knn-main(python)

import os
cwd = os.getcwd()
os.chdir('/home/student/Documents/KNN')

import csv
with open('iris.csv') as csvfile:
    lines = csv.reader(csvfile)
    for row in lines:
        print(','.join(row))

import csv
import random
def loadDataset(filename, split_ratio, trainingSet=[] , testSet=[]):
        with open(filename) as csvfile:
            lines = csv.reader(csvfile)
            dataset = list(lines)
            for x in range(len(dataset)-1):
                for y in range(4):
                    dataset[x][y] = float(dataset[x][y])
                if random.random() < split_ratio:
                    trainingSet.append(dataset[x])
                else:
                    testSet.append(dataset[x])


import math
def euclideanDistance(instance1, instance2, length):
        distance = 0
        for x in range(length):
            distance += pow((instance1[x]-instance2[x]), 2)
        return math.sqrt(distance)



import operator
def getNeighbors(trainingSet, testInstance, k):
        distances = []
        length = len(testInstance)-1
        for x in range(len(trainingSet)):
            dist = euclideanDistance(testInstance,trainingSet[x], length)
            distances.append((trainingSet[x], dist))
        distances.sort(key=operator.itemgetter(1))
        neighbors = []
        for x in range(k):
            neighbors.append(distances[x][0])
        return neighbors

#akurasi
def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][-1] == predictions[x]:
            correct += 1
        return (correct/float(len(testSet))) * 100.0


#Response
import operator
def getResponse(neighbors):
    classVotes = {}
    for x in range(len(neighbors)):
        response = neighbors[x][-1]
        if response in classVotes:
            classVotes[response] += 1
        else:
            classVotes[response] = 1
        sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
        return sortedVotes[0][0]

# prepare data
trainingSet=[]
testSet=[]
split = 0.67
loadDataset('iris.csv', split, trainingSet, testSet)
print('Train set: ' + repr(len(trainingSet)))
print ('Test set: ' + repr(len(testSet)))
# generate predictions
predictions=[]
k = 3
for x in range(len(testSet)):
    neighbors = getNeighbors(trainingSet, testSet[x], k)
    result = getResponse(neighbors)
    predictions.append(result)
    print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))
accuracy = getAccuracy(testSet, predictions)
print('Accuracy: ' + repr(accuracy) + '%')