kNNlibrary.py

#-*- coding: utf-8 -*-

import csv
import math
import operator

def loadDataset(trainfn, testfn, trainingSet=[], testSet=[]):
    with open(trainfn, 'rb') as csvfile:
        lines = csv.reader(csvfile)
        dataset = list(lines)
        #print dataset
        for x in range(len(dataset)):
            for y in range(3):
                dataset[x][y] = int(dataset[x][y])
            trainingSet.append(dataset[x])
        #print trainingSet

    with open(testfn, 'rb') as csvfile:
        lines = csv.reader(csvfile)
        dataset = list(lines)
        for x in range(len(dataset)):
            for y in range(3):
                dataset[x][y] = int(dataset[x][y])
            testSet.append(dataset[x])
        #print testSet

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