supervised_classification_neural_network_&_XGBoost.ipynb

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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import losses\n",
        "import sklearn\n",
        "import random\n",
        "import time\n",
        "\n",
        "from sklearn import preprocessing, model_selection\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.utils import shuffle\n",
        "\n",
        "import xgboost as xgb\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import plotly.express as px\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.feature_selection import SelectKBest, mutual_info_classif"
      ]
    },
    {
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4719e63-85e7-4e7d-b32f-a63430770917')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e4719e63-85e7-4e7d-b32f-a63430770917 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e4719e63-85e7-4e7d-b32f-a63430770917');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a34e9f4f-0104-43dd-a434-f489653e9e02\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a34e9f4f-0104-43dd-a434-f489653e9e02')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a34e9f4f-0104-43dd-a434-f489653e9e02 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y_train",
              "summary": "{\n  \"name\": \"y_train\",\n  \"rows\": 47992,\n  \"fields\": [\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "y_train = pd.read_csv(\"/content/full_y.csv\")\n",
        "y_train.drop('id', axis = 1, inplace = True)\n",
        "y_train['label'].replace(-1, 0, inplace=True)\n",
        "#y_train['x'].replace(-1, 0, inplace=True)\n",
        "y_train.info()\n",
        "y_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Neural Network"
      ],
      "metadata": {
        "id": "6WLjamkwcW7P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split data into training, testing and validation set"
      ],
      "metadata": {
        "id": "OSuA7tLChRUs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vBL6poYeVGV7"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(X_train,y_train,test_size = 0.2, random_state = 42)\n",
        "# Create a validation dataset from training data\n",
        "X_train, X_valid, y_train,y_valid = model_selection.train_test_split(X_train, y_train ,test_size=0.1,random_state=55)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a normalization layer\n",
        "normalizer = layers.Normalization()\n",
        "# adapt the layer to the training data\n",
        "normalizer.adapt(X_train.to_numpy())\n",
        "# apply\n",
        "normalized_X_train = normalizer(X_train)"
      ],
      "metadata": {
        "id": "ZegdOj-51Buy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1oLDtjwnv_W",
        "outputId": "f0ebb542-6c46-4742-d089-dc049ea8ba5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(34553, 30), dtype=float32, numpy=\n",
              "array([[ 0.03503059, -1.6508241 ,  0.5852843 , ...,  0.5802922 ,\n",
              "        -0.34934026, -0.46572292],\n",
              "       [ 0.32043818, -0.6615093 ,  0.08249158, ..., -1.1004384 ,\n",
              "        -1.5750778 , -0.46572292],\n",
              "       [ 1.1766609 , -0.6615093 , -1.4258865 , ...,  0.02004869,\n",
              "        -0.65577465, -1.0451391 ],\n",
              "       ...,\n",
              "       [-1.9628224 ,  1.3171203 , -0.9230938 , ..., -1.1004384 ,\n",
              "        -1.5750778 , -1.6245553 ],\n",
              "       [ 1.1766609 ,  1.3171203 ,  1.088077  , ..., -1.6606818 ,\n",
              "        -0.34934026, -1.6245553 ],\n",
              "       [ 0.8912533 ,  1.3171203 , -0.4203011 , ...,  0.86041397,\n",
              "        -0.34934026,  0.9828176 ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "normalized_X_train"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model building:"
      ],
      "metadata": {
        "id": "LqyOIa0_gCPC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-r8PUlusARt7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c521f22-e098-4176-ce27-068ca70d4305"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(441)\n",
        "\n",
        "input_dim = len(X_train.columns)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(normalizer)\n",
        "model.add(Dense(30, input_dim = input_dim , activation = 'relu'))\n",
        "model.add(Dense(40, activation = 'relu', kernel_initializer = \"he_normal\"))\n",
        "model.add(Dense(40, activation = 'relu'))\n",
        "model.add(Dense(5, activation = 'softmax'))\n",
        "\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy' ,\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "              metrics = ['accuracy'] )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.05, patience=3, min_lr=0.001)"
      ],
      "metadata": {
        "id": "R_7SUT_4dq0S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting the model\n",
        "\n"
      ],
      "metadata": {
        "id": "a48GvJiagIay"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m = model.fit(X_train, y_train, epochs = 30, batch_size = 100,\n",
        "          validation_data=(X_valid, y_valid),\n",
        "          callbacks = [callback,reduce_lr ])"
      ],
      "metadata": {
        "id": "6nhNFHcFdVlY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e847a98a-5b32-4441-ced9-934c163c48f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.5315 - loss: 1.0647 - val_accuracy: 0.5807 - val_loss: 0.9559 - learning_rate: 0.0100\n",
            "Epoch 2/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5959 - loss: 0.9439 - val_accuracy: 0.5974 - val_loss: 0.9361 - learning_rate: 0.0100\n",
            "Epoch 3/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.5988 - loss: 0.9350 - val_accuracy: 0.5747 - val_loss: 0.9530 - learning_rate: 0.0100\n",
            "Epoch 4/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6052 - loss: 0.9264 - val_accuracy: 0.5956 - val_loss: 0.9336 - learning_rate: 0.0100\n",
            "Epoch 5/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6105 - loss: 0.9181 - val_accuracy: 0.6115 - val_loss: 0.9130 - learning_rate: 0.0100\n",
            "Epoch 6/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6101 - loss: 0.9159 - val_accuracy: 0.5984 - val_loss: 0.9304 - learning_rate: 0.0100\n",
            "Epoch 7/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6128 - loss: 0.9116 - val_accuracy: 0.6089 - val_loss: 0.9109 - learning_rate: 0.0100\n",
            "Epoch 8/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6166 - loss: 0.9047 - val_accuracy: 0.5951 - val_loss: 0.9322 - learning_rate: 0.0100\n",
            "Epoch 9/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6191 - loss: 0.8988 - val_accuracy: 0.5943 - val_loss: 0.9278 - learning_rate: 0.0100\n",
            "Epoch 10/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6132 - loss: 0.8985 - val_accuracy: 0.6109 - val_loss: 0.9152 - learning_rate: 0.0100\n",
            "Epoch 11/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6315 - loss: 0.8689 - val_accuracy: 0.6151 - val_loss: 0.9013 - learning_rate: 0.0010\n",
            "Epoch 12/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6355 - loss: 0.8579 - val_accuracy: 0.6161 - val_loss: 0.9008 - learning_rate: 0.0010\n",
            "Epoch 13/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6386 - loss: 0.8571 - val_accuracy: 0.6143 - val_loss: 0.9010 - learning_rate: 0.0010\n",
            "Epoch 14/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6427 - loss: 0.8465 - val_accuracy: 0.6091 - val_loss: 0.9046 - learning_rate: 0.0010\n",
            "Epoch 15/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6411 - loss: 0.8526 - val_accuracy: 0.6174 - val_loss: 0.9052 - learning_rate: 0.0010\n",
            "Epoch 16/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6457 - loss: 0.8465 - val_accuracy: 0.6169 - val_loss: 0.9044 - learning_rate: 0.0010\n",
            "Epoch 17/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6454 - loss: 0.8412 - val_accuracy: 0.6091 - val_loss: 0.9054 - learning_rate: 0.0010\n",
            "Epoch 18/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6441 - loss: 0.8431 - val_accuracy: 0.6112 - val_loss: 0.9071 - learning_rate: 0.0010\n",
            "Epoch 19/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6440 - loss: 0.8438 - val_accuracy: 0.6135 - val_loss: 0.9073 - learning_rate: 0.0010\n",
            "Epoch 20/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6475 - loss: 0.8398 - val_accuracy: 0.6151 - val_loss: 0.9069 - learning_rate: 0.0010\n",
            "Epoch 21/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6492 - loss: 0.8317 - val_accuracy: 0.6146 - val_loss: 0.9065 - learning_rate: 0.0010\n",
            "Epoch 22/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6442 - loss: 0.8408 - val_accuracy: 0.6148 - val_loss: 0.9084 - learning_rate: 0.0010\n",
            "Epoch 23/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6498 - loss: 0.8352 - val_accuracy: 0.6156 - val_loss: 0.9084 - learning_rate: 0.0010\n",
            "Epoch 24/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6484 - loss: 0.8348 - val_accuracy: 0.6109 - val_loss: 0.9114 - learning_rate: 0.0010\n",
            "Epoch 25/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6494 - loss: 0.8355 - val_accuracy: 0.6133 - val_loss: 0.9113 - learning_rate: 0.0010\n",
            "Epoch 26/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6500 - loss: 0.8325 - val_accuracy: 0.6167 - val_loss: 0.9137 - learning_rate: 0.0010\n",
            "Epoch 27/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6496 - loss: 0.8353 - val_accuracy: 0.6182 - val_loss: 0.9133 - learning_rate: 0.0010\n",
            "Epoch 28/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6493 - loss: 0.8264 - val_accuracy: 0.6083 - val_loss: 0.9160 - learning_rate: 0.0010\n",
            "Epoch 29/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6521 - loss: 0.8272 - val_accuracy: 0.6120 - val_loss: 0.9146 - learning_rate: 0.0010\n",
            "Epoch 30/30\n",
            "\u001b[1m346/346\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6463 - loss: 0.8321 - val_accuracy: 0.6076 - val_loss: 0.9178 - learning_rate: 0.0010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "hA6qEobkgJo3",
        "outputId": "2490eab9-f27d-4ab6-924a-61cbb8d12998"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xyPV9fHzw5ptv4vPPP8ft27fh6uqK/fv3Y8eOHfj444+VOmRXtG+++Qa9evWCh4cHRo8erRjmb2Zmpli/7smTJ6hduzb69+8PV1dXGBsbIzg4GGfPnsW3334LQJpLadKkSXjvvffQsGFD5OXlYd26ddDW1oavr6/iM/nyyy8REBCA27dvo0+fPjAxMUF0dDS2bduGsWPHYtq0aaU6F1GxKn/gHFH1UdIw/2bNmhVb/sSJE+KNN94QBgYGwt7eXkyfPl3s27dPABCHDh1SlCtpmP8333xT5Jx4bth5ScP8ixtW7eDgoDTsXAghQkJCRKtWrYSenp5wdnYWv/76q/jkk0+Evr5+CZ/CU9euXROenp7C2NhYWFlZiTFjxiiGjT87HNzPz08YGRkVOb642B8+fCiGDRsmTE1NhZmZmRg2bJhi6HZphvkX2rVrlwAg7OzsigytLygoEAsXLhQODg5CLpeLVq1aiZ07dxb57yDEy4f5CyFEfn6+mD9/vrCzsxMGBgaia9eu4sqVK0U+76ysLPHJJ58oynXo0EGEhoaKLl26iC5duihdd8eOHaJp06aKKRcK7724GJ88eSKmTp0q7O3tha6urmjQoIH45ptvlKYdKLyX0n4vnlfcMH8hhAgODhYdOnQQBgYGwtTUVPj4+Ihr164p3s/OzhaffvqpcHV1FSYmJsLIyEi4urqKH3/8UVHm1q1bYtSoUcLZ2Vno6+sLCwsL8eabb4rg4OAicWzZskV07NhRGBkZCSMjI9G4cWMxceJEERERUeZzET1LJkQV+vOViKqkPn364OrVq7h586a6QyEiqhTsg0RESp5fFuTmzZvYvXs3unbtqp6AiIjUgDVIRKTEzs4OI0aMgJOTE+7cuYOVK1ciOzsb58+fLzK3DxFRdcVO2kSkpGfPnti4cSMSEhIgl8vh4eGBhQsXMjkiotcKa5CIiIiInsM+SERERETPYYJERERE9Bz2QSqngoIC3Lt3DyYmJmVaBoCIiIjURwiBJ0+ewN7eHlpaJdcTMUEqp3v37qFOnTrqDoOIiIjKITY2FrVr1y7xfSZI5VS4CGRsbCxMTU3VHA0RERGVRmpqKurUqaO0mHNxmCCVU2GzmqmpKRMkIiIiDfOy7jHspE1ERET0HCZIRERERM9hgkRERET0HPZBIiKiKiE/Px+5ubnqDoM0nK6uLrS1tV/5PEyQiIhIrYQQSEhIQHJysrpDoWrC3Nwctra2rzRPIRMkIiJSq8LkqGbNmjA0NOTku1RuQghkZGTg/v37AAA7O7tyn4sJEhERqU1+fr4iObK0tFR3OFQNGBgYAADu37+PmjVrlru5jZ20iYhIbQr7HBkaGqo5EqpOCr9Pr9KnjQkSERGpHZvVqCJVxPeJCRIRERHRc5ggERERVRH16tXDsmXLSl3+8OHDkMlkKh8BuHbtWpibm6v0GlUNEyQiIqIykslkL9zmzZtXrvOePXsWY8eOLXX59u3bIz4+HmZmZuW6HpWMo9iqmOy8bNx6fAu1TGvBVM5FcImIqqL4+HjF802bNmHOnDmIiIhQ7DM2NlY8F0IgPz8fOjov/8m1trYuUxx6enqwtbUt0zFUOqxBqmI6r+2Mpj82xcHog+oOhYiISmBra6vYzMzMIJPJFK+vX78OExMT7NmzB25ubpDL5Th+/DiioqLQu3dv2NjYwNjYGG3btkVwcLDSeZ9vYpPJZPj111/Rt29fGBoaokGDBvjnn38U7z/fxFbYFLZv3z40adIExsbG6Nmzp1JCl5eXhylTpsDc3ByWlpb47LPP4Ofnhz59+pTpM1i5ciWcnZ2hp6eHRo0aYd26dYr3hBCYN28e6tatC7lcDnt7e0yZMkXx/o8//ogGDRpAX18fNjY26N+/f5muXRnUmiAdPXoUPj4+sLe3h0wmw/bt2196zOHDh9G6dWvI5XLUr18fa9euLVJmxYoVqFevHvT19eHu7o4zZ84ovZ+VlYWJEyfC0tISxsbG8PX1RWJiYgXd1atxruEMALj58KaaIyEiUg8hBNJz0it9E0JU6H3MmDEDX3/9NcLDw+Hi4oK0tDR4e3sjJCQE58+fR8+ePeHj44OYmJgXnmf+/PkYMGAALl26BG9vbwwdOhSPHj0qsXxGRgYWL16MdevW4ejRo4iJicG0adMU7//vf//Dhg0bsGbNGpw4cQKpqaml+v191rZt2/DRRx/hk08+wZUrV/Dhhx9i5MiROHToEABgy5YtWLp0KX766SfcvHkT27dvR4sWLQAA//33H6ZMmYIFCxYgIiICe/fuRefOnct0/cqg1ia29PR0uLq6YtSoUejXr99Ly0dHR+Ptt9/GuHHjsGHDBoSEhOCDDz6AnZ0dvLy8AEhVnf7+/li1ahXc3d2xbNkyeHl5ISIiAjVr1gQATJ06Fbt27cLmzZthZmaGSZMmoV+/fjhx4oRK77c0Glg0AADcfMQEiYheTxm5GTAONH55wQqWFpAGIz2jCjvfggUL0KNHD8VrCwsLuLq6Kl5/8cUX2LZtG/755x9MmjSpxPOMGDECgwcPBgAsXLgQ33//Pc6cOYOePXsWWz43NxerVq2Cs7P0B/ekSZOwYMECxfvLly9HQEAA+vbtCwD44YcfsHv37jLd2+LFizFixAhMmDABAODv749Tp05h8eLFePPNNxETEwNbW1t4enpCV1cXdevWRbt27QAAMTExMDIywjvvvAMTExM4ODigVatWZbp+ZVBrDVKvXr3w5ZdfKv4jvcyqVavg6OiIb7/9Fk2aNMGkSZPQv39/LF26VFFmyZIlGDNmDEaOHImmTZti1apVMDQ0xG+//QYASElJwerVq7FkyRJ069YNbm5uWLNmDU6ePIlTp06p5D7LoqFlQwBMkIiINF2bNm2UXqelpWHatGlo0qQJzM3NYWxsjPDw8JfWILm4uCieGxkZwdTUVLGURnEMDQ0VyREgLbdRWD4lJQWJiYmKZAUAtLW14ebmVqZ7Cw8PR4cOHZT2dejQAeHh4QCA9957D5mZmXBycsKYMWOwbds25OXlAQB69OgBBwcHODk5YdiwYdiwYQMyMjLKdP3KoFGdtENDQ+Hp6am0z8vLCx9//DEAICcnB2FhYQgICFC8r6WlBU9PT4SGhgIAwsLCkJubq3Sexo0bo27duggNDcUbb7yh+ht5gQaWUg3SjYc31BoHEZG6GOoaIi0gTS3XrUhGRsq1UdOmTcOBAwewePFi1K9fHwYGBujfvz9ycnJeeB5dXV2l1zKZDAUFBWUqX9HNhy9Tp04dREREIDg4GAcOHMCECRPwzTff4MiRIzAxMcG5c+dw+PBh7N+/H3PmzMG8efNw9uzZKjWVgEZ10k5ISICNjY3SPhsbG6SmpiIzMxNJSUnIz88vtkxCQoLiHHp6ekX+IzxbpjjZ2dlITU1V2lShsInt3pN7SM9JV8k1iIiqMplMBiM9o0rfVD2b94kTJzBixAj07dsXLVq0gK2tLW7fvq3Saz7PzMwMNjY2OHv2rGJffn4+zp07V6bzNGnSpEi3lBMnTqBp06aK1wYGBvDx8cH333+Pw4cPIzQ0FJcvXwYA6OjowNPTE4sWLcKlS5dw+/ZtHDxYtQYnaVQNkjoFBgZi/vz5Kr9ODYMasDSwxMPMh4h8FAlXW9eXH0RERFVegwYNsHXrVvj4+EAmk2H27NkvrAlSlcmTJyMwMBD169dH48aNsXz5cjx+/LhMCeKnn36KAQMGoFWrVvD09MS///6LrVu3KkblrV27Fvn5+XB3d4ehoSHWr18PAwMDODg4YOfOnbh16xY6d+6MGjVqYPfu3SgoKECjRo1UdcvlolE1SLa2tkVGmyUmJsLU1BQGBgawsrKCtrZ2sWUK54mwtbVFTk5OkVlHny1TnICAAKSkpCi22NjYirmpYhQ2s7EfEhFR9bFkyRLUqFED7du3h4+PD7y8vNC6detKj+Ozzz7D4MGDMXz4cHh4eMDY2BheXl7Q19cv9Tn69OmD7777DosXL0azZs3w008/Yc2aNejatSsAwNzcHL/88gs6dOgAFxcXBAcH499//4WlpSXMzc2xdetWdOvWDU2aNMGqVauwceNGNGvWTEV3XE6iigAgtm3b9sIy06dPF82bN1faN3jwYOHl5aV43a5dOzFp0iTF6/z8fFGrVi0RGBgohBAiOTlZ6Orqir///ltR5vr16wKACA0NLXW8KSkpAoBISUkp9TGlNWzrMIF5EAuPLqzwcxMRVSWZmZni2rVrIjMzU92hvLby8/NFw4YNxaxZs9QdSoV50feqtL/fam1iS0tLQ2RkpOJ1dHQ0Lly4AAsLC9StWxcBAQGIi4vDH3/8AQAYN24cfvjhB0yfPh2jRo3CwYMH8ddff2HXrl2Kc/j7+8PPzw9t2rRBu3btsGzZMqSnp2PkyJEApPbX0aNHw9/fHxYWFjA1NcXkyZPh4eGh9g7ahTjUn4iIVOXOnTvYv38/unTpguzsbPzwww+Ijo7GkCFD1B1alaLWBOm///7Dm2++qXjt7+8PAPDz88PatWsRHx+vNPzR0dERu3btwtSpU/Hdd9+hdu3a+PXXXxVzIAHAwIED8eDBA8yZMwcJCQlo2bIl9u7dq9Rxe+nSpdDS0oKvry+ys7Ph5eWFH3/8sRLuuHTYxEZERKqipaWFtWvXYtq0aRBCoHnz5ggODkaTJk3UHVqVIhOiksf+VROpqakwMzNDSkoKTE0rds20c/Hn4PazG2yMbJAwreSRdUREmi4rKwvR0dFwdHQsUx8Yohd50feqtL/fGtVJ+3VR2MSWmJ6I1GzVTCdAREREJWOCVAWZyE1gYyQ1CXJNNiIiosrHBKmKYj8kIiIi9WGCVEUpRrKxBomIiKjSMUGqojjUn4iISH2YIFVRbGIjIiJSHyZIVRSb2IiIqr969eph2bJlitcymQzbt28vsfzt27chk8lw4cKFV7puRZ3nZUaMGIE+ffqo9BqqwsVqq6j6FvUBAA8zH+JR5iNYGFioOSIiIlK1+Ph41KhRo0LPOWLECCQnJyslXnXq1EF8fDysrKwq9FrVCWuQqigjPSPUMqkFgLVIRESvC1tbW8jlcpVfR1tbG7a2ttDRYT1JSZggVWHsh0REVDX9/PPPsLe3R0FBgdL+3r17Y9SoUQCAqKgo9O7dGzY2NjA2Nkbbtm0RHBz8wvM+38R25swZtGrVCvr6+mjTpg3Onz+vVD4/Px+jR4+Go6MjDAwM0KhRI3z33XeK9+fNm4fff/8dO3bsgEwmg0wmw+HDh4ttYjty5AjatWsHuVwOOzs7zJgxA3l5eYr3u3btiilTpmD69OmwsLCAra0t5s2bV6bPLTs7G1OmTEHNmjWhr6+Pjh074uzZs4r3Hz9+jKFDh8La2hoGBgZo0KAB1qxZAwDIycnBpEmTYGdnB319fTg4OCAwMLBM1y8Lpo5VWAOLBjh8+zBrkIjotSIEkJFR+dc1NARkstKVfe+99zB58mQcOnQI3bt3BwA8evQIe/fuxe7duwFIC7J7e3vjq6++glwuxx9//AEfHx9ERESgbt26L71GWloa3nnnHfTo0QPr169HdHQ0PvroI6UyBQUFqF27NjZv3gxLS0ucPHkSY8eOhZ2dHQYMGIBp06YhPDwcqampikTDwsIC9+7dUzpPXFwcvL29MWLECPzxxx+4fv06xowZA319faUk6Pfff4e/vz9Onz6N0NBQjBgxAh06dECPHj1K9blNnz4dW7Zswe+//w4HBwcsWrQIXl5eiIyMhIWFBWbPno1r165hz549sLKyQmRkJDIzMwEA33//Pf755x/89ddfqFu3LmJjYxEbG1uq65YHE6QqjEP9ieh1lJEBGBtX/nXT0gAjo9KVrVGjBnr16oWgoCBFgvT333/DyspKsQi7q6srXF1dFcd88cUX2LZtG/755x9MmjTppdcICgpCQUEBVq9eDX19fTRr1gx3797F+PHjFWV0dXUxf/58xWtHR0eEhobir7/+woABA2BsbAwDAwNkZ2fD1ta2xGv9+OOPqFOnDn744QfIZDI0btwY9+7dw2effYY5c+ZAS0tqcHJxccHcuXMBAA0aNMAPP/yAkJCQUiVI6enpWLlyJdauXYtevXoBAH755RccOHAAq1evxqeffoqYmBi0atUKbdq0ASB1Yi8UExODBg0aoGPHjpDJZHBwcHjpNV8Fm9iqMDaxERFVXUOHDsWWLVuQnZ0NANiwYQMGDRqkSCbS0tIwbdo0NGnSBObm5jA2NkZ4eDhiYmJKdf7w8HC4uLgoLbbq4eFRpNyKFSvg5uYGa2trGBsb4+effy71NZ69loeHB2TPVKF16NABaWlpuHv3rmKfi4uL0nF2dna4f/9+qa4RFRWF3NxcdOjQQbFPV1cX7dq1Q3h4OABg/Pjx+PPPP9GyZUtMnz4dJ0+eVJQdMWIELly4gEaNGmHKlCnYv39/me6xrJggVWHPDvUXQqg5GiKiymFoKNXmVPZmaFi2OH18fCCEwK5duxAbG4tjx45h6NChivenTZuGbdu2YeHChTh27BguXLiAFi1aICcnp8I+qz///BPTpk3D6NGjsX//fly4cAEjR46s0Gs8S1dXV+m1TCYr0g/rVfTq1Qt37tzB1KlTce/ePXTv3h3Tpk0DALRu3RrR0dH44osvkJmZiQEDBqB///4Vdu3nsYmtCnO2cIYMMqRkpyApIwnWRtbqDomISOVkstI3damTvr4++vXrhw0bNiAyMhKNGjVC69atFe+fOHECI0aMQN++fQFINUq3b98u9fmbNGmCdevWISsrS1GLdOrUKaUyJ06cQPv27TFhwgTFvqioKKUyenp6yM/Pf+m1tmzZAiGEohbpxIkTMDExQe3atUsd84s4OztDT08PJ06cUDSP5ebm4uzZs/j4448V5aytreHn5wc/Pz906tQJn376KRYvXgwAMDU1xcCBAzFw4ED0798fPXv2xKNHj2BhUfFT4bAGqQrT19FHHbM6AIAbD2+oORoiInre0KFDsWvXLvz2229KtUeA1Edn69atuHDhAi5evIghQ4aUqbZlyJAhkMlkGDNmDK5du4bdu3crEoVnr/Hff/9h3759uHHjBmbPnq00KgyQ+vFcunQJERERSEpKQm5ubpFrTZgwAbGxsZg8eTKuX7+OHTt2YO7cufD391c0Gb4qIyMjjB8/Hp9++in27t2La9euYcyYMcjIyMDo0aMBAHPmzMGOHTsQGRmJq1evYufOnWjSpAkAYMmSJdi4cSOuX7+OGzduYPPmzbC1tYW5uXmFxPc8JkhVXEPLhgDYD4mIqCrq1q0bLCwsEBERgSFDhii9t2TJEtSoUQPt27eHj48PvLy8lGqYXsbY2Bj//vsvLl++jFatWuHzzz/H//73P6UyH374Ifr164eBAwfC3d0dDx8+VKpNAoAxY8agUaNGaNOmDaytrXHixIki16pVqxZ2796NM2fOwNXVFePGjcPo0aMxa9asMnwaL/f111/D19cXw4YNQ+vWrREZGYl9+/YpJsfU09NDQEAAXFxc0LlzZ2hra+PPP/8EAJiYmGDRokVo06YN2rZti9u3b2P37t0VlsA9TybYuaVcUlNTYWZmhpSUFJiamqrsOhN2TcDK/1ZiZseZ+Kr7Vyq7DhGROmRlZSE6OhqOjo5KnZGJXsWLvlel/f1mDVIVx6H+RERElY8JUhXHof5ERESVjwlSFceh/kRERJWPCVIV51jDEVoyLaTnpiMhLUHd4RAREb0WmCBVcXraeqhnXg8Am9mIqPpiDTlVpIr4PjFB0gCFzWycC4mIqpvCmZkz1LE6LVVbhd+n52f+LgvOpK0BGlo2xL6ofbj5kDVIRFS9aGtrw9zcXLGel6GhodJ6YERlIYRARkYG7t+/D3Nzc2hra5f7XEyQNACH+hNRdVa4ynxpFz0lehlzc3PF96q8mCBpAA71J6LqTCaTwc7ODjVr1ix2GQyistDV1X2lmqNCTJA0QGENUuSjSBSIAmjJ2HWMiKofbW3tCvlhI6oI/KXVAA7mDtDR0kFWXhbiUuPUHQ4REVG1xwRJA+ho6cCphhMANrMRERFVBiZIGoJD/YmIiCoPEyQN8eySI0RERKRaTJA0REPLhgDYxEZERFQZmCBpCA71JyIiqjxMkDREYRPbrce3kF+Qr+ZoiIiIqjcmSBqijlkdyLXlyMnPQUxKjLrDISIiqtaYIGkILZkWnC2cAbCZjYiISNWYIGkQjmQjIiKqHGpPkFasWIF69epBX18f7u7uOHPmTIllc3NzsWDBAjg7O0NfXx+urq7Yu3evUpl69epBJpMV2SZOnKgo07Vr1yLvjxs3TmX3WFE4FxIREVHlUGuCtGnTJvj7+2Pu3Lk4d+4cXF1d4eXlVeKKzrNmzcJPP/2E5cuX49q1axg3bhz69u2L8+fPK8qcPXsW8fHxiu3AgQMAgPfee0/pXGPGjFEqt2jRItXdaAXhSDYiIqLKodYEacmSJRgzZgxGjhyJpk2bYtWqVTA0NMRvv/1WbPl169Zh5syZ8Pb2hpOTE8aPHw9vb298++23ijLW1tawtbVVbDt37oSzszO6dOmidC5DQ0Olcqampiq914rAuZCIiIgqh9oSpJycHISFhcHT0/NpMFpa8PT0RGhoaLHHZGdnQ19fX2mfgYEBjh8/XuI11q9fj1GjRkEmkym9t2HDBlhZWaF58+YICAhARkbGK96R6hU2sUU/jkZufq6aoyEiIqq+dNR14aSkJOTn58PGxkZpv42NDa5fv17sMV5eXliyZAk6d+4MZ2dnhISEYOvWrcjPL35eoO3btyM5ORkjRoxQ2j9kyBA4ODjA3t4ely5dwmeffYaIiAhs3bq1xHizs7ORnZ2teJ2amlrKO6049ib2MNQ1REZuBm4n31Y0uREREVHFUluCVB7fffcdxowZg8aNG0Mmk8HZ2RkjR44ssUlu9erV6NWrF+zt7ZX2jx07VvG8RYsWsLOzQ/fu3REVFQVnZ+dizxUYGIj58+dX3M2Ug0wmQ32L+riUeAk3H91kgkRERKQiamtis7Kygra2NhITE5X2JyYmwtbWtthjrK2tsX37dqSnp+POnTu4fv06jI2N4eTkVKTsnTt3EBwcjA8++OClsbi7uwMAIiMjSywTEBCAlJQUxRYbG/vS86oCh/oTERGpntoSJD09Pbi5uSEkJESxr6CgACEhIfDw8Hjhsfr6+qhVqxby8vKwZcsW9O7du0iZNWvWoGbNmnj77bdfGsuFCxcAAHZ2diWWkcvlMDU1VdrUQZEgsaM2ERGRyqi1ic3f3x9+fn5o06YN2rVrh2XLliE9PR0jR44EAAwfPhy1atVCYGAgAOD06dOIi4tDy5YtERcXh3nz5qGgoADTp09XOm9BQQHWrFkDPz8/6Ogo32JUVBSCgoLg7e0NS0tLXLp0CVOnTkXnzp3h4uJSOTf+Cgqb1TgXEhERkeqoNUEaOHAgHjx4gDlz5iAhIQEtW7bE3r17FR23Y2JioKX1tJIrKysLs2bNwq1bt2BsbAxvb2+sW7cO5ubmSucNDg5GTEwMRo0aVeSaenp6CA4OViRjderUga+vL2bNmqXSe60orEEiIiJSPZkQQqg7CE2UmpoKMzMzpKSkVGpzW2JaImy/tYWWTAsZMzMg15FX2rWJiIg0XWl/v9W+1AiVTU2jmjDRM0GBKMCtx7fUHQ4REVG1xARJw8hkMi45QkREpGJMkDQQh/oTERGpFhMkDcSO2kRERKrFBEkDsYmNiIhItZggaaDCGiTOhURERKQaTJA0UGEN0t3Uu8jIzVBzNERERNUPEyQNZGlgiRr6NQAAUY+i1BwNERFR9cMESQNxqD8REZFqMUHSUBzqT0REpDpMkDQUh/oTERGpDhMkDcUmNiIiItVhgqShONSfiIhIdZggaajCGqSEtAQ8yX6i5miIiIiqFyZIGspc3xxWhlYAgMhHkWqOhoiIqHphgqTBGlo2BMB+SERERBWNCZIG41B/IiIi1WCCpME41J+IiEg1mCBpMA71JyIiUg0mSBqMTWxERESqwQRJg9W3qA8AeJDxAMlZyeoNhoiIqBphgqTBTOQmsDW2BcBaJCIioorEBEnDsaM2ERFRxWOCpOEUcyGxBomIiKjCMEHScKxBIiIiqnhMkDQch/oTERFVPCZIGo5D/YmIiCoeEyQN52zhDAB4nPUYDzMeqjkaIiKi6oEJkoYz1DVEbdPaAIAbD2+oORoiIqLqgQlSFZOXB+zaBeTnl/4YdtQmIiKqWEyQqhAhgNatgXfeAfbtK/1x7IdERERUsZggVSEyGdC9u/T8559Lf5xiLiTWIBEREVUIJkhVzNix0uPOnUBcXOmO4VB/IiKiisUEqYpp0gTo1Enqg/Tbb6U75tkmNiGECqMjIiJ6PTBBqoI+/FB6/PXX0nXWdqrhBC2ZFp7kPMH99PuqDY6IiOg1wASpCvL1BSwsgJiY0nXWluvIUdesLoBXa2bLzQX++0/qLE5ERPQ6Y4JUBenrA35+0vPSdtYubGZ7lbmQpkwB2rYFfvqp3KcgIiKqFpggVVFjxkiPpe2s/apD/WNjpSY9AFi6lLVIRET0elN7grRixQrUq1cP+vr6cHd3x5kzZ0osm5ubiwULFsDZ2Rn6+vpwdXXF3r17lcrMmzcPMplMaWvcuLFSmaysLEycOBGWlpYwNjaGr68vEhMTVXJ/5VXWztqvOpLt22+lSSoB4MYN4ODBcp2GiIioWlBrgrRp0yb4+/tj7ty5OHfuHFxdXeHl5YX794vvaDxr1iz89NNPWL58Oa5du4Zx48ahb9++OH/+vFK5Zs2aIT4+XrEdP35c6f2pU6fi33//xebNm3HkyBHcu3cP/fr1U9l9lldhZ+1ffnl5Z+1XmU07KUm6BgC0aiU9rlxZ5tMQERFVH0KN2rVrJyZOnKh4nZ+fL+zt7UVgYGCx5e3s7MQPP/ygtK9fv35i6NChitdz584Vrq6uJV4zOTlZ6Orqis2bNyv2hYeHCwAiNDS01LGnpKQIACIlJaXUx5RVZqYQFhZCAELs2vXisjeSbgjMgzD8ylAUFBSU6Tpz5kjXaNVKiMuXpefa2kLExb1C8ERERFVQaX+/1VaDlJOTg7CwMHh6eir2aWlpwdPTE6GhocUek52dDX19faV9BgYGRWqIbt68CXt7ezg5OWHo0KGIiYlRvBcWFobc3Fyl6zZu3Bh169Yt8brqUpbO2vXM60Fbpo2M3Azce3Kv1NdISwOWL5eeBwQAzZs/bdor7JNERET0ulFbgpSUlIT8/HzY2Ngo7bexsUFCQkKxx3h5eWHJkiW4efMmCgoKcODAAWzduhXx8fGKMu7u7li7di327t2LlStXIjo6Gp06dcKTJ08AAAkJCdDT04O5uXmprwtIyVlqaqrSVhlK21lbV1sXjjUcAZStme3nn4HHj4EGDYDCVsZx456+V9gviYiI6HWi9k7aZfHdd9+hQYMGaNy4MfT09DBp0iSMHDkSWlpPb6NXr15477334OLiAi8vL+zevRvJycn466+/XunagYGBMDMzU2x16tR51dsplSZNgM6dS9dZu6xD/bOzpc7ZADB9OqCtLT339QWsraWEbOfO8kZORESkudSWIFlZWUFbW7vI6LHExETY2toWe4y1tTW2b9+O9PR03LlzB9evX4exsTGcnJxKvI65uTkaNmyIyMhIAICtrS1ycnKQnJxc6usCQEBAAFJSUhRbbGxsKe/01RWuz/ayztplHeq/fj1w7x5gbw8MG/Z0v1wOjBolPWdnbSIieh2pLUHS09ODm5sbQkJCFPsKCgoQEhICDw+PFx6rr6+PWrVqIS8vD1u2bEHv3r1LLJuWloaoqCjY2dkBANzc3KCrq6t03YiICMTExLzwunK5HKampkpbZSmcWTs29sUza5dlqH9+PvC//0nP/f2lpOhZH34IyGTA/v3A/+eWRERErw21NrH5+/vjl19+we+//47w8HCMHz8e6enpGDlyJABg+PDhCAgIUJQ/ffo0tm7dilu3buHYsWPo2bMnCgoKMH36dEWZadOm4ciRI7h9+zZOnjyJvn37QltbG4MHDwYAmJmZYfTo0fD398ehQ4cQFhaGkSNHwsPDA2+88UblfgCl9Gxn7RfNcl1YgxTxMOKl59y6Fbh5E6hR42kN1bMcHYGePV9+TSIioupIrQnSwIEDsXjxYsyZMwctW7bEhQsXsHfvXkXH7ZiYGKUO2FlZWZg1axaaNm2Kvn37olatWjh+/LhSh+u7d+9i8ODBaNSoEQYMGABLS0ucOnUK1tbWijJLly7FO++8A19fX3Tu3Bm2trbYunVrpd13eRQmMS/qrN3StiV0tHRwPek6jtw+UuK5hAC+/lp6PnkyYGJSfLnx46XHNWuArKxyBk5ERKSBZEJwUYnySE1NhZmZGVJSUiqtua1LF+DoUWD+fGDOnOLLTNw1ET/+9yM8anvgxKgTkMlkRcrs3w94eQGGhsCdO4CVVfHnys8HnJykRXP/+EO5nxIREZEmKu3vt0aNYnvdFdYi/fpryZ21Z3WeBQMdA4TeDcXOG8UPQSusPRozpuTkCJBGtRVek521iYjodcIESYOUprO2nYkdPnL/CAAw8+BM5BcoZ1KnTwOHDgE6OsAnn7z8mqNHS2VDQ4GLF1/1DoiIiDQDEyQNUtrO2tM7TIe5vjmu3L+CjVc2Kr0XGCg9vv8+UJqpnGxtn04gyVokIiJ6XTBB0jCl6axdw6AGPuvwGQBgzqE5yMnPAQBcuwbs2CEN339m4N9LFXbWXr8eqKQJxImIiNSKCZKGadxYmlm7oABYvbrkclPcp8DW2BbRydH49Zy0qFrhvEd9+kgzdJdWly5S+fR0KUkiIiKq7pggaaAPP5QeX9RZ21DXELM7zwYALDiyAOE3MxAUJL33zNRSpSKTPV2fbdUqaZoAIiKi6owJkgbq1w+wtJQ6a+/dW3K5D1p/AEdzRySmJ2J0wDXk5QHduwNt25b9msOHAwYGwOXLwMmT5Y+diIhIEzBB0kDPdtb++eeSy+lp62HBmwuAdCuE/tMUADBjRvmuaW4O/P9k5OysTURE1R4TJA01Zoz0uHMncPduyeUGNx8M6ytfArmGsG14F927l/+ahZ21N28GHjwo/3mIiIiqOiZIGurZztq//VZyufQ0bWSckNa2e+T2KRLS4ksu/BJt2khbTo60/AgREVF1xQRJg5Wms/ZPPwHpqXrQt72DnAab8MXRL17pmoW1SD/9JCVnRERE1RETJA32ss7aWVnAkiXS84/8MwEtgV/O/YKoR1HlvuagQVJ/pFu3pDXdiIiIqiMmSBrsZZ21//gDSEgAatcGFnzUGD3r90ReQR7mHp5b7msaGj69JjtrExFRdcUEScOV1Fk7Lw9YtEh6/skngJ4esLDbQgBA0OUgXEq8VO5rFs6JtHMnEBNT7tMQERFVWUyQNFzjxtJM18931t6yBYiKkprgCpOoVnatMKDZAAgIfH7w81e65ptvStf85ZdXvAEiIqIqiAlSNVC4PlthZ20hni5KO2UKYGT0tOwXb34BbZk2dt7YiRMxJ8p9zcLO2r/+CuTmlvs0REREVRITpGrg+c7a+/YBFy9KidGkScplG1o2xKhWowAAMw/OhCjnuiF9+gC2tlIfp+3bXy1+IiKiqoYJUjXwfGftwtqjDz8ELCyKlp/TZQ7k2nIcvXMU+6L2leuaurrABx9Iz9lZm4iIqhsmSNVEYTPbv/8CR49KCczUqcWXrW1aG5PaSVVLASEBKBDlm9BozBhASws4dAi4fr1cpyAiIqqSmCBVE40aSZ21C1vMhg+XhveXZEbHGTDRM8GFhAvYfHVzua5Zty7w9tvS81WrynUKIiKiKokJUjVSWIskkwHTp7+4rJWhFT5t/ykAYPah2cjNL19P68LO2r//DmRklOsUREREVQ4TpGqkf3+p2evbb4GGDV9e/uM3Poa1oTVuPrqJtRfWluuaXl6AoyOQnAz8+We5TkFERFTlMEGqRvT0pE7aJfU9ep6J3ASfd5LmQ5p/ZD4yczPLfE0tradrwrGzNhERVRdMkF5z49qMQ12zuoh7EocVZ1eU6xyjRknJ2X//SRsREZGmY4L0mpPryDGvyzwAQODxQKRkpZT5HNbWUvMewFokIiKqHpggEYa5DkMTqyZ4lPkI34Z+W65zFHbW3rgRSCl7jkVERFSlMEEi6Gjp4MtuXwIAloQuQfyT+DKfo0MHoGlTIDOTnbWJiEjzMUEiAEDfxn3R1r4t0nPT4fazG3bd2FWm42UyqS8SoLxo7uvgr7+Ajz+WFgcmIqLqgQkSAQBkMhl+7/M7Glk2QnxaPN7Z+A5G7xhdpj5Jw4YBOjrAmTPAlSsqDLYKSU0FRowAvvsOaNIEmDYNePxY3VEREdGrYoJECk2sm+D8h+fh/4Y/ZJDhtwu/ocXKFgi+FVyq42vWBHx8pOevSy3S5s1Ss6KeHpCbK81BVb8+sHy59JqIiDQTEyRSYqBrgG+9vsWREUfgVMMJsamx6LGuBybsmoC0nLSXHj96tPS4bh2Qk6PiYJ+Rnw/k5VXe9Qr9/rv0OH8+sGcP0KwZ8OgRMGUK0Lw58M8/T5d/ISIizcEEiYrVyaETLo67iAltJgAAVv63Eq6rXHHszrEXHuflBdjZAUlJUnJQGfLygDfekGpuKnMEXVQUcOyY1P/q/feBnj2BCxekdemsrYEbN4DevYHu3YHz5ysvLiIienVMkKhExnrGWPH2ChwYdgB1zeri1uNb6LK2C/z3+Zc467aOjtQnB6i8ZrYtW6QJKu/cAdavr5xrAsAff0iPPXo8XRhYR0eaWTwyEggIAORy4NAhwM1N6sR+717lxUdEROXHBIleytPJE5fHX8boVqMhILD01FK0+qkVTt89XWz5wtFs+/YBd++qNjYhgG++efp65crKadIqKHiaIPn5FX3f1BRYuBCIiAAGD5ZiWrMGaNBAao5LT1d9jEREVH5MkKhUTOWm+PXdX7Fz8E7YGdsh4mEE2v/WHjNDZiI7L1upbP36QOfOUhJR2EdHVY4cAcLCAH19wNAQuHoVOHFCtdcEpKa127cBExOgT5+Syzk4AEFBwKlTQPv2QEYGMG+etJjw2rXSZ0RERFUPEyQqk7cbvo0rE65gaIuhKBAFCDweiLa/tMX5eOVONoWdtX/7TbVJQGHt0ciRUk0NIPUBUrW1a6XHgQOlxOxl3N2B48elOZMcHaWmtpEjgTZtpCY4IiKqWmRCcIxNeaSmpsLMzAwpKSkwNTVVdzhqsS18Gz7c+SEeZDyAjpYOZnWahZmdZkJXWxfp6VJn7SdPpASga9eKv/7Vq9JIMZlM6hCdnAy0bSsNub97V+oorQrp6YCtLZCWJtUkdexYtuOzs6VpAL74QppHCZD6Ky1cWPGxEhGRstL+frMGicqtb5O+uDrhKnyb+CKvIA/zjsxDpzWd8CD9AYyMntborF6tmut/+//LxvXtKzXrtWkjdYbOyXlaw6MKW7dKyZGzs7TESlnJ5dKEkpGRwARpkCC+/hoIDa3YOImIqPzUniCtWLEC9erVg76+Ptzd3XHmzJkSy+bm5mLBggVwdnaGvr4+XF1dsXfvXqUygYGBaNu2LUxMTFCzZk306dMHERERSmW6du0KmUymtI0bN04l91fdWRtZY/N7m7HRdyNq6NfA6bjT6LimI24n31Z01v77b6l2pyLdu/d0xNqnnz7dX7ho7k8/qa5pr7BflZ+fVHtVXtbWwIoVUlObEMDYsZxckoioqlBrgrRp0yb4+/tj7ty5OHfuHFxdXeHl5YX79+8XW37WrFn46aefsHz5cly7dg3jxo1D3759cf6ZSWaOHDmCiRMn4tSpUzhw4AByc3Px1ltvIf25YUNjxoxBfHy8Ylu0aJFK77U6k8lkGNR8EE59cAoOZg648fAGOvzWAYb1rqBZMyArq+IXsP3+eymZ6NhRmgOp0KBB0giyqCggJKRirwkAMTHAwYPS82HDKuac33wDWFlJy7MsXlwx5yQiolck1Khdu3Zi4sSJitf5+fnC3t5eBAYGFlvezs5O/PDDD0r7+vXrJ4YOHVriNe7fvy8AiCNHjij2denSRXz00UevFHtKSooAIFJSUl7pPNXN3ZS7otmKZgLzIMy/NheTZ98SgBBt2lTcNVJThTAzEwIQYvv2ou9PmiS917dvxV2z0JdfSufu2rViz7tunXRefX0hIiMr9txERPRUaX+/1VaDlJOTg7CwMHh6eir2aWlpwdPTE6EldMbIzs6Gvr6+0j4DAwMcP368xOuk/P/UyhYWFkr7N2zYACsrKzRv3hwBAQHIyMgo763QM2qZ1sLRkUfRvk57JGcl45e8LtDRLcB//wGXLlXMNX79VZoxu1Gjp2u/PauwtfSff4C4uIq5JiA1gz3bvFaRhg4FPD2l2rZx47g8CRGRuqktQUpKSkJ+fj5sbGyU9tvY2CAhIaHYY7y8vLBkyRLcvHkTBQUFOHDgALZu3Yr4+PhiyxcUFODjjz9Ghw4d0Lx5c8X+IUOGYP369Th06BACAgKwbt06vP/++y+MNzs7G6mpqUobFc/CwAIHhh3A2w3eRpY8FnkNtgGomJm1c3OBpUul5598AmgV8w1u1gzo1Elan60iO4iHhgI3bwJGRkD//hV3XkDqy7RypTSfU3AwsGFDxZ6fiIjKqJJqtIqIi4sTAMTJkyeV9n/66aeiXbt2xR5z//590bt3b6GlpSW0tbVFw4YNxYQJE4S+vn6x5ceNGyccHBxEbGzsC2MJCQkRAETkC9o25s6dKwAU2djEVrKcvBwxbOswgSG9BCCEoVmGyMp6tXNu2CA1RdWsKURm5svL1aolRG7uq12z0Nix0jmHD6+Y8xXnq6+ka1hZCZGUpLrrEBG9rqp8E5uVlRW0tbWRmJiotD8xMRG2trbFHmNtbY3t27cjPT0dd+7cwfXr12FsbAwnJ6ciZSdNmoSdO3fi0KFDqF24UFYJ3N3dAQCRkZEllgkICEBKSopii42NfdktvvZ0tXWxts9aTH2/GWByFxkpBhj45QaIcrYfPbusyOTJUm1LSXx9pY7PcXHArl3lupySzExg0ybpeUU3rz1r2jSpBiwpSXl0HhERVS61JUh6enpwc3NDyDNDjQoKChASEgIPD48XHquvr49atWohLy8PW7ZsQe/evRXvCSEwadIkbNu2DQcPHoSjo+NLY7lw4QIAwM7OrsQycrkcpqamShu9nJZMC0t6fYNufaWEcsdGS3zwzwfIK8gr87lCQoALF6SZqwuH85dELn+6JlxFzKy9Y4fU76luXdVMellITw/4+Wfp+Zo1wOHDqrsWERG9QKXUZ5Xgzz//FHK5XKxdu1Zcu3ZNjB07Vpibm4uEhAQhhBDDhg0TM2bMUJQ/deqU2LJli4iKihJHjx4V3bp1E46OjuLx48eKMuPHjxdmZmbi8OHDIj4+XrFlZGQIIYSIjIwUCxYsEP/995+Ijo4WO3bsEE5OTqJz585lip2j2MomMlJqOoIsX2BqbfHuxndFRk5Gmc7h5SWdY9Kksl1TJhMiKqocQT+jZ0/pXLNmvdp5SmvcOOl6DRu+uCmRiIjKprS/32pNkIQQYvny5aJu3bpCT09PtGvXTpw6dUrxXpcuXYSfn5/i9eHDh0WTJk2EXC4XlpaWYtiwYSIuLk7pfCimnxAAsWbNGiGEEDExMaJz587CwsJCyOVyUb9+ffHpp5+WOdFhglR2XbtKP/o63ecJzIPo9Fsn8TjzcamOvXhROlZLS4hbt0p/zbfeko777LPyxSyEEHFx0nUBIW7cKP95yuLxYyFsbaVrzplTOdckInodlPb3m2uxlRPXYiu7deuA4cMBuzqZSB9nj9TcZLjYuGDv0L2wMym5eROQjlu3Dhgw4GlfoNLYtg3o10+atTo2Vmp6K6tvvgGmT5eWFXnBjBIVbvNm6X51dYGLF4EmTSrv2kRE1ZVK12L7/fffseuZnq/Tp0+Hubk52rdvjzt37pTnlPQa8PWVZrmOjzXA4ob/wdbYFpcSL6Hjmo6IehRV4nF37wIbN0rPp00r2zV9fAB7e+DBAylZKishnq7rpsrO2cXp3x94+21paoMPP1Td0ilERFRUuRKkhQsXwsDAAAAQGhqKFStWYNGiRbCyssLUqVMrNECqPgwNgSFDpOdHtjvjxKgTcK7hjFuPb6HDbx1wPv58scd99x2Qlwd06QK0bVu2a+roAGPGSM9Xrix7zGFhwLVr0oi5AQPKfvyrkMmktdoMDYFjx6RO20REVDnKlSDFxsaifv36AIDt27fD19cXY8eORWBgII4dO1ahAVL1UjiybMsWwELmhBOjTqClbUskpieiy9ouCL4VrFQ+JUVaeBYo/7D3Dz4AtLWBo0elZKcsCmfO7tsXMDMr3/VfhYMD8MUX0vNPPwVKWKaQiIgqWLkSJGNjYzx8+BAAsH//fvTo0QOANPw+MzOz4qKjaqdNG6BFC2lJjaAgwMbYBof9DqNrva54kvMEPdf3xIozKxRzJf3yC/DkidT/plev8l2zdu2nS5IUJlulkZ0txQhUfvPas6ZMAVq3Bh4/BlhBS0RUOcqVIPXo0QMffPABPvjgA9y4cQPe3t4AgKtXr6JevXoVGR9VMzLZ01qkwqVHzPTNsHfoXgx3HY58kY9JeyZh/K7xSMvMwbJlUplp04pfVqS0Ctdn+/13oLTL7u3aBTx6JPVhembJwEqnoyPNjaSlJSVs+/apLxYiotdFuX5yVqxYAQ8PDzx48ABbtmyBpaUlACAsLAyDBw+u0ACp+nn/fWlkVliYNDoLAOQ6cqztvRbf9PgGMsjwU9hPaPvRN4iLA2xtpcVcX0WPHoCjo9Rk9+efpTumsHlt2DCpiU6d3NykmiRAmiSTaysTEakWh/mXE4f5v5oBA6Rh7FOmSJ2wn7X75m4M+nswniw7Btx3wUczE7Dsq+KXnymL//0PmDFD6uh95syLy96/D9SqJXUOv3atagyxT0sDmjaVpiv47DPg66/VHRERkWpcvSqNXp4/v+L/QFXpMP+9e/fi+DMTwqxYsQItW7bEkCFD8Pjx4/Kckl4zhc1s69dLfX2e5d3AG4udLgH3XQDdNPyq3Rb/RPxTIdfU1QXOnpVqr14kKEhKjtq2rRrJEQAYG0uj2gBg8WLg0iX1xkNEVJEyM6X57jp2BJo3B776Cti/X33xlCtB+vTTT5GamgoAuHz5Mj755BN4e3sjOjoa/v7+FRogVU89ekidpx89ktY5e97mXx0AAHXe3It07bvo82cfBB4LLPdCt4A0WWT//tLzl63PVti8ps7O2cXx8ZHmk8rPB8aOlR6JiDTZ1avARx9J/T2HDwdOnJBqjfr2BWrWVF9c5UqQoqOj0bRpUwDAli1b8M4772DhwoVYsWIF9uzZU6EBUvWkrQ2MGCE9X71a+b3z54HgYKnMwZW9MbHtRAgIzDw4E+9vex+ZueUfKVnYWTsoSOqPVJyLF6VFcfX0gEGDyn0plfn+e2nCzdOnK2YhXiKiyvZ8bdH33wPJydLUJl9+KXUl2LpV6n+pLuVKkPT09JDx/71Eg4OD8dZbbwEALCwsFDVLRC8zcqT0eOAA8OwE7IsXS48DBgD1nXTxg/cPWPn2Suho6SDochC6rO2Ce0/uleuanTpJ/XgyMqTmveIU1h75+AD/P/6gSrG3BwIDpecBAUBcnHrjIaLq5fp1qe9Ps2ZAjRpSEjNxojSa9tQpID29/Od+UW3Rnj1AVBTw+eeA3YtXn6oc5VnozcfHR3h5eYkFCxYIXV1dcffuXSGEEPv27RMNGjQozyk1DherrRjdukkLss6fL72+c0cIbW1pX1iYctmDtw4Ki/9ZCMyDsP/WXpy5e6Zc1/z+e+n8zZsLUVCg/F5OjhA1a0rv//NPuU5fKfLzhXjjDSnOfv3UHQ0RabqoKCEWLhTCxUX6d+VFm0wmRIMGQvTvL8QXXwixY4cQt28X/fe0UEaGEH/8IUSHDsrncXAQ4ssvpQXBK5NKF6uNiYnBhAkTEBsbiylTpmD06NEAgKlTpyI/Px/ff/99BadxVQ9HsVWMDRukYf/16kl/OUybBixdCnTrBoSEFC0f9SgK7/75Lq49uAZ9HX389u5vGNyibFNLJCdLf71kZkpLeHTs+PS9nTulmqOaNaU14HR1X+n2VOryZWkCybw8ac6m/5+OjIioVGJjgb/+khYAP3v26X4dHeCtt6QuBi1aSLU+Fy9K26VLQEJC8eczMwNcXABXV2lzdAT++Qf44w/p311Aqi16912pD2WPHuqZQqW0v98c5l9OTJAqRmamVJWakgL8/bfULyktTapq7dmz+GNSs1MxdOtQ7LyxEwAws+NMfNHtC2jJSt9iPHq0NFHl0KHKTW39+0vLoEydCixZ8go3VkmmTwe++QZo1EhKmKpyQkdE6peQIP1bu2kT8MxgdGhpSX+YDhwoNXe9qHvB/ftPE6bCpOnaNemPtZI4OEjrYo4cKf2Bqk4qT5Dy8/Oxfft2hIeHAwCaNWuGd999F9rqnlGvkjBBqjgTJkgLyZqYSMuKNG8u/Q8nk5V8TH5BPmaGzMSik4sAAL0b9ca6vutgIjcp1TX/+08awq+nJ/XhsbKSRtTZ2QE5OVInbVfXCrg5FUtNBRo0kP7BWrZMatsnourh4UNg714gPl769/FFm5FRyf9mPnwodXj+80/g8GGgoODpe506STVFvr6AjU35Y83JAcLDlZOmGzekWu4PP1RfbVFxVJogRUZGwtvbG3FxcWjUqBEAICIiAnXq1MGuXbvg7Oxc/sg1BBOkihMWJq3RVmjt2tIPr193cR0++PcD5OTnwKmGE0a3Go3BzQfDsYbjS49t00a69jffSE17P/4odURs2VIaSacpfvlFqq42Nwdu3pSSPSLSTBERwL//Sk1TJ04oJzMvIpNJc6WZmEijXAsTp/x86TzP1u60ayclRe+9J0238rpRaYLk7e0NIQQ2bNgACwsLAMDDhw/x/vvvQ0tLC7t27Sp/5BqCCVLFEQJo1Ur6q6NWLeDWLalmp7RO3T2Fvpv6IiHtacO4R20PDGkxBAOaDUBNo+In0vj1V6nK19lZ+kvnjTekdvilS4GPP37Fm6pE+fnSUNiLF6UE74cf1B0REZVWXh5w8uTTpOjGDeX3W7SQ+vWkp0s1xk+eFN1K8yvesqXUfDZgAODkpJJb0RgqTZCMjIxw6tQptGjRQmn/xYsX0aFDB6SlpZU9Yg3DBKlibdok/UWzevXTWbbLIjU7FVuubUHQlSAcjD6IAiH92aUt04ankyeGtBiCvo37KjXBpadLbeGpqdJyJx99JHVOjItT7+Rk5XH4MPDmm1IV9sWL0vBcIqqaUlOlRaf/+QfYvVtq3i+kqwt07Sp1ZH7nHWkAy4sIIU1bUlLylJkJdOgANG6syjvSLCpNkCwsLLBz5060b99eaf+JEyfg4+ODR8/+166mmCBVvNzciulkHP8kHpuubkLQ5SCcvfd0aIa+jj7ebfQuhjQfgp71e0KuI8fkyVKNi46O9Jfcu+8WP7O3JvD1lfoZ9Ogh/eP7oj5cRFS5bt+Waon+/Vf6gyY39+l7FhbSKNR33wW8vKQmMlIdlSZIw4cPx7lz57B69Wq0a9cOAHD69GmMGTMGbm5uWLt2bbkD1xRMkDTDzYc3sfHKRgRdDkLEwwjFfnN9c/Rv0h8eemMwulc7xf4tW4B+/dQR6au7dUtaNy4nR/pH+J131B0RkWZLTZUW1d6wQZr2QyaTRnsV9/ii95KTpQ7Mz2rYUEqIfHyA9u2lP9Kocqg0QUpOToafnx/+/fdf6P7/n/y5ubno3bs31qxZA3Nz83IHrimYIGkWIQTOJ5xH0OUgbLyyUWkmbr3fTyEn2h3mNfKREK8NuVyNgb6iGTOA//1P+sf38uWy9eUiIqlT9KFD0mCRLVukJqqKoKUlzbnm4yNt/z++idSgUuZBioyMVAzzb9KkCerXr1/eU2kcJkiaK78gH8dijiHochA2X9uM5EseQNBO6HT7CnPmAJ92+BT6OvrqDrNcUlOl5CgxUZrHaepUdUdEpBkiI6Vlhv74A4iJebq/cWNpfjYPD+m1EFISVTgfdOHzkh6FkGqHPDyq5tJFr6MKT5D8/f1LffElmjDD3itiglQ9ZOdlY1/UPnxzaBWOJ+wBZIBzDWd81/M7vN3wbXWHVy6rVwMffCDNahsZyWH/RCUpbEJbu1Z50kQzM2DwYCkxateO/fmqmwpPkN58881SXVgmk+HgwYOli1KDMUGqXoQQ+OvqX/Df769ofnu30btY5rWsVHMqVSX5+dIcTxcuAOPHS/M7EZGkpCY0LS1peY0RI4DevQF9zaxEplLgUiMqxgSpenqS/QRfHP0CS08tRV5BHvR19DGjwwxM7zAdBroG6g6v1I4ckYYKa2lJw/6bN1d3RESqUfgL9uxjcc/v3JGaz37/XVqDrFBhE9r770vzsFH1xwRJxZggVW/XHlzD5D2TcTBaqg11quGE73p+h3caas7QsMJ15Tw9gf372UxAmiU3Fzh3TlpQ+uhRaTLFx4+Vk57yMjeXmtD8/NiE9jpigqRiTJCqPyEENl/bDP99/oh7EgcAeKfhO/iu53dwqlH1p6KNjpb+Os7JkeZ2evdddUdEVLLMTOD0aSkZOnZMSogyMiru/Fpa0hxDI0ZI/y+wCe31xQRJxZggvT7SctLwxZEvsOTUEuQV5EGuLceMjjPwWYfPqnyzW0AA8PXXQP36wNWrHPavyVJSpATi3DlphuTc3LJvhetxGRu/eDMxefF7cvmr17qkpEhrhBXWEJ09qzx5IiBNoNixI9C5s7Soat260v7CaxfOP1Sa53p6gEHV/t+VKgkTJBVjgvT6CX8Qjsl7JiMkOgQA4GjuiO96fgefRj5qjqxkT54ADRpIw/4XLwY++UTdEVFp5OcD164Bp0493cLDX71pqaLo6EizPRcuilrcY3H70tOl0WJHj0p9456/H3v7p8lQ585A06ZSzQ9RRWKCpGJMkF5PQgj8fe1v+O/3x93UuwCAtxu8je96fgdnC2c1R1e8334DRo+WfqAiIwFra3VHRM978EA5GTpzBihuSUtnZ6nPjLW1tCyPjo70WJZNCClRSUsr+5aeXrH3Xb/+02SoUydpEVX2ByJVY4KkYkyQXm9pOWn48uiXWBK6BLkFudDT1sNH7h9hZqeZMNc3V3d4SgoKpGH/588DH34IrFql7oheb1lZwJUryglRVFTRcsbGgLs78MYb0uburv7ktqBASpSePJHmECpcIPXZxxc9l8mke+nUSdrs7dV7P/R6YoKkYkyQCACuJ13HlD1TcODWAQCApYEl5naZi3FtxkFXuwJW3q0gx45Jf6VraUmJkouLuiNSHSGkH+MHD4rfkpKkR5kMsLMrfrO1RbmWnBFCGml15440G/Ozj4XPExOLP7Zp06fJ0BtvSK+1tV/tsyCiopggqRgTJCokhMDum7vx6YFPEZ4kLb3T0LIhFnkuwruN3oWsirQZDBggzRrcrRsQHKzZTRnXrgHbtwMJCcUnQM939i0PC4uSEyhLS+D+/eIToOKaxp5Xo4aUBHl4SI9t20pDz4lI9ZggqRgTJHpeXkEefgn7BXMPz8WDjAcAgK71umJxj8Vws3dTc3TA7dvSsP/sbCm56N1b3RGVjRBSYrdkCbB378vLGxtLy6xYWxe/5ecD8fFPt4SEp89zcl4t1po1pRFXDg7S9vxzCwvNTlCJNBkTJBVjgkQlSc1OxdfHv8aS0CXIzs8GAAxzGYavun2FOmZ1Kuw6yVnJOHrnKLLzstG/af9S1VR9/jmwcKHU2ffq1fI1I1W2rCxg40YpMbpyRdonkwFvvw20aFF88mNlVf4h3YXNZM8mT89vSUnSdYpLgOrU4XByoqqMCZKKMUGil4lJicHMkJnYcHkDAEBfRx/+b/hjRscZMJGblPl86TnpOBF7AgejD+Jg9EGExYehQBQAAPYM3YOe9Xu+9BxPngANG0q1Jd98A0ybVuYwKs2DB8DKlcCKFVJzFgAYGUkj8qZMkZI8IqKyYoKkYkyQqLTOxp3FJ/s/wbGYYwCAmkY1saDrAoxuPRo6WjolHpedl43TcacVCdGpu6eQW6DcucZAxwCZeZmY3n46/tfjf6WKZ+1aYORIadj/zZtSc1BVcu0asGyZtG5WtlQBh9q1paRozBj21SGiV8MEScWYIFFZCCGwI2IHPj3wKSIfRQIAmlo3xeIei9Gzfk/IZDLkFeThXPw5RUJ0POY4MvMylc5Tx7QOujt1R7d63fCm45sIvhWMkTtGomPdjjg28lipYikokObSCQsDxo4Ffvqpwm+3zErqX9SmjTS5pa+vNIcPEdGrKu3vt9rnKF2xYgXq1asHfX19uLu748yZMyWWzc3NxYIFC+Ds7Ax9fX24urpibzG9NV92zqysLEycOBGWlpYwNjaGr68vEksae0tUAWQyGfo07oOrE67iu57fwcLAAtceXIN3kDd6rOuBdze+C8tFlnD/1R0BIQE4cOsAMvMyUdOoJgY1H4Sf3/kZkZMjcefjO1jTew2GuQ5DbdPaaF+nPQCplionv3Q9i7W0pBoaAPj1VylRUtefSVlZwJo10rQDb70lJUcyGdCvnzQ1wZkzwKBBTI6IqPKptQZp06ZNGD58OFatWgV3d3csW7YMmzdvRkREBGoWU+//2WefYf369fjll1/QuHFj7Nu3D/7+/jh58iRatWpV6nOOHz8eu3btwtq1a2FmZoZJkyZBS0sLJ06cKHXsrEGiV/E48zG+OvYVlp9ZrpTYmOubo2u9ruhWrxu6OXZDU+umL+x8LYSA9TfWeJj5EKdGn4J7bfdSxzBwIPDXX9Jzbe2ny0OUZTM0lNb3ysmRttzcp89f9jo9Hdiyhf2LiKhylfr3W6hRu3btxMSJExWv8/Pzhb29vQgMDCy2vJ2dnfjhhx+U9vXr108MHTq01OdMTk4Wurq6YvPmzYoy4eHhAoAIDQ0tdewpKSkCgEhJSSn1MUTPi3oUJWYcmCEWHV8k/ov7T+Tl55X5HO8EvSMwD2LJySVlOu7OHSHs7YWQ6o/Ut9WuLcSiRUI8flzmWyciKrPS/n6X3ENUxXJychAWFoaAgADFPi0tLXh6eiI0NLTYY7Kzs6Gvr6+0z8DAAMePHy/1OcPCwpCbmwtPT09FmcaNG6Nu3boIDQ3FG2+8UeK1swt7jELKQIlelVMNJwR6Br7SOdrXbo+dN3bi5N2TmIqppT6ubl3g7l2pJqdwKYiybhkZUvOXnt7Tx8Lt2dfFPdfVBVq2BPr2ZRMaEVU9akuQkpKSkJ+fDxsbG6X9NjY2uH79erHHeHl5YcmSJejcuTOcnZ0REhKCrVu3Ij8/v9TnTEhIgJ6eHsyfGwpjY2ODhISEEuMNDAzE/Pnzy3qbRCpX2A/pZOxJCCHKNHO3TCZNqGhszHWxiIiepfZO2mXx3XffoUGDBmjcuDH09PQwadIkjBw5Elpaqr+NgIAApKSkKLbY2FiVX5OoNNrWagttmTbuPbmH2FR+L4mIKoLaEiQrKytoa2sXGT2WmJgIW1vbYo+xtrbG9u3bkZ6ejjt37uD69eswNjaGk5NTqc9pa2uLnJwcJCcnl/q6ACCXy2Fqaqq0EVUFhrqGaGUnDVI4GXtSzdEQEVUPakuQ9PT04ObmhpCQEMW+goIChISEwMPD44XH6uvro1atWsjLy8OWLVvQ+/8XlSrNOd3c3KCrq6tUJiIiAjExMS+9LlFV1b7202Y2IiJ6dWrrgwQA/v7+8PPzQ5s2bdCuXTssW7YM6enpGDlyJABg+PDhqFWrFgIDpU6sp0+fRlxcHFq2bIm4uDjMmzcPBQUFmD59eqnPaWZmhtGjR8Pf3x8WFhYwNTXF5MmT4eHhUWIHbaKqrn2d9vj+zPdMkIiIKohaE6SBAwfiwYMHmDNnDhISEtCyZUvs3btX0ck6JiZGqX9RVlYWZs2ahVu3bsHY2Bje3t5Yt26dUofrl50TAJYuXQotLS34+voiOzsbXl5e+PHHHyvtvokqWmFH7QsJF5Cekw4jPSM1R0REpNm41Eg5caJIqmrqLK2Du6l3ccjvELrW66rucIiIqiSNWWqEiCrGs8P9iYjo1TBBIqom2FGbiKjiMEEiqiY86kijMEPvhqJAFKg5GiIizcYEiaiaaGnbEvo6+niU+Qg3Ht5QdzhERBqNCRJRNaGnrYe29m0BsJmNiOhVMUEiqkYKO2qHxha/4DMREZUOEySiakQxku0ua5CIiF4FEySiasSjttRR+9qDa3ic+VjN0RARaS4mSETViLWRNRpYNAAAnLp7Ss3REBFpLiZIRNUMJ4wkInp1TJCIqhn2QyIienVMkIiqmcIE6fTd08gryFNzNEREmokJElE109S6KUzlpkjPTcflxMvqDoeISCMxQSKqZrRkWnij9hsA2A+JiKi8mCARVUOKhWvZD4mIqFyYIBFVQxzJRkT0apggEVVD7rXdIYMMt5NvI/5JvLrDISLSOEyQiKohU7kpWti0AACE3uW6bEREZcUEiaiaUvRDYjMbEVGZMUEiqqbYD4mIqPyYIBFVU4UJUlh8GLLystQcDRGRZmGCRFRNOdVwQk2jmsjJz8G5+HPqDoeISKMwQSKqpmQyGZvZiIjKiQkSUTXGjtpEROXDBImoGvOo4wFASpCEEGqOhohIczBBIqrG3OzcoKuli8T0REQnR6s7HCIijcEEiagaM9A1QGu71gDYzEZEVBZMkIiqucKO2qGxnFGbiKi0mCARVXOKkWx3WYNERFRaTJCIqrnCBOlS4iU8yX6i5miIiDQDEySias7exB4OZg4oEAU4E3dG3eEQEWkEJkhErwFOGElEVDZMkIheA+yHRERUNkyQiF4Dz45kKxAFao6GiKjqY4JE9BpwsXGBoa4hUrJTEP4gXN3hEBFVeUyQiF4DOlo6aFerHQD2QyIiKg0mSESvCcXCteyHRET0UkyQiF4THMlGRFR6ak+QVqxYgXr16kFfXx/u7u44c+bF87QsW7YMjRo1goGBAerUqYOpU6ciKytL8X69evUgk8mKbBMnTlSU6dq1a5H3x40bp7J7JKoK3qj9BgDgxsMbSMpIUnM0RERVm1oTpE2bNsHf3x9z587FuXPn4OrqCi8vL9y/f7/Y8kFBQZgxYwbmzp2L8PBwrF69Gps2bcLMmTMVZc6ePYv4+HjFduDAAQDAe++9p3SuMWPGKJVbtGiR6m6UqAqwNLREY6vGAIBTd0+pORoioqpNrQnSkiVLMGbMGIwcORJNmzbFqlWrYGhoiN9++63Y8idPnkSHDh0wZMgQ1KtXD2+99RYGDx6sVOtkbW0NW1tbxbZz5044OzujS5cuSucyNDRUKmdqaqrSeyWqChT9kNjMRkT0QmpLkHJychAWFgZPT8+nwWhpwdPTE6Ghxa863r59e4SFhSkSolu3bmH37t3w9vYu8Rrr16