def spamTest():
docList = []; classList=[]; fullText=[]
for i in range(1, 26):
wordList = textParse(open('./email/spam/%d.txt' % i).read())
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)
vocalList = bayes.createVocabList(docList)
trainingSet = range(50); testSet=[]
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat=[];trainClass=[];
for docIndex in trainingSet:
trainMat.append(bayes.addWordInList(vocalList, docList[docIndex]))
trainClass.append(classList[docIndex])
p0V,p1V,ps = bayes.trainNB0(array(trainMat), array(trainClass))
errorCount = 0
for doc in testSet:
wordV = bayes.addWordInList(vocalList, docList[doc])
if bayes.classifyNB(array(wordV), p0V, p1V, ps) != classList[doc]:
errorCount+=1
print 'err count is %d' % errorCount
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
wordList = textParse(open('./email/spam/%d.txt' % i).read())
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)
vocalList = bayes.createVocabList(docList)
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClass = []
for docIndex in trainingSet:
trainMat.append(bayes.addWordInList(vocalList, docList[docIndex]))
trainClass.append(classList[docIndex])
p0V, p1V, ps = bayes.trainNB0(array(trainMat), array(trainClass))
errorCount = 0
for doc in testSet:
wordV = bayes.addWordInList(vocalList, docList[doc])
if bayes.classifyNB(array(wordV), p0V, p1V, ps) != classList[doc]:
errorCount += 1
print 'err count is %d' % errorCount
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
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)
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainingMat = []
trainingClasses = []
for docIndex in trainingSet:
trainingMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
trainingClasses.append(classList[docIndex])
p0V, p1V, pSpam = trainNBO(trainingMat, trainingClasses)
errorCount = 0
for docIndex in testSet:
wordVector = setOfWords2Vec(vocabList, docList[docIndex])
if classifyNB(wordVector, p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print ("the error rate is: ", float(errorCount)/len(testSet))
def spamTest():
docList = []; classList = []; fullText = []
for i in range(1,26):
wordList = textParse(open('email/spam/%d.txt' %i).read())
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 = bayes.createVocabList(docList)
# range对象不支持del，所以要转成list
trainingSet = list(range(50)); testSet = []
# 随机取10组数据作为测试机
for i in range(10): # 循环10次
# 在0到49之间（包括0,49）取随机整数
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex]) # 删除数组的引用
# 把剩下的40组数据作为训练集
trainMat = []; trainClasses = []
for docIndex in trainingSet: # 由于上面删除了10个引用，还剩40个
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNBO(array(trainMat), array(trainClasses))
errorCount = 0
for docIndex in testSet:
# 转成词向量
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(wordVector, p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print('the error rate is: ', float(errorCount)/len(testSet))
def testingNB():
# 1. 加载数据集
listOPosts, listClasses = bayes.loadDataSet()
print('listOPosts: ', listOPosts,
'\n************************************\nlistClasses: ', listClasses)
# 2. 创建单词集合
myVocabList = bayes.createVocabList(listOPosts)
# 3. 计算单词是否出现并创建数据矩阵
trainMat = []
for postinDoc in listOPosts:
# 返回m * len(myVocabList)的矩阵，记录的都是0，1信息
# print('postinDoc:', postinDoc)
trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))
# 4. 训练数据
p0V, p1V, pAb = trainNB0(np.array(trainMat), np.array(listClasses))
# 5. 测试数据
testEntry = ['love', 'my', 'dalmatioin']
thisDoc = np.array(bayes.setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))
testEntry = ['stupid', 'garbage']
thisDoc = np.array(bayes.setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
# 导入并解析文本
wordList = textParse(open('email/spam/%d.txt' % i).read())
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 = bayes.createVocabList(docList)
trainingSet = range(50)
testSet = []
# 随机构建训练集
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
# 对测试集分类
for docIndex in testSet:
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is:', float(errorCount) / len(testSet)
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
wordList = textParse(open('xxx/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('xxx/%d,txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
