Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('IRIS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>0.222222222</th>\n",
       "      <th>3.5</th>\n",
       "      <th>0.625</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.06779661</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.041666667</th>\n",
       "      <th>setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   5.1  0.222222222  3.5     0.625  1.4  0.06779661  0.2  0.041666667  setosa\n",
       "0  4.9     0.166667  3.0  0.416667  1.4    0.067797  0.2     0.041667  setosa\n",
       "1  4.7     0.111111  3.2  0.500000  1.3    0.050847  0.2     0.041667  setosa\n",
       "2  4.6     0.083333  3.1  0.458333  1.5    0.084746  0.2     0.041667  setosa\n",
       "3  5.0     0.194444  3.6  0.666667  1.4    0.067797  0.2     0.041667  setosa\n",
       "4  5.4     0.305556  3.9  0.791667  1.7    0.118644  0.4     0.125000  setosa"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn as sl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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smb+SdW0JBvYrp7pymL4QlZyzFJ2SnKioqPD6+vq8XU8kY/PmhfFvy5eHpYfTp2tvcikY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\nqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2361858b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import arange,array,ones,linalg\n",
    "from pylab import plot,show\n",
    "\n",
    "xi = arange(0,8)\n",
    "A = array([ xi, ones(8)])\n",
    "# linearly generated sequence\n",
    "y = [19, 20, 20.5, 21.5, 22, 23, 23, 25.5]\n",
    "w = linalg.lstsq(A.T,y)[0] # obtaining the parameters\n",
    "\n",
    "# plotting the line\n",
    "line = w[0]*xi+w[1] # regression line\n",
    "plot(xi,line,'r-',xi,y,'o')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import random\n",
    "def loadData(filename,split,trainingSet=[],testSet=[]):\n",
    "    with open(fileName,'rb') as csvfile:\n",
    "        lines = csv.reader(csvfile)\n",
    "        dataset = list(lines)\n",
    "        \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: 106\n",
      "Test set: 44\n",
      "[5.1, 3.5, 1.4, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.5, 1.4, 0.3, 'Iris-setosa']\n",
      "[5.0, 3.5, 1.3, 0.3, 'Iris-setosa']\n",
      "[5.1, 3.4, 1.5, 0.2, 'Iris-setosa']\n",
      "[5.2, 3.5, 1.5, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.8, 1.5, 0.3, 'Iris-setosa']\n",
      "[5.1, 3.7, 1.5, 0.4, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[5.1, 3.4, 1.5, 0.2, 'Iris-setosa']\n",
      "[5.0, 3.3, 1.4, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.5, 1.4, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.5, 1.4, 0.3, 'Iris-setosa']\n",
      "[5.0, 3.4, 1.6, 0.4, 'Iris-setosa']\n",
      "[4.8, 3.4, 1.6, 0.2, 'Iris-setosa']\n",
      "[5.2, 3.5, 1.5, 0.2, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[5.5, 4.2, 1.4, 0.2, 'Iris-setosa']\n",
      "[5.4, 3.9, 1.3, 0.4, 'Iris-setosa']\n",
      "[5.7, 4.4, 1.5, 0.4, 'Iris-setosa']\n",
      "[5.7, 3.8, 1.7, 0.3, 'Iris-setosa']\n",
      "[5.4, 3.7, 1.5, 0.2, 'Iris-setosa']\n",
      "[5.5, 3.5, 1.3, 0.2, 'Iris-setosa']\n",
      "[5.3, 3.7, 1.5, 0.2, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[5.4, 3.4, 1.7, 0.2, '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",
      "[5.5, 3.5, 1.3, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.5, 1.4, 0.3, 'Iris-setosa']\n",
      "[5.4, 3.7, 1.5, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.4, 1.5, 0.2, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[4.9, 3.1, 1.5, 0.1, 'Iris-setosa']\n",
      "[4.9, 3.0, 1.4, 0.2, 'Iris-setosa']\n",
      "[4.8, 3.1, 1.6, 0.2, 'Iris-setosa']\n",
      "[4.8, 3.0, 1.4, 0.1, 'Iris-setosa']\n",
      "[5.0, 3.0, 1.6, 0.2, 'Iris-setosa']\n",
      "[5.0, 3.3, 1.4, 0.2, 'Iris-setosa']\n",
      "[4.7, 3.2, 1.6, 0.2, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[4.9, 3.1, 1.5, 0.1, 'Iris-setosa']\n",
      "[4.9, 3.0, 1.4, 0.2, 'Iris-setosa']\n",
      "[4.8, 3.1, 1.6, 0.2, 'Iris-setosa']\n",
