqvjyYy1OdHffRzoFKntmUTQf/0o+gQeFqPthrIMJDjpfWEcxcKfH2Zp+iQLeqGcDxbVwY38bx7xe2ZDO8qYb4HCtLTxqZ0rVoDPRupOL9vQa2xWQzrKkapbzmMzAANzVx54udCUy6yaF93SVEx6RlZ8ucHze4uQfvb4kjwkeD96XxeKW829tDisZfVTOAm05lcj7dyEfbHIHn64Ma46tTlpJxG9nam7c2XGDewRQe6uq4jyw8nMrN4e409yt6Vr76Rwwmiw0Z8Fxfx9SSzHwLCokKs4ZVoW+HZnz8+2aeu9txb+3eKoxHP5xL58jSn86H92zLWz+vpWkjX7zdHfa9UuZt5hOjC4+vTgZwx6Ez9O3QrPDvO27pyNs/r8Vmt9Mk0IfwIB9W7DyMm6uG/p1bFB734zTHVLPVu48glyQCvfU8OKQHr/+4BovVykv3DapSPyrDdS0FVx2yj20nfdd8Aka+ULgSuDjpexaha3kTKs+qF7FN3f4L2ce2Ef70r2Xuvx6k4KqL6eQO8v/+Hd3g51AElLZb/v7FqJrdhNwjsMptGzZ+hi09Dvdxn5S5/3qQgqsKh3dtYvOSnxn79GuENGt19R8Aq3+dxaC7HymRAawsP777PEq1hgemzijcdj1IwVWX7dHZzD+Uxgt9A2juq736D4BF/6ZxUxM3gtwrfgv+5UAK26Jy+HWcY3rA9SgFVx1qc3zm7f4V88nteEz8ucz915sUXHXYccHE78fyea6brnAl8JX79WqJDv4VzzddfcbIslNGPunvjpfWkdG63qTgqsq2qEzm/5PCi7cElwjeqsoXO+OZ0icIWRU/VRZYbTyz4iztAl15oldgnUnBVZfNB07y2/q9vDJ+CC3DKjf+vlq2nQnDepXIAFaWmXPXcyE5g/97wTG//Fqk4G64ALAuqc8BYF1S3wLA64H6HAA6kxslAKxLboQAsDap7wHg9UZ9CwDrGqEFLBAIBAKBQCCoNLUSANam0kdx7FYL8YvfdNQZ/P4J8mOPVasdw5m/yY3eX63f1uS11qaSQHHsVgs5K98ma/6zZP32FAXxx6vVjvnsPsznDlTrtzV1rc5SyLBaCvjlg5eZPW0inz5/P+dPHqlWOycO7Obkob3V+q0z1UBqUwmkOInZZm7/9TTProph8ooYNpxyrIZOy7Pw+6XizhWx7mQmG09nXfW4mkCMz9qhNhVAipNksHLH0nSe35zFM5uy2HjWsYgiPd/GguP5V/m1Q01k07nyi/g6k9pUBilOYraZ2346zjMrzjJpWTTrTzoWTqTlFjD/n4tX/f0fJ9LZcCqjtrtZaWpTEaQ4CamZDH5hFk9+PJ/HPpzL2r1HAUjNMvDb+r+u+vvVu48UqoHUJjW+CMSZ5EbtQ+PfFO+7xmO3WbEVXH1w2m22wrqCl9E1615bXbwuKTi3H4VfBNqer2O3WaEydrPbCuuWXUYV3q22unjdceKfvQQ3iWTgyx9gs1oxm65ejNZmsxXWF7xMyy69a6uL9ZaejXW83C8Is9XG6xviCfNS0dxXyz0dK55OcaMixmft0SNYxdQeOsxWO2/9mUOYh5xILwXjWlVuLmpDpGeYG9NuDXGMz3UxNPFS09zPhXs71V7ppRuBm9o2ZfpDwzAXWHj5m+VEBPnSIjSAB4b0qOuuFXLNAaDNlEfC8vew5uegDW6J36AnC/el7viV3OgDYLMSdOfrKNz9iF80HWt+DgqdN8F3vUHSmk8xXTyLTCan8YQvqnRumVKNMfEUFkM6Cp0XcrULmYfWIZPk6NsPImXbT7iGd8Fus5Lx12LsVgvakDYo3H3x6DgUw5m/MV08h9xFj0ySU5CZhLZxW1ybdCRj/0rkrnrUvmEkr/0cu8WMvtNwPDoNJ/PAKjIPrUMb3OLqnSwHuzkfw9r3seVnowhqiestRdmdvD1zKDjvsJtu5GtI7n4YVryJLT8bSeeN26jpGDZ+hjXlPEgS+ns+q5rdFCosSaex5aY7JNrULhiPrkcmyVG3Hkjerl9QhnUGm5X8A0vAakER3AbJzQdN2yGYz+7DmnoemdYdmSTHmpWMslEblI07YDy0CpmLHrl3GLmbvwCLGXW7YWjaDXOsED66HkVg9exmys9j7ievkWfIIjSyLaMefrZw34YF33Pq0F/YrBYeeOl9PHwC+HnGi+QZsnD39OXBl2ey6Kv3SIqJRiZJTJ75Q5XOrVSpiY06QXZGGu6e3mhcXPl700okuYKutw5n3byviezQA5vVwo6V87BaLDRp2R69tx/dB97GiQO7SbwQjaubHkmuIONiAk1adaBZu67s+mMxOr0nASHhLPlmJhaziR6DRtNj