贝叶斯是搞概率论的。学术圈上有个贝叶斯学派。看起来吊吊的。关于贝叶斯是个啥网上有很多资料。想必读者基本都明了。我这里只简单概括下：贝叶斯分类其实就是基于先验概率的基础上的一种分类法，核心公式就是条件概率。举个俗气的例子，通过我们的以往观察，鲤鱼中尾巴是红色的占比达90%，鲫鱼中尾巴是红色的占比只有1%不到，那么新来了一条小鱼，他是鲤鱼还是鲫鱼呢？我看一下他的尾巴，发现是红色，根据过去的先验概率经验，它是鲤鱼的概率比较大，我认为它是鲤鱼。
这当时是个最简单的例子，实践中的问题就复杂了。比如说特征不止是尾巴红不红，还有鱼嘴巴大不大，鱼肥不肥，鱼身子长还是宽，各种，而且不是一个特征就能分辨出来的，还需要多方分析，然后贝爷感觉这个那个的真麻烦，就先假定每个特征都是独立的，如果一条鱼红尾巴大嘴巴肥得很还是长身子，就这样求她是鲤鱼的概率：鲤鱼中红尾巴0.9*鲤鱼中大嘴巴0.3*鲤鱼中肥猪0.6*鲤鱼中长身子0.4=0.27*0.24.。。。。
闲话少扯。上代码分析。我代码干的不是鱼的分类了，而是一篇文档。
from numpy import *
def loadDataSet():#这个函数呢，他建立了一个敏感词典，并打了标签，共6个词集合，其中2、4、6词集合中的词是敏感词
postingList = [['my','dog','has','flea',\
'problems','help','please'],
['maybe','not','take','him',\
'to','dog','park','stupid'],
['my','dalmation','is','so','cute',\
'T','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]
return postingList,classVec
def createVocabList(dataSet):#这个函数呢，它是把输入的dataset(就是一个新文档嘛)进行分解处理，返回的是这个文档没有重复词的列表
vocabSet = set([])
for document in dataSet:
vocabSet = vocabSet | set(document)
return list(vocabSet)
def setOfWords2Vec(vocabList,inputSet):#这个函数呢，他就是根据输入的新文档，和词汇表，来对新文档打标签，看他有多少敏感词，只要是出现了词汇表里的词，就将标签打1，没有就默认为0
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)
pAbusive = sum(trainCategory) / float(numTrainDocs)
p0Num = zeros(numWords)
p1Num= zeros(numWords)
p0Denom = 0.0;p1Denom = 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
p0Vect = p0Num /p0Denom
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
def testingNB():
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
trainMat=[]
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList,postinDoc))
p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
testEntry = ['love','my','dalmation']
thisDoc = array(setOfWords2Vec(myVocabList,testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry = ['stupid','garbage']
thisDoc = array(setOfWords2Vec(myVocabList,testEntry))
print (testEntry,'classified as :',classifyNB(thisDoc,p0V,p1V,pAb))
def bagOfWords2VecMN(vocabList,inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] +=1
return returnVec
def textParse(bigString):
import re
listOfTokens = re.split(r'\W*',bigString)
return [tok.lower() for tok in listOfTokens if len(tok) >2]
def spamTest():
docList = []; classList = [];fullText = []
for i in range(1,26):
wordList = textParse(open('E:/数据挖掘/MLiA_SourceCode/machinelearninginaction/Ch04/email/spam/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
# print('zhe li de i shi %d,', i)
wordList = textParse(open('E:/数据挖掘/MLiA_SourceCode/machinelearninginaction/Ch04/email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)
trainingSet = list(range(50));testSet=[]
for i in range(10):
randIndex = int(random.uniform(0,len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat=[];trainClasses=[]
for docIndex in trainingSet:
trainMat.append(setOfWords2Vec(vocabList,docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
errorCount=0
for docIndex in testSet:
wordVector = setOfWords2Vec(vocabList,docList[docIndex])
if classifyNB(array(wordVector),p0V,p1V,pSpam) !=classList[docIndex]:
errorCount +=1
print ('the error rate is :',float(errorCount)/len(testSet))
