机器学习之k近邻算法

from numpy import *import operatordef createDataSet():    group=array([          [1.0,1.1],          [1.0,1.0],          [0,0],          [0,0.1],          [1,0],          [1.1,0]             ])    labels=['A','A','B','B','C','C']    return group,labelsdef classify0(inX,dataSet,labels,k):    dataSetSize=dataSet.shape[0]    diffMat=tile(inX,(dataSetSize,1))-dataSet    print(diffMat)    sqDiffMat=diffMat**2;    print(sqDiffMat)    sqDistances=sqDiffMat.sum(axis=1)    print(sqDistances)    distances=sqDistances**0.5    print(distances)    sortedDistIndicies=distances.argsort()    print(sortedDistIndicies)    clasCount={}    for i in range(k):        print(clasCount)        voteIlabel=labels[sortedDistIndicies[i]]        print(voteIlabel)        print(clasCount.get(voteIlabel,0))        clasCount[voteIlabel]=clasCount.get(voteIlabel,0)+1    print(clasCount)    sortedClassCount=sorted(clasCount.iteritems(),                     key=operator.itemgetter(1),reverse=True)    print(sortedClassCount)    return sortedClassCount[0][0]group,labels=createDataSet()print(group)print(group.shape)print(group.shape[0])print(classify0([1,0],group,labels,3))