Scikit-Learn study.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# use seaborn for plot defaults\n",
      "import seaborn\n",
      "seaborn.set()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The Scikit-Learn Estimator Object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = LinearRegression(normalize=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(10)\n",
      "y = 2 * x + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print x,y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9] [ 1  3  5  7  9 11 13 15 17 19]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, y, 'o')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7885b30>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7816890>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# the input data for sklearn is 2D\n",
      "X = x[:, np.newaxis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print X, y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[0]\n",
        " [1]\n",
        " [2]\n",
        " [3]\n",
        " [4]\n",
        " [5]\n",
        " [6]\n",
        " [7]\n",
        " [8]\n",
        " [9]] [ 1  3  5  7  9 11 13 15 17 19]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fit the model with train data\n",
      "model.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print model.coef_\n",
      "print model.intercept_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 2.00000002]\n",
        "0.999999902313\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot Iris KNN"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import neighbors, datasets, linear_model\n",
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris = datasets.load_iris()\n",
      "X = iris.data[:, :2]  # we only take the first two features. We could\n",
      "                      # avoid this ugly slicing by using a two-dim dataset\n",
      "y = iris.target\n",
      "\n",
      "knn = neighbors.KNeighborsClassifier(n_neighbors=3)\n",
      "knn.fit(X, y)\n",
      "\n",
      "x_min, x_max = X[:, 0].min() , X[:, 0].max() \n",
      "y_min, y_max = X[:, 1].min() , X[:, 1].max() \n",
      "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
      "                     np.linspace(y_min, y_max, 100))\n",
      "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "# Put the result into a color plot\n",
