(转载请注明出处：http://blog.csdn.net/buptgshengod)
嗯，如今最终用到了。朴素贝叶斯分类器据说是好多扫黄软件使用的算法。贝叶斯公式也比較简单，大学做概率题常常会用到。核心思想就是找出特征值对结果影响概率最大的项。
公式例如以下：
[[‘my‘,‘dog‘,‘has‘,‘flea‘,‘problems‘,‘help‘,‘please‘], ? ?0
?[‘maybe‘,‘not‘,‘take‘,‘him‘,‘to‘,‘dog‘,‘park‘,‘stupid‘], ?1
?[‘my‘,‘dalmation‘,‘is‘,‘so‘,‘cute‘,‘I‘,‘love‘,‘him‘], ? ? ? ? ?0
?[‘stop‘,‘posting‘,‘stupid‘,‘worthless‘,‘garbage‘], ? ? ? ? ?1
?[‘mr‘,‘licks‘,‘ate‘,‘my‘,‘steak‘,‘how‘,‘to‘,‘stop‘,‘him‘], ?0
[‘quit‘,‘buying‘,‘worthless‘,‘dog‘,‘food‘,‘stupid‘]] ? ? ? ? ? 1
我们通过分析每一个句子中的每一个词，在粗口句或是正常句出现的概率，能够找出那些词是粗口。
#以矩阵形式创建数据集
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] #1 is abusive, 0 not
return postingList,classVec#将矩阵内容加入到列表，set获取list中不反复的元素
def createVocabList(dataSet):
vocabSet = set([]) #create empty set
for document in dataSet:
vocabSet = vocabSet | set(document) #union of the two sets
return list(vocabSet)#推断list中每一个词在总共词语list中的位置
def setOfWords2Vec(vocabList, inputSet):
returnVec = [0]*len(vocabList)
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 returnVecdef trainNB0(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory)/float(numTrainDocs)#脏句的比例
p0Num = zeros(numWords); p1Num = zeros(numWords) #zero是numpy带的函数。zeros(i)长度为i的list
p0Denom = 0.0; p1Denom = 0.0
for i in range(numTrainDocs):
if trainCategory[i] == 1:#假设是粗口句，每一个词在p1num加一
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i])
else:
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p1Vect = p1Num/p1Denom #粗口字概率
p0Vect = p0Num/p0Denom
return p0Vect,p1Vect,pAbusive[ 0.? ? ? ? ? 0.? ? ? ? ? 0.? ? ? ? ? 0.05263158? 0.05263158? 0.? ? ? ? ? 0.
? 0.? ? ? ? ? 0.05263158? 0.05263158? 0.? ? ? ? ? 0.? ? ? ? ? 0.
? 0.05263158? 0.05263158? 0.05263158? 0.05263158? 0.05263158? 0.
? 0.10526316? 0.? ? ? ? ? 0.05263158? 0.05263158? 0.? ? ? ? ? 0.10526316
? 0.? ? ? ? ? 0.15789474? 0.? ? ? ? ? 0.05263158? 0.? ? ? ? ? 0.? ? ? ? ? 0.? ? ? ? ]
出现概率最大项：
0.157894736842
相应的词是：stupid
[‘cute‘, ‘love‘, ‘help‘, ‘garbage‘, ‘quit‘, ‘I‘, ‘problems‘, ‘is‘, ‘park‘, ‘stop‘, ‘flea‘, ‘dalmation‘, ‘licks‘, ‘food‘, ‘not‘, ‘him‘, ‘buying‘, ‘posting‘, ‘has‘, ‘worthless‘, ‘ate‘, ‘to‘, ‘maybe‘, ‘please‘, ‘dog‘, ‘how‘, ‘stupid‘, ‘so‘, ‘take‘, ‘mr‘, ‘steak‘, ‘my‘]
【机器学习算法-python实现】扫黄神器-朴素贝叶斯分类器的实现
原文：https://www.cnblogs.com/xfgnongmin/p/10809394.html