Practical-1 Regression-solutions.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["In this notebook, you will:\n","\n","1. Implement a Linear Regression model from scratch using the closed-form solution (Normal Equation).\n","2. Solve the same problem using Scikit-learn's Linear Regression module.\n","3. Extend this to a Multiple Linear Regression scenario.\n","4. Apply it to a real-world dataset.\n","\n","Let's get started!"],"metadata":{"id":"gbvS6s1tP8tG"}},{"cell_type":"code","source":["# Import Libraries\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.linear_model import LinearRegression\n","from sklearn.model_selection import train_test_split"],"metadata":{"id":"nV6OXa82OqpS","executionInfo":{"status":"ok","timestamp":1732364208359,"user_tz":0,"elapsed":238,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":["We will first generate a synthetic dataset for a simple linear regression problem."],"metadata":{"id":"lndihVHTRvEh"}},{"cell_type":"code","source":["np.random.seed(42)  # For reproducibility\n","\n","# Simple Linear Regression Data\n","X_simple = np.random.rand(100, 1) * 10  # Feature\n","y_simple = 3 * X_simple + np.random.randn(100, 1) * 2  # Target with noise\n","\n","# Convert to DataFrame for visualization\n","df_simple = pd.DataFrame({'X': X_simple.flatten(), 'y': y_simple.flatten()})\n","df_simple.head()\n"],"metadata":{"id":"0HDZ7WBoRl4A","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1732364210837,"user_tz":0,"elapsed":251,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"b8e7ca0f-360c-452f-a1fc-64a53b4bd7b1"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          X          y\n","0  3.745401  11.410298\n","1  9.507143  27.923414\n","2  7.319939  22.143340\n","3  5.986585  13.984617\n","4  1.560186   4.241215"],"text/html":["\n","  <div id=\"df-84a2a6fa-9ec6-4dc3-b977-1d03dc8bf0a6\" 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>X</th>\n","      <th>y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3.745401</td>\n","      <td>11.410298</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9.507143</td>\n","      <td>27.923414</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>7.319939</td>\n","      <td>22.143340</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5.986585</td>\n","      <td>13.984617</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.560186</td>\n","      <td>4.241215</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84a2a6fa-9ec6-4dc3-b977-1d03dc8bf0a6')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-84a2a6fa-9ec6-4dc3-b977-1d03dc8bf0a6 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-84a2a6fa-9ec6-4dc3-b977-1d03dc8bf0a6');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? 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","\n","\n","<div id=\"df-be336a3c-7ef1-4666-a6cc-d2cfc2862534\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be336a3c-7ef1-4666-a6cc-d2cfc2862534')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-be336a3c-7ef1-4666-a6cc-d2cfc2862534 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_simple","summary":"{\n  \"name\": \"df_simple\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"X\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9748941101531927,\n        \"min\": 0.05522117123602399,\n        \"max\": 9.868869366005173,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.6355835028602363,\n          8.948273504276488,\n          7.722447692966574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.837472820668715,\n        \"min\": -0.4820298011514643,\n        \"max\": 31.17025384157014,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          3.2198577258483683,\n          25.837869204597066,\n          20.693441657143556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["plt.scatter(X_simple, y_simple, color='blue', label='Data')\n","plt.title(\"Simple Linear Regression Data\")\n","plt.xlabel(\"X\")\n","plt.ylabel(\"y\")\n","plt.legend()\n","plt.show()"],"metadata":{"id":"pQFy7FDzR3LZ","colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"status":"ok","timestamp":1732364230147,"user_tz":0,"elapsed":629,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"ca02b237-626a-4feb-9639-ac0998a481b2"},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Implement Closed-Form Solution"],"metadata":{"id":"SNIp5LRBR6cv"}},{"cell_type":"code","source":["# Add bias (intercept term)\n","X_simple_bias = np.c_[np.ones((X_simple.shape[0], 1)), X_simple]\n","\n","# Compute weights\n","w_simple = np.linalg.inv(X_simple_bias.T @ X_simple_bias) @ X_simple_bias.T @ y_simple\n","\n","# Predicted values\n","y_pred_simple_closed = X_simple_bias @ w_simple\n","\n","\n","# Print weights\n","print(f\"Weights (Closed-Form Solution):\\nIntercept: {w_simple[0][0]}, Slope: {w_simple[1][0]}\")"],"metadata":{"id":"QPwuiKatR8EK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732364484167,"user_tz":0,"elapsed":734,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"189c304c-2128-4208-efcd-500ae532424d"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["Weights (Closed-Form Solution):\n","Intercept: 0.4301923150935001, Slope: 2.9080453545753935\n"]}]},{"cell_type":"code","source":["# Compute the error between the predicted and true values\n","\n","def mean_squared_error(y_true, y_pred):\n","  return np.mean((y_true - y_pred) ** 2)\n","\n","mse_simple_closed = mean_squared_error(y_simple, y_pred_simple_closed)\n","print(f\"Mean Squared Error (Closed-Form): {mse_simple_closed}\")\n"],"metadata":{"id":"9-0LO8F4SLyJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732364563280,"user_tz":0,"elapsed":248,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"ea3bc223-8a66-4d19-dce9-81e08357fa34"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (Closed-Form): 3.2263382558682125\n"]}]},{"cell_type":"code","source":["plt.scatter(X_simple, y_simple, color='blue', label='Actual')\n","plt.plot(X_simple, y_pred_simple_closed, color='red', label='Prediction (Closed-Form)')\n","plt.title(\"Simple Linear Regression - Closed Form\")\n","plt.xlabel(\"X\")\n","plt.ylabel(\"y\")\n","plt.legend()\n","plt.show()"],"metadata":{"id":"bwx9sf_pRoG2","colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"status":"ok","timestamp":1732364565168,"user_tz":0,"elapsed":585,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"0e140a0b-802e-4848-d0e5-ecb031534c1f"},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Solving with scikit learn"],"metadata":{"id":"4jTGtd3HScTd"}},{"cell_type":"code","source":["# Scikit-learn Solution for Simple Linear Regression\n","\n","# fit the model\n","model_simple = LinearRegression()\n","model_simple.fit(X_simple, y_simple)\n","\n","# make predictions\n","y_pred_simple_sklearn = model_simple.predict(X_simple)\n","intercept = model_simple.intercept_[0]\n","coeff = model_simple.coef_[0][0]\n","\n","# Print weights\n","print(f\"Weights (Scikit-learn):\\nIntercept: {intercept}, Slope: {coeff}\")\n","\n","# Evaluate\n","mse_simple_sklearn = mean_squared_error(y_simple, y_pred_simple_sklearn)\n","print(f\"Mean Squared Error (Scikit-learn): {mse_simple_sklearn}\")"],"metadata":{"id":"_6GzWNLJSeC6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732352224354,"user_tz":0,"elapsed":15,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"82b0c665-6013-4a26-cf9c-eea55a2e98a3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Weights (Scikit-learn):\n","Intercept: 0.4301923150934961, Slope: 2.9080453545753935\n","Mean Squared Error (Scikit-learn): 3.2263382558682125\n"]}]},{"cell_type":"markdown","source":["## Multiple regression problem"],"metadata":{"id":"FCzafNvISzrp"}},{"cell_type":"markdown","source":["Let's try this on a multiple linear regression problem now."],"metadata":{"id":"id2VCtuYSsCU"}},{"cell_type":"code","source":["# Multiple Linear Regression Data\n","X_multi = np.random.rand(100, 2) * 10  # Two features\n","y_multi = 3 * X_multi[:, 0:1] + 2 * X_multi[:, 1:2] + np.random.randn(100, 1) * 2  # Target with noise\n","\n","df_multi = pd.DataFrame({'X1': X_multi[:, 0], 'X2': X_multi[:, 1], 'y': y_multi.flatten()})\n","\n","df_multi.head()"],"metadata":{"id":"NFLumKOJRoQz","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1732366426797,"user_tz":0,"elapsed":262,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"651d5ba2-925f-4d2b-8cf7-42c69fd9ed64"},"execution_count":49,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         X1        X2          y\n","0  8.623637  9.495206  43.972737\n","1  1.470735  9.265876  23.698558\n","2  4.921163  2.582444  21.442354\n","3  4.591358  9.800326  31.530394\n","4  4.926181  3.287516  23.092787"],"text/html":["\n","  <div id=\"df-a71ca2ec-29f4-4d9c-9700-dd77c5e5053b\" 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>X1</th>\n","      <th>X2</th>\n","      <th>y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>8.623637</td>\n","      <td>9.495206</td>\n","      <td>43.972737</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.470735</td>\n","      <td>9.265876</td>\n","      <td>23.698558</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.921163</td>\n","      <td>2.582444</td>\n","      <td>21.442354</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.591358</td>\n","      <td>9.800326</td>\n","      <td>31.530394</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4.926181</td>\n","      <td>3.287516</td>\n","      <td>23.092787</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a71ca2ec-29f4-4d9c-9700-dd77c5e5053b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-a71ca2ec-29f4-4d9c-9700-dd77c5e5053b button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-a71ca2ec-29f4-4d9c-9700-dd77c5e5053b');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? 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","\n","\n","<div id=\"df-e467325e-84cf-4287-b38a-6623df6549ad\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e467325e-84cf-4287-b38a-6623df6549ad')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-e467325e-84cf-4287-b38a-6623df6549ad button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_multi","summary":"{\n  \"name\": \"df_multi\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7130612531018006,\n        \"min\": 0.20071197777726368,\n        \"max\": 9.69470433275369,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          4.626797856696064,\n          8.1707207094928,\n          3.4886826654299528\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7718408346225347,\n        \"min\": 0.40868616266478863,\n        \"max\": 9.899602323899453,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          3.0137787416414206,\n          2.579028270449398,\n          9.29529144247826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.272922716390273,\n        \"min\": 3.104371056062541,\n        \"max\": 50.09216770603136,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          17.649847510936574,\n          31.923348728472714,\n          23.754691264460355\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":49}]},{"cell_type":"markdown","source":["Implement closed-form solution"],"metadata":{"id":"wg7gLKp0SyEJ"}},{"cell_type":"code","source":["# Add bias (intercept term)\n","X_multi_bias = np.c_[np.ones((X_multi.shape[0], 1)), X_multi]\n","\n","# Compute weights\n","w_multi = np.linalg.inv(X_multi_bias.T @ X_multi_bias) @ X_multi_bias.T @ y_multi\n","\n","# Predicted values\n","y_pred_multi_closed = X_multi_bias @ w_multi\n","\n","# Print weights\n","print(f\"Weights (Closed-Form Solution): {w_multi.flatten()}\")"],"metadata":{"id":"6NsG_1jqSxTR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732366437209,"user_tz":0,"elapsed":236,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"9207d3d6-3473-4f51-a0c8-931a47e27d86"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["Weights (Closed-Form Solution): [0.35749243 3.03090926 1.915687  ]\n"]}]},{"cell_type":"code","source":["# Evaluate Multiple Linear Regression\n","mse_multi_closed = mean_squared_error(y_multi, y_pred_multi_closed)\n","print(f\"Mean Squared Error (Closed-Form): {mse_multi_closed}\")"],"metadata":{"id":"GbfeoT-7TDWg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732366459111,"user_tz":0,"elapsed":261,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"2a0876fd-b526-462a-d75e-1569f579f2d0"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (Closed-Form): 4.430966542274645\n"]}]},{"cell_type":"code","source":["# Scikit-learn Solution for Multiple Linear Regression\n","\n","# fit the model\n","model_multi = LinearRegression()\n","model_multi.fit(X_multi, y_multi)\n","\n","# make predictions\n","y_pred_multi_sklearn =model_multi.predict(X_multi)\n","intercept = model_simple.intercept_[0]\n","coeff = model_simple.coef_[0][0]\n","\n","# Print weights\n","print(f\"Weights (Scikit-learn):\\nIntercept: {intercept}, Coefficients: {coeff}\")\n","\n","# Evaluate\n","mse_multi_sklearn = mean_squared_error(y_multi, y_pred_multi_sklearn)\n","print(f\"Mean Squared Error (Scikit-learn): {mse_multi_sklearn}\")"],"metadata":{"id":"2m5bxVXKTFRy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732366487543,"user_tz":0,"elapsed":222,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"31e4d17e-ffe6-4dae-a725-e474fdb979ad"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["Weights (Scikit-learn):\n","Intercept: 0.4301923150934961, Coefficients: 2.9080453545753935\n","Mean Squared Error (Scikit-learn): 4.4309665422746445\n"]}]},{"cell_type":"code","source":["# Visualization - Observed vs. Predicted\n","plt.figure(figsize=(10, 6))\n","plt.scatter(y_multi, y_pred_multi_sklearn, alpha=0.6, edgecolors='k')\n","plt.plot([y_multi.min(), y_multi.max()], [y_multi.min(), y_multi.max()], 'r--', lw=2)\n","plt.xlabel(\"Observed Values\")\n","plt.ylabel(\"Predicted Values\")\n","plt.title(\"Observed vs. Predicted Values\")\n","plt.grid(True)\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"Zys8TxCTz4gZ","executionInfo":{"status":"ok","timestamp":1732366491811,"user_tz":0,"elapsed":608,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"05fb7d59-82c7-44cc-9d8b-e8dccbf01452"},"execution_count":53,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Now let's work with a real-world dataset: the New York Stock Exchange dataset.\n","\n","While we focus on this specific dataset, you can find several new datasets to apply and play around with [here](https://www.kaggle.com/datasets?tags=13405-Linear+Regression).