      "Z = Z.reshape(xx.shape)\n",
      "pl.figure()\n",
      "pl.pcolormesh(xx, yy, Z)\n",
      "\n",
      "# Plot also the training points\n",
      "pl.scatter(X[:, 0], X[:, 1], c=y)\n",
      "pl.xlabel('sepal length (cm)')\n",
      "pl.ylabel('sepal width (cm)')\n",
      "pl.axis('tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "(4.1001639131028238,\n",
        " 8.0998360868971755,\n",
        " 1.8619671937298403,\n",
        " 4.5380328062701603)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ovcVM/qWd7/NZa/8tCvGISJjiPRwdrZW8nCze10R6lvYeI3O1+trVxtciIiIS\nB+2tRva9tl43xiQBI6IVkIiIiHQsnOfA/w54HMji8573DvyPl3X0vW7gWWAs4AP+1lq7rdn+G4B/\nApqA31hrf93ZE5CeSbPOJRFoyFziKZzVyL4BTAVeAUYCXwXCLRd2PeC11l4OfBf/BwEAjDEpwE+B\nhcAVwP82xvQLP/Su8/l8lJeXc+rUqbCOP336NMeOHXPMs5tNTU2sX7+esrKyDo/1+XwcP36cysrw\nVoutrKzk+PHjjvlZeL1eTp8+TX195EsZnDhxgqNHj4asdNdcXV0dZ86cCetYSSxer5dDhw6F/W9E\nJNrCSeDl1tp9+KuwTbbWPg/MDadxa+0bwAOBzeHA6Wa7xwN7rLVVgRXPPgHmhRl3l3m9Xl5//Y/s\n2bOH7du388orr7R7/BtvvMHGjRs5fPgwzz//fMLXKy4rK+OHP/whXq+XtWvXtjvhyOPx8OKLL3Lg\nwAFKS0t57bXX2m37tddeo7S0lAMHDvDiiy8m/KMv586dY+3atdTU1LBnz55gPetIWLFiBadPn6au\nro633nqr3d+LHTt2sH//fqqqqvjss88iVrQnJSWF7Oxs3G432dnZEXsUEfyPI7rdbtLS0oIVA7sL\nl8vF3Llzefjhh7ntttvo1atXyGPPnTvH97//ff7zP/+T//iP/+B3v/tdDCMVaVs4j5HVGmOuArYA\nNxpj1uNf1CQs1lqPMeZ54GZaLkOaC1Q1264BQj+sHGGffPIRS5bcTO/evQE4dOgQa9euZc6cOV84\ndteuXQwbNoypU6cCMHnyZN5++22uv/76WIXbaS+88ALf/va3g5XY/vznP1NWVtZm9aiVK1dy6623\nBiux7dq1i82bN7f5yExpaSkTJkxg3LhxAEyaNIl3332Xa665Jopn07Uhc2stl1xySXCRmNLSUhob\nG0lJSelSTGvWrGH27Nn06dMHgAEDBvDRRx+xYMGCLxxbW1uLy+Vi/PjxAAwePJgtW7YEf6e6Iisr\nizNnzgTfJy8vLyIfDrKzs6mpqQH8H/K8Xi//8A//wJgxYy66zUQq9DJhwgRuv/12XC4Xo0aN4ty5\nc/h8vjYXE3rppZeCI1lNTU1s2LCBNWvWxCxWkbaE0wP/e2AxsALoA+wE/qszb2KtvRf/ffBnjTEZ\ngZergOYf6XNo2UOPqvPnzweTN8DAgQOpqKho89hTp061KNyQkZGR8EOgWVlZweQNMGjQoJBD6Y2N\njS1WKhu9nMtFAAAgAElEQVQ8eDAnT55s89iTJ0+2+FlkZ2fT2NgYoaijIzk5ucUf5aysLM6fP9/l\ndqurq1v02rKyskKORtTV1ZGbmxvcdrvdLa5PJEXqd7N1+dDGxkbKy8sj0nYiyMrKavF7kZeXF/Jn\n1/p3PFKjJyJdEc5qZFvxr0A2Ffg3oLe1NqxVLowx9xhjvh3YrAO8+Cezgf+DwBhjTG9jTCr+4fOY\nfaQdP34iK1asCG4vXbq0zd43QFFREe+8807wfu+qVaswxsQkzouVn5/Pxo0bAf8f9JUrV4bs7U2c\nOJH3338/uL18+XJmzZrV5rGzZs1i+fLlwe0PPviAiRM7nM/Yoaeffrrd/7oiKyuLY8eOAf6eZHl5\nOVlZWV2Oefr06axbty64vXnz5pBlcPPz8zl06FAwQRw6dKjdIdvO8Hg8YS1B+9RTT3H33Xe3GHmY\nPHkyTz31VJs948WLF5OZmRnc7tOnD9OmTYtIzIlg3759wfkvTU1NpKSkhFwJbNKkSaSnpwe3u9vt\nBHGmcGahLwReAI7hT/i9jDG3W2uLw2j/j8DzxpiPgBTg68DNxphsa+2zxpiHgXcC7T5nrT12sSfS\nWYMHD6GhoZ7XX38dr9fLzJkzg0OhrWVlZbFgwQJee+01UlJSGDFiRMIn8LvvvpsXX3yRTz75hLNn\nz3LfffeRnNz25R45ciT19fW88cYbeDweLrvsspClV3Nzc7n88st57bXXcLvdjBkzhlGjRkXzVLps\n5MiR7Nu3j9LSUjweDzNmzIjImusFBQXU1tby3nvvkZSUxIABAxg5cmSbx7rdboqKiti8eTNut5u8\nvDyGDRvW5RjAX/c+OzublJQUGhsbg8Pebbn00ktJTk7GWkt2djbXXXddyGMvueQSGhsbKSkpISkp\niVtuuaVFQne6U6dOcfr0acrKyvB6vcyYMSPksXPnzsXj8bBy5Uq8Xi/V1dGpey3SGR0uZmKM2Qbc\nZa0tCWzPBH5prZ0Zg/ha6I6LmYifHgvrvM7eL+7s/ed4VFqL9WNZnT1HPTYmocRjMZNwEvgGa+2M\njl6LBSXw7ksJXNoS6YQZ6Q8lSuhyQaKuRvapMeYZ4JeAB7gL2GeMmQ0Q5lC6iIiIRFA4CXwy/oln\nTwS2XYHtHwa2r4pCXCIiItKODhO4tfbKGMQhIh3oaLg2EVcH62zMGpIWCV84s9CH469nPgL/o17/\nA3zVWrs/uqGJiIhIKOEMof8K+AnwA+A4/gT+AjEse5oIDh06xLp160hOTmbQoEHMnBnzSfiSgMrL\nyykrK8PlctG3b18GDRoU8tiamhp2795NcnIyWVlZcXv8bufOnTQ0NNDU1MSECRNaPN/c2v79+6mp\nqcHj8TBixIh2n10/ceIEx48fB6Bfv34MGDCgw1ief/55ioqKOny+fOTIkfTq1YvDhw+HLDLUWT6f\nj507d9LY2EhTUxOTJk2KaBlakWgLJ4H3tda+Y4z5gbXWC/w6sEJZj1FTU8PatWtZsmQJAGvXrmXb\ntm0RKWAizlVTU8OJEyeCBXJ27drFyZMnKSgo+MKxHo+H7du3M3v2bFwuF2VlZezfv58RI2K7Mu/O\nnTspKCigT58+wTr5l156aZvHHjlyBLfbzZQpUwBYv349U6ZMaTPJnTlzhlOnTgXL7+7cuZO0tLQO\n41m3bl2LYjhtKSoq4m/+5m9ISkri5MmTPP300yErwnXmNsL27dsZNGgQvXr1wuv1UlxczCWXXBL2\n97f1froFILEUTi3Hc8aYwRc2jDGXA5Ff0imBlZaWMn/+/OD2nDlz2Lt3bxwjkkRw8OBBJkyYENwe\nO3ZssOJbaxUVFQwdOjRYQGbgwIHtFlyJloaGhmDBoqSkJHr37h2yrGxlZSVDhw4Nbo8ePTrk+R06\ndKjFz2LcuHEcOXIkIjFPmjQpWHa2oKCA4cOHR6Rdj8cTHFFISkoiOzs74RfmEWkunAT+MLAcGG2M\n2Qy8jL+iWo/Rv39/9u///JZ/TU1NlxfCEOfLzc1tUT+/trY2ZK8zOzs7uOAI+Et3xmMp1qamphb1\nvs+dOxfyd9nlcrWo+V1ZWdminntzrc+vqqqKjIyMNo/trObLd/p8vohVQWtsbGxxDRoaGqJWn14k\nGsKZhb4uUH3N4E/4O621PaqS/6hRo1ixYgVvvvkmmZmZHD16lLvvvjveYUmcDR06lM2bN1NeXo7b\n7ebs2bMhy3FmZmbidrvZvHkzGRkZVFRURHweRTiV1MaPH8/atWvp27cvtbW19O7dO2TSmjBhAuvW\nrQv20lNSUlosANTcyJEj2bRpU3A+QF1dXbulSTtj9erVuN1uBgwYQGlpKTt37oxIu8YY1q5dS58+\nfaipqaFv374RKbErEivhzEKfA1wGPAW8CUwzxjxorf1jtINLJNdccw3nz5//wspd0rMVFRXR2NiI\n1+vt8J7v2LFj8Xg8NDQ0MHbs2BhF2FJGRgaXXHIJ9fX1pKWltdvjdLvdwWOTk5ND1tK/YNq0acEe\neyQng1VXV7Ns2bKItXdBdnY2c+bMoa6ujvT0dPW+xXHCmcT2C/yrkd2Kf0WxGcBr+Bcq6VHS0tLC\nmpgjPUtnbqe43e6LHlru7ISpUD1yl8vVqRjam6XeWrxncXe2nrvL5epWC7RIzxLOR84ka+1HwHXA\nn6y1h4C219wTERGRmAh3Fvr/ARYAy4wxXwdiP31WREREgsIZQr8L+Cpwi7W20hjTH/ir6IYlIq05\nsZRqT6PnwiWWwpmFfgT4t2bb345qRCIiItIhTbsU6aITJ05w7NixsJ7rPnjwIJs3b27xfHUotbW1\nHDp