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  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Cree0618/BlackFriday-GPTs-Prompts/blob/main/most_uptodate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "source": [
        "!pip install shap\n",
        "!pip install tensorflow\n",
        "!pip install -U tensorflow"
      ],
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        "colab": {
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          "height": 1000
        },
        "id": "9TvLby91K0EA",
        "outputId": "92ef67a3-c140-4371-894a-317d158e6210"
      },
      "execution_count": null,
      "outputs": [
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          "text": [
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            "Installing collected packages: namex, optree, ml-dtypes, h5py, tensorboard, keras, tensorflow\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.2.0\n",
            "    Uninstalling ml-dtypes-0.2.0:\n",
            "      Successfully uninstalled ml-dtypes-0.2.0\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.9.0\n",
            "    Uninstalling h5py-3.9.0:\n",
            "      Successfully uninstalled h5py-3.9.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.15.2\n",
            "    Uninstalling tensorboard-2.15.2:\n",
            "      Successfully uninstalled tensorboard-2.15.2\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.15.0\n",
            "    Uninstalling keras-2.15.0:\n",
            "      Successfully uninstalled keras-2.15.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.15.0\n",
            "    Uninstalling tensorflow-2.15.0:\n",
            "      Successfully uninstalled tensorflow-2.15.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.16.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed h5py-3.11.0 keras-3.3.3 ml-dtypes-0.3.2 namex-0.0.8 optree-0.11.0 tensorboard-2.16.2 tensorflow-2.16.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "h5py",
                  "keras",
                  "ml_dtypes",
                  "tensorboard",
                  "tensorflow"
                ]
              },
              "id": "1e116e3c9b4a43cebd471c0214f2b045"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "POB3r0SBhYhI",
        "outputId": "f02e1968-c603-4bbe-9dcc-1eba05c2330e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-a5b4ff4f380a>:28: 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",
            "  X['date'] = X['date'].map(pd.Timestamp.toordinal)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizer: adam, MAE: 360.12273754732513, MSE: 199035.60466050665, R2: 0.21715936533048186\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/extmath.py:189: RuntimeWarning: overflow encountered in matmul\n",
            "  ret = a @ b\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/extmath.py:189: RuntimeWarning: invalid value encountered in matmul\n",
            "  ret = a @ b\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-a5b4ff4f380a>\u001b[0m in \u001b[0;36m<cell line: 43>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;31m# Initialize and train the neural network model with the current optimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0mnn_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMLPRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_layer_sizes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mnn_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;31m# Predict on the test set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    747\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 749\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincremental\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_solver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, incremental)\u001b[0m\n\u001b[1;32m    489\u001b[0m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoefs_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintercepts_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    492\u001b[0m                 \u001b[0;34m\"Solver produced non-finite parameter weights. The input data may\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m                 \u001b[0;34m\" contain large values and need to be preprocessed.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed."
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load your data\n",
        "data = pd.read_csv('/content/data_analyze_modified.csv')\n",
        "\n",
        "# Clean and preprocess your data\n",
        "data = data.drop(columns=['Unnamed: 3', 'Unnamed: 4'])\n",
        "data['date'] = data['date'].str.strip(',').apply(lambda x: pd.to_datetime(x, errors='coerce'))\n",
        "data = data.dropna(subset=['date']).reset_index(drop=True)\n",
        "data['ma_7'] = data['ma_7'].fillna(method='ffill')\n",
        "data['ma_7'] = data['ma_7'].fillna(data['ma_7'].mean())\n",
        "\n",
        "# Feature engineering\n",
        "data['day'] = data['date'].dt.day\n",
        "data['month'] = data['date'].dt.month\n",
        "data['year'] = data['date'].dt.year\n",
        "\n",
        "# Prepare features and target variable\n",
        "X = data[['date', 'ma_7', 'day', 'month', 'year']]\n",
        "y = data['spot_price']\n",
        "\n",
        "# Convert 'date' to numerical format (ordinal)\n",
        "X['date'] = X['date'].map(pd.Timestamp.toordinal)\n",
        "\n",
        "# Standardize the features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the standardized data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Define different optimizers to compare\n",
        "optimizers = ['adam', 'sgd', 'lbfgs']\n",
        "\n",
        "# Dictionary to store the results\n",
        "results = {}\n",
        "\n",
        "for optimizer in optimizers:\n",
        "    # Initialize and train the neural network model with the current optimizer\n",
        "    nn_model = MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, solver=optimizer, random_state=42)\n",
        "    nn_model.fit(X_train, y_train)\n",
        "\n",
        "    # Predict on the test set\n",
        "    y_pred_nn = nn_model.predict(X_test)\n",
        "\n",
        "    # Evaluate the neural network model\n",
        "    mae_nn = mean_absolute_error(y_test, y_pred_nn)\n",
        "    mse_nn = mean_squared_error(y_test, y_pred_nn)\n",
        "    r2_nn = r2_score(y_test, y_pred_nn)\n",
        "\n",
        "    # Store the results\n",
        "    results[optimizer] = {'MAE': mae_nn, 'MSE': mse_nn, 'R2': r2_nn}\n",
        "\n",
        "    # Print the results for the current optimizer\n",
        "    print(f\"Optimizer: {optimizer}, MAE: {mae_nn}, MSE: {mse_nn}, R2: {r2_nn}\")\n",
        "\n",
        "# Visualize the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "for optimizer, result in results.items():\n",
        "    plt.plot(y_test.values, label=f'Actual - {optimizer}', color='blue')\n",
        "    plt.plot(y_pred_nn, label=f'Predicted - {optimizer}', color='red')\n",
        "    plt.title(f'Actual vs Predicted Spot Prices ({optimizer} Optimizer)')\n",
        "    plt.xlabel('Sample Index')\n",
        "    plt.ylabel('Spot Price')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Print the overall comparison\n",
        "print(\"Overall comparison of different optimizers:\")\n",
        "for optimizer, result in results.items():\n",
        "    print(f\"Optimizer: {optimizer}, MAE: {result['MAE']}, MSE: {result['MSE']}, R2: {result['R2']}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load your data\n",
        "data = pd.read_csv('/content/data_analyze_modified.csv')\n",
        "\n",
        "# Clean and preprocess your data\n",
        "data = data.drop(columns=['Unnamed: 3', 'Unnamed: 4'])\n",
        "data['date'] = data['date'].str.strip(',').apply(lambda x: pd.to_datetime(x, errors='coerce'))\n",
        "data = data.dropna(subset=['date']).reset_index(drop=True)\n",
        "data['ma_7'] = data['ma_7'].fillna(method='ffill')\n",
        "data['ma_7'] = data['ma_7'].fillna(data['ma_7'].mean())\n",
        "\n",
        "# Feature engineering\n",
        "data['day'] = data['date'].dt.day\n",
        "data['month'] = data['date'].dt.month\n",
        "data['year'] = data['date'].dt.year\n",
        "\n",
        "# Prepare features and target variable\n",
        "X = data[['date', 'ma_7', 'day', 'month', 'year']]\n",
        "y = data['spot_price']\n",
        "\n",
        "# Convert 'date' to numerical format (ordinal)\n",
        "X['date'] = X['date'].map(pd.Timestamp.toordinal)\n",
        "\n",
        "# Apply MinMax scaling to features\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the standardized data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Define different optimizers to compare\n",
        "optimizers = ['adam', 'sgd', 'lbfgs']\n",
        "\n",
        "# Dictionary to store the results\n",
        "results = {}\n",
        "\n",
        "for optimizer in optimizers:\n",
        "    # Initialize and train the neural network model with the current optimizer\n",
        "    nn_model = MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, solver=optimizer, random_state=42)\n",
        "    nn_model.fit(X_train, y_train)\n",
        "\n",
        "    # Predict on the test set\n",
        "    y_pred_nn = nn_model.predict(X_test)\n",
        "\n",
        "    # Evaluate the neural network model\n",
        "    mae_nn = mean_absolute_error(y_test, y_pred_nn)\n",
        "    mse_nn = mean_squared_error(y_test, y_pred_nn)\n",
        "    r2_nn = r2_score(y_test, y_pred_nn)\n",
        "\n",
        "    # Store the results\n",
        "    results[optimizer] = {'MAE': mae_nn, 'MSE': mse_nn, 'R2': r2_nn}\n",
        "\n",
        "    # Print the results for the current optimizer\n",
        "    print(f\"Optimizer: {optimizer}, MAE: {mae_nn}, MSE: {mse_nn}, R2: {r2_nn}\")\n",
        "\n",
        "# Visualize the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "for optimizer, result in results.items():\n",
        "    nn_model = MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, solver=optimizer, random_state=42)\n",
        "    nn_model.fit(X_train, y_train)\n",
        "    y_pred_nn = nn_model.predict(X_test)\n",
        "    plt.plot(y_test.values, label=f'Actual - {optimizer}', color='blue')\n",
        "    plt.plot(y_pred_nn, label=f'Predicted - {optimizer}', color='red')\n",
        "    plt.title(f'Actual vs Predicted Spot Prices ({optimizer} Optimizer)')\n",
        "    plt.xlabel('Sample Index')\n",
        "    plt.ylabel('Spot Price')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Print the overall comparison\n",
        "print(\"Overall comparison of different optimizers:\")\n",
        "for optimizer, result in results.items():\n",
        "    print(f\"Optimizer: {optimizer}, MAE: {result['MAE']}, MSE: {result['MSE']}, R2: {result['R2']}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "40s_aatQiamP",
        "outputId": "ebd37dca-5b12-4045-a324-b2f55968347b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-b82ced944863>:28: 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",
            "  X['date'] = X['date'].map(pd.Timestamp.toordinal)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizer: adam, MAE: 385.23545419472305, MSE: 222167.515402628, R2: 0.1261776552119903\n",
            "Optimizer: sgd, MAE: 392.20813381980304, MSE: 255391.4091501261, R2: -0.004497527812913349\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizer: lbfgs, MAE: 354.01199998383794, MSE: 191380.89886228534, R2: 0.24726661551574014\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RVFSEAQMG4NChQzh9+jSA8Mt0UVERACAlJSXoc8aOHQutViv8fuihhxAXFxdwajs1CVG1hYK+RymSk5Px999/48yZM0Gnq7y8HABgMpn8Hkf3l5WVybYHKl/hoG3btujWrZvwm85w7t27t6x+0u3Bztq1Wq249dZbhbpD/a2+/vprtGnTBq1bt5bVX+paoHSJuO6669C2bVuv96Bl4vz580Gl6VIDM9tdInC5XPjyyy9x/fXXCz4aAKm006dPx9q1a9G/f38AxAw1dOjQWknXggULMH36dOzfvx8Oh0PYnpubG/K10tPT0adPHyxZsgRTp04FQEx2cXFxuO2224TjpkyZgltuuQUtW7ZE+/btMXDgQIwYMQIdO3YM6j4vvfQSrr32Wmg0GqSnp6NNmzZezU7KZzh06BA4jkOLFi28Xpd2SCdOnAAAj+NoaAR/oI1vuDJ7baSRQq/X44UXXsALL7yA/Px8rF+/HjNnzsSSJUug1WqxcOFC2fHKezVr1gxqtVrwMzlx4gTUarWHGS07OxvJyclCmsPFLbfcggkTJkClUsFkMqFdu3ZeJwEEU3apqdffezp8+DA4jsN//vMfn9PGz507h/r161e7THMh+LUo30NiYiJycnICxjii70eZP8r3BQBvvfUWRo0ahYYNG6JLly648cYbMXLkSL9li5IiSqJ8wRfJClS+woFyAEMHFkqzNt0u9R3zhwceeABHjhzBhg0bZO4Chw4dwr59+zz8vSjOnTsn++2vrNIyEY1YU3UBjDxdIvj111+Rn5+PL7/8El9++aXH/kWLFgnkqbrwVdlcLpdsRsrChQsxevRoDBkyBE8//TQyMzOh0Wgwbdo0oXMJFXfccQfuvfde7NixA5dddhmWLFmCPn36CH49APF7OHLkCH744Qf88ssvmDdvHmbMmIG5c+dizJgxAe/RoUOHoBxsleqD2+2GSqXCzz//7HVmTqRi7FQH0UpjTk4O7rjjDgwdOhTt2rXDkiVLMH/+fL++UL7KWU019g0aNAjrvYcLt9sNAHjqqacwYMAAr8dQ4hFumaYdb3FxMRo0aBCRdEcCw4YNw7XXXovvvvsOv/zyC95++228+eabWLp0qc+ZjUlJScjJycGuXbv8XnvXrl2oX78+zGaz3+MiUY58zcDztT0YEjtz5kwsXrwYCxcu9PALdbvd6NChA9555x2v5ypJm7+ySomctO1kEMHI0yUC6lQ4a9Ysj31Lly7Fd999h7lz5yI+Ph7NmjXDv//+6/d6/hqWlJQUr2aXEydOyEaO33zzDZo2bYqlS5fKrled0AlDhgzBgw8+KJjuDh486DV8QGpqKu69917ce++9sFgs6NmzJyZPnhwUeQoXzZo1A8dxyM3NRcuWLX0eR52pDx06JJsm7HA4cOzYMXTq1MnnuTR/w31/tZFGf9BqtejYsSMOHTokmAspDh06JBspHz58GG63W3Bybdy4MdxuNw4dOoQ2bdoIx509exYlJSVCmoHoj6apo/G///7rk5DRd6nVaoMibeGU6datWwMgM0U7dOgQVNoPHTokmP4BMsEkPz8fN954o9/z6Ps5duyYTOU5fPiw1+NzcnLw8MMP4+GHH8a5c+fQuXNnvPbaaz7JEwAMHjwYH330Ef7880/ZDFeKP/74A8ePH8eDDz7o9bn8lS8g+uXmjz/+wFNPPYWJEyfi7rvv9tjfrFkz7Ny5E3369Kl2WqmFQlqXGEQwn6dLAJWVlVi6dCkGDx6M22+/3eNvwoQJKC8vF6Y/Dx06FDt37hRmJ0lBR0bUXOGNJDVr1gybNm2C3W4Xti1btsxjqjsdfUlHW3///Tc2btwY9rMmJydjwIABWLJkCb788kvodDoMGTJEdgz186BITExE8+bNPabyRhq33XYbNBoNXnnlFY8RJsdxQrq6du2KjIwMzJ07V5aH8+fPDxj/KCMjAz179sQnn3yCvLw8j3tQ+Hp/tZFGgHRUyvTR9GzcuBEpKSkepgcl8f/f//4HAEJnSjvvd999V3YcHYVLZ3UZjcaoxpLq3LkzcnNz8e6773qkg+Z7ZmYmevXqhQ8++AD5+fke1ygsLBS+h1umu3TpAp1OF/SSKwAJTyE1sc+ZMwdOp9MvqQEgqGezZ8+WbafvkcLlcqG0tFS2LTMzE/Xq1Qv4PE8//TTi4+Px4IMPeuTJhQsXMG7cOCQkJAihOqQIVL6A6Jab/Px8DBs2DD169MDbb7/t9Zhhw4bh9OnT+Oijjzz2VVZWwmq1Bn2/bdu2ISkpCe3atQs7zRczmPJ0CeDHH39EeXk5br75Zq/7r776amRkZGDRokUYPnw4nn76aXzzzTf4v//7P9x3333o0qULLly4gB9//BFz585Fp06d0KxZMyQnJ2Pu3LkwmUwwGo246qqrkJubizFjxuCbb77BwIEDMWzYMBw5cgQLFy70mNY7ePBgLF26FLfeeisGDRqEY8eOYe7cuWjbtq1sja1QMXz4cNxzzz2YPXs2BgwY4OGQ27ZtW/Tq1QtdunRBamoqtm7dKkyLrkk0a9YMr776KiZNmoTjx49jyJAhMJlMOHbsGL777juMHTsWTz31FLRaLV599VU8+OCD6N27N4YPH45jx47h008/Dcqf6L333kOPHj3QuXNnjB07Frm5uTh+/DiWL18uLKfTpUsXAMALL7yAO+64A1qtFjfddFOtpXHnzp246667cMMNN+Daa69FamoqTp8+jQULFuDMmTN49913PUwbx44dw80334yBAwdi48aNWLhwIe666y5B5erUqRNGjRqFDz/8ECUlJbjuuuuwefNmLFiwAEOGDJGpJV26dMGcOXPw6quvonnz5sjMzKzVYIBqtRpz5szBTTfdhMsuuwz33nsvcnJysH//fuzZswerVq0CQDr0Hj16oEOHDnjggQfQtGlTnD17Fhs3bsSpU6ewc+dOAOGXaYPBgP79+2PNmjWYMmVKUGm32+3o06ePMLV+9uzZ6NGjh8/2haJLly4YOnQo3n33XRQVFQmhCg4ePAhAVHXKy8vRoEED3H777ejUqRMSExOxZs0abNmyBdOnT/d7jxYtWmDBggW4++670aFDB9x///1C+f/4449x/vx5LF682GuIgUDliz7DmjVr8M4776BevXrIzc2t1jJXoeDRRx9FYWEhnnnmGQ/Xi44dO6Jjx44YMWIElixZgnHjxuG3335D9+7d4XK5sH//fixZsgSrVq3ymNjiC6tXr8ZNN90UdbUtZlH7E/wYahs33XQTZzAYOKvV6vOY0aNHc1qtljt//jzHcRxXVFTETZgwgatfvz6n0+m4Bg0acKNGjRL2cxzH/fDDD1zbtm25uLg4j+ny06dP5+rXr8/p9Xque/fu3NatW70uLfL6669zjRs35vR6PXf55Zdzy5Yt40aNGsU1btxYlj6EsDxLWVkZFx8fzwHgFi5c6LH/1Vdf5a688kouOTmZi4+P51q3bs299tprsunX3kCnm3/99dd+j6NTjAsLC73u//bbb7kePXpwRqORMxqNXOvWrbnx48dzBw4ckB03e/ZsLjc3l9Pr9VzXrl2533//3SMPvYUq4DiO+/fff7lbb72VS05O5gwGA9eqVSvuP//5j+yYqVOncvXr1+fUarXHNOtIptEbzp49y73xxhvcddddx+Xk5HBxcXFcSkoK17t3b+6bb77xmp979+7lbr/9ds5kMnEpKSnchAkTPJavcDgc3CuvvMLl5uZyWq2Wa9iwITdp0iTZVH+O47iCggJu0KBBnMlkCiq0AoCAU+f9lQ9lqAKKP//8k+vXrx9nMpk4o9HIdezYkfvf//4nO+bIkSPcyJEjuezsbE6r1XL169fnBg8eLMuncMs0x3Hc0qVLOZVKxeXl5cm2+wpVsH79em7s2LFcSkoKl5iYyN19991cUVGR7FxvoQo4juOsVis3fvx4LjU1lUtMTOSGDBnCHThwgAPAvfHGGxzHcZzNZuOefvpprlOnTkK+dOrUiZs9e3bAZ6HYtWsXd+edd3I5OTmcVqvlsrOzuTvvvNNrSIZQytf+/fu5nj17Cu0LzR9foQq8hY/wVpZoPX777bc90kVBQ3t4+5O2jXa7nXvzzTe5du3acXq9nktJSeG6dOnCvfLKK1xpaanfdFDs27ePA8CtWbPG634GjlNxHAsfysDAELuYPHkyXnnlFRQWFjLn1RqAy+VC27ZtMWzYMGGWam1ix44duPzyy7Fw4UKvfjw1DVa+PDFx4kT8/vvv2LZtG1OefID5PDEwMDBcwtBoNJgyZQpmzZpVLXN5MKisrPTY9u6770KtVqNnz541em+G4FBUVIR58+bh1VdfZcTJD5jPEwMDA8MljuHDh8sCydYU3nrrLWzbtg3XX3894uLi8PPPP+Pnn3/G2LFjAy7pw1A7SEtLq3ESfTGAkScGBgYGhlrBNddcg9WrV2Pq1KmwWCxo1KgRJk+eLKwxyMBQV8B8nhgYGBgYGBgYQgDzeWJgYGBgYGBgCAGMPDEwMDAwMDAwhADm8xQE3G43zpw5A5PJxGYfMDAwMDAw1BFwHIfy8nLUq1cPanXk9CJGnoLAmTNn2EwQBgYGBgaGOoqTJ09GdPFrRp6CgMlkAkAyP9BK3AwMDAwMDAyxgbKyMjRs2FDoxyMFRp6CADXVmc1mRp4YGBgYGBjqGCLtcsMcxhkYGBgYGBgYQgAjTwwMDAwMDAwMIYCRJwYGBgYGBgaGEMB8nhgYGBgYqgW32w273R7tZDBcotDpdBENQxAMGHliYGBgYAgbdrsdx44dg9vtjnZSGC5RqNVq5ObmQqfT1do9GXliYGBgYAgLHMchPz8fGo0GDRs2rPXRPwMDDWKdn5+PRo0a1Voga0aeGBgYGBjCgtPpREVFBerVq4eEhIRoJ4fhEkVGRgbOnDkDp9MJrVZbK/dkwwQGBgYGhrDgcrkAoFbNJQwMStDyR8tjbYCRJwYGBgaGaoGt+ckQTUSj/DHyxMDAwMDAwMAQAhh5YmBgYGBgiCGoVCp8//331brG5MmTcdlllwm/R48ejSFDhvg9h+M4jB07FqmpqVCpVNixY0e10nAxg5EnBgYGBoZLEhs3boRGo8GgQYNCPrdJkyZ49913I5+oKGLlypWYP38+li1bhvz8fLRv3z7aSYpZMPLEwBBjqKiIdgoYGC4NfPzxx3jkkUfw+++/48yZM9FOTtRx5MgR5OTk4JprrkF2djbi4tiEfF9g5ImBIYbw88+A2QzMnh3tlDAwXNywWCz46quv8NBDD2HQoEGYP3++xzE//fQTrrjiChgMBqSnp+PWW28FAPTq1QsnTpzA448/DpVKJTgsK01lAPDuu++iSZMmwu8tW7agX79+SE9PR1JSEq677jr8888/NfWYHnjllVeQkZEBs9mMcePGCZHhR48ejUceeQR5eXlQqVRCmsvLy3H33XfDaDQiJycHM2bMQK9evTBx4kThmrNnz0aLFi1gMBiQlZWF22+/vdaeJ1pg5ImBIYawcSPgcgF//RXtlDAwhA6OA6zW6PxxXGhpXbJkCVq3bo1WrVrhnnvuwSeffAJOcpHly5fj1ltvxY033ojt27dj7dq1uPLKKwEAS5cuRYMGDTBlyhTk5+cjPz8/6PuWl5dj1KhR+PPPP7Fp0ya0aNECN954I8rLy0N7gDCwdu1a7Nu3D+vWrcPixYuxdOlSvPLKKwCAmTNnYsqUKWjQoAHy8/OxZcsWAMATTzyBv/76Cz/++CNWr16NP/74Q0b2tm7dikcffRRTpkzBgQMHsHLlSvTs2bPGnyXaYJocA0MMoayMfJaWRjcdDAzhoKICSEyMzr0tFsBoDP74jz/+GPfccw8AYODAgSgtLcX69evRq1cvAMBrr72GO+64QyAXANCpUycAQGpqKjQaDUwmE7Kzs0NKZ+/evWW/P/zwQyQnJ2P9+vUYPHhwSNcKFTqdDp988gkSEhLQrl07TJkyBU8//TSmTp2KpKQkmEwmaDQa4ZnKy8uxYMECfPHFF+jTpw8A4NNPP0W9evWEa+bl5cFoNGLw4MEwmUxo3LgxLr/88hp9jlhAVJWnOXPmoGPHjjCbzTCbzejWrRt+/vlnYX+vXr0ESZT+jRs3TnaNvLw8DBo0CAkJCcjMzMTTTz8Np9MpO2bdunXo3Lkz9Ho9mjdv7lWeZWCIBdDBJyNPDAw1hwMHDmDz5s248847AQBxcXEYPnw4Pv74Y+GYHTt2CIQhkjh79iweeOABtGjRAklJSTCbzbBYLMjLywvq/EWLFiExMVH4++OPP4K+d6dOnWSR4Lt16waLxYKTJ096Pf7o0aNwOByC4gYASUlJaNWqlfC7X79+aNy4MZo2bYoRI0Zg0aJFqLgEHDejqjw1aNAAb7zxBlq0aAGO47BgwQLccsst2L59O9q1awcAeOCBBzBlyhThHOmLd7lcGDRoELKzs7Fhwwbk5+dj5MiR0Gq1eP311wEAx44dw6BBgzBu3DgsWrQIa9euxZgxY5CTk4MBAwbU7gMzMAQAJU9UgWJgqEtISCAKULTuHSw+/vhjOJ1OmYLCcRz0ej3ef/99JCUlIT4+PuQ0qNVqmekPABwOh+z3qFGjUFRUhJkzZ6Jx48bQ6/Xo1q2b4HsUCDfffDOuuuoq4Xf9+vVDTmckYTKZ8M8//2DdunX45Zdf8NJLL2Hy5MnYsmULkpOTo5q2mkRUydNNN90k+/3aa69hzpw52LRpk0CeEhISfMqiv/zyC/bu3Ys1a9YgKysLl112GaZOnYpnn30WkydPhk6nw9y5c5Gbm4vp06cDANq0aYM///wTM2bMYOSJIebAzHYMdRkqVWims2jA6XTis88+w/Tp09G/f3/ZviFDhmDx4sUYN24cOnbsiLVr1+Lee+/1eh2dTuexHEhGRgYKCgrAcZzgRK6MlfTXX39h9uzZuPHGGwEAJ0+exPnz54NOv8lkgslkCvp4KXbu3InKykqBGG7atAmJiYlo2LCh1+ObNm0KrVaLLVu2oFGjRgCA0tJSHDx4UObXFBcXh759+6Jv3754+eWXkZycjF9//RW33XZbWOmsC4gZh3GXy4Uvv/wSVqsV3bp1E7YvWrQI6enpaN++PSZNmiSTAzdu3IgOHTogKytL2DZgwACUlZVhz549wjF9+/aV3WvAgAHYuHGjz7TYbDaUlZXJ/hgYagPMbFf3sGsX0LQp8MUX0U4JQzBYtmwZiouLcf/996N9+/ayv6FDhwqmu5dffhmLFy/Gyy+/jH379mH37t148803hes0adIEv//+O06fPi2Qn169eqGwsBBvvfUWjhw5glmzZslcUQCgRYsW+Pzzz7Fv3z78/fffuPvuu8NSucKB3W7H/fffj71792LFihV4+eWXMWHCBKjV3qmAyWTCqFGj8PTTT+O3337Dnj17cP/990OtVgvkcNmyZXjvvfewY8cOnDhxAp999hncbrfMtHcxIurkaffu3UhMTIRer8e4cePw3XffoW3btgCAu+66CwsXLsRvv/2GSZMm4fPPPxcc/ACgoKBARpwACL8LCgr8HlNWVobKykqvaZo2bRqSkpKEP1+snIEh0qA8vaws9NlDDNHBr78Cx44BP/wQ7ZQwBIOPP/4Yffv2RVJSkse+oUOHYuvWrdi1axd69eqFr7/+Gj/++CMuu+wy9O7dG5s3bxaOnTJlCo4fP45mzZohIyMDALFszJ49G7NmzUKnTp2wefNmPPXUUx73Ly4uRufOnTFixAg8+uijyMzMrNmH5tGnTx+0aNECPXv2xPDhw3HzzTdj8uTJfs9555130K1bNwwePBh9+/ZF9+7d0aZNGxgMBgBAcnIyli5dit69e6NNmzaYO3cuFi9eLFiPLlpwUYbNZuMOHTrEbd26lXvuuee49PR0bs+ePV6PXbt2LQeAO3z4MMdxHPfAAw9w/fv3lx1jtVo5ANyKFSs4juO4Fi1acK+//rrsmOXLl3MAuIqKCq/3qaqq4kpLS4W/kydPcgC40tLS6j4uA4NfNG7McYQ2cVx5ebRTwxAMpk8n7+u226KdktpHZWUlt3fvXq6ysjLaSWGoJVgsFi4pKYmbN29etJMiwF85LC0trZH+O+qhCnQ6HZo3bw4A6NKlC7Zs2YKZM2figw8+8DiWOskdPnwYzZo1Q3Z2tmwkAJCZDAAEP6ns7Gxhm/QYs9nsUyrV6/XQ6/XVezAGhjAgtRCXlkZv2jdD8HC7yafCL5iB4aLA9u3bsX//flx55ZUoLS0VJnDdcsstUU5ZdBF1s50SbrcbNpvN6z7qeJeTkwOATLPcvXs3zp07JxyzevVqmM1mwfTXrVs3rF27Vnad1atXy/yqGBhiARwn+jwBzO+proD6DCsipDAwXDT473//i06dOqFv376wWq34448/kJ6eHu1kRRVRVZ4mTZqEG264AY0aNUJ5eTm++OILrFu3DqtWrcKRI0fwxRdf4MYbb0RaWhp27dqFxx9/HD179kTHjh0BAP3790fbtm0xYsQIvPXWWygoKMCLL76I8ePHC8rRuHHj8P777+OZZ57Bfffdh19//RVLlizB8uXLo/noDAwesNnkHTCbp1A3wJQnhosZl19+ObZt2xbtZMQcokqezp07h5EjRyI/Px9JSUno2LEjVq1ahX79+uHkyZNYs2YN3n33XVitVjRs2BBDhw7Fiy++KJyv0WiwbNkyPPTQQ+jWrRuMRiNGjRoliwuVm5uL5cuX4/HHH8fMmTPRoEEDzJs3j4UpYIg5KMkSU57qBih5YsoTA8Olg6iSJ2k0VyUaNmyI9evXB7xG48aNsWLFCr/H9OrVC9u3bw85fQwMtQnl0laMPNUNMOWJgeHSQ8z5PDEwXKpQKk/MbFc3wHyeGBguPTDyxMAQI2DKU90EU54YGC49MPLEwBAjYOSpboL5PDEwXHpg5ImBIUbAzHZ1E9Rsx5QnBoZLB4w8MTDECJjyVDfBlCcGfxg9ejSGDBki/O7VqxcmTpxY6+lYt24dVCoVSkpKqnUdZfqbNGmCd9991+85BQUF6NevH4xGI5KTk6t1/1gBI08MDDECFqqgboL5PNU9jB49GiqVCiqVSljlYsqUKXDWAgNeunQppk6dGtSxkSI80caMGTOQn5+PHTt24ODBg9FOTkQQ9eVZGBgYCKjylJREiBMjT3UDTHmqmxg4cCA+/fRT2Gw2rFixAuPHj4dWq8WkSZM8jrXb7dDpdBG5b2pqakSuU5dw5MgRdOnSBS1atIh2UiIGpjwxMMQIKHlq0IB8Mp+nugHm81Q3odfrkZ2djcaNG+Ohhx5C37598eOPPwIQTW2vvfYa6tWrh1atWgEATp48iWHDhiE5ORmpqam45ZZbcPz4ceGaLpcLTzzxBJKTk5GWloZnnnkGHMfJ7qs0e9lsNjz77LNo2LAh9Ho9mjdvjo8//hjHjx/H9ddfDwBISUmBSqXC6NGjAZBlzKZNm4bc3FzEx8ejU6dO+Oabb2T3WbFiBVq2bIn4+Hhcf/31snRGGuXl5bjzzjthNBpRv359zJo1S9jXpEkTfPvtt/jss89kz7B//3706NEDBoMBbdu2xZo1a6BSqfD9998DIIR1woQJyMnJgcFgQOPGjTFt2rQae4ZQwZQnBoYYASVLDRoAe/Yw5amugClPEnAcUFERnXsnJAAqVdinx8fHo6ioSPi9du1amM1mrF69GgDgcDgwYMAAdOvWDX/88Qfi4uLw6quvYuDAgdi1axd0Oh2mT5+O+fPn45NPPkGbNm0wffp0fPfdd+jdu7fP+44cORIbN27Ee++9h06dOuHYsWM4f/48GjZsiG+//RZDhw7FgQMHZIvZT5s2DQsXLsTcuXPRokUL/P7777jnnnuQkZGB6667DidPnsRtt92G8ePHY+zYsdi6dSuefPLJsPMmEN5++208//zzeOWVV7Bq1So89thjaNmyJfr164ctW7Zg5MiRMJvNmDlzJuLj4+FyuTBkyBA0atQIf//9N8rLyz3S99577+HHH3/EkiVL0KhRI5w8eRInT56ssWcIFYw8MTDECKjy1LAh+WTkqW6A+TxJUFEBJCZG594WC2A0hnwax3FYu3YtVq1ahUceeUTYbjQaMW/ePMFct3DhQrjdbsybNw8qnqR9+umnSE5Oxrp169C/f3+8++67mDRpEm677TYAwNy5c7Fq1Sqf9z548CCWLFmC1atXo2/fvgCApk2bCvupiS8zM1NwtLbZbHj99dexZs0aYYH7pk2b4s8//8QHH3yA6667DnPmzEGzZs0wffp0AECrVq2we/duvPnmmyHnTzDo3r07nnvuOQBAy5Yt8ddff2HGjBno168fMjIyoNfrER8fj+zsbADAypUrceTIEaxbt07Y9tprr6Ffv37CNfPy8tCiRQv06NEDKpUKjRs3rpG0hwtGnhgYYgRS5Un6myG2wSKM100sW7YMiYmJcDgccLvduOuuuzB58mRhf4cOHWR+Tjt37sThw4dhMplk16mqqsKRI0dQWlqK/Px8XHXVVcK+uLg4dO3a1cN0R7Fjxw5oNBpcd911Qaf78OHDqKiokBENgJi5Lr/8cgDAvn37ZOkAIBAtXxg3bhwWLlwo/LZYLEGnSXntbt26+Z2Bd+DAATRs2FAgTgBw5ZVXyo4ZPXo0+vXrh1atWmHgwIEYPHgw+vfvH3SaahqMPDEwxAiUPk9VVYDdDkTIT5WhhsCUJwkSEogCFK17h4Drr78ec+bMgU6nQ7169RAXJ+8OjQoVy2KxoEuXLli0aJHHtTIyMkJPLyCY4UIBJTXLly9H/fr1Zfv0en1Y6QCAKVOm4Kmnngr7/Eijc+fOOHbsGH7++WesWbMGw4YNQ9++fT18u6IFRp4YGGIElDxJ28PSUiDMdpmhlsB8niRQqcIynUUDRqMRzZs3D/r4zp0746uvvkJmZibMZrPXY3JycvD333+jZ8+eAACn04lt27ahc+fOXo/v0KED3G431q9fL5jtpKDKl4vKmwDatm0LvV6PvLw8n4pVmzZtBOd3ik2bNvl9vszMTGRmZvo9xheU1960aRPatGnj8/hWrVrh5MmTOHv2LLKysgAAW7Zs8TjObDZj+PDhGD58OG6//XYMHDgQFy5ciIkZi2y2HQNDjICa6ZKTRbcRZrqLfVDyxHGiCY/h4sPdd9+N9PR03HLLLfjjjz9w7NgxrFu3Do8++ihOnToFAHjsscfwxhtv4Pvvv8f+/fvx8MMP+43R1KRJE4waNQr33Xcfvv/+e+GaS5YsAQA0btwYKpUKy5YtQ2FhISwWC0wmE5566ik8/vjjWLBgAY4cOYJ//vkH//vf/7BgwQIAxAR36NAhPP300zhw4AC++OILzJ8/v8by5q+//sJbb72FgwcPYtasWfj666/x2GOP+Ty+X79+aNasGUaNGoVdu3bhr7/+wosvvggAgj/ZO++8g8WLF2P//v04ePAgvv76a2RnZ8dMkE1GnhgYYgRUeTKZSKwngDmN1wVICRNTny5eJCQk4Pfff0ejRo1w2223oU2bNrj//vtRVVUlKFFPPvkkRowYgVGjRqFbt24wmUy49dZb/V53zpw5uP322/Hwww+jdevWeOCBB2C1WgEA9evXxyuvvILnnnsOWVlZmDBhAgBg6tSp+M9//oNp06ahTZs2GDhwIJYvX47c3FwAQKNGjfDtt9/i+++/R6dOnTB37ly8/vrrNZY3Tz75JLZu3YrLL78cr776Kt555x0MGDDA5/EajQbff/89LBYLrrjiCowZMwYvvPACAMBgMAAATCYT3nrrLXTt2hVXXHEFjh8/jhUrVkCtjg3aouJ8ebIxCCgrK0NSUhJKS0t9yrUMDNWFTkf8ZvLygAEDgH37gF9/BfhQLwwxipEjgc8/J9/Ly6M32SwaqKqqwrFjx5Cbmyt0egwM4eCvv/5Cjx49cPjwYTRr1iykc/2Vw5rqv5nPEwNDDMBmEx2OmfJUt0DNdgBTnhgYgsV3332HxMREtGjRAocPH8Zjjz2G7t27h0ycogVGnhgYYgDSRYGl5In5PMU+pGY7NuOOgSE4lJeX49lnn0VeXh7S09PRt29fIS5VXQAjTwwMMQBKkhISAI0GoOoyU55iH0x5YmAIHSNHjsTIkSOjnYywERueVwwMlzikzuIAM9vVJTDyxMBw6YGRJwaGGABVnqjixMx2dQdS8nSpmu3YvCOGaCIa5Y+RJwaGGABTnuouLuVQBRqNBgBZGoSBIVqg5Y+Wx9oA83liYIgBUPJElSfm81R3cCkrT3FxcUhISEBhYSG0Wm3MxOBhuHTgdrtRWFiIhIQEjyV2ahKMPDEwxACoeU6pPDGzXezjUvZ5UqlUyMnJwbFjx3DixIloJ4fhEoVarUajRo2E6OS1AUaeGBhiAMxsV3dxqYcq0Ol0aNGiBTPdMUQNOp2u1lVPRp4YGGIASodxZrarO7iUlScKtVrNIowzXFJgBmoGhhgAU57qLi5lnycGhksVjDwxMMQAlA7jzOep7oApTwwMlx4YeWJgiAH4chgvL5d3zgyxh0vd54mh7uDAAeCWW4CtW6OdkroP5vPEwBADUJrtqALFcWQfJVMMsQemPDHUFSxeDPz4I1C/PtC1a7RTU7fBlCcGhhiA0mHcYAB0Ovk+htgE83liqCuwWMhnZWV003ExgJEnBoYYgFJ5ApjTeF0BU54Y6gooaaqqim46LgYw8sTAEANQOoxLvzPyFNtgPk8MdQUVFeSTkafqg5EnBoYYgNJhHGAz7uoKmPLEUFdAlSebLbrpuBjAyBMDQwzAm/LEzHZ1A8zniaGugJntIgdGnhgYogy7XRwJMp+nugep2Y4pT3J88w3QoweQlxftlDAAotmOKU/VByNPDAxRBlWdADl5Yj5PdQNMefKNTz4B/voLWL062ilhAJjyFEkw8sTAEGVQ8hQfD8RJIq8xn6e6Aebz5Bu0s2ZrBscGmM9T5MDIEwNDlOHNWRwQlSdGnmIbTHnyDdpZM1IZG2Cz7SIHRp4YGKIMb87iAJCQQD5pg8cQm2A+T75BO2lGKmMDzGwXOUSVPM2ZMwcdO3aE2WyG2WxGt27d8PPPPwv7q6qqMH78eKSlpSExMRFDhw7F2bNnZdfIy8vDoEGDkJCQgMzMTDz99NNwKlqwdevWoXPnztDr9WjevDnmz59fG4/HwBAUfClPRiP5tFprNz0MoSGSytPFZt6inTUjT7EBZraLHKJKnho0aIA33ngD27Ztw9atW9G7d2/ccsst2LNnDwDg8ccfx08//YSvv/4a69evx5kzZ3DbbbcJ57tcLgwaNAh2ux0bNmzAggULMH/+fLz00kvCMceOHcOgQYNw/fXXY8eOHZg4cSLGjBmDVatW1frzMjB4g7fo4gAjT3UFkfJ5ev55ICUF2L+/+mmKFTDlKbbAzHYRBBdjSElJ4ebNm8eVlJRwWq2W+/rrr4V9+/bt4wBwGzdu5DiO41asWMGp1WquoKBAOGbOnDmc2WzmbDYbx3Ec98wzz3Dt2rWT3WP48OHcgAEDgk5TaWkpB4ArLS2tzqMxMHjFvHkcB3Dc4MHy7YsWke19+kQnXQzBoXFj8p4Ajnv++fCv06MHucaiRRFLWtSRkUGe6aWXop0SBo7jOI2GvA+tNtopqT3UVP8dMz5PLpcLX375JaxWK7p164Zt27bB4XCgb9++wjGtW7dGo0aNsHHjRgDAxo0b0aFDB2RlZQnHDBgwAGVlZYJ6tXHjRtk16DH0Gt5gs9lQVlYm+2NgqClQZYn6OFEw5aluIFLKEzWlXEymO6Y8xQ4cDtE/z+GQl1uG0BF18rR7924kJiZCr9dj3Lhx+O6779C2bVsUFBRAp9MhOTlZdnxWVhYKCgoAAAUFBTLiRPfTff6OKSsrQ6WPpaWnTZuGpKQk4a9hw4aReFQGBq+gnaVeL9/OyFPdQKR8nmg5uJj8URh5ih0oJ55cTOUsGog6eWrVqhV27NiBv//+Gw899BBGjRqFvXv3RjVNkyZNQmlpqfB38uTJqKaH4eIG7Vh0Ovl2Sp7YbLvYBlOevMPlEss2I0/Rh1IrYH5P1UNc4ENqFjqdDs2bNwcAdOnSBVu2bMHMmTMxfPhw2O12lJSUyNSns2fPIjs7GwCQnZ2NzZs3y65HZ+NJj1HO0Dt79izMZjPi4+O9pkmv10OvlAEYGGoItLPUauXbqRmPKU+xDWmoAqY8iZB2ziyEQ/TByFNkEXXlSQm32w2bzYYuXbpAq9Vi7dq1wr4DBw4gLy8P3bp1AwB069YNu3fvxrlz54RjVq9eDbPZjLZt2wrHSK9Bj6HXYGCINmin6Ut5YuQpthEp5YmWg4tFeZJ21kx5ij6Y2S6yiCp5mjRpEn7//XccP34cu3fvxqRJk7Bu3TrcfffdSEpKwv33348nnngCv/32G7Zt24Z7770X3bp1w9VXXw0A6N+/P9q2bYsRI0Zg586dWLVqFV588UWMHz9eUI7GjRuHo0eP4plnnsH+/fsxe/ZsLFmyBI8//ng0H53hEkRhIdCtG/DRR/LtwZjtOK7m08cQHiLl80Q7s4ulU5MqG4w8RR9MeYosomq2O3fuHEaOHIn8/HwkJSWhY8eOWLVqFfr16wcAmDFjBtRqNYYOHQqbzYYBAwZg9uzZwvkajQbLli3DQw89hG7dusFoNGLUqFGYMmWKcExubi6WL1+Oxx9/HDNnzkSDBg0wb948DBgwoNafl+HSxh9/AJs2ke8PPCBuD2S2c7nIMcySHJuIVIRxpjwx1CSU5OliIenRQlTJ08cff+x3v8FgwKxZszBr1iyfxzRu3BgrVqzwe51evXph+/btYaWRgSFS8NU5BjLbAcR0x8hTbIIpT97BlKfYgtJsx5Sn6iHmfJ4YGC5WUFUiWPIUFyduY35PsQvm8+QdTHmKLTCzXWTByBMDQy2BdiDKztGXzxPAFgeuC4iE8uR0itdhyhNDTYCZ7SILRp4YGGoJvsiTL58ngM24qwuIhM+TtExcLJ0aI0+xBWa2iywYeWJgqCUEIk/elCdGnmIfkVCepGXiYjTbsThP0QdTniILRp4YGGoJ4ZAnZraLfUTC50nakV0snRpTnmILzOcpsmDkiYGhluDLYdyfzxNTnmIbHMeUJ19gDuOxBRYkM7Jg5ImBoZbAfJ4uPiiDlzLlSQRTnmILTHmKLBh5YmCoJTCz3cUHqeoEMOVJCqY8xRYYeYosGHliYKgl0A7E7ZbP0GIO43UXSvLEZtuJYMpTbIGZ7SILRp4YGGoJ0g5E2lkyn6e6CykJBsInCdKOjClPDDUBpjxFFow8MTCEiJ07gd27Qz/PF3ny5/PEzHaxjWgqT+fPA3l54d2vNsCUp9gCbUPogIwpT9UDI08MDCHAZgOuvRbo2TP0DkHasXojT0x5qj4mTgS6dq29UXWkfJ7CUZ66dwfatAHKysK7Z02DKU+xBfo+UlLIJ1OeqgdGnhgYQkB5OfkrKQFKS0M7N5DyxMhT9fHFF8C2bcCePbVzP6XZrraUJ4cDOHiQqAmFheHds6Yh7ZxZkMzoQ0memPJUPTDyxMAQAqQNTqgj/kA+TyxUQfVB87W2OoZoKU/FxdW/Z02Dme1iC9Rsx5SnyICRJwaGECDt5MrLQzs3HOWJ+TyFBpqX0TLb1ZbydOFC9e9Z02Bmu9gCM9tFFow8MTCEAGmDo1SelCYcJZjZruZB87iuKU/VIU+xSkyY8hRbYGa7yIKRJwaGEODLbFdRATRvDgwZ4vtcFqqgZuF2iypMbY2qI+XzFKrZjilPDKGCme0ii7hoJ4CBoS7Bl9nuwAHg+HHgxAnAYgESEz3PDTTbjoUqqB6kHXRdNttREhjnp3Wua8qT00mWslGpopeeSx1MeYosmPLEwBACfClPdOYdxwH//uv9XG/Kk8sldsBMeaoepPlb18x2yvQGUp/qmvIExG46LxUwn6fIgpEnBoYQEIg8AcCuXd7P9UaepJ1kbZCnn38G/vtfzwVtLwZI8zJaZjuO8yRUwUBJlgKRv7qmPAGxm85LAS6XWKaSk8knI0/VAyNPDAJWrgTuuEPeMDPIEQx52rnT+7neyJN0W23Mtnv4YeDpp4H9+yNzvVhCNMiTN6IUDknwtVi0LzDliSEUSOtDair5ZGa76oGRJwYB06cDX30FrFoV7ZTELnz5PEVCeQoU5ykSahGNDySNE3SxIJo+TxqNuC0ckqDsyJjyxBBJSIksU54iA0aeGASUlJBP5YiRQYSvUAU07wBCnrwRHX/kSaMB1F5qIyVPbndkRor03V6MDWc468NVF5Q86fXiNqY8ESjbEUaeogeqXOt0oprNlKfqgZEnBgFUSblYVnWvCQRjtisrI7PulPA2285fjCdAbOiA6pvuXC7xfhcjQY6mz5P0/THliYApT7EDWt8TEkSifzEOoGoTjDwxCKDk6VJu5KZMAfr3900ggzHbAd5Nd/58nnyRp7g4cV91ncaljeXFSJ6iabbTakXlkClPRHlVEsBLuV2JNmh9j48HDAbynSlP1QMjTwwCmPIEzJ0LrF7tO9xAIOWJxrHx5jTuz2znzd+JIlIz7qSE6WIcdUbTbKdWi3GZwiEzF9tsO2n5qg6pZIgMqGodH8+Up0iBkScGAGSkaLGQ75cyefLmyC1FIPLUsSP5DFZ5CmS2A0TTXSTJ08WoPEXTbKdWiwQ4HJIQitnO5ZL72MWi8iTNf7OZfDLyFD1IzXZUeXK5YrPs1BUw8sQAgIxMqJPzpdzI0Q7YV6MSyGx37bXkM1TlyR95ospTdX2eLnbyFO3ZdpFUnvwNYEpL5RMSYrG+0vKl0RC1A4jNdF4q8Ga2A5jprjpg5IkBgJwIXMrKE23gfTX0gZQnSp4OH/ZUirw5jNP7MLNd9RHNOE+1qTwp47DFonpA899gEPMlFtN5qcCb2Q64ONuB2gIjTwwAGHmiCJU8UQWAmlFatgQyM8n2PXu8XxtgZruawMXk8+SvDirJUywqOlKlozqkkiEykJrtpCopU57CByNPDAAYeQJIR0h9WHw19NKRmtstjuio8pSUBHTqRL4rTXfMbFeziIbZLho+T3VVeWLkKXqQklmAOY1HAow8MQCQk6dLtZGTPncwyhNA8s3hEBunpCTfTuPVJU9MefKPaJrtatPnqS4pT4w8xQbowIuq2CxcQfURF+0EMMQGmPIkf+5gyVNZmdhpAmRmUSjKUzA+TzVhtrsYR5zRNttVhyRII81LF3H1hrqkPDGzXWyAKU+RByNPDADEMAXApUuewlGeysrEdc0SEwmRatmS/FZGGWdmu5pFtM12NMZXdSKMm81k3cG6rjxJzXYszlP0oSRPVHli5Cl8MLMdAwBmtgPCN9tJ/Z0AsYGSHstxYkcLMLNdTSDaZrtIKE8mE/ms68qTtLOmyuyl2q7EApjZLvJg5IkBADPbAfLnDibOE0CUJzrTjpInSoT8XY92JNEiTxfjiDPaoQqq4/NEy1ViIvkMRnmiSlcskhLmMF5z4Dhg5kzgr7+CP4eZ7SIPZrZjAMDIExC+2Y52oP7Ik8MBJMCKGXgcS3Eb7PaBwvZMnMVT2x8HBhR53tBoRMPGrwFow8x2ASB9Z3XV5ykU5Sk9HSgsjH3lqc4G3z1wAJg2DXj+edEWHwP45x9g4kSgQwfvKxl4gy+zHVOewgcjTwwAGHkCgiNPdKQWF0c6rfJy0RwXiDz1w2qMxUdoi734L0+e7HZgKL7FtScXAye93/PKng0AvMfMdgEQ7eVZIjHbLhTylJlJyFMskhKp8kTTF4skzy8+/hhYsIBk9FtvRTs1Aor4MZbSfOsPSrMdU56qD0aeGAAwnycgtNl26elAQQFRnuixSvLkcJBRt0pFvieBOEeZUSbzeaLbcd11wJgx4s3WrgXmz0eS9QwA5vMUCHXZ50nqMA4EZ7bLyiKBWGORlEhDFdB3UefaFerMSD9jBDRvQ1GimcN45MHIEwMANtsOCM1sl5EhkieaX8nJ5FO6/IHdTn47HIARhP0YYZWFKqDb0aEDcM894sl6PTB/PoyWswCYz1MgSN+Z00lUIToTsqYQ6QjjoSpPQGySEmmoAlpuYzGdfkETLm0cYwDhkCfp8iyA2EYxs134YA7jDACY2Q4ITXnKyCCfZWWes+2kzt/ShYa9kSe7XUKeqGc4RXY2ACChrAAAC1UQCMpyWxsdgzezXagkgeOCdxjnOLnyBMS+8lRnHcZphZM2jjEAmrc2m3wGbzDnKGfbXYyDqNpCVMnTtGnTcMUVV8BkMiEzMxNDhgzBgQMHZMf06tULKpVK9jdu3DjZMXl5eRg0aBASEhKQmZmJp59+Gk5Fi7Ju3Tp07twZer0ezZs3x/z582v68eoUmNkuNOUpPZ18lpf7nm0HwKvCFDR54ntHQ2nkladLgTzVRsfgzWwXKplxuUSn6kDKk9THrq4oT3WWPMW48qT8Hsw5zGE8cogqeVq/fj3Gjx+PTZs2YfXq1XA4HOjfvz+sil7igQceQH5+vvD3lsR5z+VyYdCgQbDb7diwYQMWLFiA+fPn46WXXhKOOXbsGAYNGoTrr78eO3bswMSJEzFmzBisWrWq1p411sGUJ0+zjzcEozxpNGJgQJ/kycYJ+wMpT3EV5UiAlZntAkDZOdcmeaqO8iTtwCh58lUHqepkMIj+UbGuPNXZOE8xSp6kCnSwajRzGI88ourztHLlStnv+fPnIzMzE9u2bUPPnj2F7QkJCcjmOxIlfvnlF+zduxdr1qxBVlYWLrvsMkydOhXPPvssJk+eDJ1Oh7lz5yI3NxfTp08HALRp0wZ//vknZsyYgQEDBtTcA9YhMPIUvtmOEiVKngCiPlVVeSdPanBQ2aoAxMt9npTkyWQSPG6zcBYVFU3DfzhcespTbYyqvYUqCJXMSNMdSHmi5Ck1NbZJyUWhPMW42Q4InjzVpPJkt5MBJG0TLxXElM9TKT+ET01NlW1ftGgR0tPT0b59e0yaNAkVkhKzceNGdOjQAVnUAQDAgAEDUFZWhj179gjH9O3bV3bNAQMGYOPGjV7TYbPZUFZWJvu72MHIU+gO44D3COOAp0OmjCQBiLOR736VJ5VKUJ+ycDaiypPdHry/RF1BNMx2kfB5oulWqcQiEEh5Sk0Nn6zVBi6KIJkhKk8//wyMGlXzXCsSZrtIKk+33AI0aADk51f/WnUJMUOe3G43Jk6ciO7du6N9+/bC9rvuugsLFy7Eb7/9hkmTJuHzzz/HPZIZSQUFBTLiBED4XVBQ4PeYsrIyVHopfdOmTUNSUpLw17Bhw4g9Z6xC2j7UZiPHccD33wOHD9fePX0hkPIkdez1Z7YDPGM9ORxAIsRM1tgqhP0J4AcDSvIECOQpGwWwWkXfmHCgLOoXm79DNM121fF5ou9Bpws8C6quKE/SzjqWSZ5fhEieXnsN+Owz4KefajBNCE958rU8SyTqyO7dpB07cqT616pLiJlQBePHj8e///6LP//8U7Z97NixwvcOHTogJycHffr0wZEjR9CsWbMaScukSZPwxBNPCL/LysouagLFcdELVbB9O3DrrUD37oDi1dc6AilP0nyRkidlhHHAkzxJZ9sBQSpPgOA0noWzAnmjDV+oUJKnykqxMb0YEG2zXXWVJ53Oe4BVKZjyFBhOJzBhAtCrF3DHHdW4EGUcFosYsM0Pzp8nn8oFwSONUMkTx/lWniJRR+g1LjX/qZhQniZMmIBly5bht99+Q4MGDfwee9VVVwEADvNSRXZ2Ns6ePSs7hv6mflK+jjGbzYinpUkCvV4Ps9ks+7uYoVQ0apM8neSjap86VXv39IVA5Ena0HibbUfjPAHelScpedI5rJ7bAyhPQPVm3HkjTxcTom22q67ypNdffMpTNMjTli3ABx8AL79czQvRyuZ0BsUyiovJ50kfKwVECqGSJ7tdbN9rIkgmI09RAMdxmDBhAr777jv8+uuvyM3NDXjOjh07AAA5OTkAgG7dumH37t04d+6ccMzq1athNpvRtm1b4Zi1a9fKrrN69Wp069YtQk9St6G00dcmeaL3jgWfzEBmO2n7SZUni0XcHorPk9buRXnyJgPxylM9dfXDFVzs5CnaZruaUp7cbqJwAnVPeYqGwzitI/7qitsN/Pabn+DhTqf8JQQw3XGcSJ7y8oJPazgIlTxJj1Ga7ZjyFD6iSp7Gjx+PhQsX4osvvoDJZEJBQQEKCgoEP6QjR45g6tSp2LZtG44fP44ff/wRI0eORM+ePdGxY0cAQP/+/dG2bVuMGDECO3fuxKpVq/Diiy9i/Pjx0PM92Lhx43D06FE888wz2L9/P2bPno0lS5bg8ccfj9qzxxKUxKU2R7K0Uygrq54/TyQQrPIUFycnShR0phQQvPIU0GzHK0/1NNUPlKkkSxdbYxfNOE+RmG3nS3n6v/8D6tUjZm2mPAUGfe/+3v+KFUDv3sBTT/k4QFnRApCnigrx+WpaeQo1VAF9F1K/vEg5jLvdYvm92NqTQIgqeZozZw5KS0vRq1cv5OTkCH9fffUVAECn02HNmjXo378/WrdujSeffBJDhw7FTxKPPI1Gg2XLlkGj0aBbt2645557MHLkSEyZMkU4Jjc3F8uXL8fq1avRqVMnTJ8+HfPmzWNhCnhQ8kQrlMtVezOx6L2dzuhXPmnn660DVJpXaEMEEOIkXQokEHnSO63gOMBu44LyecpWMeUpEOqqz1Mgh/G//ybvfdgwYP9+so0pT74RjBJC1SGf/knKihZAGqeqk/TaNYVQlSelvxMQOeUpGutJxgqi6jDOBZAaGjZsiPXr1we8TuPGjbFixQq/x/Tq1Qvbt28PKX2XCuigKi0NOEPWoIXDUfPrggGi8gSQ9smLC1qtgRAcC5JRAofD0/eONjT1dOehuqCC2ZwmrHCuVKICOYwnwEoIqt2OOPBM1Y/ylMVVz+dJ6jSalETMFXWaPFVWksUFJab+aJjtIuHzRMuIUWtH4rlTAJrKOiXab+fni9PB64zyFOdEWskJAM2ql86iIpLZNKy6HwSjPNF9PuuTckcA5UlKnkpKyDuTKtGRRLhmO6lXQKSUJ5sNiEcF0lCEqqqLd1KVN8SEwziDb3AcMHs2cYKsKdDGWRpeq7YaZOmALtrhtBwOYCUG4iiaItFS4LG/qgqIgwN/lrYHOnZEUqIozwUiT8pQBXSJFnWlpJH2Q57SnQUAuLDJk9RplL7nOj1SvPNOoGlT4N9/hU1S8xdQd3yeKCmfnP8gOt7aDFfib2Ebx4l1RLrsT11RnprMfBz3vd4c1+PX8NsUtxu4/HKgXbugpBJ6b5fLd94EXFw3RLOdlDwBNWu6i6TyFAnytAyDcQy5iCuIgVk/tQhGnmIcW7YA48cDt90mNtSRhjfyVFtO41LCFG3yZLcD7bAHOjiQWXrIY7/NBqTiAjLdZ4EzZ5BjFBOsJE9K84ur0g4txJY8ARWw28V4T+44rdwOSMGb7eK5SiTCErbPk7TBTUnx3Fan4HYDa9aQ7wcPCptpmaWTY+uaz1MzGwnq2wb7hG3SmbAffSSek5FRN5Sn+GPkmdphT/gkz2olbOT8eUAyMcgXpPzKVxmg233Wp2qY7YDYJE/elKfqmu1sNvJu4+CCMT8GgvXVIhh5inHQ2CGnTgFbt9bMPWi7kJzsuSZbTUPaJkV7xp3DzsEEkgiDzXMajs0mV4+yjOJ3aZgCwFN54izyxpgqTzTek8vgRXUCiBqVmAhADJQZDmgDqlKJRK/OkqdDh7xOqaIkItASJ5FEJCOMG92k7JlRJqSdDijUamDECEKgnnoK6NAhdpUnjhPJiaaS1BEzysIneVLVR8lSvEBKmHyVgZDJU4jKU036PYUaYZw+Y00pT2aQQqps4y52xEyQTAbvkFaO778Hrrwy8vegpMVk8lyTraYRS8oTV1Ep+B8FQ57SDeL3QGY7ZWNMyRMNWeD2RZ4Aoj5ZLPwSLS2CfBo5pNI9bUTrrNlu2zbxu6RTo3lNyVNdizBudJEKYEaZ8CzSuqlSAWPGiOfFqvLkcIhqmaaCPEASSsNPp7Tu0KBqfiB974GUJ5+DkRg224U7264mHMZt5XbEg2SmyhpbCyjXNJjyFOOQkqfvvgvt3M2bga5dgd9/93+ctIGu7ZkxseTzpLaKiTHYA5OnNH31yRNVntwJfsiTJFBmdc12UvIUbeVpzRqgYUNg2bIQT/znH/G7F/IULbOdksz8/TfQrRvgYwlNATTdCS5S/kwoF2a80vrhLU5vrCpP0nKltohqWm0pT7Fgtqst5am6Zrvq1hHHBTFf1BWMPDHEEKSFe/9+capyMPj+ezJI//pr/8fRtokqT0B0lKdom+3iKsTEJNhLPPZXhzypKryTJxrviTP4WSclAosDS8kTHXVGmzytWkXM0TNnhniiD/IUDbOdP5+nL78ENm0Clizxfw2bDVDBjQSnSDQAUnZo/fA2cytWlSdZhxwJ8qRQntxu/zHhQlGebDYfYVnCNNs1bkw+a0p5ks6aBUKbbVcTZjtXsdhmMvLEEFNQdnChqE++4p0cPw5MmyZG11Wa7YDo+DxFXXmqEBNjdARWnpLjfJMnpUNmQPLkT3misZ5QEHZjF4vKE+1M162TR3rmODJy99pBclxA5ak2zXb+fJ5oeQ6Uz7JAqRDJk81Wt5Ung56Din8/1TLbSd6xu6gYXboAPXv6JlChkCdpemUI02zXqRP5rCnlyeGQTxwK12wXKYdxV4nYaGuqGHliiCHQgk8L+/ffB38u7UyUFeTNN4Hnnxdn79AGOjGx9s12seTzpJUqT9UkT0oSqhyVeZAnb2EKKCTKUyTJU7R9nmgZczqBlSvF7c8+S0bwP//s5aSjR+VMK0bMdt58nmgyA5EnqdMtcPEoT2nxFUIGRUp5qjhTgh07SLR1Xx1/KGY7wAcBCdNsxy98gZMna2bFBGVZCoY80XIoJeBS5ak66eQk5CmOkSeGWAKt5AMHEofRzZuB06eDO9dX2Hxa0ffuJZ/RUp6kMWyk6agtKEM/aKvEBCQ6SzyOr6qSkyezOgTyVOldedI7yXaVP/IkUZ7CHSlKIz7HitlO2pn++CP5LC4G3n+ffJeEcBIhVZ0AuMp8m+2i7fNEiU+gdNjtEGZ5AkCSylN58kaeYl15StVJ6keEHMadhaJzUTDEKJhjvJrC6UaVinwGqTy1b09OsdmAwkK/p4SFcMgTnbFNFzMHxME4x1Wv/HClInnS+iFPH31EFmu+mMDIU4yDVpbcXOJ8CgSvPvlSnujvQ3woo2iRJ2kMG6B2lafnniPBiqW+CdpKMQFGV2DlyaSKHHlCYnAO4+GSp1g22wFkrTGHA/jkEzFdXssgT56qVIQBFp0g74DjxOvREXZthypQkplQzHYy5UlFKmQgsx0la05n9NeFlIISk3S9SAjNKAu/k5aa7S6UeNzH1/2BwKEKAB8EhG6kjCNI8pSVJVTXGvF7UqY1GPJEV0FISxO30QEUUL1BBidptLV273m0Zw8wdiwwbpwYIf9iACNPMQ5ppzdkCPm+fHlw5/oiT7SyHOZjmkWLPCnJUm2Sp5UrSaMinfWutUmUpyDIk8yElyw/lo7saD5qqghJshhIC5aACthsgN5NWj+VP/LEK08Xq9kOIDPQ168HZs0St3nt+HjytEVzNfldbvG4VrRCFYTr82SzyZWnYM120piqtbUWZTCgz5sSJ1HTIqQ8ccWBlaeImu34uhes2S4lBWjUiHyvCb+nSCtPQDUHGWVivuh9kKcPPxS/79hRjXvFGBh5inFIVydv3558L/BcOcQrAilPBQWkTZDOtqtNnydle1SbZjvaNkrzRl8lGf27SzzOUZKnBHdg5Ylen4YksBrJ2lxGWFFRIToKq4NVnqrCkxhiWXmipGPiRODYMXG/B4HnOIHt/ursCUD0JYs2eYqo8hSkwzjNN0D+/Pv2iapyNEDzXUqezCiDwx6mPCZRfVSlJR738XX/YI8Jijz5UZ44Tk6eGvJLvNWE8hTO4t7elCdpea1OPVFZxHKrc3jmUUUFsGCB+JuRJ4Zag9RXhcbpCDbWjy+fJylhOHLk0lSevJEnnV1s7E3uwMpTfBDkSak8WY2kMTbCCotFQp5MgZUnPezQWDzTFQxiMVQB7fB79yafe8hKHr7L4MmTQFERuLg4bASxYav5CNbSY2vTbOfN50lJngJ1TkrlyeQOXXmi96yqAq66CrjmmuipUd7IkwZuxNnDDFImUZ40ZZHxeZKWfa8+T7SRDYI8VVaK5a+2lCfqihWu8gREJlyBWkKeDE7PPPrqK/n8jp07w79XrIGRpxiHtNOj5CnYTi+Q2Q4gI1TpbLvaJE9Kpak2yRNtdKR5YZCQJzMXmDwZHMGTJ6o8VZhE5UlKnjQmP3GeDAbYE8gNjOVByo4KRFN5crvJ0kLKckjJ0403imYEtZqs+Qt4KYO8ya68UTucB+kJ6Awf6bH8ajbV6hTy8oIrj958nhwOeYDLUJUnPWzQwh7QYdyb8lRWRs45f95P9OwaBn3eJI28M433Eng2KEiIS5ylRPgeK2Y7qjppNKTs1YbyRNenDNfnCYhMuAKNVSy38V7I09y55JMOkJjyxFBrkJrtwlWefJntALKuarSCZNLOid6zNs12DosNjXFclhcGu9gQJMLqYbtUkic9T57Uas/OTZmPWhs5tpInTwbYUFHuCk55AlCVTEx3RsvZIJ7OE2H7PJ09G9SSGP7w/ffAFVeQ8BhS0OxNTgb69iXfb7oJaNmSfPdFnvJzusACwpDoDB96La22+iPqU6eAXs1O4saeloDqjTefJ6dTLlQEQ56kyhNAftvtwZvtqPIkLc8BfJxrDDTfk9TyZ4p3hDk6krBAnSXys+2qa7aj5CkjyQ7VieO1ojxRImS3+5ktd/Qo7BVOoQzVhPIUJyVPLnkebd9OZodrtWIg3IMHo0fqIw1GnmIcNWG2k/6WjgSi5fNUvz75rC3lyW4HPnWNwHHkwnR8t7Dd4JA39tJpuIBnqIJ4twXjxgEvvywuqEyhHNXRNexspgwxHcVWgTz5dRgHYE8hjbjJWn3lKWizncUCtGpF7EDVwIED5PPECfl2KeGZOhW47Tbg7bc9/cUEbN8OADiUeLlInuxy5Umnq36ncHRdHg46c/H6zhsDBqX15vPkcMjLcqhmO0BcHNif2U6lIqSN3hOQE85oK0/KZ/IWOy0oSIiL1m5FHMjD1ih58ma28zGlkZKn2Y4xQG4uWlvICu41qTxJVSSv9XjNGqBZMzgeewoAKZ+BAvmGgzjJDGWpDygghiYYOpT462ZnkyzcvRsXBRh5inF4UwwqKoKbmhyM8sT3R1CryfVrW3mKgwPTKx/GLfge5eW1M+XaagU6g6gYSXliTY53ysmS64K8sVcqTyqLBXPmAC+9BJLwZ54hc+3hRXnig2HaTWlwgzgsSMkT/MV5AuBMI8qTuTJyylNA8kQDUh48WC02TYUr5SWk5Onyy4FvvwVatAAaFGzFJ7gXxtIz8hPOkN8HHE0F8hTnsgN2u1fyFHansGsX4uBCT/yB9yZf8FsmvUUYdzrl5ClUsx0gLg7sT3nCBx/gNbwg3BOQP3PI5MliAR54AFi92vcxJ04A994L7Nrl8xBKTEwqBXlyVl95AoBklMjuo4Q0D8IOVaBUnjjO56hVCJDpJG1K/SKSN2fORH4QSpNAzXbSbTLwQdI43skoLY0f4B0/Tt7fv/9GRHmShndJ4OTk6auvyOfYseTzssvI58ViumPkKcbhzecJCK5jCMbn6cgR8klXba9tn6dr8QduLZiD1/E8OK52RstWC4cckIAj2vILwna6thiF83yJ7LeSPMmk/AMHiGzy2GMAx3nkI40k7opPhD2OvEhnaQjkKZ004km2WjTbSYOyVEMWpA6jyjIlJU9SdPl9Bu7FfFx99Av5Dt5540hJGqyQ5JfVKrtWdRc95c6Iz52850+sWOH7WF+hCkIhT2EpTxwHTJyIZ12vozGOC89fLfK0YgUwbx7w6qu+j1mwAJg/X4xk6gX0eRO5CJEnhcksEHkKpDw5nXJner9BMjNEpdiX6Y6Sp3QnUYVNjiLo9eQVnTnj9ZSwIV3k168lgq90Kj5Sp6BUffopeX9z5lR/kAFAJ5mhbIRVqBBOpzhoolHXKXm6WJzGwyZPdrsdBw4cgDPWwtteZKCV32CQr00UjOkuGOWJgjbOtWm2KysD0kA6xCycFbbVNKoKy2EEyUBdeZGwPcEVmvIka0zplBaLBbhwwSd54hKMsMWRjt9VXoEEPh2ByBNSSetnshX5P84HwjLbSclTaZgmF8mp/pQnKYwW0gklViiIIk+eDpxPgxNa2MBnssUSUbOd6qxoGr0Wf+DVV30ror5CFUizy2bzjGYvhTflSenz5EGeiouFB0zFBUF5qpbZ7iyf30V+yhg95qxvEk/zPdGtJISlfvPBJxQPkoJi2X2UCOQwrtzm12yXmCjWTT/kKQ4OJDlIvqkvFKFBA7Iv0n5P3gbTNKmVlcSvcPNmCAVQU0LaJcHfidbp0tJqDzIAQG+TLAwMDs7ySlmaADH7LnnlqaKiAvfffz8SEhLQrl075PGl45FHHsEbb7wR8QRe6pBWFq1WbKBDIU++fJ7odFdAnKFU28pTEkglT0ExNBCdG91uYNky4Ny5yN/Xnid2jjqrqDwZXeTmDhAJwVUcAnmSOlWfOOGRj3qePLmNibBrSWviLg9eeaJDR7Oz+uQpaLOdNKBYLZKneCtp8BMrz4sbbTYhv/cUkLygpjtf5CncEXXceZE09lL9jk2bgN9+836sr+VZlIMAf2nxpTxVVfkx20nIizQAZbWUJzoA8PeuKbE6f97nIUJZU/jAhL2+XYhmu0DKU1DkySqpl5S5+pjRUlwMZECyFktRkSBYXbjg9ZSw4Y88LV9OFnx/9lkI71BXdh4AJypPdM0YiyUiZjspeQIAWxF559LVbShJo4sm79oVW0Fdw0XI5GnSpEnYuXMn1q1bB4Mkxnvfvn3xFTVyMkQMyhWxpX5PgUA7FOlK3G632Gk1by4eS9uH2vZ5ouRJDQ6puCB0OitWkJlXjz0W+fs6T4qdo0FKnvj4OqdAho1uJXmq4nyTJ0nkY+TleThjCsuwJBjhCIM8qdJJ65cUQfIUktkuAuQpWLOdoZw08IlSlY3vtDm1GufsxPNVSp68zbaz28VyP2sWWRA7GMQXi8/dGdtghAXTp3s/1tfyLEry5I+oypQnnoGZUYaSEjH9HsqThDxJlz6plvIUCnnyo07RckXN4Bzv1R52lHFaz/gw/oGUp2qTJ6dTzEijURxZ+lGesiEZaBQVCc7Z1ag2XuHPbEfHOsePQyiAarcLSSgVlScJeYqEw3i83T95MhrFQXqLFqTtqagQV7eoywiZPH3//fd4//330aNHD6gk0kW7du1whDrQMEQM0lAFQGgz7qSVwpsJj0YsB6JDnqTKEwCk47zQ6ezbRz4PHoz8fbl8saGLr+Q7AY4TfDROgg/UUlwiO89dZYcWEjN1RYXYe0qVp7w8j3wUAsgZjXDoeKJkDZ48aTIJeUpxRVZ58uugL1WeIuDzFJTyxHHQl5NO3GyXqBt8Z+1MSgPHN1sCebJaZcqTcukJmw149FGynqE/ixSFsVwkTxrOhauxyWdjH4zPExCYPAnKU04OAEKIKJdRqbwUj5pUnsrLfUsDoShP1IewXj0AEVCeeFuYP+VJ6c8UDHnyyCdp45qQEBR5om4HAIDz5wWlMNJuCP7i/lGV6/RpgCsR29UMFNaM8uR2C+/YxddJRzHJowov3ggajej/dDGY7kImT4WFhcjMzPTYbrVaZWSKITKQhioAQguUKSVA9DqByFNt+zzRhhAglZwq49TR0o9rRfgoEDvHhEq+xamqQhxIq0uVJ2kDBACaSi+NJ20lApntXCJJcuhJi6KpKEcCJENJP6DkKZXz3Wn5gzefJ44LQJKj4fNUVga1k+wwOzzJU2WCOEc7kNkOIOW+sFAkOcGErEqykucuy24BAOiJ333GIPPl8xQKebLZJMoTH7fDhHKBn9DJHDIoyJM35SnkOE9SQuSr16fk6cIFnwSL5rGBkif+mcJSnjhOfBCePPlTnoLx7wyoPFE2pVYTJh6E2U5GnoqKaow80bQqZ18DInlyOABHkXxQ6kGerNbqK0+SAlYAMhvYUWKllwfg2axdTH5PIZOnrl27YrlkZVpKmObNm4du3bpFLmUMcLvFgq0kT6GY7QDxOlJ/p7Ztxf2xoDxloFBobGi/fe5c5MMXaM6JiorRxrc4klbuNEhjz5V6J08urV4MsEMbEIXZTpmP8W6RPDl58pRQIemsAihPcVmk9UtDEZyO0DPEm/Ik3e4VEfJ5op1pUGY72rgDSHZ6kqdyHcmH+HjvZjudjihANO6WzSbnBAEDsXIcUuzkuc/1Gg6AkCdfnaC3UAUOh2d2+Rvdy5QnnmhIlSdvMZ6UZruIKk+A7/ddJCq1sjIvAY3jQ5VcFU96wlKeqqrEBoDPGzrg8lZ2/cW087XNoy2VyiYqVUya7bz5PElVVelkl3ScJ2Y7h0N8Z5FQnvhK4UCcEPHfWeJptpPiYppxFxf4EDlef/113HDDDdi7dy+cTidmzpyJvXv3YsOGDVi/fn1NpPGShWzpkDDMdt7Ik+CDoyc2aIpo+zwBcrMdVZ5ofU9Njdx9tUWiopJo51scvlctgwnFIEFUVAqZgi4F4jaaoOH4HpI2qArliY7q7HbyL44j0oDKZIRLT16iqVLiDS9lNF6gyyGkwQAbrMUViMsM4GCugLTR1elIn8BxARrOCChPLpeYRUEpT5IOPMlVTGScuDihZyhWk3xo2RKw7JQoTxrxWtRJtbJSVJ4oApKnCxeg40jhrxj0f8CXr+JqbILDaoPbrfcIhio129Hn4DhPhcuv2a7KDbMf8uQ1xpOE2EqVpxolTzab/KLnz3uErS4rE8OfCKZqyTOFTJ6khIW/TijKU1jkSSmbhGq2KyqC2cQBUNWK2U6pPAGAqtSL8iRlV5EgT0KbaUY5SAdCyZM3sx0gOo3T+IJ1GSErTz169MCOHTvgdDrRoUMH/PLLL8jMzMTGjRvRpUuXmkjjJQtpoQ7VYZzj5J2VkjwZDHKHcdo+1LbZTqk8Kc12QORNd/oLEvLkKAbcbiGaeDlMKFORYaOqTN6B0CVW3AmJYobRBPvwebLZIOtw1IlGuAykRUm2E/JUqU7wDFGuTHNaojA131EQut+TtNFVqYIIV2CxyDuLMMmTtPNQEnIhgKgP5QmA2CPwDf85l4Q8+TDbAfJwBSGRJ54wFiEV+q4dwGVmwgAbumKr177T22w7SXIF+CNPdN1DAIJpKlzlKWyHcY4LTJ6UD+XF74mqTg0aABqr3OcpLLMdfQiDQSBq/nyeglGelO/CJ3miPX+oZjunE+l6nlhEiTxprPJ2NT0d8oogcRivrvJUBrNQF91l/pWnNm3IZ0FB9Wb5xQLCivPUrFkzfPTRR9i8eTP27t2LhQsXokOHDpFO2yUPWlHi4sSGOVifJ2UjRQsq/dTrSbh+OqU2WmY7qc8TVZ6UweUiTZ7iS8VRuxpEJnCVkMauHCZU6XjyVO6dPHFS8uTNbHf2LHRuktF2O4SWxIE4xCXo4ObJU4qLNGZVmsAqUpxWhSIQ4mCvJnmSfvosR1KTHRA2eZKe5kt5omUOgCd5oh00/3m6inSgrVoFR55CNdu5TpPnzkcOUlJVQM+eAHyb7rz5PAGeU9T91Ve6piKnVgsRraU+T16VJx8+T2ErTxaLvNJ7e99KsuSFPFFflk4dPX2VpLMCQ0oXQOpbELPtQjPbEXOgT4dx2vOHqjwByFCTOuq32nBcyD4J/mbbUW4bBwe0DrHACcqTtG5VVSHR4JRdM2QEQZ6UPk9JSWJfFszkjVhGyORpxYoVWLVqlcf2VatW4eeff45IohgIlDPtgODNdkry4015AkTTXW2TJ7fbt89Taam8QkeaPBnL8uUbLlyAq1hsCKoMyQAATVmJ7DAdv44al+iFPCnsNPHnycJWdjvAWUhLYkEitFrAHU8aZRobhgbNDIRiFSFPrrORI08+R3/5ijwKcwgdDHnyZbaT/eZb2jyrd+VJea1wlaeKo+S5C5CNlBRAde21AHw7jXvzeZIkV4C/UbbOxk/pTzQLTMmMMoGPB1KepIpO2MqTMt+9edYHoTxR8nRFuwqRWVbHYVwiYbhMyQD8K0/Bmu06YifOIx0TMaNaZrvKSnJPmc8TgDR+YoffajNkCLFjhdDYBqM8KQOuCj5PioFJUpzcuTtkeCFPXLl/s51KJbpgXHLk6bnnnoPLyywLjuPw3HPPRSRRDATKDg+oPnmSKk8AcMMNpMO54gryu7bIE62wSp+n8nLPJQ0iGijT4RCCL1rBZ+aFCzLlyZFAlCe1RT5s1DtIw6AKgjzpz4qhhZ2l/GgMRsTFkSjjAJAJ8mD2YMmThidPhaG1Oi6XxHGdL0sBzXY1oDxJy5TUrOzXbKcgT8fLSR4EUp6kJgnpJQNxQNsxQp4KNTkkXbwrQhvs80qevPk8AWJHRucV+MpnjgPiHbzylGiSkScKr0uz+IjzFLbypMz3MM121BH48hZ8ZqlUsvALYZOnxERUGogvYiSUpxuxAmm4gNuwtFpmO0pwBeWJL4DJfEgRn+XN5QJ+/JHYOUOIx+KLPLndYlqkbSpABmkpKfAkTxo50QkZEvJUqZaTJ19mO0BcKuaSI0+HDh1CW+k0LR6tW7fG4Ysh8lUMQRmmQPo9VPKkDFVAO88XXyT9fvfu5Hcgn6eHHiId19atAZPvF6TecV6VJyV5iqjyxF/MgTgcBu/0VVQkU54oeYqTkCeOAwwunjyZ/JjtmjQBAOjyTwjnOor5ER6M0Go9yZNNGxx5Ko0jrQ53PrRWx5/vnE/yRJUnykYibLaTjr9CUZ7OIw16PdCwoUieuAj6PNEAqiXxpMNHo0YAgPo4jbJiz0Gj1GynVoshBWjHQM3ivvLZ6QQSqbO42eyVPHmY7UpLZRW8RpSnMMiT0yn6PHVswj+TxNxmRjkcVSGGlqb1y2iEVUuuE4rPk69QBS1wCADQECf9z7ajzyBNiwR0aZZ0fpkptG4NQAxm67PaSHecOuXjIE9IQxVIyVNZmVgWleQpW3OekPgaVJ4cepJHKqt/sx1wCZOnpKQkHD161GP74cOHYQy0xARDSPCnPAWzUrsUvpQn6TUB/8qT203WBT14ELjuOuCnn/ynwR/Ky4EEVAixlYBaIk+8onIWWcL0Wly4AHcZaewtKpNgtouzlAin2e3i0iwe5MntFoeY/HQSbb6oPLnKvJMnOsPKHiR5KosLr9WRlpWQzXbUrhth8iT97k15coJINlyhnDwVIQ0NG5IyK/hZlPo224Xq88Txz11u4slTTg5cUEMLJxynPAui1GwnvT/dTkPi+aqvsjAF5iCVJ0WF8OXzFFKcpwiQp0OHSHkyGoFGqfzNTSYZ+6M+MUFDojxZtFLlifNrtqNdkS+C1RJE7amP07BXueQhq0Iw2xUXi4MgaDTEngwxOr5P5UnqIxkCefIVJFP6ajyUJzX/nhQSvkklJzohQ0KeXPFy8uTLbAdcwuTplltuwcSJE2XRxA8fPownn3wSN998c0QTd6mjNnyelPBHnk6eFNNUUUFM9nPm+E+HLygDZAK82a6ME8gT7ZAiSp74zjEfOYIDNi5cEGbbWdVmVOl5s53TLrS+VVUieVInKcgT9XIHhBC6qpN5ggrhVJAnZYvi1PoPkElBYxypikILlEnfmU4n5mnQDuOtWpHPMMmT1JrpcomjY5/kie+Qj6AZAMBNyRO/vQhpaNRITp5cZcGb7QKRJ805Uj6qknnyFBeHCwYyW4zLO+lxvNRsxx8uA+//7XcRW0qUVElmgSkRQkXKlM917fibRiTOUyg+TykpXs+h/k4dOwJqq0R5MhhgV5EXQ+tZ0JAoT2XqZABAHFwwwupXeaJxlnwdQ5UnLZzIwll5PQjRbCeY7DIzBakxoVIkT159wiNAnqRWCGomrlcPSFGTumqLJ5mQ6ubfk0J5ou1ZJMx27gRSF9UVzGznE2+99RaMRiNat26N3Nxc5Obmok2bNkhLS8N///vfmkjjJQtvZrtI+zwpQTsfb2a7AwfIZ8uWwJgxpON45JHwyI3MWZx/QANscJVaBPJE++2aIE8FyMYFiJ6LHK88VWhMcBhMcINnPjxpkC4KrDEryBNtCOPjBaVGlSdGGXcryJPKKCdLwnItAWDRk1ZHXRye8iQtRwF9nqjyVE3ypDyNlqtAytM+kDnN7nPnCevi8/g80tGwITmnUk3yjSuPnNlOx8cAs6flCNsuJJDlelSnfJMnpfJEkU0CLwelPKkkypMGbiSAVHKfylNuLgDe58nuFq5HERZ5ouzPn/JEy4SCPFF/p8sug5jRfOItarPv6/qDRHkqdyXADpLBKSj2S554S6EsxiYFV1KKLIgqjIfpLkSznUCesrIEZmCo4JcTcvp49xFUnqTkKSMDaJxMSE1BAhmAmF0lpML5IE/VVZ7KYRLySEmemNlOgqSkJGzYsAHLly/Hww8/jCeffBJr167Fr7/+imRaYhkigkg6jPvyeVKCNv7elCdKntq2BT78kHQMLhdZSylUyGI8ZWfDbSAPGVdyXui3L7+cfAZDnjZvJs7ve/YEOLBAnIouVZ5oY2+NMyNOp0YZ5I29lDx5mO3oKD05WfCRkS4O7CpXkKdEOVmiy7UEgtVA0qspqT55Clp54n04UF4uMoUQEAx5oqoNAA/yxBWeJ/nL94AXkCpksUNHY235NttVVMgb6UDkiYaxcGWK5KnUTMiTtiAweZIqTzqd2In7I0+C8mQ2AwkJ4FTkYnS7T/LEm4jUEMMCVFt5atyYfIZBnoQwBZ3gQZ4q4sJcr0SiPFmsKpQgGQBRrf2Z7ajyBHi2ZYn5h2S/PchTiGY7YaZddrbADLSl5wXl2esjh0GeOM53qAJKnlJTgUZJ5N0dcTURB4FFRSJ54gtrgrt6ypO7VFSeYCJ5pKliypNfqFQq9O/fH08//TQmTJiAnnwsFIbIwpvZLlyH8VCVJ2/kaf9+8tmqFXGMpep9sIPJuXOBlSvJd5nylJQEVyqRu+MthYLyJCVPgcKhvP8+ufYXXwRIhBflibtwASq+hauKM0GrBUrBt748MZKSJyhn21HylJIidj55edBrSc9KlScLEsnSISZ5i+IKkjxVJBAfLW1p5MhTQJ8n2lECQUSY9ISybNByJSU7wrptNptwj4MqnrSdPy+0stY4M5zQoiG/brPTwL8Dq2/lKT9fXnb8PoLVCj0fNkBVTyRPllRyQ0OhJ3ny5fMEEBEpkMJns0l8nvhF7BwJcr8nn2a7Jk3gVBG2prGSY6utPDUjaoVf8tS6tfwcHpQ8eVOerHHeA88GhNTnyQIh+n+wypN0G4W5QD67zSd5qobypAq0vl0Y5MnhEMm6UnmiryYtDaiXSPL4RFmKqK6fPy+SJz7uVryresoTVyKSJ42Z5JG26tLxeQpqeZb33nsPY8eOhcFgwHvvvef32EcffTQiCWPwb7YL12FcujyLN/gjT1R5ov1pKItfHj1KZuqZzWSUJPN5Sk6Gyq0GzuTBWHUe//J9FCVPVVWkLfYaLJDH3r3kMyCRkyhPVSA9G1dYBJWF9LAVcWbExUnIkxflCYmJYu8vNdslJ5OYNmo1YLejXtw5FCJbnL7LK09K8uQ0BEee6KK42vIaNts5nWJD26QJKRR2O8kL6ZA+CARSnrzOtNNocELTHLAD6iKRPJVq0gCn2PhS8qTyQp5o+T6p4Dt+yRNPGK1IQEJmorC5Mp2Qp4QLofk8mc2BSapUeaIF3JVgAqwlAqnyqTxlZaFSlwSTrQjq8lIADWTKk81GyJ1M2fOFUMiT1JTrcABaLQoKSLJUKn7B8Q0Sh3GIypPKUg3lyQKZ8nTAD3mSthVVVfJim3LeU3mSEQhlzx/A56mJlDzR5Wr49e1KS320SWGQJynB82W2S00FsvTkhhfcSTiPdDIT8OxZ8f3l5gJ5eTC4qjfbzl1aBg0IeYpLJvUlznbpmO2CIk8zZszA3XffDYPBgBkzZvg8TqVSMfIUQUTTbOfP54m2naEsfkmFjLIyMitHqTyptaS3y0Ch0OE1a0Z4isVC6r4v8uR2A/v2ke/BLr9RgGy46IyuogtQ2cj3Ki1RnmgjHRR5kprttFritXnqFJqo87AT2R5mO405POWpykhaHV0EyJNfsx1djVmjIZ1BUhIhU2H4PYVFntLSUGzNBOyAqrhI2E7jXNGyS2f4aCp8m+2U/VIw5EmILs7DnkXIk7k0NJ8nKXkKWnkC4DKagcIglKesLFRpzTDZihDHL8ehrPdWq/9BhwCa93TNJn8O482bkwd2u0mvnZUl+Du1bMlzDprRvGpTqeUnYZSHSJ4kypPVGlh5krZvBgNp95ThCtKKiPJUbq4HU9mZ4M12FRUebLS4GLjai9kOgZQnaQj60lKSX16joYqgZUilIoMEX+QpXUvKQimSUIgMtMYBMkWaSrC8Ok7XHqyuw3g5zNAkkTyigYQvBbNdUOTp2LFjXr8z1CxqYrZduGY7q1XsiMIhT9K24p9/FD5PSUlQ8w0UjboNkNh6WVkieZIuZCzFiRNifgRUwSTKUxzI/G6uqAhqFenlbHpzcGY7b+SJ2jEbNQJOnUJjnABwJdwByBONOB4IdhNpdfSVpeKCuUEgZLMdZbqZmaSjMJsjRp68me0EULUrIwPlznTACmgsZUJ66KLAtD7QGT6aKiscNjcAtYfZjpZZo5GUYb/lQ0qeUsTN7nqEPKVYApvtfClPwfg80d7WnSg32/lVnvRJgEU02ymJQsjkSaI8nT5N+IBGA5nTPjIySC99/jz5k5AnuvCr0mxXqeMd4S0hliFJL6xUnvyZ7aTkSXlcRglRnk627IO2Wz9HQ5xEcTBmO4A0NJIXUljo3WyHoiIk8eblgGY7gDiOUnOoDyjXp/RFnpLVInkSwrFQaT4lRbBp6vigvw6HICCGBv4dV+nMUPNmO7390jHbheTz5HA40KxZM+yjw3yGGoW/Tk9GniwW4NlnZd7SkQ5VQIPgpqWJhT9c8rRtm6fyRKf4puO8cB+9Xpzq7c9pnLYLQABlgeOEOD4FyBYcxlXFF8TOR6fweeIfThqqwMPnSWq2AwSn8YYcifVEl2eh5CkuKTzy5EhMER1AlYun+UHIZjvqLM5Hhg7pRSugFDD8Kk8S8lSlT4KLNk984SviOwKads6o6NQgN9vdgcW47cjbAICmTcn2oJUnCXmi7zOlKt9DklWa7aTPk5Qkn/XnDbI4T3ynzIVAnmx8WA1thaiQShFUrCe3W+zJKHmqqEBuAweefZY/pqREfNjUVNE8xZMuGh+ZLvyqJE90vUi1NcDoZulSYOpUUSWRrG2n9Hmy2z3nMEjJk9eFbzkO2eWkPJ1t2xtAELPt4uNFdlxeTtrZJ58EV1yCf/7xQZ6sVqQZyY0Dmu2AoEx3ynrsy+fJ5BZ9kQTyRPvsjAyh7aJrdfLJDRlURazSmQVXBL0zeOWpuDisOSgxg5DIk1arRVVdXwq5DiFon6cPPgDeeovEDeARaeVJabIDQvN58qY8SX2eaGNMlSd+IfagyJN0hp3ftBQXQ8U/WAGyUaIizpTqshIhIKaH8uTLbOdrth0gyOINXHyUcat/8kSDZgaCLl4jmhNDGLaFbLajylNODiorAZeJz4sw1rcLy2yXng6dQS3OhuQL33lOrjypjfECmaSzfASznZ7DhxiLV+3PoCmOCOSJWl68wgd50tXPgA06MqtNEcHV32y7YM12SuWJUwTKlClH0qVZsrJg05OdcRWeDuNAkJ2ilBjRjAJgQhl27eJ/0PJmMpFGQkGe6H2EtHqQJz6d/pQntxu47z7gpZeAf/+VP4AX5QnwJItSn06vxPX8eRgdpXBDhaIOvQAAOchHRamEFEvMdufPg8g80vp+//3AO++g9Pk3ce6cYrZdUpLApOvp/QTKrAZ5on2AtC+QKk8JDi/KEyVPmZnCs2gqLALpD9l0x3GC/5pNbyax7wDEuR2A3e7X54mubed2e7cO1xWEPNtu/PjxePPNN+EMeXlsT0ybNg1XXHEFTCYTMjMzMWTIEBygvTSPqqoqjB8/HmlpaUhMTMTQoUNxVtGT5uXlYdCgQUhISEBmZiaefvppj/StW7cOnTt3hl6vR/PmzTF//vxqp7+mEbTP0+bN5PPPP4WRWqR9nryRp+qY7UpL/StPoZCnoJUnXlEpRjJsMIBLJj2kiuOgpZ2PXuHzFOpsO0BQKuo5ifKkqiAtSQWMUKsBXUIcbNAJyeLigwuSaTBAJBRe1hXzhZDJE59PzvRsNGsG/LotfOUpXLOdTgex4ecLX6FbTp7iE1RCoEwNH1+Gkv9ETSVM/PtqhiM0JBIAP2qMxKQrJU+mJDVOgcxQUnqgV9fnyZvyJA+UqRi9WyzixbKyhECIukrvylNQ5ImWJT5Ugp0P2pqEUvF8qbQBeJAn2h4JnaWCPNkMcpLnFcePiwXmBD/wkC7PovB5AnyvZUfNdh7H8CpmHhrBWa8RHCot1OCgypeQYv6hf/rViIwM4NNPIdb39euBv/8mz7L0K2hhRxr4xi0rixAtPo9ydEGQJ6ruhqE80U+HQ2wfU1MBbaXc5wmASPolypPKahHeV8jKk80GtZNUYrvBjDipK4LF4tdsp9OJRb0um+5CJk9btmzB0qVL0ahRIwwYMAC33Xab7C8UrF+/HuPHj8emTZuwevVqOBwO9O/fH1bJm3z88cfx008/4euvv8b69etx5swZ2X1cLhcGDRoEu92ODRs2YMGCBZg/fz5eeukl4Zhjx45h0KBBuP7667Fjxw5MnDgRY8aMwapVq0J9/FpF0D5P27aRT4cDWLcOQO0oT+GSp7IyElDPG3mKiPK0YgXQtatn0CeJsmAwAFqjDmWQ20TshjCUJx9muxwH6QDUlaQ8V/ELAOt0QAUkhCnIZY30egl5Cld5ev554PrrYVLzafLj81RsyEF+PnDaEh55cjo9FYmglScpeeKXgzrnUpCneDHKuKaK3IiW32S3WOAa4wQaNBBVIV8Emzsjlg86OgZIQ38SvAOLgjxV1+fJm/KkkihPJpN4bQBiRTAaAaMRdgNvtqv07TAeEJS0pqfj8GGg0JEMgKg7wZInpSoiNbcB8DAvegV1nAJEMqEIVUAHNSk+1rcLSJ4OEX+nQ2gBQ4IaxUZCiuPyJe+Vb1z//pfUyz//hNjbSwJBJ547hhvwM/mh0YiSCp9HmRo/69vRNqNDB/nz+oF0XTtArurQWHupqYCq1IvyRCEhT7BYhKZHs3olmd68fXvAdACQMUJXfCJ0iTphQOgstQrl0FfTdjH4PYVMnpKTkzF06FAMGDAA9erVQ1JSkuwvFKxcuRKjR49Gu3bt0KlTJ8yfPx95eXnYxpOB0tJSfPzxx3jnnXfQu3dvdOnSBZ9++ik2bNiATZs2AQB++eUX7N27FwsXLsRll12GG264AVOnTsWsWbNg59/g3LlzkZubi+nTp6NNmzaYMGECbr/9dr8zB2MBgSKMcxxIJZQslYNffgEQeZ+nSJIngAwsZeSJb4yp8kQHZJQ8KZZlEiCdaQfwHeM77xBCOXOm/GBeWShANoxGQkaEOCj0evFGr6EK7BVOxIN/Ib7IE5UreMfPxta9iEeF0LFXafgZKTpiwhMQJHmSKU/hkqePPgLWrUOzQytl+2TgydMFHQmPTfPiwJbQyJN0xE0by7CUJ56hFDh8kyfqv0GvZ3bKyVNGht8Z5+Q2p0V/OGmcILPZN3mqCZ8ndbKcPMkgMdkBgCOeHKur8u0wHhAS0vrEE+L7TkKpOEirpvJk55UnbZUf5ckbeVKEKqDKU6rau/IU0GzHK08H0RIGA1BiIu9Vf07yXvlMO36OPMzJkxDrO3X+5AOUPoL/kd9ZWSLL5fMoXRWE8hQCeVIqT3q9GCON1qu0NAhtVimSUJnghzxZraJP/I+LSKCu778PmA4AkqVZTNAZ1DAYxLpYWShKu97MdkI6UbfJU3DTdST49NNPayIdAAhZAoBUnsFv27YNDocDffv2FY5p3bo1GjVqhI0bN+Lqq6/Gxo0b0aFDB2TRXhbAgAED8NBDD2HPnj24/PLLsXHjRtk16DETJ070mg6bzQabpBUqC8PXIxLwZ25xu0kHpKOqEwWvpikbUdqABFKeaOPPceKsXI4T24zq+jyZTGK7Ggnl6eRJeQdhLXeB27yZeMKsWEEST1sYifKUkCAqOU1AFKIymBCnU0OrBQoVZju6uC8A+Ww7jhN9hGiP26wZ0LAhdCdP4lr8gTjeH4cuAKwkT8qI474QEeWJf1mNDq4BMNSv2e6shjDYcj7a+l/LS5FyTlzsNhAoqY6PF/lhsA7jej08Rs2FnG/yRPOYkn+TQ06e0nnyVFzsmzyp+PdYGp8jS5dUeeLyTlKXfQDV93myV7lFRZOvUEGRJ37dFztvttNXyZUnOrswFPJUrM3ATz8Bz0vI00Gl8kRJkw/yJLRVSvIUTxWyIMkTlVIUypONKk+qEgDBKU+ytlCiPLUzAGVJDYF8IL4wTzyGv+ehM6TQnjwJIEsyOaFjRzgmvQTtnbejL9aSbZL+hzKDVM6H8uRyiRurQZ7ojDvpO04xu4S8L0USnMnpgNRKoVCeKLlR0/Uygw2EK1mahRJVCxKRhguwFZHyrFb77mcuBvIUtPLkdrvx5ptvonv37rjiiivw3HPPoTJQpMYQ4Ha7MXHiRHTv3h3t27cHABQUFECn03ks+5KVlYUCqiIUFMiIE91P9/k7pqyszOszTJs2TaamNaQhjWsZ/sx2AN9gbd1KfgwYQJjOwYPA8ePVVp4AsSE+c4YMADUacTIOEJ7ydP314jZvDuMpKEEcHEGTJ2qZo+lqg31Q0Qbg9Glg927xYIXyZDDIladymKDTwavZjga6dKri5EFWALHho+VUpQL69QMA9MNqaO2kdbPFeSdPoZjtBEIRBnlK1NqEl5qzZzUA/2a7My7SQTdsz/vVVJUilDBupRJurPSlC8lsR3crZtt5U54E8mQX86cJjgdWnux2aIrJ/atScmS7pOTJddy/2S5Unyd3uZU4otMbAdAkiz5P/mI8AYDTSN6NoUru80QtSKGQpyIVyV9NqkieIuXz5OSjplOS5xWCdzq8Kk8ynycusM+T19l2CuXJmqIIgOp0CnXkSIFInjgpi33oIWzPuREWaR32Qp6SnSRvPAaX0gazGuQJkDdDCQli7CaAzLbj+EGpgIwMsb2Rmu3oqgXBCgWSRYGl5AmAQJ6MRsnqAQpcUuTptddew/PPP4/ExETUr18fM2fOxPjx4yOWkPHjx+Pff//Fl19+GbFrhotJkyahtLRU+DupDFNcS/BmttNqIZ8hQZWnPn2Aq68m33/5RSA+9NhQfZ4AkTxRk11urnx/OORJKgDKlKfUVLj5Nb3SUCSQJ6pyCOTpn39IMJmffgIgOot36UJG/d2wUX7j5cvF7xLlyZvZrgzE38kfebJpE0mLoFZ7yilSL2OePPXFGuh48kSVJ41GTp6UEcd9ISSz3XvvAT16AKWlQqObrBYbxsSCI2iCY56dOscJJPOEnZAIfRbJCzPKsGJFUEkFIPrRS8lTWGY7HvTZadmVkicas4aWT6NNoTyli0qp4c81gm+gAL6AORAntuw8EhKA0yrSybpP5Mn2BRth3Bd5ogtSu1Qa4WBNih/liYaRoOSJkhI7b7ar4vAZRuB/lnsBcCGRJ4ue5LctIRkAGdz4NNvRT/7cLkW/YAc6If0k7zOjIE+OBErAfXTOZWWCbxsAQibsdrGgKHyezFwJgBDNdm63EFPhEFrAYBCjx5tL+DZe4kxq4QgzsVol6yiaTMDdd2Pjjnj8iJvFG9MVoAGBWFIC78FHqMkuIUGc3VhUJBaSFSuAtWuhhIdfmeJ7aiqE9soVp4MNBsRlBefzpC3hBy4RIE/2CxaPtClxSZGnzz77DLNnz8aqVavw/fff46effsKiRYvgjkCghgkTJmDZsmX47bff0IBfdwcAsrOzYbfbUaKYz3j27Flk84U1OzvbY/Yd/R3oGLPZjHgpM+Gh1+thNptlf9GAt5GGMjiaoDx16ULUJwBYtUropGjSg12eRdqZKcmT1GQHhEeeevbk1zODW3SUTUoC1Gph+ZEMFHooTxYL/7xTp5IR6ssvAxDJU7t2pF27GsQXTnCaor290ynMSjyN+gJ5EsgIiPJEyZNytp2MPFFIg+cB8gW1+vQBAFyGnTA4+Jl8WpEkVapDJ09Bm+0qKoAXXgD++gtYu1YoR0J+8+iH1Z6d+okTpLdRqXDESuqPPkNUIsrLSVYGA6nyREmNT+VJGmvID3mSDh5k5Mku93lKqBLJU32cRkayAyYTkIILuPrVQcCNN8plGT7470k0RHKqvFlUqYALRtLJqk/7n22nJE+BfJ5oJ1SlNQnD9LhUkTwFqzzF20lmZ1WdwAgsxC3F89EVW4OL88QToBItyW9KdJJQKgRQDKQ8PXphMjphF+ot+4AQcIXDOCV5BpuPxoIqxPTlnj4tfz+8zxMtE4mcBY1wIjSH8TNngIoKOBCH42gCgwGwZZHJHUnl/Hvl78mp1bBBbCTLEnhyNHIkYDLh77+BJRgm3tiL8pRo82G2k/pIJiWJg7DTpwmBHDwYGDgQyJMT9UDKk9TfCUlJuPVW4MEnE+UjXh9mO2HVggiQJ0exqDz5wiVFnvLy8nDjjTcKv/v27QuVSoUzirgnoYDjOEyYMAHfffcdfv31V+RK5xMD6NKlC7RaLdZKWPiBAweQl5eHbt26AQC6deuG3bt345zEo3j16tUwm81o27atcMxaBZNfvXq1cI1YhTezHSBWHtuZIjK9FwA6dwb69yff166Fs4r0cHTkGmyoArVabL9oB+eLPNGGvbzcT+wckH2U/+bkkLWvEmERzRU8C6tKFJ3GaVtkMolpLdxbCCxbRn5s3w4cOCCY7dq2JekRyNPzz5PPDRtIY7V4MXD4MKqMqViOQT6VJw+zXVmZrDOw+yNP0p4uIwN5aZcBgPCcDp3YmlSFQZ6CVp5WrBA7L4nypCRPfbHGs1P/6ivyed11OFVEClpCjtiZAor29cIFUvbS08lfVhYwaxa9NQBx1RrAD3kqKRELUVqah8+TW6dHBRI8FsmmCh6NbEz7ifhKkTxp4Iax+BRMJqADdkPjtJPKJZ1pwBekvWgrD5DJo9RMyFNc8XmZjOQvVEFSklhXb61YBK5zZw9FQWUhCk2lViw7qqTgHcZdiXLyVM8mrgBxB74MzedJTfLbZZS/b6sV/snTqVO4wkkU38R/N0lms0BccoZPp85Z6X3tJ2qy696dfJaXi76EWi2g08FiAUqRDGs3Il8/ivd8Kk9eyRPvU3VclQsntDAYAEc2ea+pVrny5NQlABLvtp39nyYTUd54AwCwaROwEgPhTOBfkBfyFF8RQHlKSSGEmQoGp06RdorjyAhFsY5sIPIkVZ40KUlYuhTo118lvivAU3lK4KCFXVQEI0CenKWMPMngdDphUPS4Wq0WDm8VIUiMHz8eCxcuxBdffAGTyYSCggIUFBQIfkhJSUm4//778cQTT+C3337Dtm3bcO+996Jbt264mjdR9e/fH23btsWIESOwc+dOrFq1Ci+++CLGjx8PPS+vjBs3DkePHsUzzzyD/fv3Y/bs2ViyZAkef/zxsNNeG/BWWQCxwmh28Ca7Fi1ID9W1K6lBpaXIOkFUFtr4Bmu2Azxn3AVSngD/kYylwmFKChHJqL8Tp9MJrZw9idjnm5kKhTSoVGK7xC36QiZ7cIu/FJSntm2BegklaAd+w7BhRI5yuwmZeOUVAMD2Pk+jHGafPk8eZjuXC7BaoeLjCNn1PsiTJDgexYFG/WS/nZI17Co15LsDcdAmaBEMglaepKZvCXlK5CMP056+D9bCVqFgvYsXk8+77hL66cT6JC+SVaRhlgnBv/xCiGxREfk7dw74+GN6awBBmu2oyc5sBvR6D+XJlZwGQOVBnmiDTX09hOVZKjynd5pMQHv8K26ThrLgC5Iv8uROSoGVhpeQ+KcEE6pgEJZhAUZCtX07MHy47Hw1H2jQrjPJT0Rw5Iku5RLPq5sNHCJ5Go6vUGERLQOrVomxJ2VQ+DzRoKj0fVdUwDd5sljAfbFYuJTuwG6R9KhUQg/qNkoexFsHTZ3Fr7lGVHBpw8Nfg7YxlQ89AQB4AB/BWSSXdaTtm4w8nToFjB0LAPiT6wGA7HfXJ+Qpyc6TYp5t2jTynv9wRT3g8ceBxEScO0eESrvKANeYceQ5e/YUD+bzSG8JgjwBInk6eRJYtEg87qOPZCd7OOXDN3mSNc5S8pSeLrZbLhfMBjtSIakrYZAnSlRpXXQx8iQHx3EYPXq0LKZTVVUVxo0bF3acpzlz5qC0tBS9evVCTk6O8PcVHf2CLEo8ePBgDB06FD179kR2djaWLl0q7NdoNFi2bBk0Gg26deuGe+65ByNHjsSUKVOEY3Jzc7F8+XKsXr0anTp1wvTp0zFv3jwMoGauGIU3nydArDC6XbzJrmtX8qnRCE5FzY+QWXe+zHa+lCfAkzxRcatloyrS+HfsCFitMBjEY/2Z7qQz7bRaIlTQUa0qKUkwV7iSSSVvZi6UnU/JU/IP88kXXjF0LvoSFguHuDiyVulVKkIYLVlNibMUVUqffJKEc0hPx6auEwBANtuOgipPcXEkDpNTxfeEJSVQW0mD4PBFnhSTGgDgSK5InhyIk8nndg3vTwGjTFX3B1l6fQXJLCsT1TnAO3m67DK4Es1IwwW0tEriuuzZQzoxrRYYOlTop5MakkJE35mMPO3fTz7/7/+Ab74h3/kTQzLbSZzFAXiQJ0eS3Fkc8E6e6PV0Vu/kqQMkEwhCIE8ms8pruAJ/oQrMZiDh3834CsOhgRtcQgLpLe66SxgEqK1EeaKRwsnNfDiM5+eLYUkoeeKJToKjFC4X0IQT/YYa4DTqHf0TAKnDAwcCXptoPu8LOT6/k5IBAGkaP8qTZLDAffihcCmV2w389hv5kZgo1G21XosK8A2ZP/LUqRNQvz75TslTYiLsdnHcFDd4II4b28KMctRf+bHsMt7Mdu7ScmIKO3MG7jZt8TjeEY6Jy1CQYp48VajlPb/Un5uPkYk2bQD9jDeIStali3gAn0facpE8USEOgG/ytGIFUUP1etKglZUJAxHA/+xr4bbeyBN1GucHJlJWk6K1IA0SBhMB5YkrI3XxleOjiO+nVGBxuYCBA9Hn/SFQwR1KrN+YQ9DkadSoUcjMzJTNQrvnnns8Yj2FAo7jvP6NHj1aOMZgMGDWrFm4cOECrFYrli5dKvgyUTRu3BgrVqxARUUFCgsL8d///hdxikVTe/Xqhe3bt8Nms+HIkSOye0QL588Dd9wB3H679/2+zHZCeP69En8nioEDAQBtjxKHaqXZLhTliZb5khJABxs6T70VWLKE+CfwjV0wfk/SpQMAwn1kzuI80tuQSj6gi7xGZWUBnbADycd2kMR98QWg10N7eD86YSdatiSbL7cRk93Zprw5lpInygKefRYlTlLBvZntpMoToMKFeL5RO3gQmkp+tp0hePJ0qkkPVPF+ExYkyjpWOvMuFPIkM9tduKBokXn88IN8braEPCU4+YYxNRX2a8i0xx5Vq8Vjqeo0cCC4lFQhtlZqLnlHJq4MAOedPF15JfkDiPrkdoc2207iLA6Q9ykltnRR5GCVJ52FFDohCKo35Ukqw/BEag/aeSVPvmI9ud2AGaVo+OkrwMGDMuUpqfg44m4dDCMq8DMG4vyqf0iF/OMPQQkVllUxeCpPRlTAnMAzhnXrSBDDc+dIL8nP0qLkSee2wV5uQy6I8uTUkIzocpC8U8q5jh/3Umz4Huysi5AnVTK5Zoq6BABgtXCe5EklmoPUR4gT9iZcRfat5suURDbzMIVL4XaLPk+dOolkQqI8SZVtY6IKPzYlVoOWP8+UqdFKs50GTgz/dhhpr7KyYF2yAqW8P2N8PJBgVJBiXt6xuEkj266duIuCDzFI5udIJ45Q8PlCZ2+63YpZj77I05Il5HPQIOCZZ8j3meLzBWW2o3krZd1UeaIkSiNOTkjRWoTYegAiQp5cpRZkoQD9Cz4D1qyRh6DYuxdYtQpZG3/AQKy8NJSnTz/9NKg/huDhchEXk2+/9d4PBjLbmQ7xZjuqPAGk4qlUaHR+OxoiT2i/rir9BWjbFm0tZNjkT3lSmlgqSuz4BrfDvGGleBDfg9I6Ggx5ou3uZZcBb07yJE+GhqRyd27oqTyNxnzy45ZbgCZNyHOC+HXwrm1obyWt2qn6/KzD7t3FBjwrC3j4YdmClTIyAiV5Ag6m8J3Bpk0CeXL5Ik9eely1MR5/gpgIrDDKOlYpeQp2NXOZ8uR0em/oqMlOwmo9yJPJBHdvolD2dq8hbTPHieTpzjtRXCwSnLSm5FpxcCEBFd7JU5s24tRIpxMoLg7NbEeHoBLyVAYzXGqSaTajf/IU75KTJ20ZaZV34DKy4fhxmBI572a7oiKBYO9Ha1l0cQpfUcZdLuBOLEbDeZOBKVNE5UsH6D+dC1VhIXaoLsf/4WtY6rcC5s0jB7z2GrBuHTQVRHlySMmThHSk68tJB9qnD0lj+/ZkIgCteJJj7efLBPJ0qNsoAMBVp74BHA7BkuZwKDpyh0Ooy/kOngylys12VRcqxFGXdCaixBz0N67ENyregZr6dUnqh1ZL3icAz8bi6FGSKL2euCAoyRM/047mq1YLbGx2D84hA4kX8kgDykM6ONTrOLyPCWh9fCUpLD/9hIqMxsKxOh1pB2Tvlc+cUgepn9deK+6ioHN06FjBAzSPSkqgVRO7rqyqUvJECxp9Xipj3nkncM89pC6cOEEWS0Zos+28mu2kYQt4wpek8aI8eeuMAODnn8m6fqdOCbMpfZGnK7BFPE8aLkby/THMvDTIE0PkIS34SsdHjvNttouPBzJwDsbzeWQEePnl4s7MTOI3AOAm/MS3sRyeK3kW2LcPN1iIaUWmPJWVEYZz4QJQVSUz2zkqHFhQNQw3YRk4gwGgMa/4BiDJx2BS+mD631biPTyCpQfaggYK6t6uhOyXKjaKGTwU9dLtuBu8LwBVDO+4g3zgS7RvxwEch1YXCHk6ksGTJ62WkC0AePFFICFBtuaSP4dxANiXxF9n0yZh4VlXfPDKk14PrAYx3SlJEp15F6rZrgrxqFTxBULZ8hQVCRHmMWIE+SwtFdpqo0sclar6k3T1wJ9wrF5HZiIePUoK5c03C2JdcjKgTzUKJpoklIrkyeUSO7jWrXHwhB4WLZ8PZ8/KQhX4MtsZYSXljs4s4ssAKZ8qWOPJ76pA5MktN9tpSglj/wedyYYTJ5DjOoVklMJFm728PFJwecfxs/pGsMDk3WxnIuuhAfBQnnKQL9yDEmSzGcIzfRt/D6xIJJ3fsGHAAw+QCj52LOItZKBAI4UDAHQ6VKnIg17153Rg4kRyo5Ejic1I4nwYp9egnM5yOl8qkKfTA+/HOWQQX561a4UIB4Ai2j/9oVIhv4o8eByN88SRjthRUCSkS1bmJeTpWwzFjvir5df0pTwpV4OlykT79sRmrjTbSZQnenuN0YBZ4EPljBhBCmpyMhZcGIxmOAyDAbhu638xDh+QxaMXLwauuEJm1qMzl72Rp2KePPXoIe5SJveyy+AdPClScRwam0nlk/FFX8oTQPJs0CBSuB9+mGy75x4gORlzvkzGO3g8fJ8naXRbPiPNaoXyxHG+g4M99xzwySfEdMA7+FPypNdLzHblFlyFv8XzfJCnAfgFuVV7Q1+UOEbAyFMUIa0EsgJUVgbnspWo787zOA4gFaYLeNWpVSt4zGceMgQAcAt+gMkEdMY/6ODcAQAwuUjFFTqhGTNIRUtLI38pKRhveQNquOCocMB9x50Ygh9QBT1cS38Uh1t8A+jXbGezAW3bovfbN+ARvI/G1n3A+++TDsufbX7rVnFhUIcDQzY/jwycxwV9tjijcNAgWNWJaIITGKhZDRw8CKO9GJUw4EhiJ/Gas2YRkwcfk0yqPPk32wF7TCJ50lby8XgSgidPOh3pVCphwFZ0rTZ5ou+sWO3D2/Lbb4nqc/nlwntyXSgVGv50nUieDB1bYjOugB52xN94PfFlAwjZNBrlfskqlVDGZOQpL48wfJ0Of51ugm7dgFMOMappILPdLfgesxcnk3I3dSrZIVGeAMBiIA1/Rbx/8pTglitPlDxtBz+wOHEC9YuJ6nTS2EYMZbF3r+DvdEhHbDShmu3oIrU4c0ZcHsYMYTHWIj2JuyFM0nv7bXL/Q4fQ69//AQBc8XLP8Ao1+d3xp9fIhkmTgPnzPYLnSJcScp0uQA4IS3I1b4mv8X/koLlzYd97GGoQFURGnuhAJTUVZVZCkLUZyQAAE0+enGdFk922f1To1IlfyEBBng4nXi53+lKQpyNoRn4oYxhJ/Z0AkUzQhCYmSgONAyDlYDYeRkVCGilMpaVAaSkGOpfjX7RHvRfuRZ+VxPS1qMsMYRAlJU+Agjz9/LPwbq1cArRasbk7dUoMgXb2LLHW0fiWHoiLE9q1l1wv4yhy0bRXQ7G++iNPt94qNvgPP0zqBv98RkcpHsIcJOjFSR7+QhUI6NaN1GE6k1GSkWa1QnkCvI+ErVbRzH3qFFnKBSJ5UqvF5adgteBKbBbP9UKeOP4FPIr36qz6xMhTFBEn8SGuqACx4fXvD6SnQ3vzDfgZNwDw7vPUDLwTA7VZScE3FL2wDunaUjyAj4RdZp48CcoTVSooqqrw1PlJWIs+aPnyndD/9C1s0GG47nvE3dBPrPDBkKd//wWOHYNTo8eHeACl8VmkBdq2zXsl79WL9DpHjpCG9P33gR49cPmv0wEAM7VPg9OQob3FnYDv3OQ5r3ppgKCvb0VXlFgVXrvXXSc4rtJGOCHBc7adNEgmAOyPv5y8oMJCNCjhK70v8uSlx9XpgCNojiycxQh8LutXKvXJJN+QFJLyBIizojxaHT5wKIYPF/LVdo7kc2qqxGxnNkOtUWGw9hfMxkPgVCqRrN51FwCPSV3C9WTkiTfZlWa1RJ/+Gly4AJwFOaHwX5E8NbDsx+SfOuNWLJWZ7XrjV8RxkqBRRqMQq0wgT/rgyJORk5CnykqoeKYikKe8POScI530YUN70Zllzx4xTIGK1CVfylMBeF9LSVgUGXnKz0echpg8zGYIs85KEwhRE9TlpCTg3XcBAIl8ME+nUT4AskuVqDFjiJnPS7hmqTlM9S9RA0qQBH1WChbjTnLQDz/g+U9bwAoj3sQzcvJEPaHT04W4lgY+KGqii7xA93mRPP3wAxEdliyBQJ6sLS/HUTSD2hgvl2MU5GkBiCkRn38ul9ppmAIleaLwojwZDMB5ZOCdCcdI1PCDB8Ft3oJf0A8G2GD8ej4A4D08gh+aPCZcyht5+hE3kwkdGzYAzz5LnglGNGworPGNykrC5XjOgBYt/AeBpKa7EeWzkYvj0J87Bfz+O9nnjzzx9Q8AUYqO8c934ADsaj0MsCHNKsZ/Ckp5GjCAtNfUj0qSkUZOoTwB3snT9u2ksGdmykhYOUziMjh8GJe4ijK52U7qW8h/V73wAgBgJD5DyVHF5I46AkaeogzB13DFCmKKWr1aGJ63w140xgkP5+6EBKAB+EbP29IxLVogL7ENdHCg6/FvcBe+EHbRhl7ohGhLunQpsdN98gkq1Eb0wnpk/vEt3HFaDMW32JxKHNEFhYVvAPz6PPHmkONZV+FBfIi8xvx03r//9l7J69UjEcSvvprsf+QRYPNmcMnJuEvzFaZYnhBm/u3YAUzFf/CnrjcxKfEOx3+hu1+fR6Xy5EKcEGNHujwLAFS49IJJtH3RegAAZwxNeSLXNYODWkaetmQNxhyMwzRMCtrnib6zIs6H8kRjrnXsKOSr8wLJ59atIUZ95l+a25yM8ZiNo59vIA1iv36CsudBnrwpTzx5+uVkG9hswM03A/YUcsKRDSJ5yt2+FI3Ob8dIfCZTnoQp0m++STaUlQnBRWneHU0nQ//8+l1leQDIyRNdH06rhVA2ndBgH9rArdYADoewJM1eTQc5eeKVp512/+RJINq08wOxXArLDFVWwuQuFbOLfx+lRoXyBJDZifzkDkAxlR9AZnvexHLrrcCcOT7XuZAqT9p/dwAATmpyYTSSujDH9AzQqRNsagMMsOEZvA3N77+JF/gfUb5w1VUe5EnvroIONkBCnmjdslgg2LTybiKqbkICxFUOaKbx0GqJCfusoRHJPzpjuqgIWE/qlkC8qNmOQuLzRNtLWg7KOBNhMi1awNahKwZgFe7EF3DnNsOha0biccyQ8TQleTIagX/QBd2wERy/2C9AyFOTJqSNoNaukyeDMNlR8JN4dph7Ygt4n1QaV0xJntLSxPLQpw84TuJ2ZOKfr2VLnNQ3BwCkFx0QbuOTPCmtEcrflDyprMEpT9TR6+qrSR81YgQKzC2wCVcL/RMN49K4ZCdSUAJnHL+joIAonGVl4iDt4YexX98JCaiEYdE8z/vVAYRMnn7//Xc4vYQYdjqd+J0ya4agkZAAaGFHxusTyYa77wb274etE+k0+mrXe7Sb8fES8qQcpfH4K52oMtevfAZmlBO7P0TyJBAySp4yMkgLd++9GNFhJ/7CNXAajNj3ytdYjsFi3aMkQaE8eSUsfGNxIqENAKCoOe+A7Ys8AWSRuj/+ACZPJj1D795Q7dqFI12IM+pGEosP27YBB9EKbw1YSyrm4sXYNvhlvIVn/K5tqSRPAFCuJ2SEKk/Ub8XhgNAZxLsUdgPldz/kiULqMG5PSMbDmIO/0CNk5em8L/IkndbI56u6nORzq1bwmIlD/VpPNbga+PNPokLyiQlKeeLf7z60RseOpD80NScnFOw+J7xiUykxhaTignfylJFBMkctNkf0WRd3egM4dw7HGxHiHYg86XRiPhQjBU5oYc8kdSR5F2mfdrnbE/8aQEaetlX5Jk9ms4Q8SaQbmfIEIKWSEKZsY7kQmMhiIsqTjDypVMCsWbCpyQO5FcqT+r2ZxLz3xRfygqOA1JdIt28HAOCkNpcnGSq8qH0T2LEDXVpaMAfjAABtFjxLeue//iLLF2k0wH/+I9QbY7ZIepJQCk0Rr7RJyFN5OUhbVVSEI73uBxCYPLmhwcqc+8iGj3g1/D//IW1Jx46Cr2awyhMgF7DITDsVvsSdcO4/jC3jF8ANjV/yRMnHNnRFxR//AOPGwa3WYAOuQZMmZB8dn548KSpPVCTziYULgfx8vHjtenyHW8k2GpROSZ5UKiLl/fwznIhD165kDhB18qc4piO+bimFfsgTfUGBZr7zGZng8qI8eWtAt/BK0hVXkIr32Wd4/MaDKEK6UFfpEjapTjKQPdugq7j8zO7dogJVvz6QmoqljScCAOotfT/4ZQtiCCGTp+uvvx4XLnjKbKWlpbheuuorQ1BISCCzDgx5h0hPNXs20KoVLF2uAwD0Uq33ek4g8vR7MiFPBj7eDV2HiTb0QoetjCMAoNDcDD3wF378pAhHO5DrCHUxFLMd37ke1hLyZG0vIU+0B/ZCOhAXR5ZfKS8n/hENGwptspQ8AfwALzkZuOMOHLxrMoqR6ld5kjqM0wZ0ZfMJ+DejFzbhapnyJCVPFCpT8GY7pWIoVZikhClU8nTOHTx50lVKlCcFeaIuK958DnyRJzPKPJSn/WiN3FzSB2d1ICdYj4rKU0KRSJ6kZjuBPHmZ3kbzxGZXARkZHp0eEAR5UpHruhuSGVZqF2mg/7FLzHabNwsK0V4Q8uStSMqUJ0k0dA/yVEV6vIZx+cKJVK30WAqnaVPMaPMRNqAbCq8aLN93xRXAU0/5nxYLUlWo2S7+MDEtn9Y1FRQaOlg4XaDBZExGORKReXwLicnFm05w331wNmku5HFikkamNGYf4NugDh2EftViAen0U1PlwRul9UVSPyj/+ynjPkKS160jPnoffEB2vPeeeFBKitzRk18UWHpJb+RJ+l2rFeuLNHKHshxJs9cKIzBnDh4eacUnuN+DPElcfQIrTzodkJ0NsxnYB9L++VSeJNi/n4jv//wD9O4NmaP/IU1rAEBSfhDKU7DkyR2kzxNVnq64QtikXOpLFsYFQFGzK0XHsN27Rd8nftuOVnegEOkwFp2Ur0FaRxAyeeI4DiovEnJRURGMQa4OzyCikTYfL4EP6Pnmm0KjVdyRkKfuLk81LxjytFN/JfJ5Hw0nNJgBEhclFReg0/GDfLfbc9osxE7eBr1nXVSY7YIhT3vcpPHgLu9Metj8fHGauL9KLmnZ6Eo6SvIkjdIgXS7GF7wpT0sbP4EXuv2GCn5GXFjkKQjlqbrkSTDbeQuUSc1egJw8uaqghd2v8uQtUF0oPk/70Vrw9W/QhZxgrjwrdKr6s8RHIw1F3pUnP+SJkq3gyBMJmErJk7FhGh58EIhvLU5PtyIB/1pzwbXhfQX5h3FkN0A5zEhMhFczqtksWe9Qcp7LJSdPuQZCxDqk8SbUevUEHuBtfbsfzfegOzZA1dB7PQ4EqfKktpEb5BtyBfJks5EyX1ICnEMW/ounyI4HHyTmMp0O+M9/ZHGUTCYI7zsDhcg9RALuYvBgufLEg77nhASQ1cNpYVAoTwBwRtNQNFfecQdpg4YNI36JFCqV3HQnMdsFQ57oTLpAxwCkHaTvhz7H4ZOkYWjMFxvaxPKuVQCCIE88kpJEUo79+4nCQhtLH+RJ+v3668W6eBBEeUo8LR5E0059OEMlTwanhDxRJqYkTyUl4oNLYgoqYwYqyVNpK//kyZxpwMcgqiVmz/af3hhE0OSJRhBXqVQekcZvueUWDBgwANdQ2ZUhaDxeOAkmWHChxZXi9HIARW16wA0Vcl2HRV8WHgnxXEDyZHOoBbXpZ9UgoQKbUY4EHS+RSmN6SCqytOPyiLmmMNv59HlyOIQVzLfbyL2TchKIPA+IFSnIwKqUPO3cSdyb6CBOGh+UttWh+DwBpIORxh2iDb3TCdKCStatijR5Uqk8VnXxCcFh3NsSLRI/HCQny3wcklAaMeVJIE9FRYKf2QG0EvpLbQNyQhbOCtfSnJEoTzZS3hwOCemIAHnSwgljnJ2YuPkHqt8hFXPnAqrGInnag3ZwuNSwGZJk/oLF2aSMSg6VwWQCnNDCws+CowRNqTz1aJaPbduAu3vzylNOjtDBeShPUBCPMCD1eaI4m5ArK5pHxaDjeAdPoMyQKZaXhx8GGjYUyJCg2PDv+yb8RNYNzMoCOneWK0+QP1dCAkiBppXVy4DM6QQJ1UB/xMcT86QS0nZNYrZT+jx5mu08VSV/5El6TfouqFuOUnn6+WfyvjMyAEWcZp8wm8ksQ6daS26wZ4/XNpeCtmu9e5P77t9PYv263cBeFyFP8XmeypOQ1cGSJ/6h9Q6J2Y6a2JQN6D//kM/cXNkMS6XyJJuJDMDazgd54k3maWnAB3iQuJT88gtw6JD/NMcYgiZPNII4x3EwmUyyqOLZ2dkYO3YsFi5cWJNpvfjw998YVLgAALBlxP9kPh8WTZIY4I86VPJI5YpgAF9y69Xzemm7HXgZr+DkrY9iUsJM2ag5S19CvlAzD12rhIe04/Koi8Ga7Q4fJo2jyYR9ZWQUmZoKz8hyQZKnRo3I7G6nk8QZ5Djy6NJGLBTlSRipgTQCtJP2MNupVDL1SZ3sPZihr9l2UngjT8GqTkAA8kTfZXIyYWNxcXAnkAYyTVNK2sVIKk/8ELkwoREqYBTb1Cw5ecqIt0DFlxU97FBVkh7KaXeLypMfk6c/8mQwiAsDA0CKzirPC9qj0F4QwL8gDXd5OUTTHYDDevJdYpWQgb5qagqk99C6qhAPsXdW559B585A3DlP5akmyJMs+CSPc8ZcYfo4IIxhAAAWmPBlq5fJD6ORhECAWGeEIs2/b2GyyY03Amp1YOUJAF59lcSm4mOx0XQCfJ0aNEisuM89J05pk6IayhMtO8GSJ5ruigpCUnyRJxp26rLLfPrve8BsJpNS/r+9M49vqkr//ydL032HblB2hMouaK0iOtKxoAMozFdhEBcYGRFGEMeF1wzgiIqiMiOOyriBOvBTcWEURxRBVlkLCAICsiO0hdK9dM39/XFy7pab5aY3TZo+79cL2iY3yTnJPed+8nme85yiuO7shh9+kF5UY/Bz8XTLLWyXm9hYpl2++w74qZ6Jp7Cic+IHwOe8tm3BJkWdOU/hNWXSgofOndlPtXji+U5ymx/O4kmIlMbiBbSBqUtnSTz99JOT85ScDJxEZ+xr59gJYvFi920OMlxnIqrg1cM7deqEv/zlLxSiM4Irr8Sn3Z7AhV9KYUlTiorLl4F9uBFXYQ8TT+PGifclX2au0yVbKpJcXH3r6oBCpOHUzFdQuAloqALqI2IQVlOJttYSAG00850AZUFDT86Ty4Rxxywg9OyJknyT9DLZ2VKeg/z5PMC/0H72GVt8BChdJ3kb9TpPNTXS5O4UtgOYePrvfwEA1nh9RTLlNFU8mc3sOYrr3YgnWQXouqgERFRX4cp2Zey1vXSeBMGL1XYO8XQqoidQLSterBBPAnpGnwFkoiGiqhhANCzVFbDAUVHZjfB0J55MJiAswoqaGraEO8FaCSDJ+byW2UmHw/oA9eza07ZXL2D1agDAzirmPKmuDyL83LqEJGTilBS2tpcoD+RZvucl5ynikrIPcvzhPBXHdhL35K2okLZm4XwUPxmTnytjKsCxlEwtTvj53BGOZfG/YzlZWs6TUx/69GH142QoxlRYGKuEv2GDWBrACZXzpCfnSa/zJBdP58+zNlos0vdS9YJmj8niMvj8eDo6CxmlByXxpLUqAZJ46tmTrZ25/36WDvbqq8D5ywkoRApSUcTCaAMH4sYbWdHvkSPBJje+U7V6dZ0axxtpKzoDM5gTZu/QibkprsST6puF2umTr0TegWsQH2NiKwVtNta2qir2xmaxNA4+Va1Mn4L+v34FLFnC6r35OhiaGd05T3PnzkV0dDQuXLiAzZs3Y/PmzbjA96Ui9BEbi08GPY8peMOpympNDbABjjwA1SrGxComni7YVEt6ZcidFFEkRLIBmxLmmPBdiCf5Vhouc55KSwG73bXz5JgF6rtmibsOJCaCiSc5OvZD5NEAXqNQfaHj35rlc4icxkZpwKvDdi6dJ/kLA7C4Ek9eOE/yRVPiNiJelingKLZo0RJPss+y2sre2yvblbHO8yuQB+epvFx6n7Scp4oKwH6QiacjZpbEqhZPEahFHMrRI1KqSQNIe86FOxYy1IdFOleBhSxh3NEOrYsewOZZHrpj4knjvZCJpxPR2s7Tt2fdiyd+bl1oVDpPsY2lygN5iP2cd86TXMz7gqJyN4BzSIcpKlLxnNx54n9fLLUyx2n4cPFxrpwnAKg3hbEyFpCuq1VV0m4i3ghApzF1443AnDmuN9mUiycvnSdfw3a83VVVkuuUmSmNV7V48jbfCZA0zPFwR9K4G/Fkt0vulkNf8Nq++Oor9qXmZ7Dxxg+MjATefqMeI0fIXCeLxfMJ5Xgjrb+yDpciHnVxjglBLZ4cyeJ7LIMwfbr0eaudJ1NUpLiqeweuYe+r1Sp1BmBiyvHm8/nnW/MwZvOVlLBahy0E3eKpuroaEydORHp6OoYMGYIhQ4YgIyMDkyZNQnVLrbMeQNjANTmJp8uXgU1wbKx06JCiMF98JRNPBVbXSaZyMcAnimobG7DJFvfiyauwnd0OVFa6znlyiKeKzCyxnxERYF+p5N+KfBBPHFfOE6D8ZsyRv8fy1XbqnCdFqQIAGDQItbChEWZY28omveRkZn/ExGheOYwO2wGq/fg8iCd+Ue2eUqaMsziukK6cJ+46xcbKdI1stR0ANPzEPt99DVmK50JkpPj8qShE5zDZvhYAIqvZi0VUs/bWRjvnOwHeOU/85UTxZHH0Uf1edOjAntBkwplElnNXUQGpXAGALaVXwmqVUvLUcFGhLlfg5DypxVMz5DzJw3Yn0Fl879TiiV/DNBZLuxVP+xOHsFWDgvI04sLPJ/HkCXnYzsucJ3XYTu4sq4/R2uKkuhpiHTlZpBcZGcownR7nic9Jh82ON58noGmIp9On2Tlis0npR1dcwWpc8jSpw46kcTGz/MgRNg/deqtU8DQuznNc0SGeTKdOAmBzSl24hnV/4QJTlCYTHvnPQCxaJJq1TuIpItIkjsUduEbSb/JS7LLfuXi6cMkCPPgg+6MFJY7rFk+PPPIINmzYgC+//BKlpaUoLS3Ff//7X2zYsAGPPvqoP9oY0sgHrpzLl4FLSMbJWMfJJnOf4srYIDln8k488ZO7MowN2DZm78WTU9guIkJ6wtJShfOk2E/SIZ5KUrOUL2E2K+1fHeLpqquUAkQtnsLDpftd7TAASCtx5JOrVsK4ONFHR2Os5RPcg/dhS5VNem3aAO+/z74taUxW/hBP4eHARbSROlRbyyYxjc/yYj17bzsnlUlvCN+ICq6dJ6eQHSB+TolmppJNjsk7v1LlPMkemIpCdLaqxNPlS4qfdQaIp1/BLrR8Xzen9yI8nNXRWb5cLOJZXg52FbzqKpy75naUIQG9e2uaYADYRdtkYrWj5K/BxZM9wXH7+fNsIPCwnRvnqaFB6l9Tcp7kztMJdBbPa7V44kabXvG0Oe42sf1yR5cLGkWpAjftBHSIJx+cJ1dhO3mpAv4ZuEoY56WI5AsHwsKkFK3wcMXWgh7hb+NPjVnKO9wki3fvrnSpp02Tfj9icrw4t6jefpt9eKtXi9tyeTWncvHk+PAuog0u2zTEk8N1Enr0wM7D7H5+amvlmH2OO/ATemETbvBaPBUXA5g4kQ36mhrtEzQI0S2ePv30U7zzzjsYPnw44uLiEBcXh1tvvRVvvfUWPvnkE3+0MaRxJZ74iflzqiN0J0sajyph4uksXIsnPmHIxVO5lQ3YRJN3YTu+ZRSgGo+ycgX89sZG2cXBbhe/GRUkqsQTIIXuIiN1qYdI2e4P6mRxjrukcXmyuMnkfdiusRFY2TgCyzHeOcpw993sW58G/hJPZYiH4Fiit2huMWJjgbM/Ouwj2Rt9/jL7cNrHlmmoYM/Ok5Z4Ssd55GE1rGeZUNnf4Fo8PXh7IX7bU1s8RdSwc7AuRls8eZMwDrBzgieBZzU6rnwa+V8YNQoYO1YUBxUVYG/+rl3419DPAbgO2QFM88fEKJ0nQQDiBdaPhiscy9EvX2aDRha207rQ80M5RuU8aTlPPMzNd3KqrnZui6ucJwBYG8nyndRfSPgY84vz5EPOk6uwXX29JPrche1KSti+t4Ci+DsAKXTXq5e+UDsfbvtqeyi/YLkpU9Czp/L24cMlJ+qkTSae7Ha24THAzmWuanSIJ04xklETpiGeHPlOVVmDxM+ZB0GcnKcI4D68hz7YjyrEKHPg4Pw7r9xeUgLUxbdlCeX79mmuvg1GfArbpSpmVUZKSgqF7XxAvUyWwyfWY+0d4mnTJvG+yGImnk7b9YXtSk1swCbxpdUaNZ74Y/hzaFxzFSvuYmKkOUEM3Z05wzpks+HX8C7OL8HFkw7XicNDd2rXieOuXIE6v8RTqQJ+m3yTcfkCO094kzCuN+eJfZYm1MeyN3T/BlY76ew+pRCuqgLOV7P3Ny1SWzxxbVFSoizwqymeHM+bbj+H1RgOk92Oxth4FCIVkZGqC6fjgeNzC9HGsQ9XYxh7M6JrmFKLqnHkPMW6d5485TzJxVPPesdqnmJnIclRiCcAMJnE+n/uxBN/rFo88TIFQnqGJDiOHJHUiJuwHR/z8ppEelGvtjuOLk7OE89N6tFDWoEnr2wBaDhPjr4cQXccauiuOIbDu6goVeACp1C4J1JSpJMgPr5Jq+0A9+cRb/eSJezcb9cOGDNG2RwunvTkOwHScCuqjFLaWW6cpyyVSWWxsIoSAHAmyiGejhxh0YizZ9mL7NwprVr0RnyocqIuog2qLBriac8e9rqp0uBwJ54YJuVLuBBPSUnSeVFUBBaj9HYZYxCgWzzl5ORg7ty5qJGdtZcvX8bf//535KiTUgiPyJMV5fAJ6Wx7xzL5n34SZ9vwIiaeTtRriydB0A7bXRLYoOLflsVv6KqB7DbnCVAkjZtMbOzGohyVJx3xH5n/XFzGRodiPOfmsupvf/qTZvvd8fDDLHfV1SIdb5wnrbwJLeeJCwo+cVssrvNbtfCX8wQAdbFM+ZgcQqHmnFI8HT0qORJR9driSf6ZyC+mmuIpKwt49FHsiRuCH9EXVSmdcG7cowBMStdJ/sDCQtH2KM9kAifasRFutEM8NcRqrzrSE7bj4ql7rcp58kI8CYJm8WRNFFu0lJQoazwlJLJaGoBUFyc2FoiJcSme1E6oL7hznlTmAjIypKGujow4iaeRI1HZNwezMU9spxHOk9e7cFgsWNRtEV5L+CuO12fqynnSqh7O73Mnnnj18GnTnL/U3HQT+3nbbV623wGfNysqAKGnTBXpEE8A2xt6yBDg5kmdWeMuX5bqY40Zw5L1tmxhB/LK8e7QcJ40xZNj/B5p6CrexNeHuXL6ADZXinNbu3Ysp2niRMlCAxPyfKqQV1JvKXhdqoDzyiuvIC8vD+3bt0c/R+bcjz/+iIiICHzzzTeGNzDUcZfzBAC1bdqx+FRBAfsWcN11sBYy8XSsVls8NTZK+Udy8XTRzgasmOSqo1SBq7AdAMTHCVhbdhU65xYBWzYqZgHNl4iKAtat02y7J7p2ZfXUXOHOeZJvzQJ4dp74+yj/1qvnIufNajtfxVNtdDJiAFhLmWAVVG/0zz/LLqplZRpXR9aehAS2cPLiRSn0pimeTCbgpZfwt0NsD+t35jts9zfhnXjq0h+Jx/MRXcfaGeP42RDX9JynXQ7x1L7uODun+dXeC/F0/Dg7jW02Rf64JmrnSS6ehMREpk4OHZLK3zvElCfnqSkrs7Vynq5UOU+c9HT2lhQXeyGe0tPx68c/4OOeQEK18hiOOufJyLDdmTPA9IPsy1WX30rt1RO2s1rZRbyx0b14kr9PkZFSDU8506YB99zjuQKAGn68IAB13bIQjq/ZDW7CdlriKT6eZ25Yga+6sfPsf/9jd/7hD+xn+/bSnoGeUImni2iDSrOGeHKEn/dfkuoJenaepBxBAOwXXl9GRVoa8OuvLVM86XaeevfujaNHj2L+/Pno378/+vfvj+effx5Hjx5FL9nSX8I7POU8RUaZpK/Eu3YBZWUwV7OLw6nGdpqTEb/gAErxVFTPBmxco3c5T7W1LjbpVtV66hhTjG44Bkt1BSs4sn49u9+VePIjepwneV4Nf8/k4glgk71TPoiX+Gu1HQDURDPnyVbBnCcuRngs7vBh2UW1tNRF/FXKe5InjWuKJwfyj54/RlZ0WPnAgwdFxVDdjS1ji61Ttrcxvuk5TxfRFgVwvObmzeynyaQZFlaLJ+469evn+bNQbw4s35rFxMWT/Ekdf7vKeTJCPFmtQC0isAh/xtae9+M0OjiF7TgpKdI4VIsnrXNc7Yqrv5D4UzzJshRw/Li0FaaesJ3Wce6cJ4Bt8iBPlZOjVzjxtvC+V3Vw7TxdvCiNpyuu8PCk8oz11FTm4utFw3kSw7/8g25sFCeD/F/TxWOLipiDyMPBWuLJ23Oa5622RPGk23kCgKioKDygJc8J3XhyniIiwMTTl1+yuPbQoQCAYiThMqJw+bKzxawWT/ykLqxlAza2wTvnSbb/qfI6pKoy3iX8V+m+M2ekDNWsLFz6RvMl/Ia7QpnyMAmgHOz8QmCzKR0if4snX+o8AUB1FJvhoxw5ROp94g4fBixy58mFeEpOZqux5Enj3oonfm64dJ54+ColBQ0pTEjE1TucJ8dPe0LTc54AYD/6IA2F0sKKxERFxX6OK/HkKd+JP/YXlfPEqzObEhOksB1fsuUQT/52ngBgOhbhvmsB/Cy9d3Lx1KYNu52PQ485T7LH19ezf+ovJL6G7QTBs4PLNfAdd7DSSPycVIsnngxusWifI+HhbNx7K56mT3ffLr1wDX/xIlCalgXxbFeJJ27Wd+zoRc0vuXgaO9b7/Z3kaDhPZYJjbqirYwPv0iWmkMxmbD2WIh5bVKQUra6cJ29oyeJJt/MEAIcPH8a0adMwdOhQDB06FNOmTcPP8h0NCa/xJJ4iIyE5Tzt3irU8+Eo7rRx9uXiS7y5+7jIbsDH13okn/k3IbFYNBlXYrqOViafKxPbKSSEAzpNTQrAMV84T4B/nyegK44CsZlcEE098U09xc0/HG717typs5wfniec+uBRP/OTMzISQyNoV2+AoLtnAl/h7DtsJgmfxxPOexJIeLuyDpooneakCe6Mg5TzJnSee2OMhbNfUApmA8rrJn0/LeeIXKVfOk5Z4kouK6mrPzpM3pQoA7/KeuPN0993AN9+w865tW+lj1UoGV4ft5L+7E+H8Y8vLk1YkGgmfEs/Gunae3IXsnJCLJ9nOE7qIiFB8uShGMsoaZRNcRYW4es/eNhWFF6UTraREmaNL4slLPv30U/Tu3Rv5+fno168f+vXrh927d6NPnz749NNP/dHGkMbVajtFMTc+sx85In6rPW/xLJ7CwpRL8n/l4qnuErsieQjb8QuqU801Vdgu08QEXVFaP+CTT9iMHhcH9OgRsLCdN6vttISLPGEcaJp4sqp8Xfnz3nwzW7nD0xW8hX+WVTLxZEEDEuCIryYl4ehR5jxVWrwXT744T/z8cCmeOB06iCdAQiN7oTiHiOKiSo38s2lo0CGeuNvl4oST58TZ7VJ6kjfiSRG2a2hAY1mlFLZLkiWMc5rBeTKZpPOMn99azhNvmh7xJN8fr6rKtfPkzWo79Zhyx6VLknk3eDALqR47xqLAvG/uksH1hu3GjWOpQsuWuW+Xr/DCqzuPJbGEaZPJaQdq+bYsHrn6avYcWVnOe4V6Cy/u66AYyai8LKtMXl4u5jtVJbDzuH176Xzg9TjNZun8o7CdBx5//HHMmjULTz/9tOL2uXPn4vHHH8cY9RpPwi2eVttFRIBd4Tp3Bk6cEPdYKwprDzS6F0/qiYZ/a46qLWEvwA/04Dw5xfpVYbt0O3OeiqPao8vNN7Ov8yYTEBkZ1M6T2cz6qnbqzGb2z25vmngymZTPLxdTHTuKq4B1IdbssjHVk4xiaWNPAEhMxJcfsF+7D4wHdsBj2A6QPuvycumc8iSe+KIElzlPnMxM8YXiG5lwj290f2KoXUFvwnYApEQMF88rz4n76iv2MzraO8chNha4jEjUW8IR1lgLlJSI4smcnAjEqawXh2LxZ84TwM6rhoamOU/8HJeLJ/n+eN44T0aJJ76DSY8eUi0g9cdptUr95u+rO+fJnXiKiGCL1PzFwIHAp586XM7Vq5lSkFdQh/uVdk707s2suQ4dmra0PyZG/FAvog37HOPi2IlUXi46TyXh7Dy+8ko2FouKJPGkJVQBcp40OX/+PO655x6n2++++26c50W6CK/xKmwHSF+Nt2wBAFwIb684To5aPPETnIuniLoKacmE1ep0pqvFk1PerSpsl1Kn2muvf39xD4Ngcp7Uq+0A16E1eY6Gr+IJUDooevObtOATVIVNcp76tWdvchniUFJhxZdfsmOuzdPvPPFNZNu21e6vV2E79XY1mZkwt2EngA31QFWVKJ5MSe5LFQDeiaeDUKkfD85TWRnb3g1gK6nULqHrx5pQZWPPLRRfUoonHv/hNIPzBMgSkg0I26k/c/mXO61SBYLgXT/UeYTu4CG7G25wf5y3wsjTMf6GT927doGVD9fomC7xBADXX++86Z5eZCdIMZLZ+SOfQB3O069Chtg2LmZ5WqvWe616are0KvF00003YZN8KYSDzZs34wZPZzvhhNfiiec9Ob7uF0d6DtupxVMpEqSD+B5LSUlO317Uy8RdiieH85RUw5ynAouydIKbyKDf0FMkE3AWT+pE7qY4T+rnN0I88ecrs0riqVc6e5MvIQnr1kkXn9/c7vjgampc2ohq5+nIEfaze3ft1/dKPAFK9ykzE9a4KNTC8eaePYtIsKuYKVn7xLBYpNNSvtGzK/FUhRgUxUo1ZDyJp717gQMHmInqqmaYGvG6Esae21RUiBiwk0qRMM5phlIFgOuwnfx85U3zus6TAz5W5GE7LrgrK9kcwc0+d/2Qf57eiqfBg90f50oY6Q3b+Rte0PeXX5wT9QHlhsReiycjcJwgtbYY1CHcWTw5zJDjl9PFtqnFkyvnicJ2GowcORJPPPEE8vPzce211wIAtm3bhhUrVuDvf/87vvjiC8WxhHvk4km+CsVpA0tVBb/SGP3iqQFhqEAMYlEpWQwaFxn1Rd5T2C7BsVHxWUFpRVdVSRNlMJUqkA9s9SSqdp6aKp6Mdp5EIWyRxFN6BLsSFiMZzz3HhEavXkCnvrIPjs92HpwnLp5cLZeWiydexsIpbAcw8XTCsddchw4Is5lQjGRk4Ly42VoDLLAkaJds5yHP2lqlEHYlngDgXFJvpFTIvhRooBYHs2Zpltxx+9gyi0M8HT8m3ZmQwBQCL5wFNEvCOGCs8+RKPMnDdhkZTGxXVirnH08XzLAwNje5Sxi/fFlK4tfrPGmF7dSbA2ttDOxvkhypTsePs5Q8x4JpEe46paS4GEv+wjGh1UQnA3WQwnaAwnk6UOLaeTIqbFdVxc4nX+bYQKFbPD3kqBP/+uuv43XVDsj8PgAwmUxolO8iSWjCJxy7nU0s/GR02sBy4EB2RXE4T+Vx3osn+UldgkSP4kmdSO0pbBdTxpynkw1K52n7dunwpn679pamOk989VKwiicxf83MxFMSLiHDyiygS0gS86VHjoQUkq2qUu64LkPtPB09yn56Ek8XLkgXIm+cJ5vA2peB8+K5dwlJCLO5ztnQK54K2/QGTn2h7JgKuTho31656aon+OfPtzkyn2JCrQxxiOcnTno6E0+xseKL8TY3NjLhwJ0ifztP3iaMy/elVIsnediOC6yMDLYFGc+F4m3wdH5z8eTOedqxg92fkcHSPN3hS9hOa2Pg5mDQICaedu1yFk88Od5TkVbD4c5TbBuAr6DTcJ5+KpacJz7WjRJPMTHSFFVQAHTr5mtnmh/dYTu73e7VPxJO3qFeDsxxCtvFxiqWYlQmeM554ie2/AQXl1ofd/0NXS2enJwnfgWtrATKyhBeXQoAOFGndJ7efpv9HDu2+bYs0lMkE1C+Nzab1E75XlzBJJ7ErXYcq77MEJBZf1xxGwCMGOH4hStffqJ46Tx5CtvxixE3W5zg4sliAdLTERYma59DoV1Cktv3REyOd4gnm825dJNCPKXK9tDywnl6+ml9DgRva5mZPbfFIZ5KIbOueN6TLIQnfw35eDU654mfp3qdJ/44wPkcl4ft5M4Tf5zTPOVFO+vr2fTz2WfOx8jznTzNGfxizdvgLmznqV6Yv1HkPakItHhqiGNfNFw5T78iA0lJbK4w2nkCWm7ozqc6T4RxyOsKyVfcadrLPHQXFydeBfSE7QCZeDLCeQLY+mEAFYjBuUrpwnzxojQ5Nmc9VXcJ43xwyi8Q8gEvv5D7w3nyJinZE2KdpwYbLlvZOZBRyRQPFycpKbIVzGpl48J5unSJORCewnbqcyE5WbMWpSSeMjIAi0UhnoSjv4jtdSee+HvHw4NaFzz5+ChOk119XIintm2BO+8Efv97tt2GHnh7Skzsua0n2RjiThQASVnIksfl7faHeOLnFRcI3jhPZWVS+Ix/0bBanZ1YediOH8efS+48edMH+ZgaMYJtycaLYXK8zXcCvAvbBUPOExDk4imBfYNSOE8lJWLNkvNIR1YWE7NcPP3qqIvc1JwnoBWIp61bt2LVqlWK295//3107twZKSkpmDx5Mmr52UvoQitpXNNe5iOwfXuXieaA+7Cd+O1fR86Tk3iyWiUl4Rj5v6Idysqlr4r/+Q9rx4ABwFVXObfRX7gqVXD0KAsjms1sg02O2nniBHvCeG0tUBbGlE+bEubk1EWzz/K222TFE9UfngvxJAjslOAJra7sc5tNOTFqhuwASTw5dnq32VhOFgAIv+gTT1wIexJPZalXSG+yC/FkMgEffQSsWKG/MDN/alE8nXaIJ7NMPHFlIXOeTCZnlwQwPueJw8+Rtm3Z+Z6cLH3s8vwunpolz3dSuz1aq+3kzpMv4mnbNvE7lxhmBtg5uGMH+/366z0/n96wHU+NUB/THPA58ORJZU01IPDiyZ7ExqVCPB07BjQ2wm4yowgpYiI7H+9ceDd1tR3QCsTT008/jQMHDoh/79+/H5MmTUJubi6efPJJfPnll5g/f75fGhnquBNPCufp9ttZgaBx4xSPOXgQuPZa4PPP2W1eOU/867xGtqzHsB0gORqOkX8W7cWnFAQpZNfcu/jwtlZXKxNTeXuGDVOu8HUlboI150meAMvznuIvMPGUmsUu6nffLXuAB/EUFibdtHUr+9m+vfuLodzMcimecnPZGz12rPg6XLibTp0EwM5FI8WTJdIG3H8/uwr16eN8cBPhbRVDpo49JstNCdJBI0awGj6jRyseq1XryWjniSMXT//9L9vZSR6O5p83D925ShYHtFfbcfHkq/P03nvSbdzpBJjRUVrKBJ83dbf0rraTf7dvbvEUHy+FwnlhVoB9WeEuTrNvDTt8ONCuHSqG/A6AKmx3+DAAoCw8BY2wiuKJO0+c1hy28zqQsHfvXsybN0/8+8MPP0R2djbecuzinJmZiblz5+Kpp54yvJGhjlo8NTZKSZUK8ZSZyb66AIh6WHrMo48yV+Xdd9leUF6JJ44vYTuAia6zZ9l6bzDnqaaGvXZ+Prs5MlJ/Be2mIr8AVFayC319PbB0KbtNLeY8he2Ctc5TbS1wUWDiKayaqYvRf0zCz/9R7t6g+PDMZs2rXJs2TKBw8eRpY9KEBDEdwvXqoB49gNOnxT8V4smRD6k358mTeLLZAPz73+4b3wT4Z1ksKMeMwnkaPFhKzpcRGcmEgT9zntTtBIDf/c75+KQk9p5y8eTu/PaH8yQP1Tmu0QCkLUq6dHEOH2qhN2wnF67NLZ4AFjg4epSF7m65hd3G/YgOHXzbeLhJDB8OnD2LOoeYUzhPDlVbZM0Q2wd4L54obCejpKQEqbIVNBs2bMDw4cPFv6+++mqc4VlkhC7UW7R4M8j5RWP9ela0FpDMpKaKJ4+lCgAn5+lXx157/fsDDzuE3Z13uhBefsRmc77ofvklqwmalsZCWnL8Hbbzp/NU2KhULuHpSUrhBCg/AKd9dhg8dMcrO7tKFud45TypsFiAEpNyBZzROU9GvL/uEJ0nQTmGyszO7q0arXIF/naeXKHeHNgb5+nCBamiPC+OXVcnhf686YNWzp/cedK1RQm8C9vJxwu/X76lSHOilfcUsJCdDMWXdz7ZO96s82DhZ37OkPMk4bV4Sk1NxQlH3Za6ujrs3r1brPMEABUVFQjz9+wVoqidJ/kE62oVC3+Mo+A4AGki81SqQIGvzhO/gjqSCrN+2w7h4WwC5JODP7c8cIc678lhjuL++13nhwAtL2x3vla1HF9reb5aPGnA3SM+kXvjPHG8FU+AVB+JcwlJbi9iesN2ejdZ1ou456Nd2Y/yAIsnd86TFuoVd96IJ755hNmsvIDyQql6nCdAimqePi29D9x5aqp4chW2k4ur5lr9KydYxZM8NKueI842MueJnzNxccrPUf5ey38n8STj1ltvxZNPPolNmzZh1qxZiIqKUlQU37dvH7p27eqXRoY66v3t+AQbFuY6qVVrsnIlnvwStlOt4rpjWnsUFrLQ4bBhwJQp3iV9+gP5irtTp9iu7AAwaZLzsfILstbKOCMTxo1cbXfhAnARKrGklSTthXiSJ40D+sSTnqJ+5VZl+8rMSW4vYsEqnooaVOLJ4lk8aeU8GZUw7qvz5I144vMMv7Dxiyd/Db7Lk55SBQBzp3k7HOsHmiye3IXtamsDt9KOM2AAE21nzkhh72AQT/wzrq0FGqOVc8TJWqXzJF9xByjPNb5XqPw5vSHkxdO8efNgtVpx44034q233sJbb70Fm2y2evfdd3ELD+QSulA7T95UwZWfnFykGBW28yphXJ1o3q4d4uOZu/P118Drrwfm2x2gLJT58stMFNx8M6Cl7T05T3V1wek8nTsnrV4T8SSetK6OcBZA/gjbAdJ+fBy1mFKjN+fJ38Y3/ywvNOoXT8HsPGltCszhwo5f2PgxfCw4jGddzlO7dqyOExfpPO9J7/5uelfbBVo8xcZKJUTefZfNS8EgnuTivcamnOzP2JnzJDe15eJJ/V7yv31xngoLpa1+WgJefxdu06YNNm7ciLKyMsTExMCiskRWrFiBmJZUWz2IcBW2czfI+WMsFmDBAiagystZsrm63ovesJ3HUgWAc/2g9u01DgoMXOzNmSMtff7zn7WP9SSeysslRyYYxJN8ybuTeNLaZ0SH8wSw88lTZWdfxVOlTSU6PIgnvTlPzeU8FdfHsa/Zjpm+wpLg8bHBmPOkdp60zm9+EeQiiZ9CMTFsyT13nvSIp3Hj2NvXowcrW3D4MBNwPGXWKW/PBU0J2wWKP/+ZLe557TVgwgT2HprN3rtt/oCHMQUBqLbEQq57ziPdbXkS9bk2cCCwZw9L+vcWLsYaGtg52axb1DQB3UUy4+PjnYQTACQlJSmcKMJ71OKJfxN0p95zctiEPHOmtPEkwC72upwnDWWkq1QBwGZFPVdRP8Pby4XTSy+xKg9aeEoYl2/k6ctFzl/OE6ASTzEx2upBR84TwISTp2Hsa9hOLZ6qbO4dm2AN29U1mBVCtcLqvfPkj1IF7vL4tPAlbMdX//Jj+E89ztP48WxBCd8ShztPR45IieNt27rcWccJuTBqaJBci2B1ngDg//6PrVYsKABmz2a3devWvHvtqTGZpM+v0qycI845qovLowiuwnYA8O23LI9Nz0Ihm036zFtS6I4qjAcB6tV2fJ8xd3qkZ092UVmwgJ3AfEIoK9MhnvhmpirkF6GICBcXJbnLkZHhosx0YOATu9kMvPMOK+XgCk+lCngeWXS0b130V5FMQCWeXO28rFM8eQrZAb47T43hUaiB1AG1mFLjjXiyWKT31d/iiT+/3Q4IOsWTukim3CE22nnyR8I4R+48AfqcpwcfZK5Ex47sb+4wHT6sf6Ud4F0ZAq3VdoEUTzabJB4/+ID9DGTIjiMmjVuUc8R5pDuJWXfiyWp1mR3glpaY9xQ8V7xWjNp54itYPH2rl0+a8t3u3YmnUiRIf7i44Mov8i5rj8ivoHztcpBw550sb2LFCmDiRPfHeus8+RqR9tf2LIBx4kk+OXpKFgd8d57CbCbF/nvVtgTXB8M78QRI39qbq1QBAAgJUj/0OE9cPMkL4vqrwrgruO7Tk/PEUTtPesSTGrnzpDffCXAtnrTCdjt3ApMns98D6fIArB3yNgSDeBKdp/pwceAJJhMKkeo0tbgL2/kKiSfCJ9Sr7bjzpOfC5E48yS86DQiDwJWAF+LJpf0qv4IGUb4TwAqFHjzoVORZE085T0aKJ786T65iHTqdJz3iKS5On9tjs0mFMksRD7PNvZrkfeXOiCfx1FxhOwCwy8RTVViCx8eqx7hcPDXVCVGLck8iXR0icZfzpBZFaueJixZfxFO3biwcVFoq7Wnnq/PEXbywMKVDzKemqiqpdml2tv62GklysnJfxWAQT4roh+NDvhzLqourLxPunCdfIfGkk40bN2LEiBHIyMiAyWTCypUrFfffd999MJlMin/Dhg1THHPp0iWMHz8ecXFxSEhIwKRJk1Ap3yYcrIzCDTfcgIiICGRmZmLBggX+7pou1M6T0eJJfoJbLICJf/V0s/8Xv1C4FE/ysF2QOU96cBW24xegYBZPlYhBg9n9Xm56nSdvwnY8GdTbxF5OWJgk+DwVyASk944n7LsSGXz8NKfz1BjP3u8qRKHR4lm1qUNl8nynpq5KVdfd8fR8vXszgXHyJNsaRE/YTu08cXxxcyIjpcrVvOp4U8N26nNk4EBg1Spg+XJWUPiXX1gOZKCZMUP6PZjEk7zWU3k0K1OgJ2znKy1RPAWgzqpEVVUV+vXrh4kTJ2K0C5tg2LBhWLJkifh3uOrTGj9+PM6fP481a9agvr4e999/PyZPnozly5cDAMrLy3HLLbcgNzcXixcvxv79+zFx4kQkJCRgMvdxA4wr8aQnn4RfIz3lPEVEgAmfM2dcX3Adj62v9zJsF2TOkx6aK2xnMunfiFYL5cXBhMvRbRBbcd71Zyn/AN2IJ77axhvnqWtXtkpK78cu36JFj3jiuBJPo0cDK1cC/frpa49eLBbpfWqIT0I4WA6hN7lw/ALEN4U1KlkcUDpN3rhviYlso9pdu4Dvv/cuYZyjdp5cHectPXqwWmw82dvXsB0P7aov5iaT864CwUDPnsC//sXOh0CutOMonFHHh1wSoSyQyXFXqsBX5OUKWgoBFU/Dhw9XbPGiRXh4ONL4O6vi0KFDWL16NXbu3IlBjvKtr776Km699Va89NJLyMjIwLJly1BXV4d3330XNpsNvXr1wt69e7Fw4cKgFU/e5jzJcec8mc3swlVf75hc+GhwI548Ok9BnPOkB3+H7fjzG+WKqC8O9bHJgDvxZLWyr5VVVS7FU3g4MH8+uwDxZF5P+BL6kIftjBRPL70EvPii/+uKcUe2rg5oiGX9KEGiV6KYj2X+xcioApmA64rP7hg6lImntWt9y3kySjxdcQVboQWwz5c7Ud4gF08ffsh+v+oq39oRCKZODXQLJLTCdhesygKZHMp5YgR9ztP69euRkpKCHj16YMqUKSjmX90AbN26FQkJCaJwAoDc3FyYzWZs375dPGbIkCGKMgp5eXk4fPgwSuTr0GXU1taivLxc8c+fuFptZ5R4AqSTXHSeAO26QFA+1qXzFBsrJRe0YOfJVYVxo50no8STWlA0JjgsDTdCWFTAbnYefeIJ4Nlnm9g4D8jDdiVI9PieqCdmd99ym6sgq7htTwwbO8HmPHl7Mbv5ZvZz7Vrv6jxx+CmkFlpNcZ7kv+tZ0crPh5IS4O232e/BJEhaElphu/Mmz84TiacgZdiwYXj//fexdu1avPDCC+JmxI2OXdkLCgqQotqp0Gq1IikpCQWOT6GgoECxoTEA8e8CF5/U/PnzER8fL/7LzMw0umsK/J3zBEgneXg4JJ/YjV/MH+vSeTKb2dfG8HD9yS9BhCfniRdobKp4MmojUpNJ2ebaro44hzvvn38+Ad4+KSwM+BmsnT+jp2HOU3PC23S5I+vHYfTw6oLPx7I/xJP8ffQ2aX7wYHbsmTNSqETLeVLnUBkdtpOHifWGr3ie1YEDLJesU6fgDNG1BMTVdpUQEx9/MvcB4JzzFB0tHd+axVNAw3aeGDt2rPh7nz590LdvX3Tt2hXr16/H0KFD/fa6s2bNwsyZM8W/y8vL/Sqg/J3zBKicp7lzgVGjpJ0qNfAongCWMFFa2nJKwmrgSTw1pbo4YLzzBLA289VFF598Ge3/dj9w9dWuH/DJJ2ypkTfZ4H7EZgPew72IuaYX/r2jP65rgeJJrP+VnYcz/9yBGTOuRJYXYTt+AeJjO9DOU1QUK7S7YYN0m5Z4MpnYxVId2vOH86Qn3wlwPh8eesiYvMLWCPcgCgsBPP8ccNdd+Px+Nqdomdpt27JcNaPEU6dOwJtvAunpxjxfcxDUzpOaLl26oE2bNvjFsZNkWloainihEQcNDQ24dOmSmCeVlpaGQlUWGv/bVS5VeHg44uLiFP/8iTxZr65OcjuMdJ74RBMe7vgvO9vtTMMvEm67npYWHNmOTcBT2I4TTOJJ3ubEdlFswyx3caukJKBvX+Ma4CNhYYAdFuRbs1GH8BbpPIlhuwYTSrpdjWpE6wrbVVUx4ctzngLlPAFS6I7jqrihPHRntPOUmSl9rnqnEnUlca2Nvwnv4N7AmTNgb2Z2NopL2ImtJZ6uuYaJdqOCDjExwAMPAL/7nTHP1xy0KPF09uxZFBcXI90hT3NyclBaWor8/HzxmHXr1sFutyPbkdGak5ODjRs3op7vLwBgzZo16NGjBxLd5Pw0J3Lnidv6ZrPz9nHu0JXz5AVeOU8hgCvnSR1mC5aEcflzAu5TnYIN/h5w18XInKfmgo+LujpphZg34ik+XvquUlwsvQdGJIz74jwBLGmcY7G4fqxcGBlZqgBg791NN7HXzsnR91j5+TB+fMsaC8GGQjyBOe78WqRVQm75crY5eYAzAQJKQMVTZWUl9u7di7179wIATpw4gb179+L06dOorKzEY489hm3btuHkyZNYu3YtRo0ahW7duiEvLw8AkJWVhWHDhuGBBx7Ajh07sGXLFkybNg1jx45FRgZLdvvDH/4Am82GSZMm4cCBA/joo4/wyiuvKMJygUaeMM5t/aQkfRa0rpwnL/CYMB4ieCpVwAkm54m32Wr1vV2BgL8X3oqnoHae6tkWK4B34slkUobu/JXzpEc8XXONNPfExro2L/3pPAHAF1+wqLKelXaAUrDxLU8I3+Brfrh4qqqS9jPUEqVWa1BtZxoQAiqedu3ahQEDBmDAgAEAgJkzZ2LAgAGYM2cOLBYL9u3bh5EjR+KKK67ApEmTMHDgQGzatElR62nZsmXo2bMnhg4diltvvRWDBw/Gm2++Kd4fHx+Pb7/9FidOnMDAgQPx6KOPYs6cOUFTpgBQOk++5DsBknhylfOkCNt5wYABbFLu319fO1oanva24wRLwjggtVm9YWeww99THrJq6eKJO0/efsmRr7gLZJ0nTlgYMGQI+93dfmRy8WR0qQLeDl/SJjt1YtsvzZoV+vOUv+HOU2Ehu37wYq42mzHnaCgS0ITxm266CQLPyNXgm2++8fgcSUlJYkFMV/Tt2xebeP3/IISfnA0NzAoF9E8mPLxWWipNbE0J2735Jquf01rDdkaJJ/44I8IzHN7mlham0Bu2C2bxpDdsByhX3AWD8wSw0N3XX7sXT/I2Gl2qoCmYTGzjb6LptG3LxltdHbsG8Q3RW9oXtOakReU8hSryief0afZTr3iSO098JVZTwnYmU+gLJ8D/YbvrrwdmzgSeeca3x2shd55aEqEQtuNtkjtP3oonedjOyIRxX50nABgzhpV7c7d4mQt/i0X6DOTjwWr1/9Y4hH8xmZShO3f5TgQjqEsVtBbCwtjE1NjIln8CvounxkapsGNTnKfWgr/DdlYr8PLLvj3WFS3deZJv4uqOYEwY18p5akrYLlAVxjmdOrEdDdz1gQu8uDjJhZA7TxTWCQ0yM4Hjx5l44udUS5tjmhNynoIAk0magLjzpDfnKTJSOuF53lRTcp5aC/4O2/mDli6eXP2tJhidp2AM2zXFeQI8iz95UjlH3m4ST6EBz3s6e1bKeWppc0xzQuIpSOATlK/OkzzMxid1cp484ypsZ1SpAn/Q0sN2nJYonowK2wVLzpM38LlJvvLWbJbGhK9lCojgQl6ugMJ2niHxFCSonSdfVp+o60I1JeepteAqz4mcJ+MJJedJb6kCQBm281fOkz/GtzxsJ4ePCXKeQgN5zhM5T54h8RQkKPYWgjHiST6R+mPVVyhgNksXaX8kjPuDm25i5wtfZt5S0Os8BXvOk95SBVphO6NznnwJ23lCK2wHkHgKNShspw9KGA8S1BOQLwXI3DlPDzzAlp/ec4/+5w11wsNZDos75ymYROef/sS2ojCydlRz0FTnKRhcU3nOExdNvoTtuBBpCc4TF0lq58nIPhCBRx62a9eO/U5hO9e0sOk3dFFPQEaH7Xr1ApYu1f+crYHwcKCiwrV4Cg8PvqXYLU04AU0TTzab9yLFn8hznrhQ8SVsx4VXIPe285bbbwdWrwbUdYXJeQoteNiuqAg4f579Ts6Ta1rgFByaGCGe1HWZ/DGRhiI8HOQqbBdMIbuWTFMSxoMhZAc0rVQBH9OlpVJ/WoLz1KUL8O23zreT8xRaJCez87KmBvjpJ3YbiSfXBMF3OQJQhoXCw30LE7lzngjXaG3eS+LJePQ6T3IhEGziyZdSBYmJUp2kwkL2syU4T64g5ym0MJmk0N3ly+wniSfXkHgKEuQTUNu2vpXEV4unYAs1BSu5ucwV6N1buk3+bZ7EkzE0JWwXLOKpKaUKLBZpjPJdqYzIpfO38+QKKlUQenDxxKGcJ9eQeAoS5OLJl5AdoBRPFov34YTWzuLFLMafkiLdRs6T8ahdEU8uSTCKp6aUKgCcx3ZLqPPkCgrbhR4874lDzpNrSDwFCUaIJ3nOE4Xs9KFOwCbxZDyh4Dxphe30fElRf5MPhgrjvjJ6NHNrb7+9+V6T8C9y58lmI2HsDkoYDxKMdp5IPDUNEk/Go1c8yUVBsIinpoTtAGfxZES/AuU8DRkC7N/ffK9H+B+5eEpO9i19pLVAzlOQIM998KXGE0DiyUhIPBmP3tV2JlPwbSukVSTT17BdZKQx5RcC5TwRoYdcPFHIzj0knoIEcp6CCxJPxqPXeQKk8zgYxZPeUgWA0nkyqvBqoJwnIvSQ5zyReHIPiacggXKeggsST8YTSuLJl1IFgFI8GZVPQs4TYRTqsB3hGhJPQQI5T8EFiSfj0Ru2kz8mWMRTU3Oe5GPbKPFEzhNhFAkJkiNKzpN7SDwFCeo6T74QEyNN5CSemgbVeTIeX5ynYM558qVUgb+dJxJPRFMwmaTQHYkn95B4ChKMcJ7MZil0R+KpaZDzZDyhGrbTk/MkH9v+yHmicU80FR66I/HkHhJPQYJ8IvVVPAFS6I4m0aZB4sl4KGxHzhMR/OTmsi8EOTmBbklwQ+IpSJBPpE1J1CPnyRhIPBlPKDlPwSSeyHkijOSJJ4DycuCmmwLdkuCGxFOQwN2m1NSmTYDkPBkD5TwZjy/OU7DmPNXVNb1UATlPRLBClcU9QxXGg4T27YH333femFEvJJ6MwWRiF6WGBhJPRkHOE+tPbCxQUUGr7QiiJUPiKYiYMKHpz0HiyTjCwkg8GUkoiKem5jwBzH2qqDA2YTwqirlhdK4SRPNA4inEoJwn4+jeHTh2rOluIMEIhYTxppYqAFiI/uRJ45wniwX49FOgtpbEE0E0FySeQgxynoxj0yagslJZuZ3wHV+cp+HDgZ07gcGD/dMmvTS1VAEg5T0ZmVcybJhxz0UQhGcoYTzEuOkm9u2TVko0nbg4ICMj0K0IHXwRT1OnAgUFQJ8+/mmTXowI23XowH6mpBjXLoIgmhdynkKMm24CSkv1fxsmCH9jNrPzkoe7vBFPAEveDxaamjAOAHPnAgMGAH/4g7FtIwii+SDxFIKQcCKClbAw/eIpmGhqqQIAaNcOmDLF2HYRBNG8UNiOIIhmQy6YWqJ4MiJsRxBEy4eGPUEQzYZ8IUNLFE9GhO0Igmj50LAnCKLZkAsmawtMGtAK25F4IojWBw17giCaDS4+rNbgSgT3Fi3niXIMCaL1QeKJIIhmg4ftWmLIDpDa39hIzhNBtGZo2BME0Wxw0dRSxZO83bW17CeJJ4JofdCwJwii2Qgl8VRTw35S2I4gWh8kngiCaDZCJWwHSOKJnCeCaH3QsCcIotlo6c6TfIUghe0IovVCw54giGajpYsnk0kSUOQ8EUTrJaDDfuPGjRgxYgQyMjJgMpmwcuVKxf2CIGDOnDlIT09HZGQkcnNzcfToUcUxly5dwvjx4xEXF4eEhARMmjQJlZWVimP27duHG264AREREcjMzMSCBQv83TWCIDRo6WE7QGo75TwRROsloOKpqqoK/fr1w2uvvaZ5/4IFC7Bo0SIsXrwY27dvR3R0NPLy8lDDZy0A48ePx4EDB7BmzRqsWrUKGzduxOTJk8X7y8vLccstt6Bjx47Iz8/Hiy++iKeeegpvvvmm3/tHEISSlu48AZIApLAdQbReAlrjd/jw4Rg+fLjmfYIg4J///Cf+9re/YdSoUQCA999/H6mpqVi5ciXGjh2LQ4cOYfXq1di5cycGDRoEAHj11Vdx66234qWXXkJGRgaWLVuGuro6vPvuu7DZbOjVqxf27t2LhQsXKkQWQRD+JxSdJxJPBNH6CNphf+LECRQUFCA3N1e8LT4+HtnZ2di6dSsAYOvWrUhISBCFEwDk5ubCbDZj+/bt4jFDhgyBTbZMJi8vD4cPH0ZJSYnma9fW1qK8vFzxjyCIphMKzhNvO3eeKGxHEK2PoBVPBQUFAIDU1FTF7ampqeJ9BQUFSElJUdxvtVqRlJSkOEbrOeSvoWb+/PmIj48X/2VmZja9QwRBhIR44t/DyHkiiNYLDXsNZs2ahbKyMvHfmTNnAt0kgggJQilsRzlPBNF6Cdphn5aWBgAoLCxU3F5YWCjel5aWhqKiIsX9DQ0NuHTpkuIYreeQv4aa8PBwxMXFKf4RBNF0QsF5IvFEEETQDvvOnTsjLS0Na9euFW8rLy/H9u3bkZOTAwDIyclBaWkp8vPzxWPWrVsHu92O7Oxs8ZiNGzeivr5ePGbNmjXo0aMHEhMTm6k3BEEAoSGe5FXGAcp5IojWSEDFU2VlJfbu3Yu9e/cCYEnie/fuxenTp2EymTBjxgw888wz+OKLL7B//37cc889yMjIwO233w4AyMrKwrBhw/DAAw9gx44d2LJlC6ZNm4axY8ciIyMDAPCHP/wBNpsNkyZNwoEDB/DRRx/hlVdewcyZMwPUa4JovYRS2I5DzhNBtD4CWqpg165d+M1vfiP+zQXNvffei6VLl+Lxxx9HVVUVJk+ejNLSUgwePBirV69GRESE+Jhly5Zh2rRpGDp0KMxmM8aMGYNFixaJ98fHx+Pbb7/F1KlTMXDgQLRp0wZz5syhMgUEEQBCwXki8UQQREDF00033QRBEFzebzKZ8PTTT+Ppp592eUxSUhKWL1/u9nX69u2LTZs2+dxOgiCMIRTFE4XtCKL1Qd+ZCIJoNkIhbKfOeSLniSBaHzTsCYJoNqKi2M/IyMC2oylQ2I4giICG7QiCaF2MGwccOgRMmRLolvgOiSeCIEg8EQTRbHToACxdGuhWNA0qVUAQBH1nIgiC0AE5TwRB0LAnCILQAYkngiBo2BMEQeiAShUQBEHiiSAIQgdUqoAgCBr2BEEQOqCwHUEQNOwJgiB0QOKJIAga9gRBEDqgUgUEQZB4IgiC0AE5TwRB0LAnCILQAYkngiBo2BMEQeiAwnYEQZB4IgiC0AE5TwRB0LAnCILQAYkngiBo2BMEQeiAxBNBEDTsCYIgdEA5TwRBkHgiCILQATlPBEHQsCcIgtABiSeCIGjYEwRB6IDCdgRBkHgiCILQATlPBEHQsCcIgtABiSeCIGjYEwRB6IDEE0EQNOwJgiB0QDlPBEGQeCIIgtABOU8EQdCwJwiC0AGJJ4IgaNgTBEHogMJ2BEGQeCIIgtABOU8EQdCwJwiC0AGJJ4IgaNgTBEHoQB22I/FEEK0PGvYEQRA6UDtPlPNEEK0PEk8EQRA6oLAdQRA07AmCIHSgFk8mU2DaQRBE4CDxRBAEoQN5zhOF7AiidULiiSAIQgdy54lCdgTROqGhTxAEoQOzWRJNJJ4IonVCQ58gCEInPHRH4okgWic09AmCIHTCQ3eU80QQrRMSTwRBEDrh4omcJ4JondDQJwiC0AmJJ4Jo3QT10H/qqadgMpkU/3r27CneX1NTg6lTpyI5ORkxMTEYM2YMCgsLFc9x+vRp3HbbbYiKikJKSgoee+wxNDQ0NHdXCIIIIXjOE4XtCKJ1Yg10AzzRq1cvfPfdd+LfVqvU5EceeQRfffUVVqxYgfj4eEybNg2jR4/Gli1bAACNjY247bbbkJaWhh9++AHnz5/HPffcg7CwMDz33HPN3heCIEIDcp4IonUT9OLJarUiLS3N6faysjK88847WL58OW6++WYAwJIlS5CVlYVt27bh2muvxbfffouDBw/iu+++Q2pqKvr374958+bhiSeewFNPPQWbeodPgiAILyDxRBCtm6Af+kePHkVGRga6dOmC8ePH4/Tp0wCA/Px81NfXIzc3Vzy2Z8+e6NChA7Zu3QoA2Lp1K/r06YPU1FTxmLy8PJSXl+PAgQMuX7O2thbl5eWKfwRBEBwqVUAQrZugHvrZ2dlYunQpVq9ejTfeeAMnTpzADTfcgIqKChQUFMBmsyEhIUHxmNTUVBQUFAAACgoKFMKJ38/vc8X8+fMRHx8v/svMzDS2YwRBtGioVAFBtG6COmw3fPhw8fe+ffsiOzsbHTt2xMcff4zIyEi/ve6sWbMwc+ZM8e/y8nISUARBiFDYjiBaNy1q6CckJOCKK67AL7/8grS0NNTV1aG0tFRxTGFhoZgjlZaW5rT6jv+tlUfFCQ8PR1xcnOIfQRAEh8QTQbRuWtTQr6ysxLFjx5Ceno6BAwciLCwMa9euFe8/fPgwTp8+jZycHABATk4O9u/fj6KiIvGYNWvWIC4uDldeeWWzt58giNCAShUQROsmqMN2f/nLXzBixAh07NgR586dw9y5c2GxWDBu3DjEx8dj0qRJmDlzJpKSkhAXF4c///nPyMnJwbXXXgsAuOWWW3DllVdiwoQJWLBgAQoKCvC3v/0NU6dORXh4eIB7RxBES4WcJ4Jo3QS1eDp79izGjRuH4uJitG3bFoMHD8a2bdvQtm1bAMA//vEPmM1mjBkzBrW1tcjLy8Prr78uPt5isWDVqlWYMmUKcnJyEB0djXvvvRdPP/10oLpEEEQIQOKJIFo3JkEQhEA3ItgpLy9HfHw8ysrKKP+JIAiMGQN89hmQlQUcPBjo1hAE4Qp/Xb/pexNBEIROqFQBQbRuSDwRBEHohMJ2BNG6oaFPEAShE6owThCtGxr6BEEQOqGwHUG0bkg8EQRB6ITCdgTRuqGhTxAEoRMSTwTRuqGhTxAEoRPKeSKI1g0NfYIgCJ1QzhNBtG5IPBEEQeiEwnYE0bqhoU8QBKETCtsRROuGhj5BEIROKGxHEK0bEk8EQRA6obAdQbRuaOgTBEHohMQTQbRuaOgTBEHohHKeCKJ1Q0OfIAhCJzfeCHTvDoweHeiWEAQRCKyBbgBBEERLo1s34MiRQLeCIIhAQc4TQRAEQRCEDkg8EQRBEARB6IDEE0EQBEEQhA5IPBEEQRAEQeiAxBNBEARBEIQOSDwRBEEQBEHogMQTQRAEQRCEDkg8EQRBEARB6IDEE0EQBEEQhA5IPBEEQRAEQeiAxBNBEARBEIQOSDwRBEEQBEHogMQTQRAEQRCEDkg8EQRBEARB6MAa6Aa0BARBAACUl5cHuCUEQRAEQXgLv27z67hRkHjygoqKCgBAZmZmgFtCEARBEIReKioqEB8fb9jzmQSj5VgIYrfbce7cOcTGxsJkMhn63OXl5cjMzMSZM2cQFxdn6HMHG9TX0IT6GppQX0OT1tJX3s/Tp0/DZDIhIyMDZrNxmUrkPHmB2WxG+/bt/foacXFxIX0iy6G+hibU19CE+hqatJa+xsfH+6WflDBOEARBEAShAxJPBEEQBEEQOiDxFGDCw8Mxd+5chIeHB7opfof6GppQX0MT6mto0lr66u9+UsI4QRAEQRCEDsh5IgiCIAiC0AGJJ4IgCIIgCB2QeCIIgiAIgtABiSeCIAiCIAgdkHgKIK+99ho6deqEiIgIZGdnY8eOHYFuUpOZP38+rr76asTGxiIlJQW33347Dh8+rDimpqYGU6dORXJyMmJiYjBmzBgUFhYGqMXG8fzzz8NkMmHGjBnibaHU119//RV33303kpOTERkZiT59+mDXrl3i/YIgYM6cOUhPT0dkZCRyc3Nx9OjRALbYNxobGzF79mx07twZkZGR6Nq1K+bNm6fYG6ul9nXjxo0YMWIEMjIyYDKZsHLlSsX93vTr0qVLGD9+POLi4pCQkIBJkyahsrKyGXvhHe76Wl9fjyeeeAJ9+vRBdHQ0MjIycM899+DcuXOK5wiFvqp58MEHYTKZ8M9//lNxeyj19dChQxg5ciTi4+MRHR2Nq6++GqdPnxbvN2JeJvEUID766CPMnDkTc+fOxe7du9GvXz/k5eWhqKgo0E1rEhs2bMDUqVOxbds2rFmzBvX19bjllltQVVUlHvPII4/gyy+/xIoVK7BhwwacO3cOo0ePDmCrm87OnTvx73//G3379lXcHip9LSkpwfXXX4+wsDB8/fXXOHjwIF5++WUkJiaKxyxYsACLFi3C4sWLsX37dkRHRyMvLw81NTUBbLl+XnjhBbzxxhv417/+hUOHDuGFF17AggUL8Oqrr4rHtNS+VlVVoV+/fnjttdc07/emX+PHj8eBAwewZs0arFq1Chs3bsTkyZObqwte466v1dXV2L17N2bPno3du3fjs88+w+HDhzFy5EjFcaHQVzmff/45tm3bhoyMDKf7QqWvx44dw+DBg9GzZ0+sX78e+/btw+zZsxERESEeY8i8LBAB4ZprrhGmTp0q/t3Y2ChkZGQI8+fPD2CrjKeoqEgAIGzYsEEQBEEoLS0VwsLChBUrVojHHDp0SAAgbN26NVDNbBIVFRVC9+7dhTVr1gg33nijMH36dEEQQquvTzzxhDB48GCX99vtdiEtLU148cUXxdtKS0uF8PBw4f/9v//XHE00jNtuu02YOHGi4rbRo0cL48ePFwQhdPoKQPj888/Fv73p18GDBwUAws6dO8Vjvv76a8FkMgm//vprs7VdL+q+arFjxw4BgHDq1ClBEEKvr2fPnhXatWsn/PTTT0LHjh2Ff/zjH+J9odTXu+66S7j77rtdPsaoeZmcpwBQV1eH/Px85ObmireZzWbk5uZi69atAWyZ8ZSVlQEAkpKSAAD5+fmor69X9L1nz57o0KFDi+371KlTcdtttyn6BIRWX7/44gsMGjQI//d//4eUlBQMGDAAb731lnj/iRMnUFBQoOhrfHw8srOzW1xfr7vuOqxduxZHjhwBAPz444/YvHkzhg8fDiC0+irHm35t3boVCQkJGDRokHhMbm4uzGYztm/f3uxtNpKysjKYTCYkJCQACK2+2u12TJgwAY899hh69erldH+o9NVut+Orr77CFVdcgby8PKSkpCA7O1sR2jNqXibxFAAuXryIxsZGpKamKm5PTU1FQUFBgFplPHa7HTNmzMD111+P3r17AwAKCgpgs9nECYrTUvv+4YcfYvfu3Zg/f77TfaHU1+PHj+ONN95A9+7d8c0332DKlCl4+OGH8d577wGA2J9QOKeffPJJjB07Fj179kRYWBgGDBiAGTNmYPz48QBCq69yvOlXQUEBUlJSFPdbrVYkJSW16L7X1NTgiSeewLhx48RNZEOpry+88AKsVisefvhhzftDpa9FRUWorKzE888/j2HDhuHbb7/FHXfcgdGjR2PDhg0AjJuXrUY2nCDkTJ06FT/99BM2b94c6Kb4hTNnzmD69OlYs2aNIp4eitjtdgwaNAjPPfccAGDAgAH46aefsHjxYtx7770Bbp2xfPzxx1i2bBmWL1+OXr16Ye/evZgxYwYyMjJCrq8ESx6/8847IQgC3njjjUA3x3Dy8/PxyiuvYPfu3TCZTIFujl+x2+0AgFGjRuGRRx4BAPTv3x8//PADFi9ejBtvvNGw1yLnKQC0adMGFovFKbu/sLAQaWlpAWqVsUybNg2rVq3C999/j/bt24u3p6Wloa6uDqWlpYrjW2Lf8/PzUVRUhKuuugpWqxVWqxUbNmzAokWLYLVakZqaGjJ9TU9Px5VXXqm4LSsrS1zBwvsTCuf0Y489JrpPffr0wYQJE/DII4+I7mIo9VWON/1KS0tzWtTS0NCAS5cutci+c+F06tQprFmzRnSdgNDp66ZNm1BUVIQOHTqI89SpU6fw6KOPolOnTgBCp69t2rSB1Wr1OFcZMS+TeAoANpsNAwcOxNq1a8Xb7HY71q5di5ycnAC2rOkIgoBp06bh888/x7p169C5c2fF/QMHDkRYWJii74cPH8bp06dbXN+HDh2K/fv3Y+/eveK/QYMGYfz48eLvodLX66+/3qnkxJEjR9CxY0cAQOfOnZGWlqboa3l5ObZv397i+lpdXQ2zWTk1WiwW8VttKPVVjjf9ysnJQWlpKfLz88Vj1q1bB7vdjuzs7GZvc1Pgwuno0aP47rvvkJycrLg/VPo6YcIE7Nu3TzFPZWRk4LHHHsM333wDIHT6arPZcPXVV7udqwy7BulMbicM4sMPPxTCw8OFpUuXCgcPHhQmT54sJCQkCAUFBYFuWpOYMmWKEB8fL6xfv144f/68+K+6ulo85sEHHxQ6dOggrFu3Tti1a5eQk5Mj5OTkBLDVxiFfbScIodPXHTt2CFarVXj22WeFo0ePCsuWLROioqKE//znP+Ixzz//vJCQkCD897//Ffbt2yeMGjVK6Ny5s3D58uUAtlw/9957r9CuXTth1apVwokTJ4TPPvtMaNOmjfD444+Lx7TUvlZUVAh79uwR9uzZIwAQFi5cKOzZs0dcYeZNv4YNGyYMGDBA2L59u7B582ahe/fuwrhx4wLVJZe462tdXZ0wcuRIoX379sLevXsVc1Vtba34HKHQVy3Uq+0EIXT6+tlnnwlhYWHCm2++KRw9elR49dVXBYvFImzatEl8DiPmZRJPAeTVV18VOnToINhsNuGaa64Rtm3bFugmNRkAmv+WLFkiHnP58mXhoYceEhITE4WoqCjhjjvuEM6fPx+4RhuIWjyFUl+//PJLoXfv3kJ4eLjQs2dP4c0331Tcb7fbhdmzZwupqalCeHi4MHToUOHw4cMBaq3vlJeXC9OnTxc6dOggRERECF26dBH++te/Ki6qLbWv33//veb4vPfeewVB8K5fxcXFwrhx44SYmBghLi5OuP/++4WKiooA9MY97vp64sQJl3PV999/Lz5HKPRVCy3xFEp9feedd4Ru3boJERERQr9+/YSVK1cqnsOIedkkCLKyuQRBEARBEIRbKOeJIAiCIAhCBySeCIIgCIIgdEDiiSAIgiAIQgckngiCIAiCIHRA4okgCIIgCEIHJJ4IgiAIgiB0QOKJIAiCIAhCBySeCIJoNZhMJqxcuTLQzcBTTz2F/v37B7oZBEH4CIkngiAM48KFC5gyZQo6dOiA8PBwpKWlIS8vD1u2bAl00wzh5MmTMJlM2Lt3b6CbQhBEALEGugEEQYQOY8aMQV1dHd577z106dIFhYWFWLt2LYqLiwPdNIIgCMMg54kgCEMoLS3Fpk2b8MILL+A3v/kNOnbsiGuuuQazZs3CyJEjxeMWLlyIPn36IDo6GpmZmXjooYdQWVkp3r906VIkJCRg1apV6NGjB6KiovD73/8e1dXVeO+999CpUyckJibi4YcfRmNjo/i4Tp06Yd68eRg3bhyio6PRrl07vPbaa27bfObMGdx5551ISEhAUlISRo0ahZMnT3rd5/Xr18NkMmHt2rUYNGgQoqKicN111znt6v78888jNTUVsbGxmDRpEmpqapye6+2330ZWVhYiIiLQs2dPvP766+J9EydORN++fVFbWwsAqKurw4ABA3DPPfd43VaCIIyDxBNBEIYQExODmJgYrFy5UrzIa2E2m7Fo0SIcOHAA7733HtatW4fHH39ccUx1dTUWLVqEDz/8EKtXr8b69etxxx134H//+x/+97//4YMPPsC///1vfPLJJ4rHvfjii+jXrx/27NmDJ598EtOnT8eaNWs021FfX4+8vDzExsZi06ZN2LJlC2JiYjBs2DDU1dXp6vtf//pXvPzyy9i1axesVismTpwo3vfxxx/jqaeewnPPPYddu3YhPT1dIYwAYNmyZZgzZw6effZZHDp0CM899xxmz56N9957DwCwaNEiVFVV4cknnxRfr7S0FP/61790tZMgCINo+h7HBEEQjE8++URITEwUIiIihOuuu06YNWuW8OOPP7p9zIoVK4Tk5GTx7yVLlggAhF9++UW87U9/+pMQFRWl2OU9Ly9P+NOf/iT+3bFjR2HYsGGK577rrruE4cOHi38DED7//HNBEAThgw8+EHr06CHY7Xbx/traWiEyMlL45ptvNNt64sQJAYCwZ88eQRCkHd6/++478ZivvvpKACBcvnxZEARByMnJER566CHF82RnZwv9+vUT/+7atauwfPlyxTHz5s0TcnJyxL9/+OEHISwsTJg9e7ZgtVqFTZs2abaRIAj/Q84TQRCGMWbMGJw7dw5ffPEFhg0bhvXr1+Oqq67C0qVLxWO+++47DB06FO3atUNsbCwmTJiA4uJiVFdXi8dERUWha9eu4t+pqano1KkTYmJiFLcVFRUpXj8nJ8fp70OHDmm29ccff8Qvv/yC2NhY0TVLSkpCTU0Njh07pqvfffv2FX9PT08HALFthw4dQnZ2tst2VlVV4dixY5g0aZLYjpiYGDzzzDOKduTk5OAvf/kL5s2bh0cffRSDBw/W1UaCIIyDEsYJgjCUiIgI/Pa3v8Vvf/tbzJ49G3/84x8xd+5c3HfffTh58iR+97vfYcqUKXj22WeRlJSEzZs3Y9KkSairq0NUVBQAICwsTPGcJpNJ8za73e5zOysrKzFw4EAsW7bM6b62bdvqei5520wmEwB43Tae7/XWW285iSyLxSL+brfbsWXLFlgsFvzyyy+62kcQhLGQ80QQhF+58sorUVVVBQDIz8+H3W7Hyy+/jGuvvRZXXHEFzp07Z9hrbdu2zenvrKwszWOvuuoqHD16FCkpKejWrZviX3x8vGFtysrKwvbt2122MzU1FRkZGTh+/LhTOzp37iwe9+KLL+Lnn3/Ghg0bsHr1aixZssSwNhIEoQ8STwRBGEJxcTFuvvlm/Oc//8G+fftw4sQJrFixAgsWLMCoUaMAAN26dUN9fT1effVVHD9+HB988AEWL15sWBu2bNmCBQsW4MiRI3jttdewYsUKTJ8+XfPY8ePHo02bNhg1ahQ2bdqEEydOYP369Xj44Ydx9uxZw9o0ffp0vPvuu1iyZAmOHDmCuXPn4sCBA4pj/v73v2P+/PlYtGgRjhw5gv3792PJkiVYuHAhAGDPnj2YM2cO3n77bVx//fVYuHAhpk+fjuPHjxvWToIgvIfEE0EQhhATE4Ps7Gz84x//wJAhQ9C7d2/Mnj0bDzzwgLgqrF+/fli4cCFeeOEF9O7dG8uWLcP8+fMNa8Ojjz6KXbt2YcCAAXjmmWewcOFC5OXlaR4bFRWFjRs3okOHDhg9ejSysrLEMgJxcXGGtemuu+7C7Nmz8fjjj2PgwIE4deoUpkyZojjmj3/8I95++20sWbIEffr0wY033oilS5eic+fOqKmpwd1334377rsPI0aMAABMnjwZv/nNbzBhwgRFuQaCIJoHkyAIQqAbQRAE0VQ6deqEGTNmYMaMGYFuCkEQIQ45TwRBEARBEDog8UQQBEEQBKEDCtsRBEEQBEHogJwngiAIgiAIHZB4IgiCIAiC0AGJJ4IgCIIgCB2QeCIIgiAIgtABiSeCIAiCIAgdkHgiCIIgCILQAYkngiAIgiAIHZB4IgiCIAiC0AGJJ4IgCIIgCB38f3S5zKD+2cjBAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall comparison of different optimizers:\n",
            "Optimizer: adam, MAE: 385.23545419472305, MSE: 222167.515402628, R2: 0.1261776552119903\n",
            "Optimizer: sgd, MAE: 392.20813381980304, MSE: 255391.4091501261, R2: -0.004497527812913349\n",
            "Optimizer: lbfgs, MAE: 354.01199998383794, MSE: 191380.89886228534, R2: 0.24726661551574014\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Liked the lbfgs optimizer the most, lets improve it\n"
      ],
      "metadata": {
        "id": "ts7D7bBCiwhv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMBiNQ3sjH30",
        "outputId": "59cf9272-d82c-4c0c-dfc9-95decf30677e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.25.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.0)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.11.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.63.0)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.43.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.0.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## using tensorflow\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load your data\n",
        "data = pd.read_csv('/content/data_analyze_modified.csv')\n",
        "\n",
        "# Clean and preprocess your data\n",
        "data = data.drop(columns=['Unnamed: 3', 'Unnamed: 4'])\n",
        "data['date'] = data['date'].str.strip(',').apply(lambda x: pd.to_datetime(x, errors='coerce'))\n",
        "data = data.dropna(subset=['date']).reset_index(drop=True)\n",
        "data['ma_7'] = data['ma_7'].fillna(method='ffill')\n",
        "data['ma_7'] = data['ma_7'].fillna(data['ma_7'].mean())\n",
        "\n",
        "# Feature engineering\n",
        "data['day'] = data['date'].dt.day\n",
        "data['month'] = data['date'].dt.month\n",
        "data['year'] = data['date'].dt.year\n",
        "\n",
        "# Prepare features and target variable\n",
        "X = data[['date', 'ma_7', 'day', 'month', 'year']]\n",
        "y = data['spot_price']\n",
        "\n",
        "# Convert 'date' to numerical format (ordinal)\n",
        "X['date'] = X['date'].map(pd.Timestamp.toordinal)\n",
        "\n",
        "# Apply MinMax scaling to features\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the standardized data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Build the neural network model\n",
        "model = Sequential()\n",
        "model.add(Dense(100, input_dim=X_train.shape[1], activation='relu'))\n",
        "model.add(Dropout(0.2))  # Dropout layer for regularization\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1))  # Output layer\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])\n",
        "\n",
        "# Implement early stopping\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train, y_train, validation_split=0.2, epochs=500, batch_size=32, callbacks=[early_stopping], verbose=2)\n",
        "\n",
        "# Predict on the test set\n",
        "y_pred_nn = model.predict(X_test).flatten()\n",
        "\n",
        "# Evaluate the model\n",
        "mae_nn = mean_absolute_error(y_test, y_pred_nn)\n",
        "mse_nn = mean_squared_error(y_test, y_pred_nn)\n",
        "r2_nn = r2_score(y_test, y_pred_nn)\n",
        "\n",
        "print(f\"MAE: {mae_nn}, MSE: {mse_nn}, R2: {r2_nn}\")\n",
        "\n",
        "# Visualize the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(y_test.values, label='Actual', color='blue')\n",
        "plt.plot(y_pred_nn, label='Predicted', color='red')\n",
        "plt.title('Actual vs Predicted Spot Prices (Optimized Neural Network with Early Stopping and Dropout)')\n",
        "plt.xlabel('Sample Index')\n",
        "plt.ylabel('Spot Price')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bH-PRPXxi1dR",
        "outputId": "d665530b-be4a-49ee-cb47-47db56f6b819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-c5c5d4cde771>:31: 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",
            "  X['date'] = X['date'].map(pd.Timestamp.toordinal)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "16/16 - 2s - loss: 3945674.0000 - mae: 1926.6118 - val_loss: 4194931.0000 - val_mae: 1979.2721 - 2s/epoch - 122ms/step\n",
            "Epoch 2/500\n",
            "16/16 - 0s - loss: 3941038.2500 - mae: 1925.4064 - val_loss: 4187853.0000 - val_mae: 1977.4873 - 84ms/epoch - 5ms/step\n",
            "Epoch 3/500\n",
            "16/16 - 0s - loss: 3930979.0000 - mae: 1922.7700 - val_loss: 4172588.5000 - val_mae: 1973.6304 - 91ms/epoch - 6ms/step\n",
            "Epoch 4/500\n",
            "16/16 - 0s - loss: 3910110.0000 - mae: 1917.2830 - val_loss: 4142091.5000 - val_mae: 1965.8879 - 78ms/epoch - 5ms/step\n",
            "Epoch 5/500\n",
            "16/16 - 0s - loss: 3869922.7500 - mae: 1906.8861 - val_loss: 4087808.7500 - val_mae: 1952.0214 - 111ms/epoch - 7ms/step\n",
            "Epoch 6/500\n",
            "16/16 - 0s - loss: 3803283.0000 - mae: 1889.2314 - val_loss: 4000385.5000 - val_mae: 1929.4658 - 102ms/epoch - 6ms/step\n",
            "Epoch 7/500\n",
            "16/16 - 0s - loss: 3699475.5000 - mae: 1861.2598 - val_loss: 3870611.7500 - val_mae: 1895.4532 - 104ms/epoch - 7ms/step\n",
            "Epoch 8/500\n",
            "16/16 - 0s - loss: 3547675.0000 - mae: 1819.9100 - val_loss: 3691124.2500 - val_mae: 1847.3264 - 125ms/epoch - 8ms/step\n",
            "Epoch 9/500\n",
            "16/16 - 0s - loss: 3347213.0000 - mae: 1763.3269 - val_loss: 3457450.5000 - val_mae: 1782.6669 - 118ms/epoch - 7ms/step\n",
            "Epoch 10/500\n",
            "16/16 - 0s - loss: 3095758.7500 - mae: 1689.8135 - val_loss: 3171933.5000 - val_mae: 1700.2339 - 107ms/epoch - 7ms/step\n",
            "Epoch 11/500\n",
            "16/16 - 0s - loss: 2805241.0000 - mae: 1601.8462 - val_loss: 2840217.2500 - val_mae: 1598.9707 - 124ms/epoch - 8ms/step\n",
            "Epoch 12/500\n",
            "16/16 - 0s - loss: 2445680.2500 - mae: 1482.9172 - val_loss: 2470227.2500 - val_mae: 1478.2904 - 112ms/epoch - 7ms/step\n",
            "Epoch 13/500\n",
            "16/16 - 0s - loss: 2084261.8750 - mae: 1354.5308 - val_loss: 2080450.3750 - val_mae: 1342.3506 - 415ms/epoch - 26ms/step\n",
            "Epoch 14/500\n",
            "16/16 - 0s - loss: 1716607.2500 - mae: 1209.8945 - val_loss: 1693808.8750 - val_mae: 1192.1244 - 387ms/epoch - 24ms/step\n",
            "Epoch 15/500\n",
            "16/16 - 0s - loss: 1361129.3750 - mae: 1056.9951 - val_loss: 1332106.1250 - val_mae: 1030.1248 - 143ms/epoch - 9ms/step\n",
            "Epoch 16/500\n",
            "16/16 - 0s - loss: 1033595.5625 - mae: 883.6898 - val_loss: 1026448.3750 - val_mae: 872.1733 - 124ms/epoch - 8ms/step\n",
            "Epoch 17/500\n",
            "16/16 - 0s - loss: 779155.8750 - mae: 737.6674 - val_loss: 781247.5625 - val_mae: 726.0978 - 121ms/epoch - 8ms/step\n",
            "Epoch 18/500\n",
            "16/16 - 0s - loss: 584636.9375 - mae: 616.3483 - val_loss: 605203.7500 - val_mae: 617.4288 - 113ms/epoch - 7ms/step\n",
            "Epoch 19/500\n",
            "16/16 - 0s - loss: 464670.5000 - mae: 538.5538 - val_loss: 492410.4375 - val_mae: 541.2489 - 111ms/epoch - 7ms/step\n",
            "Epoch 20/500\n",
            "16/16 - 0s - loss: 400621.4062 - mae: 500.5431 - val_loss: 422105.3750 - val_mae: 491.2426 - 112ms/epoch - 7ms/step\n",
            "Epoch 21/500\n",
            "16/16 - 0s - loss: 356785.3125 - mae: 477.1006 - val_loss: 385663.1875 - val_mae: 469.3005 - 138ms/epoch - 9ms/step\n",
            "Epoch 22/500\n",
            "16/16 - 0s - loss: 339489.2812 - mae: 455.2636 - val_loss: 362842.0625 - val_mae: 455.9935 - 125ms/epoch - 8ms/step\n",
            "Epoch 23/500\n",
            "16/16 - 0s - loss: 337035.7500 - mae: 456.8185 - val_loss: 348876.5000 - val_mae: 446.9313 - 117ms/epoch - 7ms/step\n",
            "Epoch 24/500\n",
            "16/16 - 0s - loss: 329646.5000 - mae: 458.3877 - val_loss: 341075.5312 - val_mae: 441.4155 - 117ms/epoch - 7ms/step\n",
            "Epoch 25/500\n",
            "16/16 - 0s - loss: 335807.9062 - mae: 457.2741 - val_loss: 334226.6562 - val_mae: 436.5409 - 113ms/epoch - 7ms/step\n",
            "Epoch 26/500\n",
            "16/16 - 0s - loss: 321980.9375 - mae: 447.9568 - val_loss: 328016.5000 - val_mae: 431.9992 - 73ms/epoch - 5ms/step\n",
            "Epoch 27/500\n",
            "16/16 - 0s - loss: 323475.6250 - mae: 454.3330 - val_loss: 322199.2500 - val_mae: 428.1880 - 85ms/epoch - 5ms/step\n",
            "Epoch 28/500\n",
            "16/16 - 0s - loss: 307977.1875 - mae: 439.4337 - val_loss: 317364.7812 - val_mae: 425.5978 - 78ms/epoch - 5ms/step\n",
            "Epoch 29/500\n",
            "16/16 - 0s - loss: 298375.0000 - mae: 433.0894 - val_loss: 310760.9688 - val_mae: 422.0258 - 74ms/epoch - 5ms/step\n",
            "Epoch 30/500\n",
            "16/16 - 0s - loss: 303628.9062 - mae: 442.5477 - val_loss: 305573.1250 - val_mae: 418.9517 - 85ms/epoch - 5ms/step\n",
            "Epoch 31/500\n",
            "16/16 - 0s - loss: 316882.8438 - mae: 451.0596 - val_loss: 302539.7188 - val_mae: 417.2278 - 74ms/epoch - 5ms/step\n",
            "Epoch 32/500\n",
            "16/16 - 0s - loss: 303452.3750 - mae: 450.2980 - val_loss: 303065.0625 - val_mae: 417.7704 - 84ms/epoch - 5ms/step\n",
            "Epoch 33/500\n",
            "16/16 - 0s - loss: 292445.1250 - mae: 435.8451 - val_loss: 298022.7500 - val_mae: 414.6120 - 95ms/epoch - 6ms/step\n",
            "Epoch 34/500\n",
            "16/16 - 0s - loss: 289507.0625 - mae: 436.5092 - val_loss: 293101.8125 - val_mae: 411.5237 - 85ms/epoch - 5ms/step\n",
            "Epoch 35/500\n",
            "16/16 - 0s - loss: 302592.1875 - mae: 441.8122 - val_loss: 291236.0625 - val_mae: 410.3914 - 89ms/epoch - 6ms/step\n",
            "Epoch 36/500\n",
            "16/16 - 0s - loss: 304896.1562 - mae: 444.4182 - val_loss: 290444.3750 - val_mae: 409.9958 - 78ms/epoch - 5ms/step\n",
            "Epoch 37/500\n",
            "16/16 - 0s - loss: 287776.7188 - mae: 425.5179 - val_loss: 287724.8438 - val_mae: 408.3160 - 76ms/epoch - 5ms/step\n",
            "Epoch 38/500\n",
            "16/16 - 0s - loss: 276008.0312 - mae: 419.5872 - val_loss: 286342.1562 - val_mae: 407.5656 - 85ms/epoch - 5ms/step\n",
            "Epoch 39/500\n",
            "16/16 - 0s - loss: 278859.0312 - mae: 425.8397 - val_loss: 284894.1250 - val_mae: 406.8549 - 89ms/epoch - 6ms/step\n",
            "Epoch 40/500\n",
            "16/16 - 0s - loss: 274132.2812 - mae: 418.4466 - val_loss: 283538.3438 - val_mae: 406.2764 - 74ms/epoch - 5ms/step\n",
            "Epoch 41/500\n",
            "16/16 - 0s - loss: 274174.6250 - mae: 418.1178 - val_loss: 282244.4375 - val_mae: 405.6415 - 86ms/epoch - 5ms/step\n",
            "Epoch 42/500\n",
            "16/16 - 0s - loss: 280379.0000 - mae: 431.7452 - val_loss: 280189.6250 - val_mae: 404.4702 - 87ms/epoch - 5ms/step\n",
            "Epoch 43/500\n",
            "16/16 - 0s - loss: 264987.8125 - mae: 421.1817 - val_loss: 279516.5625 - val_mae: 404.3087 - 85ms/epoch - 5ms/step\n",
            "Epoch 44/500\n",
            "16/16 - 0s - loss: 279821.3750 - mae: 424.6881 - val_loss: 278214.5625 - val_mae: 403.6621 - 75ms/epoch - 5ms/step\n",
            "Epoch 45/500\n",
            "16/16 - 0s - loss: 275564.4688 - mae: 423.0852 - val_loss: 278394.1250 - val_mae: 404.0848 - 71ms/epoch - 4ms/step\n",
            "Epoch 46/500\n",
            "16/16 - 0s - loss: 277711.3750 - mae: 427.1420 - val_loss: 275880.5938 - val_mae: 402.4675 - 72ms/epoch - 4ms/step\n",
            "Epoch 47/500\n",
            "16/16 - 0s - loss: 276332.6250 - mae: 420.5854 - val_loss: 274592.8750 - val_mae: 401.6255 - 87ms/epoch - 5ms/step\n",
            "Epoch 48/500\n",
            "16/16 - 0s - loss: 270348.6875 - mae: 417.9633 - val_loss: 272916.6875 - val_mae: 400.4484 - 83ms/epoch - 5ms/step\n",
            "Epoch 49/500\n",
            "16/16 - 0s - loss: 276852.3438 - mae: 423.0290 - val_loss: 272731.3125 - val_mae: 400.5421 - 95ms/epoch - 6ms/step\n",
            "Epoch 50/500\n",
            "16/16 - 0s - loss: 268345.7812 - mae: 418.8024 - val_loss: 273071.7812 - val_mae: 400.9174 - 84ms/epoch - 5ms/step\n",
            "Epoch 51/500\n",
            "16/16 - 0s - loss: 265643.1250 - mae: 411.3795 - val_loss: 271645.8438 - val_mae: 399.9261 - 89ms/epoch - 6ms/step\n",
            "Epoch 52/500\n",
            "16/16 - 0s - loss: 265102.9688 - mae: 416.4276 - val_loss: 272013.8438 - val_mae: 400.3161 - 85ms/epoch - 5ms/step\n",
            "Epoch 53/500\n",
            "16/16 - 0s - loss: 277724.7812 - mae: 418.0591 - val_loss: 270507.9062 - val_mae: 399.2672 - 77ms/epoch - 5ms/step\n",
            "Epoch 54/500\n",
            "16/16 - 0s - loss: 273007.9062 - mae: 420.9440 - val_loss: 270294.0312 - val_mae: 399.2186 - 87ms/epoch - 5ms/step\n",
            "Epoch 55/500\n",
            "16/16 - 0s - loss: 280732.2188 - mae: 428.5061 - val_loss: 271135.1875 - val_mae: 399.9478 - 77ms/epoch - 5ms/step\n",
            "Epoch 56/500\n",
            "16/16 - 0s - loss: 269869.1562 - mae: 418.2750 - val_loss: 272882.5938 - val_mae: 401.2229 - 88ms/epoch - 6ms/step\n",
            "Epoch 57/500\n",
            "16/16 - 0s - loss: 284454.9375 - mae: 432.7213 - val_loss: 270785.8125 - val_mae: 399.8260 - 88ms/epoch - 5ms/step\n",
            "Epoch 58/500\n",
            "16/16 - 0s - loss: 278081.6875 - mae: 429.1483 - val_loss: 268677.0000 - val_mae: 398.3232 - 76ms/epoch - 5ms/step\n",
            "Epoch 59/500\n",
            "16/16 - 0s - loss: 278808.5312 - mae: 424.0676 - val_loss: 269105.2812 - val_mae: 398.7014 - 77ms/epoch - 5ms/step\n",
            "Epoch 60/500\n",
            "16/16 - 0s - loss: 272019.5000 - mae: 424.2790 - val_loss: 269038.0312 - val_mae: 398.6649 - 72ms/epoch - 4ms/step\n",
            "Epoch 61/500\n",
            "16/16 - 0s - loss: 275236.9375 - mae: 422.9108 - val_loss: 268468.7188 - val_mae: 398.2927 - 75ms/epoch - 5ms/step\n",
            "Epoch 62/500\n",
            "16/16 - 0s - loss: 274949.1875 - mae: 422.7366 - val_loss: 268855.8750 - val_mae: 398.6202 - 88ms/epoch - 5ms/step\n",
            "Epoch 63/500\n",
            "16/16 - 0s - loss: 262128.6406 - mae: 411.2466 - val_loss: 268608.2188 - val_mae: 398.4635 - 89ms/epoch - 6ms/step\n",
            "Epoch 64/500\n",
            "16/16 - 0s - loss: 282766.0312 - mae: 432.3362 - val_loss: 267449.0625 - val_mae: 397.6392 - 91ms/epoch - 6ms/step\n",
            "Epoch 65/500\n",
            "16/16 - 0s - loss: 272751.0312 - mae: 428.4665 - val_loss: 267782.5312 - val_mae: 397.9045 - 82ms/epoch - 5ms/step\n",
            "Epoch 66/500\n",
            "16/16 - 0s - loss: 276394.6250 - mae: 419.1878 - val_loss: 266377.4688 - val_mae: 396.8268 - 81ms/epoch - 5ms/step\n",
            "Epoch 67/500\n",
            "16/16 - 0s - loss: 277057.9375 - mae: 420.7190 - val_loss: 266280.8125 - val_mae: 396.7939 - 74ms/epoch - 5ms/step\n",
            "Epoch 68/500\n",
            "16/16 - 0s - loss: 274989.2188 - mae: 422.7307 - val_loss: 265003.0938 - val_mae: 395.7923 - 90ms/epoch - 6ms/step\n",
            "Epoch 69/500\n",
            "16/16 - 0s - loss: 268155.1875 - mae: 420.0222 - val_loss: 265137.0625 - val_mae: 395.9108 - 73ms/epoch - 5ms/step\n",
            "Epoch 70/500\n",
            "16/16 - 0s - loss: 267234.2812 - mae: 415.8643 - val_loss: 265346.5312 - val_mae: 396.0935 - 81ms/epoch - 5ms/step\n",