/HqFGjIJPJlN7bsGEDrKys0Lx5cwQEBCAjI+OF8WZnZyM1NVVpI9I07IdERFQ6Ouq6cFJSEvLz82FjY6O038bGBtevXy/2mCFDhiApKQkdO3aEEAJ5eXkYN26cUhPbs7Zv347k5GSMGDGiyHkcHBxgb2+PS5cu4bPPPkNERAS2bt1aYryBgYGYP39+2W6SqIopTJDOxJ1Bbn4udLV11RwREVHVpPZO2mVx+PBhLFy4ED/++CPOnTuHrVu3YteuXfjiiy+KLb969Wr06tUL9vb2SvvHjh0LLy8vtGjRAkOHDsUff/yBbdu2ISoqqsRrBwQEICUlRbHFxsZW6L0RVYZGVo1QQ78GMvMycTHxorrDISKqstRWg2RlZQVtbW0kJiYq7U9MTIStrW2xx8yePRvDhg3DBx98AABo0aIF0tPTMXbsWHz++efQ0nqa7925cwfBwcEvrBUq5O7uDgCIjIyEs7NzsWXkcjnkcnmp7o2oqtKSacGjjgd239yNk7En0ca+jbpDIiKqktRWg6Snpwc3NzeEhIQo9hUUFCAkJAQeHh7FHpORkaGUBAGAtrY2AEAIobR/zZo1qFmzJt5+++2XxnLhwgUAgJ2dXVlugUgjedSW/v9iPyQiopKprQYJAPz9/eHn54c2bdqgXbt2WLZsGdLT0zFy5EgAwPDhw1GrVi0EBgYCAHx8fLBkyRK0atUK7u7uiIyMxOzZs+Hj46NIlAAp0VqzZg38/Pygo6N8i1FRUQgKCoK3tzcsLS1x6dIlTJ06FZ07d4aLi0vl3TyRmrCjNhHRy6k1QRo4cCAePHiAOXPmICEhAS1btsTevXsVHbdjYmKUaoxmzZoFmUyGWbNmIS4uDtbW1vDx8cFXX32ldN7g4GDExMRg1KhRRa6pp6eH4OBgRTJWp04d+Pr6YtasWaq9WaIqol2tdtCSaSE2NRaxKbGoY1ZH3SEREVU5MvF82xSVSmpqKszMzJCSksI5lEjjtP6pNc4nnMem/pswoNkAdYdDRFRpSvv7rVGj2IioYhQ2s4XGFj/nGBHR644JEtFrSNEP6S77IRERFYcJEtFrqDBBOhd/Dpm5mWqOhoio6mGCRPQacjBzgJ2xHfIK8vDfvf/UHQ4RUZXDBInoNSSTyTjcn4joBZggEb2mChOkozFH1RwJEVHVwwSJ6DX1Zr03AQC7b+7GL2G/qDkaIqKqhQkS0WuqlV0rzOgwAwAwbtc4bA1/+bqFRESvCyZIRK+xhd0X4oNWH6BAFGDwlsE4GH1Q3SEREVUJTJCIXmMymQyr3lmFfk36ISc/B73/7M1RbUREYIJE9NrT1tLGhn4b8Ga9N5GWk4ZeG3ohIilC3WEREakVEyQigr6OPrYP2g43OzckZSThrfVv4W7qXXWHRUSkNkyQiAgAYCo3xZ6he9DQsiFiUmLgtd4LDzMeqjssIiK1YIJERArWRtbY//5+1DKphWsPruHtoLeRlpOm7rCIiCodEyQiUuJg7oD9w/bDwsACp+NOw/cvX+Tk56g7LCKiSsUEiYiKaGrdFLuG7IKhriH2R+2H33Y/FIgCdYdFRFRpmCARUbHeqP0Gtg7YCl0tXfx55U9M2TMFQgh1h0VEVCmYIBFRibzqe+GPvn9ABhlWnF2B+UfmqzskIqJKwQSJiF5oUPNB+MH7BwDA/CPz8cOZH9QcERGR6jFBIqKXmtB2AuZ1mQcAmLJnCjZe3qjegIiIVIwJEhGVypwuczCp7SQICAzfPhx7I/eqOyQiIpVhgkREpSKTyfBdr+8wuPlg5BXkwfcvX4TGhqo7LCIilWCCRESlpiXTwto+a+Hl7IWM3Ay8HfQ2/r72N0e3EVG1wwSJiMpET1sPWwZswRu138DjrMd4b/N76PBbB5yMPanu0IiIKgwTJCIqMyM9IwQPC8bszrNhqGuI0Luh6PBbB/T/qz9uPryp7vCIiF4ZEyQiKhcjPSMseHMBbk6+idGtRkNLpoUt4VvQ9MemmLJnCpIyktQdIhFRuTFBIqJXYm9ij1/f/RUXx11Er/q9kFeQh+VnlsP5e2d8ffxrZOZmqjtEIqIyY4JERBWiec3m2D10N4KHBaOVbSukZqciICQADX9oiN8v/M613IhIozBBIqIK1d2pO/4b+x/W9V2HumZ1cTf1LkbsGIHWP7XGgagD6g6PiKhUmCARUYXTkmnhfZf3ETEpAv/z/B/M5Ga4mHgRb61/Cz3X98SlxEvqDpGI6IWYIBGRyujr6GN6h+mInBKJj9w/gq6WLvZF7UPLVS0xasco3Em+o+4QiYiKJROc4a1cUlNTYWZmhpSUFJiamqo7HCKNEPUoCgEhAdh8bbNiX/s67TGg6QD0b9oftUxrqTE6InodlPb3mwlSOTFBIiq/U3dPYdbBWTgYfRACT/8J6lCnAwY0GwDfJr5MlohIJZggqRgTJKJXF5cahy3hW7D52mYcjzmu9F7Huh3xXtP30L9pf9ib2KspQiKqbpggqRgTJKKKdTf1LrZck5KlE7EnFPtlkCmSJd+mvkyWiOiVMEFSMSZIRKpTmCz9de0vpTXeCpOlwmY4OxM7NUZJRJqICZKKMUEiqhyxKbHYEr4Ff139C6F3QxX7ZZChs0NnDGw2EL5NfVHTqKYaoyQiTcEEScWYIBFVvtiUWPx97W/8de0vnLp7SrFfS6aFbo7dMLDZQPRr0g8WBhZqjJKIqrLS/n6rfR6kFStWoF69etDX14e7uzvOnDnzwvLLli1Do0aNYGBggDp16mDq1KnIyspSvD9v3jzIZDKlrXHjxkrnyMrKwsSJE2FpaQljY2P4+voiMTFRJfdHRBWnjlkdTPWYitDRobj90W180+MbtLFvgwJRgOBbwRjz7xjYLLaB9wZv/H7hd6Rkpag7ZCLSUGqtQdq0aROGDx+OVatWwd3dHcuWLcPmzZsRERGBmjWLVpcHBQVh1KhR+O2339C+fXvcuHEDI0aMwKBBg7BkyRIAUoL0999/Izg4WHGcjo4OrKysFK/Hjx+PXbt2Ye3atTAzM8OkSZOgpaWFEydOFLlmSViDRFR1RD2Kwl9X/8Kmq5twMfGiYr+eth561u+Jgc0G4t1G78JYz1iNURJRVaARTWzu7u5o27YtfvjhBwBAQUEB6tSpg8mTJ2PGjBlFyk+aNAnh4eEICQlR7Pvkk09w+vRpHD8uDRGeN28etm/fjgsXLhR7zZSUFFhbWyMoKAj9+/cHAFy/fh1NmjRBaGgo3njjjVLFzgSJqGq6nnQdf139C39e+RPhSeGK/fo6+ni7wdsY2Gwg3m74Ngx1DdUYJRGpS5VvYsvJyUFYWBg8PT2fBqOlBU9PT4SGhhZ7TPv27REWFqZohrt16xZ2794Nb29vpXI3b96Evb09nJycMHToUMTExCjeCwsLQ25urtJ1GzdujLp165Z4XQDIzs5Gamqq0kZEVU9jq8aY02UOrk64ikvjLuHzTp+jvkV9ZOVlYUv4Fgz4ewBqflMTff7sg4XHFmJ/1H48zHio7rCJqIrRUdeFk5KSkJ+fDxsbG6X9NjY2uH79erHHDBkyBElJSejYsSOEEMjLy8O4ceMwc+ZMRRl3d3esXbsWjRo1Qnx8PObPn49OnTrhypUrMDExQUJCAvT09GBubl7kugkJCSXGGxgYiPnz55f/homoUslkMrSwaYEWNi3wxZtf4HzCeUUz3O3k29gRsQM7InYoyjuaO6KNfRvF1tquNcz1zdV3A0SkVmpLkMrj8OHDWLhwIX788Ue4u7sjMjISH330Eb744gvMnj0bANCrVy9FeRcXF7i7u8PBwQF//fUXRo8eXe5rBwQEwN/fX/E6NTUVderUKf/NEFGlkclkaG3XGq3tWiOweyD+u/cfjsUcw3/3/sN/9/7DzUc3EZ0cjejkaKV14hpYNFBKmlrZtoKJ3ESNd0JElUVtCZKVlRW0tbWLjB5LTEyEra1tscfMnj0bw4YNwwcffAAAaNGiBdLT0zF27Fh8/vnn0NIq2mJobm6Ohg0bIjIyEgBga2uLnJwcJCcnK9Uivei6ACCXyyGXy8t6m0RUxchkMrSt1RZta7VV7EvOSsa5+HOKhOm/e/8hOjkaNx/dxM1HN7HxykbpWMjQ2Kox3Ozd0NKmJVxsXOBi4wIbY5uSLkdEGkptCZKenh7c3NwQEhKCPn36AJA6aYeEhGDSpEnFHpORkVEkCdLW1gYAlNTXPC0tDVFRURg2bBgAwM3NDbq6uggJCYGvry8AICIiAjExMfDw8KiIWyMiDWOub45ujt3QzbGbYt/DjIcIiw9TSppiU2MRnhSO8KRwrMd6RVkbIxtFslS4NbFqArkO/6gi0lRqbWLz9/eHn58f2rRpg3bt2mHZsmVIT0/HyJEjAQDDhw9HrVq1EBgYCADw8fHBkiVL0KpVK0UT2+zZs+Hj46NIlKZNmwYfHx84ODjg3r17mDt3LrS1tTF48GAAgJmZGUaPHg1/f39YWFjA1NQUkydPhoeHR6lHsBFR9WdpaIm3nN/CW85vKfYlpiUiLD4MYffCcOn+JVxMuIjIR5FITE/EgVsHcODWAUVZbZk2Gls1houNC1xtXBWJk72JPWQymTpuiYjKQK0J0sCBA/HgwQPMmTMHCQkJaNmyJfbu3avouB0TE6NUYzRr1izIZDLMmjULcXFxsLa2ho+PD7766itFmbt372Lw4MF4+PAhrK2t0bFjR5w6dQrW1taKMkuXLoWWlhZ8fX2RnZ0NLy8v/Pjjj5V340SkkWyMbeDdwBveDZ6OnE3PScfVB1dxKfGS0vY46zGuPriKqw+uKproAMDCwAKt7VqjY52O6OTQCW/UfoNTDhBVQVxqpJw4DxIRlUQIgbgncYpk6WLiRVxKvISIpAjki3ylsjpaOnCzc0Nnh87oVLcTOtTtwKVSiFRIIyaK1GRMkIiorLLysnDtwTWciTuDo3eO4ljMMdxNvVukXPOazdGpbidF0lTLtJYaoiWqnpggqRgTJCJ6VUII3Em5g2N3jikSpoiHEUXKOZo7opNDJ3SqK231LepDW0tbDRETaT4mSCrGBImIVOF++n0cjzkuJU0xR3Eh4QIKRIFSGV0tXdQzrwfHGo5wNHeEUw0nOJo7wrGG9LyGfg12BCcqARMkFWOCRESVITU7FaGxoTgWcwzHYo7h9N3TyM7PfuExpnLTp0lTYQL1/8mUYw1H6OvoV1L0RFUPEyQVY4JEROqQX5CPu6l3pZm/H0fj1uNbiE5++piQVvKSSYA0/UBT66ZobdcabnZuaG3XGi1tW8JIz6iS7oBIvZggqRgTJCKqijJyM3A7+bZS8qRIoB5H40nOkyLHPDtDeGvb1tJM4bYtYSrnv21U/TBBUjEmSESkaQqnHzgXfw5h98JwLkF6jE+LL7Z8Q8uGSjVNXMCXqgMmSCrGBImIqov4J/E4F39OSpziw3Au/hxiU2OLLdu8ZnO85fQWejj3QGeHzpzkkjQOEyQVY4JERNXZ/fT7OB9/XpEwhcWH4XbybaUyetp66Fi3oyJhamnbElqyoouGE1UlTJBUjAkSEb1uHqQ/wMHogzhw6wD2R+0vUstkZWgFTydPRcJU27S2miIlKhkTJBVjgkRErzMhBG48vIH9Uftx4NYBHLp9CGk5aUplmlg1QQ+nHnjL+S10qdcFxnrGaoqW6CkmSCrGBImI6Knc/FycuntKkTCdvXdWaYJLXS1dtLZrDStDK5jpm8FMbgZTuSnM5GYw03/63FRuqvy+vhl0tNS6rjpVM0yQVIwJEhFRyR5nPsbB6IPYH7Uf+2/tL9J/qSwMdQ1hKjeFo7kjWtm2Qkvblmhl1wrNazbnpJdUZkyQVIwJEhFR6QghEPU4CufjzyM1OxUp2SnSY1bK0+fF7MvIzXjheXW0dNDEqgla2bVCK9tWiuTJTN+sku6MNBETJBVjgkREpFq5+bl4kvMEKVkpSM5KxvWk6zifcF7a4s/jYebDYo9zquGklDC1smsFO2M7rk9HAJggqRwTJCIi9RFC4G7qXUWyVJg4xaTEFFve2tAaDSwbwKmGE5zMnaTH/9/sTOw4PcFrhAmSijFBIiKqeh5mPMSFhAtKNU0RDyOUOow/T19HX7Gob3EbJ8OsXpggqRgTJCIizZCRm4HwB+G49fjW0y1ZeryTfAf5Iv+Fx9sa2z5NmFj7pPGYIKkYEyQiIs2XV5CHmJQY5eTpme1x1uMXHi/XlsOxhmOxyZNTDScY6RlV0p1QaTFBUjEmSERE1d/jzMe49fgWopOjlRKnqMdRpap9sjGygVMNJzSxaoJmNZuhmXUzNKvZDLVMarHTuJowQVIxJkhERK+3vII8xKbEFtt0d+vxLTzKfFTisWZys6cJ0/8nTc2sm8HW2JaJk4oxQVIxJkhERPQiyVnJiH4cjZuPbuLag2u4cv8Krj64ipsPb5ZY81RDvwaa12yulDQ1sGwAGyMb6GrrVvIdVE9MkFSMCRIREZVHdl42bjy8oUiYrj64iqv3ryLqcdQLR9tZGVrBztgOtsa2sDOxg63R/z8a20r7/v89U7kpa6FegAmSijFBIiKiipSZm4nrSdcVCdPVB1dx5f4VxKTEvLSv07MMdAwUSZSdsR0czR3hbOGM+hb14VzDGXXN6kJbS1uFd1K1MUFSMSZIRERUGQpEAZIykpCQloD4J/FISEuQnqfFKz0mpCUgNTv1pefT1dKFYw1HONd4mjTVt6iP+hb1Uc+8HuQ68kq4K/VhgqRiTJCIiKiqSc9JR2J6oiKRinsSpxh1F/koErce30JOfk6Jx8sgQ12zulKNU436aGjZEC42LnCxcYGNsU0l3onqMEFSMSZIRESkafIL8hH3JA6RjyIR9UhKmgqTp8hHkUjPTS/xWBsjG7jYuMDVxlWRNDWxbgI9bb1KvINXxwRJxZggERFRdSKEwP30+4qk6ebDm7j+8DouJlxE5KNICBRNF3S0dNDEqkmRxKkqT1fABEnFmCAREdHrIj0nHVcfXMXFhIu4lHgJl+5fwsWEi0jJTim2vLWhNVxsXOBo7gg7EzvYm9jD3sQedsbScxtjG+ho6VTyXUiYIKkYEyQiInqdCSEQmxorJUyJl3AxUUqebjy88cLpCgCpr5ONsY0iYXo2ebI3sVckVTZGNhU+4o4JkooxQSIiIioqMzdTMUXB3dS7uPfknmKLT4tH/JP4Uk9bsGXAFvRr0q9C4yvt77d66reIiIioWjLQNUAb+zZoY9+m2PfzC/KRlJGkSJieT6AKnyekJcDexL6So3+KCRIRERFVGm0tbdgY28DG2Aat0KrEcvkFpZ8cUxWYIBEREVGVo+7ZvrXUenUiIiKiKogJEhEREdFzmCARERERPYcJEhEREdFz1J4grVixAvXq1YO+vj7c3d1x5syZF5ZftmwZGjVqBAMDA9SpUwdTp05FVlaW4v3AwEC0bdsWJiYmqFmzJvr06YOIiAilc3Tt2hUymUxpGzdunEruj4iIiDSPWhOkTZs2wd/fH3PnzsW5c+fg6uoKLy8v3L9/v9jyQUFBmDFjBubOnYvw8HCsXr0amzZtwsyZMxVljhw5gokTJ+LUqVM4cOAAcnNz8dZbbyE9XXkBvjFjxiA+Pl6xLVq0SKX3SkRERJpDrcP8lyxZgjFjxmDkyJEAgFWrVmHXrl347bffMGPGjCLlT548iQ4dOmDIkCEAgHr16mHw4ME4ffq0oszevXuVjlm7di1q1qyJsLAwdO7cWbHf0NAQtra2qrgtIiIi0nBqq0HKyclBWFgYPD09nwajpQVPT0+EhoYWe0z79u0RFhamaIa7desWdu/eDW9v7xKvk5IiLaRnYWGhtH/Dhg2wsrJC8+bNERAQgIyMjBfGm52djdTUVKWNiIiIqie11SAlJSUhPz8fNjY2SvttbGxw/fr1Yo8ZMmQIkpKS0LFjRwghkJeXh3Hjxik1sT2roKAAH3/8MTp06IDmzZsrncfBwQH29va4dOkSPvvsM0RERGDr1q0lxhsYGIj58+eX406JiIhI02jUTNqHDx/GwoUL8eOPP8Ld3R2RkZH46KOP8MUXX2D27NlFyk+cOBFXrlzB8ePHlfaPHTtW8bxFixaws7ND9+7dERUVBWdn52KvHRAQAH9/f8Xr1NRU1KlTp4LujIiIiKoStSVIVlZW0NbWRmJiotL+xMTEEvsGzZ49G8OGDcMHH3wAQEpu0tPTMXbsWHz++efQ0nraYjhp0iTs3LkTR48eRe3atV8Yi7u7OwAgMjKyxARJLpdDLpeX+v6IiIhIc6mtD5Kenh7c3NwQEhKi2FdQUICQkBB4eHgUe0xGRoZSEgQA2trSWi1CCMXjpEmTsG3bNhw8eBCOjo4vjeXChQsAADs7u/LcChEREVUzam1i8/f3h5+fH9q0aYN27dph2bJlSE9PV4xqGz58OGrVqoXAwEAAgI+PD5YsWYJWrVopmthmz54NHx8fRaI0ceJEBAUFYceOHTAxMUFCQgIAwMzMDAYGBoiKikJQUBC8vb1haWmJS5cuYerUqejcuTNcXFzU80EQERFRlaLWBGngwIF48OAB5syZg4SEBLRs2RJ79+5VdNyOiYlRqjGaNWsWZDIZZs2ahbi4OFhbW8PHxwdfffWVoszKlSsBSJNBPmvNmjUYMWIE9PT0EBwcrEjG6tSpA19fX8yaNatMsRfWWHE0GxERkeYo/N0u/B0viUy8rAQV6+7du+ykTUREpKFiY2Nf2EeZCVI5FRQU4N69ezAxMYFMJlN6r3CEW2xsLExNTdUUoebh51Z2/MzKh59b+fBzKzt+ZuWjys9NCIEnT57A3t6+SL/mZ2nUMP+qREtL66Wj40xNTfk/RDnwcys7fmblw8+tfPi5lR0/s/JR1edmZmb20jJqX6yWiIiIqKphgkRERET0HCZIKiCXyzF37lxOLFlG/NzKjp9Z+fBzKx9+bmXHz6x8qsLnxk7aRERERM9hDRIRERHRc5ggERERET2HCRIRERHRc5ggERERET2HCVIFW7FiBerVqwd9fX24u7vjzJkz6g6pSps3bx5kMpnS1rhxY3WHVeUcPXoUPj4+sLe3h0wmw/bt25XeF0Jgzpw5sLOzg4GBATw9PXHz5k31BFuFvOxzGzFiRJHvX8+ePdUTbBURGBiItm3bwsTEBDVr1kSfPn0QERGhVCYrKwsTJ06EpaUljI2N4evri8TERDVFXDWU5nPr2rVrke/buHHj1BRx1bBy5Uq4uLgoJoT08PDAnj17FO+r87vGBKkCbdq0Cf7+/pg7dy7OnTsHV1dXeHl54f79++oOrUpr1qwZ4uPjFdvx48fVHVKVk56eDldXV6xYsaLY9xctWoTvv/8eq1atwunTp2FkZAQvLy9kZWVVcqRVy8s+NwDo2bOn0vdv48aNlRhh1XPkyBFMnDgRp06dwoEDB5Cbm4u33noL6enpijJTp07Fv//+i82bN+PIkSO4d+8e+vXrp8ao1a80nxsAjBkzRun7tmjRIjVFXDXUrl0bX3/9NcLCwvDff/+hW7du6N27N65evQpAzd81QRWmXbt2YuLEiYrX+fn5wt7eXgQGBqoxqqpt7ty5wtXVVd1haBQAYtu2bYrXBQUFwtbWVnzzzTeKfcnJyUIul4uNGzeqIcKq6fnPTQgh/Pz8RO/evdUSj6a4f/++ACCOHDkihJC+W7q6umLz5s2KMuHh4QKACA0NVVeYVc7zn5sQQnTp0kV89NFH6gtKQ9SoUUP8+uuvav+usQapguTk5CAsLAyenp6KfVpaWvD09ERoaKgaI6v6bt68CXt7ezg5OWHo0KGIiYlRd0gaJTo6GgkJCUrfPTMzM7i7u/O7VwqHDx9GzZo10ahRI4wfPx4PHz5Ud0hVSkpKCgDAwsICABAWFobc3Fyl71vjxo1Rt25dft+e8fznVmjDhg2wsrJC8+bNERAQgIyMDHWEVyXl5+fjzz//RHp6Ojw8PNT+XeNitRUkKSkJ+fn5sLGxUdpvY2OD69evqymqqs/d3R1r165Fo0aNEB8fj/nz56NTp064cuUKTExM1B2eRkhISACAYr97he9R8Xr27Il+/frB0dERUVFRmDlzJnr16oXQ0FBoa2urOzy1KygowMcff4wOHTqgefPmAKTvm56eHszNzZXK8vv2VHGfGwAMGTIEDg4OsLe3x6VLl/DZZ58hIiICW7duVWO06nf58mV4eHggKysLxsbG2LZtG5o2bYoLFy6o9bvGBInUqlevXornLi4ucHd3h4ODA/766y+MHj1ajZHR62DQoEGK5y1atICLiwucnZ1x+PBhdO/eXY2RVQ0TJ07ElStX2C+wjEr63MaOHat43qJFC9jZ2aF79+6IioqCs7NzZYdZZTRq1AgXLlxASkoK/v77b/j5+eHIkSPqDoudtCuKlZUVtLW1i/SuT0xMhK2trZqi0jzm5uZo2LAhIiMj1R2Kxij8fvG79+qcnJxgZWXF7x+ASZMmYefOnTh06BBq166t2G9ra4ucnBwkJycrlef3TVLS51Ycd3d3AHjtv296enqoX78+3NzcEBgYCFdXV3z33Xdq/64xQaogenp6cHNzQ0hIiGJfQUEBQkJC4OHhocbINEtaWhqioqJgZ2en7lA0hqOjI2xtbZW+e6mpqTh9+jS/e2V09+5dPHz48LX+/gkhMGnSJGzbtg0HDx6Eo6Oj0vtubm7Q1dVV+r5FREQgJibmtf6+vexzK86FCxcA4LX+vhWnoKAA2dnZav+usYmtAvn7+8PPzw9t2rRBu3btsGzZMqSnp2PkyJHqDq3KmjZtGnx8fODg4IB79+5h7ty50NbWxuDBg9UdWpWSlpam9FdmdHQ0Lly4AAsLC9StWxcff/wxvvzySzRo0ACOjo6YPXs27O3t0adPH/UFXQW86HOzsLDA/Pnz4evrC1tbW0RFRWH69OmoX78+vLy81Bi1ek2cOBFBQUHYsWMHTExMFH09zMzMYGBgADMzM4wePRr+/v6wsLCAqakpJk+eDA8PD7zxxhtqjl59Xva5RUVFISgoCN7e3rC0tMSlS5cwdepUdO7cGS4uLmqOXn0CAgLQq1cv1K1bF0+ePEFQUBAOHz6Mffv2qf+7pvJxcq+Z5cuXi7p16wo9PT3Rrl07cerUKXWHVKUNHDhQ2NnZCT09PVGrVi0xcOBAERkZqe6wqpxDhw4JAEU2Pz8/IYQ01H/27NnCxsZGyOVy0b17dxEREaHeoKuAF31uGRkZ4q233hLW1tZCV1dXODg4iDFjxoiEhAR1h61WxX1eAMSaNWsUZTIzM8WECRNEjRo1hKGhoejbt6+Ij49XX9BVwMs+t5iYGNG5c2dhYWEh5HK5qF+/vvj0009FSkqKegNXs1GjRgkHBwehp6cnrK2tRffu3cX+/fsV76vzuyYTQgjVp2FEREREmoN9kIiIiIiewwSJiIiI6DlMkIiIiIiewwSJiIiI6DlMkIiIiIiewwSJiIiI6DlMkIiIiIiewwSJiKicZDIZtm/fru4wiEgFmCARkUYaMWIEZDJZka1nz57qDo2IqgGuxUZEGqtnz55Ys2aN0j65XK6maIioOmENEhFpLLlcDltbW6WtRo0aAKTmr5UrV6JXr14wMDCAk5MT/v77b6XjL1++jG7dusHAwACWlpYYO3Ys0tLSlMr89ttvaNasGeRyOezs7DBp0iSl95OSktC3b18YGhqiQYMG+OeffxTvPX78GEOHDoW1tTUMDAzQoEGDIgkdEVVNTJCIqNqaPXs2fH19cfHiRQwdOhSDBg1CeHg4ACA9PR1eXl6oUaMGzp49i82bNyM4OFgpAVq5ciUmTpyIsWPH4vLly/jnn39Qv359pWvMnz8fAwYMwKVLl+Dt7Y2hQ4fi0aNHiutfu3YNe/bsQXh4OFauXAkrK6vK+wCIqPwqZUlcIqIK5ufnJ7S1tYWRkZHS9tVXXwkhpNXVx40bp3SMu7u7GD9+vBBCiJ9//lnUqFFDpKWlKd7ftWuX0NLSEgkJCUIIIezt7cXnn39eYgwAxKxZsxSv09LSBACxZ88eIYQQPj4+YuTIkRVzw0RUqdgHiYg01ptvvomVK1cq7bOwsFA89/DwUHrPw8MDFy5cAACEh4fD1dUVRkZGivc7dOiAgoICREREQCaT4d69e+jevfsLY3BxcVE8NzIygqmpKe7fvw8AGD9+PHx9fXHu3Dm89dZb6NOnD9q3b1+ueyWiysUEiYg0lpGRUZEmr4piYGBQqnK6urpKr2UyGQoKCgAAvXr1wp07d7B7924cOHAA3bt3x8SJE7F48eIKj5eIKhb7IBFRtXXq1Kkir5s0aQIAaNKkCS5evIj09HTF+ydOnICWlhYaNWoEExMT1KtXDyEhIa8Ug7W1Nfz8/LB+/XosW7YMP//88yudj4gqB2uQiEhjZWdnIyEhQWmfjo6OoiP05s2b0aZNG3Ts2BEbNmzAmTNnsHr1agDA0KFDMXfuXPj5+WHevHl48OABJk+ejGHDhsHGxgYAMG/ePIwbNw41a9ZEr1698OTJE5w4cQKTJ08uVXxz5syBm5sbmjVrhuzsbOzcuVORoBFR1cYEiYg01t69e2FnZ6e0r1GjRrh+/ToAaYTZn3/+iQkTJsDOzg4bN25E06ZNAQCGhobYt28fPvroI7Rt2xaGhobw9fXFkiVLFOfy8/NDVlYWli5dimnTpsHKygr9+/cvdXx6enoICAjA7du3YWBggE6dOuHPP/+sgDsnIlWTCSGEuoMgIqpoMpkM27ZtQ58+fdQdChFpIPZBIiIiInoOEyQiIiKi57APEhFVS+w9QESvgjVIRERERM9hgkRERET0HCZIRERERM9hgkRERET0HCZIRERERM9hgkRERET0HCZIRERERM9hgkRERET0HCZIRERERM/5P+F+TIpC97jrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_loss = m.history['loss']\n",