0KKxjo6WhoYHS0tIWdRBC8fl8wXKq8XjGvbl4zyi/8LMQiaZwhtBFIu7pp5+Odwhd5vP5eOml\nlyguLsbj8VBUVMT9998fMnmsXLmS4cOHM2DAAD744ANmz54d8rnqtWvX8vrrr1NdXc3AgQO57777\nonkqbaqtreXjjz9mxowZnD17lnfeeYdFixa1eazP5+PFF19kw4YNgP+Rsr/+67+OZbgAZGVl8ZWv\nfIWioiI2b97MI488wtmzZ4P7Y3Gb4UKJ2mHDhvHII4/w+uuvs2vXrqi/r/Q86oGLXKStW7eyZs2a\nYFW1kpISVq9e3eaxx44dY8iQIYwZM4Z+/fqxcOFC1q5dG7Ltd999N1hxrKysjBUrVkTlHNqzevVq\nFi1aRGFhISNHjmTcuHFYa9s8dsOGDcEPMh6Ph/Xr11NcXBzjiGH69OnMnz+fPn36MH/+/A4XSYmG\nnTt3Mm3aNAYOHMiwYcO47777yMrKinkc0v0pgYtcpJqami8MFZ87dy7ksc0LACUlJYWsKeDz+b4w\nbN7U1NTFaDvP7Xbjdn/+xGhubm7IMqa1tbVfeK15zzdWWj/fHo9nvJuamlpc2+zsbJVelqjQELrE\nRHcYMm9t6tSpfPTRR8FFO/r16xeyfOjIkSNZuXIlAwYMwO12s2vXrpDD5y6Xi3HjxvHpp58GXyst\nLe0wns4WMenIoEGDgqvueb1e1q1bx7x5ba8iPGPGDFavXk1ZWRngXz9gxowZvPbaa12KobO2b9/O\n3Llzyc/Pp7Kyku3bt8f0/QFGjBhBaWkpRUVF+Hw+Nm3axGOPPRb8MKGZ6RIpSuAiFykzM5MHH3yQ\n999/H5/Px+WXX07fvn3bPDY5OZm5c+eycuVKUlJSKCgoCC5D2pY777yzRQKPh/Hjx7Nt2zb+8pe/\n0NTUxJw5c0JWZcvJyeHBBx/kgw8+wOVyMXfu3HbXDo+WI0eO8MQTT9CnTx9OnTrVYiGUWMnJyQnW\nyff5fIwePVrV3iQqXPGeLdoZJ0/WRDzYgoKcSDfZI3XHHnasRbtnFo/nxBOtt5mIz8on2s9ILk55\neWRWyWutoCAn5Ao7ugcuIiLiQErgIiIiDqR74BIWDZHHXyIO/7aW6MPBkZ7oF4kYRC6WeuAiIiIO\npAQuIiLiQBpCly+I13B5fX0927dvJyUlBa/Xy+TJk+Ne07ojZWVllJeX43K56NWrF8OGDQv7ezsa\nSk1PT8fr9eJ2u6mrq+tqqIC/SMy2bdvw+Xw0NjYyduzYFgVmRMQ5lMAlYWzevJlZs2aRlJREfX09\npaWl7T4rHW9nzpyhqqoqGOP+/fs5fvw4/fv373LbKSkp1NfXB7dbVxi7WNu3b2fo0KHk5ubi8/ko\nLi5mzpw5EWlbRGIrsbs30qNkZGQEe9zp6ekJ3/s+fPgwxpjg9ogRIygvL49I242NjS22PR5PRNr1\neDzk5uYC/opveXl5LT4oiIhzqAfeAyXqjPLmSevCEG8iy8/P58SJEwwYMADw98g701NuPQO6+ZC6\n2+1ukbRdrpC1HDqlqakJr9cb/HB09uzZkDXZO9IdZlMnwqx0kYulBC4JY/Dgwaxfv560tDTq6uqY\nMGFCvENq16BBg9i6dWvwHrjH44nY6lcXFr/weDykpKRw/vz5iLQ7ceJE1q1bR2ZmJo2NjfTr1y9i\nHw5EJLaUwCVhFBYWUlhY2KKHmOgmTZqEz+fD5/NFNObmw9qRHIlIS0tjzpw5jvoZi0jblMB7gEQd\nMg/FaYnF5XI5rhfrtJ9xd9LerRORztC/YhEREQdSD7wbclqPW/w0gSr+NKlNnEQ9cBEREQdSAhcR\nEXEgDaF3Axoyj6ympiYOHTpEamoqgwYNiugEtePHj1NbW8vQoUNJTU1t99iKigoqKysZNGgQmZmZ\nEYuhMxobG9m1axdZWVkMHz48LjGISNvUAxdppqGhgXXr1lFQUEBaWhrr16/H5/NFpO2SkhK8Xi/9\n+/enpKSE2trakMdaa6murmbQoEHs3buXEydORCSGzqirq+MXv/gFTz/9ND/96U/5wx/+EPMYRCQ0\nJXCRZnbs2MHs2bPJycmhoKCAYcOGcfTo0S63W1VVRU5ODgMHDiQ7O5tZs2axZ8+eNo/1+XzU1dUx\nYsQIMjMzmTx5MmVlZV2OobPee+899u3bB/gLyqxevZpjx47FPA4RaZuG0LuBhx56qMW2htS7xu12\nB7/OyMhot6ccrsbGxhYlS10uV7vPYrfe1zymWGlqamqx7fF4VDc9CvRcuFws9cBFmhk6dCilpaWA\nP2Ft2bKFoUOHdrnd/Px8Dh8