def testingNB():
listPosts, listClasses = loadDataSet()
vocabList = Bayes.createVocabList(listOPosts)
trainMat = Bayes.words2Mat(vocabList, listOPosts)
p0V, p1V, pAb = Bayes.trainNB(trainMat, np.array(listClasses))
testEntry = ['love', 'my', 'dalmation']
thisDoc = Bayes.setOfWords2Vec(vocabList, testEntry)
print(testEntry, 'classified as: ', Bayes.classifyNB(thisDoc, p0V, p1V, pAb))
testEntry = ['stupid', 'my', 'garbage']
thisDoc = Bayes.setOfWords2Vec(vocabList, testEntry)
print(testEntry, 'classified as: ', Bayes.classifyNB(thisDoc, p0V, p1V, pAb))
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26):
wordList = textParse(open('testDemo/email/span/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('testDemo/email/ham/%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.trainNBO(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('the error rate is: ', float(errorCount) / len(testSet))
def spamTest():
hamemail = loadDataSet("D:\学习资料\machinelearninginaction\Ch04\email\ham")
hamclassList = [0] * len(hamemail)
spamemail = loadDataSet("D:\学习资料\machinelearninginaction\Ch04\email\spam")
spamclassList = [1] * len(spamemail)
Allemail = []
Allemail.extend(hamemail)
Allemail.extend(spamemail)
AllList = []
AllList.extend(hamclassList)
AllList.extend(spamclassList)
VocalbList = Bayes.createVocabList(Allemail)
# print(VocalbList)
testMat = []
realclass = []
for i in range(10):
randIndex = int(random.uniform(0, len(Allemail)))
testMat.append(Bayes.bagOfWords2Vec(VocalbList, Allemail[randIndex]))
del (Allemail[randIndex])
realclass.append(AllList[randIndex])
del (AllList[randIndex])
trainMat = []
for i in range(len(Allemail)):
trainMat.append(Bayes.bagOfWords2Vec(VocalbList, Allemail[i]))
p0vect, p1vect, pA = Bayes.trainNB0(trainMat, AllList)
# print(p0vect,'\n',p1vect,'\n',pA)
for i in range(10):
print("test_result=", Bayes.classifyNB(testMat[i], p0vect, p1vect, pA),
",real_result=", realclass[i])
def spamTestOfbag():
docList =[];classList = [];fullText = []
for i in range(1,26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList) #extend()方法使得fullText中的元素都是单个的单词(list类型),参考:https://www.cnblogs.com/tzuxung/p/5706245.html
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 = []
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.bagOfWord2VecMN(vocabList,docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = Bayes.trainNB0(array(trainMat),array(trainClasses))
errorCount = 0
for docIndex in testSet:
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))
def spamTestOfvoc():
docList =[];classList = [];fullText = []
for i in range(1,26):#总共有50份文件,垃圾邮件25份,非垃圾邮件25份
wordList = textParse(open('email/spam/%d.txt' % i).read())
docList.append(wordList) #加入一个词向量样本到docList
fullText.extend(wordList) #extend()方法使得fullText中的元素都是单个的单词(list类型),参考:https://www.cnblogs.com/tzuxung/p/5706245.html
classList.append(1) #spam中的样本都是垃圾邮件
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = Bayes.createVocabList(docList) #得到所有单词(特征)的词汇表
trainingSet = range(50) #0-49
testSet = []
for i in range(10):#交叉验证,10个样本用于测试
randIndex = int(random.uniform(0,len(trainingSet)))#生成一个在[0,len(trainingSet)的随机数
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])#在训练集中去掉测试集
trainMat =[];trainClasses = []
for docIndex in trainingSet:#得到训练集和其对应的类别
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:#测试,得到错误率
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 testingNB():
listOPosts, listClasses = loadDataSet()
myVocabList = Bayes.createVocabList(listOPosts)
trainMat = []
for postinDoc in listOPosts:
#trainMat.append(Bayes.setOfWords2Vec(myVocabList, postinDoc))
trainMat.append(Bayes.bagOfWords2Vec(myVocabList, postinDoc))
p0V, p1V, pAb = Bayes.trainNB0(trainMat, listClasses)
testEntry = ['love', 'my', 'dalmation']
#thisDoc = array(Bayes.setOfWords2Vec(myVocabList, testEntry))
thisDoc = array(Bayes.bagOfWords2Vec(myVocabList, testEntry))
print testEntry, 'classified as: ', Bayes.classifyNB(
thisDoc, p0V, p1V, pAb)
testEntry = ['stupid', 'garbage']
#thisDoc = array(Bayes.setOfWords2Vec(myVocabList, testEntry))
thisDoc = array(Bayes.bagOfWords2Vec(myVocabList, testEntry))
print testEntry, 'classified as: ', Bayes.classifyNB(
thisDoc, p0V, p1V, pAb)
def localWords(feed1, feed0):
docList = [] #以二维数组形式存储所有样本的词汇表
classList = [] #存储所有样本的类别信息
fullText = [] #以一维数组形式存储所有样本的词汇表
minLen = min(len(feed1['entries']), len(feed0['entries'])) #获取两个RSS源的最小长度
for i in range(minLen):
#解析feed1['entries'][i]['summary']，将长度大于2的单词提取出来，并全转换为小写
wordList = Bayes.textParse(feed1['entries'][i]['summary'])
docList.append(wordList) #将该样本词汇添加到docList中
fullText.extend(wordList) #将该样本词汇追加到fullText中
classList.append(1) #将样本类别信息添加到classList
wordList = Bayes.textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = Bayes.createVocabList(docList) #获取docList中所有不重复的单词列表
#由于语言中大部分都是冗余和结构辅助性内容，导致词汇表中一小部分单词却占据了所有文本用词的一大部分。需要去除冗余词汇。
#另一个常用的方法是不仅移除高频词，同时从某个预定词表中移除结构上的辅助词。该词表称为停用词表（stop word list）。
top30Words = calcMostFreq(vocabList, fullText) #获取在fullText中出现次数最多的30个词汇信息
for pairW in top30Words: #从词汇表vocabList中去除出现次数最多的30个单词
if pairW[0] in vocabList:
vocabList.remove(pairW[0])
trainingSet = range(2 * minLen)
#定义列表变量存储训练样本id
print 'minLen : %d' % minLen
if minLen < 20:
print 'the len is too small.'