        "val_loss = m.history['val_loss']\n",
        "train_acc = m.history['accuracy']\n",
        "val_acc = m.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(val_acc) + 1)\n",
        "\n",
        "\n",
        "plt.plot(epochs, train_loss, 'g', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='validation loss')\n",
        "plt.title('Training and Validation losses')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "zRlCDrnGhvaE",
        "outputId": "b6848954-0939-46da-8fa3-9b64416959a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IiICjoyM2b95cpFqS/PTt2xdt27bFe++9h7CwMNSvXx9btmwpdn2Mvb09+vfvr60Dyjk8uU+fPtiyZQsGDBiAF154AaGhoVixYgXq16+PxMTEYp1LM5/RwoUL0adPH/Tu3Rvnz5/H7t27dVp1NOedP38+Ro0ahTZt2uDy5ctYv369TssRIH3xOzs7Y8WKFXBwcICdnR1at26dZ41L37590blzZ3zwwQcICwtDkyZNsG/fPvz555+YNm2aTsFzaRT1mnXu3BnDhw/HN998g1u3bqFnz55Qq9X4559/0LlzZ0yePBn+/v744IMP8NFHH6F9+/Z46aWXoFQqcebMGXh7e2vn0xkzZgzGjx+PgQMHolu3brh48SL27t2b67oWFvcvv/wCJycn1K9fHydOnMCBAwdyDfl/5513sGnTJgwaNAijR49GQEAAnj17hu3bt2PFihVo0qSJdt+hQ4fi3XffxdatWzFhwgROTmnKynjUGZFs8hsG36BBgzz3P3bsmHjuueeEjY2N8Pb2Fu+++652KO3hw4e1++U3DH7RokW5jokcQ4PzGwY/adKkXK/NOXxYCCEOHjwomjVrJqysrISfn59YvXq1ePvtt4W1tXU+VyHLtWvXRNeuXYW9vb1wd3cXY8eO1Q6rzj6EOygoSNjZ2eV6fV6xP336VAwfPlw4OjoKJycnMXz4cHH+/PkiD4PX2LlzpwAgvLy88hxqvWDBAlG9enWhVCpFs2bNxF9//ZXr30GIwofBCyGESqUS8+bNE15eXsLGxkZ06tRJXLlyJdf1Tk1NFW+//bZ2v7Zt24oTJ06Ijh075hpG/eeff4r69etrpyTQvPe8YkxISBBvvfWW8Pb2FpaWlqJWrVpi0aJFOkPKNe+lqJ+LnIpzzTIzM8WiRYtE3bp1hZWVlfDw8BC9evUSZ8+e1dnvxx9/FM2aNRNKpVK4uLiIjh07iv379+tc15kzZwp3d3dha2srevToIW7fvp3vMPgzZ87kijsmJkaMGjVKuLu7C3t7e9GjRw9x48aNPN/z06dPxeTJk0WVKlWElZWVqFq1qggKChLR0dG5jtu7d28BQBw/frzA60YVm0III/qTlohKrX///rh69Spu3boldyhERmnAgAG4fPlykerlqOJiDRBROZZz2Ypbt25h165d6NSpkzwBERm5R48eYefOnRg+fLjcoZDM2AJEVI55eXlh5MiRqFmzJu7du4fly5cjLS0N58+fzzW3DZEpCw0NxbFjx7B69WqcOXMGd+7cQeXKleUOi2TEImiicqxnz57YuHEjIiMjoVQqERgYiAULFjD5IcrhyJEjGDVqFKpVq4aff/6ZyQ+xBYiIiIhMD2uAiIiIyOQwASIiIiKTwxqgPKjVajx8+BAODg6lWt2YiIiIyo4QAgkJCfD29oaZWcFtPEyA8vDw4UP4+PjIHQYRERGVwP3791G1atUC92EClAfNooT379+Ho6OjzNEQERFRUcTHx8PHx0dnceH8MAHKg6bby9HRkQkQERFROVOU8hUWQRMREZHJYQJEREREJocJEBEREZkc1gCVgkqlQkZGhtxhEOmVpaUlzM3N5Q6DiMigmACVgBACkZGRiI2NlTsUIoNwdnZG5cqVOQ8WEVVYTIBKQJP8eHp6wtbWll8SVGEIIZCcnIzHjx8DkFabJyKqiJgAFZNKpdImP25ubnKHQ6R3NjY2AIDHjx/D09OT3WFEVCGxCLqYNDU/tra2MkdCZDiazzdr3IioomICVELs9qKKjJ9vIqromAARERGRyWECRCXm6+uLJUuWFHn/4OBgKBQKjp4jIiLZMQEyAQqFosDb3LlzS3TcM2fOYNy4cUXev02bNnj06BGcnJxKdD4iIiJ94SgwE/Do0SPt/d9++w2zZ89GSEiIdpu9vb32vhACKpUKFhaFfzQ8PDyKFYeVlRUqV65crNdUFOnp6bCyspI7DCIi2aVlpuFhwkPYWdnB085TtjjYAmQCKleurL05OTlBoVBoH9+4cQMODg7YvXs3AgICoFQqcfToUdy5cwf9+vVDpUqVYG9vj5YtW+LAgQM6x83ZBaZQKLB69WoMGDAAtra2qFWrFrZv3659PmcX2Jo1a+Ds7Iy9e/eiXr16sLe3R8+ePXUStszMTLz55ptwdnaGm5sbZs6ciaCgIPTv3z/f9/v06VMMGTIEVapUga2tLRo1aoSNGzfq7KNWq/H555/D398fSqUS1apVwyeffKJ9/sGDBxgyZAhcXV1hZ2eHFi1a4NSpUwCAkSNH5jr/tGnT0KlTJ+3jTp06YfLkyZg2bRrc3d3Ro0cPAMDixYvRqFEj2NnZwcfHBxMnTkRiYqLOsY4dO4ZOnTrB1tYWLi4u6NGjB2JiYrB27Vq4ubkhLS1NZ//+/ftj+PDh+V4PIqKykKnOxMOEhzgTcQZ/3vgTy88sx4eHPsToP0ej57qeaLy8Mdw/d4f1J9ao+U1NfH/2e1njZQuQHgghkJyRXObntbXU3ySM7733Hr744gvUrFkTLi4uuH//Pnr37o1PPvkESqUSa9euRd++fRESEoJq1arle5x58+bh888/x6JFi7B06VIMGzYM9+7dg6ura577Jycn44svvsAvv/wCMzMzvPbaa5gxYwbWr18PAPjss8+wfv16/PTTT6hXrx6+/vprbNu2DZ07d843htTUVAQEBGDmzJlwdHTEzp07MXz4cPj5+aFVq1YAgFmzZmHVqlX46quv0K5dOzx69Ag3btwAACQmJqJjx46oUqUKtm/fjsqVK+PcuXNQq9XFuqY///wzJkyYgGPHjmm3mZmZ4ZtvvkGNGjVw9+5dTJw4Ee+++y6+++47AMCFCxfQpUsXjB49Gl9//TUsLCxw+PBhqFQqDBo0CG+++Sa2b9+OQYMGAZDm6tm5cyf27dtXrNiIiEpCCIHLjy/j4N2DuPn0Jh4mPkREfAQeJjxEVFIU1KJovyetLayRmplq4GgLxgRID5IzkmG/0L7wHfUscVYi7Kzs9HKs+fPno1u3btrHrq6uaNKkifbxRx99hK1bt2L79u2YPHlyvscZOXIkhgwZAgBYsGABvvnmG5w+fRo9e/bMc/+MjAysWLECfn5+AIDJkydj/vz52ueXLl2KWbNmYcCAAQCAb7/9Frt27SrwvVSpUgUzZszQPp4yZQr27t2L33//Ha1atUJCQgK+/vprfPvttwgKCgIA+Pn5oV27dgCADRs24MmTJzhz5ow2cfP39y/wnHmpVasWPv/8c51t06ZN09739fXFxx9/jPHjx2sToM8//xwtWrTQPgaABg0aaO8PHToUP/30kzYBWrduHapVq6bT+kREpE+PEh7hwN0D2Hd3Hw7cPYDIxMh89zVXmMPLwQveDt7Szd4bVRyrZD128EYVhypwtnaWfboNJkAEAGjRooXO48TERMydOxc7d+7Eo0ePkJmZiZSUFISHhxd4nMaNG2vv29nZwdHRUbusQl5sbW21yQ8gLb2g2T8uLg5RUVHaVhsAMDc3R0BAQIGtMSqVCgsWLMDvv/+OiIgIpKenIy0tTTu53/Xr15GWloYuXbrk+foLFy6gWbNm+bZaFVVAQECubQcOHMDChQtx48YNxMfHIzMzE6mpqUhOToatrS0uXLigTW7yMnbsWLRs2RIRERGoUqUK1qxZg5EjR8r+i4SIKo7kjGT8fe9v7L+zH/vv7sflx5d1nrexsEEn305o4d0CVRyq6CQ4HrYeMDcrH7PHMwHSA1tLWyTOSix8RwOcV1/s7HRbkmbMmIH9+/fjiy++gL+/P2xsbPDyyy8jPT29wONYWlrqPFYoFAUmK3ntL4QoZvS6Fi1ahK+//hpLlizR1ttMmzZNG7tmqYf8FPa8mZlZrhjzmjE55zUNCwtDnz59MGHCBHzyySdwdXXF0aNH8frrryM9PR22traFnrtZs2Zo0qQJ1q5di+7du+Pq1avYuXNnga8hIuOhFmpcf3IdR8OP4uj9owiPC4efix/quNVBXfe6qONeB34ufrA0tyz8YHqM6ULkBey/sx/77u7D0fCjSFdl/a5XQIHmXs3R3a87utXshjY+baC0UJZZfIbCBEgPFAqF3rqijMWxY8cwcuRIbddTYmIiwsLCyjQGJycnVKpUCWfOnEGHDh0ASK07586dQ9OmTfN93bFjx9CvXz+89tprAKSC55s3b6J+/foApK4pGxsbHDx4EGPGjMn1+saNG2P16tV49uxZnq1AHh4euHLlis62Cxcu5Ermcjp79izUajW+/PJLmJlJ4w9+//33XOc+ePAg5s2bl+9xxowZgyVLliAiIgJdu3aFj49PgeclIvmkZabh34f/ahOeY+HHEJMao7PP3/f+1nlsrjCHn6uUFGVPjOq614W7rXuJ44hLi0Ncapz2Z1hsGA6EHsCBuwcQnRyts7+Po4824elSs0uJz2vMmABRnmrVqoUtW7agb9++UCgU+PDDD4tdBKwPU6ZMwcKFC+Hv74+6deti6dKliImJKbDLp1atWti0aROOHz8OFxcXLF68GFFRUdoEyNraGjNnzsS7774LKysrtG3bFk+ePMHVq1fx+uuvY8iQIViwYAH69++PhQsXwsvLC+fPn4e3tzcCAwPx/PPPY9GiRVi7di0CAwOxbt06XLlyBc2aNSvwvfj7+yMjIwNLly5F3759cezYMaxYsUJnn1mzZqFRo0aYOHEixo8fDysrKxw+fBiDBg2Cu7v0C2jo0KGYMWMGVq1ahbVr15byChORPsWkxOD4/ePahOdMxBmkqXRHbtpa2uK5qs+hnU871HKrhbsxdxHyNAQ3om8gJDoESRlJuPn0Jm4+vYkd2KHzWlcb16ykyK0OrMytdBObHEmO5mfOGHKyt7JHZ9/O2qSntlvtCt+1zgSI8rR48WKMHj0abdq0gbu7O2bOnIn4+Pgyj2PmzJmIjIzEiBEjYG5ujnHjxqFHjx4FrlD+v//9D3fv3kWPHj1ga2uLcePGoX///oiLi9Pu8+GHH8LCwgKzZ8/Gw4cP4eXlhfHjxwOQ5ivat28f3n77bfTu3RuZmZmoX78+li1bBgDo0aMHPvzwQ7z77rtITU3F6NGjMWLECFy+fDnPeDSaNGmCxYsX47PPPsOsWbPQoUMHLFy4ECNGjNDuU7t2bezbtw/vv/8+WrVqBRsbG7Ru3VpbWA5ILWMDBw7Ezp07C5wOgIgMLzwuHP/c+0eb8Fx5fCXXPp52nmhXrR3a+bRDu2rt0LRy03y7uIQQeJjwUEqGnoYgJDoEN55KidG9uHt4lvIMJx6cwIkHJ0oUr6PSEU5KJzhZO8Hd1h3tq7VHt5rd8FzV58q0280YKERpCy4qoPj4eDg5OSEuLg6Ojo46z6WmpiI0NBQ1atSAtbW1TBGaLrVajXr16uGVV17BRx99JHc4sunSpQsaNGiAb775xiDH5+ecKG/xafE4HHoY++/ux747+3Dr2a1c+9R2q61NdtpVawd/V3+9tKYkZyTj1tNb2sQo5GkI1EKtTWjy+uls7ay976B0gJmiYk//V9D3d05sASKjdu/ePezbtw8dO3ZEWloavv32W4SGhmLo0KFyhyaLmJgYBAcHIzg4WGeoPBEZRqY6E2cizmDfnX3Yf3c/Tj44CZVQaZ83V5gjwDtAm/C0rdbWYLMb21raoknlJmhSuUnhO1OhmACRUTMzM8OaNWswY8YMCCHQsGFDHDhwAPXq1ZM7NFk0a9YMMTEx+Oyzz1CnTh25wyGqcIQQuBNzRzsE/FDoIcSlxensU8u1FrrV7Ibuft3RybcTnKy5vmF5xASIjJqPj4/OTMqmrqxH4hEZs+SMZGy6tgnbbmyDWqjhoHSAg9V/N2XWT0elY65tmp9W5laISYnBodBD2lae0NhQnfO4WLuga82u6FazG7r5dYOvs688b5j0igkQERGVG0IInH10Fj+c+wEbrmxAfFrpBmdYmVshU52ps4SDpZkl2vi00Y6Iau7VvNxM7kdFxwSIiIiM3rOUZ1h/aT1+OP8DLkZd1G6v4VwDI5uORGX7ykhIS0BCeoL2Z3xavM7j7D9TMlMAQDvhX32P+tpurQ7VO8DequyXN6KyxQSIiIiMklqoERwWjNXnVmPL9S3auWyU5kq8VO8lvN7sdXSu0blEI5sy1ZlITE9EfFo8lOZKVLKvpO/wycgxASIiIqPyIP4B1lxYgx/P/6hTj9OkUhO83ux1DGs8DK42pVurz8LMAs7WznC2di5ltFReMQEiIiLZZagy8NfNv7D6/Grsub1HW5PjqHTE0IZDMab5GDT3al7hZyemssMEiIiIDEqlVuFpylM8SXqC6ORoPEl+ons/+QkOhR7C46TH2td0qN4BY5qNwcD6A/W68DORBhMgKjJfX19MmzYN06ZNAyAtArt169Z8l2MICwtDjRo1cP78+QIXLy2Mvo5DRPlLy0zD46THyFBnIF2VjgyV9DNdlV7gNs321MxUPE15qk1qopOj8SRJSm5iUmIgUPiiA5XtKyOoSRBGNxuN2m61y+BdkyljAkQl9ujRI7i4uOj1mCNHjkRsbCy2bdum3ebj44NHjx5pFwMlIv1QCzWOhB3B2ktrsenaJiSmJxrsXAoo4GrjCndbd3jYeUg/bT3gYSvdr+NeB91qdjO59ahIPkyAqMQqV65cJucxNzcvs3MZm4yMDFha8guB9Ov6k+v45dIvWHdpHe7H39dutzSzhNJCCUszS1iZW8HK3AqW5tnu57E9+zY3GzdtgqNJbDTJjquNKyzM+JVDRkRQLnFxcQKAiIuLy/VcSkqKuHbtmkhJSZEhspJZuXKl8PLyEiqVSmf7iy++KEaNGiWEEOL27dvixRdfFJ6ensLOzk60aNFC7N+/X2f/6tWri6+++kr7GIDYunWr9vGpU6dE06ZNhVKpFAEBAWLLli0CgDh//rwQQojMzEwxevRo4evrK6ytrUXt2rXFkiVLtK+fM2eOAKBzO3z4sAgNDdU5jhBCBAcHi5YtWworKytRuXJlMXPmTJGRkaF9vmPHjmLKlCninXfeES4uLqJSpUpizpw5BV6n06dPi65duwo3Nzfh6OgoOnToIM6ePauzT0xMjBg3bpzw9PQUSqVSNGjQQOzYsUP7/NGjR0XHjh2FjY2NcHZ2Ft27dxfPnj3L8/oJIUSTJk104gIgvvvuO9G3b19ha2sr5syZU+h10/jhhx9E/fr1tddk0qRJQgghRo0aJV544QWdfdPT04WHh4dYvXp1nteiPH7OqWCPEx+Lb05+I1p+31JgLrQ3p4VOYtz2ceLovaNCrVbLHSZRqRT0/Z0T03E9EAJITi7789raAkUZEDFo0CBMmTIFhw8fRpcuXQAAz549w549e7Br1y4AQGJiInr37o1PPvkESqUSa9euRd++fRESEoJq1aoVeo7ExET06dMH3bp1w7p16xAaGoqpU6fq7KNWq1G1alX88ccfcHNzw/HjxzFu3Dh4eXnhlVdewYwZM3D9+nXEx8fjp59+AgC4urri4cOHOseJiIhA7969MXLkSKxduxY3btzA2LFjYW1tjblz52r3+/nnnzF9+nScOnUKJ06cwMiRI9G2bVt069Ytz/eQkJCAoKAgLF26FEIIfPnll+jduzdu3boFBwcHqNVq9OrVCwkJCVi3bh38/Pxw7do1mJtLM8ReuHABXbp0wejRo/H111/DwsIChw8fhkqlyvN8+Zk7dy4+/fRTLFmyBBYWFoVeNwBYvnw5pk+fjk8//RS9evVCXFycdgmRMWPGoEOHDnj06BG8vLwAAH/99ReSk5MxePDgYsVG5UtaZhr+uvkX1l5ai123diFTnQlAGgLey78Xhjcejr51+sLawlrmSIlkYPh8rPwpbgtQYqIQUhpUtrfExKK/p379+onRo0drH69cuVJ4e3vnahXKrkGDBmLp0qXaxwW1AK1cuVK4ubnpXJfly5fnarnJadKkSWLgwIHax0FBQaJfv346++RsAXr//fdFnTp1dP5aXbZsmbC3t9e+n44dO4p27drpHKdly5Zi5syZ+caSk0qlEg4ODtoWnr179wozMzMREhKS5/5DhgwRbdu2zfd4RW0BmjZtWqGx5bxu3t7e4oMPPsh3//r164vPPvtM+7hv375i5MiR+e7PFiB5hMeGi0uRl0RoTKiITooWaZlpxT6GWq0Wx8KPiTd2vCGcP3XWae0JWBkgvj75tYhKjDJA9ETyYwsQ5TJs2DCMHTsW3333HZRKJdavX49XX30VZmbSDKqJiYmYO3cudu7ciUePHiEzMxMpKSkIDw8v0vGvX7+Oxo0bw9o66y/JwMDAXPstW7YMP/74I8LDw5GSkoL09PRij+y6fv06AgMDdeYDadu2LRITE/HgwQNti1Xjxo11Xufl5YXHjx8jP1FRUfjf//6H4OBgPH78GCqVCsnJydprcOHCBVStWhW1a+c9OuXChQsYNGhQsd5LXlq0aJFrW0HX7fHjx3j48KG2dS8vY8aMwffff493330XUVFR2L17Nw4dOlTqWKl0VGoVTj44iR03d+Cvm3/h6pOrufZRmivzXMRTs+ino9JR+zg+LR4br2zEnZg72tdXcaiC4Y2HY3iT4ajvUb8s3x6RUWMCpAe2tkCi4QZPFHjeourbty+EENi5cydatmyJf/75B1999ZX2+RkzZmD//v344osv4O/vDxsbG7z88stIT0/XW7y//vorZsyYgS+//BKBgYFwcHDAokWLcOrUKb2dI7ucxcMKhQJqtTqfvYGgoCA8ffoUX3/9NapXrw6lUonAwEDtNbCxsSnwfIU9b2ZmBiF0hwJnZGTk2s/Ozk7ncWHXrbDzAsCIESPw3nvv4cSJEzh+/Dhq1KiB9u3bF/o60r/4tHjsu7MPO27uwK5buxCdHK19zlxhDlcbVySkJyA1MxUAkKZKQ1pyms5+hbGztMPA+gMxovEIdPLtxIU8ifLABEgPFAogx3eW0bG2tsZLL72E9evX4/bt26hTpw6aN2+uff7YsWMYOXIkBgwYAEBqEQoLCyvy8evVq4dffvkFqamp2lagkydP6uxz7NgxtGnTBhMnTtRuu3Pnjs4+VlZWhdbM1KtXD5s3b4YQQtsKdOzYMTg4OKBq1apFjjmnY8eO4bvvvkPv3r0BAPfv30d0dNaXTuPGjfHgwQPcvHkzz1agxo0b4+DBg5g3b16ex/fw8MCjR4+0j+Pj4xEaGprnvjnjKui6OTg4wNfXFwcPHkTnzp3zPIabmxv69++Pn376CSdOnMCoUaMKPS/pT1hsGHaE7MCOmzsQHBaMDHVW4uts7Yxe/r3Qt3Zf9PTvCRcbaWqJDFVGvgt5ahf6zLFNJVTo5d8LA+oOgJ2Vkf9SIpIZEyATMmzYMPTp0wdXr17Fa6+9pvNcrVq1sGXLFvTt2xcKhQIffvhhga0lOQ0dOhQffPABxo4di1mzZiEsLAxffPFFrnOsXbsWe/fuRY0aNfDLL7/gzJkzqFGjhnYfX19f7N27FyEhIXBzc4OTk1Ouc02cOBFLlizBlClTMHnyZISEhGDOnDmYPn26tkuvJGrVqoVffvkFLVq0QHx8PN555x2d1pWOHTuiQ4cOGDhwIBYvXgx/f3/cuHEDCoUCPXv2xKxZs9CoUSNMnDgR48ePh5WVFQ4fPoxBgwbB3d0dzz//PNasWYO+ffvC2dkZs2fP1hZQFxZXYddt7ty5GD9+PDw9PbWF2seOHcOUKVO0+4wZMwZ9+vSBSqVCUFBQia8TFU6lVuFUxClt0pOza6uWay30rd0Xfev0RVuftnnOfWNpbglXG9dSr3lFRHljAmRCnn/+ebi6uiIkJARDhw7VeW7x4sUYPXo02rRpA3d3d8ycORPx8fFFPra9vT127NiB8ePHo1mzZqhfvz4+++wzDBw4ULvPG2+8gfPnz2Pw4MFQKBQYMmQIJk6ciN27d2v3GTt2LIKDg9GiRQskJibi8OHD8PX11TlXlSpVsGvXLrzzzjto0qQJXF1d8frrr+N///tfyS7Mf3744QeMGzcOzZs3h4+PDxYsWIAZM2bo7LN582bMmDEDQ4YMQVJSEvz9/fHpp58CAGrXro19+/bh/fffR6tWrWBjY4PWrVtjyJAhAIBZs2YhNDQUffr0gZOTEz766KMitQAV5boFBQUhNTUVX331FWbMmAF3d3e8/PLLOsfp2rUrvLy80KBBA3h7e5fqWlEWlVqF+/H3cfPpTdx6egtnHp7Brlu78CT5iXYfc4U52lVrp016OMsxkfwUImdRAiE+Ph5OTk6Ii4uDo6OjznOpqakIDQ1FjRo1dAp+iYxdYmIiqlSpgp9++gkvvfRSgfuW58/5ifsncDjsMJytnaWZhrPNOuxm61aiyfiEEHiU+Ai3nt6SEp1nWT9vP7uNdFXuWjknpRN61crq2mJLDpHhFfT9nRNbgIgqOLVajejoaHz55ZdwdnbGiy++KHdIBvEg/gHe3f8uNl7ZWOB+LtYuujMV55EkRSdH6yY6T28hKSMp32NamVvBz8UPtd1qo557PfTw75Fv1xYRGQcmQEQVXHh4OGrUqIGqVatizZo1sLCoWP/tUzNTsfjEYnzyzydIzkiGAgr0q9sPALQLckYnR+NZyjMICMSkxiAmNQY3n94s1nnMFGao4VwDtdxqobZrbemnW23Ucq2Fak7VONKKqJypWL8JiSgXX1/fXMPvKwIhBHbc3IG39r6FuzF3AQBtfNpgaa+laO7VPNf+mepMPEt5prNKefYE6Uly1jZXG1fUcs1KcGq71UYNlxqwMrcq67dJRAbCBIiIyp2Q6BBM2zsNe27vAQB42XthUbdFGNpoqM4EmdlZmFnA084TnnaegEdZRktExogJUAlVxL+oiTSM9fMdnxaPj458hCWnliBTnQkrcytMf2463m//PhyUDnKHR0TlCBOgYtLMLpycnFykGXiJyqPk/1b3zTmbtlzUQo1fLv6CmQdmIiopCgDQp3YfLO6+GLXcaskcHRGVR0yAisnc3BzOzs7aNaVsbW3zbXInKm+EEEhOTsbjx4/h7OxcpIkaDe1MxBlM2T0FpyKkpT9qudbCkp5L0LtWb5kjI6LyTPYEaNmyZVi0aBEiIyPRpEkTLF26FK1atcp3/9jYWHzwwQfYsmULnj17hurVq2PJkiXa5Quy+/TTTzFr1ixMnToVS5Ys0VvMlStXBoACF9YkKs+cnZ21n3O5RCVG4f2D7+PHCz8CAOyt7DG7w2xMfW4qi5GJqNRkTYB+++03TJ8+HStWrEDr1q2xZMkS9OjRAyEhIfD09My1f3p6Orp16wZPT09s2rQJVapUwb179+Ds7Jxr3zNnzmDlypW5VgTXB4VCAS8vL3h6eua5mCVReWZpaSlry09ieiK+P/s95h2Zh/g0aTbyEU1G4NMun8LLwUu2uIioYpE1AVq8eDHGjh2rXZhxxYoV2LlzJ3788Ue89957ufb/8ccf8ezZMxw/flxbm5BzmQRAmvF22LBhWLVqFT7++GODxW9ubm4UXQREFcGFyAtY+e9KrL+8HgnpCQCAAK8ALO21FIE+gTJHR0QVTclXjiyl9PR0nD17Fl27ds0KxswMXbt2xYkTJ/J8zfbt2xEYGIhJkyahUqVKaNiwIRYsWJBr9fBJkybhhRde0Dl2QdLS0hAfH69zIyLDS0pPwo/nf0Tr1a3RbGUzrDi7AgnpCajtVhur+67G6bGnmfwQkUHI1gIUHR0NlUqFSpUq6WyvVKkSbty4kedr7t69i0OHDmHYsGHYtWsXbt++jYkTJyIjIwNz5swBAPz66684d+4czpw5U+RYFi5ciHnz5pX8zRBRsVyOuoyVZ1fil0u/aLu5LM0s8VK9