\n"],"metadata":{"id":"IDh41wGDTXuJ"}},{"cell_type":"code","source":["! pip install kagglehub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8sw-wCRrfG0E","executionInfo":{"status":"ok","timestamp":1732366530797,"user_tz":0,"elapsed":3745,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"07ed8de0-9694-4dfb-83dd-7d0da8584b7c"},"execution_count":54,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: kagglehub in /usr/local/lib/python3.10/dist-packages (0.3.4)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from kagglehub) (24.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kagglehub) (2.32.3)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kagglehub) (4.66.6)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kagglehub) (3.4.0)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kagglehub) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->kagglehub) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->kagglehub) (2024.8.30)\n"]}]},{"cell_type":"code","source":["import kagglehub\n","path = kagglehub.dataset_download(\"dgawlik/nyse\")"],"metadata":{"id":"5kftg-Dre_fl","executionInfo":{"status":"ok","timestamp":1732366533231,"user_tz":0,"elapsed":2438,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":55,"outputs":[]},{"cell_type":"code","source":["print(\"Path to dataset files:\", path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mE_aH2WyfRTN","executionInfo":{"status":"ok","timestamp":1732366533232,"user_tz":0,"elapsed":3,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"f06be9df-a62e-498e-f0da-aa6f0f42ff6a"},"execution_count":56,"outputs":[{"output_type":"stream","name":"stdout","text":["Path to dataset files: /root/.cache/kagglehub/datasets/dgawlik/nyse/versions/3\n"]}]},{"cell_type":"code","source":["%cd /root/.cache/kagglehub/datasets/dgawlik/nyse/versions/3"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZbeRH_zLfV6c","executionInfo":{"status":"ok","timestamp":1732366533551,"user_tz":0,"elapsed":4,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"ba1f763b-00ad-44d5-e2ae-0ab1839425bf"},"execution_count":57,"outputs":[{"output_type":"stream","name":"stdout","text":["/root/.cache/kagglehub/datasets/dgawlik/nyse/versions/3\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import mean_squared_error, r2_score\n"],"metadata":{"id":"GxrcfWRDfdZv","executionInfo":{"status":"ok","timestamp":1732366535115,"user_tz":0,"elapsed":224,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":58,"outputs":[]},{"cell_type":"markdown","source":["# Preparing the data\n"],"metadata":{"id":"p8sCjPLdTezL"}},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import mean_squared_error, r2_score\n","\n","# Load the datasets\n","prices_split = pd.read_csv(\"prices-split-adjusted.csv\")\n","\n","# Step 1: Exploratory Data Analysis (EDA)\n","print(\"Prices-split-adjusted dataset:\")\n","print(prices_split.head(), \"\\n\")\n","\n","# Check for missing values\n","print(\"\\nMissing values in prices-split-adjusted:\\n\", prices_split.isnull().sum())\n"],"metadata":{"id":"jHQTZ-kiTcJ1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732366542716,"user_tz":0,"elapsed":5843,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"30d5cab7-0fc9-4216-bb20-b45f059f95d8"},"execution_count":59,"outputs":[{"output_type":"stream","name":"stdout","text":["Prices-split-adjusted dataset:\n","         date symbol        open       close         low        high  \\\n","0  2016-01-05   WLTW  123.430000  125.839996  122.309998  126.250000   \n","1  2016-01-06   WLTW  125.239998  119.980003  119.940002  125.540001   \n","2  2016-01-07   WLTW  116.379997  114.949997  114.930000  119.739998   \n","3  2016-01-08   WLTW  115.480003  116.620003  113.500000  117.440002   \n","4  2016-01-11   WLTW  117.010002  114.970001  114.089996  117.330002   \n","\n","      volume  \n","0  2163600.0  \n","1  2386400.0  \n","2  2489500.0  \n","3  2006300.0  \n","4  1408600.0   \n","\n","\n","Missing values in prices-split-adjusted:\n"," date      0\n","symbol    0\n","open      0\n","close     0\n","low       0\n","high      0\n","volume    0\n","dtype: int64\n"]}]},{"cell_type":"code","source":["prices_split"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"VaG-q0LVF63e","executionInfo":{"status":"ok","timestamp":1732366546433,"user_tz":0,"elapsed":245,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"971e9cb6-eaa0-47a0-c632-54993b439440"},"execution_count":60,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              date symbol        open       close         low        high  \\\n","0       2016-01-05   WLTW  123.430000  125.839996  122.309998  126.250000   \n","1       2016-01-06   WLTW  125.239998  119.980003  119.940002  125.540001   \n","2       2016-01-07   WLTW  116.379997  114.949997  114.930000  119.739998   \n","3       2016-01-08   WLTW  115.480003  116.620003  113.500000  117.440002   \n","4       2016-01-11   WLTW  117.010002  114.970001  114.089996  117.330002   \n","...            ...    ...         ...         ...         ...         ...   \n","851259  2016-12-30    ZBH  103.309998  103.199997  102.849998  103.930000   \n","851260  2016-12-30   ZION   43.070000   43.040001   42.689999   43.310001   \n","851261  2016-12-30    ZTS   53.639999   53.529999   53.270000   53.740002   \n","851262  2016-12-30    AIV   44.730000   45.450001   44.410000   45.590000   \n","851263  2016-12-30    FTV   54.200001   53.630001   53.389999   54.480000   \n","\n","           volume  \n","0       2163600.0  \n","1       2386400.0  \n","2       2489500.0  \n","3       2006300.0  \n","4       1408600.0  \n","...           ...  \n","851259   973800.0  \n","851260  1938100.0  \n","851261  1701200.0  \n","851262  1380900.0  \n","851263   705100.0  \n","\n","[851264 rows x 7 columns]"],"text/html":["\n","  <div id=\"df-d3b2dde0-295c-41e2-9a2f-5aaa535f0b9c\" 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>date</th>\n","      <th>symbol</th>\n","      <th>open</th>\n","      <th>close</th>\n","      <th>low</th>\n","      <th>high</th>\n","      <th>volume</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2016-01-05</td>\n","      <td>WLTW</td>\n","      <td>123.430000</td>\n","      <td>125.839996</td>\n","      <td>122.309998</td>\n","      <td>126.250000</td>\n","      <td>2163600.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2016-01-06</td>\n","      <td>WLTW</td>\n","      <td>125.239998</td>\n","      <td>119.980003</td>\n","      <td>119.940002</td>\n","      <td>125.540001</td>\n","      <td>2386400.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2016-01-07</td>\n","      <td>WLTW</td>\n","      <td>116.379997</td>\n","      <td>114.949997</td>\n","      <td>114.930000</td>\n","      <td>119.739998</td>\n","      <td>2489500.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2016-01-08</td>\n","      <td>WLTW</td>\n","      <td>115.480003</td>\n","      <td>116.620003</td>\n","      <td>113.500000</td>\n","      <td>117.440002</td>\n","      <td>2006300.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2016-01-11</td>\n","      <td>WLTW</td>\n","      <td>117.010002</td>\n","      <td>114.970001</td>\n","      <td>114.089996</td>\n","      <td>117.330002</td>\n","      <td>1408600.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>851259</th>\n","      <td>2016-12-30</td>\n","      <td>ZBH</td>\n","      <td>103.309998</td>\n","      <td>103.199997</td>\n","      <td>102.849998</td>\n","      <td>103.930000</td>\n","      <td>973800.0</td>\n","    </tr>\n","    <tr>\n","      <th>851260</th>\n","      <td>2016-12-30</td>\n","      <td>ZION</td>\n","      <td>43.070000</td>\n","      <td>43.040001</td>\n","      <td>42.689999</td>\n","      <td>43.310001</td>\n","      <td>1938100.0</td>\n","    </tr>\n","    <tr>\n","      <th>851261</th>\n","      <td>2016-12-30</td>\n","      <td>ZTS</td>\n","      <td>53.639999</td>\n","      <td>53.529999</td>\n","      <td>53.270000</td>\n","      <td>53.740002</td>\n","      <td>1701200.0</td>\n","    </tr>\n","    <tr>\n","      <th>851262</th>\n","      <td>2016-12-30</td>\n","      <td>AIV</td>\n","      <td>44.730000</td>\n","      <td>45.450001</td>\n","      <td>44.410000</td>\n","      <td>45.590000</td>\n","      <td>1380900.0</td>\n","    </tr>\n","    <tr>\n","      <th>851263</th>\n","      <td>2016-12-30</td>\n","      <td>FTV</td>\n","      <td>54.200001</td>\n","      <td>53.630001</td>\n","      <td>53.389999</td>\n","      <td>54.480000</td>\n","      <td>705100.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>851264 rows × 7 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3b2dde0-295c-41e2-9a2f-5aaa535f0b9c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-d3b2dde0-295c-41e2-9a2f-5aaa535f0b9c button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-d3b2dde0-295c-41e2-9a2f-5aaa535f0b9c');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? 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","\n","\n","<div id=\"df-2bf265ab-34cd-4c48-bd28-fbe3d86fc0ec\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2bf265ab-34cd-4c48-bd28-fbe3d86fc0ec')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-2bf265ab-34cd-4c48-bd28-fbe3d86fc0ec button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","  <div id=\"id_e51e9b8b-8856-4077-9289-1933b4b85783\">\n","    <style>\n","      .colab-df-generate {\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-generate: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-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate: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","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('prices_split')\"\n","            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_e51e9b8b-8856-4077-9289-1933b4b85783 button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('prices_split');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"prices_split"}},"metadata":{},"execution_count":60}]},{"cell_type":"code","source":["prices_split.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"4vrr3u-9gX5S","executionInfo":{"status":"ok","timestamp":1732366582254,"user_tz":0,"elapsed":244,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"df4f86f9-6bb9-4a6b-fe1d-3be8a0b3d5bb"},"execution_count":61,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         date symbol        open       close         low        high  \\\n","0  2016-01-05   WLTW  123.430000  125.839996  122.309998  126.250000   \n","1  2016-01-06   WLTW  125.239998  119.980003  119.940002  125.540001   \n","2  2016-01-07   WLTW  116.379997  114.949997  114.930000  119.739998   \n","3  2016-01-08   WLTW  115.480003  116.620003  113.500000  117.440002   \n","4  2016-01-11   WLTW  117.010002  114.970001  114.089996  117.330002   \n","\n","      volume  \n","0  2163600.0  \n","1  2386400.0  \n","2  2489500.0  \n","3  2006300.0  \n","4  1408600.0  "],"text/html":["\n","  <div id=\"df-344f04ed-0aa7-49f0-9222-aaaa060507f9\" 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>date</th>\n","      <th>symbol</th>\n","      <th>open</th>\n","      <th>close</th>\n","      <th>low</th>\n","      <th>high</th>\n","      <th>volume</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2016-01-05</td>\n","      <td>WLTW</td>\n","      <td>123.430000</td>\n","      <td>125.839996</td>\n","      <td>122.309998</td>\n","      <td>126.250000</td>\n","      <td>2163600.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2016-01-06</td>\n","      <td>WLTW</td>\n","      <td>125.239998</td>\n","      <td>119.980003</td>\n","      <td>119.940002</td>\n","      <td>125.540001</td>\n","      <td>2386400.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2016-01-07</td>\n","      <td>WLTW</td>\n","      <td>116.379997</td>\n","      <td>114.949997</td>\n","      <td>114.930000</td>\n","      <td>119.739998</td>\n","      <td>2489500.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2016-01-08</td>\n","      <td>WLTW</td>\n","      <td>115.480003</td>\n","      <td>116.620003</td>\n","      <td>113.500000</td>\n","      <td>117.440002</td>\n","      <td>2006300.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2016-01-11</td>\n","      <td>WLTW</td>\n","      <td>117.010002</td>\n","      <td>114.970001</td>\n","      <td>114.089996</td>\n","      <td>117.330002</td>\n","      <td>1408600.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-344f04ed-0aa7-49f0-9222-aaaa060507f9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-344f04ed-0aa7-49f0-9222-aaaa060507f9 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-344f04ed-0aa7-49f0-9222-aaaa060507f9');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? 