linear_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear model using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model, datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## generate random values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random\n",
    "regdata = datasets.make_regression(100, 1, noise=20.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin = linear_model.LinearRegression()\n",
    "lin.fit(regdata[0], regdata[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## coefficients, intercept, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 75.5447381]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients: \\n', lin.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -1.80915887051\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', lin.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score : 0.939722450557\n"
     ]
    }
   ],
   "source": [
    "print(\"score :\", lin.score(regdata[0], regdata[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc88e0ed908>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bxgnOOOM05fj7IOX8RXqx6EHdysp/4l5Pv34jYgbuIUNyefhh+PGP4ayz4IYbtjNu3DFU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\nRFLQ/wfc3k343qdVVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8a2fc8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xr = [-2.5, 2.5]\n",
    "plt.plot(xr, lin.coef_ * xr + lin.intercept_)\n",
    "plt.scatter(regdata[0], regdata[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}