      "[4.8, 3.0, 1.4, 0.1, 'Iris-setosa']\n",
      "[5.0, 3.0, 1.6, 0.2, 'Iris-setosa']\n",
      "[5.0, 3.3, 1.4, 0.2, 'Iris-setosa']\n",
      "[4.7, 3.2, 1.6, 0.2, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[5.0, 3.4, 1.6, 0.4, 'Iris-setosa']\n",
      "[5.1, 3.3, 1.7, 0.5, 'Iris-setosa']\n",
      "[5.1, 3.7, 1.5, 0.4, 'Iris-setosa']\n",
      "[5.1, 3.5, 1.4, 0.3, 'Iris-setosa']\n",
      "[5.0, 3.5, 1.3, 0.3, 'Iris-setosa']\n",
      "[5.1, 3.4, 1.5, 0.2, 'Iris-setosa']\n",
      "[5.1, 3.8, 1.5, 0.3, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[4.6, 3.1, 1.5, 0.2, 'Iris-setosa']\n",
      "[4.7, 3.2, 1.3, 0.2, 'Iris-setosa']\n",
      "[4.4, 3.2, 1.3, 0.2, 'Iris-setosa']\n",
      "[4.6, 3.4, 1.4, 0.3, 'Iris-setosa']\n",
      "[4.7, 3.2, 1.6, 0.2, 'Iris-setosa']\n",
      "[4.4, 3.0, 1.3, 0.2, 'Iris-setosa']\n",
      "[4.8, 3.0, 1.4, 0.1, 'Iris-setosa']\n",
      "> predicted='Iris-setosa', actual='Iris-setosa'\n",
      "[7.0, 3.2, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.7, 3.1, 4.7, 1.5, 'Iris-versicolor']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "[6.8, 2.8, 4.8, 1.4, 'Iris-versicolor']\n",
      "[6.7, 3.1, 4.4, 1.4, 'Iris-versicolor']\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.4, 3.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.5, 2.6, 4.4, 1.2, 'Iris-versicolor']\n",
      "[5.7, 2.9, 4.2, 1.3, 'Iris-versicolor']\n",
      "[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']\n",
      "[5.7, 3.0, 4.2, 1.2, 'Iris-versicolor']\n",
      "[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.4, 3.0, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.1, 2.8, 4.7, 1.2, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[6.4, 3.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.1, 3.0, 4.6, 1.4, 'Iris-versicolor']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.7, 3.1, 4.7, 1.5, 'Iris-versicolor']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.7, 3.1, 4.4, 1.4, 'Iris-versicolor']\n",
      "[6.8, 2.8, 4.8, 1.4, 'Iris-versicolor']\n",
      "[6.7, 3.1, 4.7, 1.5, 'Iris-versicolor']\n",
      "[6.4, 2.9, 4.3, 1.3, 'Iris-versicolor']\n",
      "[6.4, 3.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.2, 2.9, 4.3, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.0, 2.3, 3.3, 1.0, 'Iris-versicolor']\n",
      "[4.9, 2.4, 3.3, 1.0, 'Iris-versicolor']\n",
      "[5.5, 2.4, 3.7, 1.0, 'Iris-versicolor']\n",
      "[5.5, 2.4, 3.8, 1.1, 'Iris-versicolor']\n",
      "[5.5, 2.3, 4.0, 1.3, 'Iris-versicolor']\n",
      "[5.6, 2.5, 3.9, 1.1, 'Iris-versicolor']\n",
      "[5.5, 2.5, 4.0, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']\n",
      "[5.6, 2.5, 3.9, 1.1, 'Iris-versicolor']\n",
      "[5.5, 2.4, 3.8, 1.1, 'Iris-versicolor']\n",
      "[5.5, 2.3, 4.0, 1.3, 'Iris-versicolor']\n",
      "[6.3, 2.3, 4.4, 1.3, 'Iris-versicolor']\n",
      "[5.5, 2.4, 3.7, 1.0, 'Iris-versicolor']\n",
      "[5.5, 2.5, 4.0, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.7, 2.6, 3.5, 1.0, 'Iris-versicolor']\n",
      "[5.6, 3.0, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.6, 2.5, 3.9, 1.1, 'Iris-versicolor']\n",
      "[5.2, 2.7, 3.9, 1.4, 'Iris-versicolor']\n",
      "[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']\n",
      "[5.5, 2.5, 4.0, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.4, 3.0, 4.5, 1.5, 'Iris-versicolor']\n",
      "[5.7, 2.9, 4.2, 1.3, 'Iris-versicolor']\n",
      "[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']\n",
      "[5.9, 3.0, 4.2, 1.5, 'Iris-versicolor']\n",
      "[5.7, 3.0, 4.2, 1.2, 'Iris-versicolor']\n",
      "[5.6, 3.0, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']\n",
      "[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.6, 2.5, 3.9, 1.1, '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",
      "[6.1, 2.8, 4.0, 1.3, 'Iris-versicolor']\n",
      "[5.6, 3.0, 4.1, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.6, 1.4, 'Iris-versicolor']\n",