0O3sWbeEvzevonFk62rZrDLkFdh4b0sCOSYrLf20PNmz6Mb+68FUDsTmYrXbeX1AMH46BdM3xJNjsuLtouCNgcF8ujORs+km5DIZX1xDDUCVXGJsey/+upCLu1rObwdTeblfEMv+S2drVDZymYxp/QLxcVXyxsY4TFY7GoWMvuHXXk/sSsT4rB3yC+y8v9dAtslGSx8Fj3csWuk652geB5IKsNrgtd46/Fwl3vzTcay3VmL6TW58ts/A+UwrkgSfDaj8yt8rUcll3NVSy9/xZtxVMub8l8/UHjqWnzKyPcaEJMHLPXR4ayXe2pWDyQoaOdzc2Pkyg3lmK+9tjiXbaKVVgAtP9ipaaPDr/mT2xzpWR78xqDF+bkqmr4sh22jF21XBm4ND+WR7POfSjUgymDW6ajUBi6OSS4zt4MtfMTm4axT8eiCZabeGsOxIKlujspBk8Er/EHxcFby+/gImi6M8TN+I6v8/XQt5RjNv/LiarFwjbcKDmHJnv8J9P6zZzb7j57BYbbz32G34e7rz8tfLyMo14uPhyozHbmfm3PVEx6ciSTK+nVr1WoCXUSkV3DewG7uORuPuquHHNXuY/tAwFm09yKb9J5AkGW9MGI6vhxvTvlmOqcCCRqXk1k6lF3vVNNccAGYeXI1biz7oOwzBbispB+PVcyw+fR8k9+xBMg+uwbPHnchkckInzCoswmhOiaHxhNml2rUVmIidO7XENrV3CAGjira5hnfGlBRN7JypyDU6gu6cXmFfQ8Z/iNVoIGnVR44A8MROvPrcR955h/yLW8ubyTy4CtcmHcmN2kfQHdNJXDmzpDJJu0FkHdlI6MSvyI87hmnbT9Wym/HwalTNeqNuMxi7vaTdtF3vwqXX/RTE/IPp3z/QdLkDZBL6ez8vtJs17QLuZZSOsBeYyF78coltcq+QEiVZlGGdsaScJXvRy8g0OtxGVqw44X7XTGwmA7nrPnY8YE7/ibbHvRTEOuymirwJ0+E1KBt3wHxuP24jX8Xwx4cllQ9aD8R0bDPu42djiT9O/q6yVxxWxJ71S2nbsx/d+o/EdoWv9bt9PIPHPcrpf/exd8Ny+o66F0mSmDzzx0KbJceeY/IHpUu8mE1Gvn3j6RLb/ILDGDu5yJ+ad+hOwrnTfPP6U2hd3Xjgpfcr7Ovjb31Jfm4OC2a9TfeBt3Fk71YG3PUwUUcdn+Ta9bqVPeuX0qxdV07+s4cHXpzB/C/eLKFQ0qXfcA5s+4NnPvqFmJNH+CPm6yrbrDKsPp5BnyY6hjT3KFVtfmw7Lx7s7MPBuFzWnMjgzrZeyGUw67bQQrvGZJiZXUbgZ7LYmLq2ZA2yEA8VU/uWv1LO20VBRl7R4rLMfAv/JuTx5e1hnM8wseDfdNoHutAu0IVxHbz5aHtiuW1dC2J8Vn18VobVUUZ6N1IxOFxdytfuaqnl/rYu/JNUwB/RJu5orkGSwecD9UVFe7OtfD6wdMBvsth5eVvJT8kh7nJe6F5+oWsvrUSGsagPmUYb/14s4ItBemKyLCw8kU87PyXt/JTc3VLLx39XXnGlJll1LJ0+4XqGtPAsPT47+PJgV38OxhlYczydO9v7IMlkzB4TUWx8Gpk9OrxUuyaLjRdXlVyI0thTzdR+5ZdT8nZVkF5sfGbkWzickMuXYyI4n25kwaEU2ge50j7IlXEdfflwa9y1XPo1sWznYfp2jGREr7aFMmyXGT+wG4+M6M3+E+dZ8edh7unfFUmS+O6l+wrtdi4xrcwSMEZzAVM+X1RiW2iAF68+MLTcvvh46EjPLqp3mZGTxz+nL/D9y+M5l5DKnA1/0ymyMR2bhTB+cHfe+23dtVx6pbnmANCcFodXc8dnrSs/rWYdWkv2f9uw2yyo/cJRuHrgEtaBhCVvowlugVfPu/HoOorEZe+i9AjE59aJhUvIJaW6UjX/vHrdjVevu8k5voP0vYtR+4UV7Sz2f64JjARArtFht1qwmvIoyLqIyjOoMABU+4VhTonBmp+DTK5AUmlKKZNYcjNQegQik6Qyy8xUFmtGPKpml+x2xacb45F1mE9uA6sFuW84koseZeP25Kx+F0VAC7Rd70TTYSSGNTOQ6wPR9plQaDeZUl2pmmLarneh7XoXplM7yT+wBLl3sQd4sZuMwt9Rz0hS67DbrNhNediyLyL3CCx8wCh8wshLi8FmNCCTK5ApNaWUD2y5mUh6f2QyCUVAZLVslpJwgTbd+zr6c4Wv/bVpJYf+3IDVYiEorCk6vSdN23Xht49eoXGz1txy+3h6D7uLOR+/indAMMPGP1VoM5VaU6naf/1G30+/0ffz7+4t7Fgxj4DGRTfV4