bayes python 源代码

#coding: utf-8#date: 2016-07-10#mail: [e-mail]#author: xinwangzhong -version 0.1from numpy import *def trainNB0(trainMatrix,trainCatergory):    #适用于二分类问题，其中一类的标签为1    #return    #p0Vect：标签为0的样本中，出现某个特征对应的概率    #p1Vect：标签为1的样本中，出现某个特征对应的概率    #pAbusive：标签为1的样本出现的概率    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: 在训练样本标签为0的数据中，所有特征的对应value值之和，为矩阵    #p1Num: 在训练样本标签为1的数据中，所有特征的对应value值之和，为矩阵    p0Num = ones(numWords)    p1Num = ones(numWords)    #p0Denom：在训练样本标签为0的数据中，所有特征的value值之和，为标量    #p1Denom：在训练样本标签为1的数据中，所有特征的value值之和，为标量    #为什么初始化为2？？    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])    #出于精度的考虑，否则很可能到限归零，change to log()    p1Vect = log(p1Num/p1Denom)    p0Vect = log(p0Num/p0Denom)    return p0Vect,p1Vect,pAbusivedef classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):    #element-wise mult,只算分子的log值，因为只需比较大小，所以正负无关    p1 = sum(vec2Classify * p1Vec) + log(pClass1)        p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)    if p1 > p0:        return 1    else:         return 0####################3#from numpy import *#import os#os.chdir(r"/home/luogan/lg/Python728/bayes/classical-machine-learning-algorithm-master/bayesian")#import bayesdef 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,classVecdef 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 returnVecdef 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 bagOfWords2VecMN(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in vocabList:            returnVec[vocabList.index(word)] += 1    return returnVecdef textParse(bigString):    #input is big string, #output is word list    import re    listOfTokens = re.split(r'\W*', bigString)    return [tok.lower() for tok in listOfTokens if len(tok) > 2] if __name__ == "__main__":    listOPosts,listClasses = loadDataSet()    myVocabList = createVocabList(listOPosts)    #print (myVocabList)    trainMat = []    for postinDoc in listOPosts:        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))    p0V,p1V,pAb = trainNB0(trainMat, listClasses)    testingNB()    # spamTest()