wine_quality_09072022.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNl1AhpQy2P00LtS5+25Cmf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cjsutton77/ML_toy_examples/blob/master/wine_quality_09072022.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Z9UB208gNuiy"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_wine"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X,y = load_wine(return_X_y=True,as_frame=True)"
      ],
      "metadata": {
        "id": "qdq7O3N6N9kd"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "PjtA2sj8N7aR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "metadata": {
        "id": "7vmnHDQIOJb4"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_train == 0),sum(y_train == 1),sum(y_train == 2)\n",
        "# check the balance of classes in the training targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKNzavDUOyrS",
        "outputId": "59dc8f16-b4df-45a4-d3f9-89b3deb23bf7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39, 47, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_test == 0),sum(y_test == 1),sum(y_test == 2)\n",
        "# check the number of classes in the test targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbO9oY2dO1Ci",
        "outputId": "856b6d00-b9ba-4428-a5db-645e8680c51b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 24, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "u2T-uX9vPJLs"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(X_train.corr())\n",
        "# let's get a measure of the correlation for each feature, for co-linearity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "I9itT6GNPMso",
        "outputId": "0f44db74-0c2a-4d17-bb96-3f7e5cb7cd43"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41ec9aba10>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.corr() > 0.8\n",
        "\n",
        "# same thing, but I just want to mark high correlation with True, for co-linearity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "uW2vT_zFQGQC",
        "outputId": "22ac3d55-570a-4dd3-c764-db44767bdc7b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              alcohol  malic_acid    ash  alcalinity_of_ash  \\\n",
              "alcohol                          True       False  False              False   \n",
              "malic_acid                      False        True  False              False   \n",
              "ash                             False       False   True              False   \n",
              "alcalinity_of_ash               False       False  False               True   \n",
              "magnesium                       False       False  False              False   \n",
              "total_phenols                   False       False  False              False   \n",
              "flavanoids                      False       False  False              False   \n",
              "nonflavanoid_phenols            False       False  False              False   \n",
              "proanthocyanins                 False       False  False              False   \n",
              "color_intensity                 False       False  False              False   \n",
              "hue                             False       False  False              False   \n",
              "od280/od315_of_diluted_wines    False       False  False              False   \n",
              "proline                         False       False  False              False   \n",
              "\n",
              "                              magnesium  total_phenols  flavanoids  \\\n",
              "alcohol                           False          False       False   \n",
              "malic_acid                        False          False       False   \n",
              "ash                               False          False       False   \n",
              "alcalinity_of_ash                 False          False       False   \n",
              "magnesium                          True          False       False   \n",
              "total_phenols                     False           True        True   \n",
              "flavanoids                        False           True        True   \n",
              "nonflavanoid_phenols              False          False       False   \n",
              "proanthocyanins                   False          False       False   \n",
              "color_intensity                   False          False       False   \n",
              "hue                               False          False       False   \n",
              "od280/od315_of_diluted_wines      False          False        True   \n",
              "proline                           False          False       False   \n",
              "\n",
              "                              nonflavanoid_phenols  proanthocyanins  \\\n",
              "alcohol                                      False            False   \n",
              "malic_acid                                   False            False   \n",
              "ash                                          False            False   \n",
              "alcalinity_of_ash                            False            False   \n",
              "magnesium                                    False            False   \n",
              "total_phenols                                False            False   \n",
              "flavanoids                                   False            False   \n",
              "nonflavanoid_phenols                          True            False   \n",
              "proanthocyanins                              False             True   \n",
              "color_intensity                              False            False   \n",
              "hue                                          False            False   \n",
              "od280/od315_of_diluted_wines                 False            False   \n",
              "proline                                      False            False   \n",
              "\n",
              "                              color_intensity    hue  \\\n",
              "alcohol                                 False  False   \n",
