机器学习实战 kNN算法

#!/usr/bin/env python3 # -*- coding: utf-8 -*- #Remove dict.iteritems(), dict.iterkeys(), and dict.itervalues(). #Instead: use dict.items(), dict.keys(), and dict.values() respectively. # 线代和矩阵操作库 from numpy import * import operator #路径相关的 from os import listdir #画图的 import matplotlib import matplotlib.pyplot as plt #测试数据 书上代码都只有2，3个特征 def createDataSet(): group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]]) #array 遍于矩阵操作 labels = ['A', 'A', 'B', 'B'] return group, labels # 分类器 def classify0(inx, dataSet, labels, k): dataSetSize = dataSet.shape[0] #获得一维长度 diffMat = tile(inx, (dataSetSize,1)) - dataSet #获得二维长度 sqDiffMat = diffMat**2 #平方 sqDistances = sqDiffMat.sum(axis=1) # axis 1 代表行 0 代表 列 distances = sqDistances**0.5 #同上 sortedDistIndicies = distances.argsort() #排完序从小到大的索引值 classCount={} for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #dir 计数 sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True) #迭代， 从大到小 以第一个域大小为主 return sortedClassCount[0][0] #读数据 def file2matrix(filename): fr = open(filename)#打开文件 arrayOLines = fr.readlines()#读取 numberOfLines = len(arrayOLines) #一维长度 returnMat = zeros((numberOfLines, 3)) # n * 3 的 0矩阵 classLabelVector = [] index = 0 for line in arrayOLines: line = line.strip() #删除字符串前后空白符 listFromLine = line.split('\t') #以'\t' 切割 returnMat[index,:] = listFromLine[0:3] classLabelVector.append(int(listFromLine[-1])) index += 1 return returnMat,classLabelVector #图形化 def picture(): fig = plt.figure() ax = fig.add_subplot(111) #把图像分成1行1列 左到右 上到下 第1块 #ax.scatter(datingDataMat[:,1], datingDataMat[:,2])#x 为 数组1， y为数组2 ax.scatter(datingDataMat[:,1], datingDataMat[:,2], 15.0 * array(datingLabels), 15.0 * array(datingLabels)) # 形状 颜色 plt.show() #归一化 def autoNorm(dataSet): minVals = dataSet.min(0) #0 代表列 maxVals = dataSet.max(0) range = maxVals -minVals normDataSet = zeros(shape(dataSet)) m = dataSet.shape[0] normDataSet = dataSet - tile(minVals, (m,1)) normDataSet = normDataSet/tile(range, (m,1)) #矩阵除法 return normDataSet, range, minVals #检验分类器错误率 的测试数据 def datingClassTest(): hoRatio = 0.10 datingDataMat,datingLabels = file2matrix("/Users/yinfeng/Downloads/rar/machinelearninginaction/Ch02/datingTestSet2.txt") #路径啊！！！ 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 rate is: %f" % (errorCount/float(numTestVecs))) #约会网站测试函数 def classifyPerson(): resultList = ['not at all', 'in small doses', 'in large doses'] percentTats = float(input("percentage of time spent playing video games?")) ffMiles = float(input("frequent flier miles earned per year?")) iceCream = float(input("liters of ice cream consumed per year?")) datingDataMat,datingLabels = file2matrix("/Users/yinfeng/Downloads/rar/machinelearninginaction/Ch02/datingTestSet2.txt") normMat,ranges, minVals = autoNorm(datingDataMat) inArr = array([ffMiles, percentTats, iceCream]) classifierResult = classify0((inArr-minVals)/ranges, normMat, datingLabels, 3) print("You will probably like this person: ", resultList[classifierResult - 1]) #把图像转换成特征值 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 #测试数字 mac os 有一个隐藏文件 .DS_Store def handwritingClassTest(): hwLabels = [] trainingFileList = listdir("/Users/yinfeng/Downloads/rar/machinelearninginaction/Ch02/digits/trainingDigits") m = len(trainingFileList) #print(trainingFileList[1]) trainingMat = zeros((m,1024)) for i in range(m): if(trainingFileList[i] == ".DS_Store"): continue fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] #切割函数 classNumStr = int(fileStr.split('_')[0]) hwLabels.append(classNumStr) trainingMat[i,:] = img2vector("/Users/yinfeng/Downloads/rar/machinelearninginaction/Ch02/digits/trainingDigits/%s" % fileNameStr) testFileList = listdir('/Users/yinfeng/Downloads/rar/machinelearninginaction/Ch02/digits/testDigits') errorCount = 0.0 mTest = len(testFileList) for i in range(mTest): if(testFileList[i] == ".DS_Store"): continue fileNameStr = testFileList[i] fileStr = fileNameStr.split('.')[0] classNumStr = int(fileStr.split('_')[0]) vectorUnderTest = img2vector('/Users/yinfeng/Downloads/rar/machinelearninginaction/Ch02/digits/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 += 1.0 print("\nthe total number of errors is %d" % errorCount) print("\nthe total error rate is: %f" % (errorCount/float(mTest)))