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，也是自己的一点心得，交流一下。

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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)无锡人流医院 http://www.bhnkyy39.com/

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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