03_iris_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "iris.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "k-vaWEycVCsf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gINukgsDey0J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kCu0Mym5VKUc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-slXbDTbVSIP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "iris = load_iris()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0_W5mx1_VbNt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(iris.data, columns=iris.feature_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1ZM7TyzfVmEX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df['target'] = iris.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Txg0EoAhVsh3",
        "colab_type": "code",
        "outputId": "2aaf9384-c915-4b2e-bc23-0dcca0fbd1c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
              "1                4.9               3.0                1.4               0.2   \n",
              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   target  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "T4nWkP8KVtYS",
        "colab_type": "code",
        "outputId": "f1690d7a-543e-42cc-86fb-d7d1570d3a8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "# visualization\n",
        "df.keys()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
              "       'petal width (cm)', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "Lm-9yYMUVw9t",
        "colab_type": "code",
        "outputId": "09b85593-3e11-4ff9-e9f0-70872cf77309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        }
      },
      "cell_type": "code",
      "source": [
        "sns.pairplot(df, hue='target', vars=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)','petal width (cm)'])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f21140e9ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
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6+B91PYM/EC99jLS0VMPvD2CSt2KsqsI3OxvdphuNjH//u+G2v/DIkYT2xfn1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\nXHh3TLu5isf+4BPk5OiYm/PxuRN/R59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7EtACbwQCgBjUCwQZTqbZmsXaUgL8+lQfB5vL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buwwvRsKPvNI0frczJQNqzXj9bdo6m3373/EZ6+/pymbv9AYQfN33wlQbfVve+9jH3/h7rVYBs+\n9AtMPPc8JfX1FDc1sjgwyMLICNbWVsra2xj4ylcTjKL6X/x5hr/19ykFIKarCni2yWxYnrTm95//\nqf1878WbCXL68fe185Xv92raD9tp2K91bySrZPza0IXIMr2g2PcceDsDt4eYnJ/BUVrB/Su1mvbA\n0L98iKeWuiPL/qv1vYx+/W8TK73+yscwHeuMGaOerO1+33uZvzUQEwBbVFfL8P/+dqRd/eM/y+j3\nf6BZUXbu+o1NJebYUYGyd96u/xBAUZTTwH8GfnetbVVVfTLqZwMQqRygKEoLMKOq6tCd308Tcu9J\nZtTrEh0EEx/kGiZcwfCDb2tNCKAF/Wqzw5NeDrdW8kzXUMz6cH/JbnK9wKlLE914fHP4AitYjGaW\nA8u67Y6dHV2zGmzM33cqucUb9eHglOmv/6V2f73XKS+1MK+1bmkJg8HA8FP/QM37HqX1k/82xuUm\nHCizViXEZIEr6ehDEIStQ0vvArr6d3DMg2tmAdfMIssrAYoLzTnx9jOervFLALp6e9Qb8o+P1+0W\no5nmfk+CbgNYGhlJWg3We+06BSYTs91vMn/zJn7vPCZrKRgMzF5+I8agDzN/vT/lAMR817fx83tZ\nSaj4lFbl+devT+raD9ksr3r2xqJ/MVL1PVlQ7MDtIa5P36LUXML16Vu89eacplw03LiNpSnUX421\nisWeq9qVXt/opjLOqNeTtflbA8yp1zBZSyMBsJWdpzFZrfi9XkxWK4sjw9r7ef0ylR/715KY4w5r\nGvWKovwaIV/6ewkZ3T8CfjPVHSiK8hqhANv3Ri2uBaIjfiaA1rX6cji0i0f1Oe8GwSSraKg63bp9\n6FWbHXZ5WVoOaN78yfoDuH5RLzhqiomF6dB4kwSpTM5PEVyjGuzSuCvmbwhVctMb19CgU3P5gnMY\ni0M74j+6/7nubvb9ykdj+7wTwJIskGteVTmQ5Fylo49k12K7sNtLMJlSC7rK5PivZaznrWU7rnE2\nylWmSUVutfRuMv3rmlnE57+bZUxPf2b7+b5+8VZSvT3quVtlO7qNvagcy4CLePPbUmFnYXhUs6+w\n7l0YGsZcbmPB68XvDXnA+r1eCuvq8E1PJ2wX3kaLeD06pBOouJa+zTb0ZNbhKEuY36MDZWP6sBXq\nVppda77fCjZmb8xgLyrHNT+1hr0xQ6m5BNf8VCj+Q0NW4U4VeSXU3/G6e1j4p9c0+1sYGuZg3Hj1\nZG1pYhKTtTRiwwAsDI9Q0tyEp6eXkuYm3XtkwTmUsJ+dTCo+9VXAl4CzqqomWrZroKrq/YqiHAH+\nVlGUe1VV1XqUSunTh96np4ONuxgc8wChyrEdrZU4XYltlUa7bh8NNWWa29RXW3Wf/pL1B7Dfvhfn\n7EjCckdpFYXmQoY9Y7iXZmlzHGDYM6bZzrDPB87ErDRF1Q5m3+xJ+BtCldz0xlVSv4fFocT+Shrr\nWSzRLo0d3X+poiT0bT2gsDjoxDfjxtbRrtm/1nbp7GMN9xvd/WYat3shpXa54HaQDWz1Odpm95tt\n2S+kJrdaerenf1pX/zrsxfT03zVAtfRnLtwH++17eXnonK7e3m2r4fJYL0BMG/fSLL7m2gR97ptx\nYz92n6bOC+te+/FjzHa/mbDebLNhKilhUbPPoynp0bDuXatdKmSbzIblKX5+HxjzaMqp27PMsUPV\n67YftoK17g19e6OC3onQa53k9kZsOy1ZhXAV+dD2F8be5FT9bm2boqFe11aIJ96GgZCtEl62MDCo\naxeUNDZs+rps98NaOkklpeVnVVV9db0GvaIoxxRFabjTx2VCDxDhUm2jhN7Wh9lzZ9mG6GyvjUSu\nL68EIhUNo1mrAuyp9hrNbXY7rBiNhnX3B6HKbVqlwo9WH+Z47b2RT2JFpkIsRnMkI0L476PVh7Ge\nPhmJ8g4TrhIYdlcJ/x1eV37/3SCR+Kj18js5XRP6a9/P7UO719yXVgCKrbMz4v4TDuSK78N26nTS\nTAKp9iEIQnagpXcBXf1bZDHFuOtkc6BsMl0VDj6M19tWSwn1tjoaynYDxOj28O+B1vIE3QZQtGeP\nru4FKL/3cILrgcFiwXb0KOUnE+cIgNID+1LSxWHdq9UuX4if3+cWVmioLkuQU4Aj+x0bmu+3Gz17\no9hUjMVopt2xH4vRTIm5WLdd2C3HF1hhaJ9dUy6G9u2KtHN5pyi+p12zXfm9sS7AoC9rxuLixOr1\ne3bHfJUqqde+R8pPJlau3cmsGSi7URRF+Y+EAmX/g6IoNcB5oFlV1eCd9b3Aewj52p8BPqyqajIv\ngaQVZaMryB5ssrOvYRdvXJtkcHyOxtoyTrbV0NFk190eoGfQTdcVF8472xw54KB/+Dbq4G1Od9Qy\nMbPAjZHZmADbtdCqBhvOatM9280briuMe1080HiC/plBhj3jNNjqOF53JFL0YfZmN96zXQT6Bylu\n3YvF4WC26wLGvQ3YOtqZ7e0leGMQ0769zHbU87Rf5VHTIexXRli6FsoeYzhxD88xyIBnkH/tv5el\n7issDA1T0lBP6ZF7+I51hGHvKD9XfATT69cJ9g9S3LKX4ppa3F3nse7fnzQAJVJF9vp1Kk+dZHli\nMpS55k7FuLneK3jVvrUzMOj0sVbwiwTKJkcCZTeGBMomJ17vtu2t4Mqt0Od9z4KP4Qkv9dVW7t3n\n4NboLH2D7qT6c7vf1CfLVhbNNC5euXWBXSVlOGdHsFpKmfN5GfVMsNtWw4HKvdyYHmBkbpyjdfcw\nveBm4PYw9bY6TvqqKO2+RbDfSUl9PcYyK/P+JdjXANcGQ8v37MFYVkZgfp7yEycwtR/B33uZ2a6u\nUIabhnqKmxtZnr6N7dQpVuc8ietue7C3H8Ld3RMJItTTxemqAp5tMhstT/Hz++m2GhZ9AV6/Phmp\nKHtkv4Pj+6s0K9Jvtz99KveGlr0xsTxBz0QfIx4Xe2w1dFQfxFJg4nXXFYY9Y9Tb6ri3po3V1SDd\nE1cZ9oxTb6vlcPUhOjy2BLkYc1gS9lHbO8hsd0/Epig/3IHp+AOaY4yvKGs7dZpV9zSzFy/d3f7o\nfRRUVCXsO0bOGxsoP3kSU/uRdJzbvAmUzaRRXwz8FaEg2WLgc0AlMKuq6ncURXkI+C93mn9bVdU/\nXKPLpEZ9GIOhgEHXHL//jYtAyEfO7QkFG6UavW4yGWJy0UcH1cYH2KZK/HbhSHWL0czDTad55ubL\nmtlvoieUiRUXf3QuFN1uLyrHvTQLwP0Nx9hVZOPZm6/g9S3oZlgY+PBb+M5y6HNWjbWK3zj1SeaW\nF/iDM38SOld3+rRaSviNU/8Gu6Fy3cccf65WBm9y6wtfWFdmhY2cbzHqkyNG/cYQoz41ou/ToUkv\nf/XDq8wv+dhdZWVgzINvJchv/9IxmmrKkt7P23m+k2UrizfsHY4yXndeTZoF5/jue+mfGcC9NIvF\naObBxlP85NYrkYDF6tIqfvmeD1JfvCcmH3d0Vd74miiB4QGmvvcd5q/3R95iRuvT8DbhPsLnM1Vd\nvNH57f+w9+bhbV3ngfePAAnupLmT4ipS0pVFbdZCibKdOE4mi5PYTmJPOknTNk2+9EucTpMm843z\ndcZtMm3jadp0OtM2jb/ESbO12RzHTdJsXiQ7Wqh919FCiQQoriLEnQQB8PsDBITlArgAAQIg39/z\n+LF47znnnnvvi/e+99x38bsuaSWzevLkvUbe7E2WHBMtdSU+OfW3E5Z6PRJJLL8N77xP3j7BN07/\nMEQ2/9OWR/nhhZ/SXFpPz1gfW2s2cezmaYAA28Ir+3rXwbvNef4UN/7hH7GUl1O2eyf2o8dxjI7S\n8sTHIhrceudjsZgDknD4H8cfvd/GUlhJRn1Yn3pN074JhJVmpdTvRBpYKTUDvC/C/gNAZ7j98eJ2\nL3Dw3J2sDAO37vjZGY1eDxYWf4GK9wce3M8/Uv3m5KBuNPrRgZM0rL3zMHnNdoRJh+d8BqdGfNun\n5mcYm5tg0jEdNsOC2