机器学习实战_kNN算法python3.6实现与理解

标签（空格分隔）： kNN算法

from numpy import *import operatorfrom os import listdir#创建数据集和标签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#k-近邻算法def classify0(inX, dataSet, labels, k):dataSetSize = dataSet.shape[0]diffMat = tile(inX, (dataSetSize, 1)) - dataSetsqDiffMat = diffMat ** 2sqDistances = sqDiffMat.sum(axis=1)distances = sqDistances ** 0.5sortedDistIndicies = distances.argsort()classCount = {}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]]classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)#Python3.5中：iteritems变为itemsreturn sortedClassCount[0][0]#将文本记录转换为Numpy的解析程序#将原始数据转为为计算机可以分析的numpy数据(输入一些字符串——————————得到矩阵)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 returnfr = 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, classLabelVector#测试数据处理为numpy的结果datingDataMat,datingLables = file2matrix('datingTestSet2.txt')#print(datingDataMat)#print(datingLables)'''import matplotlibimport matplotlib.pyplot as pltfig = plt.figure()ax = fig.add_subplot(111)'''#ax.scatter(datingDataMat[:,1],datingDataMat[:,2])#用色彩参数个性化标记散点图上的点ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLables),15.0*array(datingLables))plt.show()#归一化特征值norm规范化def autoNorm(dataSet):minVals = dataSet.min(0) #从列中选出最小的值maxVals = dataSet.max(0) #从列中选出最大的值ranges = maxVals - minVals #取值范围（最大值-最小值）normDataSet = zeros(shape(dataSet))m = dataSet.shape[0]normDataSet = dataSet - tile(minVals,(m,1)) #tile()将变量的内容复制成输入矩阵同样大小的矩阵normDataSet = normDataSet/tile(ranges,(m,1)) #这里是具体特征值相除 在numpy库中，矩阵的除法是函数：linalg.solve(matA,matB)return normDataSet,ranges,minVals#print(autoNorm(datingDataMat))#分类器针对约会网站给定的数据进行分类的测试代码(k-近邻算法)def datingClassTest():hoRatio = 0.50 # hold out 10%datingDataMat, datingLabels = file2matrix('datingTestSet2.txt') # load data setfrom filenormMat, ranges, minVals = autoNorm(datingDataMat)m = normMat.shape[0]numTestVecs = int(m * hoRatio)errorCount = 0.0for i in range(numTestVecs):classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)print("分类器划分的类型: %d, 实际的类型是: %d" % (classifierResult, datingLabels[i]))if (classifierResult != datingLabels[i]): errorCount += 1.0print("错误率: %f" % (errorCount / float(numTestVecs)))print("错误的总是:%d"%(errorCount))#约会网站预测函数def classifyPerson():resultList = ['一点都喜欢','喜欢一点','很喜欢']percentTats = float(input("打电子游戏的时间：?"))ffMiles = float(input("坐飞机的时间：?"))iceCream = float(input("吃多少冰淇淋：?"))datingDataMat,datingLables = file2matrix('datingTestSet2.txt')normMat,ranges,minVals = autoNorm(datingDataMat)inArr = array([ffMiles, percentTats, iceCream])classifyResult = classify0((inArr-minVals)/ranges,normMat,datingLables,3)print("你的喜欢程度：",resultList[classifyResult -1 ])classifyPerson()

数据集下载：链接：/s/1eSvpjLS 密码：e1ni