Build_regression_and_discretizer_models.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNnTe7zMAtgRFo5vJ9JDzYD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JeanMichelDeveloper/Training/blob/main/Build_regression_and_discretizer_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compare decision tree and linear regression models"
      ],
      "metadata": {
        "id": "Y-Th31nEsmbC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xPZgIR8TrChW"
      },
      "outputs": [],
      "source": [
        "import numpy\n",
        "#create data\n",
        "def create_data():\n",
        "  random = numpy.random.RandomState(42)\n",
        "  x = random.uniform(-3, 3, size = 100)\n",
        "  y_no_randomness = (numpy.sin(3*x) + x)\n",
        "  y = (y_no_randomness + random.normal(size=len(x))) / 2\n",
        "  return x.reshape(-1, 1), y\n",
        "\n",
        "X, y = create_data()  "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adm2Pinxo9Vb",
        "outputId": "33ec2943-dc97-4831-b092-da4515325149"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.75275929]\n",
            " [ 2.70428584]\n",
            " [ 1.39196365]\n",
            " [ 0.59195091]\n",
            " [-2.06388816]\n",
            " [-2.06403288]\n",
            " [-2.65149833]\n",
            " [ 2.19705687]\n",
            " [ 0.60669007]\n",
            " [ 1.24843547]\n",
            " [-2.87649303]\n",
            " [ 2.81945911]\n",
            " [ 1.99465584]\n",
            " [-1.72596534]\n",
            " [-1.9090502 ]\n",
            " [-1.89957294]\n",
            " [-1.17454654]\n",
            " [ 0.14853859]\n",
            " [-0.40832989]\n",
            " [-1.25262516]\n",
            " [ 0.67111737]\n",
            " [-2.16303684]\n",
            " [-1.24713211]\n",
            " [-0.80182894]\n",
            " [-0.26358009]\n",
            " [ 1.71105577]\n",
            " [-1.80195731]\n",
            " [ 0.08540663]\n",
            " [ 0.55448741]\n",
            " [-2.72129752]\n",
            " [ 0.64526911]\n",
            " [-1.97685526]\n",
            " [-2.60969044]\n",
            " [ 2.69331322]\n",
            " [ 2.7937922 ]\n",
            " [ 1.85038409]\n",
            " [-1.17231738]\n",
            " [-2.41396732]\n",
            " [ 1.10539816]\n",
            " [-0.35908504]\n",
            " [-2.26777059]\n",
            " [-0.02893854]\n",
            " [-2.79366887]\n",
            " [ 2.45592241]\n",
            " [-1.44732011]\n",
            " [ 0.97513371]\n",
            " [-1.12973354]\n",
            " [ 0.12040813]\n",
            " [ 0.28026168]\n",
            " [-1.89087327]\n",
            " [ 2.81750777]\n",
            " [ 1.65079694]\n",
            " [ 2.63699365]\n",
            " [ 2.3689641 ]\n",
            " [ 0.58739987]\n",
            " [ 2.53124541]\n",
            " [-2.46904499]\n",
            " [-1.82410283]\n",
            " [-2.72863627]\n",
            " [-1.04801802]\n",
            " [-0.66793626]\n",
            " [-1.37190581]\n",
            " [ 1.97242505]\n",
            " [-0.85948004]\n",
            " [-1.31439294]\n",
            " [ 0.2561765 ]\n",
            " [-2.15445465]\n",
            " [ 1.81318188]\n",
            " [-2.55269614]\n",
            " [ 2.92132162]\n",
            " [ 1.63346862]\n",
            " [-1.80770591]\n",
            " [-2.9668673 ]\n",
            " [ 1.89276857]\n",
            " [ 1.24114406]\n",
            " [ 1.37404301]\n",
            " [ 1.62762208]\n",
            " [-2.55573209]\n",
            " [-0.84920563]\n",
            " [-2.30478564]\n",
            " [ 2.17862056]\n",
            " [ 0.73978876]\n",
            " [-1.01461185]\n",
            " [-2.6186499 ]\n",
            " [-1.13410607]\n",
            " [-1.04890007]\n",
            " [ 1.37763707]\n",
            " [ 0.82534483]\n",
            " [ 2.32327646]\n",
            " [-0.16671045]\n",
            " [-2.28243452]\n",
            " [ 1.27946872]\n",
