Python Bayes

from numpy import *    def loadDataSet():    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],                 ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],                 ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],                 ['stop', 'posting', 'stupid', 'worthless', 'garbage'],                 ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],                 ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]    classVec = [0,1,0,1,0,1]    #1 is abusive, 0 not    return postingList,classVec                                    def createVocabList(dataSet):    vocabSet = set([])  #create empty set    for document in dataSet:        vocabSet = vocabSet | set(document) #union of the two sets    return list(vocabSet)  def setOfWords2Vec(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in vocabList:            returnVec[vocabList.index(word)] = 1        else: print "the word: %s is not in my Vocabulary!" % word    return returnVec  def trainNB0(trainMatrix,trainCategory):    #numTrainDocs训练集总条数    numTrainDocs = len(trainMatrix)    #每条训练数据总单词数    numWords = len(trainMatrix[0])    #侮辱类的概率(侮辱类占总训练数据的比例)    pAbusive = sum(trainCategory)/float(numTrainDocs)        #拉普拉斯平滑，所有单词一开始全部置为1，防止概率为0    #正常类向量置为1    p0Num = ones(numWords);    #侮辱类向量置为1        p1Num = ones(numWords)     #分母置为2        p0Denom = 2.0;     p1Denom = 2.0                   #遍历训练集数据        for i in range(numTrainDocs):        #该条训练数据为侮辱类        if trainCategory[i] == 1:            #侮辱类所含单词，次数加1            p1Num += trainMatrix[i]            #p1Denom侮辱类总词数            p1Denom += sum(trainMatrix[i])                    #该条训练数据为正常类        else:            #正常类所含单词，次数增加            p0Num += trainMatrix[i]            #p0Denom正常类总词数            p0Denom += sum(trainMatrix[i])       #数据取log，即单个单词的p(x1|c1)取log，防止下溢出            p1Vect = log(p1Num/p1Denom)             p0Vect = log(p0Num/p0Denom)     #返回正常类、侮辱类的类条件概率向量，p0Vect,p1Vect    #每一项为拉普拉斯平滑处理后词的类条件概率取log以2底    #返回侮辱类的概率pAbusive    return p0Vect,p1Vect,pAbusive def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):    #在对数空间中进行计算，属于哪一类的概率比较大就判为哪一类    p1 = sum(vec2Classify * p1Vec) + log(pClass1)        p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)       if p1 > p0:        return 1    else:         return 0 def testingNB():    #获得训练数据，类别标签    listOPosts,listClasses = loadDataSet()    #创建词典    myVocabList = createVocabList(listOPosts)    #构建矩阵，存放训练数据    trainMat=[]        #遍历原始数据，转换为词向量，构成数据训练矩阵    for postinDoc in listOPosts:        #数据转换后存入数据训练矩阵trainMat中        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))    #训练分类器    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))        #测试    testEntry = ['love', 'my', 'dalmation']    #测试数据转为词向量    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))    #输出分类结果    print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)        #测试    testEntry = ['stupid', 'garbage']    #测试数据转为词向量    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))    #输出分类结果    print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)  #文档词袋模型，词向量 值为该词在本篇文章中出现的次数def bagOfWords2VecMN(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in vocabList:            returnVec[vocabList.index(word)] += 1    return returnVec #准备数据，按空格切分出词 #单词长度小于或等于2的全部丢弃def textParse(bigString):        import re    listOfTokens = re.split(r'\W*', bigString)    #tok.lower() 将整个词转换为小写    return [tok.lower() for tok in listOfTokens if len(tok) > 2]      def spamTest():    #文章按篇存放    docList=[];     #存放文章类别    classList = [];    #存放所有文章内容        fullText =[]    for i in range(1,26):        #读取垃圾邮件        wordList = textParse(open('D:/email/spam/%d.txt' % i).read())        #docList按篇存放文章        docList.append(wordList)        #fullText邮件内容存放到一起        fullText.extend(wordList)        #垃圾邮件类别标记为1        classList.append(1)                #读取正常邮件        wordList = textParse(open('D:/email/ham/%d.txt' % i).read())        docList.append(wordList)        fullText.extend(wordList)        #正常邮件类别标记为0        classList.append(0)            #创建词典        vocabList = createVocabList(docList)    #训练集共50篇文章    trainingSet = range(50);    #创建测试集    testSet=[]    #随机选取10篇文章为测试集，测试集中文章从训练集中删除        for i in range(10):        #0-50间产生一个随机数        randIndex = int(random.uniform(0,len(trainingSet)))        #从训练集中找到对应文章，加入测试集中        testSet.append(trainingSet[randIndex])        #删除对应文章        del(trainingSet[randIndex])              #准备数据，用于训练分类器        trainMat=[]; #训练数据    trainClasses = [] #类别标签        #遍历训练集中文章数据    for docIndex in trainingSet:        #每篇文章转为词袋向量模型，存入trainMat数据矩阵中        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))        #trainClasses存放每篇文章的类别        trainClasses.append(classList[docIndex])    #训练分类器    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))        #errorCount记录测试数据出错次数    errorCount = 0    #遍历测试数据集，每条数据相当于一条文本    for docIndex in testSet:        #文本转换为词向量模型            wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])        #模型给出的分类结果与本身类别不一致时，说明模型出错，errorCount数加1        if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:            errorCount += 1            #输出出错的文章            print "classification error",docList[docIndex]        #输出错误率，即出错次数/总测试次数    print 'the error rate is: ',float(errorCount)/len(testSet)            #return vocabList,fullText     if __name__ == "__main__":    #获取数据    listOPosts,listClasses = loadDataSet()      #构建词典    myVocabList = createVocabList(listOPosts)    trainMat = []    for postinDoc in listOPosts:        #构建训练矩阵        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))    p0V,p1V,pAb = trainNB0(trainMat, listClasses)    #网站论坛内容分类        testingNB()        #垃圾邮件分类    spamTest()