            "Epoch 71/500\n",
            "16/16 - 0s - loss: 279314.0312 - mae: 427.5952 - val_loss: 265149.0625 - val_mae: 395.9508 - 74ms/epoch - 5ms/step\n",
            "Epoch 72/500\n",
            "16/16 - 0s - loss: 262878.1562 - mae: 411.4311 - val_loss: 264145.9062 - val_mae: 395.1715 - 79ms/epoch - 5ms/step\n",
            "Epoch 73/500\n",
            "16/16 - 0s - loss: 273604.5625 - mae: 421.7008 - val_loss: 266608.8750 - val_mae: 397.0924 - 84ms/epoch - 5ms/step\n",
            "Epoch 74/500\n",
            "16/16 - 0s - loss: 275203.8438 - mae: 417.2756 - val_loss: 265238.1562 - val_mae: 396.0712 - 73ms/epoch - 5ms/step\n",
            "Epoch 75/500\n",
            "16/16 - 0s - loss: 270754.1562 - mae: 423.8885 - val_loss: 264048.0000 - val_mae: 395.0843 - 88ms/epoch - 6ms/step\n",
            "Epoch 76/500\n",
            "16/16 - 0s - loss: 282305.4375 - mae: 431.4044 - val_loss: 263743.0312 - val_mae: 394.8579 - 74ms/epoch - 5ms/step\n",
            "Epoch 77/500\n",
            "16/16 - 0s - loss: 273055.3125 - mae: 421.2801 - val_loss: 263405.4062 - val_mae: 394.5768 - 86ms/epoch - 5ms/step\n",
            "Epoch 78/500\n",
            "16/16 - 0s - loss: 270980.2812 - mae: 418.2415 - val_loss: 266090.7500 - val_mae: 396.6521 - 101ms/epoch - 6ms/step\n",
            "Epoch 79/500\n",
            "16/16 - 0s - loss: 275295.4062 - mae: 417.1198 - val_loss: 262843.4375 - val_mae: 394.1662 - 102ms/epoch - 6ms/step\n",
            "Epoch 80/500\n",
            "16/16 - 0s - loss: 262814.6562 - mae: 416.4091 - val_loss: 263279.7500 - val_mae: 394.5652 - 87ms/epoch - 5ms/step\n",
            "Epoch 81/500\n",
            "16/16 - 0s - loss: 267128.7500 - mae: 419.7856 - val_loss: 264694.7188 - val_mae: 395.6276 - 74ms/epoch - 5ms/step\n",
            "Epoch 82/500\n",
            "16/16 - 0s - loss: 268025.7812 - mae: 412.2119 - val_loss: 264397.2500 - val_mae: 395.3808 - 76ms/epoch - 5ms/step\n",
            "Epoch 83/500\n",
            "16/16 - 0s - loss: 271036.7812 - mae: 415.8523 - val_loss: 262908.8750 - val_mae: 394.2226 - 69ms/epoch - 4ms/step\n",
            "Epoch 84/500\n",
            "16/16 - 0s - loss: 270090.0625 - mae: 418.9163 - val_loss: 263297.9375 - val_mae: 394.5536 - 80ms/epoch - 5ms/step\n",
            "Epoch 85/500\n",
            "16/16 - 0s - loss: 269691.3438 - mae: 422.1841 - val_loss: 264627.5000 - val_mae: 395.5022 - 73ms/epoch - 5ms/step\n",
            "Epoch 86/500\n",
            "16/16 - 0s - loss: 270718.9062 - mae: 416.0737 - val_loss: 263576.0312 - val_mae: 394.7247 - 71ms/epoch - 4ms/step\n",
            "Epoch 87/500\n",
            "16/16 - 0s - loss: 288276.3125 - mae: 430.0293 - val_loss: 263322.8438 - val_mae: 394.4986 - 85ms/epoch - 5ms/step\n",
            "Epoch 88/500\n",
            "16/16 - 0s - loss: 269535.3438 - mae: 423.9496 - val_loss: 261041.6094 - val_mae: 392.6956 - 73ms/epoch - 5ms/step\n",
            "Epoch 89/500\n",
            "16/16 - 0s - loss: 273101.2500 - mae: 421.8302 - val_loss: 261510.3750 - val_mae: 393.0586 - 85ms/epoch - 5ms/step\n",
            "Epoch 90/500\n",
            "16/16 - 0s - loss: 267896.8438 - mae: 419.0904 - val_loss: 262284.5312 - val_mae: 393.6427 - 95ms/epoch - 6ms/step\n",
            "Epoch 91/500\n",
            "16/16 - 0s - loss: 271052.9375 - mae: 421.9711 - val_loss: 260899.6719 - val_mae: 392.5598 - 88ms/epoch - 5ms/step\n",
            "Epoch 92/500\n",
            "16/16 - 0s - loss: 279520.7188 - mae: 427.5347 - val_loss: 261728.2344 - val_mae: 393.1761 - 76ms/epoch - 5ms/step\n",
            "Epoch 93/500\n",
            "16/16 - 0s - loss: 261775.2188 - mae: 412.2516 - val_loss: 262842.7500 - val_mae: 394.0046 - 101ms/epoch - 6ms/step\n",
            "Epoch 94/500\n",
            "16/16 - 0s - loss: 271246.7812 - mae: 421.2004 - val_loss: 262974.1562 - val_mae: 394.0881 - 90ms/epoch - 6ms/step\n",
            "Epoch 95/500\n",
            "16/16 - 0s - loss: 262183.3438 - mae: 408.9335 - val_loss: 261044.1562 - val_mae: 392.6386 - 84ms/epoch - 5ms/step\n",
            "Epoch 96/500\n",
            "16/16 - 0s - loss: 275314.9062 - mae: 423.7988 - val_loss: 260678.2188 - val_mae: 392.3227 - 76ms/epoch - 5ms/step\n",
            "Epoch 97/500\n",
            "16/16 - 0s - loss: 256383.4062 - mae: 411.5731 - val_loss: 259571.1562 - val_mae: 391.4435 - 73ms/epoch - 5ms/step\n",
            "Epoch 98/500\n",
            "16/16 - 0s - loss: 267285.5625 - mae: 419.1268 - val_loss: 261271.8125 - val_mae: 392.7723 - 73ms/epoch - 5ms/step\n",
            "Epoch 99/500\n",
            "16/16 - 0s - loss: 264392.2188 - mae: 407.6903 - val_loss: 261385.9219 - val_mae: 392.8680 - 85ms/epoch - 5ms/step\n",
            "Epoch 100/500\n",
            "16/16 - 0s - loss: 264886.3438 - mae: 416.1749 - val_loss: 262780.7188 - val_mae: 393.8129 - 86ms/epoch - 5ms/step\n",
            "Epoch 101/500\n",
            "16/16 - 0s - loss: 269179.1250 - mae: 419.6493 - val_loss: 261658.7656 - val_mae: 392.9953 - 80ms/epoch - 5ms/step\n",
            "Epoch 102/500\n",
            "16/16 - 0s - loss: 266529.0312 - mae: 417.3154 - val_loss: 262237.3438 - val_mae: 393.3264 - 77ms/epoch - 5ms/step\n",
            "Epoch 103/500\n",
            "16/16 - 0s - loss: 268982.3438 - mae: 420.0846 - val_loss: 259039.8750 - val_mae: 390.9546 - 90ms/epoch - 6ms/step\n",
            "Epoch 104/500\n",
            "16/16 - 0s - loss: 269952.3438 - mae: 419.6147 - val_loss: 258873.6875 - val_mae: 390.7400 - 94ms/epoch - 6ms/step\n",
            "Epoch 105/500\n",
            "16/16 - 0s - loss: 265150.0312 - mae: 413.1638 - val_loss: 261118.4688 - val_mae: 392.3839 - 76ms/epoch - 5ms/step\n",
            "Epoch 106/500\n",
            "16/16 - 0s - loss: 270839.8438 - mae: 420.1661 - val_loss: 259126.6562 - val_mae: 390.9688 - 84ms/epoch - 5ms/step\n",
            "Epoch 107/500\n",
            "16/16 - 0s - loss: 272456.1562 - mae: 418.2872 - val_loss: 258303.5625 - val_mae: 390.3143 - 85ms/epoch - 5ms/step\n",
            "Epoch 108/500\n",
            "16/16 - 0s - loss: 272672.6562 - mae: 424.3878 - val_loss: 258214.6875 - val_mae: 390.2466 - 98ms/epoch - 6ms/step\n",
            "Epoch 109/500\n",
            "16/16 - 0s - loss: 276186.6250 - mae: 425.4893 - val_loss: 259202.1406 - val_mae: 391.0049 - 104ms/epoch - 6ms/step\n",
            "Epoch 110/500\n",
            "16/16 - 0s - loss: 263062.6250 - mae: 411.5859 - val_loss: 260283.7656 - val_mae: 391.7313 - 86ms/epoch - 5ms/step\n",
            "Epoch 111/500\n",
            "16/16 - 0s - loss: 269728.9375 - mae: 415.4036 - val_loss: 259677.5938 - val_mae: 391.3445 - 75ms/epoch - 5ms/step\n",
            "Epoch 112/500\n",
            "16/16 - 0s - loss: 273170.1562 - mae: 424.9750 - val_loss: 258677.1406 - val_mae: 390.5958 - 74ms/epoch - 5ms/step\n",
            "Epoch 113/500\n",
            "16/16 - 0s - loss: 280433.7500 - mae: 428.3693 - val_loss: 259496.0625 - val_mae: 391.1714 - 82ms/epoch - 5ms/step\n",
            "Epoch 114/500\n",
            "16/16 - 0s - loss: 279707.4375 - mae: 434.9297 - val_loss: 258809.7812 - val_mae: 390.6665 - 91ms/epoch - 6ms/step\n",
            "Epoch 115/500\n",
            "16/16 - 0s - loss: 269484.1562 - mae: 419.5584 - val_loss: 259882.6719 - val_mae: 391.3817 - 94ms/epoch - 6ms/step\n",
            "Epoch 116/500\n",
            "16/16 - 0s - loss: 258094.3750 - mae: 412.7397 - val_loss: 259486.5781 - val_mae: 391.0182 - 87ms/epoch - 5ms/step\n",
            "Epoch 117/500\n",
            "16/16 - 0s - loss: 268940.5312 - mae: 414.8979 - val_loss: 256634.2500 - val_mae: 388.7394 - 88ms/epoch - 5ms/step\n",
            "Epoch 118/500\n",
            "16/16 - 0s - loss: 268361.0625 - mae: 415.9219 - val_loss: 258445.5625 - val_mae: 390.1624 - 75ms/epoch - 5ms/step\n",
            "Epoch 119/500\n",
            "16/16 - 0s - loss: 279777.9062 - mae: 430.0393 - val_loss: 261659.6875 - val_mae: 392.2649 - 72ms/epoch - 4ms/step\n",
            "Epoch 120/500\n",
            "16/16 - 0s - loss: 268157.2812 - mae: 414.6084 - val_loss: 258509.0938 - val_mae: 390.1930 - 83ms/epoch - 5ms/step\n",
            "Epoch 121/500\n",
            "16/16 - 0s - loss: 265567.9375 - mae: 422.1562 - val_loss: 257341.8281 - val_mae: 389.2393 - 84ms/epoch - 5ms/step\n",
            "Epoch 122/500\n",
            "16/16 - 0s - loss: 272028.4688 - mae: 417.6697 - val_loss: 258408.6562 - val_mae: 389.9745 - 90ms/epoch - 6ms/step\n",
            "Epoch 123/500\n",
            "16/16 - 0s - loss: 265007.6875 - mae: 411.6256 - val_loss: 259658.9688 - val_mae: 390.7822 - 103ms/epoch - 6ms/step\n",
            "Epoch 124/500\n",
            "16/16 - 0s - loss: 263564.4375 - mae: 412.1963 - val_loss: 256928.9375 - val_mae: 388.7960 - 91ms/epoch - 6ms/step\n",
            "Epoch 125/500\n",
            "16/16 - 0s - loss: 261163.5156 - mae: 415.4812 - val_loss: 258925.4375 - val_mae: 390.2251 - 91ms/epoch - 6ms/step\n",
            "Epoch 126/500\n",
            "16/16 - 0s - loss: 270017.3125 - mae: 416.1470 - val_loss: 258716.9375 - val_mae: 390.1274 - 72ms/epoch - 5ms/step\n",
            "Epoch 127/500\n",
            "16/16 - 0s - loss: 264242.4062 - mae: 414.0084 - val_loss: 255774.5000 - val_mae: 387.7495 - 74ms/epoch - 5ms/step\n",
            "Epoch 128/500\n",
            "16/16 - 0s - loss: 264898.8750 - mae: 411.2918 - val_loss: 257105.6719 - val_mae: 388.9203 - 76ms/epoch - 5ms/step\n",
            "Epoch 129/500\n",
            "16/16 - 0s - loss: 262085.9062 - mae: 411.2033 - val_loss: 256965.7500 - val_mae: 388.7242 - 87ms/epoch - 5ms/step\n",
            "Epoch 130/500\n",
            "16/16 - 0s - loss: 271349.2812 - mae: 417.5382 - val_loss: 259346.1094 - val_mae: 390.3455 - 90ms/epoch - 6ms/step\n",
            "Epoch 131/500\n",
            "16/16 - 0s - loss: 254073.4375 - mae: 408.8134 - val_loss: 257216.4531 - val_mae: 388.8682 - 74ms/epoch - 5ms/step\n",
            "Epoch 132/500\n",
            "16/16 - 0s - loss: 269634.2812 - mae: 419.5843 - val_loss: 257642.7031 - val_mae: 389.1318 - 84ms/epoch - 5ms/step\n",
            "Epoch 133/500\n",
            "16/16 - 0s - loss: 264350.9688 - mae: 417.9856 - val_loss: 256962.2969 - val_mae: 388.5595 - 75ms/epoch - 5ms/step\n",
            "Epoch 134/500\n",
            "16/16 - 0s - loss: 270991.8750 - mae: 422.4358 - val_loss: 256994.0625 - val_mae: 388.5273 - 72ms/epoch - 4ms/step\n",
            "Epoch 135/500\n",
            "16/16 - 0s - loss: 272903.7812 - mae: 418.6080 - val_loss: 257208.1562 - val_mae: 388.6064 - 85ms/epoch - 5ms/step\n",
            "Epoch 136/500\n",
            "16/16 - 0s - loss: 261610.1406 - mae: 413.3005 - val_loss: 257997.5625 - val_mae: 389.1714 - 91ms/epoch - 6ms/step\n",
            "Epoch 137/500\n",
            "16/16 - 0s - loss: 269248.7188 - mae: 414.7500 - val_loss: 256851.4531 - val_mae: 388.2945 - 90ms/epoch - 6ms/step\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "MAE: 386.8043883801745, MSE: 223421.1376976779, R2: 0.12124694708684514\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the parameter grid for hyperparameter tuning\n",
        "param_grid = {\n",
        "    'hidden_layer_sizes': [(50, 50), (100, 100), (100, 100, 100)],\n",
        "    'activation': ['relu', 'tanh', 'logistic'],\n",
        "    'alpha': [0.0001, 0.001, 0.01],\n",
        "    'max_iter': [500, 1000]\n",
        "}\n",
        "\n",
        "# Initialize the model with lbfgs solver\n",
        "nn_model = MLPRegressor(solver='lbfgs', random_state=42)\n",
        "\n",
        "# Perform grid search with n_jobs=1 to avoid multithreading issues\n",
        "grid_search = GridSearchCV(estimator=nn_model, param_grid=param_grid, cv=3, n_jobs=1, verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Best model from grid search\n",
        "best_nn_model = grid_search.best_estimator_\n",
        "\n",
        "# Predict on the test set with the best model\n",
        "y_pred_nn = best_nn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the neural network model\n",
        "mae_nn = mean_absolute_error(y_test, y_pred_nn)\n",
        "mse_nn = mean_squared_error(y_test, y_pred_nn)\n",
        "r2_nn = r2_score(y_test, y_pred_nn)\n",
        "\n",
        "print(f\"Best parameters found: {grid_search.best_params_}\")\n",
        "print(f\"MAE: {mae_nn}, MSE: {mse_nn}, R2: {r2_nn}\")\n",
        "\n",
        "# Visualize the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(y_test.values, label='Actual', color='blue')\n",
        "plt.plot(y_pred_nn, label='Predicted', color='red')\n",
        "plt.title('Actual vs Predicted Spot Prices (Optimized Neural Network)')\n",
        "plt.xlabel('Sample Index')\n",
        "plt.ylabel('Spot Price')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "HFVWz2UDnnmd",
        "outputId": "bb25b8aa-a601-42ab-bc4a-9e2412a292b7",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'GridSearchCV' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-481ecae8656e>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Perform grid search with n_jobs=1 to avoid multithreading issues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mgrid_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'GridSearchCV' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ONLY WORKING NEURAL NETWORK**"
      ],
      "metadata": {
        "id": "E_2oQGmHFbe0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load your data\n",
        "data = pd.read_csv('/content/data_analyze_modified.csv')\n",
        "\n",
        "# Clean and preprocess your data\n",
        "data = data.drop(columns=['Unnamed: 3', 'Unnamed: 4'])\n",
        "data['date'] = data['date'].str.strip(',').apply(lambda x: pd.to_datetime(x, errors='coerce'))\n",
        "data = data.dropna(subset=['date']).reset_index(drop=True)\n",
        "data['ma_7'] = data['ma_7'].fillna(method='ffill')\n",
        "data['ma_7'] = data['ma_7'].fillna(data['ma_7'].mean())\n",
        "\n",
        "# Feature engineering\n",
        "data['day'] = data['date'].dt.day\n",
        "data['month'] = data['date'].dt.month\n",
        "data['year'] = data['date'].dt.year\n",
        "\n",
        "# Prepare features and target variable\n",
        "X = data[['date', 'ma_7', 'day', 'month', 'year']]\n",
        "y = data['spot_price']\n",
        "\n",
        "# Convert 'date' to numerical format (ordinal)\n",
        "X['date'] = X['date'].map(pd.Timestamp.toordinal)\n",
        "\n",
        "# Apply MinMax scaling to features\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the standardized data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Define the parameter grid for hyperparameter tuning\n",
        "param_grid = {\n",
        "    'hidden_layer_sizes': [(50, 50), (100, 100), (100, 100, 100)],\n",
        "    'activation': ['relu', 'tanh', 'logistic'],\n",
        "    'alpha': [0.0001, 0.001, 0.01],\n",
        "    'max_iter': [1000, 2000, 3000]  # Increased max_iter\n",
        "}\n",
        "\n",
        "# Initialize the model with lbfgs solver\n",
        "nn_model = MLPRegressor(solver='lbfgs', random_state=42)\n",
        "\n",
        "# Perform grid search without parallel processing\n",
        "grid_search = GridSearchCV(estimator=nn_model, param_grid=param_grid, cv=3, n_jobs=1, verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Best model from grid search\n",
        "best_nn_model = grid_search.best_estimator_\n",
        "\n",
        "# Predict on the test set with the best model\n",
        "y_pred_nn = best_nn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the neural network model\n",
        "mae_nn = mean_absolute_error(y_test, y_pred_nn)\n",
        "mse_nn = mean_squared_error(y_test, y_pred_nn)\n",
        "r2_nn = r2_score(y_test, y_pred_nn)\n",
        "\n",
        "print(f\"Best parameters found: {grid_search.best_params_}\")\n",
        "print(f\"MAE: {mae_nn}, MSE: {mse_nn}, R2: {r2_nn}\")\n",
        "\n",
        "# Visualize the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(y_test.values, label='Actual', color='blue')\n",
        "plt.plot(y_pred_nn, label='Predicted', color='red')\n",
        "plt.title('Actual vs Predicted Spot Prices (Optimized Neural Network)')\n",
        "plt.xlabel('Sample Index')\n",
        "plt.ylabel('Spot Price')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Rhs8pwysjcyr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0610f4df-5d23-4888-947f-6cc5d5f3aed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-7810497c5c45>:28: 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",
            "  X['date'] = X['date'].map(pd.Timestamp.toordinal)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 81 candidates, totalling 243 fits\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.8s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.8s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.3s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.3s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   1.6s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   4.2s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   7.9s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   2.1s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   4.2s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   9.5s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   2.7s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   3.0s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  10.2s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  10.2s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  12.9s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   2.3s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  11.1s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  12.0s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   2.5s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  11.1s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  12.3s\n",