            " [ 1.56471029]\n",
            " [ 0.36766319]\n",
            " [ 1.62580308]\n",
            " [-0.03722642]\n",
            " [ 0.13639698]\n",
            " [-0.43475389]\n",
            " [-2.84748524]\n",
            " [-2.35265144]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dl6_k1k7o-rg",
        "outputId": "364ac9e3-7b3d-45e2-bb20-cc52cfc2396a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.71927944  1.68597842  0.31211026 -0.20828425 -1.09608346 -0.80797977\n",
            " -1.08427853  0.99096301  0.38364414  0.08948738 -1.33758189  1.98552794\n",
            "  0.58506141 -0.15955191 -0.64207511 -0.18958672 -0.7518895   0.12594502\n",
            " -0.87062019 -1.06906714  0.93544887 -1.05322723 -0.33876911 -0.85391158\n",
            " -1.19491248  0.18881958 -0.68782375 -0.23172249  0.69445564 -1.6348037\n",
            "  1.73278742 -0.72846005 -1.6759149   1.79672361  0.86957103  0.57774349\n",
            " -0.37274008 -0.38459003  0.36966058 -0.46909922 -1.39973612 -0.64216159\n",
            " -1.25757664  2.04598179  0.13794205  0.14012881  0.25907318 -0.46401154\n",
            "  0.80614282  0.43645662  1.32656481  0.05658084  1.8675105   1.29957912\n",
            "  0.00923734  1.78306762 -2.21658146 -0.31280946 -2.2967383   0.25218889\n",
            " -1.17944769 -0.43337845  1.21407147 -1.31209946 -0.18423636  1.12919902\n",
            " -1.97057161  0.62537411 -1.63684377  2.15845174 -0.29292947 -1.18513794\n",
            " -1.4727128   0.8105451   0.4710376   0.4448476  -0.01895328 -1.65304233\n",
            " -0.5578967  -1.80461406  2.14719565  1.00528505 -1.15175841 -1.48104718\n",
            " -0.92550304 -0.12835397  0.84974196  0.31108039  1.96029897 -0.11673516\n",
            " -0.99752263  1.26724222  0.15974443  0.25324322 -0.12506319 -0.48224198\n",
            "  0.22858122 -0.52914567 -1.67150543 -1.11250961]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create and score decision tree model\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "decision_model = DecisionTreeRegressor(min_samples_leaf = 3).fit(X,y)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X_test = numpy.linspace(-3, 3, 1000, endpoint = False).reshape(-1, 1)\n",
        "\n",
        "score = decision_model.predict(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "0x9Ooh9FpDWF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create and score linear regression model\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "linear_model = LinearRegression().fit(X,y)\n",
        "\n",
        "linear_results = linear_model.predict(X_test)"
      ],
      "metadata": {
        "id": "IcuvkrDOqtjj"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_test, score, label = \"Decision tree\")\n",
        "\n",
        "plt.plot(X[:, 0], y, 'o', c='k')\n",
        "\n",
        "plt.plot(X_test, linear_results, label = \"Linear Regression\")\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "eMNYQewlqcDe",
        "outputId": "871296d8-e244-4ac0-a09a-d2feb1db00f6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb0a7e49b10>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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k9BOOIFRWft97nM837KZl/erUTPBPwpKTErm8faMKfSbGokg+tw/L33yszL4iuyZG4sgdaK09FrARKhcSySL4i734V+eZa9px3un1Q3Zeq0V5LHvriCP3wSLXGoKgcRETtZKQkMDBgwflD7wKoLXm4MGDJCQkhHsqQhRiK1byUNt8MRblOndpiux27wlBRfmwbi5MPw8O/mX+3Ewf0U+aNWtGZmYmWVlZ4Z6KEAISEhLcIoQEwVecxp41xEputShyCz0Iuc2DayXnEKyZCT/PgOx9cEo7x7ak00ydW8QIeWxsLC1btgz3NARBiHCcRrElxOF+3i3yUnHph/6GVdNh3btQmAOn9YZr34BTewblUSJihFwQBMEXbMUWuafOPsHCarGQW2Bj79G8MvsKbcWulcw18ONk2PI5KCu0vwG63weN2gV1biLkgiBEFXaXkIf2vNXirGzbn02355a6bbdg5xLLWob+/iys2QAJteG8UdD1bqjVOCRzEyEXBCGqsNvDY5E/0qcVPU5Lcr232nJpmfkZrf5+l1o5O7CpFtB3Apx1G8TXCOncRMgFQYgqnG5qa4hN8ub1ErmlawvIzoLVb8LPb0LuIWhyNvR7AuuZ/cEaHkmNmPBDQRAEX3C6VkKecnJgG3w+Cia1hf+bAC26wdAv4a5vSd+QS8ppp2OxWEhJSQl5QxARckEQogqna6WiFnl6ejopKSkVE1utIWMFvHczTO0C69+HTrfCiDVwy/uQ3IP0994Le3cnca0IghBVuMIPK2CSGzVXHjZsGIBxhUlbEWxZAD9Ogd3