1.perceptron.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "uLqnnpRTWCRH"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/placement.csv\")\n",
        "df.head()"
      ],
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          "height": 206
        },
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        "outputId": "0719aec9-4da1-4eda-85dd-7f040941ec07"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  resume_score  placed\n",
              "0  8.14          6.52       1\n",
              "1  6.17          5.17       0\n",
              "2  8.27          8.86       1\n",
              "3  6.88          7.27       1\n",
              "4  7.52          7.30       1"
            ],
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              "      <th>cgpa</th>\n",
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              "      <td>8.86</td>\n",
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              "    <tr>\n",
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              "      <td>7.52</td>\n",
              "      <td>7.30</td>\n",
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        "sns.scatterplot(data=df, x=\"cgpa\", y=\"resume_score\", hue=\"placed\")"
      ],
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='cgpa', ylabel='resume_score'>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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osXN+05GURvxg36M8LkZIoiKEEEJEi5qS0D4uCkiiIoQQQkSLhMzQPi4KyK6fGOep91Fa7WRPZR3ueh/tUuJJSzBgMcqPXgghok7BMGV3j72YwOtUNEp/wbCQv3RNTQ1btmxpuL99+3ZWrlxJSkoK7dq1C/nrHSRXqxhW5/Eyf8t+7vxwBbVuZWGVXqvh7tM7c+WQApItBpUjFEIIcVS0OmUL8idXAxoaJysa5T9nPak8LsSWLl3KKaec0nD/3nvvBWDcuHG8/fbbIX+9g2TqJ4btqXBw07vLGpIUgHqfn//M2MTynRUqRiaEEKLFevwFRr8DidmN2xNzlPZWqqMyYsQI/H5/k1trJikgIyoxy+fz8/GSIry+QEOD8NzMzQxol0xyvIyqCCFE1OnxF+h2rrK7p6ZEWZNSMKxVRlLUJolKjPL4fGwurQ7aX1RRh6veF8aIhBBChJRWBx1OVDuKVidTPzHKoNMyqCA5aH/3LCsWQ+xl3kIIIWKLJCoxSqPRMKpvTtBk5K9ndiXRHBfmqIQQQoijI4lKDMtNMvPRTUMoTItvaEtLMPDy2AF0ybKqGJkQQrRtfn/g9YOxJFTfo6xRiWF6nZY+eUl8dPMQKms91Pv9JFviyLSa0Go1aocnhBBtTlycMpLtcDgwm80qR9O6HA4HcOh7bilJVNqADKuJDKtJ7TCEEKLN0+l0JCUlUVpaCoDFYkGjia0Pjn6/H4fDQWlpKUlJSeh0x7YeUhIVIYQQIoyysrIAGpKVWJWUlNTwvR4LSVSEEEKIMNJoNGRnZ5ORkYHH41E7nFYRFxd3zCMpB0miIoQQQqhAp9OF7GIey2TXjxBCCCEiliQqQgghhIhYkqgIIYQQImJJoiKEEEKIiCWLaYUQkcfrgepicNohzgSWNDAnqR2VEEIFkqgIISJL7QFY+T7MeQrcNUpbh5PgLy9AcntVQxMi5KqLwV4MNaWQlA8JWRCfqnZUEUUSFSFE5PD5YN2X8OODjdu3/wLvXgjXfAeJ2erEJkSo7d8E718KFTsOtbU/ES56FRJzVAsr0sgaFSFE5KguhtkTA/eVb4PyreGNR4jWUl0M713SOEkB2PErTP8HuGpUCSsSSaIihIgc9XVQuz94/77fwheLEK2pqggqdwbuWz8VasvCGk4kk0RFCBE5dEaIswTvT+4QvliEaE3VJcH7fF7w1IYvlggniYoQIji/X1k3Ei4JGTD4+sB9piTI7Bm+WIRoTckFwfvizGCwhi+WCCeLaYUQTdWUwv7NsHyK8umu/1jI6AHWzNZ9Xb0Rht4O5TtgwzeH2hMy4MrPwJbXuq8vRLgkZEHeYCha0rTvuFvAeuynDscKjd/v96sdREvZ7XZsNhtVVVUkJiaqHY4QsaGmFL67D9ZNbdzeYQRc9Ep43kAdFVBbCuXbwZICibnKLgiNpvVfW4hwqSqC7/4Om75TRi/1Jjj+ZiVZT8hQO7pWdTTXbxlRESISOSrAcUBZXGpKUpIDXVx4XnvvyqZJCsD22bBtNvS9vPVjsCQrt/Surf9aQqjFlgcXvqwsnPU4wJgICZlKkUPRQBIVISJN+XaYeivsmq/cNyTAyQ9A/yvA0sqFoNy1sGhy8P5FL0PnM5VRDiHEsTMlKjcRlCQqQkQS+1545/zG2xbdNfDjP8BkhQHjWnf6w1evfLILxl2rrFmJVa4a8PvUv3DUVUH1Xlj/jfJv3u0cZcdTjE8HCBGIJCpCRJKyTcFrK8x6QhnNaM2KlcZE6HUp7FoYuL/nhWBObr3XV0t1CexZCotfBa8b+o6BjqeBLTf8sdRVwKJXYfYTh9rmPavEc8H/ZJGlaHMkUWmj6r0+SqtdONxeTHFaMqwmDHrZra66fauC99WUgKeudV9fo4GuZ8H855smTAkZ0P9K0MXY20Z1CXx1G2z58VDbzvmQ2gmu/ir8O40qdjROUg7aOhPWf6ts35ZFxaINibF3HHEkDtS4+GTpbibP3ordWY8pTsvY4wu46aRCMhJlEZeqUgqD9xmtyvbd1mbLg2umweLXYNX7ylRPr0tg2O2Q1Ezth2i177fGScpBB7bAqo/hhLtBqwtPLD4fLH0zeP/Cl6D7qNbfJi5EBJFEpY1x1Xt5a94OXpy1paHN6fHx+tztFNvreOKC3tgsBhUjbOOy+4HJBs6qpn3H36LsCAiHpHw47UEYcoty35IaniQp3OpdsPSN4P0r3lVqyIQrMfB7my+d7qxUHiNEGyJj/W1Mqd3Fa79uC9g3bfU+9te6wxyRaCQxF67+uumiyV4Xw+Abw7dFGZTXSsxRbrGYpMDvlXfrm+kPc1Kgi4MeFwTvLzwVjLawhdMiPq9Si6emNLxVjUXMkhGVNqaqzoOrPvibR3FlHR3TE8IYkWhEq4XsvnDjbLDvUT5BJ7VXEhdzkrqxxaI4k7KTavOMwP29R7f+lvA/an8CJLWDyl2N2/UmOPnvYIwPbzxHo3I3rP4YVn0IGq3yb9vzQnUWJYuYIYlKG2M2ND/XbjOH8RO7CEyjUd7Y5c09PHIHQu5g2POHUuaJuTBwXPgXD9vy4JpvYc7TykXf64bCU+DMf0FyM2uY1FZVBFPOUxYDHzTj/2DFe3DVF627W03ENFWnfrxeLw8++CAdOnTAbDbTsWNHHnvsMaK4qn/ES403MLh94O2leclmMqyymFa0MYnZcNm7MOp5yOqjVMM99UG4broysqGGpAI452m4cwXcvQYunQJZvUAfoR8kfD747fPGScpBZeth689hD0nEDlVHVJ566ikmT57MlClT6NmzJ0uXLuXaa6/FZrNx5513qhlazEqyGHhmdD/GvbmY7fsPHSOenmDkzXGDybRJoiLaoMRsZfSk23ng8yjrLDZOU6oEFwyHnP7hH+GKM0fPIYx15bD6o+D9K96D7n9Rv5CeiEqqJirz58/n/PPP59xzzwWgffv2fPjhhyxevFjNsGJeuxQLH900hF3lDjaXVFOQGk9hWjzZSeaGx3h9fvbXuPD5/ViNcSSYZJYwqjjKlZorOgMkpKsdTfQwJsLOefDBJeD1KG0L/6dMA13zbfPbx9s0DWibeY/Q6qX2i2gxVa8+w4YN49VXX2XTpk106dKFVatWMXfuXCZNmhTw8S6XC5fL1XDfbreHK9SYk5loIjPRxOD2Tc9sKbE7+XxZEW/P34Hd6eHEzmncN7Ib7VMtGPRhqichWqauCopXwE+PQuk6SC5QzgnqcDLEh3lRaDSqKYaPxhxKUg6y74Fv71WmYMwRvuvmWPl8UF2sHN2gN4Il/c8X8ManwqDr4Nu7A/cPvlGpAyREC6iaqDzwwAPY7Xa6deuGTqfD6/Xy+OOPc+WVVwZ8/MSJE3n00UfDHGXbUlbt5Pb3l7NkZ0VD24/rSvll036m3jac7tlROHTrKIfa/coOGpMN4tNj81A9rxc2fQ9f3nyorWwjfHYtnHifUrjMKDu6mlW2MfhZR9tmKSdax3Ki4iiHjd/BT48o9Vy0euh5EZz+8J9PQ3UZqUyR7V3RuL39SdDu+FYLWcQ+VROVTz75hPfff58PPviAnj17snLlSu6++25ycnIYN25ck8dPmDCBe++9t+G+3W4nPz8/nCHHvK1ltY2SlINc9T6e+G49L10xgMRo2hlUtUcpj75t1qG2guFw0avRM/9/pGqK4YcHAvfNm6SUv5dEpXl1lc33e2O4zpDfDxu/V/5eDvLVw2+fwP5NcOWnzR+KmJgDl38IuxbA8img0Snl/nMHyvlE4piomqjcd999PPDAA1x++eUA9O7dm507dzJx4sSAiYrRaMRojNHCUxFi+tp9QfvmbtlPtas+ehKVukr49q7GSQooaxC+uEnZ6RHuGhmtyVGuHGgXiM8LFTshpUN4Y4o2Wb2D91mzYnsxaHUxzAwyYl28Uvn9+bPTmxOzoddFyugKgCGCa76IqKHq9mSHw4FW2zgEnU6HT6oZqsZmCp6EmPQ6tNG0Hq62DDYHOMMFlGSldn9442ltf1a11mBuvl8oF+KeFwXuGzkRrNnhjSec3LXKwZfBNHdg5h8Z4iVJESGjaqIyatQoHn/8caZNm8aOHTv48ssvmTRpEhdeeKGaYbVp5/QO/kZ82eA8UuOj6Bwg158stnZWhiWMsLGkQnq3wH2mJGXnimieJQXOfhJO/+eh0baM7jD2c+h4WmzvXNEZlFsw8vsjVKLq1M8LL7zAgw8+yK233kppaSk5OTncfPPNPPTQQ2qG1aZl2Uzcf1ZXnvphY6P2junx3HRSx+ja9WP6k0WPpsCF76JWQgZc/Dq8dU7jJE0XB6OnQIKsEzgiCZnKSdF9LlWmzOJMygLsWBefAX0uhxXvNO0zWiGzZ/hjEgLQ+KO4DKzdbsdms1FVVUViYgzPHYdZVZ2bvZVOPl26m7JqF6P65tAnz0aWLcqmDuqqYOotyi6GP+owAi59K/Z2//h8SinzzdOV6a2s3sohd7Z80EfRaJhQh30vfHINFC061Ga0wtgvIGdA+I8TEDHraK7fkqiI2GbfC9P+2jhZ6XS6Ui5dztIRoqmaMrAXwb7flDU56d2UHT3aKBpNFRHvaK7fkh6L2JaYAxe8DI4ycFYplUfj0+UkYiGCSUhXbjn91Y5ECEASFdEWmG2xXaRLCCFimKq7foQQQgghmiOJihBCCCEiliQqQgghhIhYskZFiDCpdLgpr3XjcHuxmeNItxoxxclOCiGEaI4kKkKEQVGFg/s/W828rQcAMOi0XDO8PTeeWEi6Vc6vEkKIYCRREaKVlVY7uWHKUjbsq25oc3t9vPrLNox6LXec2im6Kv6KtqGmBPZvgfVfK0Xfel6olNGXrf0izCRREaKVFVc6GyUph3tj7nYuG5xPXrIlzFEJ0Qx7MXx2LexacKjtl6dhxAQ4/mYwx9jxEyKiyWJaIVrZjgO1Qfscbi+1Lm8YoxHiT/h88NunjZOUg2ZPhIqdx/78VXugbAOU7wBXzbE9n4h5MqIiRCvLSQp+RpJBp8VskGkfEUFqS2HxK8H7l0+BnH4te25HOWyYBjMfgdr9oNFC97/Amf+CpPyWPaeIeTKiIkQry082k2MzBey7aEAu6VY5LLBN8Xmh3qV2FMH5feC0B++v3a+Mihz18/ph8wz4+nblOQ6+1rqp8MFoqN7XonBF7JNERYhWlmUz8+71x1OQ2ngdyhndM7nnjC6Y42Rgs01wlEPRUvjqNvjkKlj1kXLSdaQx2aDjacH7e10M2hZcOqqL4adHAveVroPy7Uf/nKJNkHdIIcKgY0YCn9w8lLJqF5UON9k2M6kJBpIsMprSJtRVwLznYN6zh9o2TYfk9jDu28ia9jDEw4gHYNP3UO9s3JfaEfIGt+x5PQ4lWQlm73IoGNqy5xYxTUZUhAiTzEQTvXJtnNA5nY4ZCZKkhJrXA5W7oXgVlK6HmjK1IzqkqqhxknJQxQ6Y+2zThEBtqZ3gplnQ+QzQaCDOAsfdBFd9Bbbclj2nzgj6wFOgANgiKFkTEUVGVIQQ0c9RDms+h5mPguv3reAZPeCSNyC9u3KxVdOaL4L3rXofTry35QlAa9DplX+/i98Elx3QQnwq6I+hOGFCOvS/Gpa82rTPEN/yBboi5smIihAtVOuqp6LWTb23BQsLRWjtnAff/e1QkgLKuoe3zoGq3erFdZDbEbyv3gX4Q/M6TruStPlD9HymRLDlgS3n2JIUUEZTTrwH2p/UuN2YCFd9CdacY3t+EbNkREWIo1Re62J9cTWv/LKVAzVuRnTNUIq2JZnRalX+5N4W1ZQEX6RZVwG7FkJSu7CG1ETP82Hxy4H7upylLGA9FtUlsHsRLHoZPHXQ+xLo8ZfIm05JzIFL3wL7XihZCwkZkNZFadfKNn0RmCQqQhyFSoeb/83ayutzD+1QWLvXzrsLdvDFrcPolGFVMbo2yuuBA1uC9+9eDH1Ghy+eQFI7QYcRsH124/Y4C5z2sFKivqVqSuHrO2HzD4fa9i6Hhf+Da79XP0n7o/g05ZbdR+1IRJSQqR8hjkKp3dUoSTnI7qznX9+ux17nUSGqNk6rV86gCSazR/hiCSYhAy56Bc75j7JzJj4d+l4JN/+iJDHHonR94yTloKoiWPyaksgJEcVkREWIozBnU/CdJHM2l1FV5yHRHBfGiATWLDj5AfjmjqZ9cWboeGr4YwrEmgWDb4Ae5ytF38w2ZUTlWPh8SqXYYFZ/BENuhcTsY3sdIVQkiYoQR6G5JYqhWr8oWqDbOXBgEyx4Sal2Csr0wmUfQGKeurEdTqNRRldCqblfvLb6S+l2gKsKtHHK74GIapKoiBardLixO+vRAjZLHFZT7I8knNwljSe+C9x3Yuc0bDKaoo74NDj5fhh0vTLlEWdRRjCs2S2rohottFoYcDWsDbL9ufdosKSGNyY11buhfBv8+gzs+AUsaXDC3dDh5NAniCJsJFERR63e62NTSQ2PfL2GxTsq0Gjg1G4Z/N853SlMT1A7vFaVmWhi3LACpsxvfIJsglHPg+f1kGkfNRmtyi2lg9qRhFdmD6Xk/daZjdsTc2DIzaBvprBgTSk4DihbpC0pkJDV/OMjXelaeONM8LqV+9X74PMboM/lcNZE5XsUUUfj90fv2KDdbsdms1FVVUViYqLa4bQZ28pqOOf5X3F6GtcPSYk38PVtw8lLOcZ59whXXuPmtz2VvPLLNspr3ZzcJZ0rjm9HfrJFticLdVTvg53zYdFkZXtyr0uUM3maK81fugE+u1apNwNK0bVT/g/6jonOC7qjHN6/FPYsDdx/yzzI6hXemERQR3P9lhEVcVScHi+vzNnaJEkBKK91892aYm44oTCmL9gpCQZO7prBgIJkPF4fVpOeOJ3UgBAqsmZBr4uUhcM+L5iTm5/yqtwNb5+jjKYc5K6F6f8P4jOgz6WtH3OoOauCJykA22ZLohKlYnjyVrQGe52HeVsPBO3/eUMpDk99GCNSj9UUR0q8UZIUETnMSUqp+z9bl7NnaeMk5XA/P6YUkIs2Gk3zRyUca2VdoRpJVMRRidNpSYkPPoednmDEIBduISJb0bLgfZU7wesKXyyhYk6BziOD9xeeEr5YREhJoiL+1IEaF5v2VbNw6wGq6tzcdFJh0MfecnJHDHr5tRIiojVXBM+apWzrjTamRBj5RODtyGc8BgmZ4Y9JhESL16jU19cze/Zstm7dyhVXXIHVamXv3r0kJiaSkBDbOz/akqIKB7d/sJyVu6sa2l4c059LBuby2bI9DW1DC1O5/6yubNhnZ9bGUnrnJWGJ0+Fw19M500pOklmN8IVou7xe8LlAb246JVIwHAwJ4K5p+nUn/k1JVqJRake4cRZs/B42TVe+j+NuhOQOYJLjLaJVi3b97Ny5k7POOotdu3bhcrnYtGkThYWF3HXXXbhcLl5+OcjhWyEmu35aV3mti+unLGXFrspG7VoNPHFhb/rk2fh1835yk0xYTXHc9O4yXPWHFtl2zbTywNnd+MfU35hy3XFyDo4Q4eCuhcpdsPQt5QykgmG/7wBqd+jgP58XilfCh2OUQx0BNFo4/mY44a+QkK5a+CHjcYDWADrZMxKJjub63aJE5YILLsBqtfLGG2+QmprKqlWrKCwsZPbs2dx4441s3ry5xcEfDUlUWtfmkmrO+O8vQft//uvJFKYnUFTu4LRJcxolKQeN6pON2aBn+a4KPrxxCOlWWdAmRKupd8HG75Rtx4e/tRvilQMKs/seavP7obpYSVTctUrdlfgMMMqIuGh9R