Python Bayes.untrain方法代碼示例

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import untrain [as 別名]
 def untrained(self, cr, uid, ids, context=None):
     for id in ids:
         record = self.read(cr, uid, id, ['category_id','description'])
         if record['description']:
             group_obj = self.pool.get('crm.bayes.group')
             cat_obj = self.pool.get('crm.bayes.categories')
             cat_rec = cat_obj.read(cr, uid, record['category_id'][0],[])
             guesser = Bayes()
             data = ""
             for rec in group_obj.browse(cr, uid, [cat_rec['group_id'][0]]):
                 if rec['train_data']:
                     data += rec['train_data']
             if data :
                 myfile = file(file_path+"crm_bayes.bay", 'w')
                 myfile.write(data)
                 myfile.close()
                 guesser.load(file_path+"crm_bayes.bay")
             guesser.untrain(cat_rec['name'],record['description'])
             guesser.save(file_path+"crm_bayes.bay")
             myfile = file(file_path+"crm_bayes.bay", 'r')
             data= ""
             for fi in myfile.readlines():
                 data += fi
             group_obj.write(cr, uid, cat_rec['group_id'][0], {'train_data': data})
             cat_obj.write(cr, uid, record['category_id'][0], {'train_messages':int(cat_rec['train_messages']) - 1 })
             cr.execute("select sum(train_messages) as tot_train,sum(guess_messages) as tot_guess from crm_bayes_categories where group_id=%d"% cat_rec['group_id'][0])
             rec = cr.dictfetchall()
             if rec[0]['tot_guess']:
                 percantage = float(rec[0]['tot_guess'] *100)  / float(rec[0]['tot_guess'] + rec[0]['tot_train'])
             else :
                 percantage = 0.0
             group_obj.write(cr, uid, cat_rec['group_id'][0], {'train_data': data,'automate_test':percantage})            
             self.write(cr, uid, id, {'state_bayes':'untrained'})
     return True    

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import untrain [as 別名]
def mark(request, flag):
    
    id = request.GET.get('post', None)
    feed = request.GET.get('feed', None)
    category = request.GET.get('category') 
    tag = request.GET.get('tag') or None
    
    try:
        if feed:
           posts = Post.objects.filter(feed=feed)
        else:
           posts = Post.objects.filter(id=id)
    except Post.DoesNotExist:
        return HttpResponseRedirect('/')
    
    bayes = Brain.objects.get(user=request.user) #login required
    brain = Bayes()
    brain.loads(base64.decodestring(bayes.data))
    
    if flag in ('read', 'unread'):
        flag = flag == 'read'
        posts.update(read=flag) 
    else:
        for post in posts:
            text = "%s %s %s" % (post.title, post.author, post.summary)
            t1 = Tag.objects.get(id=flag)
            if t1 in post.tags.all() and not feed:
                post.tags.remove(t1) 
                brain.untrain(t1.name, text)
            else:
                post.tags.add(t1)
                brain.train(t1.name, text)
            post.save()    
        
    bayes.data = base64.encodestring(brain.saves())
    bayes.save()
        
    if category:
       return HttpResponseRedirect('/?category=%s' % category)
    elif feed:
       return HttpResponseRedirect('/?feed=%s' % feed)
    elif tag:
       return HttpResponseRedirect('/?tag=%s' % tag)
    else:
       return HttpResponseRedirect('/')

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import untrain [as 別名]
 def untrain(self,bucket,words):
   """
   Remove nominated words from the relevant bucket
   """
   Bayes.untrain(self,bucket,words)
   Bayes.save(self,self.brain)

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import untrain [as 別名]
from reverend.thomas import Bayes

guesser = Bayes()
guesser.train('fish', 'salmon trout cod carp')
guesser.train('fowl', 'hen chicken duck goose')

guesser.guess('chicken tikka marsala')

guesser.untrain('fish','salmon carp')

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import untrain [as 別名]
brain = Bayes()
brain.load('fish.db')

tag = 'Dead'
posts = Post.objects.filter(read=read)
posts = posts.filter(tags__in=tag)
#brain.train('Dead', post.summary)
t1 = Tag.objects.get(id=flag)

for post in posts:
  t1 = Tag.objects.get(id=flag)
  if t1 in post.tags.all() and not feed:
    post.tags.remove(t1) 
    post.read = not t1.read
    brain.untrain(t1.name, post.summary)
  else:
    post.tags.add(t1)
    post.read = t1.read
    brain.train(t1.name, post.summary)

post.save()    

flag = "Weather"
t1 = Tag.objects.get(name=flag)
keyword = "weather"
for post in posts:
  if keyword in post.title.lower():
    post.tags.add(t1)
    post.dead = True
    brain.train(t1.name, post.title+post.summary)