+Gg5dq4L2p9cHKEX3UfwGwy3fmUtjjm6MwYr/Qc5F1r3xnzOQdfq/FDh2LO\nrJCI6y0IwvLh/zs9eG7A55PsrdLp2T5IY1XmZQ8J1sP+7SF8FpwZ54zHN9k1j8M1z8DUUMB+23g/\nR24eZ81aj7uO9xp6/69ntIwfPMjYydMB2/z1qbdPsN40qotXg771XiNv9qa5eRdn/WI9/O2ETL0e\n3nmfG1K6sqlGrlJoKeD88JWQjDj+toVX9vWug3fbWFfXYpXXAfr/7ae+/WNdXVTEuIoebND7H8ef\nRBr0K41IgbK/jrAvbSVdryS0F29Z8lT/UP1LNUeKRr882o2p7c7qTbhS4w7XPLem7b7xjESte8ev\nyNd3SfI/drxEKwEtKahWNzNdb42904OJn4eQODJB/+oRSb+G04VXRiNnwfHPOqL3d6Sxw80xXn0q\nujiQTJXTWMjPz8E2PqC7zzY+wK66Lfz0ysuGbRA9srNNTOsE04InK01NglfUheiEDZRVSv2z3n/A\nvwDvXL4pxoZ/Sehg0qUsuX+p5qn5adaVt+gGyu6s2xqweuPtE0yu2cKa4hrfeFnbNniCrILKKS9s\nbKa2uJoiSwEAG8pbqS7QT2O5obw1wAUm3vPUKwHtKWi12/CDTBCEzCAT9K8ekfSrvy705+6KdeSY\nc6gt0nc7qyos97kxeP+emp+mprCS8vxS2qvWs62mPaRfdrb+Y9lfn2YXFXkqahZ5VpSLNC3qtS3e\nFHqscH1Xut7NVDk1ismUxczMPPUlHrvA37YAaCip5fTgJWoKK5man6ayoFy3XTjZ9+J0uiloavQc\nM8jeKGhq9LmCxTJvI9uM9l2NGMlT/wHgi0D54iY38GIyJ7VUOttreeVEX0hFuHSKXu+o20HZwDiN\nV+3kvnSCvW3NjLbX80v3VR7J3Uf5+T448DL2DTd9wUwddTt41XokxD+urriaaccMj+VtpfGqHXqv\nUvvQW5kbHGLa1kfJ5nZKt7QzcfUab/t+D7mNa5hpb+Z01gIlecW+LDz+Y+6uvcdw0FgkSjo7ubV/\nv+ezr8lExd49uObmsB85wvztsVVbtlwQViqZoH/1CKdfd9fe4/vbNmPjaP9Jsm7A9LwnH7/ZlMW+\nxl0ctp3AveD29cs15wYWnrqrkawFE/e768g/1Q3dveQ1uBgtGWJhIYvi9W2MnTwVMQiwpLOT3NJi\npm19zPTdpGRzOwUN9eRu2qx7TmMXLmJ/ab9Hj7a1UXnfvYwcPARuzzyDs9ysJr2bqXIaieBn9pbq\njZizspmen2FkepRNVRsoyMlnY2Ubs04HI9OjrCteS2tZE+tum2m8asdyYwhHSy3WdWWs95P9cJTu\n6SArKwvn9DRzwyOel83CQkq2bcX+3W+HyJI34NvqlfM9HWQVlYTIHWBIFleTzBohaqCspmlH8azM\n/yuebDXvxxPs+t2IHROPoUBZL+kYve5PuHLJ2b/zKM5vPB82mMkb3X5ltJvmuxpwul0ctp3g0dx2\nmr/1qqcS275O7MeO65Ycv3Xw0J1j/e6j/M30AfY2eFJhddt7fdHsgOGgMSPnOn7kMFlZMPLSK0kp\nWy6BspFJx0DZeNxvnn1yef1vJFA2PuLVv+mQ/SZctjJvIG24wNiH1j/I8ZtnaSippbW8mYvDVxia\nukVDSR1ryxr50cWf88G77iXvn54L0W11D7+D/hd+ErI9ONjQG5QYrR2E16Nr3vMubh06HJLlJh69\nG0y6yWw0eUp3O8GfaOeiF+h9f1MHR/pOhshqR/12Xus96tv2eP52Gr4Z+myu+vTHQ6rTB6MnN5X3\n3cto19FQOf3Ih7nxzFeitg3XPxG2gh6rIlDWjzGl1ICmaWal1BTwjKZpvwCW26iPiabqIpqqi9LW\nNy5cueTCi72MBbX1D2ZqyG+gYW0DVR3F/J9Dz3LQegyLOYfmq2O+XPLuubmwJce9ueDdDgeFF3op\nWJ/HQesx3tL2AJ/Z/QnftXru+gsxBY1FwtzUSllTK7e//x0pWy4Iq4B017/h8OpXPT/iaIGxk3PT\n/Le9f8yPr/+Mb5/5ERZzDmV5pZwbusSca45sk5mSsz3MBuk2gNm+Pl2dFxxs6A1KjNYOwuvRuVE7\nzei5ZC8AACAASURBVE99LuT8VqPezVQ51SM40NtizmFyfkpXVqfmp31f6C3mHBqujOre+8nDXVGN\n+mC5MVksuGZm9OX0xMmAbXptI/UXWyE6UYtPAS5N094BWDVN+zNN0x4HmpM8r4SRjj/USEFLs719\nWMpDg1cnlQrxGbt+21MG3BscC57y4LNDw/pjL5Yc9z9Wc2k9ABdHrgTML2LQWBy+ayZTFhOXwgdq\neceMFtAlfnOCkDmko/41gp6PebTA2Muj3YAn4wh4jKfBqREKcwoYnhr16NprtpB+lvIypm36tRen\ne60+H/toQYn+vvjR9Ggwq13vZqqcetF7ZhsJ4va289oPwbiu9YSN8fAeN1huItkg01ZbgA2i1zZS\nf7EVomPEqP8AngJRnwDWAL8N/GEyJ7XSCRdACpDXVI9j1B6yXS+YqbGkDvCWdPa4PjhG7eRWVeqP\nXV0VMHZekydHLQQGxMQTNBaNSOfsf25G2wmCICwXXp1onx3zBRUGs6Hck1IyWHd6+/SM9UFr6FdO\nx6idgoY1umN6gw0hMCgxUjvvfGPRo6J3Mxu9Z3YkWfUP4va3H4IxtzVHzF6jJzeRbJCCxoYAG0Sv\nbaT+YitEJ6r7jVJqSNO0eWA98P95NqnxpM8sA4nlE15AAKm3v8XC1N1NcPQEJosFS3mZ7wegV7K7\nY80Oji/6dt5oK6XlsMe1xpyX53Oz8R/blJvr2+Y91uR0d0gwGBgLGouVcOccfG5G2wnJZTmKSQlC\nprBnzU7U6DUKcvLDJheAUN3pcM1TkJOPwzXP+NZm8g4eD3EZyK+v92UN8df7pR0dAc+V0o4O7EeP\nhejG0o6OkPnGqkdF72Y2XrmDOwWkwslqfnZ+gHxa15XReDjUZija65GrSLZNsNy4HQ6yCwr05XnH\nPdiPHff19W+r119shdgxEij7SeC/AQrPyn4b8JRS6kvJn14AMQXKLifxZokZ6z7DxOEu3Nd6yF6/\nltubGviFW/F/Obcxd/oC07Y+ChrqKbxnK3k79ukea11FC2eHLmEbv8k7cjZSdXGQuWs3qNi9m9n+\nm0xd7/FEmG/ZzNjZc74y4nnbNvGM+TQtpc0BwWDB5xUuaCxejJYjj7VsuQTKRiaeQNlkG/USKBv1\n2Gkvt4km1YGy4bg0cYlj/aewjfdTX1JHU+kaesdu0jc+QH1JLcWWQhbcsLvOoyP9dWdrWRPVBZXY\n58aYdsywa66cgjPdLFzrpaC+AXNxMa6ZGYo3bWTi9Fmf3i/etoWpG1YmL17QzR4SKUuOF8uwjcFX\nDhjWo7Hq3WDSTWbTVZ5iIRb7wiun1vF+Gkvq2FW3nVnXLGeHLmIbH6ChpJYt1XeTZ84LaVc37PGh\nd13rwdzWTNHeDibqyg0dO0Qm93TAzAxjp0775Ll0+zayd3bqts0qKgmROyAptoIeKylQ1ohRfwp4\nvVJqbPHvMuBlpVRspcKWTloa9bGUFtfrB3feqi3mHJ4qfRv9X/pqyJtn7f/9+9xe3xD2WC3FTXc+\nkw3Y6F6MCPd/S664717mhoaYvtGD2+Gg9TOfwdSo72bjTzKCiIyOabSdGPWREaM+PsSoX17S0Qi7\nNHGJL5/4ZojefVh7M4dtJxiaGglIXemv+/31l/ffJlMW8z3d3Hz2q8wNDsWUscw/q0e2gcI+3usZ\nqw6PV+enm8ymozzFQiz2hV7bfY27OHbTU4HYa2cA7FqzjWM3Twds847plSujx/ZmoIE7q/LlHbvD\nZr/J3u5Z/de7N3pyl2hbQY+VZNQb8akf8Br0AEopO6AfRbkKiVRa3Eg/bzCVd4zpY6d0o7lnjp/m\n9Mi5sMfyV+72AwdwTk4ulm4e9GW7cY6NMXn5im/f2OFDhs4xGb5pRsdcrX5xgiCkB8f6T4XoXYDe\n8T5s4/0B+4J1v7/+8vcFHj90iBmrzXDGMu/f40cO+/bHUqkzVj0qejc9iMW+0Mt+M+OcCbEzHK55\nZpyeGgv+27xjeuXK6LG9GWi89gZgOPtNMHpyJ7ZCbBhJadmtadrzwC/xvAS8AbiladrvAyilnk3i\n/NKaeEqLR+rXXFrPbG+v7nizvTZm5vWLYvgfK2JmncXsN94f3mosDy4IgmCU7GwT1vH+kO1leaXc\nHNfPGBJJ90No1g4jGctEZ68+YrEv4s1+Mzg1ojum0WPHk/2mzmLG4XDp7heWjpGV+nzADuwGdgLj\ngBm4H7gveVNLf5aSJUavX89YH3mN9brt85oayM/O0913d+X6gDmFzawTlP0mEyLEV2taKkEQlp9g\nfeN0un1ZxrxYzDnkmHKoL9ZfZNlQHtmfN1hHx5KxzF9nL6duFD28/PjbFxZzDjWFlVjMOUCofeHf\ntshSQHvVeubd8xGz30zNT4cd06htE1f2mwQY9CKP4TGS/eaDmqaZgGql1MAyzCmjiCVLjH/Ay576\ne0Ki0h2ueXJ2bsZ07ESIL1r+zm1sq2rg19df9fUxZZl4d+5mNh8epucb/90XTBUuIjw4+006R4hL\n6WdBEJaLSMGIu+q2c7z/LE63i4767cw65xiZHqWuuFo3s0hZfimfP/q/IgYW+uvoWDKWlezZu6y6\nUfRwaumo28GMa5bp+RlGpkfZVLWBgpx8Xfuio24HJflF9I0PcHNikHXla1lfsZYLw5cDZDQvO5d9\n87U80D2O5cYQjpZarOvKWB9nBrxI2W9CsjTtiD97Hog8GsFIoOyDwFeBOaXURk3T/hb4tVLqp8sx\nQT/SMlAWjGWJCQ46MWWZ2NuwA4s5h257LxUFd5FrziUbM/vshRRc6GW2t4+8pnqmNzVh3ro1IKvC\nldFuHs3djOkfv6tbIhkCI8eLN21i4uJFJi9dijtCfLmIp/SzBMpGRgJl40MCZZeXVFxvIwGBlyYu\nYZu8yU8vvxiiw7OysrCO3aS1rAmHa57DthO4F9y64/gTkLVj40YKW1sYP32GmV4b+U0NlGzbylT3\njQCdDcSkG5dyPePUw2kls6spUFYvoDsvO5ff3fY410Z7fPbJgwvNDP/13xu6r0Yz4OlloFkYHWHs\nxEmmrTYKGhso3XGPL0gWYr838cijUVZSoKwRn/q/BPYC/7r4918APwGW26hPWyKVFvcSHHTiXnBz\n0HqMt7Q9QEf9dl5QvwSgvXoDfzV1lKL1BTTvaqJnrI/JqW7eNOCiYW2D71hVHcVc/ocvMxyuRPLj\n76OsqTXA/7KsfXtG+GNK6efEIznnBUGfSAGBDWs9BszG4o1cGLkcVod/ZvcneP76T3jlxqGI4/hj\nbmoN0dGVOzqx+PkcW3Z0Buy3f+87y6YbRQ+nHiOy6UUvoHvWOceZoYv89vr3+uyTWGTIiG0D+rJM\nUysV2zsS5kMv8mgMIz71k0opX0SQUmoEcERov2qJFBgVLujk4sgVro324HDNBwS2TDqmOT98hUnH\nNLAYnBLkR2akRLJe5cB0Rko/C4KwXEQNCPQrSR9Jh5tMWVwcuRp1HD2CdXKwAeTvQ79culH0cOox\nKpsQPqAbwDp2k+xsU9REGpHu61Iy0CTKh17k0RhGjPoZTdNeD2RpmlamadpHgdkkzyutiCQwRoQp\nWtBJdWEFAFPz06wrb/EFrgS3C/7BGCmRnJ1t5BaHkqofiZR+FgQhkUQzqI0GBEZq53S6Q/Z7gxvv\nrlwfUW955xdNVxvRjYnS26KHU49R2fTmlQ8O6PbSWLoGp9Pty+OeKBnSa2d0W6yIPBrHiPvNx4Av\n4cl+cw14FfiIkcE1TfsrPFlysoHPK6We89t3A7AC3te49yul+oxOfDmIFJQRaxXZcEEnZfmljM2O\n81jeVhqv2sl96QQ7Wuq43lbCjx0XcC+4wwbeRiqRPNZ9hslDR3Bd68Xc1kRR5x5KW7cu6ZyXCyn9\nLCSKWGMJNnzl60mZh7D8GNXRRgICbTM2ygvu0g2M9bbzjuN0u3jEsomWa2NYbgyRrw3jyurW9Vc+\n1n+K3aO5WM5cY9baF7VCbDjdWLxpE/bvfjuhelv0cOqJJJvB1WM3V2/k5MB5Zp1zAW23Vt/Nc90v\n+H4Hb9y9BZNeIo2dmwPahfu96NkIgKFt5qZWX39rjLIq8miMqIGywWiaZlJKRa16oWnaG4D/opR6\nSNO0CuCkUqrJb/8NYLNSatLgoZc1UDZSUEZ/lSXmKrK2GRv7bQeZcc4wPDVKVWE5+dn5LCwsUDfi\noPlbr4Yc69bvv5WbFTm6wSneIBO9AJVJ56RuIEzVpz8e0bBPZiBKrMRa+lkCZSPzxEv/T9LGjpfl\nCJRdqlEvgbLLS6Kud6yVviMFBHrH2lm3laysrBAd/vqGfQFtp6+psAkM/BeF/qbrS/xR7r04v/F8\naOXNJz4W1rAP1o3FmzZx40v/hHt2NmCMtU8+Se3ObUu6nnHo4bSS2UwPlAUCkmOsX5TNSeekbpXj\n3932OOeGFdaxmzSWrmFr9d1888wPmZ6f8bXLy87lqYbHcR8/57uvpp2b+Zzt+yEvBOGqx/rLa+V9\n94ZUj9Xb5pXrG//wj3HbGLHKo1FWVaCspmm/BxQAXwb2A42apj2tlPpSlK4HgK7Ff98GCjVNMyul\nMqLqQKSgjBMdpYaDV7x0DZzgoPWYZ3U+r5TzQ56gq93122i5OoZL51ja9Vn27Hp3xHnqBahMfucr\nunOfPNwV0ahPp0AU3cAbQRAEA8QSYAiRAwK7Bk4AMOua5WT/+RAdXpCd7xuzIb8B+4UD4RMYLOrR\nroETWMw5FF7sxa5XebOri4owRn2wbrR/7zsBBr3/8Wp3bot2qSIiejj1+CfH8L6gfOvyv+rK97lh\nxW+vf6/PJee56y8EGPTgCZ59KauHdz3+Pt99fe76CwEGvXe84N9LsI1gslhCqsfqbYM7ch1MLDaG\nyGN0jLjf/AHwAPAu4BzwOuAlPC45YVk03qcW//wQ8DMdg/6fNE1rAV4DPqOUiniXqqqKDUw3MVjD\nBGVMKcVQe5vuviuj3VR16M/xynFPwIu3XLMXh2uenBuD6L3pTCrF+gjnHO569F7t0d3uutYT8RpG\nOucNy3jt42U55cMoZWUFZGebDbVNx/mnG7Feo8sJGH813pdY5DbRJOJ6e/VtyPYIOjrSWP5JDIJ1\nePCYRvTolePXI1YQn+61stHgdYh0PFg98htOZlfS+XvPxXosfFCs//ka/R0YbRcsa3rVYyNWlO21\nBlRI9pIpNkYmYMSon1FKzWma9hDwLaWUW9M0w69ImqY9gseof3PQrqeAnwOjwPPAe4AfRBprOT+j\nFW3QmOkJVbiFmkZ1Qalun/XlrWHnuL5sLb1joSEDFnMO8y010GvVPVYEl5Kw+8xtTbrjmduaI17D\nSOec7p8wo7jfLPNs7mC3TxtqtxI+E8fjThMrv3nkPUkdP/gepNj9JiXHBeNym2gSdb3D6dtIOjrS\nWK9aj7CpagM2nQwjwWMa0aPry9Zy5OYJchvXMGMN1dUFTY2G5xnpeLC8z810k9mVoFe9+J9LY0md\nriw2lq4JOF+jvwOj7YJlzTFqp2Rze4AM623zUtDUiP3osZDtqbYxVtKLn6HUKJqm/QNwL7Bf07RO\nIM9gv7cAfwK8TSk15r9PKfUNpdSQUsoJ/AzYEtPMk0xJZycmiyVgmzcoY0fNtpAMNeGCWb101O3Q\n7WPOMnO9rSTsseKhqHOP7nhFezvC9PAQ6ZwFQRAyhXD6NlhHR8vMYTJlsbd+F9WFlRTk5Bsa04ge\n7ajbgcM1z/SmZt22pR2RdXWsxxNWFrvqtuvK4q7aQHcro78Do+2CZc2/emykbRBerkVWE4uRirJ1\nwHuBf1dKKU3T/hNwQSl1Okq/UjyZct6klBrS2fc94J1KKYemad8FfqCU+n6EIZe9ouxY9xkmDnfh\nvtaDqa2Z4r0dPp90o5XW/DkzdobTgxewjffTUFLHluqN9NpvouxXecTSTtn5m8xeubrkwFDv3CcP\nd+G61oO5rZkiv7lHIlmBKMlGAmUjsxyBssuxUv/k1W8kdXwJlPWQ6YGyED34NVJmHO/+y6PdNJTU\nUmQpZNoxQ31pLdbxfvrGB2KusKmX/eb4wGl23bKQe7abmV5b1Ow34Qh3vOWW33ST2ZWwUh+c5WZX\n3XY2Fm8MsSe21Wxia2noM96orbKU6rGAoW2+7DdHDjOlFIVpYmOspEDZmLPfGEXTtI8Af0agW+tL\nwFml1I80Tfsj4HeBGeAk8IdRfOqX1aj3ZicAKMsrxT7r+dAQHA1uMhiw4S3hHDzeH+z4AJtK7w4o\nMGJkPKPKyhswEytG55EuiFEfGTHqjSFGvYeVYNR7CdZl0TLjhNu/o24LJ/rP8uk9H2N740ZD8zSi\nR71t4tXVkY4nRn1mG/VeuyFYFj+4/b187dR3gcj2iT9Gn+lLaWd0G6TXvVlJRr0Rn/q4UEo9AzwT\nYf/fAX+XrOMvFf/sCf5BUcHR4EYNX/8Szv7jHRs4zcbijTGPZ5R4HxKZZNALq4en1/1OUsd/Nqmj\nC6lAL5tNpMw44fbPuTzZQbr6T7C9cSNGMKJHvW2WatAbPZ6QOfjbDV4crnlOD17w/TuSfeLPUqrC\nGm1ndJuQPOIrN7rCiaU8sxGMlHAWBEEQkks03Z6dbQq7f3hqlLK8Ui6PdidzioIARLYbbOP9lOWF\nJuyIxz4RVharypo0KuzhyjNbzDnsrNsa85unkRLOyUJ+4IIgLDfpqnf8dbvFnENNYaUvQHBDeStO\np1tX9wNUFZZjnx1jQ3l0/990PX8hNlJ5H/3thmBZbSip87nc+LOhvDWsfSKLh6uDpLnfpBO9Q5Mc\nOj/ApZ7bbGy+i872WpqqiyL28S/PbMoy0VG/nTnnHMdunmZ8diJsCeVw7KrbzvH+syG+ccHR6onC\naIl0QRCERBGPrl1uOup2MOOaZXp+hpHpUTZVbaAgJ9+X6cNf93uxmHPINecCUJZfyqd//ucRA2xF\n72Y26SLHu+q2k5VlCpHVrdUbOdF/NqBtuAx84QJthZXJijfqe4cm+fw3jzM37ynv1DMwzisn+vjM\nB3ZG/JE25DfwqY6PcnTgJFlZsL/nsE/JW8dv8qr1SMSglGCKsot4+4Y30jcxwM3xQdaU1FBfXEtR\nduIVRXCgV+9YX8zzFQRBiIV4dW0qOHbztE8/2sb7sZhzeH3DPiBQ918e7aa+pJainAKcbhe71mzj\nhxd/hnvBHaJXRe+uDNJJjouyi8LKqr+MhstWExxoaxvv53j/Wf5gxwfEsF+hrHij/tD5Ad+P08vc\nvItD5wej/kC95Zl/dP2FmEqO69E1cIIXr79GkaWA5tJ6Lgxdpst2ijetnTI8hlFiLZEuCIKwVJai\na5cTI/rRq/tNbZ7MHSZTFs9f/wkv3zgYtp/o3ZVBOslxJJl619p3BsioHuECbYMTdAgrhxXtZGUy\nZXGp57buPtVrN+QvZzJlcXmJQbP+wVmTjmnOD19h0jEd0xhGSXSQryAIQjQSoWuXg1j1o7+xdHHk\nath+kQJsRe9mDukkx0ZlNZIPvSToWH2s6JV6t3uBjc130TMwHrJPayoznG4sXAnlSEEpiR7DKMt5\nLCF1LEfeeUEwSiJ07XIQr36M1s8bYCt6N7NJJzle6rPcG2hr0zHsk52gQ0gdK/5VrbO9ltwcc8C2\n3Bwzne01hscwWkI52WMYPtaa5TuWIAgCJEbXLgfx6mJvP/9MJP79lqrjZTU/PUgnOV6qTO2q267b\nP1KCDlnBz2ySVlE2CcRdUdYTyT6I6rWjNZXR2V4Ts2+c0RLKyR5jrPsMk4eO4LrWi7mtiaLOPZS2\nbvWN39V/gmu3b7B7zXaGp2/Rbe+N+1iZxGqrKJuOK/XLUVE22Tz75IMBf0tF2dhYqq5drusdry6O\nlknEf9z64hqKLIUsuGF3XfjxXb3djB86xORlRdEGjZLOTsxN0dNmGkEqysZ3/omwGRLFUrPXXJq4\nxLGB01jHbtJYuoZdtdt0+0eyLZKBVJRNDqvCqPditPxxuo4x1n2G4b/+e9wOx52xLBaqPv1xJurK\nQ0qbF1kK+NSej1Kdk14rZclAjPrUI0Z9YskEuQ1HvDpuua93LPMMzm4DnlXP4Ow2fbN9fOPc9xia\nGvG11WsHHoP++tNPh+j0tU8+mRDDXoz6pZ1/Ip73S8ErcwBleaW+3PTxZFTKzjaFdbmJZFsky7AX\noz45rKrvLIn4caZyjMnDXQE/OgC3w8Hk4S5ODJ0OiXKfdEzzG1tX3PMUBEGIh0zxIY9lnpEykfhz\npP84tvH+gLZ67QDGDx/W1enjRw4bnpeQPFItx16Zc7jmGVx8SQwnS9GI5EMfybYQMotVZdRnMtnZ\nJlxXe3T3ua71MDR1S3efZF4QBEFYGkYzkcSSXcdkymJSXdJtO6mU6O1VznJlsotmW4iPfWYhdytD\ncDrdmNuadPeZ25qpLqjQ3SeZFwRBEJaGNxOJHv461mg7b9uiDZpu2yJNE729yolFlpZCNNtCsuRk\nFmLUZxBFnXswWSwB20wWC0V7O9hRs00y3giCICQJo5lIYslYUtLZqavTS/bsTdCshUxmubLmRbIt\nhMxiReepX2mUtm6FT3+cycNduK71YG5rpmhvB6WtWykFQ2WjBUEQhNhpyG/w6dgro92sD6Nj/dtF\n08XmplbWPvkk40cOM6kURZpGyZ69Cct+I2Q2RmVuqUSyLYTMQoz6DKO0dSulrVt1I8eDS5sLgiAI\nicOrY6s6ImfuiEUXm5taKWtqpSLFmVaE9MSozC0Vr20RKUuOkP6I+80KRB4MgiAIqScWXSx6W0gH\nxKDPbGSlPkZSnbdWWJn85pH3xNbhfdXJmYggrGJEvwuCkMkk1ajXNO2vgPsXj/N5pdRzfvveBPwl\n4AJ+ppT6H8mcy1LxVmu9Yr/O+rK1dNTtEH91QRCEFYDodyFd8cnmcZFNITpJM+o1TXsDsFkp1alp\nWgVwEnjOr8n/Bt4C9AH7NU37oVLqQrLmsxSCKwn2jvXxqvVIXFXdBEEQhPRB9LuQrohsCrGSTJ/6\nA8Dji/++DRRqmmYG0DStFRhVSlmVUm7gZ8AbkziXJWG0kqAgCIKQWYh+F9IVkU0hVpK2Uq+UcgFT\ni39+CI+LjWvx71pg2K/5ENAWbcyqquKEztEoV47rV3W7MtpNVUdq5gSpux7pSjpej7KyArKzzVHb\nPSE+8mmBngylo1wlG6NymwyW+3rHq98zRS4yZZ5LJZzMZvL5p6vtkSgy+d6kK0kPlNU07RE8Rv2b\nIzQzVO84memcIrG+bC29Y32h28tbUzYnvZSWq5lI1yOVisNun07ZsYXYCZahVP7OVqPcpuJ6x6Pf\nM0X/Lvc8001mM+U+hSMdbY9EkU73ZiW9XCQ1paWmaW8B/gR4m1JqzG/XTTyr9V7qF7elJctV1U0Q\nBEFYXkS/C+mKyKYQK8kMlC0FvgC8SSk16r9PKXVD07QSTdNaABvwDuD9yZrLUomlQqAgCJnL7z/9\nUtKP8eyTDyb9GIJxRL8L6cpyVZQVVg7JdL95L1AJfE/TNO+2l4CzSqkfAR8F/mVx+3eVUpeTOJcl\nI9VaBUEQViai34V0Zbkqygorg2QGyj4DPBNh/wGgM1nHTxai8AVBEFYmot8FQchkkupTLwiCIAiC\nIAhC8kl69htBEBLPTNdbY2qf3/HzpB9DEARBEITUISv1giAIgiAIgpDhiFEvCIIgCIIgCBmOGPWC\nIAiCIAiCkOFkLSxItL8gCIIgCIIgGEXTtGrgz5VSH0ngmC8opR6Ot78Y9YIgCIIgCIIQA5qmfQl4\nAPgLPLWZ5oAepdSnNE37OuAErgM/Bv4OuATkA93A5/GkfTcBRcDngBrgh8A/KaX+azxzEvcbQRAE\nQRAEQYiN7wK/wWPM/65S6jHgzZqm5S3u/41S6i+APwY+p5R6Aphd3Pcu4JZS6oPAp4D/qZT6NTAc\nr0EPktJSEARBEARBEOLFCfy1pmnjQDngNeqvLv6/AehZ/PcZoBJoA/YtrugDuBMxEVmpFwRBEARB\nEITYWMBjR38R+CjwacDBHdva698+BNQv/nvr4v+vAb9SSv0e8BHgE95BNU3LindCYtQLgiAIgiAI\nQmxcA+4DLMDXgS8ALwOfCWr3d8BfaJr298BdeIz9HwFNmqY9u/jvLYttTwI/iHdCEigrCIIgCIIg\nCElA07T1gEkppTRNewroU0p9NRnHEp96QRAEQRAEQUgOZuBLmqb14Fmp/2KyDiQr9YIgCIIgCIKQ\n4YhPvSAIgiAIgiBkOGLUC4IgCIIgCEKGI0a9IAiCIAiCIGQ4YtQLgiAIgiAIQoYj2W8EQRAEQRAE\nIY3QNO1vgb148tr/kVLqaLQ+slIvCIIgCIIgCEujAGhb/P+S0DTt9cB6pVQn8CHgfxvpJyv1giAI\ngiAIghAf2T87eP0Lp68MP2obnGxsqCmybltf9fxD+9b+F8AZ55hvBJ4HUEpd1DStTNO0EqXUeMSJ\nxHkwQRAEQRAEQVjV/Ozg9S88+8L5T8zNuwDoHZxoOX5x6BMAD+1b+8k4h60Fjvv9Pby4LaJRL+43\ngiAIgiAIghA7BWeujDzqNei9zM27OHNl5BES4IqzSJaRRmLUC4IgCIIgCELs1FkHJxr1dliHJhqB\nujjHvYlnZd7LGqA/Wicx6gVBEARBEAQhdvobaoqsejsaq4utGDDEw/BL4DEATdN2ADeVUhPROolR\nLwiCIAiCIAixM71tfdXzuTnmgI25OWa2rq/8MTAdz6BKqYPAcU3TDuLJfPOEkX5ZCwsL8Rxv2XE6\nXQt2e1zXZkVSVlaAXI87RLoeVVXFhnzRksHw8IShH9hKup9yLokhE+Q20WSK7Mg89Uk3mc2U+2SE\nlXQukF7nkwC5zf7ZwetfOHNl5BHr0ERjY3Wxdev6yh8vMftNfBNZzoMthexsc/RGqwi5HoFk+vXI\n9Pn7I+cixEumXG+ZZ2awks5/JZ0LrLjzcT60b+0nH9q39k/w+ND3E+cK/VLJGKNeEARBEARBZisb\nRAAAIABJREFUENKUaeBaKicgPvWCIAiCIAiCkOGkbKVe07QPAR/w27RLKVWUqvkIgiAIgiAIQqaS\nMqNeKfVV4KsAmqa9HviPqZqLIAiCIAiCIGQy6eJT/xTw/lRPQkg/TKYs3O7MyNCUqcg1FgRBEDIF\neWaFJ+VGvaZpuwGrUmog1XMR0gdXbzfjhw4xeVlRtEGjpLMTc1Nrqqe1opBrLAiCIGQKq/GZpWna\nZuDHwN8qpf4+WvuU56nXNO3LwL8opV6J0lRey1YJYxcucuFPP4fb4fBtM1ksbPrsU5RuujueIVOW\nO9npdC2kY+quJFxjIfGI3AqZhsiskBSS/MxKlNwWkMCUlpqmFQI/Aa4AZ4wY9SlfqQceAP7QSMPh\n4agVclcNVVXFK/Z62F8+EPDDBXA7HAy+cgBHVYNun0jXo6qqOOFzNIrR4hrLfT/jucZGWUmymcpz\nyQS5TTSZIjsyz/DHSxV6Mpsp98kIK+lcIPbzSfYza4lk//LqgS+cHbz0aN/4QGN9Sa11S83G59+8\n7nVLLT41BzwE/FfDE1nCwZaMpmlrgEmllCNqY2FVYDJlMakuef5tsWApL8MxasftcDCpFBXiS7dk\n/K9xMJGu8UryY1xJ5yIIgrBSMZk8i+jxPLOWi19ePfCFb5z6wSccrnkAbOP9LSf7z30C4M3rXvfJ\neMdVSjkBp6ZphvukeqW+DhhK8RyENMLtXqBI20hBfT2u2Vnmhkco2dyOOS8PU0mJGGIJwO1eoGiD\nxkxPb8i+Ik0LucYryY9xJZ2LIAhCpuHVwdYoOthfVxdvaqeorc3wM2uZKTg3qB71GvReHK55zg2q\nR9687nV/wjJWl02pUa+UOg68LZVzENKP4g3ruPHMV3yf2masVkwWCy0f+XCKZ7ZyKOns5Nb+/SH+\niSV79ga0c/V2c/3pp+/ci55ebu3fz9onn8w4Y3glnYsgCEKmEU0He7+g6rWrvO9eTBZL1GdWCqiz\njfc36u3o82yvYxmrzKZ6pV4QfLh6u5k8eQLnyIiu79zYqVNUbO9I0exWFuamVtY++STjRw4zqRRF\nmkbJnr0hxu344cO692L8yGHKgtoaXYFJFbGciyAIgpBYwurg37xG1tGjTFw4T/GmdhbmZkPajRw8\nRMN7H2du1B7xmZUC+utLaq228f6W4B31JXVWPEGzy4YY9UJa4H0zL9qwnvmxcd020z1WarJNOJ3u\nZZ7dysTc1EpZU2tEH3qjfozpvgoebxyBEJnff/qlmNo/++SDSZqJIAjpTEQdfPkKC/MOZgcGWZib\nIyvHEtrI7ebWocM0P/U5KiCd9PX0lpqNz5/sP/cJfxccizmHzTXaj1mC642maTuBvwFagHlN0x4D\n3q2UGg3XR4x6IS3wvsFP3+ihZHM7M1ZrSJuCpkYx6JNAOOUYi+99uq+CxxpHIAiCICSOSDo4r7qK\nsbPnAHCM2sPaAOmqqxez3HBuUD3SN97fWF9SZ91co/3Yuz1eFl3UH4iljxj1Qsrxf4N3Tk6S31Cv\n6ztX2iGuN8uNEd/7TFkFNxpHIAiCICSecDrYlJvr2+Z2ODyJMdLTfz4czjeve90nF4NiE5anPh7E\nqBdSTvAbfN/zL1D/6MPM9t1k2tZHQVMjpR0dZLdvD+gnaQmTjxHf+0xZBfeey8TxY8wNDJBbW0vx\nzl1p4R4kCIKw0vHXwY6BASy1tViKi7F97/sB7W4dPpKu/vPRmGYZg2L1EKNeSAsC3uCdTvp+8ByW\n8nJan/yvuMtrAtrqpSWkaluKZr7yieZ7D5m1Cr7gmMcxMoKlvCLVUxEEQVh1LDjmmRsZIae8Aktd\nnWdVfnbWt9+UnU3ueo2CKM8dIRQx6oVlI9LKergVYT2DXi8gM/+zT8ESq8oJkYlUkMr//k0pRWEa\nrqykezCvIAhCJhPt63k4HdzyxMeYuHBBd1VeDPrYEKNeSDpGC/4YWREOF5A58uprlLz7t5IyfyGU\ncPe0rKmVDWlazjzdg3kFQRAyEaPP+HA6eOLCBcoef5+syicAMeqFpBLP6mi4H3WkgMzxCxe56zFR\nCMtBJq54Z0owryAIQiZh9HkgOnh5MKV6AsLKJtLqaKx4AzL1KNl0tyiEZSKR93S5iCQ76RTMKwiC\nkEkYfR6IDo4PTdP+StO0Q5qmHdU07d3R2otRLySN7GwTU92hgeAmiwXHyAgmU1bUMYLblHR2YrIE\nFqYwWSxU3n/f0iYrRMVkyoq62mLkniaTSMcPJzvpGMwrCIKQ7hh5Hvjr5ETpYKPPmRQ8jwqAtsX/\nLxlN094AbFZKdQJvBf5XtD7ifiMkHH//uoKGBvKrq7l1+AgAFXv34JqdZW54mFv/8q2wvnfhfPTC\nBdSWbro7Lf24VwLB96KisxOb1QZuv0JgJhMVnXu59S/foieKX+VyzFHv2EbScwqCIAjGCJvO2O95\nEKyTl5JQwajvvtF2CSS7/99/8YWx02cenenra8yvr7eWbtv6fN3b3vJfAOcSxj0AdC3++zZQqGma\nWSnlCjuRJRxMEELQ868zWSxU7N0DgP3Y8Tv7eq26vnfRfPSMBNQKiSHc/azc18nIa7/xtavc18nN\nH/4oJX72sfj4i+wIgiAkDr10xtGeB/EkVDCq51MR89X/77/4wo1nv/4J7zGne60t9uMnPgFQ97a3\nfDLecReN96nFPz8E/CySQQ8pdr/RNO39mqad1jTtuKZpb0/lXFYqy/35yetfZ7JYyKut8VWFy8rJ\nwe1yGfK9i8VHT0gu4e5FVm4u1W9/iPyWZqrf/hBZQdX/vO0i+dnryWY88hqPj7/IjiAIwtLxrr5X\nv/0hSnfuoObhd8b1PIiGUT2fgpivgrEzZx/VO+bYmbOPkABXHE3THsFj1H88WtuUrdRrmlYB/Cmw\nEygCPgv8NFXzWWmk4POTx7/uymUq9nUuutiMULK5HXNeHvPT0ziGhnT7+Ue+S4R8+hDxXly7RvNT\nn6P8PZ6/ez773/Xb6dwz3eJhEJe8irwIgiCkHm9Rv8KWtYxfvarbJl6dbFTPp+h5UDdjszXq7Vjc\nXscSqsxqmvYW4E+AtyqlxqK1T6X7zZuAXyulJoAJ4CMpnMuKItGfn6IVlPDidi9QsWcPN5/z++xm\ntWKyWKj/j48xe1dZqO8dgZHvYX30kAj55SaWe2G0nVc2ASzlZdzav59b+/dT3rHb584Ti7yKvAiC\nIKSOYHtjrn+Aki2bmem1hrSNppPD2Rr+et5ksWApL8MxasftcITaD21t+s+DtrZkPQ/68+vrrdO9\n1pbgHfkNDVagP96BNU0rBb4AvEkpNWqkTyqN+hagQNO0F4Ay4M+UUi+mcD4rhkQV2Ylltd/V283E\n0aO4xsd0jz3bP0DJvfeF+N7pRb7r+ehJlpLUYPReGG03fuQIZbt2hnzJcc3O+ly1IDZ5FXkRBEFI\nDcH2htvhwJybG6DPIbJONmJrlHR2sjAzjXN62vfsyC4oCBkzt7pa99i51VWJOF09pku3bX3efvzE\nJ4KPWbp1y4+B6SWM/V6gEviepvnSgf6OUir0rWWRVBr1WUAF8C6gGXhZ07RmpVTYV6mqquLlmltG\nEO56WMN8fppSig0Gr+HYhYtc0Fnt3/TZpyjddLduW0t5GVk5nnRVwW/TU9euseE/P0H+Z59i5NXX\nGL9wkZJNd1N5/30h41G1zVi7INJRPsrKCsjONhtqm47zN3ovxoYtlHfsxjU7y+zgEHk11Zjz8sjP\nt1Dqd163sxYCg6UXv+RUvuEBLOVlzA4M+toaltc45cXwJUjH+5JkYpHbWDByLTPless804twMruS\nzj8dz0XP3rh1+Ag1b34TWSZTRJ1cVVVs2NYYG7ZwvetoyLOj9u1vC3jGnFhcOHLPzTE7NExedRWm\n3FxGj3TR/Fv/MRmXgMUsN4ydOfvIjM3WmN/QYC3duuXH3u3xopR6Bngmlj6pNOoHgYNKKSdwTdO0\nCaAK0He8BklZ6EdVhMjxcO4IhZrG8PBExLdi74r7/K0R3RX3wVcO4KhqCNhuf/kAbocDx6idki2b\nKWhsCFmJNZWUeOZb1UDJu3/LV/3VQZj7arSdgeuRSkVotxt7SY80/0SwpBgLA/fCvv81cmtrmB0Y\nxFJZgSkvj9zaGgYPvBYgL66JSV25co2P45ycCtjulddEzTEekn1foh07VRiV21iJdi1Teb1jQeYZ\n/nipQk9mM+U+GSEV52LkuaFrb7jduBegLIJO9p6P134I6O50cvvMWQZf2s/kZUXxpnYW5mYN2SRF\n69Yz/Ktf+RYWx86ew+1wUPWWNyfTRnDWve0tn6x721v+BI8PfT9LW6GPm1Qa9b8Evq5p2v/E435T\nBIykcD4rhkjuCJH87QGuP/00eQ315JSWhnzCAk+wSVW2CafTk6PcPzDF7XBQuLaF/hd+EvI23fLE\nxwLGMerbJj7RSydRMRbh7oXJlEVueSk3f/CjEJlb89i7fH6SJlMW09ZQP0uA6b4+LNVVFLQ0M32j\nB7fDEZf7jMiLIAjC0jH63Cje3K5rbxRv2gRE1sn+9oP/1/2yXTsDUmIuzM35vACCCQ6A9do/AcdZ\nPnfMaZYQFJsIUmbUK6X6NE37AeDNM/SHSil3pD6CMSIV2bF/7zu6b7sTx4+x4HJT9/A7mO3rY9p2\n07fKfuvwEV+hoYL6em78+WcpXNvqyVrS1BoQwDJ9/Yb++BcuUNa+fdmugXCHRMVYhMPtXmDmRq/u\nMWZu9FLgH8SkbQwNoDKZqNi7hxlbH9NWG6VbNlO6c4cUhRIEQUgRRp8bExcu6rq7TFy8GPWZ730m\nFNTX3/m6v2UzudVVuJ13ajY5Ru2UbG5nRmdRKDj41tzUSssTH2Osq4vpXitlu3dR2tGxap4nKS0+\npZT6MvDlVM5hpRAcNe4t8FBjMeNwuHxtcDvJLirCOTkZ0H9uYIBibX3Aaqt3lb1i7x5uHTzkKe+8\nsMD01WtMX73me2v3vhlbysuYHRrWnZ+kF0wNRlJ8gbEV7my/LzT+43tW4G26faatNmqyTbjdC7jd\nC7qrOpX7Orn5/AsBcmc/foKWJyxkLz4UjGZgEgRBEIwRTq/GlELy0sWArDRed5f8lmZDz/zi9k3c\n+Id/BDwZ0cbPnmP87Dkq9u7Bfuy4b/W+sHUt4+fOh/0i4H0+uXq7ufEP/xj4PDl6bFkKIaYDCTHq\nNU3bCrwVT0YbgBvAz5VSZxIxvhCecD5vzvOnfG+qBU2NlG7dwtjZc0z39FKyuZ38hnr6nn8BFt+G\n85uawq62up1OKh94PQsOh2fV3m/f+JHDlD3+PtY++SQTx48xPzxs6G1aWB7iKeMdTIgsdXSQVVzi\nk7uSrVspaGwIve8mE+V7djP87W/6jpFbWUHZ7l24Z2eZHRomv76OhSx05W7saBd3lZQyfvDgstZb\nEARBWMlE85U3mirYv53b4QhIdGD0me9d6Q+Iw8vPJ7e6ipItm5kbGqZky2YWXK6AZ0dedRWm/Dxc\nY7cZ+9ozTPdaKWxdS5bJlNQv0+nOkox6TdPqgK8CtcCvgfOLu5rx+Mv3Ax9WSsWdp1MITzift5aP\nfJgbz3wl5E21bNdOZqzWO7njH32Yvh88h8li4a7OTnr+/u91jzM7MEDR3RsZeWV/yD7fam9TK3c1\nteK2Xmfs1GlJL5hGxFPG24vz/CndVY81D7+D4V/9yte34bF3h8RgVO7rDIyvWFzNKdu1k7Gz57CU\nl+