TCC.ipynb

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      "authorship_tag": "ABX9TyPsbEjdPNANKEEQPjUYnLhP",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luciodias/TCC_PUC/blob/main/TCC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "sHLlN8puQySY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "_zya5NIIQ3Ws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Carga de Dados"
      ],
      "metadata": {
        "id": "wxmiGAm-LN0q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S67v44xaLHkh"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00422/wifi_localization.txt\", #local do arquivo no Gdrive\n",
        "                 sep='\\t',                                               #arquvio sepado por tab\n",
        "                 names=['W1','w2','W3','W4','W5','W6','W7','Room'],      #nome dos atributos\n",
        "                 header=None)                                            #arquivo sem cabeçalho"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estatíscicas básida do dataframe e verificação de dados ausentes e/ou falhas no esquema de importação."
      ],
      "metadata": {
        "id": "Ol2Ef6xkSlqB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe() #estatíscicas básida do dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mhl2nkKjSdIO",
        "outputId": "caa536c3-ae51-448b-d231-c2e9916fed6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                W1           w2           W3           W4           W5  \\\n",
              "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
              "mean    -52.330500   -55.623500   -54.964000   -53.566500   -62.640500   \n",
              "std      11.321677     3.417688     5.316186    11.471982     9.105093   \n",
              "min     -74.000000   -74.000000   -73.000000   -77.000000   -89.000000   \n",
              "25%     -61.000000   -58.000000   -58.000000   -63.000000   -69.000000   \n",
              "50%     -55.000000   -56.000000   -55.000000   -56.000000   -64.000000   \n",
              "75%     -46.000000   -53.000000   -51.000000   -46.000000   -56.000000   \n",
              "max     -10.000000   -45.000000   -40.000000   -11.000000   -36.000000   \n",
              "\n",
              "                W6           W7         Room  \n",
              "count  2000.000000  2000.000000  2000.000000  \n",
              "mean    -80.985000   -81.726500     2.500000  \n",
              "std       6.516672     6.519812     1.118314  \n",
              "min     -97.000000   -98.000000     1.000000  \n",
              "25%     -86.000000   -87.000000     1.750000  \n",
              "50%     -82.000000   -83.000000     2.500000  \n",
              "75%     -77.000000   -78.000000     3.250000  \n",
              "max     -61.000000   -63.000000     4.000000  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W1</th>\n",
              "      <th>w2</th>\n",
              "      <th>W3</th>\n",
              "      <th>W4</th>\n",
              "      <th>W5</th>\n",
              "      <th>W6</th>\n",
              "      <th>W7</th>\n",
              "      <th>Room</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-52.330500</td>\n",
              "      <td>-55.623500</td>\n",
              "      <td>-54.964000</td>\n",
              "      <td>-53.566500</td>\n",
              "      <td>-62.640500</td>\n",
              "      <td>-80.985000</td>\n",
              "      <td>-81.726500</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.321677</td>\n",
              "      <td>3.417688</td>\n",
              "      <td>5.316186</td>\n",
              "      <td>11.471982</td>\n",
              "      <td>9.105093</td>\n",
              "      <td>6.516672</td>\n",
              "      <td>6.519812</td>\n",
              "      <td>1.118314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-74.000000</td>\n",
              "      <td>-74.000000</td>\n",
              "      <td>-73.000000</td>\n",
              "      <td>-77.000000</td>\n",
              "      <td>-89.000000</td>\n",
              "      <td>-97.000000</td>\n",
              "      <td>-98.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-61.000000</td>\n",
              "      <td>-58.000000</td>\n",
              "      <td>-58.000000</td>\n",
              "      <td>-63.000000</td>\n",
              "      <td>-69.000000</td>\n",
              "      <td>-86.000000</td>\n",
              "      <td>-87.000000</td>\n",
              "      <td>1.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-64.000000</td>\n",
              "      <td>-82.000000</td>\n",
              "      <td>-83.000000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-46.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-46.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-77.000000</td>\n",
              "      <td>-78.000000</td>\n",
              "      <td>3.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-45.000000</td>\n",
              "      <td>-40.000000</td>\n",
              "      <td>-11.000000</td>\n",
              "      <td>-36.000000</td>\n",
              "      <td>-61.000000</td>\n",
              "      <td>-63.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificação do balanceamento das classes."
      ],
      "metadata": {
        "id": "UysyZtIGS5op"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Room'].value_counts() #verificação do balanseamento das classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Enz6YoePPHC6",
        "outputId": "307e54bf-6cbc-4a6e-a871-2b864f9f1bc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    500\n",
              "2    500\n",
              "3    500\n",
              "4    500\n",
              "Name: Room, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Modelagem\n",
        "Divisão do banco de dados em dados de treino e teste.\n"
      ],
      "metadata": {
        "id": "bBZ4CGleR5uc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = train_test_split(df, test_size=0.2)"
      ],
      "metadata": {
        "id": "5hE50QIlRuyb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain = train.loc[:, 'W1':'W7']\n",
        "ytrain = train[['Room']].values.ravel()  # values.ravel() converte de Coluna pra Array\n",
        "xtest = test.loc[:, 'W1':'W7']\n",
        "ytest = test[['Room']].values.ravel()  # values.ravel() converte de Coluna pra Array"
      ],
      "metadata": {
        "id": "v1gJJCdiUmWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corrM = xtrain.corr().abs()\n",
        "sns.heatmap(corrM, cmap=\"Blues\", annot=True,fmt=\".2f\")\n",
        "plt.xlabel('Módulo da correlação entre os sinais')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "dagPGHG8fo8d",
