KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import random\n",
    "import math\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['5.1', '3.5', '1.4', '0.2', 'Iris-setosa']\n",
      "['4.9', '3.0', '1.4', '0.2', 'Iris-setosa']\n",
      "['4.7', '3.2', '1.3', '0.2', 'Iris-setosa']\n",
      "['4.6', '3.1', '1.5', '0.2', 'Iris-setosa']\n",
      "['5.0', '3.6', '1.4', '0.2', 'Iris-setosa']\n",
      "['5.4', '3.9', '1.7', '0.4', 'Iris-setosa']\n",
      "['4.6', '3.4', '1.4', '0.3', 'Iris-setosa']\n",
      "['5.0', '3.4', '1.5', '0.2', 'Iris-setosa']\n",
      "['4.4', '2.9', '1.4', '0.2', 'Iris-setosa']\n",
      "['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']\n",
      "['5.4', '3.7', '1.5', '0.2', 'Iris-setosa']\n",
      "['4.8', '3.4', '1.6', '0.2', 'Iris-setosa']\n",
      "['4.8', '3.0', '1.4', '0.1', 'Iris-setosa']\n",
      "['4.3', '3.0', '1.1', '0.1', 'Iris-setosa']\n",
      "['5.8', '4.0', '1.2', '0.2', 'Iris-setosa']\n",
      "['5.7', '4.4', '1.5', '0.4', 'Iris-setosa']\n",
      "['5.4', '3.9', '1.3', '0.4', 'Iris-setosa']\n",
      "['5.1', '3.5', '1.4', '0.3', 'Iris-setosa']\n",
      "['5.7', '3.8', '1.7', '0.3', 'Iris-setosa']\n",
      "['5.1', '3.8', '1.5', '0.3', 'Iris-setosa']\n",
      "['5.4', '3.4', '1.7', '0.2', 'Iris-setosa']\n",
      "['5.1', '3.7', '1.5', '0.4', 'Iris-setosa']\n",
      "['4.6', '3.6', '1.0', '0.2', 'Iris-setosa']\n",
      "['5.1', '3.3', '1.7', '0.5', 'Iris-setosa']\n",
      "['4.8', '3.4', '1.9', '0.2', 'Iris-setosa']\n",
      "['5.0', '3.0', '1.6', '0.2', 'Iris-setosa']\n",
      "['5.0', '3.4', '1.6', '0.4', 'Iris-setosa']\n",
      "['5.2', '3.5', '1.5', '0.2', 'Iris-setosa']\n",
      "['5.2', '3.4', '1.4', '0.2', 'Iris-setosa']\n",
      "['4.7', '3.2', '1.6', '0.2', 'Iris-setosa']\n",
      "['4.8', '3.1', '1.6', '0.2', 'Iris-setosa']\n",
      "['5.4', '3.4', '1.5', '0.4', 'Iris-setosa']\n",
      "['5.2', '4.1', '1.5', '0.1', 'Iris-setosa']\n",
      "['5.5', '4.2', '1.4', '0.2', 'Iris-setosa']\n",
      "['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']\n",
      "['5.0', '3.2', '1.2', '0.2', 'Iris-setosa']\n",
      "['5.5', '3.5', '1.3', '0.2', 'Iris-setosa']\n",
      "['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']\n",
      "['4.4', '3.0', '1.3', '0.2', 'Iris-setosa']\n",
      "['5.1', '3.4', '1.5', '0.2', 'Iris-setosa']\n",
      "['5.0', '3.5', '1.3', '0.3', 'Iris-setosa']\n",
      "['4.5', '2.3', '1.3', '0.3', 'Iris-setosa']\n",
      "['4.4', '3.2', '1.3', '0.2', 'Iris-setosa']\n",
      "['5.0', '3.5', '1.6', '0.6', 'Iris-setosa']\n",
      "['5.1', '3.8', '1.9', '0.4', 'Iris-setosa']\n",
      "['4.8', '3.0', '1.4', '0.3', 'Iris-setosa']\n",
      "['5.1', '3.8', '1.6', '0.2', 'Iris-setosa']\n",
      "['4.6', '3.2', '1.4', '0.2', 'Iris-setosa']\n",
      "['5.3', '3.7', '1.5', '0.2', 'Iris-setosa']\n",
      "['5.0', '3.3', '1.4', '0.2', 'Iris-setosa']\n",
      "['7.0', '3.2', '4.7', '1.4', 'Iris-versicolor']\n",
      "['6.4', '3.2', '4.5', '1.5', 'Iris-versicolor']\n",
      "['6.9', '3.1', '4.9', '1.5', 'Iris-versicolor']\n",
      "['5.5', '2.3', '4.0', '1.3', 'Iris-versicolor']\n",
      "['6.5', '2.8', '4.6', '1.5', 'Iris-versicolor']\n",
      "['5.7', '2.8', '4.5', '1.3', 'Iris-versicolor']\n",
      "['6.3', '3.3', '4.7', '1.6', 'Iris-versicolor']\n",
      "['4.9', '2.4', '3.3', '1.0', 'Iris-versicolor']\n",
      "['6.6', '2.9', '4.6', '1.3', 'Iris-versicolor']\n",
      "['5.2', '2.7', '3.9', '1.4', 'Iris-versicolor']\n",