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","\n","\n","<div id=\"df-087c4228-ec4b-434f-abe1-7fac77770d42\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-087c4228-ec4b-434f-abe1-7fac77770d42')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-087c4228-ec4b-434f-abe1-7fac77770d42 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"prices_split"}},"metadata":{},"execution_count":61}]},{"cell_type":"code","source":["plt.figure(figsize=(10, 6))\n","prices_split.groupby('date')['close'].mean().plot(title=\"Average Closing Prices Over Time\")\n","plt.ylabel(\"Average Closing Price\")\n","plt.xlabel(\"Date\")\n","plt.xticks(rotation=45)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":617},"id":"CipXkyZFf-zf","executionInfo":{"status":"ok","timestamp":1732352249065,"user_tz":0,"elapsed":1201,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"f2806a53-891d-4f49-f372-e9b55ffbc581"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0kAAAJYCAYAAAC3hK3KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADY9ElEQVR4nOzdd3hTZfsH8G+Spkm69y5Q9p6ylwKKskQRRXGAiltURH/yKgqKoijDiYtXFFFE5UVFAVkie+9ZoIXS0pbOdKYZ5/dHmtOcjLZp0/39XBeXOc85OXlaSs2d+37uRyYIggAiIiIiIiICAMjregJERERERET1CYMkIiIiIiIiKwySiIiIiIiIrDBIIiIiIiIissIgiYiIiIiIyAqDJCIiIiIiIisMkoiIiIiIiKwwSCIiIiIiIrLCIImIiIiIiMgKgyQiIpJYvnw5ZDIZEhMTa+T+LVq0wJQpU2rk3jXtn3/+gUwmwz///FPXU2ky+D0norrAIImIGo3PPvsMMpkMffv2reup1EtGoxHffPMNbrzxRgQFBUGlUqFFixaYOnUqDh48WNfTqxUymUz8I5fLERUVhVtuuaXRvAG/cuUKnnjiCbRo0QIqlQphYWEYP348du3aVddTk5gyZYrk78LZn4YaTBNRwycTBEGo60kQEbnDwIEDkZKSgsTERMTHx6N169Z1PaV6o6ioCHfeeSc2bNiAIUOGYOzYsQgKCkJiYiJWr16N8+fP48qVK4iJicHy5csxdepUJCQkoEWLFm6fi06ng1wuh1KpdPu9KyKTyXDzzTfjwQcfhCAISEhIwGeffYb09HT8+eefuO2228p9vslkQklJCTw9PSGX16/PGXft2oVRo0YBAB599FF07NgRqampWL58OS5evIgPP/wQzz77bB3P0mzPnj24ePGieJyQkIDXX38djz32GAYPHiyOt2rVCn379q2333MiasQEIqJG4NKlSwIAYc2aNUJoaKgwZ86cWp+D0WgUioqKav11K+Ppp58WAAiLFy+2O2cwGIT3339fSEpKEgRBEL755hsBgJCQkFC7k6wFAISnn35aMnb8+HEBgHDLLbc4fV5RUZFgNBprenpVlpWVJURERAjh4eHChQsXJOcKCwuFwYMHC3K5XNi1a1etzquy37cDBw4IAIRvvvmm5idFRFQJ/EiGiBqFlStXIjAwEKNHj8Zdd92FlStXiuf0ej2CgoIwdepUu+dptVqo1WrMnDlTHNPpdHjjjTfQunVrqFQqxMbG4uWXX4ZOp5M8VyaT4ZlnnsHKlSvRqVMnqFQqbNiwAQDwwQcfYMCAAQgODoZGo0GvXr3wyy+/2L1+UVERpk+fjpCQEPj6+mLcuHFITk6GTCbDnDlzJNcmJyfj4YcfRnh4OFQqFTp16oT//ve/FX5vrl69ii+++AI333wznn/+ebvzCoUCM2fORExMTLn3+eyzz8SvMyoqCk8//TRycnIk18THx2PChAmIiIiAWq1GTEwMJk2ahNzcXPEa2zVJljVQu3btwowZMxAaGgpvb2/ccccduH79uuT+JpMJc+bMQVRUFLy8vHDTTTfh9OnT1Vrn1KVLF4SEhCAhIQFA2RqYVatW4bXXXkN0dDS8vLyg1Wqdro/Zt28fRo0ahcDAQHh7e6Nr16748MMPJdecPXsWd911F4KCgqBWq3HDDTfg999/l1yj1+sxd+5ctGnTBmq1GsHBwRg0aBA2bdpU7tfwxRdfIDU1Fe+//z5atWolOafRaPDtt99CJpPhzTffBAAcPHgQMpkM3377rd29Nm7cCJlMhnXr1oljlfnZK+/7Vh2Ovuc33ngjOnfujOPHj2Po0KHw8vJC69atxX9j27dvR9++faHRaNCuXTts3rzZ7r5V/fdERE2DR11PgIjIHVauXIk777wTnp6euPfee7F06VIcOHAAvXv3hlKpxB133IE1a9bgiy++gKenp/i8tWvXQqfTYdKkSQDMb8LHjRuHnTt34rHHHkOHDh1w4sQJLF68GOfPn8fatWslr7t161asXr0azzzzDEJCQsTytA8//BDjxo3D5MmTUVJSglWrVmHixIlYt24dRo8eLT5/ypQpWL16NR544AH069cP27dvl5y3SEtLQ79+/cTALDQ0FOvXr8cjjzwCrVbrMPixWL9+PQwGAx544IEqf3/nzJmDuXPnYsSIEXjyySdx7tw58Xu8a9cuKJVKlJSUYOTIkdDpdHj22WcRERGB5ORkrFu3Djk5OfD39y/3NZ599lkEBgbijTfeQGJiIpYsWYJnnnkGP/30k3jNrFmzsGDBAowdOxYjR47EsWPHMHLkSBQXF1f5a8vOzkZ2drZdeeZbb70FT09PzJw5EzqdTvJzY23Tpk0YM2YMIiMj8dxzzyEiIgJnzpzBunXr8NxzzwEATp06hYEDByI6OhqvvPIKvL29sXr1aowfPx6//vor7rjjDvH7PH/+fDz66KPo06cPtFotDh48iMOHD+Pmm292+jX88ccfUKvVuPvuux2ej4uLw6BBg7B161YUFRXhhhtuQMuWLbF69Wo89NBDkmt/+uknBAYGYuTIkQBc/9mr7PeturKzszFmzBhMmjQJEydOxNKlSzFp0iSsXLkSzz//PJ544gncd999eP/993HXXXchKSkJvr6+VfqaiKgJqutUFhFRdR08eFAAIGzatEkQBEEwmUxCTEyM8Nxzz4nXbNy4UQAg/PHHH5Lnjho1SmjZsqV4vGLFCkEulws7duyQXPf5558LACTlSgAEuVwunDp1ym5OhYWFkuOSkhKhc+fOwrBhw8SxQ4cOCQCE559/XnLtlClTBADCG2+8IY498sgjQmRkpJCRkSG5dtKkSYK/v7/d61l74YUXBADCkSNHnF5jzbbcLj09XfD09BRuueUWSenUJ598IgAQ/vvf/wqCIAhHjhwRAAg///xzufdv3ry58NBDD9m93ogRIwSTySSZt0KhEHJycgRBEITU1FTBw8NDGD9+vOR+c+bMEQBI7ukMAOGRRx4Rrl+/LqSnpwv79u0Thg8fLgAQFi5cKAiCIGzbtk0AILRs2dLu+2o5t23bNkEQzKWKcXFxQvPmzYXs7GzJtdZfy/Dhw4UuXboIxcXFkvMDBgwQ2rRpI45169ZNGD16dIVfh62AgAChW7du5V4zffp0AYBw/PhxQRAEYdasWYJSqRSysrLEa3Q6nRAQECA8/PDD4lhlf/bK+75VpLxyO9vvuSAIwtChQwUAwg8//CCOnT17Vvw3uXfvXnHc8m/f+t7V+fdERE0Dy+2IqMFbuXIlwsPDcdNNNwEwl8Hdc889WLVqFYxGIwBg2LBhCAkJkWQlsrOzsWnTJtxzzz3i2M8//4wOHTqgffv2yMjIEP8MGzYMALBt2zbJaw8dOhQdO3a0m5NGo5G8Tm5uLgYPHozDhw+L45bSvKeeekryXNvF9YIg4Ndff8XYsWMhCIJkXiNHjkRubq7kvrYs5U6WT9FdtXnzZpSUlOD555+XLJyfNm0a/Pz88OeffwKAmCnauHEjCgsLXX6dxx57DDKZTDwePHgwjEYjLl++DADYsmULDAZDhd+viixbtgyhoaEICwtD3759xTI/2+zBQw89JPl7dOTIkSNISEjA888/j4CAAMk5y9eSlZWFrVu34u6770ZeXp74d5eZmYmRI0ciPj4eycnJAICAgACcOnUK8fHxLn1NeXl5Ff79Ws5bfh7uuece6PV6rFmzRrzm77//Rk5Ojvhvoio/e5X5vrmDj4+PmAEGgHbt2iEgIAAdOnSQdLi0PL506VKVvyYianpYbkdEDZrRaMSqVatw0003iWtKAPMbo4ULF2LLli245ZZb4OHhgQkTJuCHH36ATqeDSqXCmjVroNfrJUFSfHw8zpw5g9DQUIevl56eLjmOi4tzeN26deswb948HD16VLKWyToIuHz5MuRyud09bMu+rl+/jpycHHz55Zf48ssvKzUva35+fgDMb6SrwhKktGvXTjLu6emJli1biufj4uIwY8YMLFq0CCtXrsTgwYMxbtw43H///RWW2gFAs2bNJMeBgYEAzEGm9Txsvz9BQUHitZVx++2345lnnoFMJoOvry86deoEb29vu+uc/d1as3Ro69y5s9NrLly4AEEQMHv2bMyePdvhNenp6YiOjsabb76J22+/HW3btkXnzp1x66234oEHHkDXrl3LnYevr2+Ff7+W85ZgqVu3bmjfvj1++uknPPLIIwDMpXYhISHihwJV+dmrzPfNHWJiYiT/ngBzoB4bG2s3BpT9HFX33xMRNQ0MkoioQdu6dSuuXbuGVatWYdWqVXbnV65ciVtuuQUAMGnSJHzxxRdYv349xo8fj9WrV6N9+/bo1q2beL3JZEKXLl2waNEih69n+wbM0SfmO3bswLhx4zBkyBB89tlniIyMhFKpxDfffIMffvjB5a/RZDIBAO6//3679SMW5b2Jbt++PQDgxIkT6N69u8uv74qFCxdiypQp+O233/D3339j+vTpmD9/Pvbu3VthYwiFQuFwXHDzThUxMTEYMWJEhde5Kxti+fubOXOmuM7HliXwGzJkCC5evCh+/77++mssXrwYn3/+OR599FGnr9GhQwccOXJE/ADAkePHj0OpVKJNmzbi2D333IO3334bGRkZ8PX1xe+//457770XHh4ekrm78rNXG1kkwPnPS0U/R9X990RETQODJCJq0FauXImwsDB8+umndufWrFmD//3vf/j888+h0WgwZMgQREZG4qeffhIXsb/66quS57Rq1QrHjh3D8OHD7T6lrqxff/0VarUaGzdulLxh/eabbyTXNW/eHCaTCQkJCZI3rhcuXJBcFxoaCl9fXxiNxkq9ubd12223QaFQ4Pvvv69S84bmzZsDAM6dO4eWLVuK4yUlJUhISLCbU5cuXdClSxe89tpr2L17NwYOHIjPP/8c8+bNc/m1Hc3jwoULkmxFZmammCWobZZOcidPnnT6d2P5nimVykr9/Vk6MU6dOhX5+fkYMmQI5syZU26QNGbMGOzZswc///wz7r//frvziYmJ2LFjB0aMGCEJYu655x7MnTsXv/76K8LDw6HVaiUlbNX92auPGuPXRETuxzVJRNRgFRUVYc2aNRgzZgzuuusuuz/PPPMM8vLyxDbLcrkcd911F/744w+sWLECBoNBUmoHAHfffTeSk5Px1VdfOXy9goKCCuelUCggk8nE9VCA+U2qbWc8S1bhs88+k4x//PHHdvebMGECfv31V5w8edLu9WzbZNuKjY3FtGnT8Pfff9vdGzB/sr5w4UJcvXrV4fNHjBgBT09PfPTRR5KszrJly5Cbmyt249NqtTAYDJLndunSBXK53K59elUMHz4cHh4eWLp0qWT8k08+qfa9q6pnz56Ii4vDkiVL7NqhW75XYWFhuPHGG/HFF1/g2rVrdvew/vvLzMyUnPPx8UHr1q0r/P49/vjjCAsLw0svvSSuvbEoLi7G1KlTIQgCXn/9dcm5Dh06oEuXLvjpp5/w008/ITIyEkOGDBHPV/dnrz5qjF8TEbkfM0lE1GD9/vvvyMvLw7hx4xye79evH0JDQ7Fy5UoxGLrnnnvw8ccf44033kCXLl3QoUMHyXMeeOABrF69Gk888QS2bduGgQMHwmg04uzZs1i9ejU2btyIG264odx5jR49GosWLcKtt96K++67D+np6fj000/RunVrHD9+XLyuV69emDBhApYsWYLMzEyxBfj58+cBSNcvvfvuu9i2bRv69u2LadOmoWPHjsjKysLhw4exefNmZGVllTunhQsX4uLFi5g+fboYWAYGBuLKlSv4+eefcfbsWUkGwVpoaChmzZqFuXPn4tZbb8W4ceNw7tw5fPbZZ+jdu7eYudi6dSueeeYZTJw4EW3btoXBYMCKFSvEN6XVFR4ejueeew4LFy7EuHHjcOutt+LYsWNYv349QkJCqpz5qw65XI6lS5di7Nix6N69O6ZOnYrIyEicPXsWp06dwsaNGwEAn376KQYNGoQuXbpg2rRpaNmyJdLS0rBnzx5cvXoVx44dAwB07NgRN954I3r16oWgoCAcPHgQv/zyC5555ply5xEcHIxffvkFo0ePRs+ePfHoo4+iY8eOSE1NxfLly3HhwgV8+OGHGDBggN1z77nnHrz++utQq9V45JFHJM05gOr/7NVHjfFrIiI3q5umekRE1Td27FhBrVYLBQUFTq+ZMmWKoFQqxVa/JpNJiI2NFQAI8+bNc/ickpIS4b333hM6deokqFQqITAwUOjVq5cwd+5cITc3V7wOgPD00087vMeyZcuENm3aCCqVSmjfvr3wzTffCG+88YZg+2u3oKBAePrpp4WgoCDBx8dHGD9+vHDu3DkBgPDuu+9Krk1LSxOefvppITY2VlAqlUJERIQwfPhw4csvv6zU98tgMAhff/21MHjwYMHf319QKpVC8+bNhalTp0rag9u2ALf45JNPhPbt2wtKpVIIDw8XnnzySUnb60uXLgkPP/yw0KpVK0GtVgtBQUHCTTfdJGzevFlyH2ctwA8cOCC5zlHrZ4PBIMyePVuIiIgQNBqNMGzYMOHMmTNCcHCw8MQTT1T4PSjv78z2dR21Mnc0J0EQhJ07dwo333yz4OvrK3h7ewtdu3YVPv74Y8k1Fy9eFB588EEhIiJCUCqVQnR0tDBmzBjhl19+Ea+ZN2+e0KdPHyEgIEDQaDRC+/bthbffflsoKSmp8GsTBEFISEgQpk2bJjRr1kxQKpVCSEiIMG7cOLuW9tbi4+MFAAIAYefOnQ6vqczPXnnft4pUpQV4p06d7K5t3ry5wxbqjv7eq/vviYgaN5kguHlFLBERVcvRo0fRo0cPfP/995g8eXJdT6fey8nJQWBgIObNm2e3xoyIiKgquCaJiKgOFRUV2Y0tWbIEcrlcsjaEzJx9vwDgxhtvrN3JEBFRo8U1SUREdWjBggU4dOgQbrrpJnh4eGD9+vVYv349HnvsMbt242Tex2f58uUYNWoUfHx8sHPnTvz444+45ZZbMHDgwLqeHhERNRIstyMiqkObNm3C3Llzcfr0aeTn56NZs2Z44IEH8Oqrr4p71VCZw4cP4+WXX8bRo0eh1WoRHh6OCRMmYN68efDx8anr6RERUSPBIImIiIiIiMgK1yQRERERERFZYZBERERERERkpdEXvJtMJqSkpMDX17dONhokIiIiIqL6QRAE5OXlISoqym7zbGuNPkhKSUlhhygiIiIiIhIlJSUhJibG6flGHyT5+voCMH8j/Pz86ng2RERERERUV7RaLWJjY8UYwZlGHyRZSuz8/PwYJBERERERUYXLcNi4gYiIiIiIyAqDJCIiIiIiIisMkoiIiIiIiKwwSCIiIiIiIrLCIImIiIiIiMgKgyQiIiIiIiIrDJKIiIiIiIisMEgiIiIiIiKywiCJiIiIiIjICoMkIiIiIiIiKwySiIiIiIiIrDBIIiIiIiIissIgiYiIiIiIyAqDJCIiIiIiIisMkoiIiIiIiKwwSCIiIiIiIrLCIImIiIiIiMgKgyQiIiIiIqq2Yr0R4z7ZidlrT9b1VKqNQRIREREREVXbtO8O4vjVXKzYe7mup1JtDJKIiIiIiKhaivVG7IjPEI9NJqEOZ1N9DJKIiIiIiKhazqXmSY5nrD6K7/YkIimrEILQ8AImj7qeABERERERNWxnrmklx2uPpmDt0RQAp/DunV0wqU+zuplYFTGTRERERERE1XLaJkiy9vpvp2pxJu5Rp0FSXl4enn/+eTRv3hwajQYDBgzAgQMHxPOCIOD1119HZGQkNBoNRowYgfj4+DqcMRERERER2Tqfluf0XEygphZn4h51GiQ9+uij2LRpE1asWIETJ07glltuwYgRI5CcnAwAWLBgAT766CN8/vnn2LdvH7y9vTFy5EgUFxfX5bSJiIiIiMiKtsjg9Fw0g6TKKyoqwq+//ooFCxZgyJAhaN26NebMmYPWrVtj6dKlEAQBS5YswWuvvYbbb78dXbt2xXfffYeUlBSsXbu2rqZNREREREQ2CkvMQdI9N8QCAFqGeIvnjA2w012dBUkGgwFGoxFqtVoyrtFosHPnTiQkJCA1NRUjRowQz/n7+6Nv377Ys2eP0/vqdDpotVrJHyIiIiIiqjkFJUYAwIMDmiPx3dHYNGMobu4YDgDQFuvrcmpVUmdBkq+vL/r374+33noLKSkpMBqN+P7777Fnzx5cu3YNqampAIDw8HDJ88LDw8VzjsyfPx/+/v7in9jY2Br9OoiIiIiImrqi0iDJ29PcPFshl+HxIS0BACeTtcjM19XZ3KqiTtckrVixAoIgIDo6GiqVCh999BHuvfdeyOVVn9asWbOQm5sr/klKSnLjjImIiIiIyJogCCgoLbfz8lSI4+F+ZRVj289fr/V5VUedBkmtWrXC9u3bkZ+fj6SkJOzfvx96vR4tW7ZEREQEACAtLU3ynLS0NPGcIyqVCn5+fpI/RERERETkXlvOpGH1gSToDCZY9ov1UpVtwxob5CUGTcnZRXUxxSqrF/skeXt7IzIyEtnZ2di4cSNuv/12xMXFISIiAlu2bBGv02q12LdvH/r371+HsyUiIiIiatr0RhMe+fYgXv71OM6llrX/1igVkuseH9IKAHC1gQVJHhVfUnM2btwIQRDQrl07XLhwAS+99BLat2+PqVOnQiaT4fnnn8e8efPQpk0bxMXFYfbs2YiKisL48ePrctpERERERPVKck4RTlzNxchO4ZDJZDX+epeuF4iPEzPNj9VKORRy6Wtb9khKzmGQVGm5ubmYNWsWrl69iqCgIEyYMAFvv/02lEolAODll19GQUEBHnvsMeTk5GDQoEHYsGGDXUc8IiIiIqKmymA0YeC7WwEAr47qgB8PXMFjg1tiUp9mNfaaJ5Jzxcf/O2Le49RHpbS7rltsAJ4f0QbtIxrWEhiZIAgNr3G5C7RaLfz9/ZGbm8v1SURERETU6Hyx/SLmrz9rN5747mgAQG6RHr4qD8jl7sswPfbdQfx9Wto74MZ2oVg+tY/bXqMmVDY2qBdrkoiIiIiIyDWCIOCTrfEOAySLXRcy0OutTXhvo/NrquL41Vy7sa7R/m59jbrEIImIiIiIqAE6m5qHD/4+7/Ccn9oDgiDggWX7YDAJ+GL7Jbe9bm6hHqnaYrvxML/GsySmTtckERERERGRa0oMJrzw01FklLNBq7bYgO5vboLJamGNwWiCh0IOncEIo0mAl2fVQoGk7EIAQKivCqE+Kpy+pgUAhPh4Vul+9REzSUREREREDciuCxn488Q17EvIsjvn6VH29j63SC85d700qLr9k13o+dYmfL3jEpKyCl1+/ZxC832DvDzF7nUAEOyjcvle9RWDJCIiIiKiBkAQBMz94xSmLj8gGR/YOhgn547Ep/f1xN5Zw9Ey1Nvh8/UGASUGE86m5qFYb8K8P89g8IJtuJrtWqD0wuqjAAA/jQci/ctK7IK8mUkiIiIiIqJatPdSFr7ZlWg3HuWvgY/KA6O7RiLI2xO3dopw+PwSowk5hSV24499d6jSc8gt0uN6njkjlZhZKOmY1yzIq9L3qe8YJBERERERNQD3frXX4bjRJN3RZ1DrEIfX6Y0mZDkIkixriio1hy/L5lBcYsTUAXGICdTg9TEdoVQ0ntCi8XwlRERERESNVL7O4PTcmG6RkuMBrUOw+J5uCPNVoXO0H2SlyR690YTsAr2DOwDrT1xDQkZBhfOwDqiiAzVoFuyFnf83DA8PiqvEV9FwsLsdEREREVE9ZjQJWHsk2en5Ye3D7cbu6BGDO3rEAACGLNiGK1mF0BsFh+V2APDkysMAyjagrYwlk7pX+tqGhpkkIiIiIqJ67NvdiXht7Unx+KZ2oVj37CBolAq8MKJthc9XKsypJL3RZNfxzhVGkyBmpdY/NxjtI/yqfK/6jpkkIiIiIqJ67M11pyXH703oijA/NY7PuaVS64As16zcdwXdYvzLvVYQBMhkMofnlv5zAULp8qc2YT6VmHnDxUwSEREREVE9dTpF2lThs8k9EeZnbrtd2UYJZ1PzAAB/HEtBYYmx3Gv1RsHheLHeiA/+Pi8eezSiJg2OMJNERERERFRP7UvIFB8fff1mBHhVby+iAqsGELPHdER+sQGLN5cFP0V6o2RDWovEzIqbOjQmjTsEJCIiIiJqwCx7EvWNC6p2gASUdcl7bngbPDIoDs+NaIOE+aPE88V6x5mmi+llQdJXD95Q7XnUdwySiIiIiIjqqYx8c5A0uI3jvY9ctXLfFQCAt0ohjslkMviozAVmRaXleLsvZuBCer7dPG7rHIGbO9p302tsWG5HRERERFRPWTJJIT4qt97XWyUNA9RKBfJ1BmQW6PDa2pPYeSEDQFlLcG1pVzx/jdKt86ivGCQREREREdVT13KLAQDhpc0a3EUGaQc7jae5wGzen2dw5EqOOG4yCZDLZWLr8KYSJLHcjoiIiIhcojea8OT3h7D0n4t1PZVGzWgScCnDvBaoVah7W253jJLucZSZb95k1jpAAoCc0uDIEiT5MUgiIiIiIrK360IG1p9MxXsbzoqNAMj9rmYXosRggspDjuhAjdvu+9G9PdA9NkAy5qw1eGbpWiQGSURERERUpy5dz0eJwVSte+QW6p12KquKohIjvvz3Il76+Rj+OHZNHD90Odttr9HUJGUV4s0/TotNEWxZApNgb08o5I43eK2Kcd2i7MZevLmtw2szSjNMLLcjIiIiojqz5Uwahi3cjhd/Pub0GkEQ8OYfp3HnZ7uQU1giOac3mvDfnQno9ubfePC/+yEIjjcHrUix3ih57pIt5/HOX2fx86Gr+PXwVXE8TVtcpfsT8OyPR/DfXQl49NuDDs8X682BslqpcHjerXMZ3gYDWgWLx+3CfQEAyTlFAIDMAvPPWbB39duQNwQMkoiIiIjqka92XAIA/HEsxeF5QRBw+EoO/rsrAYev5GDvpUzJ+T+PX8Ob604DAPYnZOFcWp7D+6TnFWNnfAb0RvuM1ZXMQgx8dyumfXdIHNt+7rrD+1i6r5HrjiblSP5ry5IJVFUzSLLOHIX5Ou+SN8iqzXivFoEAILYBTy8Nhst7fmPC7nZERERE9UhsoBf2IguA+U2yJYuw6XQanlt1BIIAtA4rW8SfbhOknErJlRzvT8hC+wg/u2tGf7QTAHBf32Z4544ukvNvrjuNzIISbD6ThqSsQsQGecFocpyRYpBUNQYHwWmx3og7P9uNXs0D8db4zmKQpFZWL6+x4K6uCPL2xM8HkzBtcEun1/VuESQ+tmSSLl7PR7HeCG2xee1ZmK97u+zVV8wkEREREdUj1qVVV7OLxMfTvjuIwhIjivRGnEguC4Rsg5SEjELJ8eu/nUJ2gbQk7+HlB8THG0+m2s0hPr0s+3THZ7uQXVCCeKuNRa1dd7Kehsr3ZWnG0EIQBGw4mYrT17RYsfcyAKC4dF2a2qN6mSS1UoE54zrh5NyRmDak/CBpwV1dseqxfoj0NwdD6Xk68WfM00MOP03TyLEwSCIiIiKqI1vPpuGvE9ckY6sPJomPr+WagySdwXkDhrOpZQGNwWjC0SRzIwWlomyh//bz0lK5NG1ZYGO7EL9Yb8TlzLJAKyO/BHd9vtvp6xeyu53LkrIKsWDDOclYsd6EghKDzZh7MkkWMlnFzR/uviEW/VoGI9jHvPYoq0AHbXFZ04bK3KMxYJBEREREVAcMRhMeXn4QT608LDY/OJ2ihc6qq921HPP4BSdZHADYejYdvx66iu/2JGLEou3IyC+Bv0aJHrGB4jWrDlzBrgsZDp+vN0nLvo5fzbW75uL1AqevX+KgbKyqzlzT4q6lu7Hw73MVX9yAHUjMshvL1xnsOhrqxCCp5hs32Ar2Nq89Ss4uEn8mPRVNJ3RoOl8pERERUT1xLbcIHd/YKB5byupsO8W9/Otx3PvlXuyItw9wxnWLgqdCDqNJwIs/H8Prv51CYmkGKNJfDQFla4j2XsrC5K/3iW2cu0T7i+dyCvWS+979xZ4K5983LgifTe4JANVuVW7tq38v4eDlbHy89QLOXNO67b71TbbN9xwACksMkgAZKOtup/Ko/bfsQaWZJJMATFi6u87mUVeazldKREREVA8kZRWi//ytkuDC0mbZ4KA5wp5LmXh3/VkAwMu3thPHlQo5WoZ6O3wNP40SeqP9vf48fg0Tlu6WrGnKKzaITRls24V7WO3N88igOPGxv0YpvmF2Z5BknZWavfak3fmiEiN2Xchw62vWBUeZQdtM0pYzaXj7rzMA6iaT5KsqW3tk+bHwZJBERERERDVh4yn7RgnTfzyCrIISFJaUv76nRXBZUOQhlyHcz3GnMT+10uEb2v/874TDzV8zC8xrlLKsGjy0DvOBt9Ub5UAvJdY+PRC3dorArFEdxPvbZj+qI99qfdPZ1Dy7jnov/XIMk7/eh4Wb6m85ntEklLs3VUJGAX7cfwUA8NnknogLMf+dFuiMkrVnj1jtnVQXQZJMJsPn9/eUjDGTREREREQ1wlnL7F8OJUmCBEeiAzTiYw+FDOF+jves8dN44M3bO1U4Fy9P85vvF1ebN661ZLQA4LuH+8DHOkjy9kT32AB8/kAvxIV4i+tT3JnVyS8u+/rzdQak50nLD9cdNze5+HpHgtte0x2OJeXg+VVH8Om2Cxj47lY8/cNhp9duOZMmPh7SNlT8OyjQGaDTO/5eWncbrE23do6ExipAa0qZpKbRw4+IiIionnAWJGk8PZCY4bxBAgBEB5YFSUqFXHyDbUvloUD7CD8kvjsa+xOy8Mqa47hk03xBJgNubBeKv06k4lxphzxLkNImzAdRARp4q8ruH+jlKXl+TWeSAHN2xZH61F9NEATc/ukuydhfJ+yzhRaWjWOnDGgBH5WH2F0wt0iPy1mFDp8ztmuUw/HaEOTtKQbPTSlIajpfKREREVE9YNlXaO64TmgW5CWOqzzk+KqcDMmw9mEI8VFhyoAW8FV74LEhLeGjUjq81nqj0j5xQegc5W93TcdIPzw3vC2AsrVAliDFUmZnXW5n2yrc8obZnd3t7IOksmPrdTz1qQt1npPsnyAIOJmci8dXHMTF6+a5Z+TrxGyYZUPgQG9z8Hk2NQ+bTqc5vNfEG2LdPe1KC/EpC47Z3Y6IiIiI3E4QBMSnmd8wtwn3wdYXh4rncgpLnD0NAHBPb/Mb5TnjOuHI7JsRFaCBj9q+KKhjpB+mDoyTjEVZlelZdIj0E/ffsZTMWfbpsZTZWZfbWQdMANzeuOFCep5k81zr+QDApC/3io9l9SiXlJnv+O9NZzBhwtLd2HgqDdNK1xedt9rTqltMAAAguDRI+uXQVaevoZDX3dcb5G0VJDGTRERERETudipFi1RtMTRKBXo2C4SHQo47e0YDcP5m20LStKH0E32FzXvnAC8l/npuMDpG+UnGHx0sDZoA8xteT5tAJ7+0vM1SZuftWRYY+aikpX2eCoXkudX10ZYL4uNIf3NDCktp4sp9l5GRX1amWGI0IT6tbtbp2LKelzVtkV4sRbxUWkZ5Lde8xirCT40uMebsnqWM0dl9oh0EuLUpyLts3ZunR+03kKgrDJKIiIiIasnfpZ3tBrcJETuWWRbGW3eWmze+s91zWzlo9y2zqTtz1n3Mdj0RAChkMrF8ymASYDIJYnmbo3I720ySu8vtLOV00QEaaErXWj236ij0RhPeWnfa7vqbF//rltetru3nrjsc7//uVrux1NJ9sAa2DhHHgn3s/26srXlqQDVmV33W5XbsbkdEREREbmUyCfhhfxIAYHTXSHHcEiT9XFpuFeClRKtQH8lzH+jXXMweWbOtwnIWr9iWa3kq5HhkUJykfOrrnZfE/Zh8xCCpLHPgrNzOaBIka6CqSlts3mD14/t6SJpMfPnvJXFT1fqiWG/OuF3LLcIn28wZsNFdIvH3C0PEa2zblwNAemmQZN2VsH2En911FkPbhjpt815bWG5HRERERDUmVVuMjHwdPOQy3No5Qhy3bULgpVRIgpOXRrbDWw4yS4B5XZG1Hs0CKpxHq1BvHJo9Ai1CvCVvet/566z42GEmydNxJglwTzZJW2QOkvzU0gYR728s2xPp/25tX+499EaTGMDUlGNJOeg6528s3nQeSVlla6ju7dMMbcN94etgnRgA5BbqxXJGX6uv8YbmgZK1X9Y0dbA/kq1QX6tyOzZuICIiIiJ3srT3jg3ygspqbUeqVroWJSW3GO0ifMXjYG/n5Vg3tAjCJ/f1wMf39sC9fWIx/84uTq/95L4e8FV74P5+zcU36c7e9Dpq3GCXjbIOkqq5LslkEsTOdn4aDwxoFWx3TcsQb4zqEmE3bu2Oz3Zh0HvbajRQen/jOZQYTfhwS7zYbEPlIcegNuYSOkcZJADo9ubf4roj69btcrkMf04f5PA5Gict3muTdVazKZXbcZ8kIiIiolpg2QOnebCXZDy/tMzMmspDgUV3d8OWs+kY3yO63PuOKd1DZ2y38vfSGdM1CqO7RErWMdmuabKwBEcxgc6bBnjIZZDJAEGofpC08VQqLLGFn1qJDyf1QO+3N0uueWt8Z7vMis5gFANOncGIk8laAMCZa1r0aBZYrTk542HVLeOxFYcAmNusW6iVChSWOA7Sjl/NAWAf/DQP9sbh2Tfj4vV8PLz8APJK96tS14NMUpvwsiDJWZasMWo6XykRERGRG81ffwbrjl3D2qcHSkqSnMkpNAdDwd7Sa2fc3A7bHCz+v7NnDO7sGeOeyZZyFhTZspTZje4SiX/OXUdPB2V8MpkMSrkcJUYT9E6yJ5X15MrD4mOVhxxqXxVGdAjH5jNl+waF+qqgtgku0rU6FJQYEOytkpQtFtVgJsmy4a416z2k/NQekiYc1izBj23pImBe+xPkHQSlVXbP0qK9Lnl5euCJoa2QmFGAB/q1qOvp1BoGSUREREQuMhhN+GL7JQDAmsNX8fjQVhU+x9KYwE8jffvVJcYfcSHeSMgocPS0OmFp9+2hkGPxPd2dXuehkKHEiGo1brAtjbMEcl42AZFGqbDLJP19Ok3sfLdt5o3iuLMgpbqu5Rbh4OVsu3E/qyCpS0wAEjMLHT7fUBpM2n5t1qzLGuvDmiQAeOW28teCNUZ1H54SERERNTBnrpXt0VPZHEpeaZDka9OYAJC+aY4Nqtt9cQD7TnbOWLIeemPVM0nWAY11YwbbQEKtVEiyLADww77L4mNL4wcAyMhzvOdQde25mOlw3DqTNGdsxwrvU95aI496GCQ1RQySiIiIiFy09Wy6+Di1dIPQimiLShsTOFjXYf3G+IdH+1VzdtVX+SDJPG99NTJJlk10I/zUePLGsoyc7XocR4HFdatgyNKKGwAyayiTZFnzZMu6I1+wjwoL7upa7n0cldtZSDJJ9aBxQ1PFIImIiIjIBbmFenz570XxuLJBkqVxg22LawCwXtIT6V+3++IAcNqS2paHvHQz2mpkkjIKzIGO7aaqdpkkB53VtFbrgzadLlu/ZFn/5W5ZBY4zVLYllEEONu+1VtlMkoqZpDrDIImIiIjIBRtPpaLAqntZTlH5WYuL1/PR861NOJaUA8D+DTUACFZFe442ja0N1i29K51J8ijNJJmqn0kK8nYeJCkVMpe+L7lF7g2S9l7KxBMrDmHt0RSH563L7QAgyCrge2lkO7vrywuS6uOapKaIjRuIiIiIXJCQaW6w0DLEG5cyCirMWsxbd1qy7qZNuK/dNdWIMaptxSN98M+569AbTfhuj3mNT3l7M1lTlmaS9FVsAZ6Rr8O/582d/WwDDY1VSZqra55y3BgkmUwCHvzv/nLbnNtmBztG+qF7bAAi/dWYNrglHhrQAp3f2CiejyonW2jJzgEMkuoSM0lERERELriaXQQA6BTtD0DaMMDiQno+vt97Gfk6Ay5Zda2L8FNLNue06N3CvKdPXIh3TUy5XIPbhGL2mI5IySkrG6zs/jyWRgqGKrQAN5kEjPt4J34/Zs7O2Jb4OesA90C/5hXeO7fQfWuSUrXFFe4DZRvgqZUKrH16IJbe3wueHnLJ1/Zg/+bltmKXrkniW/W6wu88ERERUTls21snZ5vXFnWK8gNgn7XYczETIxZtx2trT+L5VUdw2aoddMtQx0HQS7e2x+J7uuHbqX3cOXWXDGkbAqD8DWRteVSxccPm02loP3sDUqzWc9mW+FkfT+odKz5+a3xnbJ4x1OF9LRmwbDeuSbrsoJ330sk90cJqU2BXNn3tHhtQ7nnrzWrVHswk1RWW2xERERHZuJCeh7OpeUjOLsL89WcxuE0Ilj3UG54e8rJMUmmQVFhiRInBJK7pWbL5vHifzWfSJfcVnCRcfFQeuKOHezeOddW9fZoh2FuFPnFBlX6ORzktwItKjPjjWAoGtQlBVIA08Hr0u4N219sGSb5WXQD7twqWnGsd5gN/jdJu7VGEvxqZBSUo0Nlv+FpVV7Ls969qF+GLbTNvxND3/0FhiQHNrQImZzbPGIL9CdkY3z263OusGzfYbp5LtYdBEhEREZEVg9GE8Z/uRr7VG+0d8RnYdSED/VsFI7207XSHSD94yGUwmASk5xUjyl8DuVyGMD/n603ydDXTdc0dlAo5RneNdOk5nqVZD0ebyU777iB2XsjAHT2iJRvS2m4ea6FWSgucrFulO2qZ3b9lMDacSpWMhfioAABFTl6jKhxlkkJ9VZDJZNg0Ywj0RqFSmaTWYb5oHWa/Hs2WdQMNrkmqOyy3IyIiIrJyLbdYEiBZpGmLkZJjziJ5eyoQ7O2JZkHmDMKg97Zh1Ec7YDCaEOarcnrvvGL3ZTjqA0uTAb2DNUk7L2QAANYeTZaMbz6TZnctAOj00kDLetNdR932bIMqoCxIKtYbIThL27nI0rr9ts4RCPVV4aWR7cS5qTwUlW6XXlkqqxI7V8r4yL0YJBERERFZsZTT2XplzQmxS12wjzmTYN1o4WxqHi5cz3eYVbFobEGS0qPi7nY+NlkgS6BpyzbDZF1u5ygQUTlYrxPuZw6STAJQUo0Nbi02nLyGP49fAwCM7xGNA6+OwNM3ta72fcvDTFL9wCCJiIiIyMrVbPvyKovk0jf4liyGbWldYYkRhSXOS72eurGVG2ZYfyhL188s25mAUR/uwPGrOQAAo1VmyXZdjbM9jGzXLVlnkqw7von3dZBJig0qWxtUXFK9IEkQBDzx/WHxOLycMkp3Usi4T1J9wDVJRERERFaSnGSSgLJ235ZP+23fqOcW6VHoYD3MuG5ReHRwHDpH+btxpnXP0ont9DUtAOC3oynoGhMgaYvu6HtkrW9cELrG+OPePs0k415WAUKwj/2+TY5K0SL81eI6sSK9Ef5Q2l1TWbbNKEIczKEmmATrAJP5jLrCIImIiIjISnmZJMsbfEupl+0b9az8EhQ6WM/kr1Gia0yA+yZZT1j2SbKwlCNmW+1TpDdIg43cIun3Z0jbUIclbHK5DCsf7Yt8ncFhFkflIEgK9VFBo1QgT2eodvOGYoP0+Zb1TjXNenmXp4JBUl1hkERERERU6nqeDmsOJ9uNR/mrkZJbbBUklWaSbNbFZBWUOCy3c7YxakNnGyRZ1hVZ7x1lG6zYZpJ05QQzA1uHOD2n8rAPIEJ9VZCXluadT8ur1ua8to0kaquJgnUmqbxNZ6lmMTwlIiIiKvXu+rPi4xvbhQIwBziWN8i5FZTbZToJkjSNNkiSvonPKzbggWX78J7V9zG3SA9daVamxGDCv+evS56jK6fpQ3ky80vsxoK8PcW/o8dXHKrSfcvmVfb3+FD/5tW6lytMburKR9XDIImIiIio1N9W++58el9P/N+t7fHn9MF2QZKYSbIttyvQiSVn1qybEDQmHjaZpJ0XMrAjPgP7ErIk419svwQAmL32pN09qlrGZlsOB9hntqqjuDST5Kf2wNzbO7vtvhVx0E2d6gCDJCIiImry/jiWgv7ztyCvdD3R+3d1hbfKA0/e2ApxId5ixmjbWXMWpGxNkk0mKb8E6XnFdvf31zTSIMlB1zlHvtuTCAD46WCSOPbSyHa4+4YYPFDFLM2TQ1uhW2xAlZ5bGZZMUm3vVeSu/Z2oehgkERERUZP3+m8ncS23LLjpEOknOX/4Sg6Asr13LOV2tnv1XLieb9cVDWi8QZKj1tyO2H4/AWBCzxgsuKtblYOQ2CAv/Pb0QAxpGyoZf2NsRwBAy9Cqr0faeCoVEz/fAwBQOWg1XpNYblc/MEgiIiKiJk1nMCK7UNpMoKI9cVRO1iRdzjR3xrNtF91Yg6TKZpIcfT/d9T35z6j2CPL2xOtjzMFRr+aBAIDicvarKk9RiRGPrzgkri2zbc5R04yst6sX2N2OiIiImrSkrLKW31MHtoC3pwdCfctfJyNmkpxkQfq3CsEfx1LEYz9N43zLpZBX7vP2whL7tuiONoOtivYRfjj02gixE5yXp/l7XVDFIGlvQqbkuLYzSbbZSaobjfNfLBEREVEl3ffVPgDm7NAbYztV6jnimiQnb2i7xwagRbAXPt56AUDj3e+mspmkAp19wOLO9tbW9/JWmf9OHAVmlXE6RSs5ru1M0htjO+Khb/bjiaGtavV1Sapx/oslIiIiqqT0PB0AoHmwl9Nrfn2yv+TYUm7nrLW3j0qBF29ph7HdojC0bShaBFd9fUx95qEoP9CZMqAFgLKApVXpOqHJfZvV2JwsmSS9UUBJFdqLX7yeLzmu7UxSy1Af7Hh5GCb3rb2242SPmSQiIiJqks5c02LKN/vF4w8mdnN6bc9mgVDIZeJ6EUu5na+67K2Un9oD2mJzMGB5o/7xvT3cPu/6pLxM0ogO4RjWPgzLdyeKmSRLW+2JN8TW2JysN+4tLDHA08OznKvtXUyXBkk+Kr5dboqYSSIiIqImaffFTKRpdeJxpyh/p9fKZDLJm29LJsm6+UB0YFkmylLy1diVtybp+RFt7ErfivTmYElTg221lQq5uMmto419y1NiMOFMap5kbFSXSLfNjRqOOg2SjEYjZs+ejbi4OGg0GrRq1QpvvfWWpD+8IAh4/fXXERkZCY1GgxEjRiA+Pr4OZ01ERESNQYFOumalonbW3p5lGQVLwwbrIMnXKuOgUTaN7IOzTJKf2gOdo/0R6mPuapeSU4zCEgOKSoMWLydliu5iWTPmarnd5cwCu+fc0CLIbfOihqNOg6T33nsPS5cuxSeffIIzZ87gvffew4IFC/Dxxx+L1yxYsAAfffQRPv/8c+zbtw/e3t4YOXIkiovtN2ojIiKipuNkci76vbMFq/ZfqdLzbYOkikgySaWNGJRWDRm8rLJHTSeT5DhICvAyl7jFBmkQHaBBidGEQ5ezxUxSTW/Qasn06VwMkvId/ExEVtAOnhqnOv2YY/fu3bj99tsxevRoAECLFi3w448/Yv9+c32wIAhYsmQJXnvtNdx+++0AgO+++w7h4eFYu3YtJk2aVGdzJyIiorpx4moufjmUhG/3XAYAvLLmBCb1cb0RgPUb4so0abNef+RoMb91psnLs4lkkpw0btCXbrork8nQOdoPyTlFOJGcK56v6UySZc2Yq5kky/XRARpE+qvRs3kg5JXs4EeNS51mkgYMGIAtW7bg/PnzAIBjx45h586duO222wAACQkJSE1NxYgRI8Tn+Pv7o2/fvtizZ4/De+p0Omi1WskfIiIiahz2XcrE2E92igGSRWGJAQajCXnFeifPtGcdJH1UiQYLYVYZBUumwlqfuLKyLGUFXd8aC2eZJL2xbOlEy1AfAMCp5LL3ZDW5JgmwziS5uCapNLjzVXvglycH4D+jOrh9btQw1GmQ9Morr2DSpElo3749lEolevTogeeffx6TJ08GAKSmpgIAwsPDJc8LDw8Xz9maP38+/P39xT+xsTXXPYWIiIhq16oDSQ7Hr+fpMPPnY+j7zhbEp+U5vMaWpdzuxZvbYkzXqAqvD/cr22DW0ypI+uOZQXh9TEdJW+sQn/I3o20snK1JMpjKMjgtQ8xtv0+mmDNJKg95jWdnqptJ8nQQBFPTUqc/AatXr8bKlSvxww8/4PDhw/j222/xwQcf4Ntvv63yPWfNmoXc3FzxT1KS41+mRERE1LBk5OvwvyPJ4nHXmLJudFkFJVh7NAWFJUbM+eNUpe5nySQ1D6ncHkYRkkxSWSakS4w/Hh4UBw+FHDv/7yb8M/NGeDeRttHOutvprYKTVmHmTNLlzEIANV9qB5T9/bi6JkkMkhrp5r9UeXX6L/ill14Ss0kA0KVLF1y+fBnz58/HQw89hIiICABAWloaIiPL2i+mpaWhe/fuDu+pUqmgUjWNT2+IiIiakuk/HhEf39e3GZ4d1hoPLtuP+PR83PHZbvGcwarUqzyWvXt8KtlkIdS37P2Fo3I7AIgJdL4hbWPkrKzQuutfqxAfybmaLrUDyjJBVS23YyaJ6vQnoLCwEHKbTyAUCgVMpSnauLg4REREYMuWLeJ5rVaLffv2oX9/6c7XRERE1HjpjSbsvpgpHs+6rT0i/TWIt9n4EwBMQuWCpOzCEgCAv6Zym41aX8c30WbO1iR98cAN4mN/L6XkOk2tZJKq1t1Ox3I7KlWnPwFjx47F22+/jT///BOJiYn43//+h0WLFuGOO+4AYO6I8vzzz2PevHn4/fffceLECTz44IOIiorC+PHj63LqREREVA25hXpkF5RIxs6manHrkn/xx7EUu+vPWW3w+fszA+GrNmcq2oT52F2bV1xxa+/cQj2uZhcBAEJ8KhckBXiVZUesy+2aMkdrku7t0wxdYqQb81pnnGqj819VgySW25FFnZbbffzxx5g9ezaeeuoppKenIyoqCo8//jhef/118ZqXX34ZBQUFeOyxx5CTk4NBgwZhw4YNUKvZs56IiKghMpoE9H5nM4wmAafmjhT3zJn4+R7kFRswY/VRjO0mbaRw8bo5Y9SnRRC6xgSI40vv74kRi/6VXJtVUAJBECCTOW8O8PxPZaV7lW2yIA2S+CYacLwmyVFySamQo1hvDkBqs9zOlcYNeqMJy3cnSp5PTVed/gT4+vpiyZIluHz5MoqKinDx4kXMmzcPnp5ln+jIZDK8+eabSE1NRXFxMTZv3oy2bdvW4ayJiIioOtK0xSgxmGA0CbiWa94cXhAEMQOkd7Cm6OL1AgBAy1Bpk4XWYb54YYT0fUF6ng7PrTpa7hy2nbsuPq5sk4UAq3I7Jd9EA3CcSeoWG2A3Zr3pbu2U27neuGHjqVRcKC3fdHWjYWp8+C+ciIiIatW8P0+LjzPzdQAglr5ZnErJlRxfzTZ3RmsWbN8YITpQYzf2u4OSPWvOWleXxzqTxP1FzazXGt3cMRxvje+Mu3rG2F1nXW5n3SWwplQlk5RaGrAD5mwkNW0MkoiIiKhW/XWibK/D9DxzkHQlq1ByzfoT0v0QcwvNm8QGe9uvH+pqs/6lMno2DwQAPH1Tq0o/R61U4PGhLXFf32aI9LcPzJoi62Czc5Q/HujX3OEeSB5WZXl9WwbZnXc3tdL8ekX6yne3u14asAOVW9dGjVvTaOJPRERE9dJfJ64hJafIruTtk20X8NyINmKZVk6ROUiybi1t0SrUB61CvaEzmOwyUs5Yyql6t3DtDfus2zq4dH1jZ51J8nDSDhyQrvEJr4VMkndpc4jCSpbN/bDvCr7Yfkk81jJIavIYJBEREVGtsd23Zt3xa1h3/JrDa/89fx3DO4QDAHLKadetkMuw4fkhMJoEtJ+9QRzffSEDA1qHOLx3YYl5Hk1l09eaYh0YOdszyfacJctTkyx/rwUllQt2/vO/E5JjbbHe7XOihoXldkRERFQrCnSGCtd6+KrLghajSRD/a2nc4CiTBJgbA6htuqbd9/U+CE72TMovzTB410I76sbMurudh4NOd47O1Ub7dEuQVNWM0Pt3dXXndKgBYpBERERENe7rHZfQ6Y2N6D9/a7nX3dopAnEh5g52ls5k646XNWGwbp7gSJivtJ33NavF+BbFeiOul66F8lZxv6PqsG6FXm4myeq62sgk+ZYGSX8ev4av/r0kOReflic2DHHkjbEdcXv36BqdH9V/DJKIiIioxn2141LFFwF46dZ2aBZk7mBnCZKOXMkRz4f6lr+n0c9P9JccW/ZXsvbb0WTxcW1sbNqY+ViVK3qUswGr0mrtUm1mkgDg7b/OiI/Ppebh5sX/YsLS3U6fGxto30GRmh4GSURERFSjruUWIU0r/eR+7dMDHV4b5qsWMw3FpZ3JknPMzRheHdVBst+OI82Dpfsozfz5GBIyCiRjCRllnfRCfOzXOFHl+anLMnsmJ6WNgHTtkqpW1iQ5DsR+OpAEAEjMLHR4HgBigxgkEYMkIiIiqgHHknIw5Zv9+OnAFUnLbwBoFuSF7rEBODl3JPb/ZzhahkgDG8vaIksmKam0PXibcB+X55Gm1eGmD/6RrE1K05pL8Gbd1h4yGTc8qg7rYKRY73xPIhlqN5NkvbbNWkKGfWbRVjMGSQQGSURERFQDPvvnAv45dx3/9+sJpORI23Lf2dO83sNH5YEwPzVs4xTLOhdLJsmyfqiyraOHtg21G5v23UHxsWU+Ef4134q6sbMusSsuZ08io1WQWhtrksJ8K/67tQTOx5JyJOMaT65TIwZJREREVAMsgQ1QtgZIIZehTZgPHurfQnKtwmbzUUsm6fPtF3Hiai4ySzviBVeyNG7ZQzdg6kDpa2w+ky4+tnTYC/Upf30TuaaoxHmQZDKVBUmeFZRMuoOzkjm5VURuyVTe/ukucezz+3vW7MSowWCQRERERG6XatVVLiPfHJTMGdcJm2YMRaC3NNh5d0JXaJQKvD6mI4CyTFJesQFjP9kpXhfkVbkgyUMhR0w5i++5R1LNKKpkJqm2Shw/vrcHACDCKgNp/dKOgrpusQE1PS1qIBgkERERkVsZjCakau1bbzsLcno2C8SJObfg4UFxAGC335FFed3TbPk4Wbi/8VSq2AiC7b/dY1j7MADAvX1inV5jnUmqLVEBGgBAqrZYzGYareZRbLAPkipTpkdNAz9CISIiIrdKz9PB0XvicD/n5W3WAZD13jsWk3o7fwPuiLMs0eMrDomP2f7bPb5+8AbkFRvgX84eVhV1JawJ1j9Hz606iqvZ0i6LiRmFiPTXiMdD2obalX5S08VMEhEREbmVbaMGAAj29kT3SpYy2WaSZDLgnTu6uDQHbwcBkMFoqvAacp1cLis3QAKAMV0jAQAvjGhbG1MCYB+Yvb/xHE5f04rH9361F0UlRviVdsKbPbpDrc2N6j/+diAiIiK3Sildj9Q82AuXS/ejeXxoy0qXywXYlOX5eHpA7uIn/I5K9kpsgiQvltvVmikD4zCuezSCvGtvXypPBxlJWxn5OhSXNnBgVzuyxkwSERERuU1mvg7TfzwCwLzWyGJM16hK32NImxDJsULheglU99gAdI3xl4zpbPbxqYsSsKasNgMkoHJBks5gQklpkORsLRw1TfztQERE1IQU643I1xlq7P5f70wQHw9oFYxfnxyA1Y/3FxfRV0aYnxqf3NdDPM4p1Ls8D42nAr8/M0gyZmn5TE2DshLBtba47GeLQRJZY7kdERFRE2EyCejz9mZoiw2IC/HGmicH2LXjro5ivRHLSoMkmQwY2y2qym88x3SNwvQfjzhsAFFVOgfdzKjxUikq/tnLLbIKkiqReaKmgz8NRERETUR+iQHaYnMWKSGjAH+euGZ3TUpOET7ddgGFJa5nm9K0xSgxmOAhl+HSO6Oq/cl8YCX3RaosncGEtuE+AICnbmzl1ntT/aP0qEQmqTRI8pDLXGoxT40ffxqIiIiaCNvNM/deykRhiQEFVuV3Dy8/gPc3nsNr/zvp8v33XsoEAMQEatyyYeik0n13OkX5VfkeCyd2Ex/r9Cax5M6ytw81Xp7lBD2W9uCWTBJL7cgWgyQiIqImotAmSLqSVYghC7ah3ztbxMXrZ1PzAABrjiS7fP//+/UEACAlx34j2aqYPrwNFk7shm+m9q7yPSb0ikGzIC8A5nI7S/MGvilu/MrLDPmqzS3LcwstQRLfEpMUfyKIiIiaiAKbhg3Hr+YiI78EeToD/jiWItlHqDqbat7UPrTKz7Wm8lBgQq8YhPmqq3UfTWlAVKw3ieuSHG1YS43PwwPjHI6H+po3NrY0blB5MGgmKf6GICIiaiJ2XsgAAHHzTGsv/nxMXK8EAN4u7hljMgmwxFVv3t656pOsAZb9bwpLDChmJqlJuad3rN3YggldxSCprNyOb4lJij8RRERETUCJwYR3158FAEkwZC2roKTseqNr7bJzivRiJ7ra3g+nIj4qc1BYUGJgJqmJsTTqsJjYKwZ3947lmiSqEH9DEBERNQGHr2RLjp8d1trumrOpWvFxsd4Ekwv9tzPydQCAAC9lvduk1VtlfgOcVVAWyKn4prhJkMlkGNetbCPjZ4e1AcDGDVSx+vVbjIiIiGpEfFqe5NhbZV9yd/aa9JoifeX3FUrTmps1hJWWMdUnlq/1Wk4RAPMeTr4Ovn5qnHysykstpZeeYpBkzqqy3I5s8SeCiIiokcst0mPlviuSMUdB0pWsQsmxbTc8W78dTcYLPx2FtliPq9nmACQ6QFPN2bqfpdzuWm6xeCyvRmMKalhyCsvKSEN8zKWglkYNln2S1GzcQDb4MQoREVEjt/ZIstjaGzDvHSR38DHp1WxpkHQ5s0Bc4O7Ic6uOAjCvd4oL8QYAxAR6VX/CbmYJCFNyzYGcX2n7Z2oarPdLsuzfxXI7qggzSURERI1ccmmZWd+4ICS+OxoTesXA29NRJqlIcnzX53uwthL7Je25lInLpVmomMB6nEkq3b/JT8MgqSl54ea26BDpJ9lYWFVaXpdf2hZfxXI7ssGfCCIiokbOsl5oeIcwccx6nYalBMnSfMHa23+dAQAkZhRI9lGypi3S40J6PgCgdZiPw2vqkqWdeWrp98FRC3RqvJoHe2P9c4MxoVeMOGa7LxIzSWSLQRIREVEjZ1mLE+5XtimrdTCjNzrvYqfykGPT6TTc+ME/mPvHaQBAel4xDiRmidcYTALOXNPa3be+0Njs+cRMEtm2gOeaJLLFIImIiKgR23Q6DfsTzAFNs6Cy9UJhvmp8el9P3N+vGT66t4fT53t6yLFo03kAwIq9l6E3mjBi4XZM/HyPw+uj6mHjBtssAdckkV2QxHI7ssF8MxERUSNVrDdi2ncHAQChvip0jw2QnB/dNRKju0YCALw8FQ672Xkq5Ai22hz2k60XnG5GC6De7ZEEOAiSNHz709TZ7pPFcjuyVf9+kxEREZFbWNYJAeZgx9LZyxHrMjmN1RtGlYdcsl/Sh1vi3TzLmsdMEtlSKZhJovLxoxQiIqJGyjpIMpgcN12w8Ldap9MqzBsnk81rjI5dza2ZydUijV0miUFSU2fbzY6ZJLLFsJmIiKiROp9WtjeSo5bfzrQMqX/NF6rDNkvA7nbExg1UEQZJREREjVS8VSbp/Yldy722fYSv+NgoOO92V56fHutXpefVNPs1ScwkNXW2LcC5TxLZ4kcpREREjVRCRgEAYMUjfdCreVC51z43oi2uZhehf6tgbDqdZnd+RIcwbD6TDgCI8lcjpbSt+H+n3IB1x65h5sh29bKzHWBfbmfZF4qaLvvudswkkRSDJCIiokbKsolspL+6gisBH5UHlt7fCwDw66GrducjrO5xW5dIvDa6g9gIYlj7cHdMt8bYZglah/o6uZKaCq5Jooowt0hERNSA5RSWYMLS3fhws7TrXLHeiLzSVt2hvhUHSda62bQKB4BI/7IsUUygptxOefWN7Rtgfy+W2zV1tuV2ag++JSYp/kQQERE1YIs2ncehy9lYvPm8ZDxdqwNgLitytVHBzJHt0CXaXzLWNabsONzPtaCrrlmX271/V/lrs6hpaB3mA0+rwIiZJLLFIImIiKiBKtYb8YuD0jgAOH3N3Lo7wl/tctbHT63Ey7e2k4y1DPXBnLEdMax9GG5qF1a1CdcRpUKO9yZ0wZu3d8LEG2LrejpUD6iVCmyZMVRyTGSNa5KIiIgaoOyCEkxZfgCFJWUbvRaVGKHxNL/Z23Ta3GRheBXXC3nabLbpo/LAlIFxmDIwroozrlv39G5W11Ogesa6y2EDqh6lWlKlTNKKFSswcOBAREVF4fLlywCAJUuW4LfffnPr5IiIiMie3mhCj7c24VhSjmQ8u7BEfJxZYC63s27t7QpvVdnnqH5qD+4tRI2Or9XPuG0HRCKXg6SlS5dixowZGDVqFHJycmA0mj/BCggIwJIlS9w9PyIiIrJxJavQ4XhWQVmQlFOoBwAEVLFJgZ+67HlxoT4NqlEDUWXI5TIsursbXh/TEbFBXnU9HapnXA6SPv74Y3z11Vd49dVXoVCURd033HADTpw44dbJERERUZmFf5/DzYu2SzJIfmoPtAnzAQBoi/TieE5pVinQu2p7Avlpyj5ljw5oWI0aiCrrzp4xeHhQwywhpZrlcu48ISEBPXr0sBtXqVQoKChwy6SIiIhIKjGjAB9vvQAAeP23U+K4TCYT93zRGUzieLYlk6SpWibJR1KKxFI7ImpaXM4kxcXF4ejRo3bjGzZsQIcOHdwxJyIiIrKiMxhx4wf/iMf5OoP4WCYr2/PFEiTpDEZoiy3ldlXLJHkorNsjsxkuETUtLn80NGPGDDz99NMoLi6GIAjYv38/fvzxR8yfPx9ff/11TcyRiIioSbuQnu/0nAzmvZAAc3AEANvOpkMQgAg/NYKrWG5nje2RiaipcTlIevTRR6HRaPDaa6+hsLAQ9913H6KiovDhhx9i0qRJNTFHIiKiJu3stTyn52QymbgpZklpJulyprmxQ/9WwZDLq99woV/L4Grfg4ioIalSkfHkyZMxefJkFBYWIj8/H2FhDWtTOSIiooZk18UMp+ekmSRzkFRQWo5nva6oKra8OBSnU7QY0YH/nyeipsXlIuOEhATEx8cDALy8vMQAKT4+HomJiW6dHBEREQH7LmUBAG7vHmV3TiYDPEvXJFkySQWlG8x6qapXJtcq1Adju0Wx/TcRNTkuB0lTpkzB7t277cb37duHKVOmuGNOREREVKpAZ0ByThEAYNZt9g2SHurfQpJJ0hmMKCwpzSR5sisdEVFVuBwkHTlyBAMHDrQb79evn8Oud0RERFR1F6+bmzaE+Hgiwl+NJ29sBQBYck93/PBoXzx5YysxSHpvw1n0emszzqeZn+NVzXI7IqKmyuXfnjKZDHl59gtIc3NzYTQa3TIpIiIiMkvNLQYARAd6AQBeHtkOUwa0QLhf2QavlsYNgLk9+KHL2QAAb092pSMiqgqXM0lDhgzB/PnzJQGR0WjE/PnzMWjQILdOjoiIqKlLy9MBAEJ9VADMH1ZaB0hA2T5JtryZSSIiqhKXf3u+9957GDJkCNq1a4fBgwcDAHbs2AGtVoutW7e6fYJERERNgSAIACBpkmAyCZi99iQAwE/t/H/Z1pkka97VbNxARNRUuZxJ6tixI44fP467774b6enpyMvLw4MPPoizZ8+ic+fONTFHIiKiRm/57kS0+s9f2Blf1u47v7QBAwBoi/VOn6tyEiR5sXEDEVGVVOm3Z1RUFN555x13z4WIiKhJ2RF/HfsTsmA0Cfjsn4sAgJk/H8Onk3viZHIuhrUv25/ohZvbOr2PsyDJm0ESEVGVVOq35/Hjx9G5c2fI5XIcP3683Gu7du3qlokRERE1dg8s2283lqotxoSl5q02TqXkAgB81R7oFOXv9D4BXp4Ox1luR0RUNZUKkrp3747U1FSEhYWhe/fukMlkYu20NZlMxg53RERElZBb6Lx8zmLXhUwAFWeEogLUDsfZuIGIqGoq9dszISEBoaGh4mMiIiKqnvPp0u00Ar2UGNg6BOuOXxPH0rTm9t9eFbTyjvLXOByv6HlERORYpYKk5s2bAwD0ej3mzp2L2bNnIy4urkYnRkRE1JhZAiCLCH8NWof5SMYMJnPVhlcFZXMR/o4zSWzcQERUNS51t1Mqlfj1119rai5ERERNRrpWJzkO8laiT4sgh9dqlOUHSWqlAuueHYS54zpJnqOQy8p5FhEROeNyC/Dx48dj7dq1NTAVIiKipuN6vjRICvTyRJ84x0HSgcTsCu/XOdofva2CrCI91wgTEVWVy3n4Nm3a4M0338SuXbvQq1cveHt7S85Pnz690vdq0aIFLl++bDf+1FNP4dNPP0VxcTFefPFFrFq1CjqdDiNHjsRnn32G8PBwV6dNRERUr9hmkgQB8FDIMb57FNYeTanSPVVKlz/7JCIiB1wOkpYtW4aAgAAcOnQIhw4dkpyTyWQuBUkHDhyQdMM7efIkbr75ZkycOBEA8MILL+DPP//Ezz//DH9/fzzzzDO48847sWvXLlenTUREVKe+33sZJkHAg/1bALDPJKXkFgEAPpjYDXffEIv7vt4nnls+tXelXsNTURYk3de3WTVnTETUdLkcJLmzu52lY57Fu+++i1atWmHo0KHIzc3FsmXL8MMPP2DYsGEAgG+++QYdOnTA3r170a9fP7fNg4iIqCZpi/V4be1JAMCbf5zG7DEdcTWrEADQu0UgDl7OxvRhbQCYs0l9WwZLnn9juzBUhvWmsmO7Rrlj6kRETZJLQdLevXvxxx9/oKSkBMOHD8ett97qtomUlJTg+++/x4wZMyCTyXDo0CHo9XqMGDFCvKZ9+/Zo1qwZ9uzZ4zRI0ul00OnKPp3TarVumyMREVFVpOQUiY8NJgFv/H5KPJ47rjOiAzTw91KKYwq5DA/2b47v9lzG9OFtKv06nlZBkr9GWc6VRERUnkoHSb/88gvuueceaDQaKJVKLFq0CO+99x5mzpzplomsXbsWOTk5mDJlCgAgNTUVnp6eCAgIkFwXHh6O1NRUp/eZP38+5s6d65Y5ERERucOLq485PRfmp5IESBb/GdUBd/aMQbcY/0q/jnWQ5Ktm+28ioqqq9ArP+fPnY9q0acjNzUV2djbmzZuHd955x20TWbZsGW677TZERVWvPGDWrFnIzc0V/yQlJblphkRERFVzKsVxVUOrUG8Ee3s6PKdWKtA9NgAyWeXbeHt5euCuXjEY3z0KMYGON5glIqKKVfpjpnPnzuGnn36CQmHeq+HFF1/E66+/jvT0dISFVa5W2pnLly9j8+bNWLNmjTgWERGBkpIS5OTkSLJJaWlpiIiIcHovlUoFlUpVrfkQERG5S1Lp2iNHPrmvp0tBUGV8MLGbW+9HRNQUVTqTVFhYCD8/P/HY09MTarUa+fn51Z7EN998g7CwMIwePVoc69WrF5RKJbZs2SKOnTt3DleuXEH//v2r/ZpEREQ1zWA0YfCCbU7P+6hYEkdEVB+59Nv566+/ho+Pj3hsMBiwfPlyhISEiGOutAAHAJPJhG+++QYPPfQQPDzKpuPv749HHnkEM2bMQFBQEPz8/PDss8+if//+7GxHREQNwrXcYvGxv0aJrx68ARtOpuK/u8ydYrluiIiofqr0b+dmzZrhq6++koxFRERgxYoV4rGr+yQBwObNm3HlyhU8/PDDducWL14MuVyOCRMmSDaTJSIiagiSrbra9W8ZjD5xQTh+NUccYyaJiKh+qvRv58TExBqZwC233AJBEByeU6vV+PTTT/Hpp5/WyGsTERHVpFd+PS4+nn9nFwCAyer/eR6KSle9ExFRLeJvZyIiohqiN5oDorHdohBY2sXO5PhzQSIiqkcYJBEREdWQ3CI9AOCFEWUbwnaLCaij2RARUWWxGJqIiKgG6I0m5OsMAIAgq72Q+rcKxlcP3oBWod51NTUiIqoAgyQiIqIakFNoziLJZICvWik5d3PH8LqYEhERVRLL7YiIiGpATmEJAMBPrYRC7t4NY4mIqGa5nEnSarUOx2UyGVQqFTw9PR2eJyIiakqySzNJAV7KCq4kIqL6xuUgKSAgADKZ80/EYmJiMGXKFLzxxhuQy5moIiKipilNa95INtxXXcczISIiV7kcJC1fvhyvvvoqpkyZgj59+gAA9u/fj2+//RavvfYarl+/jg8++AAqlQr/+c9/3D5hIiKihkAMkvwZJBERNTQuB0nffvstFi5ciLvvvlscGzt2LLp06YIvvvgCW7ZsQbNmzfD2228zSCIioiYrNdccJEX4qep4JkRE5CqX6+F2796NHj162I336NEDe/bsAQAMGjQIV65cqf7siIiI6ql8nQG7LmTA5GR32Ix8HQAg1JdBEhFRQ+NykBQbG4tly5bZjS9btgyxsbEAgMzMTAQGBlZ/dkRERPXUh5vPY/LX+zDzl2MOz1v2SLJt/01ERPWfy+V2H3zwASZOnIj169ejd+/eAICDBw/i7Nmz+OWXXwAABw4cwD333OPemRIREdUjaw4ni/9ddHd3u/N5xeYgyVvFLQmJiBoal39zjxs3DmfPnsUXX3yB8+fPAwBuu+02rF27Fi1atAAAPPnkk26dJBERUX0T4KVEZoF5LySD0QQPRVlxxpEr2diXkAUA8GWQRETU4FTpN3dcXBzeffddd8+FiIiowVB5KMTHaXk6RAdoxONp3x0UHzOTRETU8FTpN3dOTg7279+P9PR0mEwmybkHH3zQLRMjIiKqzyxrjgAgObsIfxxLwfU8HZ4b0QYZ+SXiOW+VwtHTiYioHnM5SPrjjz8wefJk5Ofnw8/PT7KxrEwmY5BERERNQl6xXny8I/46Pt56AQCwar+0u6sPM0lERA2Oy93tXnzxRTz88MPIz89HTk4OsrOzxT9ZWVk1MUciIqJ6RRAESSbJEiABQEGJUXKtRslMEhFRQ+NykJScnIzp06fDy8urJuZDRERU7+XpDNAbHe+PZC02SINgH+6TRETU0LgcJI0cORIHDx6s+EIiIqIGIjmnCP/3y3FsO5deqevPXsur8JpmQV7Y9MJQKOSyCq8lIqL6xeVC6dGjR+Oll17C6dOn0aVLFyiV0k3yxo0b57bJERER1YbX/ncC285dx9qjyTj6+i3QeJZfInc2VQsAGN4+DMk5RTibah80+Wk8oGapHRFRg+RykDRt2jQAwJtvvml3TiaTwWg02o0TERHVZ+fT8gEAOoMJSdmFaBvuW+71iRmFAICWod5YdE93PL3yMHZeyJBcw7VIREQNl8vldiaTyekfBkhERNQQFZSUNWFIyios99oDiVn4764EAEBskBf8NUqseKQPNr0wRHIds0hERA2Xy0ESERFRY1OoK/uQ75FvD+JabpHTayd+vkd8HBtobmIkk8nQJtwXCyd2E89ZbzZLREQNS6XK7T766CM89thjUKvV+Oijj8q9dvr06W6ZGBERUW0oMZhQYpRujN5//lbsfmUYogI05T63U5Sf5NhHXfa/1YrWNRERUf1VqSBp8eLFmDx5MtRqNRYvXuz0OplMxiCJiIgahGU7EyAIAib0jHF4/ueDV/HciDaSMesNZD0VcoT5qSXnfa02jlV7sFiDiKihqlSQlJCQ4PAxERFRQ5SQUYC31p0GAHS0yQZZZBbo7MbStMXi4+0v32h33lvFTBIRUWNQ7Y+5jEYjjh49iuzsbHfMh4iIqMZtOp0qPp7yzQEAgLenAoNahyDK35wdyioosXteaq45cGoT5oNIf/tSPOtyOzZuICJquFwOkp5//nksW7YMgDlAGjJkCHr27InY2Fj8888/7p4fERGRWxlNAjafLts0tsRgXo8U6qvC94/2xaxRHQAA1/PsM0mppZmkCH+13TmA5XZERI2Fy7/Bf/nlF3TrZu7e88cffyAxMRFnz57FCy+8gFdffdXtEyQiIrKVV6zH1ezyW3U7kppbjOEL/8H+xCy7cze2CwMAhPioAADX8+2DpIzSsVBflcP7+6rLNliXy2Uuz4+IiOoHl4OkjIwMREREAAD++usvTJw4EW3btsXDDz+MEydOuH2CRERE1gp0BnSZ8zcGvbetwj2NbK06cAWJmY6f80D/5gAAP405G5RfbLC7JqfQ3Lgh0MvT4T00ngpMGxwHtVKOfi2DXZobERHVH5Vq3GAtPDwcp0+fRmRkJDZs2IClS5cCAAoLC6FQsP6aiIhq1pt/nBYfH7uag9ggr0o/92RyruT47hticPF6AcZ2jUSrUB8AgK/KnA3K1zkKkszrlAI0SrtzFq+O7oiXb20PpYLldkREDZXLQdLUqVNx9913IzIyEjKZDCNGjAAA7Nu3D+3bt3f7BImIiCyyCkrw08Ek8dhoElx6/qXrBeJjL08FFtzVze4aS/OFwhIjjCYBJkGAh1wGmUwmZpICvJwHSQAYIBERNXAuB0lz5sxB586dkZSUhIkTJ0KlMtdlKxQKvPLKK26fIBERkcXstSclx3kOSuLKo7Xa5+inx/o7vMZbVVYVsel0Kp5aeRiv3NYejw1phezSTJK/k3I7IiJqHFwOkgDgrrvusht76KGHqj0ZIiKi8mw5myY5tg56KkNbGlTt/L+bEBPouExP5aGAp4ccJQYTnlx5GIIAvPPXWdzWORL7EswNHwIryCQREVHDVqV6gO3bt2Ps2LFo3bo1WrdujXHjxmHHjh3unhsREZFEsd4kOV6w4Rw+2RpfqefqDEax3bdl3ZEzllbeglU138K/zwEw75HUN45NGYiIGjOXg6Tvv/8eI0aMgJeXF6ZPn47p06dDo9Fg+PDh+OGHH2pijkRERE598Pf5Sl1n3a3OetNXRxydP3MtDwDwyKA4eHIPJCKiRs3lcru3334bCxYswAsvvCCOTZ8+HYsWLcJbb72F++67z60TJCIislB5yKEzmOzGcwpLEFDBOiHL+iVvTwUUFexh1DzYG5dtWoVfzjI3fQjzc7xHEhERNR4ufxR26dIljB071m583LhxSEhIcMukiIiIbOmNJjFA+u+UGyTnHvvukPi4WG/Ek98fwo/7r0iusQRJFWWRAKBbjL/dmKXUz7LZLBERNV4uB0mxsbHYsmWL3fjmzZsRGxvrlkkRERHZyi0qa9IwpE2o5Nz+xCzx8Q/7rmD9yVTMWiPd4HzPpQwAQLifusLXahfh6/QcgyQiosbP5XK7F198EdOnT8fRo0cxYMAAAMCuXbuwfPlyfPjhh26fIBEREQAkZpjL3TzkMngo5Pj3pZsw5P1tdtclZRfajQHA7ouZAIDbu0dX+Fotgr2dngv2YftvIqLGzuUg6cknn0RERAQWLlyI1atXAwA6dOiAn376CbfffrvbJ0hERAQA7204CwAwlG4g2yzYC3f2jMaaw8mIC/HG3D9O4VSK1unzL17PBwB0jvKr8LVahJQFSUqFDHpjWZs7lYfC0VOIiKgRqdI+SXfccQfuuOMOd8+FiIjIIUEQcCI5FwAQZxXATB0QhzWHk5GmLcY3uxLtnmcyCZDLZSjWG3E1uwgA0CrMp8LX81F5YMUjfWASgEeWH3DPF0FERA0Ge5gSEVG9t/FUqtg4YcFdXcVxP435s77CEqPD5xXpzeOJmQUQBMBP7YFg78qVyw1uE4qhbUPRo1mAOPb5/T2rMn0iImpgKpVJCgwMhExWfrtUi6ysrIovIiIicsF7G86JjyP9yxov+KnL3xS2QGeAt8oDF9PN65lahflU+v9nFu9O6IpfDl3FE0NbwV9T/usREVHjUKkgacmSJTU8DSIiIuf8rNp2R1h1pwvwqiBIKs0wWZo5lNeQwZlWoT74v1vbu/w8IiJquCoVJD300EM1PQ8iIiKHBEHA5SxzkPPzE/3hoSirFJfJZHj/rq546ZfjDp9boDPvjZRdUAIAlS61IyKipq3Sa5JSUlIwc+ZMaLX2nYNyc3Px0ksvIS0tza2TIyIiup6nQ06hHnIZ0CXafpPX6ACN0+da1ipllQZJgQySiIioEiodJC1atAharRZ+fvatU/39/ZGXl4dFixa5dXJERERHknIAmLvaqZX27bcDvJwHPjqDEcV6I7afvw4ACGKQRERElVDpIGnDhg148MEHnZ5/8MEHsW7dOrdMioiIyOLf0gBncJtQh+f9y1mXpNObsHjzeaTn6QAAgeUEVERERBaVDpISEhLQrFkzp+djYmKQmJjojjkRERGJ0rTmAKddhK/D89ZNHWwdTcrBF9svicfWnfGIiIicqXSQpNFoyg2CEhMTodE4rwsnIiKqCm2xHoDzdt/ens6DpE+2XRAf39unGbrG2K9pIiIislXpIKlv375YsWKF0/Pfffcd+vTp45ZJERERWWiLSoMkjeNgSC6X7nsU5O2JnlYbwFrMvKWty3skERFR01TpIGnmzJn45ptvMHPmTEkXu7S0NLz44otYvnw5Zs6cWSOTJCKipiuv2NzG27eCjWMB4O07OmP/f4YjyqbjXYSfGsE+qhqZHxERNT6V2icJAG666SZ8+umneO6557B48WL4+flBJpMhNzcXSqUSH3/8MYYNG1aTcyUioiZIzCSVs/Zo4cRu2BF/HRN7xcJDIYfKQ9oF79XRHWp0jkRE1LhUOkgCgMcffxxjxozB6tWrceHCBQiCgLZt2+Kuu+5CTExMTc2RiIiaqMNXspFXuiGsn8Z5JmlCrxhM6FX2/yGVUlooEerLLBIREVWeS0ESAERHR+OFF16oibkQERFJ3PnZbvGxs8YNjqg8pEFSCEvtiIjIBZVek0RERFRTTCYBD/13Px5Ytg9GkwAAyMzXSa7x9Kj8/7Jsy+2YSSIiIle4nEkiIiJyt5TcImwv3TR236VMDGgdgsdXHKry/awzSZ4KebnrmYiIiGwxk0RERHXuSlah+Pj0NS0y83U4eDm7yvdLsrqfxlPB1t9EROQSBklERFTnrIMancGEpOyiat1veIdw8THjIyIiclWVgqScnBx8/fXXmDVrFrKysgAAhw8fRnJyslsnR0RETUO6tmz9kU5vlARNAPD1gze4dL9bO0eIj+WMkoiIyEUuF2kfP34cI0aMgL+/PxITEzFt2jQEBQVhzZo1uHLlCr777ruamCcRETViuaV7IQHmTNKWM+ZNyxVyGU7NHQm1UuHsqQ4p5GWBkZwxEhERucjlTNKMGTMwZcoUxMfHQ61Wi+OjRo3Cv//+69bJERFR02AdJJ1JzcP+BHOVwrdT+7gcINnieiQiInKVy0HSgQMH8Pjjj9uNR0dHIzU11S2TIiKipsHS7ts6SPr3/HWk5BYDAJoHe1X53vf2aQYAeOmWdtWYIRERNUUul9upVCpotVq78fPnzyM0NNQtkyIiosZvxZ5EzF9/Fl8+cIMkSLIW5O1Z5fu/dXsnPDo4Di1DvKt8DyIiappcziSNGzcOb775JvR68//QZDIZrly5gv/7v//DhAkT3D5BIiJqHLaeTcMdn+1CQkYBAGD2b6dQWGLE/cv2IT493+56tVIOL8+ql9p5KORoFerDcjsiInKZy0HSwoULkZ+fj7CwMBQVFWHo0KFo3bo1fH198fbbb7s8geTkZNx///0IDg6GRqNBly5dcPDgQfG8IAh4/fXXERkZCY1GgxEjRiA+Pt7l1yEiorr18PKDOHIlB6//dtLuXFZBid1YsLeKAQ4REdUJl8vt/P39sWnTJuzcuRPHjx9Hfn4+evbsiREjRrj84tnZ2Rg4cCBuuukmrF+/HqGhoYiPj0dgYKB4zYIFC/DRRx/h22+/RVxcHGbPno2RI0fi9OnTksYRRERUf/3nfyfExzviMzDm4x0VPqd1mE9NTomIiMgpmSAIQl29+CuvvIJdu3Zhxw7H/7MUBAFRUVF48cUXMXPmTABAbm4uwsPDsXz5ckyaNKnC19BqtfD390dubi78/PzcOn8iIqpYmrYYfd/Z4vLzXrmtPZ4Y2qoGZkRERE1VZWMDlzNJH330kcNxmUwGtVqN1q1bY8iQIVAoKq4j//333zFy5EhMnDgR27dvR3R0NJ566ilMmzYNAJCQkIDU1FRJlsrf3x99+/bFnj17HAZJOp0OOl3ZpoSOmkwQEVHtOZiYXaXnhfup3DwTIiKiynE5SFq8eDGuX7+OwsJCsSwuOzsbXl5e8PHxQXp6Olq2bIlt27YhNja23HtdunQJS5cuxYwZM/Cf//wHBw4cwPTp0+Hp6YmHHnpIbCkeHh4ueV54eLjTduPz58/H3LlzXf2yiIioBhhNAv53JBkAMLB1MHZdyHR6bZivCul5ZR9yBXhVvbMdERFRdbjcuOGdd95B7969ER8fj8zMTGRmZuL8+fPo27cvPvzwQ1y5cgURERF44YUXKryXyWRCz5498c4776BHjx547LHHMG3aNHz++edV+mIAYNasWcjNzRX/JCUlVfleRERUPX8cS8HmM2kAgFs7RSDERxr4vH1HZ3SL8cd9fZvh7xeGSM4FMkgiIqI64nKQ9Nprr2Hx4sVo1aqsTrx169b44IMPMGvWLMTExGDBggXYtWtXhfeKjIxEx44dJWMdOnTAlStXAAAREREAgLS0NMk1aWlp4jlbKpUKfn5+kj9ERFQ3zqfliY/v6d0Mn9/fSzz29lRgct/m+O2ZQXjnji4I8PLEn9MHiecDvZS1OlciIiILl4Oka9euwWAw2I0bDAaxBC4qKgp5eXl219gaOHAgzp07Jxk7f/48mjdvDgCIi4tDREQEtmwpW/Cr1Wqxb98+9O/f39WpExFRDSvQGbBsZwJScooAABn55vK5mbe0haeHHDe0CMLuV4Zh7rhOOPCafVfUcL+yrqUBGmaSiIiobrgcJN100014/PHHceTIEXHsyJEjePLJJzFs2DAAwIkTJxAXF1fhvV544QXs3bsX77zzDi5cuIAffvgBX375JZ5++mkA5mYQzz//PObNm4fff/8dJ06cwIMPPoioqCiMHz/e1akTEVENm/P7Kby17jQeXn4AAJCRb97/KMSnrAlDVIAGDw1oAS9P+2WxIT4qPHVjKzx1Yyv4M5NERER1xOXGDcuWLcMDDzyAXr16Qak0/w/MYDBg+PDhWLZsGQDAx8cHCxcurPBevXv3xv/+9z/MmjULb775JuLi4rBkyRJMnjxZvObll19GQUEBHnvsMeTk5GDQoEHYsGED90giIqpnDEYTfj50FQBwNtVcTWDJJFkHSRV5+db27p8cERGRC6q8T9LZs2dx/vx5AEC7du3Qrl07t07MXbhPEhFRzTt0ORsTlu6WjCXMH4Ueb21CTqEe654dhM7R/nU0OyIiIrMa2yfJon379mjfnp/2ERER7AIkTw85knOKkFOoh1IhQ5twnzqaGRERkeuqFCRdvXoVv//+O65cuYKSkhLJuUWLFrllYkRE1DAYTfYFCeF+Knyy9QIAoE2YL1QeFW8wTkREVF+4HCRt2bIF48aNQ8uWLXH27Fl07twZiYmJEAQBPXv2rIk5EhFRPfXb0WQ8t+qoeLzu2UEY8/FOJGUVYVWWeZ86H3WVixaIiIjqhMvd7WbNmoWZM2fixIkTUKvV+PXXX5GUlIShQ4di4sSJNTFHIiKqZz7ZGo8Wr/wpCZAAxw0a7r4htpZmRURE5B4uB0lnzpzBgw8+CADw8PBAUVERfHx88Oabb+K9995z+wSJiKj++eDv83Zji+7uBn+NfdvuO3tE18aUiIiI3MblIMnb21tchxQZGYmLFy+K5zIyMtw3MyIiqpf0RpPD8Tt7xkDjKV171DLEG3K5rDamRURE5DYuF4r369cPO3fuRIcOHTBq1Ci8+OKLOHHiBNasWYN+/frVxByJiKgeSdMWV/parkciIqKGyOX/ey1atAj5+fkAgLlz5yI/Px8//fQT2rRpw852RERNwLVcc5AU4uOJjPyScq9VKlwuWCAiIqpzLgVJRqMRV69eRdeuXQGYS+8+//zzGpkYERHVTxfTzR+UdYj0w+S+zfHE94fw9E2txPMbnx+CkUv+BQAoZCy1IyKihselIEmhUOCWW27BmTNnEBAQUENTIiKi+iy+NEhqE+aLWztH4MCrIxDi4ymebxfhKz7297Jv5EBERFTfuVwH0blzZ1y6dKkm5kJERA3ArgvmJj2dovwAAKG+KsicZIwi/dW1Ni8iIiJ3cTlImjdvHmbOnIl169bh2rVr0Gq1kj9ERNR4ZeTrcDY1DzIZMKJDuNPrLA3tbukYUUszIyIich+XGzeMGjUKADBu3DjJJ4eCIEAmk8FoNLpvdkREVK9Y1iPFBnqVW0q3beaNuHg9H4PahNTW1IiIiNzG5SBp27ZtNTEPIiJqAM6XBkmtQr3Lva55sDeaB5d/DRERUX3lcpA0dOjQmpgHERE1ANvPpQMAuscG1vFMiIiIak6VNrDYsWMH7r//fgwYMADJyckAgBUrVmDnzp1unRwREdUvx6/mAgCGtGUZHRERNV4uB0m//vorRo4cCY1Gg8OHD0On0wEAcnNz8c4777h9gkREVD9cySxEep75d35skFcdz4aIiKjmVKm73eeff46vvvoKSmXZot2BAwfi8OHDbp0cERHVDyk5RRjyftma1CAvz3KuJiIiathcDpLOnTuHIUOG2I37+/sjJyfHHXMiIqJ65vEVhyTHcrnjfZGIiIgaA5eDpIiICFy4cMFufOfOnWjZsqVbJkVERPWDySTg3/PXcSI5Vxz74oFedTgjIiKimudyd7tp06bhueeew3//+1/IZDKkpKRgz549mDlzJmbPnl0TcyQiojry+7EUPP/TUQBAqK8KB14dUbcTIiIiqgUuB0mvvPIKTCYThg8fjsLCQgwZMgQqlQozZ87Es88+WxNzJCKiOrLpdJr4OMRHVYczISIiqj0uB0kymQyvvvoqXnrpJVy4cAH5+fno2LEjfHx8amJ+RERURwRBwJ5LmeJxTKCmDmdDRERUe1xek/T999+jsLAQnp6e6NixI/r06cMAiYioETqVokVWQQkAwFMhx+tjOtbxjIiIiGqHy0HSCy+8gLCwMNx3333466+/YDQaa2JeRERk40pmIYwmoUZfIzmnCLsvZgAAUnOLxfGzb93KvZGIiKjJcDlIunbtGlatWgWZTIa7774bkZGRePrpp7F79+6amB8REQHYcDIVQ97fhg+3xNfo6wxf+A/u+2offjuajDydHgAwuE0IW34TEVGT4nKQ5OHhgTFjxmDlypVIT0/H4sWLkZiYiJtuugmtWrWqiTkSETV5b607DQD4aEs80rTFFVxddcV6EwDguVVHkVVgDpJ8VC4vXyUiImrQXA6SrHl5eWHkyJG47bbb0KZNGyQmJrppWkREZM1bpRAf931nC/KKzQFMck4RNp5KRbHeCEEQsObwVZxN1VbpNWxL+c5eM9+HQRIRETU1VQqSCgsLsXLlSowaNQrR0dFYsmQJ7rjjDpw6dcrd8yMiIgDaIoPk+IXSvYueXnkYj684hNd/O4nt569jxupjuHXJDpiqsHYpp7BEcnwyxRwk+aqVVZs0ERFRA+Xyx4OTJk3CunXr4OXlhbvvvhuzZ89G//79a2JuRERUKqdIGsBsPpMOncGIo0k5AIDVB68iyLtsH6MzqVp0ivJ36TWybYKkM5ZMkpqZJCIialpcziQpFAqsXr0a165dwyeffCIJkE6ePOnWyREREVCsN4prhY7MvlkcP5+aL7nuaFK2+DgjXxrwVIZlDZItPwZJRETUxLgcJFnK7BQKc318Xl4evvzyS/Tp0wfdunVz+wSJiJo6SytuhVyGAC8lwnzNGaNNp1Ml151NzRMfW9YsCYKA+LS8CluHbzmThie+PwQACPL2lJzjmiQiImpqqty44d9//8VDDz2EyMhIfPDBBxg2bBj27t3rzrkREbmNIAi4eD0f6dpivP7bSZxMzrW7Rm804eVfjuGWxdux5UxaHczS3j/n0nHjB/8AAPw1SshkMoT4mIOkj7ZekFybU1iWCcovNq9h+n7vZdy8+F/M+b38NaPP/3RU3Di2U5Sf5BzL7YiIqKlx6f98qampWL58OZYtWwatVou7774bOp0Oa9euRceO3ImdiOqvDSdT8eTKw+Lxd3su4+I7o6Cw2v/nf4eTsfrgVQDAI98exLl5t0LlobC7V236z5oT4uMAjbmBQrCPp7PLRXnFBhSVGDH7N3NwtGLvZbw1vrPddYIgoKDEiLzissYQEX5qyTVs3EBERE1NpTNJY8eORbt27XD8+HEsWbIEKSkp+Pjjj2tybkREbvPNrkS7MdtGBYmZBZLj63m6mpxSpRiFsjK5Aa2DAQABXmVB0vD2YRhYOm4tr1iPl3897vy+JgF3Ld2Nnm9twpt/SLNMGk8FYgI14jHL7YiIqKmpdJC0fv16PPLII5g7dy5Gjx4trkkiImoIWoV5243ZBkG5RdLGBen1IEiyzmQ9eWNrANKg5euHbnDYxU5bbMAfx1Kc3vd8Wh4OXs5GdqFezJ5ZCAIwzyrr5MtyOyIiamIqHSTt3LkTeXl56NWrF/r27YtPPvkEGRkZNTk3IqJqS8oqxKPfHsDOC/a/rzLypUHQ1ewiyXG6trhG51YZeqO5q917E7ogOsCc3elotWZIJpMhNsjL7nl7LmY6vefuixm47cMdkjFvTwU+m9wTfVoE4bEhLcV1TwCDJCIianoqHST169cPX331Fa5du4bHH38cq1atQlRUFEwmEzZt2oS8vLyKb0JEVMueWnkYm8+kIymryO7c23+ekRynlQZFGqU5e1Mfyu3ydea1Qr1bBIljk3rHYsqAFvj6wRsAAP1bBtk971ya9HdybFBZ+dzUbw7YXX9371iM6hKJ1U/0R2yQl6TDnTfL7YiIqIlxubudt7c3Hn74YezcuRMnTpzAiy++iHfffRdhYWEYN25cTcyRiKhKsgtKcMJBFzuLIr1RcmwJktpG+AIwl6zVppzCEuRadagTBAGFJeY5WgcqSoUcc8Z1woiO4QCA1mG+WDChK2QyYMFdXRHgZd9ooVBX9rXqDCbx8YgO4XhtdAe8clt7yfWR/mqM6xaFO3pEw4+NG4iIqImpcgtwAGjXrh0WLFiAq1ev4scff3TXnIiI3OK13+w3uP5xWj9seXEoACArvwSCIODIlWxkF5QguzRAaR3qAwDQFjveXLUmFOgMuGXxv7jtw39RVBoY6QwmcX8jL8/y14He3TsWCfNH4+4bYhFqVSon3r+kLOCz3hx2aLtQPDq4pV0XP5lMho/u7YHF93Sv6pdERETUYLmlhkKhUGD8+PEYP368O25HRFRtecV6/Hn8mt1482AvMeDI0xkQN+svAICnwvyZkUIuQ3RpZ7e8Wswk/XPuutgoYuHf5/DamI5iFgkAvDwr/+s61FeF+PR8yVix3gSD0QQPhRzW+8p2i7Fv+kBERNTUsdCciBql7IKyLFCEnxqp2mI0C/JCVIAGJpMAhVwmZmkAoKS0QYKf2kPMtNRmkGRdFvj1zgR8vTMBgaVlc2qlXLKfU0WiAsrWH/VoFoAjV3IAmMsHZShb5/TpfT3RNSag2nMnIiJqbBgkEVGjZF0q9+3DfaA3mhDpb94kVS6XIdDL0667HQD4a5TiGpwcm32UalJSdqHdmKX8r1hvsjtXnm4x/vjlkLmt95QBLXAh7STydAbkFJZg/clUAECHSD+M7hpZzVkTERE1TtVak0REVF9ZgqTWYT5oF+GLztH+CLZaq+OvcfwZkZ9GCZ/STNKO+AyHgZS7FJUYMe27g/hx/xW79uPVMb5HNAa3CcGs29rj9u7RCPA2B33ZhXr8djQZAPDIoDi3vR4REVFjw0wSETVK2iJzSZmfkz1+nLW1VnnIEe6nFo/3J2RhVJeaybj8fiwZm06nYdPpNHFsUOsQuz2dRnQIc+m+vmolVjzSVzwO0HgiCUXYfv46sgrM2bFOVnstERERkRSDJCJqNLIKSnDX57txa6cIcZ8fP43j9tXeThohFOtN6NksQDyOT8sHurh9qgCAfJ20BXmQtyeW3t8Tvxy6ChmAOX+cxp09ovHm+M5ueb2PtsSLj7lBLBERkXP8vyQRNQoGowk939oEAPjsn4viuLOMkVoprTb2VXsgr9iA3i2CIJPJ8J9R7fHOX2cRn+7+jbKvZBZCJoNdKd/LI9vBV63E1IHmUrhbO0ci3E8FmazyTRsc6RztZ7dflC/3PiIiInKKQRIRNQp/HE9xOO6sxbV1G+wtLw6F0STgXGoebu0cAQBoE2beUHbd8WuYeUsBWoR4uzSfz/65gD0XM/Hp5J7wUytxPi0P//frcTx1Y2u88NNR5OsM6BYbAAB45qbWePGWtnbBUIS/2sGdXTfj5nZIzCjEnkuZ4piPk+CRiIiI2LiBiBqBpKxCfLrtot14bJAGjw1p5fA51t3vWoX6oG24L8Z2i4KydL+k1mE+4vlX155weU4LNpzDjvgMfLL1AgDg0W8P4siVHEz77qDYgvtYUg4AYGy3qGpni8oT6qvCQwOai8fengqXWooTERE1NQySiKhB+3H/FQxesA0XbDZPBYDmQc6zP7lFeqfnACAmUIMOkebmBvFp9vcuT2FJ2f5K59PM5XpXsuxbfAPmTWzbRfi6dP+qsF6bxVI7IiKi8jFIIqIGbdaasizPkLahePnWduJxqK/K0VMAAAaj4PQcAMhkMnz5QC8A5oBKEMq//mRyLn4/Zi75S8kpa+etq2CPIy+Votzz7uJnFRj5OWl/TkRERGb8PyURNVjp2mLJcVywF5oFeYnH5QVJ703oike/PYDZYzo6vcbSIU9nMKFIb4SXk454ADDm450AzC3E9caywCgjX4eiEqOzpzntsudu/laZpNhAr3KuJCIiIgZJRI2E0SSgWG902s2tsbmQnocRi/4Vj29qF4pnh7fBqRStOBbq4zxI6t8qGCfmjIS8nLU5Xp4KeHrIUWIwITO/BF5Bjr+31lmm9SeuQWMV+GTk65Cc47jUzvIatSGwNOCzfUxERET2WG5H1EhM++4g+r6zxa6tdGNUYjBJAiQA+GZqH4T4qCT7/0QGlN8drrwACTCX3AWXBhTXy/m+Wq9vKjGacD2v7NrsQr0kcLNVoDM4PedOPioP3NUrBgBwW2kHPyIiInKMQRJRI7H1bDrydQasPZJc11OpURn5OnSdu9HpeT+rIMkdZWWWLneWQOfDzfGYsHS3pIQu3SooMhgF6AzS8rrnVh11ev9rNiWDNWnBhK7496WbMKx9WK29JhERUUPEIImoEci3ykaUt/6lMfhuz2UU2zRDWHxPN/GxdalbdKCm2q/Xo1kgAODI5Wzza20+j0OXs/Hb0bJgNDm7rFFDkd4o/h0EeFXcRa6CfhBuJZfL0CzYq0bbjRMRETUGTWPxAlE9VWIw4XxaHjpF+VXrjWuaVTaisZfb2TZrGNM1Enf0iBGPI/3U6NMiCBpPhVgqVx09mgUAAI4k5UjWHumtdqM9V9rmGwAKS4xiJik6QIOcwvJbjVvuT0RERPUHgySiOiIIAh759gB2xGdg+rDWaBnqg6NJOXh9TMcK18rYss5kWNbHaIv1OJiYhX4tg8vtytbQWOKUZ4e1Rq/mgejdIkhyXi6XYfUT/d32el2i/QEACRkFyCwoEceVVn9H522CpJLSICkqQCNZj9QtNkDcQPbuG2LQJswXo7tGum2uRERE5B6N550TUQMTn56PHfEZAIBVB5LEdS39WwVjZCfXFtZ/v/ey+DhfZ36D/sDX+3Dsai4eH9ISs0Z1cNOs654lCAz1VeHGdjW/tsa6dfaaw1fFxyVWbb7TtWXZu3ydHqbSU9EBZeV+naP9EOmnxjFYznlh2pCWNTNpIiIiqhauSSKqI3suZoqPrRf+z/39lOS69Lxiyb47tjLzdfj7dJp4bOmWduxqLgBgTSNr5KAtNgdJ1sFLTVIq5PBUmH9VvvPXWXE8r9iADSdTcfunu3DwcpY4npRVhOTSzWRjrNZEeSk9JPs2VWa9EhEREdUNBklEdSQ+Pc/heEpuMU6naHE1uxCnUnLR5+0tmPbdQaf3ybZZ81JQYkBhSVkjh+t5OphMtdgdoIZZMkl+6toLMrxU9nsZaYv1eOL7QziWlGPXSMJiUJsQ8fGVrEKE+5UFSbUV5BEREZHrGCQR1ZHLmc43GB310Q5MWLoby3YmAAD+OXfd6bWWzIpFgc4gKf8CgKtWa5YasnydAYkZBQBqNxPjpbQPkirzPQ3y8sSSe7pDLgOmDWmJMN+yfZv8mUkiIiKqt7gmiaiOXMkyB0nB3p6ShgAWaVod4tPyK7yPtsg2SDLi5V+PS8ZOX9OiWXD19wyqawcSslBQYkSkvxqdSxsq1AaNZ1mQ1DHSD6evafHn8WsVPk/tqcD4HtEY0TEcPioP7IgvC3ZbBHvXyFyJiIio+phJIqoD2mK9mEka2i7U6XUnU3IrvJel/CzS35ylKNAZAJvqurRa3LC0JuQV63Hvl3uxYOM5AECHSD8oFbX368tbVfZ50n/KaYLRPTZAcmzJQPmUPr9fy2BMGxyHJfd0R1wIgyQiIqL6ikESUR14euVh8XEfmxbW1iqz0ai22Lz+KKq0k1qezoD9ieZGAn5q85vzwga+weyaw8nYcykTZ66Z22mHWTVAqA0aq3K75uVk5O7r20x8/P5dXeFhE8gpFXK8OrojxveIdv8kiYiIyG0YJBHVsJPJuVhz+Kokm2Np/Q1AklEY0SHc6X2MTpovWMrtHGUmIv3NgVORVSOHhiaroAQnkqUZtdBaDpIUVnsiBXgp4WzfX0tnQQC4s2eM44uIiIio3qvTIGnOnDmQyWSSP+3btxfPFxcX4+mnn0ZwcDB8fHwwYcIEpKWllXNHovrlZHIuxny8EzNWH8PNi7YjNbdY0nlu8T3d0CrMRzyeOrCF03sVOgl0LMFXuJ8KvVsESs6Fl5bgNeRM0p2f7cIvh65KxqKs9h+qDXKrqMjb00OSWbL4cFJ3jOoSCT+1Byb1jpUEVkRERNSw1Hnjhk6dOmHz5s3isYdH2ZReeOEF/Pnnn/j555/h7++PZ555BnfeeSd27dpVF1Mlcpl1BkRbbMC64ym4paN5o1iNUoE7epizDcun9oZGqUDflsGYMqAFlu9OtLtXUYkRvg7aXqfmmoOkCH8NPp3cE33e3iKeiyhtOV2ob5hBkt5oQqJNF8BmQV4Y3TWyVufhbdUCXC6XQa1UiIHnuG5R+M+oDogoDUgPzb65VtdLERERkfvVeZDk4eGBiIgIu/Hc3FwsW7YMP/zwA4YNGwYA+Oabb9ChQwfs3bsX/fr1q+2pEgEANp1Ow/m0PDx1YyvInNVdlbJ0sLM4m5qHvnHBAAA/Tdk/vxvbhYmP54zrhPYRvnhlzQnJc51lg1JLM0mRfmpJi2kAiPAzHxc10ExSks3377uH++CGFoHw8qzdX10+KmlwqvYoC4JGdAwXAyQADJCIiIgagTr/v3l8fDyioqLQsmVLTJ48GVeuXAEAHDp0CHq9HiNGjBCvbd++PZo1a4Y9e/Y4vZ9Op4NWq5X8IXKX63k6TPvuIN7feA6nr5X/syUIAnaWrj3q19LcnGHvpUz8cy4dQPmbiQZ6e9qN/XwoyeG1KTnm/Xosb9RvaF5WcmdZu+OsVK++sw4yHx/aEkPahtZ6gAQAvmrpa6qtyu0c7aFEREREDVudBkl9+/bF8uXLsWHDBixduhQJCQkYPHgw8vLykJqaCk9PTwQEBEieEx4ejtTUVKf3nD9/Pvz9/cU/sbGxNfxVUFPy3oaz4mNtUfmBR0pusVhu99btneGr9sDV7CIs3HQeQPlBUt+4IAR6KSGXlXWoc7T5bF6xHhn55j2WLF3Xfn6iP754oBd+nNZPbF3dUNckZZZ+bf1bBmPWbc5bb9e0aUNawstTgYm9zOWRkiBJxSCJiIiosanTcrvbbrtNfNy1a1f07dsXzZs3x+rVq6HRVG1h9qxZszBjxgzxWKvVMlAit1ix97KkgUCRvoIgqTTDExukQZtwX3SM9MO+hCzxfHlBUoCXJ9Y/NwRp2mJczirE9B+P4Hqezu46S+AU7O0prleSyWQY2clcwrrhpHnD0wYbJBWYv2brcra6EB2gweHZN0NVWmanVpZ9vuTnYJ0YERERNWx1Xm5nLSAgAG3btsWFCxcQERGBkpIS5OTkSK5JS0tzuIbJQqVSwc/PT/KHyB1mrz0pOS4sMeKHfVcwYP4WnE/Ls7veEiRZ2nDrjSbJ+YreXEf4q9EtNgChPuaSuev59kGS5TViAh1/qBDoZS7bS8goQInB5PCa+sySSQp2UH5Y29RKhcM1aNG13GmPiIiIal69CpLy8/Nx8eJFREZGolevXlAqldiypaxT17lz53DlyhX079+/DmdJTVGxg+5wqbnF+M//TiAltxjPrTrq8DwARJVmQe7r21xy3q+cTJI1y7oi60xSsd6IGauPYtUB8zolR2uYAKBn80CE+aqQVVCCv087L1OtjwRBwPbz1wEAwT61uy9SRbIL9eLjAC9mkoiIiBqbOg2SZs6cie3btyMxMRG7d+/GHXfcAYVCgXvvvRf+/v545JFHMGPGDGzbtg2HDh3C1KlT0b9/f3a2o1p3NbtIfGxZ+zPvzzPi2JlrWuQV6yXPuVYaJEWWZhru7BGNP6cPsrtPRSxBUl6xQQzWfj18FWsOJ2PrWXMTiAAnAZdSIceE0nU0W8+kV+r16ovVB5NwNtWcoYsKqNtyO1ttSve2Mm8sy/2QiIiIGps6XZN09epV3HvvvcjMzERoaCgGDRqEvXv3IjQ0FACwePFiyOVyTJgwATqdDiNHjsRnn31Wl1OmJmrXBXOXug6Rfugc5eewicLtn+7C1hdvBGBuXW3Z68iSSZLLZegU5Y+HB8Zh76VMTLyhcmvl/NQe8PSQo8RgwvU8HWKDvFCsl5bOBXg5L0frEu0PAEjILKjU61VFsd6Il345Di+lAu/c2aXaG6maTAL+79eyFuhdYwKqOUP3enV0B7QO88GtnZ2X/hIREVHDVadB0qpVq8o9r1ar8emnn+LTTz+tpRkR2csr1uON308BALpE+zltQX3punndj6eHHN/vvSyOB3lLS8VeH9vRpdeXyWQI81XhanYRBi/Yhh+n9YNtCFJeyVdciDcA87qkmrI/IQt/HEsBANzXtxm8VR5QKmRoHuxdpfsdSCxrcBHmq0LzoMpl3WpL82BvvHxr+7qeBhEREdWQOt9Mlqi+s7TxBoCRnSJw8HK202vzdQYEeXiKG7wCQPtI32rPwbrc796v9tqdd1ZuBwBRpY0jcgr10BtNNbLZaZHVmq1ruUV44vvDAIBL74yCvApZJcvX279lMH58jOW1REREVLvqVeMGovrodIp509i4EG8M7xBut7GoNcu6JMub/EcGxaFVqE+159A2vPx7RPg777CmsmpX7agBhTvorDrnnblW1umvxFi1jnqWTn6Rddz6m4iIiJomBklEFThVGiTd0SMaQFlmxpHVB5NQYjDhXGnDgVFdIt0yh2UP9S73fLNyytFUHnJYegvYrmVyF+v24mlWWbSq7M+Uka/Du+vNm/ZamlYQERER1SYGSUQVOJViLrfrFGXec6u87Man2y5iyjf7ka8zbzTrrk1QY4O80D02oJzzzgM3mUwGtYcCQM1lkqyDJEtXPwDo+dYmZBeUuHSvOaXrvwAGSURERFQ3GCQRlSMjX4fzafkAgG6lQUqUzeahb97eSXK8+2Km+DjMjW/y543vbDcWHaDBwwPj4FvBxrTq0pK7miu3K7uvZW8jix5vbar0fS5nFmDd8Wvi8chO7B5HREREtY+NG4icMJoE3DBvMwCgfYQvQko3NI0J1GDa4DiolQpMH94GSoUcr/92yu75fmoPtzZJcNTB7qfH+yEmsOLOb2qlAoC+VsrtHCnWG0vnALy7/iy+33sZy6f2xg0tgsRr/j1/HQs3nRePN88Ygth61tWOiIiImgZmkoisrNp/BX8cS4HeaMILPx0VxztE+omPZTIZXh3dES/e0k4Mgt5w0NZ7/fND3Dq3CD/70r3IctZHWbMEKEW10LjBEa3VRrufb7+IfJ0Bd32+ByaTAMDcFfDB/+7HsaQcAMDNHcPROqz6XQGJiIiIqoJBElGpi9fz8cqaE3j2xyP48t9L+L103x8AePqm1uU+d+rAONzSMVw89vJUIDqgcgFMZXko5Nj1yjDxeGKvmEpv2moJkmqi3C4jX4dFVhkgR7RF5jVa1mV5ALA3wVyauNeqRBEAgr2db45LREREVNMYJBGVOnG1bD+k/x1JFh93jvZD67CK23jHhZZtnFrevkXVER2gwb7/DMfSyT0x12YtVHlSc80tyWesPub2Ob30c8X3tLRG/3TbRcn4fV/tAwAkZko3us0obQFOREREVBe4Jomo1Pm0sv19LqTni48DvSqX1QjzLSuH86uhIAkAwv3UuM3F1uLZheYgpSaCj72Xsiq8RltsQF6xHh9tiXd4/kpWoeS4RbC3w+uIiIiIagMzSUSlEjIKHI5XtvTLOntUXrvuxsYkCBVek1esd7pnkiAIuJwpDZKeGVZ+eSMRERFRTWKQRAQgt0iP9SdTHZ6rbFbI3+q6Aa1D3DIvdxnSNrROX19bZBA74GlK10dZ5OsMuGxVbtcq1BsBlczeEREREdUEBklEMLefdqay64usW3RHB7hnE1l3efHmtgDg9mYSAFBxHsmcSbJ0wPP0kP7a+f1YChJLM0lTBrTA94/2dfcUiYiIiFzCNUnUpGmL9VDK5U5L7YDKZ5KsN3StbGvu2mJpVV5irJl9kiqiLdaLmSTbIGnz6TTx8ewxHSvdsY+IiIiopjBIoiarWG/E0AXbkF2oR6vSznSD24RgR3wGvD0VCPZR4UpWIUZ2iqjU/QK9y4KkMF9Vjcy5qpQKc+BhqIkgqRKpJG2RQQzQPG022E3JKQYATOodywCJiIiI6gUGSdRkJWQUiF3fLl43Z5IeG9ISD/VvgU7RfvDXKJFbpK90VijMV42lk3vCW+UBD0X9qmS1ZJIMxsoUx7nGYLIPvOaM7YgDidmIDtTgy38vIa9YD72xLJP02eSeeGrlYQBASo65PXkQ90YiIiKieoJBEjU5izadx7lULXq3CJKM+6o80CcuCCqPssYCXp6u/RNxtTV3bfEozSSVV26XVVACf43S5WyOWqmw61x3c6cITBkYh58OXAFgbgEultsp5BjVJRJ39IjG/44kI09n3mjWtqEDERERUV2pXx93E9WwY0k5+GhLPDaeSsO8P89Izn0yuackQGpMxEySyXEmKT4tD73mbcLzPx0Vx17+5Rhu+uAf5Bbpy723pQX4ggldxTE/tUfpf80liNmFJXZrkmwzR2oGSURERFRPMEiiJuVAouONTy+9MwpD67hNdk2yBElGkwCTg0Dp460XIAjAH8dSAJgbLaw+eBUJGQXYXk7nP4PRhGK9OfgZ1Kas7bl3aQYuOtBcqngsKQdJ2eYOdk6DJE8GSURERFQ/sNyOmpQTybkAAC9PBXzVHkjT6vDBxG6QN/KGAZZyOwDQm0xQyaUBSaq2WHJ8PClXfJxTWOLwnvk6Ay5dzxePQ3xUOPjaCHjIZeL3s0u0P6IDNEjOKcK51DwAZU0k7IIkD35mQ0RERPUDgyRqMvJ1BrHd9H+n9Ea/lsEwGE31rslCTbDuKGcwClDZ/MtPswqSCksMyMjXiceXS/cwsjV0wbb/b+++w6OqtjaAv1PSO6GEQKRDgBAgSAcpV6qgCCIW4CKoFPkgqFgQhetFERVEFKVdAQGxXwEvCtJEpNdAaKEEAmkQSJ0kk8ys748ww0waoMmck8n7ex4fM2dmwkpW9sxZs89eGylZRuv3d9VrUdXbvqufRqNBdV83XE3NRkrmrcfeuqSxcJHkwZkkIiIiUgkWSVQpHIy9gfXH4pFlNKF+NS+0r1fQtKEyFEgAoLeZKcsr1LzBmG/G1ZvZ1ttLdl6Ap03BcvjyzWK/p6VAAgAppQ+4Zf8oS+FlKdgKF0mFW4MTERERKYVFEjm9lMxcDF28B7f6C6B3syBoNM59eV1hOrsiSXA6MR0uOi0aVPPGTYPRrqHD/C0xds+NupIGk1nsvoeIfVGUV0prcUsTB0tR5VbCmqScfGU2uiUiIiIqjB/dktM7lZAB23P6OoGeygWjEI1GY52puWkwou/8P/CPub/DbBbk5JlKfa7JLMjNt39MlrH059iyzCRdudW4wbImqVqhDXfLZaNbIiIior+ARRI5NbNZcCI+ze6Yv4eLQtEoy9K8wbJ5KwCkZech+w5FEgBkFyqKCrcF9y68yMmGr0fBfZbZJg/X2+3Blz/TFq1C/NGspi/6hgXdxU9BREREVP54uR05rczcfDy04A+7xgNhtXzxgBO3+i5NQRtwEzJy8q3HUrJyrS28S1P4Uri951Psbr/zaFiJzw3wtL+srnPDQOvXPZpUR48m1e/47xMRERE5EoskcloboxKsBVINXzdsnNQVgYW6r1Umlsvcbtq09L6eWXx778IKX5L30nfH7G4/3DK4xOdammRYNKvpe1f/JhEREZFSWCSR0zqbVLAvz8MtgzHv8ZaVppNdSSxrkg7E3u5Wl5JphKfbnVtv32ndUmmNMEKD7Isidxe2+iYiIiJ1q9xnjeTUrt1qOd2ill+lL5AAIKJOAADgt5OJ1mOnEtKRW0oBZFlrZFskXS60b9KrfUNL/XfdXex/9x4skoiIiEjleOZIFdqVmwZ8vf9ysTMd1zIKiqTCXdQqqxa1/ADAbg3SH+eul7gmSasBgvzcizxn3dGr1q8PTn8Q47s3KPXf1Wg0sOkezpkkIiIiUj0WSVShzVx/Eq/9eBwT1hwucp9l89KqlXgdki3bfY4scoymErvbVfFyhdetTWVX7bmE9cfiAQC5t5o4hAb53PXv1vbftuyTRERERKRWPFuhCktEsOVUEgBg2+lkvPHf43b337i1eWnhTUsrK30xRVKeyVzieiN/T1e43Zr1+TU6EZPWHkG+zeO7Nbn7LoG2RZK2mDiIiIiI1IRFElVYSem5drfX7LsMY74Z+SYzDsTesHZus+zTU9npilmXZTSZrb/HIF93u/u8XHVFLo0z5JmsM0lu+ru/bE5XSmMHIiIiIrVhkUQVVuENTYGC2aMv/ryIoYv2WI/5uFXOzWMLK24m6crNbCz6/TwA4B9Nq6Nj/dt7GHm66uFe6NI4Q67JOpNUuCFDaYq71I+IiIhIrVgkUYVVXJGUkJaNdzeetjvmdRctriuDOxUqAZ6uWPt8B+vtKl6uRWeSjPl/aSaJ3QWJiIioIuGZC1VY6beKpPDafmhY3RsA8PqP9uuStBqeoFtYNpMtibe7/WWJdQI9i7TrNhj/2kzSsLYhAIBgP/c7PJKIiIhIeVysQRWWZSbJz8MFfh4uOJecidOJGXaPMYsSkamTTlt6UWPZE6lFLT8cv5qGIW1q48vdsXaPycjJx+aTBc0y7mUm6eXeTVDTzx2tQwLuLWgiIiIiBbBIogorPed2kdQ82A9/xFxXOCJ1K25NUnH3f/18B9w0GFE7wLPI5XbfHoyzfn2va5JGdqx798ESERERKYhFElVYhy7dBFCwdqZNneJnKAI82bTB4k5rkiyzbl5uenjdmlVyK1Qk/ffI7Y1k72UmiYiIiKgiYZFEFdbvZ64BAPqF1YR/McVQz9DqmP5QU0eHpVp3mkkqrkt34TVJtrjUi4iIiJwViySqkNKy85CRmw8AaBnih8ycfLv7w2v74bOnI4pcLlaZ3Wkm6aHwmkWOlXZJXXHdBYmIiIicAYskqlBy8kx4++eT1lmkQC9XeLrqobWZBunWuBpWjm6nVIiq5VLK1M/CpyLg6150Nq60IrNJDd8yiYuIiIhIbVgkUYXyc1QCvtp32Xq7XlUvAPYn85YubWSv8EySXqtB/q2FSB6uxRdQJc0ktazth2bBLJKIiIjIOfFsshIQEVy5mY3aAR7QFLfwpAJISs/BsMV7EJtisB7zctXhrYHNrLe1moLmA4/dX1uJEFWv8JokF50W+eZbex6V0IQhK9dU7PFezWqUbXBEREREKsIiqRL46ehVTPnmGKb2aYIXejRUOpy/ZM6vp+0KpA8eC0eP0Oqo6u1mPfbz/3VFQlo2ejSprkSIqld4JslFp4FlWZFbCTNGJTV78OJsHRERETkx9qeqBN7/9QwA4INNZ7DnfIrC0fw1Ry6n2t1+sGkNuwIJAJoF++IfTTnDURJ9oc1kXfW3b5fUzntQ61oYHFEL84e1sjvu58HW6kREROS8WCRVAp6ut0+An1y6F+NXH1Iwmnu3KToRF69nWW+3q1cFAV6uCkZUMRWdSbItkkpak6TDvMdb4ZFWwXbPD/Dk75+IiIicF4ukSuCmwb5V8y8nEiEiCkVz72ZvPGV3u3ODqgpFUrG56OyLJNtL5u60MaxGo7HbM8mPm/QSERGRE+PCAieVZzLj8x3nkW8W3MgyFrk/J88MD9eKsYeQt7v9n6mXW8WIW20KzyRVsZmNK2lNki13Fx0yb+1N5c/L7YiIiMiJsUhyUn/EXMO8386WeH+WMV/1RdK7G0/h1xOJuHzDYHe8aU22nv4rbPeSeqlXYxy7kma9XVJ3O1u2M1H+vNyOiIiInBgvt3NScTeyS73fUEJrZzVZsvOCtUCqHeCBVWPaYcbAZujUIFDhyCom2wssR3WuC2+bGbm7mUnKM93+Dr7u/HyFiIiInBfPdJxUYnpOqfdnGfMdFMlfU3jN1HNd66Nro2ro2qiaQhFVfGab36mLTotAm+6Arro7F0m2OdHfxeOJiIiIKioWSU4qMa30Ismg8iIpN99sd/uh8JoKReI8bDvYuei0eLhlMHacScYznetBW8J+SLbMFajZBxEREdHfwSLJCaXn5GHXueulPiZLpZfbpRqM+GzHebtjPQttGkt/Te0AT7zQowF83F2g02rQMsQfW1/qrnRYRERERKrDIsnJmMyCeZvP4lpGrt3xH8Z3xP+iErH7/HWcTsxAWnYePt0Wg66NqqFliL8ywRZjwprD2F1ow9svRrVVKBrnM7VP6F9+LueRiIiIqLLgwgIn88Z/j2PF7lgAQJMaPgCAJ9uFoE2dKnhrYDNU8ymYkVm26yI+3HwWjyz8U6lQ7aRk5uIfc3cUKZBIPcxmlklERERUOXAmyYnczDLi6wNx1tuv9w9Fw+reqOXvYT1Ww9cdAHAsLtXR4ZXqx8NXcf5altJhUClYIhEREVFlwZkkJxKbYl9ktKjlh9oBntDY7I8TEuBZ5HkmFcwQFG5BPSSitt3/SXns20BERESVBWeSnMjV1Nt7Iz3V/j67Fs8WIVU8ihxLycxF9VszTErJyLHvtje6S11Mf6gp/DxcFIqICivclp2IiIjIWXEmyYlcuVlQJA0Ir4lZj4QV+5gqXq5FjiUXavKghJtZRrvbPm4uCPByvavW1OQYQ9oUzOq1rRugcCRERERE5YszSRWcySxYvPM8GlX3wXu/nAYA1K/mXWJx4eGiK3IsPSevXGO8G6nZ9jF4uRWNk5Q1rX9TtKkTgG6NuaEvEREROTcWSRVYRk4ePt1+Dot/v2B3vEE1rxKf4+FaTJGUrezGskfjUvH9oSt2x7zd+aepNu4uOjzSqpbSYRARERGVO56JVmDDl+3DsStpRY6H1fIr8TmexRVJCs8kzd9ytsgxNz1nkoiIiIhIGSySKqis3PxiC6SPn2iFBtW8S3yee3GX22WXT5G05WQSzl3LxNgH6tt12LN1/lomfj97DQDwWJvaeP6B+nBngURERERECmKRVEFdKGZPobBavne8HKq4NUkfb43Bs13rl1lsZrNgzMoD2H6moPhpVtMXD5SwjuXo5VSIAM2DffHBY+ElFlNERERERI7CIqmCOnctw/r1fVU8sfb5DqhWTMvvwopbk5SRkw+TWaAro05yx6+mWQskAIhJziyxSEpMzwEANAnyYYFERERERKqgmhbg7733HjQaDSIjI63HcnJy8MILLyAwMBDe3t4YMmQIkpKSlAtSJVINRkz55hgA4MGmNfDTC51Ry98Drvo7p7OkS9luGozFHgeAhLRsnIxPv+v4Hln4p93tSylFZ70skm8VSUEK79NERERERGShiiLpwIEDWLx4McLDw+2OT5kyBRs2bMB3332H33//HfHx8Rg8eLBCUarHB5vOWL9uX69KsXsflcS2NXh1Hzf4exZs1nojq2iRlG8yo+/8neg4exsGfrqr1GIHAGKSMqzri2xl5hbtnnczy4ghn+/Gyj2XAABBfiySiIiIiEgdFC+SMjMz8fTTT2Pp0qUICLi9SWVaWhr+85//YN68eejZsyfatGmD5cuXY/fu3di7d6+CEStLRPDriUQAgKtOi0Gt/3pLZpNZEHirwLqeWXRD2fjUHJxOzLA+dmcxBZCtXh/txD+/2F/keLbRVOTY4p0XcOjSTevtFqV05CMiIiIiciTFi6QXXngBDz30EB588EG744cOHUJeXp7d8dDQUNx3333Ys2dPid8vNzcX6enpdv85k//suoiULCPcXbQ48a8+qOZz53VIJTHmmxF4ax1TSmbRmaSMXPuudycTMoo8xiLfZC5y7KHwmgAAQzFF0o2s20VZLX8PtKztf1cxExERERGVN0WLpK+//hqHDx/G7Nmzi9yXmJgIV1dX+Pv72x2vUaMGEhMTS/yes2fPhp+fn/W/kJCQsg77b0sz5CEhLRsZOXm4mpp918+7lpGLdzaeAgA80KjaXa1BKk12ngm1/D0AFL9uKDPH/jK50vZTSil0uZ6nqw79wwqKpOJmkjS4fdnfoNbBdpcBEhEREREpSbHudnFxcZg8eTJ+++03uLuX3XqU119/HS+++KL1dnp6uqoKJZNZ8NAnf+DKzdvF0d7X/3FXa3IS03IgUvD1nCHhpT/4LuSbBaFBPgCAU4kZeO+X09h/MQXhtf3RNywIWYXWEpW2n1LSrQYMFr7uLvB0K2gSYcjLx+UUAzxcddaZL0Pe7cKpug/XIxERERGReig2k3To0CEkJycjIiICer0eer0ev//+OxYsWAC9Xo8aNWrAaDQiNTXV7nlJSUkICgoq8fu6ubnB19fX7j812XIqya5AAoA/z12/q+dev3WJWvNgXwTcQ7OG0tSr6gUAuHIzG4t+P4/Dl1OxYncsnliyt0jDhfScog0YLJLS7dc0ZeeZ4HlrT6YTV9PxwAfbMcim651tUfVIq+C//XMQEREREZUVxWaS/vGPf+D48eN2x5555hmEhobi1VdfRUhICFxcXLB161YMGTIEAHDmzBlcvnwZHTt2VCLkv01E8Om2c0WO5+YXXc9THMu6ocC72A/pbnm6FvwJ5OYVvSQu41ZRVMXLFTeyjKXOJCUWmkmq5uNm/d4WV1OzkW00wcNVZ239/dWz7eHvWTYFHxERERFRWVCsSPLx8UFYWJjdMS8vLwQGBlqPjxkzBi+++CKqVKkCX19f/N///R86duyIDh06KBHy3zZ381kcv5pW5HhC2t2tS4pJKmicULWMZpEAwM2lYDIxp5gi6beTBXtS1fL3wI0sI9IKFUkigrNJmbhy04CEW2urqnq7olF1H7w7uAVMZinyPW8ajHB3cbcWVbUCPMrsZyEiIiIiKguKFUl346OPPoJWq8WQIUOQm5uLPn364LPPPlM6rL8kMS0Hi3eeBwBoNYBt/XAto2j77cJOxqdj8c4LAIAG1b3/VixfPdcekV8fxaxBYdbNZYu7lM7SortJkA+OX03DjSwjYpIy0KhGwTqmX04kYsKawwBg3atpVKe6mNizEYDii7+bBiO83PTIySuYPavBTWSJiIiISGVUVSTt2LHD7ra7uzsWLlyIhQsXKhNQGQryc8eXo9tj66kkvNynCXacuYaZ66ORmJ6Dm4ai7bcLW3fsqvXr4e3r/K1YOjWoiv1vFLRWP3trdiq1mBgsa5L+/UgYziVn4mhcKvZeSLEWSbb7HFk2o7VtwlDDxx21/D3sOvilGvLg614wI+XuooX7rXVLRERERERqofg+SZVJxwaBmD6gGdxddOgbFoTpA5oCAG4aSl7rY3H8SsFleh88Fg4/T5cyi8ntVhvxYq6MAwA0q+kLD1edtQuebayFL78DAF+P23W3Vqsp0pThWkYusm9d2ld4zRIRERERkRqwSFJQwK2GBTezSp5JyskzITffZO0GZ9nXqKzcaSbH/1ZBZmmuMO+3s9Z4U4sp7rzd7Au4pzvUQVWbRhMbjsVbN5f14CwSEREREakQP8pXkKUAKWkmKS07D73m/Q5vNz0uXC/Y7LV6Ga/hcbvDhrSergWFjI/77T+Vr/Zfxgs9GuLi9cwij7d9HFBQ1O16tQcOX76Jp5buwx/nrsN0a7Mny/cmIiIiIlITFkkKsswkpRqMEBFoNBq7+3+OikdyRi6SbRo7VPctu/bfAOCmL71Q8bh1SZztpXXxqdmIScrA+WtZRR5fuEgCCmarOtQLhK+7Huk5+dhx5hoAFklEREREpE683E5BliIp3yxFNm4FgIOxN+1u+3m4wMetbOvaO84k3bokrsmtZg1AwUaxv5+9VuzjvYspkoCC9Uk1/ewvFfRgkUREREREKsQiSUEerjprkZJqyMPsX07hqaV7rXsWRcfb76n070FhRWab/i6ttvTv5+lWUMgMal0L9at6AShYJ2Xbsc6Wr3vJTSWqFNrfiY0biIiIiEiNWCQpzFI43MgyYvHvF7D7fArWHb2Km1lGxCTbr/l5uGVwcd+iTP3nn/fbNVSwXBKn02owrnsDAEC20YSLt9ZIWbreWZQ2M+Xuwj83IiIiIlI/fpSvsIS0gq51jyz803rsdGIGqvm4QQQI8HRBSBVPDImoXe6xfP18B3SoH4hAb1dcuVkwU2Q722MpngxGExJvddurV9ULpxML9lr6blzHUme6ouPT7W5n3+pyR0RERESkJiySVCjbaML1jII2261C/LH8mXbl+u+tGtMO55Mz0aF+IICC2S1LkWQ7M2QpktJz8hF3wwAAeKl3EwBAvxY10bZulVL/nREd6mDub2ett++vG1B2PwQRERERURnh9U8KWzry/iLHDEYTbhgKiqSAQut4ykPXRtUwqnM96+0bNvs22TZXsHwdd8MAswAuOg0aVPPC58Pb3NWlgM93q4929QoKqTf6N0Xkg43L6kcgIiIiIioznElSWK9mNfD9uI54bNEe6zGD0WTdsLWKZ/kXSYWl2ezb1D+spvVry8azlk58nq76e2ok4abX4duxHYttd05EREREpBacSVKB+6p42t3OzsvHzpjrABwzk1RY05q+AAAfN73dv194XyOvv9jCmwUSEREREakZZ5JUoKq3/Qax8ak51u5xhdtmO8IHQ8Mxf0sMxnarb3fctusdwH2OiIiIiMg5sUhSgcJ7FVkKJADoFxbk6HBQJ9ALHw1rVeS4V6GNbLnPERERERE5I15upxLF7S/UuIY3/BVYk1SSQC9X+253nEkiIiIiIifEIkklfo18AEPb2O+FZFDZPkJarQYhNuunCq9RIiIiIiJyBiySVKJeVS9M6NHQ7tiHQ1sqFE3JGlbztn7txcvtiIiIiMgJsUhSkSBfd7vbls1d1WTo/bdnu3i5HRERERE5IxZJKuLhqsPgiFpKh1Gqtrc2gwUA12LWURERERERVXS8XkplZj7cHIZcEx5uFax0KMXydXdB9ybVcOJqGsZ3a6B0OEREREREZU4jIqJ0EOUpPT0dfn5+SEtLg6+vr9LhOAURgdFkhpuel9sRERERUcVxt7UBr5eie6bRaFggEREREZHTYpFERERERERkg0USERERERGRDRZJRERERERENlgkERERERER2WCRREREREREZINFEhERERERkQ0WSURERERERDZYJBEREREREdlgkURERERERGSDRRIREREREZENFklEREREREQ2WCQRERERERHZYJFERERERERkg0USERERERGRDRZJRERERERENlgkERERERER2WCRREREREREZEOvdADlTUQAAOnp6QpHQkRERERESrLUBJYaoSROXyRlZGQAAEJCQhSOhIiIiIiI1CAjIwN+fn4l3q+RO5VRFZzZbEZ8fDx8fHyg0WgUjSU9PR0hISGIi4uDr6+vorEQ86E2zIe6MB/qwnyoC/OhLsyHuqg9HyKCjIwMBAcHQ6steeWR088kabVa1K5dW+kw7Pj6+qryj6ayYj7UhflQF+ZDXZgPdWE+1IX5UBc156O0GSQLNm4gIiIiIiKywSKJiIiIiIjIBoskB3Jzc8OMGTPg5uamdCgE5kNtmA91YT7UhflQF+ZDXZgPdXGWfDh94wYiIiIiIqJ7wZkkIiIiIiIiGyySiIiIiIiIbLBIIiIiIiIissEiiYiIiIiIyAaLJCfDPhxEpTObzUqHQEREFRDfPyoXFklOwmAwAAA0Gg0LJRViTpR14sQJPP744wAArZYve0qLj4/Hvn37lA6DiOiO+P5ReTHbTiA6Ohrt2rXD119/DYCFktJiY2OxcuVKfPLJJ9i1axeAgpzwEyhlHDt2DN27d8f333+Pn3/+GQCLViVFRUWhc+fO2LZtG+Li4pQOp9K7ePEi5s+fj5kzZ+L7779XOhwqBt87lMP3D/VJTU112L+ld9i/ROVmzZo1iI2NxXvvvQez2YynnnrKWihpNBqlw6tUTpw4gW7duiE8PBzHjx9HcHAwQkJCsG7dOuj1epjNZn4S5UDHjh1Dhw4dMHbsWOzfvx/fffcdBgwYwHGhkPPnz6NXr154+umnMXXqVOj19m9BHB+OFRUVhT59+uD+++/H2bNnERgYCJ1Oh0cffVTp0Cqt8+fP48cff4TBYEDNmjXx3HPPcUwohO8f6nP69Gm0aNECb731Ft58800AKNdzXY48J+Dh4YGWLVuie/fu+Pe//401a9YAKJi9MJlMCkdXeRgMBowbNw7Dhg3D1q1bERMTg+nTpyM2NhZt2rSBwWCAVqtlThzkyJEj6NixIyIjIzF//ny8+OKL+Omnn7Bjxw6lQ6u0vvnmG3Tp0gXz5s2DVqvFwoULMWPGDLz99tvIy8vjyaADnT17Fv3798eYMWOwfv16/PnnnzAYDEhISFA6tErrxIkTaNeuHQ4cOIAdO3bgk08+sX7gxtkLx+L7hzrt378fGo0Gs2bNwhtvvFHu/x7fkZxAt27d0KZNG0ycOBEdOnTAO++8g19++QX//ve/sWfPHk7VO4jRaERGRgZ69eoFrVaLgIAADB48GKtWrYLZbEb37t0BADqdjm945ezatWsYPnw4XnjhBcyePRsAEB4ejjp16uD3338HwEtYlBAXF4eGDRsCADp27IjvvvsOW7ZswcqVKxEaGooLFy4AYG7Km9FoxJIlS9C7d2+89dZbAICqVauiRYsWOH78OCZPnow5c+YoHGXlkp6ejueeew6jR4/Gt99+i02bNmHJkiU4e/YsRowYgT179gDgpV6OwPcP9QoMDESfPn2wevVqfPjhh3jzzTets0g3b94s83+PRZITcHNzw5YtWxAcHIxXXnnFejnLjBkz0LhxY2i1Wr6wOoCvry80Gg22bt1qPabX6xEREYFFixYhIyMDr776KgBwur6cubq6YvHixfjggw+sxxo3boxBgwZh/vz5SExM5KyFQo4cOYJvvvkGgYGBWL9+PbZs2YK9e/eidu3aGDx4MAAuji5vOp0Ojz/+OCZNmgRXV1doNBq88847+OqrryAiSEhIwJdffsnL7hwoNTUVGRkZGDZsGICC17CwsDC0bdsWGRkZGD58ODIyMrjm2AFcXV2xZMkSvn+oUEREBDIyMtCtWzfMnTsXs2fPxvvvv4+XXnoJS5cuRV5eXpn+e