Underfitting_Overfitting_Check_Regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Check Underfitting and Overfitting for Stock"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import math\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_name = 'AMD'\n",
        "start = '2018-01-01' \n",
        "end = '2018-09-14'\n",
        "df = yf.download(stock_name, start, end)\n",
        "df = df.reset_index()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "        Date   Open   High    Low  Close  Adj Close     Volume\n",
              "0 2018-01-02  10.42  11.02  10.34  10.98      10.98   44146300\n",
              "1 2018-01-03  11.61  12.14  11.36  11.55      11.55  154066700\n",
              "2 2018-01-04  12.10  12.43  11.97  12.12      12.12  109503000\n",
              "3 2018-01-05  12.19  12.22  11.66  11.88      11.88   63808900\n",
              "4 2018-01-08  12.01  12.30  11.85  12.28      12.28   63346000"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>10.42</td>\n",
              "      <td>11.02</td>\n",
              "      <td>10.34</td>\n",
              "      <td>10.98</td>\n",
              "      <td>10.98</td>\n",
              "      <td>44146300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>11.61</td>\n",
              "      <td>12.14</td>\n",
              "      <td>11.36</td>\n",
              "      <td>11.55</td>\n",
              "      <td>11.55</td>\n",
              "      <td>154066700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>12.10</td>\n",
              "      <td>12.43</td>\n",
              "      <td>11.97</td>\n",
              "      <td>12.12</td>\n",
              "      <td>12.12</td>\n",
              "      <td>109503000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>12.19</td>\n",
              "      <td>12.22</td>\n",
              "      <td>11.66</td>\n",
              "      <td>11.88</td>\n",
              "      <td>11.88</td>\n",
              "      <td>63808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-08</td>\n",
              "      <td>12.01</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.85</td>\n",
              "      <td>12.28</td>\n",
              "      <td>12.28</td>\n",
              "      <td>63346000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "(178, 7)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.index.values\n",
        "y = df['Adj Close'].values"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": [
              "(178,)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": [
              "(178,)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.reshape((178,-1))"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "plt.plot(X, y, 'ro')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        " a = model.coef_ * X + model.intercept_\n",
        " a"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "array([[ 8.10233949],\n",
              "       [ 8.17837349],\n",
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              "       [ 9.62301945],\n",
              "       [ 9.69905345],\n",
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              "       [11.4478354 ],\n",
              "       [11.5238694 ],\n",
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              "       [13.27265136],\n",
              "       [13.34868535],\n",
              "       [13.42471935],\n",
              "       [13.50075335],\n",
              "       [13.57678735],\n",
              "       [13.65282135],\n",
              "       [13.72885534],\n",
              "       [13.80488934],\n",
              "       [13.88092334],\n",
              "       [13.95695734],\n",
              "       [14.03299134],\n",
              "       [14.10902533],\n",
              "       [14.18505933],\n",
              "       [14.26109333],\n",
              "       [14.33712733],\n",
              "       [14.41316133],\n",
              "       [14.48919532],\n",
              "       [14.56522932],\n",
              "       [14.64126332],\n",
              "       [14.71729732],\n",
              "       [14.79333132],\n",
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              "       [17.37848725],\n",
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              "       [17.68262324],\n",
              "       [17.75865724],\n",
              "       [17.83469124],\n",
              "       [17.91072523],\n",
              "       [17.98675923],\n",
              "       [18.06279323],\n",
              "       [18.13882723],\n",
              "       [18.21486123],\n",
              "       [18.29089522],\n",
              "       [18.36692922],\n",
              "       [18.44296322],\n",
              "       [18.51899722],\n",
              "       [18.59503122],\n",
              "       [18.67106521],\n",
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              "       [18.82313321],\n",
              "       [18.89916721],\n",
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              "       [19.0512352 ],\n",
              "       [19.1272692 ],\n",
              "       [19.2033032 ],\n",
              "       [19.2793372 ],\n",
              "       [19.3553712 ],\n",
              "       [19.43140519],\n",
              "       [19.50743919],\n",
              "       [19.58347319],\n",
              "       [19.65950719],\n",
              "       [19.73554119],\n",
              "       [19.81157518],\n",
              "       [19.88760918],\n",
              "       [19.96364318],\n",
              "       [20.03967718],\n",
              "       [20.11571118],\n",
              "       [20.19174517],\n",
              "       [20.26777917],\n",
              "       [20.34381317],\n",
              "       [20.41984717],\n",
              "       [20.49588117],\n",
              "       [20.57191516],\n",
              "       [20.64794916],\n",
              "       [20.72398316],\n",
              "       [20.80001716],\n",
              "       [20.87605116],\n",
              "       [20.95208515],\n",
              "       [21.02811915],\n",
              "       [21.10415315],\n",
              "       [21.18018715],\n",
              "       [21.25622115],\n",
              "       [21.33225514],\n",
              "       [21.40828914],\n",
              "       [21.48432314],\n",
              "       [21.56035714]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Underfitted Model\n",
        "\n",
        "Low variances and High bias\n",
        "\n",
        "Too simple and not generalize well\n",
        "\nLess Features"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear Regression\n",
        "# Underfitted \n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X, y, 'ro', X, a)\n",
        "axes = plt.gca()\n",
        "axes.set_ylim([0, 30])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.score(X, y))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6227203882581938\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "\n",
        "# Lesser the MSE => smaller is the error => better the estimator\n",
        "# [MAE] ≤ [RMSE]. The RMSE result will always be larger or equal to the MAE\n",
