KNN algorithm

#learned from online resource
import csv
import random,math,operator
def loadDataset(filename,split,trainingset=[],testset=[]):
	with open(filename,"rt", encoding='utf8') as csvfile:
		lines = csv.reader(csvfile)
		#for row in lines:
		#	print(" ".join(row))
		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:
				trainingset.append(dataset[x])
			else:
				testset.append(dataset[x])
def euclideanDistance(instance1,instance2,length):
	distance = 0
	for x in range(length):
		distance += pow((instance1[x]-instance2[x]),2)
	return math.sqrt(distance)

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

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.iteritems(),key=operator.itemgetter(1),reverse=True)
		return sortedVotes[0][0]

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
def main():
	trainingset = []
	testset = []
	split = 0.67
	loadDataset(filename,split,trainingset,testset)
	print ('Train set:' + repr(len(trainingset)))
	print ('test set:' + repr(len(testset)))
	predictions = []
	# 
	k=3
	for k in range(10):
	for x in range(len(testset)):
		neighbors = getNeighbors(trainingset,testset[x],k)
		result = getResponse(neighbors)
		predictions.append(result)
		print('predicted='+repr(result)+',actua='+repr(testset[x][-1]))
	accuracy = getAccuracy(testset,predictions)
	print ('Accuracy:'+repr(accuracy)+'%')

main()