from numpy import *

 import operator

 def createDataSet():

     # generate the samples and labels.

     group = array([[1.0,1.1], [1.0,1.0], [0,0], [0,0.1]])

     labels = ['A', 'A', 'B', 'B']

     print group

     return group, labels

 def classify(inX, dataSet, labels, k):

     dataSetSize = dataSet.shape[0]                  # get the size of one dimension.

     　　　　　　　　　　　　　　　　　　　　　　　　　　　　# calculate the distance between inX and samples.

     diffMat = tile(inX, (dataSetSize, 1)) - dataSet # repeat inX to generate a dataSetSize * 1 matrix. Then subtract the corresponding number in dataSet.

     sqDiffMat = diffMat ** 2                        # get the square of each D-value.

     sqDistances = sqDiffMat.sum(axis=1)             # get the sum of each pair of numbers.

     distances = sqDistances ** 0.5                  # get the square root of each sum. Those are distances between inX and samples.

     sortedDistIndicies = distances.argsort()        # return the index if 'distances' is sorted.

     classCount = {}                                 # make a directory {label:display times}.

     for i in range(k):                                                  # get first kth nearest samples.

         voteIlabel = labels[sortedDistIndicies[i]]                      # get the ith's label.

         classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1      # count the number of this label.

     sortedClassCount = sorted(classCount.iteritems(),                   # get the most frequent label.

                               key=operator.itemgetter(1), reverse=True)

     return sortedClassCount[0][0]                                       # return the most frequent label.

 dataSet, labels = createDataSet()

 print classify([-100.0,-100.1], dataSet, labels, 1)