text: from numpy import *
  import operator
  
  class KNearestNeighbor:
    GROUP = array([1.0, 1.1], [1.0, 1.0],
                  [0.0, 0.0], [0.0, 0.1])
  
    LABELS = ['A', 'A', 'B', 'B']
  
    def createDataSet():
      return GROUP, LABELS
  
    def classify0(inX, dataSet, labels, k)
      dataSetSize = dataSet.shape[0]
      diffMat = tile(inX, (dataSetSize, 1)) - dataSet
      sqDiffMat = diffMat ** 2
      sqDistances = sqDiffMat.sum(axis = 1)
      distances = sqDistances ** 0.5
      sortedDisIndicies = distances.argsort()
      classCount={}
      for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voiteIlabel, 0) + 1
      sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(1), reverse = True)
      return sortedClassCount[0][0]
  