knn.py

# Example of kNN implemented from Scratch in Python

import random
import math
import operator
import readtr as rd

def loadDataset(filename,s,m,n):
	a=rd.read2(filename,m,n)
	tr=[]
	te=[]
	for x in range(len(a)):
		if(random.random()<s):
			tr.append(a[x])
		else:
			te.append(a[x])
	return(tr,te)


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[1:], trainingSet[x][1:], 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][0]
		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][0] == predictions[x]:
			correct += 1
	return (correct/float(len(testSet))) * 100.0
	
def main(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][0]))
	accuracy = getAccuracy(testSet, predictions)
	print('Accuracy: ' + repr(accuracy) + '%')