# -*- coding: utf-8 -*-
"""
Created on Mon Nov 14 11:15:47 2016
Naive Bayes Clssification
"""
# -*- coding: utf-8 -*-
import numpy
as np
class NaiveBayes:
def
__init__
(
self
):
self._creteria
=
"NB"
def _createVocabList
(
self
, dataList
):
"""
创建一个词库向量
"""
vocabSet
=
set
(
[
]
)
for line
in dataList:
print
set
(line
)
vocabSet
= vocabSet |
set
(line
)
return
list
(vocabSet
)
#文档词集模型
def _setOfWords2Vec
(
self
, vocabList
, inputSet
):
"""
功能:根据给定的一行词，将每个词映射到此库向量中，出现则标记为1，不出现则为0
"""
outputVec
=
[
0
] *
len
(vocabList
)
for word
in inputSet:
if word
in vocabList:
outputVec
[vocabList.
index
(word
)
]
=
1
else:
print
"the word:%s is not in my vocabulary!" % word
return outputVec
# 修改 _setOfWordsVec 文档词袋模型
def _bagOfWords2VecMN
(
self
, vocabList
, inputSet
):
"""
功能：对每行词使用第二种统计策略，统计单个词的个数，然后映射到此库中
输出：一个n维向量，n为词库的长度，每个取值为单词出现的次数
"""
returnVec
=
[
0
]*
len
(vocabList
)
for word
in inputSet:
if word
in vocabList:
returnVec
[vocabList.
index
(word
)
] +
=
1
# 更新此处代码
return returnVec
def _trainNB
(
self
, trainMatrix
, trainLabel
):
"""
输入：训练矩阵和类别标签,格式为numpy矩阵格式
功能：计算条件概率和类标签概率
"""
numTrainDocs
=
len
(trainMatrix
)
#统计样本个数
numWords
=
len
(trainMatrix
[
0
]
)
#统计特征个数，理论上是词库的长度
pNeg
=
sum
(trainLabel
)/
float
(numTrainDocs
)
#计算负样本出现的概率
p0Num
= np.
ones
(numWords
)
#初始样本个数为1，防止条件概率为0，影响结果
p1Num
= np.
ones
(numWords
)
#作用同上
p0InAll
=
2.0
#词库中只有两类，所以此处初始化为2(use laplace)
p1InAll
=
2.0
# 再单个文档和整个词库中更新正负样本数据
for i
in
range
(numTrainDocs
):
if trainLabel
[i
]
==
1:
p1Num +
= trainMatrix
[i
]
p1InAll +
=
sum
(trainMatrix
[i
]
)
else:
p0Num +
= trainMatrix
[i
]
p0InAll +
=
sum
(trainMatrix
[i
]
)
print p1InAll
#计算给定类别的条件下，词汇表中单词出现的概率
#然后取log对数，解决条件概率乘积下溢
p0Vect
= np.
log
(p0Num/p0InAll
)
#计算类标签为0时的其它属性发生的条件概率
p1Vect
= np.
log
(p1Num/p1InAll
)
#log函数默认以e为底 #p(ci|w=0)
return p0Vect
, p1Vect
, pNeg
def _classifyNB
(
self
, vecSample
, p0Vec
, p1Vec
, pNeg
):
"""
使用朴素贝叶斯进行分类,返回结果为0/1
"""
prob_y0
=
sum
(vecSample * p0Vec
) + np.
log
(
1-pNeg
)
prob_y1
=
sum
(vecSample * p1Vec
) + np.
log
(pNeg
)
#log是以e为底
if prob_y0
< prob_y1:
return
1
else:
return
0
# 测试NB算法
def testingNB
(
self
, testSample
):
listOPosts
, listClasses
= loadDataSet
(
)
myVocabList
=
self._createVocabList
(listOPosts
)
# print myVocabList
trainMat
=
[
]
for postinDoc
in listOPosts:
trainMat.
append
(
self._bagOfWords2VecMN
(myVocabList
, postinDoc
)
)
p0V
,p1V
,pAb
=
self._trainNB
(np.
array
(trainMat
)
, np.
array
(listClasses
)
)
print trainMat
thisSample
= np.
array
(
self._bagOfWords2VecMN
(myVocabList
, testSample
)
)
result
=
self._classifyNB
(thisSample
, p0V
, p1V
, pAb
)
print testSample
,
'classified as: '
, result
return result
###############################################################################
def loadDataSet
(
):
wordsList
=
[
[
'my'
,
'dog'
,
'has'
,
'flea'
,
'problems'
,
'help'
,
'please'
]
,
[
'maybe'
,
'not'
,
'take'
,
'him'
,
'to'
,
'dog'
,
'park'
,
'stupid'
]
,
[
'my'
,
'dalmation'
,
'is'
,
'so'
,
'cute'
,
' and'
,
'I'
,
'love'
,
'him'
]
,
[
'stop'
,
'posting'
,
'stupid'
,
'worthless'
,
'garbage'
]
,
[
'mr'
,
'licks'
,
'ate'
,
'my'
,
'steak'
,
'how'
,
'to'
,
'stop'
,
'him'
]
,
[
'quit'
,
'buying'
,
'worthless'
,
'dog'
,
'food'
,
'stupid'
]
]
classLable
=
[
0
,
1
,
0
,
1
,
0
,
1
]
# 0：good; 1:bad
return wordsList
, classLable
if __name__
==
"__main__":
clf
= NaiveBayes
(
)
testEntry
=
[
[
'love'
,
'my'
,
'girl'
,
'friend'
]
,
[
'stupid'
,
'garbage'
]
,
[
'Haha'
,
'I'
,
'really'
,
"Love"
,
"You"
]
,
[
'This'
,
'is'
,
"my"
,
"dog"
]
]
clf.
testingNB
(testEntry
[
0
]
)
# for item in testEntry:
# clf.testingNB(item)
