naiveBayes - juedaiyuer/researchNote GitHub Wiki

#朴素贝叶斯#

##优缺点##

优点：在数据较少的情况下仍然有效，可以处理多类别问题
缺点：对于输入数据的准备方式较为敏感
适用数据类型：标称型数据

##贝叶斯决策##

选择高概率对应的类别

##条件概率##

P(A|B)=P(A and B)/P(B)

贝叶斯准则：p(c|x)=p(x|c)p(c)/p(x)

##使用朴素贝叶斯进行文档分类##

每个特征同等重要

##准备数据：从文本中构建词向量##

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

'''
	Task: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([])  #create empty set
	for document in dataSet:
	    vocabSet = vocabSet | set(document) #union of the two sets
	return list(vocabSet)
'''
	Task:
'''
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 returnVec

###测试###

>>> import os
>>> os.chdir("/home/juedaiyuer/mycode/researchNote/machinelearning/Ch04")
>>> import sys
>>> sys.path.append("/home/juedaiyuer/mycode/researchNote/machinelearning/Ch04")

RUN起来，没有出现重复的单词

>>> import bayes
>>> listOPosts,listClasses=bayes.loadDataSet()
>>> listOPosts
['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'](/juedaiyuer/researchNote/wiki/'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')
>>> myVocabList=bayes.createVocabList(listOPosts)
>>> myVocabList
['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']

函数setOfWords2Vec()运行效果

>>> listOPosts[0]
['my', 'dog', 'has', 'flea', 'problems', 'help', 'please']
>>> bayes.setOfWords2Vec(myVocabList,listOPosts[0])
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1]

##训练算法：从词向量计算概率##

朴素贝叶斯分类器训练函数

'''
	输入参数：
			trainMatrix:文档矩阵
	
	二类分类问题，首先计算文档属于侮辱性文档（class=1）的概率，即p(1)
'''
def trainNB0(trainMatrix,trainCategory):
	numTrainDocs = len(trainMatrix)
	numWords = len(trainMatrix[0])
	pAbusive = sum(trainCategory)/float(numTrainDocs)
	p0Num = ones(numWords); p1Num = ones(numWords)      #change to ones() 
	p0Denom = 2.0; p1Denom = 2.0                        #change to 2.0
	for i in range(numTrainDocs):
	    if trainCategory[i] == 1:
	        p1Num += trainMatrix[i]
	        p1Denom += sum(trainMatrix[i])
	    else:
	        p0Num += trainMatrix[i]
	        p0Denom += sum(trainMatrix[i])
	p1Vect = log(p1Num/p1Denom)          #change to log()
	p0Vect = log(p0Num/p0Denom)          #change to log()
	return p0Vect,p1Vect,pAbusive

###测试###

>>> from numpy import *
>>> reload(bayes)
<module 'bayes' from 'bayes.pyc'>
>>> listOPosts,listClasses=bayes.loadDataSet()

##source##

• 机器学习实战：第4章