        "# 0 to 1000 - an RMSE of 0.7 is small error\n",
        "# 0 to 1 - an RMSE is not small error \n",
        "meanSquaredError = mean_squared_error(X, y)\n",
        "print(\"MSE:\", meanSquaredError)\n",
        "rootMeanSquaredError = sqrt(meanSquaredError)\n",
        "print(\"RMSE:\", rootMeanSquaredError)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 7690.334086691011\n",
            "RMSE: 87.69454992581359\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.c_[X, X**2]\n",
        "X"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": [
              "array([[    0,     0],\n",
              "       [    1,     1],\n",
              "       [    2,     4],\n",
              "       [    3,     9],\n",
              "       [    4,    16],\n",
              "       [    5,    25],\n",
              "       [    6,    36],\n",
              "       [    7,    49],\n",
              "       [    8,    64],\n",
              "       [    9,    81],\n",
              "       [   10,   100],\n",
              "       [   11,   121],\n",
              "       [   12,   144],\n",
              "       [   13,   169],\n",
              "       [   14,   196],\n",
              "       [   15,   225],\n",
              "       [   16,   256],\n",
              "       [   17,   289],\n",
              "       [   18,   324],\n",
              "       [   19,   361],\n",
              "       [   20,   400],\n",
              "       [   21,   441],\n",
              "       [   22,   484],\n",
              "       [   23,   529],\n",
              "       [   24,   576],\n",
              "       [   25,   625],\n",
              "       [   26,   676],\n",
              "       [   27,   729],\n",
              "       [   28,   784],\n",
              "       [   29,   841],\n",
              "       [   30,   900],\n",
              "       [   31,   961],\n",
              "       [   32,  1024],\n",
              "       [   33,  1089],\n",
              "       [   34,  1156],\n",
              "       [   35,  1225],\n",
              "       [   36,  1296],\n",
              "       [   37,  1369],\n",
              "       [   38,  1444],\n",
              "       [   39,  1521],\n",
              "       [   40,  1600],\n",
              "       [   41,  1681],\n",
              "       [   42,  1764],\n",
              "       [   43,  1849],\n",
              "       [   44,  1936],\n",
              "       [   45,  2025],\n",
              "       [   46,  2116],\n",
              "       [   47,  2209],\n",
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              "       [   50,  2500],\n",
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              "       [   59,  3481],\n",
              "       [   60,  3600],\n",
              "       [   61,  3721],\n",
              "       [   62,  3844],\n",
              "       [   63,  3969],\n",
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              "       [   68,  4624],\n",
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              "       [   70,  4900],\n",
              "       [   71,  5041],\n",
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              "       [   74,  5476],\n",
              "       [   75,  5625],\n",
              "       [   76,  5776],\n",
              "       [   77,  5929],\n",
              "       [   78,  6084],\n",
              "       [   79,  6241],\n",
              "       [   80,  6400],\n",
              "       [   81,  6561],\n",
              "       [   82,  6724],\n",
              "       [   83,  6889],\n",
              "       [   84,  7056],\n",
              "       [   85,  7225],\n",
              "       [   86,  7396],\n",
              "       [   87,  7569],\n",
              "       [   88,  7744],\n",
              "       [   89,  7921],\n",
              "       [   90,  8100],\n",
              "       [   91,  8281],\n",
              "       [   92,  8464],\n",
              "       [   93,  8649],\n",
              "       [   94,  8836],\n",
              "       [   95,  9025],\n",
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              "       [   97,  9409],\n",
              "       [   98,  9604],\n",
              "       [   99,  9801],\n",
              "       [  100, 10000],\n",
              "       [  101, 10201],\n",
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              "       [  106, 11236],\n",
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              "       [  108, 11664],\n",
              "       [  109, 11881],\n",
              "       [  110, 12100],\n",
              "       [  111, 12321],\n",
              "       [  112, 12544],\n",
              "       [  113, 12769],\n",
              "       [  114, 12996],\n",
              "       [  115, 13225],\n",
              "       [  116, 13456],\n",
              "       [  117, 13689],\n",
              "       [  118, 13924],\n",
              "       [  119, 14161],\n",
              "       [  120, 14400],\n",
              "       [  121, 14641],\n",
              "       [  122, 14884],\n",
              "       [  123, 15129],\n",
              "       [  124, 15376],\n",
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              "       [  127, 16129],\n",
              "       [  128, 16384],\n",
              "       [  129, 16641],\n",
              "       [  130, 16900],\n",
              "       [  131, 17161],\n",
              "       [  132, 17424],\n",
              "       [  133, 17689],\n",
              "       [  134, 17956],\n",
              "       [  135, 18225],\n",
              "       [  136, 18496],\n",
              "       [  137, 18769],\n",
              "       [  138, 19044],\n",
              "       [  139, 19321],\n",
              "       [  140, 19600],\n",
              "       [  141, 19881],\n",
              "       [  142, 20164],\n",
              "       [  143, 20449],\n",
              "       [  144, 20736],\n",
              "       [  145, 21025],\n",
              "       [  146, 21316],\n",
              "       [  147, 21609],\n",
              "       [  148, 21904],\n",
              "       [  149, 22201],\n",
              "       [  150, 22500],\n",
              "       [  151, 22801],\n",
              "       [  152, 23104],\n",
              "       [  153, 23409],\n",
              "       [  154, 23716],\n",
              "       [  155, 24025],\n",
              "       [  156, 24336],\n",
              "       [  157, 24649],\n",
              "       [  158, 24964],\n",
              "       [  159, 25281],\n",
              "       [  160, 25600],\n",
              "       [  161, 25921],\n",
              "       [  162, 26244],\n",
              "       [  163, 26569],\n",
              "       [  164, 26896],\n",
              "       [  165, 27225],\n",
              "       [  166, 27556],\n",
              "       [  167, 27889],\n",
              "       [  168, 28224],\n",
              "       [  169, 28561],\n",
              "       [  170, 28900],\n",
              "       [  171, 29241],\n",
              "       [  172, 29584],\n",
