Python Bayes.save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 save [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 save [as 別名]
def retrain(request):
    # Retrain your brain
    user = User.objects.get(user=request.user)
    posts = Post.objects.filter(user=user)
    bayes = Brain.objects.get(user=user)
    brain = Bayes()
    #brain.loads(base64.decodestring(bayes.data))

    tagcount = 0
    # retrain the brain based on existing tags
    for post in posts:
        print post.title, "::",
        for tag in post.tags.all():
            text = "%s %s %s" % (post.title, post.author, post.summary)
            brain.train(tag, text)
            tagcount += 1
            print tag,
        print
    brain.save('%s.db' % user)
    bayes.data = base64.encodestring(brain.saves())
    bayes.save()

    message = 'Found %s tags' % tagcount
    params = {'Messages': [message,]}
    return response(request, 'mainapp/index.html', params)

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
def treinar():

    print """>>> Carregando categorias..."""
    CATEGORIAS = os.listdir('./data')
    if '.svn' in CATEGORIAS:
        CATEGORIAS.remove('.svn')


    print ">>> Instanciando treinador\n"    
    guesser = Bayes()
    try:
        for categoria in CATEGORIAS:
            print ">>> Treinando categoria %s" % categoria
            arquivos = os.listdir("%s/%s" % (CAMINHO_CATEGORIAS, categoria))
            if '.svn' in arquivos:
                arquivos.remove('.svn')
            for arquivo in arquivos:
                arquivo = open('%s/%s/%s' % (CAMINHO_CATEGORIAS, categoria, arquivo), 'r')               
                texto = arquivo.read()
                guesser.train(categoria, texto)
        print "\n>>> Salvando base de conhecimento...\n"
        guesser.save("conhecimento.bay")
        print "Voil?!\n"
    except:
        print "N?o foi poss?vel treinar a base"

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
 def train(self,bucket,words):
   """
   Nominate a bucket to which the words apply, and train accordingly
   """
   if bucket != "" and words != "":
     try:
       Bayes.train(self,bucket,words)
       Bayes.save(self,self.brain)
     except:
       print "Failed to learn"
   else:
     return None

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
  def __init__(self,name):
    Bayes.__init__(self)

    self.brain = name + '.bay'

    try:
      Bayes.load(self,self.brain)
      print "[Bayes] Brain loaded ok"
    except:
      print "[Alert] Failed to load bayesian brain - %s, creating it now" % self.brain
      Bayes.save(self,self.brain)
      Bayes.load(self,self.brain)

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
 def action_train(self, cr, uid, ids, context=None):
     cat_obj = self.pool.get('crm.bayes.categories')
     group_obj = self.pool.get('crm.bayes.group')
     message_obj = self.pool.get('crm.bayes.test.guess')
     
     for id in ids:
         cat_id = self.read(cr, uid, id, ['category_id','name'])         
         cat_id = cat_id[0]['category_id']
         if  result :
             max_list = max(result, key=lambda k: k[1])
             if cat_id:
                 cat_guess_msg = cat_obj.read(cr, uid, cat_id, ['train_messages'])
                 cat_obj.write(cr, uid, cat_id, {'train_messages' :cat_guess_msg['train_messages'] + 1})
             if max_list[1] > 0 and not cat_id:
                 cat_id = cat_obj.search(cr, uid, [('name','=',max_list[0])])[0]
                 cat_guess_msg = cat_obj.read(cr, uid, cat_id, ['guess_messages'])
                 cat_obj.write(cr, uid, cat_id, {'guess_messages' :cat_guess_msg['guess_messages'] + 1})
                 self.write(cr, uid, ids, {'category_id':cat_id})
         if cat_id :
             cat_rec = cat_obj.read(cr, uid, cat_id, [])
             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.train(cat_rec['name'], message_obj.read(cr, uid, id)[0]['name'])
             guesser.save(file_path+"crm_bayes.bay")
             myfile = file(file_path+"crm_bayes.bay", 'r')
             data=""
             for fi in myfile.readlines():
                 data += fi 
             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 not rec[0]['tot_guess']:
                 rec[0]['tot_guess'] =0
             percantage = float(rec[0]['tot_guess'] *100)  / float(rec[0]['tot_guess'] + rec[0]['tot_train'])
             group_obj.write(cr, uid, cat_rec['group_id'][0], {'train_data': data,'automate_test':percantage})            
         else :
             raise osv.except_osv(_('Error !'),_('Please Select Category! '))
     return {
         'view_type': 'form', 
         "view_mode": 'form', 
         'res_model': 'crm.bayes.train.message', 
         'type': 'ir.actions.act_window', 
         'target':'new', 
      }

