貝葉斯是搞概率論的。學術圈上有個貝葉斯學派。看起來吊吊的。關于貝葉斯是個啥網上有很多資料。想必讀者基本都明了。我這里只簡單概括下：貝葉斯分類其實就是基于先驗概率的基礎上的一種分類法，核心公式就是條件概率。舉個俗氣的例子，通過我們的以往觀察，鯉魚中尾巴是紅色的占比達90%，鯽魚中尾巴是紅色的占比只有1%不到，那么新來了一條小魚，他是鯉魚還是鯽魚呢？我看一下他的尾巴，發現是紅色，根據過去的先驗概率經驗，它是鯉魚的概率比較大，我認為它是鯉魚。

這當時是個最簡單的例子，實踐中的問題就復雜了。比如說特征不止是尾巴紅不紅，還有魚嘴巴大不大，魚肥不肥，魚身子長還是寬，各種，而且不是一個特征就能分辨出來的，還需要多方分析，然后貝爺感覺這個那個的真麻煩，就先假定每個特征都是獨立的，如果一條魚紅尾巴大嘴巴肥得很還是長身子，就這樣求她是鯉魚的概率：鯉魚中紅尾巴0.9*鯉魚中大嘴巴0.3*鯉魚中肥豬0.6*鯉魚中長身子0.4=0.27*0.24.。。。。

閑話少扯。上代碼分析。我代碼干的不是魚的分類了，而是一篇文檔。

from numpy import *

def loadDataSet():#這個函數呢，他建立了一個敏感詞典，并打了標簽，共6個詞集合，其中2、4、6詞集合中的詞是敏感詞

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):#這個函數呢，它是把輸入的dataset(就是一個新文檔嘛)進行分解處理，返回的是這個文檔沒有重復詞的列表

vocabSet = set([])

for document in dataSet:

vocabSet = vocabSet | set(document)

return list(vocabSet)

def setOfWords2Vec(vocabList,inputSet):#這個函數呢，他就是根據輸入的新文檔，和詞匯表，來對新文檔打標簽，看他有多少敏感詞，只要是出現了詞匯表里的詞，就將標簽打1，沒有就默認為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:/數據挖掘/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:/數據挖掘/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))

總結

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