              "       [  173, 29929],\n",
              "       [  174, 30276],\n",
              "       [  175, 30625],\n",
              "       [  176, 30976],\n",
              "       [  177, 31329]], dtype=int64)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X, y)\n",
        "x = np.arange(1, 179)\n",
        "x = np.c_[x, x**2]\n",
        "a = np.dot(X, model.coef_.transpose()) + model.intercept_"
      ],
      "outputs": [],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(x)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": [
              "178"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting Model"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Polynomial features\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X[:, 0], y, 'ro', x[:, 0], a)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": [
              "0.9172297439488537"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Over-fitting Model\n",
        "\n",
        "Low bias and High variance\n",
        "\n",
        "Too complex and not likely to generalize well\n",
        "\n",
        "Required More Features\n",
        "\nLeads to small in-sample errors, but large out-of-sample errors."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(1, 179)\n",
        "X = np.c_[X, X**2, X**3, X**4, X**5, X**6, X**7, X**8, X**9]\n",
        "x = np.arange(1, 179)\n",
        "x = np.c_[x, x**2, x**3, x**4, x**5, x**6, x**7, x**8, x**9]\n",
        "\n",
        "model.fit(X, y)\n",
        "a = np.dot(x, model.coef_.transpose()) + model.intercept_\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X[:, 0], y, 'ro', x[:, 0], a)\n",
        "axes = plt.gca()\n",
        "axes.set_ylim([0, 30])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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4ol+wvYfichiGiYiIiOxs4/FseKhVGNsr0N5DcTkMw0RERER2tuFYFkb1CICPJytYbY1hmIiIiMiOUvNKcTKrGJf362zvobgkhmEiIiIiO9p4IhsAcEV/1gvbA8MwERERkR1tPJaF8AANegb52HsoLolhmIiIiMiGiit0kFICAMqr9Nh2OgdX9OustFQjm2OVNhEREZGNJCTl4ZZPdyG0kzfmRIchuIMnyqsMuJwlEnbDMExERERkAxkF5bhn2V507eiFQF8PvPb7MQCAp5sKl/RkSzV7YRgmIiIiamMVOj3uWbYHZZU6fHPXaPTp0gGns4vxw55zCO7gCS93tb2H6LIYhomIiIja2POrjmB/aj4+njccfbp0AAD06uyLJ6b2t/PIiAvoiIiIiNrQz/vO4Zvdqbj/il6YGhli7+FQPQzDRERERG3ok81nMSCkI/41uZ+9h0ImMAwTERERtZHDaQVIPF+IW0aFQa1i6zRHxDBMRERE1Ea+S0iFh5sK1w3pbu+hkBkMw0RERERtoLxKjxX70nB1ZFf4adztPRwyg2GYiIiIqA38mZiJwnIdbowOs/dQqBEMw0RERERt4PuEVHTv5G39hhrx8UBEBKBSKZfx8W05PKrGMExERETUys5dKMXWUzmYEx0KlTUL5+LjgYULgeRkQErlcuFCBmIbYBgmIiIiamU/7kkDANwwItS6G8TGAqWldY+VlirHqU1xBzoiIiKiVlCh02NP8gVsPZmDb3anYFzvIIT6a6y7cUpK045Tq2EYJiIiImoBKSU+2XIG76w9ibIqPdxUAsPCOzVtq+XwcKU0wtRxalMMw0RERETNVF6lxxM/HsTK/em4ckAw5o4Mx5hegfD1bGLEiotTaoRrl0poNMpxalMMw0RERETNkJZfhruXJuBIeiH+fVU/3Hd5LwjRzF3mYmKUy9hYpTQiPFwJwsbj1GYYhomIiIiaSEqJWz/fjfMF5fhkfjSuHNil5XcaE8PwawfsJkFERETUREfSC3Eyqxix0wa0ThAmu2EYJiIiImqi1QfPQ60SmDqoq72HQi3EMExERETUBFJK/HooHZf2DoK/j4e9h0MtxDBMRERE1ASH0wqRmleG6YND7D0UagUMw0RERERNsPpQOtxUAlMGsVbYGTAMExEREVlJSolfD57Hpb2D0EnDEglnwDBMREREZKWD5wpw7kIZpkWxRMJZMAwTERERWenXQ+fhrha4aiC7SDgLhmEiIiIiKxhLJMb1DoKfxt3ew6FWwjBMREREZIWE5AtIyy/DtKhu9h4KtSKGYSIiIiILpJR468/jCPDxwNRIlkg4E4ZhIiIiIgs2HM/CzjN5eHhSH/h6utl7ONSKGIaJiIiIGqHTG/DKb8fQI8gHt4wOb707jo8HIiIAlUq5jI9vvfsmq/FPGyIiIqJG/LDnHE5mFePjecPhrm6lecT4eGDhQqC0VPk+OVn5HgBiYlrnMcgqnBkmIiIiqlZUXoWnfz6EL7edRVp+GUordXh77QmM0PrjqkGtWCscG1sThI1KS5XjZFOcGSYiIiKq9tafJ/D1rhQAwPO/JKJLR09kFVVg0bzhEEK03gOlpDTtOLUZhmEiIiIiAIfTCvDVjiT84xItbh0bgbWJmVibmImrBnXFCG1A6z5YeLhSGmHqONkUwzARERG5PINBInbFYQT4eODRKf3g5+2Ouy/zxd2X9WqbB4yLq1szDAAajXKcbIo1w0REROTylv+digOp+YidNgB+3jbYXS4mBli8GNBqASGUy8WLuXjODjgzTERERC4tt7gCr/1+DKN7BGDm0O5t+2Dx8coiuZQUpSQiLo4B2M4YhomIiMhlVej0eOz7Ayip0OGlmZGtu0iuPrZTc0gskyAiIiKXVKHT4/74vdhwPBv/nTEIfbp0aNsHZDs1h8QwTERERC7HGIT/OpqFF2dGIma0tu0flO3UHBLDMBEREbmUCyWVuGfpnotBeP4YGwRhwHzbNLZTsyuLYVgIESaE2CCEOCqEOCKEeLj6eIAQYq0Q4mT1pX/bD5eIiIioeaSUWLk/DVe+vQlbTuYgbpYNgzCgLJbTaOoeYzs1u7NmZlgH4FEp5QAAYwDcL4QYCOBJAOuklH0ArKv+noiIiMjhZBSU4/Yv/8bDy/cjNECDXx4c17TSiPh4ICICUKmUy/j4pt8uNhZYsIDt1ByMxW4SUsrzAM5X/3eREOIogO4AZgC4vPq0JQA2AniiTUZJRERE1EwJSXm4Z9lelFTo8J/pA7FgbATUqiZ0jWhuFwhTt1uyhAHYwQgppfUnCxEBYDOASAApUspOta67IKVstFQiOjpaJiQkNG+kRERERE20fHcKnl15GN07eeOTf0Q3r2NERITprZO1WiApqfVvR61CCLFHShlt6TyrF9AJIXwB/Ajgn1LKwibcbqEQIkEIkZCdnW3tzYiIiIha5JXfjuLJnw5hTM9ArLx/XPNbpzW1C4SxNMJUEG7sdmQXVoVhIYQ7lCAcL6X8qfpwphAipPr6EABZpm4rpVwspYyWUkZ37ty5NcZMRERE1Kj0/DL83+YzuH54KL64dST8NM3YYtkYas19iq5SNawhNpZGmAvCALtHOBiLNcNC2YrlMwBHpZRv17pqFYAFAF6tvlzZJiMkIiIiaqLVB9MBAA9O7A03dTM6ydav9zVFr1cua9cQm9pYozZ2j3A41vx0XApgPoCJQoj91V/XQAnBk4UQJwFMrv6eiIiIyO5WHUhHVKgfIoJ8GnaCuO8+y50hGgu1anXDY8ad5BorgWD3CIdkTTeJrQDMLbmc1LrDISIiImqZM9nFOJxWiGemDTDd0WHRopqTzXWGMBdqhQAMBtPXpaQoJRBcNNeucAc6IiIicizN7elbbdWBdAhITF84C5g3r/GyBaBmVre2xnaLa+w6bqzR7jAMExERkeOovQBNypqZWysDsZQSv2w5hlHnEtH12EHrH7f+THBjobax62JilFIIbqzRbjAMExERkeMwVatraua2mt4gUXvPhMTzhThdocZ1hzc07XHrz/Y2FmrrXxcYCHh7A/PnKzPZgFISYTAolwzCDo1hmIiIiBxHE3r6Sikxd/EOXP3eFhxJLwCglEi46XW45vg26x/TXBlDTIz5UGu8bulSoKwMyM1t1kw22R/DMBERETmOxupx69lwPAt/J11Acm4pZn64DR9uOIXVB85jfOYx+JcXmb4frRa4997WK2No4kw2OR6GYSIiInIcVi5Ak1Lif+tOIdTfG5sevxxTBnbFG38cR1p+Ga4b1cP0fSxbpszmfvRRzYxvXJwSXJu5WK/Ju9ORw2EYJiIiIsdh5QK07adzsT81H/dc1gvBHbzwwS3D8N7coZgWFYIpd822bhGbqcV6t90GBAVZH46bMJNNjolhmIiIiOyrfis1wOICtPfXn0SXjp64YUQoAEAIgRlDu+PDW4bDx9Ot8XpfI1MlDlVVTav/ZSu1do9hmIiIiOynGa3U/k7Kw84zeVg4oRe83E3sBmcta0oZLNX/spVau8cwTERERPZjYQFaSYUOu8/mYcOxLCSmFyKvpBIfrD+FQB8P3Hx2e4s257C6lMFSaLZmFpoclsXtmImIiIjajImgWalyw3P9r8XutzbiTE4JarURvujfXcqguafeNsumtlVuTFxc3a2azWH9r1NjGCYiIiL7CQ9Xgmwt27VR+GboVFzq54Vrh3TD4O5+6KRxR2ZhBTIKylFaqcNtd00zP6NsbRg2nhcbq4TygACgqAiorKw5h/W/To9hmIiIiOzHxOzs+v5j4S0kPlsw0nxN8NlTpo83taWZcUc5o/j4mnAcHl6zxTI5LYZhIiIisp96s7MyPBzrRkzBuD5dG18cZ2JG+eLxlo6H4delcAEdERER2VetBWgndh5CWpUKk/oHmz7X2IYtOVnp3lCbEMrx5iymI5fFmWEiIiJyGOuOZQIArjAVho1t2IwlFVIqAbj2JdC8xXTksjgzTERERLZXf6ON6pnc9UezMLi7H7p09Gp43oIFDRfNSQmo1WjQcsJSf2CiapwZJiIiItuqP8NbPZObpxPYm+KHByf2MX2eXm/6/swdb+piOnJJnBkmIiIi2zKz0camz3+GQQKTBgSbP88UtZmFduwPTFZgGCYiIiLbSkmBBPDxqOuxVTvk4uF1nXqicwdPRHbzu3ieRRqNMnus0TQ8zv7AZAWGYSIiIrKt8HAc6dILr15xG+bNjcMb4+ej3M0Dm3pFY2K/YKhU4uJ5JqnVyoI5rRZYvBj46CPlUqute5yL58gKrBkmIiIi24qLw4qvtsBdX4VrEzfjw7E3Yc2A8Sjy0GDigOA65zXYLlmjMR102R+Ymokzw0RERGRT+ptvwaqR03DF+SN4e827eGfHl8gIDIGHmwrjegfVnBgTwxlfanOcGSYiIiKb2nE6F1k6FWY+cTuwLBazAIzILUV2cQV8POtFE874UhtjGCYiIiKbWrE/DR083TCx1sYa4YEahAdqGrkVUdtgmQQRERHZTHmVHr8fzsDUyK7wcjfTEo3IhhiGiYiIyGb+OpqJ4godZg3rbu+hEAFgGCYiIiIbWrEvHV06emJ0z0B7D4UIAMMwERERtbKi8iro9IYGxy+UVGLTiSxcN6Qb1MZewkR2xgV0RERE1GqW7kzGcysPQwII9PFAkK8npAQKyqqQX1aJKr3EjKEskSDHwTBMRERErWLH6Vw8v+oILukViGhtALKLK5BVWAGVAPy83eHn7Y5ewb4Y1K2jvYdKdBHDMBEREbXYuQuluP/rvYgI1ODjeSPQwcvd3kMisgprhomIiKhFyir1uHvpHlTpDfjkH9EMwtSucGaYiIiIWiT250NIPF+IzxeMRM/OvvYeDlGTcGaYiIgoPh6IiABUKuUyPt7eI2o3fjt0Hj/tS8PDk/rgilo7yhG1F5wZJiIi1xYfDyxcCJSWKt8nJyvfA0BMjP3G1Q5kFZUj9udDGBLqhweu6G3v4RA1C2eGiYjItcXG1gRho9JS5TiZJaXE0z8dQmmlHm/dOBRuakYKap/4k0tERK4tJaVpx12FhdKR7/ecw19Hs/Dvq/qhdzDrhKn9YhgmIiLXFh7etOOuwFg6kpwMSKlczp8PCAFdj5744YPv8MIviRjdIwC3X9rD3qMlahGGYSIicm1xcYBGU/eYRqMcd1UmSkd0EPghciImTXkKj53zQYQox1s3DoGK2ypTO8cwTEREri0mBli8GNBqASGAwEDA21uZCXXVzhL1SkSqVGrcccN/8Ni0f8G3ohSf/PgCfnnpeoQG+tZ9jdiVg9ohhmEiIqKYGCApCVi6FCgrA3Jza8oDFi50/FDXGiG09n2oauKBBPDclfdgU89oPL/2Y6xe8k9MPrUbQq+v+xrdd1/D0or28NqRy2MYJiIiMjLXWWLBAsed7WykvrfBeM2F5ur7yM26gE0Rw1Apa0ofPouega+HXY17dn6PW/euhsmiiNJSZXadXTmoHWKfYSIiIiNzHST0euXSEXsQmwrwUiqXtccLmO+nHBuLTJUX5t78Cs4GdEdgST5mHdkAbUEG4ibeganHt+PxTV81Pg7ja1Sfq3flIIcnpPF/GBuIjo6WCQkJNns8IiKiJomIUEKiJVqtUlbhCFSqmvBrjlarXJp6blotMvOKcfNNccj0DcCTG7/A9oih+Kv3KFSp3TEk1A/L/ZLh/WysEmxVKvPB19xjO8prRS5FCLFHShlt6TyWSRARkfNqrCzA1HFTnSVMcYTZTuNzsGZSKyXF7Jizcotw87zXkdEhEEu+fw7z96/BohWvYOeHC/DGrqX44rZR8J5fXVNtMABLllj3GgHsykHtAsskiIjIOZnaZnn+fGDePKWe1lQpgbH0IdbCLKi9exDXf26W1HoeOqHC7rBBONi1Lw6G9MHusEiUuntiyffPIzrt6MWbBAod5tx5LeDjUfe+ar9Gjc2ia7VKEHaUchIiM1gmQUREzsnakgcjUx/nmwqdGo2yWMyeIa+x51Y76NciAfzZZwxev2wBTgeGAQDC8jMQlXESd+7+GcPOn6i5rbVB1tw4jK9lfHzNHxbh4QzHZFPWlkkwDBMRkXOyppa2NiGUMoD6HDHQmXtuQijt4erNbO+mqpUVAAAgAElEQVQP6YsXJ96JPaED0TM3FY9s/RrjUg/BvyS/4X00pca3sT8WAMf8Q4JcBsMwERG5ttaYGXZUlmZkjVQqrOsZjXtnPg2/8iI8sjUeNx5cCzdU/+43F6hN/VFgjrk/Fhp7/VlCQTbABXREROTarF0MB7S/hV5WbiG9Zux1uHtWLPpnn8Xaz+7DLQf+gJs0KKHVXN1zU+uhY2otrktKqgm4jS0y5IYc5EAYhomIyDnV3mYZUGY8azN+r9W2v4/u628hbeI5rNyfhgfG3YmorNNYtvwZdCovVq4whmYrA3WzWQrV3JCDHATDMBEROY/6LdMAZbZSSqWWtnZ4XLpUOV57NrM9MTcjC2BPch4e+XY/onsE4qtJXdAxpHPD0GxFoG4Ra2bmHaFFHbk81gwTEZFzsEfnB0dcXAfg9i//xv7UfGx5/Ar4eNqxi6rx9Wmsdri91GlTu8OaYSIicg3G2eB58xr23W3Lj+KN4Ts5WZlhdpA62OMZRVh/LAu3jo2wbxAGamavly1r25IMohZgGCYiIsdjboc4U+cZA6k5rflRfO1xLVhg2/BtpcWbz8DbXY35Y7R2HUcdbV2SQdQC3IGOiIgci6md4+rvEGcUG2t5F7bW2i2u/rhM7UwH2LQOVm+QUKtqFgaeLyjDqgNpiBmthX/9nePszVinTORgODNMRESOxVTANTfjail4tuZH8dYEb6AmfFs7u22tevf3zfvfY9Bzv+M/Kw+jvEoJ5l9sS4JBAneM69GyxyJyIQzDRETkWMwFXFPHG5v1be2P4q2Z8TWG79auJ651f5VCjWf7Xo2n0jQIVVXhqx3JmPXRduxLuYCvd6Vg2uAQhAVY2V+ZiBiGiYjIwTRlMwhzvXKXLWv9lmnmxqVWN6yDbcrstjWq7y/XuyPm3fQilg6fjrt3/Yg/vnoYn98ajczCcsz6aDuKK3RYOKFn8x6DyEUxDBMRkWNpymYQtlyYZW5cS5ZYv/taSkrzyidSUpDhG4g5Ma/jQEhfvPvLm3hq4xdQJydjYv8u+O2h8bi8X2fMHtYdkd39WvAkiVwPwzARETkWSwHX3MYaJjafsOm4ajM3ixwQ0KzyiXP9h+LGW15Flm8Aln37LGYmbqzzOF39vPDlbaPw9k1DW/AEiVwTwzARETkec7ur2bu3byO7vtVhbhYZaFA+kSU88fyP+7HheJbJWeOU3FLcdP1/ke/dAcu+fQYj0xJr7o99eolajGGYiIjaj9auxW1NtYNsbKzSh7j+LHJe3sXTq1RqfBo9AxPv+j982fdyPLl0F8rvvb9O0C944J+Y++46lLh74utIiaEeFezTS9TKLIZhIcTnQogsIcThWseeF0KkCSH2V39d07bDJCIiQtM6TdiSqRnrJUuUmdvas8jVZQ1n/Lth+q3v4aVJdyH6XCLe2LUUmToVlvafWOdu3x0xC+crgS8/fhCRLz3Z8P6IqMWsmRn+EsBUE8ffkVIOrf76rXWHRUREZEJTOk3YkqUZa+OscXIysnwDMP+mF5Gj6YTFP76IL1bGYc6hvzDhzB58NOYGFHl4AwCOB2nx1fDpiNm3BkPPn3CY7Z6JnI3FMCyl3Awgz9J5REREbaZWmIQQda9zhNpZS90jqmeNizy8seCG55Hv3QFffv8cplw4BSEEkJuLx7YsxQWNHz4bORMSwHNX3o0OFSV4dMuymvtzlJIQIifSkprhB4QQB6vLKPzNnSSEWCiESBBCJGRnZ7fg4YiIyKnUXyx2332mW47VLkEAlDIEYyB2gNrZU1nFqNJGmL4yPPzirHGlyg33zIrFyaBwLPr5FQz20gG+vkBlJQAgKuMUph7fhk9HzsLSYdOwUxuFf2/6Cv7lRXXv094lIURORkgpLZ8kRASA1VLKyOrvuwDIASABvAggREp5u6X7iY6OlgkJCS0ZLxEROQNjwG1se2ONRgm6sbE1Qbg2rVapnbWjI+kFmP7+Vszyq8RbL86DqP18jOOfPx+QEo9f/RC+i5qCt1a/jeuPrK8J9LV+D58MDMOUOz6EFCpEZpzCyq/+BbU01H1QB3jeRO2BEGKPlDLa0nnNmhmWUmZKKfVSSgOATwCMas79EBGRizJVY1ufsSTAURfNAYjflQIpgZ/yPbD81S9N9yAOD8eOsMH4LmoK7tvxnRKEAWXWuF6tc5/cVMw6vAEA8N+xXaD29qr7gI5QEkLkZNyacyMhRIiU8nz1t7MAHG7sfCIiojqsDbIpKUpgNDUzbOdFc8UVOqzcl4bZw7oju7gCz53NQ9T2AxjUre4OcFUvxeG5TYUIzc/AQ9uXKwdrh9p6M+Qvb/kct88ahcg75wI++po/CMLDlduwiwRRq7Kmtdo3AHYA6CeEOCeEuAPA60KIQ0KIgwCuAPBIG4+TiIicibVB1hgArd2e2YZW7U9HSaUeMWO0ePemoQjQeOC++L0oLK+qc95XEWNxIjAc/zm0El76qrqzxiZ2tfNa9KEShAHrN/kgomazqma4tbBmmIiIADStZjgmRjnfwWZIp7+/BTq9xJqHx0MIgb+T8jB38U5Ea/3xwoxI9OvaAVlF5Zj05iaMiPDHF7eOVDpHEJFNWFsz3KwyCSIiomapHWoDAgBvb2VXtvBw4JprgN9+Mx14jbOoDuLguXwcTivECzMGXQy4IyMC8MrswXjhl0RMfW8zpg0OQYXOgAqdAc9dO4hBmMhBMQwTEZFt1J8Nzs1VZn+XLnWooGuNr3elwNtdjZnDutc5fmN0GCYP6IJPtpzBl9uTUFqpx/1X9EKPIB87jZSILGGZBBER2YZx04z62lmrsKLyKox+eR2mR4Xg9RuGmD0vt7gCG45nY3pUCLzc1TYcIREBLJMgIiJH48At0ppixf50lFbqcctobaPnBfp64oYRoTYaFRE1F8MwERHZhoO2SAMAKSW2ncpFcl4JPNQqeLipEBHogyFhneqcpzdIfLblDKJC/TAk1M/MvRFRe8IwTEREthEX17CDhI1bpK0+mI7jGUWYMbQbegd3AKBsp/zC6kRsPpHd4PzlC8dgTM/Ai9+vOXweSbmlWBQznAviiJwEwzAREdmGcZGcnVqklVfpEfvzYRSUVeH99acQFeqH/l074Ke9afD2UOO5awfimsEhqNQZUKHTY8Hnf+P5VUew+sFxcFOrIKXEoo2n0bOzD64a1NUmYyaitscwTEREtmPHFmm/H85AQVkV/nfzMGQVluOnvWn4fs853BQdhseu6ocgX88658dOG4D74vfim79TMX+MFptP5uBIeiFevz4KKhVnhYmcBcMwERG1LgfcIAMAvtmdAm2gBtMHh0ClErhzfE9U6PTwdDPd6eHqyK4Y0zMAb/15HNMHh2DRxlPo2tGrQTs1ImrfLG7HTEREZDVjL+HkZEBK5XLhQuW4HZ3OLsaus3m4aWRYnVldc0EYAIQQeP66QSgsq8I9y/Zg55k83Dm+Bzzc+KuTyJnw/2hXEx+v9PpUqZRLO/+CIiInExvbcIvl0lJgwYI2e9/RGyS+S0jFwXP5Zs/59u9UuKlEk1ud9e/aEfPHaLHrbB46adxx8yj7d74gotbFMglXUn/3J+OMDeAQH2ESkRMw1zNYr1cum/m+k55fhlu/2I1+XTvipZmR8PN2B6Asint4+T78cSQTADA8vBNuvbQHro7sCne1Mt9TqTPgxz3nMGlAMII7eDX5KT0yuS/+TMzEgrER8PHkr00iZ8Md6FyJk+z+REQOzNz7TH1NeN/JLqrATf+3AxmF5ajUGdDVzwvv3zwM2kAf3LHkb+xPzcdTV/eHu1qFJduTkJRbilB/bzw7fSCmDOyC3w5l4P6v9+LL20bi8n7BzXpaUkq2UiNqZ6zdgY5h2JWoVEoNX31CAAaD7cdDRO2XuUVy9T+BMsfK950LJZW4+ZOdSM4txdI7RkGlEnjom33IKChH5w6eyCupxLs3DcXVg0MAAAaDxIbjWXj99+M4nlmEy/p2RkFZFbKLKrD58SugZhcIIpfB7ZipIQfe/YmI2gFjAE5OVsKs8Y9rU6UPxqCsUtWUSNRmxftOUXkVFnyxG2dySvDFrSMRHREAAPj1ofF4+udD2H02D1/fNRojtAEXb6NSCUwa0AUT+nbGVzuS8e7aEyiq0OGRK/syCBORSZwZdiWmZmw0GmDxYtYME1HjrJnxVauV2V5LM8X13nfS88vQrZN3g7t7Zc1RLN58Bp/Mj8aVA7s0uN5gkBb7/WYVlWPlvnTMHRWGDl7u1j1XInIK1s4Ms5uEMzLXMSImRvkFpNUqszpaLYMwEVnHVJeI+vT6hu3ULLzvbDiehbGvrscvB9Lr3FVeSSWW7kjGdUO6mQzCAKza+CK4gxfumtCTQZiIzGIYdjaWenzGxCiLVgyGmsUrbLVGRJaY6xJhTmmpEqCBhu87tf4A/2JbEgDgpV8TUVyhu3j8ky1nUFalx4MTe7ds3EREFjh9GM4vrcSyncmo0JmoWXNG5np8Gn8p1eagzfGJyAE1Z22BhQCdlFOCzSeyMXlgF2QWVuB/604CUGaFl2xPwvSobugd3KE5oyUisprTh+FfDqTjmRWHccUbGxG/KxmVOifvmmDul4+p400JzkTkemqXXBUXAx4eda83thpTm9nFzUKAXrYzGW4qgbiZkbgpOgyfbz2Lk5lF+LR6VvghzgoTkQ04fRieN0aLr24fhS5+Xoj9+TCueHMjvtmd4ryh2NwvH1PHmxKcich51F9XcN99Dcul6n9ylJurXAYG1tT+Ll2qHFuyRFkUV0tacBi2P/Wa2SGUVerxXUIqrorsiuCOXnh8aj/4eLrhiR8PYsn2JEwbHII+XTgrTERtz+nDsBACE/p2xk/3jsWXt41EUAdPPPXTIUx8ayOW705Bld7BQ7G12ycbzzO2PKpNo1FWdtfXlOBMRM7BVHnUokV1v58/H5g3r+EnR1VVgK9vw9rfeovkTg4ejZl3fYhbzvri9d+PwWBo2LXolwPpKCzX4R9jtACAQF9PPHZVP+xNyUdplR4PTerTxi8EEZHC5VqrSSmx8UQ23l17AgfOFSDU3xt3je+JG6PD4O1h5qM+e7G2FZqp84w9QLVa4JprgN9+s645PlutETk3a3eIM8fCZhnHM4pwyyc7oVIJXNorECv2p2Pa4BC8deMQeLkr77FSSlz7wVZU6gz4458TLu7spjdILPh8NyKCNHhp5uDmj5GICNyBziIplV2KPlh/CntT8uGvcceCsRFYcEkE/H08LN+BLVi7fXJj58XFNR54ze0iRUTtW/3/t41/FLckCAONbqOcmF6ImE93wsNNha/vGoOeQT74dMtZvLzmKIaEdsKtYyPQq7MvCsurEPPpLrw4MxLzq2eGjYy/k7j1MRG1FMOwlaSUSEi+gI83nsa6Y1nwdlfjppFhuHN8D4T6ayzfQVuydvvkxs4zt+tcI7/QiKids3ZLZCtIACmdukKbn9HoJ0ensopww8c74O2uxjd3jUFEkM/F6/44koFHvztQp3War6cbdj49Cb6e3AiViNoGw3AzHM8owuLNZ7ByfxokgGmDQ3DrpREYFtbJdrMUtWdzzG1jCtTM+sbEND4znJJiXaAmIufR0lKIWl6bsACLLpmDfx9YifvnXWYyCKfnl+H6RduhM0j8cM8l0Ab6NDinQqdHSm4pTmcX43R2Cfp16WB2Mw0iotbAMNwC6fll+GzrWXz7dyqKK3QY3N0P/7hEi2uHdLtY89aqjAHYuPjN2n8T4ywNYL4Uwni/9XFmmMh5mfu0yJTa6wrqvQf90WcM7p79DELcDThfpcJTV/fH3Zf1qnPz/NJKzPl4BzIKyrH87jEY1M2vtZ8NEVGzcDvmFujWyRvPTh+InU9PwoszI1Fepce/fziIS15Zh1fXHENqXss/eryo9spuwPQvMHM9PI09gRvb7jQurkHLI7PdJZyZtV05iJyBiY4wBgh8OGYO4odMRXKnrspBrRaVp87g0NOvYNXK7cgrrlDapWm1OBPQHY9NfxRR3jqse/ZqTI8KwStrjuHTLWcAAFV6A05nF+P2L/9Gcl4pFv8jmkGYiNolzgxbQUqJHWdy8dX2ZPyZmAEAuLxfMOaMCMXEAcHwdGvibLG1pRBGxhKN5pY7WLtIzlkX07FrBjmxw2kFeG/dSWgDNOiXdAT9P/sfIvdvVUq7ar1nJHQfgBvmvXHx+7CCTAR0DcTRKo+Lfde93FWYMyIMt4wOxz+X70dWUTl+eXAcQv010OkNeGj5Pvx2KAPhARqk5ZdBb5AQAlgUMxxTI0Ns/tyJiBrDMok2kpZfhq93JeOHPeeQWViBThp3XDekG66ODMHICH+4qS1MtjdnYYu2erW1uRrA2vXDzeWMgbF2+YkpLBWhdk5KiTkf78Dh9AJInR4VUvnD+V9bluGh7cvrtFh8Zda/8LmbFj/G/xv7B47BlomzURTUBVGhnRAV6ocQPy989/c5/LwvDZV6A4QAltw2ChP6dr74eFV6A15bcwznC8rRI8gHEUE+GBLqx80xiMghMQy3Mb1BYuupHPyw5xz+PJKBCp0BAT4emNQ/GOP7dkZUdz9oAzUNF941dWFLY3XBps5rbnC1to1be2HNHx1cREjt3Ppjmbj9ywS8ODMSt9x8OZILKhF71f04G9Ad2xbdDrU0AFot5NmzmPjWJoT6e2PpHaMbvc+swnIs25WCMH9vzIkOs9EzISJqfawZbgkr6kvVKoHL+nbG+zcPw95nJ+OjmOEY3ycIvx/OwEPf7MPlb27EkP/+ibuXJqCgtKrmhtZsdWwM0LVrf2vXBZtirB9uLmfbmjk21vLse0t32mMdMtmRwSDx+u/HoQ3UYO7IMKiTk9HzQjpu2f87MjoEYVfYIOXElBSczi7G2ZwSTLGie0NwRy/8a3JfBmEichkMw/WZ2qp04cJGg46PpxuuGRyC9+YOw97/TMavD43Dq7MHY1pUN2w4lo27liagfGl1cDI3E69W1yx+W7pUOa/2dqeA8t9JSQ23WzZqSXC1x9bMtcNkUJDy1VrB0tJr0dJFhM34OSFqTGpeKS57YwPmLt6B9/46ib+T8i7W8pryy8F0HMsowr9Wfwh3dzfl/x0AV57aDZ+KUqwYdIVyYng4/kzMVK5jKzMiogZcNwybm9UzNaNobtbVxH24q1UY1M0Pc0eF45XZg/HmjUOw+2weHl1xFIbkmoCW7+WL7eFR2BkWiT09hyLx46WQen3DAGxKWwRXW3edqB8mc3OVr9YKlo29FlotsGCB8m/a3PDdlJ8TIiu8vfYEMgrKUVSuw7vrTmDOxzsw5pV1ePnNH3Fm8Kg6P6tVegPe/mkP+uck4drNPyn/31QvxPXWVeCqEzuwpt+lKO/gB8TFYW1iZnVdsLd9nyQRkQNyzZrhxhaLzZ9vXdeGJiw4++SqOxA3bDZuS1iJace24uuhV2N1/3GodKu77fOsYd3x1pwhUKksbPDRVovdbNlNwpra6abWK9cef0AAUFQEVFbWXG9NX2Zrn6+1uwMSWeHo+UJc878tuHtCLzx5dX/kl1Zi55lcrFy1E2svqKBTu2FU6mEMzDyD8NJcZMy+GYtzvPD5989j4pl676lqNbaERWH+TS9iUXgJRsy7DqNeXodHJ/fFg5P62OcJEhHZARfQNaaxxWJA4wvJLHUoUKuVMFQrTEqVCi9MvBNfRM8AAPhWlGLWkfWYcnIX1Gv/RJVBYvvpHPzfpjOYNyYcL86ItLzjXXtvg2bNpgDGYGnNczX1B4K7O9CxI5CXV/d2rbFYsLEw3xrdPcil3PbFbuxJvoAtj0+En8a95oqICGTlFOD7wZPxW79LkeTfDSWeyic4I1OP4Luvn0CDdwohoNfpMeaVdRgW1gmX9wvG0z8fwh//nIB+Xdn1gYhch7VhGFJKm32NGDFCOgQhpFSiWN0vIaRctkxKjabucY1GOW7qusa+jI+jVks9hHz/khvlN1FTZLG7l3Jcq704JIPBIF/+LVFqn1gtX11z1H6vja1otZZfP6228X8Pa+6v1mt8kbl//9qPabRsmXJMiLrXWfpZMDVGcnmlFTr5wi9H5JpD6dJgMEgppdx5Okdqn1gtF208VXOi8eeu3s+VAZA53h3lvpC+8oKXb6M/8/9ddUT2efo3OfujbXL8a+svPh4RkasAkCCtyKecGa7NOKsbEKB8b5xRrL1VaWsx8bG8lBLPrDiM+F0pmDqoKzzdVSgq16FCp8fAkI4Y0zMQ0REB8PN2b+SO2wlLrc+M/VHVatObklg7U2+qbMFSiYa15RTsY2xzpZU6vLbmGC7pFYQpA7tYLimyASkl/m/zGXyXkIolt41CWIDG5HnlVXrcuSQBW0/lAACuHBCM568bhAe/2Yfz+eXY+O/Lle3em9OL3KjWz+ehcwW49oOtAIA7xvXAs9MHNvs5EhG1RyyTaIw1v2ys7e/bFCZKKOozGCT+s+owfjuUAV9PN3T0doNaCBw9X4RKvQEqAYzv0xlPTO2Pgd06XrzdvpQL+GjjaRgMEsPCO2F4uD96B/uisFyH/NJKFJZXIdRfg16dfaG2V4CoX+5g/CPDWOMLKIvo6u2cZZIQStcNS/82pgKptWHDXBA33q/x35D1w62qsLwKCz7fjUeu7FtnwwcAWLk/DQ8v3w8A6NelA+6f2BvTBofY7We6pEKHx384iF8PnQcATI8KwQe3DG9wXnmVHncv3YPNJ7Px2uwoFJRV4e21J6A3SFTqDXh19mDMHVW96LOpvciN6pXnSCkx6e1NOJNdgm8XjsHonoHNfZpERO0Sw7Al1myJbGnnt9oaC05GLQhH5VV67EvJx7ZTOVi6MxmF5VWYNaw7rh8eii+3J2FtYiYCfTzQSeOO09klZu/Hx0ONyO5+uG5oN8SMNtOzuDXVnj2tH3JNLVprShCw9Jo3tijO0qyuNYz3b+5+rPjjhxp6Z+0JvLfuJCYP7IJP/lH3PeyBr/di55k8xE7rjw83nMaprGIMCfXDonkj0K1T23ZKSM0rxbd/p8LbQ40uHb3g5+2ON/84jpNZRXh8an+UVurxv3Un8eO9YzFC63/xdhU6Pe5dthfrj2Xh9eujcONIpX/vuQul+O8viSgoq8LXd46u2b3Smnr6+sy8tyzdkYT4XSlY/eA4y7tjEhE5GYbhpmhsZg9o/BdTU2aQW+lj84LSKny06RS+2JaESp0BHbzccPeEnrjt0h7w8XRDfmkl9qfmIyWvFH7e7uik8UAHLzck5ZTgQGo+dp7Jw/HMIvzywDgMDvWz7kHj47H5/aUIOnsCA70NdcKdwSDx6u/HcEmvQFzRL7jObZr8mjQnCJi7X2sCaHNn4eo/TlM+aWjPCx/b2IWSSkx4fQPKqvRQqQT2PjsZvp5uAIBKnQEjXlyLaVEhePX6KBgMEqsOpOOZFYfh6abChzHDMaYFs59llXr8fuQ8/krMwoCQDpgxtDvCAjQor9Jj8eYz+HDDKVTqDXV+PP283fH+zcMwoW9nlFbqcPkbG9Gtkzd+vm8shBAor9Lj/vi9WHcsCy/PGoxbRlvR/rAlC3yJiOgihmFTzHUlaM4vH+P1tcNMU2dBWygtvwzbTuVgysAu6KTxsHyDakXlVZjw+gZEdvczvzVrrdeqIigYLw2/AUuHXo3Aknz8/vkD6FxWoDw/rRZfPvIGnj+vgY+HGqsfGo8eQT7KfVgTNOvPaDVWz21p5t2oKcGgJfWZgOmOF+Y+aQgMBMrKWr8lHoD3/jqJhOQ8fH7rSLi34xnA134/ho83ncaLMyLxzIrD+N/Nw3DdkG4AgK0nczDvs1349B/RdTaPOJVVjIVLE5CSW4p/XtkH/j4eSM0rQ3p+GaIj/DFvtLbR2uK0/DJ8sP4kVh84j6IKHYJ8PZBTrLTki9b6I7u4Asm5pZgWFYJnpg1AJ28PZBWVI7OwAj07+yDI1/PifX2XkIrHfziI/908DJMHdMHCpQnYcjIHcbMiG34SY65sqLH3D6Bt2ioSETkhhuH6GuvNCzTvusZ++Th467NPt5zBS78exdd3jcbYXkF1r6z1Wp3r2Bn3z3gSB7r1w40H/8SKgZfj0uQD+PyH/0IASOoUgqm3f4CoTmqckN7o3skbP903Fp5uautmeesH1+b0gK6tOcHAmnZ5jdUO1w/eTZ3dbuGsnsEgMfqVdcguqsD9V/TCv6/q3+z7sqec4gqMf20DJg/sgndvGorRr6zDyAh/fBQzAgDw/KojWP53CvY9OwXeHuo6ty0qr8Ij3x7AX0eVndbc1QIBPh7ILKzAyAh/vH7DkJo/0mqp1Bkw48NtOJtTjGsGh+DG6DCMighAWn4ZVh1Ix6r96VCrBJ6+ZgDG9QlqcPv69AaJa9/fioKyKoT6e+PvpDy8fsMQ3DAitO6J1vwRZgzE5v7odtD3FiIiR8HWavVZar1lroWWpevaqbJKnRzz8l9yxgdbG7RcKurVV/7Re7R8esp9Muqhb+Sgf34n1/S9REpAfj7iWql9YrX8aujVUg8hb7jlNRn58HKZ3n+IXHskQ2qfWC2fW3lYuSML7dNyA7rIt976QaZdKK07OHOvt7n7U6tb59+mqW31zLVPs6ZtXP0WfKae97331nwfGKh8mXie+1IuSO0Tq+WE19fLiCdXy52nc5r/GrSyKp1eVlTprTr3hV+OyB5Prpans4qklFI+8/Mh2f+ZNbKkokoaDAY59pV18o4vd5u9vV5vkInpBTI9v1Tq9AZpMBjkj3tS5eDnfpd9Y3+Tn2450+Bn/Y3fj0ntE6vln0cymv8k69l2Mltqn1gtez71q1y5P830Sdb+jJhqDUhERFYBW6vVwxX/DSzfnYInfzqExfNHYMqgrjieUYR31p7AXwfPQad2g6ayDOOS9uPJjV+g54V0AIABArfOeR67wwZh7oE/8WX0dXjj13cw58h6wGDAi6sT8dnWs/h43ghMPbCu4exX9WzX2WlPpuYAABoNSURBVEHRuG32s0iqVCPU3xvf3DXGbEuqi9pq5736j2Fu1s3CjNyxjEKk55dh4p6/mlZ6YZx5tqaLhlGt5/3GH8fw8aYz2Pz4FYj5ZCeq9BK/PTzeri34pJRYuT8dL65ORFmVHuN6B2HSgGAMDfNHSl4pTmQW4WRmEbzc1QgP1KBrRy889dMhXDukG96cMwQAsP10Dm75ZBcWxQxHRJAPrn5vS92uC1bKLCzH0z8dwrpjWZgzIhQvzx4Md7UK+1Iu4PpF2zF7eOjFx2wtn209i97BvrisXjeMi6z99MCF35+IiFqKZRL1tcauY05GpzdgyjuboVIJRHbriJUH0uHr4Ya5B37HxL3rMCLtKDwMuga3y/Lxx1W3f4ALGj9MPLUbn/34AkT161i5NB43bC7AGd/OeGfXV5g8NKymfVp1gEy49Grc9VUChBD491X98OqaY/DxUOObhWOgDWz4UTagbFcb6OOB4F9+tPlHxPtT89HNzwvBHb3MnpOcW4IZH25DcbkOu56ehMCVP7S8W4Ul1a/5lHc2IcDHA8sXXoL9qfm4ftF2XDM4BP+bO9TiToZVegPcVMLyjodNkJxbgmdWHMaWkzkYEtYJkd06YsOxLKQXlNc5r5ufFyp0BuSWKPW57mqB9Y9efvGPIp3egNEvr8PY3kHoG+yLt9aewO7YSQjuYP7fwRwpJd796yTeW3cS4/sE4Z2bhuLG/9uB8ko9fn9kAjp62egPh6Z2MXHh9yciopZiGK7PFrOK7dDqg+l44Ot98HJX4daxPXD3hJ7wX/G9+a2Nq/sAb+gxHB+NuREfrHoNXSqK6lx33jcAC2c9g0MhffDg7h/xz9snQT0vBlmF5fhxbxre+esEunfyxhe3jkREkA+OpBdg3qe74OGmwmcLRmJQt44Xw1lafhleW3MMqw6ko6OXG167PgpXDw6x2euTUVCO8a+vR6i/BqseuBQdTISmovIqzP5oO84XlKO4Qodnpg3AneN7Klc2toiwKYsCTRECydlFuOyNjXh27w+4468lQHg43n/4TbyV6Y3L+nbGvyb3xZCwTiZvnl1UgdmLtiHI1xPv3jTU7B8ilhSWV2HH6VzsTbmAfcn52J+aDw83FR6f2g8xo7VQqwSklDiWUYTE9EJEBPmgbxffi69lUXkVUvJK4a5WoW+XutsFP/XTQazan46wAA283NVYcf+lzRqj0bd/p+Dpnw/D212N4godlt0x2qpa4FbR1MWafH8iImoRhmFTuPCkASkl/kzMxLCwTnVnPq0tFwgIAIqKgMrKOvdbrnbHf6bci++ipuDSjGNQjR+HbadyYJDA+D5BeG/uMAT41HTAOJ5RhJhPdyKnuBLd/LxwSa8gdNK4Y9lOJUjedmkP7DidgwPnCnDzqDA8O30gNB5ubf76xP2aiM+3JQEAJg/ogkXzhteZRdUbJBZ+lYCNJ/6/vTuPkrI68zj+e6DZQdYGhEALIiAaWdIQAi6YKIpJxCwmOmhINMfoaCaamGOMs2iiCSYx5ySOiZKIG2iURKKTUcElhuhIWBRFZF8aQYRuIKxCb8/8UdVQ3VRVL7V0V93v5xxOdb1dVe/ty9tv/+rWfZ9bqseuGqefzV+jg0cqteCmsyOPS+GCQJe0ss/JGrR7mzpVHD7+AUVF+v1Nv9Cd2zto4f1Xa+DeyMVjVZ066fd3Pqz7952gPYcqdN6pvfX9C4ZrWN9jQbO8slr/8rtFeveDvWrbupWqql0/mnq6vjimf4NHicsrq/XYohL9+uV12vtRhdq0No3o11Vji7rrm2cNVt+ujR/BrWvh2lJ9bdZiSdL3Lxim688dkvJrvrpmp254/C19pXiA/vPzaViVraHnlWRvjIqKai9Cw/kJAFJGGEZ2JPkD75KeGHmBbj/vW+pd2FVfGN1fl4zur5MLO8d9/M59hzX/vR1atGGX3ti4S7sPluvikf10y5Th6t+tg8orq/XLF9fqgYUbdOIJ7TV6YHcN6tVJgws76eyhhbVKXCVTuv+I5vyjRC+8+6Hat2mtXp3bqlfndrp4ZD9NGHJslPCfh8o1ccYrOn9EH53Wr6vuem5VrVHf8spqzXh+tWa9vkk/vuR0XTm+SE8s3qJbn16hp/91gsYMjC680MiSfmt6FemZEefomRHnaFvXPhq3ZYVmP/kftaesREfqvzr5Zv2zQxfNn3VD7RcpKtKBNev18OubNHPhRh2uqNZtnz1VX/tUkcxMP5y3Qo//Y4vuvXy0xhR1101PLtfiTbs1dVQ/3f2lMyLLAsdYt2O/Xlq1U53bF+iE9gU6UlGt+15dr5Jdh3TWKb10/blDNGpAt+Oel6qKqmoV3/mS9n5Uofk3nl0r0KficEWV2hW0atz0kHj/j1LDP3HiugUAyCrCMLKjARcClQ8arIL165PWeq2rutq173BF3PrJr68v06zXNmlD6QG9v+cjVVW72ha00pfG9NfVZw7WkN7xw/bG0gP677+u11/e3q7yqmp9anBPFbQ2lR0o17Y9h1ReVa0/Xz9Rw/tGlrm+9+V1uufFtXrhxrM0rE8XXTf7Tb24aod+ddkordq+T08ueV9lB8p15fgi/fiS0yVFPvIfd9fLmjqqn2Z86YzkP2SdUeP3T+itO867Ri+dMl6tq6t0ZvUuDV+3XA+cOllXvPey7vy/R6Xdu4+Oxu9p1U7F356t6xbN1c1/n137tWMC1u6D5bp57tt6ZfVOnT+ijz5R1F0znl+t6yadrFsujJRhq6p2/fbV9frFgrU6Z2ihHrjyE0eD7eJNu3X1w0u0/0jt+eND+3TWDy86VZNiF1rJgNufXalFG3fp+e+clda5zfWq7xOQjh2lDh0i04PqijfXl+sWACCrCMMS0yKyob6FNTI877G8slprd+zX44u36E/LtupIZbUuPK2v7ph6mvrETPt