使用朴素贝叶斯进行分本分类

代码如下：
'''使用Python进行文本分类'''
'''词表到向量的转换函数'''
def loadDataSet():
postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
classVec = [0,1,0,1,0,1]
return postingList,classVec

'''创建包含所有文档中出现的不重复词的列表'''
def createVocabList(dataSet):
vocabSet = set([])
for document in dataSet:
vocabSet = vocabSet | set(document)

'''set of  words model（词集模型：仅将每个词的出现与否作为一个特征）'''
def setOfWords2Vec(vocabList,inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in inputSet:
returnVec[vocabList.index(word)] = 1
else:
print "the word : %s is not in my Vocabulary!" % word
return returnVec

'''bag of words model（词袋模型：该模型中每个词可以出现不止一次）'''
def bagOfWordsVecMN(vocabList,inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] += 1
return returnVec

'''训练算法：从词向量计算概率'''
'''trainMatrix:文档矩阵(numTrainDocs行*numWords列)；trainCategory：每篇文档类别标签所构成的向量'''
def trainNB0(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix)'''训练文本数'''
numWords = len(trainMatrix[0])'''总单词数（词汇表中的总单词数）'''
pAbusive = sum(trainCategory)/float(munTrainDocs) '''统计侮辱性（1）在文档中出现的概率'''

'''p0Num = zeros(numWords)
p1Num = zeros(numWords)
p0Denom = 0.0
p1Denom = 0.0变更代码如下：使用拉普拉斯校准避免计算零概率值'''
p0Num = ones(numWords)
p1Num = ones(numWords)
p0Denom = 2.0
p1Denom = 2.0
'''以下分别计算p(wi|c1)和p(wi|c0)的概率'''
for i in range(numTrainDocs):
if trainCategory[i] == 1:
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i])
else:
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])

'''p1Vect = p1Num/p1Denom
p0Vect = p0Num/p0Denom变更此行代码如下，为避免下溢出（由于太多很小的数相乘造成，在Python环境中由于太多很小的数相乘最后四舍五入会得到0）,
取自然对数，可避免下溢出或者浮点数舍入导致的错误'''
p1Vect = log(p1Num/p1Denom)
p0Vect = log(p0Num/p0Denom)
return p0Vect,p1Vect,pAbusive

def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
p1 = sum(vec2Classify*p1Vec) + log(pClass1) '''本应该是 p1Vec*pClass1但整体的都去了对数，所以最后变为对数相加的形式'''
p0 = sum(vec2Classify*p0Vec) + log(1.0 - pClass1)
if p1>p0:
return 1
else:
return 0

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']
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
testEntry = ['stupid', 'garbage']
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)