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:zhoumeixu createdate: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 np import operator #运算符操作包 from _ctypes import Array from statsmodels.sandbox.regression.kernridgeregress_class import plt_closeall def createDataSet(): group=np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels=['A','A','B','B'] return group ,labels group,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array 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\\' datingDataMat,datingLabels=file2matrix(path+'datingTestSet2.txt') import matplotlib import matplotlib.pyplot as plt fig=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array def 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,minVals normMat,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 returnVect testVector=img2vector(path+'testDigits\\0_13.txt') print(testVector[0,0:31]) import os def 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()
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