4bV2Zrpu9TFXu+vInPqbpE06qNUJduv+IPvvrv6tTuwI9c8NEtWnVShPvfkWjBnTTrK+PlRSZG3vxva9p865DamXSp4f31rTxRZo0tLBWSLt57tt6fsV2Lb7tPHVqd/xUjiOVVXptXZk6tStQ31cXqMdP79BjhSN174TLZG3a6IbJw/XVsQOOjnTPeD6yEMXRhRuiff70aefqu5/7nv786Hc1avva2jupE7DcXQ++tkl3v7BaFVWuScMK9eD0sWpd5w3KU0ve1y1Pv6Mzh/TSzCuLtXjzbn3rsaXq17WDHvrGWHVo21r7PqrU4YoqDe/bJStL/FZV+9E3PFmT6mIsUuQNSY8eka9j3sQcF6iZFwwAGUEY5oK57IjXz4kWC8iwXQeO6NE3SvTAwg1q36a1fvqFj2vKx0/U3KXv69anV+jkws566Btj1a9bh7jPX7xpty7/3SJNHtFH4wb10B3/857mXvspjT2px9HHbCo7qPkrP9TnR/ZT/wSvs2Tzbl16/xv6+ZfP0KXFA2p9b92O/fq3PyzXqu37jnvelNP76t8/N+K4162qdn3zkSX6+7oyzf7mJzV+SKHkrusuuVXL+g3Xot98Xa0U83tcc7Hj7t3HvQlcsXWv5r21Td8575SEpdf+uGyrvv/HtzXixBO0dsd+DendRY9dPa7B01DyQqrLdCeS5P8GAJBehGE+ksyeFjYCv6H0gG56crne2bpXxUXdtbRkj84c0ku/uWJMvSW0Zi7coJ88t1ptC1rpjP5d9cfrJjR6/+6uz9zzN/Xs3FZzr51wdNvsRSW6839XqXO7At1+8Wnq3rGtPtx3WDv2HdbIj3VLWtVg3+EKXXLf69pYelAdK46o58E9+rBLT136zkv6yYL7jj2wZ8+0jD7Oe2urvvfU2xo1oJse+vo4de3YfDWLm0VjVxFsDM5BAJAVhGEuVglaRVW17n1lve7763p9cXT/owst1Mfdde3sZZq/cocenF6sz5zap0n7/+2rG3T3C6s1aVih9hyqUOm+w/pg72GdM7RQP7/0jCbVyv1w72H9efk2lS1Zrl2vLda+gva6eeGjOrV087HAm6iGbRMCWMmug+rbtX1kae3QZGpkWOIcBABZQhhmZBiSDhypVOc483aT+ai8Sm9u2aMJJ/ds8gVbZQeOaHq0JFiPTm3Vo1NbjR/cU18tHtCoCwkTSjQaz5vA9Ig3/Se21nY8ZpH/i/pCNOcgAMgKwjBzhhGi+mrZMke14RpZEu9o/ya78I5zEABkTUPDcBYvz86yadMif3SKiiIjNkVF/BFC/rvrrkjgiqekJBLU5szJbpsyZc6cSDBt1Spym+6fa9q0yAhudXXktubcEa+PO3Y8FpZjzzs9e0b+cQ4CgBYrf0eGgVDVjGgm+7g+10eJm/uTnxZ20SgA4HhMkwBCV19FhFz+yJ5rAgAA9WCaBBC6gQOTf//QocjoZi7asiX+9pKSzEyZAADkLcIwkK+SzR+ukShUtnTJgn6+zY0GAGQUYRjIV7EXcyVS3+hxS1Vf0M/lUW8AQFYRhoF8VlMRYfbsxBUQclFDgn5JSeYqTQAA8ka9YdjMZpnZTjN7N2ZbDzN70czWRW+7Z7aZAFLS2FKDmS5blkyyfcd+77bbImE+WSB2P37aROxr9OoV+UdoBoBgNWRk+GFJF9bZ9gNJL7v7KZJejt4H0JLVrZsrxQ+dNWXLSkrih8lEGhOgEz022b4Tfe+ii+qfG33okHTFFZHge9VVx15j167Iv8b8nACAvNKg0mpmdpKkv7j76dH7ayRNcvftZnaipFfdfVh9r0NpNaCFSFanN1GN4mRlyxpT97cp+5ak1q2lqqr47brrrmN1f1MtF0l5NgDIC2mtMxwnDP/T3bvFfH+Pu9c7VYIwDLQQyer0JgqUZpFR5ca+Xt1gmWzJ6Kao265UXz/ZzwkAyBktps6wmV1jZkvNbGlpaWmmdwegIZLV6W2V4LQQr/JEzXSHROEz3n7SXc6tbrsaUlKuMa8HAMhrTQ3DO6LTIxS93Znoge4+092L3b24sLCwibsDkFbJAl+8qQjxKk/EzuFtzH7SGTbjtashlSYSadNGOnCAC+oAICBNDcPPSpoe/Xq6pGfS0xwAWdGQ0dPWrZNXnrjtttrzfutKFCxTHbmtr11S8pJybdpIPXtGXqNnz9pfm3FBHQAEpiGl1Z6Q9IakYWa21cyuljRD0vlmtk7S+dH7AHJFQ0ZPq6uPVZ6IDZz1TY2QkgfLhuy7qChxbeRHHonfrvp+zpoA/dBDUllZ5DXKyo593bmzVF5e+/ks3gEAea9BF9ClCxfQAS1QfRe/zZlzrFJDjx7S/v3Hh8a6z5MSh+Wa6g/TptVfhSJ23wMHHnteJrRq1fgLBwEALVaLuYAOQAsXb9pCzVzcurV9d+1KHoRrnpfsIrlEo8Txpj7UrY2cqSAsJZ7LzAV1AJDXCMNA6JIF0vrmBceKfV59ATJ2+kE2A28yyd4UAADyFtMkACSWaOpAXXXrCceb/lBXS5x+kM1pGQCAjGroNImCbDQGQI4aOLD+BSwSlTeTkq8o1xKnH0ybRvgFgMAwTQJAYvGmDsSWJmtqeTOmHwAAWgjCMIDE6itN1tTyZokCNAAAWcacYQAAAOQdSqsBAAAA9SAMAwAAIFiEYQAAAASLMAwAAIBgEYYBAAAQLMIwAAAAgkUYBgAAQLAIwwAAAAgWYRgAAADBIgwDAAAgWIRhAAAABIswDAAAgGARhgEAABAswjAAAACCRRgGAABAsAjDAAAACBZhGAAAAMEiDAMAACBYhGEAAAAEizAMAACAYBGGAQAAECzCMAAAAIJFGAYAAECwCMMAAAAIFmEYAAAAwSIMAwAAIFiEYQAAAASLMAwAAIBgEYYBAAAQLMIwAAAAgkUYBgAAQLAIwwAAAAgWYRgAAADBIgwDAAAgWIRhAAAABIswDAAAgGARhgEAABAswjAAAACCRRgGAABAsAjDAAAACBZhGAAAAMEiDAMAACBYhGEAAAAEizAMAACAYBGGAQAAECzCMAAAAIJFGAYAAECwCMMAAAAIFmEYAAAAwSIMAwAAIFiEYQAAAASLMAwAAIBgEYYBAAAQLMIwAAAAgkUYBgAAQLAIwwAAAAgWYRgAAADBKkjlyWa2WdJ+SVWSKt29OB2NAgAAALIhpTAcda67l6XhdQAAAICsYpoEAAAAgpVqGHZJC8xsmZldE+8BZnaNmS01s6WlpaUp7g4AAABIn1TD8ER3HyNpiqTrzezsug9w95nuXuzuxYWFhSnuDgAAAEiflMKwu38Qvd0paZ6kceloFAAAAJANTQ7DZtbJzLrUfC1psqR309UwAAAAINNSqSbRR9I8M6t5ncfd/YW0tAoAAADIgiaHYXffKGlkGtsCAAAAZBWl1QAAABAswjAAAACCRRgGAABAsAjDAAAACBZhGAAAAMEiDAMAACBYhGEAAAAEizAMAACAYBGGAQAAECzCMAAAAIJFGAYAAECwCMMAAAAIFmEYAAAAwSIMAwAAIFiEYQAAAASLMAwAAIBgEYYBAAAQLMIwAAAAgkUYBgAAQLAIwwAAAAgWYRgAAADBIgwDAAAgWIRhAAAABIswDAAAgGARhgEAABAswjAAAACCRRgGAABAsAjDAAAACBZhGAAAAMEiDAMAACBYhGEAAAAEizAMAACAYBGGAQAAECzCMAAAAIJFGAYAAECwCMMAAAAIFmEYAAAAwSIMAwAAIFiEYQAAAASLMAwAAIBgEYYBAAAQLMIwAAAAgkUYBgAAQLAIwwAAAAgWYRgAAADBIgwDAAAgWIRhAAAABIswDAAAgGARhgEAABAswjAAAACCRRgGAABAsAjDAAAACBZhGAAAAMEiDAMAACBYhGEAAAAEizAMAACAYBGGAQAAECzCMAAAAIJFGAYAAECwCMMAAAAIFmEYAAAAwSIMAwAAIFiEYQAAAAQrpTBsZhea2RozW29mP0hXowAAAIBsaHIYNrPWku6TNEXSCEmXm9mIdDUMAAAAyLRURobHSVrv7hvdvVzSHyRNTU+zAAAAgMxLJQz3l/R+zP2t0W0AAABATihI4bkWZ5sf9yCzayRdE717wMzWpLDPxuglqSxL+woJ/ZoZ9Gtm0K+ZQb9mBv2aGfRrZuRCvxY15EGphOGtkgbE3P+YpA/qPsjdZ0qamcJ+msTMlrp7cbb3m+/o18ygXzODfs0M+jUz6NfMoF8zI5/6NZVpEksknWJmg8ysraTLJD2bnmYBAAAAmdfkkWF3rzSzGyTNl9Ra0ix3X5m2lgEAAAAZlso0Cbn7c5KeS1Nb0i3rUzMCQb9mBv2aGfRrZtCvmUG/Zgb9mhl506/mftw1bwAAAEAQWI4ZAAAAwcq7MMwS0elhZgPM7K9mtsrMVprZd6LbbzezbWa2PPrvouZua64xs81mtiLaf0uj23qY2Ytmti56272525lLzGxYzDG53Mz2mdmNHK9NY2azzGynmb0bsy3uMWoRv46ec98xszHN1/KWK0Gf/tzMVkf7bZ6ZdYtuP8nMPoo5bu9vvpa3bAn6NeHvvZndGj1W15jZBc3T6pYvQb8+GdOnm81seXR7zh+veTVNIrpE9FpJ5ytS+m2JpMvd/b1mbVgOMrMTJZ3o7m+aWRdJyyRdIukrkg64+y+atYE5zMw2Syp297KYbT+TtNvdZ0TfxHV391uaq425LHoe2Cbpk5K+IY7XRjOzsyUdkPSou58e3Rb3GI0GjW9LukiRPv+Vu3+yudreUiXo08mSXolekH63JEX79CRJf6l5HBJL0K+3K87vvZmNkPSEIivo9pP0kqSh7l6V1UbngHj9Wuf790ja6+4/yofjNd9GhlkiOk3cfbu7vxn9er+kVWKFwUyaKumR6NePKPLGA03zGUkb3L2kuRuSq9x9oaTddTYnOkanKvIH0919kaRu0TfTiBGvT919gbtXRu8uUqRePxohwbGayFRJf3D3I+6+SdJ6RXID6kjWr2ZmigyMPZHVRmVQvoVhlojOgOi7vtGS/hHddEP0Y71ZfJzfJC5pgZkts8gKjZLUx923S5E3IpJ6N1vrct9lqn2S5nhNj0THKOfd9LhK0vMx9weZ2Vtm9jczO6u5GpXD4v3ec6ymx1mSdrj7uphtOX285lsYbtAS0Wg4M+ss6U+SbnT3fZJ+K+lkSaMkbZd0TzM2L1dNdPcxkqZIuj76cRTSwCILAF0saW50E8dr5nHeTZGZ3SapUtKc6Kbtkga6+2hJ35X0uJmd0Fzty0GJfu85VtPjctUecMj54zXfwnCDlohGw5hZG0WC8Bx3f1qS3H2Hu1e5e7Wk34mPmBrN3T+I3u6UNE+RPtxR89Fy9HZn87Uwp02R9Ka775A4XtMs0THKeTcFZjZd0uckTfPoRTzRj/F3Rb9eJmmDpKHN18rckuT3nmM1RWZWIOmLkp6s2ZYPx2u+hWGWiE6T6JygByWtcvdfxmyPnQv4BUnv1n0uEjOzTtELEmVmnSRNVqQPn5U0Pfqw6ZKeaZ4W5rxaIxYcr2mV6Bh9VtLXolUlxityUc325mhgrjGzCyXdIulidz8Us70weiGozGywpFMkbWyeVuaeJL/3z0q6zMzamdkgRfp1cbbbl+POk7Ta3bfWbMiH4zWlFehaGpaITquJkq6UtKKmfIqkH0q63MxGKfLR0mZJ32qe5uWsPpLmRd5rqEDS4+7+gpktkfSUmV0taYukS5uxjTnJzDoqUkkm9pj8Gcdr45nZE5ImSeplZlsl/ZekGYp/jD6nSCWJ9ZIOKVLBA3Uk6NNbJbWT9GL0nLDI3a+VdLakH5lZpaQqSde6e0MvEgtKgn6dFO/33t1XmtlTkt5TZFrK9VSSiC9ev7r7gzr+mgwpD47XvCqtBgAAADRGvk2TAAAAABqMMAwAAIBgEYYBAAAQLMIwAAAAgkUYBgAAQLAIwwAAAAgWYRgAAADBIgwDAAAgWP8PUjLeUV+q6KwAAAAASUVORK5CYII=\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": [
              "0.9405459998434336"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Daily Returns"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "df['Returns'] = df['Adj Close'].pct_change().dropna()"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "outputs": [],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.index.values\n",
        "y = df['Returns'].values"
      ],
      "outputs": [],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": [
              "(177,)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.reshape((177,-1))"
      ],
      "outputs": [],
      "execution_count": 26,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        " a = model.coef_ * X + model.intercept_\n",
        " a"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": [
              "array([[-3.41944131e-03],\n",
              "       [-3.30425644e-03],\n",
              "       [-3.18907157e-03],\n",
              "       [-3.07388670e-03],\n",
              "       [-2.95870183e-03],\n",
              "       [-2.84351696e-03],\n",
              "       [-2.72833208e-03],\n",
              "       [-2.61314721e-03],\n",
              "       [-2.49796234e-03],\n",
