使用python實現knn

import numpy as np
import operator
def createDataSet():
    group =np.array([[1.0,1.1],[1.0,1.0],[0.0,0.0],[0,0.1]])
    labels=['A','A','B','B']
    return group,labels
def classify0(inX,dataSet,labels,k):
    dataSetSize=dataSet.shape[0]
    diffMat=np.tile(inX,(dataSetSize,1))-dataSet
    sqDissMat=diffMat**2
    sqDistances=sqDissMat.sum(axis=1)
    distance=sqDistances**0.5
    sortedDistIndicies=distance.argsort()
    classCount={}
    for i in range(k):
        voteLabel=labels[sortedDistIndicies[i]]
        classCount[voteLabel]=classCount.get(voteLabel,0)+1
    sortedClassCount=sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0]
if __name__ =='__main__':
    group,labels=createDataSet()
    result=classify0([0.0,0.0],group,labels,3)
    print(result)

np.tile(inX,(dataSetSize,1))：將intX按行重複dataSize次

‘0’按列進行計算

‘1’按行進行計算