SVM-Iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "x = iris.data[:, 0:2]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train,x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVC(kernel = 'linear')\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81578947368421051"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makegrid(x1, x2, h = 0.02):\n",
    "    x1_min, x1_max = x1.min() - 1, x1.max() + 1\n",
    "    x2_min, x2_max = x2.min() - 1, x2.max() + 1\n",
    "    a = np.arange(x1_min,x1_max,h)\n",
    "    b = np.arange(x2_min, x2_max, h)\n",
    "    xx, yy = np.meshgrid(a, b)\n",
    "    return xx, yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IajxIaXOCxau7qfaE0T6F9qtzHquW/dbgphKdmQrbri5w0/2zDG8p4Q+4sRXC87zcVaKk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084177b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy = makegrid(x[:, 0], x[:, 1])\n",
    "predictions = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "plt.scatter(xx.ravel(), yy.ravel(), c = predictions)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}