Python是一種面向物件、解釋型計算機程式設計語，作者是Guido van Rossum（吉多·範羅蘇姆），1991年公開正式發行。粗糙進行歸納： （1）Python是純粹自由軟體，原始碼和直譯器CPython遵循 GPL協議 （2）Python語法簡潔清晰 （3）Python具有豐富和強大的庫（^_^膠水語言^_^）         NumPy系統是Python的一種開源的數值計算擴充套件，一個用python實現的科學計算包。這種工具可用來儲存和處理大型矩陣。 （1）下載對應Python版本的NumPy（請用Ctrl+F查詢numpy） http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy

（2）將.whl改為.rar    
（3）將.rar中的內容全部Copy到Python安裝目錄下的.\Lib\site-packages之下。例如：                D:\Program Files\python\Lib\site-packages Until to here, we can use it. Ok, Let's see an example about Naive Bayesian Network and itsAlgorithm. 1）Naive Bayesian Network 2）Naive Bayesian Algorithm

 
3）I will give some net-source to you http://scikit-learn.org/stable/modules/naive_bayes.html http://www.saedsayad.com/naive_bayesian.htm http://www.saedsayad.com/naive_bayesian_exercise.htm http://www.saedsayad.com/flash/Bayesian.html    Step to here, we already have prepared Programming Language, Tool of NumPy, Naive Bayesian Network and Algorithm. So, let's implement Naive Baysian Classifier by this Language & Tool &Theory.Fellow is Code-Programming, ^_^let's see it now. 1）Some Code of Python-Self-Methods about Naive Bayesian and save it as bayes.py.
''' Created on Oct 19, 2010 ''' #匯入numpy（一種開源的數值計算擴充套件，可用來儲存和處理大型矩陣） from numpy import * 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 = ones(numWords); p1Num = ones(numWords)      #change to ones()      p0Denom = 2.0; p1Denom = 2.0                        #change to 2.0     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 = log(p1Num/p1Denom)          #change to log()     p0Vect = log(p0Num/p0Denom)          #change to log()     return p0Vect,p1Vect,pAbusive def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):     p1 = sum(vec2Classify * p1Vec) + log(pClass1)    #element-wise mult     p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)     if p1 > p0:         return 1     else:          return 0 def bagOfWords2VecMN(vocabList, inputSet):     returnVec = [ 0 ]*len(vocabList)     for word in inputSet:         if word in vocabList:             returnVec[ vocabList.index(word) ] += 1     return returnVec 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 textParse(bigString):    #input is big string, #output is word list     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('email/spam/%d.txt' % i).read())         docList.append(wordList)         fullText.extend(wordList)         classList.append(1)         wordList = textParse(open('email/ham/%d.txt' % i).read())         docList.append(wordList)         fullText.extend(wordList)         classList.append(0)     vocabList = createVocabList(docList)#create vocabulary     trainingSet = range(50); testSet=[  ]           #create test set     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:#train the classifier (get probs) trainNB0         trainMat.append(bagOfWords2VecMN(vocabList, docList[ docIndex ]))         trainClasses.append(classList[ docIndex ])     p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))     errorCount = 0     for docIndex in testSet:        #classify the remaining items         wordVector = bagOfWords2VecMN(vocabList, docList[ docIndex ])         if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[ docIndex ]:             errorCount += 1             print ( ("classification error",docList[ docIndex ]))     print ( 'the error rate is: ',float(errorCount)/len(testSet))     #return vocabList,fullText def calcMostFreq(vocabList,fullText):     import operator     freqDict = {}     for token in vocabList:         freqDict[ token ]=fullText.count(token)     sortedFreq = sorted(freqDict.iteritems(), key=operator.itemgetter(1), reverse=True)      return sortedFreq[ :30 ]        def localWords(feed1,feed0):     import feedparser     docList=[  ]; classList = [  ]; fullText =[  ]     minLen = min(len(feed1[ 'entries' ]),len(feed0[ 'entries' ]))     for i in range(minLen):         wordList = textParse(feed1[ 'entries' ][ i ][ 'summary' ])         docList.append(wordList)         fullText.extend(wordList)         classList.append(1) #NY is class 1         wordList = textParse(feed0[ 'entries' ][ i ][ 'summary' ])         docList.append(wordList)         fullText.extend(wordList)         classList.append(0)     vocabList = createVocabList(docList)#create vocabulary     top30Words = calcMostFreq(vocabList,fullText)   #remove top 30 words     for pairW in top30Words:         if pairW[ 0 ] in vocabList: vocabList.remove(pairW[ 0 ])     trainingSet = range(2*minLen); testSet=[  ]           #create test set     for i in range(20):         randIndex = int(random.uniform(0,len(trainingSet)))         testSet.append(trainingSet[ randIndex ])         del(trainingSet[ randIndex ])       trainMat=[  ]; trainClasses = [  ]     for docIndex in trainingSet:#train the classifier (get probs) trainNB0         trainMat.append(bagOfWords2VecMN(vocabList, docList[ docIndex ]))         trainClasses.append(classList[ docIndex ])     p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))     errorCount = 0     for docIndex in testSet:        #classify the remaining items         wordVector = bagOfWords2VecMN(vocabList, docList[ docIndex ])         if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[ docIndex ]:             errorCount += 1     print ( 'the error rate is: ',float(errorCount)/len(testSet))     return vocabList,p0V,p1V def getTopWords(ny,sf):     import operator     vocabList,p0V,p1V=localWords(ny,sf)     topNY=[  ]; topSF=[  ]     for i in range(len(p0V)):         if p0V[ i ] > -6.0 : topSF.append((vocabList[ i ],p0V[ i ]))         if p1V[ i ] > -6.0 : topNY.append((vocabList[ i ],p1V[ i ]))     sortedSF = sorted(topSF, key=lambda pair: pair[ 1 ], reverse=True)     print ( "SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**")     for item in sortedSF:         print ( item[ 0 ])     sortedNY = sorted(topNY, key=lambda pair: pair[ 1 ], reverse=True)     print ( "NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**")     for item in sortedNY:         print ( item[ 0 ]) 2）In order to instruction how to use methods Up, I gives an example which used to check words-list and Detail as fellowing.      import bayes
    ListOPosts, ListClasses = bayes.loadDataSet()
    myVocabList = bayes.createVocabList(ListOPosts)
    #Ok, Let's see the fianlly-result.^_^     myVocabList RUNNING RESULT AS FOLLOWING