K-邻近算法

导入的包

coding=UTF8from numpy import *import operatorfrom os import listdirimport matplotlibimport matplotlib.pyplot as plt

判断数据是哪一个类型

def classify0(inX, dataset, labels, k):dataSetSize = dataset.shape[0]diffMat = tile(inX, (dataSetSize, 1)) - datasetsqDiffMat = diffMat ** 2sqDistance = sqDiffMat.sum(axis=1)distance = sqDistance ** 0.5sortedDistIndicies = distance.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)return sortedClassCount[0][0]

数据预处理

def file2matrix(filename):fr = open(filename)arrayOLines=fr.readlines()numberOfLines = len(arrayOLines)         #获取 n=样本的行数,1000个样本returnMat = zeros((numberOfLines,4))        #创建一个2维矩阵用于存放训练样本数据，一共有n行，每一行存放3个数据classLabelVector = []                       #创建一个1维数组用于存放训练样本标签。classLabelVectortou = []index = 0for line in arrayOLines:    # 把回车符号给去掉,头尾空白去掉    line = line.strip()    # 把每一行数据用\t分割    listFromLine = line.split('\t')    # 把分割好的数据放至数据集，其中index是该样本数据的下标，就是放到第几行    returnMat[index,:] = listFromLine[1:5]    # 把该样本对应的标签放至标签集，顺序与样本集对应。    classLabelVector.append(int(listFromLine[-1]))#每行的最后一个下标位置数据是类型    classLabelVectortou.append(int(listFromLine[0]))    index += 1return returnMat,classLabelVector

测试数据预处理

def file3matrix(filename):"""从文件中读入训练数据，并存储为矩阵"""fr = open(filename)arrayOLines=fr.readlines()numberOfLines = len(arrayOLines)         #获取 n=样本的行数,1000个样本returnMat = zeros((numberOfLines,4))        #创建一个2维矩阵用于存放训练样本数据，一共有n行，每一行存放3个数据classLabelVector = []                       #创建一个1维数组用于存放训练样本标签。classLabelVectortou = []index = 0for line in arrayOLines:    # 把回车符号给去掉,头尾空白去掉    line = line.strip()    # 把每一行数据用\t分割    listFromLine = line.split('\t')    # 把分割好的数据放至数据集，其中index是该样本数据的下标，就是放到第几行    returnMat[index,:] = listFromLine[1:5]    # 把该样本对应的标签放至标签集，顺序与样本集对应。    classLabelVector.append(int(listFromLine[-1]))#每行的最后一个下标位置数据是类型    classLabelVectortou.append(int(listFromLine[0]))    index += 1return returnMat

数据显示图像

def pictureshow(datingDataMat):datingDataMat1=[]datingDataMat1=datingDataMatfig = plt.figure()ax = fig.add_subplot(111)ax.scatter(datingDataMat[:,1], datingDataMat[:,2],30.0*array(datingLabels), 15.0*array(datingLabels))ax.axis([-2,25,-0.2,2.0])plt.xlabel('Percentage of Time Spent Playing Video Games')plt.ylabel('Liters of Ice Cream Consumed Per Week')plt.show()

数据归一化

def autoNorm(dataSet):minVals = dataSet.min(0)maxVals = dataSet.max(0)ranges = maxVals - minValsnormDataSet = zeros(shape(dataSet))m = dataSet.shape[0]normDataSet = dataSet - tile(minVals, (m,1))normDataSet = normDataSet/tile(ranges, (m,1))return normDataSet, ranges, minVals

计算错误率

def datingClassTest():# 将数据集中10%的数据留作测试用，其余的90%用于训练hoRatio = 0.8datingDataMat,datingLabels = file2matrix('D:\MachingLearning\KNN\machingKNN1.0\draining1.txt')       #load data setfrom fileprint datingDataMatnormMat, ranges, minVals = autoNorm(datingDataMat)m = normMat.shape[0]numTestVecs = int(m*hoRatio)errorCount = 0.0#print normMat[0],normMat[0,:],normMat[0,:1],normMat[0,:2]#normMat[0]代表list中的下标0，normMat[0,:]代表嵌套list的外部下标0，normMat[0,:1]代表嵌套list下标0的同时，截取下标1前面的数for i in range(numTestVecs):    classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],4)    #print "the classifier came back with: %d, the real answer is: %d, result is :%s" % (classifierResult, datingLabels[i],classifierResult==datingLabels[i])    if (classifierResult != datingLabels[i]):        errorCount += 1.0print numTestVecsprint "the total error rate is: %f" % (1-errorCount/float(numTestVecs))print errorCount

判断输入数据

def classifyPerson():a=0b=0c=0resultList=['not','pai','pay']datingDataMat,datingLabels=file2matrix('D:\MachingLearning\KNN\machingKNN1.0\draining1.txt')print datingDataMatnormMat,ranges,minVals=autoNorm(datingDataMat)datingDataMat1=file3matrix('D:\MachingLearning\KNN\machingKNN1.0\dest1.txt')normMat1,ranges1,minVals1=autoNorm(datingDataMat1)print len(normMat1)for i in range(len(normMat1)):    classifierResult=classify0((normMat1[i]-minVals)/ranges,normMat,datingLabels,5)    #print "result:",resultList[classifierResult-1]    if resultList[classifierResult-1]=='pai':        a+=1    if resultList[classifierResult-1]=='pay':        b+=1    if resultList[classifierResult-1]=='not':        c+=1print 'pai=',aprint 'pay=',bprint 'not=',c#return resultList[classifierResult-1]return a

函数调用

if __name__=="__main__":datingDataMat,datingLabels=file2matrix('D:\MachingLearning\KNN\machingKNN1.0\draining.txt')pictureshow(datingDataMat)#autoNorm(datingDataMat)#datingClassTest()classifyPerson()