class_10/Non_Linear_DecisionBoundary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_circles\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = make_circles(n_samples=1000, factor=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ajqrqq6p6DsDjALbEeV8qCP9RgaoTLv6jg16vQJo0r3TSfkTR7bM/1Lv9bJ6C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\nRBXE8CciqiCGPxFRBf1/DZPHO41hlB8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb404c57f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ix in range(X.shape[0]):\n",
    "    if y[ix] == 0:\n",
    "        plt.scatter(X[ix, 0], X[ix, 1], c='r')\n",
    "    else:\n",
    "        plt.scatter(X[ix, 0], X[ix, 1], c='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 2)\n",
      "(600,)\n"
     ]
    }
   ],
   "source": [
    "split = int(0.6*X.shape[0])\n",
    "X_train = X[:split, :]\n",
    "X_test = X[split:, :]\n",
    "\n",
    "Y_train = y[:split]\n",
    "Y_test = y[split:]\n",
    "\n",
    "print X_train.shape\n",
    "print Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.335\n"
     ]
    }
   ],
   "source": [
    "ls = LogisticRegression()\n",
    "ls.fit(X_train, Y_train)\n",
    "print ls.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}