8xyBWa5bCs0NBRVqlSBi4sLmjZtigsXLsBoNKJu3br4448/APCk3BFEBEOGDMHhw4etCzwtOnTogP79++PgwYMwGo0KRVh5+Pn5oXPnztbblk/9/vnPf6J27dpYsWIFRIQnGw729NNPw2Aw4OOPP0ZwcDB8fX3h5uaGatWqYe7cuUhJScH+/fuVDtPp6XQ6tGrVCq1atQJQsA5m0aJFWLduHRYtWoRvv/0WL774Io4dO4bTp08rG2wl4eLiguzsbGuzHwC4fv06bty4gSVLlsDNzY0fsjmA2Wzm+4eKaTQaxMfHIzk5GRMnTsTq1asxbdo0LFiwAMOGDYOLi0uZ5oVFUgWSkJCAHTt2YNeuXUhMTIROpwNQcELo5uaGAwcOYNSoUTh8+DC++OIL9O/fHy+88AJ++OEHhSN3TvHx8fj555/x9ddfIzY2Fjqdzto0Y+HChdi2bZv1sTqdDm3atEFcXBwyMjIUjNp5JScnY9euXdi6dSsSEhLsOgpaPvWrX78+mjVrhh9++AEajYafypajGzdu4Pjx4zh+/Lj1MojGjRujcePGOHjwIC5evAjgdm48PDzg4+MDT09PxWJ2ZoXz4erqaj3Ra9CgAY4ePYoBAwZYx0xgYCDc3Nzg7++vbOCVhJ+fH3r27IkNGzbghRdewFdffYXWrVujR48e+Mc//oFBgwbh8uXLSofptHJzcwEUX4Dy/UM9goKCEB4ebt32Zv369fDx8QEArFq1CkAZf4ggVCEcO3ZM6tSpIw0bNpTg4GAJCgqS77//XgwGg4iIPPHEE+Lj4yP16tWTI0eOiIjIkSNHJDIyUs6dO6dg5M7p2LFj0qhRI2nVqpXUr19fQkND5fz58yIiEh0dLeHh4dKnTx/5z3/+IyIiRqNRIiMjpVu3bpKZmalk6E4pKipKwsLCpFmzZlK7dm1p0aKFnDlzxu4xJpNJRETOnDkjVapUkc8//1yJUCuFqKgoiYiIkCZNmkhISIg8/PDDcvHiRRERuXz5sjz22GPi7u4uzz77rJjNZrl+/bq8/fbb0qpVK0lKSlI2eCdUXD4uXbokIiJms9nu/xYvvfSS9OvXT9LT0x0eb2WQk5MjIgW/d8trU2xsrLzyyivSqlUradu2rcycOdP6+Lfffls6dOggeXl5isTrzE6ePCldunSR7du3i0jRsSDC9w9Hs5zbFpeLUaNGyYcffijPPfecBAUFyb59+2TFihWi0Wjk3XffLdM4WCRVAMnJydK4cWN59dVXJT4+Xg4ePChTpkwRnU4ns2fPFhGRP//8Uzp27CgHDhywe67lhZjKzqlTp6RatWry+uuvS0JCguzatUuaN28uW7ZssT7m5MmTMmTIEGncuLHUqlVLevToIQEBAdYClsrO2bNnpUaNGvLqq6/KxYsXZfv27fLwww/L+PHjJS8vr8iLbEZGhnTo0EFGjBghubm5CkXtvM6cOSPVqlWTqVOnyvHjx2XlypXSs2dP+eCDD6wnGlevXpXXXntNateuLb6+vhIRESFBQUFy+PBhhaN3PiXl48MPPxSRoichKSkp8vrrr0tgYKBERUUpEbLTK+6kPD8/X0REcnNzJT8/X5KTk+2eM3z4cHnuueesY4jKxsWLF6Vhw4YSGBgoERERsmPHDhEp/uRchO8fjhAdHS116tSRdevWiUhBLmw/TPj000/F09NT6tevb33PMBqNsnLlSjl58mSZxsIiqQK4cOGCNGnSRA4ePGh3/KOPPhKNRiPz588XEbH7xK+kAU5/T3p6ujz44IMyYcIEu+O9evWSjz76SBYuXCj79+8XEZFr167JoUOHZNasWbJ8+XKJiYlRImSnZjAYZPTo0TJy5Ei7v/m33npLWrZsWeLzfvnlFzl16pQDIqxcMjMz5cknn5QxY8bYHR81apR06dLF7pjBYJCkpCRZvny5bNq0SWJjYx0ZaqVwL/kQEfntt9/k+eefl/r16/MDnXJS2kl5cQXQkSNH5LXXXhM/Pz85ceKEo8N1ajk5OTJx4kQZPHiwrF27Vh5//HEJDw+/Y6HE94/yc+nSJWnevLlUq1ZNqlatKuvXrxeR24WSiMj58+dl5MiRDnmN4mayFUB6ejouXrxovfY1Ly8PLi4uiIyMRE5ODl5++WV06dIFbdq0sT6HCzvLh4+PD8aNG4fq1atbj73zzjvYtm2bdR+kiRMn4ocffsCjjz6KqlWrIiIiQsGInd99992H6tWrW9cgabVaDBgwAN9++y3S0tLg7e1tXb9nMpmg1WrRt29fhaN2Tjk5OQgKCsL9998PoOD3rdPpMHjwYMyePRv5+fnQaDTQ6XTw8PCAh4cHRo0apWzQTuxu8qHVaq1rLsLDw5GcnIxp06ahTp06SobulHJzczF37lyEh4dj6NCh+O9//4tJkyZhwYIF6NatW5HHZ2dn49dff8X333+PHTt2oHnz5gpE7bzc3NzQv39/XLlyBU888QTq1q2LefPmlZgTubVpKd8/ykdeXp51O4i5c+fip59+wogRI7Bq1SoMHDgQIoL8/HzUr18fy5Ytg4uLS/kHVe5lGJWJhx9+WNq3b2+9Xt9yGZHZbJYBAwbIyJEjxWg0cgbJAWx/x7/++qs0atRI1q9fLxkZGSIi8vzzz0toaKhkZGQwHw5w4cIF69eW3/eBAwekQYMGkpaWZj127do1ReKrbGwv+bX87jdu3CgtW7aU3Nxc67EbN24oEl9lc7f5uH79uogIL+cqZxs3bpQlS5aIiMiePXtk6NChdrMXhVlmXMkxdu3aVWRGKScnp8wv46Li7dy5U1atWiUiBbNK48aNEz8/v2JnlByB3e0qiLFjx8LFxQVTp07F9evXodfrrZ9qBAUF4fr163BxceEMkgPY/o47dOiADRs2YODAgfD29gYA1KpVC4GBgfD29mY+HKBevXoAbn/KJyLIyspCTk6OdQ+YqVOnIjw8HLm5uexGVM4ssxaWfABAVlYWMjMzodPpoNFoMH36dPTt2xdGo5H5KGd3m4/+/fvDaDTyNauc9evXD8899xyAgvePyZMnIzQ0FJMmTbJuVJqbm4tTp04BKOj6aHvlApUPS1fHzp07Y9KkSdacbN26FVOnTkWPHj3YmdYBunbtiuHDhwMouErklVdewZNPPokRI0Zgw4YN0Gg0MBqN2L17N7Kysso9Hl5uV0H069cP58+fx5dffonx48fj008/RY0aNQAUtKf09/eH0WhkoeQAticbfn5+8PPzs7s/KSkJoaGhzIeDWC6x02g0yM/Ph16vh7e3N1xcXODm5obp06dj8eLF+O233+Dm5qZ0uE7PNh+Wy7t8fX3h4eEBnU6H6dOnY968edi5cydcXV2VDtfpMR/qZMmLZT+eBQsWYNKkSZg3bx7WrVuHb7/9FjExMdb2xlQ+LO/nWq3WupTBkpNPPvkEffr0gY+PDzZt2sRcOJAlL/Xq1cPUqVMBACNGjMCKFSuwc+dOrF69GqdOnYKXl1e5xsEiSeUsb2q5ubn4v//7P/j7+2PZsmVo3rw5+vfvj7S0NGzbtg27d+/mG5wDWPJx48YNVKlSxe6+rKwszJkzB9988w3++OMP5sMBLPm4du0aqlWrBr2+4CXN1dUVAQEBGD9+PJYvX47du3fbrdmj8lF4fFjWgrm5uSEgIAAvvvgiFi5cyHw4CPOhPjwpV4/C48OyEalGo0Hnzp3x3nvvwcfHB7t27eJ6MAex/VDHMj7q16+PV155BQAwePBg+Pn5YdOmTQgMDCz3eHi5nYoUvuzEMoAvXbqEOnXq4Mcff8SIESOwfPlyREZGAgDq1q2Lffv2oUWLFgpEXLnY5iMsLAxr1qyx3vfHH3/ghRdewH/+8x/89ttvaNq0qYKRVg75+fnWfNSqVQsLFy603peSkoKjR49izZo12Lt3L08AHaC08ZGSkoJdu3Zh6dKl2LNnD/PhAMyH+phMJmg0Gty4cQMArCflQMFlXllZWdaT8nbt2ikZqtMraXxYZlxnz56Nbdu2Yfv27SyQHMTSWOnatWsAYNeYoW7durh58yb8/Pzw559/Om58OGz1E5UoJSXF+nXhBWmXL1+W4OBgGTduHDeRc5CLFy/KRx99JC+99JJ89913dvfZ5sM2V/v27ZMPP/ywyAam9PedO3dOZs6cKSNHjpRPP/1U0tLSrPddvXpVgoKCZPz48XYLzhMSEmTEiBFsmVsO/sr4OHbsmPTr10+io6MdHa7TYz4qBss+SLGxsVKzZk1ZvXq13X3vvvuueHp6svV6Gfsr40OkoM03mzU4juX8NjY2VlxcXOTTTz+13mcymWTRokXi5+cnhw4dcmhcLJIUFh0dLXq9XiZPnmw9ZjtYp02bJlOmTLE7xo5p5ScqKkpCQkKkZ8+e0qFDB9FoNDJ37lwRKfi9F5cPCxaxZS8qKkqCg4Olf//+MmDAANHr9TJlyhQRKcjHp59+KtOnTy82H1lZWY4O1+n9nfFx8+ZNB0fr/JgP9eFJuXr8nfFB5eOvfOgpUtAJ8ty5c44Ol0WSkq5evSrt2rWTiIgI8fLyksjISOt9lkHLE2/HiY2NlQYNGsgrr7xi/dRv8eLFEhQUZB2czIfjxMXFSdOmTeXll1+2Htu0aZPdp61sV+w4f3V88ASkfDAf6sOTcvXg+7n6/J0PPZXCxg0KERFs374dderUQWRkJC5duoRnnnkGGo0G8+bNs+vUReXPbDbjq6++QuPGjTFt2jTrAucOHTpAr9db24MyH44hItiwYQPq1KmDl19+GUDBGqTw8HAEBwcjJycHAKybYFL5+jvjg90dyx7zoT6XLl3Co48+iieffBLvvvsudDodlixZghkzZuCRRx5BgwYN8K9//YvvIQ7A93P1uXLlCoYNG4annnoKH3zwAQBg8+bNePTRRzFy5Ei0atUK48ePV917Ov9CFKLRaNC1a1f4+PigU6dO6NSpE0QEo0ePhojgo48+stsLicqXVqtFx44dkZ6ebtfSu3nz5tDpdEhISECjRo0UjLBy0Wg0aN++PeLi4qyt7vV6PYKCgqDX65GQkKBwhJULx4e6MB/qwpNydeH4UJeK/KEnR6yC7rvvPoSEhFhvDxs2DBqNxm5GyWQy4ZtvvkHLli0RFhamYLTOr3v37ujevTsA+72QLK1aLbZt24bw8HBUrVpViTCdntzq9hQREYGIiAjrMdt85ObmWh+/bt06NGjQgOOjnHF8qAvzoR48KVcfjg/1qMgferJIcqC4uDicOnUK165dQ69eveDv7w9XV1frZXU6nQ5Dhw4FADzzzDMACloifv755zh37pySoTslg8EAFxcXuzaTFpbLHXNycqDVauHr6wsAmDZtGubMmYO4uDhHh+v00tLSoNVq4ePjU6QdviUfWq0WHh4e8Pf3B1CQjw8++IDjoxxwfKgL86FuPClXFseHOlX0Dz3VN7flpKKiotC2bVu8/vrrGD16NHr27Inp06cjNTUVer0eJpMJQEF1/fjjj2PZsmWYP38+Vq1ahd27d+O+++5T+CdwLidPnsTAgQOxadMm5OfnF/sYrVYLnU4HEYFer8e///1vLFiwAHv37kVwcLCDI3ZuJ0+eRK9evfDFF18gPT292EtMdTqdXbFkyceePXtQp04dBaJ2Xhwf6sJ8qI/BYLArfmxZXqeys7OLnJT36tULRqPRkaE6PY4P9UlLS0NGRkax7+WW8WE2m4t86PnYY4+paxNlx/aJqJxSU1MlIiJCXnrpJUlJSZHs7Gx5/fXXpVOnTvLII49Y90mydGAxmUwyZswY8fX1ZUvQchAbGyuhoaGi0+mkbt26snnz5hK73JjNZmnVqpW0a9dOXF1d5eDBgw6O1vldunRJwsPDpUqVKtK8eXNZvHixXVvQwjp27ChNmjQRNzc3OXDggAMjrRw4PtSF+VCf6Oho6dmzp2zYsKHEXJhMJjEYDFK/fn05fPiwvP322+Ll5SX79+93cLTOjeNDfaKjo6Vt27Yyf/78Et/LzWazmM1madmypWzatMk6PtT2ns4iyQEuXrwo9evXlx07dliP5ebmyhdffCEdO3aUp59+WtLT00Wk4A9n48aNUq9ePdX9sTiDvLw8+eijj2TQoEFy6dIl6du3rwQFBRX7wmoymeTatWvi4+MjLi4uEhUVpVDUzstkMsmyZcukf//+cubMGRkzZow0bNjQrlCytAM1mUySnp4udevWFZ1OJ8ePH1cydKfE8aEuzIf68KRcPTg+1MfZPvRkkeQA165dk7CwMPnkk09ExP6kb+HChRIRESFffvml9fGJiYmSkJCgSKzOzmw2y65du+SHH36wHuvTp4/UrFlTNm/eLEaj0e6xIiKrV6+WEydOODzWyuLs2bOyfv16623bQik1NdV63JKPrVu38g2unHB8qAvzoS48KVcXjg91ccYPPVkkOYDRaJQhQ4ZIp06dJDY2tsj9vXv3loceekiByCqn4jYg7dOnj/XNznLZ44YNGyQzM9PR4ZFIsS+uK1askBs3bigcmfPj+FAX5kM9eFKuPhwf6uJsH3qySCpnlj+EpKQkqVGjhjz88MOSlJRkt6PwJ598Iu3bt5fs7Gylwqy0bD/969u3r9SsWVM2btwozz//vDRs2FDi4+MVjK7ysbyhidx+cf38889lzJgxEhgYWOyHDFR+OD7UhflQHk/K1YvjQ50q8oeeLJIcIDc3V0REjhw5IjVq1JBevXrJ/v37rS+mY8aMkf79+1sfR+XLtkAVEbtP//r16ycajUa8vLx4/biDFD7psM3Hs88+KxqNRry9veXQoUOODq1S4vhQF+ZDvXhSrjyOD/Vyhg892QK8jEmh/V1MJhNcXV2RkpKCGjVqYPfu3bh06RLGjRuHiIgIPProo/j2228xe/ZsuLq6KhS18youHxqNBjdv3kRMTAwAwMXFxdo2tEmTJggICMD+/fvRpk0bh8db2ZhMJmi1Wly7dg27d+8GUJAPS0t8Ly8vBAQEYN++fdY9Fqj8cHwoi69X6mebI71eb20D/ssvv6BVq1Z46KGHsGbNGnz99deoWbOmUmFWChwf6mM2m61f63Q66/hYtmwZunfvjgkTJuCbb77B5s2bK8TWHSySyojlpM7CbDYjPz8fOp0OsbGxCA8Px9atW1G/fn0cOHAAkZGR6NWrF9q2bYsDBw4gPDxcocidU2n5uHTpEsLCwnDo0CHr/Xq9HsuWLcPHH3+MzZs3o1mzZo4OuVKwPcEwmUzWfLRq1QpRUVHW+3Q6HdauXYsFCxYwHw4gIhwfCrLsm2PZVJH5UIfC7yM8KVdGTEwMjh49anfM9v2D48PxbIshC6f80FPBWSyncfLkSRk/frw88sgj8tprr9lN68bFxYmfn58899xzYjabi72emcrWveTDVkxMjFy8eNHB0Tq/uLi4Ei91iIuLE29vbxk7dmyRfOTk5Mjly5cdEWKlEhMTI++8846MHDlSli5davc3HxcXJ/7+/hwfDnTq1CkZNWqU9O7dW0aOHGnX5Yn5UIZtd1nLJUOW/8fGxkpwcLCsXbvW7jlLly4VjUbDy7rK2NGjR0Wj0chnn31W5L7Lly9zfCjg3Llz8vHHH0tycrL1WOHx8fnnn9s956uvvqqQ44MzSX/T6dOn0aFDBxgMBuj1ehw6dAhdunTBqlWrAAAHDx7Es88+i0WLFkGj0UCr5a+8PN1rPmw1bNgQdevWVSBq53XmzBncf//9GD9+PHbt2lXk/oSEBEyYMAGfffZZkXy4ubkhJCTEUaFWCidOnECnTp1w7NgxxMTEYMmSJZgzZw4yMzMhIjh48CDGjBmDxYsXc3w4wPHjx9GpUye4ubmhQYMGSEpKwrx585CXlweTyYT9+/dj9OjRfL1yoPPnzyM4OBj9+/cHUDCrbZnVu3LlivWSumHDhtk9r3v37rhw4QJnkMrQsWPH0KlTJ7zyyisYP358kfst44OvV44TFRWF9u3b49KlS7h+/TqAglklnU6HuLg4hIWFYeDAgRg7dqzd8wYPHoxLly5VvPGhdJVW0U2YMEEGDRpkvZ2UlCRvvvmm6HQ6WbRokYgU3w2HygfzoR4JCQnSvXt36dy5s/Tr10969+4tO3futN7PPDjW5cuXpVmzZvLaa69Zjy1cuFDq168vV69eFRH7Rc9Uvi5cuCANGjSQN954w3ps5syZMnr0aBERa7dT28XPVP52794tISEh0qhRI+nTp4/1eF5envz4448ydepU5sQBTp06JXq93vp6ZTab5YcffpB3331X1q5dK+fOnRMRjg9Hio+PlwYNGsiLL75od9xgMIiIyL59++TVV191qpzolS7SKrrExEQEBgZab1evXh1vv/023N3dMX78eISEhKB///4QkSKfdFDZYz7U48qVK9DpdHjnnXeQnp6OBQsWYNasWZg+fTq6du3KWVUHEhFs374djRs3xrhx42A2m6HVajFmzBjMnTsX0dHRCA4OhouLi9KhVhoHDhxA586dMWnSJOux9PR0HDx4EO3atYObmxumT5+OPn368PXKQSy/Z29vb7z55puYOXMmHnroIfzvf/+DXq9H69at8eijjyodZqXw+++/w2QyoUuXLjCbzejZsycMBgOSkpLg6+uL7OxsrFq1Ch07duT4cJCoqCjUqFEDc+fOhdlsRmRkJC5cuIDk5GRMnDgRTz31FNq1a6d0mGWKZyl/U3h4ODZv3oz4+HgAtxemT506FWPHjsXLL7+MxMREDmAHYT7U4/7778fcuXPRqVMn9O3bFxMnToROp8OsWbOwc+dO6+MsC0CLWwhKZUOj0aBq1aro27cv6tSpA61WCxFBXl4ecnNzkZaWVuQ5UqjTGpWt3r17Y9q0aahevToA4IMPPsBnn32G0aNHY8yYMWjRogUefvhhHD9+nK9XDqLRaBAeHo5mzZqhW7dumDNnDs6ePYvBgwdj9OjRWLFiBQwGg9JhVgpjx47FjBkz8Mgjj6Bx48aoWrUqVq9ejQsXLuCLL75AREQEJk2ahOTkZI4PB0lJSYFeXzC30r17d8TExKBly5Zo3749Ro0ahVmzZgFwsvcOhWawKjTby4T27dsnnTt3lokTJ0pSUpLd/Vu2bJHg4GA5cuSIEmFWGsyHupR2Gd3GjRulb9++0qdPH+uld5MnT5a9e/c6KrxKp7hLH2wXObdp00bWrVtnvb1y5Uo5c+aMQ2KrjIrLR25urjz33HOyefNm67ELFy5IcHCwfPnll44Mr9LLycmR1q1by88//ywiItu2bRN/f3/RaDQSFRUlIvb7I1HZKjw+Zs2aJS1atCjyvv3dd99JYGCgNSdU/n755Rdxd3eXlStXyuDBg63nWCIiX375pWg0Gtm1a5eCEZY9ziTdg9TUVACAVqu1tjRs164dBg4ciN27d+PDDz/E1atXrZcRhYaGwsvLC1lZWUqF7NSYD3WxzUfhWSG59clSv379MHnyZGi1Wrz77rt49NFHsWDBAu4RVg4s+bAsPLdl+8mrZVYJAN544w1MnDiRn8yWA9t82LaWFhG4urpi0aJF6NWrl3XsaDQa1KxZk81Lypnta5WIwM3NDeHh4db9XZYuXQqtVouQkBC89dZbAGD9NJ3KTknj44033sCXX36J0NBQALfzFRwcjGrVqsHT09PhsVYmtuOjd+/eGDRoEGbOnIlTp07By8sLJpMJZrMZI0aMQKtWrbB//34Foy17LJLu0qlTpxAREWF9kbTdJOvVV1/FgAEDsHPnTowfPx7Hjh3DuXPn8Omnn8JoNKJBgwZKhu6UmA91KZyPwoWSRqOxnoj37dsXEyZMwL59+7Bjxw4cOXIErVu3ViRuZ1U4H3q9vtjLGXNycpCSkgIRwZw5czBv3jxs374djRo1cnTITq241yvbYsj2/5YPdZYuXYr8/HzrySGVreI+1LHkoHnz5jh69CiGDx+O7du3Y+PGjfj888+xc+fOIl3t6O8rbnzYFkqtWrWCu7s7gNvj48cff0SVKlXs1iBT2SlufGi1WgwePBj+/v64ePEizp8/D51OZ32Mt7c3AgICFIy6HCg5jVVRXL58WVq1aiWNGjWSsLAw+de//mW9Lzc31/r18uXLpV+/fqLRaCQsLEzq1Kkjhw8fViJkp8Z8qEtp+Sh86Z1lr7DIyEjx8fGx2xOGysa95MNkMkmXLl2kefPm4unpKQcOHHB0uE7vXvIhInL69GmZMmWKBAQEyNGjRx0ZaqVx8uRJqVevnrz55pvWY7a5WLZsmWg0GmnUqJEcOnRIRAouw/vf//4nMTExDo/XmZU2Poq7NPXUqVMSGRkpAQEBcuzYMUeGWmkUNz5sLzFdtWqVNGnSRHx9feWnn36SLVu2yPTp06V27dpy4cIFJUIuN5wzvgMRwdq1axEcHIzIyEj8+eefWLt2LQDgrbfegqurK4xGI1xdXTFq1CiMGjUK+/fvh7e3N6pUqYKgoCCFfwLnwnyoy53yYbkUUqfTASj4pPbEiRP47rvvsH37doSFhSkZvtO513yYzWaYzWbEx8djz549CA8PVzJ8p3Ov+YiOjsbq1auxa9cu7Nixg/koB3FxcXjqqaeg1+vx3//+F3q9vkguxowZg5iYGAwdOhQREREACvZts+ydRGXjTuPDMqNkGR/Hjx/H8uXLsXXrVmzfvp3joxyUND70er313Gr48OGoW7culi1bhuHDh+O+++6DXq/H+vXrUa9ePaV/hDLFIukONBoNRo4ciRo1aqBXr15o2bIlAGDt2rUQEcyYMQOurq7Iy8uzts91thaIasJ8qMvd5MNyaZHlMokWLVrg5MmT8PX1VTJ0p3Sv+dDr9Xj22WfRtWtXNGzYUOHonc+95qN58+YYPnw4pkyZYu16R2Xnbk7Kc3Jy4O7ujvfee0/haJ3fvY6PFi1aYOTIkXjllVf4gWc5uJcPobt06YIuXbpg+vTp8PHxgYuLC6pUqaLwT1AOlJi+quji4+NlxowZEhoaKjNnzrQe/+mnn5xqE62KgvlQl9LyYdms1La7GpWvkvLxww8/KBhV5cV8KCshIUFWrFghIgWbjReXC75vKIfjQ1l3Mz5sL71z9vdyziQVIyEhAXFxcbh58yYefPBBu0tTLB2Hnn/+eQDA119/DRFBWloaPv74Y1y5cgXBwcFKhu90mA91KYt8sHta2eH4UBfmQ92CgoLwz3/+E0DBZuNjx44FUJALANbZi3Xr1mHgwIHc9LqMcXyo292MD71eX3nGh5IVmhodO3ZM6tSpI40bNxY/Pz8JDQ2Vr776SlJSUkSkYHGnpXKOj4+Xt956SzQajQQEBMjBgweVDN0pMR/qwnyoC/OhLsyH+sTHx8u+ffvk119/tZshss3F1atXrZ+Yz5gxQyIjI0Wj0cjVq1eVCtspcXyoD8dH6Vgk2UhOTpbQ0FCZNm2anD9/Xq5evSrDhg2Tpk2byowZMyQ5OVlE7KcXR4wYIb6+vhIdHa1U2E6L+VAX5kNdmA91YT7Uhyfl6sHxoT4cH3fGIslGdHS01K1bt0jyX331VWnRooW8//77kpWVZT2+bNky8ff3Z1vpcsJ8qAvzoS7Mh7owH+rCk3J14fhQF46Pu8MiycbRo0eldu3asnPnThERMRgM1vsmTZok9erVs+vLn5iY6HQ94dWE+VAX5kNdmA91YT7UhSfl6sLxoS4cH3dHIyKi9LooNWnXrh28vb2xbds2AEBubi7c3NwAAG3btkXDhg2xdu1au979VH6YD3VhPtSF+VAX5kM9jh07hgEDBuCrr75C165dkZ2dDQ8PDwDA5MmTsWHDBvz000/WvXaSkpJgMBicbp8XNeH4UA+Oj7vj5G0pSpeVlYWMjAykp6dbjy1evBjR0dF46qmnABRsIJefnw8AeOCBB5CVlQUAHMDlgPlQF+ZDXZgPdWE+1K1ly5aoWbMmZsyYAQDw8PBAbm4uAODjjz9GYGAgZs+eDQAwmUyoUaNGpTsBLE8cH+rG8XF3Km2RdPLkSQwePBjdunVD06ZNsWbNGgBA06ZN8fHHH+O3337D0KFDkZeXZ21xmJycDC8vL+Tn54MTcGWL+VAX5kNdmA91YT7Uhyfl6sHxoT4cH3+Rclf6KSc6OloCAwNlypQpsmbNGnnxxRfFxcXFeq1lVlaWrF+/XmrXri2hoaEyaNAgefzxx8XLy0uOHz+ucPTOh/lQF+ZDXZgPdWE+1Cc6Olp69+4trVu3luDgYFm9erWIiGRnZ8vatWulatWq8thjj4nRaBSTySQiIsOHD5cnnnhC8vLynH5DTEfi+FAfjo+/rtKtSbpx4waefPJJhIaG4uOPP7Ye79GjB1q0aIEFCxZYj2VkZGDWrFm4ceMG3N3dMX78eDRr1kyJsJ0W86EuzIe6MB/qwnyoz8mTJ/HAAw9g5MiRuP/++3Ho0CF88skn2LdvH1q3bg2DwYCtW7diwoQJ8Pb2RmhoKFxdXfG///0Pe/fuRVhYmNI/gtPg+FAfjo+/R690AI6Wl5eH1NRUPPbYYwAKdnnWarWoV68ebty4AQCQgq5/8PHxwZw5c+weR2WL+VAX5kNdmA91YT7U5caNG5gyZQqefvppzJs3DwDw1FNP4fDhw1i+fDlat24NT09PDBw4EN27d7c7Kd+/fz9PyssYx4e6cHz8fZWuSKpRowZWr16NRo0aAShYkKbValGrVi1cunQJAKDRaKDRaJCeng5fX1/rMSp7zIe6MB/qwnyoC/OhLjwpVxeOD3Xh+Pj7KuVvwTKAzWYzXFxcABT8oSQnJ1sfM3v2bCxbtsy6iI2DuPwwH+rCfKgL86EuzId6WE7Ku3btCqDgpBwAatWqZT3J02g00Gq1dgvWmY/yw/GhHhwff1+lm0mypdVqISLWPwjLH81bb72FWbNm4ciRI9DrK/WvyKGYD3VhPtSF+VAX5kMd7vak3M3NDZMmTYJer+dJoANwfKgDx8ffU+n/Qi2DWK/XIyQkBB9++CHef/99HDx4EC1btlQ6vEqH+VAX5kNdmA91YT7Ugyfl6sPxoR4cH39Npf+NWP5QXFxcsHTpUvj6+mLXrl2IiIhQOLLKiflQF+ZDXZgPdWE+1IUn5erC8aEuHB/3rlKuSSpOnz59AAC7d+/G/fffr3A0xHyoC/OhLsyHujAf6lD4pHzWrFk8KVcBjg914Pi4d5Vun6TSZGVlwcvLS+kw6BbmQ12YD3VhPtSF+VCPgwcPol27djhx4gTbGKsEx4d6cHzcPRZJRERE5FR4Uk5UMo6Pu8MiiYiIiIiIyAbXJBEREREREdlgkURERERERGSDRRIREREREZENFklEREREREQ2WCQRERERERHZYJFERERERERkg0USERFVGKNGjYJGo4FGo4GLiwtq1KiBXr164YsvvoDZbL7r77NixQr4+/uXX6BERFShsUgiIqIKpW/fvkhISEBsbCx++eUX9OjRA5MnT8aAAQOQn5+vdHhEROQEWCQREVGF4ubmhqCgINSqVQsRERGYNm0a1q1bh19++QUrVqwAAMybNw8tWrSAl5cXQkJCMGHCBGRmZgIAduzYgWeeeQZpaWnWWamZM2cCAHJzc/Hyyy+jVq1a8PLyQvv27bFjxw5lflAiIlIMiyQiIqrwevbsiZYtW+LHH38EAGi1WixYsADR0dFYuXIltm3bhldeeQUA0KlTJ8yfPx++vr5ISEhAQkICXn75ZQDAxIkTsWfPHnz99deIiorC0KFD0bdvX8TExCj2sxERkeNpRESUDoKIiOhujBo1Cqmpqfjpp5+K3PfEE08gKioKJ0+eLHLf999/j3HjxuH69esACtYkRUZGIjU11fqYy5cvo379+rh8+TKCg4Otxx988EG0a9cO7777bpn/PEREpE56pQMgIiIqCyICjUYDANiyZQtmz56N06dPIz09Hfn5+cjJyYHBYICnp2exzz9+/DhMJhMaN25sdzw3NxeBgYHlHj8REakHiyQiInIKp06dQr169RAbG4sBAwZg/PjxeOedd1ClShXs2rULY8aMgdFoLLFIyszMhE6nw6FDh6DT6ezu8/b2dsSPQEREKsEiiYiIKrxt27bh+PHjmDJlCg4dOgSz2Yy5c+dCqy1Yevvtt9/aPd7V1RUmk8nuWOvWrWEymZCcnIyuXbs6LHYiIlIfFklERFSh5ObmIjExESaTCUlJSfj1118xe/ZsDBgwACNHjsSJEyeQl5eHTz75BAMHDsSff/6JRYsW2X2PunXrIjMzE1u3bkXLli3h6emJxo0b4+mnn8bIkSMxd+5ctG7dGteuXcPWrVsRHh6Ohx56SKGfmIiIHI3d7YiIqEL59ddfUbNmTdStWxd9+/bF9u3bsWDBAqxbtw46nQ4tW7bEvHnzMGfOHISFhWHNmjWYPXu23ffo1KkTxo0bh2HDhqFatWp4//33AQDLly/HyJEj8dJLL6FJkyYYNGgQDhw4gPvuu0+JH5WIiBTC7nZEREREREQ2OJNERERERERkg0USERERERGRDRZJRERERERENlgkERERERER2WCRREREREREZINFEhERERERkQ0WSURERERERDZYJBEREREREdlgkURERERERGSDRRIREREREZENFklEREREREQ2WCQRERERERHZ+H/sULoYQcd34gAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["# Filter