机器学习实战(k-近邻算法)

本文主要是介绍机器学习实战(k-近邻算法)，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

给定训练数据样本和标签，对于某测试的一个样本数据，选择距离其最近的k个训练样本，这k个训练样本中所属类别最多的类即为该测试样本的预测标签。简称kNN。通常k是不大于20的整数，这里的距离一般是欧式距离。

下面是机器学习实战书中一些代码的实现:

其中包含使用k-邻近算法改进约会网站配对效果代码和手写识别系统的代码:

#coding=UTF8 
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
import operator
from os import listdirdef createDataSet():group = array([[3,104],[2,100],[1,81],[101,10],[99,5]]) #训练集labels = ['affectional film','affectional film','affectional film','action movie',"action movie"]return group,labelsdef classify0(inX,dataSet,labels,k):dataSetSize = dataSet.shape[0]diffMat = tile(inX, (dataSetSize,1)) - dataSetsqDiffMat = diffMat ** 2; #diffMat ^ 2sqDistances = sqDiffMat.sum(axis=1) #将矩阵的每一行相加比如[[2,1,3],[1,1,1]]结果为[6,3]distances = sqDistances ** 0.5 #sqDistances ^ (1/2)sortedDistIndicies = distances.argsort()classCount = {}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]]classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1sortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)   #获取对象的第1个域的值return sortedClassCount[0][0]def file2matrix(filename):fr = open(filename)numberOfLines = len(fr.readlines())         #get the number of lines in the filereturnMat = zeros((numberOfLines,3))        #prepare matrix to returnclassLabelVector = []                       #prepare labels return   fr = open(filename)index = 0for line in fr.readlines():line = line.strip()listFromLine = line.split('\t')returnMat[index,:] = listFromLine[0:3]classLabelVector.append(int(listFromLine[-1]))index += 1return returnMat,classLabelVectordef autoNorm(dataSet):minVals = dataSet.min(0) #参数0从当前列选取最小值maxVals = dataSet.max(0) #同上ranges = maxVals - minValsnormDataSet = zeros(shape(dataSet)) #创建规模为dataSet的零矩阵m = dataSet.shape[0] #行normDataSet = dataSet - tile(minVals, (m,1)) #m行1列的minValsnormDataSet = normDataSet / tile(ranges, (m,1))return normDataSet,ranges,minValsdef datingClassTest():hoRatio = 0.10datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')normMat,ranges,minVals = autoNorm(datingDataMat)m = normMat.shape[0] #行numTestVecs = int(m * hoRatio) #算出测试数据errorCount = 0.0for i in range(numTestVecs):classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 3) #@param1:读入每行的数据,@param2:样本数据,因为测试数据是0-numTestVecsprint "the classifier came back with: %d,the real answer is : %d" % (classifierResult,datingLabels[i])if(classifierResult != datingLabels[i]):errorCount += 1.0print "the total error rate is: %f" % (errorCount / float(numTestVecs))def classifyPerson():percentTats = float(raw_input("输入玩视频游戏所消耗时间的百分比:"))ffMiles = float(raw_input("输入每年获得的飞行常客里程数:"))iceCream = float(raw_input("输入每周消费的冰淇淋公升数:"))datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')normMat,ranges,minVals = autoNorm(datingDataMat)inArr = array([ffMiles,percentTats,iceCream])classifierResult = classify0((inArr-minVals)/ranges, normMat ,datingLabels , 3)print(classifierResult)temp = classifierResult - 1;if temp == 0:print("一点都不喜欢这个人")elif temp == 1:print("一般般")else:print("非常喜欢")def img2vector(filename):returnVect = 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 #returnVect为1*1024的数组def handwritingClassTest():hwLabels = []trainingFileList = listdir("trainingDigits")m = len(trainingFileList)trainingMat = zeros((m,1024))for i in range(m):  #这是训练集fileNameStr = trainingFileList[i]fileStr = fileNameStr.split('.')[0]classNumStr = int(fileStr.split('_')[0])hwLabels.append(classNumStr)trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)testFileList = listdir('testDigits')errorCount = 0.0mTest = len(testFileList)for i in range(mTest):  #这是测试集fileNameStr = testFileList[i]fileStr = fileNameStr.split('.')[0]classNumStr = int(fileStr.split('_')[0])vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)classifierResult = classify0(vectorUnderTest,trainingMat,hwLabels,3)print "the classifier came back with: %d,the real answer is : %d" % (classifierResult,classNumStr)if(classifierResult != classNumStr) : errorCount += 1.0print "\nthe total number of errors is: %d" % errorCountprint "\nthe total error rate is:%f" % (errorCount / float(mTest))'''
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
print(datingDataMat)
print(datingLabels[0:20])
normMat,ranges,minVals = autoNorm(datingDataMat)
print('\n')
print(normMat)
print(ranges)
print(minVals)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0 * array(datingLabels))
plt.show()
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.scatter(normMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0 * array(datingLabels))
plt.show()
datingClassTest()
'''
#classifyPerson()
#testVector = img2vector('0_13.txt')
#print testVector[0,0:31]
#print testVector[0,32:63]
handwritingClassTest()

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