Gam8PRp68epnutTD/7VWYWvwIsR7lvQRCElYxRX3mjqYKj+dRHwmTKIksnI1rlffcy8NN/D9g2\nfvZcwLNj7Ow56h5+B9Zv3XEpXph3GPa9X6ksdaX+B8CfKqV+rbdT07T/AHwfuG+JxxF00PN5Axg7\ncVL3TXXB5SK/sYG5wSHPW/XNfmrf9SiFW7biLq+hoL5et2BEQUMD5nz9SsfBb+OmxrVh/fmF1BAc\nY1Hc3s7CzIyh1Yyxri59X/m+mwFuXLbnX6D5A+9n8uo1pnt6KGxrIwv9FXj33BwAswODOCenwvpK\nFjQ0YD92POocBUEQBGMY9ZWPFJvnj3elnawsTLm5Hv2+sGDYpz44I5rJYsEV5vnk/+zILipitq8v\noF0svvcrlaUa9Q9FKlurlPqVpmldSzyGEAY9nzdLeVmof/NiIKIn8CTLFwA73XeTqvUbFptkYS4p\n0S3aYC4uJq+ywnAxCa8//2p4K84U/O8JQM9n/7tuO//VjOxsE9M6L3kA07Y+ClqaGT933rfNNTdL\nlsmEpaKCnOJibp86pdt3dmjYl5PeOTlJQUM9t8PInd5L62pZcREEQUgkRn3lvUR7lptMWUxevUL5\nPduZ6etj8spV8hvWkF9fz+ip01H1tF5GtEixef7PjoKWZqZtNwP2ux0OTwrtGApfrTSWZNR7DXpN\n0z4A/DFQgqeoVBawoJRqjWT0C0tDz+fNOTlF5f33MDc46BNqb8BJcABs3Tvfzs0f/hC3w8Haz3yG\nhQV0o9jnb92id/8Byjt2YyosZG5khNzaWop37oq4Ai9GV/rhvSdG/CWdTjcFTY1hVtHrGTt7zvd3\n/aMPBwRZT5y/QNnOHfqlwte1kVVQQNb58xRpGrmbNtPS2BTot7+ng4lzF4DQQmarZcVFEAQhkRj1\nldfrF2579f33Y/3Ov4TYF43v/09R9bTbvUDRunXM9Fp9et45OUXRhjX6q+0b1mMqLCS3r4/8pibm\nBwdD2t06fIT6dz+KY2JyVXoLJCr7zVPAhwH9FBhCUgjweVtcjXfNzTF2/jwlWzZjzs3FfuIk7rk5\n3U9Zs0PDnk9aDgfjhw9Rsncv159+GsDnswaLhr7DwYLbjWtyEsfICJbyimU/XyFxGPWXLO3owH70\nWEi7/Po13DrkyUar9xkUILe2Rr9cd10tBQ++lfL3BK7iVLRvp8Yvw05JUQkL01NRy4ILgiAIxjCq\n+40yeeWyrn0xefkKufe+IWr/3JoaKu+716fnizasoah1LeMXLuKenb0zx7w8irduZeLceZ8NUrrj\nHo+N498uO5vcxiYK27evyi+6iTLqLyqlQqMohaTi7/OWlQUjL71y52158c235qG3cvv4Sd3+0zd6\nfJ+yJpWi4r3v92SxOXKYiQsXKd2yGVNuLrcOHwld7ZegxYwmmr+kN9VZdvv2gJy//tlvqt7iWQmp\nesMbGP71iwHjW8rLcM7MUt6xG9fMjO/Ljzk/H4fdTtGiG1AwwSkzR3XKgpc++MYkXBFBEISVj1Ff\neSN4XDTDpDPutQakM9bDZMrCYR8N0fPj586z5rF347g1ysTiHIs3bQpM2uCXGGTs5KmA55M3FfJq\nM+ghcUb9lzVN+yWeQlK+igFKqc8laHwhDF6ft9vf1y8q5ZqZpXjzZt1PWXnVVb7VeO+nN/NiFpvs\nf/83Bv7t33A7HJ7V1jCr/RK0mLno+UvqpTrLbt8esooOBPQtuHw5QMack1M4b99m9PCRkPzFFffu\nw/6j7zN+5kzENJXJLpolCIKwGklU3FtEF82mRm798HsR9bxeoCwsJmPo6aXigx+hfHGOYQtnXrlK\nxQc/EvJ8Wq0kyqj/azwGfRaQk6AxBYOYTFlMXNIPfplQiobHH2PkxRcDfhDZRUXk1lTDWf1Pb0X3\n3IPpF7/A7XBIUakVjr9BHynVmZ7C9Pb1uumYLBYKWppxTk8z0+cJYgrOXzxttbJw7RqzA4Nhv/jE\nGtAlCOnK5Q//Xsx9Nnzl6wmfhyAEkwgdGtZFs64W2w+eA8J/2dcLlPUybbNRFVTkSo/JS5eoMGWJ\nQb9Iooz6fqXUBxM0lhAj0YJfvDnq3XNzzI7conz3TmYHBhg7ez6khLL/Sm1Zx25ya6oZPXGS/MrK\nVZ0majWwlJXx7PbttHzog4ydOu3JjNNQT5G2gZ5vfcdX4MxLXlVVQJCt3jHiDegSBEEQlg89F82i\n9evp+da3A9q5HQ7Gu45QAowfOoR18Wtwxb592GzfB3egUV60cWNAkauCxkb9lNuNDfI88CNRRv3P\nNU37PeAgge433Qkaf1VisZhxOFy+v8OVdIbwwS9l+/Zh++dvMN3djcliofJ19wcWBPIroQyErNSa\nLBZaP/MZFhYWsB895pnXYiYSQIIWVwj+K+PB2Wa8K+OmCKshzvOnuPHVrwEe+bAfP4H9+AkaHn2Y\nmy/8JEBmTLm5IS8PeqvviQ7oEgRBECKjZ2dEsj0An4tmncWM0+n2pEx2OkOeJVks6NoYlfs6GXnt\nN3eOF6TnTaYs8psbMR0NTbyQ39SIaTFGS4z7xBn1H9XZtgCI42scOE91MXb8hGfFs7GB0m1bmbja\nzeRlRd6GNuyb6vnC/AXa7mqho24HDfkNocEvbW3kVlVh/frXKaitJb+2BvuJk8zb7WFXY7PM2br7\nxg4fouzx9wW8jQev8AuZjdu9QJG2kYL6+sBy3QX5FG3YwPBXv6wbiORl7OjR0FLfeXnMDAxQcf99\nTF6+QtnuXborOKC/+p7IgC5BEAQhPHrxVP1VFrr6T3DFfp31ZWt99ka0vhWdncw0NOCambmTuayo\nKKz/fFZuLtVvf4iJxTTHwXre7V5gptemm3J7ptfGvIEYrdVCQox6pdRaTdNMSik3gKZpOUqp+USM\nvdpwnurixjNf8Ql+QWMDN776tcA32/0W7nn/ffzo9mu8aj3Cpzo+6jPsy5paKRuw0f30075qnzPX\nb2CyWKh+04OMnT2ve9zJS5ewVOqnqZxUirIBW2Dkud8K/2r+Aa0kCtetpfcrXwu4xyaLhSyyuPWb\ng75t9qPHaHniYz7DPjvbhLmwkJGXXwnpW/mGB5jt62PGavX1rdy7J2RVJlxJcSlkJgiCkFzCxVNZ\nf/t1vDh7BoDesb4AeyNSX5PFQnnHbm6f8GTem7FayW9sgCz9rGeTV6/S/Kf/g/L36K+2m0xZTPf2\nBuSz9yZeyG9qZOHq1YgxWqsJUyIG0TTtPcCP/Ta9qmnaY1H6PKBp2rCmaa8s/vd/EjGXTGfsxEnf\njyNS1pm261MUWQpwuOY5OhCYstJ+4ADOyUlMFgt5frnCnZNT5NXV6h63oLGR3Gr9fUWaxu2DB8Ou\n8AvLhylMKkijZGeH/8mPnT4TJoPSDCaLJWDbWNedQtFOpxvXxIR+34kJZm/2B2xbcLsp39NBfmMj\nZTt3ULZrJxMX9YOgfP3EoBcEQUgK4eKpGq/exmK+k/vE397wPovC9XXNzJBdVOSzQeYGhyior9c9\nfkFDQ8TUl16feu/YswN3imsW1NfjnJwKOPZqtksS5X7zKeBtfn+/GfgF8IMo/fYrpSIa/6sFV283\n8709TFvv5HyNlHUm+9pNPl68AVW5wEn7dUxtflHiVy5Tsa8zxBViymaj6r57GTt1OsQvzVxUSElH\nR0iWHJPFQuneTvq+/jXdeUgmkuVB79NoLCsRzvOnQnLN+7vQWCxmZsPkG/Yvze3Fm4PY6XR7VlFs\nYXIV22yYLIEJsTwyvsDC/Pyd1ZaWZpEjQRCEZSZSprGcG4OUaaUMTo34tl0e7ebXlpc4PnCWnbVb\n0ML0nR0epmxPB5OXr/hsEHNJsW5BQnNJcUS/fZMpC3NxUZi+JWQXFfo8E2B12yWJMuqzlFJj3j+U\nUuOapkl+IaMM2Oj54hcBKNnc7ssy4xi1B/ztT151FWO/eJUWoOXj7/Ntd7sXqNizh5vP/SjEFWLN\ne97F7Miorl/aAln0V1mw/vbraLx6m5wbg8y31GBddxf5VRbJRJJCoqWajIbz/Cld1yl/FxqHw0Ve\nY33UegZeCpoafUGzbvcCedo63cwEwZlu/MfzV84iR4IgCMuP271AUVub7vM9Z20j9tmegG0VBXfx\n06sv4nDNMzA5xNrmOtDpm1ddxa1XX/PknF+0QeoeeZjyzr24piaZHRwmr6YKc14+lrKyiPrf7V5g\nwY2u7TJ/65YvCYOX1fw8SZRRf0zTtO8Cr+Bx6XkrcNxAv02apr0AlAOfVUr9KkHzyQh8q6/qEkUb\n1mPOyyNvTZ3vbdTtcGDOy9N9OzXl5uJ2OqnYuweOXKbnhy95VnD37WNuaEj3c9jc0DAl993P9c9/\nHsDnlwaw9sknebn/OC/OnsHSnEOZVop9th/HbC9v6i/lYclEkjKWWoRprKtLPwC6q4sKv9X6ot07\nGDt2InQlJD8/9OtNR0fAePb2Bkz7dVZRdPrqbRM5EgRBSA251dW6dkZeTTVwx6i3mHPINeficHlC\nJh2ueXrW3UXTYR0bxRKY5cztcDA3MODxqzeZPTF8JjMLbjdzQ8MURJljyd69XH/6ac88/GyX8o7d\n8jzxI1FG/X8G3g/swZP15tvA96L0uQJ8drFdK/CypmnrlFKOcB2qqooTM9s0YOzCRS74r74uBoCU\ndeym7uF3MNt3k2lbH5iyaPnwB5m+0cPY2fPkVVdhzs9nsrubyvvuZfTwkYAV3MlLFyMGo2z4z0+Q\n/9mnGHn1NcYvXKT6TQ9Sef99lG66mys//zfA80P1/9x2ZbSb6d336q7ilzUVsjFN7ks6ykdZWQHZ\n2WZDbcPN3xrm8+aUUmwwcM5WnRV08LjQ+N+7sdo1zHTsxjUz41sJMRcWkFdTQ9muHQErKwXVlZT6\n9e2aG2ddx25cszN32hUUMNPeTE62E1P3TeZbarCtL6eufiu1JcWMX7hIyaa7ffKXrqSjXCWbWOQ2\nFoxcy0y53kbneTmJYy/3WOlMOJldSeefrHM5ceSw7ir41NFTPPyh/8DRvtNsrGzDvbDAi9d/E9C3\n1VxBib/ur6slp6yMoV/9OuQ4Uz09sLDAjJ+bsclioerBB6KfW9U2XdsFIDuDnifJZklGvaZpf6yU\n+qJSagH41uJ/um2Ctyul+oDvLv55TdO0AaAeuB7ueMPDE0uZblphf/mA7uqpe2aG/hd+gsliof7x\nx8i99wFsMzZOV46xI6cds30K5+QkWeZsFuYdlO3aya3DR3yFG+YGhyjbeY+uK0R+Q72BjBO7AAAg\nAElEQVTnGlY1UPLu3+Kuxzw+Zw4813Z92Vp6x/pC+lUVVvDTyy8yX5nNT5yDlGrFvlX829dLqaAm\nKdcoFqqqisPKRyqVut0+bahdpPmHc30q1DRDv4lwbjX5TQ0B/e0vH2Dktd8EZhdwOlnz8DvAnB2w\nsjK4/1UcVXcyINRfHmXB7Q5cgXG6cF2+zrNto+Ss98iMc9YGlgbGdxQztLGN6oJidhRBQ5r+tiPd\nl+U4dqowKrexEu1apvJ6x0Ky55mosZf7eqabzGaKPBkhkedim7H5UlXeXbGOXa2N3HrxtZDMMgUP\n3ssbah/gjWvegNu9wHPdL+BeuONZXWQpoPBiLwtufLo/Kzsb1/h4SDEp8ATE2o8FOnG4HQ6cE5PG\nzk3HdgF07ZlYWEkvfktdqS/SNO0A8LfAL5VSUwCaphUC/wH4Y0DXpUbTtPcDdUqpv9Y0rRaoAUIt\nyhVIpMAUb1CiY9ROdmMTthkbf9P1JRyuedqrHmDq318K8ZWv2LuHWwcPAZ4fiLmkVPdTWnZxMdmL\nwY0QmlGko24Hr1qPAFCWV4p91hMmkW3K5tXeo1jMOeyo28Jh2wlfn8uj3b4gXSE5LKUIk8mUhWNr\nGyYdt5q5La2+4CR/mfRmFwCo2NfJwM9+HtK38sEHAjLxFObkM/LqK7rtCnKmmF/8XNtRv50fq1/4\nPt8CvNxz0JcmLVqRE0FINZc//Htxrb4LwnIRSY/62xTgSVVZ3LyVxkWbwav7TRYLtg3ltGSbfGN5\nbQRv3+bSenKC0hkDVN53r35Qa3FxyGImeBIqVMWg+/XayXPDw5KMeqXU5zRN+znw/wL/rGma925Z\ngBeBTymljobp/gLwHU3THlls/9FIrjcrCbd7Iezqa15dLTlVVRTv3IW5qZWu6y/gcM1jMefgvNyt\nv7o/NxfwA5q/dYtyfzeKmipMllym5me4K0xFUICG/Ab+YMcHONZ/Cut4P9tr21lTXMNPLr8IeNxy\n5lxzWMw5vh91dWEFz3f/hB0123