a single stock for simplicity, e.g., 'AAPL'\n","stock_data = prices_split[prices_split['symbol'] == 'AAPL']\n","print(\"\\nApple (AAPL) stock data:\")\n","print(stock_data.head())\n","\n","# Convert dates to datetime format and sort\n","stock_data['date'] = pd.to_datetime(stock_data['date'])\n","stock_data = stock_data.sort_values(by='date')\n","\n","stock_data['previous_close'] = stock_data['close'].shift(1)\n","stock_data['next_close'] = stock_data['close'].shift(-1)\n","\n","# Drop rows with NaN values (from lagging/leading)\n","stock_data = stock_data.dropna()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VEHKkn4kgEK4","executionInfo":{"status":"ok","timestamp":1732366597917,"user_tz":0,"elapsed":448,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"967429d0-121e-4c54-ebfc-a1a63b280346"},"execution_count":62,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Apple (AAPL) stock data:\n","            date symbol       open      close        low       high  \\\n","254   2010-01-04   AAPL  30.490000  30.572857  30.340000  30.642857   \n","721   2010-01-05   AAPL  30.657143  30.625713  30.464285  30.798571   \n","1189  2010-01-06   AAPL  30.625713  30.138571  30.107143  30.747143   \n","1657  2010-01-07   AAPL  30.250000  30.082857  29.864286  30.285715   \n","2125  2010-01-08   AAPL  30.042856  30.282858  29.865715  30.285715   \n","\n","           volume  \n","254   123432400.0  \n","721   150476200.0  \n","1189  138040000.0  \n","1657  119282800.0  \n","2125  111902700.0  \n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-62-26a49b30ad81>:7: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  stock_data['date'] = pd.to_datetime(stock_data['date'])\n"]}]},{"cell_type":"code","source":["# Step 3: Prepare data for Linear Regression\n","X = stock_data[['previous_close']]  # Feature\n","y = stock_data['next_close']       # Target variable\n","\n","# Split the data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"],"metadata":{"id":"y0N8vWY6gHFU","executionInfo":{"status":"ok","timestamp":1732366635608,"user_tz":0,"elapsed":211,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":63,"outputs":[]},{"cell_type":"code","source":["# Step 4: Train Linear Regression Model\n","model = LinearRegression()\n","model.fit(X_train, y_train)\n","\n","# Predict on the test set\n","y_pred = model.predict(X_test)\n"],"metadata":{"id":"cMEQ0gHlTg7X","executionInfo":{"status":"ok","timestamp":1732366643398,"user_tz":0,"elapsed":205,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":64,"outputs":[]},{"cell_type":"code","source":["# Step 5: Evaluate the Model\n","mse = mean_squared_error(y_test, y_pred)\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"\\nModel Evaluation:\")\n","print(f\"Mean Squared Error: {mse:.2f}\")\n","print(f\"R-squared: {r2:.2f}\")"],"metadata":{"id":"FvRtjKKITtxj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732366645461,"user_tz":0,"elapsed":285,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"beaa462c-cf6d-45fd-d7ed-9d2490c16310"},"execution_count":65,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Model Evaluation:\n","Mean Squared Error: 4.09\n","R-squared: 0.99\n"]}]},{"cell_type":"code","source":["# Plot predictions vs actual values\n","plt.figure(figsize=(8, 6))\n","plt.scatter(y_test, y_pred, alpha=0.6, color='b')\n","plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', lw=2)  # Perfect prediction line\n","plt.title(\"Actual vs Predicted Stock Prices\")\n","plt.xlabel(\"Actual Prices\")\n","plt.ylabel(\"Predicted Prices\")\n","plt.show()\n","\n","# Step 6: Analyze model coefficients\n","print(\"\\nModel Coefficients:\")\n","print(f\"Intercept: {model.intercept_:.2f}\")\n","print(f\"Coefficient for 'previous_close': {model.coef_[0]:.2f}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":636},"id":"lHoflMFnmU61","executionInfo":{"status":"ok","timestamp":1732366651752,"user_tz":0,"elapsed":947,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"9f0eda5c-a7d6-4d48-8cab-58536f266c46"},"execution_count":66,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Model Coefficients:\n","Intercept: 0.25\n","Coefficient for 'previous_close': 1.00\n"]}]},{"cell_type":"code","source":["# Better Visualization: Actual vs Predicted Prices\n","plt.figure(figsize=(14, 8))\n","\n","# Sort test data for smoother plotting\n","test_data = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}, index=X_test.index).sort_index()\n","\n","# Plot actual prices\n","plt.plot(test_data.index, test_data['Actual'], label='Actual Prices', color='blue', linewidth=2)\n","\n","# Plot predicted prices\n","plt.plot(test_data.index, test_data['Predicted'], label='Predicted Prices', color='orange', linestyle='--', linewidth=2)\n","\n","plt.title(\"Stock Price Prediction: Actual vs Predicted\", fontsize=16)\n","plt.xlabel(\"Index (sorted test set)\", fontsize=12)\n","plt.ylabel(\"Stock Price\", fontsize=12)\n","plt.legend(fontsize=12)\n","plt.grid(alpha=0.3)\n","\n","# Highlight differences\n","for i in range(len(test_data)):\n","    plt.plot([test_data.index[i], test_data.index[i]],\n","             [test_data['Actual'].iloc[i], test_data['Predicted'].iloc[i]],\n","             color='gray', alpha=0.4, linestyle='--')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":649},"id":"fawHD0Lcg6xc","executionInfo":{"status":"ok","timestamp":1732366662564,"user_tz":0,"elapsed":1279,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"b094267a-444b-4075-dc28-df3aa79bd55e"},"execution_count":67,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Logistic regression using a real-world dataset"],"metadata":{"id":"hq27_O_lmhdh"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score, roc_curve\n","import matplotlib.pyplot as plt\n","\n","# Load dataset\n","url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n","columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']\n","data = pd.read_csv(url, header=None, names=columns)"],"metadata":{"id":"aChaE0vXmwJZ","executionInfo":{"status":"ok","timestamp":1732366690446,"user_tz":0,"elapsed":210,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":68,"outputs":[]},{"cell_type":"code","source":["# Display the first few rows\n","print(\"Dataset preview:\")\n","print(data.head())\n","\n","# Check for missing values\n","print(\"\\nMissing values count:\")\n","print(data.isnull().sum())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LsErd9rtm8A4","executionInfo":{"status":"ok","timestamp":1732366693530,"user_tz":0,"elapsed":246,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"c189ab7d-183b-42b8-d805-dd4a38bcdd36"},"execution_count":69,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset preview:\n","   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0            6      148             72             35        0  33.6   \n","1            1       85             66             29        0  26.6   \n","2            8      183             64              0        0  23.3   \n","3            1       89             66             23       94  28.1   \n","4            0      137             40             35      168  43.1   \n","\n","   DiabetesPedigreeFunction  Age  Outcome  \n","0                     0.627   50        1  \n","1                     0.351   31        0  \n","2                     0.672   32        1  \n","3                     0.167   21        0  \n","4                     2.288   33        1  \n","\n","Missing values count:\n","Pregnancies                 0\n","Glucose                     0\n","BloodPressure               0\n","SkinThickness               0\n","Insulin                     0\n","BMI                         0\n","DiabetesPedigreeFunction    0\n","Age                         0\n","Outcome                     0\n","dtype: int64\n"]}]},{"cell_type":"code","source":["# Replace zero values in specific columns with NaN (indicates missing values)\n","columns_to_replace = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n","for col in columns_to_replace:\n","    data[col].replace(0, np.nan, inplace=True)\n","\n","# Fill missing values with the column mean\n","data.fillna(data.mean(), inplace=True)\n","\n","# Split data into features and target\n","X = data.drop(columns='Outcome')\n","y = data['Outcome']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H8TmkF-mm-TX","executionInfo":{"status":"ok","timestamp":1732366702675,"user_tz":0,"elapsed":235,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"5d630dfa-dbf8-442d-e856-bb1363e5c50c"},"execution_count":70,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-70-d11e678f13ae>:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n","The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n","\n","For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n","\n","\n","  data[col].replace(0, np.nan, inplace=True)\n"]}]},{"cell_type":"code","source":["# Normalize features\n","from sklearn.preprocessing import StandardScaler\n","scaler = StandardScaler()\n","X_scaled = scaler.fit_transform(X)\n","\n","# Split into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)\n","\n","# Train Logistic Regression Model\n","model = LogisticRegression()\n","model.fit(X_train, y_train)\n","\n","# Make predictions\n","y_pred = model.predict(X_test)"],"metadata":{"id":"EVtLYUzmm_B3","executionInfo":{"status":"ok","timestamp":1732366762473,"user_tz":0,"elapsed":232,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":71,"outputs":[]},{"cell_type":"code","source":["# Evaluate model performance\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Accuracy: {accuracy:.2f}\")\n","\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","report = classification_report(y_test, y_pred)\n","print(\"\\nClassification Report:\")\n","print(report)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0UOTsYh-nDAO","executionInfo":{"status":"ok","timestamp":1732366910663,"user_tz":0,"elapsed":235,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"8a2751e4-dc52-4f4b-8c22-2f976c20bdad"},"execution_count":73,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.74\n","\n","Confusion Matrix:\n","[[124  27]\n"," [ 33  47]]\n","\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.79      0.82      0.81       151\n","           1       0.64      0.59      0.61        80\n","\n","    accuracy                           0.74       231\n","   macro avg       0.71      0.70      0.71       231\n","weighted avg       0.74      0.74      0.74       231\n","\n"]}]},{"cell_type":"code","source":["# Sigmoid function\n","def sigmoid(z):\n","    return 1 / (1 + np.exp(-z))\n","\n","def sgd_logistic_regression(X, y, learning_rate=0.1, epochs=10, batch_size=32):\n","    np.random.seed(42)  # For reproducibility\n","    n_samples, n_features = X.shape\n","    weights = np.random.randn(n_features) * 0.01  # Small random initialization\n","    bias = 0\n","\n","    # Training loop\n","    for epoch in range(epochs):\n","        # Shuffle the data\n","        indices = np.arange(n_samples)\n","        np.random.shuffle(indices)\n","        X, y = X[indices], y[indices]\n","\n","        for start in range(0, n_samples, batch_size):\n","            end = start + batch_size\n","            X_batch, y_batch = X[start:end], y[start:end]\n","\n","            # Predictions\n","            linear_model = np.dot(X_batch, weights) + bias\n","            y_pred = sigmoid(linear_model)\n","\n","            # Gradients\n","            error = y_pred - y_batch\n","            dw = np.dot(X_batch.T, error) / batch_size\n","            db = np.sum(error) / batch_size\n","\n","            # Weight updates\n","            weights -= learning_rate * dw\n","            bias -= learning_rate * db\n","\n","        # Calculate and print loss every 100 epochs\n","        if (epoch + 1) % 1 == 0:\n","            y_preds = sigmoid(np.dot(X, weights) + bias)\n","            loss = -np.mean(y * np.log(y_preds) + (1 - y) * np.log(1 - y_preds))\n","            print(f'Epoch {epoch + 1}/{epochs}, Loss: {loss:.4f}')\n","\n","    return weights, bias\n"],"metadata":{"id":"pkc8h4sf58TR","executionInfo":{"status":"ok","timestamp":1732367194269,"user_tz":0,"elapsed":217,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}}},"execution_count":74,"outputs":[]},{"cell_type":"code","source":["# Split data into features and target\n","X = data.drop(columns='Outcome')\n","y = data['Outcome']\n","\n","# Standardize features for better convergence\n","X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n","\n","# Split data into train and test sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Train logistic regression model using SGD\n","weights, bias = sgd_logistic_regression(X_train.values, y_train.values, learning_rate=0.2, epochs=10)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xECaeNQh6sYo","executionInfo":{"status":"ok","timestamp":1732367198193,"user_tz":0,"elapsed":239,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"e592b377-82d4-4e81-a12b-23adf3142303"},"execution_count":75,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10, Loss: 0.5040\n","Epoch 2/10, Loss: 0.4799\n","Epoch 3/10, Loss: 0.4710\n","Epoch 4/10, Loss: 0.4671\n","Epoch 5/10, Loss: 0.4651\n","Epoch 6/10, Loss: 0.4642\n","Epoch 7/10, Loss: 0.4634\n","Epoch 8/10, Loss: 0.4630\n","Epoch 9/10, Loss: 0.4628\n","Epoch 10/10, Loss: 0.4627\n"]}]},{"cell_type":"code","source":["\n","# Make predictions on the test set\n","y_test_pred = sigmoid(np.dot(X_test.values, weights) + bias)\n","y_test_pred_class = [1 if i > 0.5 else 0 for i in y_test_pred]\n","\n","# Evaluate model\n","accuracy = accuracy_score(y_test, y_test_pred_class)\n","print(f'Accuracy: {accuracy:.4f}')\n","print(\"Confusion Matrix:\")\n","print(confusion_matrix(y_test, y_test_pred_class))\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_test_pred_class))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K2Zt-Sub6BV4","executionInfo":{"status":"ok","timestamp":1732367222465,"user_tz":0,"elapsed":222,"user":{"displayName":"Sweta Agrawal","userId":"10830246245677576480"}},"outputId":"978417fe-5e4f-4434-993f-09c27c300f94"},"execution_count":76,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.7597\n","Confusion Matrix:\n","[[83 16]\n"," [21 34]]\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.80      0.84      0.82        99\n","           1       0.68      0.62      0.65        55\n","\n","    accuracy                           0.76       154\n","   macro avg       0.74      0.73      0.73       154\n","weighted avg       0.76      0.76      0.76       154\n","\n"]}]}]}