+HKyqZq2lX79+bR7rcrlobGwMfnA4evRoXO6Bz5kzh759+wa3x40b\nF5GfhYhEhnrgIs307t0bgC1btuDz+Zg5cybJyV3/Z5KUlMTs2bPZvn074C8bGyqBA0yfPh1rLY2N\njfTq1YvRo0d3OYbOGjhwIA8++CDr168nJSWF+fPnx2UkQETapgQu0krv3r2DiTyS3G43kydPDutY\nl8vFuHHjIh5DZ/Xv35/rr78+3mGISBs0hC4iIuJA6oGLSFh64uQqlVaVRKYeuIiIiAMpgYuIiDhQ\n1IbQjTEpwG+AYUAa8O/W2jeb7f9H4H7gZOClB6y1u6IVj4iIE+i5cAlXNO+B3wWctNbeY4zpDZQA\nbzbbPx24x1q7KYoxSJg+++wz1q9fj9vt5sorr2T8+PERabeiooKDBw+SnJyMy+Vi0qRJEa0tHg3W\nWs6fP4/X66WgoIBBgwaFPPbQoUNUVlbicrnIyspq93GvI0eOsHz5choaGhg3bhwLFy6MRviOkJKS\nQlZWFuAvGBOJYjmx8OSTT5Kamso111yjZ+Il7qKZwF8F/hj4OgloarV/BvAdY0x/YLm19gdRjEXa\nYa3lj3/8I3V1dYC/cMjDDz9Mfn5+l9r1eDzs37+fmTNnAlBdXc3OnTsj9uEgGg4ePEheXh79+/cH\nYNu2beTl5ZGdnf2FYysrKzl//jxTp04F4PDhw5SVlTFw4MAvHNvY2MgLL7wQLIm6e/dusrKy+NKX\nvhTFs0lcaWlpnDlzBvA/XpeRkRH8/UtkO3fuBODkyZN885vfbFFdTyTWopbArbVnAYwxOfiT+WOt\nDnkZeAqoAf5sjLnOWrs8WvFIaHv27Gnxx/P06dPs2rWLSy65pEvtVldXtyhWkpuby4EDB7rUZrRV\nVVUxZcqU4PaIESM4evQoY8aM+cKxR48eZeLEicHtIUOGUFpa2mYCr6ioaFHP3OPxcPDgwYgm8EjP\nmI7G0O1TTz1FcXExL7zwQvA1j8fDqFGjePDBBzvdXryGl48dO8bDDz+sWekSV1GdxGaMGQK8D7xo\nrf19q90/t9ZWWmsbgeXAtGjGIqH179+/RYWtzMxMhg0b1uV2s7OzqaysDG43NDREbGWvaElLS6Oq\nqiq4XVZW1qKcaHMFBQUtFjqpqKhos6cO0KtXry+MaIRqt7sbMWJEcPj8gry8vDhFI+Jc0ZzEVgi8\nCzxkrf2g1b48oNQYMwE4B8wHnotWLNK+GTNmUFZWRklJCW63m3nz5jFgwIAut5uSkkKfPn3YuHEj\nKSkp1NXVMWPGjAhEHD1jx44N/hy8Xi/Z2dkhq7L179+fnTt3UlJSElycJFSltfT0dJYsWcLbb79N\nfX09Y8aMYcGCBdE8lYRVUFDAggUL+PTTT2lqamLAgAHccccd8Q5LxHFc0eoRGWN+DiwBbLOXnwWy\nrLXPGmPuBP4ROA+8Z639147aPHmyJuLBFhTkRLrJuNNqZD1TR8PJnR3ujcTwdKyHmOM9Yzsa5xvv\nc5LwlJdXR6XdgoKckLN+o3kP/OvA19vZ/zL+++AiIiLSSSrkIiIi4kCqhS7STXQ0fBuLoVjNyhaJ\nHfXARUREHEg9cBG5KInY2452GdKO2tfqZRJLSuAC+J/R3rNnD8nJyYwePZqkpJ47OFNdXc1nn31G\nWloal112GcnJ+mciIomn5/6VlqDz58+zYcMGRo8ezeDBg1m7di1erzfeYcVFZWUlxcXFXHXVVUyf\nPp233367x/4sRCSxqWshWGuZM2cOSUlJpKamMmnSJPbv38+oUaPiHVrMrVu3joULF5KUlERKSgqX\nXnopGzZsYNasWfEOrUPRHr514nBwtGO+mImDTvw5SmJSD1wAWqwQ5na78Xg8cYwmflwuV4ufRXJy\nMk1NrdfhERGJPyVwYeTIkaxfvx6fz0dTUxMlJSWMHDky3mHFxcSJE1m9ejU+n4/GxkY++eSThC//\nKiI9U9RKqUaDSqmG52JKqZ49e5Y9e/aQlJTEuHHjSElJiUJkznDs2DFKS0sBuOKKK0hPT49zRG3r\n6gxrDeU6g0qpOkO3KqUqzpKVlUVRUVG8w0gIAwYMiMhiLiIi0aQhdBEREQdSD1zEITRkLiLNqQcu\nIiLiQOqBiyQo9bgFol8eVpxLPXAREREHUg88Cvbs2cPGjRtJT0/n/Pnz3Hrrrd2qtvj+/fupqqrC\n5XKRmprK+PHj4x1SRG3bto2mpiZ8Ph+9e/dm2LBh8Q6p2zl69CgnTpwI1pmfMmVKnCMScR4l8Ajz\ner2sW7eOO++8E/AvjPHWW29x/fXXR+09L+a574t1+vRpmpqamDp1KgDl5eUcOHCA4cOHxyyGaNq3\nbx+FhYX07dsXgJ07d1JdXU1ubm6cI+s+6urqqKysZPr06QBUVVWxa9cuxo4dG+fIRJyl+3QLE0Rl\nZSVDhw4Nbufm5narxTCOHz/eokfar18/qqujU8AgHmpra4PJG2Do0KGcOHEijhF1PxUVFQwcODC4\nnZeXR319fRwjEnEmJfAIy8/P5/Dhw8Hts2fP4qRqdx0pLCzk0KFDwe1Tp06RnZ0dx4giKzMzk8rK\nyuD2kSNH6NevXxwj6n7y8/M5duxYcLumpobU1NQ4RiTiTBpCj7CkpCSmTZvGK6+8Qnp6OnV1ddx2\n220RfY9YDpm3lp+fz5kzZygpKcHlcuF2u5k0aVLc4om00aNHs2XLFo4cOYLX6yUvL4+8vLyYvHdP\nmXWemZlJXl4emzZtwu124/V6VQVQ5CIogUeBMQZjTLzDiJruvtDJ5MmT4x1CtzdkyBCGDBkS7zBE\nHE1D6CIiIg6kHrgDxHPIXGJHBTtEpDPUAxcREXEgJXAREREH0hB6AtAQuYiES7da5AIlcJEeZufO\nnaxbt46+fftyzTXXRKzdxsZGdu7cCfifxNCz3SLRpQQeB+pxSzg66mldzHPfn332GX/+85+pra0l\nKSmJAwcO8OCDD3YpTvAn7/Xr1zN79mwAiouLmTFjhpK4SBTpHrhID7JmzRpqa2sBf93+ffv2UVdX\n1+V2rbXMmjULt9uN2+1m9uzZWGu73K6IhKYELtLDuVyuiLTRnUoGiziBhtBjQEPmEgmRKJU6d+5c\nTpw4QU1NDW63mzFjxpCent7ldo0xFBcXM2vWLFwuV/BrEYkeJXCRHmTmzJnk5+ezZs0aCgsLufrq\nqyPSbnJyMrNnzw5OYps1axYpKSkRaVtE2qYELtLDjBw5Mir17JOTk7vVwjYiic7lpPtWJ0/WRDzY\ngoKcLrehIXIRSRR6Ljw+ysuro9JuQUFOyEkqmsQmIiLiQErgIiIiDqR74G3QkLiIiCQ69cBFREQc\nSAlcRETEgXr8ELqGy0VExInUAxcREXGgHt8DFxHpTrReeM+hHriIiIgDKYGLiIg4kBK4iIiIAymB\ni4iIOJASuIiIiAMpgYuIiDhQVB8jM8akAL8BhgFpwL9ba99stv8G4J+AJuA31tpfRzMeERGR7iLa\nPfC7gJPW2nnAV4D/urAjkNx/CiwErgD+tzGmX5TjkQhoaGigtLSUzZs3U1NTE+9wRER6pGgn8FeB\nf272Xk3N9o0H9lhrq6y1jcAnwLwoxyNd1NjYyPr165k4cSJTpkxh165dSuIiInEQ1QRurT1rra01\nxuTgT+aPNdudC1Q1264B8qIZj3TdgQMHmDp1Km63G5fLxfTp09m3b1+8wxIR6XGiXkrVGDMEeA14\nylr7+2a7qoCcZts5wOlox9PakiX3xPotHW3Nmk/Ytm1zcNvr9TJ58jSWLLkrjlGJSCj6G9d9RbUH\nbowpBN4FHrHWPt9q905gjDGmtzEmFf/w+ZpoxiNdd8kll1Fbe5aqqmrq6urYv/8gN9xwc7zDEhHp\ncVw+ny9qjRtjfg4sAWyzl58Fsqy1zxpjrsd/jzwJeM5a+0x77Z08WRO9YCVsPp+PTz/9hPr6Oi6/\n/ArS0tLiHZKISLdUUJDjCrUvqgk80pTARUSkJ2kvgauQi4iIiAMpgYuIiDiQEriIiIgDKYGLiIg4\nkBK4iIiIAymBi4iIOJASuIiIiAMpgYuIiDiQEriIiIgDKYGLiIg4kBK4iIiIAymBi4iIOJCjFjMR\nERERP/XARUREHEgJXERExIGUwEVERBxICVxERMSBlMBFREQcSAlcRETEgZLjHUB3YozpB2wAFlhr\ndzV7/R+B+4GTgZceaL7fCYwxG4GqwOY+a+39zfbdAPwT0AT8xlr76ziEeNE6OLfucO2+DdwApAD/\nZa19odk+R1876PD8HH39jDF/Ddwb2MwAioBCa211YL+jr18Y5+fY62eMSQJ+DYwFvMD/stbaZvu7\nfO2UwCPEGJMC/Ao428bu6cA91tpNsY0qMowx6QDW2qva2JcC/BSYCZwDVhtjllpry2Mb5cVp79wC\nnH7trgQutdZ+yRiTBTzSbJ+jrx20f34Bjr5+gQ8jLwAYY/4L+HWz5Ob469fe+QU4+fp9Gciy1l5u\njLkaeBy4DSJ37TSEHjk/Bp4BjrWxbwbwHWPMKmPMo7ENKyKKgExjzDvGmL8YY+Y02zce2GOtrbLW\nNgKfAPPiEuXFae/cwPnX7svAFmPM68CbwNJm+5x+7aD98wPnXz8AjDEzgYmtemnd