K-Nearest Neighbors algorithm

from numpy import *import operatorimport matplotlibimport matplotlib.pyplot as pltfrom os import listdirdef createDataSet():    group=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    labels=['A','A','B','B']    return group, labelsgroup, labels = createDataSet()  def classify0(inX, dataSet, labels, k):    dataSetSize = dataSet.shape[0]    diffMat = tile(inX, (dataSetSize, 1))-dataSet    sqDiffMat = diffMat ** 2    sqlDistances = sqDiffMat.sum(axis=1)    distances = sqlDistances**0.5    sortedDistIndicies = distances.argsort()    classCount = {}    for i in range(k):        voteLabel = labels[sortedDistIndicies[i]]        classCount[voteLabel] = classCount.get(voteLabel, 0)+1        sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)   # print sortedClassCount    return sortedClassCount[0][0]def file2matrix(filename):    fr = open(filename)    numberOfLines = len(fr.readlines())    returnMat = zeros((numberOfLines, 3))    classLabelVector = []    fr = open(filename)    index = 0    for line in fr.readlines():        line = line.strip()        listFromLine = line.split('\t')        returnMat[index,:]=listFromLine[0:3]        classLabelVector.append(listFromLine[-1])        index += 1    return returnMat, classLabelVector    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')def autoNorm(dataSet):    minVals = dataSet.min(0)    maxVals = dataSet.max(0)    ranges = maxVals-minVals    normDataSet = zeros(shape(dataSet))    m = dataSet.shape[0]    normDataSet = dataSet - tile(minVals, (m,1))    normDataSet = normDataSet/tile(ranges, (m,1))    return normDataSet, ranges, minVals    def datingClassTest():    hoRatio = 0.10;    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')    normMat, ranges, minVals = autoNorm(datingDataMat)    m = normMat.shape[0]    numTestVecs = int(m*hoRatio)    errCount = 0.0        for i in range(numTestVecs):        classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 4)        print 'the classifier came back with: %s, the real answer is: %s' % (classifierResult, datingLabels[i])        if(classifierResult != datingLabels[i]):            errCount += 1.0    print "the total error rate is: %f" % (errCount/float(numTestVecs))    def classifyPerson():    resultList = ['not at all', 'in small doses', 'in large doses']    percentTats = float(raw_input("percent of time spent playing video games?"))    ffMiles = float(raw_input("frequent filter miles earned per year?"))    iceCream = float(raw_input("liters of ice cream consumed per year?"))        datingDataMat, datingLabels = file2matrix('datingTestSet.txt')    normMat, ranges, minVals = autoNorm(datingDataMat)    inArr = array([percentTats, ffMiles, iceCream])    classifierResult = classify0((inArr-minVals)/ranges, normMat, datingLabels, 3)        print "You will probably like this person: ", resultList[classifierResult-1]    def img2vector(filename):    returnVect = zeros((1,1024))    fr = open(filename)    for i in range(32):        lineStr = fr.readline()        for j in range(32):            returnVect[0,32*i+j]=int(lineStr[j])        return returnVect    def handwritingClassTest():    hwLabels = []    trainingFileList = listdir('trainingDigits')    m=len(trainingFileList)    #print "m=",m    trainingMat = zeros((m,1024))    for i in range(m):        fileNameStr = trainingFileList[i]        fileStr = fileNameStr.split('.')[0]        classNumStr = int(fileStr.split('_')[0])        hwLabels.append(classNumStr)        trainingMat[i,:]=img2vector('trainingDigits/%s' % fileNameStr)            testFileList = listdir('testDigits')    errCount = 0.0    mTest = len(testFileList)        for i in range(mTest):        fileNameStr = testFileList[i]        fileStr = fileNameStr.split('.')[0]        classNumStr = fileStr.split('_')[0]        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)        print "the classifier came back with: %s, the real answer is: %s" % (classifierResult, classNumStr)        #print 'classifierResult = ', classifierResult,"classNumStr = ", classNumStr        if (int(classifierResult) != int(classNumStr)):            errCount += 1.0        print "\n the total number of error is: %d" % (errCount)    print "\n the total error rate is: %f" % (errCount/float(mTest))