机器学习实战——朴素贝叶斯

from numpy import *
# 创建一个实验样本
def loadDataSet():
    postingList = [['my','dog','has','flea','problems','help','please'],
                   ['maybe','not','take','him','to','dog','park','stupid'],
                   ['my','dalmation','is','so','cute','I','love','him'],
                   ['stop','posting','stupid','worthless','garbage'],
                   ['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

# 创建一个包含在所有文档中出现的不重复词的列表
def createVocabList(dataSet):
    vocabSet = set([])      #创建一个空集
    for document in dataSet:
        vocabSet = vocabSet | set(document)   #创建两个集合的并集
    return list(vocabSet)

#将文档词条转换成词向量
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)        #创建一个其中所含元素都为0的向量
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1     #index函数在字符串里找到字符第一次出现的位置  词集模型
            #returnVec[vocabList.index(word)] += 1      #文档的词袋模型    每个单词可以出现多次
        else: print "the word: %s is not in my Vocabulary!" % word
    return returnVec

#朴素贝叶斯分类器训练函数   从词向量计算概率
def trainNB0(trainMatrix, trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs)
    #p0Num = zeros(numWords); p1Num = zeros(numWords)
    #p0Denom = 0.0; p1Denom = 0.0
    p0Num = ones(numWords); p1Num = ones(numWords)         #避免一个概率值为0,最后的乘积也为0
    p0Denom = 2.0; p1Denom = 2.0
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            #print"------------\n"
            #print p1Num
            p1Denom += sum(trainMatrix[i])
            #print"+++++++++++++\n"
            #print p1Denom
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
   # p1Vect = p1Num / p1Denom
    #p0Vect = p0Num / p0Denom
    p1Vect = log(p1Num / p1Denom)
    p0Vect = log(p0Num / p0Denom)      #避免下溢出或者浮点数舍入导致的错误   下溢出是由太多很小的数相乘得到的
    return p0Vect, p1Vect, pAbusive
#朴素贝叶斯分类器
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify*p1Vec) + log(pClass1)
    p0 = sum(vec2Classify*p0Vec) + log(1.0-pClass1)
    if p1 > p0:
        return 1
    else: return 0

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