j简单的分类实现-K近邻

 1 __author__ = 'worfzyq'
 2 from numpy import *
 3 import operator
 4 
 5 def creatDataset() :
 6     group=array([[1.0,1.1],[1.0,1.0],[0.0,0.0],[0,0.1]])
 7     labels=['A','A','B','B']
 8     return group,labels
 9 
 def classify0(inX,dataSet,labels,k) :
     dataSetSize=dataSet.shape[0]  #返回数组得到维数
     # print(dataSetSize)
     diffMat=tile(inX,(dataSetSize,1))-dataSet  #把inX扩展datasetSize-1维然后做减法
     #print(diffMat)
     sqDiffMat=diffMat**2  #乘方
     #print(sqDiffMat)
     sqDistances=sqDiffMat.sum(axis=1) #把每一列加起来
     #print(sqDistances)
     distances=sqDistances**0.5   # 得到距离
     sortedDistIndicies=distances.argsort() #得到排好序得到下标
     classCount={}
     for i in range(k) :
         voteIlabel=labels[sortedDistIndicies[i]]
         classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
     sortedClassCount=sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)
     #print(sortedClassCount)
     return sortedClassCount[0][0]
 
 group,labels=creatDataset()
 # print(group)
 print(classify0([0,0],group,labels,3))