      "['5.0', '2.0', '3.5', '1.0', 'Iris-versicolor']\n",
      "['5.9', '3.0', '4.2', '1.5', 'Iris-versicolor']\n",
      "['6.0', '2.2', '4.0', '1.0', 'Iris-versicolor']\n",
      "['6.1', '2.9', '4.7', '1.4', 'Iris-versicolor']\n",
      "['5.6', '2.9', '3.6', '1.3', 'Iris-versicolor']\n",
      "['6.7', '3.1', '4.4', '1.4', 'Iris-versicolor']\n",
      "['5.6', '3.0', '4.5', '1.5', 'Iris-versicolor']\n",
      "['5.8', '2.7', '4.1', '1.0', 'Iris-versicolor']\n",
      "['6.2', '2.2', '4.5', '1.5', 'Iris-versicolor']\n",
      "['5.6', '2.5', '3.9', '1.1', 'Iris-versicolor']\n",
      "['5.9', '3.2', '4.8', '1.8', 'Iris-versicolor']\n",
      "['6.1', '2.8', '4.0', '1.3', 'Iris-versicolor']\n",
      "['6.3', '2.5', '4.9', '1.5', 'Iris-versicolor']\n",
      "['6.1', '2.8', '4.7', '1.2', 'Iris-versicolor']\n",
      "['6.4', '2.9', '4.3', '1.3', 'Iris-versicolor']\n",
      "['6.6', '3.0', '4.4', '1.4', 'Iris-versicolor']\n",
      "['6.8', '2.8', '4.8', '1.4', 'Iris-versicolor']\n",
      "['6.7', '3.0', '5.0', '1.7', 'Iris-versicolor']\n",
      "['6.0', '2.9', '4.5', '1.5', 'Iris-versicolor']\n",
      "['5.7', '2.6', '3.5', '1.0', 'Iris-versicolor']\n",
      "['5.5', '2.4', '3.8', '1.1', 'Iris-versicolor']\n",
      "['5.5', '2.4', '3.7', '1.0', 'Iris-versicolor']\n",
      "['5.8', '2.7', '3.9', '1.2', 'Iris-versicolor']\n",
      "['6.0', '2.7', '5.1', '1.6', 'Iris-versicolor']\n",
      "['5.4', '3.0', '4.5', '1.5', 'Iris-versicolor']\n",
      "['6.0', '3.4', '4.5', '1.6', 'Iris-versicolor']\n",
      "['6.7', '3.1', '4.7', '1.5', 'Iris-versicolor']\n",
      "['6.3', '2.3', '4.4', '1.3', 'Iris-versicolor']\n",
      "['5.6', '3.0', '4.1', '1.3', 'Iris-versicolor']\n",
      "['5.5', '2.5', '4.0', '1.3', 'Iris-versicolor']\n",
      "['5.5', '2.6', '4.4', '1.2', 'Iris-versicolor']\n",
      "['6.1', '3.0', '4.6', '1.4', 'Iris-versicolor']\n",
      "['5.8', '2.6', '4.0', '1.2', 'Iris-versicolor']\n",
      "['5.0', '2.3', '3.3', '1.0', 'Iris-versicolor']\n",
      "['5.6', '2.7', '4.2', '1.3', 'Iris-versicolor']\n",
      "['5.7', '3.0', '4.2', '1.2', 'Iris-versicolor']\n",
      "['5.7', '2.9', '4.2', '1.3', 'Iris-versicolor']\n",
      "['6.2', '2.9', '4.3', '1.3', 'Iris-versicolor']\n",
      "['5.1', '2.5', '3.0', '1.1', 'Iris-versicolor']\n",
      "['5.7', '2.8', '4.1', '1.3', 'Iris-versicolor']\n",
      "['6.3', '3.3', '6.0', '2.5', 'Iris-virginica']\n",
      "['5.8', '2.7', '5.1', '1.9', 'Iris-virginica']\n",
      "['7.1', '3.0', '5.9', '2.1', 'Iris-virginica']\n",
      "['6.3', '2.9', '5.6', '1.8', 'Iris-virginica']\n",
      "['6.5', '3.0', '5.8', '2.2', 'Iris-virginica']\n",
      "['7.6', '3.0', '6.6', '2.1', 'Iris-virginica']\n",
      "['4.9', '2.5', '4.5', '1.7', 'Iris-virginica']\n",
      "['7.3', '2.9', '6.3', '1.8', 'Iris-virginica']\n",
      "['6.7', '2.5', '5.8', '1.8', 'Iris-virginica']\n",
      "['7.2', '3.6', '6.1', '2.5', 'Iris-virginica']\n",
      "['6.5', '3.2', '5.1', '2.0', 'Iris-virginica']\n",
      "['6.4', '2.7', '5.3', '1.9', 'Iris-virginica']\n",
      "['6.8', '3.0', '5.5', '2.1', 'Iris-virginica']\n",
      "['5.7', '2.5', '5.0', '2.0', 'Iris-virginica']\n",
      "['5.8', '2.8', '5.1', '2.4', 'Iris-virginica']\n",
      "['6.4', '3.2', '5.3', '2.3', 'Iris-virginica']\n",
      "['6.5', '3.0', '5.5', '1.8', 'Iris-virginica']\n",
      "['7.7', '3.8', '6.7', '2.2', 'Iris-virginica']\n",
      "['7.7', '2.6', '6.9', '2.3', 'Iris-virginica']\n",
      "['6.0', '2.2', '5.0', '1.5', 'Iris-virginica']\n",
