def localWord(feed0,feed1):
minLen = min(len(feed1['entries']),len(feed0['entries']))
listOfPost = [];classVec = [];fullText = []
for i in range(minLen):
wordList = Bayes.textParse(feed1['entries'][i]['summary'])#数组从feed1开始
listOfPost.append(wordList)
fullText.extend(wordList)
classVec.append(1)
wordList = Bayes.textParse(feed0['entries'][i]['summary'])
listOfPost.append(wordList)
fullText.extend(wordList)
classVec.append(0)
vocabList = Bayes.creatVocabList(listOfPost)
top30Words = calMostFreq(vocabList,fullText)
for pairW in top30Words:
if pairW in vocabList:vocabList.remove(pairW)
trainingSet = range(2*minLen) ; dataSet = []
for i in range(20):
randIndex = int(np.random.uniform(len(trainingSet)))
dataSet.append(randIndex)
del(trainingSet[randIndex])
trainMat = [];trainClass = []
for docIndex in trainingSet:
trainMat.append(Bayes.bagOfWords2Vec(vocabList,listOfPost[docIndex]))
trainClass.append(classVec[docIndex])
p0V,p1V,pSpam = Bayes.trainNB0(np.array(trainMat),trainClass)
errorCount = 0.0
for docIndex in dataSet:
dataMat = Bayes.bagOfWords2Vec(vocabList,listOfPost[docIndex])
if Bayes.classifyNB(np.array(dataMat),p0V,p1V,pSpam) != classVec[docIndex]:
errorCount += 1
# print "the error rate is :",errorCount/float(len(dataSet))
return vocabList,p0V,p1V