        "outputId": "f30092d9-cc43-4c6d-a654-c7a200421e39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Módulo da correlação entre os sinais')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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NZtvRdHaeyKBFLW9+fO0RLFbJpxuOcyEnH2dHB74a8DAAl6/l8/7yg1hUeqEQcasebSHEXKAhkA38gq1jLV5KmVnqQTegluu5PdRyPbePWq7n9rjvl+vpMr3cPicnZmiFe+iy7n4o4AIcw7YERTKgXdJUoVAotKaSR7q3dLpSyi5CCIEt2m0FvA2ECSEygF1SyjEVoFGhUCjKTyV/OaLM54yCmdEPCCGygAsFWxTQHNvSFQqFQlF5+Iu/BjwYW4TbCsgDdhZsC9CwI02hUCg04y8e6dYAlgJDylrhUqFQKCoFf/Gc7tCKEqJQKBSa8BePdBUKheKvxV850lUoFIq/HCrSVSgUiopDOCinq1AoFBWGqOTphVu+BqwFV/OplK8BezV7415LKJGfv628Q5/D69w8K1dl4KgGcw3cDQK9tJmAXWucdZU3EvR0u/OZxas++XW5fc7lpS9UuteAFQqg8jpcheJGKnukq5yuQqG4r1BOV6FQKCoQB9WRplAoFBVI5Q50ldNVKBT3Fyq9oFAoFBWIcroKhUJRgSinq1AoFBWIcroKhUJRgYg7f7/irlK5x1YoFArFbSKEKPdWjnN1EUIcEUIcF0IML6E+VAixWQjxmxBivxCia1nnVE5XoVDcV2jldIUQjsBsIBJoADwthGhwg9n7wA9SyoeAvsDnZekr0+kKISKEEC8JIWrcsP/Fso69HXZs30aPbhFEdenEV/Pm3lSfm5vLu2+/RVSXTvTr+yQpKcmFdV/N+5KoLp3o0S2CHXHbtZTFnDH9OL1xEnuWjizVZtqwPhxYMYbd34/gwXrBhfv7dX+EhBWjSVgxmn7dH9FU18F98Xz4+tOMe+0p1i/75qb6uJifmfSv/kweMoBPR7zG2aQ/CuvWLfuGca89xYevP82h337RVBfAjrht9IyKoHtkJxbML7kth739Ft0jO/Hs00VtmZWVycAXnqNls4eYNGGc5rp+/3Un/3rhCd58/nF+/m7hTfWrf1zMkJee5J1BfRn37mukmYoWS5kw4k0GPN6Oj95/S3Nd8Tu38/QT3Xjq8S58s3DeTfW5ubmMHvE2Tz3ehZef78vZ1BQA1q1dzYBnnijcHm0WxrEjhzTVtmvHdvr0jOSJ7hEsWlCytpHDhvBE9wheePYpUlNS7OrPnU2lbcuHWbxogaa6bom4je3WNAeOSylPSilzge+AnjfYSMCj4LMnkFrWSW/pdIUQE4FRQCNgoxDizWLVms0YY7FYmDhhHJ/Pmc/ylWuIiV7NiePH7WyWL1uKh4cHq2PW82z/AXw6fSoAJ44fJyZ6DT+tXMPnX85n4ocfYLFYtJLGN6vi6fn67FLrI9o0oFaoH2E9P+CND//LjJF9AfDyqMKoQZGEPzeVR5/9mFGDItG7azMBitViYenc6bz676mMnLGYvXEb7JwqwMPhnRjx2X9475OFPNarH8u/ngnA2aQ/2Be3gREzvuG10dP44ctpWDW8XxaLhUkfjmP2F/P56XpbnrihLX+yteWqtet59rkBfFbQli7OLrz+5r8Y+s4wzfRcx2qx8NXMyYycOINP5i9lx+ZYkk+ftLOpUbseH83+hqlzv6NF+GMsnjejsK7Hk8/xxnva/xBYLBamT57A1BlzWLx0JRtio/njpP39Wr1iGe7uHnz/cwxPPdOfL2ZOB6BzZBQLl/zEwiU/8e9xHxEQGEyduvU11TZl0ng+mz2X739aRWzMGk7e0JYrl/+Iu4cnP62K5eln+zPrs6l29Z9Om0zL1o9qpqk8aJheCAKSipWTC/YVZyzwrBAiGYgG3qQMyop0uwMdpJRvAQ8DkUKIT67/bWWdvLwcSNhPSEh1gkNCcHJ2pkvXbmzZvNHOZvOmTfTo2QuATp0j2B2/CyklWzZvpEvXbjg7OxMcHEJISHUOJOzXSho79p0g48KVUuuj2jZmyerdAOxOOIWnuxv+vh50alWfjfGHybx4hazsHDbGH6Zz6xufTP4cp48dwi8gGF//IHROTjRp05GE3XF2Nm5VqhZ+zr2WgyhoroTdcTRp0xEnJ2d8jIH4BQRz+ph20dGBhP2EhBa0pZMzEZHd2LLJvi23bNpE94K27Ng5gt2/2NrSrUoVHmrSFGcXF830XOf4kUT8A0MwBgSjc3KiVbvO/Lpzq51N2INNcXF1BaBO/TAy0kyFdY2aNMetShXNdR1KTCA4JISgYNv96ti5K3FbN9vZxG3dRGSULcBq91hn9u6O58bZATfERvNY50hNtSUe2E9wSGihts4RXdm2ZZOdzdYtm+jW3aatQ8cIfi2mbcumDQQGBlOzVm1NdZXF7ThdIcQgIcSeYtug27zc08BCKWUw0BX4Rohbz6JeltPVSSnzAaSUWdicsIcQYingfJviSsVsMuEf4F9YNhiNmEwmexuzCX//AJsonY5q7u5kZWViMpkw+hcda/Q3Yr7h2LtJoEFP8rnMwnKKKYtAg55APz3JpmL7zVkE+uk1uWZWRhp6X0NhWe/jx4XzaTfZbYtexgev/oMVi76g90DbY/GF82l4+dgfm5Vx87F/Fls7FWsPoxGzuYy2rGZry7tJRroZHz9jYdnH10BGurlU+01rV/Bg81Z3VRNAmtmEwRhQWPYzGEm74X6lmc0YjLZ7qtPpqFrNnQsXsuxsNq6LoVNEmX04t6nNbPfdMhhL0mbCeENbXsjK4sqVy/xn4XwGvvpPTTWVBwcHh3JvUsq5Usqmxbbi+bAUIKRYObhgX3FeAn4AkFLuAlwB31vqK0P/CSFE2+sFKaVFSvkScAQo9Tmm+K9HSflZRcUQ3rU3Y+b8QI/+r7Ju6aJ7Lecvw7YN0Zw8eogeT/a/11LKReKB/bi6ulKzdp17LaWQeXNm83S/56lS7ImrwtAup/srUEcI8YAQwhlbR9nKG2zOAI8BCCHqY3O6t4xiyhqn+2TByRYDW4HtUsrDUsr3hRBflHZQwa/FXCjfJOYGo5FzZ88Vls0mE0aj0d7GYOTcubMY/f3Jz8/nUnY2er0XRqMR07miY03nTBhuOPZukmrOItjfq7AcZNSTas4iNS2LRx8u+hIEGfRs33tMk2vqvf3IKhalZZ1Pw9On9Plum7TpyA9fTgPA08ePzPP2x+q9tZsr19ZOxdrDZMJgKKMtL9na8m7i7WvgfLF0wfl0M97Fnhaus3/fLyxfsoCx0+bi5KzZw1yp+BmMmIt12KWZTfjdcL/8DAbMpnMYjLb7dflSNp6eRU9NG2Oj6ahxlHv9usW/W2ZTSdqMmM6dxWgsaktPvZ4DCfvZtD6WWZ9OJTs7GwcHB5xdXPhH336a67wRrV6OkFLmCyHeAGIBR2CBlDJRCDEO2COlXAm8DcwTQgzB1qk2QJaxMsQtI10pZY6UMgf4CggAZgohTgohlgF97vzPstEwrBFnzpwiOTmJvNxcYqLX0LZ9Bzubdu07sHLFcgDWr4ul+SMtEELQtn0HYqLXkJubS3JyEmfOnCKsUWOtpJXJmq0JPBPVHIDmjWpw8VIO59Ivsn7nITq2rIfe3Q29uxsdW9Zj/U5tcqehdeqRdjaJ86ZU8vPy2Be3gUbNWtvZmFOL8v+Je3fiF2AbVdGoWWv2xW0gLy+X86ZU0s4mUb2Odp0v19syJTmJvLxcYtfe3JZt23dgVUFbblgXS7OCtryb1KrbgLMpSZjPppCfl8fOLeto2jLczuaP44eZ9+lEho2bjqeX913Vc516DcJISjpDakoyeXm5bFgXTevw9nY2rcPbs3b1CgC2bFxHk2aPFN4vq9XKpg2xmudzARo0bETSmdOkFGhbFxvNo23ttYW3bc+aVTZtmzbE0rSZrS3nfb2YFWs3smLtRvr268+AlwZViMMFbcfpSimjpZR/k1LWklJOKNg3usDhIqU8KKVsLaX8u5TyQSnlurLOWa430qSUm4UQ24BmQHvgVaAh8Fl5ji9ThE7HiFGjeW3QQKxWC4/36k3t2nWYPfMzGjYMo12Hx+jVuw+jhr9LVJdOeHh6MmWqrT+vdu06dO4SSa8eXXF0dGTk+6NxdHTUQhYAiyYN4NGH6+Crr8bxmPGMnxONk852/vk/xhETl0hEm4YkrhzDlat5vDJ2MQCZF68waV4McYttPfET58aQebH0DrnbwdFRR5+Xh/L5B0OxWq20eKwbAaE1WbNkPqG169GoeRu2Ry/jyP49ODrqcKvmzrODRwEQEFqTh1p1YOKbz+Lo6MiTLw/FQcP7pdPpGD5yNK+9MhCrxULPgrb8fNZnNGgYRrv2j9HriT6MGvEu3SNtbTn5408Kj4/s3IHLly6Rl5fH5k0b+GLuAmpp0BHj6KjjxTfeZcKIN7FaLbSP6EFIjVp8v3AOtf5Wn6at2rJ47gyu5uQwfbxtDLyvwch7423aRg8ZSErSKa7m5PDq0115dei/ebBZyzvWpdPpGPruKIa+OQirxUq3Hr2oWas28+fMpF79hrRp24Gonr0ZP3o4Tz3eBQ8PT8ZOLBoh8Pu+PRiM/gQFh9ziKn9e27vD32fwawOxWq107/kEtWrX4cvPZ1C/QRjh7TrQo1cfxox6jye6R+Dh4cmEydM013G7VPbXgMu1RpoQYiNQFdgFbAfipJSl90IUQ62RdntU1jXSKvNyPWqNtNvjfl8jLfTNleX2OWdm9qhwD13eu78fyAXCgMZAmBCicv5HKRSK/9domV64G5Q3vTAEQAjhDgwAvgb8Ae0HVCoUCsUdUNnTC+VyugU9eI9ie0HiFLAAW5pBoVAoKhX3hdPFNvZsOrD3+ssSCoVCUSmp3D633OmFqWVbKRQKxb3nfol0FQqF4i+BQyWfxFw5XYVCcV+hIl2FQqGoQCq5z1VOV6FQ3F+oSFehUCgqkEruc+++072ap92qBFpSWV+3fbzfB/daQqmc2fbpvZZQIsNWH7zXEkpkYb+H7rWEEsnPtaJzqKyvAt+5LtWRprgvqKwOV3H7VF6Hqw3K6SoUCkUF8v8+vaBQKBQViepIUygUigpEOV2FQqGoQCq5z1VOV6FQ3F+ojjSFQqGoQFR6QaFQKCqQSu5zldNVKBT3FyrSVSgUigqkkvvcsp2uEMIfQEp5Tgjhh23ZniNSysS7LU6hUChul790pCuEeAUYbvsoJmNblPIAMEkIMUVK+ZVWQnbt2M70KZOwWi306NWH51982a4+NzeXD94fzuFDiXh66vlw8nQCg4JITUmh7xNRhFavAUBY478z/P2xWsni4L54fvrqM6xWKy07RtGp93N29XExP7N97U84ODjg4urGU/8cRkDIAwCsW/YN8RtW4+DgQO+Bb1H/oUc00zVnTD8iw8NIy8im6ZMTS7SZNqwPEa0bcuVqLoPGfMPvh5MB6Nf9EYYPjADgo/mxfLvqF810AcTv3M5nUz/CarUQ9Xhvnhtwc1t+OGYERw4l4uGpZ9ykaQQEBrFu7WqWfLOg0O7EsaMsWLyUOnXra6KraYgnr7apjqODYO1BMz/8dvYmm/Ba3jzbLBiQnEy/wkcbTgAwIaou9YzVSDybzejoo5rouc4vu+KYNW0yFquFbj2foN/zA+3qc3NzmTR2JEcOH8TTU8/oCR8TEBhEXl4e0yZ9wJFDiTgIB954ezgPPdxMM12VtR3L4q8+euENoCHgBpwGahdEvF7AZkATp2uxWPh40ofMnDMfg9HIgH5P8Wjb9tSsVbvQZuXyZbh7eLBsVSzrYqKZ/dk0JkyZDkBQcAiLf1iuhRQ7rBYLS+dO5/Wxn6D3MTB12EDCmrcpdKoAD4d3ok2XxwFI2B3H8q9n8s/R0zmb9Af74jYwYsY3XMxIZ9aYt/j37P/i4OioibZvVsUz5/utzB/fv8T6iDYNqBXqR1jPD2jeqAYzRvYlvP9UvDyqMGpQJK37TUFKyc4l77Fmy36ysnM00WWxWJg+eQKfzJ6HwWhkYP+naBPengdqFrXl6hXLcHf34PufY9gQG80XM6czbtI0OkdG0TkyCoATx48y4u3Bmn1RHQS8Hl6DEasOk34pl5l9GhJ/KoszmUV/d6CnC081CWTo8kQuXbPg6Vb09Vj621lcdA50a2jQRM91LBYLn02ZwNRZc/Ez+PPq831p/Wh7atSsVWgTvfInqrl7sOSnaDauW8vcWZ8wZhNfrukAACAASURBVOJUVv/8IwBf/3c5mRnnee+t15iz8DscNJhbobK2Y3mo5IFumVP65Espr0gpzwMnpJTnAKSUmYDUSsTBAwkEh4QSFByCk5MznSIi2bZlk53Nti2b6Nbd5tw6dOzMr7vjkVIzCSVy+tgh/AKC8fUPQufkRJM2HUnYHWdn41alauHn3Gs5iIJV8RJ2x9GkTUecnJzxMQbiFxDM6WOHNNO2Y98JMi5cKbU+qm1jlqzeDcDuhFN4urvh7+tBp1b12Rh/mMyLV8jKzmFj/GE6t26gma5DiQkEh4QUtmXHzl2J27rZziZu6yYio3oC0O6xzuwtoS03xEbzWOdIzXTVNVQj9cJVzl28Rr5VsuV4Bi0f8LKziWxgYNUBE5eu2WbGu5BTtAbr7ykXybkLM+YdTkwgKDiUwKAQnJyc6NA5kh3b7O/Xjq2b6dKtBwBtO3Ri76+/IKXk9B8naNLU9vTk5e1DtWoeHDmkTdavsrZjeRBClHu7F5TldK1CCKeCz92u7xRCuJbj2HJjNpsw+vsXlg1Gf9LMZjubNLMJQ4GNTqejWjV3LmRlAZCaksJzTz3Bqy/157d9e7SSRVZGGnrfoshG7+PHhfNpN9lti17GB6/+gxWLvqD3wLcAuHA+DS8f+2OzMm4+9m4RaNCTfC6zsJxiyiLQoCfQT0+yqdh+cxaBfnrNrptmNmEwBhSW/QxG0symG2zMGIxFbVm1mjsXLmTZ2WxcF0OniK6a6fKp6kzapdzCcvqlXHyrOtnZBHu6EqR3ZXqvBnz6REOahnhqdv3SSEsz42cs+t/3MxhJSzOVamP736/GhQtZ1KpTlx3bNpOfn8/ZlGSOHD6I2XROG12VtB3LgxDl3+4FZaUXVgCPCCHipZTJxfb7AG/fPVnlx9fPj5UxG/HU6zl0MJFhQ97kv8tWUq1atQrTEN61N+Fde7Nn2zrWLV3Es/96v8KufT+SeGA/rq6u1Kxdp0Kv6+ggCPJ05d0Vh/Ct6sy0XvV55bsELudWzjmhI7v34vQfJ3nl+b74BwQQ1vjvmqQWtOJetWNl70grq4U8gCmAWQixVQgxUQgRBeRIKTeUdpAQYpAQYo8QYs/Cr+aVKcJgMGI6V/QLbTadw89gnzvzMxgxF9jk5+dz6VI2nno9zs7OeOptkVr9Bg0JDg4h6fSpMq9ZHvTefmSlF0XcWefT8PTxK9W+SZuO7N+9HQBPHz8yz9sfq/cu/VitSTVnEexf9PgcZNSTas4iNS2LYGOx/QY9qWlZJZ3iT+FnMGI2FXVQpZlN+BmMN9gYCiOy/Px8Ll/KxtOzKNreGBtNR42jo/OXc/Gr5lxY9q3mTPrlPDub9Mu5xJ/KxGKVmLKvkZx1lSC9q6Y6bsTPz0Baseg0zWzCz89Yqo3tf/8Snp56dDodbwx9j6++/ZEJU2dyKTubkNAa2uiqpO1YHv7S6QUp5TtSylaAPzACyABeAA4IIUqdrl9KOVdK2VRK2XTASy+XZlZI/YZhJJ05TWpKMnl5uayPXUt42/Z2No+2bc+aVT8DsGnDOpo2ewQhBJkZGVgstkgkJTmJpDOnCQwOLvOa5SG0Tj3SziZx3pRKfl4e++I20KhZazsbc2pS4efEvTvxC7Bdu1Gz1uyL20BeXi7nTamknU2iep2K60xYszWBZ6KaA9C8UQ0uXsrhXPpF1u88RMeW9dC7u6F3d6Njy3qs36ldrrlegzCSks4UtuWGddG0Drdvy9bh7Vm7egUAWzauo0lBWwJYrVY2bYjVPA94xHyJIE9XjO4u6BwE7Wp7E/9Hpp3NzpOZNA70AMDDVUew3pWzF65pquNG6jYIIznpNGdTksnLy2PTurW0erSdnU2r8HbErFkJwNZN62nStDlCCK5ezSEnx5bX3/PLThwdHe064O6EytqO5cHBQZR7uxeU9+UIN2xRr2fBlgokaCZCp+Od4aMY/NrLWK1WuvfsRc3adfjy85nUb9CQ8HYd6NGrN2NHvUfv7hF4eOj5cPJUAH7bt4e5n89Ep9Ph4ODAe++Psfu1vRMcHXX0eXkon38wFKvVSovHuhEQWpM1S+YTWrsejZq3YXv0Mo7s34Ojow63au48O3gUAAGhNXmoVQcmvvksjo6OPPnyUM1GLgAsmjSARx+ug6++GsdjxjN+TjROOtv55/8YR0xcIhFtGpK4cgxXrubxytjFAGRevMKkeTHELR4GwMS5MWReLL1D7nbR6XQMfXcUQ98chNVipVuPXtSsVZv5c2ZSr35D2rTtQFTP3owfPZynHu+Ch4cnYydOLTz+9317MBj9CQoO0UwTgFXC7O2nmNi9Lg5CsO5wGqczc+jfLIijaZeJP5XFnqQLNAnxZG7fxlilZN7OM2Rfs3WmTXu8PsFebrg5ObK4/0N8svkke5Mu3LEunU7Hv94dybuDX8VqtRDZvRcP1KrNgi9nUbd+Q1qHt6drjyeYOGYEzzzRFQ8PT0ZPmAJAZkYGwwa/inAQ+PoZGPnBpDvWU1xXZWzH8lDJswuIW40AEELMxTZkLBv4BYgH4gtGL5SLrBzL3R1i8Cf55Y+Mey2hRCrrGmmVebmeZ7/Ze68llEhlXSOtMi/X4+euu2OX2WHGrnL7nE2DW1a4iy4r0g0FXIBjQAqQDGiXAFQoFAqNqeyR7i2drpSyi7AlaRoCrbCNWAgTQmQAu6SUlXNJXYVC8f8Wh0rudct8zpA2DgDRwFpgB1AL+Ndd1qZQKBS3jZYdaUKILkKII0KI40KI4aXY/EMIcVAIkSiEWFLWOcuae2Ewtgi3FZAH7CzYFqBhR5pCoVBohVaDEoQQjsBsoBO21OqvQoiVUsqDxWzqYBvZ1VpKmSmEKPM98bJyujWApcAQKeXNs4MoFApFJUPD8bfNgeNSypMF5/0O6AkUHy77MjD7+uACKaX5prPcQFk53aF/Wq5CoVDcA27H5wohBgGDiu2aK6WcW/A5CEgqVpcM3DhV4N8KzrMDcATGSiljbnVNNYm5QqG4r7g+6VR5KHCwc8s0LB0dUAdoBwQD24QQjaSUpY7yqrwD9hQKheJP4CDKv5VBClD87Y7ggn3FSQZWSinzpJR/AEexOeHS9d3en6NQKBSVGw1HL/wK1BFCPCCEcAb6AitvsPkZW5SLEMIXW7rh5K1OqtILCoXivkKrcbpSynwhxBtALLZ87QIpZaIQYhywR0q5sqCuc8FcNBbg3YL5x0tFOV2FQnFfoeW7EVLKaGzvKBTfN7rYZwkMLdjKxV13ugE9p9/tS/wpMta8e68llEhlneMgNPytey2hVLb8OOFeSyiRiGnb7rWEEnF1rbyx1u6R7e74HJV9Pt3Ke/cVCoXiT1DJfa5yugqF4v7CsZJ7XeV0FQrFfYVKLygUCkUFco8WhCg3yukqFIr7ChXpKhQKRQVSyX2ucroKheL+QkW6CoVCUYE4VvKkrnK6CoXivqJyu9w/MeGNEGLi3RCiUCgUWuAgRLm3e0FZy/XMuHEX8JwQohqAlHKwVkI6Na3B1Ncew9FBsDBmP1O/321XH2rwYM7bXfD1rEJmdg4vTl5DSvolGtc0MGNwJ9yrOGOxSqb8dxc/bj2ilSx2xG1jykcTsFqs9Or9JC8OHGRXn5uby/sjhnHoYCKeej2Tp35CUFAwWVmZvDNkMIkHDtDj8V6MGDW6lCv8OeJ3buezqR9htVqIerw3zw14+SZdH44ZwZFDiXh46hk3aRoBgUGsW7uaJd8sKLQ7cewoCxYvpU7d+promjOmH5HhYaRlZNP0yZJ/n6cN60NE64ZcuZrLoDHf8PvhZAD6dX+E4QMjAPhofizfrvpFE03X2b9nF4u/nIbVaqVtRE+6/+N5u/q1P33L1tiVODo64u6pZ+Bb/8bXGADA81EtCKlRCwAfP3+GjJmmma7WtX14r+vfcBCCn/alsGD7abv6d7v8jWYPeAHg6uSAd1Vn2kzaCsAXzz1Io2BPfjuTxZvf/k8zTQAtanrzdqfaOAjBiv+d5T+7ztxk07G+HwMfrQESjpkv8e8VhwB4s31NWtf2QQjY/Ucm09Yf11TbrajkKd0y0wu9gK3AOoqi9r7AXi1FODgIPn2jE92G/0BKejZxM59j9a4THD5TNFnPpEHt+HZDIt+uT6Ttg6GMezGcl6ZEc+VaHi9NWcOJ1CwCvKuyY3Z/1u85xYXL1+5Yl8ViYdKH45gz72uM/kb6PdWHtu07UKtW7UKb5T8txcPDg1Vr1xMTvYbPpk9lyrRPcXF24fU3/8XxY8c4fvzYHWu5Udf0yRP4ZPY8DEYjA/s/RZvw9jxQs0jX6hXLcHf34PufY9gQG80XM6czbtI0OkdG0TkyCoATx48y4u3BmjlcgG9WxTPn+63MH9+/xPqINg2oFepHWM8PaN6oBjNG9iW8/1S8PKowalAkrftNQUrJziXvsWbLfrKyczTRZbVY+M/nUxg2YRbevgbGvPU8TVo8SlBozUKb6rXq8sFni3BxdWXjmh/5bsFM3hhh++Fwdnbhw1nfaqKlOA4CRkbVZdCi3zBdvMp/X2nOlsPpnEy7XGjzcczRws9PPxJCvQD3wvLCHadxdXKkT9MgzXUNi6jDG//9H+aL11j0wsNsP5bOH+lXCm1CvNx4vmUoL//nN7Kv5uNVxQmARkEeNA725Jn5vwIw77mHaBKqZ9+ZUuf11pTK3pFWVnqhAZAOdAHWSykXAdlSykUFnzWhWd0ATqRmcurcBfLyrSzdepioVrXtbOqF+rD1d9sv7dbfzxDV0lZ/PCWTE6m2xjybcZm0rCv4erppoutAwn5CQqsTHBKCk5MzEZHd2LJpo53Nlk2b6N6zFwAdO0ew+5ddSClxq1KFh5o0xdnFRRMtxTmUmEBwSAhBwTZdHTt3JW7rZjubuK2biIzqCUC7xzqzd3c8tgmRitgQG81jnSM11bZj3wkyLlwptT6qbWOWrLY9xexOOIWnuxv+vh50alWfjfGHybx4hazsHDbGH6Zz6waa6TpxNBFDYDCGgCB0Tk60CO/Mvl32E9I0+HtTXFxdAahVrxGZ6WUud3XHhAV7ciYjh5TMHPItkpgEE+3r+ZVqH9nIyNqEc4XlX05mcvmaRXNdDQM9SM7MITXrKvlWybqDZsLr+NrZPP5gAD/uTSX7aj4AmVfyCuucdQ44Odo2naMDGZdzNddYGkKUf7sX3NLpSimzpZRvAdOAb4UQ75R1zJ8h0LcayWnZheWUtGyCfKrZ2SScNNOztW1C9p6t6+BR1QVvd1c7m6Z1/XF2cuTkWW1+Uc1mE/7+/oVlo9GI2Wwqwcb2CKrT6ahWzZ2srExNrl8aaWYThoLHXgA/g5G0G3Slmc0YjP6FuqpWc+fCBfv7snFdDJ0iut5VrTcSaNCTfK7o/qSYsgg06An005NsKrbfnEWgn16z62aeT8PH11hY9vY1kHk+rVT7bbErady0ZWE5LzeX0YP788GQF9m7c4tmuozuLpguXC0smy5exeBR8g91gKcrQV5u7D6Zodn1S8PP3QXTxaKnRXP2Nfzc7XWFelch1NuNec89xFfPN6FFTW8AElIusvd0FtGDW7F2cCviT2Zw6nzpP8Ra4+ggyr3dC8rK6b6Fbcn1fUAH4J9AXAXouokRc7fwyRsdebZzGDsSkklJy8ZiLYrc/L2r8tWwbrz8cTQ3BHSKEkg8sB9XV1dq1r7lyiL/L9mxaS1/HDvEyClzCvdNX7gCb18D5rMpfDTinwQ/UBtjQHCF6urSyMj6RDPWSvL/7eggCPF249Vvf8fo7sKXzz3I0/P2oK/iRA3fKkTN3AXArKcb82CIJ78nXagQXX/19EIw8ClgBrZgWx3zOyGE960OEkIMEkLsEULsyU+OL1NEavolgv2K8lRBfu6knL9kZ3M24zJ9x62g5T//w5ivtwMU5m3dqzjz0/jejF24nd2HtVsp3mAwcu5c0aOcyWTCYDCWYGO7Zn5+PpcuZaPXe2mmoST8DEbMpqK/M81swu8GXX4GA2bTuUJdly9l4+lZFDlujI2mYwVHuQCp5iyC/YvuT5BRT6o5i9S0LIKNxfYb9KSmaZcD9PLx43x60dNARroZL5+bH+MP/Labld9/zZAxU3Fyci7c7+1rAMAQEES9xk04fUKbzlpT9jWMnkVPbEYPV8wXS+6P6NLI3y61cDdJy76GsVjEbXB3IS3bXpc5+xrbjp3HYpWkXrjKmYwcQrzdaPc3Xw6kXCQnz0JOnoWdJzNoFORRIbrB5tTKu90LykovvCOlbAX4AyOADOAF4EDB8hSlHTdXStlUStlUF9yiTBF7jpyldpAX1f09cdI58GTbeqzZZd/b6ePhVpiDebfvIyyKTQDASefA92MeZ8mGRJZvP3rjqe+IhmGNOHPmFCnJSeTl5RK7dg1t23ews2nbvgOrViwHYMO6WJo90uKu/9LWaxBGUtIZUlOSycvLZcO6aFqHt7ezaR3enrWrVwCwZeM6mjR7pFCX1Wpl04ZYzfO55WHN1gSeiWoOQPNGNbh4KYdz6RdZv/MQHVvWQ+/uht7djY4t67F+5yHNrlvzbw0wpSaRdi6F/Lw84ret46EWj9rZnDpxhIUzJzFk9FQ89EVxxeXsi+Tl2XKS2ReyOHZwP0GhD2iiKzHlItW93QjSu6JzFHRpZGTL4ZvTHjV8q+DhquN/FRQtHkzNJsTLjUBPV3QOgs4NDGw/lm5ns+VoOg+H2n7IPd2cCPV2IzUrh3MXr9IkVI+jsD3CNwnV23XA3W2EEOXe7gXlfTnCDfAAPAu2VCBBKxEWq2TIrA2smtgHRwcHFsUmcOj0ef7dvzX7jp5jTfwJwv8ewrgXw5FSEpeQzFuzNgDQu2092jQKxtvDjWc7hwEw6OO17D95550gOp2O4SNH89orA7FaLPTs1Zvatevw+azPaNAwjHbtH6PXE30YNeJdukd2wsPTk8kff1J4fGTnDly+dIm8vDw2b9rAF3MX2I18uBNdQ98dxdA3B2G1WOnWoxc1a9Vm/pyZ1KvfkDZtOxDVszfjRw/nqce74OHhydiJUwuP/33fHgxGf4KCQ25xlT/HokkDePThOvjqq3E8Zjzj50TjpHMEYP6PccTEJRLRpiGJK8dw5Woer4xdDEDmxStMmhdD3OJhAEycG0PmRe2+qI6OOvq/9i5T3h+MtFoJ79yd4Oq1WPbNlzxQpz5NWoTz3VczuHo1h1mTRgBFQ8NSk07x9cxJCAeBtEqinuxvN+rhTrBYJRPXHOGL/g/h6CD4eV8qJ9Iu888ONTmYcpEtR2yOLrKRPzEHTDcdv/Clh6nhW5Uqzo6sf7sNY1YcZOfxO8/5WqTk43XHmNG3MQ4OglX/O8vJ9CsMCq/BobPZbD92nviTGbR4wIvvBjXDapXM2HSSCzn5bDqcRtMaXix5uSkSiD+RQdzxWy4bpimV/IU0xI092naVQswFGgLZwC9APBAvpSx3T5Fb548rSQbKnsq6XM+lgp7gyoZaruf2eXnRr/daQolU8uV67thlvr3qSLl9zrTudSvcRZd190MBF+AYtvXek4GKGWynUCgUf4LKHune0ulKKbsIW+KjIdAKeBsIE0JkALuklGMqQKNCoVCUm0o+eKHsnG7BEsMHhBBZwIWCLQpoDiinq1AoKhX3ak6F8lLWON3B2CLcVkAetjG7O4EFaNiRplAoFFpxr4aClZeyIt0awFJgiJRSuwGwCoVCcZeo5IFumTndoRUlRKFQKLRATWKuUCgUFUgl97nK6SoUivuLv3RHmkKhUPzVqOQ+VzldhUJxf6HSCwqFQlGBiEq+NOVdd7qbP3+5bKN7wNGz2WUb3QOGrS518rZ7SmWd3wCgXZ9R91pCiXz4aeUc/ONX1eleS7ir6Cr5QF0V6SoUivuKyj6JuXK6CoXivqKy53QreSCuUCgUt4eWC1MKIboIIY4IIY4LIYbfwq63EEIKIZqWdU4V6SoUivsKrcbpCiEcgdlAJ2zT2v4qhFgppTx4g5078C9sc46XrU8TdQqFQlFJcHQo/1YGzYHjUsqTUspc4DugZwl244HJwNUS6m5COV2FQnFf4YAo91YGQUBSsXJywb5ChBBNgBAp5Zry61MoFIr7iNvJ6RZfubxgG1T+6wgHYDq2xR3KjcrpKhSK+4rbGb0gpZwLzC2lOgUovnprcMG+67gDYcCWgmFq/sBKIUQPKeWe0q6pnK5Cobiv0HDCm1+BOkKIB7A5277AM9crpZQXAN/rZSHEFuCdWzlcuM30ghDiASHEE0KIerdznEKhUFQUWg0Zk1LmA28AscAh4AcpZaIQYpwQosef1VfWcj0/SykfL/jcE/gU2AJMEkJMklIu/LMXvpH9e3bx7dzpWK1W2nbuQdQ/nrerj1m+hK2xK3Bw1OHhqeelt97H1xAAwIDuLQmpXgsAbz9/hoyZqpUsfv91J19/PhWr1cpjkY/zeN8BdvWrf1zMxrUrcHR0xMPTi9feGY2f0aZrwog3OXYogXphDzL8w0810wTQNMSTV9tUx9FBsPagmR9+u3lhj/Ba3jzbLBiQnEy/wkcbTth0RdWlnrEaiWezGR19VFNdYGvLxV9Os7VlRE+639CWa3/6lq2xK3F0dMTdU8/At/6Nb8E9ez6qBSE1bG3p4+fPkDHTNNE0Z0w/IsPDSMvIpumTE0u0mTasDxGtG3Llai6DxnzD74eTAejX/RGGD4wA4KP5sXy7qlwjg8pN0oE97Px+DtJqpV6bLjwY+Y8S7U7ujWPDlxPoNfIz/Gr8jeSD+9j909dY8vNx1Ol4pM9LBNV7UDNdJ/63m3XffI60WnmwXSStejxtV793wyr2rl+BcHDE2dWVri8NxS+4Ogd2bGTX6h8K7cxJJ3npwy/wr1FbM223QstJzKWU0UD0DftGl2LbrjznLCu9UL3Y5/eADlLKP4QQvsBGYGF5LlIWVouF/3zxMcM+nIm3r4GxQwbwUItHCQqtWSSk5t8Y++kiXFxd2bhmGd8vmMXrw23zATg7uzB+1mItpNyk66uZk3l/8mx8fI2MeKM/TVuGE1y9SFeN2vX4aHYfXFxdWbfqRxbPm8GQ9ycB0OPJ57h27Sob1vykqS4HAa+H12DEqsOkX8plZp+GxJ/K4kxmTqFNoKcLTzUJZOjyRC5ds+DpVtTUS387i4vOgW4NDZrqgoK2/HwKwybMwtvXwJi3nqfJjW1Zqy4ffHa9LX/kuwUzeWOEzRE6O7vw4axvNdf1zap45ny/lfnj+5dYH9GmAbVC/Qjr+QHNG9Vgxsi+hPefipdHFUYNiqR1vylIKdm55D3WbNlPVnZOiee5XaxWC3FLZtNtyESqevmyfOK/qP73R/AKrG5nl3v1Cgc2rcDwQN3Cfa7VPIh4YyxV9T5kpJwi+rP3eXaKNt8Dq9VCzMKZPDNiMh7efiz49+vUadIKv+AiXWGtOvBwx+4AHN27kw3ffsHT731EWOvHCGv9GADmMydZ+smYCnO4UPlHB5SlTxb7rJNS/gEgpUwHrFqJOHn0IMbAYAwBQeicnHgkvBP74rfZ2dT/e1NcXF0BqF0vjIx0s1aXL5XjRxLxDwzBGBCMzsmJVu068+vOrXY2YQ8W6apTP4yMNFNhXaMmzXGrUkVzXXUN1Ui9cJVzF6+Rb5VsOZ5Bywe87GwiGxhYdcDEpWsWAC7k5BfW/Z5ykZw8i+a6AE4cTcRQrC1bhHdm3y77tmxQrC1r1WtEZgW05Y59J8i4cKXU+qi2jVmyejcAuxNO4enuhr+vB51a1Wdj/GEyL14hKzuHjfGH6dy6gWa60v44iqchEA+/ABx1TtRq1pZT/4u/yW7Piv/wYMSTODo5F+7zDa1NVb0PAF6B1bHkXsOSl6uJrtQTR/A2BuJlCMRR50SDFu04uneHnY1LlaqFn/OuXYUShmAl7tpMg5btNdFUXoQQ5d7uBWU53cZCiItCiGzgISFEAIAQwhlw1EpE5nkz3r7GwrK3r4HM82ml2m9dt5LGTVsWlvNycxnzr+cZN/RF9u7aWupxt0tGuhkfvyJdPr6GWzr7TWtX8GDzVppdvzR8qjqTdqnoy5V+KRffG2aOCvZ0JUjvyvReDfj0iYY0DfG867oAMs+n4XMbbbkt9ua2HD24Px8MeZG9O7fcRaX2BBr0JJ/LLCynmLIINOgJ9NOTbCq235xFoJ9es+tezkqnqrdfYbmq3pfLmeftbNJPH+dSRjqhjZuXep4/9sXhG1rbzinfCdkZ6bj7FD0JeXj7kX2DLoA961Ywe8hzbPzvPCKef/2m+oPxW2hY0U73NrZ7QVnphXeBHcC+gqTydaoAr9w1Vbdgx6a1nDp2iBGT5xTum/b1z3j7GjCfTWHyyNcJrlELY0BwheratiGak0cPMXZaaaNPKhZHB0GQpyvvrjiEb1VnpvWqzyvfJXA59+5EuH+GHZvW8sexQ4ycUtSW0xeuKGzLj0b8k+AHald4W1YmpNXKrqVzaTeg9KGgGamn+WXZArq9VfHTbzbt3JOmnXtyYMdG4n7+lh6vvldYl3L8EE7OLhhCHqhQTZV9uZ6yIt0gbJ1nZiHEViHERCFEFOAgpdxV2kHFBxz//N3CMkV4+RjISC96LM9IN+Pl43eTXeJvu1n1/ULeGj0Vp2K/6N6+tl9kQ0AQ9Ro14cyJI2Veszx4+xo4XyxdcD7dXHit4uzf9wvLlyxg2LjpODlrE2ncivOXc/GrVuwxs5oz6Zfz7GzSL+cSfyoTi1Viyr5GctZVgvSud12bl48f58vRlgd+283K779myJhbtGXjJpzWqC3LItWcRbB/UYomyKgn1ZxFaloWwcZi+w16UtOyNLtuVb0vlzOKngQuZ6VT1cunsJx3NYeMlNOsmjaMJSOex3zyMLGzPyDtlK0D9FJmGus/H8//tXfe8VVU6f9/PymQAKmkEQKE3nsUCEgTCd2y7iqyApZlcS1fu6KuYAFEQV2BFQERFduCkxexAQAAIABJREFUBem9Q1B6CL2ThBRIhSSQ5J7fHzOppMLl5obfeec1r5yZOTPnc+fMPPPMmTnP6fP4y7j7BVpNl5u3D2mX8p/qUhMTcCugqyitu/Xh2K7CzQ+HdmygdWhfq2kqL/bu6ZZqdJVSLyulQjE++h0HJAKPAQdFpMRo20qp2UqpEKVUSNG3/cXRsFlL4qLPkxAbQ3ZWFjs3r6Fjl56F8pw9eZSvZnzA829/hLund97yK2mpZJntWGkpyRw/vJ/A+ta5szZu3ooL0eeJvxBNdlYW2zeuJqRbYV2nTxxhzqeTePXdj/Hw8i5hT9blaPxl6nq44O9WHScHoXcTb8JPJxXKs/1UEu0C3QFwd3EiyNOFCylXb7m2Rs1aERdznoRY45iFb15Nx653Fcpz5uRR5k+fzAtvTy29Lg8doK6V6rIslm2K4JEhxuP7nW2DSb2cQezFVNZsP0y/bi3wdHPF082Vft1asGb7YauV6xvcjJT4GFIvxpKTncXJPzfRoH3XvPXVatRk1Cc/8cjkr3lk8tf4NWpB2NPj8Q1uxtX0y6ycPp47H3iMgCatraYJILBRcxJjo0mOv0BOdhaHwjfSrHPhprPE2Ki89PF9O/EKyH8iURYLh3ZuolW33lbVVR4cHKTcU2VQ3s4RroA74GFOMUCEtUQ4Ojrx6FMv89G/n8NisdDznqEENWjEL99+QXDTlnTq2pMfv5zO1cx0Zk5+A8j/NCzm/Bnmz/gAcRCURTH4wVGF3pTfrK7Hn3mFieOexWLJoU/YMOoFN+an+bNo3KwlIaG9WDD7MzIzMvj4PSPqm4+fP6+99wkAb7/wJNHnz5CZkcHY4YMY++K/6XBHt9KKLBcWBTO3nGHS0OY4iLD6SAJnkzIYeUddjiVcIfxMMrvOp9CpngezH26HRSnmbD9H2lWjhWjafS0J8nLF1dmRBSM78smGU+w+n3LTusA4ZiOfeoUP33oOZbHQs/9Qgho05udvv6BhXl1+RmZmBjMmjwPyPw2LOX+Gr6ZPzqvLIX8dabW6/HryaO7q3BQfz1qcWPke781ajrOT8Vpi7qKtrNwaSViP1kT+Pp70zCz+OcH4CiApNZ3Jc1aydcGrAEyavZKk1JJfyFUUB0dHug9/ihWfvoXFkkPz7v3xDmzArsXf4NOgGcEdupa4beSGJaTGx7Bn6ffsWfo9AIOen4ir+823OTs4OhI2+ll+mPI6FouF9r0G4BsUzKZF86nTsBnNOoeya/ViTh/cg4OjE641azFs7Kt52587cgB3b1+8rOh9l1u7zUusGKKUKnmlyGygNZCGEbYsHAhXSiWVuFERwk8kl1xAJeJazWrvAa2KvQ7X826Y/faH0cP1VAx7Hq5nZEi9m3Y//7cvptw2528dAm3u7pZ1U6gPVAdiMbrBRQHWa9DSaDQaK2PvbbqlNi8opQaI8TFbayAUI5pOGxFJBHYopcbbQKNGo9GUmyo/Rpoy2h8OikgykGJOQzAC/Gqjq9Fo7ArHqmx0ReQ5DA83FMgCtpvTPKz4Ik2j0WishX2b3LI93WBgIfCCUur6iCoajUZjZ9i5o1tmm659vn7VaDSaEijHMDyVig5irtFobiuqtKer0Wg0VQ3Rnq5Go9HYjir99YJGo9FUNezc5mqjq9Fobi/s3eiWGnvBGrR+c7Vdxl7Y+LrtQ86Vh2yL1QbksCph0zaXnamSGHW3dYLiWJu3nv+4siUUS/3+QytbQokcnRJ20yZzzeGL5bY597T0sbmJ1p6uRqO5raikiI3lRhtdjUZzW2HvI0doo6vRaG4r9CdjGo1GY0N084JGo9HYEO3pajQajQ2x8yZdbXQ1Gs3thZ3bXG10NRrN7YXuBqzRaDS2xL5tbpkjR9QH4pVSmeZYaaOBTsAhYI5SKvvWS9RoNJryU9VfpC3HGAsN4AOgMfAb0Be4A3jcWkJ6NK3N64Nb4Ogg/LwrirmbzxRa/9qg5tzZyAsAF2dHvGtWo9v7G2hRx41/D2tJrepO5CjF7I2nWBkRZy1ZhG/fwn+mfoDFksOQ+/7Co6P/UWj9tWvXeH/8OI4ejsTdw5N3J0+jTmBdVq9YyvffzsvLd/L4MeYtWEjT5i2tomvnjq3MmDaFHEsOg+99gBGjnrxO1+QJb3D0yCE8PDx5e+JH1AmsS1ZWFtMmv8PRw5E4iAPPvPQ6HTvfYRVNuXRvUpvXBjXDQYRf9kQzb8vZQutfGdCMOxrm1qUD3jWr0WPyJgA+f7QDbYM82HsumWe/229VXecP7mL7T7NQFgstegygw8C/FZvv1O6trP1iIve/8R98g5sRdWgPf/zyFTnZ2Tg6OdHlwSeo26KD1XTNGj+CgT3bkJCYRshfJxWbZ9qrDxLWvTXpmdcYM/5b9h2JAmDE0C68/mQYAB/MXcV3S3ZaTdddzXx4c1gLHERY+GcUczaeLrR+3JDmdGnsDRjXZO1a1bhjwnoCPV2YMbIjDgJOjg4s2HaWH3dGWU1XWdh560KZRtdBKZVupvsBdyilLMACEbHaFeEg8ObQlvzjq93EpWby01Nd2XA4gZMJV/LyTFl+NC/9SNd6tAx0ByDjWg7jFh3k3KV0fN2qs/Dprmw7fom0zJt3wnNycvh4ykQ+mTkHP39/nhz5ED169qFhoyZ5eZYu/hk3N3d++m0la1ct5/PpH/Pu5Gn0HziE/gOHAHDyxDHGvfSc1QxuTk4O//lwIlNnzMbXL4Cxox6m+119CG7UOC/P8t9/oZabO9//spx1q1cwe8YnjJ80laW/LQLgqx9+JSnxEq89/xSz5v+Ig4ODVbQ5CLwxpDljvt5LXGomP/zzTjYeucipAnX50cpjeenhXerRoo5b3vz8bWdxcXbkwZC6VtGTi8WSw9bvZzL4hUnU9PLh10n/R4P2XfAKbFAo37XMdA6uX4xfw+Z5y1xquRP2zARqetYmMfoMy//zFn//cIHVtH27JJxZP21i7nsji10f1qMVjev70ubed7izbTCfvfEwPUdOxcu9Bm+OGUj3ER+ilGL796+xbOMBktMyblqTg8Db97Xksbm7iEvJZNEz3Vh/KJ6T8fn1OHlp/jX599D6tAo06jEh7SoPzQwnK0dRo5ojS17ozvpDCcSnXb1pXeXBzm0uZV1p50UkNzLMGaAegIjUtqaItkEenE9MJyopg6wcxfIDsfRp6Vdi/kHt6rB8vzFk29lL6Zy7ZNwXEtKuknj5Gl41q1lF1+HICILq1aNuUD2cnavRr/8gtm7aUCjP1k3rGTjkXgB6392f3X+EUzSI0NpVy7m7/0CraAI4EhlB3aD6BNath7OzM337D2Tb5sK6tm3awIDBwwDo1fcedv+5E6UUZ0+fpFNIFwC8vGtTq5Y7Rw9HWk1bmyAPziVmEJ2UQXaOYmVEHH1a+JaYf2Bbf1ZExObN7zyVxJWrOVbTk0vC6WN4+AXi7lsHRydnGt/RizP7w6/Lt2vxN3QI+yuOzvnnkE/9JtT0NE55r8AG5Fy7Sk7WNatp27bnJIkp6SWuH9KrHd8v/QOAPyLO4OHmSoCPO/eEtmRd+BGSUtNJTstgXfgR+ndvZRVN7ep5cPZSOlGJxjW5bP8F7m5V8jU5uEMAS/cb9ZiVo8jKMa6Bak4OWOl+Xn6kAlMlUNbheBL4t4hsBqoB+0RkA7AWsNr4af7uLlxIycybj0vNxN+jerF563i6EOTtys5TidetaxvkjpOjcD6x5BO4IiTEx+HnXydv3tfPn4T4uCJ54vHzDwDAycmJmrXcSElJLpRn3eqV3BM2yCqaABIS4vE1y8zTlRBXYh4nJydq1apFSkoyjZs2Z9vmDWRnZ3MhOoqjRw4RHxeLtfB3q05ckbr0cy+hLj1cqOvlyh/F1KW1uZJ8kZre+ca/pqcPV5IuFcpz8ewJLidepH67O4tunsfpPVvxqd+kkFG+1QT6eRIVm5Q3Hx2XTKCfJ4G+nkTFFVgen0ygr6dVyvT3cCE2uUA9pmTi7+FSvD5PF4K8ahB+Iv94Bni48PvzoWwc14s5G0/bzMsFI/ZCeafKoKzmhb8ArwEZQCNgPhAF/Gk2M9icQW0DWH0wDkuR4G0+btWY/GBb3vj5ILc4WmWFiDx4ABcXFxo1aVrZUgAYOPR+zp4+xT9HPUxAnTq0adfeak0LFWVAW3/WRMZfV5eVgbJY2LFwNr1Hv1RinsSYs+z8eR6Dn59oQ2X2z+D2dVgVEVuoHmNTMhn26Xb83Kozc1RHVkXEcemy9Z4OSqOqNy8EAZ8CmzA8286AL1Dq7VRExojILhHZlbR3eZki4lIzqVPgLurv7kJcSvF3xoHtAlh+oPBo8DWrO/L5yE58tuYEB86nlFleefH18yc+Lr+shPg4fP38i+Txy/MUs7OzuXI5DQ+P/MOzbtVy+lnRywXw9fUjoYB3mhAfh6+vf4l5srOzuXz5Mh4enjg5OfHMi6/x5XeLmDh1OpfT0qhXP9hq2uLSrhbyiPzdXYhPLb4uB7QNKNS0cCup6enDlcSEvPkryRep6ZXfSpaVmUFi9FmWTHuV78eNIv7UEVbNfIeEM0b78+WkBNb89z36PP4y7n6BNtGcS0x8MkEBXnnzdf09iYlPJiYhmSD/Asv9PIlJSC5uFxUmLiWTAM8C9ejhUugJpiCD2gewbH/x9RifdpXjsWmENPQqdv0twYrNCyIyQESOisgJEXm9mPUvisghETkgIutEpEFx+ylIqUZXKfWyUioUCADGAYnAY8BBETlUynazlVIhSqkQr45lG5yD0anUr12Dul6uODsKg9oFsOFI/HX5GvrUwN3VmX3n8g2rs6Pw2YgO/L43htWR1vtqAaBFqzacP3+OmOgosrKusXb1crr37FMoT/eefVixdDEAG9etptMdXRDzscVisbB+7SqrtucCNG/VhqjzZ7kQHUVWVhbrV68g9K7ehfKE9uzNymW/A7Bp/Ro6hdyJiJCZmUFGhtH8smvndhwdHQu9gLtZIqNTaeDtSl1PF5wchQFt/dl4JOG6fME+NXB3cWK/FW+SpeEb3IyU+BhSL8aSk53FyT830aB917z11WrUZNQnP/HI5K95ZPLX+DVqQdjT4/ENbsbV9MusnD6eOx94jIAmrW2ityDLNkXwyBCjyePOtsGkXs4g9mIqa7Yfpl+3Fni6ueLp5kq/bi1Ys/2wVcqMiEoluHYNgsxrcnD7Oqw/fP012ci3Ju6uzuw9m2/s/T2qU93JMC3urk50CvbidIEXqbcaqcBfqfsRcQRmAgOBVsBwESnaaL4XCFFKtQMWAR+Wpa+8nSNcAXfAw5xigIhyblsmORbFxCVHmD26Ew4i/LonmpPxV3jm7sZERqeywbxoB7arw4oDhe+oYW0C6BzshWcNZ+7rZHggb/4cyZELaTety8nJiRdfeZMXnx2DJcfC4GH306hxE+bOmk6Llq3p0asvQ+79C++9/ToP3TcAd3cPJkyamrf9vj278PMPoG5QvZvWUlTX/73yBq88NxaLJYeBQ++nYeMmzPtiBs1btqZ7zz4MGvYAk8aP45EHBuHu7sHbE41zISkxkVefG4s4CD6+frzxzmSrasuxKCYtO8rnIzvi6CD8tieGkwlX+FffRhyKTmXj0YsADGwbwMqD198k5z/RmWCfmtSo5sial3owfvEhtp+4+TZfB0dHug9/ihWfvoXFkkPz7v3xDmzArsXf4NOgGcEdupa4beSGJaTGx7Bn6ffsWfo9AIOen4iru3XaT7+ePJq7OjfFx7MWJ1a+x3uzluPs5AjA3EVbWbk1krAerYn8fTzpmVn8c4Lx5URSajqT56xk64JXAZg0eyVJqdZ5n5FjUby7+DBzn+hsfMb5ZzQn4q7w3D1NOBiVwvrDxjU5qH1A3kvtXBr71eL1wc1Ryvh8a97mMxyLvWwVXeXBik21dwInlFKnjP3Kj8C9GP0UAFBKFXyDHQ78vUx9pQ3XIyKzgdZAGrDT3Gm4UiqpxI2KoIfrqRh6uJ6Ko4frqRi3+3A9+8+nldvmtK/nVmJ5IvIgMEAp9aQ5/yjQRSn1TAn5ZwCxSqn3SyuzLE+3PlAdOA5EY7xEs06jkUaj0dwCKtIjTUTGAGMKLJqtlJpd4TJF/g6EAL3Kyluq0VVKDTC7/7YGQoGXgDYikgjsUEqNr6g4jUajuZVUpHnBNLAlGdlozL4JJkHmsiLlST/gTaCXUqrMb+PKbNNVRvvDQRFJBlLMaQhGe4c2uhqNxq6w4idjfwJNRaQhhrF9GHikUFkiHYEvMJohrn/TWAxlBbx5DsPDDQWygO3mNA8rvkjTaDQaq2Elq6uUyhaRZ4BVgCMwTykVKSLvAruUUr8DHwG1gIXmV0vnlFLDSttvWZ5uMLAQeEEpdaGMvBqNRlPpWDPKmFJqOUbgr4LL3i6Q7lfRfZbVpmu1rr4ajUZjC/TAlBqNRmNLtNHVaDQa21HVg5hrNBpNlaKqBzHXaDSaKoWd21xtdDUazW2GnVvdW250q1e3T7tezalyYsiWiZ0O9eniYp/1COBb07myJRSLvcY4OLd6SWVLKJkpYTe9i8oKTl5e7PdK0mg0mhvAvk2uNroajeZ2w86trja6Go3mtkJ/MqbRaDQ2xM6bdLXR1Wg0txfa6Go0Go0N0c0LGo1GY0O0p6vRaDQ2xM5trja6Go3m9sLePd1Su2WJyDARcbGVGI1Go7l5pAKT7SnL0/0JuCIiK4AfgFVKqZxbISS0sTcvhzXF0UH4de8F5m87e12ee1r58c9eDVFKcSzuMm/+agw//9zdjenRtDYAczefYfWhcg1VVC52bNvCtA8nYbFYuPf+Bxn1+D8Krb927RoT3nqNI4cP4eHhycQpHxNYt27e+tgLMTz0wFD+MfZp/j7qcavpCt++hf9M/QCLJYch9/2FR0dfr+v98eM4ejgSdw9P3p08jTqBdVm9YinffzsvL9/J48eYt2AhTZu3tJq2ro28eemeJjiIsHj/Bb7Zce66PP1a+vLkXcGg4Hj8Zf69+DAAz/ZpRPcmtRGBP04nMW3NCavpOrn/D1Z/+1+UxUKH3gMJHTa80Prda5ewe81ixMGRai4uDHriRXyDGnBw2zp2LP1fXr7486d44v3PCQhuYhVddzXz4c1hLXAQYeGfUczZeLrQ+nFDmtOlsTcALs6O1K5VjTsmrCfQ04UZIzviIODk6MCCbWf5cWeUVTQBzBo/goE925CQmEbIXycVm2faqw8S1r016ZnXGDP+W/YdMcofMbQLrz9pdOn9YO4qvluy02q6yqKqBzE/AvQFHsQYCfgrEfkV+EEptclaIhwEXhvYnH8t2Etc6lUWPBnCpqMJnL6Ynpennrcrj3VvwGNf7SYtMxuvGkZ/+x5Na9OijhvDv/gTZydhzshObDtxiSvXbv7ekJOTw4eT32PGrC/x8/dn1Ii/cVevPjRqnH+x/f7rItzcPfhlySpWr1zGjP9MZdKHn+St/3TaFLp1v+umtRTV9fGUiXwycw5+/v48OfIhevTsQ8NG+bqWLv4ZNzd3fvptJWtXLefz6R/z7uRp9B84hP4DhwBw8sQxxr30nFUNroPAq2FNeeaH/cSnXuXrxzqz5fjFwnXp5cqobvX5xzd7C9Vl27rutAvy4JG5fwIw59GOdKrvyZ5zyTety2LJYeX86Twybgru3r7M+/fTNO0Uim9Qg7w8bUL70rmfES/h2O7trP3uc4a/9gFtut9Nm+53AxB/7hQLPxlvNYPrIPD2fS15bO4u4lIyWfRMN9Yfiudk/JW8PJOXHs1L/z20Pq0C3QBISLvKQzPDycpR1KjmyJIXurP+UALxaWUOSFsuvl0SzqyfNjH3vZHFrg/r0YrG9X1pc+873Nk2mM/eeJieI6fi5V6DN8cMpPuID1FKsf3711i28QDJaRlW0VUWVbp5AWMw4CSl1Byl1N1Ae+AQ8IGInLeWiDZ13YlKSic6OZNsi2JVZDy9m/sWyvNAp0D+tyuKtEwjIkxSehYAjXxqsudsMjlKkZll4Xj8ZUKb1LaKrsiDBwiqV5+6QfVwdq5G/7BBbN64vlCeTRvXM3jovQD07RfGn3+EYwygDBvXryUwMKiQkbYGhyMjCKpXL09Xv/6D2LppQ6E8WzetZ+AQQ1fvu/uzu4CuXNauWs7d/QdaVVvrQHeikjKIMety9aF4ejb1KZTnvg51WLQ75rq6BCMQkbOjMTk5OpB45ZpVdMWcPIq3fyBefoE4OjnTqmtvju3eVihP9Ro189JZVzMp7vEzcscGWnXrYxVNAO3qeXD2UjpRiRlk5SiW7b/A3a38Ssw/uEMAS/fHGhpzFFk5Rp1Wc3LAwcoxnLbtOUliSnqJ64f0asf3S/8A4I+IM3i4uRLg4849oS1ZF36EpNR0ktMyWBd+hP7dW1lXXClIBf4qg7KqqZAqpVSsUuozpVQ3oIe1RPi6VSc2Jf/uHJ96FT+36oXy1PeuQYPaNZj3WCe+frwzoebj1rG4y4Q28cbFyQFPV2dCgr3wdy+87Y2SEB+Pf0BA3ryfvz8J8XFF8sThH1AHACcnJ2rVciMlOZn09Ct8M38uT479l1W0FC3Tz79O3ryvX3G64vHzD8jTVbOWGykphT3GdatXck/YIKtq83WrTlxqgbpMu4pvMXVZ39uVOY925MtRnejayKjLiOhUdp9NZvlzoax4LpTwU4mcuVTyRV8R0hIv4lY735i5e/uSlnTpuny7Vi9m5guPsu6HOYSNevq69YfCN9LaikbX38OF2OTMvPm4lEz8PYp/jRLo6UKQVw3CT+TrDvBw4ffnQ9k4rhdzNp62mpdbHgL9PImKTcqbj45LJtDPk0BfT6LiCiyPTybQ19Nmuuy8SbfM5oVvReROYI9SqlDQQaXU9Y2utxAnB6Gedw3GfL0XP/fqzB3Vib/N+oPwU4m0DnTjq8c7k5SexYGoFCxFPLrKYM6smQwfMYoaBbwneyLy4AFcXFxo1KSpzct2dBDqebsy9rt9+LtV54tHOzB8zi48azgT7FODIdN3ADBjeDs61PNg3/kUm2kL6X8vIf3v5eC2dWz97TuGjX0tb130icM4V6uOX72GNtNTkMHt67AqIhZLgdM7NiWTYZ9ux8+tOjNHdWRVRByXLlvn6aCqYuetC2V6uoHAp0C8iGwSkUkiMkREvEvbSETGiMguEdl1cdfSMkUkpF0lwCPfG/Jzr37dHTsu9Sqbj14k26KISc7kXGI69Wu7AvDl1rMMn/0n/1qwDwHOXrJO25Gvnx9xsbF58/Fxcfj6+RfJ409crDE6fXZ2Npcvp+Hh6cnBiAPM+HQq9w68mx+/+4b5X87mfz9+ZyVd/sTHXcibT4gvTpcf8XGxebquXE7DwyPf21i3ajn9rOzlglGXBZ80/Nyqk1CkLuPTrrL5+CVyLIqYlEzOJWZQz9uV3s18OBidSkZWDhlZOWw/lUjbuu5W0eXm7UPapfwXrKmJCbh5ldwM1bpbH47tKtz8cGjHBlqH9rWKnlziUjIJ8Mz3bP09XIhLySw276D2ASzbH1vsuvi0qxyPTSOkoZdV9ZVGTHwyQQH55dX19yQmPpmYhGSC/Ass9/MkJuHm2+XLi0j5p8qgVKOrlHpZKRUKBADjgETgMeCgiBwqZbvZSqkQpVSIT8iQMkVERqdRz7sGgZ4uODkIYa392HTsYqE8G48m0DnYMBqers7U965BdFIGDgIerobD3tSvJk39axF+MrHMMstDq9ZtOX/uLNHRUWRlXWP1quXc1avwo2XPXn1YtmQxAOvXriLkjq6ICHO+WsDiFetYvGIdD48YyegnxvC3h0dYRVeLVm04f/4cMaautauX071nYV3de/ZhxVJD18Z1q+l0RxfEPMssFgvr166yensuwKGYNOp5uRLoYdRl/1Z+bDlepC6PXaRzfaMuPVydqe/tSkxyBrGpmXSq74mjCI4OQqf6noVewN0MgY2akxgbTXL8BXKyszgUvpFmnUML5UmMzX/zf3zfTrwCgvLmlcXCoZ2baNWtt1X05BIRlUpw7RoEebni7CgMbl+H9Yev//qmkW9N3F2d2Xs233j5e1SnuhmM393ViU7BXpxOuHLdtreKZZsieGTInQDc2TaY1MsZxF5MZc32w/Tr1gJPN1c83Vzp160Fa7YftpkuESn3VBmUt3OEK+AOeJhTDBBhLRE5SjFlxTFmjuiAgwi/74vhVMIVxvZuyKGYNDYfu8j2k4l0bezNoqe6kGNRfLr2BCkZ2VRzdODL0Z0BuHI1m7d+PUSOlZoXnJyceOX1t3juqSexWCwMvfcBGjdpyhf//YyWrdrQs3dfht3/IOPffI0Hhobh7u7BxCnTrFJ2WbpefOVNXnx2DJYcC4OH3U+jxk2YO2s6LVq2pkevvgy59y+89/brPHTfANzdPZgwaWre9vv27MLPP4C6QfWsri1HKT5afZzPHm6Hg4OwZP8FTl1MZ0zPYA5fSGPL8UuEn0qka0MvfhxzBxaL4rP1p0jJyGb9kQRCgr34/h8hKCD8ZCJbT1zf7nojODg6Ejb6WX6Y8joWi4X2vQbgGxTMpkXzqdOwGc06h7Jr9WJOH9yDg6MTrjVrMWzsq3nbnztyAHdvX7