demo_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = list(range(100))\n",
    "y = list(range(0,200,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_fmt = [(a,) for a in x]\n",
    "y_fmt = [(a) for a in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_fmt, y_fmt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a80db50>]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+78ssWJn6x6YCXspRttMrHwOmhK+nAI8mtV9mgdOBHRqflziqn7kgJeRHDuyrkJey1Znp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\ncwp6EZEyp6AXESlzCnoRkTL3/wE2l/jG8iGGKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a80d210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test, y_test)\n",
    "plt.plot(x_test, regr.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9999999999999996"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.coef_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
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      "text/plain": [
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      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
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   },
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   "source": []
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