Study Log---Naive Bayesian algorithm

import numpy as np# returns the sample data set Def loaddataset ():     postinglist=[[' my ',   ' dog ',  ' have ',  ' 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]     return postinglist,classvec# extracts the words from the sample data to form the Glossary def createvocablist (DataSet):     vocabset = set ([])     for document in  Dataset:        vocabset = vocabset | set (document     return list (Vocabset) #传入单词表和待分析的数据, the data is converted to vectors, where the word for each row of samples appears def  Setofwords2vec (Vocablist, inputset):     retvocablist = [0] * len ( Vocablist)     for word in inputSet:         if word in vocabList:             retvocablist[vocablist.index (wORD)] = 1        else:             print  ' word  ', word , ' Not in dict '      return retvocablist# here is the number of occurrences of each sample def bagofwords2vecmn (Vocablist, inputset):     returnvec = [0]*len (vocablist)     for word in  inputset:        if word in vocablist:             returnvec[vocablist.index (word)] += 1     return returnvec# brings in sample data and results, calculates the number of occurrences for a class # This computes the probability of each word appearing in different groups def trainnb0 ( Trainmatrix,traincatergory):     numtraindoc = len (TrainMatrix)      numwords = len (Trainmatrix[0])     pabusive = sum (trainCatergory) /float (Numtraindoc)      #防止多个概率的成绩当中的一个为0     p0num = np.ones (numwords)      p1num = np.ones (numwords)     p0Denom = 2.0     p1denom = 2.0    for i in range (NUMTRAINDOC):         if trainCatergory[i] == 1:             p1Num +=trainMatrix[i]             p1denom += sum (Trainmatrix[i])          else:            p0num += Trainmatrix[i]            p0denom += sum (Trainmatrix[i])      #处于精度的考虑, otherwise it is possible to limit to zero, because there may be too many items are 0    # Avoid errors caused by overflow and floating-point rounding     p1vect&nbSp;= np.log (p1num/p1denom)     p0vect = np.log (p0Num/p0Denom)      return p0vect,p1vect,pabusive# here is the equivalent of log def classifynb (Vec2classify, p0vec,  P1VEC, PCLASS1):     p1 = sum (Vec2classify * p1vec)  +  np.log (PCLASS1)     p0 = sum (Vec2classify * p0vec)  +  Np.log (1.0 - PCLASS1)     if p1 > p0:         return 1    else:         return 0# test Method DEF TESTINGNB ():    listoposts,listclasses =  Loaddataset ()     myvocablist = createvocablist (listoposts)      trainMat=[]    for postinDoc in listOPosts:         trainmat.appenD (Setofwords2vec (Myvocablist, postindoc))     p0v,p1v,pab = trainnb0 ( Np.array (Trainmat), Np.array (listclasses))     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) Def main ():     TESTINGNB ( ) if __name__ ==  ' __main__ ':     main ()

Study Log---Naive Bayesian algorithm