z8Aq2iJ5cci+LdxYeZ+0RnHB2En/+M5kTcFZ67pwkHo1JYfzgBMLzc5fsvFNq2sV8tXh/cHKUMr23e5jMci71sNW1fTx7NXZ2b4uNZixMr3+O9WctxdnIEYO6irazcGklYj9ZE/j6e9Mws/jlhAQBJqelMnrOSrQuM4zdp9kqSUq1z8ywP9t68IEXfaBdaKTIbaA2kATuBcCBcKZVU4kZF6PTu+spvYC2GDa/0rmwJxXIt21LZEopl8PStlS2hRJ7p37iyJRTLxIUlPgxWKvY8XE/G3hk3bTMvXckut82pXdPJ5ja6LE+3PlAdOA5EA1GA7RpnNBqNpoJU6ShjSqkBYjR8tAZCMTpItBGRRGCPArgHAAALZElEQVSHUmq8DTRqNBpNubH3zhFltukqo/3hoIgkAynmNAS4E9BGV6PR2BVV2uiKyHMYHm4okAVsN6d5WPFFmkaj0ViLKt28AAQDC4EXlFIXysir0Wg0lU6V9nSVUi/aSohGo9FYAzu3uTqIuUajuc2wc6urja5Go7mtqOptuhqNRlOlqOpBzDUajaZqoY2uRqPR2A57b14oNfaCvSEiY5RSsytbR3HYqzatq2LYqy6wX232qstesfIAH7ecMZUtoBTsVZvWVTHsVRfYrzZ71WWXVDWjq9FoNFUabXQ1Go3GhlQ1o2vP7Ub2qk3rqhj2qgvsV5u96rJLqtSLNI1Go6nqVDVPV6PRaKo02uhqNBqNDbE7oysin4jI8wXmV4nI3ALz00TkRRFZKSLJIlL2GO82QERqiMgyETkiIpEi8oGNyi3P8RovIntEZJ+pbayd6HrRTLuLSJSIzLjVuiqiTURyzGO2T0R+tyNd9UVktYgcFpFDIhJsB7peKnCs9olIpojcdyt1VVXszugC2zCCpiMiDoAPxnBBuYRiBFL/CHjU5upKZ6pSqgXQEeguItYf4/x6ynO8NgDdlFIdgC7A6yJi3WFtb0zXdjP9HrD5Fuu5EW0ZSqkO5jTMjnR9A3yklGqJMYLL9WO2217XttxjBfQF0oHVt1hXlcQeje52oJuZbg0cBNJExEtEqgMtgT1KqXUYoxTbDBF5xRxNI/fuv95M9wXmKKU2ACilrgF7gCAbyCrP8QpXSl0181THNvVernoUkc6AP7a9QMulzYZ6KqIrHXBSSq0BUEpdVkrd6vHNK3q8HgRW2EBXlcTujK5SKgbIFpH6GHfQHRjDv3cDQoAI06hVBluAu8x0CFBLRJzNZXmemoh4AkOBdbdaUHmPl4jUE5EDwHlgirldpeoCsoFpwMu3UsuNaDPPMRcR2SUi4bZ4VC7nMWsEJIvILyKyV0Q+EhHHytZV5Jp8GPjhVmqqythrwJvt5I/N9jFQ10ynYDzqVBa7gc4i4g5cxbi7h2AY3VwP2AnjhPtMKXXKRrrKPF5KqfNAO7NZ4TcRWaSUiqtkXf8CliulosT2Y6yU5xxroJSKFpFGwHoRiVBKnaxkXU4Y51tH4BzwEzAa+LKSdQEgInWAtsCqW6ynymJ3nq5JbhtSW4xHmXCMu2rBdkCbo5TKAk5jnOTbMTzfPkAT4LCZbTZwXCn1qQ2llft4mV7LQfI99srU1Q14RkTOAFOBkbZ6AVkObSilos3/p4CNGIausnVFAfuUUqeUUtnAb0AnO9CVy9+AX81rRVMM9mp0t2MM856olMpRSiUCnhiVXGlG12QLxuPwZjM9FtirlFIi8j7gATxfyva3glKPl4gEiYgrgIh4AT2Ao5WtSyk1QilVXykVjHFMv1FKvW4DXWVqK9BeiYj4AN2BQ5WtC/gT8BQRXzN/XzvRlctwdNNCqdir0Y3AeEMaXmRZilLqIoCIbMEYqfhu83OjMBtp2wLUAXaYj+eZwBYRCQLeBFphvCDaJyJP2khTWcerJbBTRPYDmzC+soiwA12VSXmO2S7zmG0APlBK2cK4lapLKZWDcYNaJyIRGCG751S2LgDz07V6GOeYpgR0N2CNRqOxIfbq6Wo0Gs1tiTa6Go1GY0O00dVoNBoboo2uRqPR2BBtdG8QEXEUkadFxKWytWg0mqqDNrpFEBElIgsKzDuJSIJcH81sKnBYKZVZwn7OmN93llbWfBF58AZ1BovIwRvZ9lYiIhNE5Ia69YrI82aX24Ui0tba2oqU1VtEQm9lGRVFROaKSKsb3HaYiNjqG2fNTWCv3YArkytAGxFxVUplAPcA0UUzKaVesLmySkJEnMzeT8XOWwuzF5+tevL1Bi5TTGebW/X7ykIpdcPfdSulfgduefhJzc2jPd3iWQ4MNtOFetiIiLeI/CYiB0yvrJ25vLYZ4zTSjDUq5vJCHqmIvCwiE4oWKCJ3mwFMIkRkXm5vqCJ5OovIfvO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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Selection"
      ],
      "metadata": {
        "id": "S72hr7fcSIPH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import GenericUnivariateSelect, f_classif\n",
        "k_vs_feature = []\n",
        "\n",
        "for k in range(7):\n",
        "  transformer = GenericUnivariateSelect(f_classif, mode='k_best', param=k+1)\n",
        "  X_new = transformer.fit_transform(xtrain, ytrain)\n",
        "  k_vs_feature.append(transformer.get_support())\n",
        "pd.DataFrame(k_vs_feature, range(1,8), xtrain.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "1bXD4reMa3TZ",
        "outputId": "6bc1b298-812c-4dca-cc87-78cf668201d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      W1     w2     W3    W4     W5     W6     W7\n",
              "1  False  False  False  True  False  False  False\n",
              "2   True  False  False  True  False  False  False\n",
              "3   True  False  False  True   True  False  False\n",
              "4   True  False  False  True   True   True  False\n",
              "5   True  False  False  True   True   True   True\n",
              "6   True  False   True  True   True   True   True\n",
              "7   True   True   True  True   True   True   True"
            ],
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3655fc91-6f90-41b4-815a-34191527c11d 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-3655fc91-6f90-41b4-815a-34191527c11d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Naive Bayes"
      ],
      "metadata": {
        "id": "r7v5nt3UsRV8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = GaussianNB()                       # 2. instantiate model\n",
        "model.fit(xtrain,ytrain)  # values.ravel() converte de Coluna pra Array\n",
        "y_model = model.predict(xtest)             # 4. predict on new data\n",
        "mat = confusion_matrix(ytest, y_model)\n",
        "sns.heatmap(mat, square=False, cmap=\"Blues\", annot=True, cbar=False, fmt=\".0f\", xticklabels=['1','2','3','4'], yticklabels=['1','2','3','4'])\n",
        "plt.xlabel('Valor predito')\n",
        "plt.ylabel('Valor Verdadeiro')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "TluD-IEAcg3s",
        "outputId": "ff4c3637-da3c-4b87-e052-78a8a961f3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Valor Verdadeiro')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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": [
        "#model.score(xtest,ytest)\n",
        "print(classification_report(ytest, model.predict(xtest)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X6AYjt0sewk",
        "outputId": "6e9473be-227b-47b8-a6ae-0c470a5d1a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      1.00      1.00       106\n",
            "           2       1.00      0.93      0.96        94\n",
            "           3       0.94      1.00      0.97       109\n",
            "           4       1.00      1.00      1.00        91\n",
            "\n",
            "    accuracy                           0.98       400\n",
            "   macro avg       0.98      0.98      0.98       400\n",
            "weighted avg       0.98      0.98      0.98       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kDic={}\n",
        "for idx, mask in enumerate(k_vs_feature):\n",
        "  model.fit(xtrain.iloc[:,mask],ytrain)  # values.ravel() converte de Coluna pra Array\n",
        "  #print(\"k={} - score {}\".format(idx+1,model.score(xtest.iloc[:,mask], ytest)))\n",
        "  kDic[idx+1] = model.score(xtest.iloc[:,mask], ytest)\n",
        "graf = pd.DataFrame(kDic.values(),kDic.keys(),['Score']).plot(title='Naive Bayes\\nAtributos vs Score')\n",
        "graf.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.1%}'.format(y))) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "a0YwpjAdwEEB",
        "outputId": "2188b88e-17bb-4164-8870-0ef69500c7b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "source": [
        "#Random Forest Classifier"
      ],
      "metadata": {
        "id": "tQ8ckT24jHa0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(max_depth=7, random_state=0)\n",
        "kDic= {}\n",
        "for idx, mask in enumerate(k_vs_feature):\n",
        "  clf.fit(xtrain.iloc[:,mask],ytrain) \n",
        "  #print(\"k={} - score {}\".format(idx+1,clf.score(xtest.iloc[:,mask], ytest)))\n",
        "  kDic[idx+1] = clf.score(xtest.iloc[:,mask], ytest)\n",
