import numpy as npimport operatorimport osdef createDataSet():    group = np.array([[1.0, 1.1],[1.0, 1.0],[0, 0],[0, 0.1]])    labels = [‘A‘, ‘A‘, ‘B‘, ‘B‘]    return group, labelsdef 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]