6. SVM Classifier for Digit Classification using sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets, svm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2797079f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = datasets.load_digits()\n",
    "plt.imshow(data.images[100], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual: 9\n",
      "Predicted: 9\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x279708399b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = svm.SVC(kernel='linear')\n",
    "\n",
    "X, y = data.data[:-20], data.target[:-20]\n",
    "clf.fit(X, y)\n",
    "\n",
    "ind = 2\n",
    "\n",
    "print('Actual:', data.target[-ind])\n",
    "print('Predicted:', clf.predict([data.data[-ind]])[0])\n",
    "\n",
    "plt.imshow(data.images[-ind], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
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}