SLUghLZylFmNzuBY6Wz7H9dx6l8GIvs7195DXVM3V3E8cqHNT7\nleEOlslIKVXnJyb4Wc8vOT10gc1VGnvDpLScn5igoWQNtvGbtFdtoKqwHKfbFdDO4ZrnUP9Rck2n\nuXjrasQiJ4IgCII+/ivw4fRo18CJgEUVgOcdF/ijj78Py0kF3TZobWB8SzM3CqYYuvpvAXr5Ux0f\n5ejASS6PdrO+fB2uicshun/k4CHq3/0ojonJgGfW+GH9VJNFGzeKUZ4gluxTr5TqAh7VNM0EeKsX\n3fIWoorQbwJ451KPn6mEW32teNtDmBrXev42ZXFl1OONVJZXiuXGILM6Y/mnHPQEqFh8bhTF7ZuY\nHRxibnCIu/74DwL6Bb/N22ZtfPnEN30/eNt4PxZzDrvWbPWtzg9PjVKW50lxZTHnkG3K5hfd+wNW\nW4XEs5QiTLvqtvM31i9hWZ9D864mesb6cMxZ+VRtYCHoYJmMlFJ11maj+7aJ3rE+5pxzbLXqf+6c\ntdq4bh9jcGoE23g/54cv01G/PeBrD8DV0RvML8ZyhCtyIgiCIOijtwLvr0e9X1Yv3+oGPEGv3i/y\nO+q28H/6fwUNULauFPtsL9h7eXvVG/nRxZ+HjPeute/E1JZFdraJa7aXQifjdjPadZS2P/9LKpxu\n3zOrBLj1SugX3dUc2JpoEhUoy6IRr28BCCGEW331GvTgWT31+rnbZ8dwtNSCXtrApgbmBgYp27kD\nU26ux8cez0qpKScH7l7L7Lvu4zfmAR4h8G1+Q1krd1et5/Kta9yatYe8wQevzjeWrmFwYph76trJ\nNefS1XfK1+7owEka1ooRlkziWc1oyG8IWF3ZW7+T3bX3hBjMwTJZ3N6OY2pc1x+ftkb6J4cAsM+O\nMddSpyubcy012GfvFJ/S+9oDUFVYzvmhywHtRJ6E/5+9N49vI7vufH/ESoAAKZAEV3ARJfFSIiWx\nJYoSu1vqbnmR7V5tt+PE/vTYiZ302J2ZvHl5mdf9YudNZjJuPzt5eZnETuLYiZe04/beTtyxk16s\nXrRQoprdEiWWqI0AuC8QSRAgQCzvD7BKKKCqUCQAYuH5fj76iKi699atqnMuDu695xyCINQhNQOf\nuAraZWdoLK9DY3kdVkIBzPrmRSuokWhEFCTj1m2XaKyOH5cjkWgsFLKjUdqHr8mBYFC8Khv/HbPM\ncShbx6ozoY6MGfXE+lEz+xq/h+3Wjgq0SoSOusTKYOreh5pvvSRKwKAxGODuacL3fBfgneXQvNqI\nPseU6Nd8g7UWf3vhO7CVVkCv1SddH7gzO+9ZWcD9TffgFzdewdA0lzSA0P76/MVhcsCx3ZHy/cTL\nJAAMv/MSNKf6k2TOv68N+uXbAGID/Ur3TpSeeSep3Er3TgRnxdvM4ld7gOQwaTwkT0Q+8hcfq1lX\n+d/77nSWekIQMeJX9ROJXwUNhAI41noELwz/UrQiL7eCmjhWA8njslwo5LKeuyT7w3/HtBeRE3M+\nQUa9CjLhvKe2jcRy8bOsb3luovmzH0Xl5XGsXL0GtDkw3GLEC4FLQAB49CO9aL2+CMPoFMLbG3B9\nexleWDgleKu3V7bhzPh5QZkNWj0C4QCC4VV4Vhawx94O9+JEUp/qLHZUmSpxoGYfHKUOVJZuSzLA\n+PbJAMsum+VIyl/jvGEO+/7jh1B+aRS47gZ2OLDY1YJ+7QSWV32oLavG8qoPZ/XTaPn4vWi9viiE\nPL21oxyj+umkWXlHeT3C0TD0Wj1qyqrQaK3Dz0eSl3Dj5YkcaAmCIKSJX9VPJH4VdHnVh9EFd8oV\n+fi6I3M3UVtWHdstEF4VxmV+TDbt7UXjZ4Dl82/B73TD1OxAWc9dMO0V5zIhNoeMGPWMsXoAvw5g\nGwAhJAbHcX+UifZzhZAc6ioXS+zU17fuZSIlx5X4c9ttTagxV+Pc+CB2bGsVlYufZb28cAUvlc5D\n07MfzttjuHl7RLjWTwKXYGjRY09PO+xmG5aCPiGekEGrR62lGidH7ziq2EorMLM8DyCm1KU6Y5JS\nG7R6vKf1fjhK72yDSPSA58sdqpP+ZU6kjxoHKCU2IssaTQnMRjO+MfE6DM16tOyN7ccP3Xbj/bse\nQDgawaxvHj32/bg2fwsXAhPixGUBJxzL9Umz8vXWGrgWJlBltkFTooVOq4NOo0UwfMcNh5endO+b\nIAhiKyD3vRy/ClqmN2N8cUqyvtQKaputBdEoMOubxx57O8r0ZhyPtsDz/HOi7xLT3l6Y9vbCYNAm\nbbkhNpdMzdT/C4ALANypChYKYecN3IxPDjXqxNzJk9j+9NOqDXspx5Wz4xfw+4c/g9VwKOkcHzLy\n5VtvSDoKXl64IjiyGrR67LG3J10zGF5FSQlwcvQsDFo9Hus4gcXAEnyrK/jRlRfRUb1TmI2Pn503\naPUYve1GT8N++EN+zC57wKp2xPZel4qNqMQ92u2VbZJ7tInMoMYBSmkWO5Usy9WPRKKwGSsEmZhZ\nnseuqu3oqNqJH115UejP9PIs7qrvgntxIilxWZutGSadCVdmR9Be2Ybd1bvwd289h5VQQChzaXoY\nTx54AldmR0TyBEDxvgmCIIgY/Pfyhel3ML08i5qyaliMZvzkyi8Ep9jlVR92WrdLrsjHr6DayyrR\nuq0JP7/6sjBWuxcn8HjpPsz841/JfpeQQZ97MmXUL3Mc99sZaisvWDxzRjJE3+LZM7CpNOrjHVc0\nJRr0NnZjJRTA37/9XTgqGnCgfi/6xwaF7TGJS2CJjoLnJwZFDityM+ulWiMO1O/FSiiAc2ODcFQ0\nIBqNJtUJhldh0pXi7qYe+Fb9mPXNIxwNoaXcgZJoCbQlGtl7U7tHm0gfKQeoUCSMaws3VM1iy8ny\n/Ok3cTpyRTGM5LRvFqdc54UvhZG5mzBqDUn9qSmrkpTFKtM2hMIRVJls0JZoMLk8nVR3JRTAldkR\nIaICL08/XgvnGg850BIEQcgTCocw5/egsnQbzDoT+hwHsbzqw6xvHjut29Fma8LlmatJYzUAvD15\nWRjnAYgmXwxaPZqu3U7bLiKyS6aM+l8xxnZzHHclQ+3lFKXkUF6OUxVWMNFxpbexGxcmLgqK5Fqb\nHU90TolfAot3SNHpNHAl/LruHxtEb2M3QpEQpryzqC6zoUxvhsVQhjec/fAGfaJrHXEcwE2PE72N\n3Vhe9WFmeR5N5fX4KSd2muFXDNSEqiSDPrvIOUAlJnGSm8VWkmUfdxWDrVbZMJIaTQlueGJOrvwM\nfG1ZNdyLkwDuhETTa/R4e/IyDtTvRSAcwMzyPOxllTBqjbgwcQkWgxlDMyNCHSmHrETnKyXHL3Kg\nJQiCECO1ohsLSb0fl2euwlZagcszVzE8ew0Ptr8Lt267MLM8jwZrLcqNFrxy85QQ/aa2rFrYmsuj\nFFZbrV1EZB/5qVgVMMZcjDEngN8E8A5jbJwx5ow7XpDwiXiksDCmSnB5xxVA7JAaT/zMPI+9rFLI\n5BrvKBgKRdBUXi++RjSCM+4LMOlLcY+jByadCctBHy5Nc9hZuR1HHAegWZttD4ZXEYqGUKozwhv0\nwaQzoa+xB3P+2ymdZs5NvpXyfonsEC9HPErylPiulGR5tbVWkDWp+lLX9qwswG6uwhHHAeyxtwtL\ntftr96B/bBBD01exGl7F0PRVnHFfQJ21Rgh9yV8jUeaBZCdrqWvLlSUIgtjqyK3oVpm3oXNtrN5j\nb0d3XSdGb7sxMncTq+FVXJu/icWAV9gxAMTG+WpzpaitWFht6chPau0iIvukZdQDuBfA0bX/dwLo\ni/v8vjTbzinlfX3QGAyiY+tNktBbf0CYzUz81ctz27+IXZWtMGj1qDRVYIct9reU42lPQ3eSMWTQ\n6tFd0wV7WTXOj7+NtyaH4F6cwODkEC5MXERvY7dQdnxxCr5VPwYnh3B+/G20bnPIzobyKwbA2syo\npkSyHJF9eDniUZInqXclJ8u3dpRLh5HUlAhtJF47GF7FnppduDBxEYNrsvbW5BB+ef0kjjgOiNoy\naPUoN5RBrxHLbLxs8eWknKwTr61UliAIYqsSv7Jp0OpRW1YtrIr+8trJJLug0rQNZXozppZnMe9f\nQIO1NmmcN+tNScdcO21p20VEdklr+w3HcaMAwBj7BcdxIiOeMXYOwKF02s8lcsmh1hP9Jt5xZc4/\nL3JO4ffYRxHFlHcaH+l8ENfmR3HGfQGdNe3oru1KipJzc2EUH+18GNfmb8G1OIGmigb01O1Hh7VD\ndv9x/Ix7fGirYHgV70xfURUGi2ZGc0uiY/Lu6l1YCa9IOjtJvSspWZ7pqMELsy+JymlKNDjceBd+\neO0F0T79+Gt31XTgxvyopKyVlJSgp2E/nAtj6K7rRIO1Fq6FcdFqAADsrGyFUWsUnGflnKzJIZsg\nCCI1kUgU7bY2NFhrhaRS+2r3AIDkWL0UXMbyqk849i9XX8ZHux7G9flRuBcn4Civxx77TtznuFs0\n/u6quwv1jrvTsouI7JKWUc8Y+ziAPwLQkrDdxgBgMp228wE1yaFS4TA54GhxwL3ixjtTVwQFi99j\n/1jHCfxg6OcAYrOwb00M4a2JITx50ACrziraJ3fD40KlqQLPHP1dmEOx2U6l/cfxiaMSE/xcmR3B\nxzs/rBgGi2ZG84NEx2S3343TrgHVYUUTZXnZ74Zu/lVRGMkjjgOy+/Q/uP1h6HbFFvb+5MyfSV7D\neXsMwXAQU8uzgm/Gg+3vwsDERVEf++oPwWFy4LHtqfWKHLIJgiBSs9u+S4iOBwCr4VUhoSS/Y4CP\nNT++OIUyvVnwu9OUlMC36sfN20701O/F+YmLuDBxURj7ReNvM9K2i4jske5M/XOMse8B+AaA/zvu\nVATAeDpt5xOZEFxH6Z1Zx5u3nShB7BezxWDGhHdaiFbDx4Mt1RlxbnwQJp0hKUrOvH8BL11/HY+0\nPCT0T27Gvc5ih0lnxGokjP6xQdG59so2Ub+uzt9Am60ZdnMVzo0P4t3bj9LMaJ7By+JGZ7Hl6u+u\n3iW5T18qys7Bhr0YW5oS7cEEgPryGlxeW90BYvI9vTyHEzvul5yVX49e0ZcHQRBbESFXx4BylLPL\ns+KINsurPnRuY3CU1yfZFiUogcVowpWZ62iqaMBOWwv+6dLPEIqE8PORV4U2+EhjciGPifwj7eg3\nHMeFGWPfAbAj4dR2xtg1juOKxrhPF37WUafT4E/O/L8AgJaKRlgNZXht9M5sOT/LeazlMCaWpjAy\nfyspYsjw7HXRTKdc4on3tN6PEpTgT89+VWSExc/qSs2GHm+4j5Q2z0l3Fju+PgB8of/Pk8pIRdnh\nIymdcp0XysX2z1tEsz9AbPb+c0f+d0S2R0meCIIg1kGqHCU8Uqv1ZXozmrc14oXh5Oh2j3acwLsb\n7wd2xIzzZ8/9fwhFQknXp0hjhUemQlr+X4g5x3IAwgA6AJxHzLB/luO4r8hVZIyZAFwC8D84jvtm\nhvqT9zgq6uFaHMeEdxoWo0Vy35s3uIxZvydpb7xBq8ehxv0iRWsua5KeuV1LHKVmVjcx+giRf6RK\nNLXR+pFIFI7yOrgW7/wGV4qyE4lGcKhxPyaWpoXwlfP+20n75xvL6xAKiWf0CYIgiNRIRbSRytUh\ntVq/vOrD6IJbsr5rYRyh2jvjstxKP/nTFR6ZMuqdAA5wHDcEAIyxPQD+E4D3ADgJQNaoB/A5ANKh\nPIqM4aVhnJ8YhGtxAgcb9sKg1UOv0cumbR5bnMLu6l3Yvq0Z40uTqDLZ0LLNgUAogP6xQdz2LWG3\nfRcuz1wVtkYcbjiID+94JEkRaW9yYSMswcY5sAJQlXxKVf3K7WiqaMBAXC4FpSg7vJMuH74SAHoa\n9ietFFkNZWn/ECEIgthqrDdXR+JqfZnerGBbTEKn0wgTLnIr/eRPV3hkyqjfyRv0AMBx3GXG2B6O\n41YYY7J5gxljHQD2APh5hvqRU5SMl+GlYZETy/jS1Foc+RKshAKiGVKe6jKbsMXhwfZ3wb+6gldu\nvim00WCtE7UptzQXDxlXhYfcEmxPw35BPpTevVR9f3gF58ffFh0r1RnxgV3HMbrgxszyPOqtNdBr\ndJJRdtpszTDpTLgyO4JjzUdgM1XAvTiBu+o7RcmnECWDniAIYr1EIlFstzVJzqC32ZpFifr4v3sa\n9sMf8gvjt6ZEIzl+N29rRCgUEepSpLHiIVNGvY8x9qcAfoWYk+zdAAyMsRMAvAr1/gzA7wL4RIb6\nkROkZkETleH8xKDoV3AkGsEp13kcatyPbaXlwtYanvgINADg8S/AqDUIn1MlIIpfmiMKG7klWH/I\nL5IbuXefWN+g1cMf8ie1uRIKYNI7A6O2FFVmGxAFWrY5cH78naT6tWV23F93TPDrcPvdeIH7JYDY\nDD8/e//7vZ/J3IMgCILYQtSYqyVtgxpztcju2F21E4FIEKdc54VIN3yWb8n6ZVX4ufMXuDTDiWwW\nWs0vfDJl1P8GgP8C4EnEEloNA3gcQBmAJ6QqMMb+A4DTHMfdZEw642Uidrs1I53NJMMz1yRnUT93\n339Gh32nUM51PvnXMgBMLE1jNRzCiZ33Yc7ngXNhXJjljI9Wc8PjRJXJJnxW2hoxMn8D9t78e1bZ\nJh/lw2YzQ6fTqior1/+RAeVwpVPLs3fKSrz7kYEb4j6l2FazGl4V2rw4PSyavedl8/z4ID6y98E7\nfcdufM70n/Hm6DkMz17HA619uKflkEgHCpV8lKtssx65XQ9qnuVWfN6JZPIZbJXnKSezhXz/5y4M\n4kD9XgTCAdH4ezuwgD/r/2sAa5Mos+KoY/z43T82iOPb78btlQVMemeF+gPj78BqKINzYUzWZtkM\nCvnd5CsZMeo5jpsH8HnGWAmAkrjjSh5yDwJoY4w9BMABIMAYc3Mc95JchZmZpUx0N6OcvHlWchb1\ntZv9qEKtcKypvF5yGcxeVoUZ7xwuz1yFxWBGT+N+/Orm6aQ2G8vrYDNW4K21z56VBeyxt0u2uauy\nLS+fVTax262y95zLgcPj8aUuBOX+q0kQBvAJze7C3559TuRj0Witg3PhzvYuJdlJbHMlFMDoQiyl\neJnejKHpWNi0d28/mtTfKtTikZaH8KmeO/dS6HKo9F4249q5Qq3crpdUzzKXzzufyNQz2OznmW8y\nW+jytGNbK16+9YYw+y74L9XvE4XBdpQ3IIpw0pgeiUYw5/ckjd+9ju6k8MOJNku2yad3U0w/LjSZ\naIQx9geMsdsAQgBW4/6XheO4j3Icd4jjuCMAvo5Y9BtZgz4fSenIohF+32Bf7R7JlPctFY1wL00g\nGF6FXqNHJJL8O8ig1cOiN+NQ/V1CG8HwKkp1Rsk2ybmluOitPyD5nk06k+jHH5886nXXWQRCAbzu\nOos/PftVNFU0pEwBzreZmKAMiK0I8CnFKSEZQRDE5iA19teUVaPMYMaFiYsYnByCe3ECNz2jsBos\nst8T3qBPNH43WGpFoYeBZJuFKEwytf3mtwDs4zjOmbJkEaGU9CkxFNSNeSc+sOs4xr1TGF+cQkN5\nLRostXAtjAt73jwrC/AF/ZLLbYiWoEZfKzizjMzfgFVvwZMHnsCV2RFybili5JyYAMCsMwnJo4Lh\noGQSs7GFSZEDlb2sEtFoFI+xE5j3LwhtWo1mvMD9e9L1W7Y5YNGXSSaQIgiCILKDw+TAkweeuBM1\nr34vehq6MRAX5ACIrb7O+W4n2Q6lWiMarLXocxyAa3ECjvJ67Kxsxfcu/SzpWhS+sjjIlFE/ko5B\nz3Hcf8tQPzYdNaGgNJoSoCSKF0deARDbAzc4MYRBDOFYy2HRvuiD9fvxtxe+I5RLdDjknVnsvXeW\nrjqsHeTcUuTIOTHFJ4/68Y2f4UJcSEo+0ch9LUdwZfYappdnRTJ1f9M9uK/uTpvDS8PQabQIhsVJ\nyg7U7kWHtUOU7IyHwlUSBEFkB7ffLYpw516cwMDERdzXckRULhhehVFnwIWJiwCQMM7fiwfq7kNp\nqQ4rKyG4/W5oSsQz8rT6Wjxkyqi/yBj7LmLRb4S0ZBzH/X2G2s9b1ISCikSi8AaXBcWMd2z0Bn2o\nMFrRZmtGT303OqwdovaONR9RNTNKhtXWQCldd7yM8QTDq/Cu+vCJvR/F2fEBSZni63dYO2KzQpNv\nw7UwjqaKBvTU7UeHtSPp2moiPhEEQRAbRy7ymTe4nBTVpn9sEB/e/QF41lZf+XEeiE34xI/VFL6y\neMmUUd8AIACgL+5YFEDRG/VA6sROGk0J3IuTknXHFidRbizDwMRFDExcFOKMU2gpYj2kkjGHqREN\n2xtSylSHtQMd1g5RYpJE1KYuJwiCIDaGks/e2NIUasqqRY6xOo0WOyva4IhbfVUaqz+4/WGyMYqQ\nTEW/+U3GmAZADcdx0pZFkaC03UDpeHtlm2yCKd4j3aDVY9gzkjSDShCpUJKx+L2Scj86E4/LGfSA\n+tTlBEEQxMZI5bPXW38A/RMXJGfb+fE81VhNNkbxkRGjnjF2HMA3EJut72CM/TmAlziOK4pMsUD6\n2w3k9t4btUaEImEccRzASiiAc+ODWAwsobeOtjNsBQS5Gkh/G8t6U31vRKbVpC4nCIIg0kdpTG8s\nbcQHtzcq7hBINVaTUV98ZGr7zRcAHAHwvbXP/xPAvwAoCqM+E9sNEvfeN1prEUVsH1xvY3eSg+Pr\nTtrOUOxkehvLelJ9b/Ta64n4RBAEQWwcb8ibFLnMpDPBG/IKZZR2CNBYvfXIlFHv5Thuis8My3Hc\nLGMsmKG2c06mthvE75V3+dz407NfhU6jRSAcoO0MW5BsbGNR64+RzrXXuyJAEMT6uPrpT66rfPvX\nv5mVfhC55fzEIE67L4iSTwXDq4giKgQwUILG6q1Hpox6P2PsPgAljDEbgF8HsJKhtnNK/BIWr1ie\nlQUEw6sbXsKKRKJoLG3E7/d+BsOeEZwbH5QsR0tkxUsml0bXG1Yy3WuvZ0WAIAiCWD86nQYuiazf\nAOBaGFcMZsBDY/XWI1NG/WcB/DWAQwCuA3gdwO9kqO2c4fa7cWHqbdSWVaPBWpuU1Meqt6RlcDtM\nDjhMDiwGlpLSOwO0RFbMZGJpVGpPPICU++QzcW2K0EQQBJE9QqEImssb4CivT7I9NCWpDXoeGqu3\nFpmKfuMC8FAm2soX4vccP9ZxAi+OvJKU1OfJA09k5Fq9dQfwupOWyLYa6SyNyu2J72nYj1Ou86Jj\nUvvkM7UsS18SBEEQ2aGzhuFbb/8gyfb4xP6PrLstGqu3BmkZ9Yyx1xGLRy8Jx3HH0mk/l/B7jg1a\nPUYX3JL7j6/Mjqja15YKWiLbmsS/95H5G9i1jvcutyfeH/KLkpLI7ZMnmSMIgshvLk1zkuP8pRkO\n3du6c9QrIp9Jd6b+cxnpRZ4Rv+fYVlqBmeV5yXKZ3PNOS2RbE/6923utmJlZUlVHaU/8zPI8bKUV\noqzFcnJKMkcQBJGfxPbUJ+cdAdTvqSe2HmkZ9RzHncxUR/KJ+D3HnpUF7LG3b9qedzKuiFQo7Ym3\nl1ViaPqq6FgqOSWZIwiCyC9CoQiayuslbY+migYy6AlJNLnuQL7SW39A2MZQqjPCoNWLzhu0evTW\nH4BGQ8l2iM2Hl894DFo9TDqT4j55kleCIIjCoKe+W3Kc76nbL3ymMZ2IJ1PRb9YNY8wM4JsAagGU\nAvgfHMf9S676k0j8nuNrnpt4lJ3AjG8ONzxOsMod6KjeibPjA/hHzw/TzgRKEOtFbk88AJh1pqR9\n8ulmRCYIgiA2lw5rB5488ATOT74N18I4mioa0FO3Hx3WDhrTCUnSdZQ9rnSe47hXFE4/DOA8x3Ff\nYoy1APh3xLLQ5g1Se441mhI4l10ZzQRKEBtBbk984rFMZ64lCIIgNocOawc6rB2w2+/4XdGYTsiR\n7kz95xXORQHIGvUcxz0f97EJgDvNvmSNeIMpEolmJRMoQWwUqT3x8cdIXgmCIF9m4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HVq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\nIvJtYBBr8FwKfNsY8zlPds1Hsa53HvAFY8x3opThH4AbPO0/B3w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            "text/plain": [
              "<Figure size 761.625x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Du28D2gToyrk",
        "colab_type": "code",
        "outputId": "cc7c3dae-84cd-4cde-b855-0ae75a257cca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "cell_type": "code",
      "source": [
        "# violinplots on petal-length for each target species\n",
        "sns.violinplot(data=df,x=\"target\", y=\"petal length (cm)\" );"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UKhTCBT6fj/nzq7B6YyNDMgA1NbUuVyZEZjIJ9wqt9Y+AGIDWei0QmehDWuvf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\nCOFBEu5CCOFBEu5CCOFB/x8mcSY8WRpLggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "48Q9U8RBV31B",
        "colab_type": "code",
        "outputId": "732331e1-8c67-40d1-ac15-545757e1a69f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(df.target)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f210fc7eef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+cHNE3AdcTe/iMMDXgL+OiHMa/y3SnLx7R5qnbk723wZuy8zpiNgAfCEzvzDk0qSD8kxf\nmr/twAnAwxHxdeBp4MvDLUnqj2f6klSIZ/qSVIihL0mFGPqSVIihL0mFGPqSVMj/A4G4BdgWALqj\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "T7-MiuoFWg-K",
        "colab_type": "code",
        "outputId": "7b0f9124-54cf-499a-a7cc-4d3411f17975",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True);"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AM/C71vqt4hyjxNOnUsoTeL6k9+ug5wFPewoqpbyA94DxxTmQ1nopUFspdWtx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\nCD0hClIIIYSeEAUphBBCT/h/nLdwpA43tXcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8-cM-uIwYIBa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X, y = load_iris(return_X_y=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tgTU5CPaYXED",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "SEED = 123"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c2ApKtaFYbNn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size = 0.3, random_state = SEED )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sPbfbXmcY6Fc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LVFTn2wcZAnz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "models = {'Logistic Regression': LogisticRegression(multi_class='ovr', solver='liblinear'),\n",
        "          'Decision Tree': DecisionTreeClassifier(), \n",
        "          'Random Forest': RandomForestClassifier(), \n",