for docIndex in testSet:
wordVector = bayes.setOfWord2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount) / len(testSet)
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,errors='ignore').read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
dataSet = list(range(50))
testSet = []
for i in range(10):
randIndex = int(np.random.uniform(0, len(dataSet)))
testSet.append(dataSet[randIndex])
del(dataSet[randIndex])
trainMat = []
trainClasses = []
for doc in dataSet:
trainMat.append(bayes.bagOfWord2Vec(vocabList, docList[doc]))
trainClasses.append(classList[doc])
pAbusive, p1Vect, p0Vect = bayes.trainNB0(trainMat, trainClasses)
errorCount = 0.0
for i in testSet:
vec2Classify = bayes.bagOfWord2Vec(vocabList, docList[i])
if bayes.classifyNB(vec2Classify, p1Vect, p0Vect, pAbusive) != classList[i]:
errorCount += 1
return errorCount/10
def test_bagOfWords2VecMN(self):
listOPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOPosts)
features = bayes.bagOfWords2VecMN(myVocabList, listOPosts[0])
expected = [
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1
]
self.assertEqual(features, expected)
def test_train():
listOposts,listClasses=bayes.loadDataSet()
myVocabList=bayes.createVocabList(listOposts)
trainMat=[]
for postinDoc in listOposts:
trainMat.append(bayes.setOfWords2Vec(myVocabList,postinDoc))
p0v,p1v,pab=bayes.trainNB0(trainMat,listClasses)
print p1v
def test_createVocabList(self):
dataSet = [
['stupid', 'garbage'],
['love', 'puppies']
]
expected = ['stupid', 'garbage', 'love', 'puppies']
word_list = bayes.createVocabList(dataSet)
self.assertTrue(set(word_list) == set(expected))
# don't want duplicate elements
self.assertTrue(len(word_list) == len(expected))
def test_setOfWords2Vec(self):
# listOPosts is actually...
# listClasses is actually a list of labels for the data in listOPosts
listOPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOPosts)
features = bayes.setOfWords2Vec(myVocabList, listOPosts[0])
expected = [
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1
]
self.assertEqual(features, expected)
def test_createVocablist(self):
data_set, _ = bayes.loadDataSet()
vocab_list = bayes.createVocabList(data_set)
print("\n vocab_list == %s" % (vocab_list))
# 根据数据集第0行输出对应的向量表
# （即，第0行中所有单词，在整个data_set词汇表中出现的单词位置设置为1）
vec = bayes.setOfWords2Vec(vocab_list, data_set[0])
print("\n vec == %s" % (vec))
vec = bayes.setOfWords2Vec(vocab_list, data_set[3])
print("\n vec == %s" % (vec))
def test_train_nb(self):
data_set, listClasses = bayes.loadDataSet()
vocab_list = bayes.createVocabList(data_set)
print("\n vocab_list == %s" % (vocab_list))
trainMat = []
for postinDoc in data_set:
trainMat.append(bayes.setOfWords2Vec(vocab_list, postinDoc))
p0Vect, p1Vect, pAbusive = bayes.trainNB0(trainMat, listClasses)
print("\n p0Vect == %s\n p1Vect == %s\n pAbusive == %s\n" %
(p0Vect, p1Vect, pAbusive))
def localWords(feed1, feed0):
import feedparser
docList = []
classList = []
fullText = []
minLen = min(len(feed1['entries']), len(feed0['entries']))
# print('minlen=%d'%minLen)
for i in range(minLen):
# visit one rss source every time
wordList = bayes.textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
# visit rss 0
wordList = bayes.textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
# remove most frequent words
vocabList = bayes.createVocabList(docList)
top30words = calcMostFreq(vocabList, fullText)
for pairW in top30words:
if pairW[0] in vocabList:
vocabList.remove(pairW[0])
trainingSet = list(range(2 * minLen))
testSet = []
for i in range(20):
randIdx = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIdx])
del (trainingSet[randIdx])
trainMat = []
trainClasses = []
for docIdx in trainingSet:
# print("doc idx:%d, len=%d" %( docIdx, len(docList)))
trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIdx]))
trainClasses.append(classList[docIdx])
p0V, p1V, pSpam = bayes.trainNB0(numpy.array(trainMat),
numpy.array(trainClasses))
errorCount = 0
for docIdx in testSet:
wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIdx])
if bayes.classifyNB(numpy.array(wordVector), p0V, p1V,
pSpam) != classList[docIdx]:
errorCount += 1
