这是学习机器学习算法实战这本书时，写的代码实战。让自己对各个算法有更直观的了解，不能一直不写啊。不管简单还是不简单都亲自一行一行的敲一遍啊。
具体的源码和和数据链接：https://pan.baidu.com/s/1G2S2pb5gfBnxGNNTFgTkEA 密码：fov0
# -*- coding: utf-8 -*- # author: Yufeng Song from numpy import* import operator import matplotlib import matplotlib.pyplot as plt import os def createDataSet(): group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels = ['A','A','B','B'] return group,labels def classify0(inX,dataSet,labels,k): dataSetSize = dataSet.shape[0] diffMat = tile(inX,(dataSetSize,1))-dataSet sqDiffMat = diffMat**2 sqDistance = sqDiffMat.sum(axis=1)# distances = sqDistance**0.5 sortedDistIndices = distances.argsort() classCount={} for i in range(k): votelabel = labels[sortedDistIndices[i]] classCount[votelabel] = classCount.get(votelabel,0)+1 #sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True) sortedClassCount = sorted(classCount.items(),key=lambda item:item[1],reverse=True) return sortedClassCount[0][0] def file2matrix(filename): fr = open(filename,"r") arrayOfLines =fr.readlines() numberOfLines = len(arrayOfLines) # print(numberOfLines) # returnMat = zeros(numberOfLines,3) returnMat = zeros((numberOfLines,3))#里面有个小括号，别忘了啊 # a=eye(3) 单位矩阵 # print(a) # print(returnMat) classLabelVector = [] index = 0 for line in arrayOfLines: line = line.strip() #删除左右两边的空格指定空格，默认是空字符串啊，lstrip(),rstrip() listFromLine = line.split('\t') returnMat[index,:] = listFromLine[0:3]#他是个二维数组所以要加这个啊 classLabelVector.append(int(listFromLine[-1])) index += 1 return returnMat,classLabelVector # def file2matrix(filename): # fr = open(filename) # numberOfLines = len(fr.readlines()) #get the number of lines in the file # returnMat = zeros((numberOfLines,3)) #prepare matrix to return # classLabelVector = [] #prepare labels return # fr = open(filename) # index = 0 # for line in fr.readlines(): # line = line.strip() # listFromLine = line.split('\t') # returnMat[index,:] = listFromLine[0:3] # classLabelVector.append(int(listFromLine[-1])) # index += 1 # return returnMat,classLabelVector def autoNorm(dataSet): minVals = dataSet.min(0)#选取列的最小值，而不是行的最小值 maxVals = dataSet.max(0) ranges=maxVals-minVals normDataSet = zeros(shape(dataSet)) m = dataSet.shape[0] normDataSet = dataSet - tile(minVals,(m,1)) normDataSet = normDataSet/tile(ranges,(m,1)) return normDataSet,ranges,minVals def datingClassTest(): hoRatio = 0.10 datingDataMat,datingLabels=file2matrix('datingTestSet2.txt') normMat,ranges,minVals=autoNorm(datingDataMat) print(normMat.shape)#(1000,3) m = normMat.shape[0] numTestVecs = int(m*hoRatio)#100 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))) def classifyPerson(): resultList = ['not at all','in samll doses','in large doses'] percentTats = float(input("percentage of time spent playing video games?"))#python3不支持raw_input ffMiles = float(input("frequent flier miles earned per year?")) iceCream = float(input("liters of ice cream consumed per year?")) datingDataMat,datingLabels = file2matrix("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]) 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 def handwritingClassTest(): hwlabels = [] trainingFileList = os.listdir('trainingDigits') m = len(trainingFileList) trainingMat = zeros((m,1024)) for i in range(m): fileNameStr = trainingFileList[i]#0_0.txt fileStr = fileNameStr.split('.')[0] classNumStr = int(fileStr.split('_')[0]) hwlabels.append(classNumStr) trainingMat[i,:] = img2vector('trainingDigits/%s' %fileNameStr) testFileList = os.listdir('testDigits') errorCount = 0.0 mTest = len(testFileList) for i in range(mTest): 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 print("\nthe total number of errors is: %d" % errorCount) print("\nthe total error rate is: %f" %(errorCount/float(mTest))) if __name__ == '__main__': # group,labels = createDataSet() # print (classify0([0,0],group,labels,3)) # print(file2matrix("datingTestSet2.txt")) # datingDataMat,datingLabels=file2matrix("datingTestSet2.txt") # normMat,ranges,minVals = autoNorm(datingDataMat) # fig = plt.figure() # ax = fig.add_subplot(211)#111,与121是左右的关系啊 这几个参数要弄明白啊 # ax2 = fig.add_subplot(212) # # ax.scatter(datingDataMat[:,1],datingDataMat[:,2]) # # ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels)) # ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels)) # ax2.scatter(normMat[:,0],normMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels)) # plt.show() # normMat,ranges,minVals = autoNorm(datingDataMat) # print(normMat,ranges,minVals) # y=pp.DS.Transac_open # 设置y轴数据，以数组形式提供 #res=[1,2,3] # x=len(res) # 设置x轴，以y轴数组长度为宽度 # x=range(x) # 以0开始的递增序列作为x轴数据 # plt.plot(x,res) # 只提供x轴，y轴参数，画最简单图形 # plt.show() # datingClassTest() # classifyPerson() # testVector = img2vector('testDigits/0_13.txt') # print(testVector[0,0:31]) handwritingClassTest()