      "[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[5.6, 2.8, 4.9, 2.0, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-versicolor'\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.1, 2.8, 4.7, 1.2, 'Iris-versicolor']\n",
      "[6.2, 2.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.3, 2.3, 4.4, 1.3, 'Iris-versicolor']\n",
      "[6.8, 2.8, 4.8, 1.4, 'Iris-versicolor']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[6.7, 3.1, 4.4, 1.4, 'Iris-versicolor']\n",
      "[6.4, 2.9, 4.3, 1.3, 'Iris-versicolor']\n",
      "[6.4, 3.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.7, 3.1, 4.7, 1.5, 'Iris-versicolor']\n",
      "[6.2, 2.9, 4.3, 1.3, 'Iris-versicolor']\n",
      "[6.8, 2.8, 4.8, 1.4, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']\n",
      "[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.6, 2.5, 3.9, 1.1, 'Iris-versicolor']\n",
      "[6.1, 2.8, 4.0, 1.3, 'Iris-versicolor']\n",
      "[5.5, 2.5, 4.0, 1.3, 'Iris-versicolor']\n",
      "[5.7, 2.9, 4.2, 1.3, 'Iris-versicolor']\n",
      "[5.6, 3.0, 4.1, 1.3, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "[6.1, 2.6, 5.6, 1.4, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-versicolor'\n",
      "[6.4, 3.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.1, 3.0, 4.6, 1.4, 'Iris-versicolor']\n",
      "[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']\n",
      "[5.9, 3.0, 4.2, 1.5, 'Iris-versicolor']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.7, 2.9, 4.2, 1.3, 'Iris-versicolor']\n",
      "[5.5, 2.6, 4.4, 1.2, 'Iris-versicolor']\n",
      "[5.5, 2.5, 4.0, 1.3, 'Iris-versicolor']\n",
      "[5.6, 3.0, 4.1, 1.3, 'Iris-versicolor']\n",
      "[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']\n",
      "[5.7, 3.0, 4.2, 1.2, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[4.9, 2.4, 3.3, 1.0, 'Iris-versicolor']\n",
      "[5.0, 2.3, 3.3, 1.0, 'Iris-versicolor']\n",
      "[5.7, 2.6, 3.5, 1.0, 'Iris-versicolor']\n",
      "[5.5, 2.4, 3.7, 1.0, 'Iris-versicolor']\n",
      "[5.5, 2.4, 3.8, 1.1, 'Iris-versicolor']\n",
      "[5.2, 2.7, 3.9, 1.4, 'Iris-versicolor']\n",
      "[5.6, 2.5, 3.9, 1.1, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-versicolor'\n",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, 'Iris-virginica']\n",
      "[6.8, 3.2, 5.9, 2.3, 'Iris-virginica']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']\n",
      "[6.7, 3.3, 5.7, 2.5, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[7.4, 2.8, 6.1, 1.9, 'Iris-virginica']\n",
      "[7.2, 3.2, 6.0, 1.8, 'Iris-virginica']\n",
      "[7.6, 3.0, 6.6, 2.1, 'Iris-virginica']\n",
      "[7.1, 3.0, 5.9, 2.1, 'Iris-virginica']\n",
      "[7.2, 3.0, 5.8, 1.6, 'Iris-virginica']\n",
      "[7.7, 2.8, 6.7, 2.0, 'Iris-virginica']\n",
      "[7.7, 3.0, 6.1, 2.3, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "[6.2, 3.4, 5.4, 2.3, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']\n",
      "[5.6, 2.8, 4.9, 2.0, 'Iris-virginica']\n",
      "[5.8, 2.8, 5.1, 2.4, 'Iris-virginica']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.2, 3.4, 5.4, 2.3, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, 'Iris-virginica']\n",
      "[6.7, 3.3, 5.7, 2.5, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.2, 2.2, 4.5, 1.5, 'Iris-versicolor']\n",
      "[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']\n",
      "[6.3, 2.3, 4.4, 1.3, 'Iris-versicolor']\n",
      "[6.1, 2.6, 5.6, 1.4, 'Iris-virginica']\n",
      "[6.1, 2.8, 4.7, 1.2, 'Iris-versicolor']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "> predicted='Iris-versicolor', actual='Iris-virginica'\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']\n",
      "[6.8, 3.2, 5.9, 2.3, 'Iris-virginica']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.7, 3.3, 5.7, 2.5, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "[7.1, 3.0, 5.9, 2.1, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']\n",
      "[6.1, 3.0, 4.6, 1.4, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-virginica'\n",
      "[7.7, 3.8, 6.7, 2.2, 'Iris-virginica']\n",
      "[7.6, 3.0, 6.6, 2.1, 'Iris-virginica']\n",
