我认为(而且 Contino 仍然不明白为什么会有所不同)这段代码:
def categories(self):
cur=self.con.execute('select category from cc');
for d in cur:
return d[0]
等同于这个:
def categories(self):
cur=self.con.execute('select category from cc');
return [d[0] for d in cur]
但是当我在我的代码中一个一个地替换另一个时，我在代码的其他地方得到了一个错误:
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 226, in post
spam_result = nb.classify(given_sentence)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 204, in classify
if cat==best: continue
UnboundLocalError: local variable 'best' referenced before assignment
为什么会这样？为什么这两段代码不等价？
完整代码:
# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
import sqlite3
import USSSALoader
import random
from pysqlite2 import dbapi2 as sqlite
import re
import math
def getfeatures(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 the unique set of words only
# return dict([(w,1) for w in words]).iteritems()
return dict([(w,1) for w in words])
class classifier:
def __init__(self,getfeatures, filename=None):
# Counts of feature/category combinations
self.fc={}
# Counts of documents in each category
self.cc={}
self.getfeatures=getfeatures
# def setdb(self,dbfile):
self.con=sqlite.connect('db_file')
# self.con=sqlite3.connect(":memory:")
self.con.execute('create table if not exists fc(feature,category,count)')
self.con.execute('create table if not exists cc(category,count)')
def incf(self,f,cat):
count=self.fcount(f,cat)
if count==0:
self.con.execute("insert into fc values ('%s','%s',1)" % (f,cat))
else:
self.con.execute(
"update fc set count=%d where feature='%s' and category='%s'"
% (count+1,f,cat))
def fcount(self,f,cat):
res=self.con.execute(
'select count from fc where feature="%s" and category="%s"'
%(f,cat)).fetchone()
if res==None: return 0
else: return float(res[0])
def incc(self,cat):
count=self.catcount(cat)
if count==0:
self.con.execute("insert into cc values ('%s',1)" % (cat))
else:
self.con.execute("update cc set count=%d where category='%s'"
% (count+1,cat))
def catcount(self,cat):
res=self.con.execute('select count from cc where category="%s"'
%(cat)).fetchone()
if res==None: return 0
else: return float(res[0])
def categories(self):
cur=self.con.execute('select category from cc');
# return [d[0] for d in cur]
for d in cur:
# print "d =", d
# print "d[0] =", d[0]
return d[0]
def totalcount(self):
res=self.con.execute('select sum(count) from cc').fetchone();
if res==None: return 0
print "res=self.con.execute('select * FROM cc').fetchall(); = ", self.con.execute('select * FROM cc').fetchall();
print 'res sum(count) = ', res
print 'res[0] = ', res[0]
return res[0]
def train(self,item,cat):
features=self.getfeatures(item)
# Increment the count for every feature with this category
for f in features:
## for f in features:
self.incf(f,cat)
# Increment the count for this category
self.incc(cat)
self.con.commit()
def fprob(self,f,cat):
if self.catcount(cat)==0: return 0
# The total number of times this feature appeared in this
# category divided by the total number of items in this category
return self.fcount(f,cat)/self.catcount(cat)
def weightedprob(self,f,cat,prf,weight=1.0,ap=0.5):
# Calculate current probability
basicprob=prf(f,cat)
# Count the number of times this feature has appeared in
# all categories
totals=sum([self.fcount(f,c) for c in self.categories()])
# Calculate the weighted average
bp=((weight*ap)+(totals*basicprob))/(weight+totals)
return bp
class naivebayes(classifier):
def __init__(self,getfeatures):
classifier.__init__(self, getfeatures)
self.thresholds={}
def docprob(self,item,cat):
features=self.getfeatures(item)
# Multiply the probabilities of all the features together
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
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 classify(self,item,default=None):
probs={}
# Find the category with the highest probability
max=0.0
for cat in self.categories():
probs[cat]=self.prob(item,cat)
if probs[cat]>max:
max=probs[cat]
best=cat
# Make sure the probability exceeds threshold*next best
for cat in probs:
if cat==best: continue
if probs[cat]*self.getthreshold(best)>probs[best]: return default
return best
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')
nb = naivebayes(getfeatures)
sampletrain(nb)
doc_test = "buy pharmaceuticals now or earn money at the online casino"
print ('\ndoc_test is classified as %s'%nb.classify(doc_test))
请您参考如下方法:
一个函数只返回一次。
当你看到
for d in cur:
return d[0]
循环在第一次迭代期间返回。
return [d[0] for d in cur]
遍历 cur 中的每个项目以创建一个列表，然后返回结果。
