import numpy as npimport osimport operatorimport matplotlibimport matplotlib.pyplot as pltdef classify(inX, dataSet, labels, k):    dataSetSize = dataSet.shape[0]#lines num; samples num    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet#dataSize*(1*inX)    sqDiffMat = diffMat**2    sqDistances = sqDiffMat.sum(axis=1)#add as the first dim    distances = sqDistances**0.5    #return indicies array from min to max    #this is an array    sortedDistanceIndices = distances.argsort()    #classCount={}    classCount=dict()   #define a dictionary    for i in range(k):        voteIlabel = labels[sortedDistanceIndices[i]]        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1#get(key,default=none)    #return a list like [(‘C‘,4),(‘B‘,3),(‘A‘,2)], not a dict    #itemgetter(0) is the 1st element    #default: from min to max    sortedClassCount = sorted(classCount.iteritems(),                              key=operator.itemgetter(1), reverse=True)                      return sortedClassCount[0][0]def file2matrix(fileName):    fileHandler = open(fileName)    numberOfLines = len(fileHandler.readlines())    #get the number of lines in the file    returnMat = np.zeros((numberOfLines, 3))           #init a zero return matrix    classLabelVector = []    #classLabelVector = list()                       #will be used to record labels    fileHandler = open(fileName)    index = 0    for line in fileHandler.readlines():        line = line.strip()                         #strip blank characters        listFromLine = line.split(‘\t‘)        returnMat[index,:] = listFromLine[0:3]        classLabelVector.append(listFromLine[-1])        index += 1    return returnMat, classLabelVector#normalize data setdef autoNorm(dataSet):    minVal = dataSet.min(0)    maxVal = dataSet.max(0)    ranges = maxVal - minVal    normDataSet = np.zeros(np.shape(dataSet))    m = dataSet.shape[0]    normDataSet = dataSet - np.tile(minVal, (m,1))    normDataSet = normDataSet/np.tile(ranges, (m,1))    return normDataSet, ranges, minValdef showMatrix():    m,l = file2matrix("datingTestSet.txt")    m,r,mv = autoNorm(m)    fig = plt.figure()    ax = fig.add_subplot(111)    ax.scatter(m[:,1],m[:,2])    plt.show()#calculate the error rate of sampledef calcErrorRate():    ratio = 0.1         #only use 10% samples to calc the error rate    matrix,l = file2matrix("datingTestSet.txt")    matrix,r,mv = autoNorm(matrix)    m = matrix.shape[0]    numTestSample = int(m*ratio)    errorCount = 0    for i in range(numTestSample):        classifyResult = classify(matrix[i,:], matrix[numTestSample:m,:],l[numTestSample:m],3)        print "the classifier came back with: %s, the real answer is: %s" % (classifyResult, l[i])        if (classifyResult != l[i]):            errorCount += 1    print "the total error rate is: %f" %(errorCount/float(numTestSample))    print errorCountdef classifyPerson():    percentTats = float(raw_input(                "percentage of time spent playing vedio games?"))    ffMiles = float(raw_input("frequent flier miles earned per year?"))    iceCream = float(raw_input("liters of ice cream consumed per year?"))    datingDataMat, datingLabels = file2matrix("datingTestSet.txt")    normMat, ranges, minVal = autoNorm(datingDataMat)    inArr = np.array([ffMiles, percentTats, iceCream])    classifyResult = classify((inArr-                               minVal)/ranges, normMat, datingLabels,3)    print "You will probaly like this person: ", classifyResult