NBC朴素贝叶斯分类器 ————机器学习实战 python代码

这里的p(y=1|x)计算基于朴素贝叶斯模型（周志华老师机器学习书上说的p(xi|y=1)=|Dc,xi|/|Dc|）

也可以基于文本分类的事件模型

见http://blog.csdn.net/app_12062011/article/details/50540429有详细介绍

代码是机器学习实战所呈现的那种方式。。。。。。

# -*- coding: utf-8 -*-
"""
Created on Mon Aug 07 23:40:13 2017

@author: mdz
"""
import numpy as np
def loadData():
vocabList=[['fuck', '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']]
classList=[1,1,0,1,0,1]#1 侮辱性文字，0 正常言论
return vocabList,classList

#对vocabList已经拆分过的句子进行筛选，筛选掉重复的单词，最后再返回list
#该list的length即属性的个数
def filterVocabList(vocabList):
vocabSet=set([])
for document in vocabList:
vocabSet=vocabSet|set(document)
return list(vocabSet)

#对测试样本进行0-1处理
def zero_one(vocabList,input):
returnVec=[0]*len(vocabList)
for word in input:
if word in vocabList:
returnVec[vocabList.index(word)]=1
else:
print "the word: %s is not in my Vocabulary!"%word
return returnVec

def trainNbc(trainSamples,trainCategory):
numTrainSamp=len(trainSamples)
numWords=len(trainSamples[0])
pAbusive=sum(trainCategory)/float(numTrainSamp)
#y=1 or 0下的特征取值为1
p0Num=np.ones(numWords)
p1Num=np.ones(numWords)
#y=1 or 0下的样本计数
p0NumTotal=2.0#每个特征可能的取值2种情况
p1NumTotal=2.0
for i in range(numTrainSamp):
if trainCategory[i]==1:
p1Num+=trainSamples[i]
p1NumTotal+=1
else:
p0Num+=trainSamples[i]
p0NumTotal+=1
p1Vec=p1Num/float(p1NumTotal)
p0Vec=p0Num/float(p0NumTotal)
return p1Vec,p0Vec,pAbusive

def classifyOfNbc(testSamples,p1Vec,p0Vec,pAbusive):
p1=sum(testSamples*np.log(p1Vec))+sum((1-testSamples)*np.log(1-p1Vec))+np.log(pAbusive)
p0=sum(testSamples*np.log(p0Vec))+sum((1-testSamples)*np.log(1-p0Vec))+np.log(pAbusive)
if p1>p0:
return 1 
else: 
return 0 
def testingNbc():
vocabList,classList=loadData()
vocabSet=filterVocabList(vocabList)
trainList=[]
for term in vocabList:
trainList.append(zero_one(vocabSet,term))
p1Vec,p0Vec,pAbusive=trainNbc(np.array(trainList),np.array(classList))
testEntry=['fuck','my','daughter']
testSamples=np.array(zero_one(vocabSet,testEntry))
print testEntry,'classified as :',classifyOfNbc(testSamples,p1Vec,p0Vec,pAbusive)
testEntry=['stupid','garbage']
testSamples=np.array(zero_one(vocabSet,testEntry))
print testEntry,'classified as :',classifyOfNbc(testSamples,p1Vec,p0Vec,pAbusive)

'''上述代码存为bayesClassify.py'''

'''控制台输入 :>>>import bayesClassify

             >>>bayesClassify.testingNbc()

'''输出结果：

the word: daughter is not in my Vocabulary!
['fuck', 'my', 'daughter'] classified as : 1
['stupid', 'garbage'] classified as : 1

'''