Esempio n. 1
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    def test_classify_by_bag_of_model(self):
        listOPosts, listClasses = self.loadDataSet()
        myVocabList = bayes.createVocabList(listOPosts)

        trainMat=[]
        for postinDoc in listOPosts:
            trainMat.append(bayes.bagOfWords2VecMN(myVocabList, postinDoc))
        pVectDict, pCateDict = bayes.train(array(trainMat), listClasses)
        print pVectDict
        print pCateDict

        testEntry = ['love', 'my', 'dalmation']
        thisDoc = array(bayes.bagOfWords2VecMN(myVocabList, testEntry))
        print testEntry, 'classified as: ', bayes.classify(thisDoc, pVectDict, pCateDict)

        testEntry = ['stupid', 'garbage']
        thisDoc = array(bayes.bagOfWords2VecMN(myVocabList, testEntry))
        print testEntry, 'classified as: ', bayes.classify(thisDoc, pVectDict, pCateDict)
Esempio n. 2
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def localWords(feed1, feed0):
    docList=[]; classList = []; fullText =[]
    minLen = min(len(feed1['entries']),len(feed0['entries']))
    for i in range(minLen):
        wordList = bayes.textParse(feed1['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1) #NY is class 1
        wordList = bayes.textParse(feed0['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)

    vocabList = bayes.createVocabList(docList)
    top30Words = calcMostFreq(vocabList, fullText)

    for pairW in top30Words:
        if pairW[0] in vocabList:
            vocabList.remove(pairW[0])

    trainingSet = range(2 * minLen); testSet=[]
    # create test set
    for i in range(20):
        randIndex = int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del trainingSet[randIndex]

    trainMat=[]; trainClasses = []
    # train the classifier (get probs) train
    for docIndex in trainingSet:
        trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])

    pVectDict, pCateDict = bayes.train(array(trainMat), trainClasses)
    errorCount = 0
    # classify the remaining items
    for docIndex in testSet:
        wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIndex])
        if bayes.classify(array(wordVector), pVectDict, pCateDict) != classList[docIndex]:
            errorCount += 1

    print 'the error rate is: ', float(errorCount) / len(testSet)

    return vocabList, pVectDict
Esempio n. 3
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def spamTest():
    docList=[]; classList = []; fullText =[]
    for i in range(1, 26):
        wordList = bayes.textParse(open('email/spam/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)

        wordList = bayes.textParse(open('email/ham/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)

    vocabList = bayes.createVocabList(docList)

    trainingSet = range(50); testSet=[]
    # create test set
    for i in range(10):
        randIndex = int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del trainingSet[randIndex]

    trainMat=[]; trainClasses = []
    # train the classifier (get probs) train
    for docIndex in trainingSet:
        trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])

    pVectDict, pCateDict = bayes.train(array(trainMat), trainClasses)
    errorCount = 0
    # classify the remaining items
    for docIndex in testSet:
        wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIndex])
        if bayes.classify(array(wordVector), pVectDict, pCateDict) != classList[docIndex]:
            errorCount += 1
            print "classification error:", docList[docIndex]
    print 'the error rate is: ',float(errorCount) / len(testSet)