print('the error rate is: %.2f' % (float(errorCount) / len(testSet)))
return vocabList, p0V, p1V
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']
tesDoc = array(setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as: ', classifyNB(tesDoc, p0V, p1V, pAb))
testEntry = ['stupid', 'garbage']
tesDoc = array(setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as: ', classifyNB(tesDoc, p0V, p1V, pAb))
def testSimpTrain():
listOPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOPosts)
trainMat = []
for postinDoc in listOPosts:
trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))
print "trainMat:", trainMat
print "listClasses:", listClasses
p0V,p1V,pAb = bayes.trainNB0(trainMat, listClasses)
print "pAb:",pAb
print "p0V:",p0V
print "p1V:",p1V
def testingNB():
listOPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOPosts)
trainMat = []
for postinDoc in listOPosts:
trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))
p0V,p1V,pAb = bayes.trainNB0(trainMat, listClasses)
testEntry = ['love', 'my', 'dalmation', 'stupid']
thisDoc = array(bayes.setOfWords2Vec(myVocabList, testEntry))
print testEntry,'classified as: ', bayes.classifyNB(thisDoc, p0V, p1V, pAb)
testEntry = ['quit', 'stupid']
thisDoc = array(bayes.setOfWords2Vec(myVocabList, testEntry))
print testEntry,'classified as: ', bayes.classifyNB(thisDoc, p0V, p1V, pAb)
def localWords(feed1, feed0):
docList = []
classList = []
fullText = []
minLen = min(len(feed1['entries']), len(feed0['entries']))
for i in range(minLen):
wordList = st.textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = st.textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
#remove top frequent words
top30Words = calcMostFreq(vocabList, fullText)
for pairW in top30Words:
if pairW[0] in vocabList: vocabList.remove(pairW[0])
#build training and testing set
trainingSet = range(2*minLen)
testSet = []
for i in range(20):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
#training
p0V, p1V, pLocal = bayes.trainNB0(array(trainMat), array(trainClasses))
#testing
errorCount = 0
for docIndex in testSet:
wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V, pLocal) != classList[docIndex]:
errorCount+=1
print 'the error rate is: ', float(errorCount)/len(testSet)
return vocabList, p0V, p1V
def spamTest():
docList = []; classList = []; fullText = []
# 读取spam下面的26个文件
for i in range(1, 26):
# 垃圾邮件的分类
wordList = textParse(open('./data/spam/%d.txt' % i).read())
# 添加到docList中
docList.append(wordList)
# extend是将list1的元素添加进来
fullText.extend(wordList)
classList.append(1)
# 正常邮件的分类
wordList = textParse(open('./data/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
# 创建词汇表：需要文档列表 postingList。 读取所有的txt并进行词汇切分
vocabList = bayes.createVocabList(docList)
docMartix = np.zeros((50, len(vocabList)))
i = 0
for document in docList:
# 需要将每个文档进行转换成向量
docVec = bayes.setOfWords2Vec(vocabList, document)
# 添加类别
docMartix[i, :] = docVec
i += 1
print('词汇表长度：', len(vocabList))
print('词汇表内容：', vocabList)
print('文档内容：', docList[0], '\n词汇数', len(docList[0]))
print('文档矩阵：', docMartix[0], '\n词汇数', sum(docMartix[0]))
# 训练贝叶斯
p0Vec, p1Vec, pAbusive = bayes.trainNBC(docMartix, classList)
# print(p1Vec)
# print(p0Vec)
# print(pAbusive)
# 测试函数
i = 0
errorCount = 0
for document in docList:
testVec = bayes.setOfWords2Vec(vocabList, document)
testClass = bayes.classifyNBC(np.array(testVec), p0Vec, p1Vec, pAbusive)
if testClass != classList[i]:
errorCount += 1
i += 1
print('error percent: ', errorCount/float(len(docList)))
def testingNB():
postList, classList = bayes.loadDataSet()
myVocabList = bayes.createVocabList(postList)
trainMat = []
for post in postList:
trainMat.append(bayes.setOfWords2Vec(myVocabList, post))
p0V, p1V, pAb = bayes.trainNB0(trainMat, classList)
testEntry = ['love', 'my', 'dalmation']
thisDoc = bayes.setOfWords2Vec(myVocabList, testEntry)
print testEntry, 'classified as: ', bayes.classifyNB(
thisDoc, p0V, p1V, pAb)
testEntry = ['stupid', 'garbage']
thisDoc = bayes.setOfWords2Vec(myVocabList, testEntry)
print testEntry, 'classified as: ', bayes.classifyNB(
thisDoc, p0V, p1V, pAb)
def test_trainNBO(self):
listOPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOPosts)
trainMat = [] # list of lists, e.g., [[...], ..., [...]]
for postinDoc in listOPosts:
trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))
# this is interesting as the names sent to the funtion imply
# different types than the names received by the function.
# Compare sending trainCategory to receiving listClasses.
# There isn't even a hint of meaning between those two names
# at the program (self-referentiall) perspective.
# p0Vect, p1Vect, pAbusive = trainNBO(trainMatrix, trainCategory)
p0V, p1V, pAb = bayes.trainNBO(trainMat, listClasses)
# print p0V, p1V, pAb
self.assertAlmostEqual(pAb, 0.5)
def localWord(feed1, feed0):
docList = []
classList = []
fullText = []
minLength = min(len(feed1['entries']), len(feed0['entries']))
for i in range(minLength):
wordList = bayes.textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = bayes.textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
top30words = calMostFeq(vocabList, fullText)
for pairW in top30words:
if (pairW[0] in vocabList):
vocabList.remove(pairW[0])
trainingSet = range(2 * minLength)
testSet = []
for i in range(20):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMatrix = []
trainClass = []
for docIndex in trainingSet:
trainMatrix.append(bayes.bagOfWordsToVec(vocabList, docList[docIndex]))
trainClass.append(classList[docIndex])
p0V, p1V, pSapm = bayes.trainNB0(trainMatrix, trainClass)
errorCount = 0
for docIndex in testSet:
calculatedClass = bayes.classifyNB(
bayes.bagOfWordsToVec(vocabList, docList[docIndex]), p0V, p1V,
pSapm)
if (calculatedClass != classList[docIndex]):
errorCount += 1
print "error rate is: ", float(errorCount) / len(testSet)
return vocabList, p0V, p1V
def spamTest():
'''
对贝叶斯垃圾邮件分类器进行自动化处理
对测试集中的每封邮件进行分类，若邮件分类错误，测错误数加1，最后返回总的错误百分比
'''
docList = []
classList = []
fullText = []
for i in range(1, 26):
#切分，解析数据，并归类为1的类别
wordList = textParse(open('email/spam/%d.txt' % i).read())
docList.append(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
#创建词汇表
vocabList = bayes.createVocabList(docList)
trainingSet = list(range(50))
testSet = []
#随机取10个邮件测试
for i in range(10):
#random.uniform(x,y)随机产生一个范围为x-y的实数
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.setWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
errorDoc = []
for docIndex in testSet:
wordVector = bayes.setWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
errorDoc.append(docList[docIndex])
print(vocabList)
print(trainMat)
print('the errorCount is: ', errorCount)
print('the testSet length is: ', len(testSet))
print('the error rate is； ', float(errorCount) / len(testSet))
print(errorDoc)
def localWords(feed1, feed0):
import feedparser
docList = []
classList = []
fullText = []
minLen = min(len(feed1['entries']), len(feed0['entries']))
for i in range(minLen):
# 每次访问一条RSS源
wordList = bayes.textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(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)
# 去掉出现频数最高的词
for pairW in top30Words:
if pairW[0] in vocabList:
vocabList.remove(pairW[0])
trainingSet = list(range(2 * minLen))
testSet = []
for i in range(20):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
for docIndex in testSet:
wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print('the error rate is:', float(errorCount) / len(testSet))
return vocabList, p0V, p1V
def testingNB():
listOPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOPosts)
print(myVocabList)
trainMat = []
for postinDoc in listOPosts:
trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))
p0V, p1V, pAb = bayes.trainNB0(trainMat, listClasses)
testEntry = ['love', 'my', 'dalmation']
print
thisDoc = np.array(bayes.setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))
testEntry = ['stupid', 'garbage']
thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))
def testNB():
listOPosts, listClasses = bayes.loadDataSet() #加载数据
myVocabList = bayes.createVocabList(listOPosts)
trainMat = []
for postinDoc in listOPosts:
trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))
p0V, p1V, pAb = bayes.trainNB0(trainMat, listClasses)
resultLabel = {0: 'Not garbage', 1: 'Garbage'}
testEntry = ['love', 'my', 'dalmation']
thisDoc = array(bayes.setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as:',
resultLabel[bayes.classifyNB(thisDoc, p0V, p1V, pAb)])
testEntry = ['stupid', 'garbage']
thisDoc = array(bayes.setOfWords2Vec(myVocabList, testEntry))
print(testEntry, 'classified as:',
resultLabel[bayes.classifyNB(thisDoc, p0V, p1V, pAb)])
def localWords(feed1, feed0): # 两份RSS文件分别经feedparser解析，得到2个字典