ovpG0W0BEDr6obVasGYl0tGShLeAcGFAWBA4wiSY8+RZ8hGoXAUjL5SocSQlY6XfxCSJFW63Ex1iMs00/tSfb0rq82vPZnJtqhsLDY74d4aPLQKOgS78PbmeFr4arm7vRejWnvy7pYEAt2VTOzqW2hXtUKqck3AtDxLYb1BgITsAk5cNDJlpWNOYoCbkoRsMy39HJ/sIn1rp5CxGJ+1Q3yOld6XVDiu9LV10Ua2xZix2CDcQ45eI9HeT8m7u3No4a3gzhZaRjbVMGOPgUCdnAnttMV8TValWoDgmPt3uc4gQILByqk0C89ucswpDdDJSTTYaHGpbEykV93MmIrLMnHTJdWMUuPzRDpbz2Q6bHZpfHYMduXtjRdo4afl7g6+3NbGm3c2xRLkrmJid/8S47OqKiFpuRa8XYuNzywzJ5PzmLwsGoAAd5VjfPo7ysk096u7T+uxyen0be+YBiFdoTazcve/bNp/AovVRtNgXzzcXOjcvDGvfb+SVmGB3DuwG3f07cjrP6wmyFfPE7fdXGg3jUpZ5dqAqZmGwrqDAPEpGRw/n8hjH84DIMhHT3xKJq2bOF6OW4RWLNVXU1yzR6u8G5EfdxyVd6NS8+uy/t1A6KPfknf2INn/bcFuteDR9TY8u48h9pLOr65FH9zb9Cdp9ceYkqPRBDQFKpcBLMhORa51Q1KqCzV+JbUOc5oj62C6GI1rxKXiwMUGjmvTbqRu+wltSOnPaurASFJ3/Iousuel6yupTAJQkJmI3WbDlHim2naTewZjSTiO3DO41Pwd07GN6O//Pwpi/sF8Yit2qwV1h1FoOo0me9FLqNsORtXsJtQtb8Ww4VOsKdEo/Bx2q0yGwZaTikzjhkypRnLRg92OpNZhzXDYzZp6DmWTy3Yr6pcqvCt5u35GEVw6GJEHRJK/+1dUET0Kz1lc+QCZDFtWMna7DUtyVLVs5hvUmPMnj+Ib1LjU/Lr9W1fz/KdzOX34b/7ZuQGrpYDeQ++iz4hxfD39Kbr1H0W7nv3odPNgFn75LgnnThMc7gjgK5MBzEq7iFbnjkqtwVXvgR07GlcdKfGOwCTxfBTNO/a8ZLKifrXs3Jt1874mrEVpWbxGES3Z8Pt3tOrap/CcxRVKkMlIT07AZrMRF32y1O9rikYeKo4n59NIr8Jmt5d4yGw4ncW3Y8I4GJ/HljNZWKx2bmvlyZg2Xry45gJDW+jp00RH/6bufLwjkeg0E019HEFZVTOABVY7i4+kM6FrUcmRQDclbQO0TB/gKCJrsdrZcS6HqDQjnRu5cibVSLvAmi/SLsZn7RDsJud4qoVgN3kpX9t4zsT/DdbzT1IBW2PMWGx2RjVTM7q5hpe2ZjO4iZqbQlTcGqbm078NRGdaaerpeIRVNQNYYLWz5GQ+D7Ut8p1AnZw2vgpevaRLbLHZ2XnBTFSGhU4BSqLSLbT1q7hOYG3QSK/mWHIejTzUpcfnyQy+vaspB+MMbD6T6RifbbwZ086HF1adZWgLT/qEu9O/mQcfb4sjOs1IUx9HUFbVDGCB1caif1N5uJt/4bYgdxVtAl15fZCj6LLFamfH2SzOpOTTuZGOMyn5tA0qXdDaGYT4e3H0bAIh/l7YbPYSQeDaPf/x66sPsu/EeTbuO4HFYuWOWzpy962dmfzZAkb0asstHSMZ1K0VM+as50zcRSJDHNdd1QxggcXK75v389ioPoXbgnw8aB/RiHcedZRvsVisbP3nFKdiL9K1ZRinLiTToWntFba/zDUHgPrOI0lc9i6Z/6wpNQdQ7RfOhZ+nFAZ1ltwMEha/BXYrSq9GSCotF357HmxWJI0OlXeRpFNlMoCWrCTiF053vNXKFQTePg1J40b67t/JizmCTCq70rhbyz5EfXonTR4vPQ/MrdXNXPhpMk1fXA6UrUzi3nYgMT8+hUvjtlW212U07UeQs3YGxn/XlppjpPBpQvbvz6Lwc7yd2fIyMKx6B2w2JM8gZEot2QtfBJsVmUaH3Kuo4nllMgzW7GTyVrwBcgUySYHrsJeR1Dry9y2gIPYolGM3VbObyNg0Fv1D35Xap47sQ9b8Z/B8eglQtvKBulV/sudMQtGoenbrOXgMcz95lb82Lis1BzAwtBmzpz1CcBNH9iInM51fP5yGzWrFN6gxaq2W/3vtCaxWK1pXN/wahRVdVyUygOkXE1kx40XkCiVyuYJ7n3sLrasbW5f+SvSxQ0jysm3Wtkc/3nxoMC9+saDUvna9bmX2yxN5+7dNQNkKJZ1uGcrnLz5IeKuOVbRW5RnZ0oN3tySw5kRmqTmA4V5qpqy8QFMfx9ynjHwLb22Kx2qHRnoVWoXE82