代码笔记--朴素贝叶斯

1.贝叶斯决策理论的核心思想：选择具有最高概率的决策。
2.贝叶斯公式

3.朴素贝叶斯的两个假设：
第一，特征之间相互独立；第二，每个特征同等重要。
4.使用python进行文本分类
4.1准备数据：从文本中构建词向量

#词表到向量的转换函数def loadDataSet():    positingList=[['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 代表侮辱性文字，0代表正常言论    return positingList,classVecdef createVocabList(dataSet):    vocabSet=set([])    #创建一个空集    for document in dataSet:        vocabSet=vocabSet|set(document) #创建两个集合的并集    return list(vocabSet)def setofWords2Vec(vocabList,inputSet):    returnVec=[0]*len(vocabList)    #创建一个其中所含元素都为0的向量    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

4.2训练算法：从词向量计算概率
伪代码：

计算每个类别中的文档数目对每篇训练文档：    对每个类别：        如果词条出现在文档中->增加该词条的计数值        增加所有词条的计数值    对每个类别：        对每个词条：            将该词条的数目除以总词条数目得到条件概率    返回每个类别的条件概率

#朴素贝叶斯分类器训练函数def trainNBO(trainMatrix,trainCategory):    numTrainDocs=len(trainMatrix)    numWords=len(trainMatrix[0])    pAbusive=sum(trainCategory)/float(numTrainDocs) #初始化概率    # p0Num=zeros(numWords);p1Num=zeros(numWords)    p0Num=ones(numWords);p1Num=ones(numWords)    # p0Denom=0.0;p1Denom=0.0    p0Denom = 2.0;p1Denom = 2.0    for i in range(numTrainDocs):   #向量相加        if trainCategory[i]==1:            p1Num+=trainMatrix[i]            p1Denom+=sum(trainMatrix[i])        else:            p0Num+=trainMatrix[i]            p0Denom+=sum(trainMatrix[i])    # p1Vect=p1Num/p1Denom    #change to log()    # p0Vect=p0Num/p0Denom    #change to log()    p1Vect=log(p1Num/p1Denom)    #change to log()   #对每个元素做除法    p0Vect=log(p0Num/p0Denom)    #change to log()    return p0Vect,p1Vect,pAbusive

4.3测试算法：根据显示情况修改分类器

def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):    p1=sum(vec2Classify*p1Vec)+log(pClass1) #元素相乘    p0=sum(vec2Classify*p0Vec)+log(1.0-pClass1)    if p1>p0:        return 1    else:        return 0def testingNB():    listOPosts,listClasses=loadDataSet()    myVocabList=createVocabList(listOPosts)    trainMat = []    for postinDoc in listOPosts:        trainMat.append(setofWords2Vec(myVocabList, postinDoc))    p0V, p1V, pAb = trainNBO(trainMat, 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)