3P9btFigl9//ZV//OMfGAyNF1W2b9+ePXv2BPkqEW3szuYPM6t2Krt7VhdVBkxSAH5Yu48RXdPZUlpDeW0ULtATIprUlMCXtzQtne+uhS9vhprDzqrSaJTkJKc/tD8BUgolSRERqUWJis/nw+v1NmkvKirCapXh/ViRZA6+u0ejoaEKa2l18ATE4/U3vGfWBai9IoQIoQNbod4ZuK90PdQFLy0gRKRqUaJy5pln8uyzzzbc12g01NTU8PDDD3POOeeEKjahsrQEAyO6BJ6rPrtXFmkJSiLTv11y0OfISzZT6XATp9OQEh+FOwmEiCYHS8cH42v6AVOISNeiROU///kP8+bNo0ePHjidTq644oqGaZ+nnnoq1DEKldgsBiZe3JuRPTMbNg1oNXBen2weHtWz4RDC3CQzgwsCJyu3jujIB4t3ccMJhaQnyPqUmOI4oBwAaC8Gn4yWRYS0zsqi2EASc5SKtUJEmRaf9VNfX8/HH3/MqlWrqKmpYcCAAVx55ZWYzeHbhiqLacOj2ulhf42bGqcHqymOtAQjCabGK+n3VdXx8pytfLRkN06Pj4JUC7ec3JEd+2vJTDRxfr8cUiVRiQ2uGti3Gmb8H+xdAfHpMPQO6HMZWKVWhapcNbDgRZg9sXG7RgNjPoHOZzRfvVWIMGnVXT8ej4du3brx7bff0r1792MK9FhJohJZ3PU+ympceLw+tIBep0Gj0ZBpNcXk2T+l1U7Kql1UOTxk2kykxhtIskTxybNHavNP8P7FTdu7ngfnPw+W1PDHJA5xlEPRUpjzpDLild0XTvl/kNZF2f0j2jZXDdSWQtUe5fchIROs2X9+7EKIteqhhHFxcTidQRZriTbNoNeSG2OF3fx+P06PjzidBr3u0B/yltIabpiyhB0HHA1tZ/XM4tHze5KZaFIj1PCo3gff/S1w38ZvwT5BEhW1WVKgy5mQN0jZrmyIV6q2ClFTBnOfgUWvgP/36dqEDLj8Q2X3lzYyjz9pUQp122238dRTT1Ff3zYOnwsHv99PWbWLfVVOnB5Z8BYJiiocvDV/Bze+s5QHvviN1UWVVNW52VdVx1VvLGqUpICyFfvFn7fE9s/PVQ0V24P3FzVzeq0IL0sKJGZLkiIUfj+s/woWTj6UpADUlMI7f1FG3yJUi0r2LVmyhJkzZzJjxgx69+5NfHzj4cQvvvgiJMG1FSV2Jz+s2cdb87ZT6/JySrd0xo/oSLuUeHQxOGUSDbaV1XDJywsorz20i+KzZUX83zndGd4pleKqwKOKnyzdzU0nFZKfYglXqOGljVPWOASbMTbbwhuPEOLIVO+DX/4TuM9dCzvmQnJBeGM6Qi1KVJKSkrj44gBz1OKolVU7ueujFSzcVt7Q9snSIqatLubr20+gY4YUYAq3aqeHx75d1yhJOeiJ79fz9e0nBL1Wu+p91MXyiIolVTmhdtMPTft0ccrwsRAi8vg8SiXiYErWhi+Wo9SiROWtt94KdRxt1pbSmkZJykG1bi/P/LiRpy/pS7xRzqoIpwqHm9mbygL2+f2wYmcFndIT2Fza9EC3eIMOiyEy53lDwmSFs56EkjWNh4o1WrjkTUiI0sPshIh1OqNyOGOwqdu8QeGN5ygc0xWwrKyMjRs3AtC1a1fS02PgIKsw+2ZV8Az3x3Ul2J0eSVTCzOcPPrMB4Pb6KEyPD5io3HBiBzJi/TyjlA5w3QzYswy2/gzJ7aHbeWDLhbgYXkgsRDSzZsJpD8Jn1zXts6RC3uDwx3SEWnQFrK2t5Y477uCdd97B93uhJ51Ox9VXX80LL7yAxRKj8/OtwGwIvp7ZqNchK1TAXe/lQK0bvx+sJn1DobnWkmiKo0+ejdVFVQH7T+ycxsieWVQ6PCzaroyGGXRarh5WwNgh7THoY3hE5SBbrnLr8Re1IzkyPh/UliiVWfUmiE9TOyIhwq/wFDhrIvz8OLh//6CV2RMufhOS8tWNrRktSlTuvfde5syZwzfffMPw4cMBmDt3LnfeeSd//etfmTx5ckiDjGUX9s/jjbk7AvZdNjiPlPg2UJejGXsr63jt1218vGQ3To+Xkzqn88A53eiYlkCcvnX2/afEG/jXBb24ZPIC3N7GFVcvHZhHhtVEcryBV64ayIFaNw63F5s5jvQEA2aDjH5FnJoSWP0pzH9O2eGQ1QfO/BfkDFCmsoRoKywpMOgGZQTUUQ56I1jSICGyZ0NaVJk2LS2Nzz77jBEjRjRqnzVrFqNHj6asLPD8fqi1RsG3qjoPB2pcFFc5STTpSbcaybK1Xm2QCoebF3/ewhtzG88bFqRa+ODGITFXl+Ro7KtycuXrC9laVtuo3ajX8s0dJ9Als/UuMm6vl537Hbw4awuLtpWTmmDglpM7MrRjKmlSYTd6OMqVui9rPm/ad9n70P288MckhGjdgm8ADoeDzMympbIzMjJwOBwBviI6lFU7eXzaeqau3NvQlmMz8eY1g+maZUXTCqWnky0Gbj+lE+f2zubdhTuodNRzfr8cju+QQnYbTlIAVu6ubJKkgLKz5r8/buLpS/qQ0ErTQAadjs6ZViZe2JtqVz1xOm3kj275/VC1G/auhNJ1kNVbqUpqy1M7MvXUlAROUgB+uB9yByq1RoQQEatFicrQoUN5+OGHeeeddzCZlMVzdXV1PProowwdOjSkAYaLu97LW/N2NEpSAPZWORnz2kKm3XkiOa2UOCTHG0iON9An34bPp1R4bet8Pj/frt4btP+XTWVUu+pbLVE5yGLUY4mWxcwla2HKeVBXcagtPg3GTYOMburFpabiVcH7qorAZQckUREikrXoHfi5555j5MiR5OXl0bdvXwBWrVqFyWRi+vTpIQ0wXMqqXUyZvyNgX4XDw6aS6lZLVA7Sa7UtrBUce7RaDSnNnJtjNcWhlcPVDrEXw0dXNE5SAGr3w6fjYNw3Sqnstsb0JwXodK2b6Aohjl2LLou9evVi8+bNTJw4kX79+tGvXz+efPJJNm/eTM+ePUMdY1i46n3UuoMX6tq2v+kUhGhdlw0Ovgr9mmHtSZe1Ioc49kPlzsB9ZRuU/rYoo4eyyyeQwlPALOcSCRHpWjymbbFYuPHGG0MZi6rMcTqSLXFUODwB+7tlye6AcMtLsXDvGV2Y9OOmRu2DC5K5cEBuTJ7I3GLuP0mkPW30IFFrNox+Fz4aA77DziZLzIHzJknJfyGiQIsSlYkTJ5KZmcl11zUuHPPmm29SVlbG/fffH5Lgwikj0cTtp3bmsW/XNenLSzZTmCbHo4ebzRzHuGEFjOyZyTer9lLtrOfsXtkUZsSTYT2ywmJen589lXXM2lDKil0V9Mq1cUaPTHKTzI1OQ456CZnKyae+AKOCeqOyLbEt0hugw0lw22LY+B2Ub1fu5w1q24uMhYgiLdqe3L59ez744AOGDRvWqH3RokVcfvnlbN/ezOmqIRTq7ckHaly8s2Anr/yyFadHqZ8xoCCJ/47uR0GqJCrRaHVRJZe/uhDHYdN6pjgt798whAHtklplJ5cqXDUw61/Kyah/dNLf4cR7Ia5t7yITImQc5eBxKEdHxGeALkoW3EeQo7l+tyhRMZlMrF+/ng4dOjRq37ZtGz169MDpDM8wc2vUUXHVeym1u7DXeTDF6Uj5fUeOAI9X+bepdtZjitORmmBo9Sqxx6LE7uTiyfMpqqhr0pdhNfL17cNbtUZO2NWWwepPYO4kZRFtQiac/IBSPVYqsQpx7NwOZXfd9P8HRYvBlATH3QSDrpNt7kep1euo5OfnM2/evCaJyrx588jJyWnJU0YMo15HfoocAfBHB2pdfLa0iBd+3kKNqx6NBk7vnsEjf+kVsUXpymvdAZMUgNJqF/tr3LGVqMSnw/HjoecFUO9Wpnys2RAro0ZCqK14Fbx9Dvh/r1jtrIRf/g0758KlU9rmzrowaFGicuONN3L33Xfj8Xg49dRTAZg5cyZ///vf+etf/xrSAIX6vF4fU1fsZeL3Gxra/H74cV0pRRVLeOe640m3Gqn3+iiucrJ0ZwU79tfQr10y3bKsZKuUDHj+UP7+j/5YHj8maLWQmKt2FKHnrgWNTg49FOqpLYPv7zuUpBxu53yo2CGJSitpUaJy3333ceDAAW699VbcbjegTAfdf//9TJgwIaQBCvWVVLt4fubmgH3ri6spqnCQEm9gVVEVY19fRJ3n0HqQHJuJD24cQnsVFiOnxBswx+kaxXOQUa8lQ7Y3R76qPbD9F1j9EejNcPzNkNkr4s8mETHIVQP7fgvev20O5B8XvnjakBYlKhqNhqeeeooHH3yQ9evXYzab6dy5M0ajvPHHIoe7nqq6wNu2ATb+Xgzv+ilLmiQFe6uc3P/5Kp69vD9Ojw/d74XcEkytv/gsw2rk/rO78sjXTXdy3XVaZ9Ks8vsa0aqK4J3z4cCWQ22bvodeF8NZT0myIsJLqwedAbzuwP1tdWddGBzT/syEhAQGDx5Mu3bt+P7771m/fn2o4hIRxKjXYWhmK2+uzUxRhYPKIDVoFm2vYNO+ak75z2xGPD2Luz9ewc4DrV9Az6DXcUG/XF4fN4huWVbidBo6ZyQweewAxhzXDlOcrtVjEC3krYdl7zROUg5a8zns39S0PRy8HqjcDXtXQPFqsO9V5kFF7LOkQZ/LA/dptFA4IqzhtCUt+lg7evRoTjrpJG6//Xbq6uoYNGgQO3bswO/389FHH3HxxReHOk6horQEAxcPyOXDJbub9CVb4uiYkcCaPVXNPkeNSxlp8fnhp/Wl/Lanii9uHd7qC3GTLAZO755J//wk3PU+4vRaOf04GtTuhxXvBO9f+ha0G6qsyQkXp12pxfLdfb+fEYSyWPmSN5W6LDrZHRjTDGY4+e+wewHsP2wqXKOBC18Ga5Z6scW4Fv2V//LLL5x44okAfPnll/j9fiorK3n++ef517/+FdIAI1G108OBGheu+uAl92OJ2aDnrtO7cEKnxuXG0xOMvH/D8WTbTBSmJwT9+kSzHt8fPnWW2F0s2nagVeINJDXBSHaSWZKUqOEDX/DpRrwu5THhVLIWvrz5UJICUF2sTE9V7gpvLEIdSflw9ddwxadw3M1w+iNw2xLodi4YpNZWa2nRiEpVVRUpKcp83A8//MDFF1+MxWLh3HPP5b777gtpgJGkotbNumI7L83aQondxXHtk7nhxELyU80YdLE9jZBlM/H8mP6U2l1s219LeoKRvGQzWTYTGo2GtAQD5/fL4auVTU88vvmkjnwcYDRm5voSLuyfGztF10ToWFKh54Ww+LXA/f2vUtYMhEtdFcx6PHCf1w0rPoBT/xHeER6hjsQc5dblTLUjaTNaXEdlwYIFpKSk8MMPP/DRRx8BUFFRgckUm9sHq50e3pq3ned/PjRnvrWshs+X7+GTm4fQr12yitGFR0q8kZR4I92ymxbnSbIY+L9zu9MpI4HXf91OVZ2HvGQzN59cSIndxdwtTQ/Fy022SJIiAtMbYejtsPZLZRrocHnHQXaf8MbjcTS/LqZ4JXidoJUaTEKEWosSlbvvvpsrr7yShIQECgoKGDFiBKBMCfXu3TuU8UWM/TVuXpjVdGGf2+tjwpe/8e71x7f5aYUMq4lbT+7IJQPy8Pj8GHValu2s4MGpawM+/pKBctaKaEZye7jhZ1jyBqybqhwBMPhGZZg93OsB4syQ0hFqSgL3Z/YCXdv++xeitbQoUbn11ls5/vjj2bVrF2eccQba34c7CwsLY3aNyqrdlUEX968vrqaqztPmExUAnU5L9mELZAd3SOac3ll899u+hjatBp68uA85EVrRVkSQ5AI47UEYeqtS8E2tLcnmJDhlAkwZ1bRPq4cBY5VDIYUQIdfiSd6BAwcycODARm3nnntuo/uJiYmsXLmSwsLClr5MxND+yRSFTGAElm418a8LenPHqZ1ZtP0ACQY9gzukkJZgJN4oB3mJI6CLi4wdFVl94Lz/Kue8eH4/msGSAhe/CbZ26sYmRAxr1StFC847jFh98mxoNcr22j/qnWsjyRK5h/OFit/vp8TuwuGux/D7Nt8jqUWSEm8gJd5A9wBrW4SIGuYk6HsldDodqkuUE3PjM5QkSkZThGg18pH2CKVbjUw4pzuPT2tc1M4cp+PJi3uTEh/b0z6VDjc/byjlqR82UGJ3YdRruXhAHnee1qlFB/uVVjspr3HjrPeRGm8g3XpkSY8QqoozQlI75SaECAtJVI5QvFHP6EH5DCxI5rVftlFc5WRIYQqXD25HXnJsr7Xw+fzMWFfC3z9b3dDmqvfxweJdbC6tZvLYgUe8Psfv97NxXzW3vLeMHQccgHLuzu2ndOLKIe1iPuETQghxdFRNVNq3b8/OnTubtN9666289NJLKkTUPJs5jgHtkvnvZX1xenzEG/XENVNaPlaUVDt5+oeNAfuW7KiguLLuiBOVPZV1XPbqwkZnB7nqfTzz4yayk0xcMjA/JDELIYSIDa16lf2zGhlLliyhuLi44fbjjz8CcOmll7ZmWMfMFKcnyWJoE0kKQK2rnrIaV9D+dcX2oH1/tHxnRdADDifN2ESJ3XnU8QkhhIhdqi6mTU9vvNXwySefpGPHjpx88skBH+9yuXC5Dl0w7fYjv0CKljPodei0GryBVhIDGYlHXuRvdTNnAu2tcuLxhrksuhBCiIh2TEMCbrebjRs3Ul9fH7D/+++/Jzc394if67333uO6664LOhIzceJEbDZbwy0/X6YJwiE13sA5vQJvD00w6umSEfycnz/qlWML2peVaGozo1RCCCGOTIuuCg6Hg+uvvx6LxULPnj3ZtUs5kOuOO+7gySefbHjcCSecgNF4ZGsXpk6dSmVlJddcc03Qx0yYMIGqqqqG2+7dTc+PEaEXb9Qz4Zzu9Mi2Nm436Jhy3WAyj2JEZVD7ZBJNgQfy7j69MxlWWUwrhBDikBZN/UyYMIFVq1Yxe/ZszjrrrIb2008/nUceeYQHHnjgqJ/zjTfe4OyzzyYnJyfoY4xG4xEnPiI0yqqd1Pv8WE163rnuOHaW17G6qJKcJDM9cxLJSjShP4pRkBybmQ9vHMLN7y2jqEIpmhWn03DzSYWc0SNTzv4RQgjRSIsSlalTp/Lxxx8zZMiQRheWnj17snXr1qN+vp07d/LTTz/xxRdftCQc0QrKql3MXF/CK79sY3+1i8Htk/nryK70zElkYEHLD2DUajX0zLXx+fhhHKhx4fT4SLMaSUswYDHIbvmQ8fuhuhjctaAzQEKGcl6NEEJEmRZdGcrKysjIyGjSXltb26JPxG+99RYZGRlNSvALdVTUuvnnN2v5ZnVxQ9vPG8uYs3k/H980hEHtU475NTITTUc1ZSSOgqMcNn4PMx9VDtHTGaDvFTDifuV4eiGEiCItWqMyaNAgpk2b1nD/YHLy+uuvM3To0KN6Lp/Px1tvvcW4cePQ6+UTdSTYZ3c2SlIO8vr8PPjVGg40s1VZqMzngw3fwle3Hjrp1+uG5W/DZ9dBTZmq4QkhxNFqUWbwxBNPcPbZZ7Nu3Trq6+t57rnnWLduHfPnz2fOnDlH9Vw//fQTu3bt4rrrrmtJKKIVLNp+IGjf+uJq7M56UuWk6MhUXayMpATi94Lj95+tJRW0ssNKCBH5WvROdcIJJ7By5Urq6+vp3bs3M2bMICMjgwULFjQ5UfnPnHnmmfj9frp06dKSUEQriG9mrYhGAzqtLHiNWO4aqN3fuM2cDJe9Bx1Phc+uhSnnwYIXoKpInRiFEOIotHiupWPHjrz22muhjEVEiMHtU4KeFH1qtwyS28BJ0VFLb1RO8vV5D7Wd91+Y9TiUHnag5o8PwbK3Ydw3YMsLe5hCCHGkjmnst7S0lDVr1rB69epGNxHdMhKNPH5h76btViMPntsDq0kSlYgVnw49Ljh0P6s3VO5qnKQcVL4N1n2lrGsRQogI1aIRlWXLljFu3DjWr1/fpEy+RqPB6/UG+UoRDSwGPaP6ZDOgXRIfL9nNnkonp3fPYFjHVHKTLWqHJ5pjiIcz/gn7N8O+1dDhJNj4XfDHr/wA+lwG8Wnhi1EIIY5CixKV6667ji5duvDGG2+QmSlFumJRgimOrllxPDSqJ/U+H3pZeHnk3LVQV6UsVo3PCP+iVVseXPkZVO5UYileFfyxWh0gf79CiMjVokRl27ZtfP7553Tq1CnU8YgIJEnKEfLWQ/lWmD0RtswEoxWOu0kZsUjMDm8s1kzlBlBXATvmBn7cwGshPjV8cQkhxFFq0RXotNNOY9WqZj6lCdEWHdgMr54Ma78Elx3se+Cnh+HTa6C6RL24CoZBuwD1jbL7QpeR4Y9HCCGOQotGVF5//XXGjRvHmjVr6NWrF3FxjRdX/uUvfwlJcEJEDWc1/PQIeOqa9u1eCPs3HhrhCDdrFlz6FuxeDEteB78PBl4DBcOlUq0QIuK1KFFZsGAB8+bN4/vvv2/SJ4tpRZvkqoItPwbvXztVWdiqFms29DgfOp4G+JVpKSGEiAItmvq54447GDt2LMXFxfh8vkY3SVJEm6TRgr6ZQ/9MieGLpTnGBElShBBRpUWJyoEDB7jnnnvIzFRpKFuISGNJgwFXB+/vdUn4YhHiWHnroXI37PsNyjYpB10KoZIWTf1cdNFFzJo1i44dO4Y6nphUaneyr8pJSbWL3CQzGYlG0uSsnNiiN8DQ25Tpn/2bG/eddL9UfxXRo65CWRD+06PgrFTacgfAha9CWmdVQxNtU4sSlS5dujBhwgTmzp1L7969myymvfPOO0MSXCzYsb+Wa99ewvb9tQ1tffNtTL5yIDlJzUwVtBF+v589lXUs3VHBmj1V9Mq1Mah9MrlJ5pDU53F6vLjrfcQbdOh0rbzN2pYHV38Ne5bBb58qB/8NGAfJBWBOat3XFiJUtv0C397TuG3Pcnj7HLhxliTdIuw0/j+Wlj0CHTp0CP6EGg3btm07pqCOlN1ux2azUVVVRWJihKwBOExZtZPLX13E1rKaJn0nd0nn+TH9sZnbdjn6tXurGPPqQuzO+oa2RJOej24aQo8cW4uft6rOzbayWt6Yu529lU6Gdkxl9KA88pIt4TlU0eeT04lF9KkugbfOUo5XCGT0u9BDdnWKY3c01+8Wjahs3769RYG1NWXVroBJCsCcTWUcqHG16USlxO7klveWNUpSAOzOem5+bxmf3zKMjETTUT9vraueT5cW8a9ph863Wb6rgrfmbefz8cPonh2GpFaSFBGNvK7gSQooW9wlURFhJu+mrajC4Wm23+Fu2zukymvd7C4PUHcE2F1ex4Fad4uet6zaxRPfNT2Ez+H2MuGL1ZS38HmFiHlavXKwZTDpXcMXixC/a/FZP8158803WxRMrMlMDL5gNk6nIdHUon/+mOGqbz5R+7P+YFYVVeILMqG5cncVVXVuUuINLXpuIWJaQhaccA9M/39N++LM6tYCEm1Wi66UFRUVje57PB7WrFlDZWUlp556akgCiwWpCUZO7pLGnE37m/RdPrgdada2vfMnNd6IQafF7fU16TPotKTGt+zfxxcsS/nd0a/KEqKN0Gqh96VQthFWvHPoj8WcDGM+hsRcdeMTbVKLEpUvv/yySZvP52P8+PGyZfkwyRYDT13ch4nfbeDb34rx+vwY9VquOL4d40d0xGKI3BEVV72XAzVuvD4/8UZ9q4xApCUYGD+iI8/N3Nyk79ZTOpKW0LLX7JufFLSve7a1Ta8LEuJPJWTAmY/B8LuUQzaNVrDlK9WNtTq1oxNtUIt2/QSzceNGRowYQXFxcaieslmRvuvnoFpXPftrXNS6vCSY9GRYjZjiIvcPfm9lHZNnb+GTpUW46n30ybPx8Kie9MhJxBziuMtr3fy0voRnf9zE3ionOTYTd5/RhdO7Z7Y4Oap2enh5zjZemrWlUbtBp+Xjm4fQv11yKEIXQgjRQkdz/Q5povLdd98xbtw4ysrKQvWUzYqWRCWalFQ5uerNRWwqabxbSauBL8YPo18rXeRL7E48Xh9xOi2ZLdjp80fltW7W7qnipdlbKLG7GNw+mZtO6ki7VDMGXeQmiUII0Ra0+vbke++9t9F9v99PcXEx06ZNY9y4cS15ShEhNpTYmyQpAD4/PDZtPW+MG0SS5dimgWpd9eyzO5mxtoQDNS5GdE2nc6aVvGTLMT3v4VLiDZzYJZ2++Um46r1YTXERPYolhBAisBYlKitWrGh0X6vVkp6ezjPPPPOnO4JEZJu1Ifho2LKdFdS6vSQdQz5R66pn2m/F/P2z1Q1tr8/dTo9sK2+MG0x2iKv1JprjAFmTIoQQ0apFicqsWbNCHYeIEBnN7ESyGvXojrGoa4ndyf2fr27Svq64mtfnbuP+s7ph0MvIhxBCCEWLCr7V1dXhcDga7u/cuZNnn32WGTNmhCywtsTv97OvysnaPVUs31XB7nIHde76P//CVjCyZ1bQvquHFZB6jIcp/rS+JOj24A8W7eZAjRRjE0IIcUiLRlTOP/98LrroIm655RYqKys57rjjMBgM7N+/n0mTJjF+/PhQxxmz6r0+fttTxS3vLaPE7gKU3Sl3ntaJK48vIDnMhcmybCaevqQPf/98daOEon+7JK4a0p64YzzYr7lEpM7jxStFToQQQhymRVed5cuXc+KJJwLw2WefkZWVxc6dO3nnnXd4/vnnQxpgtKuodVNqd+L0BK6yurfKyRWvLWpIUgDcXh//mbGJeVubFoprbfFGPef2zubnv47gofN6cNspnfjk5qG8etVAsmzHvhvnlK4ZQfv6t0siPoJrywghhAi/Fl0VHA4HVqsVgBkzZnDRRReh1WoZMmQIO3fuDGmA0Wp/jYuF2w7w8pytVDo8DO+Yys0nd6RdigX9YaMSszaUUhckiZk0YxNDOqSGvYKtxaing1FPhxOCn5LdUoXp8fTOTeS3PfZG7VoNPHRej7CPIAkhhIhsLRpR6dSpE1OnTmX37t1Mnz6dM888E4DS0lKpZ4IyivL4tPXc/sEK1uyxU1RRx8dLizjn+V/ZXNp46+9vRVVBn2fHgVo8AcrLR7OMRBOvXj2Im04qJN6gLJodUJDEF+OH0S0cpxoLIaB6H5SshX1rwL5XzpUQEa1FIyoPPfQQV1xxBffccw+nnnoqQ4cOBZTRlf79+4c0wGi0t6qOL1fsadLu9Ph49Ju1vHLVQGxmZeSgf7skPlteFPB5CtMTGo2+xIpsm5n7Rnbh2mHt8fnBYtDJSIoQ4VDvhr3L4YuboPL30W9rNpz/IrQbBobQ1TISIlRalKhccsklnHDCCRQXF9O3b9+G9tNOO40LL7wwZMFFq182Ba9FsnBbOfa6+oZE5aQu6cQbdNS6m07//G1kV9JDOO3jcNXj8fmxGvVotce4z/gYxel0Ia+ZIoT4E5U7Ycoo8B62qL26GN6/FG7+BbJ6qxebEEG0+ON6VlYWVquVH3/8kbq6OgAGDx5Mt27dQhZctDI0Mwqi1cDhKUJOkpmPbhpKXvKhi7Y5Tsc/zu3OkA4pIYlnf42LORtLueW9ZVzz5mJe+WUrRRWOP/9CIUTs8HpgyRuNk5SD/D74dRK4a8MflxB/okUjKgcOHGD06NHMmjULjUbD5s2bKSws5Prrryc5OZlnnnkm1HFGlZO7pvPYtPUB+07vnklS/KFKqTqtht55Nj4fP4zyWjfueh+pCQbSE4wYQ1DyvbzWzZPfr+ezZYemolbsruTNuTv47JahFKTFH/NrRILSaif2Og96rZZkSxy2YyzzL0TM8Thg77Lg/cUrlUTFEBvvCSJ2tGhE5Z577iEuLo5du3ZhsRya07zsssv44YcfQhZctMqwmrjz1E5N2lPjDUw4pxsJxqYl3TMTTXTPTqRvfhJ5yZaQJCkAuw44GiUpB5XVuHj+583UedQpLBcqde565m/Zz+iXF3D6pF8Y8Z/Z3PTuUraUNj2vSIg2TW+G1M7B+5MLIU6mY0XkaVGiMmPGDJ566iny8vIatXfu3Fm2J6OcL3Pd8A58MX4Yo/pmM7QwlX+c252vbh9Oh7SEsMbyzeq9wftWFXOgOrorwW4prWXsG4vYceDQVNai7RWMfmWBTG8JcTi9AYaMB02Q9Wkn/RWM1vDGJMQRaNHUT21tbaORlIPKy8sxGsNb8yNSJcUbGBBvoFeuDY/Xh8WgQxPsDaIVNbe92ef3R1Ql2BqXh1qXF5Nee0RTN/Y6D0/P2IgvwLdQXutm9sYyxg4paIVIhYhSyR3gojfg69uVqSAAvRHOfBwyeqgbmxBBtChROfHEE3nnnXd47LHHANBoNPh8Pv79739zyimnhDTAaGfQazHo1dtifHr3TN5ZEHiU6/QemarGdpDDXc+2slqen7mZNXuqyE02c+epnemdZyOpmYTF4a5n5a6KoP1zNpVx+eD8mNziLUSLGBOg+3mQPxgqd4HPC8ntISFDpn1ExGpRovL0009z6qmnsnTpUtxuN3//+99Zu3Yt5eXlzJs3L9QximOQl2zm9O4Z/LS+tFF7kiWOm08qJFXl+iV+v5/F28u59u0lDTWn9lY5uerNxdx/VlfGDW2PxRj411Sv1ZKRaMLuDLwepeAPVYCFECgjKEntlJsQUeCo38U9Hg933nkn33zzDSeccALnn38+tbW1XHTRRaxYsYKOHTu2RpyihdISjNx0UiH/PL8nA9ol0zkjgXHD2jP5ygHk2kwY9KFZtNtSJXYnD3z+W8DCmM/M2MT+Zg4xTLMauXVE4N83jQZGD84PVZhCCCFUctQjKnFxcaxevZrk5GT+7//+rzViEiGUaI6ja5aVlHgj6QlGPF4fuclmcpMsZITgkMFjVenwsM/uDNhX7/OzfX8N7VKDV8s8qUs6Y47L58PFuxva4nQanr6kL7m/F5Rzerw4PV4sBn1ETHUJIYQ4ci2a+hk7dixvvPEGTz75ZKjjEa3AZjZgMxvolBHeHUdH4s8q5P7Z1E1agpEHzu7O9ScUsmp3JRaDjp65NtKtRrxeH7/tqeLVX7axY38tffNsXDO8Pe1SLKqPJAkhhDgyLUpU6uvrefPNN/npp58YOHAg8fGNCwRNmjQpJMGJ2JdsMdAxPZ6tZU0rYpritBQ0M5pykM0ch80c1ygRc3m8fL+uhHs/WdXQ9tueKj5eupt3rz+eIYWpofkGhBBCtKoWJSpr1qxhwIABAGzatKlRnxpbcEX0SrcamTS6H5e9ugCn59BWao0Gnr6kL3qNhpW7K/H5/WRYjWRYjUc0GlJa7eL/fflbk3aP18/fPl3F5+OHkZmo/tSXEEKI5rUoUZk1a1ao4xBtWM+cRH646yQ+X17E0p0VFKbFM3ZIAbWues589hfsTqV6rjlOxyN/6cE5vbOxmppW9z3cnoq6RonP4Yoq6qiodUuiIoQQUaBFiYoQoaTXaWmfFs/dp3fB6fFi0GvZUVbL6FcWNCrmVufxcv/nv9EpI4GBBc0f2Oin+UJ2kVPmTgghRHNkC4SIGDqthnijHvDz9vwdASvOArzw8xaqnZ5mnysv2YIxyA6fbJuJZDm0UAghooIkKiLiOD0+NpVWB+3fVlYbdFrnoHSrkUf+0rNJu06r4elL+pCZKEc9CNEijnIo2wR7V0LFTvAELi8gRKjI1I+IOKY4Hb1ybCzZEbg8frcsK2ZD8zm2KU7Heb2z6ZZl5X+ztrLjQC198mzcdFJHClItIV30XVbtoriyjl3lDnKSzOQmm2X9i4hN5dvhy1tg90Llvt4IQ2+H48dDQrq6sYmYJYmKiDhxOi1XDing3YU7qQ8w/3P7qZ1IMDa/mBbAao6jf7tknr28H06Pl3ijHlNcaOun7C53cP2UJWwqOVTGPz/FzDvXHRf2k7KFaFXVxfDeRVC+7VBbvQt+fQYMCTDsTtDJJUWEnkz9iIjU7veLfYb10BRNkiWOyVcOoGP60SUA8UY9qQnGkCcpFQ43936yslGSArC7vI4b31lGWbUrpK8nhKrKdzROUg4371moKQ5nNKINkfRXRCSDXsfQjql8ffsJHKh14fdDSryBDKsxYg4aLK9xB52e2lJaw/4aF+lWWQujOqcdHPuh3g1GK1izQRsZv0NRpWxD8D5nFbgd4YtFtCmSqIiIpdFoyLKZyIqAM4kCcbjrm+23/8nOJBEGFTvh+/tg8wzw+yEhA07/J3Q9G8xJakcXXZI7BO+LMys3IVqBfKwQooUSzXHomzmrKD1BRlNUZS+Gdy+ATdNpOJ67phSm3gLb56gaWlRK6wwJmYH7Bl0PCVnhjUe0GZKoCNFC6QlGxgzOD9h3dq8sUiVRUVfZhuBrKn58CKr3hTeeaGfLhau/gqSCxu09L4Zhd4BeahOJ1iFTP0K0kMWo587TO2M26nlnwQ6cHh8GnZZLBuZy1+ldsJn/fGeSaEW7Fwfvq9gBnrqwhRIzMrrDddOhtlRZl2LNhvh0mUYTrUrj9/ujtpq43W7HZrNRVVVFYmKi2uGINspd76PE7sThrsds0JNuNWCOk88Aqlv/Ldj3KCMBPq8y/bP8Hdg6U1lUe+tCsOWpHaWIBF43VJeA4wDoDGBJA2uG2lHFtKO5fsu7aStw13spsbvYUlZDrbOe7tmJpFmN8gk7Rhn0WvJTLGqHIQ7n8ykJyk8Pw4EtSpsxEU68FzJ7gNYQfL2FUKrP1pRCyVpltCStM1hzYrNOSl0FrP1SmQ50/V4RO60zXPI2ZPZUjnIXqpIRlRBzerzM27Kf2z5Y3qjM++WD8/nbmV1Jk+2qQrS+ip3w8gngsjftu+h1KBwhlVSDqS6BHx6AtV8cajMkwJgPod0QZcQhlmz8AT68rGm7yQY3/wrJBU37xDE7muu3LKYNsb2Vddz07rImZ9F8tGQ3M9Y1XrxX4/JQXFnHvqo63PXNn10jhDgKW2YGTlJAKU4mn5ID83lh5fuNkxQAdw28dzFU7VEnrtZSUwYzHw3c56yCbbPDGo4ITBKVEJu2uhhvkGN//zd7K6XVTrw+P1tKa7jv09Wc9PQsTntmDk98t549lbK4T4iQOHgWTSBlG5TS76KpmhKY/3zgPq8btsXYtm6vu/lCdrsXhS8WEZQkKiG2fX9t0L4Su5Kk7Cqv5fwX5/L9mn14vH5q3V7enr+DMa8uZK8kK0Icu6zewfuS24NO1osF5KtX1mwEU741fLGEg1av/D4Ek9krbKGI4CRRCbHhndKC9vXKtaHXapg8eyu1bm+T/l3lDhZtP9Ca4QnRNnQ7VznZN5ARE5QKtaIpvRHSugTvLxgWvljCwZqp/D4EojcpFYyF6iRRCbEhhSmkJQRebPbA2d3w+2HWhrKgX//1ymJZryLEsUrMh6umgiXlUJtWDyfdpyykFYElZMKZjwXus+VBVp/wxhMOHU+FE/8G2sMOLbWkKr8/tsAFHUV4xeBeM3XlJlv45Oah3P/56oYD67JtJh79S096ZidS5/GRYNJTVhN4jjzJopfz0oQ4Vvo4yD8ebp6rVKCtdyrblePTwRCvdnSRrd1QZWfUjP+nbFEG6DACRj2r/BvGmvg0OOEe6D8W7HuVM4sSMuXwyggi25NbSaXDTbnDTb3Xj80UR0aiEY1Gg9/v592FO3noq7UBv+7z8cMYWJAc5miFEOIwPp+S4Lmqfi+AlirVZ0VIScG3CJBkMZBkaToFpNFoGNkzi+9/K2bBtvJGfTec0IHCNPm0J4Q4RvY9UL5D+W9qZ2Uk5GjW5Wi1YMsBclorQiGOmCQqKshMNPHcmP5sK6vlm1V7sRh0XNAvl9wkM0nxMVZMSQhxdGr3K2cRrf4E/F7oPRpSCo+8QF3JOuXU6JqSQ21ZfWHM+7LmQkQlmfoRQohIUVMKMx6E1R81bu82Cs59Rtml0hz7Xnj9NOW/f9T1bLjwVTDJe6VQn1SmFUKIaFS8smmSArDhG9i14M+/vqoocJICsOkHqA2+41CISCWJihBCRAJXLSx4KXj/wpegrrL552guEfH7wSMFJUX0kURFCCEigc9z6PTeQFzVSuXY5iR3CN5niJdpHxGVVE9U9uzZw9ixY0lNTcVsNtO7d2+WLl2qdlhCCBFexkTocX7w/m7ngSmp+edIyIQOJwfuG3an0i9ElFE1UamoqGD48OHExcXx/fffs27dOp555hmSk6WOiBCijdFqoeeFYM1q2mdJgf5Xge5PNmrGp8KFL0O/Kw+dZ2S0wqkPwuDrgx8rIEQEU3XXzwMPPMC8efP49ddfW/T1sutHCBFzKnbAr/+F3z5W1pX0vBhO/puyRflIuR1QW6qsSTEkKMmPHMQoIsjRXL9VTVR69OjByJEjKSoqYs6cOeTm5nLrrbdy4403Bny8y+XC5TpUet5ut5Ofny+JihAitnjqoK4c/IAlGeIsakckREhFzfbkbdu2MXnyZDp37sz06dMZP348d955J1OmTAn4+IkTJ2Kz2Rpu+flSvEgIEYPizJCYq1SUlSRFtHGqjqgYDAYGDRrE/PnzG9ruvPNOlixZwoIFTWsGyIiKEEKIiOCsAlcNaDRgSQO9VBU/GlFz1k92djY9evRo1Na9e3c+//zzgI83Go0YjdG5GMxV76XU7qLWXY/FoCfdasAcJycYCCFEVPG6oWyTUkF4+yyIi4eB18CQW2PzdOkIoOqVcvjw4WzcuLFR26ZNmygoKFApotZRWu3k1V+28e6CnbjqfcTpNFw6MI+7Tu9CZqJJ7fCEEEIcqf2b4bVTlIQFwF0DC16ELTPhqi8hMVvd+GKQqmtU7rnnHhYuXMgTTzzBli1b+OCDD3j11Ve57bbb1AwrpGpd9Tz302Ze/3U7rnofAB6vnw8W7+bRr9dSVedROUIhhBBHxGmHnx45lKQcrmw97Fsd9pDaAlUTlcGDB/Pll1/y4Ycf0qtXLx577DGeffZZrrzySjXDCqn9NS4+WrI7YN93a/ZxoMYVsC/SOT1e9lU5Ka124vNF7bmWQghx5FzVsG128P51U8MVSZui+iKJ8847j/POO0/tMFpNVZ0HbzMX8v01LgrTE8IY0bHx+fzsLHfw8uwt/LS+FItRx7ih7RnVN0emsYQQsU2jVQroOQ4E7rekhTeeNkL1EvqxzmJoPhdMNEdXEaadB2r5ywtz+XhpEQdq3ewur+Nf09Yz/r1llNqdaocnhBCtJz4DBgeu8wVA38vDF0sbIolKK0tNMDC4feAjATpnJJAaHz27mBzuep6duZlqV9OD0ZbvqmTDvmYOVBNCiGin08HAcZAzsGnf6f9Uat+IkFN96ifWJVsMTBrdj+unLGFTSU1De0GqhdeuHkS6NXoSlao6Dz+uKwna/8WKPZzUJT2MEQkhRJgl5sCYD6BsI6z/FsxJ0OtCsOaC2aZ2dDFJEpUwyE+x8P4NQyiuqmNXuYPcJDM5SeaoW9OhRYNRr8Xh9gbsjzfowhyREEKowJql3AqDnFQtQkoSlTBJtxpJtxrpk5ekdigtlpJgYMxx7fjf7K0B+0cPkiMNhBBChJasURFHLE6nZeyQAjqmxzfpGzesgHYpciaJEEKI0JIRFXFUcpLMvHfD8SzbWcGXK/aQaNIzdkh7OqTFkxwvZ10IIYQILUlUxFHLtpk5r4+ZM3tkodOCThv6gTmnx0ulw41GoyEtwYhOqwn5awghhIh8kqiIFjPoQ5+g+Hx+dpU7mDx7C0t2VnDxgFyGFqaRaI4jJd5AiozaCCFEmyKJiogoO8sdnP/iXLJsJh46rwcvz9nK09M3AdAvz8a/LuxN10wrca2QJAkhhIg88m4vIkadp56XZm2h2lXP/zunO3d+tIKF28ob+lcWVXHx5PnsKneoGKUQQohwkkRFRIxKh4cZ6/YxpDCVBVsPYK9rWgHXVe/j1V+24vQEruUihBAitkiiIiKGBg0mvY5uWVaW76oI+riF28updjZNYoQQQsQeSVRExEj/vaCcvc5DWkLwowVS443E6WQXkBBCtAWSqIiIodNpufy4fLYfqOG8PjlBH3frKR1JssjuHyGEaAskURERJdtm5n9XDCTbZuL2Uzo16b96aAH985PCH5gQR6KqCNZPg5mPwW+fQsUu8PnUjkqIqCbbk9uo8lo37novpjhdxI1OZNnMZNnMdEqP56IBuSzaVo7H52NoYSrpVmPExSsEAGWbYMq5UFN6qM1ohXHfQHY/0Mh0pRAtIYlKG1PpcLNiVyWTftzEzgO1dMxI4L6RXemVYyPRHKd2eI0kWgwkWgwUpieoHYoQzaspg8+va5ykALiq4cPL4cZZkBh8OlMIEZxM/bQhTo+Xz5YVce3bS/htTxV2Zz0rdlVyxWuLmL52Hx6vDFEL0SKOA7Dvt8B91fugpiS88QgRQ2REpQ0pq3bx9PSNAfv++c06hnVKIzfJHOaohIgBXlfz/a6a8MRxJFw1ysjPnqXgq4e8wZCQCaZEtSMTIiBJVNqQ0monrvrAoybVrnrKa1ySqAjREuZkiLOAJ0DVZI0WbLnhjymQukpY8T78+A/wH/ZecMK9MPR2iE9VLTQhgpGpnzYkTtf8j1v3J/1CiCASMmHEA4H7Bl0L8enhjSeYA1tgxv9rnKQAzJ0Ee1eoE5MQf0KuTG1IWoIx6OnDeclmUuVkYiFaRm+EfmPhgpfBlqe0xafByCfg5AeU3T9q8zhh/ovB++dOgrqq8MUjxBGSqZ82JDPRxEtX9OfqNxfj8fob2k1xWp6/vD+ZiSYVoxMiysWnQr8x0HEE1LtAZ4CELNBGyOdBrwuq9wbvr9n352tthFCBJCptiE6rYWBBMjPuOYmvVu5l7V47/fOTOLdPdqO1KQ53PfjBYpRfDyGOmjVb7QgCi0uADifD7kWB+9sNi4yRHyH+QK5EbYxBr6NDWgJ3n94Fj9fXaN1Kid3Jil0VvLdwF378jBncjkEdUsiSkRYhop9OB/2ugIX/A/cfdiHpDDDsToiTxfQi8kii0ob9MUm544MVLN5R3tA2b8sB+ubZeOWqQWTZJFkRIuoltYPrpsO3d0PREqUtsxeMeg5S2qsZmRBBSaIiAFiyvbxRknLQqqIqftlUxujB+SpEJYQIKa0OsnrBFZ9AXYWy+8ecrCz8FSJCRcgqL6GmaqeHdxbuDNr/zsIdVNS6wxiREKJVWVIgtSOkdZYkRUQ8GVER+P1Qf9guoD/y+vz4Cd4vhBAiBrkdynomvUnVysWSqAgSzXGMHpTH8l0VAfsvHpBHspxYLIQQbYOnDsq3wa+TYO9ysOXDSfcp65ksyWEPRxIVAcDJXdLplJHAltLGuwEKUi2c0zsbjRxRL4QQbUPREnj3AvB5lfvl22D7HDjjMRh8PRjiwxqOJCoCgOwkM+9efxzTVhfz0ZLd+P1+LhmYz/n9csiR83+EEKJtqN4HX91+KEk53MxHoftfIEUSFaGSbJuZ64Z34IL+ygFqKRYDWq2MpIRDRa2bvVV1zFhbAsCZPTLJTjKREm9UOTIhRJtSVw6VQTZX+Oph/4awb2WXREU0otVqSEuQi2M4Hahx8Z8ZG/lw8e6Gtudmbmb0oDz+flY3+XmIY1dXBZ4a0OqVAxSFCOpPNgNrwp82yPZkIVS2Zk9VoyTloE+WFrG6SA6JE8fA7YA9y+HTcfDicfDmWbD8XagpVTsyEaksKZDWJXCfzqBsaQ8zSVSEUFGNq57X524P2v/aL9uodnrCGJGIKXuWwOunwbZZyjbT8m3w9e3w40PgaFrgUQgSMuCCycqW5D8671mlP8xk6kcIFXm8PiocwYvpVda58Xh9YYxIxIzqEvj2XqX67B+t+hCG3618ehbij7L6wvj5sOxt2L0Qkgth6K3Kf1U4D0oSFSFUlGjUc1q3TNbssQfsP7VbJlZTXJijEjHBZYcDW4L3Fy2GjG7hi0dED32cUrn4tIfBU6uMrujVWysnUz9CqEin03LJwDySLE2TkUSznssG5zc6PFKII6b9k8+hhoTwxCGil04PJpuqSQpIoiKE6vKSzXwxfhhn98pCqwGtBkb2zGLqrcPJT5YaNqKFzCnQYUTgPq0ecgeEMxohWkzj9/uj9hAXu92OzWajqqqKxET1ziEQIhRqXB6q6urBDzaLngSjTPmIY3Rgi7LTp7bsUJtGAxe+Ct1HqbLeQAg4uuu3rFERIkIkGOMkORGhldoJbpyl7PrZ/CMkt4d+V0JSviQpImpIoiKEELEsKR8GXA39xoJWZvtF9JFERQgh2oJjSVIc5VBXAX4/mG0Qnx66uIT4E5KoCCGECMzng7IN8M1dynZmgMxeMOo5yOoDeoO68Yk2QcYBhRBCBFa5E94881CSAlCyBt46GyqCV1QWIpQkURFCCNGUzwurPwFXddM+rxvmPgvuurCHJdoeSVSEEEI05a5VdgsFs2uBUv1WiFYmiYoQQoim9EZIzA3en5ChesVS0TZIoiKEEKIpvVE5iC6YE/8K5qSwhSPaLklUhBBCBJbSEUY+AZo/XCqG3QG5g9SJSbQ5sj1ZCCFEYOYkGDAOup4Nu5eAzwP5xyvTPiab2tGJNkISFQFAqd2J3VmPXqchxWIg0Syl3IUQgDFBuaUUqh2JaKMkUWnjal31LNtZwT+mrmFXuQOAEzun8c/ze9EhLV7l6IQQQrR1skaljduwr5pxby1uSFIAft28n8teWcDeSqmRIIQQQl2SqLRhlQ43E79fj9/ftK+02sXi7QfCH9RhvF4fZdUu9le78PkCBCmEECLmydRPG1bn9rJqd2XQ/jmb9nNB/7zwBXSYPZV1fLZ0N1+u2INOq+GywfmM6ptDtk2OphdCiLZEEpU2TKfVkGE1sSfIFE9BqiXMESn2VNZx2SsLKKo4FNcT323gs2VFTLnuOElWhBBHzl4Mjt9Hhy2pkJitbjziqMnUTxuWbjUy/uSOAfu0GhjVNyfMEYHX52fq8qJGScpBm0pqmL9F3ekoIUSU8Dhh+xx443R4ebhye+N02P6L0ieihiQqbZhGo2Fkrywu6Nc4IYnTaXjpigHk2Exhj6nC4ebLlXuD9n+ydDc1zvowRiSEiEoV2+Hdi6Cq6FBbVRG8e6FyKrSIGjL108alW4088pee3HpKJ1bursRq1NMz10aG1YgpThf2eLQaDXE6TdB+vU6DNni3EEJAvRPmvwi+AB9qfPWw4CU459+gD/+HMXH0JFERJFkMJFkMdMm0qh0KKfEGrjiuHQ9+tTZg/7ih7bEY5ddWCNEMVy0Urwzev3e5cjq0JCpRQaZ+RMQ5o0cWvXMTm7Sf2CmNfvlJ4Q9ICBE+1aVQshZ2LYIDW8FpP/rniDNDcjOVdFM6Ko8RUUE+moqIk2Uz8dq4wSzbUc5HS3aj02i4amgBvXNtZCTKJyAhYtb+LfDxFVC2Ubmv0UDvy+CMR8GadeTPY7DAiffAhq8D9w+/C+LU2dUojp4kKiIiZSWaOLdPDqd2ywSNH3Oc/KoKEdPsxfDuBVC1+1Cb3w+rP4L4NDjtIdAbj/z5UjvB+S/BtL8qa1ZAmeo5d5LSJ6KGvPtHkSqHG7uzHo0Gki0G4tvAWg2zIfwLeoUQKqjY3jhJOdzSN+D4myGp3ZE/nykRel0C7U+Eyl1KW1I7SMiEOBmZjSaxf6WLAZ56H5tKq/nnN2tZtL0CnVbDyJ6Z3H9WNwpS5eBAIUQMKN8WvM9TB25H8P5g4kyQXKDcRNSSxbRRYGe5g4v+N59F2ysApSjad7/t45KXF7CnogV/vEIIEWmam44xxCvrTkSbJIlKhHO463lp1hZc9b4mfWXVLmZtLFMhKiGECLHkAkgJslPn+PGQcBSLaUVMkUQlwlU765m3ZX/Q/h/XleD0SKVWIUSUs2bD2C8gZ8ChNq0eBt+krE/RG9SLTahK1TUqjzzyCI8++mijtq5du7JhwwaVIoo8eq2GZIuB0mpXwP7UBAN6reSbQogYkNIBrvwUaveDxwHmZIjPAKOsxWvLVF9M27NnT3766aeG+3q96iFFlNQEIzedVMhfP10VsH/c0PbodZKoCCFiRHyachPid6pnBXq9nqwsmXtszkld0ji3dxbTftvXqP1vZ3ahfZp80hBCCBG7VE9UNm/eTE5ODiaTiaFDhzJx4kTatQu8V97lcuFyHZoCsdtbUFo5CqVbTTx2fi/Gj+jE7I2lmOJ0jOiaQabViNUcp3Z4Qgi11LugrgI0WkjIUDsaIVqFxu/3+9V68e+//56amhq6du1KcXExjz76KHv27GHNmjVYrU0PyAu0pgWgqqqKxMSmZ8MIIURM8vuhYgcs/B9s/E7ZvnvczdDt3KMrNS+ESux2Ozab7Yiu36omKn9UWVlJQUEBkyZN4vrrr2/SH2hEJT8/XxIVIUTbcmArvHYqOCsbtxcMh0velGRFRLyjSVRUn/o5XFJSEl26dGHLli0B+41GI0bjUZz1IIQQscbjgF/+0zRJAdg5D0o3SKIiYkpEbRepqalh69atZGdnqx2KEEJEprpKWB/kVGCAVR+GLRQhwkHVROVvf/sbc+bMYceOHcyfP58LL7wQnU7HmDFj1AxLCCEim66ZRfRx5vDFIUQYqJqoFBUVMWbMGLp27cro0aNJTU1l4cKFpKenqxmWEEJErvh06Dc2eH//ZvqEiEKqrlH56KOP1Hx5IYSIPro4GHIzbJzW9MThAeMguYM6cQnRSiJqMa0QQogjYMuHcd/C9l/gt0/AYFXOw0nvCvGpakcnREhJoiKEENHIlgv9xkCP80GrA73siBSxSRIVIYSIZgaL2hEI0aoianuyEEIIIcThJFERQgghRMSSREUIIYQQEUsSFSGEEEJELElUhBBCCBGxJFERQgghRMSSREUIIYQQEUsSFSGEEEJELElUhBBCCBGxJFERQgghRMSK6hL6fr8fALvdrnIkQgghhDhSB6/bB6/jzYnqRKW6uhqA/Px8lSMRQgghxNGqrq7GZrM1+xiN/0jSmQjl8/nYu3cvVqsVjUajdjjid3a7nfz8fHbv3k1iYqLa4YgjJD+36CM/s+gjPzOF3++nurqanJwctNrmV6FE9YiKVqslLy9P7TBEEImJiW36DzFayc8t+sjPLPrIz4w/HUk5SBbTCiGEECJiSaIihBBCiIgliYoIOaPRyMMPP4zRaFQ7FHEU5OcWfeRnFn3kZ3b0onoxrRBCCCFim4yoCCGEECJiSaIihBBCiIgliYoQQgghIpYkKkIIIYSIWJKoiJDas2cPY8eOJTU1FbPZTO/evVm6dKnaYYkg2rdvj0ajaXK77bbb1A5NBOH1ennwwQfp0KEDZrOZjh078thjjx3RmSlCPdXV1dx9990UFBRgNpsZNmwYS5YsUTusqBDVlWlFZKmoqGD48OGccsopfP/996Snp7N582aSk5PVDk0EsWTJErxeb8P9NWvWcMYZZ3DppZeqGJVozlNPPcXkyZOZMmUKPXv2ZOnSpVx77bXYbDbuvPNOtcMTQdxwww2sWbOGd999l5ycHN577z1OP/101q1bR25urtrhRTTZnixC5oEHHmDevHn8+uuvaociWujuu+/m22+/ZfPmzXJ+VoQ677zzyMzM5I033mhou/jiizGbzbz33nsqRiaCqaurw2q18tVXX3Huuec2tA8cOJCzzz6bf/3rXypGF/lk6keEzNdff82gQYO49NJLycjIoH///rz22mtqhyWOkNvt5r333uO6666TJCWCDRs2jJkzZ7Jp0yYAVq1axdy5czn77LNVjkwEU19fj9frxWQyNWo3m83MnTtXpaiihyQqImS2bdvG5MmT6dy5M9OnT2f8+PHceeedTJkyRe3QxBGYOnUqlZWVXHPNNWqHIprxwAMPcPnll9OtWzfi4uLo378/d999N1deeaXaoYkgrFYrQ4cO5bHHHmPv3r14vV7ee+89FixYQHFxsdrhRTyZ+hEhYzAYGDRoEPPnz29ou/POO1myZAkLFixQMTJxJEaOHInBYOCbb75ROxTRjI8++oj77ruPp59+mp49e7Jy5UruvvtuJk2axLhx49QOTwSxdetWrrvuOn755Rd0Oh0DBgygS5cuLFu2jPXr16sdXkSTxbQiZLKzs+nRo0ejtu7du/P555+rFJE4Ujt37uSnn37iiy++UDsU8Sfuu+++hlEVgN69e7Nz504mTpwoiUoE69ixI3PmzKG2tha73U52djaXXXYZhYWFaocW8WTqR4TM8OHD2bhxY6O2TZs2UVBQoFJE4ki99dZbZGRkNFroJyKTw+FAq2381q3T6fD5fCpFJI5GfHw82dnZVFRUMH36dM4//3y1Q4p4MqIiQuaee+5h2LBhPPHEE4wePZrFixfz6quv8uqrr6odmmiGz+fjrbfeYty4cej18pYQ6UaNGsXjjz9Ou3bt6NmzJytWrGDSpElcd911aocmmjF9+nT8fj9du3Zly5Yt3HfffXTr1o1rr71W7dAinqxRESH17bffMmHCBDZv3kyHDh249957ufHGG9UOSzRjxowZjBw5ko0bN9KlSxe1wxF/orq6mgcffJAvv/yS0tJScnJyGDNmDA899BAGg0Ht8EQQn3zyCRMmTKCoqIiUlBQuvvhiHn/8cWw2m9qhRTxJVIQQQggRsWSNihBCCCEiliQqQgghhIhYkqgIIYQQImJJoiKEEEKIiCWJihBCCCEiliQqQgghhIhYkqgIIYQQImJJoiKEEEKIiCWJihBCCCEiliQqQgghhIhYkqgIIYQQImJJoiKEaFU+n49///vfdOrUCaPRSLt27Xj88ccBmD9/Pv369cNkMjFo0CCmTp2KRqNh5cqVAMyePRuNRsO0adPo06cPJpOJIUOGsGbNmobnP3DgAGPGjCE3NxeLxULv3r358MMP1fhWhRCtQBIVIUSrmjBhAk8++SQPPvgg69at44MPPiAzMxO73c6oUaPo3bs3y5cv57HHHuP+++8P+Bz33XcfzzzzDEuWLCE9PZ1Ro0bh8XgAcDqdDBw4kGnTprFmzRpuuukmrrrqKhYvXhzOb1MI0Urk9GQhRKuprq4mPT2dF198kRtuuKFR38svv8w//vEPioqKMJlMALz++uvceOONrFixgn79+jF79mxOOeUUPvroIy677DIAysvLycvL4+2332b06NEBX/e8886jW7du/Oc//2ndb1AI0er0agcghIhd69evx+VycdpppzXp27hxY8N0zkHHHXdcwOcZOnRow/+npKTQtWtX1q9fD4DX6+WJJ57gk08+Yc+ePbjdblwuFxaLJcTfjRBCDZKoCCFajdlsbvXXePrpp3nuued49tln6d27N/Hx8dx999243e5Wf20hROuTNSpCiFbTuXNnzGYzM2fObNLXtWtXfvvtN1wuV0PbkiVLAj7PwoULG/6/oqKCTZs20b17dwDmzZvH+eefz9ixY+nbty+FhYVs2rQpxN+JEEItkqgIIVqNyWTi/vvv5+9//zvvvPMOW7duZeHChbzxxhtcccUV+Hw+brrpJtavX8/06dMb1pRoNJpGz/PPf/6TmTNnsmbNGq655hrS0tK44IILACUZ+vHHH5k/fz7r16/n5ptvpqSkJNzfqhCilcjUjxCiVT344IPo9Xoeeugh9u7dS3Z2NrfccguJiYl88803jB8/nn79+tG7d28eeughrrjiikbrVgCefPJJ7rrrLjZv3ky/fv345ptvMBgMAPzjH/9g27ZtjBw5EovFwk033cQFF1xAVVWVGt+uECLEZNePECJivP/++1x77bVUVVVhNpsbdv1UVFSQlJSkdnhCCBXIiIoQQjXvvPMOhYWF5ObmsmrVKu6//35Gjx4dlkW4QojoIImKEEI1+/bt46GHHmLfvn1kZ2dz6aWXNlStFUIIkKkfIYQQQkQw2fUjhBBCiIgliYoQQgghIpYkKkIIIYSIWJKoCCGEECJiSaIihBBCiIgliYoQQgghIpYkKkIIIYSIWJKoCCGEECJi/X+OsHruB8DirgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, 0:2]\n",
        "y = df.iloc[:, -1]"
      ],
      "metadata": {
        "id": "tzrzT-HaX6wl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Perceptron\n",
        "p = Perceptron()"
      ],
      "metadata": {
        "id": "sfALTv5PYJpZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "MhxOsZCYYcbb",
        "outputId": "19b518e1-89da-465a-ff83-6f93e638cee1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Perceptron()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Perceptron()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Perceptron</label><div class=\"sk-toggleable__content\"><pre>Perceptron()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fe-VSWf6Yga-",
        "outputId": "d72e8853-7f51-4e1b-a513-61d6b773220f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 40.26, -36.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4gmFlLqYk0v",
        "outputId": "73ea845d-9e18-41fb-da0b-4e226c414e40"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-25.])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mlxtend --upgrade --no-deps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lV7shMaFZt26",
        "outputId": "6066ac88-e9d3-457b-f87b-ec15a3c9826e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.10/dist-packages (0.22.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "metadata": {
        "id": "k9-PA6NoY0cI"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X.values, y.values, clf=p, legend=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "BT54gdu9ZBIA",
        "outputId": "d08014fb-c47b-49e3-b0ae-9b3e12b057a0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but Perceptron was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}