scKgQ6tZwQj6LCppXNAO69YODZVTHY7DC8hQfNfDQkZjtk5zxdFLQLdGHyihgkGQyM1DMo0p3XN8Tz94Vc3NS1U8lKjM/aYURTDTP25LA2ylhqDmATDwXPbs4m4lJQl2G08c4uAzY7BLlJaJUyXtySjdUOOqWMxu5FdqhsBvCveDPPb87CZodhERqaeilIMjhkPj01Em19lTyzyTGfbUCYmoFN1Lz1Zw77EwpqRQu4Moxq7cU7m2JZcyy91BzAcG8Nk5efLXzpysi38OYGx1gK1qvQKuU8t/IsVrsdnUpOiEfRHMbKZgD3ns/hmRVnsdnsDG/lRTNfbcnxGeTKpGXRSDIY1NyTQc09eH1dDPsu5JSQbnQ2o29uz+s/rGbFn/+WmgPYtJEvj380j2aNHPe69Jw8/vfdCmw2O438PHFRq3jq0wVYbTbcXNSEBhQtSKtsBnDX0Sie/Hg+Vpud2/q0o3ljfxJSMwHwcnelfbNGPPbhXGQyGUN7tGFYj9a8/M1y9v4XjbuLczKnohB0DSIKQVcPUQi66ohC0JVDFIK+dkQh6IoRhaBrFlEIumqIQtACgUAgEAgEgkojAkCBQCAQCASCBoYIAAUCgUAgEAgaGNVeBBLiXq9FRGqFytgkTC/sdiVXs4mrUgJsFR7T0HDYpPIovGp/Rdn1SFWvW4zP0lyLTRQ+YTXXkeuU6lyj3DO45jtyg1AZ20guHrXfkXrCtdiiWotArlyKLiiiIttYbXbkkrBbWZRnG7vdXlh/SVCSytqmLPnDhkRlr1+Mz/Kpjm3sNmu5lRhuNKpyrQ19PFaGimxUluxhQ6e6Nqn2KmCBQCAQCAQCQf1EhNECgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDQwRAAoEAoFAIBA0MEQAKBAIBAKBQNDAEAGgQCAQCAQCQQNDBIACgUAgEAgEDYz/B7jkYnH37b8nAAAAAElFTkSuQmCC","text/plain":["<Figure size 800x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fn = ['Pclass',\t'Age', 'SibSp',\t'Parch', 'Fare',\t'Sex_female', 'Sex_male', 'Embarked_C', 'Embarked_Q'\t,'Embarked_S']\n","cn = ['Died', 'Survived']\n","\n","plot_tree(clf_plot_tree,\n","            feature_names = fn, \n","            class_names=cn,\n","            filled = True);"]},{"cell_type":"markdown","metadata":{"id":"_wyOPR9NWXrc"},"source":["#### 🔋Melhorando o modelo: fine tuning "]},{"cell_type":"markdown","metadata":{"id":"Euc7Mr3B73yi"},"source":["Existem inúmeros hiperparâmetros que podem ser ajustados em modelos, e encontrar a combinação ideal deles pode ser considerado um desafio de busca. Atualmente, há diversos algoritmos de otimização disponíveis para essa tarefa, mas uma técnica simples é a GridSearch! \n","\n","Para ajustar os hiperparâmetros no scikit-learn, pode-se utilizar o método GridSearchCV, que encontra de maneira eficiente os valores ideais dos hiperparâmetros dentre os fornecidos."