              "malic_acid                              False  False   \n",
              "ash                                     False  False   \n",
              "alcalinity_of_ash                       False  False   \n",
              "magnesium                               False  False   \n",
              "total_phenols                           False  False   \n",
              "flavanoids                              False  False   \n",
              "nonflavanoid_phenols                    False  False   \n",
              "proanthocyanins                         False  False   \n",
              "color_intensity                          True  False   \n",
              "hue                                     False   True   \n",
              "od280/od315_of_diluted_wines            False  False   \n",
              "proline                                 False  False   \n",
              "\n",
              "                              od280/od315_of_diluted_wines  proline  \n",
              "alcohol                                              False    False  \n",
              "malic_acid                                           False    False  \n",
              "ash                                                  False    False  \n",
              "alcalinity_of_ash                                    False    False  \n",
              "magnesium                                            False    False  \n",
              "total_phenols                                        False    False  \n",
              "flavanoids                                            True    False  \n",
              "nonflavanoid_phenols                                 False    False  \n",
              "proanthocyanins                                      False    False  \n",
              "color_intensity                                      False    False  \n",
              "hue                                                  False    False  \n",
              "od280/od315_of_diluted_wines                          True    False  \n",
              "proline                                              False     True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f039f996-c166-40f4-b1cb-bc396c863a4d\">\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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malic_acid</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ash</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>magnesium</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_phenols</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flavanoids</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proanthocyanins</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color_intensity</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hue</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proline</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f039f996-c166-40f4-b1cb-bc396c863a4d')\"\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",
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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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f039f996-c166-40f4-b1cb-bc396c863a4d 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-f039f996-c166-40f4-b1cb-bc396c863a4d');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "dCfAwrgbPSqQ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "# got to scale for PCA"
      ],
      "metadata": {
        "id": "vh0FThaCRJiw"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(X_train)\n",
        "X_train_sc = scaler.transform(X_train)"
      ],
      "metadata": {
        "id": "hNP2YiIkRkLt"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_sc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1HtxxuoR1yY",
        "outputId": "16e489e3-294e-4859-c78d-d97cae89c973"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=2)\n",
        "pca.fit(X_train_sc)\n",
        "# 13 features, knock down to 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1m0OypnVQop4",
        "outputId": "889b4581-cbeb-42d6-93ab-f4deb23eb260"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pc = pca.transform(X_train_sc)\n",
        "# get PC1, PC2 on scaled training data"
      ],
      "metadata": {
        "id": "5fhHbNNfQt-f"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C788dYp5S1kc",
        "outputId": "c9e8c6f6-8565-4e42-9ed6-36b1f0404dd0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsDvKm82Sa3g",
        "outputId": "551600c6-106e-47a9-da9c-d823e21655dc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.36351132, 0.18633855])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(pca.explained_variance_ratio_)\n",
        "# ok..  is almost 60% of explained variance."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzPpt7X7VtaG",
        "outputId": "4ce88bbe-cedd-4424-caf4-68bf9c44500d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5498498695633061"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "9nzz3iTJSoRS"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(pc[:,0],pc[:,1],c=y_train)\n",
        "# plot the first and second PC's.. Look seperable."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "mZpJbeD6Sww-",
        "outputId": "290c0ba8-f2c3-4dae-d746-000b8173500f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f41e8e36b10>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "ThY4gGfkTcCD"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_sc = np.array(X_test - scaler.mean_)/np.sqrt(scaler.var_)\n",
        "# scale  test data with mean and std from the training."
      ],
      "metadata": {
        "id": "6UCN9pUHTMXK"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pc2 = pca.transform(np.array(X_test_sc))\n",
        "# now project.."
      ],
      "metadata": {
        "id": "L1gGuYlSTkLD"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(pc[:,0],pc[:,1],c=y_train,s=4)\n",
        "plt.scatter(pc2[:,0],pc2[:,1],c=y_test,marker='*')\n",
        "# plotted the training data as tiny dots, stars as test data.  Did a pretty decent job!  Note the PC's show seperability\n",