4foBIc8PnH39\n6oA8Y4wLyAMamu2LyLVTAo8AY8y9wElr7buBl1ytDnkZeACYD1xujLkuhuFFwlngx9baRcDfAv8T\nGB4CyOXz4WeAGvy/rE7R3rmB869dAf4/grcROL9m+5x+7aD98wPnX78LvgN8r9Vr3eH6XdDW+YGz\nr99qIB3YiX909slm+yJy7ZTAI+M+YKEx5gNgKvBC4H74BT+31lYGPmktB6bFI8gu2EXgD6O1djdQ\nAQwI7KsCcpodmwOcjml0XdPeuYHzr90p4F1rbVPg3mG9MaZvYJ/Trx20f37g/OuHMaYXMNZa+1Gr\nXd3h+rV3fuDs6/cIsNpaa/g8L6QG9kXk2ukeeARYa6+48HUgiT9w4V6GMSYPKDXGTMB/r2M+8Fxc\nAr149wFTgK8ZYwbi//R4PLBvJzDGGNMbf292Hv7bCU4R8ty6ybX7BPg68NPA+WUBlYF9Tr920M75\ndZPrB/7r8pc2Xu8O1w9CnF83uH5ZwIX7+afxT7JMxj+UHpFrpx54dLiMMXcaY/6XtbYKeBT4APgY\n2GqtfTu+4XXac0CuMeZj4Pf4k97/Ezi/RuBh4B3gU+A5a21b8wASVXvn5vhrZ61dDmwyxhTjvz/8\nEHB7N7l2HZ2f469fwFhg74WNZn9bHH/9AkKdn9Ov34+BS4wxq/B/QPk2cGMkr51WIxMREXEg9cBF\nREQcSAlcRETEgZTARUREHEgJXERExIGUwEVERBxICVxERMSBlMBFujFjzPeMMf/SxuveKLzXB51t\n3xjz94FVmbryvjcZY77WlTZEnEgJXKR7i2Whhys6PuRzxphC4AZr7ZtdeVNr7evALcaYgq60I+I0\nKqUqEkfGmMH4a7Fn4l8z+O+ttWuNMbPwLzeYib/e9wPW2gPGmA+BLcCX8C+U8A/W2pXGmEnAL4Bs\noB/wn9baJ7/whl98/2zgKWAi4AZ+aK39fWCBnq8AvYGR+OuNfy3wPd8Hbg3EdQx/BbTpgX1rrLWX\nBr5+Brg08Fa3WmuD1bYCvga8GjjWBfwAuAn/+si/stb+InC+G4Gr8a8X/Xf4S6dOAH5mrX0i0NZr\ngfa+19E5i3QX6oGLxNdXgTettbPwL35wWWCt4F8Dd1prZ+BP5M8GjvcByYHX78K/QEIKcD/wf621\ns/HXjH48cHzrlfFa+y6w3lo7E38P+jFjzIjAvkuBW/DXir/BGDMpMNx9Gf4Eei3+xSV81tqvA1xI\n3nvV148AAALHSURBVAErrbVTgZX4V5Rq7Qb8JTLBv5rYl4BJwGzgvkAP3Rdofwrw/+Ff0elmYC7w\nz83a+hhY3MG5inQr6oGLxNd7wGvGmGn4V1t6CjD4e71vGmMuHNd85aJfAlhrS4wxx4DJwDeAawJr\nJhfhX0ghHFcDGcaYrwa2M/H3xn3Ap9baswDGmH1AfuD4P1hrm4AzgXW4Q7mwbxttr3U8BjgS+Hpe\noN1GoJHAqlOB818ROOYQ8Jm1th44FFjFimb7xoR1xiLdhBK4SBxZaz8NrLZ0PXA7cC/wf4B91toL\nSSwJ6N/s2zzNvk4KbL+KfynUN/EvynJ7YH9H98CTgLustSWB9+ofaOevgPpmx/nw9+Y9+IfaLwjZ\nw7fWejs4zot/uBz8STt4jDFmOHAysNnQ7HuaaFtjoD2RHkND6CJxFLiffI+19kX893en4V9qMN8Y\nc3ngsK8SWLMcf5K7K/C9M4Fe+O+JXw38S2BC2JWB/Ul0PIT+Pv4VvDDGDAA2AUPa+b6VwK3GmBRj\nTC5wHZ9/SPAYY9whvq8te4Hhga8/xj8RLdkYkwm8DQzsRFsjgD2dOF7E8ZTAReLrKfwJcRP+iVgP\nWmsbgCXAfxpjNgP/L/4kDv5kOdoYswH/UPrtgZ7u94BPjDGrgXHADvxJzUfbvfALr/0r/iH0LfiX\nPHzEWrsvxPf5rLUr8CfbTcAyoAyoC+x/AygxxqS1+t5QMbwJXAXBmeSr8U9YK8Y/QW13GzG3bveC\nq/h8yF6kR9ByoiIOEnjW+lvW2uI4vf8lwFhr7YuByXOfAvdZa7deRFuFwCvW2k49fhairVXAzdba\nU11tS8Qp1AMXkc6wwJ3GmBJgA/DyxSRvAGvtCeDPxpgbuxKQMeZW4FUlb+lp1AMXERFxIPXARURE\nHEgJXERExIGUwEVERBxICVxERMSBlMBFREQcSAlcRETEgf5/FQ+w7CjnJMwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8ab5e10>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print x_min, x_max"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4.3 7.9\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Regression Example"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "np.random.seed(0)\n",