              "       [-2.38277747e-03],\n",
              "       [-2.26759260e-03],\n",
              "       [-2.15240773e-03],\n",
              "       [-2.03722286e-03],\n",
              "       [-1.92203799e-03],\n",
              "       [-1.80685311e-03],\n",
              "       [-1.69166824e-03],\n",
              "       [-1.57648337e-03],\n",
              "       [-1.46129850e-03],\n",
              "       [-1.34611363e-03],\n",
              "       [-1.23092876e-03],\n",
              "       [-1.11574389e-03],\n",
              "       [-1.00055901e-03],\n",
              "       [-8.85374143e-04],\n",
              "       [-7.70189272e-04],\n",
              "       [-6.55004400e-04],\n",
              "       [-5.39819529e-04],\n",
              "       [-4.24634658e-04],\n",
              "       [-3.09449786e-04],\n",
              "       [-1.94264915e-04],\n",
              "       [-7.90800437e-05],\n",
              "       [ 3.61048276e-05],\n",
              "       [ 1.51289699e-04],\n",
              "       [ 2.66474570e-04],\n",
              "       [ 3.81659442e-04],\n",
              "       [ 4.96844313e-04],\n",
              "       [ 6.12029184e-04],\n",
              "       [ 7.27214056e-04],\n",
              "       [ 8.42398927e-04],\n",
              "       [ 9.57583798e-04],\n",
              "       [ 1.07276867e-03],\n",
              "       [ 1.18795354e-03],\n",
              "       [ 1.30313841e-03],\n",
              "       [ 1.41832328e-03],\n",
              "       [ 1.53350816e-03],\n",
              "       [ 1.64869303e-03],\n",
              "       [ 1.76387790e-03],\n",
              "       [ 1.87906277e-03],\n",
              "       [ 1.99424764e-03],\n",
              "       [ 2.10943251e-03],\n",
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              "       [ 2.33980225e-03],\n",
              "       [ 2.45498713e-03],\n",
              "       [ 2.57017200e-03],\n",
              "       [ 2.68535687e-03],\n",
              "       [ 2.80054174e-03],\n",
              "       [ 2.91572661e-03],\n",
              "       [ 3.03091148e-03],\n",
              "       [ 3.14609635e-03],\n",
              "       [ 3.26128123e-03],\n",
              "       [ 3.37646610e-03],\n",
              "       [ 3.49165097e-03],\n",
              "       [ 3.60683584e-03],\n",
              "       [ 3.72202071e-03],\n",
              "       [ 3.83720558e-03],\n",
              "       [ 3.95239045e-03],\n",
              "       [ 4.06757532e-03],\n",
              "       [ 4.18276020e-03],\n",
              "       [ 4.29794507e-03],\n",
              "       [ 4.41312994e-03],\n",
              "       [ 4.52831481e-03],\n",
              "       [ 4.64349968e-03],\n",
              "       [ 4.75868455e-03],\n",
              "       [ 4.87386942e-03],\n",
              "       [ 4.98905430e-03],\n",
              "       [ 5.10423917e-03],\n",
              "       [ 5.21942404e-03],\n",
              "       [ 5.33460891e-03],\n",
              "       [ 5.44979378e-03],\n",
              "       [ 5.56497865e-03],\n",
              "       [ 5.68016352e-03],\n",
              "       [ 5.79534839e-03],\n",
              "       [ 5.91053327e-03],\n",
              "       [ 6.02571814e-03],\n",
              "       [ 6.14090301e-03],\n",
              "       [ 6.25608788e-03],\n",
              "       [ 6.37127275e-03],\n",
              "       [ 6.48645762e-03],\n",
              "       [ 6.60164249e-03],\n",
              "       [ 6.71682737e-03],\n",
              "       [ 6.83201224e-03],\n",
              "       [ 6.94719711e-03],\n",
              "       [ 7.06238198e-03],\n",
              "       [ 7.17756685e-03],\n",
              "       [ 7.29275172e-03],\n",
              "       [ 7.40793659e-03],\n",
              "       [ 7.52312147e-03],\n",
              "       [ 7.63830634e-03],\n",
              "       [ 7.75349121e-03],\n",
              "       [ 7.86867608e-03],\n",
              "       [ 7.98386095e-03],\n",
              "       [ 8.09904582e-03],\n",
              "       [ 8.21423069e-03],\n",
              "       [ 8.32941556e-03],\n",
              "       [ 8.44460044e-03],\n",
              "       [ 8.55978531e-03],\n",
              "       [ 8.67497018e-03],\n",
              "       [ 8.79015505e-03],\n",
              "       [ 8.90533992e-03],\n",
              "       [ 9.02052479e-03],\n",
              "       [ 9.13570966e-03],\n",
              "       [ 9.25089454e-03],\n",
              "       [ 9.36607941e-03],\n",
              "       [ 9.48126428e-03],\n",
              "       [ 9.59644915e-03],\n",
              "       [ 9.71163402e-03],\n",
              "       [ 9.82681889e-03],\n",
              "       [ 9.94200376e-03],\n",
              "       [ 1.00571886e-02],\n",
              "       [ 1.01723735e-02],\n",
              "       [ 1.02875584e-02],\n",
              "       [ 1.04027432e-02],\n",
              "       [ 1.05179281e-02],\n",
              "       [ 1.06331130e-02],\n",
              "       [ 1.07482979e-02],\n",
              "       [ 1.08634827e-02],\n",
              "       [ 1.09786676e-02],\n",
              "       [ 1.10938525e-02],\n",
              "       [ 1.12090373e-02],\n",
              "       [ 1.13242222e-02],\n",
              "       [ 1.14394071e-02],\n",
              "       [ 1.15545920e-02],\n",
              "       [ 1.16697768e-02],\n",
              "       [ 1.17849617e-02],\n",
              "       [ 1.19001466e-02],\n",
              "       [ 1.20153314e-02],\n",
              "       [ 1.21305163e-02],\n",
              "       [ 1.22457012e-02],\n",
              "       [ 1.23608861e-02],\n",
              "       [ 1.24760709e-02],\n",
              "       [ 1.25912558e-02],\n",
              "       [ 1.27064407e-02],\n",
              "       [ 1.28216255e-02],\n",
              "       [ 1.29368104e-02],\n",
              "       [ 1.30519953e-02],\n",
              "       [ 1.31671802e-02],\n",
              "       [ 1.32823650e-02],\n",
              "       [ 1.33975499e-02],\n",
              "       [ 1.35127348e-02],\n",
              "       [ 1.36279196e-02],\n",
              "       [ 1.37431045e-02],\n",
              "       [ 1.38582894e-02],\n",
              "       [ 1.39734743e-02],\n",
              "       [ 1.40886591e-02],\n",
              "       [ 1.42038440e-02],\n",
              "       [ 1.43190289e-02],\n",
              "       [ 1.44342137e-02],\n",
              "       [ 1.45493986e-02],\n",
              "       [ 1.46645835e-02],\n",
              "       [ 1.47797684e-02],\n",
              "       [ 1.48949532e-02],\n",
              "       [ 1.50101381e-02],\n",
              "       [ 1.51253230e-02],\n",
              "       [ 1.52405078e-02],\n",
              "       [ 1.53556927e-02],\n",
              "       [ 1.54708776e-02],\n",
              "       [ 1.55860625e-02],\n",
              "       [ 1.57012473e-02],\n",
              "       [ 1.58164322e-02],\n",
              "       [ 1.59316171e-02],\n",
              "       [ 1.60468019e-02],\n",
              "       [ 1.61619868e-02],\n",
