# -*- coding: cp936 -*-
#朴素贝叶斯分类器的实现
#运行
#reload(docclass)
#c1=docclass.naivebayes(docclass.getwords)
#docclass.sampletrain(c1)
#c1.classify('quick rabbit',default='unknown')
#构建训练样本
def sampletrain(c1):
c1.train('Nobody owns the water.','good')
c1.train('the quick rabbit jumps fences','good')
c1.train('buy pharmaceuticals now','bad')
c1.train('make quick money at the online casino','bad')
c1.train('the quick brown fox jumps','good')
import re
import math
#从文本中提取特征
def getwords(doc):
splitter=re.compile('\\W*')
#根据非字母字典进行单词拆分
words=[s.lower() for s in splitter.split(doc) if len(s)>2 and len(s)<20]
#只返回一组不重复的单词
return dict([(w,1) for w in words])
#分类器
class classifier:
def __init__(self,getfeatures):
self.fc={}
self.cc={}
self.getfeatures=getfeatures
#classifier.__init__(self,getfeatures)
self.thresholds={}
def setthreshold(self,cat,t):
self.thresholds[cat]=t
def getthreshold(self,cat):
if cat not in self.thresholds:
return 1.0
return self.thresholds[cat]
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
def catcount(self,cat):
if cat in self.cc:
return float(self.cc[cat])
return 0
def totalcount(self):
return sum(self.cc.values())
def categories(self):
return self.cc.keys()
def train(self,item,cat):
features=self.getfeatures(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)
def weightedprob(self,f,cat,prf,weight=1.0,ap=0.5):
basicprob=prf(f,cat)
totals=sum([self.fcount(f,c) for c in self.categories()])
bp=((weight*ap)+(totals*basicprob))/(weight+totals)
return bp
def classify(self,item,default=None):
probs={}
max=0.0
for cat in self.categories():
probs[cat]=self.prob(item,cat)
if probs[cat]>max:
max=probs[cat]
best=cat
for cat in probs:
if cat==best:
continue
if probs[cat]*self.getthreshold(best)>probs[best]:
return default
return best
class naivebayes(classifier):
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 docprob*catprob