      "X = np.random.random(size=(20, 1))\n",
      "y = 3 * X.squeeze() + 2 + np.random.randn(20)\n",
      "\n",
      "plt.plot(X.squeeze(), y, 'o');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VZmt60v2IktSD62LyGdup5lMLc22211eA6ldpSpLUz1h3vKdcpSlJvXl6WT5jE7yuwpOk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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x86a8270>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = LinearRegression()\n",
      "model.fit(X, y)\n",
      "\n",
      "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
      "y_fit = model.predict(X_fit)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(X.squeeze(), y, 'o')\n",
      "plt.plot(X_fit.squeeze(), y_fit)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "[<matplotlib.lines.Line2D at 0x86506f0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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54J6SCFvdG8s/tXH+5drGpTgvxg+6x/sIzdL86DImw9vjAyS8JJe9FOML2f+J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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8650130>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print model.coef_\n",
      "print model.intercept_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 3.93491126]\n",
        "1.46229078904\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Fit a Random Forest\n",
      "from sklearn.ensemble import RandomForestRegressor\n",
      "model = RandomForestRegressor()\n",
      "model.fit(X, y)\n",
      "\n",
      "# Plot the data and the model prediction\n",
      "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
      "y_fit = model.predict(X_fit)\n",
      "\n",
      "plt.plot(X.squeeze(), y, 'o')\n",
      "plt.plot(X_fit.squeeze(), y_fit);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KluM1vFwcupyV59PmGQujVc0iWTIVvLkdaga4f9Vd/PDsT/nu6R/zZueRtB+/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\nHqc5XhFJl4JXBFhVPh68l4Y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       "text": [
        "<matplotlib.figure.Figure at 0x2db3230>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}