都是在python3下面的:

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

postingList,classVec = loadDataSet()


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

vocabList = createVocabList(postingList)
vocabList.sort()
vocabList

#setOfWords2Vec(vocabList, postingList[0])

from numpy import * 

def trainNB0(trainMatrix,trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs)
#     print("pAbusive :")
#     print(pAbusive)
    p0Num = ones(numWords); 
#     print("p0Num :")
#     print(p0Num)
#     print(type(p0Num))
    p1Num = ones(numWords)      #change to ones() 
    p0Denom = 2.0; 
    p1Denom = 2.0                        #change to 2.0
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
#             print("trainMatrix[i] :")
#             print(trainMatrix[i])
#             print(type(trainMatrix[i]))
            p1Num += trainMatrix[i]
#             print("p1Num :")
#             print(p1Num)
#             print(type(p1Num))
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
#     print("p1Num/p1Denom :")
#     print(p1Num/p1Denom)
#     print(type(p1Num/p1Denom))
    p1Vect = log(p1Num/p1Denom)          #change to log()
    p0Vect = log(p0Num/p0Denom)          #change to log()
    return p0Vect,p1Vect,pAbusive

traimMat = []
for postinDoc in postingList:
    traimMat.append(setOfWords2Vec(vocabList, postinDoc))

#traimMat

p0V, p1V, pAb = trainNB0(traimMat, classVec)

pAb

p0V

p1V

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("thisDoc :")
    print(thisDoc)
    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)
    print(testEntry, "classified as: ", classifyNB(thisDoc,p0V,p1V,pAb))
   

testingNB()

def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

def textParse(bigString):    #input is big string, #output is word list
    import re
    regEx = re.compile("\\W")
    listOfTokens = regEx.split(bigString)
    #print(listOfTokens)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2] 

#textParse('ngaib faibga i aig baibgaigag abi baigba i')

   
def spamTest():
    docList=[]; classList = []; fullText =[]
    for i in range(1,26):
        wordList = textParse(open('email/spam/%d.txt' % i).read())
#         if i == 1:
#             print(wordList)
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('email/ham/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)#create vocabulary
    #trainingSet = range(50); 
    #print(vocabList)
    trainingSet = list(range(50))
    #print(trainingSet)
    testSet=[]           #create test set
    #随机的找10个 0-49的数字
    for i in range(10):
        randIndex = int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])  
    #print(testSet)
    trainMat=[]; trainClasses = []
    for docIndex in trainingSet:#train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
#     print("trainMat :")
#     print(trainMat)
#     print("trainClasses :")
#     print(trainClasses)
    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
    print(pSpam)
    errorCount = 0
    for docIndex in testSet:        #classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
            errorCount += 1
            print( "classification error",docList[docIndex])
    print('the error rate is: ',float(errorCount)/len(testSet))
    #return vocabList,fullText


spamTest()

import feedparser

ny = feedparser.parse("http://newyork.craigslist.org/stp/index.rss")

len(ny['entries'])

def calcMostFreq(vocabList,fullText):
    import operator
    freqDict = {}
    for token in vocabList:
        freqDict[token]=fullText.count(token)
    sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True) 
    return sortedFreq[:30]     

def stopWords():
    import re
    wordList =  open('stopword.txt').read() # see http://www.ranks.nl/stopwords
    return textParse(wordList)

def localWords(feed1,feed0):
    import feedparser
    docList=[]; classList = []; fullText =[]
    minLen = min(len(feed1['entries']),len(feed0['entries']))
    #print(minLen)  #4
    for i in range(minLen):
        wordList = textParse(feed1['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1) #NY is class 1
        wordList = textParse(feed0['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)#create vocabulary
    print(len(vocabList))
    
    #两种方法对词汇表进行剪枝
#     top30Words = calcMostFreq(vocabList,fullText)   #remove top 30 words
#     for pairW in top30Words:
#         if pairW[0] in vocabList: 
#             vocabList.remove(pairW[0])
#     print(len(vocabList))        
    stopWordList = stopWords()
    for stopWord in stopWordList:
        if stopWord in vocabList:
            vocabList.remove(stopWord)
    print(len(vocabList))      


    trainingSet = list(range(2*minLen)); 
    testSet=[]           #create test set
    for i in range(5):
        randIndex = int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat=[]; 
    trainClasses = []
    for docIndex in trainingSet:#train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
    errorCount = 0
    for docIndex in testSet:        #classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        classifiedClass = classifyNB(array(wordVector),p0V,p1V,pSpam)
        originalClass = classList[docIndex]
        result =  classifiedClass != originalClass
        if result:
            errorCount += 1
        print ('\n',docList[docIndex],'\nis classified as: ',classifiedClass,', while the original class is: ',originalClass,'. --',not result)
    print ('\nthe error rate is: ',float(errorCount)/len(testSet))
    return vocabList,p0V,p1V

ny = feedparser.parse("http://www.nasa.gov/rss/dyn/image_of_the_day.rss")
sf = feedparser.parse("http://sports.yahoo.com/nba/teams/hou/rss.xml")
#print((ny['entries'][0]["summary"]))
#print(len(sf['entries']))

#ny
#vocabList, psF, pNY = localWords(ny, sf)

#stopWords()

vocabList, psF, pNY = localWords(ny, sf)


def getTopWords(ny,sf):
    import operator
    vocabList,p0V,p1V=localWords(ny,sf)
    topNY=[]; topSF=[]
    for i in range(len(p0V)):
        if p0V[i] > -6.0 : topSF.append((vocabList[i],p0V[i]))
        if p1V[i] > -6.0 : topNY.append((vocabList[i],p1V[i]))
    sortedSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)
    print ("SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**")
    for item in sortedSF:
        print (item[0])
    sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)
    print ("NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**")
    for item in sortedNY:
        print( item[0])

getTopWords(ny,sf)