testSet = [] #用于存储测试样本id
for i in range(20): #从训练样本中随机获取20个样本信息作为测试样本集，并从训练样本中去除这些样本
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
#从文本样本集中获取训练样本集，将相关文本样本的词汇出现次数信息存储到矩阵trainMat中，样本分类信息存储到trainClasses中
for docIndex in trainingSet:
#获取样本docList[docIndex]在词汇表vocabList中各个单词出现次数情况
trainMat.append(Bayes.bagOfWords2Vec(vocabList, docList[docIndex]))
#获取当前样本的分类信息classList[docIndex]
trainClasses.append(classList[docIndex])
#通过贝叶斯分类器对训练样本进行学习
#获取两个类别各自单词的出现频率，以及样本集的概率
p0V, p1V, pSpam = Bayes.trainNB0(array(trainMat), array(trainClasses))
errorCount = 0
#使用测试样本集对学习结果进行测试
for docIndex in testSet:
#获取样本docList[docIndex]在词汇表vocabList中各个单词出现次数情况
wordVector = Bayes.bagOfWords2Vec(vocabList, docList[docIndex])
#对当前测试样本进行分类，判断是否与已知类型相同
if Bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount) / len(testSet) #打印出错误率
return vocabList, p0V, p1V #返回词汇表和各个词汇的出现概率
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(docList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(docList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
"""
trainingSet = [1, 49]
生成10个50以内的随机数，加入testSet
从trainingSet中删掉这些数。
结果就是把【1...49]，1分为2，10个作为 testSet， 其他作为 trainingSet
trainingSet = [0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 35, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49]
testSet = [36, 3, 40, 31, 10, 42, 7, 37, 15, 34]
"""
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = int(np.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(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 'the error rate is: ', float(errorCount) / len(testSet)
def spamTest():
docList = []
classList = []
fullText = []
for i in range(1, 26): #将文件夹spam和ham下所有文本文件解析出来
#从对应文本文件中读出字符串，将其解析为单词列表
wordList = Bayes.textParse(open('email/spam/%d.txt' % i).read())
docList.append(wordList) #将当前文本的词汇列表添加到docList变量中
fullText.extend(wordList) #将当前文本的所有单词追加到fullText变量中
classList.append(1) #分类列表变量classList中增加一个1类信息
wordList = Bayes.textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0) #分类列表变量classList中增加一个0类信息
vocabList = Bayes.createVocabList(docList) #获取docList中所有出现过的单词的词汇表
trainingSet = range(50) #创建拥有50个元素的list变量，存储0-49个数字，对应spam与ham目录下所有文本
testSet = []
for i in range(10): #从0-9循环，产生10个测试样本id
#uniform() 方法将随机生成下一个实数，它在 [x, y) 范围内。
#在[0, 50)之间产生一个随机整数
randIndex = int(random.uniform(0, len(trainingSet)))
print randIndex
#将trainingSet中对应训练样本id添加到测试集testSet中，并从trainingSet中删除该id
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
trainMat = []
trainClasses = []
#在40个训练样本中，逐个文本循环处理，获取1类和0类各个单词出现的概率及1类样本的概率
for docIndex in trainingSet:
#获取当前文档中单词在词汇表vocabList是否出现的列表，添加到列表变量trainMat中
trainMat.append(Bayes.setOfWords2Vec(vocabList, docList[docIndex]))
#将对应文档的分类信息添加到trainClasses中
trainClasses.append(classList[docIndex])
#获取训练样本中1类和0类各个词汇的出现概率，及所有样本中1类样本所占概率
p0V, p1V, pSpam = Bayes.trainNB0(array(trainMat), array(trainClasses))
print classList
errorCount = 0
#使用10个测试样本，对贝叶斯分类效果进行检测
for docIndex in testSet:
#获取当前测试样本中单词在词汇表vocabList是否出现的列表
wordVector = Bayes.setOfWords2Vec(vocabList, docList[docIndex])
#使用贝叶斯分类器对当前测试样本进行分类，判断分类结果是否正确
if Bayes.classifyNB(array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print 'the error rate is: ', float(errorCount) / len(testSet) #打印出分类错误率
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