        "# the three classes."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BBlrRsD4T88m",
        "outputId": "5e21f616-a923-49cd-847a-3880581a9366"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f41e8dbfed0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Kernel SVM to the Training set with best parameters\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(C=1,\n",
        "                 kernel = 'rbf', \n",
        "                 random_state = 0, \n",
        "                 gamma = 0.2)\n",
        "classifier.fit(X_train_sc, y_train)"
      ],
      "metadata": {
        "id": "aPpo_iaRwwv6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91fe3c81-f2da-4a73-bb1b-f7b3c08b0855"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, gamma=0.2, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the Test Set\n",
        "y_pred = classifier.predict(X_test_sc)"
      ],
      "metadata": {
        "id": "8BUyoSo38dm5"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm,annot=True,fmt='2.0f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qr3bAZQ28f7r",
        "outputId": "ab975c6f-7aaf-45c1-eb70-ede7b110bcf0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41e8de02d0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_pred == y_test)/len(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HP_AUkLb8oXo",
        "outputId": "388b79f5-4a52-492e-b032-82ceb5de12ca"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9661016949152542"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.score(X_test_sc,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2z_4pFal8yZV",
        "outputId": "39704caf-0b1e-45d4-a106-a626a1fb27e0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9661016949152542"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(pc[:,0],pc[:,1],c=y_train,s=4)\n",
        "plt.scatter(pc2[:,0],pc2[:,1],c=y_test,marker='*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BGRn_gFs9NDF",
        "outputId": "ebb036ff-70fe-4c98-dd6f-789ccaba104a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f41e8c88210>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pc = np.array([pc[:,0],pc[:,1]]).T\n",
        "X_train_pc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_urJgej5-N-B",
        "outputId": "3817403f-0e15-4690-f12e-359c9a7feef2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pc = np.array([pc2[:,0],pc2[:,1]]).T\n",
        "X_test_pc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jCwpcs6-6Wd",
        "outputId": "5e21f7c0-7168-41f5-df6c-1f8f20064485"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(59, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Kernel SVM to the Training set with best parameters\n",
        "from sklearn.svm import SVC\n",
        "classifier2 = SVC(C=1,\n",
        "                 kernel = 'rbf', \n",
        "                 random_state = 0, \n",
        "                 gamma = 0.9)\n",
        "classifier2.fit(X_train_pc, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "te2K9BPM-PMp",
        "outputId": "8b4977d1-0660-4192-c373-a73bae9a827c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, gamma=0.9, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the Test Set\n",
        "y_pred = classifier2.predict(X_test_pc)"
      ],
      "metadata": {
        "id": "YhUKSodT-joQ"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm,annot=True,fmt='2.0f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "24Kg1efc-lv5",
        "outputId": "a2bbce6d-abe9-498d-a942-9ed152adf863"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41e92b9ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier2.score(X_test_pc,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFcw_RCM-n0N",
        "outputId": "45da0703-4050-46e1-8893-2a45c5f02936"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9830508474576272"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_test_pc[:,0],X_test_pc[:,1],c=y_pred,marker='*',alpha=1)\n",
        "plt.scatter(X_test_pc[:,0],X_test_pc[:,1],c=y_test,marker='o',s=200,alpha=.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "aBMgkASt_HKE",
        "outputId": "90bfac29-13e6-43a1-fc1b-58b29f11535a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f41e8c78d10>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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5PgLBUDqD6yshX29cs0kkpZwEJtv/rgohDgHDwEvXOrZi9SOEYCST5aW52a6qg3/yznd3/v2Jt7y96xo3CLANfcWSgkxNpzeRIO6ZzDRq5OwEldYCVacFRNaKT+RXdnyfrBXjaHGOF2emOLkQbUx++qUXSVoWuwr9pG0bIQS6pnNwZgZb1/nZhx6i6jj8wTPfY6HZ7LhDTC3yxz84MorZDkP83rlxUrbF4QsKcaUsizsLfRxbmMcNAnRN45/fsZs7+/qZqdfZ0vazSxk1qBACtubynCzNU3VcfmzvPfzOk99itu0n74nF+Kl799H0PXRNZ2dvgZbvo2nd0UOKW58VdaYJITYD9wDfvci5DwMfBhgdHV3J2ypuMvlEgrRlUXGcl63AF4QhlVaL3f0DyzIZXykZ28bSdbKxGDEj2vATQpCLxXnD1q2EEr566gTzrQYbUllyMZvnpiY5Wyl3xnh2aopCPMHDI5tp+T5x08QQgg2ZDP/9Xe/pbEo+smlzV82YmuuyIZ3GWlIjPHJxJKK2c1KiCYEMJd8eH8MQAjcIMITAD0O8IPLLLzQbDKczSKLa6jHDaNc219ne08tCq8mJ+XlmGw0ylo0mBAutFrONBtvzvRQSCUxdp+G5xK9DCQTF6mbFhFwIkQL+DviYlLJy4Xkp5R8Cfwiwb98+VTz5FsLQNO7s7+eF6SlKzSaZWOyiIu34PlXHYUdv7yVrm7wSdE1jJJvl9MIC/akUhUSC0UyOjZkME5UqThDwxi1bGUhlMHQNPwz5vtHNfO30Sc4tRpLoOp982zsIwpDDc7OYi9EkiI6IA53ji+8nbLtaFik7LXRNR2u3ZHv63Dk+/dIBfBlGdVvaIp4wLbble1hoNqOIHyFo+j5BGHJboUDGtrENHTcI2u6iJKbQ+MHbd/HY9tsQwBeOH+GOvj562+UQvCDA0o2rbmShWPusiJALIUwiEf8LKeVnVmJMxdoiZpjcPbiBM6UFJqpVJJE4irblGYQBKctmz8DAsjosK8FgKs1UtUrNdUhZdpR0k0hw10DAVK3K986N4/g+jh+5LgrJBPPNBqamkY3FmGs0mKxW2NKTZ2tPnhPz8/ihx2Dq4n78xeqOewcGu0oSeEHQWcR64wk2pFLs7u/ne+fGOxuvoZS8dvMWXrtpcydsU8jz1/bGowzZ0WyOI3OzFNp7Cdl4nB+7+77OvT645N8AFafF9nzvij3pKNYO1yzkIvpL/BPgkJTyd659Soq1iqXr7OgtsCnXw3yzQc1xCQmxNKPjfrkw+3Ml771nYJAXp6eYbzbI2jF0TcPUdUayOeqei+tHae/RRmUPD4yM8v7de8nGYvzFi893Cl/1xOPsLPRyaHYGPwwpt1ptS1ziBiF+GNATi3NnX/+ySoNR79BonLhpMpTJ8KZt23luchI/DAiA4Uy2E0+/SCglZafFvg3DHat/IJmi2Giw0GjQ8zLlaUvNBj2xBEOp9Mp8oIo1xUpY5A8BHwBeFELsbx/7ZSnlF1ZgbMUaxNJ1BlNpWJmglCsmbprsHRxislplvFrGD0OEEAgElmYw2ayyI1+gL5nENgw+9sBDnWs/dE+3dRtKeHh0MxvSGeYadZrtQlSJhEn/ZTrYZ2NRydpFhjNZTs7P44QBdw0MUm61OFMu0fS8TmPmludRdlrcv3GEjUvcNLqmsauvn8Nzs8zW68RNc9l9G