              "       [ 1.62771717e-02],\n",
              "       [ 1.63923566e-02],\n",
              "       [ 1.65075414e-02],\n",
              "       [ 1.66227263e-02],\n",
              "       [ 1.67379112e-02],\n",
              "       [ 1.68530960e-02]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.score(X, y))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.031971390677791645\n"
          ]
        }
      ],
      "execution_count": 29,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear Regression\n",
        "# Underfitted \n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X, y, 'ro', X, a)\n",
        "axes = plt.gca()\n",
        "axes.set_ylim([-0.2, 0.2])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 30,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": [
              "0.031971390677791645"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 31,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "meanSquaredError = mean_squared_error(X, y)\n",
        "print(\"MSE:\", meanSquaredError)\n",
        "rootMeanSquaredError = sqrt(meanSquaredError)\n",
        "print(\"RMSE:\", rootMeanSquaredError)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 10529.870781283005\n",
            "RMSE: 102.61515863303534\n"
          ]
        }
      ],
      "execution_count": 32,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.c_[X, X**2]\n",
        "X"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": [
              "array([[    1,     1],\n",
              "       [    2,     4],\n",
              "       [    3,     9],\n",
              "       [    4,    16],\n",
              "       [    5,    25],\n",
              "       [    6,    36],\n",
              "       [    7,    49],\n",
              "       [    8,    64],\n",
              "       [    9,    81],\n",
              "       [   10,   100],\n",
              "       [   11,   121],\n",
              "       [   12,   144],\n",
              "       [   13,   169],\n",
              "       [   14,   196],\n",
              "       [   15,   225],\n",
              "       [   16,   256],\n",
              "       [   17,   289],\n",
              "       [   18,   324],\n",
              "       [   19,   361],\n",
              "       [   20,   400],\n",
              "       [   21,   441],\n",
              "       [   22,   484],\n",
              "       [   23,   529],\n",
              "       [   24,   576],\n",
              "       [   25,   625],\n",
              "       [   26,   676],\n",
              "       [   27,   729],\n",
              "       [   28,   784],\n",
              "       [   29,   841],\n",
              "       [   30,   900],\n",
              "       [   31,   961],\n",
              "       [   32,  1024],\n",
              "       [   33,  1089],\n",
              "       [   34,  1156],\n",
              "       [   35,  1225],\n",
              "       [   36,  1296],\n",
              "       [   37,  1369],\n",
              "       [   38,  1444],\n",
              "       [   39,  1521],\n",
              "       [   40,  1600],\n",
              "       [   41,  1681],\n",
              "       [   42,  1764],\n",
              "       [   43,  1849],\n",
              "       [   44,  1936],\n",
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              "       [   71,  5041],\n",
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              "       [   74,  5476],\n",
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              "       [  100, 10000],\n",
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              "       [  129, 16641],\n",
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              "       [  131, 17161],\n",
              "       [  132, 17424],\n",
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              "       [  134, 17956],\n",
              "       [  135, 18225],\n",
              "       [  136, 18496],\n",
              "       [  137, 18769],\n",
              "       [  138, 19044],\n",
              "       [  139, 19321],\n",
              "       [  140, 19600],\n",
              "       [  141, 19881],\n",
              "       [  142, 20164],\n",
              "       [  143, 20449],\n",
              "       [  144, 20736],\n",
              "       [  145, 21025],\n",
              "       [  146, 21316],\n",
              "       [  147, 21609],\n",
              "       [  148, 21904],\n",
              "       [  149, 22201],\n",
              "       [  150, 22500],\n",
              "       [  151, 22801],\n",
              "       [  152, 23104],\n",
              "       [  153, 23409],\n",
              "       [  154, 23716],\n",
              "       [  155, 24025],\n",
              "       [  156, 24336],\n",
              "       [  157, 24649],\n",
              "       [  158, 24964],\n",
              "       [  159, 25281],\n",
              "       [  160, 25600],\n",
              "       [  161, 25921],\n",
              "       [  162, 26244],\n",
              "       [  163, 26569],\n",
              "       [  164, 26896],\n",
              "       [  165, 27225],\n",
              "       [  166, 27556],\n",
              "       [  167, 27889],\n",
              "       [  168, 28224],\n",
              "       [  169, 28561],\n",
              "       [  170, 28900],\n",
              "       [  171, 29241],\n",
              "       [  172, 29584],\n",
              "       [  173, 29929],\n",
              "       [  174, 30276],\n",
              "       [  175, 30625],\n",
              "       [  176, 30976],\n",
              "       [  177, 31329]], dtype=int64)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 33,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X, y)\n",
        "x = np.arange(1, 178)\n",
        "x = np.c_[x, x**2]\n",
        "a = np.dot(X, model.coef_.transpose()) + model.intercept_"
      ],
      "outputs": [],
      "execution_count": 34,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Polynomial features\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X[:, 0], y, 'ro', x[:, 0], a)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": [
              "0.04553743583036962"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 36,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(1, 178)\n",
        "X = np.c_[X, X**2, X**3, X**4, X**5, X**6, X**7, X**8, X**9]\n",
        "x = np.arange(1, 178)\n",
        "x = np.c_[x, x**2, x**3, x**4, x**5, x**6, x**7, x**8, x**9]\n",
        "\n",
        "model.fit(X, y)\n",
        "a = np.dot(x, model.coef_.transpose()) + model.intercept_\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X[:, 0], y, 'ro', x[:, 0], a)\n",
        "axes = plt.gca()\n",
        "axes.set_ylim([-0.2, 0.2])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": [
              "0.09900733588089616"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 38,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "version": "3.5.5",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "mimetype": "text/x-python",
      "name": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}