rIDEJLhoL59wJNRq4HRoJ3Z3EIhcEAfDTYg0DNj+iVryJrRv5x2HlNJh8Fsy/HfKOwZWT4IFN0OvfZUQcIqO7k1jkgiBU3GINI9qPOPJyxfbYbvjpDVgzC/KPQosejk5lZ1wO5dRQiYTuTmKRC4Lgu8UaQjw9Idj9EHJPonpZxybwyXB4pYMjkee0XnDnUrj9S2h9RbkiDpHR3UksckGIIHIKiigs8lw4rnq8NSgtxSLBPVASb08I8a0vAiq22Fm6ufKlp1p55PxqXNLyOGz+DM65A7rdA3VTKjzXSOjuJEIuCBHCtn3HufzV5RR5qOcB0P3UJN4f1s30c0eCe6Ak3p4QXpi/HKhY+GFqaioWXcTqWWMZcsZxOpxiJcdaG3qOgs5DIbFeQPNNTU0NqwtKhFwQIoR9x/IpsmuG9EihRb3EMvs/WbeLXUdyg3Lu0hYrhLf5s7cnhApnduYegbWzuGXfG9xyQQ40aAc9RpLYfgDExJs15bAiQi4IAZCenm7aI7XT93tlh8Z0SSlrIW7cdZSfMw4FNF9PRIJ7oCTenhB8zuw8vONkBcKCbEflwaunOioRVrIGNiLkguAnZkd6OB0qnjRGKUUw+66E2z1QEm9PCOVmdu76xRH/vflTUBZoN8BRgbBxhxDMPDxI1Iog+InZkR4nBcpYoSzq5DGVndTUVGbMmEFycjJKKZKTk5kxYwapqamu78CtsYTdDr9/CbP6wZu94M9voPsIGPUrXPdGpRZxEItcEPzG9EiPYo32ZGhalKoyQg6enxDcfOSFefDrB/DjVDi4DWo3hz7PwlkDIaFWqKccNkTIBcFPzI70KC8+2mIhqK6VaMGmoS7HqPbji7Dubcg5AI07wvVvQ5urwRob7imGHFNcK0qpmUqp/UqpjWaMJwjRgNmJIE6RLqnjJZNipg3vS9b6pcYfrioc+JMeW55hZfxIEn6YAE07w+CFMOz/oP2AKiniYJ6P/B2gr0ljCUJU4M2P6w+lLXLnYqqzqt6xrD3sWDApYmugBA2tYecq+CAVpnbhjN0L+MR2Pjl3/Qip86DlBaS/915U1IkJFqa4VrTW3yulUswYSxCiCTMjPUp7TYwWU3Vhfkir6oUVu83R+/LHKbBrDVSrCxc+xLuFl/LEsgNc27A1EF11YoKFRK0IQoRQuhhUpKXNh4z8bEcBq8lnwf8GQ85B6PeiowLhxf/hRJyj3ZrTBRWJdWJCTcgWO5VSw4BhEL60X0GIZJw+8kWfzKPfxGdcwl6aSvv3c3wv/DwDVr8NeUeg+bnQJw1a9QOL1XVY6czOKnvDK0HIhFxrPQOYAdClSxdZexeEUtg1ZG9axmOTp5GXa5yKr2Ljw5Y2HzT2b3GED/42D2yFcOaV0H0ktDjX8HBXZmexkEdanZhwIK4VQYgQNJoj38/xKOK1GzSm8RX3Vw6/r9aw/TuYOwCmdYONH8HZg2HkWrhprkcRh5ONJZyulXCWkY2UZhxmhR++D6wEWimlMpVSd5gxriBUJewabMcOGO5TSvHIO0up1e7ikM7JdKGyFcKv8+CNC2DO1bBnA/T6Dzy4Ga54EZJOK3cIrTUWdTID1uzoIV8pHVUUjl6dTsyKWrnFjHEEoSqjtcZaqz62Y1ll9rVo0QIV4hR9U6NB8o7C2tnw0+twbBfUbwX9p0D7GyE2Aa01y//I4lheYblD/bHveJmkqXDUiYmEXp1OJLNTEALErAqIWkOdCweRs3QauSXcK043QWaQi2YBvLV8O1v2HAfgzdEPBy5URzMdFQjXzoaC45ByAVz5Cpx+iVv3nT/3Z3PdmAkc+X4OtmMHsNaqT50LB1GjbS/DYRvUDH/52UhaZBUhF4QAMNNq1WhqtO3Ff65owyvPP1XmxvDC4q1Bt8gnLvmdOKuFWtViOX5gr+ExPgnV7vWwcips/Njxvt11jiJWTToZHv6/Dz/g0OKp6KJ8AGzHsjixdBr/vvxM+l9/Y5njI0HII2qRVWsd8lfnzp21IFQGkpOTNY5cHrdXcnJyhceav+YfnfzoQp1xINtw/8TFW3XLsQsDnLF3Tvv3Iv3C4i1aaz+uzWbT+vclWs+6QuvHa2md1lTrxeO0Pryz3PM2atrMtO8xVMydO1cnJia6zTcxMVHPnTs3aOcE1mgDTZWoFUEIADMfr522tseiWepk6F2wsGntCuvzORqkMA9+mQPTu8N7N8DBv+DSp+HBTY448DrNyz3vvt27DLdHcix4uBZZjRDXiiAEgJmP1+W5TZxRGlprjzXLA8Fh3YHFcjIaBOCRsf9md2YmDRo3YdLECSeFKucQrHkbfpoBJ/ZDo/Zw7Qxoey3ExFXo3A0bN2Xf7swy2yM9FjxSmnGIRS4IARBIDHPp0L7lX34CnBTS0lhcQh7gpD1gs5dt2JCamsqP67eQ/OjnvL5wlUO0Dm2HRQ/BpLbw7TOOpg2DFsDdy6HjTRUWcYA7HxiHKtU/M5w9Q6MNEXJBCAB/H6+NYpBnPDuW7E3LvDSWcPwbrAVPZ6JN6RuJ8wZS++A6+HAgTD4b1r4Dba+De1bCbR85+mEG8JTQ64rrqNd3BI2bNgu7myIaEdeKIASIP4/XRjHIBXm5HPl+Dhb1jOFnnAIbLD+53e74162psd1G4vYvmR/3PF1++AMS6sAFD0LXYVCzkWnnLrI7InYWvfFf2japbdq4VQWxyAUhDHhaxLMdO1CuYRssi7yoWMmtSkFBDvz8JkztQtLC22nIYda2GeuoQNj7MVNEvKRraeClXcjetIwYi0iSP8i3JghhwNMinrVWfY9CHmwfud0O9TlKl7+nO/zfXzwE1epy5Mo36VXwMluTb4X4Gqacq7RrKWvPLg4tnsoXn/7PlPGrGiLkghAGjBZJ4+KrUefCQSgPXvKg+sizfid+8Wh+iL+fTn+/CS26w9DFcOdSCltdjQ2rqS4dw6YZRfk8Pf4Rt22RUpQq0hEfuSCEAadPvWRqf98ho1mc9y88BK24LHLThFxryFjh6MCzbQnxMQmk2y6kRs/7ueaSniXO6/jXbqKSe3ItHT50kPT0dFJTU6XzTwUQIReEAFm38zBvLt/uWiz0ndb0ffojalWL4amr2/HBzztZ/PlmjzHizs0By6mtCDZ/6hDwPeshsT70HEdW61T+88oG0mq2dDvcajH5BoLn+HvAVcslkopSRToi5IIQIJ+u28WSTfs4vUHF/cfZ+UXsOpLLbd2SS2R2Gh/r8pFX+IZRTP5xRwbmqulw9B9IOt1RwKrjzRBbjaIjjkJd1lI3EqXMj5ZJS0vjtttuM9zntNYjqShVpCNCLggBciinkOZ1q7HkgQsr/NllW/cz9J3V2OzaJZSm+8iP7nKUj107G/KPQvJ50G8i/KuPWwVCZ0JQ2Tjy4vOaqOSpqamMGjWKgwcPltnnXAiOqKJUEY4sdgpCgBw+UUDd6hXPZgR3t4V2dr7x8FepKuoj3/sbfHw3vNrBUYnw9N5w17cw9AtodbmbiKenp9O9Y2t2TLiK+67q7raoGAzXCsCrr75aZsG3WolsznB2/ok2xCIXBB9Yu+Mwb36/3VDMftt1lM7Jdf0a1ymSNvvJsMLyMzu9DKg1/LXU4f/e/h3EVodz7oJuw6FuiuFHSi8qHti7y7WoCPDvceP4Z+c//PudxtR46QXT/NMlF3x37NyJtWZ9pk15ybXdaEHY31rvlR0R8gjBrOYEQnD48rc9LNm8l1an1Cyzr3HtBPq29S9Bxun3LrLb0bh3hy9NyaJZZSjKh9/mOyzv/ZuhRiO45AnoPASqeb/JeFpUHDVqFLm5ua59h/ft9itq5EB2Pqv/PmS4r16Hi5n++Uq+2LiXzzfsJjX1crf9kVKUKtIRIY8AJMwq8rFrqBEXw+LRFfeDeyPGWuy2sJ+0tMtLCHKzyHMPw5pZ8NMbkL0XGraFa6ZDuwE+F6/ytHho5L/2J2rkxSW/88Hqf8o9rlZCDDGeVnoFr4iQRwASZhX52LUOpCaURyxKkb1pGQN6Dmffnl1Ya9ZnXpuXGDJooMGxjn81Gg5