l/BGwBvo5NuJgwuIyKBkL4IuDrVaDU9PT3z//fcwNzdHQEAAIiIisGjRIsyZMwf379/H1KlTsX///mIt4Dhr1ixMnz5d+zg+Ph4+Pj6GeAtEJislIwV/XPsDK8+uxPH7x7Xba7rUxBsBb2Bk05HSJIVERGVAtgTI3d0d5ubmiIqK0tkeFRWV7+gTLy+vXAWa9erVQ2RkpLZL7fHjx2jePGsafJVKhb///hvffvst0tLS8qzZUSqVUCqVenpnRJTd9SfXsfLsSqy9uBYxqTEApFmZ+9XphzcC3kCXml1gppCtN56ITJRsCZCVlRUCAgJw8OBB9O/fH4DUwnPw4EFMnjw5z9e0bdsWGzZsgFqthpmZ9Avz5s2b8PLygpWVFbp06YLLly/rvGbUqFGoW7cuZs6cyYJlojKSlpmGTdc2YeXZlfgn/B/tdl9nX4xtPhajmo7iiC4ikpWsXWDTp09HUFAQWrRogVatWmHJkiVISkrSjgobMWIEqlSpgoULFwIAJkyYgG+//RZTp07FlClTcOvWLSxYsABvvvkmAMDBwQENGzbUOYednR3c3NxybSciwzgdcRrDtgzD7We3AQDmCnP0qd0H41uMR7ea3bhqOhEZBVkToMGDB+PJkyeYPXs2IiMj0bRpU+zZs0dbGB0eHq5t6QEAHx8f7N27F2+99RYaN26MKlWqYOrUqZg5c6Zcb4GI/qNSq/Dp0U8xJ3gOVEKFyvaVMaHFBLze7HVUcawid3hERDoUwlhXPZRRfHw8nJycEBcXB0dHR7nDITJ692Lv4bWtr+Fo+FEAwOAGg7H8heVwsXGROTIiMiXF+f4uV6PAiMj4bLy8EeN3jkd8WjwcrBywrPcyvNb4NQ5jJyKjxgSIiEokLjUOk3dPxrpL6wAAgVUDse6ldajpUlPmyIiICscEiIiK7Wj4Uby25TXci7sHM4UZZneYjQ86fAALM/5KIaLygb+tiKjIMlQZ+Ojvj/DJP59ALdSo4VwD615ahzY+beQOjYioWJgAEVGR3Hl2B8O2DMOpiFMApBXal/ZaCkclBwoQUfnDBIiICiSEwM8Xf8aU3VOQmJ4IJ6UTVvZZicENB8sdGhFRiTEBIqJ8PUt5hvF/jccf1/4AAHSo3gG/DPgF1ZyqyRwZEVHpMAEiojwdDj2MEdtG4EH8A1iYWeCjzh/hnTbvcCZnIqoQmAARkY5MdSbmBs/Fgn8WQECglmstbBi4AS28W8gdGhGR3jABIiKtiPgIDNk8RLuA6ZhmY7Ck5xLYWdnJHBkRkX4xASIiAMCe23swfOtwRCdHw8HKAav6rmKhMxFVWEyAiExcpjoTHx76EJ8e+xQA0KxyM/z28m+o5VZL5siIiAyHCRCRCbsfdx9DNg/BsfvHAACTWk7CF92/gLWFtcyREREZFhMgIhO169YujNg6Ak9TnsJR6YgfXvwBL9d/We6wiIjKBBMgIhOTocrAB4c+wKLjiwAAAV4B+O3l3+Dn6idzZEREZYcJEJEJCY8Lx6ubXsWJBycAAFNaTcGibougtFDKHBkRUdliAkRkInaE7EDQtiDEpMbASemEH/v9iJfqvSR3WEREsmACRFTBpavSMevALCw+uRgA0NK7JX57+TfUcKkhc2RERPJhAkRUgYXFhmHwpsE4HXEaADCt9TR81u0zWJlbyRwZEZG8mAARVVC7b+3G0C1DEZsaC2drZ6zptwb96vaTOywiIqPABIioAkpXpePVza8iPi0erau0xq8v/wpfZ1+5wyIiMhpMgIgqoKuPryI+LR7O1s74e9Tf7PIiIsrBTO4AiEj//n34LwCghXcLJj9ERHlgAkRUAZ15eAaANOKLiIhyYwJEVAFlbwEiIqLcmAARVTCpmam4/PgyACZARET5YQJEVMFcirqETHUmPGw94OPoI3c4RERGiQkQUQWTvftLoVDIHA0RkXFiAkRUwbD+h4iocEyAiCoYJkBERIVjAkRUgSRnJOPqk6sAmAARERWECRBRBXIh8gLUQg0vey94O3jLHQ4RkdFiAkRUgbD7i4ioaJgAEVUgTICIiIqGCRBRBcIEiIioaJgAEVUQCWkJuBF9AwATICKiwjABIqogzj06BwGBak7V4GnnKXc4RERGjQkQUQXB7i8ioqJjAkRUQfz76L8EyIsJEBFRYZgAEVUQbAEiIio6JkBEFUBMSgxuP7sNAAjwDpA5GiIi48cEiKgCOPfoHACgpktNuNq4yhwNEZHxYwJEVAGw+4uIqHiYABFVACyAJiIqHiZARBUAW4CIiIqHCRBRORedHI2w2DAAQHOv5vIGQ0RUTjABIirnzj48CwCo7VYbTtZOMkdDRFQ+MAEiKufY/UVEVHxMgIjKuTMPzwAAWnq3lDkSIqLygwkQUTnHFiAiouJjAkRUjj1KeISIhAiYKczQtHJTucMhIio3mAARlWNnH0kF0PXc68Heyl7maIiIyg8mQETlGLu/iIhKhgkQUTnGBIiIqGSYABGVU0IIJkBERCXEBIionIpIiEBUUhTMFeZoUqmJ3OEQEZUrTICIyilN609Dz4awsbSRORoiovKFCRBROcXuLyKikmMCRFROMQEiIio5JkBE5RALoImISocJEFE5FBYbhqcpT2FlboVGno3kDoeIqNxhAkRUDmlafxpXagylhVLmaIiIyh8mQETlkLb7y4vdX0REJcEEiKgc+vcR63+IiEqDCRBROaMWapx9KC2CygSIiKhkmAARlTN3nt1BXFocrC2sUd+jvtzhEBGVS0yAiMoZTf1P08pNYWluKXM0RETlk+wJ0LJly+Dr6wtra2u0bt0ap0+fLnD/2NhYTJo0CV5eXlAqlahduzZ27dqlfX7hwoVo2bIlHBwc4Onpif79+yMkJMTQb4OozLAAmoio9GRNgH777TdMnz4dc+bMwblz59CkSRP06NEDjx8/znP/9PR0dOvWDWFhYdi0aRNCQkKwatUqVKlSRbvPkSNHMGnSJJw8eRL79+9HRkYGunfvjqSkpLJ6W0QGxQJoIqLSUwghhFwnb926NVq2bIlvv/0WAKBWq+Hj44MpU6bgvffey7X/ihUrsGjRIty4cQOWlkVr+n/y5Ak8PT1x5MgRdOjQoUiviY+Ph5OTE+Li4uDo6Fj0N0RkYCq1Cs6fOSMxPRFXJlxBA88GcodERGQ0ivP9LVsLUHp6Os6ePYuuXbtmBWNmhq5du+LEiRN5vmb79u0IDAzEpEmTUKlSJTRs2BALFiyASqXK9zxxcXEAAFdXV/2+ASIZ3Hx6E4npibC1tEVd97pyh0NEVG5ZyHXi6OhoqFQqVKpUSWd7pUqVcOPGjTxfc/fuXRw6dAjDhg3Drl27cPv2bUycOBEZGRmYM2dOrv3VajWmTZuGtm3bomHDhvnGkpaWhrS0NO3j+Pj4Er4rIsPS1P8092oOczNzmaMhIiq/ZEuASkKtVsPT0xPff/89zM3NERAQgIiICCxatCjPBGjSpEm4cuUKjh49WuBxFy5ciHnz5hkqbCK9OfPwDAAWQBMRlZZsXWDu7u4wNzdHVFSUzvaoqChUrlw5z9d4eXmhdu3aMDfP+su3Xr16iIyMRHp6us6+kydPxl9//YXDhw+jatWqBcYya9YsxMXFaW/3798v4bsiMixNC1DLKi1ljoSIqHyTLQGysrJCQEAADh48qN2mVqtx8OBBBAYG5vmatm3b4vbt21Cr1dptN2/ehJeXF6ysrAAAQghMnjwZW7duxaFDh1CjRo1CY1EqlXB0dNS5ERmbTHUmzkeeB8ARYEREpSXrMPjp06dj1apV+Pnnn3H9+nVMmDABSUlJGDVqFABgxIgRmDVrlnb/CRMm4NmzZ5g6dSpu3ryJnTt3YsGCBZg0aZJ2n0mTJmHdunXYsGEDHBwcEBkZicjISKSkpJT5+yPSp2tPriE1MxWOSkf4u/rLHQ4RUbkmaw3Q4MGD8eTJE8yePRuRkZFo2rQp9uzZoy2MDg8Ph5lZVo7m4+ODvXv34q233kLjxo1RpUoVTJ06FTNnztTus3z5cgBAp06ddM71008/YeTIkQZ/T0SGoun+CvAKgJlC9jlMiYjKNdmLoCdPnozJkyfn+VxwcHCubYGBgTh58mS+x5NxWiMig9LOAM3uLyKTFxoKbN4MPPcc0K6d3NGUT7InQERUNEyAiExbejqwfTuwahWwfz8gBODsDEREALa2ckdX/rAdnagcSFel42LURQBMgIhMzc2bwLvvAj4+wKBBwL59UvJjbQ3ExgKbNskdYfnEBIioHLjy+ArSVelwsXZBDefCRzaasuvXgS+/BH77DThzBoiOlr4siMqT1FRgwwagc2egTh1g0SLg8WPAywv44APg7l3gf/+T9v3+e3ljLa/YBUZUDmTv/lIoFDJHY7wyM4G+fYE7d3S3OzgANWtm3WrUyLpfvbr0lzSRMbh2TeriWrsWePZM2mZmBvTqBYwdC7zwAmDx3zf36NHAnDnAsWPA1atAAy4NWCxMgIjKAdb/FM3vv0vJj5MT0LCh9Ffyo0dAQgJw8aJ0y0mhALy9dROkl18G6tcv+/jJNCUnS5/dVauA48eztvv4AGPGAKNGSfdz8vICXnwR2LpVeu2SJWUWcoXABIjIAIQQuBR1CXtu70EVxyoY1mhYqVpumAAVTq0GFi6U7s+YkdU9kJIChIVJydDdu9LoGc39u3eBpCSpiDQiAvjnH+k1q1YBt26xZYgMKzQU+OILYP164L91u2FuLiU1Y8cC3btLjwsybpyUAP38s/T5t7ExfNwVBRMgIj1JSk/CgbsHsPPWTuy6tQsRCRHa5w7cPYAVfVbA2qL436ipmam4/PgyACZABdm5E7hyReruyjY3KmxsgHr1pFtOQgBPnugmRcuWAQ8eAKtXA/nM0EFUakeOAAMGADEx0uOaNaXWnpEjpZadourWTerGvXdPKoYePtwg4VZITICISuHOszvYeWsndt7aieCwYKSrstaks7W0xXNVn0NwWDB+vvgzrj25hi2Dt6CqY8Fr0+V0MfIiMtWZ8LTzhI9jHu3gBnD/PnDhAtCyJZDP0nxGRYis1p8JEwAXl6K9TqEAPD2lW+vW0jY3N+kYCxYAr7/Ov6hJ/9aulZKdjAygVSvps9a5s1TrU1zm5tKxPvxQKoZmAlR0HAVGVAwZqgwcDj2Mt/e+jbrf1oX/Un9M3TMV++7sQ7oqHTWca2Byy8nYPWw3nr77FAdHHMTe1/bC1cYVZx6eQYvvW+BY+LFinbMsCqCfPZMmVZs4EahdG6hWTWqG9/UF3nhDGoZrzP7+GzhxAlAqgbfeKt2xRo+W3v+jRxxdQ/olBDB7NhAUJCU/gwYBwcFAly4lS340Ro+WEqGjR6UiaioatgARFSIqMQq7b+/Gzls7se/OPsSnxWufszCzQLtq7fBCrRfwQq0XUNe9bq4kpWvNrjgz9gz6/9oflx9fRuefO2Npr6V4o8UbRTr/v4/+S4C89Nf9lZIi/bI8eBA4cAA4d053qLiZmdSsHhoqJQGrVknN9e++m9VSYkwWLJB+jh5d+hYrKyupfmjcOKlVaexYTjJnrISQuitv3AACAwF7e7kjyl9qqvT53LhRejxrFvDxx6VLfDS8vaXRj9u2Sf9Xv/qq9Mc0BQrBtSNyiY+Ph5OTE+Li4rgyvIn78viXeGf/OxDI+m/iaeeJXv698EKtF9DdrzucrJ2KdKyk9CSM+nMU/rj2BwBgXPNxWNp7KazMrQp8XaPljXDl8RVsf3U7+tbpW6L3kZkJnD2blfAcPw6kpenuU7++9Jdo165Ax47SSKqjR4HPPwd27Mjar0MHKRHq1Us/v7xL6+xZoEUL6S/gW7ekIe6llZEhtYSFhUlzCk2fXvpjUukIIdW5nD0rJeyan0+eSM+3bg0cPmycXZbR0UD//tJwdQsLYOVKKRnSp927gd69pe7fiAjjvA5loTjf30yA8sAEiABg7+296LW+FwQEArwCpFae2i+ghXeLEi9GKoTAp0c/xQeHPoCAQBufNtg0aBO8HPKuekxKT4Ljp45QCzUipkfA28G7yOd6+BDYskVKeA4fBuLjdZ+vUkVKdrp0kW7eBRz62jVptMq6dVJyAEhzjrzzDjBkiNRqIpeXX5a67157DfjlF/0d94cfpNoKDw+pJczOTn/HNkZ37gBPn0rFuG5uUn2UXISQCtI1iY4m2dHMi5OdubmUVKSlAa++Kk0eaExTZYWESHP3aKZn2LxZ+v+mbyqV9G8XHi79P3jtNf2fozxgAlRKTIAoPC4czVY2w7OUZ3gj4A2s6LNCr8ffdWsXhm4eiri0OHg7eGPr4K1oVaVVrv2OhR9Du5/awdvBGxHTI/I4Ut4uXgQ6dZKmyddwdpYKLTWtPLVrF/+LIiIC+PprYMUKaW4dQEqk3npL6ioq6/8uN25ILVdCSCPA9DkRXEYGULeu9EX8+edSslfRpKRII4dWrcqaAgCQupLymzjS11c/0wNkZkqfz9hYaSTUnTu6yY5mWHh2lpbS/E4BAdKteXOgcWPg5ElpNFRmJjB3rjQ5oDEIDgZeekl6f76+0khFQ84v9dFHUo1R+/ZSXZwpYgJUSkyATFtaZho6rOmA0xGnEeAVgKOjj5Zo+Hphbj69if6/9sf16OtQmiuxos8KjGw6Umefr09+jWl7p+HFOi/iz1f/LNJxb9yQuqmePJG+LIYOlRKe5s0Ln1OkqOLipCRoyRIgMlLa5uQkjZ56883iDeMtjdGjgZ9+kgq2/yza5SmWNWukSejc3aVWIGOuMSmOS5ekpGfduqwk2cwMqFRJKv4uTPaJIzXJkY+PVOcSE5P3TZPoaG6aBDo/SqWU3DRvnpXsNGwobc+LpsUOkFqBhgwp6tUwjOwjvZ57Tvp8enoa9pwREVLtnkoltdrmNfVDRVes729BucTFxQkAIi4uTu5QSAaTdk4SmAvh8qmLCI0JNei54lLjxIsbXxSYC4G5EG/uelOkZ6Zrn39ty2sCcyHmB88v0vHu3hWiShUhACGaNxciNtZQkUtSU4VYvVqIOnWkcwJCWFkJMWaMECEhhj33vXtCWFhI5zxxwjDnyMgQwt9fOsfChYY5R1lJSJD+rVq3zvq3AoSoXl2Ijz4S4sEDab+UFCGuXxdi1y4hvv1WiOnThRgwQIgmTYRwcNB9rT5u9vZC+PgI8dxzQkycKMQPPwhx/rwQ6ekFvJl8vPOOdEyl0nCficKo1UJ8+GHW+3vlFSGSk8vu/P36SeedNq3szmlMivP9zRagPLAFyHRtuLwBw7YMAwDsHLoTvWv1Nvg51UKN+UfmY96ReQCATr6d8PvLv8PDzgP1ltXDjegb2DV0F3rV6lXgcR4+lJq+796VmtmPHJFaLsqCWi0VSn/2mTQcHZD+Ug8Olv76NYSpU4FvvpG69Q4dMsw5AKmeYsQIwNVVagUqb78Szp6VWns2bMhqdbGwAPr1k7otu3YtesugEFKdUPaJIzX3HzyQRsu5uGTdnJ11H+fc7uwsdWvpi0oldTlt3y61tpw+LbWIlBVDjvQqql27pJojFxfpd4KpzWbOFqBSYguQaboSdUXYfmIrMBfiw0Mflvn5t17fKuwX2AvMhaj2VTVxJOyIUMxVCMyFiEqMKvC1T54IUb++9Jefn58QERFlFHQejh4Von17KRYfHyGio/V/jsePhbCxkc6xf7/+j59dRkZWC9fHHxv2XPoSGyvEd98J0ayZbmuLv78Qn34qRGSk3BEaTkKC1FoFCNGwoRDx8WVz3sePhWjbVjqvhYXUkiWHzEzp/x0gxLp18sQgp+J8fzMBygMTINMTnxov6iytIzAXotvabiJTlSlLHFeirgj/b/wF5kKb/FT7qlqBr4mNlbq7ACGqVhUiNLRsYi1IXJwQtWpJMfXqJYRKpd/jf/CBdOwWLaQuB0Nbv146n4uL4bsVS0qtFuL4cSFGjRLC1la3S3LIECEOHdL/v4OxCg8XonJl6f2/8IKUFBjSjRtC1Kwpnc/JSYgDBwx7vsLMmyfF0qGDvHHIoTjf30YwiweRvIQQeH376wh5GoKqjlWx/qX1MDfTU7VwMTXwbIDTY06jp39P7dxDBa3/lZQkNXefOycN1z5wQBptIjdHR2l0kbW1ND/JZ5/p79jx8cC330r3Z80qmyHPgwdLBaUxMdIoOGNz4wbQowfQpo1UFJ6cLMW7eLFUGLthQ8mXWiiPfHykbjBra2nklSFH8AUHS5Mw3r0r/d87ccIww9yL4/XXpX/rv/+WPhuUD8PnY+UPW4BMy1cnvhKYC2E531KcuC9T5WQOmapM8d7+94TdJ3bi9yu/57lPSooQXbtKf+k5Owtx4UIZB1kEP/wgxWdmJkRwsH6O+dln0jHr1i3bFo1ff836Cz8mpuzOW5D4eCFmzMgqBreyEmLECKkbsixaxozd779ntYStWKHfYyckCDFzZta1f+45IaIK7qkuUy++KMX11ltyR1K22AVWSkyATMfRe0eFxXwLgbkQ35z8Ru5wclGp8/6GT0/PGu1hZyffiJfCqNVCBAVJcVauXPrak+RkISpVko63Zo1eQiwylUqIBg2kc8+eXbbnzkmtluo7vLyyvuD79hXi9m154zJGH30kXR9zc/3Ui6nVQmzcKIS3d9a1f/XVsh3pVRR//SXF5uoq/bFkKpgAlRITINMQlRglvL/0FpgL8eqmV4W6nPzJnJkpxNChWcN9Dx2SO6KCJSZmJQ7PP1+6eozvvpOOU61ayYZJl9Yff0jnd3QU4unTsj+/EFJLn6bIXFPYvHOnPLGUB2q1EMOGZbXeXb9e8mNduiREx45Z175mTSF27NBXpPqVmSnVBAJSDZupYA0QUSEy1Zl4ddOreJjwEPXc62FV31UGW2ldn4SQJhvcsEEayrx5s1TbYczs7IA//pB+HjoEzJtXsuNkZkozMgNSTYc+h08X1UsvSZPzxcdL9TVlKSYGmDJFmhDwn3+kIecLFkgzYPc2/GwN5ZZCAaxeLdVHxcUBffpIQ/mLIzZWmnahWTNpegkbG2nW5atXpeMZI3PzrIkhv/9e3liMVhkkZOUOW4AqvlkHZgnMhbD7xE5ce3xN7nCKRK2WJqXT1NT89pvcERWPZiSVQiHEnj3Ff/0vv0iv9/AQIilJ//EV1ZYtWRP4GWKIf04qlRCrVgnh7q47uV54uOHPXZFERQnh6ytdv44dhUhLK/w1KpUQP/4ofeY0137gQCHCwgwerl7cvy/9rgCkkWqmgF1gpcQEqGLbfmO7dubljZc3yh1Okc2dm/VL+Mcf5Y6mZMaPl+J3d5d+ORdV9vqbBQsMF19RqNVZ8+u8955hz3XqlBAtW2b9u9evL8TBg4Y9Z0V25UrWbNajRxdcKH7mjO6s2XXrGn7OKUPo21eKf/p0/R3z7l2p6LtPH3n/GMkLE6BSYgJUcd15dkc4LXQSmAsxZdcUucMpsi+/zPpF/PXXckdTcikpWclD27ZFr+PZti2r9sYY5uH588+sAvTHj/V//MePhXj99ax/c0dHIb76Sp66p4pm166sVpHPP8/9/JMnQowdK7VUalr6vviiaC1GxmjHDul9uLnppxj68mXd4vs+faTJQo0FE6BSYgKkX9HRQrRrJ8SiRfLGkZyeLJqtaCYwF+K51c+JtMzy8Rtt5cqsXzblZSbigty+LX2hA9IQ7sKo1Vl/ic+aZfj4ikKtFiIgQIrp3Xf1d9yMDCGWLpWmNdD8mwcFCfHokf7OQUJ8801Wd+zWrdK2zEwhli2TJrvUXPvXXhPi4UNZQy21jIysYugNG0p3rOPHsz6bdeoIYW0t3R871nimXWACVEpMgPRrxQrpP4mDg7R4plxe//N1gbkQ7p+7i/DY8lFAsW5d1l+iM2cazy+Z0tq8OetLZtu2gvc9dEjaz9rauOZZ0QwztrUtfVxqtfSXeqNGWdelWTMhjh3TT6ykS62WFl7V/Pv98IMQTZtmXfsmTYT4+2+5o9SfOXOk99WpU8mPsWdP1gzjgYFCPHsm/d/VtKbNnau3cEuFCVApMQHSryFDsn6x7N0rTww/nvtRu7zEvtv75AmimIKDpblLACEmTao4yY/GtGlZkzjevZv/fprJHidNKrvYikKtFqJVKym2t98u+XGOHMlaQ0ozb8vy5YZfvsHUZWQI0a2b7lppzs5SK5AxdenoQ3h46YqhN24UwtJSen3PntLUFhqaP3ABqVhfbkyASokJkP6o1br9xXJ8iZ1/dF5Yf2wtMBfioyMflX0AJaSZu2TQoIq5hlNamlRICUhreuXVOnjmjPS8hYVxjrzZvVuKz8am+N1U585JXyaa/xvW1lJ3mlzzC5mimBipuF6hkLpxDFHPZSz69ClZsv7dd1mt0EOG5F0L9b//ZU02Kfe8SMX5/lYIIYQcw++NWXx8PJycnBAXFwdHR0e5wynXbt4E6tTJelytGhAWVvj6TbGpsTgafhTBYcEIDgvGtSfXoFAooIACZgqzfG8KRe7nnyQ9QUJ6AnrX6o0dQ3bATFE+pr+qXh0IDwf27we6dpU7GsMID5fmVnn2DJg0KWuNL42BA4EtW4ARI4Cff5YnxoIIIc0vc/IkMG0a8NVXhb/m5k3gww+B33+XHltYSPO1fPgh4O1t0HApDykp0hxLFf3a79gBvPgi4OYmrQ+nVBa8vxDAxx8Ds2dLjydNAr75Ju/15ISQ1h/76SdpjqTDh4HWrfX/HoqiON/fTIDywARIf1atAsaNA1q2lCZsS0kBLlwAmjTR3S82NRb/3PtHSnjuBeP8o/PaxUD1wd/VH6fGnIKrjavejmlI4eFSAmRuLk3CZm8vd0SGs2uXtKArAPz6q7TwKABcvw7Ury8ly1evSot7GqP9+4Hu3aUvlLt38/8ivX8fmD9f+pJQqaT3NWSItM3Pr2xjJtOTmSkt1hoRAWzcCLz6av77qtXAW29JCQ8AzJkj3Qr6wzUjA+jXT1r82M0NOH4cqF1br2+hSIrz/W1RRjGRiQoOln726gV4eUkrNG/fDlSrHYO/7/2NI/eOIDgsGBciL+RKeGq51kIn307oWL0jWlZpCUszSwgIqIVa5yZEHtuy7SeEQHOv5lBaFPInjxE5elT62axZxU5+AGkW41mzgIULpZaQZs2kX5yaFeT79zfe5AeQWufatgWOHQM+/TTrS0MjOlp6b8uWAWlp0rY+fYBPPpFmlSYqCxYWUivN/PnSzND5JUAZGcDo0cC6ddLjb76RZiAvjKWl1KrZuTPw779Az55SElS5sv7eg96VpI9tzZo14q+//tI+fuedd4STk5MIDAwUYcbYUV9MrAHSD7U6a8HAP3fHi0kfnZNGXVS/LBRzFdrJCDW32ktri3Hbx4n1l9aLB3EP5A5fVpoJA01lJeeMDCE6dJDec6NG0npNmlW2T5+WO7rCHTwoxWpllTXBY3y8NDJGM/EeIL3Ho0fljZVM1717WcXQISG5n09KEuKFF7Lq7tatK/45oqKE8POTjtG8ufT/oCwZvAi6du3a4uB/05EeP35c2NraipUrV4q+ffuKAQMGlOSQRoUJkH7cuvXffyTLTGEz11Xg7UpZIy6me4k6S+uIN3a8ITZc2iAi4iPkDteoaGY93rxZ7kjKzsOHQnh6Zo2EAqQRYOWBWp2VwL3+uhCLF+suXdGsmVQwXdFG8lH5o0lwcs7BFRMjzdemKcjP1sZRbLdvZy0f0r172U4iafAEyMbGRty7d08IIcS7774rhg8fLoQQ4sqVK8Ld3b0khzQqTID0Y9Wq/74Aqh0RmAvh/42/8KxzV5qB9esYucMzWk+fZn1xGtO8N2Xh4MGsESdA+Vr2IThYd0g1IETt2tKabRVxFB+VT5pZzN3ds0ZePnokzX0ECOHkJMQ//5T+PGfOSDOlA0IMH152yb/BV4O3t7fH0/+W0923bx+6desGALC2tkZKSoo+euaonBNC4JvfLkkPfIMxrvk43Jh0A1NH1AAA/L3fuUziUKulFbO3bCmT0+nFsWPSzzp1AE9PeWMpa88/n7VafGCg8a90n13Hjlmj9apWlQYAXL0KvPJK3iNniOTQu7dUqB8dDWzbBoSGAu3aARcvApUqAX//LT0urRYtgD/+kAZy/PIL8P77pT+m3pUkwxo6dKho3ry5eP3114Wtra2I/m9J5D///FM0aNCgJIc0KmwBKp1MVaaYsGOigMN9aTr5L34U6v/S/0uXsppYs0+mZSjbt2fN9qqPdXDKwjvvSDGPGSN3JPJQq6VZZ8tj61dsrNR1UF4+a2SaPvwwa8ZrzTxtNWpIXVf69tNPWS2iS5fq//g5GbwFaNmyZQgMDMSTJ0+wefNmuLm5AQDOnj2LIUOG6DE9o/ImLTMNr25+Fcv37wESqsLcQoWVE0ZB8d/4yYYNpaGYqanAgQOGj+fHH6WfycnAP/8Y/nz6oBkBpo+/wsojhQLo0aN8tn45OUlD+q2t5Y6EKH9jxkj/zy5eBB49Aho1klqeDTEdw8iR0nxCAPDmm8Dmzfo/R0lxHqA8cB6gkolPi0f/X/vjcNhhmJ8fC9Wf36Ndu9yJx9Sp0tDK118HVq82XDyPHwNVqkjzXwBFn6hOTikp0pdoRgZw5w5Qs6bcERFRRdS3L/DXX9JEnn/9Bbi4GO5cQgATJwIrVkjzZe3fD7Rvb5hzFef7u0QtQHv27MFRzZ+pkFqEmjZtiqFDhyImJqYkh6RyLjIxEh3XdMThsMOwt7JHZ4VUyNGpU+59+/aVfu7YIdXoGMr69VLyo/lrfPduw51LX06dkpIfb2+gRg25oyGiimrVKmDNGikZMWTyA0itTd9+K83plZYmzUh99aphz1kUJUqA3nnnHcTHxwMALl++jLfffhu9e/dGaGgopk+frtcAyfjdeXYHbX9siwuRF+Bp54ngoCMIOesFQCoMzalDB8DRUWqhOX3aMDEJIc24C0gzmFpYACEh0ky9xix791dhy4UQEZVU5cpAUBBga1s25zM3BzZskFqcYmOliRIfPCibc+enRAlQaGgo6tevDwDYvHkz+vTpgwULFmDZsmXYXR7+zCa9Of/oPNr82AZ3Y+6ipktNHBt9DC6pzXH/vjQzaGBg7tdYWUkzQwNSK5AhnDsHXL4sNbe+8Yb0nw4w/lYgTXehoZqHiYjkYmMjrQRQt66U/IwfL288JUqArKyskJycDAA4cOAAunfvDgBwdXXVtgxRxXc49DA6rumIx0mP0aRSExwbfQz+rv7a5S9atQLs7PJ+raYbbPt2w8Smaf0ZMEBq3tUkXMacAGVmSlPHA0yAiKhicnMD9uyRWoC+/17eWEqUALVr1w7Tp0/HRx99hNOnT+OF/1YyvHnzJqpWrarXAMk4bbq2CT3X90RCegI6Vu+IIyOPoLK9tOjLkSPSPnl1f2n06iU1iV65ov9uqdRUqakVAEaNkn727i39PHRIet4YXboEJCZK3YMNG8odDRGRYVSvLv0xmt/CwWWlRAnQt99+CwsLC2zatAnLly9HlSpVAAC7d+9Gz5499RogGZ/lZ5bjlT9eQboqHS/Vewl7XtsDJ2snAFLtjaYFKK8CaA1X16xWDn13g23fDsTESJPRdekibWvUSBoRlpIiTfRljDTdX23bSskhEREZTolWg69WrRr++uuvXNu/MvYxxlQqQgjMDZ6L+X/PBwC8EfAGlvVeBnOzrG/rsDAgPFwqOtbU3eSnb18pWdqxQxoary+a7q+goKxEQqGQmlx/+AHYtQv4r9fWqLD+h4io7JR4gnaVSoXNmzfj448/xscff4ytW7dCpVLpMzYyIiq1ChN2TtAmP3M6zsHyF5brJD9AVvdXy5b51/9oaOqAjhyRRgXoQ0QEsG+fdH/kSN3njLkOSIisBMhUJ0AkIipLJUqAbt++jXr16mHEiBHYsmULtmzZgtdeew0NGjTAnTt39B0jyUwIgdHbR2Pl2ZVQQIHven+HuZ3mamd3zq4o3V8atWoB9epJxb979ugn1rVrpbmF2rcH/P11n+vaVWqZunnT+IbD374tTQtgZSUlj0REZFglSoDefPNN+Pn54f79+zh37hzOnTuH8PBw1KhRA2+++aa+YySZrT63GmsvroWFmQV+e/k3TGg5Id99i5MAAbqTIpaWEFlLX2iKn7NzcpLqawDjawXStP60asVlFIiIykKJEqAjR47g888/h6urq3abm5sbPv30UxzR9IFQhXA56jLe3CMltQueX4BBDQblu29YGHDvnlR3U1j9j8aLL0o/d+2SZkAujWPHpJYUOztgUD5harrBdu0q3bn0jd1fRERlq0QJkFKpREJCQq7tiYmJsLKyKnVQZByS0pPwyqZXkJqZil7+vfB2m7cL3D97/Y+9fdHO8dxzgLu7VAOUbXWVEtEUPw8alP/5NQnQ4cPGNRxe895ZAE1EVDZKlAD16dMH48aNw6lTpyCEgBACJ0+exPjx4/Gi5k96Kvcm756MG9E34O3gjZ/7/wwzRcEfl+J2fwFSa1GfPtL90nSDJSUBv/8u3R89Ov/9sg+HN5bGyshIqeVKoSh6yxkREZVOiRKgb775Bn5+fggMDIS1tTWsra3Rpk0b+Pv7Y8mSJXoOkeSw9uJarLmwBmYKM2x4aQM87DwKfU1RJkDMS/ZZoYUoZqD/2bRJmkTQ37/gbiSFwvhGg2m6vxo1ApydZQ2FiMhklGgeIGdnZ/z555+4ffs2rl+/DgCoV68e/HMOu6Fy6Ub0DUzcOREAMLfjXHT0LTyjuXcPCA2VWnQ0hcZF1b27NPrpzh3g+nXgv2XmikXT/TVyZOGLiPbqBaxeLdUBGUO+zu4vIqKyV+QEqLBV3g8fPqy9v3jx4pJHRLJKyUjB4E2DkZSRhOdrPI/3279fpNdpWn9atAAcHIp3Tnt7acbm3bulbrDiJkB370rnVyiAESMK318zHP7WLSnp8vMr3vn0jRMgEhGVvSInQOfPny/SfnnNDUPlx/S903Ep6hI87TyxbsC6XBMd5qek3V8afftKCdD27cDMmcV77Zo10s9u3QAfn8L3d3SUusmCg6VzTp5c3Gj1Jz4euHhRus8RYEREZafICVD2Fh6qmH6/+jtWnF0BBRRYN2AdvBy8ivzakhRAZ9e3LzBxInDihDQhoKdn0V6nVgM//yzdz2vun/z06iXFvGuXvAnQiRPSe6hRQyrOJiKislHipTCoYrnz7A7G7hgLAHiv3Xvo5tetyK+9f1/qhipJ/Y9G1apA8+ZSEXRx5ug5dEhae8zZGejfv+ivyz4cPiWlOJHqF7u/iIjkwQSIkJaZhsGbBiM+LR5tfdpifuf5xXq9pvureXOpe6mkso8GKypN8fOQIcWbQblhQynpSk2Vdzg8EyAiInkwASK8d+A9nH10Fq42rtg4cCMszIo3OLC03V8amimk9u4t2iSFsbHAli3S/eJ0fwHGMRw+LQ04fVq6z/ofIqKyxQTIxG0P2Y4lp5YAANb0WwMfpyJUEeegrwSoWTOpDiY5WeqaKsyvv0qJUsOG0uiz4pJ7WYyzZ6X4PTyAOnXkiYGIyFQxATJh4XHhGLltJADgrefeQt86fYt9jAcPpKHkZmalb8VQKIrXDabp/ho1qvC5f/LSpYs0HP72belW1rKv/8XBk0REZYsJkInKUGVgyOYhiEmNQQvvFvi066clOo6+6n80NN1gO3YUPCv0tWtS95GFBfDaayU7l6NjVu2NHN1gmgkQ2f1FRFT2mACZqNmHZ+P4/eNwVDrit5d/g5V5yRax1Vf3l0bnztJq7hERQEFTT2laf154oehD5vMiVx2QWi2tXg+wAJqISA5MgEzQ3tt78ekxqcVndd/VqOlSs8TH0iRAJZ0AMSdra2lpDCD/brCMDOCXX6T7xS1+zkmu4fBXrwIxMVKy16xZ2Z2XiIgkTIBMzMOEhxi+dTgAYEKLCRjUYFCJjxURIdXO6KP+JztNN1h+CdCePUBUlNTy07t36c7VoIE0e3RqalYyVxY09T/PPSd14xERUdliAmRCVGoVhm0ZhifJT9C4UmMs7lG6Nds09T/Nmul3FfMXXpCKgs+fl4qsc9J0f732GmBpWbpzyTUcngugEhHJS/YEaNmyZfD19YW1tTVat26N05qJUfIRGxuLSZMmwcvLC0qlErVr18auHOOYi3tMU/Hx3x8jOCwYdpZ2+P3l32FtUYyZA/Og7+4vDQ8PIDBQur9jh+5zT55kbStt95dGWQ+HF4ITIBIRyU3WBOi3337D9OnTMWfOHJw7dw5NmjRBjx498Pjx4zz3T09PR7du3RAWFoZNmzYhJCQEq1atQpVsiygV95im4kjYEcz/W5rheUWfFajjXvqJZzQtQPoqgM4uv26w9euBzExp3p+GDfVzri5dpJakO3ekFeIN7d49qWXLwgJo3drw5yMiojwIGbVq1UpMmjRJ+1ilUglvb2+xcOHCPPdfvny5qFmzpkhPT9fbMfMSFxcnAIi4uLgiv8aYRSdFiypfVhGYCzFy20i9HDMiQghACIVCiJgYvRxSx7Vr0vGtrIRISJC2qdVCNG4sbV+2TL/ne/556bhff63f4+bll1+kc7VqZfhzERGZkuJ8f8vWApSeno6zZ8+ia9eu2m1mZmbo2rUrTpw4kedrtm/fjsDAQEyaNAmVKlVCw4YNsWDBAqhUqhIfEwDS0tIQHx+vc6sohBAYs2MMIhIiUNutNpb2WqqX42paf5o21W/9j0bduoC/P5CeDuzbJ207fx64dAlQKqW1v/SpLOuA2P1FRCQ/2RKg6OhoqFQqVKpUSWd7pUqVEBkZmedr7t69i02bNkGlUmHXrl348MMP8eWXX+Ljjz8u8TEBYOHChXByctLefHyKvxyEsVp5diW23dgGSzNLbBy4EfZW9no5riG7v4C8Z4X+8Ufp54ABgIuLfs+XfTh8crJ+j51T9hmgiYhIHrIXQReHWq2Gp6cnvv/+ewQEBGDw4MH44IMPsGLFilIdd9asWYiLi9Pe7t+/r6eI5XXtyTW8tfctAMDCLgvR3Ku53o6t7wkQ86KpA9q5U0pKNmyQHuur+Dm7+vWl4fBpaYYdDh8dDVy/Lt1nAkREJB/ZEiB3d3eYm5sjKipKZ3tUVBQqV66c52u8vLxQu3ZtmJuba7fVq1cPkZGRSE9PL9ExAUCpVMLR0VHnVt6lZqbi1U2vIjUzFd39uuOtwLf0duxHj4CQEKmVxpDdOG3bSi090dHArFnSxIFVq0pFy/qmUGTNKWTIbjDN7M/16gHu7oY7DxERFUy2BMjKygoBAQE4ePCgdptarcbBgwcRqBkDnUPbtm1x+/ZtqNVq7babN2/Cy8sLVlZWJTpmRfXu/ndx+fFleNp54uf+P8NMob9/6r//ln42aaL/rqjsLC2zuqa++Ub6GRQEZMt/9Sr7cPiC1iErDdb/EBEZB1m7wKZPn45Vq1bh559/xvXr1zFhwgQkJSVh1H99HCNGjMCsWbO0+0+YMAHPnj3D1KlTcfPmTezcuRMLFizApEmTinxMU/DXzb+w9LRU7Lym3xpUts+/9askyqL7S0PTDaYxcqThzvX881LSdfeu4YbDcwFUIiLjIOsk/IMHD8aTJ08we/ZsREZGomnTptizZ4+2iDk8PBxmZlk5mo+PD/bu3Yu33noLjRs3RpUqVTB16lTMnDmzyMes6B4lPMKoP6Vkb1rraehVq5fez2GoCRDz0rOnNF9OZqbUauLvb7hzOThI5zh0SOoGq11bv8dPSgLOnpXuswWIiEheCiEM1dhffsXHx8PJyQlxcXHlqh5ILdTosa4HDtw9gCaVmuDUmFNQWij1eo6oKKByZalmJjoacHXV6+Hz1KePVAj9yy/S8heG9OWXwIwZQI8e0ppj+nTokFS/VLUqEB4uXUMiItKf4nx/l6tRYFSwL45/gQN3D8DGwgYbB27Ue/IDZA1/b9y4bJIfQFr7a/duYNgww59LUwcUHKz/4fDZu7+Y/BARyYsJUAVxJuIMPjj0AQDg655fo55HPYOcpyy7vzQ8PKSusLJIGurVA6pVk4bDHz6s32OzAJqIyHgwASpHjhyRuoFySkhLwJDNQ5CpzsTAegMxpvkYg8YAlE0BtBwMNRw+MxPQTEbOBIiISH5MgMqJzExpBuQRI4CrV3Wfm7J7Cu7E3IGPow9W9V0FhYGaSh4/Bq5dk+536GCQUxiF7Mti6KtC7sIFqQja2Rlo0EA/xyQiopJjAlROXLokTQQISGtiaWy8vBE/X5Tm+Vn/0nq42BhuYh5N60+jRoCbm8FOI7vnnwesrKTh8Ddv6ueYmu6vtm0BM/6vIyKSHX8VlxOaL1AAuHxZ+hkaE4rxO8cDAP7X/n9oX92wfSsVvftLw94+q5tKX91grP8hIjIuTIDKCc0IIgC4cgXIUGVg6JahiE+LR1uftviw44cGj6EsJ0CUmz7rgITgBIhERMaGCVA5IETuFqB5R+bh5IOTcFI6Yf1L62FhZtg5LR8/zqo9qsj1PxqaOqAjR6TandK4eRN48gRQKoEWLUofGxERlR4ToHLg9m1pAkJLS+nx/fvAJ/uWAQC+7/s9qjtXN3gMmvW/GjY0jUU869YFqlfXz3B4TfLaurWUBBERkfyYAJUDmi/Q554DvKuopAdP6mN009F4pcErZRKDqdT/aCgUuqPBSkPz78fuLyIi48EEqBzQ1I+0bSuQ6X4RAOCZ1BVf9/q6zGLQtACV5QSIcsteB1Sa4fCafz8WQBMRGQ8mQOWApgUhsfJuPLY7AADoaD0Z9lb2ZXL+1NSs+X9atSqTUxoFzXD40FCpBUylKv4xHj6UhtObmQFt2ug/RiIiKhlZV4OnwkVGSjVACgXwR8JbQCUpA4kK9SizGK5elSZidHUFfHzK7LSys7OTCr4PHAA6d5YeN2sGNG8OBARIP+vWlVarz48meW3SBChH6+oSEVV4TICMnKb7pGEjFS6rbgKetgCkkWBClM36WBcuSD+bNjW9RTznz5dawM6dk0aDHT2qOyWBjY2U3AQEZCVF9etnFaxz+DsRkXFiAmTkNC0IDVrE4DIAt2pPEGsuzQr96BHg7W34GDQzTzdrZvhzGZvAQOnfQKUCQkKkROjsWel2/jyQmAicPCndNJRKoHFjKSHas0faxvofIiLjwgTIyGkSoKoN7wLxQE0PbyTUAm7ckFqByiIByt4CZKrMzaWWnfr1gddek7ap1cCtW7pJ0blzQHw8cOaMdNNgCxARkXFhAmTE4uOBi9KgL9j6nQfOAzVcakDdMCsB6tHDsDGo1VkxmGILUEHMzIA6daTbkCHSNrVaKnrWJEUXLkjz/3h5yRoqERHlwATIiJ04IX2h1qwJPLO4AgCo6VwTNo2ATZukJTEM7fZtqZvH2lr6oqeCmZkB/v7S7ZWymaKJiIhKgAmQEcu+gObd2LsApBYgj0bSds2iqIak6f5q1Kjg0U5ERETlCecBMmLZRxCFxoQCAGo410DDhtL2a9dKNjdNcZhyATQREVVcTICMVFoacOqUdL9dO4HQWCkBqulSEzVrSsOvU1OBO3cMGwcLoImIqCJiAmSkzp6VEhwPD8DROxKpmakwU5ihmlM17YgkwPDdYGwBIiKiiogJkJHK3v0VFie1/vg4+sDSXJphr1EZ1AFFRkqr0CsUWecjIiKqCJgAGansBdDa+h+XGtrnNQmJIUeCaVp/6tSRloEgIiKqKJgAGSG1Gjh2TLrfvj1wN+a/EWDOWQmQphDakC1ArP8hIqKKigmQEbp6VVrqws5OSj6yF0BraFqAbt8GUlIMEwfrf4iIqKJiAmSENPU/gYHS3DuaBCh7C1DlyoCbm9RadP26YeJgCxAREVVUTICMUPb6HyBbF1i2GiCFwrDdYAkJ0jpXABMgIiKqeJgAGRkhshKgdu2ADFUGHsQ/AKDbBQYYdiTYpUvST29vwNNT/8cnIiKSExMgIxMeDjx4IHV9PfccEB4XDrVQw8bCBpXsKunsa8iRYKz/ISKiiowJkJHRtP4EBAC2tlndX77OvlAoFDr7GrILjAkQERFVZEyAjEz27i8g7xFgGpoE6OFD4Nkz/cbBAmgiIqrImAAZGc0IME0BdPZFUHNydASqV5fu67MbLCMj63hsASIiooqICZARefpUWuEdANq2lX7ejc09Aiw7Q3SDXb8OpKdLCZavr/6OS0REZCyYABkRTetPvXqAu7t0X9MClFcXGGCYQmhN/U/TpoAZPyFERFQB8evNiOTs/gLyngQxO0MMhWf9DxERVXRMgIxIzgkQE9ISEJ0cDaDwLrArV6Q5hPSBI8CIiKiiYwJkJJKSgLNnpfs5R4C52bjBUemY5+vq1pXmDIqLk+YPKi0h2AJEREQVHxMgI3H6NJCZCVStmjWySzsCLJ/WHwCwsgJq15bu66MbLCxMSqYsLYH69Ut/PCIiImPEBMhIZO/+0sx3qF0DLJ/6Hw191gFpWn8aNJCSKyIiooqICZCRyDkBIlDwJIjZ6XMkGOt/iIjIFDABMgKZmcCJE9L97CPAitoCpM+5gDQtQEyAiIioImMCZAQuXJCKoJ2dpa4nDe0Q+AJqgICsFqDr16VZnEsj+xxAREREFRUTICOg6f5q2zZr4kEhRKGTIGr4+gJ2dtLszbdvlzyO6OiskWRNmpT8OERERMaOCZARyDn/DwBEJUUhJTMFCihQzalaga83M8tqOSpNN5im+8vPT1oGg4iIqKJiAiQzIfKZAfq/1p+qjlVhZV74cCx9FEKzAJqIiEwFEyCZ3bwJPHkCKJVAQEDW9qKOANPQx1B4ToBIRESmggmQzDTdX61bS0mQhnYEWCEF0Br6GAnGFiAiIjIVTIBkllf3F5BtFuhChsBraFqA7t6VRpQVV3IyEBIi3WcLEBERVXRMgGSW1wSIQPG7wDw9pZsQwLVrxY/j8mVArZaO4eVV/NcTERGVJ0yAZPTwodRiY2YGtGmj+1xRJ0HMrjTdYNnrfzRLcRAREVVUTIBkpOn+atJEd9h5hioD9+PvAyh6CxBQupFgrP8hIiJTwgRIRvl1f92Pvw+1UMPawhqV7SsX+XilGQnGJTCIiMiUMAGSUV4TIAJZ3V++zr5QFKM/qqRdYCoVcOmSdJ8F0EREZAqYAMkkLi4r6chVAF3EJTBy0swGHRUlzS1UVDdvAikp0nIa/v7FOiUREVG5xARIJsePSyO2/Pxyj7rSLoJajAJoALC3B2r895Li1AFp6n8aNwbMzYt1SiIionKJCZBM8uv+Ako2AkyjJHVArP8hIiJTwwRIJvlNgAgUfw6g7EoyEkzTAsT6HyIiMhVMgGSQlgacPi3dz1n/A2SbBbqIy2BkV9xCaCE4BJ6IiEwPEyAZnDkjJUGenkCtWrrPJaYn4kmyVMFcmi6wK1ek5KYwERHA06dS7Y8meSIiIqromADJIHv9T85R7prWH1cbVzhZOxX72LVrA5aWQGIicO9e4ftrWn/q1QOsrYt9OiIionKJCZAMNPU/eXZ/lXAEmIalJVC3rnS/KN1gLIAmIiJTxASojKlUwLFj0v0CR4CVoP5HoziF0CyAJiIiU8QEqIxduSJNgmhvL60BlpN2EkTn4o8A0yjOUHi2ABERkSliAlTGNN1fbdoAFha5n78bW/oWoKKOBIuNBUKlfCvPZIyIiKiiYgJUxvJbAFVDOwS+hDVAQFYL0I0bQHp6/vtdvCj9rF4dcHUt8emIiIjKHSZAZUiIgmeAFkKUahJEjWrVAAcHIDNTWucrP6z/ISIiU8UEqAyFhQEPH0ojtVq1yv3846THSM5IhgIKVHOqVuLzKBRF6wZj/Q8REZkq2ROgZcuWwdfXF9bW1mjdujVOa6ZIzsOaNWugUCh0btY5Jq9JTEzE5MmTUbVqVdjY2KB+/fpYsWKFod9GkWhafwICAFvb3M9rWn+qOFaB0kJZqnMVZSQYW4CIiMhU5VGGW3Z+++03TJ8+HStWrEDr1q2xZMkS9OjRAyEhIfD09MzzNY6OjggJCdE+VuSYSXD69Ok4dOgQ1q1bB19fX+zbtw8TJ06Et7c3XnzxRYO+n8JokpG8ur+AbCPAStH9pVHYSLC0NODaNek+W4CIiMjUyNoCtHjxYowdOxajRo3SttTY2trixx9/zPc1CoUClStX1t4qVaqk8/zx48cRFBSETp06wdfXF+PGjUOTJk0KbFkqK59/DoSHA1On5v18aVaBz6mwLrCrV6UaIRcXwMen1KcjIiIqV2RLgNLT03H27Fl07do1KxgzM3Tt2hUnTpzI93WJiYmoXr06fHx80K9fP1y9elXn+TZt2mD79u2IiIiAEAKHDx/GzZs30b1793yPmZaWhvj4eJ2bofj4AFWq5P1caWeBzk6TAIWFAQkJuZ/PvgBqzuU4iIiIKjrZEqDo6GioVKpcLTiVKlVCZGRknq+pU6cOfvzxR/z5559Yt24d1Go12rRpgwcPHmj3Wbp0KerXr4+qVavCysoKPXv2xLJly9ChQ4d8Y1m4cCGcnJy0Nx+ZmkT0MQJMw90dqFxZup8jRwTAAmgiIjJtshdBF0dgYCBGjBiBpk2bomPHjtiyZQs8PDywcuVK7T5Lly7FyZMnsX37dpw9exZffvklJk2ahAMHDuR73FmzZiEuLk57u3//flm8nVz0sQxGdgUVQrMAmoiITJlsRdDu7u4wNzdHVFSUzvaoqChU1jRdFMLS0hLNmjXD7du3AQApKSl4//33sXXrVrzwwgsAgMaNG+PChQv44osvdLrbslMqlVAqSzfqqrQy1Zm4HyclXvroAgOkBGj//tx1QGp11iSIbAEiIiJTJFsLkJWVFQICAnDw4EHtNrVajYMHDyIwMLBIx1CpVLh8+TK8vLwAABkZGcjIyICZme7bMjc3h1qt1l/wBnA/7j5UQgWluRJeDl56OWZ+hdB37gCJiYC1NVCnjl5ORUREVK7IOgx++vTpCAoKQosWLdCqVSssWbIESUlJGDVqFABgxIgRqFKlChYuXAgAmD9/Pp577jn4+/sjNjYWixYtwr179zBmzBgA0hD5jh074p133oGNjQ2qV6+OI0eOYO3atVi8eLFs77MoNN1fvs6+MFPoJy/NrwtMU//TqFHe65ERERFVdLJ+/Q0ePBhPnjzB7NmzERkZiaZNm2LPnj3awujw8HCd1pyYmBiMHTsWkZGRcHFxQUBAAI4fP4769etr9/n1118xa9YsDBs2DM+ePUP16tXxySefYPz48WX+/opDnwXQGvXrSyO8njwBoqIATb0563+IiMjUyf73/+TJkzF58uQ8nwsODtZ5/NVXX+Grr74q8HiVK1fGTz/9pK/wyow+FkHNydYW8PMDbt+WusE0CRBHgBERkakrV6PAKrK7sfodAaaRVzcYW4CIiMjUMQEyEvpcBiO7nEtiREZKN4UCaNxYr6ciIiIqN5gAGQl9zgKdXc6RYJrur9q1ATs7vZ6KiIio3GACZAQS0xPxOOkxAMN1gV29Ks3/w/ofIiIiJkBGISw2DADgYu0CZ2tnvR7b3x9QKoHkZCA0VHcNMCIiIlPFBMgIaEeA6bn1B5Dm+alXT7p/+TILoImIiAAmQEZBuwaYnut/NDTdYCdOSEPiASZARERk2pgAGQFDTIKYnSYB2rgREALw9gY8PQ1yKiIionKBCZARMHQLkGYkmGaRe9b/EBGRqWMCZAS0Q+ANUAMEZLUAabD7i4iITB0TIJkJIQw2CaJGlSqAk1PWY7YAERGRqWMCJLMnyU+QlJEEBRSo7lTdIOdQKHRbgdgCREREpo4JkMw0rT/eDt5QWigNdh5NAuToCNQwTE8bERFRucEESGaGHgGm0aSJ9LNZM8CM/+pERGTiLOQOwNRpR4AZqABaIygIuHsXGDzYoKchIiIqF5gAyUw7C7SBhsBrWFsDn31m0FMQERGVG+wMkVlZdYERERFRFiZAMjP0JIhERESUGxMgGWWqMxEeFw7A8DVARERElIUJkIwexD+ASqhgZW4FbwdvucMhIiIyGUyAZKTp/vJ19oWZgv8UREREZYXfujIqqxFgREREpIsJkIw4AoyIiEgeTIBkxBFgRERE8mACJCNNCxBHgBEREZUtJkAy0tQAsQuMiIiobDEBkklSehKikqIAsAuMiIiorDEBkklYbBgAwNnaGS42LvIGQ0REZGKYAMlEW//D1h8iIqIyxwRIJtoRYCyAJiIiKnNMgGSiLYB2ZgE0ERFRWWMCJBMOgSciIpIPEyCZcBJEIiIi+TABkoEQgstgEBERyYgJkAyik6ORmJ4IAKjuXF3maIiIiEwPEyAZaFp/vB28YW1hLXM0REREpocJkAy4BAYREZG8mADJgAXQRERE8mICJAPOAk1ERCQvJkAy4AgwIiIieTEBkgGXwSAiIpIXE6AyplKrEB4XDoBdYERERHJhAlTGHsQ/QKY6E1bmVvB28JY7HCIiIpPEBKiMabq/qjtVh7mZuczREBERmSYmQGWMi6ASERHJjwlQGdNOgujMEWBERERyYQJUxu7GcgQYERGR3JgAlTFNCxBHgBEREcmHCVAZ4ySIRERE8mMCVIaSM5IRmRgJgF1gREREcmICVIbCYsMAAI5KR7hYu8gbDBERkQljAlSGtCPAXGpCoVDIHA0REZHpYgJUhrRrgLEAmoiISFZMgMpQUkYSbCxsWABNREQkM4UQQsgdhLGJj4+Hk5MT4uLi4OjoqNdjCyGQpkqDtYW1Xo9LRERk6orz/c0WoDKmUCiY/BAREcmMCRARERGZHCZAREREZHKYABEREZHJYQJEREREJocJEBEREZkcJkBERERkcpgAERERkclhAkREREQmhwkQERERmRwmQERERGRymAARERGRyWECRERERCaHCRARERGZHAu5AzBGQggAQHx8vMyREBERUVFpvrc13+MFYQKUh4SEBACAj4+PzJEQERFRcSUkJMDJyanAfRSiKGmSiVGr1Xj48CEcHBygUCh0nouPj4ePjw/u378PR0dHmSIsf3jdSobXrfh4zUqG161keN2Kz5DXTAiBhIQEeHt7w8ys4CoftgDlwczMDFWrVi1wH0dHR37YS4DXrWR43YqP16xkeN1Khtet+Ax1zQpr+dFgETQRERGZHCZAREREZHKYABWTUqnEnDlzoFQq5Q6lXOF1Kxlet+LjNSsZXreS4XUrPmO5ZiyCJiIiIpPDFiAiIiIyOUyAiIiIyOQwASIiIiKTwwSIiIiITA4ToGJatmwZfH19YW1tjdatW+P06dNyh2S05s6dC4VCoXOrW7eu3GEZnb///ht9+/aFt7c3FAoFtm3bpvO8EAKzZ8+Gl5cXbGxs0LVrV9y6dUueYI1IYddt5MiRuT5/PXv2lCdYI7Fw4UK0bNkSDg4O8PT0RP/+/RESEqKzT2pqKiZNmgQ3NzfY29tj4MCBiIqKkili41CU69apU6dcn7