def classifyNavieBayesianTest():
wordsList, classTypes = bayes.loadDataSet()
inputTestWords = ['my', 'dog', 'has', 'flea', 'problems', 'help', 'please']
result = bayes.classifyNavieBayesian(wordsList, classTypes, inputTestWords)
print inputTestWords, ':', result
inputTestWords2 = ['love', 'stupid']
result2 = bayes.classifyNavieBayesian(wordsList, classTypes, inputTestWords2)
print inputTestWords2, ':', result2
def trainNavieBayesianTest():
wordsList, classTypes = bayes.loadDataSet()
vocaList = bayes.createVocabularyList(wordsList)
# 将feature对应的标记为0,1
trainVocabularyMattrix = []
for words in wordsList:
trainVocabularyMattrix.append(bayes.checkSignedFeatureList(vocaList, words))
# print np.array(trainVocabularyMattrix)
p_WiBasedOnClass0, p_WiBasedOnClass1, pAbusive = bayes.trainNavieBayesian(trainVocabularyMattrix, classTypes)
print p_WiBasedOnClass0, '\n'
print p_WiBasedOnClass1
print pAbusive
def createWordSetTest():
wordsList, classTypes = bayes.loadDataSet()
print wordsList
wordsetList = bayes.createVocabularyList(wordsList)
print wordsetList
return wordsetList
