dim_reduction_model_selection.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import ListedColormap\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "!pip install --upgrade openpyxl\n"
      ],
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.9/dist-packages (3.0.10)\n",
            "Collecting openpyxl\n",
            "  Downloading openpyxl-3.1.2-py2.py3-none-any.whl (249 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.0/250.0 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: et-xmlfile in /usr/local/lib/python3.9/dist-packages (from openpyxl) (1.1.0)\n",
            "Installing collected packages: openpyxl\n",
            "  Attempting uninstall: openpyxl\n",
            "    Found existing installation: openpyxl 3.0.10\n",
            "    Uninstalling openpyxl-3.0.10:\n",
            "      Successfully uninstalled openpyxl-3.0.10\n",
            "Successfully installed openpyxl-3.1.2\n"
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              "// you may not use this file except in compliance with the License.\n",
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
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          "text": [
            "Saving Algerian_forest_fires_dataset_UPDATE.csv to Algerian_forest_fires_dataset_UPDATE.csv\n"
          ]
        }
      ],
      "source": [
        "#Load the dataset\n",
        "from google.colab import files\n",
        "file = files.upload()  #upload file into google colab session"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('Algerian_forest_fires_dataset_UPDATE.csv')\n",
        "#df=pd.read_csv('Algerian_forest_fires_dataset_UPDATE.csv')\n",
        "df=df[~df.FWI.eq(\"FWI\")]\n",
        "df = df.dropna()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "LWYrHASpsjO0",
        "outputId": "e79af811-7233-4906-f62c-a4752757a158"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "     Temperature  RH  Ws  Rain  FFMC   DMC    DC  ISI   BUI  FWI   Classes\n",
              "0             29  57  18   0.0  65.7   3.4   7.6  1.3   3.4  0.5  not fire\n",
              "1             29  61  13   1.3  64.4   4.1   7.6  1.0   3.9  0.4  not fire\n",
              "2             26  82  22  13.1  47.1   2.5   7.1  0.3   2.7  0.1  not fire\n",
              "3             25  89  13   2.5  28.6   1.3   6.9  0.0   1.7  0.0  not fire\n",
              "4             27  77  16   0.0  64.8   3.0  14.2  1.2   3.9  0.5  not fire\n",
              "..           ...  ..  ..   ...   ...   ...   ...  ...   ...  ...       ...\n",
              "239           30  65  14   0.0  85.4  16.0  44.5  4.5  16.9  6.5      fire\n",
              "240           28  87  15   4.4  41.1   6.5   8.0  0.1   6.2  0.0  not fire\n",
              "241           27  87  29   0.5  45.9   3.5   7.9  0.4   3.4  0.2  not fire\n",
              "242           24  54  18   0.1  79.7   4.3  15.2  1.7   5.1  0.7  not fire\n",
              "243           24  64  15   0.2  67.3   3.8  16.5  1.2   4.8  0.5  not fire\n",
              "\n",
              "[243 rows x 11 columns]"
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              "      <th></th>\n",
              "      <th>Temperature</th>\n",
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              "      <th>0</th>\n",
              "      <td>29</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>65.7</td>\n",
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              "      <th>2</th>\n",
              "      <td>26</td>\n",
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              "      <td>not fire</td>\n",
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              "      <th>3</th>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
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              "      <td>1.3</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>not fire</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
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              "      <td>27</td>\n",
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              "    [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-5917a738-c690-44b0-bf79-f7ea3aecd7da 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-5917a738-c690-44b0-bf79-f7ea3aecd7da');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df\n",
        "df1=df1.iloc[:,3:11]\n",
        "df1\n",
        "print(df1.columns)\n",
        "#df1=df1[~df1.FWI.eq(\"FWI\")]\n"
      ],
      "metadata": {
        "id": "3-5zngJEsSfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80eacfca-1638-427f-d89f-bae004a89172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Rain', 'FFMC', 'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe()\n",
        "print(df1.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4rlews802lD",
        "outputId": "65aede0e-bcb1-4ab5-dc44-df5503d16bdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rain       float64\n",
            "FFMC       float64\n",
            "DMC        float64\n",
            "DC         float64\n",
            "ISI        float64\n",
            "BUI        float64\n",
            "FWI        float64\n",
            "Classes     object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Some variables show strong correlation\n",
        "### 0.74, 0.68, etc. This means we can consider dimension reduction."
      ],
      "metadata": {
        "id": "MtMCAuZL1-ZV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cor = df1.iloc[:,:].corr()\n",
        "df_cor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "DURuG6IC0g0y",
        "outputId": "7bc86cab-5494-4231-a280-03122f45d2c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-a439e8aac4f8>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df_cor = df1.iloc[:,:].corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Rain      FFMC       DMC        DC       ISI       BUI       FWI\n",
              "Rain  1.000000 -0.543906 -0.288773 -0.298023 -0.347484 -0.299852 -0.324422\n",
              "FFMC -0.543906  1.000000  0.603608  0.507397  0.740007  0.592011  0.691132\n",
              "DMC  -0.288773  0.603608  1.000000  0.875925  0.680454  0.982248  0.875864\n",
              "DC   -0.298023  0.507397  0.875925  1.000000  0.508643  0.941988  0.739521\n",
              "ISI  -0.347484  0.740007  0.680454  0.508643  1.000000  0.644093  0.922895\n",
              "BUI  -0.299852  0.592011  0.982248  0.941988  0.644093  1.000000  0.857973\n",
              "FWI  -0.324422  0.691132  0.875864  0.739521  0.922895  0.857973  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b222740-34cc-4c34-8978-22b82c778d86\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rain</th>\n",
              "      <th>FFMC</th>\n",
              "      <th>DMC</th>\n",
              "      <th>DC</th>\n",
              "      <th>ISI</th>\n",
              "      <th>BUI</th>\n",
              "      <th>FWI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rain</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.543906</td>\n",
              "      <td>-0.288773</td>\n",
              "      <td>-0.298023</td>\n",
              "      <td>-0.347484</td>\n",
              "      <td>-0.299852</td>\n",
              "      <td>-0.324422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FFMC</th>\n",
              "      <td>-0.543906</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.603608</td>\n",
              "      <td>0.507397</td>\n",
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              "      <td>0.592011</td>\n",
              "      <td>0.691132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DMC</th>\n",
              "      <td>-0.288773</td>\n",
              "      <td>0.603608</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.875925</td>\n",
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              "      <td>0.982248</td>\n",
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              "    <tr>\n",
              "      <th>DC</th>\n",
              "      <td>-0.298023</td>\n",
              "      <td>0.507397</td>\n",
              "      <td>0.875925</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.508643</td>\n",
              "      <td>0.941988</td>\n",
              "      <td>0.739521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISI</th>\n",
              "      <td>-0.347484</td>\n",
              "      <td>0.740007</td>\n",
              "      <td>0.680454</td>\n",
              "      <td>0.508643</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.644093</td>\n",
              "      <td>0.922895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BUI</th>\n",
              "      <td>-0.299852</td>\n",
              "      <td>0.592011</td>\n",
              "      <td>0.982248</td>\n",
              "      <td>0.941988</td>\n",
              "      <td>0.644093</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.857973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FWI</th>\n",
              "      <td>-0.324422</td>\n",
              "      <td>0.691132</td>\n",
              "      <td>0.875864</td>\n",
              "      <td>0.739521</td>\n",
              "      <td>0.922895</td>\n",
              "      <td>0.857973</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b222740-34cc-4c34-8978-22b82c778d86')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1b222740-34cc-4c34-8978-22b82c778d86 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-1b222740-34cc-4c34-8978-22b82c778d86');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "plt.subplots(figsize=(10, 8))\n",
        "sns.heatmap(df_cor, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "OzFNDsDL0W6M",
        "outputId": "1a1f1a28-bf84-4d7b-df25-75b1386e23b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### PCA"
      ],
      "metadata": {
        "id": "Lhq97AnC0VT_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import the built-in dataset (Wine recognition) for this problem\n",
        "import sklearn\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "vQOWFY7xKW6z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#object dtype to float\n",
        "x=['Rain', 'FFMC', 'DMC', 'DC', 'ISI', 'BUI', 'FWI']\n",
        "dfx=df1[x]\n",
        "trans=dfx.T\n",
        "\n",
        "cov_mat = np.cov(trans, rowvar=False) \n",
        "cov_mat"
      ],
      "metadata": {
        "id": "sGvEca__UlP2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47e3b5e8-7928-482d-f90a-d59d8923e040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[573.51333333, 558.325     , 385.60333333, ..., 396.115     ,\n",
              "        689.46166667, 579.125     ],\n",
              "       [558.325     , 543.8247619 , 377.34809524, ..., 385.76952381,\n",
              "        671.01071429, 563.56547619],\n",
              "       [385.60333333, 377.34809524, 282.47809524, ..., 266.49952381,\n",
              "        462.65904762, 388.40047619],\n",
              "       ...,\n",
              "       [396.115     , 385.76952381, 266.49952381, ..., 274.64571429,\n",
              "        478.11309524, 402.86428571],\n",
              "       [689.46166667, 671.01071429, 462.65904762, ..., 478.11309524,\n",
              "        833.59285714, 703.18190476],\n",
              "       [579.125     , 563.56547619, 388.40047619, ..., 402.86428571,\n",
              "        703.18190476, 595.06571429]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Before Normalize, PC2 captures 98% of the data. \n",
        "### However, this can mean that some variables in greater range affect the Variance more.\n",
        "\n"
      ],
      "metadata": {
        "id": "cs2RXuCtwRYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca_ = PCA(n_components = 4).fit(cov_mat)\n",
        "\n",
        "#print('Components:\\n', pca_normal.components_)\n",
        "\n",
        "cum_explained_variance_pca = np.cumsum(pca_.explained_variance_ratio_)\n",
        "\n",
        "X_pca = pca_.transform(cov_mat) # Apply dimensionality reduction to X."
      ],
      "metadata": {
        "id": "bHMB3GfHvk7I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Explained variance ratio:\\n', pca_.explained_variance_ratio_)\n",
        "print('Cumulative explained variance without Normalization:\\n', cum_explained_variance_pca)\n",
        "print('Transformed data shape:', X_pca.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgofYyrsv1kJ",
        "outputId": "3904e4c7-b45e-4772-b230-f3604ce50a4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance ratio:\n",
            " [9.40023020e-01 5.97643528e-02 1.80404354e-04 3.19425761e-05]\n",
            "Cumulative explained variance without Normalization:\n",
            " [0.94002302 0.99978737 0.99996778 0.99999972]\n",
            "Transformed data shape: (243, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,len(cum_explained_variance_pca)), cum_explained_variance_pca, '-o')\n",
        "plt.title('Cumulative explained variance before normalize')\n",
        "plt.xticks(range(1,len(cum_explained_variance_pca)+1))\n",
        "plt.xlabel('Principal Components')\n",
        "plt.ylabel('Cumulative explained variance');\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "wAe9-ZTqwJ_T",
        "outputId": "c317095d-0292-4af6-a78f-c20fe86b154e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Scale the dataset; This is very important before you apply PCA \n",
        "# from sklearn.preprocessing \n",
        "cum_sum_exp = np.cumsum(cum_explained_variance_pca) \n",
        "\n",
        "plt.bar(range(1,len(cum_explained_variance_pca)+1), cum_explained_variance_pca, alpha=0.5, align='center') \n",
        "#plt.step(range(0,len(cum_sum_exp)), cum_sum_exp, where='mid',label='Cumulative explained variance') \n",
        "plt.plot(range(1,len(cum_explained_variance_pca)+1), cum_explained_variance_pca, '-o')\n",
        "\n",
        "plt.xticks(range(1,len(cum_explained_variance_pca)+1))\n",
        "\n",
        "plt.ylabel('Explained variance ratio') \n",
        "plt.xlabel('Principal component index') \n",
        "plt.title('Cumulative explained variance before normalize')\n",
        "#plt.legend(loc='best') \n",
        "plt.tight_layout() \n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "2KFD9Oi0bXbE",
        "outputId": "83076a11-b847-490e-d335-7c7b36275b19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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7d0vK/Vn6+vqqZs2atuk5KleunOuQt6P70cU4sr/u3r1btWrVsgugklSnTh2795LjYvtHYb4b33//fb3++utKTEzUmTNnHFrfpTz33HP69ttvtXjxYrt/Z1u3btXx48ftDs2ez5Fti8Ih2MFhISEhqlSpkjZu3OhQ//xC0YUndZ8vryuw8rsq6/zgUJB15Vi1apU6dOigNm3a6J133lHFihXl4+Oj+Ph4zZ49+5Lz56d9+/YKCAjQvHnz1LJlS82bN09eXl6287Okc8HAYrHoyy+/zPN9BgUF5bv87OxsSdJDDz2k2NjYPPvknJOU46uvvpJ07orMrVu3FuqL/UKPP/644uPjNXjwYEVGRqpkyZKyWCzq0qWLrdaLceRzzs7OVoMGDWznCV6oSpUqBSs+D126dFHPnj21fv16NW7cWPPmzdOtt95q9z/Sl156SSNGjNDDDz+ssWPHKjQ0VF5eXho8eHCe79mR0bqsrCzddtttOnLkiJ5++mnVrl1bgYGB2rdvn3r06JFrua66arEw++LFXPhvs6jWk5+8trkr9iNH9ldnXWz/KOh340cffaQePXqoY8eOevLJJ1W+fHl5e3srLi5O27dvL1CdixYt0ssvv6yxY8eqXbt2dtOys7NVvnx5zZo1K8958wvTcB2CHZxy9913a+rUqUpISFBkZORF+5YuXVqScl2pduFfqa5QmHUtXLhQfn5++uqrr+zu1xQfH5+rrzMjeIGBgbr77rs1f/58jR8/XnPnzlXr1q3tTqgPDw+XMUY1atSwjYo5Kudqz6ysLEVHR1+y/x9//KExY8bYwkrv3r21YcMGlSxZ0q5fdna2duzYYVfPli1bJOmiT+xYsGCBYmNj7a7KO336dK7PpDDCw8P1+++/69Zbb73oZ1GtWjVlZ2dr+/btdiM7SUlJDq+rY8eOeuSRR2yHY7ds2aLhw4fb9VmwYIHatm2r6dOn27UfO3Ys10iKozZs2KAtW7bo/fffV/fu3W3ty5YtK9DypHPb7auvvtKRI0fyHbUrzL54vgv/YNi2bZuys7Nt+05h11OtWjVJ5z7L80eWMzMztXPnTof+LTi6HxVWtWrV9Mcffyg7O9tu1C4xMdE2vagtWLBANWvW1Mcff2z3XvM6LO+ILVu2KDY2Vh07drRdYXy+8PBwffPNN2rVqpVDf8jA9TjHDk556qmnFBgYqN69eys5OTnX9O3bt2vixImSzo3wlS1bVt9//71dn3feecfldeUcCjh/XVlZWQ7dKNnb21sWi8VudG/Xrl15XhkXGBjoVFDp3Lmz/v77b02bNk2///673flZ0rlzuby9vTV69Ohcf+kbYy56uNnb21v33XefFi5cmOco6sGDB22/nzlzRj169FClSpU0ceJEzZw5U8nJyRoyZEiey865nUdOHW+//bZ8fHx06623XrSeC9/DW2+95dCoqaPuv/9+7du3T++9916uaadOnVJ6erok2a5KfPPNN+36nH8Y/FJKlSqlmJgYzZs3T3PmzJGvr2+uG1Pn9Z7nz5+vffv2ObyeC+WMwpy/XGOM7d9VQdx3330yxuR5C5ec9RRmXzzfpEmT7F6/9dZbkv79TAq7nujoaPn6+urNN9+0m3/69Ok6fvy4Q1c+O7ofFdadd96pAwcO2J2refbsWb311lsKCgpSVFSUS9ZzMXntTz///LMSEhKcXlZaWpruvfdeVa5c2Xbbpwvdf//9ysrKsp3CcL6zZ8+69A895I0ROzglPDxcs2fPtl16f/6TJ3766Sfbpfw5evfurXHjxql3795q1qyZvv/+e9vojyvVq1dPN954o4YPH24blZgzZ47Onj17yXnvuusujR8/Xu3atdMDDzyglJQUTZo0SREREXbny0hS06ZN9c0332j8+PGqVKmSatSooRYtWuS77DvvvFPBwcEaNmyYLYidLzw8XC+88IKGDx+uXbt2qWPHjgoODtbOnTv1ySefqG/fvho2bFi+yx83bpxWrFihFi1aqE+fPqpbt66OHDmidevW6ZtvvtGRI0ckyXb+4PLlyxUcHKyGDRtq5MiReu655/Tf//5Xd955p22Zfn5+Wrp0qWJjY9WiRQt9+eWXWrx4sZ555pmLHka5++679eGHH6pkyZKqW7euEhIS9M033+R5rllBdevWTfPmzdOjjz6qFStWqFWrVsrKylJiYqLmzZunr776Ss2aNVPjxo3VtWtXvfPOOzp+/Lhatmyp5cuXa9u2bU6tr3PnznrooYf0zjvvKCYmJtftPu6++27bKGjLli21YcMGzZo1K997GDqidu3aCg8P17Bhw7Rv3z6FhIRo4cKFhboHWNu2bdWtWze9+eab2rp1q9q1a6fs7GytWrVKbdu21YABAwq9L+bYuXOnOnTooHbt2ikhIcF2y5lGjRpJKvw+X65cOQ0fPlyjR49Wu3bt1KFDByUlJemdd97RDTfcYHdBQ34c3Y8Kq2/fvnr33XfVo0cPrV27VtWrV9eCBQv0448/asKECUV6IVqOu+++Wx9//LHuvfde3XXXXdq5c6emTJmiunXrKi0tzalljR49Wps2bdJzzz2nTz/91G5aeHi4IiMjFRUVpUceeURxcXFav369br/9dvn4+Gjr1q2aP3++Jk6cqP/+97+ufIu4UNFfeAtPtGXLFtOnTx9TvXp14+vra4KDg02rVq3MW2+9ZU6fPm3rd/LkSdOrVy9TsmRJExwcbO6//36TkpKS7+1Ozr+tgTHnbucQGBiYa/1RUVGmXr16dm3bt2830dHRxmq1mrCwMPPMM8+YZcuWOXS7k+nTp5tatWoZq9VqateubeLj4201nS8xMdG0adPG+Pv7G0m22wpc7LYADz74oJFkoqOj892eCxcuNDfddJMJDAw0gYGBpnbt2qZ///4mKSkp33lyJCcnm/79+5sqVaoYHx8fU6FCBXPrrbeaqVOnGmOMWbt2rSlRooTdLUyMOXcbhBtuuMFUqlTJHD161LZtAgMDzfbt283tt99uAgICTFhYmBk1alSu23pc+BkePXrU9OzZ05QtW9YEBQWZmJgYk5iYaKpVq2Z3+4X8bndy4eeZU8+Fn1VmZqZ5+eWXTb169YzVajWlS5c2TZs2NaNHjzbHjx+39Tt16pQZOHCgKVOmjAkMDDTt27c3e/fudeh2JzlSU1Ntn/X5t6zIcfr0afPEE0+YihUrGn9/f9OqVSuTkJBgoqKiTFRUVK73PH/+/FzLyGt7bNq0yURHR5ugoCBTtmxZ06dPH/P777/nun1Qfv8+8tp3z549a1599VVTu3Zt4+vra8qVK2fuuOMOs3btWrt+Bd0Xc9a5adMm89///tcEBweb0qVLmwEDBphTp07l6u/IevL7XjDm3O1NateubXx8fExYWJjp16+fbT/Okd9+ZYzj+1FenNlfk5OTbf8ufH19TYMGDXLdAirndievvvpqrmUW9rsxOzvbvPTSS6ZatWrGarWa66+/3nzxxRd51nrhv40Lv9dybq+T18+Ft4GaOnWqadq0qfH39zfBwcGmQYMG5qmnnjJ///13rprhWhZjCnGmJwCP0qNHDy1YsMDpv+QBAMUD59gBAAB4CIIdAACAhyDYAQAAeAjOsQMAAPAQjNgBAAB4CIIdAACAh7jqblCcnZ2tv//+W8HBwUX6KBkAAABXMMboxIkTqlSpkt3j6fJy1QW7v//+26UPCgcAALgc9u7dq2uuueaifa66YJfzCJe9e/cqJCTEzdUAAABcXGpqqqpUqeLQY+iuumCXc/g1JCSEYAcAAK4YjpxCxsUTAAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAewq3B7vvvv1f79u1VqVIlWSwWLVq06JLzrFy5Uk2aNJHValVERIRmzpxZ5HUCyFtWtlHC9sP6dP0+JWw/rKxs4+6S4GbsE8gL+8XlU8KdK09PT1ejRo308MMP6z//+c8l++/cuVN33XWXHn30Uc2aNUvLly9X7969VbFiRcXExFyGigHkWLpxv0Z/vkn7j5+2tVUs6adR7euqXf2KbqwM7sI+gbywX1xeFmNMsYjNFotFn3zyiTp27Jhvn6efflqLFy/Wxo0bbW1dunTRsWPHtHTpUofWk5qaqpIlS+r48eMKCQkpbNnAVWnpxv3q99E6XfjlYfnnv5MfasIX9lWGfQJ5Yb9wDWeyi1tH7JyVkJCg6Ohou7aYmBgNHjzYPQUBV6GsbKPRn2/K9UUtydY2/OMNOnPWyMvL8k/7xf9+vNSfl4789VnYv1Edmb3Q78OhdVxqGZdeyCV7XHJ7O7CO87pkG6OXlyZddJ94euEGHU7PlJfFkkev4qF4DHM4x5HPyl2yjdGrF9kvLJJGf75Jt9WtIG+v4rtfXGmuqGB34MABhYWF2bWFhYUpNTVVp06dkr+/f655MjIylJGRYXudmppa5HUCnuRsVrb2HDmpbSlp2pqSpoTth+0OqeTl6MkzenzOb5epQlwJjp86o2c/2XjpjrhqGEn7j5/WLzuPKDK8jLvL8RhXVLAriLi4OI0ePdrdZQCSpDeWbXF3CfnKyjY6djJTR9L//Tl8MlPH0s8oqwBDGaUDfBTg69hXzCX/Vi/kH/MFWX610MBzkwq77ksswCJp1+H0gi+/wHNeujZH152WcVYpJzIu2leSygdbFWQt3v/budQmCS8XdHkKkbT9YNplW1dROHHasf3i/Z92afWOw5ehoqIz5LZr3V2CTfH+F3aBChUqKDk52a4tOTlZISEheY7WSdLw4cM1dOhQ2+vU1FRVqVKlSOsEirOzWdk6evKMXYA7kp6pY6cyld+FaiW8LAoN9FVooK+8vSz68+9Lj3zfUru8rikd4OLqL5/L+UVdnAO/I/46elIL1+27ZL/Wtcpe0fuExH7hDEf3i0Cr92Wo5upxRQW7yMhILVmyxK5t2bJlioyMzHceq9Uqq9Va1KUBxc6ZrOxc4e1weqZST53J96wcX28vlQ70UWigr8oEWm1hLsSvhG10J9sY7T58UmkZZ/Ndd5C1hCqVyvuPLXieSqX8FWQtwT4BO+wX7uHWYJeWlqZt27bZXu/cuVPr169XaGioqlatquHDh2vfvn364IMPJEmPPvqo3n77bT311FN6+OGH9e2332revHlavHixu94C4HYZZ7Nyhbej6ZlKPZ3/l6m1hNc/4c3XFt5CA30VZC1xycNzXhaLoq4tp8Ub9ufbJ+racsX6JHm4FvsE8sJ+4R5uDXa//vqr2rZta3udc8g0NjZWM2fO1P79+7Vnzx7b9Bo1amjx4sUaMmSIJk6cqGuuuUbTpk3jHna4Kpw6k5VrBO5IeuZF/xr29/HOFd5CA30V4OtdqPOrIsoH6a4GFfXdloN26w+yllDUteUUUf7ynYeE4oF9Anlhv7j83Brsbr755oteup/XUyVuvvlm/fYbV9vBMxljdDIzd4A7nJ6pU2ey8p0v0Op9bgQuwGoX4Px9i+7clYjyQapZLlB/Hzul9IwsBVq9VamUP399X8XYJ5AX9ovL64o6xw7wFMYYpWWczXME7vTZ7HznC/YrkfsQaoCvrD7uOfnYy2K54k+Gh2uxTyAv7BeXD8EOKELZ2Ub7jp365x5wJ7RsU7ItwGVm5R/gSvr72IJbTogrHeAr3xJufbwzAKCYI9gBLpCVbbTnyEltTT6hrSlp2nbeT36HUC0WqZR/7itQSwf4qIQ3AQ4A4DyCHeCEM1nZ2n04XVuTzz2FYWtKmrYmn9COQ+nKzOcQqq+3l2qWC1RE+SAdOpFhC3ClAnx5jA4AwKUIdkAeTp/J0s5D6edG3/4ZhduakqZdh9J1Np+7+Pr5eCm8XJBqlQ9SrbBgRZQ/93vV0ADbCNyVfsNRAEDxRrDDVe1k5lltT0nX1pQTtmehbktJ0+7D6fk+hSHQ11sRYcHnAlz5INUKC1Kt8sGqXMrf9tB7AADcgWCHq8KJ02fsglvOuXB/HT2V7zwhfiV07T8jbxH/jMLVKh+kiiX9Cv2MTQAAigLBDh7l2MnMf857S7ONwm1LSdP+46fznadMoO8/we3cyFut8kGKCAtSuSArAQ4AcEUh2OGKY4zR4fRMbU1O07aUE+cFuTQdSsvId76wEKtqlQ+2hbiIcudG4soE8SxhAIBnINih2DLGKDk1Q1tTTtiCW06QO3byTL7zVS7lb7twoVZYkCL+CXMl/X0uY/UAAFx+BDu43fk38c25ke+5q1HTdCKf56BaLFLV0IBzh03/OXxaKyxI4eWCFGhltwYAXJ34PyAckpVt9MvOI0o5cVrlg/3UvEao0/dgy8o22nvk5D+3Djmhbcn/XsyQ3018vb0sql4m4Ny5b2FBtgsZwssFyc9Nj9ECAKC4ItjhkpZu3K/Rn2+yuwChYkk/jWpfV+3qV8zVP6+b+G5LSdP2g2n53sTXx9uimmXPXbRw7jYi54Jc9TKBPEYLAAAHEexwUUs37le/j9bpwlu6HTh+Wv0+Wqdn766jsGC/f89/S07TzovcxNdawuu889/+vZVItfNu4gsAAAqGYId8ZWUbjf58U65QJ8nW9sIXm/OcN9DX+5/QFvzPbUT+uYlvaX8eowUAQBEh2CFfv+w8ctH7v+W4NixITaqW5ia+AAC4GcGuCF3pzwVNOnDCoX41ywYpLMRPJ06f1brdR7Vu99Eirsz1htx2rbtLAACg0DipCfkKtDp21amj/QAAQNEi2CFflUr5K+gS94QLspZQpVL+l6kiAABwMQQ75MvLYlHUteUu2ifq2nLy4lw6AACKBYIdLiqifJDualAx18hdkLWE7mpQURHlg9xUGQAAuBAXT+CSIsoHqWa5QP197JTSM7IUaPVWpVL+jNQBAFDMEOzgEC+LRdeUDnB3GQAA4CI4FAsAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIdwe7CZNmqTq1avLz89PLVq00C+//HLR/hMmTNB1110nf39/ValSRUOGDNHp06cvU7UAAADFl1uD3dy5czV06FCNGjVK69atU6NGjRQTE6OUlJQ8+8+ePVv/+9//NGrUKG3evFnTp0/X3Llz9cwzz1zmygEAAIoftwa78ePHq0+fPurZs6fq1q2rKVOmKCAgQDNmzMiz/08//aRWrVrpgQceUPXq1XX77bera9eulxzlAwAAuBq4LdhlZmZq7dq1io6O/rcYLy9FR0crISEhz3latmyptWvX2oLcjh07tGTJEt15552XpWYAAIDirIS7Vnzo0CFlZWUpLCzMrj0sLEyJiYl5zvPAAw/o0KFDuummm2SM0dmzZ/Xoo49e9FBsRkaGMjIybK9TU1Nd8wYAAACKGbdfPOGMlStX6qWXXtI777yjdevW6eOPP9bixYs1duzYfOeJi4tTyZIlbT9VqlS5jBUDAABcPm4bsStbtqy8vb2VnJxs156cnKwKFSrkOc+IESPUrVs39e7dW5LUoEEDpaenq2/fvnr22Wfl5ZU7pw4fPlxDhw61vU5NTSXcAQAAj+S2ETtfX181bdpUy5cvt7VlZ2dr+fLlioyMzHOekydP5gpv3t7ekiRjTJ7zWK1WhYSE2P0AAAB4IreN2EnS0KFDFRsbq2bNmql58+aaMGGC0tPT1bNnT0lS9+7dVblyZcXFxUmS2rdvr/Hjx+v6669XixYttG3bNo0YMULt27e3BTwAAICrlVuDXefOnXXw4EGNHDlSBw4cUOPGjbV06VLbBRV79uyxG6F77rnnZLFY9Nxzz2nfvn0qV66c2rdvrxdffNFdbwEAAKDYcGuwk6QBAwZowIABeU5buXKl3esSJUpo1KhRGjVq1GWoDAAA4MpyRV0VCwAAgPwR7AAAADwEwQ4AAMBDEOwAAAA8BMEOAADAQxDsAAAAPATBDgAAwEMQ7AAAADwEwQ4AAMBDEOwAAAA8BMEOAADAQxToWbFZWVlatGiRNm/eLEmqV6+eOnToIG9vb5cWBwAAAMc5Hey2bdumu+66S3/99Zeuu+46SVJcXJyqVKmixYsXKzw83OVFAgAA4NKcPhQ7cOBA1axZU3v37tW6deu0bt067dmzRzVq1NDAgQOLokYAAAA4wOkRu++++06rV69WaGiora1MmTIaN26cWrVq5dLiAAAA4DinR+ysVqtOnDiRqz0tLU2+vr4uKQoAAADOczrY3X333erbt69+/vlnGWNkjNHq1av16KOPqkOHDkVRIwAAABzgdLB78803FR4ersjISPn5+cnPz0+tWrVSRESEJk6cWBQ1AgAAwAFOn2NXqlQpffrpp9q6dasSExMlSXXq1FFERITLiwMAAIDjCnQfO0mqVauWatWq5cpaAAAAUAgOBbuhQ4dq7NixCgwM1NChQy/ad/z48S4pDAAAAM5xKNj99ttvOnPmjO13AAAAFD8OBbsVK1bk+TsAAACKD6evin344YfzvI9denq6Hn74YZcUBQAAAOc5Hezef/99nTp1Klf7qVOn9MEHH7ikKAAAADjP4atiU1NTbTckPnHihPz8/GzTsrKytGTJEpUvX75IigQAAMClORzsSpUqJYvFIovFomuvvTbXdIvFotGjR7u0OAAAADjO4WC3YsUKGWN0yy23aOHChQoNDbVN8/X1VbVq1VSpUqUiKRIAAACX5nCwi4qKkiTt3LlTVapUkZeX06fnAQAAoAg5/eSJatWqSZJOnjypPXv2KDMz0256w4YNXVMZAAAAnOJ0sDt48KB69uypL7/8Ms/pWVlZhS4KAAAAznP6eOrgwYN17Ngx/fzzz/L399fSpUv1/vvvq1atWvrss8+KokYAAAA4wOkRu2+//VaffvqpmjVrJi8vL1WrVk233XabQkJCFBcXp7vuuqso6gQAAMAlOD1il56ebrtfXenSpXXw4EFJUoMGDbRu3TrXVgcAAACHOR3srrvuOiUlJUmSGjVqpHfffVf79u3TlClTVLFiRZcXCAAAAMc4fSh20KBB2r9/vyRp1KhRateunWbNmiVfX1/NnDnT1fUBAADAQU4Hu4ceesj2e9OmTbV7924lJiaqatWqKlu2rEuLAwAAgOOcOhR75swZhYeHa/Pmzba2gIAANWnShFAHAADgZk4FOx8fH50+fbqoagEAAEAhOH3xRP/+/fXyyy/r7NmzRVEPAAAACsjpc+zWrFmj5cuX6+uvv1aDBg0UGBhoN/3jjz92WXEAAABwnNPBrlSpUrrvvvuKohYAAAAUgtPBLj4+vijqAAAAQCE5fY4dAAAAiieCHQAAgIcg2AEAAHgIgh0AAICHKFSw42bFAAAAxYfTwS47O1tjx45V5cqVFRQUpB07dkiSRowYoenTp7u8QAAAADjG6WD3wgsvaObMmXrllVfk6+tra69fv76mTZvm0uIAAADgOKeD3QcffKCpU6fqwQcflLe3t629UaNGSkxMdGlxAAAAcJzTwW7fvn2KiIjI1Z6dna0zZ864pCgAAAA4z+lgV7duXa1atSpX+4IFC3T99de7pCgAAAA4z+lHio0cOVKxsbHat2+fsrOz9fHHHyspKUkffPCBvvjii6KoEQAAAA5wesTunnvu0eeff65vvvlGgYGBGjlypDZv3qzPP/9ct912W1HUCAAAAAc4PWInSa1bt9ayZctcXQsAAAAKwekRuzVr1ujnn3/O1f7zzz/r119/dUlRAAAAcJ7Twa5///7au3dvrvZ9+/apf//+LikKAAAAznM62G3atElNmjTJ1X799ddr06ZNLikKAAAAznM62FmtViUnJ+dq379/v0qUKNApewAAAHABp4Pd7bffruHDh+v48eO2tmPHjumZZ57hqlgAAAA3cnqI7bXXXlObNm1UrVo12w2J169fr7CwMH344YcuLxAAAACOcTrYVa5cWX/88YdmzZql33//Xf7+/urZs6e6du0qHx+foqgRAAAADnD6UKwkBQYGqm/fvpo0aZJee+01de/evcChbtKkSapevbr8/PzUokUL/fLLLxftf+zYMfXv318VK1aU1WrVtddeqyVLlhRo3QAAAJ6kQFc7bN26VStWrFBKSoqys7Ptpo0cOdLh5cydO1dDhw7VlClT1KJFC02YMEExMTFKSkpS+fLlc/XPzMzUbbfdpvLly2vBggWqXLmydu/erVKlShXkbQAAAHgUp4Pde++9p379+qls2bKqUKGCLBaLbZrFYnEq2I0fP159+vRRz549JUlTpkzR4sWLNWPGDP3vf//L1X/GjBk6cuSIfvrpJ9sIYfXq1Z19CwAAAB7J6WD3wgsv6MUXX9TTTz9dqBVnZmZq7dq1Gj58uK3Ny8tL0dHRSkhIyHOezz77TJGRkerfv78+/fRTlStXTg888ICefvppeXt75zlPRkaGMjIybK9TU1MLVTcAAEBx5fQ5dkePHlWnTp0KveJDhw4pKytLYWFhdu1hYWE6cOBAnvPs2LFDCxYsUFZWlpYsWaIRI0bo9ddf1wsvvJDveuLi4lSyZEnbT5UqVQpdOwAAQHHkdLDr1KmTvv7666Ko5ZKys7NVvnx5TZ06VU2bNlXnzp317LPPasqUKfnOk3PPvZyfvB6HBgAA4AmcPhQbERGhESNGaPXq1WrQoEGuq2EHDhzo0HLKli0rb2/vXE+xSE5OVoUKFfKcp2LFivLx8bE77FqnTh0dOHBAmZmZ8vX1zTWP1WqV1Wp1qCYAAIArmdPBburUqQoKCtJ3332n7777zm6axWJxONj5+vqqadOmWr58uTp27Cjp3Ijc8uXLNWDAgDznadWqlWbPnq3s7Gx5eZ0bbNyyZYsqVqyYZ6gDAAC4mjgd7Hbu3OmylQ8dOlSxsbFq1qyZmjdvrgkTJig9Pd12lWz37t1VuXJlxcXFSZL69eunt99+W4MGDdLjjz+urVu36qWXXnI4TAIAAHiyAt3HzlU6d+6sgwcPauTIkTpw4IAaN26spUuX2i6o2LNnj21kTpKqVKmir776SkOGDFHDhg1VuXJlDRo0qNBX6AIAAHiCAgW7v/76S5999pn27NmjzMxMu2njx493alkDBgzI99DrypUrc7VFRkZq9erVTq0DAADgauB0sFu+fLk6dOigmjVrKjExUfXr19euXbtkjFGTJk2KokYAAAA4wOnbnQwfPlzDhg3Thg0b5Ofnp4ULF2rv3r2Kiopyyf3tAAAAUDBOB7vNmzere/fukqQSJUro1KlTCgoK0pgxY/Tyyy+7vEAAAAA4xulgFxgYaDuvrmLFitq+fbtt2qFDh1xXGQAAAJzi9Dl2N954o3744QfVqVNHd955p5544glt2LBBH3/8sW688caiqBEAAAAOcDrYjR8/XmlpaZKk0aNHKy0tTXPnzlWtWrWcviIWAAAAruN0sKtZs6bt98DAwIs+pxUAAACXj9Pn2AEAAKB4cmjELjQ0VFu2bFHZsmVVunRpWSyWfPseOXLEZcUBAADAcQ4FuzfeeEPBwcGSpAkTJhRlPQAAACggh4JdbGysJOns2bOyWCyKiYmxPc8VAAAAxYNT59iVKFFCjz76qE6fPl1U9QAAAKCAnL54onnz5vrtt9+KohYAAAAUgtO3O3nsscf0xBNP6K+//lLTpk0VGBhoN71hw4YuKw4AAACOczrYdenSRZI0cOBAW5vFYpExRhaLRVlZWa6rDgAAAA5zOtjt3LmzKOoAAABAITkd7KpVq1YUdQAAAKCQnA52OTZt2qQ9e/YoMzPTrr1Dhw6FLgoAAADOczrY7dixQ/fee682bNhgO7dOku1pFJxjBwAA4B5O3+5k0KBBqlGjhlJSUhQQEKA///xT33//vZo1a6aVK1cWQYkAAABwhNMjdgkJCfr2229VtmxZeXl5ycvLSzfddJPi4uI0cOBA7nEHAADgJk6P2GVlZdmeG1u2bFn9/fffks5dVJGUlOTa6gAAAOAwp0fs6tevr99//101atRQixYt9Morr8jX11dTp05VzZo1i6JGAAAAOMDpYPfcc88pPT1dkjRmzBjdfffdat26tcqUKaO5c+e6vEAAAAA4xulgFxMTY/s9IiJCiYmJOnLkiEqXLm27MhYAAACXn9Pn2H300Ue2EbscoaGhhDoAAAA3czrYDRkyRGFhYXrggQe0ZMkS7lsHAABQTDgd7Pbv3685c+bIYrHo/vvvV8WKFdW/f3/99NNPRVEfAAAAHOR0sCtRooTuvvtuzZo1SykpKXrjjTe0a9cutW3bVuHh4UVRIwAAABxQ4GfFSlJAQIBiYmJ09OhR7d69W5s3b3ZVXQAAAHCS0yN2knTy5EnNmjVLd955pypXrqwJEybo3nvv1Z9//unq+gAAAOAgp0fsunTpoi+++EIBAQG6//77NWLECEVGRhZFbQAAAHCC08HO29tb8+bNU0xMjLy9vYuiJgAAABSA08Fu1qxZRVEHAAAACqlA59gBAACg+CHYAQAAeAiCHQAAgIcg2AEAAHgIhy6eSE1NdXiBISEhBS4GAAAABedQsCtVqpQsFotDC8zKyipUQQAAACgYh4LdihUrbL/v2rVL//vf/9SjRw/bjYkTEhL0/vvvKy4urmiqBAAAwCU5FOyioqJsv48ZM0bjx49X165dbW0dOnRQgwYNNHXqVMXGxrq+SgAAAFyS0xdPJCQkqFmzZrnamzVrpl9++cUlRQEAAMB5Tge7KlWq6L333svVPm3aNFWpUsUlRQEAAMB5Tj9S7I033tB9992nL7/8Ui1atJAk/fLLL9q6dasWLlzo8gIBAADgGKdH7O68805t2bJF7du315EjR3TkyBG1b99eW7Zs0Z133lkUNQIAAMABTo/YSecOx7700kuurgUAAACFUKAnT6xatUoPPfSQWrZsqX379kmSPvzwQ/3www8uLQ4AAACOczrYLVy4UDExMfL399e6deuUkZEhSTp+/DijeAAAAG7kdLB74YUXNGXKFL333nvy8fGxtbdq1Urr1q1zaXEAAABwnNPBLikpSW3atMnVXrJkSR07dswVNQEAAKAAnA52FSpU0LZt23K1//DDD6pZs6ZLigIAAIDznA52ffr00aBBg/Tzzz/LYrHo77//1qxZszRs2DD169evKGoEAACAA5y+3cn//vc/ZWdn69Zbb9XJkyfVpk0bWa1WDRs2TI8//nhR1AgAAAAHOB3sLBaLnn32WT355JPatm2b0tLSVLduXQUFBRVFfQAAAHBQgW5QLEm+vr6qW7euK2sBAABAITgd7NLT0zVu3DgtX75cKSkpys7Otpu+Y8cOlxUHAAAAxzkd7Hr37q3vvvtO3bp1U8WKFWWxWIqiLgAAADjJ6WD35ZdfavHixWrVqlVR1AMAAIACcvp2J6VLl1ZoaGhR1AIAAIBCcDrYjR07ViNHjtTJkyeLoh4AAAAUkNOHYl9//XVt375dYWFhql69ut3zYiXxvFgAAAA3cTrYdezY0eVFTJo0Sa+++qoOHDigRo0a6a233lLz5s0vOd+cOXPUtWtX3XPPPVq0aJHL6wIAALiSOB3sRo0a5dIC5s6dq6FDh2rKlClq0aKFJkyYoJiYGCUlJal8+fL5zrdr1y4NGzZMrVu3dmk9AAAAVyqnz7FztfHjx6tPnz7q2bOn6tatqylTpiggIEAzZszId56srCw9+OCDGj16tGrWrHkZqwUAACi+HAp2oaGhOnTokKR/r4rN78cZmZmZWrt2raKjo/8tyMtL0dHRSkhIyHe+MWPGqHz58urVq9cl15GRkaHU1FS7HwAAAE/k0KHYN954Q8HBwZKkCRMmuGzlhw4dUlZWlsLCwuzaw8LClJiYmOc8P/zwg6ZPn67169c7tI64uDiNHj26sKUCAAAUew4Fu9jY2Dx/v9xOnDihbt266b333lPZsmUdmmf48OEaOnSo7XVqaqqqVKlSVCUCAAC4jdMXT5zv9OnTyszMtGsLCQlxeP6yZcvK29tbycnJdu3JycmqUKFCrv7bt2/Xrl271L59e1tbzrNqS5QooaSkJIWHh9vNY7VaZbVaHa4JAADgSuX0xRPp6ekaMGCAypcvr8DAQJUuXdruxxm+vr5q2rSpli9fbmvLzs7W8uXLFRkZmat/7dq1tWHDBq1fv97206FDB7Vt21br169nJA4AAFzVnB6xe+qpp7RixQpNnjxZ3bp106RJk7Rv3z69++67GjdunNMFDB06VLGxsWrWrJmaN2+uCRMmKD09XT179pQkde/eXZUrV1ZcXJz8/PxUv359u/lLlSolSbnaAQAArjZOB7vPP/9cH3zwgW6++Wb17NlTrVu3VkREhKpVq6ZZs2bpwQcfdGp5nTt31sGDBzVy5EgdOHBAjRs31tKlS20XVOzZs0deXm6/KwsAAECx53SwO3LkiO3ecSEhITpy5Igk6aabblK/fv0KVMSAAQM0YMCAPKetXLnyovPOnDmzQOsEAADwNE4PhdWsWVM7d+6UdO6ct3nz5kk6N5KXc1gUAAAAl5/Twa5nz576/fffJUn/+9//NGnSJPn5+WnIkCF68sknXV4gAAAAHOP0odghQ4bYfo+OjlZiYqLWrl2riIgINWzY0KXFAQAAwHGFuo+dJFWrVk3VqlVzRS0AAAAoBIeC3ZtvvunwAgcOHFjgYgAAAFBwDj8r1hEWi4VgBwAA4CYOBbucq2ABAABQfBXqzr/GGBljXFULAAAACqFAwW769OmqX7++/Pz8bI/5mjZtmqtrAwAAgBOcvip25MiRGj9+vB5//HFFRkZKkhISEjRkyBDt2bNHY8aMcXmRAAAAuDSng93kyZP13nvvqWvXrra2Dh06qGHDhnr88ccJdgAAAG7i9KHYM2fOqFmzZrnamzZtqrNnz7qkKAAAADjP6WDXrVs3TZ48OVf71KlT9eCDD7qkKAAAADivQE+emD59ur7++mvdeOONkqSff/5Ze/bsUffu3TV06FBbv/Hjx7umSgAAAFyS08Fu48aNatKkiSRp+/btkqSyZcuqbNmy2rhxo62fxWJxUYkAAABwhNPBbsWKFUVRBwAAAArJ6XPsDh48mO+0DRs2FKoYAAAAFJzTwa5BgwZavHhxrvbXXntNzZs3d0lRAAAAcJ7TwW7o0KG677771K9fP506dUr79u3TrbfeqldeeUWzZ88uihoBAADgAKeD3VNPPaWEhAStWrVKDRs2VMOGDWW1WvXHH3/o3nvvLYoaAQAA4IACPSs2IiJC9evX165du5SamqrOnTurQoUKrq4NAAAATnA62P34449q2LChtm7dqj/++EOTJ0/W448/rs6dO+vo0aNFUSMAAAAc4HSwu+WWW9S5c2etXr1aderUUe/evfXbb79pz549atCgQVHUCAAAAAc4fR+7r7/+WlFRUXZt4eHh+vHHH/Xiiy+6rDAAAAA4x+kRuwtDnW1BXl4aMWJEoQsCAABAwTgc7O68804dP37c9nrcuHE6duyY7fXhw4dVt25dlxYHAAAAxzkc7L766itlZGTYXr/00ks6cuSI7fXZs2eVlJTk2uoAAADgMIeDnTHmoq8BAADgXgW6jx0AAACKH4eDncVikcViydUGAACA4sHh250YY9SjRw9ZrVZJ0unTp/Xoo48qMDBQkuzOvwMAAMDl53Cwi42NtXv90EMP5erTvXv3wlcEAACAAnE42MXHxxdlHQAAACgkLp4AAADwEAQ7AAAAD0GwAwAA8BAEOwAAAA9BsAMAAPAQBDsAAAAPQbADAADwEAQ7AAAAD0GwAwAA8BAEOwAAAA9BsAMAAPAQBDsAAAAPQbADAADwEAQ7AAAAD0GwAwAA8BAEOwAAAA9BsAMAAPAQBDsAAAAPQbADAADwEAQ7AAAAD0GwAwAA8BAEOwAAAA9BsAMAAPAQBDsAAAAPQbADAADwEAQ7AAAAD0GwAwAA8BAEOwAAAA9BsAMAAPAQxSLYTZo0SdWrV5efn59atGihX375Jd++7733nlq3bq3SpUurdOnSio6Ovmh/AACAq4Xbg93cuXM1dOhQjRo1SuvWrVOjRo0UExOjlJSUPPuvXLlSXbt21YoVK5SQkKAqVaro9ttv1759+y5z5QAAAMWL24Pd+PHj1adPH/Xs2VN169bVlClTFBAQoBkzZuTZf9asWXrsscfUuHFj1a5dW9OmTVN2draWL19+mSsHAAAoXtwa7DIzM7V27VpFR0fb2ry8vBQdHa2EhASHlnHy5EmdOXNGoaGhRVUmAADAFaGEO1d+6NAhZWVlKSwszK49LCxMiYmJDi3j6aefVqVKlezC4fkyMjKUkZFhe52amlrwggEAAIoxtx+KLYxx48Zpzpw5+uSTT+Tn55dnn7i4OJUsWdL2U6VKlctcJQAAwOXh1mBXtmxZeXt7Kzk52a49OTlZFSpUuOi8r732msaNG6evv/5aDRs2zLff8OHDdfz4cdvP3r17XVI7AABAcePWYOfr66umTZvaXfiQcyFEZGRkvvO98sorGjt2rJYuXapmzZpddB1Wq1UhISF2PwAAAJ7IrefYSdLQoUMVGxurZs2aqXnz5powYYLS09PVs2dPSVL37t1VuXJlxcXFSZJefvlljRw5UrNnz1b16tV14MABSVJQUJCCgoLc9j4AAADcze3BrnPnzjp48KBGjhypAwcOqHHjxlq6dKntgoo9e/bIy+vfgcXJkycrMzNT//3vf+2WM2rUKD3//POXs3QAAIBixe3BTpIGDBigAQMG5Dlt5cqVdq937dpV9AUBAABcga7oq2IBAADwL4IdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgBAAB4CIIdAACAhygWwW7SpEmqXr26/Pz81KJFC/3yyy8X7T9//nzVrl1bfn5+atCggZYsWXKZKgUAACi+3B7s5s6dq6FDh2rUqFFat26dGjVqpJiYGKWkpOTZ/6efflLXrl3Vq1cv/fbbb+rYsaM6duyojRs3XubKAQAAihe3B7vx48erT58+6tmzp+rWraspU6YoICBAM2bMyLP/xIkT1a5dOz355JOqU6eOxo4dqyZNmujtt9++zJUDAAAUL24NdpmZmVq7dq2io6NtbV5eXoqOjlZCQkKe8yQkJNj1l6SYmJh8+wMAAFwtSrhz5YcOHVJWVpbCwsLs2sPCwpSYmJjnPAcOHMiz/4EDB/Lsn5GRoYyMDNvr48ePS5JSU1MLU7pDTqenFfk64BqXY3+Q2CeuJJdrn5DYL64k7BfIS1HvFznLN8Zcsq9bg93lEBcXp9GjR+dqr1KlihuqQXH1jLsLQLHDPoG8sF8gL5drvzhx4oRKlix50T5uDXZly5aVt7e3kpOT7dqTk5NVoUKFPOepUKGCU/2HDx+uoUOH2l5nZ2fryJEjKlOmjCwWSyHfwdUlNTVVVapU0d69exUSEuLuclBMsF/gQuwTyAv7RcEZY3TixAlVqlTpkn3dGux8fX3VtGlTLV++XB07dpR0LngtX75cAwYMyHOeyMhILV++XIMHD7a1LVu2TJGRkXn2t1qtslqtdm2lSpVyRflXrZCQEP5RIhf2C1yIfQJ5Yb8omEuN1OVw+6HYoUOHKjY2Vs2aNVPz5s01YcIEpaenq2fPnpKk7t27q3LlyoqLi5MkDRo0SFFRUXr99dd11113ac6cOfr11181depUd74NAAAAt3N7sOvcubMOHjyokSNH6sCBA2rcuLGWLl1qu0Biz5498vL69+Ldli1bavbs2Xruuef0zDPPqFatWlq0aJHq16/vrrcAAABQLLg92EnSgAED8j30unLlylxtnTp1UqdOnYq4KlzIarVq1KhRuQ5t4+rGfoELsU8gL+wXl4fFOHLtLAAAAIo9tz95AgAAAK5BsAMAAPAQBDsAAAAPQbDDJX3//fdq3769KlWqJIvFokWLFrm7JLhZXFycbrjhBgUHB6t8+fLq2LGjkpKS3F0W3Gzy5Mlq2LCh7T5lkZGR+vLLL91dFoqRcePGyWKx2N2LFq5FsMMlpaenq1GjRpo0aZK7S0Ex8d1336l///5avXq1li1bpjNnzuj2229Xenq6u0uDG11zzTUaN26c1q5dq19//VW33HKL7rnnHv3555/uLg3FwJo1a/Tuu++qYcOG7i7Fo3FVLJxisVj0ySef2J4UAkjSwYMHVb58eX333Xdq06aNu8tBMRIaGqpXX31VvXr1cncpcKO0tDQ1adJE77zzjl544QU1btxYEyZMcHdZHokROwCFdvz4cUnn/icOSFJWVpbmzJmj9PT0fB/5iKtH//79dddddyk6OtrdpXi8YnGDYgBXruzsbA0ePFitWrXiCTDQhg0bFBkZqdOnTysoKEiffPKJ6tat6+6y4EZz5szRunXrtGbNGneXclUg2AEolP79+2vjxo364Ycf3F0KioHrrrtO69ev1/Hjx7VgwQLFxsbqu+++I9xdpfbu3atBgwZp2bJl8vPzc3c5VwXOsYNTOMcO5xswYIA+/fRTff/996pRo4a7y0ExFB0drfDwcL377rvuLgVusGjRIt17773y9va2tWVlZcliscjLy0sZGRl201B4jNgBcJoxRo8//rg++eQTrVy5klCHfGVnZysjI8PdZcBNbr31Vm3YsMGurWfPnqpdu7aefvppQl0RINjhktLS0rRt2zbb6507d2r9+vUKDQ1V1apV3VgZ3KV///6aPXu2Pv30UwUHB+vAgQOSpJIlS8rf39/N1cFdhg8frjvuuENVq1bViRMnNHv2bK1cuVJfffWVu0uDmwQHB+c69zYwMFBlypThnNwiQrDDJf36669q27at7fXQoUMlSbGxsZo5c6abqoI7TZ48WZJ0880327XHx8erR48el78gFAspKSnq3r279u/fr5IlS6phw4b66quvdNttt7m7NOCqwTl2AAAAHoL72AEAAHgIgh0AAICHINgBAAB4CIIdAACAhyDYAQAAeAiCHQAAgIcg2AEAAHgIgh0AAICHINgByFP16tU1YcIEly2vR48e6tixo8uWJ0krV66UxWLRsWPHXLpc5LZr1y5ZLBatX7++UMt5/vnn1bhxY5fUBCA3gh3g4Xr06CGLxSKLxSJfX19FRERozJgxOnv27EXnW7Nmjfr27euyOiZOnMgj6Iopi8WiRYsWXbRPlSpVtH//fp7vCRRzPCsWuAq0a9dO8fHxysjI0JIlS9S/f3/5+Pho+PDhufpmZmbK19dX5cqVc2kNJUuWdOnycHl5e3urQoUK7i4DwCUwYgdcBaxWqypUqKBq1aqpX79+io6O1meffSbp30OkL774oipVqqTrrrtOUu5DsRaLRdOmTdO9996rgIAA1apVy7aMHH/++afuvvtuhYSEKDg4WK1bt9b27dvt1pPj5ptv1oABAzRgwACVLFlSZcuW1YgRI3T+46s//PBDNWvWTMHBwapQoYIeeOABpaSkOPXejx07pkceeURhYWHy8/NT/fr19cUXX9imL1y4UPXq1ZPValX16tX1+uuv281fvXp1vfDCC+revbuCgoJUrVo1ffbZZzp48KDuueceBQUFqWHDhvr1119t88ycOVOlSpXSokWLVKtWLfn5+SkmJkZ79+61W/bkyZMVHh4uX19fXXfddfrwww/tpjuyzTdu3Kg77rhDQUFBCgsLU7du3XTo0CG77Txw4EA99dRTCg0NVYUKFfT888/bvT9Juvfee2WxWGyvL3Thodicw+DLly9Xs2bNFBAQoJYtWyopKcluvnHjxiksLEzBwcHq1auXTp8+nWvZ06ZNU506deTn56fatWvrnXfesU17+OGH1bBhQ2VkZEg694fH9ddfr+7du+dZJ3DVMwA8WmxsrLnnnnvs2jp06GCaNGlimx4UFGS6detmNm7caDZu3GiMMaZatWrmjTfesM0jyVxzzTVm9uzZZuvWrWbgwIEmKCjIHD582BhjzF9//WVCQ0PNf/7zH7NmzRqTlJRkZsyYYRITE/OsIyoqygQFBZlBgwaZxMRE89FHH5mAgAAzdepUW5/p06ebJUuWmO3bt5uEhAQTGRlp7rjjDtv0FStWGEnm6NGjeb73rKwsc+ONN5p69eqZr7/+2mzfvt18/vnnZsmSJcYYY3799Vfj5eVlxowZY5KSkkx8fLzx9/c38fHxtmVUq1bNhIaGmilTppgtW7aYfv36mZCQENOuXTszb948k5SUZDp27Gjq1KljsrOzjTHGxMfHGx8fH9OsWTPz008/mV9//dU0b97ctGzZ0rbcjz/+2Pj4+JhJkyaZpKQk8/rrrxtvb2/z7bffOrzNjx49asqVK2eGDx9uNm/ebNatW2duu+0207ZtW7vtHBISYp5//nmzZcsW8/777xuLxWK+/vprY4wxKSkpRpKJj483+/fvNykpKXluy507dxpJ5rfffrPb9i1atDArV640f/75p2ndurXde5w7d66xWq1m2rRpJjEx0Tz77LMmODjYNGrUyNbno48+MhUrVjQLFy40O3bsMAsXLjShoaFm5syZxhhjTpw4YWrWrGkGDx5sjDFm2LBhpnr16ub48eN51glc7Qh2gIc7P1BlZ2ebZcuWGavVaoYNG2abHhYWZjIyMuzmyyvYPffcc7bXaWlpRpL58ssvjTHGDB8+3NSoUcNkZmZesg5jzgWO88OQMcY8/fTTpk6dOvm+lzVr1hhJ5sSJE8aYSwe7r776ynh5eZmkpKQ8pz/wwAPmtttus2t78sknTd26dW2vq1WrZh566CHb6/379xtJZsSIEba2hIQEI8ns37/fGHMu2Ekyq1evtvXZvHmzkWR+/vlnY4wxLVu2NH369LFbd6dOncydd95pe32pbT527Fhz++232y1j7969RpLtPUdFRZmbbrrJrs8NN9xgnn76abv1fPLJJ3luoxz5BbtvvvnG1mfx4sVGkjl16pQxxpjIyEjz2GOP2S2nRYsWdsEuPDzczJ49267P2LFjTWRkpO31Tz/9ZHx8fMyIESNMiRIlzKpVqy5aK3A141AscBX44osvFBQUJD8/P91xxx3q3Lmz3eG4Bg0ayNfX95LLadiwoe33wMBAhYSE2A6Nrl+/Xq1bt5aPj4/Ddd14442yWCy215GRkdq6dauysrIkSWvXrlX79u1VtWpVBQcHKyoqSpK0Z88eh5a/fv16XXPNNbr22mvznL5582a1atXKrq1Vq1Z2NUj27zssLEzSuW12Ydv5h4lLlCihG264wfa6du3aKlWqlDZv3nzRdedMz2vdF27z33//XStWrFBQUJDtp3bt2pJkOwR+4TIkqWLFik4f0s7P+cuuWLGipH+3w+bNm9WiRQu7/pGRkbbf09PTtX37dvXq1cvuPbzwwgt29UdGRmrYsGEaO3asnnjiCd10000uqR3wRFw8AVwF2rZtq8mTJ8vX11eVKlVSiRL2//QDAwMdWs6Foc1isSg7O1uS5O/v75pi/5Genq6YmBjFxMRo1qxZKleunPbs2aOYmBhlZmY6tAxX1XT++84Jonm15WwLV7rYNk9LS1P79u318ssv55ovJ2RdahmurM/Z7ZCWliZJeu+993IFQG9vb9vv2dnZ+vHHH+Xt7a1t27YVtmTAozFiB1wFAgMDFRERoapVq+YKda7SsGFDrVq1SmfOnHF4np9//tnu9erVq1WrVi15e3srMTFRhw8f1rhx49S6dWvVrl3b6VGmhg0b6q+//tKWLVvynF6nTh39+OOPdm0//vijrr32WrtgURBnz561u6AiKSlJx44dU506dS667rp16zq8jiZNmujPP/9U9erVFRERYffjaFiXzoWz80coXaVOnTp5fsY5wsLCVKlSJe3YsSNX/TVq1LD1e/XVV5WYmKjvvvtOS5cuVXx8vMtrBTwFwQ6ASwwYMECpqanq0qWLfv31V23dulUffvhhrqskz7dnzx4NHTpUSUlJ+r//+z+99dZbGjRokCSpatWq8vX11VtvvaUdO3bos88+09ixY52qKSoqSm3atNF9992nZcuWaefOnfryyy+1dOlSSdITTzyh5cuXa+zYsdqyZYvef/99vf322xo2bFjBN8Q/fHx89Pjjj+vnn3/W2rVr1aNHD914441q3ry5JOnJJ5/UzJkzNXnyZG3dulXjx4/Xxx9/7NS6+/fvryNHjqhr165as2aNtm/frq+++ko9e/Z0KqhVr15dy5cv14EDB3T06FGn32t+Bg0apBkzZig+Pl5btmzRqFGj9Oeff9r1GT16tOLi4vTmm29qy5Yt2rBhg+Lj4zV+/HhJ0m+//aaRI0dq2rRpatWqlcaPH69BgwZpx44dLqsT8CQEOwAuUaZMGX377bdKS0tTVFSUmjZtqvfee++i59x1795dp06dUvPmzdW/f38NGjTIdlPkcuXKaebMmZo/f77q1q2rcePG6bXXXnO6roULF+qGG25Q165dVbduXT311FO20NOkSRPNmzdPc+bMUf369TVy5EiNGTNGPXr0KNA2OF9AQICefvppPfDAA2rVqpWCgoI0d+5c2/SOHTtq4sSJeu2111SvXj29++67io+P18033+zwOipVqqQff/xRWVlZuv3229WgQQMNHjxYpUqVkpeX41/vr7/+upYtW6YqVaro+uuvd+ZtXlTnzp01YsQIPfXUU2ratKl2796tfv362fXp3bu3pk2bpvj4eDVo0EBRUVGaOXOmatSoodOnT+uhhx5Sjx491L59e0lS37591bZtW3Xr1q1IRhmBK53FmPNuGgUAl8nNN9+sxo0bu/SxZcXFzJkzNXjwYB51BuCyY8QOAADAQxDsAAAAPASHYgEAADwEI3YAAAAegmAHAADgIQh2AAAAHoJgBwAA4CEIdgAAAB6CYAcAAOAhCHYAAAAegmAHAADgIQh2AAAAHuL/AW8EGB7+GIwzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### After Normalize, PC1, PC2 captures 93.5% of the data\n",
        "### If we choose up to PC2, then Transformed data shape would be (243, 2)"
      ],
      "metadata": {
        "id": "6Ka10_OjuHUn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "#standardized\n",
        "cov_mat_std = StandardScaler().fit_transform(cov_mat)\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "pca_normal = PCA(n_components = 4).fit(cov_mat_std)\n",
        "\n",
        "#print('Components:\\n', pca_normal.components_)\n",
        "\n",
        "cum_explained_variance_normal_pca = np.cumsum(pca_normal.explained_variance_ratio_)\n",
        "\n",
        "X_pca_normal = pca_normal.transform(cov_mat_std) # Apply dimensionality reduction to X."
