{
 "metadata": {
  "name": "",
  "signature": "sha256:258f05a16dc65d895f4b5bacce18a1a3d606aab57c408d7fdff3cea91c3c368b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " data = loadtxt(\"kankore.csv\",delimiter=\",\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([[  0.00000000e+00,   5.00000000e+03],\n",
        "       [  4.00000000e+00,   1.00000000e+04],\n",
        "       [  2.20000000e+01,   2.00000000e+04],\n",
        "       [  3.90000000e+01,   3.00000000e+04],\n",
        "       [  4.50000000e+01,   4.00000000e+04],\n",
        "       [  5.00000000e+01,   5.00000000e+04],\n",
        "       [  5.40000000e+01,   6.00000000e+04],\n",
        "       [  5.90000000e+01,   7.50000000e+04],\n",
        "       [  7.00000000e+01,   7.50000000e+04],\n",
        "       [  7.20000000e+01,   1.00000000e+05],\n",
        "       [  7.90000000e+01,   1.20000000e+05],\n",
        "       [  8.40000000e+01,   1.60000000e+05],\n",
        "       [  8.70000000e+01,   2.00000000e+05],\n",
        "       [  1.00000000e+02,   2.80000000e+05],\n",
        "       [  1.02000000e+02,   3.00000000e+05],\n",
        "       [  1.12000000e+02,   6.50000000e+05],\n",
        "       [  1.42000000e+02,   9.50000000e+05],\n",
        "       [  1.73000000e+02,   1.14000000e+06],\n",
        "       [  2.57000000e+02,   1.50000000e+06],\n",
        "       [  3.05000000e+02,   1.70000000e+06],\n",
        "       [  3.96000000e+02,   2.00000000e+06]])"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_act= data[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_act"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([   0.,    4.,   22.,   39.,   45.,   50.,   54.,   59.,   70.,\n",
        "         72.,   79.,   84.,   87.,  100.,  102.,  112.,  142.,  173.,\n",
        "        257.,  305.,  396.])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_act = data[:,1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.plot(x_act, y_act)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10d0a7450>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I+iYSjbT8JdLL1DeRaKZQEelF6ptItNNEW6QXqG8isUKhInIFndk38XjgzTfVN5HopuUv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\n/lv5vfIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d06f690>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import Ridge"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "degree = 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ridge = Ridge()\n",
      "ridge.fit(np.vander(x_act, degree + 1), y_act)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
        "   normalize=False, solver='auto', tol=0.001)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_pred = x_act"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_pred2 = array([400, 450, 500])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_pred = hstack([x_pred, x_pred2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_pred"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([   0.,    4.,   22.,   39.,   45.,   50.,   54.,   59.,   70.,\n",
        "         72.,   79.,   84.,   87.,  100.,  102.,  112.,  142.,  173.,\n",
        "        257.,  305.,  396.,  400.,  450.,  500.])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " pl.plot(x_pred, ridge.predict(np.vander(x_pred, degree + 1)),\n",
      "            label=\"degree %d\" % degree)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10e534c10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Dh4goxoz320E/CkOHiCiGnD0L9PQA12cUm3AMHSKiGFJZCTz6KDAjSmd/DiQgIooRV68C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\nIpow/x9B6bDU3EBL3wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e458350>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ridge.predict(np.vander(x_pred, degree + 1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "array([   52125.60313607,    27247.20053156,   -34956.40239624,\n",
        "         -26854.83993328,   -10468.52639396,     8029.64951834,\n",
        "          25804.46966244,    51517.00425806,   120485.66017525,\n",
        "         134689.65643936,   187986.865246  ,   229171.57815363,\n",
        "         255001.96411426,   375122.76891706,   394599.55182788,\n",
        "         494966.71584137,   809946.63503406,  1119014.89312694,\n",
        "        1608270.27241016,  1637737.09592139,  2005562.18440967,\n",
        "        2065596.50944486,  3517286.65328894,  7004381.43211605])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.scatter(x_pred, ridge.predict(np.vander(x_pred, degree + 1)), label=\"training points\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.collections.PathCollection at 0x10cc54b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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itnbtWqmpqRERkerqaqmsrBQRkZMnT0pWVpYEAgFxuVxiNBolGAyKiEh2drY4nU4REVm4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\nyEMweAaK4kFfnw/x8TNRWGj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       "text": [
        "<matplotlib.figure.Figure at 0x10cc54c90>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}