前两篇已经将算法思想实现，这次对其进项下更新修正一些小的细节，我们知道计算概率乘积时候如果某个概率为0，那么概率相乘结果为0，这显然不是我们想要的结果，还有就是如果出现很多非常小的数相乘会向下溢出。
实现起来就修改4行代码：
p0Num = ones(numWords)
p1Num = ones(numWords) #change to ones()
#print(p0Num,p1Num )
p0Denom = 2.0
p1Denom = 2.0 #change to 0.0
同样后面的值也要取对数：
p1Vect = log(p1Num/p1Denom) #change to log()
p0Vect = log(p0Num/p0Denom) #change to log()
对了还有个词袋模型：
def bagOFWords2VecMN(vocabList,inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] +=1
return returnVec
这样我们的朴素贝叶斯的理论和实际代码就都完成了。一定觉得非常简单，下面给出我的全部代码，方便大家使用：
from numpy import *
import time
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
def createVocabList(dataSet):
vocabSet = set([]) #create empty set
for document in dataSet:
vocabSet = vocabSet | set(document) #union of the two sets
return list(vocabSet)
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
def trainNB0(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory)/float(numTrainDocs)
p0Num = zeros(numWords)
p1Num = zeros(numWords) #change to ones()
#print(p0Num,p1Num )
p0Denom = 0.0
p1Denom = 0.0 #change to 0.0
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 = p1Num/p1Denom #change to log()
p0Vect = p0Num/p0Denom #change to log()
return p0Vect,p1Vect,pAbusive
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
p1 = sum(vec2Classify*p1Vec)+log(pClass1)
p0 = sum(vec2Classify*p0Vec)+log(1.0-pClass1)
if p1>p0:
return 1
else:
return 0
listOposts,listClasses = loadDataSet()
myVocabList = createVocabList(listOposts)
print(myVocabList)
tmp= setOfWords2Vec(myVocabList, listOposts[0])
print(tmp)
trainMat =[]
for postinDoc in listOposts:
trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
p0V, p1V, pAb = trainNB0(trainMat, listClasses)
print(p0V)
print(p1V)
print(pAb)
testEntry = ['love', 'my','dalmation']
thisDoc = setOfWords2Vec(myVocabList, testEntry)
print(classifyNB(thisDoc, p0V, p1V, pAb))
来张截图：
