def spamDict():
docList = [
] # 切分好的词组成的列表[['his','xx','xx'],['xx','xx','xx'],['xx','xx','xx']]
classList = [] # 邮件的类别，垃圾邮件和正常邮件，垃圾邮件为1，正常邮件为0
for i in range(1, 26):
with open('ham/%d.txt' % i) as f:
wordList = textParse(f.read())
docList.append(wordList)
classList.append(1)
with open('spam/%d.txt' % i) as f:
wordList = textParse(f.read())
docList.append(wordList)
classList.append(0)
vocabList = Bayes.createVocabList(docList) # 将docList组成词典
'''
从50封电子邮件中随机选出10封作为测试集，剩下的作为训练集
'''
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del trainingSet[randIndex]
trainMarix = [] # 训练集数据组成的训练矩阵
trainingClass = [] # 训练数据集中的类别
for docIndex in trainingSet:
trainMarix.append(Bayes.setOfWord2Vector(vocabList, docList[docIndex]))
trainingClass.append(classList[docIndex])
pAb, p1v, p0v = Bayes.TrainingNB1(array(trainMarix), array(trainingClass))
errorCount = 0.0
for docIndex in testSet:
thisDoc = array(Bayes.setOfWord2Vector(vocabList, docList[docIndex]))
if classifyNB(array(thisDoc), p0v, p1v, pAb) != classList[docIndex]:
errorCount += 1
print 'the error rate is :', float(errorCount) / len(testSet)