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7T4B6CdHWXcf"},"outputs":[],"source":["from sklearn.model_selection import GridSearchCV\n","\n","param_grid = {'max_features': ['auto', 'sqrt', 'log2'],\n","              'ccp_alpha': [0.1, .01, .001],\n","              'max_depth' : [5, 6, 7, 8, 9],\n","              'criterion' :['gini', 'entropy']\n","             }"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":3700,"status":"ok","timestamp":1681222183156,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"VFgzARjKXhRr","outputId":"fa4cf0c2-f8ec-4d80-9fa4-e7259c6ee475"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;ccp_alpha&#x27;: [0.1, 0.01, 0.001],\n","                         &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [5, 6, 7, 8, 9],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;ccp_alpha&#x27;: [0.1, 0.01, 0.001],\n","                         &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [5, 6, 7, 8, 9],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={'ccp_alpha': [0.1, 0.01, 0.001],\n","                         'criterion': ['gini', 'entropy'],\n","                         'max_depth': [5, 6, 7, 8, 9],\n","                         'max_features': ['auto', 'sqrt', 'log2']})"]},"execution_count":152,"metadata":{},"output_type":"execute_result"}],"source":["grid_search = GridSearchCV(clf, param_grid, n_jobs=-1, cv=5)\n","grid_search.fit(X, y)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1681222183157,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"2qfR-xbGWTs9","outputId":"fb42c7fd-c0ed-4d9d-ac98-6bd5716f3358"},"outputs":[{"name":"stdout","output_type":"stream","text":["Best parameters found:\n"," {'ccp_alpha': 0.001, 'criterion': 'entropy', 'max_depth': 7, 'max_features': 'sqrt'}\n"]}],"source":["print('Best parameters found:\\n', grid_search.best_params_)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rJjumfloXgOl"},"outputs":[],"source":["clf_tuned = DecisionTreeClassifier(max_features = 'log2', \n","                            ccp_alpha = 0.001,\n","                            max_depth = 7,\n","                            criterion = 'entropy')"]},{"cell_type":"markdown","metadata":{"id":"MzbMLbCUZCp5"},"source":["#### 🏋️ Treinando..."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T9tLvhhxZDSt"},"outputs":[],"source":["clf_tuned = clf_tuned.fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{"id":"6ex2rLYLZKWf"},"source":["#### 🤔 Analisando o Classification Report do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ym88xn0GZGGx"},"outputs":[],"source":["y_pred_tuned = clf_tuned.