55L0/PoTSS5vbdw2TK5K0kQRlkAuhDXbXH2w2jRFAgMTbth720tshJRK0/QlRKiUNw8bMNgc08PI9kspVYLt113xNB0NudylJ3WyzYqrrsuejvhJmqnduX1vbOxGJZu4AUBph75yvcODvGzD7yGQiJJy/M4PDeLF4R4YQspQUNw7+AGdvX1LxvP0DTu6OunnG4xVilTbDQ6XzZJFLmyI99LLha/7i4VLwiYbzY4WynTcD0Wa38NptIMptIr0mpOSknVdZmsVZiq1jrHhYANqQyD6fRlSwGvV8TNaNq6b98++fTTT9/w+yrWN34YcmRulplGnYRpLSu36wUBNdfB1g32XOD7vhrOVSocmZ+j74K9ACkldc+l4fmEYYihacRMk5bvcffAED1LOgNdipbv4QWRt93UtevSpONizDXqHJqdJZQhScvuLIahlNRcBzcIKLSfCpZuCF8NizXtJ2vVdtkDq7M4BWFIzXXxwoCRTJatPfl1uRcghHhGSrnvwuMql1exbjDaroqBZpOz5RJzjXqni5EQUTx6FMqX7IQTvhKG0mkqTouZelRMrLOhKaKWd4t1WaJiXw229uSvSMQh2lSO3eBv7Wy9zoGZabKx2DKR1oTo5AOUm01enJlmT//AVYt5KCWH52aZazQ6m71L0dub0lJKxiplgjBkZ2/hurl11hpKyBXrCk0ICokEhUSCetvCkzISiqUW4LXeY2fbXz1RqWDoOinL6vh4Fy1/KWFnvsBwJnPN97xeNNtdkS4m4lLKLiHNxuMsNBucKi2ws7dwVfeZqlWZadSXRTT57cSppYthbzzBRLVKPh5vN89WqN0DxbolaVnkYnF64nEytr2ij+q6prGzt8CrhjeyIZ2m5rjMNxvMNxu0fJ+tuTwPbBxhYza7qq3K6VoNDbFMxA/OTPMr//SVZa/PxuJM1qo4/pXXRA+l5EypRPYimb6ffOq7/PkL+7uOCSFI2TZjSxK61jvKIlcoriNJy2KrlWdLrqcTRXM9Iz1WkiAMGa+WL9rM+UsnjnFwdoaZWo3+JbH2mohq0sw26l0ROJej3Grh+AGpJfdpuC6hlHx77Cxx0+Ddu+5ECNGJ9IkZBnONOlXHUc2mUUKuUNwQhFhu1a52okzT863swjDkJ//n31NutZBEjS3+xec/iyY0PnT3vbz9ttuByI+/0GpdsZDXXRddP7+wHZ6b5Zce/1J7LIOG5/G+z3wKgD942zvYkI5cUToi6leqhFy5VhQKxcUJpWTpg4OmafzcAw8Ra1vFfrtf6UOjo7xx2/bzrxMCP7zyCox+GLZrS0bcXujj5x54DZYe9WF12vVofvWRRzsiDiBFNEeFEnKFQnEJ9HbRr6XsHhjkgeERAimJ6QZeGPJje+/pqnwZSImhXfnTh2noBPJ85Xg/DLmj0I+QEo0oVj1pWsubdUtUpcc2SsgVCsVFiRkGpq7jh93tOZ6ePMfmbI4funM3uhA8NXGu63zL8+i9wnBKgKwdixqRhCHjlTIvTk/x3YkxnDDk9Vu28sDGEYrNBt86e4bpWhUpZbsMgiStkoMA5SNXXCXnE0CiL7ela6Qte01s3imuDl3TGMlkOV1a6DTXBvjF1zzC7YU+dE3rqsMObVeH4KoKo6Usi6Rp8tLMDG4YkLZttvf08isPP0o+ES0Ir920mR47zlilTMPz6E0kKCzpV7reUUKuuCK8IGC2UedMqYQT+Et8mhJLN9iUy9GXSK65DT3F5RlIpjhdKnXK6QLcuaS581KfNUCp2WQ4nbmqhCopJX4QUmw12JDKoAmBpusdEQc68eI5I85svU7dddmzUzWZXkS5VhQvS8v3eH56imPFIrZh0JtIdsrE5hNJLMPgaLHIC9NTVxU/rFj92IbB7r5+qk7rZX+3C80m2ViMLT09V3WPquvihgG3F/ooOy2CMPLLe0HAV04c7+oJu+jmiRkGtq7s0EWUkCsuixsEvDA9hRsE5NtdaC7E0qNWa60g4MWZaTzV/PeWIp9IcPfgEE7gM9eo0/C8jrgGYUip1aRYr9OfTLC7f+CqqxROVCuYusFgKs3Wnh6avku51eKF6Sm+eOIYk9UqLd9vx5v77Ogt0BtPMFuvX4+3uyZRS5riskxUKjQ9v9OjcpFiu07JUt9pxraZb9SZrFUZzeZu9FQV15FcLM4DwyMstKIqjKVWEymj+jXD6QyDqfQrKjIWhCHT9Rq5dpPq3kSSx0+e4MT8fKdJ9V8deIH+ZJJ9Gzbyth070TQNNwiYqFbYmL2yWPVbHSXkikuymNl3sdZhn/zedzENjX/38KNdxzOxaENqOJ1R9aNvMXRN69SpgeW1Vl4Ji66SpeUR4obJdyfGOz9PtOuwvHXHbWjtvylD02i47jXd+1ZCCbnikpRarU5HHQDP93luapIQyfMzU2hC8OT4WXQ07h4cxDQMjLa1VHZa5OOX72qjWNusRKSSaFefXMo7bt+FoWv8t/3P4gYBpqbxa9/3OvYODnVesxKLyK2EEnLFJWkFPpo4b1VP1Wv8v995ovPlklLym098k5hh8Dvf/5ZOE2JNCLXpqbgizHY7vsVGHIscmJ7GDQK25HKcKpU4WpzrEnIn8FUM+RLUs6/iksiwO0V7JJvj997ydjK2jReGeGFILhbj9x57e1cneYG40MhSKC6KEILRTJaa63QdH8lm+Y1H38Dvvvlt/Mz9D3Zqni/S8LwrruWyHlAW+Sql5rrM1GvU270i47pBfyp5Q5NvbMMgDLsluTcWp+l5aERPxE3PI39BFp+UoYonV1wxfckkJxbmCZa48d63Z2/n/Ou3bOt6/WJMe+4iezfrFSXkq4yq43BsvkjFaaFreiexouK0GK+WSVoW23vyXdEi14uMbUO7MNHiZtRUrUbGtvnZBx4ilJL/9OS3mGnUO4khUW0OcdHa0ktRPk7FIrZhsD3fy5HiLPl44rKb5F4QUG212Ds4pDbTl6B6dq4iSq0mz09NETMNEubF/X8t36fuONzR30//DeiOcqQYtd+68NH2UpRbLQaSSXZc0CHGDQKKjQZjlTIt30NKiBsGG7NZeuOJl22IrLj1Ga+UOVYsYhnGsm5NQRhSdR1CKbmzr/+qSgDcSqienauchhdlTyYt67KiFmtHhhycmSG2wbhigX2ljGRyTNfqOL7/smLr+D5SymW+y/FKmRML8yDPd+UB8MKQ4/NFjlFkay7PxkxGWenrmI2ZLBk7xlStymSt2olmkUSVGIczrzxe/VZHCfkq4VyljC60ZWJ5cGaahWaThzdt7hwzNI24aXK6VOKugcHrOq+EabJ3YJD905N4YUDStJaJrZSSmuvihyF3Dw52urgAnCmVOLEwTz4eX/YobOk6VjzR7p4+hxcGbO3JX9f3o1jdZGybjG2zKZuj5fuEUqJrgoRpYShXyiVRQr4KiLLUqhdNvPnLF59nul7vEnKILNtiO136elso2ViM+4aGOV1aYK7R6HScB/DCAKSkkEixOZcjuSQkbL7Z4MRCkd5E8rL9MHVNozeR5HRpgYxtr9vHZsV5bMNQ7rarYEU+KSHEfwXeDsxIKXevxJjriarjRI+PbYtjodnkj555CjcMODw3C0LwG1//Kqau84G77maknf4uhGCh1bwhj5opy2J3/wBNz6PYbNDyojjxmGlQSCQuWk70bLlM0lre1Phzhw/hBAH//M7zfyqaEKQsm7PlkhJyheIqWakl70+B3wf++wqNt64IZNgVr50wTeq+y/6pqeiAlDw7NcmWXK7LJ25oGq5/YwtUxU2TjebLx+/WXZeFVvOiovy5o4fxwoD33HFnl5smbpqqoe4rwAuCTmSRoWlqn2EdsiJCLqX8phBi80qMtR4RCFgSPWQbBv/+tW/g57/0BU6UFoCoi8pvf/9bu/zMUoK+Sltd1T0XbUkfxvFyiV/52uN4QYAbBAgheP9nPoWh6fzGo69nS9s3rglBzVVC/nJEVQdbnCmXqLpOZ2MwYZlsyuToicdVLP86Qu0erAJsQ+fCKFAnCDhTLpGyLAqJBGWnRbHR6HqNFwaXDFO82Xhh2GUZbkhneMv2HbR8Hy8McYOAlu/zpq3buiolakLDu6C1mKKbuuvyzMS5qGRwGJKPJzr14QWCQ8VZvndunHKr9YrGDzut1BRrhRu2myCE+DDwYYDR0dEbdds1QdqySZhmV4hfEIY8tn0n7929B0s3+NTBFwmWfLmCMMTUtFWb3bZYi2URTdN47+69fO30KWZqNYQQ5GIxPrD3nq7rQhl2tQ5TdNPwPPZPTWLoGr2J5Ulhi5uEju+zf2qCuwc3XHQT/UJqrstktcJUvRYldUlI2zaj2Rw9sZhKvlnl3LDfjpTyD6WU+6SU+/r6+m7UbdcEQghGc7noEblN0rL4qfteRdqOYRsGH9h7D0PpdOd8pdViOJ1ZtTLwEBAAACAASURBVCFZccMkvKDiymy9xlStxt7BIe4d2kCx2WSiWul6jZSyK/JFcR4pJYdmZ9A1bdmTWHjBU4xtGCQtmwOz08uaJy/FCwIOzEzz1MQ40/U6acsmH0/QE4/jhSEvzkzx3Wuw7hU3BhXfs0roT6aYbTRYaDTouYiltZRKq0XSslZ10aC0bZO1bZqe14krz9gxfvWRR9m3YRiAZycn6Imdr9Pi+D5JyyJtKf/4xag4DlXHoTfZvYHs+D7/4vOf5eNvfIz+1PlsX9swqLku841G1/FFvHZHp7rrLNuUFkKQMM3Ok+JzkxPcPTTUSeZSrC5WxJwTQvwV8B3gNiHEuBDiJ1Zi3PWEJgS7Cn30JBLtdlrLi+a3/KjVVtw02d0/sOo3s0YyOWqu23Gx2IbBq4Y3IoRACMF9G4Y7Ii+lpOo4bMrkVNTFJZioVrCWxFZLKQnCkGcmz7HQavHE2GmCMOxyaSUti7OV8kXHO7kwT811yL1M3R7bMEjZNgemZ1R54lXKSkWtvG8lxlnvGJrGnX39LDSbjFXKFBv1Trd6iSRt2ezu63/ZwkKrhUIiwaZsjrOVEvl44pJJQaGUzDcbDGcy9CVVDPmlWGi1unIGfu+73+Grp092fv4fz+/nz57fz65CH7/1xjcDUUmH+WYDPwy73HCO7zNVq9ETX25h/+o/fYWfuOe+TiRRGAQYgBf4zDbqq/pJcL2iXCurDE0IehMJehMJmp6HFwad3ohrzXcshGBrTw+agLPlEkII0nasIyh+GFJzHAIp2ZTNsSXXo6zxy+CHAZo4/zfw4/fcx0KrxUuzM7QCH1M3GE6n+fkHH1p2bSgv3K+oI8TyLj/nKmVenJnm8RPHed/2OyhOLFAvRa/1wpDZ1DRv3HsH2d40+ip/IlxPKCFfxcRNkzhru0CQEIItPXmG0mlm6nXGKxW8IAAButAYzeboTya76rMoLo6h6QRSYrTFN2XbvG3nTvZPTRBrR6q8Ycs2+i6siilZ9jQ012wQX7Jh+tnDL/HXB16ILHdP8vjXn+MbX91PLp3g3zz6aCdhbbZa4+DTx8kk4uy4byuJtPKZrwZW//O54pYgZpiMZnM8uHGEh0Y38dDIJh4aGWVTLqdE/ArpTySpX9Bw+HvnxklaNh/Z92oGkim+PXa263zDc+mJx5dFN/lhgL5E3N+weSu3FQrghmjnaiBBJE1+8K7dXVnHVswk3ZtCaIJDTx6lUW2u/BtVXDXKIlfcUIQQHYtScXUMptOMVbs3Lt+9azc/etc9pGyb12wcZbpW6zrf9Dx25HuXjWVoGv6S7k/pWIyP3Hc//+qpv8AyDRxdcv/GEba368pLSSTobes+lowii44/e5Ldj+zqdLdX3BzUp69QrBGiLN8kpdZ5K3gglSLVLmdgGQYjufNZsjXXJWlaFw0ZzMcTyyKjnjhyAt2TPLpzO/l4gheno1o/TtPlv/zcf6NcrmFqGla78mUsadNqulTna8vGV9xYlJArFGuI23oLxA2zS8wvRs11CGXI7v6Bi0YL9SeTyAtS8beIOB996DW8efsOfumhh3nPHVF1yhP7T1OZq3Fw/0kGYt0lie24xeTJ6RV6d4pXinKtKBRrCEvXuWtgkCPFOYqNBrqmkTRNdE0jlJKG5+EFPhk7xq5C3yX3H2KGyUAqxWy93okjT3hQ6ItCDk3d4MinnuEfvncMGUqkLvj6Xz3B937zi+