docList = [] # 一条条帖子组成的List, 帖子拆成了单词
classList = [] # 标签列表
fullText = [] # 所有帖子的所有单词组成的List
# entries条目包含多个帖子，miNLen记录帖子数少的数目，怕越界
minLen = min(len(feed1['entries']), len(feed0['entries']))
for i in range(minLen):
wordList = bayes.textParse(feed1['entries'][i]['summary']) # 取出帖子内容，并拆成词
docList.append(wordList) # ['12','34'].append(['56','78']) ==> [ ['12','34'], ['56','78'] ]
fullText.extend(wordList) # ['12','34'].extend(['56','78']) ==> ['12','34','56','78']
classList.append(1) # 纽约的标签是1
wordList = bayes.textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0) # 旧金山的标签是0
vocabList = bayes.createVocabList(docList) # 创建词汇表
# 从fulltext中找出最高频的30个单词，并从vocabList中去除它们
top30Words = calcMostFreq(vocabList, fullText)
for (word, count) in top30Words:
if word in vocabList:
vocabList.remove(word)
trainingSet = range(2 * minLen);
testSet = [] # 创建训练集、测试集
for i in range(minLen / 10): # 随机选取10%的数据，建立测试集
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = [];
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIndex])) # 将训练集中的每一条数据，转化为词向量
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(np.array(trainMat), np.array(trainClasses)) # 开始训练
# 用测试数据，测试分类器的准确性
errorCount = 0
for docIndex in testSet:
wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIndex])
if bayes.classifyNB(np.array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount) / len(testSet)
return vocabList, p0V, p1V
def spamTest():
"""
将文件夹spam和ham中分别的25篇右键导入解析为词列表，再构建一个测试集与训练集,
50篇中再随机选10篇作为测试集，其余20篇作为测试集（留存交叉验证）
:return:
"""
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 = list(range(50))
testSet = []
for i in range(10): #随机选出10篇
randIndex = int(random.uniform(
0,
len(trainingSet))) #random.uniform(x, y) 方法将随机生成一个实数，它在 [x,y] 范围内。
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet: #遍历训练集中所有的文档
trainMat.append(bayes.bagOfWords2VecMN(vocabList,
docList[docIndex])) #构建词向量
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat),
array(trainClasses)) #计算分类所需的概率
errorCount = 0
for docIndex in testSet: #遍历测试集
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print('the error rate is :', float(errorCount) / len(testSet))
return vocabList, p0V, p1V
def spamTest():
docList=[]; classList = []; fullText =[]
for i in range(1,26):
str=open('email/spam/%d.txt' % i).read()
print "str"
print str
wordList = bayes.textParse(str)
print "wordlist"
print wordList
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)
print "doclist"
print len(docList)
print docList
print "fulllist"
print len(fullText)
print fullText
print classList
vocabList = bayes.createVocabList(docList)#create vocabulary
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 = []
for docIndex in trainingSet:#train the classifier (get probs) trainNB0
trainMat.append(bayes.bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = bayes.trainNB0(array(trainMat),array(trainClasses))
errorCount = 0
for docIndex in testSet: #classify the remaining items
wordVector = bayes.bagOfWords2VecMN(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
errorCount += 1
print "classification error",docList[docIndex]
print 'the error rate is: ',float(errorCount)/len(testSet)
def spamTest():
docList = []
classList = []
fullText = []
# parse text from email
for i in range(1, 26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
#parse text
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
#build training set and test set
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
#select testset and remove the testset from all dataset
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
#classify and test precision
errorCount = 0
for docIndex in testSet:
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount) / len(testSet)
def localWords(feed1, feed0):
docList = []
classList = []
fullText = []
minLen = min(len(feed1['entries']), len(feed0['entries']))
for i in range(minLen):
wordList = textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)
top30Words = calcMostFreq(vocabList, fullText)
for pairW in top30Words:
if pairW[0] in vocabList:
vocabList.remove(pairW[0])
trainingSet = list(range(2 * minLen))
testSet = []
for i in range(20):
randomIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randomIndex])
del trainingSet[randomIndex]
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bagOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = trainNB0(trainMat, trainClasses)
errorCount = 0
for docIndex in testSet:
wordVector = bagOfWords2Vec(vocabList, docList[docIndex])