      ],
      "metadata": {
        "id": "Ld6x6IIsLAhe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Explained variance ratio:\\n', pca_normal.explained_variance_ratio_)\n",
        "print('Cumulative explained variance with Normalization:\\n', cum_explained_variance_normal_pca)\n",
        "print('Transformed data shape:', X_pca_normal.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZ5zJAQ3rfyx",
        "outputId": "b594a94d-c316-4694-c913-7114c5384d0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance ratio:\n",
            " [7.08399582e-01 2.90841245e-01 6.26087172e-04 1.32506379e-04]\n",
            "Cumulative explained variance with Normalization:\n",
            " [0.70839958 0.99924083 0.99986691 0.99999942]\n",
            "Transformed data shape: (243, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,len(cum_explained_variance_normal_pca)), cum_explained_variance_normal_pca, '-o')\n",
        "plt.title('Cumulative explained variance normalized')\n",
        "plt.xticks(range(0,len(cum_explained_variance_normal_pca)))\n",
        "plt.xlabel('Principal Components')\n",
        "plt.ylabel('Cumulative explained Normalized variance');\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "acRz5kelsezn",
        "outputId": "fd7b903b-e8ab-4a52-887e-16c7128fc553"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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tyb4KHDlyBL1790aHDh3w448/wsnJCUZGRli9ejV+++23165fnMDAQJibm2Pjxo1o27YtNm7cCKlUquo/AuR/gEokEuzatavIx2lpaVns9pVKJQBgxIgRCAoKKnKZJk2aqN3es2cPgPwrkG7cuKHxB7ImPvjgA6xevRqTJk2Cr68vZDIZJBIJhgwZosr6Kpq8zkqlEl5eXqp+TC97+YOvNIYMGYLg4GCcP38e3t7e2LhxI9588021Yvyrr77CzJkzMWbMGHzxxReoXr06pFIpJk2aVORj1uToj0KhQNeuXZGamoqpU6eifv36sLCwQEJCAkaPHl1ou7q6+rA070VN8ggvdaLX9MvFq56z4vb1ugy3bt3Cm2++ifr162PBggVwdXWFsbExdu7cie+//16j9+vLYmNjMXz4cHTt2hVffvml2n0F21u7di0cHR0LrWtoyI/j8sZnnLTWq1cv/Pzzz4iMjISvr+8rly345peWlqbW/vI3PF0ozb42b94MU1NT7NmzR20cn9WrVxdaVpsjQhYWFujVqxc2bdqEBQsWYMOGDWjfvr1ax9jatWtDEAR4enqqjrJoquDqJoVCAX9//9cuf/HiRcydO1f1oT5u3DhcunSp0DddpVKJ27dvq+W5fv06ALxyJNo//vgDQUFBalfoZWVlFXpNSqN27dq4cOEC3nzzzVe+Fu7u7lAqlbh165baUZ+YmBiN99W3b1/85z//UZ0Gu379OqZPn662zB9//IHOnTtj5cqVau1paWmFjlpq6tKlS7h+/TrCw8MxatQoVfvevXtLtD0g/3nbs2cPUlNTiz0KVJr3ojYKXpsbN26gQYMGqvbk5GSkpaXB3d29zPZd4O+//0Z2dja2bdumdhRJ09N8L3v27Bn69+8PGxsb/P7776rTogUKjkTa29u/8v9qwWMvOGL0Im3eu/R67ANEWvv0009hYWGBcePGITk5udD9t27dwqJFiwDkHzGytbXF4cOH1Zb58ccfdZ6r4A/Mi/tSKBQaDchoYGAAiUSidrQoLi6uyBGfLSwstPpAHzx4MO7fv48VK1bgwoULav1HgPy+JgYGBpgzZ06hb8iCILzyNJ+BgQEGDBiAzZs3F3lU7uHDh6rfc3NzMXr0aDg7O2PRokVYs2YNkpOT8fHHHxe57YLLvAty/PDDDzAyMsKbb775yjwvP4bFixdrdBROU4MGDUJCQgKWL19e6L5nz54hMzMTAFRXjf3vf/9TW+bF04+vY2Njg4CAAGzcuBHr16+HsbFxoQEwi3rMmzZtQkJCgsb7eVnB0YsXtysIgur/VUkMGDAAgiAUeWl/wX5K817URo8ePQAUfi0KjuppcqVeaRX1HMvl8iK/9Gji3XffxfXr1/Hnn3+qvoy9KCAgANbW1vjqq6+Qm5tb6P6C/6tOTk7w9vZGeHi42qnOvXv34urVqyXKRkXjESDSWu3atfHbb7+pLld/cSTo48ePY9OmTaqxcYD8Ts3z5s3DuHHj0LJlSxw+fFh1NEGXGjVqhDZt2mD69Omqb7nr169X6/RYnJ49e2LBggXo1q0bhg0bhgcPHmDJkiWoU6eOWv8TAGjRogX27duHBQsWwNnZGZ6envDx8Sl22z169ICVlRWmTJmiKlheVLt2bXz55ZeYPn064uLi0LdvX1hZWSE2NhZ//vkn3nnnHUyZMqXY7c+bNw8HDhyAj48Pxo8fj4YNGyI1NRVnz57Fvn37kJqaCgCq/k0RERGwsrJCkyZNMGvWLMyYMQNvv/226kMJyO9Tsnv3bgQFBcHHxwe7du3Cjh078NlnnxXb9wbIPzq4du1ayGQyNGzYEJGRkdi3b1+RfWFKauTIkdi4cSPeffddHDhwAO3atYNCoUB0dDQ2btyoGi/G29sbQ4cOxY8//gi5XI62bdsiIiICN2/e1Gp/gwcPxogRI/Djjz8iICCg0GXgvXr1Uh1Va9u2LS5duoRff/212DGgNFG/fn3Url0bU6ZMQUJCAqytrbF58+ZCfYG00blzZ4wcORL/+9//cOPGDXTr1g1KpRJHjhxB586dMXHixFK/FzXVtGlTBAUF4eeff0ZaWho6duyIU6dOITw8HH379kXnzp1LvY/Xeeutt2BsbIzAwED85z//QUZGBpYvXw57e3skJiZqta0dO3bgl19+wYABA3Dx4kW1vxmWlpbo27cvrK2tsXTpUowcORLNmzfHkCFDYGdnh/j4eOzYsQPt2rVTfekICwtDz5494efnhzFjxiA1NRWLFy9Go0aNkJGRodPnQa+V09VmVAVdv35dGD9+vODh4SEYGxsLVlZWQrt27YTFixcLWVlZquWePn0qjB07VpDJZIKVlZUwaNAg4cGDB8VeBv/iZdaCkH+Zr4WFRaH9d+zYUWjUqJFa261btwR/f3/BxMREcHBwED777DNh7969Gl0Gv3LlSqFu3bqCiYmJUL9+fWH16tWqTC+Kjo4WOnToIJiZmQkAVJfEF3cZviAIwvDhwwUAgr+/f7HP5+bNmwU/Pz/BwsJCsLCwEOrXry+8//77QkxMTLHrFEhOThbef/99wdXVVTAyMhIcHR2FN998U/j5558FQRCEqKgowdDQUO3SdkHIH2agVatWgrOzs/D48WPVc2NhYSHcunVLeOuttwRzc3PBwcFBCA0NLXS598uv4ePHj4Xg4GDB1tZWsLS0FAICAoTo6GjB3d1dbeiA4i6Df/n1LMjz8muVk5MjzJ8/X2jUqJFgYmIiVKtWTWjRooUwZ84cQS6Xq5Z79uyZ8OGHHwo1atQQLCwshMDAQOHu3bsaXQZfID09XfVar1u3rtD9WVlZwuTJkwUnJyfBzMxMaNeunRAZGVno0umCx7xp06ZC2yjq+bh69arg7+8vWFpaCra2tsL48eOFCxcuFLr8urj/H0W9d/Py8oRvvvlGqF+/vmBsbCzY2dkJ3bt3F6KiotSWK+l7sbj/w0X938jNzRXmzJkjeHp6CkZGRoKrq6swffp0tb8dgpB/GXzPnj2Lfc5efj4L9nX69OnXZtu2bZvQpEkTwdTUVPDw8BDmz58vrFq1qlDW110GX7DPon5efu8eOHBACAgIEGQymWBqairUrl1bGD16tHDmzBm15TZv3iw0aNBAMDExERo2bChs2bKlyP8LVHISQXjN0K5EpFdGjx6NP/74g980iahKYx8gIiIi0jssgIiIiEjvsAAiIiIivcM+QERERKR3eASIiIiI9A4LICIiItI7HAixCEqlEvfv34eVlVWpJsIkIiKi8iMIAp48eQJnZ+dC05G8jAVQEe7fv6/TCRWJiIio/Ny9exc1a9Z85TIsgIpgZWUFIP8JtLa2FjkNERERaSI9PR2urq6qz/FXYQFUhILTXtbW1iyAiIiIKhlNuq+wEzQRERHpHRZAREREpHdYABEREZHeYQFEREREeocFEBEREekdFkBERESkd1gAERERkd5hAURERER6hwUQERER6R2OBE1ERIUolAJOxabiwZMs2FuZorVndRhIOTk0lU5Fel+JegTo8OHDCAwMhLOzMyQSCbZu3fradQ4ePIjmzZvDxMQEderUwZo1awots2TJEnh4eMDU1BQ+Pj44deqU7sMTVRAKpYDIW4/w1/kERN56BIVSEDsSVXK7LyfCb/5+DF1+Ah+tP4+hy0/Ab/5+7L6cKHY0qsQq2vtK1AIoMzMTTZs2xZIlSzRaPjY2Fj179kTnzp1x/vx5TJo0CePGjcOePXtUy2zYsAEhISEIDQ3F2bNn0bRpUwQEBODBgwdl9TCIRFPR/qBQ5bf7ciImrDuLRHmWWnuSPAsT1p3le4tKpCK+rySCIFSIr4sSiQR//vkn+vbtW+wyU6dOxY4dO3D58mVV25AhQ5CWlobdu3cDAHx8fNCqVSv88MMPAAClUglXV1d88MEHmDZtmkZZ0tPTIZPJIJfLORkqVVgFf1Be/g9ccDB56Yjm6NbYqbxjUSWmUArwm7+/0IdUAQkAR5kpjk7tUu6nLV7+qHrx5sv/BwotW+j+F+8rfrtF53jp9gvrF75P81yF9vuK/bxuX9o8/qK2jVc+t5rnKvhVoRQwYOlxPHySjaLo8n2lzed3peoDFBkZCX9/f7W2gIAATJo0CQCQk5ODqKgoTJ8+XXW/VCqFv78/IiMji91udnY2srP/fWHS09N1G5xIxxRKAXP+vlrojxPw7x+dz/68DBNDA0gkz9sEQCkIEIT824IgQPn8jn/bni+Df/+IqtZ5Yb383/9d78Xt4vl2BdV2il5P+cLveCGP2rbVtqG+Hgr2rSy8zMvrFWxX9VjVHmdx6+UvA7V9qz9X/y7/73rK5w9ItW+l+nMFQf05zt9P/gpCofWKeB1e2t/L+37x+Xz5tXllZgHIVSqRlass9n0nAEiUZ6HejF2QSiRafSgXZCr+vmJ3S1VcwfvqVGwqfGvXKLf9VqoCKCkpCQ4ODmptDg4OSE9Px7Nnz/D48WMoFIoil4mOji52u2FhYZgzZ06ZZCYqC6diU4v9ll4gNTMHwWtOl1Mi0id5+RWb2DGoFCQvHWh5+biL5IUFCt/38rrFb0yC/C9seRr0TXzw5NV/03StUhVAZWX69OkICQlR3U5PT4erq6uIiYheTdM/FM42ZrAxM4JUmv9HSiLJ/4Mkkbz0OwDp84b8tvzb+U3P/32+3Ivr5R+t/ndbqnVesd6/+5E838+Ly768vESVBS+0SQst++Lj+XcZvLAt6YvbffmxvbAM1J4T9W3j+WOWvPSY/33eXsj2fBmg8HP5uucNL+dVPR715+TF7FA9L5IiHs/zbRR6H6g/h+fjH+PjjRde+75aMqwZmrtXw/NHoXbf6z5Y1T8ctVtX8tICErX7Xl731RuTSIq965X7ed2+Xr7vZdqsW5pcLy8rpshbjzB0+YnXLmdvZVoOaf5VqQogR0dHJCcnq7UlJyfD2toaZmZmMDAwgIGBQZHLODo6FrtdExMTmJiYlElmorKg6R+K7wY2LddDylS5uVU3x9d7YpAkzyry+I4E+X01ujV24iXxpLHWntXhJDN97fuqtWf1cs1VqQZC9PX1RUREhFrb3r174evrCwAwNjZGixYt1JZRKpWIiIhQLUNUFRT8QSmOBICTCH9QqHIzkEoQGtgQQBFHF57/GxrYkMUPaaWivq9ELYAyMjJw/vx5nD9/HkD+Ze7nz59HfHw8gPxTU6NGjVIt/+677+L27dv49NNPER0djR9//BEbN27Exx9/rFomJCQEy5cvR3h4OK5du4YJEyYgMzMTwcHB5frYiMqSgVSCz3rUL/I+flBRaXRr7ISlI5rD8aUC21FmyisLqcQq4vtK1FNgZ86cQefOnVW3C/rhBAUFYc2aNUhMTFQVQwDg6emJHTt24OOPP8aiRYtQs2ZNrFixAgEBAaplBg8ejIcPH2LWrFlISkqCt7c3du/eXahjNFFl9+z51TpSyfOriJ5zlJkiNLAhP6ioxLo1dkLXho4VZsReqhoq2vuqwowDVJFwHCCq6ARBQLeFRxCT/ARTu9WDt2u1CvEHhYhITFV2HCAiynfkRgpikp/AwtgAw3zcITMzEjsSEVGlUqk6QRNRvuVHbgMABrVyZfFDRFQCLICIKpnopHQcuZECqQQY085T7DhERJUSCyCiSmbFkVgAQPfGTnCtbi5yGiKiyokFEFEl8iA9C3+dTwAAjGvPoz9ERCXFAoioEgmPjEOuQkAL92po5lZN7DhERJUWCyCiSuJpTh5+PZk/LtZ4Hv0hIioVFkBElcTmqHtIe5oL9xrm6Nqw+LntiIjo9VgAEVUCCqWAlUfzOz+PaefJgQ6JiEqJBRBRJbDvWjLiHj2FzMwIA1vWFDsOEVGlxwKIqBJY8Xzgw+E+bjA35gDuRESlxQKIqII7fzcNp+Mew8hAgqC2HmLHISKqElgAEVVwBUd/Aps6w8HaVOQ0RERVQ4kKoFu3bmHGjBkYOnQoHjx4AADYtWsXrly5otNwRPru3uOn2HU5CQAwzq+WyGmIiKoOrQugQ4cOwcvLCydPnsSWLVuQkZEBALhw4QJCQ0N1HpBIn60+FgeFUoBfHVs0dLYWOw4RUZWhdQE0bdo0fPnll9i7dy+MjY1V7V26dMGJEyd0Go5In6Vn5WLD6bsAOO0FEZGuaV0AXbp0Cf369SvUbm9vj5SUFJ2EIiJgw6m7yMjOQ117S3R8w07sOEREVYrWBZCNjQ0SExMLtZ87dw4uLi46CUWk73IVSqw+lj/w4bj2npBIOPAhEZEuaV0ADRkyBFOnTkVSUhIkEgmUSiWOHTuGKVOmYNSoUWWRkUjv7LyUiPvyLNhaGqOPN79YEBHpmtYF0FdffYX69evD1dUVGRkZaNiwITp06IC2bdtixowZZZGRSK8IgoAVR/KP/ozy9YCpkYHIiYiIqh6th5Q1NjbG8uXLMWvWLFy6dAkZGRlo1qwZ6tatWxb5iPTOydhUXEqQw8RQihFt3MWOQ0RUJZV4TH1XV1e4urrqMgsR4d+BDwe0qInqFsavWZqIiEpC61NgAwYMwPz58wu1f/311xg4cKBOQhHpq9sPM7DvWv7gomP9eOk7EVFZ0boAOnz4MHr06FGovXv37jh8+LBOQhHpq5VH8/v++DewR207S5HTEBFVXVoXQBkZGWoDIBYwMjJCenq6TkIR6aPUzBz8EXUPADCuPae9ICIqS1oXQF5eXtiwYUOh9vXr16Nhw4Y6CUWkj9aduIPsPCW8XGTw8awudhwioipN607QM2fORP/+/XHr1i106dIFABAREYHff/8dmzZt0nlAIn2QlavAL5FxADjwIRFRedC6AAoMDMTWrVvx1Vdf4Y8//oCZmRmaNGmCffv2oWPHjmWRkajK23b+PlIycuAkM0UPLyex4xARVXklugy+Z8+e6Nmzp66zEOklQRCw4mj+pe/B7TxgZKD1mWkiItJSiccBysnJwYMHD6BUKtXa3dzcSh2KSJ8cuv4Q15MzYGliiCGt+f+HiKg8aF0A3bhxA2PGjMHx48fV2gVBgEQigUKh0Fk4In1QMO3F4FausDY1EjkNEZF+0LoAGj16NAwNDbF9+3Y4OTmxsyZRKVy9n46jN1MgleSf/iIiovKhdQF0/vx5REVFoX79+mWRh0ivFPT96e7lhJrVzEVOQ0SkP7TubdmwYUOkpKSURRYivZKcnoW/L9wHAIznwIdEROVK6wJo/vz5+PTTT3Hw4EE8evQI6enpaj9EpJnw43HIVQho5VEN3q42YschItIrWp8C8/f3BwC8+eabau3sBE2kuac5efj1ZDwATntBRCQGrQugAwcOlEUOIr2y6cw9yJ/lwqOGOfwbOIgdh4hI72hdAHG0Z6LSUSgF1azvY/08YSDllZREROWtxAMhPn36FPHx8cjJyVFrb9KkSalDEVVle68mIz71KWRmRhjQoqbYcYiI9JLWBdDDhw8RHByMXbt2FXk/+wARvdqKI/mXvo9o4wZz4xJ/ByEiolLQ+iqwSZMmIS0tDSdPnoSZmRl2796N8PBw1K1bF9u2bSuLjERVxrn4xzhz5zGMDaQI8vUQOw4Rkd7S+uvn/v378ddff6Fly5aQSqVwd3dH165dYW1tjbCwME6SSvQKBdNe9PZ2hr21qchpiIj0l9ZHgDIzM2Fvbw8AqFatGh4+fAgA8PLywtmzZ3WbjqgKuZv6FLsuJwIAxrX3FDkNEZF+07oAqlevHmJiYgAATZs2xU8//YSEhAQsW7YMTk5OOg9IVFWsOhYLpQC0r2uL+o7WYschItJrWp8C++ijj5CYmP8tNjQ0FN26dcOvv/4KY2NjrFmzRtf5iKoE+bNcbDx9FwAHPiQiqgi0LoBGjBih+r1Fixa4c+cOoqOj4ebmBltbW52GI6oq1p+KR2aOAvUcrNChLv+fEBGJTetTYC8zNzdH8+bNS1z8LFmyBB4eHjA1NYWPjw9OnTpV7LK5ubmYO3cuateuDVNTUzRt2hS7d+9WW2b27NmQSCRqP5y5nsSUq1BizfE4AMDY9p6QSDjwIRGR2DQ6AhQSEoIvvvgCFhYWCAkJeeWyCxYs0HjnGzZsQEhICJYtWwYfHx8sXLgQAQEBiImJUXW0ftGMGTOwbt06LF++HPXr18eePXvQr18/HD9+HM2aNVMt16hRI+zbt09129CQY62QeHZcTESiPAu2libo4+0sdhwiIoKGBdC5c+eQm5sLADh79myx32C1/Wa7YMECjB8/HsHBwQCAZcuWYceOHVi1ahWmTZtWaPm1a9fi888/R48ePQAAEyZMwL59+/Ddd99h3bp1/z4oQ0M4OjpqlYWoLAiCgOXPBz4M8nWHiaGByImIiAjQsAB6cQLUgwcP6mTHOTk5iIqKwvTp01VtUqkU/v7+iIyMLHKd7OxsmJqqj51iZmaGo0ePqrXduHEDzs7OMDU1ha+vL8LCwuDm5qaT3ETaOHE7FVfup8PUSIoRbdzFjkNERM9p1QcoNzcXhoaGuHz5cql3nJKSAoVCAQcH9ZmwHRwckJSUVOQ6AQEBWLBgAW7cuAGlUom9e/diy5YtqqvSAMDHxwdr1qzB7t27sXTpUsTGxqJ9+/Z48uRJsVmys7ORnp6u9kOkCwXTXrzdoiaqWRiLnIaIiApoVQAZGRnBzc1NtPm+Fi1ahLp166J+/fowNjbGxIkTERwcDKn034fRvXt3DBw4EE2aNEFAQAB27tyJtLQ0bNy4sdjthoWFQSaTqX5cXV3L4+FQFXfzQQYioh9AIgHG+vHSdyKiikTrq8A+//xzfPbZZ0hNTS3Vjm1tbWFgYIDk5GS19uTk5GL779jZ2WHr1q3IzMxUXX5vaWmJWrWK/3CxsbHBG2+8gZs3bxa7zPTp0yGXy1U/d+/eLdmDInrByqP50174N3CAp62FyGmIiOhFWl8e9cMPP+DmzZtwdnaGu7s7LCzU/7BrOh2GsbExWrRogYiICPTt2xcAoFQqERERgYkTJ75yXVNTU7i4uCA3NxebN2/GoEGDil02IyMDt27dwsiRI4tdxsTEBCYmJhrlJtLEo4xsbDl7DwAwngMfEhFVOFoXQAXFii6EhIQgKCgILVu2ROvWrbFw4UJkZmaqrgobNWoUXFxcEBYWBgA4efIkEhIS4O3tjYSEBMyePRtKpRKffvqpaptTpkxBYGAg3N3dcf/+fYSGhsLAwABDhw7VWW6i11l74g6y85RoUlOGVh7VxI5DREQv0boACg0N1dnOBw8ejIcPH2LWrFlISkqCt7c3du/ereoYHR8fr9a/JysrCzNmzMDt27dhaWmJHj16YO3atbCxsVEtc+/ePQwdOhSPHj2CnZ0d/Pz8cOLECdjZ2eksN9GrZOUqsDbyDoD8aS848CERUcUjEQRBEDtERZOeng6ZTAa5XA5ra05aSdpZfyoe07ZcgouNGQ590gmGBqUecJ2IiDSgzee31keAFAoFvv/+e2zcuBHx8fHIyclRu7+0naOJKjOlUsCK552fg9t5sPghIqqgtP7rPGfOHCxYsACDBw+GXC5HSEgI+vfvD6lUitmzZ5dBRKLK49D1h7j5IAOWJoYY3IrDKRARVVRaF0C//vorli9fjsmTJ8PQ0BBDhw7FihUrMGvWLJw4caIsMhJVGgXTXgxp5QorUyOR0xARUXG0LoCSkpLg5eUFALC0tIRcLgcA9OrVCzt27NBtOqJK5Mp9OY7fegQDqQTBfp5ixyEiolfQugCqWbOmauqJ2rVr459//gEAnD59mmPpkF5beSS/708PLye42JiJnIaIiF5F6wKoX79+iIiIAAB88MEHmDlzJurWrYtRo0ZhzJgxOg9IVBkkybOw7cJ9AMD49jz6Q0RU0Wl9Fdi8efNUvw8ePBju7u44fvw46tati8DAQJ2GI6os1hyPQ55SQGvP6mhS00bsOERE9BpaF0BZWVkwNTVV3W7Tpg3atGmj01BElUlmdh5+O/l84EP2/SEiqhS0PgVmb2+PoKAg7N27F0qlsiwyEVUqG8/cRXpWHjxtLeDfwEHsOEREpAGtC6Dw8HA8ffoUffr0gYuLCyZNmoQzZ86URTaiCk+hFLDqWH7n5zF+npBKOe0FEVFlUKJO0Js2bUJycjK++uorXL16FW3atMEbb7yBuXPnlkVGogrrnytJuJv6DNXMjfB285pixyEiIg2VeJx+KysrBAcH459//sHFixdhYWGBOXPm6DIbUYVXMPDhiDbuMDM2EDkNERFpqsQFUFZWFjZu3Ii+ffuiefPmSE1NxSeffKLLbEQVWtSdxzgbnwZjAylG+rqLHYeIiLSg9VVge/bswW+//YatW7fC0NAQb7/9Nv755x906NChLPIRVVgrnh/96ePtDHsr09csTUREFYnWBVC/fv3Qq1cv/PLLL+jRoweMjDjfEemf+EdPsedKEgBgXPtaIqchIiJtaV0AJScnw8rKqiyyEFUaq47FQikAHd6wQz1H/n8gIqpstO4DxOKH9J38aS42nrkLgNNeEBFVViXuBE2kr347FY+nOQrUd7SCXx1bseMQEVEJsAAi0kJOnhJrjucPfDjWzxMSCQc+JCKqjFgAEWlh+8X7SE7Php2VCXp7O4sdh4iISogFEJGGBEHAiiP5R39Gt/WAiSEHPiQiqqw0ugqsf//+Gm9wy5YtJQ5DVJFF3nqEq4npMDMywHAfN7HjEBFRKWh0BEgmk6l+rK2tERERoTYBalRUFCIiIiCTycosKJHYCqa9GNiyJmzMjUVOQ0REpaHREaDVq1erfp86dSoGDRqEZcuWwcAg/xSAQqHAe++9B2tr67JJSSSymw+e4EDMQ0gkwJh2vPSdiKiy07oP0KpVqzBlyhRV8QMABgYGCAkJwapVq3QajqiiKOj707WBAzxsLUROQ0REpaV1AZSXl4fo6OhC7dHR0VAqlToJRVSRpGRkY8u5BADA+A6c9oKIqCrQeiqM4OBgjB07Frdu3ULr1q0BACdPnsS8efMQHBys84BEYlsbeQc5eUo0dbVBS/dqYschIiId0LoA+vbbb+Ho6IjvvvsOiYmJAAAnJyd88sknmDx5ss4DEokpK1eBtSfuAMif9oIDHxIRVQ0SQRCEkq6cnp4OAFWu83N6ejpkMhnkcnmVe2yknd9OxuOzPy/BxcYMhz7pBEMDDp1FRFRRafP5XaK/5nl5edi3bx9+//131Tfi+/fvIyMjoySbI6qQlEoBK47mX/oe3M6DxQ8RURWi9SmwO3fuoFu3boiPj0d2dja6du0KKysrzJ8/H9nZ2Vi2bFlZ5CQqdwevP8Dth5mwMjHE4FauYschIiId0vor7UcffYSWLVvi8ePHMDMzU7X369cPEREROg1HJKblh/MvfR/q4wYrUyOR0xARkS5pfQToyJEjOH78OIyN1UfC9fDwQEJCgs6CEYnpcoIckbcfwVAqwei2HmLHISIiHdP6CJBSqYRCoSjUfu/ePVhZWekkFJHYVjyf9qJnEyc425i9ZmkiIqpstC6A3nrrLSxcuFB1WyKRICMjA6GhoejRo4cusxGJIlH+DNsv5g/xMM6PAx8SEVVFWp8C++677xAQEICGDRsiKysLw4YNw40bN2Bra4vff/+9LDISlas1x+KQpxTg41kdXjU5wS8RUVWkdQFUs2ZNXLhwARs2bMCFCxeQkZGBsWPHYvjw4Wqdookqo4zsPPx2Kh4AML49j/4QEVVVWhdAAGBoaIjhw4dj+PDhus5DJKqNp+/iSVYeatlZoEt9e7HjEBFRGdG6D5CBgQE6d+6M1NRUtfbk5GS1GeKJKps8hRKrjuVf+j7WzxNSKae9ICKqqrQugARBQHZ2Nlq2bIkrV64Uuo+ostpzJRn3Hj9DNXMjDGheU+w4RERUhrQugCQSCTZv3ozAwED4+vrir7/+UruPqDISBAHLn1/6PrKNO0yNeDSTiKgqK9ERIAMDAyxatAjffvstBg8ejC+//JJHf6hSOxv/GOfvpsHYUIqRvh5ixyEiojJWok7QBd555x3UrVsXAwcOxOHDh3WViajcFUx70c/bBXZWJiKnISKisqb1ESB3d3e1zs6dO3fGiRMncPfuXZ0GIyovdx5lYs/VJADAuPaeIqchIqLyoPURoNjY2EJtderUwblz55CcnKyTUETladXRWAgC0KmeHeo6cDoXIiJ9oPURoOKYmprC3d1dV5sjKhdpT3Ow8cw9AJz2gohIn2h0BKh69eq4fv06bG1tUa1atVde7fXy+EBEFdmvJ+PxLFeB+o5WaFenhthxiIionGhUAH3//feqmd5fnAhVF5YsWYJvvvkGSUlJaNq0KRYvXozWrVsXuWxubi7CwsIQHh6OhIQE1KtXD/Pnz0e3bt1KvE3SXzl5SoQfjwOQP+0Fh3EgItIfGhVAQUFBRf5eWhs2bEBISAiWLVsGHx8fLFy4EAEBAYiJiYG9feFpCGbMmIF169Zh+fLlqF+/Pvbs2YN+/frh+PHjaNasWYm2Sfrr7wv38eBJNhysTRDY1FnsOEREVI4kggYD+KSnp2u8QWtra42X9fHxQatWrfDDDz8AAJRKJVxdXfHBBx9g2rRphZZ3dnbG559/jvfff1/VNmDAAJiZmWHdunUl2mZR0tPTIZPJIJfLtXo8VHkIgoDui44gOukJPu1WD+91qiN2JCIiKiVtPr81OgJkY2Pz2tMDgiBAIpFAoVBoFDInJwdRUVGYPn26qk0qlcLf3x+RkZFFrpOdnQ1TU1O1NjMzMxw9erTE2yzYbnZ2tuq2NgUfVU7Hbj5CdNITmBkZYFhrN7HjEBFROdOoADpw4IDOd5ySkgKFQgEHBwe1dgcHB0RHRxe5TkBAABYsWIAOHTqgdu3aiIiIwJYtW1RFV0m2CQBhYWGYM2dOKR8RVSYF014MalkTNubGIqchIqLyplEB1LFjx7LOoZFFixZh/PjxqF+/PiQSCWrXro3g4GCsWrWqVNudPn06QkJCVLfT09Ph6upa2rhUQV1PfoJD1x9CIgHG+HHgQyIifVTiqTCePn2K+Ph45OTkqLU3adJEo/VtbW1hYGBQaPDE5ORkODo6FrmOnZ0dtm7diqysLDx69AjOzs6YNm0aatWqVeJtAoCJiQlMTDj9gb5YeSR/MM+Aho5wr2EhchoiIhKD1gMhPnz4EL169YKVlRUaNWqEZs2aqf1oytjYGC1atEBERISqTalUIiIiAr6+vq9c19TUFC4uLsjLy8PmzZvRp0+fUm+T9MPDJ9n481wCAGB8Bx79ISLSV1oXQJMmTUJaWhpOnjwJMzMz7N69G+Hh4ahbty62bdum1bZCQkKwfPlyhIeH49q1a5gwYQIyMzMRHBwMABg1apRah+aTJ09iy5YtuH37No4cOYJu3bpBqVTi008/1XibpN/WRsYhR6FEMzcbtHCvLnYcIiISidanwPbv34+//voLLVu2hFQqhbu7O7p27Qpra2uEhYWhZ8+eGm9r8ODBePjwIWbNmoWkpCR4e3tj9+7dqk7M8fHxkEr/rdGysrIwY8YM3L59G5aWlujRowfWrl0LGxsbjbdJ+utZjgJrT9wBwGkviIj0nUbjAL3I2toaFy9ehIeHB9zd3fHbb7+hXbt2iI2NRaNGjfD06dOyylpuOA5Q1bTuxB3M2HoZNauZ4eCUTjA00NlUeEREVAFo8/mt9SdAvXr1EBMTAwBo2rQpfvrpJyQkJGDZsmVwcnIqWWKiMqZUClh1NL/z85h2nix+iIj0nNanwD766CMkJiYCAEJDQ9GtWzf8+uuvMDY2xpo1a3Sdj0gn9kc/wO2UTFiZGmJQKw5xQESk77QugEaMGKH6vUWLFrhz5w6io6Ph5uYGW1tbnYYj0pWCgQ+H+bjB0qTEoz8QEVEVUepPAnNzczRv3lwXWYjKxKV7cpyMTYWhVILRbT3EjkNERBWA1gWQIAj4448/cODAATx48ABKpVLt/i1btugsHJEuFBz96dXECU4yM5HTEBFRRaB1ATRp0iT89NNP6Ny5MxwcHF47SSqRmO6nPcOOS/l91sa156XvRESUT+sCaO3atdiyZQt69OhRFnmIdGrN8TgolAJ8a9VAYxeZ2HGIiKiC0PpaYJlMppp7i6gie5KVi99PxgPgtBdERKRO6wJo9uzZmDNnDp49e1YWeYh0ZsPpu3iSnYfadhbo9Ia92HGIiKgC0foU2KBBg/D777/D3t4eHh4eMDIyUrv/7NmzOgtHVFJ5CiVWH4sDAIz1qwWplH3ViIjoX1oXQEFBQYiKisKIESPYCZoqrF2Xk5CQ9gw1LIzRv7mL2HGIiKiC0boA2rFjB/bs2QM/P7+yyENUaoIgYMXzS99HtHGHqZGByImIiKii0boPkKurKycIpQrtzJ3HuHBPDmNDKUb6uosdh4iIKiCtC6DvvvsOn376KeLi4sogDlHpLT+cf/RnQHMX2FqaiJyGiIgqohLNBfb06VPUrl0b5ubmhTpBp6am6iwckbZiUzKx91oyAGCsHy99JyKiomldAC1cuLAMYhDpxqqjsRAEoHM9O9SxtxI7DhERVVBaFUC5ubk4dOgQZs6cCU9PfrumiiXtaQ42Rd0FAIzntBdERPQKWvUBMjIywubNm8sqC1Gp/HoyHlm5SjR0soZv7RpixyEiogpM607Qffv2xdatW8sgClHJZecpsOZ4HID8aS84PhUREb2K1n2A6tati7lz5+LYsWNo0aIFLCws1O7/8MMPdRaOSFPbzt/HwyfZcLA2QU8vZ7HjEBFRBScRBEHQZoVX9f2RSCS4fft2qUOJLT09HTKZDHK5nGMeVQKCIKD7oiOITnqCqd3qY0Kn2mJHIiIiEWjz+a31EaDY2NgSByMqC0dupCA66QnMjQ0wrLWb2HGIiKgS0LoP0IsEQYCWB5CIdG7F0fyifFBLV8jMjV6zNBERUQkLoF9++QVeXl4wMzODmZkZmjRpgrVr1+o6G9FrxSQ9weHrDyGVcOBDIiLSnNanwBYsWICZM2di4sSJaNeuHQDg6NGjePfdd5GSkoKPP/5Y5yGJilMw6Wm3xo5wrW4uchoiIqostC6AFi9ejKVLl2LUqFGqtt69e6NRo0aYPXs2CyAqNw+eZOGv8/cBAGP9OPAhERFpTutTYImJiWjbtm2h9rZt2yIxMVEnoYg08cvxO8hRKNHczQYt3KuJHYeIiCoRrQugOnXqYOPGjYXaN2zYgLp16+okFNHrPMtRYN3JOwA47QUREWlP61Ngc+bMweDBg3H48GFVH6Bjx44hIiKiyMKIqCz8cfYe0p7mwq26Od5q5Ch2HCIiqmS0PgI0YMAAnDx5Era2tti6dSu2bt0KW1tbnDp1Cv369SuLjERqlEoBq55f+j6mnQcMpJz2goiItKP1ESAAaNGiBdatW6frLEQa2XctGbEpmbA2NcTAlq5ixyEiokqoVAMhEolhxZH8oz/DfNxhYVKiGp6IiPScxp8eUqn0tTNsSyQS5OXllToUUXEu3E3DqbhUGEolGN3WQ+w4RERUSWlcAP3555/F3hcZGYn//e9/UCqVOglFVJyCaS96N3WGo8xU5DRERFRZaVwA9enTp1BbTEwMpk2bhr///hvDhw/H3LlzdRqO6EUJac+w81L+WFNj23PaCyIiKrkS9QG6f/8+xo8fDy8vL+Tl5eH8+fMIDw+Hu7u7rvMRqaw+GguFUkDb2jXQyFkmdhwiIqrEtCqA5HI5pk6dijp16uDKlSuIiIjA33//jcaNG5dVPiIAQHpWLtafvguAAx8SEVHpaXwK7Ouvv8b8+fPh6OiI33//vchTYkRlZcOpu8jIzkMde0t0fMNO7DhERFTJSQRBEDRZUCqVwszMDP7+/jAwMCh2uS1btugsnFjS09Mhk8kgl8thbW0tdhy9l6dQosPXB3BfnoV5/b0wpLWb2JGIiKgC0ubzW+MjQKNGjXrtZfBEZWHn5STcl2fB1tIYfZu5iB2HiIiqAI0LoDVr1pRhDKKiCYKAFUduAwBGtvGAqVHxRx+JiIg0xZGgqUI7FZuKi/fkMDGUYkQbnvoiIiLdYAFEFdry59Ne9G9eEzUsTUROQ0REVQULIKqwbj/MQER0MgBgrB8HPiQiIt1hAUQV1qpjsRAE4M369qhjbyl2HCIiqkJYAFGF9DgzB39E3QMAjOPAh0REpGMaXQW2bds2jTfYu3dvrQIsWbIE33zzDZKSktC0aVMsXrwYrVu3Lnb5hQsXYunSpYiPj4etrS3efvtthIWFwdQ0f2LM2bNnY86cOWrr1KtXD9HR0VrlInGtO3EHWblKNHK2Rpta1cWOQ0REVYxGBVDfvn3VbkskErw4fuKL4wMpFAqNd75hwwaEhIRg2bJl8PHxwcKFCxEQEICYmBjY29sXWv63337DtGnTsGrVKrRt2xbXr1/H6NGjIZFIsGDBAtVyjRo1wr59+/59kIYaX+1PFUBWrgLhkXcA5E97wfGniIhI1zQ6BaZUKlU///zzD7y9vbFr1y6kpaUhLS0NO3fuRPPmzbF7926tdr5gwQKMHz8ewcHBaNiwIZYtWwZzc3OsWrWqyOWPHz+Odu3aYdiwYfDw8MBbb72FoUOH4tSpU2rLGRoawtHRUfVja2urVS4S17bz95GSkQ0nmSl6NnESOw4REVVBWvcBmjRpEhYtWoSAgABYW1vD2toaAQEBWLBgAT788EONt5OTk4OoqCj4+/v/G0Yqhb+/PyIjI4tcp23btoiKilIVPLdv38bOnTvRo0cPteVu3LgBZ2dn1KpVC8OHD0d8fLy2D5NEIggCVhzNH/hwdFsPGBmwmxoREeme1ueGbt26BRsbm0LtMpkMcXFxGm8nJSUFCoUCDg4Oau0ODg7F9tcZNmwYUlJS4OfnB0EQkJeXh3fffRefffaZahkfHx+sWbMG9erVQ2JiIubMmYP27dvj8uXLsLKyKnK72dnZyM7OVt1OT0/X+HGQbh2+kYLryRmwMDbgnF9ERFRmtP563apVK4SEhCA5OVnVlpycjE8++eSVnZd14eDBg/jqq6/w448/4uzZs9iyZQt27NiBL774QrVM9+7dMXDgQDRp0gQBAQHYuXMn0tLSsHHjxmK3GxYWBplMpvpxdXUt08dBxSuY9mJQK1fIzIxETkNERFWV1keAVq1ahX79+sHNzU1VKNy9exd169bF1q1bNd6Ora0tDAwM1AopIL+YcnR0LHKdmTNnYuTIkRg3bhwAwMvLC5mZmXjnnXfw+eefQyotXM/Z2NjgjTfewM2bN4vNMn36dISEhKhup6enswgSwbXEdBy5kQKpBBjTjgMfEhFR2dG6AKpTpw4uXryIvXv3qk5VNWjQAP7+/lpdrWNsbIwWLVogIiJCdZWZUqlEREQEJk6cWOQ6T58+LVTkGBjkT4754lVpL8rIyMCtW7cwcuTIYrOYmJjAxITTLIhtxfNpL7o3doJrdXOR0xARUVVWouvDJRIJ3nrrLXTo0AEmJiYlvkw5JCQEQUFBaNmyJVq3bo2FCxciMzMTwcHBAIBRo0bBxcUFYWFhAIDAwEAsWLAAzZo1g4+PD27evImZM2ciMDBQVQhNmTIFgYGBcHd3x/379xEaGgoDAwMMHTq0RBmpfDxIz8K2CwkAgHHtefSHiIjKltYFkFKpxH//+18sW7YMycnJuH79OmrVqoWZM2fCw8MDY8eO1XhbgwcPxsOHDzFr1iwkJSXB29sbu3fvVnWMjo+PVzviM2PGDEgkEsyYMQMJCQmws7NDYGAg/vvf/6qWuXfvHoYOHYpHjx7Bzs4Ofn5+OHHiBOzs7LR9qFSOwiPjkKsQ0NK9Gpq5VRM7DhERVXESobhzR8WYO3cuwsPDMXfuXIwfPx6XL19GrVq1sGHDBixcuLDYS9grk/T0dMhkMsjlclhbW4sdp8p7mpMH37D9kD/LxbIRLdCtcdF9wIiIiF5Fm89vra8C++WXX/Dzzz9j+PDhqtNOANC0aVNON0El8kfUPcif5cK9hjm6NnR4/QpERESlpHUBlJCQgDp16hRqVyqVyM3N1Uko0h8KpYCVR/M7P49p5wkDKae9ICKisqd1AdSwYUMcOXKkUPsff/yBZs2a6SQU6Y+9V5Nx59FTyMyMMLBlTbHjEBGRntC6E/SsWbMQFBSEhIQEKJVKbNmyBTExMfjll1+wffv2sshIVdjK59NeDPdxg7kxJ60lIqLyofURoD59+uDvv//Gvn37YGFhgVmzZuHatWv4+++/0bVr17LISFXU+btpOB33GEYGEgS19RA7DhER6ZESfeVu37499u7dq+sspGeWP5/2IrCpMxysTUVOQ0RE+qTE5xxycnLw4MEDKJVKtXY3N05gSa93N/Updl1KBACM86slchoiItI3WhdAN27cwJgxY3D8+HG1dkEQIJFIoFAodBaOqq7Vx+KgFAC/OrZo6MyxloiIqHxpXQCNHj0ahoaG2L59O5ycnEo8DQbpr/SsXGw4HQ+A014QEZE4tC6Azp8/j6ioKNSvX78s8pAeWH8qHpk5CrzhYImOb3CKEiIiKn8lGgcoJSWlLLKQHshVKLH6WByA/L4/PIJIRERi0LoAmj9/Pj799FMcPHgQjx49Qnp6utoP0avsvJSIRHkWbC2N0dvbWew4RESkp7Q+Bebv7w8AePPNN9Xa2QmaXkcQBNWl76N8PWBqZPCaNYiIiMqG1gXQgQMHyiIH6YETt1NxOSEdpkZSjGjjLnYcIiLSY1oXQB07diyLHKQHCqa9GNC8JqpbGIuchoiI9JlGBdDFixfRuHFjSKVSXLx48ZXLNmnSRCfBqGq59TAD+649AACM9eOl70REJC6NCiBvb28kJSXB3t4e3t7ekEgkEASh0HLsA0TFWXk0FgDg38AetewsRU5DRET6TqMCKDY2FnZ2dqrfibTxKCMbm6PuAQDGtee0F0REJD6NCiB3d/cifyfSxLoT8cjOU8LLRQYfz+pixyEiIir5ZKhXr15FfHw8cnJy1Np79+5d6lBUdWTlKrD2RByA/GkvOPAhERFVBFoXQLdv30a/fv1w6dIltb5ABR9s7ANEL/rrfAJSMnLgLDNFDy8nseMQEREBKMFI0B999BE8PT3x4MEDmJub48qVKzh8+DBatmyJgwcPlkFEqqwEQcCKI/l9xka384CRgdZvNyIiojKh9RGgyMhI7N+/H7a2tpBKpZBKpfDz80NYWBg+/PBDnDt3rixyUiV08PpD3HiQAUsTQwxp7SZ2HCIiIhWtv5IrFApYWVkBAGxtbXH//n0A+Z2jY2JidJuOKrUVz6e9GNzKFdamRiKnISIi+pfWR4AaN26MCxcuwNPTEz4+Pvj6669hbGyMn3/+GbVq8RJnynflvhzHbj6CgVSC4HYeYschIiJSo3UBNGPGDGRmZgIA5s6di169eqF9+/aoUaMGNmzYoPOAVDkVDHzYvbEjalYzFzkNERGROq0LoICAANXvderUQXR0NFJTU1GtWjVe4kwAgOT0LPx9If/UKAc+JCKiiqjE4wC9qHp1Dm5H/1pzPA65CgGtPKrB29VG7DhERESFaFQA9e/fX+MNbtmypcRhqPLLzM7DryfuAODRHyIiqrg0KoBkMllZ56AqYtOZu0jPyoNHDXP4N3AQOw4REVGRNCqAVq9eXdY5qApQKAWsOhYHABjr5wkDKfuEERFRxVTiPkAPHjxQjftTr1492Nvb6ywUVU57ryYhPvUpbMyNMKBFTbHjEBERFUvrgRDT09MxcuRIuLi4oGPHjujYsSNcXFwwYsQIyOXysshIlcTy59NeDPdxg7mxTvrXExERlQmtC6Dx48fj5MmT2L59O9LS0pCWlobt27fjzJkz+M9//lMWGakSOBv/GFF3HsPYQIogXw+x4xAREb2S1l/Tt2/fjj179sDPz0/VFhAQgOXLl6Nbt246DUeVR8G0F729nWFvbSpyGiIiolfT+ghQjRo1irwqTCaToVq1ajoJRZXL3dSn2H05CQAwrr2nyGmIiIheT+sCaMaMGQgJCUFSUpKqLSkpCZ988glmzpyp03BUOaw6FgulALSva4v6jtZixyEiInotrU+BLV26FDdv3oSbmxvc3NwAAPHx8TAxMcHDhw/x008/qZY9e/as7pJShSR/louNp+8C4MCHRERUeWhdAPXt27cMYlBl9fupeGTmKFDPwQod6tqKHYeIiEgjWhdAoaGhZZGDKqGcPCXWFAx82N6Tk+ESEVGloXUfoAMHDhR734unv6jq23HpPpLSs2BnZYI+3s5ixyEiItKY1gVQt27d8MknnyA3N1fVlpKSgsDAQEybNk2n4ajiEgQBK54PfBjk6w4TQwORExEREWmuREeA/vzzT7Rq1QpXr17Fjh070LhxY6Snp+P8+fNlEJEqosjbj3DlfjpMjaQY7uMudhwiIiKtaF0AtW3bFufPn0fjxo3RvHlz9OvXDx9//DEOHjwId3d+EOqLgqM/b7eoiWoWxiKnISIi0o7WBRAAXL9+HWfOnEHNmjVhaGiImJgYPH36VNfZqIK6+eAJ9kc/gEQCjPXjpe9ERFT5aF0AzZs3D76+vujatSsuX76MU6dO4dy5c2jSpAkiIyPLIiNVMCuP5h/98W/gAE9bC5HTEBERaU/rAmjRokXYunUrFi9eDFNTUzRu3BinTp1C//790alTpzKISBVJSkY2Np9NAACM58CHRERUSWldAF26dAndu3dXazMyMsI333yDf/75R+sAS5YsgYeHB0xNTeHj44NTp069cvmFCxeiXr16MDMzg6urKz7++GNkZWWVapukuXUn7iAnT4mmNWVo5cG534iIqHLSugCytbVFWloaVqxYgenTpyM1NRVA/rQXderU0WpbGzZsQEhICEJDQ3H27Fk0bdoUAQEBePDgQZHL//bbb5g2bRpCQ0Nx7do1rFy5Ehs2bMBnn31W4m2S5rJyFVgbeQcAMLZ9LQ58SERElZZEEARBmxUuXrwIf39/yGQyxMXFISYmBrVq1cKMGTMQHx+PX375ReNt+fj4oFWrVvjhhx8AAEqlEq6urvjggw+KHFNo4sSJuHbtGiIiIlRtkydPxsmTJ3H06NESbbMo6enpkMlkkMvlsLbm5J4Ffj8Vj+lbLsHFxgyHPukEQ4MS9aEnIiIqE9p8fmv9Cfbxxx9j9OjRuHHjBkxNTVXtPXr0wOHDhzXeTk5ODqKiouDv7/9vGKkU/v7+xXambtu2LaKiolSntG7fvo2dO3eiR48eJd4mAGRnZyM9PV3th9QplQJWHLkNAAhu58Hih4iIKjWt5wI7c+YMfv7550LtLi4uSEpK0ng7KSkpUCgUcHBwUGt3cHBAdHR0kesMGzYMKSkp8PPzgyAIyMvLw7vvvqs6BVaSbQJAWFgY5syZo3F2fXTw+gPcepgJKxNDDG7lKnYcIiKiUtH6a7yJiUmRR0iuX78OOzs7nYQqzsGDB/HVV1/hxx9/xNmzZ7Flyxbs2LEDX3zxRam2O336dMjlctXP3bt3dZS46igY+HBIa1dYmRqJnIaIiKh0tD4C1Lt3b8ydOxcbN24EAEgkEsTHx2Pq1KkYMGCAxtuxtbWFgYEBkpOT1dqTk5Ph6OhY5DozZ87EyJEjMW7cOACAl5cXMjMz8c477+Dzzz8v0TaB/KLOxMRE4+z65sp9OY7fegQDqQSj23mKHYeIiKjUtD4C9N133yEjIwP29vZ49uwZOnbsiDp16sDKygr//e9/Nd6OsbExWrRoodahWalUIiIiAr6+vkWu8/TpU0il6pENDPIn4RQEoUTbpNcrOPrTw8sJLjZmIqchIiIqPa2PAMlkMuzduxfHjh3DhQsXkJGRgebNm6t1PNZUSEgIgoKC0LJlS7Ru3RoLFy5EZmYmgoODAQCjRo2Ci4sLwsLCAACBgYFYsGABmjVrBh8fH9y8eRMzZ85EYGCgqhB63TZJO4nyZ/j7wn0AwPj2PPpDRERVg9YFUIF27dqhXbt2pdr54MGD8fDhQ8yaNQtJSUnw9vbG7t27VZ2Y4+Pj1Y74zJgxAxKJBDNmzEBCQgLs7OwQGBioduTpddsk7aw5Hoc8pYDWntXRpKaN2HGIiIh0QutxgPQBxwHKl5mdhzZhEXiSlYflo1qia0MWkUREVHGV6ThApD82nrmLJ1l58LS1wJv17cWOQ0REpDMsgKhICqWAVcfyOz+P8fOEVMppL4iIqOpgAURF2nMlCXdTn6GauRHebl5T7DhEREQ6VaIC6NatW5gxYwaGDh2qmmR0165duHLlik7DkXiWP5/2YkQbd5gZG4ichoiISLe0LoAOHToELy8vnDx5Elu2bEFGRgYA4MKFCwgNDdV5QCp/UXdScS4+DcYGUoz0dRc7DhERkc5pXQBNmzYNX375Jfbu3QtjY2NVe5cuXXDixAmdhiNxFAx82LeZM+ytTF+zNBERUeWjdQF06dIl9OvXr1C7vb09UlJSdBKKxBP/6Cn2XMmf1HasXy2R0xAREZUNrQsgGxsbJCYmFmo/d+4cXFxcdBKKxLPqWCyUAtDhDTvUc7QSOw4REVGZ0LoAGjJkCKZOnYqkpCRIJBIolUocO3YMU6ZMwahRo8oiI5UT+dNcbDxzFwCnvSAioqpN6wLoq6++Qv369eHq6oqMjAw0bNgQHTp0QNu2bTFjxoyyyEjl5NdTd/A0R4H6jlbwq2MrdhwiIqIyo/VcYMbGxli+fDlmzpyJy5cvIyMjA82aNUPdunXLIh+Vk5w8JcKPxwEAxrWvBYmEAx8SEVHVpXUBdPToUfj5+cHNzQ1ubm5lkYlEsP3ifSSnZ8PeygS9mzqLHYeIiKhMaX0KrEuXLvD09MRnn32Gq1evlkUmKmeCIGD580vfg9p6wNiQA4QTEVHVpvUn3f379zF58mQcOnQIjRs3hre3N7755hvcu3evLPJROTh+6xGuJabDzMgAw314VI+IiKo+rQsgW1tbTJw4EceOHcOtW7cwcOBAhIeHw8PDA126dCmLjFTGCqa9GNiyJmzMjV+zNBERUeVXqnMdnp6emDZtGubNmwcvLy8cOnRIV7monNxIfoKDMQ8hkQBj2vHSdyIi0g8lLoCOHTuG9957D05OThg2bBgaN26MHTt26DIblYOVR/P7/rzV0AEethYipyEiIiofWl8FNn36dKxfvx73799H165dsWjRIvTp0wfm5uZlkY/K0MMn2dhyLgFA/qXvRERE+kLrAujw4cP45JNPMGjQINjacrC8ymztiTvIyVOiqasNWrpXEzsOERFRudG6ADp27FhZ5KBylpWrwLoTdwDkT3vBgQ+JiEifaFQAbdu2Dd27d4eRkRG2bdv2ymV79+6tk2BUtjafvYfUzBy42JihWyNHseMQERGVK40KoL59+yIpKQn29vbo27dvsctJJBIoFApdZaMyolQKqs7PY/w8YWjAgQ+JiEi/aFQAKZXKIn+nyulAzAPcfpgJKxNDDGpZU+w4RERE5U7rr/6//PILsrOzC7Xn5OTgl19+0UkoKlsFAx8O9XGDlamRyGmIiIjKn9YFUHBwMORyeaH2J0+eIDg4WCehqOxcTpDjxO1UGEolGN3WQ+w4REREotC6ABIEocgrhu7duweZTKaTUFR2Co7+9GziBGcbM5HTEBERiUPjy+CbNWsGiUQCiUSCN998E4aG/66qUCgQGxuLbt26lUlI0o37ac+w/WIiAGA8Bz4kIiI9pnEBVHD11/nz5xEQEABLS0vVfcbGxvDw8MCAAQN0HpB0J/x4HBRKAW1qVUdjFx6tIyIi/aVxARQaGgoA8PDwwODBg2FqalpmoUj3MrLz8NupeADAOD8e/SEiIv2m9UjQQUFBZZGDytiG03fxJCsPtews0KW+vdhxiIiIRKV1AaRQKPD9999j48aNiI+PR05Ojtr9qampOgtHupGnUGLV84EPx/p5QirltBdERKTftL4KbM6cOViwYAEGDx4MuVyOkJAQ9O/fH1KpFLNnzy6DiFRau68kISHtGapbGGNAcw58SEREpHUB9Ouvv2L58uWYPHkyDA0NMXToUKxYsQKzZs3CiRMnyiIjlYIgCFh+JP/oz4g27jA1MhA5ERERkfi0LoCSkpLg5eUFALC0tFQNitirVy/s2LFDt+mo1KLuPMaFu2kwNpRiZBt3seMQERFVCFoXQDVr1kRiYv5YMrVr18Y///wDADh9+jRMTEx0m45KrWDgw37eLrCz4utDREQElKAA6tevHyIiIgAAH3zwAWbOnIm6deti1KhRGDNmjM4DUsnFpWTin6vJAIBx7T1FTkNERFRxaH0V2Lx581S/Dx48GG5uboiMjETdunURGBio03BUOquOxUIQgE717FDXwUrsOERERBWG1gXQy3x9feHr66uLLKRDaU9zsOnMPQCc9oKIiOhlGhVA27Zt03iDvXv3LnEY0p1fT8bjWa4CDZys0bZ2DbHjEBERVSgaFUAF84C9jkQigUKhKE0e0oGcPCXCj8cBAMb5eUIi4cCHREREL9KoAFIqlWWdg3Ro24X7ePAkGw7WJghs6ix2HCIiogpH66vAqGITBAErnl/6HtTWA8aGfImJiIhepnUn6Llz577y/lmzZpU4DJXe0ZspiE56AnNjAwxvzYEPiYiIiqJ1AfTnn3+q3c7NzUVsbCwMDQ1Ru3ZtFkAiW/F82otBLV0hMzcSOQ0REVHFpHUBdO7cuUJt6enpGD16NPr166eTUFQy15Of4ND1h5BIgOB2HmLHISIiqrB00kHE2toac+bMwcyZM3WxOSqhgr4/AQ0d4V7DQuQ0REREFZfOesjK5XLVxKhU/h48ycLWc/cBAOM7cNoLIiKiV9H6FNj//vc/tduCICAxMRFr165F9+7dSxRiyZIl+Oabb5CUlISmTZti8eLFaN26dZHLdurUCYcOHSrU3qNHD9Vs9KNHj0Z4eLja/QEBAdi9e3eJ8lUGayPvIEehRDM3G7Rwry52HCIiogpN6wLo+++/V7stlUphZ2eHoKAgTJ8+XesAGzZsQEhICJYtWwYfHx8sXLgQAQEBiImJgb29faHlt2zZgpycHNXtR48eoWnTphg4cKDact26dcPq1atVt6vyTPXPchRYd+IOAE57QUREpAmtC6DY2FidBliwYAHGjx+P4OBgAMCyZcuwY8cOrFq1CtOmTSu0fPXq6kc31q9fD3Nz80IFkImJCRwdHXWataLafPYeHj/NRc1qZniroYPYcYiIiCo8UUfJy8nJQVRUFPz9/VVtUqkU/v7+iIyM1GgbK1euxJAhQ2Bhod7p9+DBg7C3t0e9evUwYcIEPHr0qNhtZGdnIz09Xe2nslAqBaw6ml+UjmnnCUMDDnxIRET0OlofAcrKysLixYtx4MABPHjwoNA0GWfPntV4WykpKVAoFHBwUD9q4eDggOjo6Neuf+rUKVy+fBkrV65Ua+/WrRv69+8PT09P3Lp1C5999hm6d++OyMhIGBgYFNpOWFgY5syZo3HuiiQi+gFup2TCytQQg1q5ih2HiIioUtC6ABo7diz++ecfvP3222jdurWoE22uXLkSXl5ehTpMDxkyRPW7l5cXmjRpgtq1a+PgwYN48803C21n+vTpCAkJUd1OT0+Hq2vlKCaWP7/0fZiPGyxNtH45iYiI9JLWn5jbt2/Hzp070a5du1Lv3NbWFgYGBkhOTlZrT05Ofm3/nczMTKxfv/61U3MAQK1atWBra4ubN28WWQCZmJhUyk7SF++l4VRsKgylEoxu6yF2HCIiokpD6w4jLi4usLKy0snOjY2N0aJFC0RERKjalEolIiIi4Ovr+8p1N23ahOzsbIwYMeK1+7l37x4ePXoEJyenUmeuSAqmvQhs6gwnmZnIaYiIiCoPrQug7777DlOnTsWdO3d0EiAkJATLly9HeHg4rl27hgkTJiAzM1N1VdioUaOKvLx+5cqV6Nu3L2rUqKHWnpGRgU8++QQnTpxAXFwcIiIi0KdPH9SpUwcBAQE6yVwRJKQ9w45LiQCAsX4c+JCIiEgbWp8Ca9myJbKyslCrVi2Ym5vDyEh9ws3U1FSttjd48GA8fPgQs2bNQlJSEry9vbF7925Vx+j4+HhIpep1WkxMDI4ePYp//vmn0PYMDAxw8eJFhIeHIy0tDc7OznjrrbfwxRdfVMrTXMVZcywWCqUA31o10NhFJnYcIiKiSkUiCIKgzQr+/v6Ij4/H2LFj4eDgUKgTdFBQkE4DiiE9PR0ymQxyuRzW1tZixynkSVYu2obtx5PsPKwa3RJd6nPsHyIiIm0+v7U+AnT8+HFERkaiadOmJQ5IpbPh9F08yc5DbTsLdHqj8GjZRERE9Gpa9wGqX78+nj17VhZZSAN5CiVWH4sDAIxrXwtSqXjDEBAREVVWWhdA8+bNw+TJk3Hw4EE8evSo0o6gXFntupyEhLRnqGFhjH7NXMSOQ0REVClpfQqsW7duAFBoPB1BECCRSKBQKHSTjAoRBAErng98OKKNO0yNCo9qTURERK+ndQF04MCBsshBGjgd9xgX7slhbCjFSF93seMQERFVWloXQB07diyLHKSBgmkvBjR3ga1l1bmkn4iIqLxpXQAdPnz4lfd36NChxGGoeLEpmdh3LX/KEA58SEREVDpaF0CdOnUq1PbiWEDsA1Q2Vh2NhSAAnevZoY69bqYiISIi0ldaXwX2+PFjtZ8HDx5g9+7daNWqVZEjM1PpPc7MwaaouwCA8e1riZyGiIio8tP6CJBMVnjaha5du8LY2BghISGIiorSSTD6168n7yArV4mGTtbwrV3j9SsQERHRK2l9BKg4Dg4OiImJ0dXm6LnsPAXCI/Mnnh3fwbPQ1CNERESkPa2PAF28eFHttiAISExMxLx58+Dt7a2rXPTcX+fv4+GTbDham6JXE2ex4xAREVUJWhdA3t7ekEgkeHkO1TZt2mDVqlU6C0b5xeXKI7EAgNHtPGBkoLMDdkRERHpN6wIoNjZW7bZUKoWdnR1MTU11ForyHbmRgpjkJzA3NsDQVm5ixyEiIqoytC6A3N05AnF5KRj4cFBLV8jMjUROQ0REVHVofE5l//79aNiwYZETnsrlcjRq1AhHjhzRaTh9Fp2UjiM3UiCVcOBDIiIiXdO4AFq4cCHGjx8Pa2vrQvfJZDL85z//wYIFC3QaTp+teN73p1tjR7hWNxc5DRERUdWicQF04cIF1UzwRXnrrbc4BpCOPEjPwl/nEwAA4zjwIRERkc5pXAAlJyfDyKj4fiiGhoZ4+PChTkLpu18i7yBXIaC5mw2au1UTOw4REVGVo3EB5OLigsuXLxd7/8WLF+Hk5KSTUPrsaU4e1p18PvAhj/4QERGVCY0LoB49emDmzJnIysoqdN+zZ88QGhqKXr166TScPtocdQ9pT3PhVt0cbzVyFDsOERFRlaTxZfAzZszAli1b8MYbb2DixImoV68eACA6OhpLliyBQqHA559/XmZB9YFCKWDl0fzOz2PaecBAymkviIiIyoLGBZCDgwOOHz+OCRMmYPr06aqRoCUSCQICArBkyRI4ODiUWVB9sO9aMuIePYW1qSEGtnQVOw4REVGVpdVAiO7u7ti5cyceP36MmzdvQhAE1K1bF9WqsaOuLhRMezG8jTssTLQeo5KIiIg0VKJP2WrVqqFVq1a6zqLXLtxNw6m4VBhKJQjy9RA7DhERUZXG2TUriIJpL3o3dYajjPOqERERlSUWQBXAvcdPsetyEgAOfEhERFQeWABVAKuPxUGhFNCuTg00dC481QgRERHpFgsgkaVn5WLD6bsAePSHiIiovLAAEtmGU3eRkZ2HOvaW6FjXTuw4REREeoEFkIhyFUqsPpZ/6fs4P09IOfAhERFRuWABJKKdlxJxX54FW0tj9G3mInYcIiIivcECSCSCIGDF84EPR7bxgKmRgciJiIiI9AcLIJGcjE3FpQQ5TAylGNHGTew4REREeoUFkEgKjv70b14TNSxNRE5DRESkX1gAieD2wwxERCcDAMb6eYqchoiISP9wxs1ypFAKOBWbih/234AgAF3q2aGOvaXYsYiIiPQOC6BysvtyIub8fRWJ8ixV2/l7cuy+nIhujZ1ETEZERKR/eAqsHOy+nIgJ686qFT8A8DgzBxPWncXuy4kiJSMiItJPLIDKmEIpYM7fVyEUcV9B25y/r0KhLGoJIiIiKgssgMrYqdjUQkd+XiQASJRn4VRsavmFIiIi0nMsgMrYgyfFFz8lWY6IiIhKjwVQGbO3MtXpckRERFR6LIDKWGvP6nCSmaK4aU4lAJxkpmjtWb08YxEREek1FkBlzEAqQWhgQwAoVAQV3A4NbAgDzgRPRERUblgAlYNujZ2wdERzOMrUT3M5ykyxdERzjgNERERUzipEAbRkyRJ4eHjA1NQUPj4+OHXqVLHLdurUCRKJpNBPz549VcsIgoBZs2bByckJZmZm8Pf3x40bN8rjoRSrW2MnHJ3aBb+Pb4NFQ7zx+/g2ODq1C4sfIiIiEYheAG3YsAEhISEIDQ3F2bNn0bRpUwQEBODBgwdFLr9lyxYkJiaqfi5fvgwDAwMMHDhQtczXX3+N//3vf1i2bBlOnjwJCwsLBAQEICtL3CutDKQS+NaugT7eLvCtXYOnvYiIiEQiEQRB1BH4fHx80KpVK/zwww8AAKVSCVdXV3zwwQeYNm3aa9dfuHAhZs2ahcTERFhYWEAQBDg7O2Py5MmYMmUKAEAul8PBwQFr1qzBkCFDXrvN9PR0yGQyyOVyWFtbl+4BEhERUbnQ5vNb1CNAOTk5iIqKgr+/v6pNKpXC398fkZGRGm1j5cqVGDJkCCwsLAAAsbGxSEpKUtumTCaDj4+PxtskIiKiqk3UyVBTUlKgUCjg4OCg1u7g4IDo6OjXrn/q1ClcvnwZK1euVLUlJSWptvHyNgvue1l2djays7NVt9PT0zV+DERERFT5iN4HqDRWrlwJLy8vtG7dulTbCQsLg0wmU/24urrqKCERERFVRKIWQLa2tjAwMEBycrJae3JyMhwdHV+5bmZmJtavX4+xY8eqtResp802p0+fDrlcrvq5e/eutg+FiIiIKhFRCyBjY2O0aNECERERqjalUomIiAj4+vq+ct1NmzYhOzsbI0aMUGv39PSEo6Oj2jbT09Nx8uTJYrdpYmICa2trtR8iIiKqukTtAwQAISEhCAoKQsuWLdG6dWssXLgQmZmZCA4OBgCMGjUKLi4uCAsLU1tv5cqV6Nu3L2rUqKHWLpFIMGnSJHz55ZeoW7cuPD09MXPmTDg7O6Nv377l9bCIiIioAhO9ABo8eDAePnyIWbNmISkpCd7e3ti9e7eqE3N8fDykUvUDVTExMTh69Cj++eefIrf56aefIjMzE++88w7S0tLg5+eH3bt3w9SUE44SERFRBRgHqCLiOEBERESVjzaf36IfAaqICmpCXg5PRERUeRR8bmtybIcFUBGePHkCALwcnoiIqBJ68uQJZDLZK5fhKbAiKJVK3L9/H1ZWVpBIdDtfV3p6OlxdXXH37l2eXiOd4fuKygLfV1QWyvJ9JQgCnjx5Amdn50L9h1/GI0BFkEqlqFmzZpnug5fbU1ng+4rKAt9XVBbK6n31uiM/BSr1SNBEREREJcECiIiIiPQOC6ByZmJigtDQUJiYmIgdhaoQvq+oLPB9RWWhoryv2AmaiIiI9A6PABEREZHeYQFEREREeocFEBEREekdFkBERESkd1gAlaMlS5bAw8MDpqam8PHxwalTp8SORJXc4cOHERgYCGdnZ0gkEmzdulXsSFQFhIWFoVWrVrCysoK9vT369u2LmJgYsWNRJbd06VI0adJENQCir68vdu3aJVoeFkDlZMOGDQgJCUFoaCjOnj2Lpk2bIiAgAA8ePBA7GlVimZmZaNq0KZYsWSJ2FKpCDh06hPfffx8nTpzA3r17kZubi7feeguZmZliR6NKrGbNmpg3bx6ioqJw5swZdOnSBX369MGVK1dEycPL4MuJj48PWrVqhR9++AFA/nxjrq6u+OCDDzBt2jSR01FVIJFI8Oeff6Jv375iR6Eq5uHDh7C3t8ehQ4fQoUMHseNQFVK9enV88803GDt2bLnvm0eAykFOTg6ioqLg7++vapNKpfD390dkZKSIyYiIXk8ulwPI/7Ai0gWFQoH169cjMzMTvr6+omTgZKjlICUlBQqFAg4ODmrtDg4OiI6OFikVEdHrKZVKTJo0Ce3atUPjxo3FjkOV3KVLl+Dr64usrCxYWlrizz//RMOGDUXJwgKIiIiK9f777+Py5cs4evSo2FGoCqhXrx7Onz8PuVyOP/74A0FBQTh06JAoRRALoHJga2sLAwMDJCcnq7UnJyfD0dFRpFRERK82ceJEbN++HYcPH0bNmjXFjkNVgLGxMerUqQMAaNGiBU6fPo1Fixbhp59+Kvcs7ANUDoyNjdGiRQtERESo2pRKJSIiIkQ790lEVBxBEDBx4kT8+eef2L9/Pzw9PcWORFWUUqlEdna2KPvmEaByEhISgqCgILRs2RKtW7fGwoULkZmZieDgYLGjUSWWkZGBmzdvqm7Hxsbi/PnzqF69Otzc3ERMRpXZ+++/j99++w1//fUXrKyskJSUBACQyWQwMzMTOR1VVtOnT0f37t3h5uaGJ0+e4LfffsPBgwexZ88eUfLwMvhy9MMPP+Cbb75BUlISvL298b///Q8+Pj5ix6JK7ODBg+jcuXOh9qCgIKxZs6b8A1GVIJFIimxfvXo1Ro8eXb5hqMoYO3YsIiIikJiYCJlMhiZNmmDq1Kno2rWrKHlYABEREZHeYR8gIiIi0jssgIiIiEjvsAAiIiIivcMCiIiIiPQOCyAiIiLSOyyAiIiISO+wACIiIiK9wwKISM95eHhg4cKFOtve6NGj0bdvX51tD8gf8FEikSAtLU2n2yUi/cUCiKiKGD16NCQSCSQSiWrCwblz5yIvL++V650+fRrvvPOOznIsWrRItFGoz507h4EDB8LBwQGmpqaoW7cuxo8fj+vXr4uSp6LSddFLVBmxACKqQrp164bExETcuHEDkydPxuzZs/HNN98UuWxOTg4AwM7ODubm5jrLIJPJYGNjo7PtaWr79u1o06YNsrOz8euvv+LatWtYt24dZDIZZs6cWe55iKhiYwFEVIWYmJjA0dER7u7umDBhAvz9/bFt2zYA/56a+u9//wtnZ2fUq1cPQOGjARKJBCtWrEC/fv1gbm6OunXrqrZR4MqVK+jVqxesra1hZWWF9u3b49atW2r7KdCpUydMnDgREydOhEwmg62tLWbOnIkXZ+FZu3YtWrZsCSsrKzg6OmLYsGF48OCBxo/76dOnCA4ORo8ePbBt2zb4+/vD09MTPj4++Pbbb/HTTz+plj106BBat24NExMTODk5Ydq0aWpHyTp16oQPPvgAkyZNQrVq1eDg4IDly5erJi+2srJCnTp1sGvXLtU6BafoduzYgSZNmsDU1BRt2rTB5cuX1XJu3rwZjRo1gomJCTw8PPDdd9+p3e/h4YGvvvoKY8aMgZWVFdzc3PDzzz+rLXP37l0MGjQINjY2qF69Ovr06YO4uDjV/QXP/7fffgsnJyfUqFED77//PnJzc1WP786dO/j4449VRwwB4M6dOwgMDES1atVgYWGBRo0aYefOnRq/BkSVDQsgoirMzMxMdaQHACIiIhATE4O9e/di+/btxa43Z84cDBo0CBcvXkSPHj0wfPhwpKamAgASEhLQoUMHmJiYYP/+/YiKisKYMWNeeaotPDwchoaGOHXqFBYtWoQFCxZgxYoVqvtzc3PxxRdf4MKFC9i6dSvi4uK0mnRzz549SElJwaefflrk/QVHpBISEtCjRw+0atUKFy5cwNKlS7Fy5Up8+eWXhfLa2tri1KlT+OCDDzBhwgQMHDgQbdu2xdmzZ/HWW29h5MiRePr0qdp6n3zyCb777jucPn0adnZ2CAwMVBUeUVFRGDRoEIYMGYJLly5h9uzZmDlzZqHThd999x1atmyJc+fO4b333sOECRMQExOjep4CAgJgZWWFI0eO4NixY7C0tES3bt3UXucDBw7g1q1bOHDgAMLDw7FmzRrVfrZs2YKaNWti7ty5SExMRGJiIoD8GeCzs7Nx+PBhXLp0CfPnz4elpaXGrwFRpSMQUZUQFBQk9OnTRxAEQVAqlcLevXsFExMTYcqUKar7HRwchOzsbLX13N3dhe+//151G4AwY8YM1e2MjAwBgLBr1y5BEARh+vTpgqenp5CTk/PaHIIgCB07dhQaNGggKJVKVdvUqVOFBg0aFPtYTp8+LQAQnjx5IgiCIBw4cEAAIDx+/LjI5efPny8AEFJTU4vdpiAIwmeffSbUq1dPLcuSJUsES0tLQaFQqPL6+fmp7s/LyxMsLCyEkSNHqtoSExMFAEJkZKRavvXr16uWefTokWBmZiZs2LBBEARBGDZsmNC1a1e1PJ988onQsGFD1W13d3dhxIgRqttKpVKwt7cXli5dKgiCIKxdu7ZQ/uzsbMHMzEzYs2ePIAj5z7+7u7uQl5enWmbgwIHC4MGD1fbz4msuCILg5eUlzJ49+5XPH1FVwiNARFXI9u3bYWlpCVNTU3Tv3h2DBw/G7NmzVfd7eXnB2Nj4tdtp0qSJ6ncLCwtYW1urTkmdP38e7du3h5GRkca52rRpozrVAgC+vr64ceMGFAoFgPyjI4GBgXBzc4OVlRU6duwIAIiPj9do+8ILp9Ne5dq1a/D19VXL0q5dO2RkZODevXuqthcfv4GBAWrUqAEvLy9Vm4ODAwAUOk3n6+ur+r169eqoV68erl27ptp3u3bt1JZv166d2vPw8r4lEgkcHR1V+7lw4QJu3rwJKysrWFpawtLSEtWrV0dWVpbqFCQANGrUCAYGBqrbTk5Orz2l+OGHH+LLL79Eu3btEBoaiosXL75yeaLKjgUQURXSuXNnnD9/Hjdu3MCzZ88QHh4OCwsL1f0v/v4qLxc3EokESqUSQP5pNV3KzMxEQEAArK2t8euvv+L06dP4888/AUDttM6rvPHGGwCA6OhonWQq6vG/2FZQQBU8J7r0quc+IyMDLVq0wPnz59V+rl+/jmHDhmm0jeKMGzcOt2/fxsiRI3Hp0iW0bNkSixcv1tGjIqp4WAARVSEWFhaoU6cO3NzcYGhoWCb7aNKkCY4cOaLq26KJkydPqt0+ceIE6tatCwMDA0RHR+PRo0eYN28e2rdvj/r162vVARoA3nrrLdja2uLrr78u8v6C8YMaNGiAyMhItSNGx44dg5WVFWrWrKnVPoty4sQJ1e+PHz/G9evX0aBBA9W+jx07prb8sWPH8MYbb6gdrXmV5s2b48aNG7C3t0edOnXUfmQymcY5jY2N1Y46FXB1dcW7776LLVu2YPLkyVi+fLnG2ySqbFgAEZFWJk6ciPT0dAwZMgRnzpzBjRs3sHbtWlVH3aLEx8cjJCQEMTEx+P3337F48WJ89NFHAAA3NzcYGxtj8eLFuH37NrZt24YvvvhCq0wWFhZYsWIFduzYgd69e2Pfvn2Ii4vDmTNn8Omnn+Ldd98FALz33nu4e/cuPvjgA0RHR+Ovv/5CaGgoQkJCIJWW/s/h3LlzERERgcuXL2P06NGwtbVVXRE3efJkRERE4IsvvsD169cRHh6OH374AVOmTNF4+8OHD4etrS369OmDI0eOIDY2FgcPHsSHH36odgrvdTw8PHD48GEkJCQgJSUFADBp0iTs2bMHsbGxOHv2LA4cOKAq3oiqIhZARKSVGjVqYP/+/cjIyEDHjh3RokULLF++/JV9gkaNGoVnz56hdevWeP/99/HRRx+pBl+0s7PDmjVrsGnTJjRs2BDz5s3Dt99+q3WuPn364Pjx4zAyMsKwYcNQv359DB06FHK5XHWVl4uLC3bu3IlTp06hadOmePfddzF27FjMmDGjZE/GS+bNm4ePPvoILVq0QFJSEv7++29Vn6vmzZtj48aNWL9+PRo3boxZs2Zh7ty5Wl3tZm5ujsOHD8PNzQ39+/dHgwYNMHbsWGRlZcHa2lrj7cydOxdxcXGoXbs27OzsAAAKhQLvv/8+GjRogG7duuGNN97Ajz/+qNXjJ6pMJIKmvQeJiEqgU6dO8Pb2rtIjDx88eBCdO3fG48ePRRkEkoi0xyNAREREpHdYABEREZHe4SkwIiIi0js8AkRERER6hwUQERER6R0WQERERKR3WAARERGR3mEBRERERHqHBRARERHpHRZAREREpHdYABEREZHeYQFEREREeuf/LRrin5a/9vUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Same graph but with the bar"
      ],
      "metadata": {
        "id": "SvNnJKZqw6Ac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Scale the dataset; This is very important before you apply PCA \n",
        "# from sklearn.preprocessing \n",
        "cum_sum_exp = np.cumsum(cum_explained_variance_normal_pca) \n",
        "\n",
        "plt.bar(range(1,len(cum_explained_variance_normal_pca)+1), cum_explained_variance_normal_pca, alpha=0.5, align='center') \n",
        "#plt.step(range(0,len(cum_sum_exp)), cum_sum_exp, where='mid',label='Cumulative explained variance') \n",
        "plt.plot(range(1,len(cum_explained_variance_normal_pca)+1), cum_explained_variance_normal_pca, '-o')\n",
        "\n",
        "plt.xticks(range(1,len(cum_explained_variance_normal_pca)+1))\n",
        "\n",
        "plt.ylabel('Explained variance ratio') \n",
        "plt.xlabel('Principal component index') \n",
        "plt.title('Cumulative explained variance normalized')\n",
        "#plt.legend(loc='best') \n",
        "plt.tight_layout() \n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "2PtEkRfhm1Ul",
        "outputId": "005753ad-e97e-48c8-ce8c-d412b029b2e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(df['Classes'])"
      ],
      "metadata": {
        "id": "PHPfkiA45Ss4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "894ac012-c3d7-4da8-b9d0-d6d140bbbf90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire',\n",
              " 'not fire']"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = df['Classes']\n",
        "classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bO6lxyiD5lJR",
        "outputId": "65d76560-97d7-4afa-8da2-4bd6984cd421"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      not fire\n",
              "1      not fire\n",
              "2      not fire\n",
              "3      not fire\n",
              "4      not fire\n",
              "         ...   \n",
              "239        fire\n",
              "240    not fire\n",
              "241    not fire\n",
              "242    not fire\n",
              "243    not fire\n",
              "Name: Classes, Length: 243, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cdict = {'fire ': 0,'fire': 0, 'fire   ':0, ' not fire':1, 'not fire   ':1, 'not fire    ': 1, 'not fire':1, 'not fire ':1, 'not fire     ':1}\n",
        "classes = df['Classes'].replace({'fire ': 0,'fire': 0, 'fire   ':0, ' not fire':1, 'not fire   ':1, 'not fire    ': 1, 'not fire':1, 'not fire ':1, 'not fire     ':1})\n",
        "list_class = list(classes)"
      ],
      "metadata": {
        "id": "9l8H4gag2mlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "axs[0].scatter(X_pca[:, 0], X_pca[:, 1], c = list_class, cmap = ListedColormap(['r','b']), label=['F','N'])\n",
        "axs[0].set_title(\"PCA\")\n",
        "axs[0].legend(loc=\"upper right\")\n",
        "\n",
        "axs[1].scatter(X_pca_normal[:, 0], X_pca_normal[:, 1], c = list_class, cmap = ListedColormap(['r','b']))\n",
        "axs[1].set_title(\"PCA_normalize\");\n",
        "\n",
        "scatter_x = np.array([1,2,3,4,5])\n",
        "scatter_y = np.array([5,4,3,2,1])\n",
        "group = np.array([1,3,2,1,3])\n",
        "cdict = {1: 'red', 2: 'blue', 3: 'green'}\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "for g in np.unique(group):\n",
        "    ix = np.where(group == g)\n",
        "    ax.scatter(scatter_x[ix], scatter_y[ix], c = cdict[g], label = g)\n",
        "ax.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gG3Gz1PRSsUl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "5cdd05e2-965d-4b8f-c33f-d5021473a971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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9ZsyYgaefftpvrxPqHnzwQbuf3377bUydOhVbt25FmTJl8Nlnn2HevHlo0aIFAGDWrFmoVq0atm7dikaNGgWjyc5NmSJ/7M5GDYxGYOpUoHHjwLaLiCgEcGbKCxYLMGiQdKS0hpJbLMDhw/5tFxF578MP5ZoSsCWPsV5bbt8u6yHzgpiYGLz88st48803ce3atWA3JySYzWYsWLAA165dQ0JCAnbt2oX09HS0bNkyY5uqVauiXLly2LJli9P9pKamIiUlxe4WEHv3up5+NZkknp2IiLJhZ8oLmzYB//zj+Zrc/+pWElEuk54OvPee88fNZpmNzrycxN8qVKgA5eAgU6ZMGRQsWDDjdvHiRQCy3un48eMevcbs2bPt1kxZPf3008iXLx8+/PBDh8/r1asXNmzY4NFrhaN9+/ahYMGCiIqKwoABA7BkyRJUr14diYmJiIyMzDaDGBcXh8TERKf7Gzt2LGJjYzNuZcuW9fM7+I+W2mys30ZE5BA7U144edLz55QsKWuriCj32bcPOHfO9TZ6PfDTT4Fpjys///wz9u7dm3ErUqSIz18jKioKb775Jt5//31cuHDB5/sPF1WqVMHevXuxbds2DBw4ED179sTBgwe93t+oUaOQnJyccTvpzcnGG48+KmF+zuj1QOfOgWkLEVGIYWfKC8WLe/6cl1+WsHMiyn3S0txvo9Np287fKlasiMqVK2fc9Hr/HMa7d++O8uXLY8yYMX7ZfziIjIxE5cqVUb9+fYwdOxZ16tTBpEmTEB8fj7S0NCQlJdltf/bsWcS7WDgbFRWVkR3QeguIgQNl5snR35LBIIsF+/QJTFuIiEIMO1NeaNECuOUW99vp9XIB9sorkh2MiHKnatWAqCjX25jNUuYgr9Dr9Rg7diymTp3qcfhgXmWxWJCamor69esjIiICa9euzXjs8OHDOHHiBBISEoLYQifKlAFWrZLiiICM/FlH/0qUANatk+KJRESUDedKvBAZKZliXQ3UNWkCNG8O9OwJVKwYuLYRkediY4EePYDPP3e8Dt9gAG69Vb7TeUnbtm3RsGFDTJ8+HXFxccFuTq4yatQotG7dGuXKlcOVK1cwb948bNiwAStXrkRsbCz69u2L5557DkWLFkVMTAyGDBmChISE3JfJz6pRI4lhX7gQ+PlnGQls3hzo2NH9SAMRUR7GzpSXeveWi67hw4GkJDnvKAUULSoFfHv0CHYLicgT770HbNsG7N9vK68DSEcqJgZYtEi+56Hi+PHjqFixItavX29XYNdT48ePx1133eW7hoWJc+fOoUePHjhz5gxiY2NRu3ZtrFy5Evfddx8AYMKECdDr9ejYsSNSU1PRqlUrTLGmi8ytoqNlBLBnz2C3hIgoZLAzlQP9+km65BUrpIZUqVJA69Yyc0VEoSU2VuqSTpkCTJsGnDgh9/XsCQwdCpQrF9z2NWvWzGF2P2eOHTuGwoULo06dOpqf4yhDX0JCgkevm1d89tlnLh/Ply8fJk+ejMmTJweoRUREFAxcM5VD+fIBjzwi63cffpgdKaJQVrAgMGIEcPSolNa5eFHqTwWiIzVlyhQULFgQ+/bt88n+li9fjpdeeskv2f6yeuedd1CwYEGcOHHC769FRESUm3BmiogoyObOnYsbN24AAMr5qOf2nqvCWT42YMAAdP4vdXZxb9KdEhERhSh2poiIgqx06dLBbkKOFC1aFEWLFg12M4iIiAKOYX5EREREREReYGeKiIiIiIjIC+xMEVFYsWTOa065Bn8vREQUjrhmiojCQmRkJPR6PU6fPo3ixYsjMjISulAqDBWmlFJIS0vD+fPnodfrEcmUp0REFEbYmSKisKDX61GxYkWcOXMGp0+fDnZzKIv8+fOjXLly0OsZEEFEROGDnSkiChuRkZEoV64cTCYTzGZzsJtD/zEYDDAajZwpJCKisMPOFBGFFZ1Oh4iICERERAS7KURERBTmGG9BRERERETkBXamiIiIiIiIvMDOFBERERERkRfYmSIiIgpHZjOQnAyYTMFuCRFR2GJnioiIKJz8+y8waBAQEwMULgwUKgT07w8cOxbslhERhR12poiIiMLF338D9eoB06cD16/LfTdvArNmAfXrAwcPBrd9RERhhp0pIiKicPHkk8ClSxLil5nJBKSkAD16BKddRERhip0pIiKicHDkCLB+ffaOlJXZDOzaBezdG9BmERGFM3amiIiIwsG+fb7djoiI3DIGuwFERETkA/nza9suOtr+Z4sFWLMG+PFHIDVV1lw9/rgkriAiIpfYmSIiIgoHTZoABQsCV6863yYqCmjZ0vbzqVNA69YyW2U0AjodMGMG8MILwIIFQNu2/m83EVEIY5gfERFROMifXzpBzuh0wODBki4dANLTpWN16JD8bDLJfUoB164B7dsDe/b4u9VERCGNnSkiIqJw8eqrwMCB8n+jETAY5F9AMvmNG2fb9vvvgT/+cFzUVyn59/33/dteIqIQF7DO1Lhx46DT6TB06NCM+27evIlBgwahWLFiKFiwIDp27IizZ8/aPe/EiRNo27Yt8ufPjxIlSmD48OEwZTnwb9iwAfXq1UNUVBQqV66M2bNnB+AdERER5TJ6PTBlitSTev55oGtXYOhQ4PffgdmzbR0rAFiyRDpbzphMwOLFto4VERFlE5A1Uzt27MD06dNRu3Ztu/uHDRuGZcuWYdGiRYiNjcXgwYPRoUMH/PrrrwAAs9mMtm3bIj4+Hps3b8aZM2fQo0cPRERE4J133gEAHDt2DG3btsWAAQMwd+5crF27Fv369UPJkiXRqlWrQLw9IiKi3KVaNftZKEeuXXOeRt0qNVU6Uzqd79pGRBRG/D4zdfXqVXTr1g0zZsxAkSJFMu5PTk7GZ599hg8//BAtWrRA/fr1MWvWLGzevBlbt24FAKxatQoHDx7EV199hbp166J169Z46623MHnyZKSlpQEApk2bhooVK+KDDz5AtWrVMHjwYHTq1AkTJkzw91sjIiIKXTVquJ6Z0umA226T2S4iInLI70fIQYMGoW3btmiZOXsQgF27diE9Pd3u/qpVq6JcuXLYsmULAGDLli2oVasW4uLiMrZp1aoVUlJScODAgYxtsu67VatWGftwJDU1FSkpKXY3IiKiPKVfP/chfIMHB6YtREQhyq+dqQULFmD37t0YO3ZstscSExMRGRmJwtasQv+Ji4tDYmJixjaZO1LWx62PudomJSUFN27ccNiusWPHIjY2NuNWtmxZr94fERFRyKpQAbBGcWSdfdLrgRYtgAEDAt4sIqJQ4rfO1MmTJ/Hss89i7ty5yJcvn79exiujRo1CcnJyxu3kyZPBbhIREVHgPfOMZPVr1Mh2X8mSwJgxwPLlQGRk8NpGRBQC/JaAYteuXTh37hzq1auXcZ/ZbMamTZvwySefYOXKlUhLS0NSUpLd7NTZs2cRHx8PAIiPj8f27dvt9mvN9pd5m6wZAM+ePYuYmBhEZ63y/p+oqChERUXl+D0SERGFvAcflFtyMpCWBhQr5pd1UikpwJw5wOrVkijwrrsk0jBLcAkRUUjx28zUvffei3379mHv3r0ZtzvuuAPdunXL+H9ERATWrl2b8ZzDhw/jxIkTSEhIAAAkJCRg3759OHfuXMY2q1evRkxMDKpXr56xTeZ9WLex7oOIiIg0iI0Fihf3S0dq+3aJKnzmGeCHH2TS67XXgHLlJEM7EVGo8tvMVKFChVCzZk27+woUKIBixYpl3N+3b18899xzKFq0KGJiYjBkyBAkJCSg0X/hBvfffz+qV6+OJ554Au+++y4SExPxyiuvYNCgQRkzSwMGDMAnn3yCESNGoE+fPli3bh0WLlyIZcuW+eutERERkUYXLwKtWsnMVOZ8FxYLkJ4OdO4M7NkDZLlkICIKCUHNdzphwgS0a9cOHTt2RJMmTRAfH49vv/0243GDwYAff/wRBoMBCQkJ6N69O3r06IE333wzY5uKFSti2bJlWL16NerUqYMPPvgAM2fOZI0pIiKiXGDWLOlIWSzZH7N2rj76KLBtIiLyFZ1SLG2ekpKC2NhYJCcnIyYmJtjNISLKU3gMdixcPpemTYFNm1xvU6IEkGX5MxFR0Hhy/GUlPiIiIvKb1FT326Sn+78dRET+wM4UERER+U1CAmB0sULbYAAaNAhce4iIfImdqSA4ehQYPhyoUQOoWlVSw+7dG+xWERER+d6AAYDZ7PxxsxkYMiRw7SEi8iW/ZfMjx378EejYUU4e1pPL338Dn38uC3AHDw5u+4iIKA/bsQPYskWmi+69V0b8cqhKFWDyZODpp2W31nOf9f9DhwJt2uT4ZYiIgoKdqQA6dQro1EliwzOn/TCZ5N8hQ4B69aSQIRERUcAcOyY5ynfulDpTSsmtVStg7lwp5JsDAwcC1asDH3wgRXvNZqBRI+lIPfIIoNP55m0QEQUaO1MB9Omn0nFylj/RaAQmTWJnioiIAujiReCee2zp9DLnMF+zBmjZEti2DYiMzNHLNG0qNyKicMI1UwG0bp3ruHGTCVi7NnDtISIiwtSpwJkztjCJzMxmWdS7eHHAm0VEFArYmQogLRW9WPWLiIgCavZsxxV1rfR64MsvA9YcIqJQws5UADVvLgtunTEagRYtAtceIiIiXLrk+nGLBTh3LjBtISIKMexMBVD//tKZcrbQ1mQCnn02sG0iIqI8rnx51xkgjEbg1lsD1x4iohDCzlQAlS0LLFwo56XMM1TW/3/wAXD33cFpGxER5VFPPeX6cZMJ6Ns3MG0hIgox7EwF2MMPAwcOAE8+CcTGymCgNSnFxInAlClcN0VERAHUqxfQoIHjOHSdTooj3ndfwJtFRBQK2JkKgpIlpSbi1av2HaeTJ4FBg4CRI4PXNiIiymPy5ZMU6P37y/+tYmKAl14C5s/3byGoDRuk2FTx4kB8PNC7N7Bnj/9ej4jIh3RKcR4kJSUFsbGxSE5ORkxMjN9fb9w44OWXXSdP2r8fqFHD700hIgq6QB+DQ0VQPpekJOD332WWql49IDrav6/3zjtyQjQabanZjUY5QX7xBdC9u39fn4jIAU+Ov5yZCoKpU113pIxG4LPPAtceIiIiAEDhwkCTJkDjxv7vSK1fLx0pwL7GlckkJ8levYCjR/3bBiKiHGJnKsCUAk6ccL2NyQT8/Xdg2kNERBQUkybJ6KErU6cGpi1ERF5iZyrAdDqgUCH32x0/zkQUREQUxn75xX5GKiuzGdi0KXDtISLyAjtTQdCtm/vBuN9/l1TpREREYclVFXsrdydLIqIgY2cqCJ5/HoiKcr/dmDHAzZv+bw8REVHAPfCA686SXg+0ahW49hAReYGdqSCoXBl4/XX32yUny/pcIiKisPPss86zMel0kqb9yScD2yYiIg+xMxUEGzcCb7+tbduUFP+2hYiIKCjq1QNmz5Zwv8whf3q9ZBL8/nspzEhElIsxGDnA9u2TqIX0dG3b3367f9tDREQUNE88Adx1FzBtmow0RkTISfLJJ9mRIqKQwM5UgL3zjiQoclVnCpBBulq1gP/9LzDtIiIiCopbbwXeey/YrSAi8grD/ALIZAIWL3adCRaQUPGoKBbuJSIiIiLKzTgzFUA3bmgL7ytdGlixAqhZ0/9tIiIiyiYxEfj2WyApSbImPfSQJIQgIiI77EwFUMGCQNGiwKVLzrcxGIA+fdiRIiKiIDCZgBdeAD75ROLRDQa5r0gRYMYMoGPHYLeQiChXYZhfAOl0wIABrusUWizSmSIiIgq4554DPvpIFvcqZYtLT0oCHn0UWL06qM0jIspt2JkKsOHDgdtuc96heukloHz5wLaJiIgIp04BkydLJyorpWRE8OWXA98uIqJcjJ2pACtcGPjpJwn3y0qnk5Ibx44FulVERJTnffON68ctFmDHDuD48YA0h4goFLAzFQRvv+143ZRSwNmzQJcugW8TERHlcZcvu45Dt3K18JeIKI9hZyrAkpKAOXMkHN0Rk0kG/nbuDGiziIgor6tUyX3KWYMBKFcuMO0xmWQW7N9/HYceEhHlAuxMBdhvvwGpqa630emALVsC0x4iIiIAkqmvYEHnjxsMQPv2wC23+LcdaWnAW28BpUoBFSsCZcsCVasCs2axU0VEuQ47UwGmJYLCk+2IiIh8okABYMoU+b9OZ/+YwQDExgLjx/u3DenpwIMPAqNHA+fP2+4/ckRS3Y4a5d/XJyLyEDtTAVavHlCokOttlAJatgxMe4iIiDI88QTw3XdA9eq2+3Q6oG1bYNs24NZb/fv6c+YAq1ZJsovMrDNS48cDe/b4tw1ERB5gZyrA8ucHhgzJPuhnZTAAbdoAt98e2HYREREBAB5+GNi3D/jjD2DrVuD0aWDpUqByZf+/9uTJgN7FpYnRCHz6qf/bQUSkkTHYDciLRo8GDh8GFi+W84LJJOcOiwWoUwf48stgt5CIiPI0nQ6oUiXwr/vnn9lnpTIzmYADBwLXHiIiN9iZCoKICGDRImDNGmDmTODoUaBECYmu6NABiIwMdguJiIiCoEAB4No154/r9bJ2i4gol2BnKkh0OuC+++RGREQUitLSgBUrgH/+AYoXl9wRrhICutW1K/DJJ87rh1gswKOP5uAFiIh8i2umiIiIyGMLFkj28vbtgWHDgMcfB+LjgQ8+yEEG82efBfLlc5zS1miUdVudO+ek2UREPsXOVC7w00/AAw9Ilr+YGCn18euvwW4VERE5M3bsWNx5550oVKgQSpQogfbt2+Pw4cN229y8eRODBg1CsWLFULBgQXTs2BFnz54NUot967vvZBLp4kX52brM6do14IUXgAkTvNxxxYrA2rW2WlYREdKJAiTD4Lp10tkiIsol2JkKstGjgdatZf3U1avAlSvA998Dd98NTJ0a7NYREZEjGzduxKBBg7B161asXr0a6enpuP/++3Et03qfYcOG4YcffsCiRYuwceNGnD59Gh06dAhiq31DKWD4cOdZaQHgtddcL31yqWFD4MQJYOFC4JlngOefl07U3r1SwJeIKBfRKcVy4ikpKYiNjUVycjJiYmIC9rrr1wMtWjh/XKcD9u+3L/dBRBRugnUM9qXz58+jRIkS2LhxI5o0aYLk5GQUL14c8+bNQ6dOnQAAf/zxB6pVq4YtW7agUaNGbveZqz6XHTuAzz8Hjh3DLt0duOOnMW6f8vXXjMgjotDkyfHXrzNTU6dORe3atRETE4OYmBgkJCRgxYoVGY9rCYE4ceIE2rZti/z586NEiRIYPnw4TCaT3TYbNmxAvXr1EBUVhcqVK2P27Nn+fFs+89FHtugFRwwGzk4REYWC5ORkAEDRokUBALt27UJ6ejpaZqrAXrVqVZQrVw5btmxxuI/U1FSkpKTY3YLOYgH69wcaNJD0sytX4sKq3W6fptMB588HoH1EREHm185UmTJlMG7cOOzatQs7d+5EixYt8PDDD+PAfzUi3IVAmM1mtG3bFmlpadi8eTO++OILzJ49G6+99lrGNseOHUPbtm3RvHlz7N27F0OHDkW/fv2wcuVKf741n9iyRUpmOGMyce0UEVFuZ7FYMHToUDRu3Bg1a9YEACQmJiIyMhKFCxe22zYuLg6JiYkO9zN27FjExsZm3MrmhpC28eOBGTPk//+dsMpZjrl9mlJA+fL+bBgRUe4Q8DC/okWL4r333kOnTp3chkCsWLEC7dq1w+nTpxEXFwcAmDZtGl588UWcP38ekZGRePHFF7Fs2TLs378/4zUee+wxJCUl4aefftLUpmCFUpQpA5w65XqbBg2AbdsC0x4iomDIVeFsXhg4cCBWrFiBX375BWXKlAEAzJs3D71790Zqaqrdtg0aNEDz5s0xfvz4bPtJTU212z4lJQVly5YN3ueSmirp+i5dyvZQQ2zFTtSHxUmFleLF5fwWEeHvRhIR+V6uCfPLzGw2Y8GCBbh27RoSEhI0hUBs2bIFtWrVyuhIAUCrVq2QkpKSMbu1ZcsWu31Yt3EWRgHknlCKBx90Hean1wPt2gWuPURE5JnBgwfjxx9/xPr16zM6UgAQHx+PtLQ0JCUl2W1/9uxZxMfHO9xXVFRURli89RZUe/Y47EgBwEd4BhEwwQD78AqdTm5TprAjRUR5g987U/v27UPBggURFRWFAQMGYMmSJahevbqmEIjExES7jpT1cetjrrZJSUnBjRs3HLYpt4RSDBki/zrKiKTXS/bXfv0C2yYiInJPKYXBgwdjyZIlWLduHSpWrGj3eP369REREYG1a9dm3Hf48GGcOHECCQkJgW6ud9LSnD7UENuxEU1xJ3ba3V+limSk/S/ghIgo7LmYF/GNKlWqYO/evUhOTsY333yDnj17YuPGjf5+WZdGjRqF5557LuNnayhFoFWvLplfu3SRNb7Wgu96PZA/P/Djj0DJkgFvFhERuTFo0CDMmzcPS5cuRaFChTIG+GJjYxEdHY3Y2Fj07dsXzz33HIoWLYqYmBgMGTIECQkJmjL55Qo1a8r0Unq6w4cbYju2IAGH5+7EieL1UaIEULu265TpOfLvv5LVolQpIMsgKhFRsPi9MxUZGYnKlSsDkJG6HTt2YNKkSejSpUtGCETm2anMIRDx8fHYvn273f6s2f4yb5M1A+DZs2cRExOD6Ohoh22KiopCVFSUT95fTj3yCHDsmCRJ+vln6Ui1bAn06WOrWUhERLnL1P9SrTZr1szu/lmzZqFXr14AgAkTJkCv16Njx45ITU1Fq1atMGXKlAC3NAeKFgW6dQO+/NI22peZwQBUrYoqXeuhir86UACweTMwahSwaZP8rNNJpfvx44Fatfz4wkRE7vm9M5WVxWJBamqqXQhEx44dAWQPgUhISMDbb7+Nc+fOoUSJEgCA1atXIyYmBtX/K76UkJCA5cuX273G6tWrQyeMAkDp0sDrrwe7FUREpJWW3E358uXD5MmTMXny5AC0yE8+/FBqTB06JCEUVgYDEBsr4RV+m4qCFOtt1cr+tZUCVq2SztUvvwB16/rv9YmI3PDrmqlRo0Zh06ZNOH78OPbt24dRo0Zhw4YN6Natm10IxPr167Fr1y707t3bLgTi/vvvR/Xq1fHEE0/gt99+w8qVK/HKK69g0KBBGTNLAwYMwNGjRzFixAj88ccfmDJlChYuXIhhw4b5860RERGFvyJFpI7H229LrnODQcImnn0W2LvXv1XlLRagb1/5N3NnCpCZsps3gaef9t/rExFp4NeZqXPnzqFHjx44c+YMYmNjUbt2baxcuRL33XcfAPchEAaDAT/++CMGDhyIhIQEFChQAD179sSbb76ZsU3FihWxbNkyDBs2DJMmTUKZMmUwc+ZMtGrVyp9vLUe2bpWwvj//BIoVAx57TML9IiOD3TIiIqIsChUCRo6UWyBt2AAcP+78cbNZOnp//AFUrRqoVhER2Ql4nancKFA1TiwWGUSbPl1SoptMskbKYpGw77VrpTYHEVFeEup1pvwlz38un34KPPWU++1+/BFo29b/7SGiPCNX1pkiYOJE6UgBGYXkMyIXDh0COncOSrOIiIhynyylU3K8HRGRH7AzFSAmE/Dee64f37AB+OEH4OrVgDWLiIgod2rdWuqEuFKqFBAqqeaJKCyxMxUghw4B/5Uhcemhh2QdVe/ewIkTkrSIiIgozylUCHjpJdfbjBkjSTGIiIKEnakAsYb1aZGWBsyeLYmToqKA9u0l+ysREVGe8tJLwKuvykJjnU6KCOt0QL58wKRJMvJIRBRETECBwCzyvX5dCrZ7E8JnNErSopkzpZgvEVE4yfOJFpzg55LJ+fNS0+rcOaBMGVlkHBsb7FYRUZjy5Pgb8KK9eVX+/MCTTwIffeS4kLwr1lmtJ58E7r1XZqyIiIiC6tgxIClJTkpFi/r3tYoXBwYN8u9rEBF5gWF+AfTWW0CDBhKh4G3B+E8/9W2biIiIPLJ8OfC//wGVKgH16knYxWOPAf/8E+yWEREFHDtTAVSgALBuHfDJJ0CNGhL67QmLBVi61D9tIyIicmvePKBdO+D33233mUzA4sUyWsgOFRHlMexMBVi+fFK4d98+Keyu9/A3cOCAZPkjIiLS7MYNKW47dy6wY4d3qWKvXZMiukrZiiRamUzApUvAqFG+aS8RUYhgZyqISpUC+vf3/HlPPOH7thARURhSSoocxscDDz4IdO8uM0i1agHbt3u2r2++cZ1FyWQCFi0CLl/OWZuJiEIIO1NBNmwY4GmSpl9/Ba5c8U97iIgojLzxBjBiBJCSYn//oUNAs2bA3r3a9/XXX+7j000m4ORJT1tJRBSy2JkKohs3gFatJG26J8xm4O+//dMmIiIKE2fPAm+/7fgxi0WKGr78svb9FS6sLR1t4cLa90lEFOLYmQqihQtl3ZQnBX2t8uXzeXOIiDyXlgb8+y9Du3KjBQuyr23KzGwGVqyQGk5adOrkeq2VXg/ceSdQrpxn7SQiCmHsTAXRt996noACACpWBG6/3fftISLSLCkJGD5c6v+ULSt1hpo1A9auDXbLyCoxETAYXG+jlBTC1aJ8eaBfP8e1PXQ62ddbb3neTiKiEMbOVJAoJRlkXQ0aOvPyy951woiIfCIpCWjcGJgwwX4tzi+/APfdJ+mzKfhKlnQflqfTSZ0orSZPlgryOp2ciKxrqAoVAubPl9h1IqI8hJfkQWAyAY8/Dvz2m/bnWAcCe/eWGxFR0IwZAxw+nP1C3WyWkaJ+/YDk5OC0jWwee8z1yJvBALRtC9xyi/Z9RkQA06dLjPr77wMvvQR8+aXMgnXpkuMmExGFGnamgmD8eODrrz17jjVMfdYs4I47WGuKiIIkLQ2YMcP1jMfNm1LPiIKrRAng9dcdP2YwAJGRzhNUuFOunKSjHT1a0q1HR3vdTCKiUMbOVIClpwMTJ3pXL9Fq716gaVOmRyeiIDh7Nnua7ayMRuDgwcC0h1x7+WUJx8yaYa9WLeDnn4HatYPSLKdMJuDMGQklJSIKAexMBdihQ8CFCznbh3W91Vdf+aZNRESaFSjgfhulgIIF/d8Wck+nA4YOlTC8FSskLGL3bmDPHqB+/WC3zubaNeDVV2X9VqlSQJEiQJMmwKpVwW4ZEZFL7EwFWE5mpLLuh1E0RBRwRYvKRa6rLHEmk6TRptwjKgp44AGgc2fgf/8LdmvsXbsGNG8OjB0LXLpku3/zZmnzrFnBaxsRkRvsTAVYlSpAbKxv9uWqrEtioqwRfvdd4LvvJLyQiMgnXnvNeSpSg0Ey+t1xR2DbRIFhMsns1r33ApUqAQ0bAlOnAlever/P994Ddu1yntDkqae018IiIgowdqYCLF8+YNCgnKc21+mkY5aVyQQ884yUfRk4UBItPfIIUKYMsHx5zl6TiAiAXEh/+aUc0HQ6yfBmNMpjzZsDixYFt33kHzdvykzRY48BGzYAx44BO3bISa1ePVnr5CmLBZgyxX1x4dmzvW01EZFfsTMVBK+9JgO3gPedKqWA/v2z3z9kCPDJJ9KpUso20Hf+PPDQQ1IGhogox7p1k4vnjz+Wg9HzzwPbtwOrV/tu+p1yl1GjgPXr5f/Wzo9Scjt2TGp+eCo52f2sk17PhCZElGsZg92AvCgqCvjxR2DhQmDaNODIEUm0dPKkhI5r8fjj2WsjHj8uoX2O1mVZ73v1Vdu5MBAuXwb+/FMGsGvWdL3MgohEejqwdCnw669yHdmihUwI5LrvT+HCMitB4e/KFTnBOJtBMplktmrfPskUqJV1dtPdgmItiU+IiIKAM1NBYjRKh2jTJhncPXRIMthai/M6ExUl9TLnzMm+7cKFrme6zGY51509m+Pmu3XhghQXjosDGjUC6tYFypeX0HpfJeEgCke7dwMVKgCPPgpMniwTP+3aAVWrysAEUVDs3QvcuOF6G51OTmqeiI52P1JgMgEdO3q2XyKiAGFnKhcZMcJWQN7oZM4wNVVmlhxFRVy8qC1sMHOyJH9ISgIaN5YlFZkTX5w6BTz9NPDKK/59faJQdfq0LEeyDnikp9u+Q8eOyXIkdyWeiIJGKeDffz1/3ksvyXMdjSYajTIi16xZjptHROQP7EzlIgYDMG+elAJp1UqK0zuycaMU7c2aPKliRRnAc8VolBIe/vTBB8Dff2dPzGT1zjsS2khE9qZMkWgqR98ds1lmsb/4IvDtIkLdujKL5M7kycCJE57t++67gQULHCc0adAA+OEH92EbRERBws5ULqPTScTDnXcCaWmOtzGZpDMyZ479/Y89JmGAzhiNEjrkz7XhSsk6MGcdKUA6jZ9/7r82EIWqBQtcf3cAyUpNFHCFCkmKcnedmuvXgYkTPd//o4/KaMEnnwD9+kla2p9/lqxJt9ziVZOJiAJBpxRXsKSkpCA2NhbJycmIiYkJdnOQlibnLWedKUDOZ/XrS1bazKZNk5ToWdfzGgyyVnzHDpnB8pcbN4D8+V1vo9dLPU9eFBLZi493v6axVi3g998D055AyW3H4NwiR5/LpUvAqlXSualTR04YOXXzpix+PXfO9XbFisnCWSKiADt7VpI3WSxSBq9sWe/248nxlzNTudCSJa47UoB0lBxddA0YIKPbt95qu0+nA9q0kazF/uxIATIz5mp2DJDOVNGi/m0HUSiqXt31Onyj0bNEaZQHpacDQ4cCJUsCXbsCfftKAeX//U8y7eVEvnza/gCTk3P2OkREHrp6FejVS+qqduwok93ly8vg/cWL/n1tdqZyodWrtW1Xrpzj+7t0kaxfv/8uERKnTgHffy/F6v1Nr5fzt7MEGoCEKXpTjoQo3D39tOswP5NJBkyInOrbF/joo+wjcvv2AU2aAEeP5mz/t9/u+gAPSDpKIqIASU8HWrcGvvrKPneAUsB330meAa2lh7zBzlQuZDZry8rXs6fzx3Q6GUBs3FgGKANp5EhJnuFohN1gAFq2lPXGRGSvQwcZDMm6LMX68+DB/O6QC7/9JmlUHUXvm80ydDt2bM5eo18/15mO9Hr2+IkooJYskckDZ8mbDh70b/ImdqZyoUaNnNdFtIqOBnr0CEx7PFWlCrBunUy1AtKBsnYOH3lE/uiZmIkoO70emDsXeP99+zjvypWBTz+VCQd+d8ipL790Hxbw1Vfus5y4Uq+e60LNSjkPmyAi8oNZs9wXtZ8503+vzwQUyH2Ln69eBUqXlhTJzn47kyZJsqPczGKRkMW9eyXUvl07+7VcROScxSLJzfR6SUwRzp2o3HYMzi08/lyeeAKYP999ZykpKWdpXW/ckNEyZ0ULjUbJdlS3rvevQUSkUa1awP79rrcpUcJ9gqfMPDn+ugl8pmAoWFBiPNu0kYFEa0SFXi8XWE88IeE+uZ1eL/WyWrUKdkuIQo9eL4MqRJqVLu2+112ggJxkcmLJEvfV399/X2bBiIj8rHRp4NAh5+NIOp1/a6wyzC+Xat5cEkgMHCij0oULy1qJhQuB2bO1rakiInLJYgHWr5dg8mXLgNTUYLeIcqJXL9frmQwGoE8f9/Ew7ixd6vokZDIB337r3b6Vksr0H30EzJghGZSIiFzo3dv1hLxSsl7fXxjmB4aYEFEetHy5jNacOGG7r2hRYNw44MknA9oUHoMd8+pzGTJECt9mZTRK/addu3I+5fngg8CPP7reRq+XTpUn8am7d0s62D//tIVi6PWyQHjqVIkXJyLKIj0daNYM2LrVec6BqCgZO0xI0LZP1pkiIiLnVq2SC+KTJ+3vv3QJ6N9fLlwpNE2aBLz1VvY1Uc2aAVu2+CZ2tFYt17NbOh1QrZpnHakjR6SNf/8tP1uviCwWYM4c6WQRETkQESHBFa7GW9LTpfyIP7AzRUTkSxcvAh98IIseW7eWVNTnzgW7VTZKAcOGyb/OAhNGjpQkAxR69HrglVcke8mqVRKS9/ffkg3IV1Xb+/d3/rdjNWSIZ/scO1b+5hzF6lgsspB4+3bP9klEecauXcD1684ft1gkIdpvv/n+tdmZIiLylQ0bpGDpiBHAihXATz/JhW358hJWlxvs2ydFN1xdDKekuA/jotwtOhq47z7goYd8X7G9QgWZAQOyz1DpdEDbtlI8WCuzGZg3z/V6L6ORCS2IyKnjx327nSfYmSIi8oVTp+Qi8vp1+6Bti0USOzzyCPDXX8Frn5WW3LA6HZCYmP3+Y8eAN96Qwq2jRkmnjPKmwYNlgKBxY9t9FSrIrOy337qud5XVjRvuk59YLMCFC141lYjCX7Fivt3OE0yNTkTkC9OnywWho9WvSsn9kycDEyYEvm2ZackPq5T92hqlgJdeAsaPlzAy61qYceMkg9ynn0rQOuUtrVvL7cYNIC0NiInxriBa/vySsjYpyfk2er3M8BIROdCqlRyCUlKcb1OmjPYEFJ7w68zU2LFjceedd6JQoUIoUaIE2rdvj8OHD9ttc/PmTQwaNAjFihVDwYIF0bFjR5zNMnJ64sQJtG3bFvnz50eJEiUwfPhwmLKEA2zYsAH16tVDVFQUKleujNmzZ/vzrRER2fvxR9e5WU0m4PvvA9ceZ2rUkGKqrlJbFy4ss2xWEyZIx0kpeY+ZC+B98QUwfLg/W0y5mVIyi/nvv64XLLii18tsp6ukFiaTpHUnInIgOhp4803X24wfn/PKEI74tTO1ceNGDBo0CFu3bsXq1auRnp6O+++/H9euXcvYZtiwYfjhhx+waNEibNy4EadPn0aHDh0yHjebzWjbti3S0tKwefNmfPHFF5g9ezZee+21jG2OHTuGtm3bonnz5ti7dy+GDh2Kfv36YeXKlf58e0RENlpqNKWl+b8dWkyaJBewzjpUEydKHllA2vz22873pRQwZYok3qC8ZcEC6ZxXqgTUrAkULw4MGgRcvuz5vkaMkGFjZ+GBL7wA3HZbztpLRGHtmWekXnh0tPxs7TjFxAAzZwKPP+6nF1YBdO7cOQVAbdy4USmlVFJSkoqIiFCLFi3K2ObQoUMKgNqyZYtSSqnly5crvV6vEhMTM7aZOnWqiomJUampqUoppUaMGKFq1Khh91pdunRRrVq10tSu5ORkBUAlJyfn6P0RUR7Wt69SRqM1R172m9Go1KOPBruVNuvXK1Wtmn0bS5dWat48++02bHD+njLfvvrK66bwGOxYrv5cPvhAfu86nf3fgcEgf1dJSZ7v8/RppTp1Ukqvt+2veHGlPvxQKYvF9++BiMJSSopSc+Yo9d57Ss2fr9T1657vw5Pjb0ATUCQnJwMAihYtCgDYtWsX0tPT0TJTWeKqVauiXLly2LJlCwBgy5YtqFWrFuLi4jK2adWqFVJSUnDgwIGMbVpmKW3cqlWrjH1klZqaipSUFLsbEVGODBrkOhuZySSL9nOLZs2AAwck3fQ33wAbNwL//JO9nk+mSAKXvA3xotDz77+20M6sWSHNZim6++67nu+3ZElg0SJJ5rJuHbB5s/x/2DDv1mIRUZ5UqBDwxBMyof3YY7aZKn8JWGfKYrFg6NChaNy4MWrWrAkASExMRGRkJAoXLmy3bVxcHBL/yySVmJho15GyPm59zNU2KSkpuOGgVsrYsWMRGxubcStbtqxP3iMR5WH/+59kMgPsQ5WscQajRwNNmgS8WS7pdMCddwIdO0rbHAWTV6+u7UK2Rg3ft49yp1mzXP9NmM0SSupqcMGV+HigeXNZKc7EJkSUywWsMzVo0CDs378fCxYsCNRLOjVq1CgkJydn3E6ePBnsJhFROHjuOWDtWkkrFB0t5dhbtJAU0q+/HuzWeadCBeD++52v2jUYgGrV/JMiiXKnI0fcb3PtGtCli+PslkREYSQgqdEHDx6MH3/8EZs2bUKZMmUy7o+Pj0daWhqSkpLsZqfOnj2L+Pj4jG22Z6l6bs32l3mbrBkAz549i5iYGEQ7mNuLiopClHVxdYg5fhyYM0eiLEqUALp3B6pWDXariChDixZyC4b0dGDPHkkaUb068F9IdY5NmQI0aiSJBTLPNhiNkqjiyy8ZhpWXxMZq+31/+60U2u3Rw/9tIiIKEr/OTCmlMHjwYCxZsgTr1q1DxYoV7R6vX78+IiIisHbt2oz7Dh8+jBMnTiDhv1HOhIQE7Nu3D+fOncvYZvXq1YiJiUH16tUztsm8D+s2CWE0UqoUMHKkJE16802Jshg/XgaEn3zS+2gKIgoDFoukMCpVCmjYELjnHgmV6tULuHQp5/uvVAnYvVvSV1sHqCIiZOZh506gfv2cvwaFji5dtJ109Hrgo4/83x4ioiDSKZV19ajvPP3005g3bx6WLl2KKlWqZNwfGxubMWM0cOBALF++HLNnz0ZMTAyGDBkCANi8eTMASY1et25dlCpVCu+++y4SExPxxBNPoF+/fnjnnXcASGr0mjVrYtCgQejTpw/WrVuHZ555BsuWLUOrVq3ctjMlJQWxsbFITk5GTEyMrz8Gn3j/feelXHQ6iS56//3AtokoLCkF/PILMH++zMTceqvUt6lUKdgtc27wYCkInJXBAFSpAmzZIrlhfSEtTYqrxsRIGKMPhMIxOBhy7eeiFNCypSSJcCciIveUBCAi0sij46/nyQK1A+DwNmvWrIxtbty4oZ5++mlVpEgRlT9/fvXII4+oM2fO2O3n+PHjqnXr1io6Olrdcsst6vnnn1fp6el226xfv17VrVtXRUZGqkqVKtm9hju5Ov2sUurmTaWKFHGdlTgqSqlLl4LdUqIQd+WKUvfea0tlrtdLqmedTqnXX8+d6Zn37nV9cNDrlXrrrWC30qXcfgwOllz9uSQnKxUX5z5lfsGCwW4pEZHHPDn++nVmKlTk2tG//6xfr20Jxrx52bMaE5EHOnYEli6VbGSOzJghoW65yZAhwLRprsOuypQBcnGindx+DA6WXP+5fP450Lev88eNRgkJ/OqrwLWJiMgHPDn+BrTOFHmHZV6IAuDPP2XBvLOOFACMGZP7spMdP+5+/cqpU9nrARHl1GOPAaVLO870aE1Q8fzzgW0TEVGAsTMVAv7Ls+EWy7wQ5cCPP8qCeVf++Qc4eDAw7dGqWDH7ulaOxMQw2x75Xv78UgqgVCn52WiU75BeL1keFy2S+mvumEzy/fvoI5nFSk72b7uJiHwoIKnRKWcqVZIwv40bHQ+aGwySHr1hw8C3jShs3LghF4HuZp5u3gxMe7R6/HHgiy+cP240Aj17Bq49lLdUqQL89ZfM6i5fLskm7rxTMkkWK+b++T/8IClpz561ff/y5QNGjQJefZWDAESU67EzFSKmT5cyL8nJ2cu85Msntad4ziHKgTp13IfLRUUBlSsHpj1atWwJNG0qGQizjrYYDEDBgpLuk8hfIiMl5O+xxzx73tq1QPv2thBU60DGzZtS5NpkklogRES5GMP8QkTlylLmpU8fWzZia5mXHTuAevWC2z6ikNe6taz/cBbqZzAA3boBmQqM5wp6vYzut28vIyo6ne093HabTGmXLx/UJhI5NHKk/OtsPd+4ccDFi4FrDxGRF5jNDyGQMSmL1FQpfxMba6ufSUQ+sHmzzPSkp9vPUhkMMqLx66/aQpeC5e+/gZ9+koNE/fpAkyYhMWUdasfgQAnrz+Xvv93P8up0kqmyf//AtImI6D/M5hfmoqKA+Hh2pIh87q67gJ07pcZARITcV6yYjKBv3Zq7O1KAFBgeNEjC+po2DYmOFOVRFy6438ZgAM6f939biMgrZ84AI0YAxYvL17V0aWD0aBnwz0u4ZoqIKLPq1WUR4qxZMsMTHc1OCZGvlSkj3ytXwTEmE0NUiXKpv/4CGjeWSFzrct3Tp6WCyJdfSiBHfHxw2xgonJkiovCVlgZ8/bUsjH/oIeDll6UukxYGg6R+ZkeKyL39++W79uOP2oojli4N3Hef4xpVVoUKAR06+K6NROQz3brZd6SszGbgxAlg4MDgtCsY2JkiovB04oQUX3vsMeCbbyRJw/jxEgr38cfBbh1ReDh4EEhIAGrVku/agw/KcPSbb7ovM/D++5JRyVmHauJEGdAgolxl715g+3bnNe5NJmDpUuDffwParKBhZ4qIwo/ZDDzwgG0WynrEN5vlAu+ZZ4Bly4LWPKKwcPSoxPns2GF//9Wrktp82DDXz69VS5K+NG5sf3+lSsDChZK+lohynV273G+jFPDbb/5vS27AzlQI2rFDBgALFJDyHg0aSNF4d4OARHnGTz8Bhw45rxtlMEja5dwoORlYt05q8CQlBbs1RM699ZZ0nJwNT3/0EXDkiOt91K4t6fv/+gtYvVqu0v76C3j0Uds2N24Ahw/LbDMTEBMFnFKSg2ncOGDsWODYMW3Pi4z0b7tyCyagCDFffw08/riUkbFeJ+7aBTzxhJyHZs1yXiaHKM9YtkwqWjvrTJnNUuT26lUpautDR49KFOHChcD160DNmsDgwXJt6PK7eeOGpEWaOVOKlgISAtWrF/DBBwx3otzl5k1g3jz3ha47dpSYIHcnpltvlVtmyckywzVzpm0dVs2awGuv2Xe2iMhvTp6UpYs7d9oicp2Nn2RWsKAkyM0LeNkdQhITpdNksdifv6wzUnPmyAwVUZ6Xlubb7TT65ReJXPrkE8lqlJQEbNkiM8mPP+7iBGQyAW3bAlOm2DpSgPx/xgwpKJye7tO2EuVIUpK278++fVIrylNXrgD33CNfpswJLQ4cADp3BiZM8HyfRKSZxSIBHnfdBezZI/eZzbbzmKvcTDod8OyzEkGVF7AzFUI++8z1aIBOJ+t1ifK8evXcD52VLQsUKeKzl7xxA3j4Yen/ZB7ssDZj4ULpKzm0eDGwfr3jWF2zGdi0SXZAlFsULmyrxebOhAmeh+e9954kt8j6Pbbu54UX8s7qdqIAUkrGP267TSqF/Puv49Np5q+00Wj/b7duUm8qr2BnKoTs2OH6fKSUjB689562KViisNW9u+u05jqdJKHwYdrzr78GLl1yvXbR6TXljBmuU0Tr9bINUW6RL59Mt2r5Dv31l2dVPJUCpk51P3o4a5b2fRKRJs88I2nNta6LqlRJTrmtW0vOmK1bJVLKmIcWErEzFUIiI7Wdt0aMkD9sJqSgPCsmBliwQDoomY/oOp3cHnhAYhB8aOtW1ycPpeTkdPGigwePH3d94WixaD+zUUBs2rQJDz74IEqVKgWdTofvvvvO7nGlFF577TWULFkS0dHRaNmyJY64S8YQal55RfvslKvBgqyuXgUuXHC/3Z9/at8nEbn1668SWQton0xOS5NxjeXLgenTgYYN8155RnamQkibNto7SAsWAN9/79/2EOVq7drJdG6XLkB0tMzuVKsGTJ4sBTC0XgRqpPVa0WGHKy7OfQB6XJxX7SL/uHbtGurUqYPJkyc7fPzdd9/FRx99hGnTpmHbtm0oUKAAWrVqhZuZ18SFusqVpXabK3o9ULcuEBurfb+uak9Z6XQyaEJEPjN9umczSgaDhAPmdexMhZAuXYCSJbVdtOn1LtZnEOUVdetKVpbr12Uh04EDEr/g444UANx3n+vEZno9UKeOLDXJpmdP9y/Qu7e3TSM/aN26NcaMGYNHHnkk22NKKUycOBGvvPIKHn74YdSuXRtz5szB6dOns81ghbxBg4By5Zxn67NYgBdf9GyfERHAI4+4vqozmSQRBRH5zIED7hN0ZmY2A0895b/2hAp2pkJIdLSkPy9e3P22FotkoyWi//g57qBdO6BiReeDHRYLMHKkkyd37w5Urer4yUYjcPvtksqTQsKxY8eQmJiIli1bZtwXGxuLhg0bYsuWLUFsmR9EREhdtxIlbGG0gK0j9Morks7SU6NGyb4cddIMBqBJE7kRkc/Exmo/Ver1EjHVqZN/2xQK2JkKMTVqyFreihXdb3vxorawc28pxWzNRFZGo1xTWiP2sl5Tvvqqi2vK/PmBDRuAe+/N/ljTpvKYj+thkf8kJiYCAOKyhGbGxcVlPOZIamoqUlJS7G4hoVo14I8/pEhvkyZA/fpAjx4SZvvWW97ts1494McfbeGBERG2wYYWLSRUN68tzCDysy5dtK2Vio2VwcElSzxbDhmu8lCujfBRoAAwZAjw3HOut1MK+PxzSUjhS7t3S5j8kiXSmapQQSI9Bg+WUHeivOr22+Wa8quvJNv51atA7drAgAFybehSiRLAypWyg02b5AvcpIlcqFKeMHbsWLzxxhvBboZ3YmPlJDB4sO/2ef/9UrBt8WKpVxUdDTz0EPC///nuNYgow+23u348Xz7gu+9kjI/XezY6pTwt/hB+UlJSEBsbi+TkZMSEyILWpCSgWDH3CSkaN5ZCor6ybBnQvr38P3NcrV4vr7VypZzviPK0Cxdk0eLnn8v/y5QB+veXG2eYsgnFY3BmOp0OS5YsQfv/Do5Hjx7Frbfeij179qBu3boZ2zVt2hR169bFpEmTHO4nNTUVqampGT+npKSgbNmyIfu5BERqKrBokYxgnDsnSTH69ZNFjJy5IvJIw4bAzp3Ory2LFgUSE/2y7DjX8eS8xDC/EFW4sLbkSL5MHHX1qoQpmc3ZFyhaLJJS8733fPd6RCHpn39k5PzNN+X/165JCufhw4FGjaQYFYW1ihUrIj4+HmvXrs24LyUlBdu2bUNCQoLT50VFRSEmJsbuRi5cuAA0aCDrCVevlkKL334LtGol8UqMQyfS7NAhYPt214P0ly4Bq1YFrk2hgp2pEJaQ4DpW1WiUUQZfmT9frgudzWVaLFKfgAWDKU/r1k2G7jJ/EZSSL8gff/g2DIqC5urVq9i7dy/2/pfp59ixY9i7dy9OnDgBnU6HoUOHYsyYMfj++++xb98+9OjRA6VKlcqYvSIfePxx4OBB+b/1CtD6vfvmG+/XaxHlQf/8434bnU7bdnkN10yFsCFDpEiaMyaTRDv4yt690kFzNdh3/rxEWpQs6bvXJQoZ+/bJFK0zZrOEJE2YwLpRIW7nzp1o3rx5xs/P/beItWfPnpg9ezZGjBiBa9euoX///khKSsLdd9+Nn376Cfm40MA3Dh6U2ShnlJKEGKNGMfY8j7t2TQ67f/4pET0dO0o0KMnX5JdfgHnzJLmZlu1vucX/7Qo17EyFsFatgL59gc8+c75N//6SUMkXoqK0ZXmJivLN6xGFnG3b3G9jMkk40gMP+L895DfNmjWDqyXHOp0Ob775Jt58880AtioX+fdfuYK9fBm49VbJn1yggO/2v3atDJO7OiklJ8sooIvQSgpvCxfKoPKVK7LOx1qiont3YMaMvJ1E4do1oEMHCdszGrVFFRUoALRt6/+2hRqG+YUwnU5GWlzZuVOSRvjCgw+6L0raqJEsUCTKk7SWjvekxDxRKDGZJJS1fHnghReAceOAXr2A+Hjgyy999zpms7YEE4w7z7NWr5Z13levys/p6bY/h3nzfBu5E4r69gXWrJH/m0zaBsvffNO3YyLhgp2pEGadnnXHV5l2mzWT8iHOrgMtFomoIMqzWrRwf4GXP7+MOhCFo+eek0yWFovcrHHhV69K7akffvDN6yQkuE9nmy+f1CZwJz1d1lh17SrVt4cPdz9SSbne6687n7y0WIC5c4EjRwLfrtzg6FGZtXP3FbLWzM6fXxKMDRvm/7aFInamQpirZBCZnT7tm9fT6aSGYtWqzrf54gsmUKIgUErbl8HfypUDHn3UeWYYnU6KsjE9OoWLK1eAd9+VUL7ISODjj51/F3U64KWXfPNdbdBAirc5G90zGGTo3V1GxDNngLp15Xu7aJGEckyYAFSpAowdm/N2UlCcPg1s2eK6s2AwyK88L1q+XNvE7rBhUnUgMVEmmlltwDF2pkKY1jW1hQr57jXj44EaNWyjFVktWQK8/LLvXo/IKaXkTNi4sVxQRUZKkc9g522dOdO2RsPaqbJe8D3yCPD228FpF5GvXb4sf+ujRslQt7uRNKWA/fslxiqnHSqdTr7/JUrYn5CsV3sNGkh1eXftadfONgtljQGz/vvSS8DXX+esnRQUycnut9HrtW0Xjm7e1NYx6tBBEtT68joyHLEzFcIMBuC229xv99RTvnvNf/91PTWsFDB5si1GmcgvlJJwos6dJemDxSJB3+vWSWaWDz4IXtsKFQI2bACWLpUK140bSzvXrpVQorxQ7ZDyhmHDJN2/u1ihrLp3B2rWBA4fztnrV6oE/P67LOSoXBkoUkRqvE2fDqxf735xx8aNwO7dzhcD63Qy+JEbZr3JI2XKyPiaKyaTtmuocFSnjvvlhEajTNCSezrlKh1RHuFJlePcZsUKoE0b54/HxEiRNVf1qDzxxReyltidVaukAD2RX/z0E9C6tettfvtN23oJCrpQPgb7U67+XC5elFAFV1mJXDEYJMfyvn1A8eK+bZtWI0ZISJ+793DmjLxXCim9e0uImrNfb/78Er4W7rMu584BJ08CxYoBFSrIfRaLdCT/+cdxp8pgkDJuc+YEtKm5iifHX85MhbjWrWWtrDMGA3DihO9eT+t509vzK5EmH3/svmL11Km+eS2TCfj+e1k/8dFHrFhIBEgnKCcHerNZChNOn+67NnkqLU1brFNamv/bQj739ttSzi/rsjq9Xn7t06aFd0fq8GHg4YdlHOCOO4CKFYGGDSVIQq8HFiyQHC1ZPx+DQbYNZoBHqGFnKsRZLK7rTF2+DDz9tO9er0ED99sYDBJpQeQ3O3a4jlEwmbTVfHJn3TqgbFk5I732moQ1VawI9OwpQedEeZW7GCotLJbgDn3Xr+9+ndcttwClSgWmPeRTpUoB27fLmp/Mf6716kmekSeeCF7b/O2PP6TjtGyZfZTqzp2ytHjpUuDOOyXKtUcPW72tYsWkDtf27cGbMA5FDPNDLg+lcOPrr6WOgjsnT0oMsS/ccw+wdavjQUmDQWozLljgm9cicqhMGeDUKdfb3HUX8Ouv3r/G7t2yuN5kyr4mRK+Xlbl5NRWUj4XyMdifcvXncvMmULIkkJSUs/0ULy5xSP5y9qzUtzp2TNZUde0qWZQAeQ+lS8t7cLTuS68HXn0VGD3af+2jgEhJkeugmBgZHwt3rVtLnS1HY446nXSaTp+2LeG1WIDUVOlUMWOfYJhfHvLVV9q2c7XO99IlSUA2bpwkWbp+3fW+vvxSps6zRlnp9cDtt0sCCiK/euQR14Vv9XrgoYdy9hpvvCFnIkcXWRaLJJP4/fecvQZRqMqXT5LAOLvyMhgcnygy0+slpbq/TJggAy8vvgjMmCHZ/WrWlA6V9crxm29k2iLz8USnk1uTJjJMTyEvJkb60HmhI/Xvv8DKlc6DN5QCLlyQWSsrvV4yRLMj5R12pkLchQvatnM0iK+UXC+WLAn07y8DcN26SXztF18431eFCsDevbJ92bLyBaxcWc5T27bJiEdeYjYDe/YAmzdLx5QCYMgQW+B7Vnq9nDn79PF+/9euSVE1V6GERiMwf773r0EU6l56SUJeAVtnxNp5SkiQ9VCuvkMWCzBggH/aNneudPasM8vp6bZwioULbfHvzZsDu3ZJzFeBArZRwUmTJNGNNf7JA8nJcmiYNk3Wp3ia7JAoJ44edZ+A0mAA/v47MO3JC9iZCnG3365tu507s9/39tsSvZCWJl8863nmyhXJ2PfNN873d8stUl38xAmZyTpyRAq6hfNizqyUAqZMkTqt9epJBuz4eIk/Pn8+2K0Lc7ffDnz3nfTkrZ0q661wYRmWy0nAd0qKtisg9p4pLzMYgM8/l+qovXoBLVtKGYBly6Q8wEMPSUowZ4MeLVrI476mlKxxdMZiAWbNsmVnql5d3sfVq3Ii/OMPGbCJivLoZS0WOS/Gx8vbGjhQPpJbb5WPgygQihRxv43ZLKdK8g2umUIuj0t3Y/VqWUzozt13Az//bPs5OVkO+M7W0Ot0UsLjyBFO+zrz8svAO+9kv99olNm77du1HdQoBy5elIuizZvl4uzee6WGTU579Wlp8stzFfOq18sfwIsv5uy1KKSPwf4UFp+L2SxhCxMm2EIpYmKkpzF6tFczP27t3w/UquV6G70emDhROk0+MnKk4zrBer30O3/+WZICaHHkiPTvjh8HihaVztldd/F8TO4pBVStKn9Dzq7wIyJkzdQttwS2baHEk+Ovi0UHFAruvVcO0u6Kr2UdYPvhB9fJyJSSKeA9e2TWhez9+afjjhQgA5vHjgHvved8G/KRYsVkStTXIiOlSMm0ac6/XDqdTEMSkXMGg4QDDh8OHDwo0zdVq8qssr9cu+Z+G73ep9XlT58G3n/f8WMWixwuXnlFBkBdUUoG6saOlY9OKWnqlCmSVGDRIve1iClv0+nk2qNTJ+ePP/ccO1K+xDC/EKfXa8vmt2uX/fqqS5fkue4wismxWbNcr6s2m2W5AOd9Q8jZs5Kmefp0SVf5yiuSW9dZootx42TBIRG5FxEB1KkjdTP82ZECJK7OVYIaQEa9qlXz2UvOmeM6MthsBtaskSKxrkyeLB0p63MsFlsI/qpVQL9+vmkvhbeOHeU6xdrxNhptM6TPPy/LPMh3/NqZ2rRpEx588EGUKlUKOp0O3333nd3jSim89tprKFmyJKKjo9GyZUscOXLEbptLly6hW7duiImJQeHChdG3b19czTKa9Pvvv+Oee+5Bvnz5ULZsWbz77rv+fFu5zosvuj9vJCUBTZvafq5YUduSkIoVc9S0sHXsmPuO0qVLwI0bgWlPWFEK+OUXORMsWeI+vWROpabKIvgyZWQx/YABsnj+vvukDY8+av8Fq1RJMrT4Y0aMiHLullvkatLZiJdeL5kG27XL8UvduCGj/K++qm3wzFXSKLPZdTSD2SzlUI4d87ydFPqUknG+adPk1HT6tOvte/WSMcLZs2VscNIkyfT33nuuB4PJC8qPli9frl5++WX17bffKgBqyZIldo+PGzdOxcbGqu+++0799ttv6qGHHlIVK1ZUN27cyNjmgQceUHXq1FFbt25VP//8s6pcubLq2rVrxuPJyckqLi5OdevWTe3fv1/Nnz9fRUdHq+nTp2tuZ3JysgKgkpOTc/yeg2X+fKXkq+b6tnOnbJ+WplSJEkrpdI63MxiUuuee4L6n3GzgQKWMRtefdVSUUmZzsFsaYjZuVOq22+w/yEKFlBo/XimLxT+v2amTUnq94y9BbKxSf/+t1IULSm3dqtS+ff5rRx4WDsdgf+DnkgP//qtU6dLZD9QGg1IREUqtXp3jl0hLU6p5c8eHD0c3vV4OJc7s2qVtHx9/nOOmU4jZv1+pWrXkb8B63abXK9Wrl1LXrwe7deHJk+OvXztTdi+UpTNlsVhUfHy8eu+99zLuS0pKUlFRUWr+/PlKKaUOHjyoAKgdO3ZkbLNixQql0+nUqVOnlFJKTZkyRRUpUkSlpqZmbPPiiy+qKlWqaG5bOJywJk7UdjB/6inbc5YulS9l1hOBwaBU/vxK7d0bvPeT2/38s+vP2WhUqnfvYLcyxGzbplRkpPMrk9Gjff+a27e7/0X27+/71yU74XAM9gd+Ljl0+rSc9KKjbVehbdvK994H5s7Vdt61nlfbt3e9v19+0bafTJdNFObWrFGqbl3Xnet27TjG5w+eHH+Dtmbq2LFjSExMRMuWLTPui42NRcOGDbFlyxYAwJYtW1C4cGHccccdGdu0bNkSer0e27Zty9imSZMmiIyMzNimVatWOHz4MC5fvuzwtVNTU5GSkmJ3C3X//KNtu8xv9aGHJIN0nTr22zRvLplus95PNo0bA61aOV53ZjBIwg8mefPQyJHOi+QCEuSttbCaVl995TpG1mSSKtUsFEMUekqWlJioS5ckDXpSktSPu/NOn+z+00+1rT02GID8+W1roZypWtV9yL7ZzHNzbnbwoJQwK19eIscfewz49VfP95OWBjz8sKTW37vX+XYWi/xJr1njdZPJB4LWmUr8bxVmXFyc3f1xcXEZjyUmJqJEiRJ2jxuNRhQtWtRuG0f7yPwaWY0dOxaxsbEZt7JhUBI7NVXbdlnXQN13H7B7N3D4sHzhT5yQbEO1a/u+jeFEpwMWL5blNNbyRtYY5LJlgXXrgCpVgtvGkHL6NLB+veu0lCaTFNv0pfPn3S90uHHD/+u2iMh/8uWTA7OP08sfPaptnOWuu6R6Q9WqrrcrVgzo0sX5ehaDQcpu3Huvx02lAFi8WDq6M2bItdSpU3Lf3XdLviJPvPAC8P332rdv3VrWRbnL7Ez+kSdTo48aNQrPPfdcxs8pKSkh36EqX17bdq1aOb7/9tu1FwAmUaAAsGCBLBhetkxSzdepIyNJWkYrATkR//uvXM+XKZOHF4WeO+d+G6PRfSosLU6dkiHlTZu0lYqPiZFhZSJyLi0N+PZbYO1aObA1bizD8pm/O0rJwIS12HaIu+UW2/Hbmf/9Tw41Wk2YIEkGjh+3vzA2GiXi4euvw+KjCzsnTgBdu8rvLPPfgzUT46hRUmOseXP3+7p4USZUPWFNXnLsmORPSkuTCdi772ZtskAI2lcyPj4eAHD27Fm7+8+ePZvxWHx8PM5lucgymUy4dOmS3TaO9pH5NbKKiopCTEyM3S3UabkW1emA227zf1vymkqVpO7j8OFSQFnLic5iAT7+WGYKy5eX0cYKFYAPP8yjI0slS7o/4ptMQOnSOXud776TX9jbbwMbNsgZ0NXQssEguYi9uXq5eVPyJXfsKMOGo0YxDReFp/375XvVtaukDpszB+jbV0aIfvkFOHMGGDYMiI0FChaU28CB2uPTc6mePV0/rtMBTz3l2T6LF5eC7yNG2Iq+R0VJLfJdu4AGDbxrK/nXp5/KqcRZx9polGx6WqxdC6Sne94GpYB582zXI02aADVqAAcOeL4v8lAA1nAppZwnoHj//fcz7ktOTnaYgGKnNQWdUmrlypUOE1CkpaVlbDNq1Kg8lYBizhxtGYAeeijYLSWlZKFonz6Of086nVJdu+bRLIAPPCCrq12lR7x0yfv9HzokCSWcpbB0lHyidGmlEhM9f62//lKqXDnbl8+6clyvV+qTT7x/D2Eq1I/B/hISn8ulS0rdcovj765er1S+fJI6NuvjRqNSRYoodfBgsN+B15KTlapY0XFmV6NRqdtvV+rqVe/3b7HI8/Pk+SDE3HOP+1NK4cLOn2+xKHX0qCSMnTZNe2ITLQlLihZV6uTJwH0W4SLXJKC4evUq9u7di73/rZ47duwY9u7dixMnTkCn02Ho0KEYM2YMvv/+e+zbtw89evRAqVKl0L59ewBAtWrV8MADD+DJJ5/E9u3b8euvv2Lw4MF47LHHUKpUKQDA448/jsjISPTt2xcHDhzA119/jUmTJtmF8YWz1FTgmWfcb1e0KDBxot+bQxqsWgV8/rnjx5QC5s+XBaV5zvjxQGSk81jHt9+2DdV64+OP5V93YX2ADCm3bi3xNlnWZLqVni5TlNYiINaZL2tyjcGDJfMLUTiYPVvikhxNqVssMkN7/nz2x00myYj0xBMBaaZmZjOwfDnQvz/QrZvETjkJL46JkRC+hg3lZ73eNonduLFMfluLpnpDp5PnM6wv99MSSufs97hoEVCrlkzu1qql7ZpOK7MZSE7m9Z/f+bNXt379egUg261nz55KKZmdevXVV1VcXJyKiopS9957rzp8+LDdPi5evKi6du2qChYsqGJiYlTv3r3VlStX7Lb57bff1N13362ioqJU6dKl1bhx4zxqZ0iM/jmxeLG20QkflNQgH2nf3nWNKoNBJmnypJ07lapf3/4DKVFChuq88e+/Sg0bJkOCWr4olSsrtWKFUidOeP8eFi1yP1TYvLn3+w9DoXwM9qeQ+FwaN8758PmuXcF+F+LMGaXq1LFNLVlnk41GpWbOdPnU3bulRMmkSUr99ltgmku5x1tvua43ZjQq1aVL9ud99JEtKsVXs1GObsWLB/4zCXWeHH91SmkZpg1vKSkpiI2NRXJycsitn/r4Y2DoUPcZhVatksx9FHy33w4cOeJ6m7JlZTlPnrVvH/D337LG4u67gYgIz/dx5IgMD1+6pH0hWs2a8to50bevrBmxrjx2JjVVZuIopI/B/hQSn0vdusBvv+VsH7NmAb16+aI1AOR8uHIlsHGjXEo2aQI88ICbBD8WC3DHHfL9d/bdXblSZp2JskhMlJml1FTH12M6nWR0bNTIdt/Zs7Ks0N2pwhciIiQpBWnnyfE3T2bzCydxcdpSswbiy0raxMa63ya3XjcFTK1acsuJbt0860gZjdJxy6m0NG2hhOnp7ExR6KtRQzogOanFli+fz5rz559A27bAX3/ZxmDefVeS/fz4I1C9upMnrlsH7NnjfMcGg4T8sTNFDsTHA0uXSm2otDTbacdolP9PnWrfkQJkzC1QJQxDPGF1rsdI3BDXrp0kRnKnTRuZmfrzT/+3iVzr0sV1DLxeL0mxKAf27AF27PAsNaLZLNUWc+qOO1yfIXU6oHJlplun0HfxomTry8kVYWSkz8ImLl8Gmja1Jc1MT7dlRTtxAmjWzEXd7++/d10x12yWqa4rV3zSVgo/1muskSOBevVkPLB/fxlrcJTV8cgRbeNuOaXXe55VkjzDzlSIy59fezG49eul/sDRo/5tE7nWp4+kunUmKkoOwJQDrkaYs7JeQH3ySc5nwwCgRw8ZaXe1IvnZZ1n8g0Lf8OFSt82VunWd/61bc4cXK+aT5nz+uZQJcTSGYjZL32/GDCdPvnFD24ukpnrdPgp/ZcoAY8ZIGvvffwcmT5bJW0diY33Tmfr0U6B3b8ePGQzy+r4YJyTn2JkKA4MGAVOmAIULu97ObJbkSa+9FpBmkRM7drg+b9+4ARw6FLj2hCWt4XMFCgDt28vouq/ONkWKSDVng8F+pNs6HfnII1JjhyiUJSUBc+e6n/0dM8Y2LG40yvfA+r3o0gX44AOfNWnBAteTZBaLZEt1qE4d9+8lLk5S4xL5QJcuvtlPiRLAzJkysJ55XCIyUpYibtqkLYKJvMfOVJgYOFDqIvbp43rA22SSCup5OVLh8mVgxQqJn89S7zkgpkxxHU1iNMo2HrNWehw4UPKgXrzobRND3333uf6QATm7nDsneWkbN/bt6z/0kPSau3aVDpvRCNSuDXz2GbBwoZuV8EQh4M8/3a9oNxolxmnqVPl36FCgc2cZAdy5U3o23iSXcSI5OQfbdO/uekZZr5d2M0855cDx48ArrwCdOkkwhC/WR+fPL3+WL74oFTm2bQN+/lmSYsyc6X6gnXKOCSjCSL588sU0Gl1XzzaZ5BqyUKHAtS03uHEDeOEFuZ61RmoYDDI69MknOSth5Ik9e1wnBDGZgP9Ks2lz5YocmVetkl++TicjrC++KBcxffrktMm+Y02ztXgxcO0aUK2atK9MGd++TlycxD189pnz1EpDh/p33VLdurLCeM4c/70GUbC4ilW2slhsySVq1gTee8+vTapRQ9ZLOTu+WkOeHCpcGPjyS9t0QeZZKr1eBlyGD/dlcymP+fBD+RPS6eSrodd7tqzXkdhY4J57bD9HRgINGuRsn+Q5DrGEmeLF3a8F1unyXqSC2SzRXNOm2Ye8m80yU9e8OXD9emDaoqWIo0fX+F26AGvXyv9NJulJWywyaty3L7BsmVft9Llz54A775RsKF98ITM0b7wBlC8vgeW+9tFHktYLsM1SWf/t0QMYPdr3r0mUV9SsCZQu7Xobi0WyJHljyxbJyFmxoiRs6dcP+PVXl4tMBgxwPVBlNss2TnXsKCG/bdvaZqDKlJEsfqtW+TTrIOUtixcDzz8vXwmzWf6Mc9qRAmTMlH+WwcfOVJjp2NH9F7R69byXkXnZMjkXOupoms2yUHT27MC05dFHXUd56fWyjSZ790rMorNful4PvPWWp030zM2bsnZi6FCZ+luzJvsFj1IS+vb77/KzySS/DOtt8GDghx+0v2ZqqnTImjUDbrtNMug99JDsZ+ZM4OpVOcMsXSrxDr17y+P9+0t40ezZDLUjygmDAXj5ZdePd+ggHSFPjRsH3HWXjHQdPy415z77TEoXlC0rdakcdKruvx/o2dP5bgsWlKVRLjVqJMeN1FSZPT9xgleslGNvvun7fT7/vGQOpFzA7yWEQ0BIVJnX4PBhpW69VVs17Fq1lLp4MdgtDpyHHpJi9s4+D51OCt8HwqlTSsXEOK6WbjAoVaSIUufOadzZq69KaXV3v/AzZ/zzZjZtUqpYMXmNiAhbW2rVUurECdt2Gze6bp9er1SjRtpe89IlperVc1w2XqeTW8GCSi1d6p/3TD4XLsdgX8v1n4vFotSoUfLdMxrle2w9BrRooVRKiuf7XLNG20lsxAiHT1+wwPlTDAY5NJnNOXzfRB5ITNT2J+3pbfPmYL+z8ObJ8ZczU2Hi3XeBKlVkAE+LgwfzVqrMEydcz9gpBZw8GZi2lCoFrF5ty7pjNNqiz265RSL2ihfXuLNr17Sl2L52Tdv+jh2TUdg6dSRN+JAhzlMLHjkCtGolGT0ACS+0xtgcOgTce68tpvKHH1wnhLBYgK1btSXNePJJ4Lff5P+OZsCUkvfbsaOsxCUi/9DpJATu8GHguedkJqpPH2DDBpmh9mZh7oQJ7pPHAHLS27kz293jxzvPEWE2Sx4Ma1Q0USDs3u37fRqN8lWh3IEJKMLAN9/I9a8nzGZ53pkzQMmS/mlXblKqlJxEXXWo4uMD154GDYB//pFEcps2yTVJs2aSR0LLuu4M1au7XiQAyCItd2sbAOnwdOxoC+oGgD/+kNSCM2dmL2QxYYJtfVZWJpN0thYvBh5/XLJ/aOn03bzp+vETJ4Bvv3VfnMP6+NixwHffuX9dIvLe7bdLL8YXNm50f0wD5Gpy+nQJ8f3PxYvuS8wZjcDy5T6rE0zk1pYt2rYzGOR0ajC4/wqYTPJ3TLkDO1Nh4J13ZCTO0yL0ZrNk0374Yf+0Kzfp2dP1gUevl1wNgRQdLXkQevTIwU66dJG1SteuOe5gGAzyxtzF+//zj/TkTCb7/ViP6H37Sna6//3P9tiCBa6P+Hq99Ngff1ye6+7sUKyYZOFz5eeftVc5NJmkg5iWlvcWCRKFgnPnZP3T77/LAdGTk5HJBOzfb3eXu0ztgIzpaNmOyFdSU7Vdo61dK+OX589LfqZ9+1xv74sEFuQb7EyFuPPn3Y/EuZJX1uA/8gjQsKFEhWQ9ABmNklAu0J0pnyhYUJIpdO6cPc+qwSDJGbRkrZs+3ZZiyBGDQbLjzZplu89d+kOLxVbU5bHHgGHDnHf69HqJO3UX3uNpuXiLRWa72Jkiyl3mzJEMfWaz9HB0OkkyUaiQHG+0XCmePCmDNVWrAn36oETJMoiPl/o6zqSn201mEfldzZruO1JRURKx0rSp/JyeLssxnH0NDAZJjku5A9dMhbicjLBFRkrCpLwgIkLKG3XsmD3arHlzmfCIjQ1O23KsY0dZo9Cihe2+2FjpvGzZoq2A1urVri9eTCb5ADOrUsV16J7RKGcRQDp98+fLGSBrh0mvl56ulrREjRtrCxe0iovzT0G11FTg44+lTlZkpNQaGDRI+6JFonB14YLMGLmqiL5hA9Crly1M2Gy2zVxfu6Z9yP3MGcn49+abQPnyMEz+CEOGOF8zpddLOSlrKSmiQOjUyXXhXL1eEs1GR9vue/JJ1/WhzWbg2Wd91kTKIXamQlx8vAfJCjLR6WSdcOZ6U0rJZIOn4YKhIjZWzrv//CODorNny7rpVavCYN3YPffIG0lOlhLo58/L0XjCBGDgQODtt2W9kTNaZnyybjNokOvtTSY5Q1i1ayedu/btbVOiZcpI29au1VZcq2JF4MEHtU2pWme7POl8aXHjBtCypZzJDh+WC8LLl4FPP5XEHVu3+vb1iELB/v3y3SxRQpLXxMdLEpoXX5Tvy113yTFj3z5Zy+jsStGTE5C1tILZLP8++yyG37YE995rm+yyMhplUO2bb/xbq9tfrlyRmsIffCDnsRs3gt0i0io6WqqHZE42ZWUwyMRq1tTpZcpI9Q+93v451lPfkCGS74VyiQBkF8z1cn36WTfeeMNxmm1Xt+bNlbp+XZ5//rxSL74oKbkBpfLlU6pvX6WOHAnu+yIvpacr1b+/LRdwRIT8q9Mp9corks44q+efd5073mhUqmtX++ekpSl1333Z//is6cpff915G00m+QN01BZ3zp9XqkaN7GnRs+ZArl9fqatXPd+/OyNHOv/CGQxKlSwpvwPSLNSPwf4SMp/Lnj1K5c/v+hhiPY5oOUHp9Up16KBUu3a2fer17vev1ytVr55KS1Nq6lQ5TBiNUoqiTx+lDhwI9gflOYtFqfffVyo62naIAZSKjVXq88+D3TryxPbtSj38sO30UaSIVBZISnL+nJ07lereXanChZUqUECpZs2UWrLEu1MnecaT4y87UyqETlhO3LwpnSNPOlNr18pzz5xRqkKF7Ocoo1GpQoXkHEkh5vnnXXc0JkzI/py//nJ/obNlS/bnpaYqNWaMUnFxtu1q1lTqq6/8+x6vXZOrpTvvlDpXBQrYXr9wYenwXLni+9e9eVOuYtx9wRYv9v1rh7FQPwb7S8h8Lnfe6b6j4+nt/vtl36mpUjxq6FClGjbU9jr+qqsXBBMnun6r8+YFu4XkqRs3pM6nyRTslpArnhx/dUopFbx5sdwhJSUFsbGxSE5ORkxMTLCb45XUVMkh8O67wKlTrrc1GiX6avJkyVuwZInjRGsGgxSvP3TI95FS5CeXLknMoqvFdMWKSShg1qQM8+cDTzwhv2zrH4TRKP+fNAl45hnn+zSbZf1CRISE+QTjD+bCBYl9iY+XdvjD4cMSk+FKRISUph871j9tCEPhcAz2h5D4XPbvl7A+X7vtNuDPP+3vGzZMTlzp6a6fe+wYUKGC79sUYNevy+E8JcX5NmXLytsNh2RSSgE7dshyuzJlJAksrz0oWDw5/nLNVJiIipJr3YMH3W+rlMRfJyZKyR5nGavNZrl2/Pln37aV/Oinn9xnJbl40XHhi65dpRhu375yIVK2rGTh277ddUcKkDN5mTKS8CFYZ79bbpE2+6sjBWjLCqgUswdS3nHkiP/2e+WK/X116rjvSBUuLIUFw8DKla47UoAkNAyHZZpLl0r/uWFD4KGHgHr1pI++fn2wW0bkHjtTYaZgQfcJKcxmue3f7z5pkl4v19cUIq5d07bd1auO769RA5g2TYY6T5yQFc/Mv2pToQJw662uO4wmE9C6dcCaRBRU/kyDmrX8QufO8nrOvn8GAzBggG8GM06cAIYPB8qVk9n8pk2l+E8AMzRdvOjb7XKrxYulfMnRo/b3HzoE3H8/sG5dcNrliMUCHDggM2iXLwe7NZRbsDMVZvR6Sd7mKqUmAMybpy2tpsXivt4r5SLVq2vbrlo1/7bDW5cvS9XCo0elgNqff8pMT26h0wGjRjlvk8EAJCTI8CpRXnD33TIr7GvR0dKJySx/ftclFurVA155JeevvW2bDCxNmCBTP5cuAb/+KjnVu3YNWLVUrZGKoRzRaDIBgwfbVoFlZk3WOGRI7jgNzJolY2k1a0pNqLg4oGdPSZ5LeRs7U2HohRe0pUs/fFhbRJS7JSLkAykpsuht8GBgxAhg82bvzh533SW/MGcB9AaDpCmuVCln7fW1Q4ckz2uxYtLRu/VWuTCqUkU6iAsXBruFNn36SIcKsF3QWUcvqlWTRYgM9Ke8IjISeOMN3+9XKaBAAZmJ6t1bQikAmfXdulWmMqzfv5IlJbf0t9/KmqpatSTUr0kTyUntLJbdkdRUSfF+/bp9p8n6/0WLpMZcADRvLtHTzg4nej3wv/8BtWsHpDl+sXat6yLLFossX9izJ3BtcuTtt+XQ/88/tvvS0+XPKyEh9GcHKWeYgAIhssjXAxcuyLnFk/OHMzqdlPY5fDj7QCD5yLffSuKHGzfkQ1ZKfnn33AN89519MTAtduwAmjWTi4LMFwNGo1yYbN0qmUVyi99/l2K8zoqc6XTymXz0kQxR5hb79gEzZshMWuHCEoL08MP+XbMVpsLtGOwrIfO5KCXZj159VY5d1sQ1Stm+v4AM5ngzq2M0yn6WLrUPoTWb5TgXHS2hyU2aSCIc63FEr5f/t2wJ/PCDtjCLefOAbt1cb1OunLyeuxAQH1i+XPp2gP3hUa+XQ8369XIxH0qUknYvXQrs3g388ov753z3nRxeg+HECbkOchbhaTBIbpT33gtsu8i/mIAij1u40LPzVcGCzh9TSiKuli3LebvIgc2b5SL8xg35sNPTbb3gzZvlLOrpeMedd0rSiE6dbDNUUVFAjx7Arl25qyMFAE89Je/f2ZnK+v6ffx44dy5w7XKnVi3p4K1aJV+6Tp3YkaK8SaeT4rxnzsiszQsvABMnSmjc009LPFRsrMycz50rgw+eMJnk9uij9hkZDAZbBd6OHSUNXObjiPX/69YBr7+u7bU2b3b/PT5xImDHojZtJBFF1oSJCQnAxo2h15E6d06ioO+9F5gyRT5uLeLi/NsuV2bNch1sYDbLuFqAoj8pF+JcQxhKTJSBPHdJj6yc5SKwMhqB1auDNyoU1t5+W/511GEym+VMs3GjzDR5okYNYMECSUiRlCSzW9HROW2t7x08qD0VldksCTGef96/bSIi7xQrBgwaZH/fXXcBn3xi+/mTT4DkZM/3rZTMXn/5ZfbX2LIF2LvX+XMtFkmsM3q0++Og1hzjAcxF3rKlvL1Dh6QzUqaMREKHGotFOofWpFZaomes0TENGvi3ba4cPeo+cjs5WZJPejpOQOGBM1NhqHRp34T4Zebr/RGAmzeBFStcD2cZjZLqyFsFCsgfRG7sSAHZ68i4YjAAf//tv7YQkf9NmeL9cw0GCWPO6pdf3HduUlIkDZs7LVu6HonU6WQdpz+SbrhRrZokFQzFjhQg66N27fLsekIp4P33AxJR6VSRIu63MRptk6SU97AzFYY6d/ZtmRuTKfRCCUKCNbTPHXdTh6GsUCHt2yrFYT+iUHfkSM5Sszk6uWlN+KJluzZtpLfirHOmlCQJYpIZjy1Z4tna6xIlgK+/llwjwdS1q+sOoNEoUd4sL5h3sTMVhooUsUWP5ZROJ2t2//rLdRQFeSE21n3aRYsl96Yx94V77sme/tgZk0lSExNR6MrJ8L2zGm7Nm7tfsFKkiOS0dsdgkIiB+Hg5AVo7TdZewIgRsv6UPJa1bJgzkyZJ4o1Tp2RwONgaNADatnXcv9br5U/jpZcC3y7KPdiZClPPPy9ZpbUqUED+zXqwsCaWGztWUrDef78swSEf0Otlcbar+AWDAejVy+FDhw7JOu8OHYC+fYE1awJaT9I3IiO1LQzX62V4sk4d/7eJiPync2fvUsMaDDJjZE1tl9kdd0AlJMCsd7xfpdNJJtCoKG2vddttcoD95BOJq6tfH+jeXepPjR/PWSkv1ajh/hwVHy+/qtatc08GYZ1OZsg6dZL/WztQgLTXUYIQyluYGh0hlH7WQ/ffL4kj3DEa5fzUubOU6PjtN1lI6Yi1JummTTyf+MS1a5LO97ff7EdWrSmEZ8wA+vWze4pSUuZo/Hj53ZnNsrnJJLv64QcgpP6MlQLeeUcWh5vN9iFA1j+yxx4DPvss9679ohwJ12NwToXl5/LHHzIyl5bm2ehPuXKSlc/JgqGXe51Czy+aozL+ggJggEI6DIiAGd9HdECdQ1+j/K255Oo8jzp/XpbwOluSptdLyTJf1F32l7//Br7/XqL0a9aUqNDc0ukj3/Lk+MvOFML0hAVZtPnii9rOV2vWSKpSQK5bv/nGddTEunUSWUE+cPWqdCamTQMuX5b7GjeWM8oDD2TbfMqU7MmsrAwGCUdYutSP7fXE339Lsc38+YG773bdGTp3Dpg/X6oiXrggAfMlSwLt24fuimvSJFyPwTkVtp/Lxo2SyvziRW21p7p3Bz791Onx448/JBo6P66hG+aiO77CLbiAI7gNn6I/Vhtao3dfPaZP98N7IY988YXUYNbr7X/ter2kTF+7lmNmlDuwM+WhcD1hXbwIVKggkx+ufssDB8qMlE4n5TMqVHC9vdEoYWXTpvm6xXlcerp0KKKjnRbqNZuB8uUlltyVP/7wLMzT544elfpRa9bY7ouNlfUGI0cGNzUT5TrhegzOqbD+XFJTJVPpiy/KAc3ZSUenA+rWlequTrz0khRMdZUkIF8+SejHUnDBt3atrOtev15+LlFCBghfeCF4GfGuXJExvGLFQiyyg/zGk+MvJyfDWLFiUmy3TRtZ+Jn1XBUXJwe0Pn3kfHX2rITwueteK2VfN5F8JCJCYiBcOHDAfUdKr5ffe8A6U5cvy3Dj6tXS26tZE5g9O/viuuRk4OWX5Q9t0qQANY6IcqWoKODxx4G33nJ90lFKjhkunDnj/uVu3pTzltZ8N+Q/994rt6tX5fdSpEjgynadOwdMny511q9eBSpVknHMzZttIfOPPCLhhtWrB6ZNFPrYmQpzTZpIpNXMmXKBnZoK1KsnHahGjezXPY0bJwV/tbj9dv+0lxw7eFDC+1audL+tXi+/54DYvFlWCl+5YrsgWrXK9cXRRx/JMCT/iIiofHlJl+4s1E+vB8qWdbmL+Hj3LxMVxRmH3KZgQbkFyt69QIsWMq5nXf5w/Lj9NmazpHBfsQL4+WdZ3kfkDmNt8oC4OJkQ2LxZRmN695alKJk7UmazrO/Xsr5KKemMeePQIcnUU6uWRG6MHClLZMi5GTNksmf6dElR747JFKATwLlzsqbr6lX7zpO7qU2jUWayiIj69XO9ZspiAfr3l1HBCRMknGLpUruYvh493NcB6t6dIX55lckEzJsnA8iXL7u/zjGbZcbM2+scyns4M5VHbNoEDB8ObN9uu++ee4APPgDuvFMmFpxl8Mtq3DhJrOSpzz8HnnxSBhqtJ779++X8+O23kjghp5SS0aQtW2S6/v77gdq1c77fYNm+XZYeWVPUu2MwAGXKyPv2u88+kwV5nuZjVwr491//tImIQkv79kCzZnKSynosMRhk1O2nn6TTZc1LbTLJiOC8eUCzZqhWTfpbM2ZkH8sxGKQ2+MsvB+j9UK5y7Zosddi0ybPnmc0yk7V7t0TzELnCmak8wJqpb+dO+/t//VUSrG3dKnWmtIzatWkjnTJP7dol50KLxb5TYDZLvHLHjsDJk57vN7M//pAZr6ZN5cQ5cqSUJWrRQiZRQtHEidpjyY1GyV2xaFGA8jssW+ZdYSudTqZLiYiMRjmW9O9vXwfKaASeeEKyEyxeLL2kzCeQs2dlZvy33wBIGPTIkdkzwdWvD/zyC1CxYoDeD+Uqw4bJ799bhw75ri0UvtiZCnMWi8wGWSzZr3stFin10batnJcee8x9vYS33vKuHZMmOe8UWGddcpK29uxZWR/2xx/ys9lsixz5+WegZcsAriPyoVWrtM1I5c8vIQm7d8tMY0B4+4GaTAGaOiOikJA/PzB1qmSSWL5cOldnzkhR8xUrHA/aWDtWb78NQM4v77wj636XLAHmzpV+1rZtThIJZD5JUK5hMsnvbccO7dEyzly8KLmQclLMvlChnLWB8gZ2psLcpk2ywNLVweTSJYmk6NVLBgaddXri4mRf3px/Vq923Skwm6Xj4K3Jk+V9OGqbyQTs2yeDm36TnAyMHSv1kPLnl0XVb74pR/Mc0FK44K67ZNnS9OnAbbfl6OU8k5DgXbVCnU6yeP39t+/bREShq0gRSWjTpg1wyy3AggWujzFms8SI37yZcVdMjEQOPv64gxBvpWThcKNGst+ICAnP+O47f7wb8oBSwIcfSph63bpAgwZyzTF4sOedKrNZ8hzdc4/zAsFaFCwoA7FE7rAzFeaOHdO23eXLMnu0YYOkCnXk/HkJx2vTxu7cpYmWTkFOKp7NmeO6k6fXy0ilX5w7J9NBr7wi9ZVu3JCCXW+8IcHWOVgf1KSJ62sJg0GWG2ROJhIwAwZ417NWSnq+vXv7vk1EFD6sRcxdMZtlYYwWI0YAXbrItAcgx6ItWyQX9ujRXjeTcm7wYOD55+2z4N+4IfUsmzeX8i5a7Nghs0nPPpvzEL0XXwxe3SsKLexMhTkntV+zsViAH34ASpUCDh+WUaGs626ss1tr1gCjRnnWjqZNtXUKvOXunGuxSEE+v3jqKem1OoqjPH0a6NnT610/+6zrGT29XpYaBEX16sAnn8j/M/9ytSzyMpsl/pIB6UTkTKVK7kfZYmKkILg7a9cC778v/898rLb+/403ZAExBdzOnbLmzRGzWcLXp01zv5+kJKBxY+mEeUqnk4lKg0H+P3y4FIMm0oKdqTB3//3azjOAnLP+/FNKfmzf7jw00GKRkDJPCve66xTodMDAgdr3l1WlSq5nZ4xGP4XAnTyZLU2vHZMJWLfOtpjLQ82aSfZEwL6/YjTKbe5ciSgMmqefBjZulIV3+fMD+fJJz/mFF7Q9//ff/ds+IgpdvXq5Dzno10/+76gyfWaTJ7se0TMaZRsKuJkz3UeMa+lMvfii52F9cXFyHn3nHbkGGTNGyrW8+26AEjlRWAirP5XJkyejQoUKyJcvHxo2bIjtmfOA51HR0TLgplXBghLq586NG9mzA7py112SAh3I3ikwGICvvnIeXqjFgAGuHzeZbOdcn9q9W1t8ojWsxAsvvijZiDp2lJnDcuWAvn0lbeujjzp4glKSPnHpUglhycnqWy2aNJE1B9euyR/G2rWyDkGLfPn82jQiClHLlzs5wGViscixLl8+SUlbpoysXXUUE7Zzp+sRPZPJs5Ma+cxff7n+1SiVvbiuI0uWaHs9nU5uVavKeN7jj0smyEmT5F83NaKJsgmbztTXX3+N5557Dq+//jp2796NOnXqoFWrVjgXqjmxfeiZZ4BXX3W/XenSUuxV69olT9c4DR0qURRdusg5r3x56RT89pvclxO9ekk+BEcRZjqdFHVs2jRnr+GQ1gQM3iRqyKRxY1mLfeqUjJpNmwbUqOFgw3Xr5IE77pBV2HfdJUkxvv02R6/vsWbN3HeU8uWTYHgiosw++URmu7dtc7/tpk222avTp2XtaosW2TtUWgZusuZV9zOt9QPDXbFi7qPDCxd2/filS9qXztWtC3z8sfSdS5TQ9hwiV8KmM/Xhhx/iySefRO/evVG9enVMmzYN+fPnx+effx7spgWdTieJ5fr2dR0K9/rrckDTMqkQFeVdIbuGDWUW6uRJGWly2inwUFSUZAMcPFgGKK1uuUWm7T//3E9JGho3dn+SNhp922nYu1fiERISgPvukw/xyhXpSN1/f/aQwuPHZVqrSxfPYjNzIjZWevHOPnSdTmI/Y2IC0x4iCg3HjsmxAfAuc5HFIpEA77xjf3+HDq6v2PV6SUQRAH/+KZES1vqOpUtL2ZFAHZ5zm65dXUdzGgxScsyR9evlGqJYMW1JKqKjJaBk0CD7awWiHFFhIDU1VRkMBrVkyRK7+3v06KEeeuihbNvfvHlTJScnZ9xOnjypAKjk5OQAtTg4UlOV6tFDKUApo1GpiAil9HqlDAal3n7bfttmzWQbOVPZ3wwGpfr3D8570OLqVaW2b1dq1y55z373zDPyQTr6sPR6pXr39t1rjR5t+wUCSul0citVSqlbb3XeDuutalWlLlxw/zppaUotWqRU165KPfSQUiNHKvX33561NT1dqZ49be3V623t7tlTHidSSiUnJ+eJY7Cn8uTnMmqUnGRcHce03IoWleOY1T//KJU/v+NjpMGgVEyMUmfO+P3tbdumVIEC2c+ver1SNWoodemS35uQ66SnK3XnnY5/7QaDUkWKyK8vq4kTPf+z6NEj8O+PQpMnx9+w6EydOnVKAVCbN2+2u3/48OGqQYMG2bZ//fXXFYBst7xywjp4UKlXXlFq4EClxo1T6vTp7NucOiXX5jqd/cEeUOquu5S6ciXw7c61bt5U6sEH7Ts51rNCy5ZKXbvmm9f5+mvnZwitFx96vfuzyb//Sqcr834NBvlj+OADz9u9Z49SQ4cq1aWL/LtnjzfvnsJYnuw0aJAnPxfrsdQXt5Mn7fe9YYN0mnQ620iiTicdry1b/P7WTCalypVzfrjO7QOV/nTxolKtWtlOU9bP6LbblPrtN/ttLRa5hvGmf+2r0zGFP0+OvzqlPF35kvucPn0apUuXxubNm5GQkJBx/4gRI7Bx40ZsyxJ3nZqaitTU1IyfU1JSULZsWSQnJyMmjMOOrl8H5s+XaDClJELtiSecR1qlpEj18FmzpPZDhQqShrtrVwmro0yUksQLs2dLjanSpWWhVqtWvksJVL++hPjlNKFERARw5ozERWRlsUj85oEDzoP5lyyR9VhEPpKSkoLY2NiwPQZPnjwZ7733HhITE1GnTh18/PHHaNCggdvnhfvn4tBjjwHffONdDbuszp+XWO/MUlKAL7+U0gw6nYRgd+sWkJiv5ctlKZgr+fLJ+Tav/LqzOnAA+OknIC1NSrS0aJE9WnzUKFuWWy10Oing+8MPefdzJc95cvwNi85UWloa8ufPj2+++QbtM13k9ezZE0lJSVi6dKnL5+eFE9bOncADDwAXL0r8sXWspmBB4Pvvc1bjiQIgOdn9ClxPbNwoWfiyWr1a1l05o9dLp46ZMsmHwvkY/PXXX6NHjx6YNm0aGjZsiIkTJ2LRokU4fPgwSrhZ/R7On4tTCxbIiF1O6PWShGfzZmDlSrlCL1AAeOghyX4UJO+8I2uT3SWd2LFDmk/20tMlJ0mTJnL9osXEibb1aUSe8OT4GxYJKCIjI1G/fn2sXbs24z6LxYK1a9fazVTlVefPS56CpCT52WyWCQilJPtNmzay5pdyMV+M0mYWGen4/mXLZObKGevibndVkokIAJMjeaxDB6By5ZxlQLVYgIcflpSxbdvKVMbgwbYUspkiUwIpKkpbJ4CRH/YuXgTuvVdm7e65R3tHCpCZLXakyN/CojMFAM899xxmzJiBL774AocOHcLAgQNx7do19O7dO9hNC7qZMyWywdH1uMUi5xV/1SpUStKhf/AB8OGHwJ49/nmdsFekiPvKxFoVK+Y8FWNamrZ9NGokI7z33gssWuT7zh5RGEhLS8OuXbvQsmXLjPv0ej1atmyJLVu2BLFluVhkpIRMV64sP1uLEWqh08m2I0dKCtszZ+R+s1lORhaLxK136+aftrvRtq37Q2XZskD16oFpTyi4dEk+k3XrPI9wj44Gatb0T7uIMgubzlSXLl3w/vvv47XXXkPdunWxd+9e/PTTT4iLiwt204JuyRLXByGLRdKV+9rx48Cdd0oG7xEjgOHD5Rr+nntkJmzyZKBWLRk1KlVKitOeOuX7duRmJ04A3804j709PsS1nk8DL70khbey0umkUJczer2EAc6b5z5V+4gRzmem6tXTVkL+zz/ll7VxI9C5s4wme1p6nijMXbhwAWazOdt5KC4uDomJidm2T01NRUpKit0tTypXDti/Xxa5dOsGFC/u/jk6nZxs/vlHykOkpzs+8SkFLF4sBc0DrGpV4MEHXfcNR43S3nfMCxo0kFrw3ujf308lUYiy8nMyjJAQ7hmTatXSluTNZNK2vytXJLu22ex8m0uXlCpTxnF6dYNBqXz5bFm9s6ZA/f1337xvazuOHVPq+nXf7dMXLlxQ6pFHlHoak1UqIpQJevlX998H1qFD9kabTJIRL2v2PqNRqehoyVSllLxp6y/dmoLR+osYOND1L+7qVcl25S7FuqM/oDfe8N8HRmEtXI/BzDSbQ1evSmZRZ3U6st5+/FGeo+X4deedQXlLyclK3XOP7bCs09ne3siRkqmOxJIl3idybNyYnyXljCfnpbCZmSLnKlRwv43FAmzY4Pixs2eBd9+VBBZlygCFCkmCpNKlgbffBm7ezP6cmTOlGL2jhbZmszzHetjLfH9Kikxy5DRh3ZYtkkivaFGgYkX5d8AAW9RHMF2/LgmkjEu/wWQMQiTSYYBF/lXyganvvgP69LF/osEgM0/z58sIbGwsEB8v1Qf37QOaNpXtihSRrH+rVkm6xrZtZYju119lOtBVdsECBYCFCyW0JvOaBXfDexYL8NFH2sMEifKAW265BQaDAWfPnrW7/+zZs4iPj8+2/ahRo5CcnJxxO3nyZKCamjvNmQMcPuw+Y4PBIGHQDzwgJxEtJ5A9eySxT4CPWTExMqG/Zg3Qs6fUCR46VCbTxo7lTEpmWesua6HTAW+8YUvWSBQQAejc5XrhOipqNX68tpGcWbOyP3faNCnum3kGKeuERJMmUmops5o1vR9RApRavdr797t8uYz0Za3lYTQqVbq0lFEKpqlTldLBovajujLByQdrvR05krMXO3pUqaeekpkrQGadnntOqcRE18/77TelnnhCnmetw6JltPfAgZy1l/KkcD4GN2jQQA0ePDjjZ7PZrEqXLq3Gjh3r9rnh/Llocuedzk8+mW9xcVJAUSmp1K51Jst6a9RIqcWLg/teyU5qqufXDXXrKrV7d7BbTuGCM1Nk5667tG2XNUvvDz/IbE56uv0MUmYWC/DLLzIpkdnFi56308polPSn3khLk8kYszn7Ql+TSWbZRozwvm2+8NlnwK34GzVwEAY4+WABGW1dssT7F9q/X9ZAffaZLeg8JQWYNEnudzXqXbu2jApfvy4f5JAh2ob5OBRIZIfJkXLg7FnnJx+rYsWAgweBatXk58hIoG5dz15nxw6gY0fJW+6l48eB8ePl/DJlChOe5pQn0Sldukj5lz17gP/9z39tInKGnak84K67JBuOK7fcAmRKOAUAeOstbfVmLRbgk0/s77v1Vu9r1SrlfVbc77+Xjpyz86/JJFFsly55t39fOH0ayI9r7jfU64GrV717EaWA7t2BK1eyh8iYzcC5c8DAgdr2deqUdKzcpaGKiwNuu8279hKFKSZHyoHy5V2fSPR66TgVLWp//6uvevY61mPbm29K+lkPmExyKK1UCXj5ZalrNHgwULKkdKrIO/nyATVquN8uIkISaNWv7/82ETnDzlQeoNdLWnJX3n3XPsFbYqIM1mkdHTpxQtZB7d8P9O4to0Ternsym2W9kzcOHXLfETOZgKNHvdt/NhcvAqNHy2KyyEj5d/Rol1NzpUsD/6ACUuEko55Verr3OXJ37pSsgM46QCYTsHy5ZL5y14aWLeUX7IpOBwwblrPaMERhavDgwfjnn3+QmpqKbdu2oWHDhsFuUmjo1899Ktp+/Ww/m0ySDu/xx717PaPR4x7Qc88B06fL+JXZbIvkSE2V5azz53vXlLzm5k05HVln9K5flyXB7mzYwNMOBR87U3lEp05yUM+aYbZIEYkCyxpxcv26Z/vX62VBbb16MkrkKCmFFgYD0KyZ51EaVoUKaevEFSrk3f7tnDolb3jMGPl/err8O2aM3P/vvw6f1q8fkKKLxTw8jnQ4zoGrdDoZbW3f3vFr79oloXcdO8oZe9s223TczZvaLgiUkt6vK999JwvA3c1Kde0KvPCC+9ckItLqsccktMJRrnC9HmjSRI6BgBzPnnhCYu2uaZj5d8RkkmOrRomJwNSpriMRX33VfaSiL1y/Dpw/H3ol/86fl5m8YsUkWVbRolK+MCEBWL/e+fMMBmDFCu3LGIj8KgBruHK9vLTINy1NssdOn67U0qXZE0dY3bypVKFC2hZ9Go1KtWunVIEC2tYKZ01gkfnf2rWVOnfO+/d37JjrNuh0kmnXJylT77/f+UJno1Gp++5z+LTr12WhbEl9ojqKCiod9pky0mFQFqNRqWXLsj85PV2pHj1sr5E5r27nzkrt3ClJJrT+Atascf0eu3bNnskj6y0ykjloKUfy0jHYE/xclNTi6NvX/lgbESGJda5ds23388/uj3fWRDyubvXra27atGnaznm//eaHz+U/W7fK+dd6Di1cWKkRI5S6fNl/r+krZ88qVaFC9lOMls/0s8+C3XoKd0xAQU5FRNgyZT/0EBAV5Xi7qCiZQdFSPFApWft7/bpnI3A6nWxfvbq0ZeFCiU7TUp/RmQoVZHDSWZi9UpI2Ncd5Ev7+W1KPO0vZazIBq1cDf/2V7aHoaKnm3rRzHBrptmMqBuIqCkj7oAPuux+6n38G2rTJvt9XXwW+/NL2GkrZ2rBwoVQ41FroMzYWaNzY9TbXrrkf6mSxXiLylwMHZArCZJITkl4vx5x9+2RNqNXnn7uP94qIcH1S0+ulNodGV65oWxvsr9rLP/4I3H23fDzWiIykJOCDD2RmJ5hrg7UYNUoCOLKeYtxdRxiNwMqV/msXkafYmSKnXn9dOkmuzj3R0cDXXwMXLnhetd16wLx8GfjmG+DRR+Vcp9X27UC3bpKFsHhxef4vv0j8eufOso3RKPvU6+XfTz6xPZYjWkNBdu50eHeRIhJ2uftkcZT65mOsWXAR5/f8C11yEoyrlgONGmV/0pUrkonP1ZnGk4Vqw4fLKl9XatRw/YvV6YDbb2cWPyLyvb//lpgva50us9l2jNu+HbjvPttgzj//uK9HdeOGjBQ66gEZDFIEKvMaLDduv939WJNOJwmZfO3GDeeZa81m4MiRHCUn1GzbNlkmUK+elDr8+GMp3+VIWppcLzz6qCzF/eIL978yR0wmeX9EuQU7U+RUbKzUeX3xRVuyJJ1OZn8efFDSoZ8+LSHrkW5yKTijlBTSnTDBs+fNmCH9jYULJeb6wgVZ3nPPPbJcaP58GdB86SXJtPThh/I6gwZ5185stL5hN9uVLi2fX/suUShet7SczJ3ZtMmW4jynChWSD71ZM/kQnXXCnnzS/TDh4MG+aRMRUWYTJkgmB0c9FpNJZqe+/15+jo93P6JXvDjw009y/NPpZHvrc4oUkWiCrDVCXGjTRpKYOhtLMhiAdu0ks5+vffONzEI5OzybzTJZ5+n6Z62UkmuDRo1knfSePVIo99lnZRD28GH77U+dAurUkWVw334LrF3r/fouvT5nESxEPheAsMNcj3Hp7plMSl24IOt9HPnuO8/WSjlaYrRrl7a27N/vPqZ6yxbfvXeHLl6UtULu1hJdvOi711y8OGcfsqOAdGuweseOsh7LkQkTZJusRXv1eqVatpTqikQ5wGOwY3n+cylSxP2x7NFHZdsVK1xvazAo9fLLsu2VK7LgqVs3pbp3lwU4mddfeWDFCudF4osVU+rvv330WWQxYoQsHXN3uD982D+vP2eO64+6YkXbKcVikXXCntZSdnX78kv/vC8iK66ZIp8zGCTbTnS048fbtgUqV/Y+RanFor00yJQprgcgjcbsda98rmhRmbVxFjCv10u4SNb6JzlRp45v9pN5KNM6NLh4scwwOSrkO3QosHQpkDmdc6lSwNtvA8uWeT8tSUTkirs6e0rJ7BQA3H8/0Ly545OD0ShTGUOGyM8FCwJPPSVTKl9+CfTpA+TPr61N585JiOHhw4BSeOABSc/dtKn9y3XuLFHelSpp262nChTQFtWt9W1pde4cMHas64AEsxk4dkzWdAHy+ezd611IX1YGg0SfP/pozvdF5CvsTJFPWBeElikjP1v7GFoL91ossojWRXmmDJs2uT4om0zAxo3aXjdHPvhA4h0BWy/S+m/btu6Le3lKr/f9mTGz6dOlSGabNtkTZzz0ELB5s8SVnDsnna6RI9mRIiL/0bLY6MgRWaSj1wM//CBxZFlPPHfcITnMhw6VXkhkpCTrmTtX+zrT48clJrtkSRlYqloVqFkT+O47NG4sYWtnz0qtwwsXZNcVKnj4fj3Qvr3rMDlrPWPrOdkXvv4aKFsWeOUV90k1jEb5TACJrPR0TbVOJ0sNAHkv1l9pkyaSwMlZ8iyiYGCpM/KZSpXkRLJokaxfunYNqFVLDuZDh7p/vlLSmSpWzPV2Wma//F7Ez2KRIhjFi8sC6UuXZLSzUiWgVy8ZpswcSP/HH9ILVErOBtWq2R4zm+XMO2WKbFeggFwQPPOMdG4ACXxv3lzWD/iTUpKlsGFDGVatWNH+cevZjYjI37p1cx+yYDZLIorbb5fZqZkzpdbUmjWSnOKOO+S42rGjHJOtPZBdu4Du3eV4N2uWPHbtmoRfZL3y/+cf6XxdumTf+Tp0CHjkEXl+r14AgN27JdHCHXdIX8tfateWca+VKx13qiwW4LXXfPd627ZJLWStfc/MH3VamvZMv3q9vMYjjwBz5kjt+c2b5Vdy773yvolynQCEHeZ6eT4uPQC6dtW2biopyf2+Ro50XfrIaFRq4EAfNdxikaJbmesoJSYq9b//2V5Mr5eb0Shx+JmdPSv1prI2smVLpc6ckcJfDz6YfU2SwSCFvrZtk/3MmJHzIHN39aKyfojduvnoQyRyjcdgx/L857J5s/Z1oNbjW8mSsrDWauJE989v3VoKNAFKRUUp1auX/WKjxx5zueDHEp1fDe6Zkm2Txo2V+usv/308yclyKrEesq2nI6NRqU8+8X6///6r1AcfKDVqlFJTp0rNqk6dPF/z9NVXsr+ZM7U/JzJSamcRBZsnx192phRPWCtXKnXvvUrVqqVUmzZSx9XXNVjPnnXfAercWdu+jh+X852jJBSRuKl66b9QV+9sptRttynVooUc0T1NknD8uFJPPy2ViAFZCD1ihLyRO+5w/WaWL5d9XLumVLVqjs9ARqNSt9+u1OjRzrNpGAxKxcdLh+v++z2viJx5P/fe6/nzjEY5WxP5WV4/BjuT5z+X0aO9O97FxSmVkiI9Am+OmUajUgULShH0y5fd9iIs0Kl+upkOdxMXJ+Nm/mKxSOdj2DCpbTx+vJymvGE2K/X887bxwYgIOe1ERXk2FmcwKFW8uIxDLl8u44Jan6vTydghUbCxM+WhvHrCunRJrucdHdD+9z9ts0SeGDvW+YE3NlapP//Uvq/ly7Mf4IvqL6sduEN+sM7yWP9NSJCTqxb790vnKesJ1GBQqkQJ92eDRo1kP9Onu+4A6XRKxcS439/ChUo1bOjdRYFOJ2fEPXukc+np0KK/UkERZZJXj8Hu5OnP5ZNPvDvmWY9748fnLH2cwSDHzN9/d7ttKiLUGLzkdDcvvhjsD1Obl17yfswu8/stUECpZcsksMLTX4FOp9SgQcH+JIiYzY80MJuBO+8E/vzT8eN79gAPP+zb1xw5Epg2TepyZNa4scRE33abhKxPmSJtK1dOlhfNmSMx15m1bi31HF9+WSq9N2wIrLv9KdQ37JENrIHdmQs8aqmHpJTE6aekZM9yYTZLUSt3tm6VBdGzZ7vf1t0q3ogIYMsWCb53tRBMr7cV4DUa5abTScKKpUtlJfLy5a6LomSl0/k2GyERkRY3b0qWg5yYNcuzIuZZWSvf/vGH200NMCMJhZ3u5vPPtb9sYqLURyxVSg7plSsD774rNdv96fJl4P335RTorQIFJOnG9euSg+nuuz2vJaWUnEKJQolOqZx8dcJDSkoKYmNjkZycjBhXRVPDyA8/SII2d377zfcLPk0mOVimpEgH6rbb5P6zZyVvg7WDp5RtMWrjxrLQtkABJzv9919J1uDq5Gk0SuVAV0UZt2+3TwHurd27gU6dgKNHc7YfoxEYNkzywDZo4Hrb2bOl87N8uW0FdPfuUqDS6soVSQU8ezawY4fzfRkMUqL+p59y1n4iDfLiMViLPPu5fP99zkfz4uIkYUR6uvf70OulcPCCBZKBwcn5xQIdKuIYTqC8092YTO7HsY4ckQ7IxYv2nRC9XpIH/vyz/8a35swBevb0/vl6ve39eVuM1yoyUpLGMt8RBZMnx1/OTOVRM2dq285aXN6XjEY5YbRpY+tIAUCPHpKR2zrhD9jOXVu3As8952Knv/zifhTSZJIpMFf27nXXfG0OHpQhOq254Z0xmaRTc+edwAsvyH1Zz8g6naRo79ZN/p06FfjsM2DgQPuOFCA/P/20dBoHDnR8drfmoX3jjZy1nYjIG1pqZLhz9mzOOlKAnFPy5QPGjJGfHRwvLdBhhq6/044UoC0gQClJ4pq1I2VtxuHD2rLieis52bOgBSvrKc5ikXbntCMFyFjg9u053w9RoLAzlUdduqRtu5s3/dsOq8OHJUOtswOx2Sz9A2v28Gy0TrC6285XxSt0Oinqm5MwE6MRqF5dOlOAxHp89pmkALYqWRJ45x0puutpPviPPpKzs/V51rNiyZJS9MsXM3RERJ4q77xjElB6vYz6tWghx9jCheV+ozFj0Olch4EYrD52uguDQU4F7uzcKQENrs6B8+cDp0/7psOSVYkS2k6jY8bILBkgpzmLRXsnzBOMmaJQws5UHqW1/kWgajr8/LP7bcxmYNAgKdHUooVE9mVo3Nj9Ed1gABo1kv9fviyVFbMesVu18ry6YFY6HXDXXUCHDraTrzfKlAGWLbN1cnQ6oE8fqW1y6hRw4oSteG5EhOf7NxqlsPDp0xLU/+GH0on65x8p6EFEFAzNmsmiWX9cpWul1wNPPGGretu+PXDmjFSuHT0amDgROHECcd9MRqfHIhw21WiUIrfPPOP+5XbscP92TSagdGkJg2vXTtt5UystEemlS8s65YMHga++sp0+fd3xiYyUKHWiUMHOVB41cKD7bQoUkPNHIHh6zvz5Z+k/ZUSDlCsnjXXWETIYZN3Rxo2SjKFoUSm4e+utMkNjHeqLjwd69/Y+PM9gkMVoFSpImfakJO/2U6iQhCRWqJD9MZ1OVieXLZvzjh8gn0Pv3sCzzwIPPOCbfRIReUuvl2xFOl3OQ6U9eU2DwTZT366dhExnFhUFdO4sPYohQ4DSpaHTyXqjESMk34+VTieJkn791X0hekBeVmunxGKR5axNm8oSWG8cPSr1kLt3l87e/Pnun5M5avLjj/3zqzEYpA/L3EcUStiZyqNq13ad3E6nkwG4yMjAtKdJE8+2N5lkZmrqVBnRmzgRmH7HDNyoVF0ab+2dWY/2depI56NrV2DfPtuOjh+XULdu3eQM9fffEtZx113yuDUrntYQuqpVbQvSvvrK+47JlSvAokXePZeIKNS1bi2x374Kj6hUyfXjFouciAYOlGQT330HREdr2nVEBDBunCzTWrFCnnr8uKw5LlVKW/Puu8+zQUWzWTpffftKBkCtlAJef12yBI4dK7k1pk61Py06Y41aV0rOu95GsdeuLdHkjk7Vd94p53OiUMJsfsi7GZOUklSob71ln3a1cmXgiy9s/YlAadMGWL06e0ZyVyIjZbGq9YCcT13HW7d/hSEFPkPk+dMSl/Dkk7L2yN0bqlEDOHDA9nNsLFCrlqwevuUW6SS5ClYvXlyG+woWlJ9btgTWrtX+ZrK6+27fxnEQ5VJ59RjsDj+X/9x1l5SI8LeaNR32KpTZgj/Wn0HSZYVSd5RC+Yr+GYd+9FFgyRLP1kTp9XIOf+klbdtPm6YtMiUrg0Gi4Jctk2uHiAjP127pdMDbbwOjRsk1x+zZksH+7FkJLunfH3j8cd8tXSbKCWbzI010OmD4cFk+9PvvkhDv8mVJz6qlI5WaCqxZIwf/zH0Qb33xBVClivxfa/iAKc2MhXgU+1QN/KLuQj/MxNt/dUHdm9tw48+Tkgawb19ZE+Rudinrm0hOlg+lRg2gfn33w3DnzwPHjtl+rlDB86QQmbmrQUVElBfkJOOCXu9+Vspq/35ZF2VlseC3/pPxb3RlVLuvDBI6lwUqVcSUKhNxYF8Okgs58dlncqqxNlsrrUlozWbpzHjDbLZFs+h0sm7ZU0rZklcUKiSRkrt3yxLgLVsk2pwdKQpF7EwRDAaZgGncWFu+BKWASZMkfOG++yTPQs2akttBS6iAM8WLS+jAzJnSmStY0F3Yg0IlHEV7fIcaOIhG2IqJGIrfLTWQeuhvLFiQadPff/dsyiuzMWMkKYOWs1vmNVJ9+nj/mkZj4LJ/EBHlZtWrez8wpZRUdtfKOoilFI60eAp1ZgxG6fTjGQ+XxQkM+PM57PtfDxzYL4E9p0/Luc/bjO67d0tOi7vvtpX/05olT6ez1Wt3Z9++LImbnMh8qrNGqg8bJktqrayVOjxRpIhEoBCFG3amyGNjxsgyo6zp1XfulA6ZhoLxTkVHy0TSzz8D337rekGuHhb0x6eIgOm/nxX0UIhDIn5EO8yZnWnksFAh7zND6XSSOc/d6KhOZz8CmpAga7Ecva6WtE3exGIQEYWbAQO8H5hSCpg7V9u2kZESGg4gddka3LZR1r/qYTsR6f/7+THzXLzf4Vc0bSpPqV1b0ot36iTRHWazdK6uX3f+cmfOSAeqfn0p67dvn/05T8siDLNZygtqceOG+230eqn/GBEhHamEBOCbb4APPrA/bd1/vyRd9MTEiZx5ovDEzhRBKWD5ckleVL68zFK99ZZUIM8qMdF5LVezGbh2TZLZPfOMZBlyV6dq3z4JE1y/Pnt9xZYtZV+OJoQMMOE2HMFTmJ7tsQiYUQ1/oNShNbY7O3Rw3RBX9HpJbViihPNOkMEgQ27/nYgB2Kbpsiah0OslI1SHDvYrcAHb/597LvCL1oiIcqOGDaXYuL91756x5vWPYdOQDuezYSvwAOYcScAvv9jus1gk+UStWrLM9pZbZHetW8NuO0A6Ns2bS64LbxmNkpBWa9bdKlXcT/BZLNLpSU2Vc/LPPwMdO8r/f/wR+PRTSayRmio5lmJj3Y8Nli0riS569NDWTqKQo0glJycrACo5OTnYTQk4s1mpXr2UApQyGORfQCm9Xqmi/2/vzsObrLI/gH+zNF0obYFuFAq0bBUoIEVqERGGSkFGRRERUUAYQCgjSEVBEFAH4QeICyK4QRk3REbQYa+sjlTAStm3shWBsNOWtU1yfn8ckzZtkiZp0nQ5n+fJ0ybvzZt70/S9Oe9777m1ifbsMS8/dy5vM5azdlOr+WedOkRbt5Z83YwMorg48+eEhBAtXGhe7s4dorFjiXx8itRNYaA+WEYXEWy1Anehpk/rTCjcUW4uUd269lW++E2lIpo2jWjVKv69+D5UKqJatYiOHi18vXXriDSawjcCIFIo+PbAA9wwnY7oww+JoqIKy7RoQbRoEZHB4Lo/shAVXHU+Btsi70sR8+c7fux25KZUEp08aXq5LHUzq2V1UFIE/iQFdHZ3IUol0fffFzZn0aKyV7lRI7Mq2+W558z7+uL1bNCAvxfo9UTLlxP97W9EQUElu71atbgNhw4RxcYWvoUKBf/erRvR118T/fILd3VCVDaOHH8lmKLq3WHNm2e7A6hfn6igoLB8SgqRl5dj/ZOvr3mcsWcPP2btgD5nTsl6Xr9OtHo10Y8/Em378lSpL3wXXrTu3tcKd3DxIlF0tPO9VlYW72frVqIHHzR/k55+unA7EdGNG0QBAdYDN6WS6O23C8sbDEQXLhBduiRBlKiWqvMx2BZ5X4ro2bPwm7q7bo89RmQw0J07RDvRnvRWyq1GT4d3rVAQ+fkRGf+U3bo5d26vaDfSrBnR3buOvY0XLnBXWLz/Vau5X96+nYOfvn0LX8dWPVJTudvavp3ogw+IFiwgOnHC9X9+IcqbI8dfGeZXjREBc+dav0Sv1/Nk1R9/LHwsLMyxxEoGAw8PeO+9wsdee43TmVvbz6RJnFWwqMBAHkX32GNAp371cEkVXmQUe0kaFKD9mE6FDwwZYp5pzxFKJWfHAHgdkm3bOP3Qvn2cwe+773ishdF33/EkZmvZ/wwG4KOPCt8AhYKHEAYHOz+vSwghqrK7d7nTcqeffgK2bIHBAHyHfiArMyGOoSmUcCzDIBEP7fvmG75/+bLz6zQB/NyjR3mYvHF/s2bxQr4PPMAJIrKySj4vNBTYuZO316rFj3l5Ac88w/OeExK4v16+vPB1bHn1VZ7OlpDAw/tffBGIinK+XUJURhJMVWNaLccXtvonpZLHTxvjkC5dHO/PdDqOL4yvuX697YAsPx9Ytsz6doWXGuf7jQXBcuChgwqXazZCned68gMnTvBgb2c7YoOBswEWFRHBKQyNvRHAmfzefx+YPLn0oOjCBcuT0oQQQpTUvr3zi6DbS60GUlPh6wuk3zMUlxACHUq+pj/yYHDi65NazdnXAaBZs7KtnAFw//yf/wDbt/P5vIkT+Vzf9u3cFTVvzquCFFenDi8yfPkyrwBy6xbPcW7Rgvvm99+3v7u8eBHYtKls7RCispNgqhqz5yKIwcATZxs35rNdjmSYLcqY0UirLf0grUYBzqbMBdq2BT78ELhxo0SZ1ktScKodJ5Uo2tnpocRtn1oI2vbfwo7311+dq3RRttKiGwy8EmK9epw/9vx5+3oiSWskhBD2GTHC/VemdDpT7vChr9RCF2zBaTQEAOTDC/nwAgDEIQMKm2MjLCMC/Pz492HDnE9QaGQwcMbAnj25myx6FUmv5/tDh/IFN0uUSiAgwDyoO3uWb46Q84KiupNgqhoLC+MgyZ6giojPdun1jvdnSmXhYryhoaWX15EK4Tez+GrQ2LF8RvLCBfNCajWidy3DzSXLcaH5Q8j1j8CV0BjkjZuGmqcPQN22VWHZsg6d02j4VJ5xnEbRy2oHDnAe2ZEjbefALUqpBO67D6hdu2z1EkKI6iI6Gvj4Y/7dkRVtHaFWmzKyvvAC0GloDJrhKB5X/IR5+Cc+wmg8qViBTt4ZeKa/0uGuRacrzLyXmAg8+2zZuie1mrujvDzbw/GefJKz8tnDmfrUr+/4c4SoSiSYqsYUCiAlxf0n+wyGwpXTIyI4Hayt0Rpq6PA0lhXOcc3K4p6tOKUSNQb2Qb3DGxGQdxZ1LhxC0LtvcMR25Qov9d60KZCcXLYG5OcX5rr18+PFsPr357ENDz3k+Fwsg4HHYwghhLDfiBG8jkbPnu7Zv04HDBoEgPvHzz4Dflipws2/PYoZdd7F3Hpz0eCl3thzQI0vv+RzfUol37y8bO9apeIpt8bRHQoF8O9/8zIkdeoUlqtRA+jVC0hNBd55x3Zwo9PxnOTS+nC9npc+Ma5HbEu9ejznyd4FgyMjuRsUojpTELn7q3TFl5ubi8DAQOTk5CAgIMDT1SlXRDxh9NNPuUMoy4RYSxQKXjX9p58KhxLs3Ak8+CB3BJZebxqmYireKrnh2DGgSRMAfBHol1/4Z2ys6WF2+jSvhHjunOsbZKRWFwZ79r6GSsVlZ8/mKFYIAaB6H4NtkffFir17gTZtXL5b6vkINo1bhX37FfDz46Cm6NKBlpw9y3N8L1/mdRoLCni0t15feAFNp+Nh8j/9ZHlAQn4+cPAgdw8xMYVDAfPzOfHS5s3m3Yyxr54+Hdi40b45SwoFj5o3nti05eOP7TsHqVBwm/7+99LLClHZOHL8LeP0R1HZKRQ83ScgAJgzx7X7VqmAt94Cxo83H5PdoQOQlsZjuYtmG6qJXEzGvzAesy3vcPt2GKKbYPp0rmvRs2x/+xsHhI0bg68aabWOBVJeXnxFy97B4s4Mdh8zhnuo6GjHnyuEEIJ9+SV3KmWddFTEreiWiDv0Hxx+WAGlks+TKRTA4MHA/PmAj4/l5xmnyhb19NN81engQV60t08fPoFo7WqPRsNThC09vno1n3+bN69wblJsLA9u6NePR55v2VJ6d6dQAFu3Wg+mDAbez9693NbnnuNFea29zc2acda/Rx6x/bpCVAcSTAnMnQu8O8eAZjgGb9zFCUTjJvyd3p9SyanMt27lg74lnTtzWtf//Q84/sMeBL4/DUlYDz/ctrnjMWM4q3hxW7fy8Il9X2YiLD3dsQrXqMEpkXr0cOx59lIo+HTju++6Z/9CCFGd2JPJyEH/yh6IY8QRkzEwIeLhdjk5hanC7RES4rrBB97enCB24kTObaTR8P6Ngdnw4ZyZryyDMH7/nVOjHz8OUyAJAElJfKL14EH+2akTEBfHJy3j4mQlDyGMZM5UNXfoIOHgK18gC01wBDHYiza4iFDMw2gEIMehffn68lm6117j9K/WAikjhYLP1g1+IxJPaNbYDqQUCmTVe8hiIAXwkIpr14BN7/zm+BH+5k1edMNdiEqeuhRCCOGciAiXfpMnAG/qJqKx/kiJbQYDn2v744+/HsjL4+ilUSMeflG7Nk+eys4GwCtkHDzIgY8rqVSc6CE01LzpkZF8Bak0RHwSs7jPPwfuv58DKYDbaxzBnpbGwxb37+cEVLNm8dWw9u0lkBKiKAmmqrnTL0zDF/gHGuGU6TE/3MaLWIit6Ax/5JV4TtFESmo13//mG56/9OefPGk2IsKBStSuzWMprGVoUqmAPn2wKC3S5rocOh3wv3QVnJoGePgwn/Jzh8GDeUyjEEKIshs0yPYQPwe/6SsAqGFACiyPdVerga+/Bp+x69iRV5Y/fZojj2vXgPnzcTr273j2kWsICQFatuQ+0LjGu7v168cjTKxRKoGaNYGBA80ff+cdTtFubd1HgwFYuRLYtctlVRWiSpJgqjo7ehQ9dnKiB2WxNTPU0CMW+zEGH5geCwjgE3Bt2vDB2ccHeOIJPmPVv38Z6/Lee4WnzYyp/ozBVbt2wGef2TWdaVdBaytL+ZbCmBbJlerW5VOaixa5L5WvEEJUNy1acOYkS0GTSuXUEEAFgP5YanX75cvgEQyHDpUYU3dSVx/tczfi+7U1zWK8X3/l+byrVjlcHYe9/DIPBwTM5yirVNxX//e/PPzeKD2dY8LSmAJJIYRVbvuGN336dHTs2BF+fn4ICgqyWCY7Oxu9evWCn58fQkNDMX78eOiKnW3asmUL2rVrB29vbzRp0gSpqakl9jN//nw0atQIPj4+iI+Px86dO93Qoiroiy+gV1i/1KOEASOxwHT/xg1eGf3JJzn2uH2bsxh5eQEzZvCB+YcfrJ/lssnPj8cULF/OA7VbtuRe6LPPePbvyZNoGHDNZiepwV2MV851YinFvxDxbGFXeOklvkz35JMyHkIIIVzto4+AN94oecyOj3d6l364gdbYjamYiiUYiA/wEhKwHWQgxIRf5/F0Fjq4cXgX1xAEXbFp6AYD3wYP5sx87vb225zltm9fznN0zz3AhAnAkSMlh/jNn297iRIjor8CSSGEdeQmU6ZMoblz59K4ceMoMDCwxHadTketWrWixMRE2r17N61Zs4aCg4Np4sSJpjInTpwgPz8/GjduHB08eJDmzZtHKpWK1q1bZyqzdOlS0mg0tGjRIjpw4AANGzaMgoKC6MKFC3bXNScnhwBQTk5Omdpc6Tz5JBkUCuPwaKs3NfJLPLxjB5FWS9SpU8mnaDRE06cTGQxlqJtWSzR4MJFKZbbzcwijr/EMdcXPpIDetKkTttJl1Cm1LaXeAgPLvg+1mqhvX1f9lYSo8qrtMbgU8r7Y4cYNotWriZYvJzpyhCgnx6njtqHY7zooKB/c/6xDdzr/ZZrF52kRSkroSn2J5cs9/UaZi4qy761RqYgmTfJ0bYUof44cf90WTBktXrzYYjC1Zs0aUiqVpNVqTY8tWLCAAgIC6O7du0RE9Oqrr1LLli3NntevXz9KSkoy3e/QoQMlJyeb7uv1eoqIiKAZM2bYXcdq22ENGUIGtdrmkfQWfAgwlIgVnn2WqEUL2wfhESOcrNfFi0SNGhEplTY7veOIos7YTCmYRXqUHhSWWzAFENWo4cq/lBBVWrU9BpdC3hcntWtXpmCq+E2vUBF17Wpx23bcX+ru1WoiB76SlIuYGPvfnqwsT9dWiPLnyPHXYxM50tPTERsbi7CwMNNjSUlJyM3NxYEDB0xlEhMTzZ6XlJSE9L9SX+fn5yMjI8OsjFKpRGJioqmMsKFfPyhsTOItgBr/xnNAsVlIOh0vFHjwoO3df/IJz6dy2FtvcWYkK7lejbVphFPYhG6Yg1ehcH5wn7kcxzIYWuXC9U+EEEI4YMYMh59iazC2kvS8cq6FFXcD7ch6q9fznOOK5NFH7RvmN3HiX+s3CiGs8lgwpdVqzQIpAKb7Wq3WZpnc3Fzcvn0bly9fhl6vt1jGuA9L7t69i9zcXLNbtZSYyMuyWzii6qBELmriZbxv8anGxQNLs2BB6WXM5OdzwgY7Fs1QgqACl6tQs5JUKs4dK4QQovx17845v105X1Wp5MUMi7kHh9Ach6GA9T5LqeRkTRXJqFE839nWWzRtGjB9erlVSYhKy6FgasKECVAoFDZvhw8fdlddXWbGjBkIDAw03SIjIz1dJc9QKjnN0MMPAwBIpYJBxRNoLyIMsdiP26hh8alk54WgPXscrNOlS5xjvTLT64ExYzxdCyGEqL769uVERq5CxCvVjhrF9/9KmadQqfAvTAZBCVgYIaFQcOLBunX5vl4PbNkCLF3KadPLsthuWTRqBPz0E68PWTTZrELBCwWvXw9MnSr5k4Swh41Ve0pKSUnB4MGDbZaJjo62a1/h4eElsu5duHDBtM340/hY0TIBAQHw9fWFSqWCSqWyWMa4D0smTpyIcePGme7n5uZW34AqKAhYuxbYvx+KVauguHMHF+vdi2b/7IWbdx36eFjk5eXgEwIC+Ohtb7TmaUplYW+oUnFPOWoU8NRTnq2XEEJUZ9OmcRpzVyEC7tzhNHj/+AfwxRfAiRNAcDCeevZZfHragDEvK3HnDsdZxkx+w4dzFlyAV8oYO5YTvRo1aAB8+CHQqxcv+Ovvz6nMy8PDD/NyWYsWAVu38mNdugBDhgB16pRPHYSoChz6thwSEoKQkBCXvHBCQgKmT5+OixcvIjQ0FACQlpaGgIAAtGjRwlRmzZo1Zs9LS0tDwl+X2jUaDeLi4rBx40b07t0bAGAwGLBx40aMHj3a6mt7e3vD29vbJe2oMlq14huAUADTb/FBv6z++INTtH71Fa91WKqaNYEePYB169wXUCmVhXNrHWEM8tRqnhP1/PMcPK1axWtU3Xcfp0SXdOhCCOE5t2/zpF2n1umwISODf957L6dmL2IYgKefAb77jmOs2rWBp5/mK0AAr/rRt2/JXWZnA7178xWi27f5nNwTT/CaUW3auLb6lgQHA6++yjchhHPKfunBiuzsbFy9ehXZ2dnQ6/XIzMwEADRp0gT+/v7o3r07WrRogeeffx6zZs2CVqvF5MmTkZycbAp0XnzxRXz00Ud49dVXMWTIEGzatAnLli3D6tWrTa8zbtw4DBo0CO3bt0eHDh3w/vvv4+bNm3jhhRfc1bRqwZUH8ZMngU6deGhD8bUuLJo6FdiwwfUdoVFwsP2Tvoq6/35eDysmhpeNL4+eTgghhGOOHXPPcPGjR21uDgzkK1HF6fV8ns2W27cLy65YwYvsbthgX59JBHzzDU8Tu3mTu6bXXweiokp/rhDCBdyVUnDQoEEEHkBsdtu8ebOpzKlTp6hnz57k6+tLwcHBlJKSQgUFBWb72bx5M7Vt25Y0Gg1FR0fT4sWLS7zWvHnzqEGDBqTRaKhDhw7022+/OVRXST9b0ooVrskQXvQWHl7kBe7cIfrlF6KNG4ksrQm2di1RUJDrK+HsTaUiev758nr7hahW5Bhsmbwvdjp1iigzk+jKFb4/Z457+oHYWKeq9/PPjr+UUkkUEUFU7CtRCUePEtWubXkfo0aVcb1HIaoxR46/CqLKMjnFfXJzcxEYGIicnBwEVLT8pR6Smgq44+Le7gwD2q6bCcyezQPEAR4299RTPHC86DDS/HwevP31166thDNzshQKHr63fLlr6yKEkGOwFfK+lGLDBh4Pt2sX31ergT59gKtXgbQ0176WUgm88w7w2msOP/Wrr3hUuDP++1/g73+3vO36dSAyErhxw/rzp0/nq1RCCMc4cvz1WGp0UbEZMw+5ki9uIezpzsCkSYWBFMDzj77/ntO0Gx8nAv79b0435GrOnD9QKk1zyoQQQnjYsmU8v9Y4jwngvuQ//+E1oVxJpeITff/4h1NPd7Y/VauB/futb//wQ9uBFMDxX36+c68vhLCPBFPCom7dgBYhlzAQqeiJ1aiDy7CU9tVetXAVe9AadY//armAXs/j3Dt2BH79lVcKHDYMyMtz+jUt8vNz7nlEwNChrq2LEEIIx92+XTg5qXhucZ3ONYumq1Sm9Odo3JjT3TmZ4q5LF86t5CiDwXaX9cUXpe/j5k0gPd3x1xZC2M9tCShEJabTQT1+PPZd/QgK6GCAEioYcBoNMAhLsE3RxeGLOwvwIhrjROkFDx0CHnzQ9Zn8VCrOT7tvH+eBtbezNaY7nz+fx1MIIYTwrB9+AHJy3Psaycm8VEfnznx2Uen8uWeVihMAbtvm2POIgEcftb7d3nONlX3pRiEqOrkyJUoaPRr44AMo9TooAKj+Wtm9Pv7EeiThkdDfMX++/buLwFk8hf9Aae+VLVcGUhERQEoKrw8yejQvpqtUWk5drlTy6cN69Qof69SJ07S/+KLr6iSEEMJ5x445sYihg4YNA95+mxdjKkMgZdSqlWO7USqB/v1tZ+SzN1tfTIz9ryuEcJwEU8Lc8ePAp59aDGhUMMBLqcd/46Zh+HDAxrrIZjpgpykgK1dRUcDZs8CcOYXDNVq04HlYfn4cUBUdylG/PrBzJ3DmDHDtGo+P2LIFSEoq/7oLIYSwLCjIfUtnAByoGUciaLXAvHm8CHBqaumTlKx46qmSIxJt6dWLU53bYs/aUG3bSop0IdxNgilhbulSm6fPlAY9FGvXQJ13DSkpttem1Wh4iML7H3jgY6ZQcEILS5KSgHPnuIMcMAAYOJAnM2dl8Sk8hYI7a2fnVwkhhHCfPn3ct6i7SgV0785rEY4fzyfZxo7lTA5DhgBhYfZNViqmSxfuklQq+8q/8AIv5GvLU09Z7+YA7oO//97uKgohnCTBlDB3+XLpYxGIgKtX8fLLwHPP8UNFOwiFgk/qHT7MF4Ea9u9YePWnvBDxsD5rAgJ4TPySJdwx9u3r/mEjQgghyi4ykhNQ2Dqb5yy9Hli9GmjWjEc16PV8SamggPuVW7c4q5+DUYpCwf1hQkLpZVUq4MsvuTs2uniRB04cO1YYR6pUwKZNwIgRJbuvdu14CnKTJg5VUwjhBAmmhLmGDUsfPuHlBYSFQaXiWGTdOuCxx4CmTYEGDXhseJs2wJo1QG4uAG/v0k+xuVrz5kB8fPm+phBCiPIxb15hQKVUlu/JMIWCl/iwcHWMiBNNDB4MtG/PV6Q++4zjsdq1gZUrS9+9Xg+sWMEp1R9/HOjZk3+Pj+cYr21bjvcAvvq0cCH3tdu38/JaubmcMT462oVtFkJYJYv2QhZGNHPxIidgsJbtTq0Gnn2Wo6giVq/mIQf5+dxpGE8YBgUBa5/7CvEfDXTfsAxLVq7kXkgIUeHJMdgyeV/skJ3Ni6lfv85Ds2fP5kx/7pxTZZSZyWcO/1JQwIvzfvddyaIBAdxPPvAAZ1o/dcr5LlGp5Od+/TUnqRBCuJ4jx18JpiAdVglz5vBY8eJUKo6Ofv8daNTI9PDhw0Dr1hx/GT9NgbiOp7EMDZGNPEVNjKeZqIPr5VF7IC4O2LHD/sHpQgiPkmOwZfK+OOHMGeDdd4HFi/8aGuE+JxZtwfhVD0Gr5ZGHgYF8Fcratyqlkq8Y/fILJ5Yt67evgADOj1HeAz+EqA4cOf7KOlOipFde4fEIU6cCf/7JjykUPCl33jyzQArgVdiJCjuGZHyE2RgPb9yFDmqoSA9FGRb8ddiePdyz9e0LvPQSnwYUQghR9UVG8pqC773HoyzOn3fbS3UZEoUzDpQ3GIBRoziY+uUXnnalVDqW5a+o3FxecmvAAOeeL4RwDZkzJSwbMoTHIezcCWzcCJw+zZOgLAQmP/5YOCrweSzBR/gnfHEHShA0KIAKBvvXmHIFnY470I8/BmJjeeV6IYQQ1ceNG24LpAjAenTHGTRw+Lnp6XyOculS4JtvgPvvd366l1oNnDjh3HOFEK4jV6aEdSoVcN99pRbLz+efXriLBRjl5ko5QKfjU369e3PvVaOGp2skhBCiPHh7l+2yjxUEIB8ajMX7Tu/jzBmu1oULwCOP8PSuHTsc34/BwCPvhRCeJcGUKLMOHYDf113GakMSauCWp6tjzmDgicnffsvpbIUQQlR9Gg3QsiWwb5/Ld90Hy3EY9zj9/FmzgFWrChMRWsv3VBqFgpfcEkJ4lgzzE6UjAv74gxe23bCh8FLUXyb1yMAhQ1PE4Q8PVbAUajWPrRBCCFH1Xb0K7Nrl8qtSUCrxWZevsRqPOr0LHx8OpIi4emUJpJKTgYgIp6sihHARuTIlbNu5k9fy2LOn8LHatYE33+Qj+e3bSHirBwi5cMPyidYpFDwM0d6eqLwXDRZCCOE+ej2wfj1w5AhQsybw6KP82PjxfOLP2SjFlg0bYDjWDdji/C7u3LG1ldAZ29AK+3ELfliDR3ARYaatanXh0iPJyZy0UAjhefINU1iXmckrDt69a/741avAP//JK8HXqQPF5cvlG0jVrg1s3gx88gnw3//yJGNbHadOByQmll/9hBBCuM/GjcDAgcC5c3xSzWDg8XI+PhytuGuNqS+/xPAv/oaXXlKgoMC1u74PO/EVnkMzHIMBCihBKIAan2I4xuI96KDBwIFAu3bAk0/yIr5CiIpBhvkJ6yZMKFyF15I33gDWri3f9ZwUCg7kWrcG5s/nBRu//dZ6eZWKU+X27l1uVRRCCOEmO3cCPXvyAksAB05E/PPmTfcu1rtkCZQvDscH77l2+GAzHMZmdEU0ODWfMfutF3QYiQVYhCEAOIhKTpZASoiKRoIpYZlWy/OjbHVMBQWcMr08131+6CFg4kTzx556iocdAoXD+RQKvoWE8FAQZ3PPCiGEqDimTOETfK6eD2Wvzz/HSP1HLj2HmIifoUE+1CjZ3ypBeB5foyX2o3lz172mEMJ1JJgSlmm1pQdJajVQp075BVOPPcaBkbd3yW1TpgC7dwNDhwLx8UDXrrzA8JEjwD3OZ10SQghRQVy9WvpJPjczALgxbQ58fFzV7xEa4wS8YH2oegHUeD3yKzRp4qKXFEK4lMyZEpaFhZVeRqcDuncHfv2V50+5+0zh+vW8AO/DD1ve3rYtsHChe+sghBDCM65dK9+REBYoAfhfOwMfzQ3cVtZ0QbenMA3vs+WR+y6V9YWEEG4iV6aEZXXrctIGW2MZ1Gpg0CBg5Uq+WuTuuVMFBTz36fJl976OEEKIiicszL1Dttu04QRHdvAvuGYzkNJoOCdG6QhdsNlmCbWKENSmoV31EkKUPwmmhHUzZnDAZK1HmDKFh/l168YLI770EtCwIT9mXy/iGIOBMzUtXuz6fQshhKjY/P2BZ55x/VIXKhXQpAkPI7RjVMYlBOMs1TU91djdKf5KaxsWxucZFXakuX37bQV8Xhhgs00Kg4F3KISokCSYEta1bw9s2gQ0a2b+eGAgL3AxaVLhY40bA3Pncspyvd59Q/4MBmDdOvesISKEEKJie/ttICjI+kiIGjUc36deD2RlAWfOAIcO2SxqAPAhXoIOXlAqgUce4Sr17s25kBYtAk6cACY/fRSz9ePwKxLwMUagLs6W2NfjjwOTJwM+b08CgoOtt2niRD5RKYSokBREHh6AXAHk5uYiMDAQOTk5CAgI8HR1Kh4iTkeblcWBVLdugK+v5bKjRgGffWZfsGM8nedM4BUSAoweDbz2muWEFEKISkOOwZbJ+2LFiRPA2LHAqlWFc6gaNeKsrv378/qDGzYAP/5YmELdRfRQohauIg+BUCg4IFqxolihTz4BRo2CnpRQkQ4EwAAldqMtnsNXOKa8B/XqARkZ3JUB4My4o0cDq1cXtik0lE9a/vOf9l3mEkK4jCPHXwmmIB2WyxDxSvQ3b9pXvn59zrz3n/8493pKJadKX7eOB6gLISqlyngMnj59OlavXo3MzExoNBpcv369RJns7GyMHDkSmzdvhr+/PwYNGoQZM2ZAbecwtcr4vpSrc+eAY8eAgACe71R8eDkRsGsXMGYM8NtvLnvZfliKZegHpRJ46y3zQRr45Regc2eLz9NBiTwEYGzvU5gxPxARERYK/fkncPgw4OcH3HefLOshhIc4cvyVYX7CdQoK7A+kAKBBA2D5cuDvf3cueYXBAGzZAnz6qePPFUKIMsjPz0ffvn0xcuRIi9v1ej169eqF/Px8bN++HUuWLEFqaiqmTJlSzjWtwiIi+ITavfcWBlJHjvAIidBQDrLGjuX7kybxfRcYhFQoFNxtDR1abOO771qd/6SGAUGKHCzpusRyIAXwScbERKBjRwmkhKgkJJgSrqPRcPIJez30EDBzJg/TM3ZyziSumD/f8ecIIUQZvPnmm3j55ZcRGxtrcfuGDRtw8OBBfPXVV2jbti169uyJt99+G/Pnz0d+fn4517aa+PlnXiLjs8+AS5eAGzeAHTuAgQN5mPrZs0Dr1mV+mYeRBhX0+PJLIDy82MYNG2wOc1cAQFpamesghKg4ZJ0p4TpEQFQUcOVK6WVVKmD2bL66VHQsuI8PT8QNDuZhDhkZwO3btl/z6FH+KWPKhRAVRHp6OmJjYxFWJDtcUlISRo4ciQMHDuDee+/1YO2qoBs3gD59gPx883m4xt+XLeMF3PfuLfNLeUGP9K35aP+ghbnDpc0BJpIESkJUMXJlSrjOG28Av/9uX1m9njsUg4F/N65on5/PSS527eKx548/XvoQQF9fCaSEEBWKVqs1C6QAmO5rrSRFuHv3LnJzc81uwk5ffw3k5dkOZj74wDV9Rb16aN/Jx/K2+HjbfZZSyUP4hBBVhgRTwjVu3QJmzSq93AMPAO3aWe9sdDpes2rDBr7fp09hoGWJWg306+d4fYUQopgJEyZAoVDYvB0+fNhtrz9jxgwEBgaabpGRkW57rSpnxw7bQQwRcO1aYaY8ZymVQHKy9aDs5Zet91nGiVb/+EfZ6iCEqFAkmBKukZbGCShK07Qp8McfpQdIP/7Ivz/+OA/NsDShV6nkjiklxbk6CyFEESkpKTh06JDNW3R0tF37Cg8Px4ULF8weM94PLzHRhk2cOBE5OTmm25kzZ8rWoOrE3oV8HbkyVXwOr0rFyS5u3eJEESoVD0l/5RXOLAhwnzVuXGH5ovVTqYBvvwXq1rW/DkKICk+CKeEaZ0suSGjR6dP2lbtzh396efGkYuMkb7W6MMNRYCCwZg3QooVjdRVCCAtCQkIQExNj86axcxmGhIQE7Nu3DxcvXjQ9lpaWhoCAALSwcszy9vZGQECA2U3YqXt323ORlEqgSRP7r0y98QYQF1d4v1YtXgfq+nXgnXe4zzMYeI7w++9z4oujRzlYmzOH14tKTOQFhkNCOAnGH3/waAshRJUiCSiEa9ibISkqijscW8GXXm++v4gITkSxdSt3UHfvAu3bA337Wl88WAgh3Cg7OxtXr15FdnY29Ho9MjMzAQBNmjSBv78/unfvjhYtWuD555/HrFmzoNVqMXnyZCQnJ8NbFhp3vccfBxo25HWaLI18MBj4ilBWln37e+opXkTq0iU+uRceDgwYwCcEi8/L0uuBq1d5weCMDA6oHnmEb0KIKk8W7YUsjOgygYFAaROmMzOBtWt5zQ9LE4UVCk6xfu4cULu2W6ophKhYKuMxePDgwViyZEmJxzdv3owuXboAAE6fPo2RI0diy5YtqFGjBgYNGoSZM2fKor3ucvQo0LUrcP483yfi0Qw6Hc+t/e670vehVAItWwJ79pgPCTx/nof2lZatb9cuPtknhKjUHDn+ypUp4ToffggMHmx9e5cuvEp9TAywfj2wbZt5x6RScee3ZIkEUkKICi01NRWpqak2yzRs2BBr1qwpnwoJoFkzXrT3m2+AH37guU1t2gAvvmj/8Dql0nLWv717Sw+kAL4yJcGUENWKBFPCdQYNAm7e5BXniyej6N4dWLGCf/f2BtatA+bN41t2NndgPXsCEyZwxj8hhBDCUf7+wPDhfCvq2LHSn6tQcN/UtWvJbXbOlbO7nBCiypBhfpChFC53+zbw5Zc8TCI4mMeRx8RYLkvE5b28ChNLCCGqFTkGWybviwup1bazyBrLWMtKe+sWz7myNZRdqeSTg/XqOV9PIUSFIMP8hGf5+vJZQb0eyMkBatSwXlahAPz8yq9uQgghqj4i7n+8vLgPqlePAx1bQkKsb/Pz41EXb79tOSOgSgU8+6wEUkJUQ5IaXbjelSvA+PFAnTp8q1GDJ//u3evpmgkhhKjKCgqAuXOBRo04nbm/P9C5s32Z9caMsb19yhQezg4Urmtl/NmtG7BwodPVFkJUXjLMDzKUwqUuXgQSEjh9bNEhFWo139av545NCCH+Isdgy+R9cVBBAadIX7fO/OqRSsX9Uf36nDrdksaNOdusv3/pr7NrF/DFF3ylKywMeP55nmflyILAQogKTYb5Cc8ZP547mOJj03U6zoTUvz8HWvauVi+EEELY4/PPSwZSQGF/dPYsr0/4ww+FjykUwJNPAp9+al8gBQD33cc3IYSAG4f5nTp1CkOHDkVUVBR8fX3RuHFjTJ06Ffn5+Wbl9u7diwcffBA+Pj6IjIzErFmzSuzr+++/R0xMDHx8fBAbG1si1SwRYcqUKahbty58fX2RmJiIY/Zk7hGude0a8O231lehNxh4/ai1a8u3XkIIIaq+efNsb1cqOX365cvAmjXATz9xgLV8uSzHIYRwmtuCqcOHD8NgMOCTTz7BgQMH8N5772HhwoV4/fXXTWVyc3PRvXt3NGzYEBkZGZg9ezamTZuGTz/91FRm+/bt6N+/P4YOHYrdu3ejd+/e6N27N/bv328qM2vWLHz44YdYuHAhduzYgRo1aiApKQl37txxV/OEJceOWc+EZKRWA0X+dkIIIUSZEQGHD1tODmGk13P/ExTES3E8+ihn6BNCiDIo1zlTs2fPxoIFC3DixAkAwIIFCzBp0iRotVpo/lqbYcKECVi5ciUOHz4MAOjXrx9u3ryJVatWmfZz//33o23btli4cCGICBEREUhJScErr7wCAMjJyUFYWBhSU1PxzDPPlFovGZfuIvv3A7GxtssolcC773JWJCGEgByDrZH3xQFEnHHP1klUlYqH+X37bfnVSwhRKTly/C3XbH45OTmoXeRSenp6Ojp37mwKpAAgKSkJR44cwbVr10xlEhMTzfaTlJSE9PR0AMDJkyeh1WrNygQGBiI+Pt5URpSTFi2AqCjbk3CJgMceK786CSGEqPoUCuCJJ2zPx9XruYwQQrhQuQVTWVlZmDdvHkaMGGF6TKvVIiwszKyc8b5Wq7VZpuj2os+zVKa4u3fvIjc31+wmXECpBN54w/owC5WKU6RHR5dvvYQQQlR948fzT0sn9NRqoEkToHfvcq2SEKLqcziYmjBhAhQKhc2bcYie0dmzZ9GjRw/07dsXw4YNc1nlnTVjxgwEBgaabpGRkZ6uUtXxwgvA9OkcWKlUhSnRAR6j/sUXnq2fEEKIquneezlTn68vB1RF+5/GjYGffwaKjIQRQghXcDg/dUpKCgYPHmyzTHSRKw/nzp1D165d0bFjR7PEEgAQHh6OCxcumD1mvB8eHm6zTNHtxsfqFplIeuHCBbRt29Zi/SZOnIhx48aZ7ufm5kpA5Uqvv87rbqSmAidO8MKJ/ftLKlkhhBDu9eijnDX2q6+AjAzA2xvo1YtP5qlUnq6dEKIKcjiYCgkJQUhIiF1lz549i65duyIuLg6LFy+GUml+ISwhIQGTJk1CQUEBvLy8AABpaWlo3rw5atWqZSqzceNGjC2SsCAtLQ0JCQkAgKioKISHh2Pjxo2m4Ck3Nxc7duzAyJEjLdbL29sb3t7ejjRbOCoykof8CSGEEOUpMBBITvZ0LYQQ1YTb5kydPXsWXbp0QYMGDTBnzhxcunQJWq3WbB7Ts88+C41Gg6FDh+LAgQP47rvv8MEHH5hdNRozZgzWrVuHd999F4cPH8a0adPw+++/Y/To0QAAhUKBsWPH4l//+hd++ukn7Nu3DwMHDkRERAR6y9hoIYQQQgghhJs4fGXKXmlpacjKykJWVhbq169vts2YjT0wMBAbNmxAcnIy4uLiEBwcjClTpmD48OGmsh07dsQ333yDyZMn4/XXX0fTpk2xcuVKtGrVylTm1Vdfxc2bNzF8+HBcv34dnTp1wrp16+Dj4+Ou5gkhhBBCCCGquXJdZ6qikrU8hBDCc+QYbJm8L0II4RkVdp0pIYQQQgghhKgqJJgSQgghhBBCCCdIMCWEEEIIIYQQTpBgSgghhBBCCCGcIMGUEEIIIYQQQjhBgikhhBBCCCGEcIIEU0IIIYQQQgjhBLct2luZGJfays3N9XBNhBCi+jEee2XZQ3PSNwkhhGc40i9JMAUgLy8PABAZGenhmgghRPWVl5eHwMBAT1ejwpC+SQghPMuefklBcioQBoMB586dQ82aNaFQKDxdHZfKzc1FZGQkzpw5U+oKzlVNdW17dW03IG2vrG0nIuTl5SEiIgJKpYw+N3JF31SZPxdFVYV2VIU2ANKOikba4R6O9EtyZQqAUqlE/fr1PV0NtwoICKgQH05PqK5tr67tBqTtlbHtckWqJFf2TZX1c1FcVWhHVWgDIO2oaKQdrmdvvySnAIUQQgghhBDCCRJMCSGEEEIIIYQTJJiq4ry9vTF16lR4e3t7uirlrrq2vbq2G5C2V9e2C+uqyueiKrSjKrQBkHZUNNIOz5MEFEIIIYQQQgjhBLkyJYQQQgghhBBOkGBKCCGEEEIIIZwgwZQQQgghhBBCOEGCKSGEEEIIIYRwggRTlVSjRo2gUCjMbjNnzjQrs3fvXjz44IPw8fFBZGQkZs2aVWI/33//PWJiYuDj44PY2FisWbPGbDsRYcqUKahbty58fX2RmJiIY8eOubVtrjB//nw0atQIPj4+iI+Px86dOz1dJYdMmzatxN83JibGtP3OnTtITk5GnTp14O/vjz59+uDChQtm+8jOzkavXr3g5+eH0NBQjB8/HjqdzqzMli1b0K5dO3h7e6NJkyZITU0tj+aZ2bZtGx599FFERERAoVBg5cqVZtvt+QxevXoVAwYMQEBAAIKCgjB06FDcuHHDrIwr/h9cqbR2Dx48uMRnoEePHmZlKmO7Rfk4deoUhg4diqioKPj6+qJx48aYOnUq8vPzzcrZ8/nwpOnTp6Njx47w8/NDUFCQxTL2HOsqgsrWL7ni2OxpM2bMwH333YeaNWsiNDQUvXv3xpEjR8zK2NOfetqCBQvQunVr04K2CQkJWLt2rWl7ZWiDJTNnzoRCocDYsWNNj1XKtpColBo2bEhvvfUWnT9/3nS7ceOGaXtOTg6FhYXRgAEDaP/+/fTtt9+Sr68vffLJJ6Yyv/76K6lUKpo1axYdPHiQJk+eTF5eXrRv3z5TmZkzZ1JgYCCtXLmS9uzZQ4899hhFRUXR7du3y7W9jli6dClpNBpatGgRHThwgIYNG0ZBQUF04cIFT1fNblOnTqWWLVua/X0vXbpk2v7iiy9SZGQkbdy4kX7//Xe6//77qWPHjqbtOp2OWrVqRYmJibR7925as2YNBQcH08SJE01lTpw4QX5+fjRu3Dg6ePAgzZs3j1QqFa1bt65c27pmzRqaNGkS/fDDDwSAVqxYYbbdns9gjx49qE2bNvTbb7/RL7/8Qk2aNKH+/fubtrvq/6E82z1o0CDq0aOH2Wfg6tWrZmUqY7tF+Vi7di0NHjyY1q9fT8ePH6cff/yRQkNDKSUlxVTGns+Hp02ZMoXmzp1L48aNo8DAwBLb7TnWVQSVsV9yxbHZ05KSkmjx4sW0f/9+yszMpEceeYQaNGhg9n2ptP60Ivjpp59o9erVdPToUTpy5Ai9/vrr5OXlRfv37yeiytGG4nbu3EmNGjWi1q1b05gxY0yPV8a2SDBVSTVs2JDee+89q9s//vhjqlWrFt29e9f02GuvvUbNmzc33X/66aepV69eZs+Lj4+nESNGEBGRwWCg8PBwmj17tmn79evXydvbm7799lsXtcT1OnToQMnJyab7er2eIiIiaMaMGR6slWOmTp1Kbdq0sbjt+vXr5OXlRd9//73psUOHDhEASk9PJyLuBJVKJWm1WlOZBQsWUEBAgOkz8eqrr1LLli3N9t2vXz9KSkpycWvsV7zDtuczePDgQQJAu3btMpVZu3YtKRQKOnv2LBG55v/BnawFU48//rjV51SFdovyNWvWLIqKijLdt+fzUVEsXrzYYjBlz7GuIqjs/ZIzx+aK6OLFiwSAtm7dSkT29acVVa1atejzzz+vlG3Iy8ujpk2bUlpaGj300EOmYKoytoWISIb5VWIzZ85EnTp1cO+992L27NlmwxrS09PRuXNnaDQa02NJSUk4cuQIrl27ZiqTmJhots+kpCSkp6cDAE6ePAmtVmtWJjAwEPHx8aYyFU1+fj4yMjLM6qxUKpGYmFhh62zNsWPHEBERgejoaAwYMADZ2dkAgIyMDBQUFJi1MSYmBg0aNDC1MT09HbGxsQgLCzOVSUpKQm5uLg4cOGAqY+vvXxHY8xlMT09HUFAQ2rdvbyqTmJgIpVKJHTt2mMqU9f/BE7Zs2YLQ0FA0b94cI0eOxJUrV0zbqnK7hXvk5OSgdu3apvv2fD4qOnuOdZ5Wlfolo8r4/QDg/wEApv8De/rTikav12Pp0qW4efMmEhISKmUbkpOT0atXrxJ9T2VsCwCoPV0B4ZyXXnoJ7dq1Q+3atbF9+3ZMnDgR58+fx9y5cwEAWq0WUVFRZs8xdjZarRa1atWCVqs164CMZbRaralc0edZKlPRXL58GXq93mKdDx8+7KFaOS4+Ph6pqalo3rw5zp8/jzfffBMPPvgg9u/fD61WC41GU2L+QPG/naX3wLjNVpnc3Fzcvn0bvr6+bmqd/ez5DGq1WoSGhpptV6vVqF27tlmZsv4/lLcePXrgySefRFRUFI4fP47XX38dPXv2RHp6OlQqVZVtt3CPrKwszJs3D3PmzDE9Zs/no6Kz51jnaVWlXyqqMn4/MBgMGDt2LB544AG0atUKAOzqTyuKffv2ISEhAXfu3IG/vz9WrFiBFi1aIDMzs9K0AQCWLl2KP/74A7t27SqxrTL9PYqSYKoCmTBhAv7v//7PZplDhw4hJiYG48aNMz3WunVraDQajBgxAjNmzIC3t7e7qyrcrGfPnqbfW7dujfj4eDRs2BDLli2rEEGOcL9nnnnG9HtsbCxat26Nxo0bY8uWLejWrZsHayY8yZF+wujs2bPo0aMH+vbti2HDhrm7iqVypg1ClFVycjL279+P//3vf56uilOaN2+OzMxM5OTkYPny5Rg0aBC2bt3q6Wo55MyZMxgzZgzS0tLg4+Pj6eq4jARTFUhKSgoGDx5ss0x0dLTFx+Pj46HT6XDq1Ck0b94c4eHhJbKfGO+Hh4ebfloqU3S78bG6deualWnbtq3d7SpPwcHBUKlUNttVGQUFBaFZs2bIysrCww8/jPz8fFy/ft3s7E3xv13xTFH2/v0DAgIqTMBmz2cwPDwcFy9eNHueTqfD1atXS21r0dco7f/B06KjoxEcHIysrCx069at2rRbmHO0nzh37hy6du2Kjh074tNPPzUrZ8/nwx3K0tcVZ8+xztOqYr9U2b4fjB49GqtWrcK2bdtQv3590+Ph4eGl9qcVhUajQZMmTQAAcXFx2LVrFz744AP069ev0rQhIyMDFy9eRLt27UyP6fV6bNu2DR999BHWr19fadpSlMyZqkBCQkIQExNj81Z0bHtRmZmZUCqVpmE/CQkJ2LZtGwoKCkxl0tLS0Lx5c9PQjYSEBGzcuNFsP2lpaUhISAAAREVFITw83KxMbm4uduzYYSpT0Wg0GsTFxZnV2WAwYOPGjRW2zva4ceMGjh8/jrp16yIuLg5eXl5mbTxy5Aiys7NNbUxISMC+ffvMvmynpaUhICAALVq0MJWx9fevCOz5DCYkJOD69evIyMgwldm0aRMMBgPi4+NNZcr6/+Bpf/75J65cuWL64lJd2i3MOdJPnD17Fl26dEFcXBwWL14MpdK8y7fn8+HpNpTGnmOdp1XFfqmyfD8gIowePRorVqzApk2bSgxrtac/ragMBgPu3r1bqdrQrVs37Nu3D5mZmaZb+/btMWDAANPvlaUtZjydAUM4bvv27fTee+9RZmYmHT9+nL766isKCQmhgQMHmspcv36dwsLC6Pnnn6f9+/fT0qVLyc/Pr0RKZLVaTXPmzKFDhw7R1KlTLaZGDwoKoh9//JH27t1Ljz/+eIVLfVrc0qVLydvbm1JTU+ngwYM0fPhwCgoKMsv2VNGlpKTQli1b6OTJk/Trr79SYmIiBQcH08WLF4mIU4c2aNCANm3aRL///jslJCRQQkKC6fnGdMHdu3enzMxMWrduHYWEhFhMjT5+/Hg6dOgQzZ8/3yOp0fPy8mj37t20e/duAkBz586l3bt30+nTp4nIvs9gjx496N5776UdO3bQ//73P2ratKlZinBX/T+UV7vz8vLolVdeofT0dDp58iT9/PPP1K5dO2ratCnduXOnUrdblI8///yTmjRpQt26daM///zTLMW+kT2fD087ffo07d69m958803y9/c3/c/k5eURkX3HuoqgMvZLrjg2e9rIkSMpMDCQtmzZYvY/cOvWLVOZ0vrTimDChAm0detWOnnyJO3du5cmTJhACoWCNmzYQESVow3WFM3mR1Q52yLBVCWUkZFB8fHxFBgYSD4+PnTPPffQO++8Y/Yli4hoz5491KlTJ/L29qZ69erRzJkzS+xr2bJl1KxZM9JoNNSyZUtavXq12XaDwUBvvPEGhYWFkbe3N3Xr1o2OHDni1va5wrx586hBgwak0WioQ4cO9Ntvv3m6Sg7p168f1a1blzQaDdWrV4/69etHWVlZpu23b9+mUaNGUa1atcjPz4+eeOIJsy9JRESnTp2inj17kq+vLwUHB1NKSgoVFBSYldm8eTO1bduWNBoNRUdH0+LFi8ujeSXqAKDEbdCgQURk32fwypUr1L9/f/L396eAgAB64YUXTF+2jFzx/1Be7b516xZ1796dQkJCyMvLixo2bEjDhg0r8cWrMrZblI/Fixdb/HwVP4dqz+fDkwYNGmSxDZs3bzaVsedYVxFUtn7JFcdmT7P2P1C0r7OnP/W0IUOGUMOGDUmj0VBISAh169bNFEgRVY42WFM8mKqMbVEQEbn54pcQQgghhBBCVDkyZ0oIIYQQQgghnCDBlBBCCCGEEEI4QYIpIYQQQgghhHCCBFNCCCGEEEII4QQJpoQQQgghhBDCCRJMCSGEEEIIIYQTJJgSQgghhBBCCCdIMCWEEEIIIYQQTpBgSgghhBBCCCGcIMGUEEIIIYQQQjhBgikhhBBCCCGEcIIEU0IIIYQQQgjhhP8HXoEa8ovdlaYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "axs[0].scatter(X_pca[:, 0], X_pca[:, 1], c = list_class, cmap = ListedColormap(['r','b']))\n",
