'''
Created on 2017年8月10日
@author: fujianfei
'''
import numpy as np
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):
'''
.产生一个词汇set，里面包含了所有的词，但不重复
'''
vocaList = set([])
for vocaRow in dataSet:
vocaList = vocaList | set(vocaRow)#与每行不重复词汇取并，表示如果vocaList原先有这些词汇，则过，没有则加上
return list(vocaList)
def setOfWords2Vec(vocaList, inputSet):
'''
.将输入的词组变成0,1向量，vocaList中某个词在inputSet中出现，则为1，否则为0
'''
numVect = [0] * len(vocaList)
for input in inputSet:
if input in vocaList:
numVect[vocaList.index(input)] = 1
return numVect
def trainNB(trainMat, trainCategory):
'''
.用来训练数据，生成类别C的先验概率P(C)和条件概率P(X|C)
'''
numTrain = len(trainMat)
numWords = len(trainMat[0])
pc = sum(trainCategory)/float(numTrain)
p0Num = np.zeros(numWords);p1Num = np.zeros(numWords)
p0Denom = 0.0;p1Denom = 0.0
for i in range(numTrain):
if trainCategory[i] == 1:
p1Num += trainMat[i]
p1Denom += np.sum(trainMat[i])
else:
p0Num += trainMat[i]
p0Denom += np.sum(trainMat[i])
p1Vect = p1Num/p1Denom
p0Vect = p0Num/p0Denom
return p0Vect,p1Vect,pc
def classifyNB(vec2Classify, p0Vect, p1Vect, pClass1):
p1 = np.sum(vec2Classify * p1Vect) + np.log(pClass1)
p0 = np.sum(vec2Classify * p0Vect) + np.log(1-pClass1)
print(p1,p0)
if p1 > p0:
return 1
else:
return 0
def testingNB():
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
trainMat=[]
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
p0V,p1V,pAb = trainNB(np.array(trainMat),np.array(listClasses))
# print(p0V,p1V,pAb)
testEntry = ['love', 'my', 'dalmation']
thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry = ['stupid', 'garbage']
thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
testingNB()