fx48fLFLFxWL58ORo3bqyd8DAwMBC7d+/WPi/3Z40JUDH89ttvmD59OubMmYNz586hSZMm6NGjBx4/fix3aEarQYMGePTokfZ29OhRuUMyOklJSWjSpAmWLVuW5/Off/45vvnmG6xYsQKnTp2CnZ0devTogdTU1DKO1LgUdt0AoGfPnjqfv40bN5ZhhMbnyJEjmDRpEk6ePIn9+/cjIyMD3bt3R1JSknaft956Czt27MAff/yBI0eO4OHDh3jppZdkjFp+RbluADB27Fidz9vnn38uU8TGoWrVqvj0009x9uxZ/Pvvv3j++efRr18/XL16FYARfNYEFVmrVq3EpEmTtI9VKpXw9vYWCxculDEq4zVnzhzRpEkTucMoVwCIrVu3ah+r1WpRuXJlsWjRIu222NhYoVQqxcaNG2WI0DjlvG5CCBEUFCT69esnSzzlxePHjwUAceTIESGE9NmytLQUf/zxh3af69evCwDixIkTcoVpdHJeNyGE6Nixo5g6dap8QZUTLi4uYvXq1UbxWWMLUBGlp6fj7Nmz6Nq1q3abmZkZunbtihMnTsgYmXG7desWvL29UbNmTQwbNgzh4eFyh1SuhIaGIjIyUudz5+TkhNatW/NzVwTBwcHw9PREnTp1MGHCBDx9+lTukIxKXFwcAMDV1RUAcPbsWWRkZOh83urWrYtq1arx85ZNzuumsX79eri7u6Nhw4aYNWsWkpOT5QjPKKlUKvz6669ISkpCYGCgUXzWuBhqEUVHR0OlUqFSpUo62ytVqoQbN27IFJVxa926NdasWYM6derg0aNHmDdvHtq3b48rV67AwcFB7vDKhcjISADI83OneY7y1rNnT7z00kuoUaMG7ty5g/fffx+9evXCiRMnYG5uLnd4slOr1Zg2bRratm2Lhg0bApA+b1ZWVnB2dtbZl5+3LHldNwAYOnQoqlevDm9vb1y6dAkzZ85ESEgItmzZImO08rt8+TICAwORmpoKe3t7bN26FfXr18eFCxdk/6wxASKD6dWrl/Z+48aN0bp1a1SvXh2///47Xn/9dRkjI1Pw6quvau83atQIjRs3hp+fH4KDg9GlSxcZIzMOkyZNwpUrV1iXV0z5Xbdx48Zp7zdq1AheXl7o0qUL7ty5Az8/v7IO02jUqVMHFy5cQFxcHDZt2oSgoCAcOXJE7rAAsAi6yNzd3WFubp6rQj0qKgqVK1eWKaryxdnZGbVr18bt27flDqXc0Hy2+LkrvZo1a8Ld3Z2fPwCTJ0/GX3/9hcOHD6Nq1ara7ZUrV0Z6ejpiY2N19ufnTZLfdctL69atAcDkP29WVlbw9/dHQEAAFi5ciCZNmuDrr782is8aE6AisrKyQkBAAA4ePKjdplarcfDgQQQGBsoYWfmRmJiIO3fuwMvLS+5Qyo0aNWqgcuXKOp+7+Ph4nDp1ip+7Ynrw4AGePn1q0p8/IQQmT56MrVu34tChQ6hRo4bO8wEBAbC0tNT5vIWEhCA8PNykP2+FXbe8XLhwAQBM+vOWF7VajbS0NOP4rJVJqXUF8euvvwqlUinWrFkjrl27JsaNGyecnZ1FZGSk3KEZpbffflsEBweL0NBQcezYMdG1a1fh7u4uHj9+LHdoRiUhIUGcP39enD9/XgAQixcvFufPnxf37t0TQgjx6aefCmdnZ/Hnn3+KS5cuiX79+okaNWqIlJQUmSOXV0HXLSEhQcyYMUOcOHFChIaGigMHDojmzZuLWrVqidTUVLlDl82ECROEk5OTCA4OFo8ePdLekpOTtfuMHz9eVKtWTRw6dEj8+++/IjAwUAQGBsoYtfwKu263b98W8+fPF//++68IDQ0Vf/75p6hZs6bo0KGDzJHL67333hNHjhwRoaGh4tKlS+K9994TCoVC7Nu3Twgh/2eNCVAxLV26VFSrVk1YWVmJVq1aiZMnT8odktEaPHiw8PLyElZWVqJKlSpi8ODB4vbt23KHZXQOHz4sAOS6BQUFCSGkofAffvihqFSpklAqlaJLly4iJCRE3qCNQEHXLTk5WXTv3l14eHgIS0tLUb16dTF27FiT/2Mlr+sFQPz000/afVJSUsTEiROFi4uLsLW1FQMGDBCPHj2SL2gjUNh1Cw8PFx06dBCurq5CqVQKf39/8c4774i4uDh5A5fZ6NGjRfXq1YWVlZXw8PAQXbp00SY/Qsj/WVMIIUTZtDURERERGQfWABEREZHJYQJEREREJocJEBEREZkcJkBERERkcpgAERERkclhAkREREQmhwkQERERmRwmQERE+VAoFNi2bZvcYRCRATABIiKjNHLkSCgUily3nj17yh0aEVUAFnIHQESUn549e+Knn37S2aZUKmWKhogqErYAEZHRUiqVqFy5ss7NxcUFgNQ9tXz5cvTq1Qs2NjaoWbMmNm3apPP6y5cv4/nnn4eNjQ3c3Nwwbtw4JCYm6uzz448/okGDBlAqlfDy8sLkyZN1no+OjsaAAQNga2uLWrVqYfv27drnYmJiMGzYMHh4eMDGxga1atXKlbARkXFiAkRE5daHH36IgQMH4uLFixg2bBheffVVXL9+HQCQlJSEHj16wMXFBWfOnMEff/yBAwcO6CQ4y5cvx6RJkzBu3DhcvnwZ27dvh7+/v8455s2bh1deeQWXLl1C7969MWzYMDx79kx7/mvXrmH37t24fv06li9fDnd397K7AERUcmW27CoRUTEEBQUJc3NzYWdnp3P75JNPhBDSCt3jx4/XeU3r1q3FhAkThBBCfP/998LFxUUkJiZqn9+5c6cwMzPTrgrv7e0tPvjgg3xjACD+97//aR8nJiYKAGL37t1CCCH69u0rRo0apZ83TERlijVARGS0OnfujOXLl+tsc3V11d4PDAzUeS4wMBAXLlwAAFy/fh1NmjSBnZ2d9vm2bdtCrVYjJCQECoUCDx8+RJcuXQqMoXHjxtr7dnZ2cHR0xOPHjwEAEyZMwMCBA3Hu3Dl0794d/fv3R5s2bUr0XomobDEBIiKjZWdnl6tLSl9sbGyKtJ+lpaXOY4VCAbVaDQDo1asX7t27h127dmH//v3o0qULJk2ahC+++ELv8RKRfrEGiIjKrZMnT+Z6XK9ePQBAvXr1cPHiRSQlJWmfP3bsGMzMzFCnTh04ODjA19cXBw8eLFUMHh4eCAoKwrp167BkyRJ8//33pToeEZUNtgARkdFKS0tDZGSkzjYLCwttofEff/yBFi1aoF27dli/fj1Onz6NH374AQAwbNgwzJkzB0FBQZg7dy6ePHmCKVOmYPjw4ahUqRIAYO7cuRg/fjw8PT3Rq1cvJCQk4NixY5gyZUqR4ps9ezYCAgLQoEEDpKWl4a+//tImYERk3JgAEZHR2rNnD7y8vHS21alTBzdu3AAgjdD69ddfMXHiRHh5eWHjxo2oX78+AMDW1hZ79+7F1KlT0bJlS9ja2mLgwIFYvHix9lhBQUFITU3FV199hRkzZsDd3R0vv/xykeOzsrLCrFmzEBYWBhsbG7Rv3x6//vqrHt45ERmaQggh5A6CiKi4FAoFtm7div79+8sdChGVQ6wBIiIiIpPDBIiIiIhMDmuAiKhcYu89EZUGW4CIiIjI5DABIiIiIpPDBIiIiIhMDhMgIiIiMjlMgIiIiMjkMAEiIiIik8MEiIiIiEwOEyAiIiIyOUyAiIiIyOT8H2MMm7TOuVhoAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(epochs, train_acc, 'g', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='validation accuracy')\n",
        "plt.title('Training and Validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkpiDqzyAUD_"
      },
      "source": [
        "Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRtlJlynf-60",
        "outputId": "24a67127-4110-40da-da5e-cb4c54b97727"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m300/300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6005 - loss: 0.9490\n",
            "Loss:  0.9330831170082092\n",
            "Accuracy:  0.6056880950927734\n"
          ]
        }
      ],
      "source": [
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(\"Loss: \", loss)\n",
        "print(\"Accuracy: \", accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run on the actual testing data"
      ],
      "metadata": {
        "id": "J0sg4kNA8RGo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZgAmKaeCmP2"
      },
      "outputs": [],
      "source": [
        "test = pd.read_csv(\"/content/X_test_30.csv\")\n",
        "predictions = model.predict(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4XIzCpk7FhEy"
      },
      "outputs": [],
      "source": [
        "predictions = np.array(predictions)\n",
        "df = pd.DataFrame(predictions, columns=['no answer', 'very important', 'quite important', 'not important', 'not at all important'])\n",
        "\n",
        "df.to_csv('tf_30_result_0325.csv', index=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5WcYdOIkpu7B"
      },
      "source": [
        "## XGboosting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W0l0YW28qPy0"
      },
      "outputs": [],
      "source": [
        "X_train_xg = pd.read_csv('/content/dtrain_normalized.csv')\n",
        "X_train_xg.drop('id', axis = 1, inplace = True)\n",
        "\n",
        "X_test_xg = pd.read_csv('/content/dtest_normalized.csv')\n",
        "X_test_xg.drop('id', axis = 1, inplace = True)\n",
        "\n",
        "y_train = pd.read_csv('y_train.csv')\n",
        "y_train.drop('id', axis = 1, inplace = True)\n",
        "y_train['label'].replace(-1, 0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaaun3iWqZL_",
        "outputId": "885643f1-32b7-467b-d5c3-1ce2aaef3f01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "categorical_cols = X_train_xg.select_dtypes(include=['object']).columns\n",
        "encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "X_train_xg_encoded = pd.DataFrame(encoder.fit_transform(X_train_xg[categorical_cols]))\n",
        "X_test_xg_encoded = pd.DataFrame(encoder.transform(X_test_xg[categorical_cols]))\n",
        "\n",
        "# Concatenate encoded features with the rest of the data\n",
        "X_train_xg_processed = pd.concat([X_train_xg.drop(categorical_cols, axis=1), X_train_xg_encoded], axis=1)\n",
        "X_test_xg_processed = pd.concat([X_test_xg.drop(categorical_cols, axis=1), X_test_xg_encoded], axis=1)\n",
        "\n",
        "X_train_xg_processed.columns = X_train_xg_processed.columns.astype(str)\n",
        "X_test_xg_processed.columns = X_test_xg_processed.columns.astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "quAK6tcPqh3c"
      },
      "outputs": [],
      "source": [
        "selector = SelectKBest(score_func=mutual_info_classif, k=500)\n",
        "#X_train_selected = selector.fit_transform(X_train_xg_processed, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEdM1APQqi2l",
        "outputId": "1c8aee2d-8e9e-4559-d8f8-50a5ea36ad2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "X_selected = selector.fit_transform(X_train_xg_processed, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dJJBuHYZqkOi"
      },
      "outputs": [],
      "source": [
        "selected_indices = selector.get_support(indices=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r8ghzVCcql2A"
      },
      "outputs": [],
      "source": [
        "selected_features = X_train_xg_processed.columns[selected_indices].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "FJQ7xmO9qneb",
        "outputId": "8cdb80a2-2ee9-47f3-fbec-803e9ec6a790"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=250, n_jobs=None, num_class=5,\n",
              "              num_parallel_tree=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=250, n_jobs=None, num_class=5,\n",
              "              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=250, n_jobs=None, num_class=5,\n",
              "              num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "model_xgb = xgb.XGBClassifier(max_depth=5, learning_rate=0.1, n_estimators=250, objective='multi:softmax', num_class= 5, seed = 10)\n",
        "model_xgb.fit(X_train_xg_processed, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_predictions = model_xgb.predict_proba(X_test_xg_processed)"
      ],
      "metadata": {
        "id": "9PkG5bUACI9I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SV4l2j_GDRvS",
        "outputId": "839462e6-74c8-4061-a79a-62474f00e3e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00152706, 0.6676432 , 0.18943883, 0.12062407, 0.0207668 ],\n",
              "       [0.01269702, 0.0938268 , 0.8119569 , 0.07570276, 0.00581663],\n",
              "       [0.01384059, 0.1859661 , 0.45086572, 0.3349875 , 0.0143401 ],\n",
              "       ...,\n",
              "       [0.00704162, 0.17774007, 0.6596917 , 0.1420516 , 0.01347506],\n",
              "       [0.01940492, 0.01257045, 0.26551652, 0.6238121 , 0.07869605],\n",
              "       [0.00170761, 0.86938804, 0.11512843, 0.01228098, 0.0014949 ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ypKXWT2lqo2e"
      },
      "outputs": [],
      "source": [
        "cv_scores = cross_val_score(model_xgb, X_train_xg_processed, y_train, cv=5, scoring='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lv5mwJRrqt3L",
        "outputId": "4b04fd1e-5e76-430b-f546-a80a0b6f609b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [0.64166667 0.64375    0.648125   0.640625   0.641875  ]\n",
            "Mean CV accuracy: 0.6432083333333333\n",
            "Standard deviation of CV accuracy: 0.0026568830944882905\n"
          ]
        }
      ],
      "source": [
        "print(\"Cross-validation scores:\", cv_scores)\n",
        "print(\"Mean CV accuracy:\", cv_scores.mean())\n",
        "print(\"Standard deviation of CV accuracy:\", cv_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_predictions_df = pd.DataFrame(\n",
        "    xgb_predictions,\n",
        "    columns=[\"no answer\", \"very important\", \"quite important\", \"not important\", \"not at all important\"]\n",
        ")\n",
        "\n",
        "xgb_predictions_df.insert(0, 'id', xgb_predictions_df.index)\n",
        "\n",
        "xgb_predictions_df.head()\n",
        "xgb_predictions_df.to_csv('xgb_feature_df.csv', index=False, float_format='%.6f')"
      ],
      "metadata": {
        "id": "4RfTadykoac2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "wecnkTkUIAN7",
        "outputId": "bd678c3b-3660-4eab-b625-e31e28bc2d38"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'y_train_xg' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-be491822c7aa>\u001b[0m in \u001b[0;36m<cell line: 20>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Fit grid_search to the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_xg_processed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected_features\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_xg\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     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# Print the best parameters and best score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'y_train_xg' is not defined"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Define the XGBClassifier\n",
        "xgb_model = XGBClassifier(objective='multi:softmax')\n",
        "\n",
        "# Define the parameter grid to search\n",
        "param_grid = {\n",
        "    'max_depth': [3, 4, 5],  # Try more values based on your dataset\n",
        "    'learning_rate': [0.01, 0.1, 0.2],  # Also called 'eta'\n",
        "    'n_estimators': [100, 150],\n",
        "    'subsample': [0.8, 0.9, 1.0]  # Fraction of the training set used for each update\n",
        "}\n",
        "\n",
        "# Set up the grid search with cross-validation\n",
        "grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid,\n",
        "                           scoring='accuracy', cv=5, n_jobs=-1)\n",
        "\n",
        "# Fit grid_search to the data\n",
        "grid_search.fit(X_train_xg_processed[selected_features], y_train_xg)\n",
        "\n",
        "# Print the best parameters and best score\n",
        "print(\"Best parameters:\", grid_search.best_params_)\n",
        "print(\"Best score:\", grid_search.best_score_)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}