机器学习python，k近邻分类器，三维作图

#-*-coding:utf-8 -*-from numpy import *import Operatorimport matplotlibimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D#读取文件数据def file2matrix(filename):    fr=open(filename)#打开文件    arrayOLines=fr.readlines()#将文件读入一个字符串列表，在列表中每个字符串就是一行    numberOFlines=len(arrayOLines)#读入字符串列表的数量，即文件的行数    returnMat=zeros((numberOFlines,3))#创建numberOFlines行3列的numpy矩阵    classLabelVector=[]#创建标签数组    index=0    for line in arrayOLines:        line=line.strip()#删除每行两侧的空格        listFormLine=line.split('/t')#将每行的字符串列表以‘/t’为间隔分为序列        returnMat[index,:]=listFormLine[0:3]#将每一行数据存入returnMat数组中        classLabelVector.append(int(listFormLine[-1]))#将每一行的最后一列即标签存入classLabelVector中        index+=1    return returnMat,classLabelVector#返回样本特征矩阵与标签向量#归一化数据def autoNorm(dataset):    minVals=dataset.min(0)#列中最小值    maxVals=dataset.max(0)#列中的最大值    ranges=maxVals-minVals    normDataSet=zeros(shape(dataset))#创建与样本特征矩阵同大小的数值全是0的矩阵    m=dataset.shape[0]#m是dataset的列数，即样本特征的维数    normDataSet=dataset-tile(minVals,(m,1))#tile()是将minVals复制成m行3列，即与dataset同大小的矩阵    normDataSet=normDataSet/tile(ranges,(m,1))    return normDataSet,ranges,minVals#返回归一化的样本特征矩阵，范围，每列最小值#K近邻分类def classify(inX,dataSet,labels,k):    dataSetSize=dataSet.shape[0]#读取样本的特征矩阵的维数    diffMat=tile(inX,(dataSetSize,1))-dataSet#计算测试数据与每一个样本特征矩阵的欧氏距离    sqDiffMat=diffMat**2    sqDistances=sqDiffMat.sum(axis=1)#每一行的相加    distances=sqDistances**0.5    sortedDistIndicies=distances.argsort()#测试数据与每一个样本特征矩阵的欧氏距离从小到大排列后，将原样本的索引值赋值给sortedDistIndicies    classCount={}#创建字典    for i in range(k):        voteIlabel=labels[sortedDistIndicies[i]]#将sortedDistIndicies相对应的标签赋值给voteIlabel        classCount[voteIlabel]=classCount.get(voteIlabel,0)+1#get是取字典里的元素，                              #如果之前这个voteIlabel是有的，那么就返回字典里这个voteIlabel里的值，                              #如果没有就返回0（后面写的），这行代码的意思就是算离目标点距离最近的k个点的类别，                        #这个点是哪个类别哪个类别就加1        sortedClassCount=sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)#key=operator.itemgetter(1)的意思是按照字典里的第一个排序，                                          #{A:1,B:2},要按照第1个（AB是第0个），即‘1’‘2’排序。reverse=True是降序排序        return sortedClassCount[0][0]#返回发生频率最高的元素标签def datingClassTest():      hoRatio=0.10      datingDataMat,datingLabels=file2matrix(r'F:/ML_use/datingTestSet2.txt')      normMat,ranges,minVals=autoNorm(datingDataMat)      m=normMat.shape[0]      numTestVecs=int(m*hoRatio)      errorCount=0.0      for i in range(numTestVecs):         classifierResult=classify(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 small doses','in large doses']   percentTats=float(raw_input("percentage of time spent playing vidio games?"))   ffMines=float(raw_input("frequent flier miles earned per year?"))   iceCream=float(raw_input("liters of ice cream consumed per year?"))   datingDataMat,datingLabels=file2matrix(r'F:/ML_use/datingTestSet2.txt')   normMat,ranges,minVals=autoNorm(datingDataMat)   inArr=array([ffMines,percentTats,iceCream])   classifierResult=classify((inArr-minVals)/ranges,normMat,datingLabels,3)   print "you will probably like this person:",resultList[classifierResult-1]   dataArr = array(datingDataMat)   n = shape(dataArr)[0]   xcord1 = []; ycord1 = [];zcord1=[]   xcord2 = []; ycord2 = [];zcord2=[]   xcord3 = []; ycord3 = [];zcord3=[]   for i in range(n):      if int(datingLabels[i])== 1:         xcord1.append(dataArr[i,0]); ycord1.append(dataArr[i,1]);zcord1.append(dataArr[i,2])      elif int(datingLabels[i])== 2:         xcord2.append(dataArr[i,0]); ycord2.append(dataArr[i,1]);zcord2.append(dataArr[i,2])      elif int(datingLabels[i])== 3:         xcord3.append(dataArr[i,0]); ycord3.append(dataArr[i,1]);zcord3.append(dataArr[i,2])   fig = plt.figure()   ax = fig.add_subplot(111, projection='3d')   ax.set_title('KNN')   type1=ax.scatter(xcord1, ycord1,zcord1, s=30, c='red', marker='s')   type2=ax.scatter(xcord2, ycord2,zcord2, s=30, c='green',marker='o')   type3=ax.scatter(xcord3, ycord3,zcord3, s=30, c='b',marker='+')   ax.scatter(inArr[0], inArr[1],inArr[2], s=100, c='k', marker='8')   plt.figtext(0.02,0.92,'class1:Did Not Like',color='red')   plt.figtext(0.02,0.90,'class2:Liked in Small Doses',color='green')   plt.figtext(0.02,0.88,'class3:Liked in Large Doses',color='b')   ax.set_zlabel('frequent flier miles earned per year')   ax.set_ylabel('percentage of time spent playing vidio games')   ax.set_xlabel('liters of ice cream consumed per year')   plt.show()classifyPerson()