        "axs[0].set_title(\"PCA\")\n",
        "#axs[0].legend(loc=\"upper right\")\n",
        "\n",
        "axs[1].scatter(X_pca_normal[:, 0], X_pca_normal[:, 1], c = list_class, cmap = ListedColormap(['r','b']))\n",
        "axs[1].set_title(\"PCA_normalize\");\n",
        "#axs[1].legend(loc=\"upper right\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "cRExMDO2jcVK",
        "outputId": "62694172-0c4e-43eb-dd62-4f59d9a86913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GYXPepP3wAsA",
        "outputId": "a04218cd-f248-4f1c-b75f-3d35edb8cb11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  FWI   Classes\n",
              "0           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4  0.5  not fire\n",
              "1           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9  0.4  not fire\n",
              "2           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7  0.1  not fire\n",
              "3           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7  0.0  not fire\n",
              "4           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9  0.5  not fire"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8a5c020-8698-4f17-adff-3c52d68c63e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>RH</th>\n",
              "      <th>Ws</th>\n",
              "      <th>Rain</th>\n",
              "      <th>FFMC</th>\n",
              "      <th>DMC</th>\n",
              "      <th>DC</th>\n",
              "      <th>ISI</th>\n",
              "      <th>BUI</th>\n",
              "      <th>FWI</th>\n",
              "      <th>Classes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29</td>\n",
              "      <td>57</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>65.7</td>\n",
              "      <td>3.4</td>\n",
              "      <td>7.6</td>\n",
              "      <td>1.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>not fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29</td>\n",
              "      <td>61</td>\n",
              "      <td>13</td>\n",
              "      <td>1.3</td>\n",
              "      <td>64.4</td>\n",
              "      <td>4.1</td>\n",
              "      <td>7.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>not fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>82</td>\n",
              "      <td>22</td>\n",
              "      <td>13.1</td>\n",
              "      <td>47.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>not fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>2.5</td>\n",
              "      <td>28.6</td>\n",
              "      <td>1.3</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>not fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>77</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>3.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>not fire</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8a5c020-8698-4f17-adff-3c52d68c63e3');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "id": "TbfFAjpSV1TM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "19612b87-f7c5-4f05-bf73-1bb1451c6a4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rain  FFMC  DMC    DC  ISI  BUI  FWI   Classes\n",
              "0   0.0  65.7  3.4   7.6  1.3  3.4  0.5  not fire\n",
              "1   1.3  64.4  4.1   7.6  1.0  3.9  0.4  not fire\n",
              "2  13.1  47.1  2.5   7.1  0.3  2.7  0.1  not fire\n",
              "3   2.5  28.6  1.3   6.9  0.0  1.7  0.0  not fire\n",
              "4   0.0  64.8  3.0  14.2  1.2  3.9  0.5  not fire"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rain</th>\n",
              "      <th>FFMC</th>\n",
              "      <th>DMC</th>\n",
              "      <th>DC</th>\n",
              "      <th>ISI</th>\n",
              "      <th>BUI</th>\n",
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              "      <th>Classes</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>3.4</td>\n",
              "      <td>0.5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.3</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.1</td>\n",
              "      <td>47.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.1</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>not fire</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>28.6</td>\n",
              "      <td>1.3</td>\n",
              "      <td>6.9</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>not fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>64.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>3.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>not fire</td>\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c1048db0-1b81-4001-a3bc-6c2867cc5cb6');\n",
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              "            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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Selection"
      ],
      "metadata": {
        "id": "aqXzp95gQ68z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.cluster import KMeans # Importing KMeans Cluster library from SK Learn\n",
        "dforig=pd.read_csv('Algerian_forest_fires_dataset_UPDATE.csv').dropna()\n",
        "\n",
        "dforig['Classes'] = dforig['Classes'].replace({'fire ': 0,'fire': 0, 'fire   ':0, ' not fire':1, 'not fire   ':1, 'not fire    ': 1, 'not fire':1, 'not fire ':1, 'not fire     ':1})\n",
        "print(dforig['Classes'].values)\n",
        "dfattr=dforig.iloc[:,0:10]\n",
        "dfclass=dforig['Classes']\n"
      ],
      "metadata": {
        "id": "kduvVBvlRBP6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3531e8dd-be85-4b50-d798-924b95b2cae4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1\n",
            " 0 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0\n",
            " 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0\n",
            " 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0\n",
            " 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0\n",
            " 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining number of clusters as 4 for all the datasets\n",
        "\n",
        "km_dataset1 = KMeans(n_clusters=2)\n",
        "var_km_labels1 = km_dataset1.fit_predict(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSSK09Pk2Y3B",
        "outputId": "38c67e1a-f58f-47b5-adb6-6f16f07815b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km_df = pd.DataFrame(var_km_labels1, columns=['Kmeans Cluster'])\n",
        "km_df\n",
        "print(var_km_labels1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sh-9kXu1283D",
        "outputId": "538a20b5-4cf9-4117-e1f6-effb07696e2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 0\n",
            " 0 1 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0\n",
            " 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "li = zip(var_km_labels1, list_class)\n",
        "print(list(li))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xm3QFTzFTN55",
        "outputId": "c74c0bd4-559b-4097-8056-624e36b6c9e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, 1), (0, 1), (0, 1), (0, 1), (0, 1), (1, 0), (0, 0), (0, 0), (0, 1), (0, 1), (0, 0), (1, 0), (0, 1), (0, 1), (0, 1), (0, 1), (1, 1), (0, 1), (0, 1), (0, 1), (0, 0), (0, 1), (0, 0), (0, 0), (0, 0), (1, 0), (0, 0), (0, 0), (0, 1), (1, 0), (0, 1), (1, 1), (1, 1), (0, 1), (0, 0), (1, 0), (0, 1), (0, 0), (1, 1), (0, 1), (0, 1), (1, 1), (0, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 1), (0, 1), (0, 0), (0, 0), (0, 0), (0, 0), (1, 0), (0, 0), (0, 0), (0, 1), (0, 1), (0, 1), (1, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (1, 0), (0, 0), (1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (1, 1), (0, 0), (0, 1), (0, 1), (1, 1), (0, 1), (1, 1), (0, 1), (1, 1), (0, 1), (0, 1), (1, 1), (0, 1), (0, 0), (1, 0), (0, 0), (0, 0), (1, 0), (1, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 0), (0, 1), (1, 1), (0, 1), (0, 1), (1, 1), (0, 1), (0, 1), (1, 1), (0, 0), (0, 0), (0, 1), (0, 1), (1, 0), (0, 0), (1, 0), (0, 1), (1, 1), (0, 1), (1, 1), (1, 1), (0, 1), (1, 1), (0, 1), (0, 0), (0, 1), (0, 1), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 1), (0, 0), (0, 0), (1, 0), (1, 0), (1, 0), (0, 0), (1, 0), (0, 0), (0, 1), (0, 1), (0, 0), (1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g2BE7SFYfBwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.cluster.hierarchy import dendrogram\n",
        "# Importing Agglomerative Clustering library from SK Learn Module\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "# Generating clusters for Dataset 1\n",
        "var_hierarchical_cluster1 = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')\n",
        "var_hc_labels1 = var_hierarchical_cluster1.fit_predict(dfattr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yLP_vA12vPT",
        "outputId": "db4ce571-954a-4280-bacb-ae31ceb7bca9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hrc_df = pd.DataFrame(var_hc_labels1, columns=['Hierachial'])\n",
        "hrc_df\n",
        "#hrc_df.insert(value = list_class, column='Original')\n",
        "li_hc = zip(var_hc_labels1, list_class)\n",
        "print(list(li_hc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7GlMTKO4LRZ",
        "outputId": "90a8920f-2034-4c46-9ab5-4fcad5ce69f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 0), (1, 0), (1, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (0, 0), (0, 1), (0, 1), (0, 1), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 1), (1, 0), (0, 0), (0, 0), (0, 1), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (0, 0), (0, 0), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0), (0, 0), (0, 0), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (1, 0), (0, 0), (0, 0), (0, 1), (0, 0), (0, 0), (0, 0), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Actual_predicted = pd.DataFrame({'Actual': list_class, 'Predicted': var_hc_labels1})    \n",
        "Actual_predicted['Report']=abs(Actual_predicted['Actual']-Actual_predicted['Predicted'])\n",
        "Actual_predicted['Hierarchial Clustering']= np.where(Actual_predicted['Report']== 0,'Matched','Unmatched')\n",
        "Actual_predicted_group_df=Actual_predicted.groupby(['Hierarchial Clustering']).agg({'Hierarchial Clustering':['count']})\n",
        "Actual_predicted_group_df.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "qdbStYP6fSDo",
        "outputId": "2924c6ed-fb5b-4010-8d17-5dff3f7790a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Hierarchial Clustering      \n",
              "                         count\n",
              "0                Matched   155\n",
              "1              Unmatched    88"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y=X_pca_normal[:, 0:2], list_class\n",
        "print(X_pca_normal[:, 0:2])"
      ],
      "metadata": {
        "id": "RUC38qacFlQ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35f12290-8804-47c6-f650-b85996d59b6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 11.1471715   -3.20485569]\n",
            " [ 12.74130074  -2.45173653]\n",
            " [ 30.86665341   6.24504199]\n",
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            " [-14.20903045   3.94419902]\n",
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            " [-20.03027126  26.9603643 ]\n",
            " [-17.33167879  30.06402804]\n",
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            " [ 14.31847934  -1.31218112]\n",
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            " [ 16.13994825  -0.24138302]\n",
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            " [  4.97280092  -4.45657396]\n",
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            " [ 28.92842073   5.72377842]\n",
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            " [ -0.79125604  -7.97606154]\n",
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            " [ -8.42508707  -6.91294074]\n",
            " [ -5.97469764  -3.22730183]\n",
            " [ -9.282915    -3.16946783]\n",
            " [ -1.4422902   -2.35497901]\n",
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            " [  8.57600164  -2.91335646]\n",
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            " [ -3.30836065  -9.355316  ]\n",
            " [ -6.26748525  -9.1510793 ]\n",
            " [  1.90903444  -8.27230243]\n",
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            " [ 29.20121573   6.83712576]\n",
            " [ 18.42801171   1.01151167]\n",
            " [ 20.70802941   1.7913436 ]\n",
            " [ 26.16984059   5.07842519]\n",
            " [ -1.29841384  -8.3868531 ]\n",
            " [  0.57901328  -6.79955339]\n",
            " [ -5.07592878  -8.04989081]\n",
            " [ -5.48829554  -7.9594944 ]\n",
            " [  7.7602776   -5.38668504]\n",
            " [  1.09127877  -7.32105923]\n",
            " [ -5.46037263  -9.89760395]\n",
            " [ -3.59798438  -7.59094855]\n",
            " [ -4.3503158   -5.78538006]\n",
            " [  8.12091365  -5.21145969]\n",
            " [ 22.8575386    3.23310095]\n",
            " [ 10.1389559   -2.08170777]\n",
            " [ 27.27773663   5.97341758]\n",
            " [ 15.57612875  -1.1796762 ]\n",
            " [ 13.87083371  -2.54117141]\n",
            " [ 13.46292761  -2.29109307]\n",
            " [ 15.95064713  -0.39984419]\n",
            " [ -5.39670363 -10.35080648]\n",
            " [  1.74977019  -8.56325101]\n",
            " [  4.84006377  -6.74332679]\n",
            " [ -3.84089798  -9.84928761]\n",
            " [ -4.54189208  -8.2741971 ]\n",
            " [ -5.41900381  -6.87061264]\n",
            " [ -5.85752327  -5.1996316 ]\n",
            " [ -6.10880616  -3.72519362]\n",
            " [ -4.8438077   -5.85115152]\n",
            " [  2.03143883  -8.53758428]\n",
            " [ 13.33967011  -2.01263011]\n",
            " [ -5.87156385 -10.94611099]\n",
            " [ -4.9135178   -7.93073303]\n",
            " [ -6.34237875  -6.87891488]\n",
            " [ -6.67306932  -5.23103265]\n",
            " [ -5.70905599  -4.15018753]\n",
            " [ -5.51012323  -6.33707884]\n",
            " [  1.89356379  -8.91301513]\n",
            " [ -0.69293247  -9.82586531]\n",
            " [  3.0740067   -7.86438126]\n",
            " [  4.74792251  -6.75766871]\n",
            " [ -5.90851113 -10.87167237]\n",
            " [ -2.56212635 -10.75434776]\n",
            " [ -3.8162332   -8.98761118]\n",
            " [ -2.55198702  -5.38513757]\n",
            " [ -3.47421582  -3.69796773]\n",
            " [ -4.96482173  -2.39468761]\n",
            " [ -5.69095436  -0.84466136]\n",
            " [ -5.89029469   1.06292032]\n",
            " [ -3.82737914   3.31634419]\n",
            " [ -4.23275492   5.91043241]\n",
            " [ -4.82708679   8.54198949]\n",
            " [  3.23667429   3.85797709]\n",
            " [  5.32040204  -4.68604483]\n",
            " [  0.43343271  -6.41647764]\n",
            " [ -1.34538305  -5.48331944]\n",
            " [ -6.0253306   -6.40401694]\n",
            " [ -6.34533607  -4.41837206]\n",
            " [ -6.63130076  -2.37046144]\n",
            " [ -6.93974777  -0.19766052]\n",
            " [  0.05167268  -8.79299376]\n",
            " [ -7.32137642 -11.69935379]\n",
            " [ -5.32203793  -8.53515214]\n",
            " [ -5.4029259   -6.02457452]\n",
            " [ -2.94077841  -2.13656225]\n",
            " [  9.67104419  -4.14270317]\n",
            " [  9.10894985  -4.20409279]\n",
            " [ -5.65590905 -10.14806026]\n",
            " [ -6.95226996  -8.92322867]\n",
            " [ -6.44130318  -7.37432463]\n",
            " [ -6.06432719  -5.60436521]\n",
            " [ -6.20754658  -6.65883368]\n",
            " [ -6.86262808  -5.20249859]\n",
            " [ -8.25350252  -3.93816957]\n",
            " [ -6.25598225  -2.16464934]\n",
            " [-10.0068348   -2.46701588]\n",
            " [ -9.70140752  -0.44356173]\n",
            " [ -7.61790598   3.07387511]\n",
            " [ -6.76362243   7.69222729]\n",
            " [ -8.21460615   9.26769392]\n",
            " [ -9.94168019  10.50517908]\n",
            " [-11.37902166  11.73096141]\n",
            " [-10.74300304  13.99853238]\n",
            " [-10.94848565  16.10685802]\n",
            " [ -9.60898097  18.86209962]\n",
            " [-10.3209742   20.88387866]\n",
            " [-11.31838496  23.12437445]\n",
            " [-14.80279238  20.08350277]\n",
            " [-15.40150355  15.83774056]\n",
            " [-20.0930939   14.82814018]\n",
            " [-19.26861538  17.21127088]\n",
            " [ 34.66679659  10.26465759]\n",
            " [  1.46540249  -6.56740082]\n",
            " [ -4.58650196  -8.1906843 ]\n",
            " [  1.93397418  -3.79304088]\n",
            " [ 11.89017864  -3.07127465]\n",
            " [ 13.72604716  -2.15254314]\n",
            " [ -4.01239869  -9.55933701]\n",
            " [ -4.55109057  -7.74598337]\n",
            " [ -5.56600673  -6.35965742]\n",
            " [ 17.04786831   0.14522254]\n",
            " [ -0.95783182  -7.92099903]\n",
            " [ -5.06490623  -8.38102355]\n",
            " [ -6.52982422  -7.87938268]\n",
            " [ -6.04134998  -5.11687103]\n",
            " [ -8.45287505  -4.74335704]\n",
            " [ -9.07514978  -3.8694791 ]\n",
            " [ -8.51489456  -2.32069342]\n",
            " [ -9.68189596   0.78235579]\n",
            " [ -8.83966838   4.12089576]\n",
            " [ -7.28827204   5.65999787]\n",
            " [-11.65689509   3.87502122]\n",
            " [-12.8030655    6.19604889]\n",
            " [-12.90009737   8.27787841]\n",
            " [ 14.67695159   3.48212111]\n",
            " [ -1.98980895  -3.93075982]\n",
            " [ -6.127974    -4.52374117]\n",
            " [ 34.56761154   9.56281064]\n",
            " [ 28.5275613    6.62795177]\n",
            " [ -1.89620078  -8.72473066]\n",
            " [  8.64865568  -2.45744515]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "\n",
        "# 데이터들 변환(스케일링)\n",
        "scaler = StandardScaler() # 생성자 호출 - 변환기 객체 생성\n",
        "scaler.fit(X_train, y_train) # 학습 데이터의 평균과 표준 편차를 데이터 변환할 때 이용하기 위해서\n",
        "X_train_transformed = scaler.transform(X_train) # 학습 데이터 세트 변환\n",
        "X_test_transformed = scaler.transform(X_test) # 테스트 데이터 세트 변환"
      ],
      "metadata": {
        "id": "VTBrYvxxP7N2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN"
      ],
      "metadata": {
        "id": "VwUCkAjIhJ0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "k_neigh = KNeighborsClassifier(n_neighbors=2)\n",
        "k_neigh.fit(X_train_transformed, y_train)\n",
        "y_pred_knn = k_neigh.predict(X_test_transformed)\n",
        "y_predict_knn_probs=k_neigh.predict_proba(X_test_transformed)"
      ],
      "metadata": {
        "id": "CDzUwovmhLOF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4BhEHsKhhcP",
        "outputId": "004458ae-05f2-4fc5-b16e-1be237430602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[28  1]\n",
            " [ 2 18]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred_knn)\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for KNN', fontsize=16)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "O2AwBbzQqwv0",
        "outputId": "dce64f31-aaff-4f23-a209-1923eb2eab8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict_knn_probsfire=[classes[1] for classes in y_predict_knn_probs]\n",
        "print(y_predict_knn_probsfire)\n",
        "probspace=[0 for _ in range(len(y_test))]\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_test, probspace)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_test, y_predict_knn_probsfire)\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='Baseline')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='Knn')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "kGPoKsVjAaOc",
        "outputId": "efea0a3d-a6fe-4ec0-f50e-5656bf1fbd1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.5, 0.5, 0.5, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score is {}\".format(accuracy_score(y_test, y_pred_knn)))\n",
        "print(\"MCC score is {}\".format(matthews_corrcoef(y_test, y_pred_knn)))\n",
        "print(\"F1 score is {}\".format(f1_score(y_test, y_pred_knn)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLfcxMqlnPxs",
        "outputId": "b82afb7f-e333-4aa1-cf18-e47086d0f5db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score is 0.9387755102040817\n",
            "MCC score is 0.873076487447726\n",
            "F1 score is 0.9230769230769231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gaussian Naive Bayse"
      ],
      "metadata": {
        "id": "Kk9vJ_LCQxh-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "X_train_gnb, X_test_gnb, y_train_gnb, y_test_gnb = train_test_split(dfattr, dfclass, test_size=0.2, random_state=42)\n",
        "# 머신 러닝 모델 선택 - Naive Bayes\n",
        "gnb = GaussianNB() # Gaussian Naive Bayes 모델 선택 - 연속형 자료\n",
        "gnb.fit(X_train_gnb, y_train_gnb) # 모델 학습\n",
        "y_pred_gnb = gnb.predict(X_test_gnb) # 예측\n",
        "y_predict_gnb_probs=gnb.predict_proba(X_test_gnb)"
      ],
      "metadata": {
        "id": "Yv_vvX0nFa2O"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test_gnb, y_pred_gnb))\n",
        "print(len(y_test_gnb),len(y_pred_gnb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOutVJQkQNsi",
        "outputId": "761750d5-e59a-43f8-8dc4-da4ceee050eb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25  2]\n",
            " [ 2 20]]\n",
            "49 49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_test_gnb, y_pred=y_pred_gnb)\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for GNB', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "1CGDjEpFq6JQ",