      "['6.9', '3.2', '5.7', '2.3', 'Iris-virginica']\n",
      "['5.6', '2.8', '4.9', '2.0', 'Iris-virginica']\n",
      "['7.7', '2.8', '6.7', '2.0', 'Iris-virginica']\n",
      "['6.3', '2.7', '4.9', '1.8', 'Iris-virginica']\n",
      "['6.7', '3.3', '5.7', '2.1', 'Iris-virginica']\n",
      "['7.2', '3.2', '6.0', '1.8', 'Iris-virginica']\n",
      "['6.2', '2.8', '4.8', '1.8', 'Iris-virginica']\n",
      "['6.1', '3.0', '4.9', '1.8', 'Iris-virginica']\n",
      "['6.4', '2.8', '5.6', '2.1', 'Iris-virginica']\n",
      "['7.2', '3.0', '5.8', '1.6', 'Iris-virginica']\n",
      "['7.4', '2.8', '6.1', '1.9', 'Iris-virginica']\n",
      "['7.9', '3.8', '6.4', '2.0', 'Iris-virginica']\n",
      "['6.4', '2.8', '5.6', '2.2', 'Iris-virginica']\n",
      "['6.3', '2.8', '5.1', '1.5', 'Iris-virginica']\n",
      "['6.1', '2.6', '5.6', '1.4', 'Iris-virginica']\n",
      "['7.7', '3.0', '6.1', '2.3', 'Iris-virginica']\n",
      "['6.3', '3.4', '5.6', '2.4', 'Iris-virginica']\n",
      "['6.4', '3.1', '5.5', '1.8', 'Iris-virginica']\n",
      "['6.0', '3.0', '4.8', '1.8', 'Iris-virginica']\n",
      "['6.9', '3.1', '5.4', '2.1', 'Iris-virginica']\n",
      "['6.7', '3.1', '5.6', '2.4', 'Iris-virginica']\n",
      "['6.9', '3.1', '5.1', '2.3', 'Iris-virginica']\n",
      "['5.8', '2.7', '5.1', '1.9', 'Iris-virginica']\n",
      "['6.8', '3.2', '5.9', '2.3', 'Iris-virginica']\n",
      "['6.7', '3.3', '5.7', '2.5', 'Iris-virginica']\n",
      "['6.7', '3.0', '5.2', '2.3', 'Iris-virginica']\n",
      "['6.3', '2.5', '5.0', '1.9', 'Iris-virginica']\n",
      "['6.5', '3.0', '5.2', '2.0', 'Iris-virginica']\n",
      "['6.2', '3.4', '5.4', '2.3', 'Iris-virginica']\n",
      "['5.9', '3.0', '5.1', '1.8', 'Iris-virginica']\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "with open('iris.data', 'rb') as csvfile:\n",
    "    lines = csv.reader(csvfile)\n",
    "    for row in lines:\n",
    "        #print(','.join(row))\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadDataset(filename, split, trainingSet=[], testSet=[]):\n",
    "    with open(filename, 'rb') as csvfile:\n",
    "        lines = csv.reader(csvfile)\n",
    "        dataset = list(lines)\n",
    "        for x in range(len(dataset)-1):\n",
    "            for y in range(4):\n",
    "                dataset[x][y] = float(dataset[x][y])\n",
    "                if random.random()< split:\n",
    "                    trainingSet.append(dataset[x])\n",
    "                else:\n",
    "                    testSet.append(dataset[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 406\n",
      "Test: 194\n"
     ]
    }
   ],
   "source": [
    "trainingSet=[]\n",
    "testSet=[]\n",
    "loadDataset('iris.data', 0.66, trainingSet, testSet)\n",
    "print('Train: ' + repr(len(trainingSet)))\n",
    "print('Test: ' + repr(len(testSet)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def euclideanDistance(instance1, instance2, length):\n",
    "    distance = 0\n",
    "    for x in range(length):\n",
    "        distance += pow((instance1[x] - instance2[x]), 2)\n",
    "    return math.sqrt(distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance: 3.4641016151377544\n"
     ]
    }
   ],
   "source": [
    "data1 = [2, 2, 2, 'a']\n",
    "data2 = [4, 4, 4, 'b']\n",
    "distance = euclideanDistance(data1, data2, 3)\n",
    "print('Distance: ' + repr(distance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getNeighbors(trainingSet, testInstance, k):\n",
    "    distances = []\n",
    "    length = len(testInstance)-1\n",
