Python实现的knn算法示例

本文实例讲述了Python实现的knn算法。分享给大家供大家参考，具体如下：

代码参考机器学习实战那本书：

机器学习实战 (Peter Harrington著) 中文版

机器学习实战 (Peter Harrington著) 英文原版[附源代码]

有兴趣你们可以去了解下

具体代码：

# -*- coding:utf-8 -*-#! python2'''''@author:zhoumeixucreatedate:2015年8月27日'''#np.zeros((4,2))#np.zeros(8).reshape(4,2)#x=np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) np.zeros_like(x)# 最值和排序：最值有np.max(),np.min() 他们都有axis和out（输出）参数,# 而通过np.argmax(), np.argmin()可以得到取得最大或最小值时的 下标。# 排序通过np.sort(), 而np.argsort()得到的是排序后的数据原来位置的下标# 简单实现knn算法的基本思路import numpy as npimport operator #运算符操作包from _ctypes import Arrayfrom statsmodels.sandbox.regression.kernridgeregress_class import plt_closealldef createDataSet(): group=np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels=['A','A','B','B'] return group ,labelsgroup,labels=createDataSet()def classify0(inx,dataSet,labels,k): dataSetSize=dataSet.shape[0] diffMat=np.tile(inx,(dataSetSize,1))-dataSet sqDiffMat=diffMat**2 sqDistances=sqDiffMat.sum(axis=1) distances=sqDistances**0.5   #计算距离 python中会自动广播的形式 sortedDistIndicies=distances.argsort() #排序，得到原来数据的在原来所在的下标 classCount={} for i in range(k):  voteIlabel=labels[sortedDistIndicies[i]] # 计算距离最近的值所在label标签  classCount[voteIlabel]=classCount.get(voteIlabel,0)+1 # 计算距离最近的值所在label标签，对前k哥最近数据进行累加 sortedClassCount=sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True) #排序得到距离k个最近的数所在的标签 return sortedClassCount[0][0]if __name__=='__main__': print(classify0([0,0],group,labels,4))# 利用knn算法改进约会网站的配对效果def file2matrix(filename): fr=open(filename) arrayOLines=fr.readlines() numberOfLines=len(arrayOLines) returnMat=np.zeros((numberOfLines,3)) classLabelVector=[] index=0 for line in arrayOLines:  line=line.strip()  listFromLine=line.split('/t')  returnMat[index,:]=listFromLine[0:3]  classLabelVector.append(int(listFromLine[-1]))  index+=1 return returnMat ,classLabelVector #生成训练数据的array和目标arraypath=u'D://Users//zhoumeixu204//Desktop//python语言机器学习//机器学习实战代码 python//机器学习实战代码//machinelearninginaction//Ch02//'datingDataMat,datingLabels=file2matrix(path+'datingTestSet2.txt')import matplotlibimport matplotlib.pyplot as pltfig=plt.figure()ax=fig.add_subplot(111)ax.scatter(datingDataMat[:,1],datingDataMat[:,2])plt.show()ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*np.array(datingLabels),15*np.array(datingDataMat[:,2]))plt.show()  #生成训练数据的array和目标arraydef autoNorm(dataset): minVals=dataset.min(0) maxVals=dataset.max(0) ranges=maxVals-minVals normeDataSet=np.zeros(np.shape(dataset)) m=dataset.shape[0] normDataSet=dataset-np.tile(minVals,(m,1)) normDataSet=normDataSet/np.tile(ranges,(m,1)) return normDataSet ,ranges,minValsnormMat,ranges,minVals=autoNorm(datingDataMat)def datingClassTest(): hoRatio=0.1 datingDataMat,datingLabels=file2matrix(path+'datingTestSet2.txt') normMat,ranges,minVals=autoNorm(datingDataMat) m=normMat.shape[0] numTestVecs=int(m*hoRatio) errorCount=0.0 for i in range(numTestVecs):  classifierResult=classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m],3)  print "the classifier came back with :%d,the real answer is :%d"/     %(classifierResult,datingLabels[i])  if classifierResult!=datingLabels[i]:   errorCount+=1.0 print "the total error rare is :%f"%(errorCount/float(numTestVecs)) #利用knn算法测试错误率if __name__=='__main__': datingClassTest()#利用构建好的模型进行预测def classifyPerson(): resultList=['not at all','in same doses','in large d oses'] percentTats=float(raw_input("percentage if time spent playin cideo games:")) ffMiles=float(raw_input("frequnet fliter miles earned per year:")) iceCream=float(raw_input("liters of ice cream consumed per year:")) datingDataMat,datingLabels=file2matrix(path+'datingTestSet2.txt') normMat,ranges,minVals=autoNorm(datingDataMat) inArr=np.array([ffMiles,percentTats,iceCream]) classifierResult=classify0((inArr-minVals)/ranges,normMat,datingLabels,3) print("you will probably like the person:",resultList[classifierResult-1])if __name__!='__main__': classifyPerson()#利用knn算法进行手写识别系统验证path=u'D://Users//zhoumeixu204//Desktop//python语言机器学习//机器学习实战代码 python//机器学习实战代码//machinelearninginaction//Ch02//'def img2vector(filename): returnVect=np.zeros((1,1024)) fr=open(filename) for i in range(32):  lineStr=fr.readline()  for j in range(32):   returnVect[0,32*i+j]=int(lineStr[j]) return returnVecttestVector=img2vector(path+'testDigits//0_13.txt')print(testVector[0,0:31])import osdef handwritingClassTest(): hwLabels=[] trainingFileList=os.listdir(path+'trainingDigits') m=len(trainingFileList) trainingMat=np.zeros((m,1024)) for i in range(m):  fileNameStr=trainingFileList[i]  fileStr=fileNameStr.split('.')[0]  classNumStr=int(fileStr.split('_')[0])  hwLabels.append(classNumStr)  trainingMat[i,:]=img2vector(path+'trainingDigits//'+fileNameStr) testFileList=os.listdir(path+'testDigits') errorCount=0.0 mTest=len(testFileList) for j in range(mTest):  fileNameStr=testFileList[j]  fileStr=fileNameStr.split('.')[0]  classNumStr=int(fileNameStr.split('_')[0])  classNumStr=int(fileStr.split('_')[0])  vectorUnderTest=img2vector(path+'testDigits//'+fileNameStr)  classifierResult=classify0(vectorUnderTest,trainingMat,hwLabels,3)  print("the classifier canme back with:%d,the real answer is :%d"%(classifierResult,classNumStr))  if classifierResult!=classNumStr:   errorCount+=1.0 print("/nthe total number of errors is :%d"%errorCount) print("/n the total error rate is :%f"%(errorCount/float(mTest)))if __name__=='__main__': handwritingClassTest()