#coding=utf-8 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 #创建一个带有所有单词的列表 def createVocabList(dataSet):     vocabSet = set([])     for document in dataSet:         vocabSet = vocabSet | set(document)     return list(vocabSet)     def setOfWords2Vec(vocabList, inputSet):     retVocabList = [0] * len(vocabList)     for word in inputSet:         if word in vocabList:             retVocabList[vocabList.index(word)] = 1         else:             print 'word ',word ,'not in dict'     return retVocabList #另一种模型    def bagOfWords2VecMN(vocabList, inputSet):     returnVec = [0]*len(vocabList)     for word in inputSet:         if word in vocabList:             returnVec[vocabList.index(word)] += 1     return returnVec def trainNB0(trainMatrix,trainCatergory):     numTrainDoc = len(trainMatrix)     numWords = len(trainMatrix[0])     pAbusive = sum(trainCatergory)/float(numTrainDoc)     #防止多个概率的成绩当中的一个为0     p0Num = ones(numWords)     p1Num = ones(numWords)     p0Denom = 2.0     p1Denom = 2.0     for i in range(numTrainDoc):         if trainCatergory[i] == 1:             p1Num +=trainMatrix[i]             p1Denom += sum(trainMatrix[i])         else:             p0Num +=trainMatrix[i]             p0Denom += sum(trainMatrix[i])     p1Vect = log(p1Num/p1Denom)#处于精度的考虑，否则很可能到限归零     p0Vect = log(p0Num/p0Denom)     return p0Vect,p1Vect,pAbusive     def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):     p1 = sum(vec2Classify * p1Vec) + log(pClass1)    #element-wise mult     p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)     if p1 > p0:         return 1     else:         return 0         def testingNB():     listOPosts,listClasses = loadDataSet()     myVocabList = createVocabList(listOPosts)     trainMat=[]     for postinDoc in listOPosts:         trainMat.append(setOfWords2Vec(myVocabList, postinDoc))     p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))     testEntry = ['love', 'my', 'dalmation']     thisDoc = array(setOfWords2Vec(myVocabList, testEntry))     print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)     testEntry = ['stupid', 'garbage']     thisDoc = array(setOfWords2Vec(myVocabList, testEntry))     print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)         def main():     testingNB()     if __name__ == '__main__':     main()