x+1U4+8rsfBCCeilGerRCGIZqmEQRB5H7RVRXGG4kScoVijWHpOnv6B6i7LtP1GtO1GkEYddLpSyQYTKVJW8s7OV3Itp5e6q5HqdUka9nIsLsh9uve+xALMyVmJhdwdUl62mFotJ8f/sV3dY3jewHTZ2aZOTOH23Kj2vq6Rv9ogcJwnnhKRbZcb5SQKxRrlKRlsdXKv+L2eKaus2dgkMOzsxRbDRrSJxYEGO34cCsbZ8/b7+ZLf/JPZKddwrLDG/7195EfOu+Hn58ucfKFMwhdI51LEE9nAAiDkJmzc0yenKZvpMDormEVd34dUT5yhWIdY+k6ewYGuG9omE2DfcxX65S9FiXXQRNQ/OYp8lMu7/nwYyQySZ59/IXOtfNTkYj3bcyTH8hi2ufdOJqukc6nyPZlmB0vcmL/6WWFvRQrh7LIFYp1jhCCjG3z6l3bSC54ZHIZBAJNCHre/zoyP/MOktkE+75/L8XJqNFJs95i8sQUlm3SN1LoGq8TqtgeO9eXYWGmzPTpWYa2Dtzot7cuUBa5QqEAIJlNkM6naVVanciUoW0DJLPRZmg8HWPjziEAFqZK+F5AMhMnnj5f7/zZx1/kT/7dXywbO5NPMXlyOtoMVaw4SsgVawIvCCi1mhQbDRaaTVq+d7OndMshhGDb3k0ITVAr1S/5Os/1mTo1gxU3Gbl9uCsZ6Bt/8y32f/0ATsPpukY3dALPp1JUMefXA+VaUaxqGp7HVK3KeKVMKJdEVUhJIZFiYyZzRR1wFFeGHbfZdf8OTjx/htJMGcMySKTjaLpGGIY0qy1KcxV002Db3s1YMYtmrcWvv+P/iSJWJBimzi+84TfQNI0P/of3cs/ro3h0wzapFKv09KvqiSuNEnLFqqXYaHBgdhpNaGTs2AWNpyUV1+HZqQk2Z3NsVpUTV4xFMW9UGkyfnaM0Xcb3AwxDJ9uXoX+0QDIdx4pFRbfiqRg/8uvv4U9//W9wmy6BH4n5Iz/4AHseub0zrqZr+K6qZ349UEKuWJWUWk1enJkiY8cu2kBDCEHKskiYJqdKJTShsWlJerri2hBCkMwm2bonCXuihXNxoazMVxGi2yu799E72bJ7lCNPH8ewDGQQ8s6PvhnDPC8xYRBiWEpyrgfKR65YdYRScmh2lpRlLxPxs6VSd1NnIcjH45wqzdPwlN/8erH0aSeeioHo7hPqez7HnzvF9r2bef37HibwQ448daJrDM/1yRbSKFYetTwqVh3lVgsnCDrFoBZpeh4/+6Uv8KuvfZR7Bjd0juuahq5pTNeqna42iuuHaZn0j/RSnFwglYs6OumGwU9/4kPctm8bQhPc96a9ZPvOi3bgB5imTjq/vHeo4tpRQq5YdZyrVrCX9KacrdcYr1Q4WpzDlyGfP3IYJORisY5wpy2bc9UKo9ncmmiDt9bpGykwfWaWwA/QDR0h4PZXb++cXwxTXKRSrLFJZXdeN5SQK1YdddcltkTI/+nUSf7ywAud5ggvzEzz9OQEuwoFfuuNjwGRVR6EEj8MlZDfABLpOFvu2sTJ/adJ96a6fOFLkVJSmq1QGO6hf1PfDZ7l+kEJuWLVcWF/yR/efRfZWIw/euYpAATw8OgmPvbqB5ddK5HLjimuD33Dvei6xskXzyKDkHg61olkCfyAerlBGIYMbu5n5LYNqvnEdUQJuWLVYRv6sq7vuVgMX0oE4AQBSdPEXGK1Syk7fUAVN478YA/ZQobSbIXJU9OU56ogJYZtsGHbIPmhHmIJ++UHUlwTSsgVq47hdIbDc3Nd7pWjc3PcXijwCw88zGcPH+JwcbbrmrrnUognLxqqqLi+6IZO71APvUM9nYgiFdN/Y1FCrlh15OMJhIBgib/7R+++t3P+p/a9atk1ju+zsZC5YXNUXBwl4DcH9RyqWHWYus6WXJ75ZrMrZvxSlJtN8rEEGVs9wivWJysi5EKIx4QQR4QQx4UQ/3YlxlSsbzZmMoxms8w16niXqJgXhCELzQYJy2JXX5+yBhXrlmt2rQghdOCTwJuAceApIcTnpJQvXevYivWLEIJtPXlSlsWphQUqLQfT0NBF1JvSCwM0BBszWUazua6NUYVivbESPvJXA8ellCcBhBB/DbwTUEIOSBlAWEIGM4AHaKDlEHoBIVTVvsshhGAwlaY/maLcajHfbOCFIYbQyNg2+URCCbhCwcoI+TAwtuTnceD+C18khPgw8GGA0dHRFbjt6kZKiQzOQXAGpA8iBkIHJAQlpH8CqfcjjG0IYd3s6a5qNCHoicfpiasmvgrFxbhh5oyU8g+llPuklPv6+m7tDC8pJdI/Af5xEEmE3ovQkggRQ4g4QsuBlodwHuk9h5Stmz1lhUKxhlkJi/wcMLLk543tY+sWGYxBMA5a7yU34IQQILLIsIr0XgJzL9F2g2K94zQdGtUWYRCiaYJYKkY8uT7dcJ7rEfghQoBhGapWyyVYCSF/CtghhNhCJODvBf6PFRh3TSKlF7lTtOWNDmT9ryH2ZoTe0zkmtDQyKEJYAr33Rk9XsYqoLtSYOj3L/NRCp2emBJCSTCHDhq0DZHpv/TKwYRhSna8xdXqGyly1c1zoGkOb+8lv6Fm3C9uluGYhl1L6QoiPAl8CdOC/SikPXvPM1igymAcZIjR9+fHWp0AkIPGO7ou0BDIYQyghX7dMnZnhzMFx7IRFri+zzAho1lq89ORRRnZuYMP2wVs21NJ1PI4/d4raQg07YZPtO5/kFfgBU6dnOHd8ktFdwwxs6r9lP4erZUUyO6WUXwC+sBJjrXnCadASnR+l8wS0vgayBGjQ/Fuktx/0Pkj+S4QQCBFHBkWkdBBCJbWsN2bPFTl9YIxcXwZNv/i2VTwVw05YjB+bRNM1hrYO3OBZXn881+Po08fxHJ/cRfp66kZUzzwMQk4fHCcMJBu2Dd6Ema4+VOzWiuPQtT7qg9GmZ3AaCIEa+PtBu9CaEFF0i2Jd4Xs+Zw6OkS2kl4n4sWdOsjSxVdM0soU0Y0cmcJoOtxrnjk3Sqrsks4mu457jdX8OukauL8PYkQnq5foNnuXqRAn5VSLDGqE/TuifIvTPIMP5KFa8gwZLSqkKYzvkfnPJMQNiP4RIvHv54FdRue9KUtcVq5/SbIUwkOhGtytufrLEf/qXf8jpg2NdxzVdQ9MF85OlGznN647reMyOFUn1JJad++2f/C88/aX9Xcc0XcO0DWbG5m7UFFc1a65o1tImsDeSMChGm5hhtS24BhAi/QCEjtRHEPowiCyEM7A0NtxrbxkYt4F/DNynkYkfIBJ3DdDbY5qXnYOUzSixKJgE6UZLg5YEbQSh5xFizf061z1Tp2dIZM5v3I0dPsfUqVmOPhv1u/zyn32Ne19/F/kNObbt3QxAMptg6vQMg1tuHR9xaaYMgk7N8jAIaVSbVIpVxg6f44m/e5JdD+xAN/SoZyiQyMSZG59neMcGLPvy351bnVX/zZfSRwYLEI5DWAMkUtigDyH0/huSHRn6Y5F7REtddENSSh/8U8hwAfQRCC6IvhR5SH4UrAfBewmc/x39H0nUJiEEc0/730vHdaOIlmAC/NMQFiPh1jeBthj54oB/CBkYSOM2NL2w8h+A4rrRqrW6+lg++/iLfPnPvt6x0A8+cYQXvv4Sr37rPR0h1w0d3/UJg3CZJb9Wqc7XOk0pAL77+Wf58//4twhNYFgGZw6N80tv+g9YMZOPP/5/YdpGJPpC4DZdJeQ3ewKXIwyK4B+OfMdaMkptF6IT4if9k5ElbGxBXKeGAqE/3Rbx/CXjvIUwQO9FhqVIxEUKGTYQ7U1PYd8XWdL+oSi7M/7WdpYnIAMIihDMItkP5p2AjQzOQnAWwhDkHIQV0HpBeOCfAGGDsQWhJUGPRZ+Jd4BQ7kIzbr2NsPXCOz/6GLmBLH//n78Q9cM0dd7wI4/wjp9+bNlrbyX3WhCEXU8XD/yz+6hV6nz+D76M7/oITZAbyPLTv/shTLtbtsIwvNHTXXWsWh956M+A9yKIRJQZKWKdX7QQJkLriYQtGEf6R5By5X+ZUgYQHG8vIBeEEzrfRIaNrmNCy4EsRhucuJ2MzUjEx0CkQEt3i3hYBWMkOu4Xka0nkO6zkQUusiD86EnEyINmgIiDnovG8I8gw2rnM0HLgX8YGaoNoLWCFbPw3eWb3F7LQzM03KaLDCVLPShhEKLp2i1jjQMYpk64pMql0AT3v/VefC/AjBlIKRna0s/wju4oFSkl+iUifdYTq/ITkGE9ssS1bCRQl0AIEbk6gumorsmKT6QMMlg2BxnWofafwf3WRS6KQ1hCmHsjP7bfdotomSUCHkJYj3zdCAhmwDsO4RS4z0LzHyAoRQIeTEUifyGNz4B/FvzjSBlFMAhhgDCQwdSKfgyK60f/pgKNarPr2PSpae5/6718/Cu/zuvf/zCzY8Wu8/Vyg/7RwnXxj4dheFMs/fxgDtfpXtAOPXkMK2by/l9+D/e+YQ/H95/uil4J/ADT1ImlVHLQqnStyGAKhL5cQN1nwf02IvXR7gu0HARnkfqGFU1zl/5EVOxq8WfvJXBfiAQXCc3PRj5sLQOxt7TT7hORVS52IKx9SOdpkLK9SSro+MXdY+B8EVIfAy0ebXbKAAKie8oSeOdAemBujsTdvBuMTRA64D8Lshr9HCyA0bZURArCCaQcvewiqFgd5Ad7GDt0jjAMOxt9P/SL7+qcf/fH3t71eiklgR9QGM6vyP2llNTLDWbG5pifXECGUfvqbCHN0JYBUj3JG9I0OZ1PYZp65E5qP2nc9egd3PGanaR7UrzqLXczO17sejKplxsMbx9SafusQiGX0oNwAsRF2nY1Pw/+i8jETyK08wIrhIEMg+uQ5t6CpWIY1qD1d0RqC4STUbam9TBCvLU9F4EMaVc8NEEEYN1LVMJWgmxAUAH/KaAEchrCQrQHIBsgQqJfSwBoEC6AH4+s/2Acgun2OT1y+1Q/DtjI3O8j9BRC6MiwfR+xPKlCsbqwbJPhHUOMHZkg1788o/NCSrMVBjb1EU9deyVIt+VyfP9pags1TNvsiLaUklbd4fD3jhFL2Gy/dyuJ9PWtPKnrOkPbBzlzcLzzOcSTMUief03fxvPfbbflITRB74aei4y2/lh1Qo6M/M6LlrUMG1D+WQibQBPQYeEnkJiQ+gjCblfMFSYyXFjhNPdFC7r9k/1qpP5bUP7F9nEDYo8hkj9+iffiABKhmYAZ+fEXPgLUomvRoP7/ASHYb4+eLEQicsHIBvinwPk64EevDc62r3OX3MQH+82wTAAu3lVHsfoY2jqA7/pMnJwmlUtixZY/SXmuT22hRmE4z+jtw9d8T7flcui7xwj8YFkWpRCCeCpGPBWjVXc49ORRdj2w87qL+cBoH626w/TpGbKFS2e5thoOTt3h9vt3dEW6rGdWoY88YKkjTGgJiP8w0IrO4Ub/WXeBuXfJdXrkhlhREiDd7kNB260isoAf+b+XIGUYiaowOR9e2H4vQoP0LwM97XNhdN7+fjDvguafQzhHJyTR2AgiSbSABe3XL77H9hps7Iw+Cy7c7FWPm2sFIQQjtw+z496thEFIaaZMpVilVqpTna9RminjOR5b9mxi612bV8TVcfKFM4RBSCqXvOzrYkkb0zY59sxJgku03FsphBBs2rWR0ds3Ul2oU56t4DRdAj/Ac33q5UYUbw7c8WDkclFErD6L/CJri4i9Huk+Cd7TdKac+ugFDRlCWOGEGGEMIb0ZYMkfjHQg9nZIfADc58H9RvdFsg7aAEKYSOl2LUrRoAL04civjQH4kV/b/WZ03n0ejDroedC3Q+zN0PwzOk8H+hYIToL5qijt3z/Wvkf0uZ1fSFQThrWEEILeoR7ygznq5Qa1Ug3fjfzFyVyCVG7lfNX1SoNKsbrMEg/8gI//2O/zkd/9YNe5WNJuLy41ei5SA2WRZq2J7wUIEcV+xxJXXzdICMHQ1gH6RnopzVaYG5vDaXnt8gQZ+kcLJLOJWyYRaqVYfUIuElHZERl2YsOlDMB7AfTRKDvS+Uq06WjvO3+ddEDkVnguWSDWVcxKxF5//ry9r2sOUkqQLkIfWvJebKR0zy86YTMSYuNOMO+F5l9C6zN0LOjwFLgnQNsJqb3gTkTHtU2RtR5MQOIjYG6NNkfdlwBxPpNU1kAbVF2H1ihCCFK55MtaytfC3HgRw1r+1T/69EnGj07y3NcO8LoffqjrXCwVY+rk9DIhD4KA8myFiZPTNCoNxJIn0HQ+xeCWfjK96atehAzToLAhT2HDymzqXgrX8Qj8aPExLWPNhnSuOiEXwkJqQ5FoicWwOw1SPwPWq6KNTfu1oJ1PepEyAGF01flembkIMG9DuvuRmnbZKBApZZR5qW9EtMMFhRBIfTSymvXFP0gJqX8ddQgSAvRt4H4H3K8u3hX0OyDxI5GvXN8E1huj6BRtBPwXwdjSfqkeCbqWaz8BBCC98wuJQnER5qdKnTR3gE//9uf4zj88je9FrpPP/O7/4nOf/BLb9m7io7/3EwDEEjal2UpXVMnSkrPxdJxcX7fIt+oOR546Qc9Alq13bcIwLy43vud37m2Y+iVft1Jcrt75wGiBwnB+RTaTbySrTsgBhD6EDCaQMkAIPRJU+8Hz5807ui8IS2Bsvi61RoSWQ5q7wTuIFFbUuu2CLFIpm1FEiz6MMLZe8F56kcFppGwiRBw0E0ic35zUk6CliDY0IfKFJ9qvA8wdkZskLIIRB/uBJTdu+8y13qhMQLgAxu0ITfkOFZcm8IOujcQ3/sj3cWL/aaZOzRD4UUGqRCbBu/7VW7uuE0QZmLqh43s+x545gdN0L1pyFiKXTCxpUylWOfH8abbfs6UTKiilpFFpMH12juLEwnkXpBAUhnvoHymQyKy8C8V1PE7sP0V1fnm98zAImR0rMnlymo07NzC0dWDNuHBW4WYnkRAZO6Keli9T2lUG81E3Hn3jdZuPphcQ1n2gFUCWkMF8+79iFEeODuYehLFjmcgLYSHMPSCdKJFI64cLMkIJxkHfBcmPgfHqaGEKa+0BNNCyUfZnWI3+k0E7qagM2iDIVuRSMXahGcoaV1we3dAJg/Ob4z0DOd79sbcT+AFWzCQIAh770OvYuHP539JiFuXM2TkaleYyF1C93MBpdgcIZHrTlGYqzE8uAJE75vSBsxz41mFKM2XS+STZvgzZvgzpfJKF6TIHv32E0wfHVnSDdXHxadZa5PqzXU8lEC1gqZ4kmUKas4fPMXF87STWrUqLHEAzNhAiIDgexUVr6Y5rI3Ih1KJYbb2/LaDX17cltBRC24GUm87fGw1E7GUtYKGlwLwH6R9p106pQ2i3Mz1DiL0JpIhS++2HgSCKhglKke9bSrD2RIMFs1FGpwhB3xCFLOqj7TIGyi+ueHl6BrLMT5W6RPjZr76IZui89SffwON//k2e/vJ+Hnn3/Z3zrYZDIpNAN3SCIGDy5AypnuV+/D/9tb8hP5jjfb/8A13HU7kEEyemyQ/1cPrAWYoTC+T6s8ssXk3TSOWSSCmZGy8SBgFb9mxakY3ec8enaNZay9rleY6HYZmdh2RN08j1Zxg/NkmmkF4T0TGrVsgBNGMIqfe2KwCeRcq2P0tqoA8i9MEb7kYQwoqqGV7tdVoSYd0bNVsWZpSSr6WiMEV9tO3nXvx1aEhje7RgeMciN0u7bgt6DszdoOcRIqEaNiuumr6Nvcyc7a7j/dC7Xs33/+hr6RnM8dAP3M/kyemu806txbZ7I7dhpVgj9P2OrzwMQsYOTxCGIUefOo4ZN3nNO1+Fpgs23jaMEGDaJvVKk7OHxpkbn6dnsDswYXHDcdHlI4Qg25dh7tw8mUKGvuFryw/x3MV658sXn0985I94zTtfxWve+arOMU3TsOMmM2fnlJCvBEJYURigPkjkP44ScdaK7+pChJYG+/uQ+oaoUuIl68kEUSSOdS8Y29vWggDM61bpUbE+SGaTpPMpaqV6xypf6kZJZOJsu3tz5+dWw8GMW2QLkSXr1FtoS6I7zhw6x2//+CehHfkReAEf/+DvYxg6v/rpn/v/23vz6Mju6t73s885NVdpnlotqSV3uwe7226b9gDYkICJATMkJnmZbgiQBALhvUducpMQkxBW4CYXsjJyVxJuSHIHSIAAD8Ll4uEFcsNg47bx0O55VKtb81hz1Tln3z9OdUklqUeppZL9+6zVy64zVG1Nu361f3t/v/MTmaqcOzaybE3985/4KpYl/ORv/mjN8URjguETo7R1t1zz33whV+TY0yc5e/Q8yYYYTjhEqiWJouTTBU49P4jn+uy5dxe2YxNvCDY6Y6kYk+en6d3RXfeDR3WfyC8Q/BA3TLiXRETA2YZKsvJJY47AVKLiLqTloCfe2YrYm03iNqw6W2/t5+DjR8nO5pZYqy2kkCtSypcDU4fKRqXn+TXS+QO7e/m1T7+Pv/iVv6nWx5PNST7wl79UM1afzxQpl8o1k6vZ2RyqylOPPAsCD7zndYhINaZwNMTM2CyZmexFV8ae55GeyjA2OEEhW0REiCQiNLU3MD06zex4mtHBSXzPp1z2KBZKfP/rT/PY/whmN0IRh+FTo/zW/R/FiTh8/JHfIRKPVPx0hWK+ZBK5YXlEpPJJoxN0NtAyVzdoo7QaQRpN2cRw3QhHw+y660ZOPHOambFZwtEQsVSsuurNZwoU8yUi0TC77r6RREO85l7fqx1069nRjVeR1w3UE5WuRQbRpUIJJzz/Oz10dJg/+Nk/Awk0ZxDhN1/3+wD89mc/UJWsFUso5orLJvLJ4SkGDw5RLntEYuGqVvnM6AxPPfwMdshmYE8f8VQEy4JwxAEcbn/dLfi+z7c+9x3KFdXFhrYU7/vTdxKpGWTSYI+uznlRJPLASWcc/CmCNj4HrLYNsQEoYoE0B/rqBsMaEolF2HX3djIzWcYGJ5gamalaKaZakvTf3EuqJblkozHVkkQrcrcXEv/5YyM4js3PffSnyM3l+dzHv8LM2CzNnUEZxfd91PeJJeeTcc/2Tbznj97O333oHygVAumJcDTEuz72MzW64yISfApYxOjgOKeeH6ShNUliQe+553qMn52koTWFHbIZPHxuiYOQCOy+Zyf/8tlvBxu4rkdHbyu9O7prrlO4qOZLPbGhE7lqGXVPB2qJaoEVJShPFMA9hnrHUWsTcp16zA2GjY6IkGpOkmpOcsMtWpXTvVQ9OhqP0NTRSD5TqLbwfciNbQAAIABJREFU9e3azB8++juEKhOjd7z+tprp0dxcno4tHZQLta2Je151U9Dy6PqAEo6G2X3vrppr1NclQ0JzU2lOHzhLY1sK27EDTZbKUFF6OkO5WK5ubKaaEkycmyQ9k2NicIK73vQyAM4cHMIJ29z3c6/m9IFBjj11smraAcEbgm3bxFL1r3e+YbObahktHwha+aQZWdyeZMcC3RHvPKpZCN1skrnBcAlE5Iq1vbu3dnHwe0cIR0PYjo1YUk3iQI0dW7lYxnN9Bvb0ceypk+Tm8kyPzlLIFZg8P01mJsdr/929hKIhHv70vzA2OE7nlnYgGBxS1SV1/PMnRgnHQuTm8kwMTZHL5BHghe8eYW4qy/aXDSCWEEvFsGyLVHOK735lP0f2n+C2+24hHA1xwy1b+OU/fgeJhhgDe/p4y/teX7P6zs7m2XRDx4bQO9+QmU1V0fIR0Gxgr3YRRCywW1BvGnWPIaFdF73WYDBcOcmmBFv39nP8B6eJN8SIxJYvYeYzBUr5Ejvu3IZlWYydnSQznSHRGCcUCdHc1cQv/MefIRKPYDkWN/+X99aYZhSyRZo6GmsEuPLZAlPD02Rnc6SngwnNVGX1/dSjz6Gq7LhzKyOnx4jGozz16LPk0nnOHg50i/7hD75ENBFj3/23svXWLQDVN6QLXCj1rJaBx/VmRYlcRH4C+D1gF3Cnqu5fjaAui2bAn1yiPa5aguynIfGemk4PsZtRbxy1+wKzYoPBsGJaN7UQioQ4c3CImbFZnLBTLYGUS2XcskdDc5Jte/vxXJ+D3ztKe28rpXyJSCyMZVs4IZtIT5AsPc8nM5Nj8tw0HVva8FyPfKbADbdsqXnduck5Rk6NYTs2DS1JJs5N8dm/fASv7Fa6YHy+/tePYjk2d7+5UkZ5Yah6/8ipcRJNiZrxfMsOZAdgXu98x53biMSuXsFxPVjpivwA8CDw16sQyxUTWMEtswIoPRMoI0ZeVXGjX4DYqD+KWDcsvc9gMFwTDS0pdr9yJ7m5HBPD0xRzQQ28OdFIa3cL8VSMQq7IoScOk2iMEYqEUM/n/PFREk2xmlWwbVukmhOMnZ3ACllYlsXAnr4l3SqjgxPkMnm6KuWXxvYGWrqbOfz4seo1sxNptu7tp7WrmURDDEQ49L2j1fM/9ZtvJRINUSqUsB2bcsnFcz1mxmaJJqPc9PLtJBo3zqJvRYlcVQ8B19yof42vWTEknh8q0OxnwD1UMX0A0n+O2u0Q2ovEfzw4JklwR8AxidxgWE2Cvu/ERRPfRMVrM1TpHGnZ1Iwdsnnq0ecYPDTEK3/0TpxQkNDdsofveZw/NsJrfuYeWrpqu7l832fi3HRgA1chFHZ46/vu5/jTp3BLwao61Zrknh+7E4BoIsrpF84ittA90MW548NYlkXP9m6mK6YdpXyJnu2b2HX3dpJNCTzXIzOTrW5+xpLRupa4XbMauYi8G3g3QF9f3wqeyQPR2iEZZxsUvkrVPUcnwE1D7G0LXt9GKde0TBkMhuuL53qMnBkPVsULaGxr4MiTxzn8/eO8+ideUS1rhKNhugY6KBXKy2qmZ2ayWFbQElguuaSnMgweHCIzm6sm8URTjPRkhuxcjkRDHNu26N3ezS2vvok733AbT37jGZLNyapQl6oyN5Fm9727UF8ZPHyOscGJIFcw34LY2ddGW09rzZtIvXDZRC4ijwFdy5x6SFW/cqUvpKqfAj4FsG/fvhV02MtCG83gSOQulPdD5pNU+8hTv4aEb1v4+sCl26oMBsPqkkvn0Yr0LQSbn3/5q39PIVNg5PQYYln8t498nnA0zNs+8ABbbgpUTDMzWWbG52hoqRW4KuVLROIR5sbTDJ8cITeb5+H/+q2qq6JlC9mZPAAzY3PVQaabX7mDpvYU8YYYP/STr6h5zuxsjsb2FJnpDKeeH8QO2aRaah2ZLkjcjpwaY+ttA7R0rrKJzQq5bKe7qt6nqruX+XfFSXw1EbHBiqNarD1RegooB647lKH0g0V3FsBqwGAwrB2eWytDG01EGbhlC+eOj+C5Pm7JZfzsJOFoiE1b5ydBLduqrrAXUiqWGT4+itiCZVl0DXTwpl/+kWDsX8F3FSficM+Dd1LIlyhV+tZLxfKyaov5TCBGl2hKcOLZM6Ragp76xUNQFyRuE01xjj11gpnx2VX5/qwW9T+ytBxW77xe9wVCN0PDHyIND0HqI4Ge+UL8/HXVLDcYDEtZnBBF4Mf+7zew+56dwXlbsB2bX/3r99RuaqouW5MeH5wgly6waaCDVHOCXDpPS2cTftWYAvCV/j19NLWlGDo6zNTwNIlklKbO+Xp7uVhmZnwOVNl22wDDJ8aqw0ULmTg3VfPYCTkkmxKcfPbMdTejvhpWlMhF5MdEZAh4OfA/ReTh1QnrMq9rt4A4qJbnj0Vfh4S2B/8f3oNEX109p1oMRKgu0XNuMBhWn0g8jKKV0maA53oc3X+ChrYkW28bwC25nHjmdM195aK7ZAioVCiRS+eJp6IgQntfG8mmBKNnxnCLLvvuv5VXvS1w0JobT5NojBNNRkm2pogkonjlMrPjc8yOp3HLHgO7e7n5lTspZIuo+kuS+PkTI3z4Rz/O3GS65ngoEsIru8xO1B5fT1batfJl4MurFMsVIxJCnZ1Qfh69qAxsgGoJ/DQS3mtEqAyGNSYSi9Dc0Uh2Lk88FWx4ighv/KX7uPdtdxOJR/j+15+msa3Wck0soam9thQ6NTJdHSJKT2WIp6K097ZWRa8K2QIDe/p46/tfj1iBXV1rdzOqys67trNl52ZUwXYsoolotcwyfGK0GhsEq/DZiTm+//WgPPuvn/suN71yBy1dzVXtmEgyyuipsbqplW/IyU4Ay27FZw+4h4J3e0nVjOCrloPxfUDCt15yAtRgMFw/Ovs7OPT4MaKJCJZlYdkWr3v7/Cfmux64veb6zHSWji1tS/RVJodniCaihKNhZsdmeeJrT/GDfzkQTGEKHPzeMc4cGqJ1Uws//u/fVPEW9RgfmmLTQMey7ZGeG8jaxlLzbxpf+KOvcuDbh6syA4999t/4xt99k