        "          'Gradient Boosting': GradientBoostingClassifier() }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SpnTBpPXZPCt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#results = { 'classifier': [], 'accuracy': [], 'f1_score':[]  }\n",
        "results = { 'classifier': [], 'accuracy': []}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VBFNLEsgZU3P",
        "colab_type": "code",
        "outputId": "106ec4c9-26e2-4aa5-f58a-b1a517d08d08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        }
      },
      "cell_type": "code",
      "source": [
        "for clf_name, clf in models.items():\n",
        "  print('Training Classifier.....', clf_name)\n",
        "  clf.fit(X_train, y_train)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  acc_score = accuracy_score(y_test, y_pred)\n",
        "  #pd.DataFrame(confusion_matrix(y_test, y_pred))\n",
        "  f1_score_value = f1_score(y_test, y_pred, average='macro')\n",
        "  results['classifier'].append(clf_name)\n",
        "  results['accuracy'].append(acc_score)\n",
        "  #results['f1_score'].append(f1_score_value)\n",
        "  print(classification_report(y_test, y_pred))\n",
        "\n",
        "  print('Training complete')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Classifier..... Logistic Regression\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        18\n",
            "           1       0.91      1.00      0.95        10\n",
            "           2       1.00      0.94      0.97        17\n",
            "\n",
            "   micro avg       0.98      0.98      0.98        45\n",
            "   macro avg       0.97      0.98      0.97        45\n",
            "weighted avg       0.98      0.98      0.98        45\n",
            "\n",
            "Training complete\n",
            "Training Classifier..... Decision Tree\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        18\n",
            "           1       0.83      1.00      0.91        10\n",
            "           2       1.00      0.88      0.94        17\n",
            "\n",
            "   micro avg       0.96      0.96      0.96        45\n",
            "   macro avg       0.94      0.96      0.95        45\n",
            "weighted avg       0.96      0.96      0.96        45\n",
            "\n",
            "Training complete\n",
            "Training Classifier..... Random Forest\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        18\n",
            "           1       0.77      1.00      0.87        10\n",
            "           2       1.00      0.82      0.90        17\n",
            "\n",
            "   micro avg       0.93      0.93      0.93        45\n",
            "   macro avg       0.92      0.94      0.92        45\n",
            "weighted avg       0.95      0.93      0.93        45\n",
            "\n",
            "Training complete\n",
            "Training Classifier..... Gradient Boosting\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        18\n",
            "           1       0.71      1.00      0.83        10\n",
            "           2       1.00      0.76      0.87        17\n",
            "\n",
            "   micro avg       0.91      0.91      0.91        45\n",
            "   macro avg       0.90      0.92      0.90        45\n",
            "weighted avg       0.94      0.91      0.91        45\n",
            "\n",
            "Training complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vZjkwLUQZd90",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_results = pd.DataFrame(results).set_index('classifier')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "407bRHtKlCuB",
        "colab_type": "code",
        "outputId": "bdce6f88-0e54-4364-a6ee-1cd7efb6edb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "cell_type": "code",
      "source": [
        "df_results"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>accuracy</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>classifier</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Logistic Regression</th>\n",
              "      <td>0.977778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree</th>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest</th>\n",
              "      <td>0.933333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gradient Boosting</th>\n",
              "      <td>0.911111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     accuracy\n",
              "classifier                   \n",
              "Logistic Regression  0.977778\n",
              "Decision Tree        0.955556\n",
              "Random Forest        0.933333\n",
              "Gradient Boosting    0.911111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "annZq812mGtX",
        "colab_type": "code",
        "outputId": "f4a9d25f-7bf5-4f67-847a-abb2c4929e6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "cell_type": "code",
      "source": [
        "df_results.sort_values(by='accuracy', ascending=False )"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>classifier</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Logistic Regression</th>\n",
              "      <td>0.977778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree</th>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest</th>\n",
              "      <td>0.933333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gradient Boosting</th>\n",
              "      <td>0.911111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     accuracy\n",
              "classifier                   \n",
              "Logistic Regression  0.977778\n",
              "Decision Tree        0.955556\n",
              "Random Forest        0.933333\n",
              "Gradient Boosting    0.911111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "nc6m6GRAZoYQ",
        "colab_type": "code",
        "outputId": "b7f837db-9378-4c45-8f43-bc94cdf4e21c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "cell_type": "code",
      "source": [
        "df_results.plot(kind='barh')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21114fe860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cl3dJBZKkLtX6lo2e+tDlUaO25/jjj+aOO25lp5125tZbb2GPPfbiyCMPB2DEiO0YOnQo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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "NBvF8qBCZ30t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}