【データマイニング】ドキュメント分類の素朴ベイズアルゴリズム

#!/usr/bin/python
import re
import math
def getwords(doc):
    splitter=re.compile('\\W*')
      # Split the words by non-alpha characters
    words=[s.lower() for s in splitter.split(doc) if len(s)>2 and len(s)<20]
    return dict([(w,1) for w in words])
#     demo，     ，  Nobody owns the water features，good cat
def sampletrain(cl):
    cl.train('Nobody owns the water.','good')
    cl.train('the quick rabbit jumps fences','good')
    cl.train('buy pharmaceuticals now','bad')
    cl.train('make quick money at the online casino','bad')
    cl.train('the quick brown fox jumps','good')
#          item   。
class classifier:
    def __init__(self,getfeatrues,filename=None):
        #               
        self.fc={}
        #             
        self.cc={}
        self.getfeatures=getfeatures
    #             
    def incf(self,f,cat):
        self.fc.setdefault(f,{})
        self.fc[f].setdefault(cat,0)
        self.fc[f][cat]+=1
    #         
    def incc(self,cat):
        self.cc.setdefault(cat,0)
        self.cc[cat]+=1
    #        
    def fcount(self,f,cat):
        if f in self.fc and cat in self.fc[f]:
            return float(self.fc[f][cat])
        return 0.0
    #  category     
    def catcount(self,cat):
        if cat in self.cc:
            return float(self.cc[cat])

        return 0.0
    #item      
    def totalcount(self):
        return sum(self.cc.values())
    #    category
    def categories(self):
        return self.cc.keys()
    #   feature         ，cat  feature   
    def train(self,item,cat):
        features= self.getfeatrues(item)
        for f in features:
            self.incf(f,cat)
        self.incc(cat)
    #        
    def fprob(self,f,cat):
        if self.catcount[cat] == 0:
            return 0
        return self.fcount(f,cat)/self.catcount(cat)
    #        
    #(weight*assumedprob + count*fprob)/(count+weight)
    def wieghtprob(self,f,cat,weight=1,ap=0.5,prf):
        basicprob=prf(f,cat)
        totals=sum(self.fcount(f,c) for c in self.categories())
        bp=((weight*ap)+(totals*basicprob))/(weight+totals)
        return bp
    

#       
class naivebayes(classifier):
    #         ，    ，       ：  item   
    def docprob(self,item,cat):
        features=self.getfeatures(item)
        p=1
        for f in features: p*=self.weightedprob(f,cat,self.fprob)
        return p
    def prob(self,item,cat):
        catprob = self.catcount(cat)/self.totalcount()
        docprob=self.docprob(item,cat)
        return catprob*docprob

まとめ:
1)sampleベーストレーニング分類器
2)itemの条件確率を計算する
3)ベイズ理論に基づいてdocの条件確率を計算する