1e8dR8/+6GgnTkSC5Oeyix5vvViY9bIK1h2KxLeFzjNazbw0fQDP020AHY/Er7DKAsaDOtIQ0uK7q2dzI7N4ftLVQwXkpnOEktF6d66tFHOLbmV1XSE3l2b6b6xG3EsivlisNHp++TTRW6/75ZASKtQJp/J07O9i2giiu/7lYlTt1rqudBiuJBf+vi/4+Vv2VfVb7JEeN3bX81Pf/DB6jUigu/XlozWkw27Ir+ASBRx+lB7c8UOzSfw0owZQwaDoU7o2d6NiHD+xAh2yCbREK8KVKkqhWyRYq5IQ2uKrXv7l6zGISiJ+L5iA6nmJLe/dg/haIi//51/xLItfN/nrjfeTve2LtJTGaLJKAO7t5CZyXH+xAjTo7Oo5yMSGFB3DXTS3NWEVO69sDHrhBzaNgdSAhDornT2tdcIarlll3A0VDftzC+aTCdiB76YVir4r0niBkPdICL0bO9m9z27aOtpJTOTrW48zk2kiSYi7LzzRrbv20oovPyeV6o1RSFbqD6OpaJMj84AsPc1uwlFHI4+fZKWriZu2NvPwO4+JoenOHvkHNPDMyQb4zS2N9DQ1kA0GeXc8WGe+9eD4PtkZ3M1r3XsqRPsvncXH/6nX2fb3n6O7D9ecz43l6ejt22Vv0vXjqzHR4N9+/bp/v1ro69lMBjqD8/1KJeCEoft2EuMH5Yjl85z4NuHajw/Tx04C6oM7OkjPZnhyP4T7Lv/Vnzf59Rzg5x89gytPS00tqVwQg5NHY2kmhPV1bXv+YycGSc7k+XG2+flOzzXw7JtREAV1PdrPkHMTsxxy6turlFlXAtE5ClV3bf4+IYvrRgMho2H7dhXrV0ST8VoaGuoMY0e2N1bPZ9qTbLv/lsp5Ioc/v4xBg+dIxqLkGiMIyKUimXOHjmHZVl09LXR2t2CZVt09bdz6PEZhk+NsmmgsxrfBURAFpRVZsfTdPZ3rHkSvxSm/mAwGDYMA7t7Ecsil84vez6fKXDy2TOMDU4QT0Xp3bWZWCISyOVGQ/zbF5/g4HePMnJ6jOGTI6gGQ0v9u3spZIrMjM/hL2MrB1TUEedo7mqiZ/um6/llXjVmRb4KqOZBK1NeEq57n1CDYaMSiUXYddeNHHv6JNNjs0TjkardnFt2Ofn8GdJTGXzXp/fm3hqxLs/zOfzEUYaaU9zxhr1MjcwQiYZp3dxCoiFOQ1uKls7GSjJXwrEQlhVshJbyJWzHoWf7JroGOpZMrK43JpFfI6oe6k+Ddxb8ueDzlwIiqNWJ2JsQK3XZ5zEYDFdHNB7hppdvJz2VYeTUGLPjc4AwMzZLMVeiraeVZGO8msSf//Zhvv6pxwBwwg7pyTQf//lPggU//3s/SXNXE5ZtYTs2rZtb6d/dx8z4HHOTwQSoE7Jpam+gsa2hbqVsTSK/BlSLaPkF8NNgxWucilR98CdR7zxq9yLOwEU7aFRd1JsGcgRtk2HEajIuRoaXPG7ZZXpslvRUpjaZtjdg2za2bdPU3khTeyNu2cUtuxz4zhE2be1kemwGrzgv3zF6egwI6t4XhLicsMMbf/G1JBtiZGZyNLQmA5kWzw/aD7tbaOveGDZvYBL5VaNaQsvPgbpLrOag4hMqKVQT4J1FUXC21vSbqpZR7xx4Q0FJRhyC7QoXdRW1mhBnC7LAPMNgeCnguR7njg8zemYCVAnHwpXhG5/JoUksx6F7ayed/e01fd/lYhnf8wPvTdvGdX2+8Ef/THYux8TQFGJLjRJjY3sDu+6+kWKuxOzELA2tgWDXwl7xjYRJ5FeJumdAi0tG/tXP1gwhiVio1Roka7sNJLg+eCM4AH4WrAbEWvpRTf0cWvoB6uzEcpaTgjcYXny4ZZdjT58kM5MNpGQXJ9VUDM/1OHvkHLl0joE9W6rJfOEGZbwhhqrSu7Obf/3892qeIhIP07O9mxPPnCaXLhCKOHiuHxi6K4Eg1wbEJPKrIBDgGq4m5RrmPgyR10DsjdVDIoJKDHWHkHBTUFcvvwBaQOyLywaIFUc1DO4hfAlj2RvnI56hfvA8j+xsDq8crERDkVC1Fa/eUFVOPneG3Gy+RkBrMbZj09TRyMT5QECrb2dFmlqkOmofS0aJxMK87EduJT2V4enHnueC1c97/ujtxJJRzp0YJZaIUiqVcUTIp/M0dzVtGLPlxWzMzxHrhHqToFRX3UGN+zxaPgreSSg+Ejz2JuZvknhQM9dCoAHjzyKLDC7Um0GLj9ccE3ECIwzveN3oORg2BqVimeGTozz3rRc4/MQxjv/gFMd+cIqDjx/luX89yNjZiSWGD+tNbi7HzNgsqdZao+VSocw//9UjLP4TaGpvYOTUeNU4IhILQ6UEIyJ09LaRmytw9OmThKIhBnYH9pKnD5wFYPPWTsQCt1gmmoxSzJfYNNBx/b/Q64RZkV8NOgey4B279Dhk/hiwgRB4YzDzfiCCNv8tYsWCVTkS6MD4g2Allz5v4RtQ+Coa3lej4CgSqbx5pEGMu5Hh8uQzeY7sP4FbCvS8E4u6LMrFMqeeH2RiaJJtt99wRROVa8Ho4MSyHp0vfOcI3/j0v3Dba/bU9G6LCJYtTI1M09XfiRNy6OhrY/L8FMmmBA1tKdp7W9hzzy5ue+1uUs1JDj1+rGYqVBXKFQGt/t191SGjjYhJ5FeD+rBAK00i96DqQfY/Exg/l8HqgIbfQazYoluz4GcDUwyC9kUKXwM/B8VHgRJk/wsqTRC5C3Eq48ISRr2xJat4g2ExpUKJw0+ewLKEhtblW19DkRDNnY1kprMcf/okO+7Ytu4tdZ7nMXl+ilTL/CLn63/zGOeOjXD28DkA/vtHPk9bTyu77rqRex68C4BYKsbomQm6+oNpzLbNLYyeHsP3gnH6zi3t3P/OH2b87ATp6Sw33j6AHbJRXymX3cDTsynOTXdv31AdKsthEvnVIOH5wZ8LhG+DbMXwGQ+sRsTevOi+CzZUC2uTVuAz6h6YP1R8FAhDeO+Cex3Q5afYDIaFDJ8cxXc9Ei21n/rGz04QTUZrrNSSzQlmxmaZHJ5ed/En3/NBa23hko0JnvnmgarR+tDRYUZOj3P3Ay+rXuOEbPILJjwTDXF6d25m8NAQjW0NWLZFe08rTR2NpKfSTA5PU5gLSjHq+/Rs38Qd9+8llqxddG1ETCK/CsRuRf2h2oPlA0Ackv9PkJQL30DVq7oRqVaSvMSp/lZS2Qht+EhQmil9d/5c058j9uJaXf1tThnqi3KpzNjZSVItS8sDf/ehf2TzjV1VU4QLJBrjDJ8YpW1zy7pOKi63+fqqn3g5Yll88U/+mXLRxQk7vPdP3sHOO7dVr1HVJXF39Qd/O0NHzmPZFonGOKGwQ0tXMy1dzRRyRYqZApFklO0v21pXeikrwSTyq0EagSiqpfkx/PA+aP4UYsUhcica+/FaSzlNg70FsWLBsNDCpxNB3dMESTwFpMGfgIWJXEuwTL+6wbCQ2Yl0TWLLZwsc+t4xvLLH2SPnGR2cYNfdO7BsYc+9u7Adm1AkRGY2R3Y2V2t8vMbYjk0oGqJccgktqJMf2X+cctFly009nDk4xMlnT9ck8lK+THyRr6eIsGmgk6b2BibOTTF6Zhz15xdQ8YY4W28bqA4WvVgwifwqEBHU7gf3IGq1ImIFCX2BtsrCWrZqEbAQuzPYuLRaUD8XJH2CFQVWJyTfC/YNkPvboL98Ieoi1sbdTTesDYVsASc0n5jGzkzwXz/8OdyyGwzIlFw+/cHPkGiM89A//iqNbUEN3RKpTjuuFyJC10AHQ4fPE2qbr+1v3raJV73tbrbv28qTDz9TNXq4QCFXpG/X5sVPB0AsGaN3x2a6t3ZRKpZBFcu2Nmx74eUwifwqsZxOfPLgng4mMGX5b6H6OaCIhG5FKp0u4vSgpeeAIJGLCDR+aP6m5PuWPofViCzX6WIwLGRRe96Wm3p46LMf4D+9/S8o5IoAtPW08uuffm/NpiJQF+2tLV3NnD10Ds/1qpuvb/iF11TP33H/3prrS4UyoZC95GtZjO3YxOpUH2U1MX3k14Dl9IOzAzRX8QnNBvorWkT92aBlUEJI6LbabhNpArsj6Ce/DMFqvoA42y57rcEQjoXx3NrSXaolSTFfxHIsEChk8iQXlVAUlrVVW2vCkRD9u3uZnUhftse9XHLJzubYunfgRVUeWQnr/xPcoFjOJtTuAJ1F3RGgANhgdQWllGVW0SIWONtRQL0xsFLV1foFVD3QDKBI6BazGjdcEQ2tKVSDUfMLm4cjp0Zp72vjXR/9aWYn03z2Y18iOztvyuC5HrZtk2iKX+qp14z2njZ8XznzwlmcsEO8IVazmem5Hrm5POr7bN+39aItli9FjNXbOqDqB9Of/mClJr5g114ErO6KDG59/IEZNgbHfnCS7EyWeMOV/d7MTabZdEMnm7fVl0lCLp1n/OwEY2cnqy73SiBotam/g9bNLS+abpOrxVi91REiFuJ0oNoerL61SDD7b4OkEKmPaTvDxmLztk288J3DlItlQpeZ2Cxki9iOTXtP/XVExVMxttzUS/e2Loq5UtXhPpaMrvvwUr2yokQuIp8A3gyUgBPAO1V1ZjUCeykgIiApgtZDg2FlxFMxtu/bytH9JwhFysRSsSU92n7FMV5E2HnnjYSj9etmFQqHCIXNouZKWOmK/FHgg6rqish/Aj4I/ObKwzIsRNUFfxbVHMGHzDBiNyGyMSU3DdePxrYGbnrFDs4fH2Z6bBYRC9sJ6sye64FCW08L3Vu76qoVT1XJzGQZG5wgM53B95VQxKGtp5WWrua60YSpV1aUyFX1kQUPHwd+/GLXGq4e1RLqnV9gQGEDAuqjro9aHYjTazZEDTUkGuLcePtWCrkisxNzFPMlLBEi8TCN7Y11lxSzczlOPHuafKZAJBoiUql/e67P0OHznD10js7+dnq2d9edV2a9sJo18ncBn7vYSRF5N/BugL6+vlV82Y1BsKlcXJCQwxe1gAuuL6Dl5wPVRFlqQKGqoDNoeRx1dhvNcsMSovEI0b729Q7jkmRmshx+4ijheITmjlpHLNuxCUdD+L7P8KkxivkyW2/dYpL5Mlw2kYvIY8ByNjUPqepXKtc8BLjAZy72PKr6KeBTEHStXFO0G5BgVT1Z6VApzp+QCGr3IHb7/Lh/9Z5yxYDCRazlDSgu1NdVS+AeQOV2szI3bChKxTJH958gmoxeslZvWRbNHY1Mj8wwnIrWXZdNPXDZRK6q913qvIi8A3gT8FqthxGxOkL9ucDWTctBz7g9L2ikWgb3BOqdgtDNNQk7aE3MLPEEVW8cvBEkvKd6TCSMagR1TyLhW67/F2UwrBJTI9MVn83aJH7u2Ajf+vx3+NmH3lZzvKE1yfDJUTq3tNfFEFM9saLPKCLyeuA3gLdosBNnqKB+Gi0/AxJB7NYlq26RUKBNLnG09CzqB80+qgreIFjLdLLkvwSZP1syUi1WAvzpiizAKn8dqqifQf2ZYGrVSOoaVgHf9xk+OUasYamE7Le/9Djf/f+eJD2VqTlu2Ra+p8xOpNcqzA3DSt/WPglEgEcrbU6Pq+ovrziqDY6qj5YPgsSXTm76BcSa7zYRCaNWKiilhO+qaI8XEElUnqsUOAipC8VvAwXIfRaVGERegdiVqpfYqD+BWKuz/6BaDj4ZeINBnb46tKSo1Yw4PSDNden/aKh/irkibtElUUnkvufzx7/0V8xNppkZm8N2bD72039KJB7mjb94H3c9cDsQmCdPDk/TuuninrcvRVbatWKEQJZDZ0CLiFW7Aal+GqbfjTb9OWLPb0KJRIJVrzcdbGr6oOSAUjAslH8E9Pz8ExW+CERrDShwKoNFqxC+n0Pd58EvBCUhK7H0fPk5sDaBs61WttdguAJ8z0cXKH1ZtsUrf/RO/uEPvlzVWsnO5WjuamLHAula27Hw1lmtsR4x27/XAXWHYEGPt2oxEMoq/htQhMK3KmJbC8oUVgK8wWBj1D0O7mFwT4J3DhI/D1bPglcIQdMn5+3gVjN2LQRJWlm2JAQE0gHSCt4w6hpzaMPVI5bFYsnGl79lH/c8eCdIIOTluz6/8mfvpKl9XnjO9zUQATPUYL4j1wN/FmRB7S/zSZj5Rcj9ffC48IXgcfrjC24KQ/kwlI8R+F41LvjXDP505TqHwB+0sOhFy7WveY2oewrwlq7Cvclgg7aCiIAVJHN0GoPhaojEwli2Hdi8LeDZb75AsjHB7a8LNvQPfPtwzflSvkSjEctagtn6XWWC1alfWztOvK9i2f0UQZemA84eSP37+Xu8oSAhWjcB6cAZqLoadsFuh+g7gSYofA6882B3z9+v/pIul6uPvQj+OMgy9ce5j0DkhyD+YPWQiKASR90hJGz62A1Xju3YdPa1MX52kmTz/KLhTe/9Efb+0G5iqSh3PfAyGhbojasqvqc0dzWtR8h1jUnkq4yIoDg1vp1ixdDInVD+HhACihB+2fyqV3PgjoM0gOUAm8A9BZYEpRWnH5K/Mv8iiXcE050XJEs1A1YbssIVuXoToIJYwZuQ+gXwTgYKjf4QFB9DQztBImDfgIggVjxYrS9wPjIYroS2zS0MnxqtMZN4+Zvnhf0W2roBZKaztHY315W0QL1gSivXA7uroim+gNL+wNYt9Vtg9waPL+CPgZTBbqmULBqD9sPS9yH7qaBjZSESDjY2NVsxoPAQp3/lcWumxraO8rMw9yHIfBxwgvLO3Idg9vcq3TXVgAh00wyGKyeWjDGwu++KzCSyszlC0dBFrd1e6pgV+XVA7E7UG6o9GH8XWPGg3TC0B/w5oDIY5E8DDljtQZmk/ExQZy8+AZQh/zA4XWD3BSUWALGCVbvTVzGgWOqeftUsNoeO3IXK70L6YwQlITfYdG34vWVW3z4Gw9XS3tMGIpx6bhDLFuINseqwj6pSyBYp5ookmxJsu23AqCFeBJPIrwNiJVGrDfVnECvQjxB7vq4n4sAFbRR1wc+B3QlSSY75L4B7lOqPp/ytYH8z8ubKahyCpNqOhG9fcUmlihUBb9HKyO4FPMAGfBAnGGSq+YKVoGRkMFw97ZtbaWxNMTUyzfCJUbLlHFTsJBrbGxjY3UeyOWE0Vi6BSeTXCQltR8sHgolNaVx2cEZVKytzB5z+6jXa8FFI/xmUn5i/OPkb4GyvPLkT3CPO6iVxQKw21D1be9A9AtICqf8A5UOQ/1xN/T/oZImArMInAsNLlnA0TFd/Jx197bhlD/V9bMd+UYziX5DozUxnKZddQiGHRGN8Vd+cNv53qU4RCUFoN+qeBG8EFbvSHmgRTPzkAyVEu61yh7PgXqeiPV7pcMEFidWshNXPAqusRy4psBKoFucnUsN3Q/juQKkxtAONvbl2AMjPgDNwSSVHg+FKsSyLcOTF8bukqkycn2L4xCj5bAHHsRFLUF/xXI9wLMzmbV20bW5d8YS0SeTXEZEQEtqBOlsCs2V/giA522D3InYHIjH80vNB58rCVa2mIf6LELkH0n8adK9w64LzRSR04yrHK6g9AOXnUctBxF6SoBcmcdU8SAixO1Y1DoNho6OqnDk0xOjpcZJN8SUSvQDlksvJ586Qmc6y5ebeFa3OTSJfA0SiiNMHLK+DIk5PME3JfCKXpgXDQo2/W3O9ajmolcvq99Nadiu+3gjuMdRKLdGKCV5fK105Gmy0LjP9aTC8lDl/fITR02M0dSxfVgUIhR2aOhoZH5rCdmz6dvUse92V8OL4DLPRkUaQlqoC4qVQ9cCfqfRxX58fn+VshtAewKv0iM8F+ip+FvWnwZ8KzC7CtxkNdINhEaVCifMnRmhsb6hJ4qrwnS9/v2aaVURobE8xemacfHbxtPaVYxJ5HSBiIaGdgVGENxl4dC6Daj5Ios52LOdsH3zeAAAJSUlEQVT6ljMsuxUJ3YGE9wb975IMBpbsLUjkTqzwzau60WowvFiYGplGLFlSKhk6ep7P/scvceKZ0zXHRQTLtpg6f+1SF6a0UidUN0e9c+CdRX0v6BXHArzg7dxqQMJ7L+oatPoxSdBxYy2t7xkMhuUZPT1BLDW/yHnmmwcYOjrMqefOAPDVv3yYHXdso7O/nTvuDxRM4w0xRs+M072t65o2Pk0iryNEbMTpQ+3NoLOonyHo4Y4gVoMpYxgMdY6qUiqWiaXmO8qmR2f4X3/z/1cfn3z2DCefPcMD7543X7MdG9f18D2/KldwNZhEXoeI2CAtS/TMDQZD/bNY1vmHf+oeUs0pPvPRf6JUKBOOhnjwAw9w79vuXnrzNXYhmhq5wWAwrBIiQiwZpVQo1xwfGxynVCjT2JaiVCgzcnqs5rxbdglFHGz72kxazIrcYDAYVpGu/nZOHzhLODovW+H78OAHHuA1P3Mv3/7yE4ydGa+5Jzebp2fHpmt+TZPIDQaDYRVp7mzizMGhGnneN71nvh5+74N31Vzvez6+r7R0XXsTgymtGAwGwyrihBy23NzL7ER6iQPSYnzfZ2Z8jr5d3YSj1z5YZ1bkBoPBsMq0b27Fcz3OvDBEJB4mlowuGg4KJHoL2QJ9OzfTuWVlcyEmkRsMBsN1oGtLB4mGOCOnx5kemWFhe7iq0tTRyMCePhpaVu5BahK5wWAwXCdSzUlSzUmK+SK5dAHf87Fsi1gySjS+epZ1JpEbDAbDdSYSi1xXr9EVbXaKyO+LyHMi8oyIPCIi3asVmMFgMBiujJV2rXxCVW9R1b3A14DfvdwNBoPBYFhdVpTIVXVuwcMEFTdJg8FgMKwdK66Ri8jHgLcDs8APX+K6dwPvBujrW95gwWAwGAxXjywWeFlygchjQNcypx5S1a8suO6DQFRVP3y5F923b5/u37//amM1GAyGlzQi8pSq7lt8/LIrclW973LXVPgM8HXgsoncYDAYDKvHSrtWFrr/vhU4vLJwDAaDwXC1rLRG/ocisgPwgTPAL688JMNLBVUfdAb10wQGGg5iNYGkrsklxWB4qbKiRK6qb1utQAwvHVR91DsP3hBoCcTmgqWduqfAiqNWP5bTvt6hGgwbAjPZaVhTVD20fAj8CbAal7WvUy2C+wK+9iHOgFmdGwyXwSRyw5qi7jHwJxG77aLXiERQKwTeICohxOldwwgNho2H0SM3rBnqZ8AbgUVepCXP49ETx2uOiVhgNYN3GtXSWoZpMGw4TCI3rBnqjYCEl5RKfjB8nr948nHGspma4yI2qKLe5FqGaTBsOExpxbAmqHrgnQerqXrskePHGM1meWr4PAL89f4n6W9qZmdbO3ds3hxcZCXAPw9cu5+hwfBix6zIDWuEB1RKJhWG0nN84dABTs5MocCTw+f44qEXSBeLC+5zwC+sbagGwwbDrMgN68a7bnsZjZEon3vheQqeS9iy+O17fojbuxepIYtZbxgMl8L8hRjWCAfEDkosCzg9M03Bc2mNxin5PufTc4vuK4HE1y5Mg2EDYlbkhjVBxELtzcEQkMzXyWOhEL9+9z3c09fHPx08gKeLXMf9HBK6YY2jNRg2FiaRG9YMsTpQ9zSqfrVW/r477qqe/79231JzvWoZxKnZIDUYDEsxpRXDmiFWHJx+8KcCnZVLoOqCPwPOjqAN0WAwXBSzIjesKWJvCZK4dwaVRJDcF6CqoJlAg8XZhXWJCVCDwRBgErlhTRERJHQDajeh7lBl2GfBgJAoWJ2IvRmxUusWp8GwkTCJ3LAuiNWChFtQPweaB/xABVESiETWOzyDYUNhErlhXQlKK6a90GBYCWaz02AwGDY4lzVfvi4vKjJO4Ci0mDZgYo3DuRZMnKuLiXN1MXGuLvUU5xZVXeK4si6J/GKIyP7lHKLrDRPn6mLiXF1MnKvLRojTlFYMBoNhg2MSucFgMGxw6i2Rf2q9A7hCTJyri4lzdTFxri51H2dd1cgNBoPBcPXU24rcYDAYDFeJSeQGg8GwwanbRC4ivyYiKiJ1qZokIr8vIs+JyDMi8oiIdF/+rrVHRD4hIocrsX5ZROpSE1ZEfkJEXhARX0TqrtVLRF4vIkdE5LiI/NZ6x7McIvK3IjImIgfWO5ZLISK9IvJNETlY+Zn/v+sd03KISFREvi8iz1bi/Mh6x3Qx6jKRi0gv8CPA4HrHcgk+oaq3qOpe4GvA7653QBfhUWC3qt4CHAU+uM7xXIwDwIPA/17vQBYjgY7ufwbeANwE/LSI3LS+US3L3wOvX+8grgAX+DVVvQm4G/iVOv1+FoHXqOqtwF7g9SJy9zrHtCx1mciBPwF+A6jbnVhVXehJlqBOY1XVR1TVrTx8HOhZz3guhqoeUtUj6x3HRbgTOK6qJ1W1BPwj8NZ1jmkJqvq/gan1juNyqOqwqj5d+f80cAjYvL5RLUUDMpWHocq/uvw7r7tELiJvBc6p6rPrHcvlEJGPichZ4Gep3xX5Qt4F/K/1DmIDshk4u+DxEHWYeDYiItIP3AY8sb6RLI+I2CLyDDAGPKqqdRnnuqgfishjQNcypx4CfpugrLLuXCpOVf2Kqj4EPCQiHwTeD3x4TQOscLk4K9c8RPCR9jNrGdtCriROw0sHEUkCXwQ+sOgTbt2ggVv43sre0pdFZLeq1t0exLokclW9b7njIrIHGACeFREIygBPi8idqjqyhiECF49zGT4DfJ11SuSXi1NE3gG8CXitruPgwFV8P+uNc0Dvgsc9lWOGa0REQgRJ/DOq+qX1judyqOqMiHyTYA+i7hJ5XZVWVPV5Ve1Q1X5V7Sf4CHv7eiTxyyEiNy54+Fbg8HrFcilE5PUE+w1vUdXcesezQXkSuFFEBkQkDPwU8NV1jmnDIsEq7dPAIVX94/WO52KISPuFLi8RiQGvo07/zusqkW8w/lBEDojIcwSloLpsoQI+CaSARyutkn+13gEth4j8mIgMAS8H/qeIPLzeMV2gsln8fuBhgo25z6vqC+sb1VJE5B+A7wE7RGRIRH5hvWO6CK8Efg54TeV38hkReeN6B7UMm4BvVv7GnySokX9tnWNaFjOibzAYDBscsyI3GAyGDY5J5AaDwbDBMYncYDAYNjgmkRsMBsMGxyRyg8Fg2OCYRG4wGAwbHJPIDQaDYYPzfwCadTQETGvNyAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Kernel SVM to the Training set with best parameters\n",
        "from sklearn.svm import SVC\n",
        "classifier3 = SVC(C=1,\n",
        "                 kernel = 'rbf', \n",
        "                 random_state = 0, \n",
        "                 gamma = 0.1)\n",
        "classifier3.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "0F6kqdrV_cX9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a04eb553-d36e-4208-9a55-ef07aa6727de"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, gamma=0.1, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the Test Set\n",
        "y_pred = classifier3.predict(X_test)\n",
        "classifier3.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0PDpzC0w8LT",
        "outputId": "ed42a532-70bc-44de-cfe5-14da2e11df0c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.423728813559322"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm,annot=True,fmt='2.0f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "KLn5WkDKw-Bq",
        "outputId": "65083d42-dc22-4524-b433-c8f500c107e1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41e8b6cf50>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}