        "outputId": "74fab408-6251-46ee-da85-3fbea910bb4c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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ZskTx8fF55m/evFlbtmyRm5ubw8NrlVR4eLj69u2r0NBQtW/fvkjD9eW8JyIiIvLMM8Zo9uzZ+S6Xc/gyv3E4S4sxRg888IAOHz6swYMHa/r06Xr88cd19uxZDRgwQBcuXCi1vlz18w0ICNDo0aMlXRy/tCR7cwV56aWX5Ovrq1mzZum3335zeD0JCQn2Q+wFDR5QERCK5dyUKVPUoEEDxcXFqVOnTvr555/ztMnIyND06dMVFRWVK3CeeOIJSdIrr7yirVu32qcbY/Tqq69qy5YtCgoKKrX7oK6kR48eki7+kl96Dm7//v2KiYnJ9xDf/PnztWrVqjyHVi9cuGA/BJRfIFyuU6dOateunVJTU/Xwww/nGhj51KlT9tE+7r33XoWFhRV/40rJN998o1OnTumXX34pUvucC33mzZtnv6hGkrKysjR27FjFxsbmu1zVqlXl5eWl48ePX/GPLUdNmDBBS5YsUdOmTfX+++/bp910001au3atnnrqqVLry5U/3/Hjx6tDhw46ffq0brrpJv373//O97184sQJ+2Hk4qhdu7ZGjx6t7OxsTZ482aEaU1NT9cgjj8gYIy8vL/Xq1cuh9VwLuNCmnAsODtbq1as1YMAArVy5UjfffLPq1aun5s2by8/PT/Hx8Vq3bp2Sk5MVEBCgWrVq2Zd9+OGHFRsbq08//VRt2rRRly5d7Dfv7969W76+vpo9e7b9gh5ne+655zRv3jwtWrRIjRo1Utu2bXXy5EmtX79eHTt2VIcOHfJ8iP/000969913VaVKFUVFRalatWpKSkrSmjVrdOLECdWuXbvIH66zZ89W9+7d9e9//1v16tVT586d7Td3JyYmqlWrVnrvvfecselOc8cdd6h169bauHGjGjVqpC5duqhSpUpau3atjh49qqefflpvvPFGnuU8PT3Vp08fzZs3Ty1btlSnTp3k5+cnSfr4449LXNeqVas0duxY+fn56auvvlKlSpUkSR4eHvryyy8VFRWlSZMmqWvXrurbt2+J+5Nc9/P18vLS0qVL9Ze//EVffvml+vXrp6pVq6p169YKDQ3VhQsXFBcXZ782oF69esU+OvPss8/qo48+KtJ5y9dff91+FMgYoxMnTmjTpk06ceKE3NzcNGXKlCL9IXnNcsV9IHCOxYsXm8GDB5sGDRqYypUrG09PT1OjRg1z6623mkmTJpnTp0/nu9zs2bNN165dTVBQkPH09DRhYWFmyJAhZteuXfm2v3SYt/zkDBM2Y8aMXNOvdJ+iMcbs2LHD3HnnnSY4ONh4e3ub6667zrz66qsmIyMj3/sUN2/ebJ555hnTqVMnU7t2bePl5WWqVq1qWrdubV577TVz6tSpPH2oCMO8NWnSxPj4+Bg/Pz8TFRVlXn/99SsOA1aQwvoryOX3KV5JYfcpJiUlmeeee85cd911xsfHx1SrVs3069fPbNiwodD6T58+bR5++GETHh5uPD09Cx3m7Up1XXqf4okTJ0ytWrXyfY/kWLBggbHZbCY4OLjA99nlivL+csbPtzjWr19vRo4caW644QYTFBRk3N3dTWBgoGnWrJn9/sTLB6EwJv/7FC+Xc89uQfXmzLv8y8fHxzRo0MAMHTrUbNq0qVS2szyzGVOCy84AALiGcE4RAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRTjd16lTVrVtXPj4+ateundatW+fqkoAysWrVKt1xxx2qVauWbDabvv32W1eXhCsgFOFUc+bM0ZgxYzRu3Dht2rRJLVq0UHR0tEMPRgbKm5SUFLVo0UJTp051dSkoIoZ5g1O1a9dObdu2tQ+0nJ2drbCwMI0ePVrPPPOMi6sDyo7NZtP8+fPVr18/V5eCQrCnCKfJyMjQxo0b7Y+Eki4+ob5Hjx5FfvQRAJQlQhFOc+rUKWVlZal69eq5plevXl3Hjx93UVUAUDBCEQAAC6EIp6lSpYrc3d0VHx+fa3p8fLxq1KjhoqoAoGCEIpzGy8tLrVu31vLly+3TsrOztXz5ct10000urAwA8ufh6gJwbRszZoxiYmLUpk0b3XjjjZo0aZJSUlI0dOhQV5cGOF1ycrL27t1r/z4uLk5btmxRSEiIwsPDXVgZCsItGXC69957T2+99ZaOHz+uli1bavLkyWrXrp2rywKcbuXKlerWrVue6TExMfrkk0/KviBcEaEIAICFc4oAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEU4XTp6ekaP3680tPTXV0K4BL8DpQf3LwPp0tMTFRgYKASEhIUEBDg6nKAMsfvQPnBniIAABZCEQAAS4V4SkZ2draOHj0qf39/2Ww2V5dT4SQmJub6F6ho+B1wLWOMkpKSVKtWLbm5Fb4vWCHOKR4+fFhhYWGuLgMA4EKHDh1SnTp1Cm1TIfYU/f39JUkz5/0gP79KLq4GKHu33HS9q0sAXCYxMVF1I8LsWVCYChGKOYdM/fwqya9SZRdXA5Q9rngEVKTTZ1xoAwCAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMDi4eoCUD6diD+m2FXLtHXjWsXt+11nz56Wp4enatSqo9btOqnv3YMUElo1z3Lxx45o2L29rrj+SR98oYaNr3dG6YDTHTx4UPPnf6Mfly/Xtm1bFR8fLy8vL9WvX1/RPXvpkUceVc2aNV1dJvJBKKLYTp44rmEDesoYY5/mV6my0tJSFbfvd8Xt+11LFs7Tcy9PVItWNxa4nqCQ0ALnuXvw1kT5dOjQIUXWr5vr9yMgIEApKSnatm2btm3bpo8/+lBzv/pa3bp1c2GlyA+fPCi27KwsSVLbmzqrR88+atG6vfz9A3ThwgVt3bhG7096TfHHjujVFx7TB58uUEholXzX8/n8FWVZNlAmsqzfj969/6SYmCHqfsstCg4OVkZGhpYvX65HRo9UXFyc7rqzn3bs3K0aNWq4uGJcinOKKLbK/gGa/PFcjX/9PXXqepv8/QMkSZ6enmrT/ma99Mb78vLy1vmUZC1Z+JWLqwXKVnBwsDZs3KwFC7/TXXffreDgYEmSl5eXevXqpYXfLZKPj48SExP14YcfuLhaXI5QRLFVquyv+g2uK3B+WEQ9Xde0uSRpz+4dZVUWcFUIDAxUixYtCpzfuHFjtWvfXpK0aePGsioLRUQowikCAgMlSdnZ2S6uBLj6hFrn03MOteLqwTlFlLqszEzt+HWLJCmiXoMC2z0+4n4d2L9PWZmZCg4JVZMbotS7zz26vnmrMqoUKHuZmZmKjV0tSbq+WTMXV4PLlas9xalTp6pu3bry8fFRu3bttG7dOleXhHx89+2XOnvmlNzc3NSjZ58C2+3asU1ubhffgvHHj2rlD//RU6OH6MMpb+S6cg+4lrz//lQdP35cbm5uGjw4xtXl4DLlZk9xzpw5GjNmjP7v//5P7dq106RJkxQdHa3du3erWrVqri4Plrh9v2vmh5MlSbf3v1fhdSNzzff08taf+g1Q5+49FdmwiXz9/GSM0b49O/X5jH9pXexP+ve8zxUYFKIBD/zFFZsAOM22bdv0/HPPSpJGjhylpk2burgiXM5mysmf5O3atVPbtm313nvvSbp4riosLEyjR4/WM888U+iyiYmJCgwM1FeLYuVXqXJZlFshnTl9Uk+MHKz4Y0fU4LqmemvKTHl5exdrHRPGPaGfV34vH19fzfzqB1W2rmxFyUR3usHVJVR4x44dU+ebOyouLk6tW7fWT6t+lo+Pj6vLqhASExMVEhyohIQEBQQU/plSLg6fZmRkaOPGjerRo4d9mpubm3r06KFffvklT/v09HQlJibm+oJzJSUm6IXHH1b8sSOqVSdC41+fWuxAlKShf31MkpSWmqqtm9aWcpWAa5w5c0a9et6muLg4NWzYUAsW/odAvEqVi1A8deqUsrKyVL169VzTq1evruPHj+dpP2HCBAUGBtq/wsLCyqrUCiklOUkvPvFXHYjbq6rVa+ofEz9UcCGj1RSmRs06Cgy6eF/X8aOHS7NMwCUSEhLUu1e0tm/frvDwcC39flmezzJcPcpFKBbXs88+q4SEBPvXoUOHXF3SNSst9bzGPT1Se3b/puCQKvrHxA9VrTpjOgKSlJKSottv760NGzaoRo0aWvr9MoWHh7u6LBSiXFxoU6VKFbm7uys+Pj7X9Pj4+HyHSPL29pa3A4fuUDzp6Wl66dnR2rl9iwICg/SPiR+qdp2IEq3z+LHDSjh3VpJUvWbt0igTcInU1FT17XuHfomNVWhoqJZ+v0wNGzZ0dVm4gnKxp+jl5aXWrVtr+fLl9mnZ2dlavny5brrpJhdWVnFduHBB/3jh79q2eb0qVfbXK2//X6H3JOa40nVdOVeuenv7qEWrdqVSK1DWMjIydPddd2rlihUKCgrS4iXf6/rreepLeVAu9hQlacyYMYqJiVGbNm104403atKkSUpJSdHQoUNdXVqFk5WVpbdeeVob162Wr18lvfzm+2rQqGiXlj/z6DC1vrGjbuzQRWER9eXu7i5jjP7Ys0tfzPxAv/z8oyTp7oFD5R8Q6MzNAJwiKytL9w8aqKVLl8jf31/f/WexWrViQIryotyE4oABA3Ty5EmNHTtWx48fV8uWLbVkyRJOWLvAzu2btfqnZZIujl7zyguPFdi2atUamvThF/bvT8Qf08yPJmvmR5Pl4eEhv0qVlZ6WpvT0NHubO+4cqPti/uq0+gFnWr16tb755mtJF4+o3HVnvwLbhoWFac3a9WVUGYqi3ISiJI0aNUqjRo1ydRkVXnb2/w6BZmSkK+NMeoFtvbxyn9sd9tcx2rzhF/2+a7vOnjml5MQEeXh6qk54XTVpFqWed9ylxtZg4kB5dOl4v2lpaUpLSyuwLbdlXH3Kzc37JcHN+6jouHkfFdk1d/M+AABlgVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFgIRQAALIQiAAAWQhEAAAuhCACAhVAEAMBCKAIAYCEUAQCwEIoAAFicGopnz55VQkKCM7sAAKDUOByKR48e1axZs7RkyZI883777Te1adNGVapUUUhIiG6++Wb9/vvvJSoUAABnczgUp0+frqFDh2rlypW5pqempqp3797avHmzjDEyxmj16tXq0aOHEhMTS1ovAABO43AoLlu2TJI0YMCAXNNnzpypQ4cOKSQkRB999JE+++wz1alTR0eOHNHUqVNLVi0AAE7kcCju379fktS4ceNc07/55hvZbDa99tprGj58uAYOHKiPPvpIxhgtWLCgRMUCAOBMDofiqVOnFBAQIF9fX/u07OxsxcbGymaz6e6777ZPv/XWW+Xm5qbdu3eXrFoAAJzI4VDMyspSenp6rmm//vqrzp8/r+uvv17BwcH/68TNTcHBwUpJSXG8UgAAnMzhUKxZs6bS09MVFxdnn7Z06VJJUocOHfK0T05OVkhIiKPdAQDgdA6H4k033SRJeumll5Sdna2TJ0/qX//6l2w2m6Kjo3O1jYuLU3p6umrWrFmyagEAcCKHQ/HRRx+VJH366acKCgpSWFiYDhw4oHr16un222/P1faHH36QJLVq1aoEpQIA4FwOh+KNN96o6dOnq3LlykpOTlZGRoYaN26sb775Rh4eHrnazpo1S5LUrVu3klULAIAT2YwxpiQrSE1N1fbt2xUUFKTIyEi5ueXO2YyMDH355Zcyxqhv374KCgoqSXcOSUxMVGBgoL5aFCu/SpXLvH/A1aI73eDqEgCXSUxMVEhwoBISEhQQEFBoW49C5xaBr6+v2rZtW+B8Ly8vDR48uKTdAADgdDwlAwAAC6EIAIClSIdP69evXyqd2Ww27du3r1TWBQBAaStSKOaMc1pSNputVNYDAIAzFCkUZ8yY4ew6AABwuSKFYkxMjLPrAADA5bjQBgAAC6EIAICFUAQAwFLiUNy6daseeughNW3aVAEBAXJ3dy/w6/IxUQEAuJqUKKXee+89jRkzRllZWSrhEKoAALicw3uKa9eu1aOPPqqsrCz97W9/06JFiyRJISEhWrZsmT777DMNGTJEXl5eqlKlimbPnq0ff/yx1AoHAKC0ObynOHnyZBlj9Nhjj2nixIn26V5eXurevbskaeDAgXrkkUcUHR2tF198UZs2bSp5xQAAOInDe4qrV6+WzWazP2w4x+WHUVu2bKkpU6Zo3759euuttxztDgAAp3M4FOPj4+Xt7a2IiIj/rczNTWlpaXna9u/fX56envrmm28c7Q4AAKdz+PCpn59fnrFM/f39lZiYqPT0dHl7e9une3p6ys/PTwcOHHC8UgAAnMzhPcXatWsrMTFRmZmZ9mmRkZGSpPXr1+dqe/ToUSUkJHCFKgDgquZwKDZp0kRZWVn69ddf7dO6du0qY4xefvll+2HUjIwMPfLII5KkG264oYTlAgDgPA6H4m233SZjjBYuXGifNnLkSHl7e2v58uWqU6eOOnbsqNq1a2v+/Pmy2WwaNWpUqRQNAIAzOHxO8a677tLhw4dVq1Yt+7R69epp9uzZGjp0qM6cOaNffvlF0sULcJ588kkNGjSo5BUDAOAkNuOEE31nzpzRokWLdOjQIQUGBuq2225TgwYNSrubIktMTFRgYKC+WhQrv0qVXVYH4CrRnTh1gYorMTFRIcGBSkhIUEBAQKFtnTIYaUhIiO6//35nrBoAAKfhKRkAAFgIRQAALA4fPs0Z37Q4bDabli9f7miXAAA4lcOhuHLlyiK1yxn1xhiTZwQcAACuJg6H4rhx4wqdn5CQoLVr1+qXX35RaGioRowYIXd3d0e7AwDA6ZwWijl+/PFH3XnnndqxY4fmzZvnaHcAADid0y+06d69u959913Nnz9fH3/8sbO7AwDAYWVy9emAAQPk7u5OKAIArmpOuXn/cj4+PqpUqZJ27txZFt0V6Jabrr/iaAbAtejH3465ugTAZVKSk4rctkz2FI8cOcKjowAAVz2nh2Jqaqr+9re/SeLRUQCAq5vDh09ffvnlQuenpaXp0KFDWrp0qU6fPi2bzaaRI0c62h0AAE7ncCiOHz++SDfjG2Pk5uamF154QQMHDnS0OwAAnM7hUOzcuXOhoejh4aHg4GC1aNFCf/7zn9WwYUNHuwIAoEw4fZg3AADKC56SAQCAxeFQfPnllzVx4sQit588efIVL84BAMCVbMbBmwfd3NxUo0YNHT16tEjt69Wrp4MHDyorK8uR7kokMTFRgYGBOnM2gZv3USFx8z4qspTkJPXvcJ0SEq6cARw+BQDAUmaheObMGfn4+JRVdwAAFFuZhOJXX32lpKQkhYeHl0V3AAA4pMi3ZLz77rt69913c007efKk6tevX+AyxhidO3dOiYmJstls+tOf/uR4pQAAOFmRQ/HcuXPav39/rmlZWVl5phXklltu0dixY4tTGwAAZarIodivXz/VrVtX0sU9wGHDhikwMFCTJk0qcBk3NzcFBASoWbNmioyMLGmtAAA4VZndkuFK3JKBio5bMlCRFeeWDIeHecvOznZ0UQAArkrcpwgAgMXhUFyzZo1atWpVpGckPvjgg2rVqpU2bNjgaHcAADidw6E4e/Zsbd26VTfffPMV27Zv315btmzR7NmzHe0OAACnczgUf/rpJ0nSbbfddsW2/fv3lyStWLHC0e4AAHA6h0Px8OHDCgwMVEhIyBXbhoaGKjAwUEeOHHG0OwAAnM7hUExNTS3WFajGGCUlJTnaHQAATudwKFarVk1JSUlFuk/xyJEjSkxMVJUqVRztDgAAp3M4FNu3by9Jmjp16hXb5rRp166do90BAOB0Dofi8OHDZYzRm2++qQ8//LDAdh988IHefPNN2Ww2DR8+3NHuAABwOoeHeZOkP//5z5o3b55sNpuaNWum22+/XREREZKkAwcOaOHChfrtt99kjNFdd92lr776qtQKLw6GeUNFxzBvqMjKZJg3SZo5c6ZsNpu++uor/frrr9q+fXuu+Tl5e++992ratGkl6QoAAKcr0TBvvr6+mjNnjpYtW6aBAwcqIiJC3t7e8vHxUd26dTVo0CD9+OOPmj17tnx9fUurZgAAnKJEe4o5unfvru7duxc4Pzs7W//5z380bdo0ffvtt6XRJQAApa5UQrEge/bs0bRp0zRr1izFx8c7sysAAEqs1EPx/Pnzmjt3rqZNm6bY2FhJ/zu32KRJk9LuDgCAUlNqobhmzRpNmzZNc+fOVXJysqSLYdi4cWPdc889uueee9SsWbPS6g4AgFJXolA8efKkZs2apenTp2vXrl2S/rdXaLPZtH79erVu3brkVQIAUAaKHYrGGC1atEjTp0/Xd999p8zMTBlj5Ovrq379+ikmJkY9e/aUxOFSAED5UuRQ3Ldvn6ZPn66ZM2fq2LFjMsbIZrOpU6dOGjx4sP785z/L39/fmbUCAOBURQ7Fhg0bymazyRijevXqafDgwRo8eLDq1avnzPoAACgzxT58+sgjj+jNN9+Ul5eXM+oBAMBlijyijbe3t4wxmjJlimrVqqWRI0dqzZo1zqwNAIAyVeRQPHbsmCZPnqzmzZvrzJkz+te//qWOHTvquuuu02uvvaaDBw86s04AAJyuyKEYFBSkUaNGafPmzdq4caNGjBihwMBA7dmzRy+++KLq16+v7t27a8aMGc6sFwAAp3FoQPCoqChNnTpVx44d06effqouXbrIGKOVK1fqwQcftLf7/vvvlZmZWWrFAgDgTCV6Soa3t7f9SRh79+7V888/r9q1a0uS/RmK1apV09ChQ7Vo0SICEgBwVSvRQ4bzY4zR0qVL9fHHH2vhwoW6cOGCbDabpIuHYE+fPl2a3RUJDxlGRcdDhlGRFechwyXaU8yPzWZTz549NW/ePB05ckRvv/22mjRpImOMzp07V9rdAQBQako9FC9VpUoVjRkzRtu3b1dsbKyGDx/uzO4AACgRpz5P8VLt27dX+/bty6o7AACKzal7igAAlCeEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGDxcHUBuDYdPHhQ8+d/ox+XL9e2bVsVHx8vLy8v1a9fX9E9e+mRRx5VzZo1XV0m4JATxw7r52WLtXntf/XH7zt07vQpeXh6qmadCLXt1E39Bj2o0KrVC1z+woUMzf/0I/24eL6OHtwvdw8PhddrqNv6DVDvuwbJZrOV4dbgUjZjjHF1Ec6WmJiowMBAnTmboICAAFeXc807dOiQ6teL0KVvrYCAAKWkpCgrK0uSFBwcrLlffa1u3bq5qswK5cffjrm6hGvGieNH9ED0jbne336V/ZWWel7Z1vvbPyBIL078SC1v7Jhn+ZTkJD314D3as2ObJMnbx1fZWVm6cCFDktSucw+NnzRd7h7ss5SWlOQk9e9wnRISrpwBHD5FqcsJvt69/6Q5c77SyVNndOZsgpKSz2vhd4tUr149nT17Vnfd2U/Hjx93cbVA8WRnZUu6GF4vvP2hvv55h76N3a2F6/bp1amfqUbtcCUlntP4x4bpzKkTeZaf9NIT2rNjm/wDg/XylJlasHavFqzbpydemSQvbx+tXbVMs95/u6w3Cxb2FFHqEhIStH//frVo0SLf+bt27VKb1lFKS0vT2HHjNXbsuDKusOJhT7H0pCQl6vjRQ4q87vp85x+M26O//TlaGelpemDE43pgxOP2eXt3/qq/DYiWJL307gzd1C0617LzP/tY/3pzrLx9fDRr8ToFh1Zx3oZUIOwpwqUCAwMLDERJaty4sdq1by9J2rRxY1mVBZSKSv4BBQaiJIXXa6gmzVtJkv0QaY4fF82XJNWpG5knECWp992DVMk/QOlpaVq9fFEpVo2iIhThEqEhoZL+d6gVuJb4BwZLkrKzs3NN37o+VpLUukOXfJfz9vFVs6gbJUlb1q12YoUoSLkIxVWrVumOO+5QrVq1ZLPZ9O2337q6JJRAZmamYmMv/sJf36yZi6sBSldWZqZ2bFkvSarb4Dr7dGOMDsXtlSRFRF6X77IX5zWSJB3Y97sTq0RBykUopqSkqEWLFpo6daqrS0EpeP/9qTp+/Ljc3Nw0eHCMq8sBStWCLz/RmVMn5Obmplv7/Nk+/XxyktJSz0tSobdrhFatIUn5XqQD5ysX1/z26tVLvXr1cnUZKAXbtm3T8889K0kaOXKUmjZt6uKKgNLzx+87NH3ya5KkPvcOte/1SbIHoiR5+/gUuA5vH9+L7c+nOKlKFKZchGJxpaenKz093f59YmKiC6tBjmPHjumuO/spNTVVrVu31oTX33B1SUCpOX0yXuMfHab0tDQ1bNpcD/79eVeXBAeUi8OnxTVhwgQFBgbav8LCwlxdUoV35swZ9ep5m+Li4tSwYUMtWPgf+RTy1zJQniQmnNWzD9+n40cOqnZEfb069VN5eed+f/v4+tn/n56WVuC60tNSL7b3q+ScYlGoazIUn332WSUkJNi/Dh065OqSKrSEhAT17hWt7du3Kzw8XEu/X6bq1Qs+pwKUJylJiXrurwO1f+8uVatZW298+KWCQ6vmaedX2d8ejKdPxhe4vtMnLw5oEVKlmnMKRqGuycOn3t7e8vb2dnUZ0MWLpG6/vbc2bNigGjVqaOn3yxQeHu7qsoBSkXr+vJ4feb9+/22rQqpU0xsfzlG1mnXybWuz2RRev6F+/22rDuzbXeA6c646vfR8JMrONbmniKtDamqq+va9Q7/Exio0NFRLv1+mhg0burosoFSkp6Vq7OgY7diyQQFBwXr9wzmqHVG/0GVatO0gSdr0y6p852ekp2n75nWSpJbtOpVuwSiSchGKycnJ2rJli7Zs2SJJiouL05YtW3Tw4EHXFoYCZWRk6O677tTKFSsUFBSkxUu+1/XXFzwKCFCeXLiQoZf+/qC2rl+tyv6BmvB/X+S6J7Eg3Xr1kyQditurNT/9kGf+oq8/V0pSorx9fNSxO1fcu0K5CMUNGzYoKipKUVFRkqQxY8YoKipKY8eOdXFlyE9WVpbuHzRQS5cukb+/v777z2K1atXK1WUBpSIrK0sTnh6pDatXyK9SZf3j/c/UsGnzIi3boMkN6hJ9hyTp7Rcf07r/Lrev84cFX2napH9Iku68/yHGPXURBgRHqVu1apW6d7s4jJWPj48CAwMLbBsWFqY1a9eXVWkVFgOCl55tG9boiWF3SpK8vH1UqbJ/gW2r1qil975YnGtavo+Oys7WhYyLt5Hx6KjSV5wBwXnVUeouHe8xLS1NaYVcfs5tGShvjPnf+zsjPU0Z6QW/v73yueCvUmV/Tfp0gb759EOtWPytjh7cL08vLzVo3IyHDF8F2FMEKgD2FFGR8egoAAAcQCgCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAAALoQgAgIVQBADAQigCAGAhFAEAsBCKAABYCEUAACyEIgAAFkIRAACLh6sLKAvGGElSYmKiiysBXCMlOcnVJQAucz4lWdL/sqAwFSIUk5IufiDUjQhzcSUAAFdJSkpSYGBgoW1spijRWc5lZ2fr6NGj8vf3l81mc3U5FU5iYqLCwsJ06NAhBQQEuLocoMzxO+BaxhglJSWpVq1acnMr/KxhhdhTdHNzU506dVxdRoUXEBDABwIqNH4HXOdKe4g5uNAGAAALoQgAgIVQhNN5e3tr3Lhx8vb2dnUpgEvwO1B+VIgLbQAAKAr2FAEAsBCKAABYCEUAACyEInAV69q1q2w2m8aPH59nXt26dWWz2fTJJ5+UaU2ffPKJbDab6tatW6b9AmWBUMQ1bfz48bLZbHm+fHx8VKdOHfXp00dz584t0piI17r9+/dr/Pjx+QYwUFFUiBFtAEmqXr26/f8JCQk6cuSIjhw5ooULF+qTTz7R/Pnzy9Ul85GRkfLx8SnySB1Xsn//fr300kuSVGgwBgYG6rrrrlPt2rVLpV/gasKeIiqM48eP279SUlK0fft23XrrrZKkxYsX64UXXnBxhcWzfPly7dq1S/379y/Tfvv3769du3Zp+fLlZdovUBYIRVRIbm5uuv7667VgwQI1aNBAkvTBBx8oMzPTxZUBcCVCERWaj4+P7rnnHkkXHyuza9cu7d+/337ucf/+/dq3b58eeugh1atXT97e3nkuMMnOztbnn3+u3r17q3r16vLy8lLVqlV122236Ysvvij0fGVWVpamTJmiVq1aqVKlSgoJCVHXrl01b968K9ZelAtt1q5dq6FDh6pBgwby8/NTQECAmjZtqmHDhmnp0qW51tWtWzf795efgx0yZIh9XlEutNm3b59GjBihhg0bytfXVwEBAWrVqpVefvnlAp9runLlSnt/krR3714NGzZMYWFh8vb2Vp06dfSXv/xFR44cKbDfXbt26aGHHlKjRo3k5+cnHx8fhYWFqX379nruuee0a9euApcFJEkGuIaNGzfOSDKFvdWnTp1qb7N69WoTFxdn//7zzz83lStXNpKMn5+fqVSpkomIiLAve/r0adO5c2d7e0kmMDAw1/d9+vQx6enpefpNS0sz0dHR9nZubm4mKCjI2Gw2I8k8/fTTpkuXLkaSGTduXJ7lIyIijCQzY8aMPPMyMzPNI488kquOSpUqmeDgYPv6AwMD7e3btGljgoOD7W2rV6+e6+uRRx6xt50xY4aRlOt1uNScOXOMt7e3fV3+/v65vg8LCzM7duzIs9yKFSvsbX788Uf76+7v7288PDzs82rVqmUOHz6cZ/nvv/8+Vz+enp4mKCgo12uQ3+sIXIpQxDWtKKH45JNP2tvs3LkzVyhWrlzZtGvXzqxfv97efvfu3caYi8GTE1otW7Y0CxcuNCkpKcYYY5KTk83MmTNNtWrVjCTz2GOP5en373//u5FkbDabefXVV01CQoIxxpj4+HgzYsSIXAFb3FB86qmn7NswbNgwe83GGHPu3Dnz7bffmgEDBuRa5tJQKkxhobhx40bj6elpJJmOHTuabdu2GWOMycrKMgsWLDA1a9Y0kkxkZKRJSkoqsP/g4GDTp08fs3PnTmOMMenp6WbOnDnG39/fSDIPPPBAnr4jIyONJHPbbbeZX3/91T49NTXVbN++3bz00kv5vlbApQhFXNOuFIoJCQmmVq1aRpIJCQkxWVlZuUIxIiIiz4d3jlmzZhlJpnHjxubcuXP5ttmwYYOx2WzGy8vLxMfH26cfOXLEvvfz4osv5rvsfffdV+geTkGhuHv3buPm5mYkmaeeeirfdeenNEKxZ8+eRpJp0KCB/Q+ES23atMm+3W+99VaB/Xfr1s1kZWXlWX7y5MlGkvH19TUXLlywT4+Pj7cve/To0SJuMZAX5xRRIZ07d07Lly9X9+7ddfToUUnSo48+muep3KNGjVLlypXzXce0adMkSSNGjCjwtojWrVvr+uuvV0ZGhlasWGGfPm/ePGVmZsrX11dPPPFEvss6er/gzJkzlZ2drdDQUPstFmXh3Llz9vOUTz75pPz8/PK0iYqK0p133ilJ+uKLLwpc13PPPZfvE9L79u0rSUpNTdWePXvs0/39/e3tjx075vhGoMIjFFFhXHrhSHBwsHr06KGNGzdKku6//349//zzeZbp2LFjvuvKysrSmjVrJF0Mrxo1ahT4tXv3bknSgQMH7Mtv2LBBktSmTZsCn8TeqFEjh+4FjI2NlSTdeuut8vHxKfbyjtq0aZP9oqIePXoU2C7nNpht27bpwoUL+bZp165dvtNr1apl//+ZM2fs//f19dUtt9wiSerZs6fGjh2rtWvXKiMjo3gbgQqPm/dRYVx68763t7eqVKmiqKgoDRo0KNeVl5eqVq1avtPPnDmj9PR0SdLZs2eL1P/58+ft/z9x4oQkXTH06tSpU+jVlvk5fvy4JCkiIqJYy5VUzjZJhW9XnTp1JEmZmZk6c+ZMrp9LDn9//3yX9fD430fW5YH68ccfq0+fPtq6dateeeUVvfLKK/Ly8lLbtm3Vt29fDR8+XCEhIcXaJlQ8hCIqjJywKA53d/d8p2dlZdn/v3jxYvXs2dPhukpbzi0NFU14eLg2bdqkH374QYsWLdLq1au1detWrV69WqtXr9aECRM0b948de/e3dWl4irG4VPAAaGhofa9lksPixZVzh7olfYCi7uXKEk1atRwuK6SuHSv+vDhwwW2y5nn4eFR6ntubm5uio6O1rvvvqsNGzbozJkz+vzzzxUeHq6zZ89q4MCBHFJFoQhFwAGenp668cYbJUkLFy4s9vJt2rSRdPHcYnJycr5t9uzZU2i4FKRDhw6SpB9++EFpaWlFXu7SC1uMAwOkt2rVyr6OwoaAW7ZsmSSpRYsW8vT0LHY/xeHv76+BAwfaL4qKj4/Xr7/+6tQ+Ub4RioCDHnroIUnSokWLtGjRokLbXnpRiCTdddddcnd3V2pqqt5+++18l3n55ZcdqmvIkCFyd3fX6dOnNW7cuCIvd+kFP+fOnSt2v0FBQYqOjpYkvfXWW7nOoebYunWrvv76a0nSfffdV+w+CnKlvT9fX1/7//O7qhXIwbsDcND999+vHj16yBij/v3769VXX7Xf3iFJKSkpWrFihUaOHKn69evnWrZ27doaOXKkJOmVV17RhAkTlJSUJEk6efKkRo0apc8++8yhJ2A0aNBATz75pCTpzTff1IMPPpjr9oXExETNmTMnz0DijRo1kpeXl6SLF604srf46quvytPTU3v37lV0dLR9ryw7O1uLFi1S7969lZmZqcjISD388MPFXn9BYmNj1bx5c/3zn//Uzp07lZ2dLeniHm9sbKxGjBgh6eJFPs2bNy+1fnENculdkoCTFWVEm8tdevN+XFxcoW0TEhLM7bffnmsosYCAgFzDtUkyHh4eeZZNTU01PXr0sLdxd3fPNQxbSYd5GzlyZK66KleuXOAwbzmGDx9ub+/n52fCw8NNRESEefzxx+1trjTM25dffmm8vLxyvR4+Pj7FGuatMDltVqxYke+ysoZ4Cw0NzTU8XEBAgFm1alWh6wbYUwRKICAgQAsXLtSiRYs0YMAAhYeHKz09XefPn1ft2rV12223acKECfZ7FS/l4+OjxYsX691331XLli3l5eUlY4xuvvlmzZ07V6+//rrDdbm7u+u9997Tzz//rEGDBik8PFwXLlyQMUZNmzbV8OHD7YcxLzV16lSNHz9eN9xwgyTp4MGDOnDggE6dOlXkvgcMGKDffvtNDz/8sCIjI5Weni4PDw+1bNlSL730krZv364mTZo4vG35adu2rebOnasRI0aodevWqlKlihITE+Xj46OWLVvqqaee0s6dO3XzzTeXar+49tiM4ZHjAABInFMEAMCOUAQAwEIoAgBgIRQBALAQigAAWAhFAAAshCIAABZCEQAAC6EIAICFUAQAwEIoAgBgIRQBALAQigAAWAhFAAAs/w+ZZ75a8JvAzQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_gnb, y_pred_gnb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmyKqsOOQYBa",
        "outputId": "a5126a7b-a678-400e-ba5f-ecbab985a5b2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.93      0.93        27\n",
            "           1       0.91      0.91      0.91        22\n",
            "\n",
            "    accuracy                           0.92        49\n",
            "   macro avg       0.92      0.92      0.92        49\n",
            "weighted avg       0.92      0.92      0.92        49\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score is {}\".format(accuracy_score(y_test_gnb, y_pred_gnb)))\n",
        "print(\"MCC score is {}\".format(matthews_corrcoef(y_test_gnb, y_pred_gnb)))\n",
        "print(\"F1 score is {}\".format(f1_score(y_test_gnb, y_pred_gnb)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WU8p2vSDnGZU",
        "outputId": "1a99df27-33c9-4346-8e19-864fb9ce09ee"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score is 0.9183673469387755\n",
            "MCC score is 0.835016835016835\n",
            "F1 score is 0.9090909090909091\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict_gnb_probsfire=[classes[1] for classes in y_predict_gnb_probs]\n",
        "print(len(y_predict_gnb_probs))\n",
        "probspace=[0 for _ in range(len(y_test))]\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_test, probspace)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_test, y_predict_gnb_probsfire)\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='Baseline')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='gnb')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "nf7DjrMaDJ_u",
        "outputId": "82e96554-de23-4db5-9682-bf12cef53bc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tree map"
      ],
      "metadata": {
        "id": "wsooVaiqBTbo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "X, y=dfattr, list_class\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "clf=clf.fit(X,y)"
      ],
      "metadata": {
        "id": "zK_4J5aFA81x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "j6dLUWwDBL6R",
        "outputId": "8ecdcdb4-d0fe-4654-a346-b0d8ce83a46f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.375, 0.9166666666666666, 'x[4] <= 80.15\\ngini = 0.492\\nsamples = 243\\nvalue = [137, 106]'),\n",
              " Text(0.25, 0.75, 'gini = 0.0\\nsamples = 102\\nvalue = [0, 102]'),\n",
              " Text(0.5, 0.75, 'x[7] <= 2.65\\ngini = 0.055\\nsamples = 141\\nvalue = [137, 4]'),\n",
              " Text(0.25, 0.5833333333333334, 'x[1] <= 77.0\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.125, 0.4166666666666667, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
              " Text(0.375, 0.4166666666666667, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.75, 0.5833333333333334, 'x[7] <= 3.05\\ngini = 0.014\\nsamples = 137\\nvalue = [136, 1]'),\n",
              " Text(0.625, 0.4166666666666667, 'x[7] <= 2.9\\ngini = 0.245\\nsamples = 7\\nvalue = [6, 1]'),\n",
              " Text(0.5, 0.25, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
              " Text(0.75, 0.25, 'x[6] <= 31.65\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.625, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.875, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.875, 0.4166666666666667, 'gini = 0.0\\nsamples = 130\\nvalue = [130, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "regr_1 = DecisionTreeRegressor()\n",
        "regr_1.fit(X_train_transformed, y_train)\n",
        "y_pred_tree1 = regr_1.predict(X_test_transformed)\n"
      ],
      "metadata": {
        "id": "adUL7eAxVDsi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred_tree1)\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Tree Regression', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "glHXkhUbrFpT",
        "outputId": "4ccbc87f-8aeb-44e7-e554-3fc05fac7f03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred_tree1))"
      ],
      "metadata": {
        "id": "l6u0yWBNUlwS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c55c53fd-139a-472a-a0cf-ca940cf5fba9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97        29\n",
            "           1       0.95      0.95      0.95        20\n",
            "\n",
            "    accuracy                           0.96        49\n",
            "   macro avg       0.96      0.96      0.96        49\n",
            "weighted avg       0.96      0.96      0.96        49\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score is {}\".format(accuracy_score(y_test, y_pred_tree1)))\n",
        "print(\"MCC score is {}\".format(matthews_corrcoef(y_test, y_pred_tree1)))\n",
        "print(\"F1 score is {}\".format(f1_score(y_test, y_pred_tree1)))"
      ],
      "metadata": {
        "id": "j2FuAHB8nAXG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be2282e8-0f29-4eec-de37-a9a85d74387b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score is 0.9591836734693877\n",
            "MCC score is 0.9155172413793103\n",
            "F1 score is 0.9500000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing all the libraries needed for external validation metrics\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score"
      ],
      "metadata": {
        "id": "9-HXYKq-20lb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "line_fitter = LinearRegression()\n",
        "line_fitter.fit(dfattr, y)"
      ],
      "metadata": {
        "id": "YGWc8dtzGzOm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "f7d853d4-6453-4d2f-9a20-b3b4d5d23c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_pca_normal[:, 0], list_class, 'o')\n",
        "plt.plot(dfattr,line_fitter.predict(dfattr))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "sRU5CQO8GnA4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "e3134b8a-d191-468b-ccf9-b645b2389a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### logistic regression"
      ],
      "metadata": {
        "id": "--ZGPEnEH0yN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X_pca,y,random_state=32,test_size=.2)"
      ],
      "metadata": {
        "id": "EOCY4YAYH0bl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Feature_Scaling(X_train, X_test):\n",
        "    scaler = StandardScaler()\n",
        "    X_train_after_Standardisation = scaler.fit_transform(X_train)\n",
        "    X_test_after_Standardisation = scaler.transform(X_test)\n",
        "    return X_train_after_Standardisation, X_test_after_Standardisation"
      ],
      "metadata": {
        "id": "kAx4DXwUHtZy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_after_Standardisation,X_test_after_Standardisation=Feature_Scaling(X_train, X_test)"
      ],
      "metadata": {
        "id": "dPENhsYKHxeX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logistic_regression=LogisticRegression()\n",
        "logistic_regression.fit(X_train_after_Standardisation,y_train)\n",
        "y_pred_logi_rgre = logistic_regression.predict(X_test_after_Standardisation)\n",
        "y_pred_logi_probs=logistic_regression.predict_proba(X_test_after_Standardisation)"
      ],
      "metadata": {
        "id": "amg-qXBsIAq-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Intercept is :',logistic_regression.intercept_)\n",
        "print('Coefficient is :',logistic_regression.coef_)\n",
        "\n",
        "print(\"Training Score:\",logistic_regression.score(X_train_after_Standardisation, y_train))\n",
        "print(\"Test Score:\",logistic_regression.score(X_test_after_Standardisation, y_pred_logi_rgre))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4L0DMwCIXaT",
        "outputId": "07b77fab-1882-4686-c9c0-5231e10c68bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept is : [-0.37065586]\n",
            "Coefficient is : [[-2.95480818  2.78358764 -1.31283205 -1.42931202]]\n",
            "Training Score: 0.9742268041237113\n",
            "Test Score: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict_logi_probsfire=[classes[1] for classes in y_pred_logi_probs]\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_test, probspace)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_test, y_predict_logi_probsfire)\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='Baseline')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='gnb')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "bq5WYtCCF0FC",
        "outputId": "4f71e2d6-4712-47bd-c9dc-ef47785fa999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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kE4oNcezYMcyfPx+rVq1CREQEdu/ejX379mHOnDlP3EalUsHBwaHIg4iIzJcQAptO38Krq07h1v1seDpa47v32mJoG16/pqqQ7FRwZ2dnKBQKJCUlFVmelJQENze3YreZMWMG3njjDbzzzjsAgKZNmyIrKwvvvvsupk2bBrm8UnU1IiIysrScfEz+/gIOXCo8AhDo64pFA5rxMFQVI1kbUCqVaNmyZZHJwTqdDkeOHEHbtm2L3SY7O/uxAqNQFN5ksordIouIiIrxv78SceBSIiwVMszo3RjrhrdksamCJL2IX3BwMEaMGIFWrVqhdevWWLp0KbKysjBy5EgAwPDhw+Hp6YnQ0FAAQJ8+fbBkyRK0aNECbdq0wY0bNzBjxgz06dNHX3KIiKjqer1lTVxNzEDf5h5o7uUodRySiKTlZtCgQbh37x5mzpyJxMRE+Pv74+DBg/pJxrGxsUVGaqZPnw6ZTIbp06cjLi4ONWrUQJ8+fTBv3jypfgQiIpLQw2wNFv0vCh93bwQHK0vIZIUjNlS1SXqdGykYcp68QXidGyKichV++wHGbjuHuIc56OfvgWWDW0gdiUzIkM9v3luKiIgqFZ1OYN2Jm1h4KAoFOoHa1W0wqoOP1LGoAmG5ISKiSiM1S4OPvovE0ah7AIDezdwR+mpT2FtZSpyMKhKWGyIiqhT+ik/D2xv/RGJ6LpQWcszq0wRDWntBJpNJHY0qGJYbIiKqFNzV1gAAnxq2WDk0AL7uvCgrFY/lhoiIKqyM3Hz9IScnWyU2vd0ano7WsFXx44uejJf0JSKiCulUdAo6Lz6OXeF39csauNqz2NAzsdwQEVGFotUJLD18Df/56g/cy8jD5tO3oNNVqauWUBmx/hIRUYWRnJ6LD3dE4lT0fQDAgJY1MbtfE8jlnDRMJcdyQ0REFcKJ6/cwfkckUjI1sFEqMLe/H14NqCl1LKqEWG6IiEhysfez8eaGs9DqBBq52WPF0ADUc7GTOhZVUiw3REQkuVrVbfDfjj54kJ2Pmb0bw8qSN0Om0mO5ISIiSRyNSkZdZzvUqm4DAJjQtSEvyEdGwbOliIioXOVrdQjdfwUjN5zFmG0R0BToAIDFhoyGIzdERFRu4h7mYMzWCETEPgQANPdyhABP8ybjYrkhIqJy8cvlJEzYeR5pOfmwt7LAZ681Q4+m7lLHIjPEckNERCalKdDh04NX8XVYDACgeU01vhgSoJ9rQ2RsLDdERGRSAgJnYlIBAG+1r4PJPRpBacEpn2Q6LDdERGQSQgjIZDKoLBRYOTQAVxPT0bWJm9SxqApguSEiIqPKK9Bi/r4rcLC2xEddGwIovI4ND0NReWG5ISIio7mVkoWgbRG4FJcOuQx4LaAmvJ1tpY5FVQzLDRERGcXPF+Ix+fuLyMwrQDUbSywe2JzFhiTBckNERGWSm6/FJz9fxtY/YgEAz3lXw/IhLeCutpY4GVVVLDdERFRqQggM++oPhN9+AJkM+OCluhgf2AAWCp4NRdJhuSEiolKTyWQY/JwXbqVk4fNB/nixQQ2pIxGx3BARkWFyNFrEPcxGPRd7AMCAVl7o2tgNahtLiZMRFeK4IRERldj1pAz0WxmGN74+gwdZGv1yFhuqSFhuiIioRHb+eQd9VoThWlImCnQCdx/kSB2JqFg8LEVERE+VlVeAGXsuYXdEHADghXrO+HyQP2rYqyRORlQ8lhsiInqiq4npGL0lAtH3siCXAcEvN8AHL9WDXC6TOhrRE7HcEBHRE605Fo3oe1lwdVBh+eAWaONTXepIRM/EckNERE/0SX8/WFkqMLFbQ1S342Eoqhw4oZiIiPQuxaVh/v4rEEIAABysLLHgtWYsNlSpcOSGiIgghMC3v9/GnJ+vQKPVoZ6LHQa28pI6FlGplKnc5ObmwsrKylhZiIhIAum5+Zj8/QXsv5gIAAj0dUHXxq4SpyIqPYMPS+l0OsyZMweenp6ws7PDzZs3AQAzZszA119/bfSARERkOufvPESv5Sew/2IiLBUyTO/li3XDW8HRRil1NKJSM7jczJ07Fxs3bsRnn30GpfLvP/x+fn746quvjBqOiIhM57uzd/D6mlO4k5qDmtWssfO/7fBOBx/IZDzNmyo3g8vNpk2bsHbtWgwbNgwKhUK/vHnz5rh69apRwxERkenUrm4DrU6gexM37BvbAf5ejlJHIjIKg+fcxMXFoV69eo8t1+l0yM/PN0ooIiIyjbScfKitC+8D1canOn4c3R5NPdUcrSGzYvDITePGjXHixInHlu/atQstWrQwSigiIjIunU5g7W/R6PDpr7iRnKlf3qymI4sNmR2DR25mzpyJESNGIC4uDjqdDrt370ZUVBQ2bdqEn3/+2RQZiYioDFKzNJiw8zx+vZoMAPjh3F1M7NZI4lREpmPwyE2/fv3w008/4fDhw7C1tcXMmTNx5coV/PTTT3j55ZdNkZGIiErp7K1U9Fp+Ar9eTYbSQo55r/hhQteGUsciMqlSXeemQ4cO+OWXX4ydhYiIjESnE1h9PBpLfrkGrU7Ax9kWK4YGoLGHg9TRiEzO4JEbHx8f3L9//7HlDx8+hI+Pj1FCERFR2ewKv4uFh6Kg1Qm80sITP415gcWGqgyDR25u3boFrVb72PK8vDzExcUZJRQREZXNqwGe+OlCPPo088CAVjU5aZiqlBKXm7179+q/PnToENRqtf65VqvFkSNH4O3tbdRwRERUMlqdwI6zd/B6y5pQWshhoZBj01utWWqoSipxuenfvz8AQCaTYcSIEUW+Z2lpCW9vbyxevNio4YiI6NmSM3Lx4fZInIq+j+h7mZjRuzEAsNhQlVXicqPT6QAAderUwdmzZ+Hs7GyyUEREVDJh11Pw4Y5IpGTmwdpSgSacV0Nk+JybmJgYU+QgIiIDFGh1WHbkOlYcvQEhgEZu9lgxNAD1XOykjkYkuVKdCp6VlYXjx48jNjYWGo2myPfGjh1rlGBERFS8xLRcjN1+DmdiUgEAQ1p7IaRPE1hZKp6xJVHVYHC5OXfuHHr27Ins7GxkZWXByckJKSkpsLGxgYuLC8sNEZGJ5eZrcTk+HbZKBea/2hT9/D2ljkRUoRh8nZvx48ejT58+ePDgAaytrfH777/j9u3baNmyJRYtWmSKjEREVZ4QQv+1t7MtVgxtgZ/HdmCxISqGweUmMjISH330EeRyORQKBfLy8uDl5YXPPvsMU6dONUVGIqIqLf5hDgZ9+TvCrqfol73U0AV1nG0lTEVUcRlcbiwtLSGXF27m4uKC2NhYAIBarcadO3eMm46IqIo7fDkJPZefwJlbqZi55xK0OvHsjYiqOIPn3LRo0QJnz55F/fr10bFjR8ycORMpKSnYvHkz/Pz8TJGRiKjK0RTo8NnBq/gqrPAM1WY11VgxJAAKOa9dQ/QsBo/czJ8/H+7u7gCAefPmoVq1anj//fdx7949fPnll0YPSERU1dxJzcaAL0/ri83I9t7Y+d+2qFXdRuJkRJWDwSM3rVq10n/t4uKCgwcPGjUQEVFVFv8wB72Wn0B6bgEcrCywcEBzdGviJnUsokrF4JGbJ4mIiEDv3r0N3m7lypXw9vaGlZUV2rRpgzNnzjx1/YcPH2L06NFwd3eHSqVCgwYNsH///tLGJiKqUNzVVgj0dUWLWo7YP64Diw1RKRg0cnPo0CH88ssvUCqVeOedd+Dj44OrV69i8uTJ+Omnn9CtWzeD3nzHjh0IDg7GmjVr0KZNGyxduhTdunVDVFQUXFxcHltfo9Hg5ZdfhouLC3bt2gVPT0/cvn0bjo6OBr0vEVFFcvt+FhysLFHNVgmZTIZ5rzSFhUIGS4XR/v9JVKWUuNx8/fXXGDVqFJycnPDgwQN89dVXWLJkCcaMGYNBgwbh0qVL8PX1NejNlyxZglGjRmHkyJEAgDVr1mDfvn1Yv349Jk+e/Nj669evR2pqKk6dOgVLS0sAeOadyPPy8pCXl6d/np6eblBGIiJT+vlCPCZ/fxHP+zhh3fBWkMlksFbySsNEZVHi/xYsW7YMn376KVJSUvDdd98hJSUFq1atwsWLF7FmzRqDi41Go0F4eDgCAwP/DiOXIzAwEKdPny52m71796Jt27YYPXo0XF1d4efnh/nz50Or1T7xfUJDQ6FWq/UPLy8vg3ISEZlCbr4W0364iKCt55CZV4CH2fnIyCuQOhaRWShxuYmOjsaAAQMAAK+++