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1681222183158,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"56DQVln_ZKBC","outputId":"750031d1-8196-4257-c35a-1599841f13f5"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.83      0.89      0.86       141\n","           1       0.79      0.70      0.74        82\n","\n","    accuracy                           0.82       223\n","   macro avg       0.81      0.79      0.80       223\n","weighted avg       0.82      0.82      0.82       223\n","\n"]}],"source":["class_report = classification_report(y_test, y_pred_tuned)\n","print(class_report)"]},{"cell_type":"markdown","metadata":{"id":"s6EXMkg45WgN"},"source":["\n","Nota-se uma melhora do desempenho quando há a alteração dos hiperparâmetros."]},{"cell_type":"markdown","metadata":{"id":"P8rgrXBPZO1u"},"source":["#### 🤔 Analisando a matriz de confusão do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1681222183159,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"zwO-X9qVZQhY","outputId":"d8229ba2-e34b-455b-c146-781f3ee1b9c0"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[126  15]\n"," [ 25  57]]\n"]}],"source":["cf_matrix_nb_tuned = confusion_matrix(y_test, y_pred_tuned)\n","print(cf_matrix_nb_tuned)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":596,"status":"ok","timestamp":1681222183748,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"j2_vIZ6oZR5R","outputId":"fd9c3c4e-1d66-4a26-bb78-62616ceaff17"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":159,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["group_names = ['True Neg','False Pos','False Neg','True Pos']\n","group_counts = [\"{0:0.0f}\".format(value) for value in cf_matrix_nb_tuned.flatten()]\n","labels = [f\"{v1}\\n{v2}\" for v1, v2 in zip(group_names, group_counts)]\n","labels = np.asarray(labels).reshape(2, 2)\n","\n","sns.set(font_scale= 1)\n","sns.heatmap(cf_matrix_nb_tuned, annot=labels, fmt= '', cmap='Blues')"]},{"cell_type":"markdown","metadata":{"id":"dFpu_tn6ofQS"},"source":["#### 🌳🌳 Utilizando o Random Forest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Xy1-vOPJoVZ1"},"outputs":[],"source":["rf = RandomForestClassifier().fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4SeQoFagpePB"},"outputs":[],"source":["y_pred_rf = rf.predict(X_test)"]},{"cell_type":"markdown","metadata":{"id":"RJFC-J1jvWAx"},"source":["#### 