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根据Issue改的代码 #24

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根据Issue改的代码 #24

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4 changes: 2 additions & 2 deletions Naive Bayes/bayes-modify.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,10 +96,10 @@ def trainNB0(trainMatrix,trainCategory):
for i in range(numTrainDocs):
if trainCategory[i] == 1: #统计属于侮辱类的条件概率所需的数据，即P(w0|1),P(w1|1),P(w2|1)···
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i])
p1Denom += 1
else: #统计属于非侮辱类的条件概率所需的数据，即P(w0|0),P(w1|0),P(w2|0)···
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p0Denom += 1
p1Vect = np.log(p1Num/p1Denom) #取对数，防止下溢出
p0Vect = np.log(p0Num/p0Denom)
return p0Vect,p1Vect,pAbusive #返回属于侮辱类的条件概率数组，属于非侮辱类的条件概率数组，文档属于侮辱类的概率
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4 changes: 2 additions & 2 deletions Naive Bayes/bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,10 +97,10 @@ def trainNB0(trainMatrix,trainCategory):
for i in range(numTrainDocs):
if trainCategory[i] == 1: #统计属于侮辱类的条件概率所需的数据，即P(w0|1),P(w1|1),P(w2|1)···
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i]) ## 该词条的总的词数目 这压样求得每个词条出现的概率 P(w1),P(w2), P(w3)...
p1Denom += 1 ## 该词条的总的词数目 这压样求得每个词条出现的概率 P(w1),P(w2), P(w3)...
else: #统计属于非侮辱类的条件概率所需的数据，即P(w0|0),P(w1|0),P(w2|0)···
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p0Denom += 1
p1Vect = p1Num/p1Denom #相除
p0Vect = p0Num/p0Denom
return p0Vect,p1Vect,pAbusive #返回属于侮辱类的条件概率数组，属于非侮辱类的条件概率数组，文档属于侮辱类的概率
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