        "graf = pd.DataFrame(kDic.values(),kDic.keys(),['Score']).plot(title='Random Forest\\nAtributos vs Score')\n",
        "graf.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.1%}'.format(y))) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "GV7FE7bsjfjV",
        "outputId": "a8315204-01ad-427d-c52e-1b53e5065e9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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o1G6vriqkdtfUZ5Dg44ePSrsUM0sUVRhUVVWxbNmygvpizIfW5xlUVXX/a/UjgkfqGzhm78GMHNAr7XLMLFFUVxONGjWKTCZDY2Nj2qXscq1POuvupr29gneXr+e6E/dNuxQzy1FUYVBZWeknfXVzNXUZevco57SD90i7FDPLUVTdRNa9bdjczP/OXMjpBw+nT8+i+h1iVvAcBrbL/Gr2ItZu2sIFPnFs1u04DGyXqanPMHJAL44a65vMzLobh4HtEgtXbeD5eUs5f9JIysp8b4FZd+MwsF3i0ZcaiICPT3IXkVl35DCwnS4iqKnLUD16IGOGFMdDecyKjcPAdrrp81fyRuM6nzg268YcBrbT1dRn6FlRxhkThqddipltg8PAdqpNW5p54uWFnHrQHvQvsCc/mZUSh4HtVL+Zs4RVG5o4311EZt1au8JA0nWSZkmaLWlKMu1GSQ2SpievM7ax7mmS5kqaJ+krOdMfkDRD0ndypn1D0rldbZR1HzV1GXbv35MPjxuSdilmth07HBNA0sHAFcCRwGbgaUmtz138j4i4dTvrlgM/BE4GMsA0SY8n+90QERMkPStpN6A3cFRE/GuXWmTdRuOaTfzutUauOG5vyn1vgVm31p4jg/HAixGxPiK2AL8HPt7O7R8JzIuINyNiM/BTYDLQBPSSVAZUAs3ATcANHW2AdV+PTW+guSW44PCRaZdiZjvQnjCYBRwnabCk3sAZwJ7JvH9MunrultTW8wtHAvNzPmeAkRExB2gE6oEngHFAWUTUb68QSVdKqpVUW4rDVBeaqXUZJo7ajXHD+qVdipntwA7DIPni/nfgGeBpYDrZX/I/AvYBDgUWAt/tyI4jYkpEHBoR3wW+DVwv6euSHpZ0xTbWuT0iqiOieujQoR3Zne1isxes4tVFa3zi2KxAtOsEckTcFRGHR8RHgBXAaxGxOCKaI6IFuINsl9DWGvjbUQTAqGTaeyRNBuqAvsA+EfFJ4ILkKMQKVE1dAz3Kyzh7woi0SzGzdmjv1UTDkj/3Inu+4EFJuXcQnUe2O2lr04B9JY2V1AO4CHg8Z7uVwBTgFqAX0Pq8ynKgR8eaYt1FU3MLj01v4MTxwxjYx/8ZzQpBe58wUiNpMNkTv5+PiJWSfiDpULJf4G8DnwOQNAK4MyLOiIgtkv4R+BXZL/i7I2J2znY/D9wbEeslzQB6S5oJPBURK/PSQtvlfje3kWXrNnO+B6UzKxjtCoOIOK6NaZ/exrILyJ5kbv38FPDUNpb9z5z3AVzcnnqse6upyzC4Tw8+ur/P65gVCt+BbHm1Yt1mfvPqYiYfOpLKcv/zMisU/r/V8uqJGQtoag6PUGpWYBwGlldT6zKMH96fA0f0T7sUM+sAh4HlzeuL1zAjs4rzJ/mOY7NC4zCwvJlan6G8TEw+1GFgVmgcBpYXzS3BL15q4GP7D2Vov55pl2NmHeQwsLx4ft5SFq/e5HsLzAqUw8DyYmpdht16VXLC+GFpl2JmneAwsC5bvbGJZ2Yv4pyJI+hZUZ52OWbWCQ4D67L/nbGQTVtaPEKpWQFzGFiX1dRlGDesLxNH7ZZ2KWbWSQ4D65K3l66j9p0VnD9pFJIfbWlWqBwG1iU19RnKBOcd5nsLzAqZw8A6raUleKS+gQ+NG8Ieu1WlXY6ZdYHDwDrtL28to2HlBg9KZ1YEHAbWaTV1DfTrWcEpB+6Rdilm1kUOA+uUdZu28MtZCzlzwnB69fC9BWaFzmFgnfLLWYtYv7nZ9xaYFQmHgXVKTV2G0YN7Uz16YNqlmFkeOAyswzIr1vPCm8t8b4FZEXEYWIc9Wt8A+N4Cs2LiMLAOiQhq6jMcvfcg9hzUO+1yzCxPHAbWIXXvrODtZev93AKzIuMwsA6pqc/Qq7Kc0w8ZnnYpZpZHDgNrt41NzTz58kJOP2QP+vasSLscM8sjh4G12zOvLGbNpi1c4C4is6LjMLB2m1qXYeSAXhy99+C0SzGzPHMYWLssXr2R519v5LzDRlJW5nsLzIqNw8Da5dGXGmgJ+Pgk31tgVowcBrZDEUFNXYbDRw9k76F90y7HzHaCdoWBpOskzZI0W9KUZNogSc9Kej35s81BaiRdlizzuqTLkmk9JT2dbPPqnGVvlzQpHw2z/JnZsIrXl6z1vQVmRWyHYSDpYOAK4EhgInCWpHHAV4DfRMS+wG+Sz1uvOwi4ATgqWf+GJDROBZ4HJgCfTpadCJRHRH0e2mV5NLUuQ4+KMs6c4HsLzIpVe44MxgMvRsT6iNgC/B74ODAZuDdZ5l7g3DbWPRV4NiKWR8QK4FngNKAJ6A1UAq1nI78NXN/ZhtjOsWlLM4+/vIBTDtyd3XpVpl2Ome0k7QmDWcBxkgZL6g2cAewJ7B4RC5NlFgG7t7HuSGB+zudMMu1ZYAzwF+D7ks4B6iNiwfYKkXSlpFpJtY2Nje0o3brquVeXsHJ9k59bYFbkdngbaUTMkfTvwDPAOmA60LzVMiEp2rvT5AjjEgBJlcCvgMmSbgP2Au6LiMfbWO924HaA6urqdu/POm9qXQPD+vXkuHFD0i7FzHaidp1Ajoi7IuLwiPgIsAJ4DVgsaThA8ueSNlZtIHsU0WpUMi3X1cB9wNHAKuBC4IsdaYTtHEvXbuJ3c5dw3mEjqSj3hWdmxay9VxMNS/7ci+z5ggeBx4HLkkUuAx5rY9VfAadIGpicOD4lmda63YHAWWTDoDfQAgTQqzONsfx6bPoCtrSEu4jMSkB7f+7VSHoFeAL4fESsBP4NOFnS68BJyWckVUu6EyAilpM9MTwted2UTGv1TeDmiGghGxLHATOB+7vcMuuymroMh4zcjf1275d2KWa2k7Vr6MmIOK6NacuAE9uYXgtcnvP5buDubWz3CznvN5I9crBuYM7C1byycDXfOuegtEsxs13AHcHWppq6DJXl4pyJI9Iuxcx2AYeBfUBTcwu/mL6AEw4YxsA+PdIux8x2AYeBfcAfXmtk6dpNHn7CrIQ4DOwDauozDOrTg+P3H5Z2KWa2izgM7H1Wrt/Mr19ZwuRDR9Cjwv88zEqF/2+393lixkI2N7e4i8isxDgM7H1q6jIcsEc/DhrRP+1SzGwXchjYe+YtWcv0+Ss5f9IoJD/a0qyUOAzsPTX1GcrLxOTDfG+BWalxGBgAzS3Bo/UNfHS/oQzrV5V2OWa2izkMDIA/v7GURas3+sSxWYlyGBiQPXHcv6qCE8f73gKzUuQwMNZsbOLp2Ys4e+IIqirL0y7HzFLgMDCemrmQjU0tXODnFpiVLIeBUVPXwN5D+3DongPSLsXMUuIwKHHvLlvPX99e7nsLzEqcw6DE1dRnkODjk0amXYqZpchhUMJaWoKa+gwf2mcIw3fzY6fNSpnDoIT99e3lZFZs8IljM3MYlLKaugx9e1Zw6kF7pF2KmaXMYVCi1m/ewlMzF3LGIXvQq4fvLTArdQ6DEvWr2YtYt7nZw0+YGeAwKFlT6zLsOagXR4wZlHYpZtYNOAxK0IKVG/jzG8s4f9Ioysp8b4GZOQxK0qMvNRCBu4jM7D0OgxITEdTUZThy7CD2HNQ77XLMrJtwGJSYl+av5M2l67jARwVmlsNhUGKm1mXoVVnOGROGp12KmXUjDoMSsrGpmSdfXsBpB+9B354VaZdjZt2Iw6CE/HrOYlZv3OITx2b2AQ6DElJTl2H4blUcs8/gtEsxs26mXWEg6QuSZkuaJekhSVWS7pH0lqTpyevQbax7maTXk9dlybSekp5Otnd1zrK3S5qUn6ZZriWrN/KH15dy3mEjKfe9BWa2lR2GgaSRwLVAdUQcDJQDFyWz/yUiDk1e09tYdxBwA3AUcCRwg6SBwKnA88AE4NPJshOB8oio73qzbGu/mN5Ac0twvkcoNbM2tPcsYgXQS1IT0BtY0M71TgWejYjlAJKeBU4DVibbqQRaf6Z+G/iHdm7XOiB7b0EDh+01gH2G9k27HDPrhnZ4ZBARDcCtwLvAQmBVRDyTzL5Z0gxJ/yGpZxurjwTm53zOJNOeBcYAfwG+L+kcoD4ithsykq6UVCuptrGxcUelW2L2gtXMXbzGJ47NbJva0000EJgMjAVGAH0kfQr4KnAAcAQwCPhye3caEVsi4pKIOAz4OTAF+K6k2yRNTcKhrfVuj4jqiKgeOnRoe3dX8qbWZehRUcbZE0akXYqZdVPtOYF8EvBWRDRGRBPwCHBsRCyMrE3A/5A9J7C1BmDPnM+jkmm5rgbuA44GVgEXAl/sWDNsWzZvaeHxlxdw8vjd2a13ZdrlmFk31Z4weBc4WlJvSQJOBOZIGg6QTDsXmNXGur8CTpE0MDnCOCWZRrLuQOAssmHQG2gBAvADefPkublLWL5usx9taWbb1Z5zBi8CU4F6YGayzu3AA5JmJtOGAP8KIKla0p3JusvJnhielrxuaj2ZnPgmcHNEtJANieOS7d2fl9YZNXUZhvbryXH7Dkm7FDPrxhQRadfQKdXV1VFbW5t2Gd3a8nWbOeo7v+bvjx3D1888MO1yzKwbkFQXEdVbT/cdyEXs8ekNNDX73gIz2zGHQRGrqW/goBH9OWCP/mmXYmbdnMOgSM1dtIaZDat84tjM2sVhUKRq6jNUlIlzJvreAjPbMYdBEdrS3MKjLzXwsQOGMbhvWzeGm5m9n8OgCP1x3lIa12zy8BNm1m4OgyI0tS7DwN6VnHDAsLRLMbMC4TAoMqvWN/HsK4uZfOhIelT4P6+ZtY+/LYrMkzMXsHlLi7uIzKxDHAZFpqYuw3679+Xgkb63wMzaz2FQRN5sXEv9uys5f9IosuMHmpm1j8OgiNTUZygTnHfYyLRLMbMC4zAoEi0twaP1DXxkv6EM61+VdjlmVmAcBkXihTeXsWDVRp84NrNOcRgUiZq6DP2qKjj5wN3TLsXMCpDDoAis3bSFX85axFkTRlBVWZ52OWZWgBwGReCpmQvZ0NTsEUrNrNMcBkWgpi7D2CF9mLTXgLRLMbMC5TAocPOXr+fFt5Zz/qSRvrfAzDrNYVDgHqlvQILzfBWRmXWBw6CARQQ19RmO3WcwIwf0SrscMytgDoMCNu3tFby7fL3vLTCzLnMYFLCaugx9epRz2sF7pF2KmRU4h0GBmrdkLU/MWMDphwynd4+KtMsxswLnMChAK9dv5vJ7p9G7RzlfOHm/tMsxsyLgn5QFpqm5hc8/WE/Dyg08dMXRPnFsZnnhMCgw//rkK/xp3jL+7wUTqB4zKO1yzKxIuJuogDzw4jvc+8I7XHHcWD5RvWfa5ZhZEXEYFIgX3ljGDY/N5vj9h/KV08enXY6ZFRmHQQF4d9l6rnqgjjFD+vD9iw+jvMzDTphZfrUrDCR9QdJsSbMkPSSpStJYSS9KmifpZ5J6bGPdrybLzJV0ajJtqKTnk+2dm7PsY5JG5KdpxWHNxiY+e+80AO68tJr+VZUpV2RmxWiHYSBpJHAtUB0RBwPlwEXAvwP/ERHjgBXAZ9tY98Bk2YOA04D/llQOXAz8P+BIYEqy7NnASxGxIA/tKgrNLcF1P53OW0vX8d9/N4kxQ/qkXZKZFan2dhNVAL0kVQC9gYXACcDUZP69wLltrDcZ+GlEbIqIt4B5ZAOgKdlOT6A52e4U4JbONqQY3fL0q/z21SXceM5BHLvPkLTLMbMitsMwiIgG4FbgXbIhsAqoA1ZGxJZksQwwso3VRwLzcz63Lvcg2aB4FvgOcDVwf0Ss71wzis/Uugw//sObXHrMaD519Oi0yzGzIteebqKBZL+4xwIjgD5ku3w6LSJWRcSZEVEN1ANnA1Ml3SFpqqRjtlHLlZJqJdU2NjZ2pYRure6d5XztkZl8aNxgrj/rwLTLMbMS0J5uopOAtyKiMSKagEeADwEDku4dgFFAQxvrNgC5F8S3tdz1wM1kzyM8D1wG3NhWIRFxe0RUR0T10KFD21F64WlYuYHP3V/HiAFV/PCSSVSW+4IvM9v52vNN8y5wtKTeyj5K60TgFeA54IJkmcuAx9pY93HgIkk9JY0F9gX+2jpT0r7AqIj4HdlzCC1AACU5xsK6TVu4/N5aNm1p4c7LjmBA7zYv0DIzy7v2nDN4keyJ4npgZrLO7cCXgX+SNA8YDNwFIOkcSTcl61KXEDQAAApCSURBVM4GHiYbHk8Dn4+I5pzN3wx8PXn/EHAVMA34XpdbVmBaWoIvPvwycxet5gcXH8a4YX3TLsnMSogiIu0aOqW6ujpqa2vTLiNvbntmLt//7Ty+ceZ4Lj9u77TLMbMiJakuOV/7Pu6Q7gaeeHkB3//tPD5ZPYrPfnhs2uWYWQlyGKRsRmYl//zzlzlizEC+fe7BZE/LmJntWg6DFC1evZEr7qtlSN+e/OhTh9OzojztksysRPl5BinZ2NTMlffVsmbjFmquOpYhfXumXZKZlTCHQQoigi/XzGBGwyp+/KnDGT+8f9olmVmJczdRCv77d2/w2PQF/PMp+3PKQXukXY6ZmcNgV3tm9iL+76/mMvnQEVx9/D5pl2NmBjgMdqk5C1cz5WfTmbjnAP79/Am+csjMug2HwS6ydO0mLr+3lv5Vldzx