🤔 Analisando o Classification Report do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1681222183750,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"wgUAE4u_pfhm","outputId":"556db64a-d368-49cc-91e1-3748105bfe24"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.84      0.84      0.84       141\n","           1       0.72      0.72      0.72        82\n","\n","    accuracy                           0.79       223\n","   macro avg       0.78      0.78      0.78       223\n","weighted avg       0.79      0.79      0.79       223\n","\n"]}],"source":["class_report = classification_report(y_test, y_pred_rf)\n","print(class_report)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1681223402912,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"vhP72MtKuNOX","outputId":"46e22563-0d03-4c6a-b88c-ffc4d00e4d75"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[118  23]\n"," [ 23  59]]\n"]}],"source":["cf_matrix_rf = confusion_matrix(y_test, y_pred_rf)\n","print(cf_matrix_rf)"]},{"cell_type":"markdown","metadata":{"id":"zmrGvpXFvZYg"},"source":["#### 🤔 Analisando a matriz de confusão do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":745,"status":"ok","timestamp":1681223496379,"user":{"displayName":"Kailane Eduarda Felix da Silva","userId":"12923641319819862541"},"user_tz":180},"id":"T5V8jxyEpkdg","outputId":"f45ef24a-a534-4bb2-ee49-493f172956c1"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":192,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 800x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["group_names = ['True Neg','False Pos','False Neg','True Pos']\n","group_counts = [\"{0:0.0f}\".format(value) for value in cf_matrix_rf.flatten()]\n","labels = [f\"{v1}\\n{v2}\" for v1, v2 in zip(group_names, group_counts)]\n","labels = np.asarray(labels).reshape(2, 2)\n","\n","sns.set(font_scale= 1)\n","sns.heatmap(cf_matrix_rf, annot=labels, fmt= '', cmap='Blues')"]},{"cell_type":"markdown","metadata":{"id":"dlf1xrFg5h--"},"source":["No caso do Random Forest, esperavá-se uma melhoria em relação ao uso da árvore de decisão, o que foi notado em relação ao treinamento da árvode de decisão com os hiperparâmetros padrão. Mas em relação a árvore com os melhores hiperparâmetros, o comportamento foi diferente do esperado."]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}