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
def treino (self):    
        banco_do_jornal = Server()
        genero=[banco_do_jornal[doc] for doc in GENEROS]
         #treinando o reverend
        from reverend.thomas import Bayes
        guesser = Bayes()
        guesser.train('artigo', ' '.join(genero[0][doc]['texto'] for doc in genero[0]))
        guesser.train('resenha',' '.join(genero[6][doc]['texto'] for doc in genero[6]))  
        guesser.train('noticia',' '.join(genero[1][doc]['texto'] for doc in genero [1]))
        guesser.train('cronica',' '.join(genero[5][doc]['texto']for doc in genero[5] if 'texto' in genero[5][doc] ))
        guesser.train('horoscopo',' '.join(genero[3][doc]['texto']for doc in genero[3]))
        guesser.train('manchete',' '.join(genero[2][doc]['titulo']for doc in genero[2]))
        guesser.train('receita',' '.join(genero[4][doc]['texto']for doc in genero[4]))
        guesser.save('my_guesser.bay')
        variavel = guesser.guess('Cidad?o se descuidou e roubaram seu celular. Como era um executivo e n?o sabia mais viver sem celular, ficou furioso. Deu parte do roubo, de Quara?.? Pois ?.? Carol.? Hein?? Meu nome. ? Carol.? Ah. Voc?s s?o...? N?o, n?o. Nos conhecemos h? pouco.? Escute Carol. Eu trouxe uma encomenda para o Amleto. De Quara?. Uma pessegada, mas n?o me lembro do endere?o.')
        print  'Resultado = ', variavel

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
 def treino (self):    
     banco_do_jornal = Server()
     genero=[banco_do_jornal[doc] for doc in GENEROS]
      #treinando o reverend
     from reverend.thomas import Bayes
     guesser = Bayes()
     guesser.train('artigo', ' '.join(genero[0][doc]['texto'] for doc in genero[0]))
     guesser.train('resenha',' '.join(genero[6][doc]['texto'] for doc in genero[6]))  
     guesser.train('noticia',' '.join(genero[1][doc]['texto'] for doc in genero [1]))
     guesser.train('cronica',' '.join(genero[5][doc]['texto']for doc in genero[5] if 'texto' in genero[5][doc] ))
     guesser.train('horoscopo',' '.join(genero[3][doc]['texto']for doc in genero[3]))
     guesser.train('manchete',' '.join(genero[2][doc]['titulo']for doc in genero[2]))
     guesser.train('receita',' '.join(genero[4][doc]['texto']for doc in genero[4]))
     guesser.save('my_guesser.bay')
     variavel = guesser.guess('Bolo de chocolate :ingredientes : 6 ovos, 2 xicaras de farinha, 1 colher de achocolatado, 1 lata de leite condensado, 2 copos de leite e 3 colheres de açucar')
     print  'Resultado = ', variavel

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
def get_db(private_path, username):
  path = os.path.join(os.path.join(private_path, username), 'spam.bayes')
  guesser = Bayes()

  # load the spam DB
  try:
    guesser.load(path)
  except IOError:
    print "Creating a new spam filter database"

    parent_directory = os.path.dirname(path)
    if not os.path.isdir(parent_directory):
      os.makedirs(parent_directory)

    guesser.save(path)

  return guesser, path

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
    def treino (self):    
        banco_do_jornal = Server()
        genero=[banco_do_jornal[doc] for doc in GENEROS]
        #dicionários
        dicionario_artigo={'enunciados de opiniao':'eu acredito''eu acho''nós entendemos que'}
        dicionario_resenha={'auxiliar modal':'pode''deve','lexico':'filme''peça''livro''artista'}
        dicionario_horoscopo={'lexico':'signo''peixes''áries''capricórnio''escorpião''cancer''gêmeos''touro''libra''sargitário''aquario''planeta''mercurio''vênus''marte''jupter''saturno''urano''netuno''ascendente''amor''saúde''trabalho''carta''sorte''dinheiro'}
        dicionario_noticia={'marcadores de data':'janeiro''fevereiro''março''abril''maio''junho''julho''agosto''setembro''outubro''novembro''dezenbro'}
        