6cOpqvSVQ2bWffgE8i6waUszV/2kjmXrNvHzzx3LsP5VaZdkZvY+DoOdLCL4xqOzmPb2Cn5w8WEcMmq3tEsyM/sAdxPtZHc9/xY/r8tw7QnjOHuiH+9sZt2Tw2Anem7uEr7z1BxOO2gPppy0X9rlmJltk8NgJ5m3ZA3XPvgS++/Rn9sunEhZma8cMrPuy2GwE6xYt5nP3ltLz8oy7rysmt49fGrGzLo3f0vlWVNzC59/sJ6FKzfy0JVHMXJAST60zcwKjMMgz7795Cv8+Y1l3PqJiRw+elDa5ZiZtYu7ifLo/r+8w30vvMPnPrI3Fxw+Ku1yzMzazWGQJ3+et5QbH5/NCQcM40unHZB2OWZmHeIwyIO3l67jqgfq2XtIH7530aGU+8ohMyswDoMuWr2xicvvq6VMcNdlR9CvqjLtkszMOswnkLuguSW49qGXeHvpOu7/7FHsNbh32iWZmXWKw6AL/u2Xc/jd3EZuPu9gjtlncNrlmJl1mruJOunh2vnc8ce3uOyY0fzdUaPTLsfMrEscBp1Q+/Zyvv7oTD48bgjXn3Vg2uWYmXWZw6CDMivW87n76xg1sDc/vGQSFeX+KzSzwudvsg5Yt2kLl99by+bmFu64tJrdevvKITMrDg6DdmppCb7ws+m8tngN/3XJJMYN65t2SWZmeeMwaKfbnn2NZ15ZzDfOPJCP7jc07XLMzPLKYdAOj01v4L+em8dFR+zJZz40Ju1yzMzyzmGwAy/PX8mXps7gyDGDuGnywUgeasLMis8Ow0DS/pKm57xWS5oi6UZJDTnTz9jG+qdJmitpnqSv5Ex/QNIMSd/JmfYNSefmp2ldt2jVRq64r5ah/Xryo09NokeFs9PMitMO70COiLnAoQCSyoEG4FHgM8B/RMSt21o3Wf6HwMlABpgm6fFkvxsiYoKkZyXtBvQGjoqIf+1im/JiY1MzV95fy7pNW7jvs8cyuG/PtEsyM9tpOvpT90TgjYh4p53LHwnMi4g3I2Iz8FNgMtAE9JJUBlQCzcBNwA0drGeniAj+ZeoMZjas4j8vOowD9uifdklmZjtVR8PgIuChnM//mHT13C1pYBvLjwTm53zOACMjYg7QCNQDTwDjgLKIqN/eziVdKalWUm1jY2MHS2+/Hz43jydeXsC/nLo/Jx+4+07bj5lZd9HuMJDUAzgH+Hky6UfAPmS7kBYC3+3IjiNiSkQcGhHfBb4NXC/p65IelnTFNta5PSKqI6J66NCdc3nn07MWceszr3HuoSO46qP77JR9mJl1Nx05MjgdqI+IxQARsTgimiOiBbiDbJfQ1hqAPXM+j0qmvUfSZKAO6AvsExGfBC6QtMvHg569YBVf+Nl0Ju45gH87f4KvHDKzktGRMLiYnC4iScNz5p0HzGpjnWnAvpLGJkcWFwGP52yjEpgC3AL0AiKZVQ706EBtXda4ZhNX3FvLbr0quePTh1NVWb4rd29mlqp2hYGkPmSvCHokZ/ItkmZKmgF8DPhCsuwISU8BRMQW4B+BXwFzgIcjYnbONj4P3BsR64EZQG9JM4G6iFjZtaa136YtzfzDT+pYvn4zd1xazbD+Vbtq12Zm3YIiYsdLdUPV1dVRW1vb5e20Xjk0tS7DDy+ZxJkThu94JTOzAiWpLiKqt55e8ndR3fnHt5hal+G6E/d1EJhZySrpMHju1SV855dzOOOQPbjuxH3TLsfMLDUlGwavL17DtQ+9xIHD+3PrJyZSVuYrh8ysdJVkGKxYt5nL76ulZ2U5d1xaTe8eOxyVw8ysqJVcGDQ1t3D1A/UsXLmR2y89nBEDeqVdkplZ6kruJ/G3npjNC28u47ufmMikvdoaQcPMrPSU1JFBRDB2SF+uPn4fzj98VNrlmJl1GyV1ZCCJz354bNplmJl1OyV1ZGBmZm1zGJiZmcPAzMwcBmZmhsPAzMxwGJiZGQ4DMzPDYWBmZhTww20kNQLvdHL1IcDSPJaTpmJpS7G0A9yW7qpY2tLVdoyOiKFbTyzYMOgKSbVtPemnEBVLW4qlHeC2dFfF0pad1Q53E5mZmcPAzMxKNwxuT7uAPCqWthRLO8Bt6a6KpS07pR0lec7AzMzer1SPDMzMLIfDwMzMSisMJN0taYmkWWnX0hWS9pT0nKRXJM2WdF3aNXWWpCpJf5X0ctKWb6VdU1dJKpf0kqQn066lKyS9LWmmpOmSatOup7MkDZA0VdKrkuZIOibtmjpD0v7Jf4vW12pJU/K2/VI6ZyDpI8Ba4L6IODjtejpL0nBgeETUS+oH1AHnRsQrKZfWYZIE9ImItZIqgeeB6yLiLymX1mmS/gmoBvpHxFlp19NZkt4GqiOioG/UknQv8MeIuFNSD6B3RKxMu66ukFQONABHRURnb759n5I6MoiIPwDL066jqyJiYUTUJ+/XAHOAkelW1TmRtTb5WJm8CvYXiqRRwJnAnWnXYiBpN+AjwF0AEbG50IMgcSLwRr6CAEosDIqRpDHAYcCL6VbSeUm3ynRgCfBsRBRsW4D/BL4EtKRdSB4E8IykOklXpl1MJ40FGoH/Sbru7pTUJ+2i8uAi4KF8btBhUMAk9QVqgCkRsTrtejorIpoj4lBgFHCkpILswpN0FrAkIurSriVPPhwRk4DTgc8n3ayFpgKYBPwoIg4D1gFfSbekrkm6us4Bfp7P7ToMClTSv14DPBARj6RdTz4kh+/PAaelXUsnfQg4J+lr/ylwgqSfpFtS50VEQ/LnEuBR4Mh0K+qUDJDJOdqcSjYcCtnpQH1ELM7nRh0GBSg56XoXMCcibku7nq6QNFTSgOR9L+Bk4NV0q+qciPhqRIyKiDFkD+N/GxGfSrmsTpHUJ7k4gaRb5RSg4K7Ci4hFwHxJ+yeTTgQK7kKLrVxMnruIIHsIVTIkPQQcDwyRlAFuiIi70q2qUz4EfBqYmfS1A3wtIp5KsabOGg7cm1wdUQY8HBEFfUlmkdgdeDT7u4MK4MGIeDrdkjrtGuCBpHvlTeAzKdfTaUkwnwx8Lu/bLqVLS83MrG3uJjIzM4eBmZk5DMzMDIeBmZnhMDAzMxwGZmaGw8DMzID/DxD9UwetnKPdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kDic = {}\n",
        "for deep in range(1,10):\n",
        "  clf = RandomForestClassifier(max_depth=deep, random_state=0)\n",
        "  mask = k_vs_feature[2]  # referente à mascara para 3 sinais W4, W1 e W5\n",
        "  clf.fit(xtrain.iloc[:,mask],ytrain)  # values.ravel() converte de Coluna pra Array\n",
        "  #print(\"deep={} - score {}\".format(deep,clf.score(xtest.iloc[:,mask], ytest)))\n",
        "  kDic[deep] = clf.score(xtest.iloc[:,mask], ytest)\n",
        "graf = pd.DataFrame(kDic.values(),kDic.keys(),['Score']).plot(title='Random Forest\\nProfundidade vs Score')\n",
        "graf.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.1%}'.format(y))) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "k1HS268Y2nYn",
        "outputId": "22ead09d-b517-439c-e8c6-202a83701cf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "source": [
        "#Nearest Neighbors Classification"
      ],
      "metadata": {
        "id": "GOmirJTBnzJj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kDic= {}\n",
        "neigh = KNeighborsClassifier(n_neighbors=4)\n",
        "for idx, mask in enumerate(k_vs_feature):\n",
        "  neigh.fit(xtrain.iloc[:,mask],ytrain)  \n",
        "  kDic[idx+1] = neigh.score(xtest.iloc[:,mask], ytest)\n",
        "graf = pd.DataFrame(kDic.values(),kDic.keys(),['Score']).plot(title='KNN\\nAtributos vs Score')\n",
        "graf.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y))) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-_1gb28-oTwI",
        "outputId": "988fac0c-fe56-4075-cbf4-1103df55d483"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "kDic= {}\n",
        "for nb in range(1,7):\n",
        "  neigh = KNeighborsClassifier(n_neighbors=nb)\n",
        "  mask = k_vs_feature[2]  # referente à mascara para 3 sinais W4, W1 e W5\n",
        "  neigh.fit(xtrain.iloc[:,mask],ytrain) \n",
        "  #print(\"Neighbors={} - score {}\".format(nb, neigh.score(xtest.iloc[:,mask], ytest)))  \n",
        "  kDic[nb] = neigh.score(xtest.iloc[:,mask], ytest)\n",
        "graf = pd.DataFrame(kDic.values(),kDic.keys(),['Score']).plot(title='KNN\\nVizinho vs Score')\n",
        "graf.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.1%}'.format(y)))  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "t0p5ZlPH96FE",
        "outputId": "e851347f-b6cd-4654-9ad8-bb655e07dee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "# Plot the decision boundary. For that, we will assign a color to each\n",
        "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
        "import numpy as np\n",
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "cmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\", \"green\"])\n",
        "cmap_bold = [\"darkorange\", \"c\", \"darkblue\", \"darkgreen\"]\n",
        "\n",
        "mask = k_vs_feature[1]  # referente à mascara para 2 sinais W4 2 W1\n",
        "neigh.fit(xtrain.iloc[:,mask],ytrain)  # values.ravel() converte de Coluna pra Array\n",
        "h = 0.5 \n",
        "x_min, x_max = -80, 0\n",
        "y_min, y_max = -80, 0\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
        "Z = neigh.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "# Put the result into a color plot\n",
        "Z = Z.reshape(xx.shape)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
        "\n",
        "# Plot also the training points\n",
        "sns.scatterplot(\n",
        "    x=xtrain['W1'],\n",
        "    y=xtrain['W4'],\n",
        "    hue=ytrain,\n",
        "    palette=cmap_bold,\n",
        "    alpha=1.0,\n",
        "    edgecolor=\"black\",\n",
        ")\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "dOfMf3-u-15v",
        "outputId": "5ef9a381-ac3f-4709-8038-70bb6b2c872f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}