python源码,朴素贝叶斯实现多分类

机器学习实战中,朴素贝叶斯那一章节只实现了二分类,网上大多数博客也只是照搬书上的源码,没有弄懂实现的根本。在此梳理了一遍朴素贝叶斯的原理,实现了5分类的例子,仅供参考!

from numpy import *
'''
贝叶斯公式 p(ci|w) = p(w|ci)*p(ci) / p(w)
即比较两类别分子大小,把结果归为分子大的一类
p(w|ci)条件概率,即在类别1或0下,w(词频)出现的概率(词频/此类别总词数即n/N)
'''
# 取得DataSet中不重复的word
def createVocabList(dataSet):
    vocabSet = set([])#使用set创建不重复词表库
    for document in dataSet:
        vocabSet = vocabSet | set(document) #创建两个集合的并集
    return list(vocabSet)
 
'''
我们将每个词的出现与否作为一个特征,这可以被描述为词集模型(set-of-words model)。
在词集中,每个词只能出现一次。
'''
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)#创建一个所包含元素都为0的向量
    #遍历文档中的所有单词,如果出现了词汇表中的单词,则将输出的文档向量中的对应值设为1
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1
        else: print("the word: %s is not in my Vocabulary!" % word)
    return returnVec
'''
如果一个词在文档中出现不止一次,这可能意味着包含该词是否出现在文档中所不能表达的某种信息,
这种方法被称为词袋模型(bag-of-words model)。
在词袋中,每个单词可以出现多次。
为适应词袋模型,需要对函数setOfWords2Vec稍加修改,修改后的函数称为bagOfWords2VecMN
'''
def bagOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

def countX(aList,el):
    count = 0
    for item in aList:
        if item == el:
            count += 1
    return count
 
def trainNB0(trainMatrix,trainCategory):
    '''
    trainMatrix:文档矩阵
    trainCategory:每篇文档类别标签
    '''
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    
    pAbusive0 = countX(trainCategory,0) / float(numTrainDocs)
    pAbusive1 = countX(trainCategory,1) / float(numTrainDocs)
    pAbusive2 = countX(trainCategory,2) / float(numTrainDocs)
    pAbusive3 = countX(trainCategory,3) / float(numTrainDocs)
    pAbusive4 = countX(trainCategory,4) / float(numTrainDocs)
    
    #初始化所有词出现数为1,并将分母初始化为2,避免某一个概率值为0
    p0Num = ones(numWords); p1Num = ones(numWords)
    p2Num = ones(numWords)
    p3Num = ones(numWords)
    p4Num = ones(numWords)
    
    
    p0Denom = 2.0; p1Denom = 2.0 ;p2Denom = 2.0
    p3Denom = 2.0; p4Denom = 2.0 
 
    for i in range(numTrainDocs):
        # 1类的矩阵相加
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        if trainCategory[i] == 2:
            p2Num += trainMatrix[i]
            p2Denom += sum(trainMatrix[i])
        if trainCategory[i] == 3:
            p3Num += trainMatrix[i]
            p3Denom += sum(trainMatrix[i])
        if trainCategory[i] == 4:
            p4Num += trainMatrix[i]
            p4Denom += sum(trainMatrix[i])
        if trainCategory[i] == 0:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    #将结果取自然对数,避免下溢出,即太多很小的数相乘造成的影响
    p4Vect = log(p4Num/p4Denom)
    p3Vect = log(p3Num/p3Denom)
    p2Vect = log(p2Num/p2Denom)
    p1Vect = log(p1Num/p1Denom)#change to log()
    p0Vect = log(p0Num/p0Denom)#change to log()
    return p0Vect,p1Vect,p2Vect,p3Vect,p4Vect,pAbusive0,pAbusive1,pAbusive2,pAbusive3,pAbusive4
 
 
def classifyNB(vec2Classify,p0Vec,p1Vec,p2Vec,p3Vec,p4Vec,pClass0,pClass1,pClass2,pClass3,pClass4):
    p1 = sum(vec2Classify * p1Vec) + log(pClass1)
    p2 = sum(vec2Classify * p2Vec) + log(pClass2)
    p3 = sum(vec2Classify * p3Vec) + log(pClass3)
    p4 = sum(vec2Classify * p4Vec) + log(pClass4)
    p0 = sum(vec2Classify * p0Vec) + log(pClass0)
##    print(p0,p1,p2,p3,p4)
    return [p0,p1,p2,p3,p4].index(max([p0,p1,p2,p3,p4]))
 
if __name__ == "__main__":
    dataset = [['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'],
                    ['i','love','you'],
                   ['you','kiss','me'],
                   ['hate','heng','no'],
                   ['can','i','hug','you'],
                   ['refuse','me','ache'],
                   ['1','4','3'],
               ['5','2','3'],
               ['1','2','3']]
 
    # 0,1,2,3,4分别表示不同类别
    classVec = [0,1,0,1,0,1,2,2,4,2,4,3,3,3]
 
    print("正在创建词频列表")
    myVocabList = createVocabList(dataset)
    print("正在建词向量")
    trainMat = []
    for postinDoc in dataset:
        trainMat.append(setOfWords2Vec(myVocabList,postinDoc))
    print("开始训练")
    p0V,p1V,p2V,p3V,p4V,pAb0,pAb1,pAb2,pAb3,pAb4 = trainNB0(array(trainMat),array(classVec))
 
    # 输入的测试案例
    tmp = ['love','you','kiss','you']
    thisDoc = array(setOfWords2Vec(myVocabList,tmp))
    flag = classifyNB(thisDoc,p0V,p1V,p2V,p3V,p4V,pAb0,pAb1,pAb2,pAb3,pAb4)
    print('flag is',flag)

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