diff --git a/Naive Bayes/bayes-modify.py b/Naive Bayes/bayes-modify.py
index 93103664..ef14fcfe 100644
--- a/Naive Bayes/bayes-modify.py	
+++ b/Naive Bayes/bayes-modify.py	
@@ -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
diff --git a/Naive Bayes/bayes.py b/Naive Bayes/bayes.py
index 6c62ef6c..a7a2481e 100644
--- a/Naive Bayes/bayes.py	
+++ b/Naive Bayes/bayes.py	
@@ -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