            "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   2.4s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.5s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.8s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.2s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.8s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.2s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.4s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.8s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.2s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   2.1s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   4.1s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   0.9s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   1.6s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   2.5s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   1.1s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   1.7s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   3.2s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   1.8s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   3.3s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  13.6s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   6.4s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   4.5s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  13.2s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   7.4s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   2.9s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  13.5s\n",
            "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   8.6s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.6s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.3s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.2s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.3s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.2s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.3s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.2s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   3.8s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   0.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   5.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   4.1s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   0.6s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   3.7s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   6.0s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   0.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   3.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   4.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  14.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  13.4s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   5.8s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  15.5s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  13.6s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   3.8s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  14.8s\n",
            "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  13.1s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.2s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   6.3s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   4.7s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   0.1s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=  10.7s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   4.0s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   0.1s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=  10.6s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   3.9s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   4.5s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  20.1s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   4.8s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   5.5s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  34.5s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   4.3s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   6.5s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  53.0s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   4.8s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  27.4s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   0.3s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   0.8s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  54.6s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   0.5s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   0.7s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time= 1.3min\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   0.5s\n",
            "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   1.1s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   3.1s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   2.4s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   5.1s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   4.5s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   2.5s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   5.2s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   2.5s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   4.3s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   5.3s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   2.6s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  20.0s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  10.0s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   4.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  35.2s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  11.2s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   2.8s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  52.0s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  10.7s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  29.2s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  25.9s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   0.7s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  50.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  50.9s\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   0.6s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time= 1.2min\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time= 1.3min\n",
            "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   0.6s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   1.3s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   4.2s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   4.8s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   1.2s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   4.2s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   2.6s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   1.3s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   6.2s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   2.7s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   2.7s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  18.2s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  19.1s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   2.6s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  34.4s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  36.1s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   2.6s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  50.8s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  38.9s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  27.4s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  26.9s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  30.1s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  49.8s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  56.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  55.3s\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time= 1.1min\n",
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time= 1.2min\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time= 1.3min\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   0.9s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   3.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   3.9s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   2.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   6.0s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   6.0s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   1.9s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   8.6s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   7.2s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  12.8s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=   4.9s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  11.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  22.6s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=   6.8s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  12.1s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  32.7s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=   5.2s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  12.0s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   3.7s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  12.4s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  22.8s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   3.4s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  11.9s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  22.5s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   5.6s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  12.8s\n",
            "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  22.5s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   1.7s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   2.1s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   4.1s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   1.3s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   1.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   6.6s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   2.8s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   1.5s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   9.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  11.4s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  13.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  12.1s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  20.4s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  24.9s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  21.3s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  34.5s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  32.6s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  19.8s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   6.1s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  10.2s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   1.0s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   4.6s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  11.1s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   0.9s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   5.3s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   9.7s\n",
            "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   0.9s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   3.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   4.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=1000; total time=   4.4s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   3.8s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   7.7s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=2000; total time=   6.6s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=   3.8s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=  10.8s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50, 50), max_iter=3000; total time=  11.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  11.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  10.5s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=1000; total time=  11.3s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  22.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  21.0s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=2000; total time=  15.6s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  32.4s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  32.3s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100), max_iter=3000; total time=  15.0s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   8.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=  21.8s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=1000; total time=   2.5s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   9.3s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=  26.5s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=2000; total time=   2.6s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   8.7s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=  24.7s\n",
            "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100, 100, 100), max_iter=3000; total time=   3.3s\n",
            "Best parameters found: {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (100, 100, 100), 'max_iter': 1000}\n",
            "MAE: 350.4125266412656, MSE: 189154.04039010542, R2: 0.25602522582897524\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "# Compute permutation importance\n",
        "perm_importance = permutation_importance(best_nn_model, X_test, y_test, n_repeats=10, random_state=42)\n",
        "\n",
        "# Create a DataFrame to display the results\n",
        "feature_importance = pd.DataFrame({'feature': ['date', 'ma_7', 'day', 'month', 'year'],\n",
        "                                   'importance': perm_importance.importances_mean})\n",
        "print(feature_importance.sort_values(by='importance', ascending=False))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "A0TBNc0gBFei",
        "outputId": "ec3a1583-f1d4-4d13-904d-b940563e4122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  feature  importance\n",
            "0    date    1.209319\n",
            "1    ma_7    0.284501\n",
            "3   month    0.225333\n",
            "2     day    0.118555\n",
            "4    year    0.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shap\n",
        "\n",
        "# Create a SHAP explainer\n",
        "explainer = shap.KernelExplainer(best_nn_model.predict, X_train[:100])  # Using a small subset for the explainer\n",
        "shap_values = explainer.shap_values(X_test[:100])\n",
        "\n",
        "# Plot SHAP summary\n",
        "shap.summary_plot(shap_values, X_test[:100], feature_names=['date', 'ma_7', 'day', 'month', 'year'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389,
          "referenced_widgets": [
            "1bd220467f4e41e889aad6d37477db09",
            "6f3d4063e50442feab1eb1b6e3c445da",
            "8a1c54604e0841479904022544cb3c0d",
            "84b92a245f334d19b9d41cf9cf3bcaf2",
            "a7c39b9e2c08403abfb1bfb9b028363f",
            "4e2694c0ea404662a78a882ce2646b95",
            "a3b0378bb3994fb4823206fdd4473483",
            "a434d83f1b3c4aec8c6a92f9daeb1e23",
            "ba0daaaa807f49fc8579d4a14280ba65",
            "d85aa88818f14357a305707fc3b1dc57",
            "9f5bfa9c895c43308514ba811a78cb80"
          ]
        },
        "id": "tvpaJVCxJupC",
        "outputId": "d91b4ae8-db72-4a22-b6e9-a0b2a1e599f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1bd220467f4e41e889aad6d37477db09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x350 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the entire dataset\n",
        "all_predictions = best_nn_model.predict(X_scaled)\n",
        "\n",
        "# Calculate residuals\n",
        "residuals = data['spot_price'] - all_predictions\n",
        "\n",
        "# Detect anomalies (e.g., residuals greater than a certain threshold)\n",
        "threshold = 3 * np.std(residuals)\n",
        "anomalies = data[np.abs(residuals) > threshold]\n",
        "\n",
        "print(anomalies)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSx-yjhHCjle",
        "outputId": "a2bf722c-13d2-4b97-a18c-c7a7ab0bcb2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          date  spot_price     ma_7  day  month  year\n",
            "174 2024-01-22     3497.71  2169.52   22      1  2024\n",
            "507 2024-02-05      287.56  1899.78    5      2  2024\n",
            "510 2024-02-05     3941.20  1899.78    5      2  2024\n",
            "569 2024-02-07     3317.41  1836.54    7      2  2024\n",
            "583 2024-02-08     3225.05  1845.54    8      2  2024\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## deploy using flask\n",
        "from flask import Flask, request, jsonify\n",
        "import joblib\n",
        "import pandas as pd\n",
        "\n",
        "# Load the trained model and scaler\n",
        "model = joblib.load('best_nn_model.pkl')\n",
        "scaler = joblib.load('scaler.pkl')\n",
        "\n",
        "app = Flask(__name__)\n",
        "\n",
        "@app.route('/predict', methods=['POST'])\n",
        "def predict():\n",
        "    data = request.get_json(force=True)\n",
        "    df = pd.DataFrame(data)\n",
        "    df['date'] = pd.to_datetime(df['date']).map(pd.Timestamp.toordinal)\n",
        "    df = df[['date', 'ma_7', 'day', 'month', 'year']]\n",
        "    scaled_data = scaler.transform(df)\n",
        "    predictions = model.predict(scaled_data)\n",
        "    return jsonify(predictions.tolist())\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run(port=5000, debug=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZuoUUOKALEkK",
        "outputId": "42383029-e50e-4799-e840-6bc02d6f73c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: on\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "INFO:werkzeug: * Restarting with stat\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Final version with saving and predicting"
      ],
      "metadata": {
        "id": "mdQtkw3ZDDgp"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vi8cYs00IjAe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}