if classifyNB(wordVector, p0V, p1V, pSpam) != classList[docIndex]:
print(docList[docIndex])
errorCount += 1
print('the error rate is: ', float(errorCount) / len(testSet))
return vocabList, p0V, p1V
def localWords(feed1, feed0):
import feedparser
docList = []
classList = []
fullText = []
minLen = min(len(feed1['entries']), len(feed0['entries']))
print feed1['entries']
print 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) #create vocabulary
top30Words = calcMostFreq(vocabList, fullText) #remove top 30 words
for pairW in top30Words:
if pairW[0] in vocabList: vocabList.remove(pairW[0])
trainingSet = list(range(2 * minLen))
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 = []
for docIndex in trainingSet: #train the classifier (get probs) trainNB0
trainMat.append(bayes.bagOfWord2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
for docIndex in testSet: #classify the remaining items
wordVector = bayes.bagOfWord2VecMN(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print('the error rate is: ', float(errorCount) / len(testSet))
return vocabList, p0V, p1V
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
try:
wordList = textParse(
open('data/Ch04/email/spam/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('data/Ch04/email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
except Exception as e:
print(str(e))
traceback.print_exc()
print(i)
exit()
vocabList = createVocabList(docList)
trainingSet = list(range(50))
testSet = []
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del trainingSet[randIndex]
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
for docIndex in testSet:
wordVector = setOfWords2Vec(vocabList, docList[docIndex])
if classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print(docList[docIndex])
print('the error rate is: ', float(errorCount) / len(testSet))
def run():
print 'begin--->run()'
postingList, classVec = bayes.loadDataSet()
myVocabList = bayes.createVocabList(postingList)
# print myVocabList
# print bayes.words2Vec(myVocabList,postingList[0])
# trainMat = []
# for postinDoc in postingList:
# trainMat.append(bayes.words2Vec(myVocabList, postinDoc))
# p0V, p1V, pAb = bayes.trainNB0(trainMat, classVec)
# bayes.testingNB()
# bayes.spamTest()
# print pAb
# print p0V
# print p1V
import feedparser
ny = feedparser.parse(
'https://newyork.craigslist.org/search/res?format=rss')
sf = feedparser.parse('https://sfbay.craigslist.org/search/apa?format=rss')
# bayes.localWords(ny,sf)
bayes.getTopWords(ny, sf)
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
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 = bayes.createVocabList(docList)
trainingSet = range(50)
testSet = []
# randomly split data set into 2 sets: test set, and training set
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet))) # random int 0~len
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex]) # split
trainMat = [];
trainClasses = [];
for docIndex in trainingSet:
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
for docIndex in testSet:
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print "error word: %s" % (docList[docIndex])
print "error rate is: %f", float(errorCount) / len(testSet)
def spamTest():
"""
对贝叶斯垃圾邮件分类器进行自动化处理
:return:
"""
docList = []
classList = []
fullText = []
for i in range(1, 26):
# 1. 导入并解析文本文件
wordList = textParse(open('email/spam/%d.txt' % i).read())
docList.append(wordList)
fullText.append(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.append(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
trainingSet = range(50)
testSet = []
for i in range(10):
# 2. 随机构建训练集
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 = bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
# 3. 对测试集分类
for docIndex in testSet:
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount) / len(testSet)
def spamTest():
docList = []
classList = []
fullText = []
# parse text from email
for i in range(1,26):
wordList = textParse(open('email/spam/%d.txt' %i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
#parse text
wordList = textParse(open('email/ham/%d.txt' %i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
#build training set and test set
trainingSet = range(50);
testSet =[]
for i in range(10):
randIndex = int(random.uniform(0, len(trainingSet)))
#select testset and remove the testset from all dataset
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))
#classify and test precision
errorCount = 0
for docIndex in testSet:
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount)/len(testSet)
def spamTest():
docList = []
classList =[]
fullText = []
#导人文件夹spam与ham下的文本文件，并将它们解析为词列表
for i in range(1,26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
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 = bayes.createVocabList(docList)
trainingSet = range(50);#本例中共有50封电子邮件,其中的值从0到49
testSet = []
'''选择出的数字所对应的文档被添加到测试集， 同时也将其从训练集中剔除。 '''
for i in range(10):#10封电子邮件被随机选择为测试集。
randIndex = int(random.uniform(0,len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMatrix=[];
trainClasses = []
for docIndex in trainingSet:
trainMatrix.append(bayes.setOfWords2Vec(vocabList,docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = bayes.trainNB(array(trainMatrix),array(trainClasses))
errorCount=0
for docIndex in testSet:
#如果邮件分类错误，则错误数加1，最后给出总的错误百分比
wordVector = bayes.setOfWords2Vec(vocabList,docList[docIndex])
if bayes.classifyNB(array(wordVector),p0V,p1V,pSpam) !=classList[docIndex]:
errorCount +=1
print 'the error rate is :',float(errorCount) /len(testSet)
def spamTest():
docList = []
classList = []
#fullText = [] #没起作用
for i in range(1,26): #此案例中样本集名为1.txt~25.txt
wordList = textParse(open('email/spam/%d.txt' % i).read()) #解析邮件，分隔成一个个词汇
docList.append(wordList) #将样本内容存储到docList中
#fullText.extend(wordList)
classList.append(1) #spam下对应的类别标签设置为1
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
#fullText.extend(wordList)
classList.append(0) #ham下对应的类别标签设置为0
vocabList = bayes.createVocabList(docList) #通过docList获取全部的词汇表
trainingSet = list(range(50)) #此处共50个案例，与classList长度对应
testSet = [] #存储测试样本集
for i in list(range(10)):
randIndex = int(random.uniform(0,len(trainingSet))) #随机提取样本作为测试样本
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex]) #把测试样本从训练样本中剔除
trainMat = []
trainClasses = []
for docIndex in trainingSet:#遍历训练样本集
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex])) #获取样本中使用词汇情况向量
trainClasses.append(classList[docIndex]) #获取当前样本的类别标签
p0V,p1V,pSpam = bayes.trainNB0(array(trainMat), array(trainClasses)) #训练算法，得到概率
errorCount = 0
for docIndex in testSet: #遍历测试样本集
wordVector=bayes.setOfWords2Vec(vocabList, docList[docIndex])
resultFlag = bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) #使用分类函数进行分类
if(resultFlag != classList[docIndex]): #如果得到结果不正确，则错误数加上1
errorCount += 1
print('the error rate is: ', float(errorCount)/len(testSet))
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
wordList = textParse(open('email/spam/%d.txt' % i, 'r').read())
docList.append(wordList)
fullText.append(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i, 'r').read())
docList.append(wordList)
fullText.append(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
trainingSet = list(range(50))
testSet = [] # 创建存储训练集的索引值的列表和测试集的索引值的列表
for i in range(10): # 从50个邮件中，随机挑选出40个作为训练集,10个做测试集
randIndex = int(random.uniform(0, len(
trainingSet))) #从一个均匀分布[low,high)中随机采样，注意定义域是左闭右开，即包含low，不包含high.
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V, p1V, pSpam = bayes.trainNBO(np.array(trainMat),
np.array(trainClasses))
errorCount = 0
for docIndex in testSet:
wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])
if bayes.classifyNB(np.array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print('wrong testSet:', docList[docIndex])
print('wrong rate:%.2f%%' % (float(errorCount) / len(testSet) * 100))
def simptestTest(): listOPosts, listClasses = bayes.loadDataSet() myVocabList = bayes.createVocabList(listOPosts) print myVocabList print listOPosts[0] print bayes.setOfWords2Vec(myVocabList, listOPosts[0])
import bayes; listOPosts,listClasses = bayes.loadDataSet() print listOPosts; print listClasses; myVocabList = bayes.createVocabList(listOPosts); print myVocabList print bayes.setOfWords2Vec(myVocabList,listOPosts[0])
def testSetOfWords2Vec(self): trainSet, classVec = BayesTestCase.loadDataSet() vocabList = bayes.createVocabList(trainSet) vec = bayes.setOfWords2Vec(vocabList, trainSet[0]) theVec = [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1] self.assertEqual(theVec, vec)
#!/usr/bin/python
#encoding:utf-8
import bayes
from numpy import *
postingList , classList = bayes.loadDataSet()
myVocabList = bayes.createVocabList(postingList)
trainMat = []
for a in postingList:
trainMat.append( bayes.setOfWords( myVocabList, a ) )
pc, p0, p1 = bayes.trainNB0(trainMat, classList)
test = ['stupid', 'garbage']
thisDoc = array( bayes.setOfWords( myVocabList, test ) )
print bayes.classifyNB(thisDoc, p0, p1, pc)