nOKJPfnkXCk/Aqb3gmmlw2sUVLl7lLRTQiIpGjZwosNG0TjXeHtLF63H1a8QHpTxvVUAWOyMACbOKfLTWHsvLBsKSBfM5tHiqoxa31tiOZXHfPcMNMxgtStFR/UnthcNg8lmw+i1o0x+Gr4BBnzoWM70IoacsSU+LiklJSYbjVDRqxGa3kxBroXWjWl5f9WuEv31btCIWeQQgYVaRj1179l0HwtQXnnb1qXRS5mnMboc/FtNr5YvcGL8W+45a0GMkdL0bajf16Ty+uO8efnQse3btomHjprw88XkAt8+Af1EjNruWYlhBRr7dCEDCrCIfu9YeE3UCYa9BVxwofhorzIU1M+G1c+CDW0jM3c34f7px5ltg6fMMKR3P87n2iDf3HTjEfOGPv5L86Od8sGyta5FxxowZbpZ5tWrVKnyNNrvnZhmCOYiQRwCR1PtPMMauCYr/tnHTZmW21U9UvNy/gaMC4cIHIK4GDJjJfwuH89yc/+OPvzNdzSiGDRvmk5j74r6zG3QIAsjNzXX9/+DBgz6f0zWu1lhFaYKKfL0RQmpqKhkZGdjtdjIyMkTEIwwdJIt8zLgnXC3OzkiyMP2KBHaOrsHoTnnQ7BwYsgiGfQftrmfutBc9umHKw5ObruR2V6u3EhdaniXvCza7LnNzEMxFhFwol9KLZPfee2+VKy3qcK34JkYVKb167YAb6Tfger4YXI8t91VnSKc4MpPOh/tWw60fQsr5rgXMA3t3G47hy6K4L+47V6u3EtdpxkK8PUgLxcJJZLFT8IrRItn06dNd+6tCzHt6ejqvj36Y4wf2kvKG92Qtn3MCbEWw5TOaLJ/MwlbryI+tw0+nDGDkn51Z8+CthmPXb9SErD27ymy31KxPythF5VxFHapdfA/538/BduwA1lr1qXbhIMb/VofxpT4bF3NSdM1YiC+yaYkPDzLKMEssyHTp0kWvWbMm5OcVKk5KSopPMcbJyclkZGQEf0IhprQwg8OS9bSG4en7cn0/+dmwbi6seg2O7KSoTkueyOpJp6vuIeMoTPvuT7Y/d4XhXN5+Zw73DL+bwvw817bY+ARuGP0MnXtfFfjFAjXirQzp0ZK4GMfDekWv34gb31iJAj68u7spc6zKKKXWaq3LBuTr4mI9oXx17txZC9GBUkrjKIHt9aWUCvdUg0JycrLh9SYnJxse7+n7alLTovXXj2v9XHOtH6+l9VuXab35c73nULZOfnShTl+1Q7+weIs+9d+LvM5n7ty5Ojk5WSuldHJysp47d67p12x0ztYmLkEAACAASURBVNjaDTV+nvOcIY/rxHqNQjrnygqwRhtoqgi54BVPQuarsEU7noTZ042r9PfVtoFFz+yfoPP/U0vrJ+po/eFArXf+7Dp+37FcnfzoQj1nZYZ+/sst+vRx3oU8XJz91Fd63Me/etzv6QYzd+5cbY2Nd/tOEhMTRcz9xJOQy2JnFSCQvodGi2Slqcwx7558wfXq1TPc7vy+ere08mVqIhvvrcGNbWP5u95FMHIt3DgHmp/jOt6ZKGOz2dHacy1yXwlWj0ullMeqi6UbKZcMixw/fjy2Qv8ibYQKYKTuwX6JRR465s6dqxMTEwOyiEpbW/fcc0/IH+/Dxdy5c3VcXFwZizw2NrbsdRfma73+fX0wrY3Wj9fSe8bU0BOubKjnzX7D4/hHThTo5EcX6reWb9fPLtqszxj/RUBzDfRn7YmuaV/rR/63wXCfN/dTRZ9oBO/gwSKXxc5KTrmLbz5yIDufrzfvK1OPu33T2nRoVifQaUY09evXN6wE6PoO847C2ndg1etwfDc0aA3dR0CHGyHGe/2Q7Pwi2j2+hHH9WnMgu4B3V+5gy9N9/ZpnID/r8sood39uKeefXp+JN3Qs81mLxWJYWlcp5THqpbIujgcbT4udEn5YyTGrINeclTuYvHRbme2tTqnpV4uzaOLQIeNa2hz9BxaPg19mQ0E2tLwQ+k+G0y/xuQKhM1HGZndkVgaSN+Pvz9qXkEmLUiz6bQ+r/i57Q4ur3YD8I/vLbK9VvxFpaWkMGnoH9hLulcrsigsXIuSVHLMKcuUX2YiLsbDikV6ubf9dsJHf9x4PeI6RTunv8OzGFh7qHs8NbWMdvTDbXQ89RkDjstZqeThrSdm1o61EIIW5/P1Z+1JGecTFp3tsDlHjtlF8M+NpigpOhkUqawzZ2ScYOHAg1mo1iY2LpyDnuDRNCRIi5JWctLQ0UyrYae2wHhvWSnBtqxZrJfSOudCTlpbGkNvv5LLkIh7qHkevljEcy9f8Ue8S2gx9FWqXrZfiK87FzpV/HSSv0BbQUqe/P2tfLPlburbglq4ebgg3dSK9W7LLNVOvXj0OHTmKLfcYAEU5xyAugXfffVcEPEiYErWilOqrlPpdKfWnUmqsGWMK5mBWQS67XbvV4ACH9Wh2Q96IozCP1NZF/PlwIxbdmsjp9Sw8s7oai898mTajPwpIxAE+eP899r5xO+l3defTsdcQ8/cPfo/l78/alzosvpzbWSuoRo0aaFuR2/6igjyJVAkmRiugFXkBVuAv4FQgDtgAtPH2GYlaiT6e+Gyjbvf4YrdtD3y4Tp/3/NIwzSjIZB/Q+rsJWr9wmtaP19IZaWfriROf1rqowLRTBDPKJJzzwCBKBYlUMQWCGEfeFfhTa71da10AfABcbcK4QgShDRorWJSi0hnkB/+CRWMcJWSXpUGTs2DQZzx2yjSWV+sF1ljTTmVGZUEzMLOMcnp6usdyv9IoJXiY4SNvCpTsrJoJnFv6IKXUMGAYyA80GjFqrGBRHjq6RyM7f4IfJ8PWRQ6x7nCjI4Sw4ZkA2Jf9ZHoZ20hq8WdWt/rx48d7/J2QSJXgEbLFTq31DGAGOOLIQ3VewRyMyrgqPGf7RQV2m0O4f5wCmT9DQh24YAx0HQY1T3E71OiJJFAqY4s/bzchWegMHma4VnYBzUu8b1a8TahEGHXIsViKO7pHASVT1888PZnVrw2DKZ1h3kA4sR8unwgPbobe/y0j4hCcVm+VscWfp5tQ7QaNQzyTqoUZQr4a+JdSqqVSKg64GfjMhHGFCEKXEDKnKD4/oBPrX0iN+MYSzoSXvAM7eapnHCuuO8Q5WR+SlYuj9snIX+DcYRBX3eMYdq1Nb/VWGVv8paWlERtbah1BWblk0OjwTKiKELCQa62LgBHAEmALME9rvSnQcYWyBKsgki/YisMPSxZIQmsKju6vcA/HUDPrhbG82tvOjtE1GHdBHP+3w0aPt09wzutHoM3VYLGWO4ZdE5RWb5WxxV/pG57VauG80+uHaTZVBKNQlmC/JPzQnVkrtutTbxir4+o01KB0XJ2G+roHJ7gdE6xQNV/rW4+Zt173eG5phetzhw27Xevt/6f13AFaP15LnxhXU7/WL0GfXs/iVzjcgOk/6FtmrAzihCsHUfP7EaXgIfxQMjsjgPT09/j700no4noUBUf28+mUx0g/u6nLQnv40X8bhqrdM/phfrK08eu8v6/4gm/fesaVWu2tbZvDtRBZkRaG2Aph06ewcgrs2QCJ9XnplwSe+yaLg7nu/vyKLCrag7DY6aS8glXRRMT/flRSRMgjgFXzprpE3Im9MN+t1sWe3ZmGnz1+YC+/7Trq13lXvjfFrT4GlK2x4cQZtRGxkRZ5x+CXObBqOhzLhPpnwFWTocNNNGr6EblLhwEVS10vKbAJdRpy9vX3wJ1lImsDwucen1FCxP5+VHaMzPRgv8S14g6UX7O5TsPGpj+yVqRW9P3v/6IveuHbiMlGdHHkH62XjNf62WaOFmoz+2m99UutbTa3wyraIs3oOq1xCaZfZ2VzRUTc70clA2n1FrlUT2pU7h/zgDETtDK5ZVZFRGTEe7/oXhOXaa1PiiJK6djaDcPzR7p7vdbz79T6yXpaP1FX6/8N1TpzrWnDh0pgK2PjhXD0Fa0qeBJyafUWAXS+/l6sse4NCCyx8W6P/u0uuoImV44yNVTNKI65WjVjl0PJWtnOSIunPtvIGaPmhM4FoDVs+xpm94c3