      "[7.7, 3.0, 6.1, 2.3, 'Iris-virginica']\n",
      "[7.2, 3.6, 6.1, 2.5, 'Iris-virginica']\n",
      "[7.2, 3.2, 6.0, 1.8, 'Iris-virginica']\n",
      "[7.7, 2.8, 6.7, 2.0, 'Iris-virginica']\n",
      "[7.4, 2.8, 6.1, 1.9, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "[6.1, 2.8, 4.7, 1.2, 'Iris-versicolor']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "> predicted='Iris-versicolor', actual='Iris-virginica'\n",
      "[6.2, 3.4, 5.4, 2.3, 'Iris-virginica']\n",
      "[6.3, 3.3, 6.0, 2.5, 'Iris-virginica']\n",
      "[6.7, 3.3, 5.7, 2.5, 'Iris-virginica']\n",
      "[6.8, 3.2, 5.9, 2.3, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "[6.3, 2.9, 5.6, 1.8, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, '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",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']\n",
      "[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "[7.1, 3.0, 5.9, 2.1, '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",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.7, 3.3, 5.7, 2.5, 'Iris-virginica']\n",
      "[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.8, 3.2, 5.9, 2.3, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "[6.7, 3.3, 5.7, 2.5, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']\n",
      "[5.6, 2.8, 4.9, 2.0, 'Iris-virginica']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[5.8, 2.8, 5.1, 2.4, 'Iris-virginica']\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']\n",
      "[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']\n",
      "[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']\n",
      "[5.9, 3.0, 5.1, 1.8, 'Iris-virginica']\n",
      "[6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']\n",
      "[6.5, 3.0, 5.5, 1.8, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']\n",
      "[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']\n",
      "[6.8, 3.0, 5.5, 2.1, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.1, 'Iris-virginica']\n",
      "[6.4, 2.8, 5.6, 2.2, 'Iris-virginica']\n",
      "> predicted='Iris-virginica', actual='Iris-virginica'\n",
      "Accuracy: 88.63636363636364%\n"
     ]
    }
   ],
   "source": [
    "# Example of kNN implemented from Scratch in Python\n",
    " \n",
    "import csv\n",
    "import random\n",
    "import math\n",
    "import operator\n",
    " \n",
    "def loadDataset(filename, split, trainingSet=[] , testSet=[]):\n",
    "        with open(filename, 'rt',encoding='UTF-8') 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])\n",
    "\n",
    "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)\n",
    "\n",
    "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",
    "#         print(distances)\n",
    "        neighbors = []\n",
    "        for x in range(k):\n",
    "            print(distances[x][0])\n",
    "            neighbors.append(distances[x][0])\n",
    "        return neighbors\n",
    "\n",
    "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",
    "        sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)\n",
    "        return sortedVotes[0][0]\n",
    "\n",
    "def getAccuracy(testSet, predictions):\n",
    "        correct = 0\n",
    "        for x in range(len(testSet)):\n",
    "            if testSet[x][-1] == predictions[x]:\n",
    "                correct += 1\n",
    "        return (correct/float(len(testSet))) * 100.0\n",
    "\n",
    "def main():\n",
    "        # prepare data\n",
    "        trainingSet=[]\n",
    "        testSet=[]\n",
    "        split = 0.67\n",
    "        loadDataset('iris.data.txt', split, trainingSet, testSet)\n",
    "        print('Train set: ' + repr(len(trainingSet))) \n",
    "        print( 'Test set: ' + repr(len(testSet)))\n",
    "        # generate predictions\n",
    "        predictions=[]\n",
    "        k = 7\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()"
   ]
  },
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   "outputs": [],
   "source": []
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