    "    for x in range(len(trainingSet)):\n",
    "        dist = euclideanDistance(testInstance, trainingSet[x], length)\n",
    "        distances.append((trainingSet[x], dist))\n",
    "    distances.sort(key=operator.itemgetter(1))\n",
    "    neighbors = []\n",
    "    for x in range(k):\n",
    "        neighbors.append(distances[x][0])\n",
    "    return neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4, 4, 4, 'b']]\n"
     ]
    }
   ],
   "source": [
    "trainSet = [[2, 2, 2, 'a'], [4, 4, 4, 'b']]\n",
    "testInstance = [5, 5, 5]\n",
    "k = 1\n",
    "neighbors = getNeighbors(trainSet, testInstance, 1)\n",
    "print(neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getResponse(neighbors):\n",
    "    classVotes = {}\n",
    "    for x in range(len(neighbors)):\n",
    "        response = neighbors[x][-1]\n",
    "        if response in classVotes:\n",
    "            classVotes[response] += 1\n",
    "        else:\n",
    "            classVotes[response] = 1\n",
    "    #print(classVotes)\n",
    "    sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedVotes[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 2, 'b': 1}\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "neighbors = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]\n",
    "response = getResponse(neighbors)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getAccuracy(testSet, predictions):\n",
    "    correct = 0\n",
    "    for x in range(len(testSet)):\n",
    "        if testSet[x][-1] is predictions[x]:\n",
    "            correct += 1\n",
    "    return (correct/float(len(testSet))) * 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.6666666667\n"
     ]
    }
   ],
   "source": [
    "testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]\n",
    "predictions = ['a', 'a', 'a']\n",
    "accuracy = getAccuracy(testSet, predictions)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: 417\n",
      "Test set: 183\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-setosa': 3}\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-versicolor': 3}\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 2, 'Iris-versicolor': 1}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 2, 'Iris-versicolor': 1}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 2, 'Iris-versicolor': 1}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 2, 'Iris-versicolor': 1}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "{'Iris-virginica': 3}\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "Accuracy: 98.90710382513662%\n"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    # prepare data\n",
    "    trainingSet=[]\n",
    "    testSet=[]\n",
    "    split = 0.67\n",
    "    loadDataset('iris.data', split, trainingSet, testSet)\n",
    "    print('Train set: ' + repr(len(trainingSet)))\n",
    "    print('Test set: ' + repr(len(testSet)))\n",
    "    # generate predictions\n",
    "    predictions=[]\n",
    "    k = 3\n",
    "    for x in range(len(testSet)):\n",
    "        neighbors = getNeighbors(trainingSet, testSet[x], k)\n",
    "        result = getResponse(neighbors)\n",
    "        predictions.append(result)\n",
    "        print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))\n",
    "    accuracy = getAccuracy(testSet, predictions)\n",
    "    print('Accuracy: ' + repr(accuracy) + '%')\n",
    "    \n",
    "main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "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
}