iosLCywcOFC1KxZs1RvnJKSAq1WC1dX1yLLXV1dkZiYWOw2N2/exK5du6DVarF//37MmDEDixcvxty5c5/4PlOmTEFaWpr+wWvxEJHUbt7LxCurTmHLH4XXCfvgpbrY/u7zcLCylDgZkXko8WGpnJwc2NgUnoYok8mgUqn0p4SXF51OBxcXF6xduxYKhQItW7ZEXFwcFi5ciJCQkGK3UalUUKlU5ZqTiOhJfjwXh6k/XES2RovqtkosGeSPjg1qSB2LyKwYNKH4q6++gp2dHQCgoKAAGzduhLOzc5F1SnrjTGdnZygUCiQlJRVZnpSUBDe34s8OcHd3h6WlJRSKv49H+/r6IjExERqNBkql0pAfh4ioXOVotFj0vyhka7R43scJywa3gKuDldSxiMxOictNrVq1sG7dOv1zNzc3bN68ucg6MpmsxOVGqVSiZcuWOHLkCPr37w+gcGTmyJEjCAoKKnab9u3bY+vWrdDpdPpbQFy7dg3u7u4sNkRU4VkrFVgxNABHryZjbJf6vNowkYmUuNzcunXL6G8eHByMESNGoFWrVmjdujWWLl2KrKws/dlTw4cPh6enJ0JDQwEA77//PlasWIFx48ZhzJgxuH79OubPn1/iQkVEVN52hd+FTicw8LnCkxn8vRzh7+UobSgiM2fwFYqNadCgQbh37x5mzpyJxMRE+Pv74+DBg/pJxrGxsfoRGgDw8vLCoUOHMH78eDRr1gyenp4YN24cJk2aJNWPQERUrKy8AszYcwm7I+KgtJCjlXc1+NSwkzoWUZUgE0JUqVvMpqenQ61WIy0tDQ4ODsZ7YU0WMN+j8Oup8YDS1nivTUSVytXEdIzeEoHoe1mQy4DxgQ3wQad6PAxFVAaGfH5LOnJDRGROhBDYcfYOQvb+hbwCHVwdVFg2uAWe96kudTSiKoXlhojICIQQ+Oi789h9Lg4A0LFBDSwZ2BzV7XgpCqLyxnJDRGQEMpkM3s62UMhlmNC1Id570QdyHoYikkSpyk10dDQ2bNiA6OhoLFu2DC4uLjhw4ABq1aqFJk2aGDsjEVGFJIRAek4B1DaFVxYe3akeAn1d0djDiPP5iMhgBt9y9vjx42jatCn++OMP7N69G5mZmQCA8+fPP/EqwURE5iY9Nx9BW89h0NrTyM0vvL+dQi5jsSGqAAwuN5MnT8bcuXPxyy+/FLlwXufOnfH7778bNRwRUUV04e5D9F4ehn0XE3AjORN/3nogdSQi+geDD0tdvHgRW7dufWy5i4sLUlJSjBKKiKgiEkJg46lbmL//CvK1Ap6O1lgxtAVa1KomdTQi+geDy42joyMSEhJQp06dIsvPnTsHT09PowUjIqpI0rLzMXHXefzvcuH98Lo2dsXC15vr59sQUcVh8GGpwYMHY9KkSUhMTIRMJoNOp8PJkycxYcIEDB8+3BQZiYgkN33PJfzvchKUCjlm9WmML99oyWJDVEEZPHIzf/58jB49Gl5eXtBqtWjcuDG0Wi2GDh2K6dOnmyIjEZHkJvdohNj7WZjbvyma1lRLHYeInqLUt1+IjY3FpUuXkJmZiRYtWqB+/frGzmYSvP0CEZXEgywNDl9JwoBWXvplQgjIZLx2DZEUTHr7hbCwMLzwwguoVasWatWqVeqQREQV1Z+3UjFm2zkkpOWimo0SgY0Lb+bLYkNUORg856Zz586oU6cOpk6disuXL5siExGRJHQ6gVXHbmDQ2t+RkJaLOs62cHe0kjoWERnI4HITHx+Pjz76CMePH4efnx/8/f2xcOFC3L171xT5iIjKRUpmHt7ceBafHYyCVifQz98DP415AU08OL+GqLIxuNw4OzsjKCgIJ0+eRHR0NAYMGIBvvvkG3t7e6Ny5sykyEhGZ1O8376PnshP47do9qCzk+PS1plg6yB92Kt5+j6gyKtPf3Dp16mDy5Mlo3rw5ZsyYgePHjxsrFxFRuUnOyENyRh7qudhh5dAANHSzlzoSEZVBqcvNyZMnsWXLFuzatQu5ubno168fQkNDjZmNiMhk/nnmU9/mHsgv0KFHUzfYKDlaQ1TZGXxYasqUKahTpw46d+6M2NhYLFu2DImJidi8eTO6d+9uioxEREZ18kYKei0PQ3JGrn7Zay1rstgQmQmD/yb/9ttvmDhxIgYOHAhnZ2dTZCIiMgmtTmDZ4Wv44ugNCAEsO3wd815pKnUsIjIyg8vNyZMnTZGDiMikktJzMXbbOfwRkwoAGPycF6b3aixxKiIyhRKVm71796JHjx6wtLTE3r17n7pu3759jRKMiMhYjl+7h/E7IpGapYGtUoH5rzZFP3/e6JfIXJWo3PTv3x+JiYlwcXFB//79n7ieTCaDVqs1VjYiojLbdyEBo7dGAAB83R2wcmgL+NSwkzgVEZlSicqNTqcr9msiooquY8Ma8HG2Rft6zpjWyxdWlgqpIxGRiRl8ttSmTZuQl5f32HKNRoNNmzYZJRQRUVlExD7Ao3sC26kssCeoPeb092OxIaoiDC43I0eORFpa2mPLMzIyMHLkSKOEIiIqDU2BDvP2Xcarq07h67AY/XJ7K0sJUxFReTP4bKl/Xvjqn+7evQu1mvdgISJp3EnNxpht5xB55yGAwrOjiKhqKnG5adGiBWQyGWQyGbp06QILi7831Wq1iImJ4UX8iEgSh/5KxMSd55GeWwAHKwssHNAc3Zq4SR2LiCRS4nLz6CypyMhIdOvWDXZ2f59toFQq4e3tjddee83oAYmIniSvQIvQ/Vex8dQtAIC/lyO+GNICXk420gYjIkmVuNyEhIQAALy9vTFo0CBYWVmZLBQRUUlcT8rEt7/fBgCM6lAHE7s1gtLC4KmERGRmDJ5zM2LECFPkICIymJ+nGrP6NoG72gpdfF2ljkNEFUSJyo2TkxOuXbsGZ2dnVKtWrdgJxY+kpqYaLRwR0T/l5mux4MBVDHrOC77uDgCA/zxfW+JURFTRlKjcfP7557C3t9d//bRyQ0RkCjfvZWL01nO4kpCOE9fv4dCHL8JCwUNQRPS4EpWbfx6KevPNN02VhYioWHsi4zB190VkabSobqvEzD5NWGyI6IkM/tchIiICFy9e1D/fs2cP+vfvj6lTp0Kj0Rg1HBFVbTkaLSZ/fwHjtkciS6NFmzpO2D+uAzo2qCF1NCKqwAwuN++99x6uXbsGALh58yYGDRoEGxsb7Ny5Ex9//LHRAxJR1ZSckYv+K09i+9k7kMmAsV3qY8s7beDqwDM1iejpDC43165dg7+/PwBg586d6NixI7Zu3YqNGzfi+++/N3Y+IqqiqtuqUN1OCWc7Fb59uw2CX27AQ1FEVCKluv3CozuDHz58GL179wYAeHl5ISUlxbjpiKhKydYUQC6TwcpSAYVchqWD/QEALvYcrSGikjP4v0GtWrXC3LlzsXnzZhw/fhy9evUCAMTExMDVldeZIKLSiUrMQN8VJzHn58v6ZS72Viw2RGQwg8vN0qVLERERgaCgIEybNg316tUDAOzatQvt2rUzekAiMm9CCOw4G4u+K8JwIzkTh68k4UEWT04gotIz+LBUs2bNipwt9cjChQuhUCiMEoqIqobMvAJM/+EifoyMBwC82KAGPh/YHNVslRInI6LKzOBy80h4eDiuXLkCAGjcuDECAgKMFoqIzN/l+HQEbY3AzZQsKOQyfNS1Af77Yl3I5bxIKBGVjcHlJjk5GYMGDcLx48fh6OgIAHj48CE6deqE7du3o0YNXn+CiJ4ur0CLkRvPICk9D+5qK3wxpAVaeTtJHYuIzITBc27GjBmDzMxM/PXXX0hNTUVqaiouXbqE9PR0jB071hQZicjMqCwUmNu/Kbo0csH+sR1YbIjIqAweuTl48CAOHz4MX19f/bLGjRtj5cqV6Nq1q1HDEZH5uHg3DWk5+XihvjMA4OXGrgj0deG96ojI6AweudHpdLC0tHxsuaWlpf76N0REjwghsPFkDF5bfQpB2yIQ/zBH/z0WGyIyBYPLTefOnTFu3DjEx8frl8XFxWH8+PHo0qWLUcMRUeWWlp2P/34bjlk/XYZGq0NrbyfYKkt9HgMRUYkY/K/MihUr0LdvX3h7e8PLywsAcOfOHfj5+eHbb781ekAiqpzOxT7AmG3ncPdBDpQKOab2bIQR7bw5WkNEJmdwufHy8kJERASOHDmiPxXc19cXgYGBRg9HRJWPEAJfh8VgwYGrKNAJ1HKywcqhAWhaUy11NCKqIgwqNzt27MDevXuh0WjQpUsXjBkzxlS5iKiSkslkiL6XiQKdQK+m7gh9rSkcrB6fp0dEZColLjerV6/G6NGjUb9+fVhbW2P37t2Ijo7GwoULTZmPiCoJnU7oL8AX0qcJ2tSpjn7+HjwMRUTlrsQTilesWIGQkBBERUUhMjIS33zzDVatWmXKbERUCeh0AquPReOtb85CpxMAACtLBfq38GSxISJJlLjc3Lx5EyNGjNA/Hzp0KAoKCpCQkGCSYERU8d3PzMPIjWfx6cGrOBZ1D/+7nCR1JCKikh+WysvLg62trf65XC6HUqlETk7OU7YiInP1x837GLv9HJLS86CykOOTfk3QrYmr1LGIiAybUDxjxgzY2Njon2s0GsybNw9q9d9nQSxZssR46YiowtHqBFYdvYHPD1+DTgD1XOywcmgAGrrZSx2NiAiAAeXmxRdfRFRUVJFl7dq1w82bN/XPeXydyPxN//EStp2JBQC83rImPunXBDa8MB8RVSAl/hfp2LFjJoxBRJXFf56vhQOXEjCjV2O81rKm1HGIiB5j8O0XTGHlypXw9vaGlZUV2rRpgzNnzpRou+3bt0Mmk6F///6mDUhUhWl1AuG3H+ifN/FQ4+Skziw2RFRhSV5uduzYgeDgYISEhCAiIgLNmzdHt27dkJyc/NTtbt26hQkTJqBDhw7llJSo6klKz8XQdb9j8NrTOH/noX65rYqHoYio4pK83CxZsgSjRo3CyJEj0bhxY6xZswY2NjZYv379E7fRarUYNmwYZs+eDR8fn3JMS1R1HL92Dz2XncAfMalQKuRISs+VOhIRUYlIWm40Gg3Cw8OL3JdKLpcjMDAQp0+ffuJ2n3zyCVxcXPD2228/8z3y8vKQnp5e5EFET1ag1eHTg1cxYv0Z3M/SwNfdAT+NeQFdm7hJHY2IqEQkHVtOSUmBVquFq2vRa2O4urri6tWrxW4TFhaGr7/+GpGRkSV6j9DQUMyePbusUYmqhPiHORi77Rz+/P85Nm88XxvTevnCylIhcTIiopIr1cjNiRMn8J///Adt27ZFXFwcAGDz5s0ICwszarh/y8jIwBtvvIF169bB2dm5RNtMmTIFaWlp+sedO3dMmpGoMjt4KRF/3n4Ae5UFVg4NwJz+fiw2RFTpGDxy8/333+ONN97AsGHDcO7cOeTl5QEA0tLSMH/+fOzfv7/Er+Xs7AyFQoGkpKKXbE9KSoKb2+ND4NHR0bh16xb69OmjX6bT6Qp/EAsLREVFoW7dukW2UalUUKlUJc5EVJW92c4bSRm5GNq6FmpXt332BkREFZDBIzdz587FmjVrsG7dOlhaWuqXt2/fHhEREQa9llKpRMuWLXHkyBH9Mp1OhyNHjqBt27aPrd+oUSNcvHgRkZGR+kffvn3RqVMnREZGwsvLy9Afh6hKu/sgG8E7IpGVVwAAkMtlmNLDl8WGiCo1g0duoqKi8OKLLz62XK1W4+HDhwYHCA4OxogRI9CqVSu0bt0aS5cuRVZWFkaOHAkAGD58ODw9PREaGgorKyv4+fkV2d7R0REAHltORE/3v78SMWHneaTnFsBGpcDc/k2ljkREZBQGlxs3NzfcuHED3t7eRZaHhYWV6rTsQYMG4d69e5g5cyYSExPh7++PgwcP6icZx8bGQi6X/Ix1IrOhKdAh9MAVbDh5CwDQ3MsR771Y9+kbERFVIgaXm1GjRmHcuHFYv349ZDIZ4uPjcfr0aUyYMAEzZswoVYigoCAEBQUV+71n3fZh48aNpXpPoqoo9n42grZF4MLdNADAqA51MLFbIygt+B8IIjIfBpebyZMnQ6fToUuXLsjOzsaLL74IlUqFCRMmYMyYMabISERGcDr6Pt7d9Ccy8grgaGOJxQOao4uv67M3JCKqZAwuNzKZDNOmTcPEiRNx48YNZGZmonHjxrCzszNFPiIykro1bKGylKOhWzUsH9ICHo7WUkciIjKJUl/ET6lUonHjxsbMQkRGlpqlgZOtEgDg4mCF7e+2Re3qNrBU8DAUEZkvg8tNp06dIJPJnvj9X3/9tUyBiMg49kTGYdoPl/DZ683Qs6k7AKCeC0dYicj8GVxu/P39izzPz89HZGQkLl26hBEjRhgrFxGVUm6+FrN/+gvbzhRejXt3xF19uSEiqgoMLjeff/55sctnzZqFzMzMMgciotK7kZyJoK0RuJqYAZkMGNOpHsZ2qS91LCKicmW0A+//+c9/sH79emO9HBEZ6Pvwu+jzRRiuJmbA2U6FzW+1QXDXhrDg/BoiqmKMdlfw06dPw8rKylgvR0QGuBSXho92ngcAtKtbHUsH+8PFnn8fiahqMrjcvPrqq0WeCyGQkJCAP//8s9QX8SOisvHzVGNUhzqwt7LE6E71oJA/edI/EZG5M7jcqNXqIs/lcjkaNmyITz75BF27djVaMCJ6MiEEvo+IQ/t61eGuLrxezbRevDQDERFgYLnRarUYOXIkmjZtimrVqpkqExE9RWZeAab/cBE/RsbjOe9q2Dbqec6rISL6B4P+RVQoFOjatWup7v5NRGV3OT4dfb8Iw4+R8VDIZejUyAXyp1x3ioioKjL4sJSfnx9u3ryJOnXqmCIPERVDCIGtZ2Ix+6fL0BTo4K62whdDWqCVt5PU0YiIKhyDy83cuXMxYcIEzJkzBy1btoStrW2R7zs4OBgtHBEVHoaa9P0F7LuQAADo0sgFiwY0R7X/v60CEREVVeJy88knn+Cjjz5Cz549AQB9+/YtchsGIQRkMhm0Wq3xUxJVYQqZDDeSMmEhl2FS90Z4p0Odp94ChYioqpMJIURJVlQoFEhISMCVK1eeul7Hjh2NEsxU0tPToVarkZaWZtxRJk0WMN+j8Oup8YDS9unrEz2FEAJCAPL/P6X7RnIG0nMLEFCLE/mJqGoy5PO7xCM3jzpQRS8vRJVdWk4+Ju26gKY11RjdqR4AoJ6LvcSpiIgqD4Pm3HAonMi0Iu88RNDWCNx9kINj15IxsJUXatirpI5FRFSpGFRuGjRo8MyCk5qaWqZARFWREAJfh8Xg04NXka8VqOVkgxVDW7DYEBGVgkHlZvbs2Y9doZiIyuZhtgYTdp7H4SvJAICeTd2w4LVmcLCylDgZEVHlZFC5GTx4MFxcXEyVhajK0RTo8MqqU4hJyYLSQo4ZvRvjP21q8RAwEVEZlPgKxfzHlsj4lBZyvNXeG3WcbfHDB+3wxvO1+XeNiKiMDD5biojKJjVLg/uZeajvWngG1H+er43XW3rBWqmQOBkRkXkocbnR6XSmzEFUJZyJScWYbRFQWSjw89gX4GBlCZlMxmJDRGREvJUwUTnQ6QRW/Hodg9eeRlJ6HiwVMqRmaqSORURklgy+txQRGeZeRh6Cv4vEiespAIDXAmpiTv8msFHyrx8RkSnwX1ciEzp1IwXjdkTiXkYerC0VmNPfD6+3rCl1LCIis8ZyQ2RCX4fF4F5GHhq42mHl0AD9JGIiIjIdlhsiE1o4oDnWHI/G+MAGnDRMRFROOKGYyIh+u3YP8/Zd1j93slViak9fFhsionLEkRsiIyjQ6vD54WtYdSwaQgAta1dDdz93qWMREVVJLDdEZZSQloNx2yJx5lbhTWOHtamFlxryNiVERFJhuSEqg6NXkxH8XSQeZOfDTmWBBa81Re9mHlLHIiKq0lhuiEpp5dEbWHgoCgDQ1FONFUNboHZ1W4lTERERyw1RKfl5qiGTASPaemNKz0ZQWXDSMBFRRcByQ2SAlMw8ONupAAAdG9TAL+NfRD0XXruGiKgi4angRCWgKdDhk58uo/OiY4i9n61fzmJDRFTxsNwQPcOd1GwMWHMK60/GID23AMeuJUsdiYiInoKHpYie4sDFBHz8/QVk5BbA0cYSi15vjsDGrlLHIiKip2C5ISpGbr4W8/dfwabTtwEUXpRv+ZAW8HS0ljgZERE9C8sNUTE2nrqlLzb/7VgXH3VtAEsFj+ISEVUGLDdExRjZ3huno+/jzfbe6MSrDRMRVSr8rygRCg9Drf0tGgVaHQBAZaHAN2+1ZrEhIqqEOHJDVd6N5EwEbY3A1cQMpOcUYEK3hlJHIiKiMmC5oSptd8RdTP/xErI1WjjbqfC8T3WpIxERURmx3FCVlK0pQMiev7Az/C4AoF3d6lg62B8u9lYSJyMiorJiuaEq50ZyBt7/NgLXkzMhlwHjujRAUOd6UMhlUkcjIiIjYLmhKkcngDsPsuFir8KywS3Qti4PRRERmROWG6oStDqhH5lp4GqPL99ohSYeDvqbYBIRkfngqeBk9i7Hp6P70t9w9laqflnHBjVYbIiIzBTLDZktIQS2/HEb/VedxPXkTMzffwVCCKljERGRifGwFJmljNx8TNl9ET9fSAAAdGpYA4sH+kMm46RhIiJzx3JDZudSXBqCtkbg1v1sWMhl+Lh7Q7zzgg/kPBuKiKhKYLkhsxKVmIFXV52CRquDp6M1lg9pgZa1q0kdi4iIyhHLDZmVBq526NzIBQU6gUUDmsHRRil1JCIiKmcsN1TpXbj7EN7OtnCwsoRMJsPSwf5QWcg5v4aIqIqqEGdLrVy5Et7e3rCyskKbNm1w5syZJ667bt06dOjQAdWqVUO1atUQGBj41PXJfAkh8NWJm3ht9SlM2X1RfyaUlaWCxYaIqAqTvNzs2LEDwcHBCAkJQUREBJo3b45u3bohOTm52PWPHTuGIUOG4OjRozh9+jS8vLzQtWtXxMXFlXNyktLDbA1GbQrH3H1XkK8VEEJAo9VJHYuIiCoAmZD4wh9t2rTBc889hxUrVgAAdDodvLy8MGbMGEyePPmZ22u1WlSrVg0rVqzA8OHDn7l+eno61Go10tLS4ODgUOb8eposYL5H4ddT4wGlrfFem4oIv/0AY7ZGID4tF0qFHDN6++I/z9fmaA0RkRkz5PNb0jk3Go0G4eHhmDJlin6ZXC5HYGAgTp8+XaLXyM7ORn5+PpycnIr9fl5eHvLy8vTP09PTyxaaJKPTCaw9cRMLD0VBqxPwrm6DFUMD4OepljoaERFVIJIelkpJSYFWq4Wrq2uR5a6urkhMTCzRa0yaNAkeHh4IDAws9vuhoaFQq9X6h5eXV5lzkzTSc/Ox4WQMtDqBvs098PPYDiw2RET0mEp9ttSCBQuwfft2HDt2DFZWVsWuM2XKFAQHB+ufp6ens+BUUo42Siwf3AI3U7Iw+DkvHoYiIqJiSVpunJ2doVAokJSUVGR5UlIS3NzcnrrtokWLsGDBAhw+fBjNmjV74noqlQoqFW+QWBnpdAKrjt2AZzVrvNKiJgCgjU91tPGpLnEyIiKqyCQ9LKVUKtGyZUscOXJEv0yn0+HIkSNo27btE7f77LPPMGfOHBw8eBCtWrUqj6hUzu5l5GHEhjNY9L9rmLr7EhLTcqWORERElYTkh6WCg4MxYsQItGrVCq1bt8bSpUuRlZWFkSNHAgCGDx8OT09PhIaGAgA+/fRTzJw5E1u3boW3t7d+bo6dnR3s7Owk+znIeE5Fp2Dc9kjcy8iDlaUcs/s1gasDR9+IiKhkJC83gwYNwr179zBz5kwkJibC398fBw8e1E8yjo2NhVz+9wDT6tWrodFo8Prrrxd5nZCQEMyaNas8o5ORaXUCX/x6HcuPXIdOFN5KYeXQANR3tZc6GhERVSKSX+emvPE6NxVTgVaHERvO4OSN+wCAQa28MKtvE1grFRInIyKiiqDSXOeG6BELhRzNajriXOxDzH+lKfq38JQ6EhERVVIsNySZAq0OaTn5qG5XOJ8m+OUGGPycF2pX56gXERGVnuT3lqKqKSEtB0PW/Y63Np6FpqDwnlCWCjmLDRERlRlHbqjcHb2ajODvIvEgOx92KgtcS8rglYaJiMhoWG6o3ORrdVh0KApf/nYTAODn6YAVQwLg7czRGiIiMh6WGyoXdx9kY8y2czgX+xAA8GY7b0zp2QgqC54NRURExsVyQ+Vi8vcXcS72IeytLLDw9Wbo7ucudSQiIjJTnFBM5WJufz+8UM8Z+8d2YLEhIiKTYrkhk7iTmo3tZ2L1z72dbfHtO23g5WQjYSoiIqoKeFiKjO7AxQR8/P0FZOYVoGY1G7xQ31nqSEREVIWw3JDR5OZrMX//FWw6fRsAEFDLEd7OHKkhIqLyxXJDRnErJQujt0bgr/h0AMB7HX0woWtDWCp45JOIiMoXyw2V2b4LCZj0/4ehqtlYYslAf3Rq5CJ1LCIiqqJYbqjMsjQFyMwrQGtvJywb4g93tbXUkYiIqApjuaFSKdDqYPH/h5wGtKwJW6UFujVx1S8jIiKSCj+JyGC7I+6i+7ITeJClAQDIZDL0aubOYkNERBUCP42oxLI1BZi48zyCvzuPG8mZ2HDqltSRiIiIHsPDUlQi15IyMHpLBK4nZ0ImA8Z1qY8xnetLHYuIiOgxLDf0VEII7Ay/i5l7LiE3X4ca9iosG+yPdnV5YT4iIqqYWG7oqTb/fhsz9/wFAOhQ3xlLBvqjhr1K4lRERERPxnJDT9XP3xPrw2IwoJUX3u9YF3K5TOpIRERET8VyQ0UIIRB2IwUv1HOGTCaD2toSBz98EVaWCqmjERERlQjPliK9jNx8jN0eiTe+PoNtZ+7ol7PYEBFRZcKRGwIAXIpLQ9DWCNy6nw0LuQy5+VqpIxERGY1Wq0V+fr7UMegZlEol5PKyj7uw3FRxQghs/v025v58BRqtDp6O1lg+pAVa1q4mdTQiojITQiAxMREPHz6UOgqVgFwuR506daBUKsv0Oiw3VVhaTj4mf38BBy4lAgACfV2xaEAzONqU7Q8VEVFF8ajYuLi4wMbGBjIZT4qoqHQ6HeLj45GQkIBatWqV6feK5aYKi0rMwKG/EmGpkGFyD1+81d6bf/GJyGxotVp9salevbrUcagEatSogfj4eBQUFMDS0rLUr8NyU4W1ruOE2f380MxTjeZejlLHISIyqkdzbGxsbCROQiX16HCUVqstU7nh2VJVyMNsDcZuO4foe5n6ZW88X5vFhojMGkekKw9j/V5x5KaKCL/9AGO3nUPcwxzcvp+FH0e35194IiIySxy5MXM6ncCXx6Mx6MvTiHuYg9rVbTDvlaYsNkRE9FTe3t5YunSp/rlMJsOPP/4oWR5DsNyYsdQsDd7+5ixCD1xFgU6gdzN3/DzmBfh5qqWORkRET/Hmm29CJpPpH9WrV0f37t1x4cIFyTIlJCSgR48ekr2/IVhuzNStlCz0XHYCR6PuQWUhx/xXmuKLIS1gb1X6CVpERFR+unfvjoSEBCQkJODIkSOwsLBA7969Jcvj5uYGlapy3DiZ5cZMeVazhmc1a/jUsMWPo9tjaJuyXTOAiMicZGsKnvj49xXajbFuaahUKri5ucHNzQ3+/v6YPHky7ty5g3v37gEAJk2ahAYNGsDGxgY+Pj6YMWNGkaswnz9/Hp06dYK9vT0cHBzQsmVL/Pnnn/rvh4WFoUOHDrC2toaXlxfGjh2LrKysJ+b552GpW7duQSaTYffu3ejUqRNsbGzQvHlznD59usg2hr6HsXBCsRm5n5kHeytLKC3ksFTIsXpYAGxVFrBV8beZiOifGs889MTvdWpYAxtGttY/bznnMHKecEuaNnWcsOO9tvrnL3x6FKlZmsfWu7WgVxnSApmZmfj2229Rr149/TV77O3tsXHjRnh4eODixYsYNWoU7O3t8fHHHwMAhg0bhhYtWmD16tVQKBSIjIzUn14dHR2N7t27Y+7cuVi/fj3u3buHoKAgBAUFYcOGDSXONW3aNCxatAj169fHtGnTMGTIENy4cQMWFhZGe4/S4KeemTgVnYJx2yPR398D03o1BgC4OFhJnIqIiErr559/hp2dHQAgKysL7u7u+Pnnn/X3Xpo+fbp+XW9vb0yYMAHbt2/Xl5vY2FhMnDgRjRo1AgDUr19fv35oaCiGDRuGDz/8UP+95cuXo2PHjli9ejWsrEr2+TFhwgT06lVY3GbPno0mTZrgxo0baNSokdHeozRYbio5rU7gi1+vY/mR69AJ4Pi1ewh+WQtrJe/kTUT0JJc/6fbE78n/dQg/fEZgidcNm9SpbMH+oVOnTli9ejUA4MGDB1i1ahV69OiBM2fOoHbt2tixYweWL1+O6OhoZGZmoqCgAA4ODvrtg4OD8c4772Dz5s0IDAzEgAEDULduXQCFh6wuXLiALVu26NcXQkCn0yEmJga+vr4lytisWTP91+7u7gCA5ORkNGrUyGjvURosN5VYcnouPtwRiVPR9wEAA1vVxOy+fiw2RETPYKMs+cefqdZ9FltbW9SrV0///KuvvoJarca6devQq1cvDBs2DLNnz0a3bt2gVquxfft2LF68WL/+rFmzMHToUOzbtw8HDhxASEgItm/fjldeeQWZmZl47733MHbs2Mfet1atWiXO+M+rCD+a16nT6QDAaO9RGiw3ldSJ6/cwfkckUjI1sFEqMLe/H14NqCl1LCIiMhGZTAa5XI6cnBycOnUKtWvXxrRp0/Tfv3379mPbNGjQAA0aNMD48eMxZMgQbNiwAa+88goCAgJw+fLlIuXJ2MrjPZ6EZ0tVQmk5+fhgSwRSMjVo5GaPvUEvsNgQEZmZvLw8JCYmIjExEVeuXMGYMWOQmZmJPn36oH79+oiNjcX27dsRHR2N5cuX44cfftBvm5OTg6CgIBw7dgy3b9/GyZMncfbsWf2hoEmTJuHUqVMICgpCZGQkrl+/jj179iAoKMho+cvjPZ6EIzeVkNraEvNeaYrT0fcR0qcxrCx5GIqIyNwcPHhQP4/F3t4ejRo1ws6dO/HSSy8BAMaPH4+goCDk5eWhV69emDFjBmbNmgUAUCgUuH//PoYPH46kpCQ4Ozvj1VdfxezZswEUzpU5fvw4pk2bhg4dOkAIgbp162LQoEFGy18e7/EkMiGEMPm7VCDp6elQq9VIS0srMvGqzDRZwHyPwq+nxgNKW+O9NoCjUclQWcjRrq6zUV+XiMhc5ebmIiYmBnXq1DHpmTlkPE/7PTPk85sjNxVcvlaHRf+LwpfHb8LZToUD4zqghn3luEIkERGRFFhuTCE9HnCu/+z1niHuYQ7GbI1AROxDAEDPpm6wt+JvGRER0dNwQrGxRG79++uVrYGITWV6uV8uJ6HnshOIiH0IeysLrB4WgE/6+XF+DRER0TNwGMAY0uKAAx///VzogJ8+BOp2AdSeBr2UVicwf/8VfB0WAwBoXlONL4YEoFZ1GyMGJiIiMl8sN8aQGl1YaP5JaIHUmwaXG7ms8B5RAPBW+zqY3KMRlBYcYCMiIioplhtjcKoLyORFC45MATj5lPglCrQ6WCjkkMlkmPtKU/Rr4YlODV1MEJaIiMi8cUjAGNSeQI/P/n4uUwB9lpZo1CavQIuQPZfw328j8OisfDuVBYsNERFRKXHkxlj8hwL7JxR+PfqPEp0tdSslC0HbInApLh0AcPbWA7Su42TKlERERGaP5cYUHDyeucpP5+MxZfdFZOYVoJqNJRYPbM5iQ0REZAQ8LFXOcvO1mPrDRYzZdg6ZeQV4zrsa9o/rgM6NXKWORkREZuTWrVuQyWSIjIyUOkq548hNOQvaeg6HryRBJgM+eKkuxgc2gIWCHZOIiMhYWG7K2ehOdXEpLg2fvd4MLzaoIXUcIiIqqbS4wkt/ONU1+DIfVL44ZGBiORotfr95X/+8Ra1qOP7xSyw2RERSEKLwRseGPs6sA5b6Ad/0Kfz1zDrDX8PA+1RnZGRg2LBhsLW1hbu7Oz7//HO89NJL+PDDDwEA3t7emD9/Pt566y3Y29ujVq1aWLt27WOvc/XqVbRr1w5WVlbw8/PD8ePHjbEnKzSO3JjQ9aQMjN4agdv3s/Hj6PbwdS+8i6nKgrdQICKSRH42MP/ZJ308ldAVnh376AzZkpoaDyhtS7x6cHAwTp48ib1798LV1RUzZ85EREQE/P399essXrwYc+bMwdSpU7Fr1y68//776NixIxo2bKhfZ+LEiVi6dCkaN26MJUuWoE+fPoiJiUH16tUNy1+JVIiRm5UrV8Lb2xtWVlZo06YNzpw589T1d+7ciUaNGsHKygpNmzbF/v37yylpyQgh8N2fd9BnRRiuJWXCwdoSmXkFUsciIqJKIiMjA9988w0WLVqELl26wM/PDxs2bIBWqy2yXs+ePfHBBx+gXr16mDRpEpydnXH06NEi6wQFBeG1116Dr68vVq9eDbVaja+//ro8f5xyJ/nIzY4dOxAcHIw1a9agTZs2WLp0Kbp164aoqCi4uDx+IbtTp05hyJAhCA0NRe/evbF161b0798fERER8PPzk+AneFzojl+x9nLh6EyH+s5YMtAfNexVEqciIiJY2hSOoBgiPb7whsj/vgr96D9KdOmPIu9dQjdv3kR+fj5at26tX6ZWq4uMyABAs2bN/o4kk8HNzQ3JyclF1mnbtq3+awsLC7Rq1QpXrlwpee5KSPKRmyVLlmDUqFEYOXIkGjdujDVr1sDGxgbr168vdv1ly5ahe/fumDhxInx9fTFnzhwEBARgxYoV5Zz8X/5xV/BJ0cMxWHEUE7o2wDcjW7PYEBFVFDJZ4aEhQx7O9YE+ywoLDfD3Veid6xv2OjKZ0X8cS0vLf/14Muh0uiesXXVIWm40Gg3Cw8MRGBioXyaXyxEYGIjTp08Xu83p06eLrA8A3bp1e+L6eXl5SE9PL/Iwun/dFVwhEwhVrkdQSxvI5cb/w0xEROUsYDjw4UVgxM+FvwYMN+nb+fj4wNLSEmfPntUvS0tLw7Vr1wx+rd9//13/dUFBAcLDw+Hr62uUnBWVpIelUlJSoNVq4epa9AJ2rq6uuHr1arHbJCYmFrt+YmJiseuHhoZi9uzZxgn8JMXcFVxWyruCExFRBaX2LLd/0+3t7TFixAhMnDgRTk5OcHFxQUhICOTywhssG2LlypWoX78+fH198fnnn+PBgwd46623TJS8YpD8sJSpTZkyBWlpafrHnTt3jP8mj+4K/k8G3hWciIjon5YsWYK2bduid+/eCAwMRPv27eHr6wsrKyuDXmfBggVYsGABmjdvjrCwMOzduxfOzs4mSl0xSDpy4+zsDIVCgaSkpCLLk5KS4ObmVuw2bm5uBq2vUqmgUpl4zovas/B47E8fAkJr0F3BiYiIimNvb48tW7bon2dlZWH27Nl49913ARTeXuHf/nmrBW9vb4j/v7bOkCFDTJq1opF05EapVKJly5Y4cuSIfplOp8ORI0eKzO7+p7Zt2xZZHwB++eWXJ65fbsr5eCwREZm3c+fOYdu2bYiOjkZERASGDRsGAOjXr5/EySo+yU8FDw4OxogRI9CqVSu0bt0aS5cuRVZWFkaOHAkAGD58ODw9PREaGgoAGDduHDp27IjFixejV69e2L59O/78889ir8pY7srxeCwREZm/RYsWISoqSj8YcOLECbM/pGQMkpebQYMG4d69e5g5cyYSExPh7++PgwcP6icNx8bGQi7/e4CpXbt22Lp1K6ZPn46pU6eifv36+PHHHyvMNW6IiIiMoUWLFggPD5c6RqUkE8LAm11Ucunp6VCr1UhLS4ODg4PUcYiIyERyc3MRExODOnXqGDwJl6TxtN8zQz6/zf5sKSIiqtqq2P/hKzVj/V6x3BARkVl6dPXe7OxsiZNQSWk0GgCAQlG2G0xLPueGiIjIFBQKBRwdHfX3WrKxsTH4AnhUfnQ6He7duwcbGxtYWJStnrDcEBGR2Xp0DbR/30ySKia5XI5atWqVuYSy3BARkdmSyWRwd3eHi4sL8vPzpY5Dz6BUKoucIV1aLDdERGT2FApFmedxUOXBCcVERERkVlhuiIiIyKyw3BAREZFZqXJzbh5dICg9PV3iJERERFRSjz63S3KhvypXbjIyMgAAXl5eEichIiIiQ2VkZECtVj91nSp3bymdTof4+HjY29sb/WJO6enp8PLywp07d3jfKhPifi4f3M/lg/u5/HBflw9T7WchBDIyMuDh4fHM08Wr3MiNXC5HzZo1TfoeDg4O/ItTDrifywf3c/ngfi4/3NflwxT7+VkjNo9wQjERERGZFZYbIiIiMissN0akUqkQEhIClUoldRSzxv1cPrifywf3c/nhvi4fFWE/V7kJxURERGTeOHJDREREZoXlhoiIiMwKyw0RERGZFZYbIiIiMissNwZauXIlvL29YWVlhTZt2uDMmTNPXX/nzp1o1KgRrKys0LRpU+zfv7+cklZuhuzndevWoUOHDqhWrRqqVauGwMDAZ/6+UCFD/zw/sn37dshkMvTv39+0Ac2Eofv54cOHGD16NNzd3aFSqdCgQQP+21EChu7npUuXomHDhrC2toaXlxfGjx+P3NzcckpbOf3222/o06cPPDw8IJPJ8OOPPz5zm2PHjiEgIAAqlQr16tXDxo0bTZ4Tgkps+/btQqlUivXr14u//vpLjBo1Sjg6OoqkpKRi1z958qRQKBTis88+E5cvXxbTp08XlpaW4uLFi+WcvHIxdD8PHTpUrFy5Upw7d05cuXJFvPnmm0KtVou7d++Wc/LKxdD9/EhMTIzw9PQUHTp0EP369SufsJWYofs5Ly9PtGrVSvTs2VOEhYWJmJgYcezYMREZGVnOySsXQ/fzli1bhEqlElu2bBExMTHi0KFDwt3dXYwfP76ck1cu+/fvF9OmTRO7d+8WAMQPP/zw1PVv3rwpbGxsRHBwsLh8+bL44osvhEKhEAcPHjRpTpYbA7Ru3VqMHj1a/1yr1QoPDw8RGhpa7PoDBw4UvXr1KrKsTZs24r333jNpzsrO0P38bwUFBcLe3l588803popoFkqznwsKCkS7du3EV199JUaMGMFyUwKG7ufVq1cLHx8fodFoyiuiWTB0P48ePVp07ty5yLLg4GDRvn17k+Y0JyUpNx9//LFo0qRJkWWDBg0S3bp1M2EyIXhYqoQ0Gg3Cw8MRGBioXyaXyxEYGIjTp08Xu83p06eLrA8A3bp1e+L6VLr9/G/Z2dnIz8+Hk5OTqWJWeqXdz5988glcXFzw9ttvl0fMSq80+3nv3r1o27YtRo8eDVdXV/j5+WH+/PnQarXlFbvSKc1+bteuHcLDw/WHrm7evIn9+/ejZ8+e5ZK5qpDqc7DK3TiztFJSUqDVauHq6lpkuaurK65evVrsNomJicWun5iYaLKclV1p9vO/TZo0CR4eHo/9haK/lWY/h4WF4euvv0ZkZGQ5JDQPpdnPN2/exK+//ophw4Zh//79uHHjBj744APk5+cjJCSkPGJXOqXZz0OHDkVKSgpeeOEFCCFQUFCA//73v5g6dWp5RK4ynvQ5mJ6ejpycHFhbW5vkfTlyQ2ZlwYIF2L59O3744QdYWVlJHcdsZGRk4I033sC6devg7OwsdRyzptPp4OLigrVr16Jly5YYNGgQpk2bhjVr1kgdzawcO3YM8+fPx6pVqxAREYHdu3dj3759mDNnjtTRyAg4clNCzs7OUCgUSEpKKrI8KSkJbm5uxW7j5uZm0PpUuv38yKJFi7BgwQIcPnwYzZo1M2XMSs/Q/RwdHY1bt26hT58++mU6nQ4AYGFhgaioKNStW9e0oSuh0vx5dnd3h6WlJRQKhX6Zr68vEhMTodFooFQqTZq5MirNfp4xYwbeeOMNvPPOOwCApk2bIisrC++++y6mTZsGuZz/9zeGJ30OOjg4mGzUBuDITYkplUq0bNkSR44c0S/T6XQ4cuQI2rZtW+w2bdu2LbI+APzyyy9PXJ9Kt58B4LPPPsOcOXNw8OBBtGrVqjyiVmqG7udGjRrh4sWLiIyM1D/69u2LTp06ITIyEl5eXuUZv9IozZ/n9u3b48aNG/ryCADXrl2Du7s7i80TlGY/Z2dnP1ZgHhVKwVsuGo1kn4Mmna5sZrZv3y5UKpXYuHGjuHz5snj33XeFo6OjSExMFEII8cYbb4jJkyfr1z958qSwsLAQixYtEleuXBEhISE8FbwEDN3PCxYsEEqlUuzatUskJCToHxkZGVL9CJWCofv533i2VMkYup9jY2OFvb29CAoKElFRUeLnn38WLi4uYu7cuVL9CJWCofs5JCRE2Nvbi23btombN2+K//3vf6Ju3bpi4MCBUv0IlUJGRoY4d+6cOHfunAAglixZIs6dOydu374thBBi8uTJ4o033tCv/+hU8IkTJ4orV66IlStX8lTwiuiLL74QtWrVEkqlUrRu3Vr8/vvv+u917NhRjBgxosj63333nWjQoIFQKpWiSZMmYt++feWcuHIyZD/Xrl1bAHjsERISUv7BKxlD/zz/E8tNyRm6n0+dOiXatGkjVCqV8PHxEfPmzRMFBQXlnLryMWQ/5+fni1mzZom6desKKysr4eXlJT744APx4MGD8g9eiRw9erTYf28f7dsRI0aIjh07PraNv7+/UCqVwsfHR2zYsMHkOWVCcPyNiIiIzAfn3BAREZFZYbkhIiIis8JyQ0RERGaF5YaIiIjMCssNERERmRWWGyIiIjIrLDdERERkVlhuiIiIyKyw3BBRERs3boSjo6PUMUpNJpPhxx9/fOo6b775Jvr3718ueYio/LHcEJmhN998EzKZ7LHHjRs3pI6GjRs36vPI5XLUrFkTI0eORHJyslFePyEhAT169AAA3Lp1CzKZDJGRkUXWWbZsGTZu3GiU93uSWbNm6X9OhUIBLy8vvPvuu0hNTTXodVjEiAxnIXUAIjKN7t27Y8OGDUWW1ahRQ6I0RTk4OCAqKgo6nQ7nz5/HyJEjER8fj0OHDpX5td3c3J65jlqtLvP7lESTJk1w+PBhaLVaXLlyBW+99RbS0tKwY8eOcnl/oqqKIzdEZkqlUsHNza3IQ6FQYMmSJWjatClsbW3h5eWFDz74AJmZmU98nfPnz6NTp06wt7eHg4MDWrZsiT///FP//bCwMHTo0AHW1tbw8vLC2LFjkZWV9dRsMpkMbm5u8PDwQI8ePTB27FgcPnwYOTk50Ol0+OSTT1CzZk2oVCr4+/vj4MGD+m01Gg2CgoLg7u4OKysr1K5dG6GhoUVe+9FhqTp16gAAWrRoAZlMhpdeeglA0dGQtWvXwsPDAzqdrkjGfv364a233tI/37NnDwICAmBlZQUfHx/Mnj0bBQUFT/05LSws4ObmBk9PTwQGBmLAgAH45Zdf9N/XarV4++23UadOHVhbW6Nhw4ZYtmyZ/vuzZs3CN998gz179uhHgY4dOwYAuHPnDgYOHAhHR0c4OTmhX79+uHXr1lPzEFUVLDdEVYxcLsfy5cvx119/4ZtvvsGvv/6Kjz/++InrDxs2DDVr1sTZs2cRHh6OyZMnw9LSEgAQHR2N7t2747XXXsOFCxewY8cOhIWFISgoyKBM1tbW0Ol0KCgowLJly7B48WIsWrQIFy5cQLdu3dC3b19cv34dALB8+XLs3bsX3333HaKiorBlyxZ4e3sX+7pnzpwBABw+fBgJCQnYvXv3Y+sMGDAA9+/fx9GjR/XLUlNTcfDgQQwbNgwAcOLECQwfPhzjxo3D5cuX8eWXX2Ljxo2YN29eiX/GW7du4dChQ1AqlfplOp0ONWvWxM6dO3H58mXMnDkTU6dOxXfffQcAmDBhAgYOHIju3bsjISEBCQkJaNeuHfLz89GtWzfY29vjxIkTOHnyJOzs7NC9e3doNJoSZyIyWya/7zgRlbsRI0YIhUIhbG1t9Y/XX3+92HV37twpqlevrn++YcMGoVar9c/t7e3Fxo0bi9327bffFu+++26RZSdOnBByuVzk5OQUu82/X//atWuiQYMGolWrVkIIITw8PMS8efOKbPPcc8+JDz74QAghxJgxY0Tnzp2FTqcr9vUBiB9++EEIIURMTIwAIM6dO1dknREjRoh+/frpn/fr10+89dZb+udffvml8PDwEFqtVgghRJcuXcT8+fOLvMbmzZuFu7t7sRmEECIkJETI5XJha2srrKysBAABQCxZsuSJ2wghxOjRo8Vrr732xKyP3rthw4ZF9kFeXp6wtrYWhw4deurrE1UFnHNDZKY6deqE1atX65/b2toCKBzFCA0NxdWrV5Geno6CggLk5uYiOzsbNjY2j71OcHAw3nnnHWzevFl/aKVu3boACg9ZXbhwAVu2bNGvL4SATqdDTEwMfH19i82WlpYGOzs76HQ65Obm4oUXXsBXX32F9PR0xMfHo3379kXWb9++Pc6fPw+g8JDSyy+/jIYNG6J79+7o3bs3unbtWqZ9NWzYMIwaNQqrVq2CSqXCli1bMHjwYMjlcv3PefLkySIjNVqt9qn7DQAaNmyIvXv3Ijc3F99++y0iIyMxZsyYIuusXLkS69evR2xsLHJycqDRaODv7//UvOfPn8eNGzdgb29fZHlubi6io6NLsQeIzAvLDZGZsrW1Rb169Yosu3XrFnr37o33338f8+bNg5OTE8LCwvD2229Do9EU+yE9a9YsDB06FPv27cOBAwcQEhKC7du345VXXkFmZibee+89jB079rHtatWq9cRs9vb2iIiIgFwuh7u7O6ytrQEA6enpz/y5AgICEBMTgwMHDuDw4cMYOHAgAgMDsWvXrmdu+yR9+vSBEAL79u3Dc889hxMnTuDzzz/Xfz8zMxOzZ8/Gq6+++ti2VlZWT3xdpVKp/z1YsGABevXqhdmzZ2POnDkAgO3bt2PChAlYvHgx2rZtC3t7eyxcuBB//PHHU/NmZmaiZcuWRUrlIxVl0jiRlFhuiKqQ8PBw6HQ6LF68WD8q8Wh+x9M0aNAADRo0wPjx4zFkyBBs2LABr7zyCgICAnD58uXHStSzyOXyYrdxcHCAh4cHTp48iY4dO+qXnzx5Eq1bty6y3qBBgzBo0CC8/vrr6N69O1JTU+Hk5FTk9R7Nb9FqtU/NY2VlhVdffRVbtmzBjRs30LBhQwQEBOi/HxAQgKioKIN/zn+bPn06OnfujPfff1//c7Zr1w4ffPCBfp1/j7wolcrH8gcEBGDHjh1wcXGBg4NDmTIRmSNOKCaqQurVq4f8/Hx88cUXuHnzJjZv3ow1a9Y8cf2cnBwEBQXh2LFjuH37Nk6ePImzZ8/qDzdNmjQJp06dQlBQECIjI3H9+nXs2bPH4AnF/zRx4kR8+umn2LFjB6KiojB58mRERkZi3LhxAIAlS5Zg27ZtuHr1Kq5du4adO3fCzc2t2AsPuri4wNraGgcPHkRSUhLS0tKe+L7Dhg3Dvn37sH79ev1E4kdmzpyJTZs2Yfbs2fjrr79w5coVbN++HdOnTzfoZ2vbti2aNWuG+fPnAwDq16+PP//8E4cOHcK1a9cwY8YMnD17tsg23t7euHDhAqKiopCSkoL8/HwMGzYMzs7O6NevH06cOIGYmBgcO3YMY8eOxd27dw3KRGSWpJ70Q0TGV9wk1EeWLFki3N3dhbW1tejWrZvYtGmTACAePHgghCg64TcvL08MHjxYeHl5CaVSKTw8PERQUFCRycJnzpwRL7/8srCzsxO2traiWbNmj00I/qd/Tyj+N61WK2bNmiU8PT2FpaWlaN68uThw4ID++2vXrhX+/v7C1tZWODg4iC5duoiIiAj99/GPCcVCCLFu3Trh5eUl5HK56Nix4xP3j1arFe7u7gKAiI6OfizXwYMHRbt27YS1tbVwcHAQrVu3FmvXrn3izxESEiKaN2/+2PJt27YJlUolYmNjRW5urnjzzTeFWq0Wjo6O4v333xeTJ08usl1ycrJ+/wIQR48eFUIIkZCQIIYPHy6cnZ2FSqUSPj4+YtSoUSItLe2JmYiqCpkQQkhbr4iIiIiMh4eliIiIyKyw3BAREZFZYbkhIiIis8JyQ0RERGaF5YaIiIjMCssNERERmRWWGyIiIjIrLDdERERkVlhuiIiIyKyw3BAREZFZYbkhIiIis/J/epV0RHeKLRAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score is {}\".format(accuracy_score(y_test, y_pred_logi_rgre)))\n",
        "print(\"MCC score is {}\".format(matthews_corrcoef(y_test, y_pred_logi_rgre)))\n",
        "print(\"F1 score is {}\".format(f1_score(y_test, y_pred_logi_rgre)))"
      ],
      "metadata": {
        "id": "61DiEDZnmfYe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b1e9874-42f6-4240-97c0-efb6f2776831"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score is 0.9183673469387755\n",
            "MCC score is 0.839206575741442\n",
            "F1 score is 0.9130434782608695\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Actual_predicted = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_logi_rgre})    \n",
        "Actual_predicted['Report']=abs(Actual_predicted['Actual']-Actual_predicted['Predicted'])\n",
        "Actual_predicted['Classes']= np.where(Actual_predicted['Report']== 0,'Matched','Unmatched')\n",
        "Actual_predicted_group_df=Actual_predicted.groupby(['Classes']).agg({'Classes':['count']})\n",
        "Actual_predicted_group_df.reset_index()"
      ],
      "metadata": {
        "id": "Js6tqDKiI6qn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "077c7546-7b8b-4b7a-afc1-2dccf3b59ef3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Classes      \n",
              "             count\n",
              "0    Matched    79\n",
              "1  Unmatched     2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2031764f-b6de-4f9e-8031-4614fc9778a4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Classes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Matched</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Unmatched</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2031764f-b6de-4f9e-8031-4614fc9778a4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2031764f-b6de-4f9e-8031-4614fc9778a4 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-2031764f-b6de-4f9e-8031-4614fc9778a4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score"
      ],
      "metadata": {
        "id": "rpNOZUElK4HF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YK8-0GTWKv8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred_logi_rgre)\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Logistic Regression', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZxyNvyEgJHRT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "989001e9-a348-427a-a2fc-beacb6171823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LDA"
      ],
      "metadata": {
        "id": "S6RCKMpoY3yR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_lda, X_test_lda, y_train_lda, y_test_lda = train_test_split(dfattr, dfclass, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "rlM91O3neD1I"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "import matplotlib.pyplot as plt\n",
        "probspace2=[0 for _ in range(len(y_test_lda))]\n",
        "lda = LinearDiscriminantAnalysis()\n",
        "lda.fit(X_train_lda, y_train_lda)\n",
        "y_pred_lda = lda.predict(X_test_lda)\n",
        "y_pred_lda_probs=lda.predict_proba(X_test_lda)"
      ],
      "metadata": {
        "id": "OaA98W7kKsWx"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_test_lda,y_pred_lda)\n",
        "\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Linear Discrimination Analysis', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "FnzZwo3yrjsh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "b7665266-b298-4f27-f276-acf9ac3e918b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score is {}\".format(accuracy_score(y_test_lda, y_pred_lda)))\n",
        "print(\"MCC score is {}\".format(matthews_corrcoef(y_test_lda, y_pred_lda)))\n",
        "print(\"F1 score is {}\".format(f1_score(y_test_lda, y_pred_lda)))"
      ],
      "metadata": {
        "id": "z9rHB0a-l2UY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffb3690c-c487-46ce-c9d0-27b97cacb388"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score is 0.9387755102040817\n",
            "MCC score is 0.8775217193155135\n",
            "F1 score is 0.9333333333333332\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "y_predict_lda_probsfire=[classes[1] for classes in y_pred_lda_probs]\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_test_lda, probspace2)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_test_lda, y_predict_lda_probsfire)\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='Baseline')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='gnb')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OoH5LDI5lzMl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "04294e8d-7e64-48a0-c457-42199ce9b679"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}