LoTfv4Cud8Oo9TBgJjQ927RThcrXa0bBKkEQIY8AWnbrQ8dbHnGJdM36jWnQuQ/jx493hRpuWLaQeh0uNjVUrXQcs7VWAyZOnmo4rnFmZ4hS9IvyYd1cmNYd0gfAgW1wyZPwwCbo+yzUMV/0QiWwlS0pyFv/TiGIGJnpwX6Ja8WdgW//pK+eusL1/vIRz2pLKTdKTHyCbnHdo0Gbw4erd+qkK8fops2aGz4SD5uzWveZ9H9un0lbtFm3/s+XQZuTPnFQ6/+bqPXEfzn839N6aL3+fUdvzCBj5OuNCYKP3HmuyuKKqGw+/0gDCT+MXErX+v7hgylurbEAivLz2Lt0FvB8UObww+JPObR4KrrIcd7S0RNGKfqKIKXoH/rbEX2y7l0ozIHTesO1r8OpvXxuoRYozqcSZ9RKfJ2G9LhpRFDcSGYVrIoEJPwwPIhrpRxCkU1Zuo7HsYN7DY8rOJpl+rmdvD9tgkvEneTk5DB48GAsFgvvP3gVe9Z+7bZfKZOLZmWugXmDYMrZsGYmtLkGhv8AAz+G0y4OmYg7KZl1ef5/PqTV+ZeH9PzRiPj8w4MIuRdC5e8rXcejVv1GhsfF1Wlg6nlLcmDvbsPtNpsNrTUnDu5l4wcvuF27KWVs7TbYshDe7gNv9Ybt38F5o2D0b3DtdGjULrDxTSIYtVYqI5XN5x8tiGvFC740pTUDux0318rFt41iwdTH3ZKEYuISSO5zhynnM8okrN+oCVl7vBettJVKUlLKEUjiFwU5sOF9WPkaHPrLsWDZdwKcdRvE1/BzUGMWrN/Fb5n+JU05yTqeT5smIuTlUdolFe2ZqtGCCLkXQuXvs2tNbAkh79jzStbsOIR17YeuP4bWV95FbnKPgM/lKZOw11U38MVHH5Rxr5Sm5LX71bMzOwtWvwk/vwm5h6DJ2TBgFpzZH6zB+XV8euFmjuQUEh8T2ANouya1TJpR5aYy+fyjBqMV0GC/oiVqJVQr8Ne+tkLf9tYq1/unPt+k2/zXPRrkvvS1roScQPB0TQ0bN9NJV47RjZs6olasVmu51/7Skq06ZexC306c9YfWn92v9VMNHBEo792s9d8rHMWtAsCXiI92jy/WT3y20e8xKlNUiRDdIAlBFSdU/j5bqYJMVovCVsrStWuNxYQ6qp6eJrL27qJG214s+nEDdrud2bNnl7l2a5x7kpIqr2en1pCxAt67GaZ2gfXvQ6dbYcQauOV9SDkvoAVMX9cwimyaGA/fXXljSFy0EA2IkHshVIX/damoFYtS2O3uxxTZNFYTFts8RQ80bNwUwOUqKX3tifVOoXPqWLdrd05Hl1ZzWxFs/Aje7AXvXOFoo3bRWEcCz1WvQP1/+T3/klFEgwcP9ql5sc2uibEa/6qX1wA5UhokC4I3xEdeDqHw95XOmrRaCJpFnpaW5uYjB8dTxt1jxjN7P9hK3EBKXvuNb6w0aL6siucGVgXkH3dkYK6cBkd3Qr3T4MpJ0PEWiK0W8NxL+/dtNpvhcaWfOgrtdrc1CG/Hlt4ucdFCNCAWeQRgs+Mm0lalsJUK0HZ06An8XJ6eMvpefYPrPEZoDyn6AProLvj6cXi5LSweC7Wbws3vkV7vAVIGPIUlvropMfhG1rERJZ86bHaN1mC1GH955cU9S1y0EA2IkEcAZVwrxW/sJUTV5kWMKopRezFn+KMnIbfZywp5g5w/eSl2OtYpneDHyXBaL7hzKdy+mPS1Rxh293BTfcu+WMGl1zCKin1UMVZji7y8dRCJixaiARHyCKCMa6X4/yXdK3a7xoMWmYJLyD2sXjpS9HEsYP65FN69lpvX3kxfy8/YOg+F+9fBjbOhWRcgOL5lT1aw1Wr1uIZRZHNcj6fFzvLWQSpjg2Sh8iE+8gjAZnf3f1tKWMex1pPHWIPR/bcYq8FTgNt+ewEXnPgOpj8A+zdBjUasajmCu7d04KfLBhAT697AOBi+ZU/+fW/C6hJyL36p8tZBJC5aiHTEIo8AtIZtP3zpisZ47JaLyN60zC3ZxmbgozYT51NAUWkhzz0CKyYx49AdDDv0Amg7XD0NRv/K+pTbOUoNw6SgYPiW/bGOna6VWC+PM6GopyMIwUQs8ghg99qv+evjl139Mw/t241aPJX33+vAHUMGAQ5LOS7AzERvON3vLh/54R3w0+uOPpgF2eyM6cScpDE8MPxeV9yh8wHByBvjyXoO1LdcUevYeWPy9DRT2XpmClUTscgjgIzFb5VpgqyL8nlkzAMuS/GL8dex8+clQZtDTLGS1zz0K/xvKEzuBD/PgNZXwN3LGVfzGTZXP9ctgUfhDD8sq+SR4lsuLI6njPWwUCxx4kJlQIQ8TJR8nM8/st/wmEOHDrmiPnIP72PVnOeD89hvt1Nz59d8GPcU5317A/rPrynoeh8n7vmF7CumkV2vDUU2e5k4cldCkIdhjaJjQu3GsJVjkUucuFAZECEPA6XTvn3FVpBnrqVYmAdr34HXutLki6E0VQd4uvA22h+dxBn/153k26dQp2ETaibEsezJG/h7lfsTgdNnr+0GYxsQjnT3Qtdip7GQS5y4UBkQIQ8Dvia2GGGKpXjiIHw3ASa1hc9HQVwi+rq3+PnKb2jUZwyj+nXmktg/OP71NGzHsgCN7VgWy2eluYnuSYvct5tRONwYJxc7jX/VJU5cqAzIYmcY8CbGSikaNG7KgcNHseceL7M/IEvxwJ+w6jVH8aqiXPhXH+gxElLORynFdSUOTRv0KgX5uW4fz8vNdatHXjJF3xfC4cZwhh96cq1I/WyhMiBCHiJKNnOwWCyGdUKSk5PJyMgA4M1Zsxl1373k5gYY9aE1/PMT/DgFti4Cayx0vBm63QcNW3v8mC+i67FolgdatGjBjh07DLd74vEFG9myt+wNzVdO5BcB3sMPJU5ciHbEtRICSvuGjUQ8Nj7BTaTvGjqYN98MIOrDboNNn8Jbl8DMPrDjB7jwIRi9EfpP8Sri4JvvWFXQIq+oG8Nu18xeuYPdR3KxKPx61UyI4eLWDWnXtLZvkxSEaMSoSHmwX9HSWMIsPDVzsFqtWimlY2s31Nc9+Lw5J8s7rvWq17We1N7RwOGVjlr/NEPr/OwKDTN37lydmJjoNt/ExES3pgrvrszQyY8u1PuO5vo0nvN7cDatKK9Jw4n8Qp386EI9/bs/KzR3QaisII0lwocnN4Xdbsdut9Nq1Bw69rwqsJMc3wtLn3IsYH75CNRsBDfNhZFroetdEFe9QsP5EgfuilopZ6ySTyTgKD/rtMS9PWGcyHc8uVSPs5bZJ9mYgnAS8ZGHgPJ8w/ZS1Q8rxP4t8ONU+G0e2ArhzCuh+0hocW4AM3ZQnu/Y6SMvr2+nv02scwscQl4tzv3XVLIxBcEdsciDzPG8QppcMhRLbLzbdktsPHUuHMQVk5eTU2irWNMIrWH7dzB3AEzr5ujGc/Zgh/V901xTRLwknqxfbyn6JfE3WuVEgWOhsrRFLtmYguCOWORBJuNADrvrd6HTLY/w+6I3OXFwH9WTTqHL9fdyeo++ADSpU40+bRuVP5itEDZ94qj9vfc3qN4Qev0HzrkDEusFZf7erN/YMy4EyrfIPT2RNG/enPwi4y4/AMdyCwFIjHf/NZVsTEFwR4Q8yDjre78w9j56z37Kv0HyjsLa2Y4iVsd2Qf1WjsiT9jdCbIKJsy2LN+t34vzlQPkWeVpaGoOG3oG9MN+1TcXEc6LDDbT6z+Jy51CjlJD7E8YoCJWZgIRcKXUD8ARwJtBVa73GjElFCyVjwz0lkjitVb/6bR7NhFXTHSJecBxSLoArX4HTLzlZrjDIeLN+neGH5Qn51QNuou4H68hbmc6JQ/uo26Ax/e8cwzmX9C/3/DUTYujYzD10MFiVFQUhWgnUIt8IXAe8YcJcogpfF9ycjRqsqgJCvns9rJwKGz92vG97LfQYAU3OMmfyFcCb9evykZcTt3L4RAE12vZi2uOjuLFL84DnJNmYguBOQEKutd4CJxNDqhK+RmKUV33Phd0Of37j8H9nLIe4mtDtHjh3ONQJXPz8xZv16/yxZ+cXuTIojdh9xJHqXy8xzrR5STamIJwkZD5ypdQwYBhUDl+mN5dDSZdLw8ZNKTr7Jiyqm/FAhXmO0MGVr0HWVqjZBC59GjoPhoTQZiN6cxUZbV/4624Arpi8wqfxk2qYJ+SCIJxE6XIcnEqpbwCjkIrxWusFxcd8Bzzkq4+8S5cues2a8LjTffFr+0JKSoqhyyEpKYnc3Fw3C1bFxPPExMk8NnrYyQNzDsGat+GnGXBiPzRq74j/bnstxIRe8Eq7iqD8fpgn8ouYvzbTa+SJkxrxsdx0TvOg9h0VhMqOUmqt1rpLmR1G6Z4VfQHfAV18PT5cKfq+pJ2XprDIpo/nFerjeYX67Xfm6OYtWmillE5KStKxsbFlxkpKSjJMx69dt55OTk7Wp9Wz6HdubKALn6jvSKF/9zqt/1qmtd0esu/BCE9lBJKTk8M6L0EQToKHFP1yLXIf7xLfEQUWuScrumTVwdL0feV7tu49TvamZRxaPBVddDKEDmsMKrYa5Ge7rPuBAwcaVgPs1szKmO5xXHdmDEV2+GCzpt4Vj3Hl7Q+bdXkBYbFYDOetlMJu97FzhCAIQcWTRR5QDJtS6lqlVCbQHViklApeU0kT8CeRZOehHLq2rIde/b67iAPYirDEJfDK17+7WpmV9P9bFFzTOoYVQxNZeUd1Lm4Zw/MrCkh5JZvBH2cz4qnXTLkuM/CnU47UOxGEyCAgIddaf6K1bqa1jtdan6K17mPWxIKBP2Jl15qzmtfh8P49hvttxw5QZDtpsaalpZFUK5F7usSy9b7qfHJTIo1rWhj5ZS4tJh1n/Lf57Ml2WL6RlIlY0RKzZrZtkxuCIASIkb8l2K9o8pH/a/wX+rkvtnj0IcfUaqAnfLnFcfDxfVovfUbnPtlY68dr6VV3Vtd39GioY6rV9Mv/7Cz9qpQqt+SrGVTkfGb51P35mZh5HYIQTeDBR16lhFzriv+Rn/bvRXrCl1s8Ck7jax7Wr89fpPWCEVo/1UDrx2tr/d4tWmf8qLXdrj/fsEsnXTlGJ1SrViGxCobAmYlSylDIlVIVGsfsRdZI/94EIRBEyP2k5diF+sUlW7XWpW8CLfRXMx7T3z3W0xF98nRDrT8frXXWNrfPL1i/Syc/ulC/NO3tCt1AIj2KxKz5mXVDMHteghCJeBLySlnG1kyfq12fzFxNTU0l468/sW+YR8a/U7h01yu0V3+yrNEd8MAmuHIS1D/d/fPFmZ3X3nATGRkZ2O1218KoNyK9wp9Z3ef9WbfwRqR/b4IQDCqdkJu5COcUYYsC8o87si8nd4KP7oCCbLjyFa6yvs7XpwyF6vUNx7D5U2sF8wXObHzpIOQLZt0QnET69yYIwaDSCbmZTQfsWtOIg1yQMRlebgtLxkGdFnDLB3DfaugyFJs1AZvNcyy+s4xtRYsVmi1wwSA1NdXrU4YvT0Zm3RCcRMP3JgimY+RvCfbLTB956cVLDPyj+ONz3fOrLpp/ly54rK62PV5H63lDtM5cU+awHs8t1Q98uM7jMO//tEMnP7pQ7z6SU9FLi+roi3AuOkbz9yYI3iCYmZ0VxazMTqP6IEopjK7JW/amC63hr6Xw4xTY/h06tjqzci8g9rx7GXj5RYYf6TlxGR2b1+HVm41LzKb/tIPxn2zk53G9aVgruE0gIgl/smgFQfBOUDI7w42RG0VrXaasbrmP1kX5sC4dpveAudfD/q1wyRPkjviVp4oGcSKxmcePWi2KIrvnm6HLz17FikXJoqMghI6oFnJPoqC19s3nmnsYlr8Mr3SABfcCCq6ZDqN/g/MfwJ5QB/C8UJmens6PaTczLbWLRx+wzbVgWrWEXBYdBSF0RHXPTk/da8p9fD+c4Wih9su7UHgCTu0F10yD0y6GEoLrFGEjDXa6dfLK6RDkXAetaNRKtCPt2AQhdES1RV7hCIXMtfC/ITD5LFj9FrTpD8NXwKBP4fTeZRTb6Ws3sqZ9jY456VqpyJVFP2ZHowiC4JmosshP5BeRnp7O0088xq7Mf2jarDk333obXy1Z7Hr/3yeeot81N3D4RAEAtRKsWLctcSxg7vwR4mtDj5HQ9W6o3dTr+Zyub6NmCL76gJ3hh1WxoYK0YxOE0BBVQn7b2JdYMPVxVznZzH92Muud2dTrO4IWt/YC4Nk/4NmnvyaeAq63Luf+xCU0KsyE2s2hz3Nw9kCIr+lTpyBbyYSgUnhrSmw8RtUTckEQQkNUCfnKeVPL1ATXRfmw+n2eGHsfAPEFhzjznw9p/c88EgoP84f9dBoNmAlnXg1Wx+WWDlv05N92ulaMmksb+YDjE6px4z2PsHjjXte2P/YdB6qmRS4IQmiIKiHfv2eX4fZD+/cwpFWRI4V+w/tQlAdn9GVKXl/+l5XM9+0udjvem3+7pJB7c604jxs++iGyD+zDWqs+NS4cxLzDycybu9bt2JrxMVVusVMQhNARVUJu5M44v4WVx3rXganngDUOOt4M3UdAgzPInP8reXv2lxmnov5tT8Z0amoq1994M9uzTnidd4Oa8VUujlwQhNARVULudGfk5+Zw3ZkxjOkez7nNrORZ4uCCB+GcO6FGQ9fxCbEW8ovK9pv01b9tt3t2rZw8h5U2TWr5e0mCIAgBE1VCnjrgalodWcYp2z+ieU07fx+z8HODG+l61ySISyxzfEKslbxCW5ntvsY46yoaAy4IQnQRVULOwgfokvU/aNMNeoykZavLaWmxejw8PtZKfpG9TNq+079dbtSKn5ULBUEQQkl0SdT5D8AdX8MdS+DMK8GLiIPDtZK9aRnJBqVUS5Zg7devH4MHD0YpRUxMDPfeey/gKGMLEjooCEJkE10W+SltPO4yigtf93sWhxZP5WBxyOKOHTu48667yDjoWJx87YWn2bMr020cm83G9OnTAXjwiQmAdx+5IAhCuInqMrZOjMrZJiYmYo2N4/jRI2WOVwk1oaigTEx6SaxWK5syD9Pnle957dazuaJDY9PmKwiC4A+eythGl0XuAU9x4ZBjeLzOO17umDabrYRrJeApCoIgBI3o8pF7IBg1rq1Wq0vIxbUiCEIkUymE3FON66SkJMPqiElJSeWOOWzYMOzFIeiSXi8IQiRTKYTcUznbV1991bCU6quvvlrmeCdWq5V77rmHadOmiWtFEISooFIIubfa10ad3ksfb63VgKlvzkRrTVFREdOmTQM8hx/60h1eEAQhVFSKxU6oeO1r5/GfrMvkgQ830O+anmWOcQl5CZPc18qJgiAIoaJSWOSBkBDjSCrKLyqbyu+sfljSteJrZyBBEIRQUWkscn9JiHUI+YptB8g8lOu27/fiWuIlXSvSHV4QhEijygt5/RrxADyzaIvHY2pXi3X939fKiYIgCKGiygt5+2a1WTrmInLyy7pWABLjrZzWoIbrvXSHFwQh0qjyQg64CXV5+Fo5URAEIVRU+cVOfzAKaSyJhCcKghBKxCI3GQlPFAQh1ARkkSulJiqltiqlflVKfaKUqmPWxKIVCU8UBCHUBOpa+Rpop7XuAPwB/DvwKUU3Ep4oCEKoCUjItdZfaa2Lit+uApoFPqXoxlMYooQnCoIQLMxc7Lwd+NLTTqXUMKXUGqXUmqysLBNPG1l4KuAl4YmCIASLcoVcKfWNUmqjwevqEseMB4oAj+EZWusZWusuWusuDRo0MGf2EYi3Al6CIAjBIOBWb0qpIcDdQG+ttXFLnlKY3epNEAShKhCUVm9Kqb7AI8BFvoq4IAiCYC6B+sinAjWBr5VS65VSr5swJ0EQBKECBGSRa61PN2sigiAIgn9Iir4BkmIvCEI0ISn6pZAUe0EQog2xyEshKfaCIEQbIuSlkBR7QRCijUov5BX1d0uKvSAI0UalFnKnv3vHjh1orV3+bm9iLin2giBEG5VayP3xd0uKvSAI0UbAKfr+EKoUfYvFgtH1KaWw2+1BP78gCIKZeErRr9QWufi7BUGoClRqIRd/tyAIVYFKLeTi7xYEoSpQqX3kgiAIlYkq6SMXBEGoCoiQC4IgRDki5IIgCFGOCLkgCEKUI0IuCIIQ5YQlakUplQXs8PPj9YEDJk4nnMi1RCaV5Voqy3WAXIuTZK11g9IbwyLkgaCUWmMUfhONyLVEJpXlWirLdYBcnK0eKAAAA2VJREFUS3mIa0UQBCHKESEXBEGIcqJRyGeEewImItcSmVSWa6ks1wFyLV6JOh+5IAiC4E40WuSCIAhCCUTIBUEQopyoFHKl1NNKqV+VUuuVUl8ppZqEe07+opSaqJTaWnw9nyil6oR7Tv6glLpBKbVJKWVXSkVlmJhSqq9S6nel1J9KqbHhno+/KKVmKqX2K6U2hnsugaKUaq6UWqaU2lz8+zUq3HPyB6VUglLqZ6XUhuLreNLU8aPRR66UqqW1Plb8//uBNlrr4WGell8opS4DvtVaFymlJgBorR8N87QqjFLqTMAOvAE8pLWOqjrFSikr8AdwKZAJrAZu0VpvDuvE/EApdSGQDczRWrcL93wCQSnVGGistf5FKVUTWAtcE20/F6WUAqprrbOVUrHACmCU1nqVGeNHpUXuFPFiqgPRdzcqRmv9lda6qPjtKqBZOOfjL1rrLVrr38M9jwDoCvyptd6utS4APgCuDvOc/EJr/T1wKNzzMAOt9R6t9S/F/z8ObAGahndWFUc7yC5+G1v8Mk23olLIAZRSaUqpf4BU4LFwz8ckbge+DPckqihNgX9KvM8kCgWjMqOUSgHOAn4K70z8QyllVUqtB/YDX2utTbuOiBVypdQ3SqmNBq+rAbTW47XWzYF0YER4Z+ud8q6l+JjxQBGO64lIfLkOQQgGSqkawEfA6FJP5FGD1tqmte6E46m7q1LKNLdXjFkDmY3W+hIfD00HvgAeD+J0AqK8a1FKDQGuBHrrCF60qMDPJBrZBTQv8b5Z8TYhzBT7lD8C0rXWH4d7PoGitT6ilFoG9AVMWZCOWIvcG0qpf5V4ezWwNVxzCRSlVF/gEaC/1jon3POpwqwG/qWUaqmUigNuBj4L85yqPMWLhG8DW7TWL4d7Pv6ilGrgjEhTSlXDsahumm5Fa9TKR0ArHFESO4DhWuuotJ6UUn8C8cDB4k2rojECRyl1LTAFaAAcAdZrrfuEd1YVQynVD3gFsAIztdZpYZ6SXyil3gd64iiXug94XGv9dlgn5SdKqfOB5cBvOP7eAcZprb8I36wqjlKqAzAbx++WBZintX7KtPGjUcgFQRCEk0Sla0UQBEE4iQi5IAhClCNCLgiCEOWIkAuCIEQ5IuSCIAhRjgi5IAhClCNCLgiCEOX8P+10S6X/95gjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bin data with Kbins Discretizer"
      ],
      "metadata": {
        "id": "8hPDb58ws6wY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import and train discretizer\n",
        "from sklearn.preprocessing import KBinsDiscretizer\n",
        "\n",
        "discretizer = KBinsDiscretizer(n_bins = 10, strategy = 'uniform')\n",
        "\n",
        "discretizer.fit(X)\n",
        "\n",
        "print(discretizer.bin_edges_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3kpBrw_s-6j",
        "outputId": "77411483-4ecf-46a3-bba2-9f45c230819a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([-2.9668673 , -2.37804841, -1.78922951, -1.20041062, -0.61159173,\n",
            "        -0.02277284,  0.56604605,  1.15486494,  1.74368384,  2.33250273,\n",
            "         2.92132162])                                                   ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bin data\n",
        "X_binned = discretizer.transform(X)\n",
        "\n",
        "print(X_binned)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vmTNDMxsOS7",
        "outputId": "a7cf73f9-3953-43ab-9330-0f1a1b7e65f0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 3)\t1.0\n",
            "  (1, 9)\t1.0\n",
            "  (2, 7)\t1.0\n",
            "  (3, 6)\t1.0\n",
            "  (4, 1)\t1.0\n",
            "  (5, 1)\t1.0\n",
            "  (6, 0)\t1.0\n",
            "  (7, 8)\t1.0\n",
            "  (8, 6)\t1.0\n",
            "  (9, 7)\t1.0\n",
            "  (10, 0)\t1.0\n",
            "  (11, 9)\t1.0\n",
            "  (12, 8)\t1.0\n",
            "  (13, 2)\t1.0\n",
            "  (14, 1)\t1.0\n",
            "  (15, 1)\t1.0\n",
            "  (16, 3)\t1.0\n",
            "  (17, 5)\t1.0\n",
            "  (18, 4)\t1.0\n",
            "  (19, 2)\t1.0\n",
            "  (20, 6)\t1.0\n",
            "  (21, 1)\t1.0\n",
            "  (22, 2)\t1.0\n",
            "  (23, 3)\t1.0\n",
            "  (24, 4)\t1.0\n",
            "  :\t:\n",
            "  (75, 7)\t1.0\n",
            "  (76, 7)\t1.0\n",
            "  (77, 0)\t1.0\n",
            "  (78, 3)\t1.0\n",
            "  (79, 1)\t1.0\n",
            "  (80, 8)\t1.0\n",
            "  (81, 6)\t1.0\n",
            "  (82, 3)\t1.0\n",
            "  (83, 0)\t1.0\n",
            "  (84, 3)\t1.0\n",
            "  (85, 3)\t1.0\n",
            "  (86, 7)\t1.0\n",
            "  (87, 6)\t1.0\n",
            "  (88, 8)\t1.0\n",
            "  (89, 4)\t1.0\n",
            "  (90, 1)\t1.0\n",
            "  (91, 7)\t1.0\n",
            "  (92, 7)\t1.0\n",
            "  (93, 5)\t1.0\n",
            "  (94, 7)\t1.0\n",
            "  (95, 4)\t1.0\n",
            "  (96, 5)\t1.0\n",
            "  (97, 4)\t1.0\n",
            "  (98, 0)\t1.0\n",
            "  (99, 1)\t1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create and train discretizer with onehot encoding\n",
        "encoding_discretizer = KBinsDiscretizer(n_bins = 10, strategy = \"uniform\", encode = \"onehot-dense\")\n",
        "\n",
        "encoding_discretizer.fit(X)\n",
        "\n",
        "X_binned = encoding_discretizer.transform(X)\n",
        "\n",
        "print(X_binned)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMXOwwSbsmhd",
        "outputId": "3d954c35-2f23-455c-d84c-7823e7d2f22d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compare binned regression models"
      ],
      "metadata": {
        "id": "ITqhIiWEtVV4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Bin test data\n",
        "\n",
        "X_test_binned = encoding_discretizer.transform(X_test)\n",
        "\n",
        "#create linear regression model\n",
        "\n",
        "model = LinearRegression().fit(X_binned, y)\n",
        "\n",
        "#create decision tree model\n",
        "\n",
        "decision_model = DecisionTreeRegressor(min_samples_split= 3).fit(X_binned, y)"
      ],
      "metadata": {
        "id": "mDPDQEjgtaj4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#score models\n",
        "\n",
        "linear_results = model.predict(X_test_binned)\n",
        "\n",
        "decision_results = decision_model.predict(X_test_binned)"
      ],
      "metadata": {
        "id": "YMqWHhbwuiOa"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot results\n",
        "plt.plot(X[:, 0], y, 'o', c='k')\n",
        "\n",
        "plt.plot(X_test, linear_results, label=\"Linear Results\")\n",
        "\n",
        "plt.plot(X_test, decision_results, label=\"Decision Results\")\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "9WHkIA6YuUAt",
        "outputId": "7e85fac3-94e4-47eb-df7d-c581d177f461"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb0a74a8d90>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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": [
        "Build a linear regression model on staked data"
      ],
      "metadata": {
        "id": "Sc0rm1SmvisY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#stack data\n",
        "\n",
        "X_stacked = numpy.hstack([X, X_binned])\n",
        "\n",
        "X_test_stacked = numpy.hstack([X_test, X_test_binned])\n",
        "\n",
        "print(X_stacked)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0OCjPMxFvnPd",
        "outputId": "1a252710-8eae-47c3-d8b5-19ffc27df25e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.75275929  0.          0.         ...  0.          0.\n",
            "   0.        ]\n",
            " [ 2.70428584  0.          0.         ...  0.          0.\n",
            "   1.        ]\n",
            " [ 1.39196365  0.          0.         ...  1.          0.\n",
            "   0.        ]\n",
            " ...\n",
            " [-0.43475389  0.          0.         ...  0.          0.\n",
            "   0.        ]\n",
            " [-2.84748524  1.          0.         ...  0.          0.\n",
            "   0.        ]\n",
            " [-2.35265144  0.          1.         ...  0.          0.\n",
            "   0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#use linear regression model on stacked data\n",
        "model = LinearRegression()\n",
        "\n",
        "model.fit(X_stacked, y)\n",
        "\n",
        "results = model.predict(X_test_stacked)\n"
      ],
      "metadata": {
        "id": "sZKn_md2v5jE"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot results\n",
        "\n",
        "plt.plot(X[:, 0], y, 'o', c='k')\n",
        "\n",
        "plt.plot(X_test, results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Mvs1hcCZwhoP",
        "outputId": "db762722-5c3b-4b4a-99d3-f57613f06416"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb0a7dc6610>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}