         #treinando o reverend
        from reverend.thomas import Bayes
        guesser = Bayes()
        guesser.train('artigo', ' '.join(genero[0][doc]['texto'] for doc in genero[0]) )
        guesser.train('resenha',' '.join(genero[6][doc]['texto'] for doc in genero[6]))  
        guesser.train('noticia',' '.join(genero[1][doc]['texto'] for doc in genero [1]))
        guesser.train('cronica',' '.join(genero[5][doc]['texto']for doc in genero[5] if 'texto' in genero[5][doc] ))
        guesser.train('horoscopo',' '.join(genero[3][doc]['texto']for doc in genero[3]))
        guesser.train('manchete',' '.join(genero[2][doc]['titulo']for doc in genero[2]))
        guesser.train('receita',' '.join(genero[4][doc]['texto']for doc in genero[4]))
        guesser.save('my_guesser.bay')
        variavel = guesser.guess('Lía, Claudia e Dourado se enfrentam no oitavo paredão do BBB10, que acontecerá nesta terça (2)Lia foi a escolha do líder Michel, que justificou que sua opinião vem sendo formada ao longo do jogo. Cacau foi eliminada, pois foram 80% dos votos contra ela, então ela saiu, muitas pessoas não queriam que ela saisse mais foram os votos que decidiram a derrota da cacau (Cláudia)')

        print  'Resultado = ', variavel

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
from reverend.thomas import Bayes
guesser = Bayes()
guesser.train('french', 'le la les du un une je il elle de en')
guesser.train('german', 'der die das ein eine')
guesser.train('spanish', 'el uno una las de la en')
guesser.train('english', 'the it she he they them are were to')
guesser.guess('they went to el cantina')
guesser.guess('they were flying planes')
guesser.train('english', 'the rain in spain falls mainly on the plain')
guesser.save('my_guesser.bay')

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
from reverend.thomas import Bayes
guesser = Bayes()

f = open("spam.log",'r')
for line in f:
  guesser.train('spam', line.strip())

f = open("notspam.log",'r')
for line in f:
  guesser.train('notspam', line.strip())

guesser.save('spam.bay')

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

  POSITIVE = POSITIVE
  NEGATIVE = NEGATIVE
  NEUTRAL  = NEUTRAL

  THRESHHOLD = 0.1
  guesser = None

  def __init__(self):
    self.guesser = Bayes()

  def train(self, example_tweets):
    for t in example_tweets:
      self.guesser.train(t.sentiment, t.text)

    self.guesser.train(POSITIVE, "cool")
    self.guesser.train(POSITIVE, "Woo")
    self.guesser.train(POSITIVE, "quite amazing")
    self.guesser.train(POSITIVE, "thks")
    self.guesser.train(POSITIVE, "looking forward to")
    self.guesser.train(POSITIVE, "damn good")
    self.guesser.train(POSITIVE, "frickin ruled")
    self.guesser.train(POSITIVE, "frickin rules")
    self.guesser.train(POSITIVE, "Way to go")
    self.guesser.train(POSITIVE, "cute")
    self.guesser.train(POSITIVE, "comeback")
    self.guesser.train(POSITIVE, "not suck")
    self.guesser.train(POSITIVE, "prop")
    self.guesser.train(POSITIVE, "kinda impressed")
    self.guesser.train(POSITIVE, "props")
    self.guesser.train(POSITIVE, "come on")
    self.guesser.train(POSITIVE, "congratulation")
    self.guesser.train(POSITIVE, "gtd")
    self.guesser.train(POSITIVE, "proud")
    self.guesser.train(POSITIVE, "thanks")
    self.guesser.train(POSITIVE, "can help")
    self.guesser.train(POSITIVE, "thanks!")
    self.guesser.train(POSITIVE, "pumped")
    self.guesser.train(POSITIVE, "integrate")
    self.guesser.train(POSITIVE, "really like")
    self.guesser.train(POSITIVE, "loves it")
    self.guesser.train(POSITIVE, "yay")
    self.guesser.train(POSITIVE, "amazing")
    self.guesser.train(POSITIVE, "epic flail")
    self.guesser.train(POSITIVE, "flail")
    self.guesser.train(POSITIVE, "good luck")
    self.guesser.train(POSITIVE, "fail")
    self.guesser.train(POSITIVE, "life saver")
    self.guesser.train(POSITIVE, "piece of cake")
    self.guesser.train(POSITIVE, "good thing")
    self.guesser.train(POSITIVE, "hawt")
    self.guesser.train(POSITIVE, "hawtness")
    self.guesser.train(POSITIVE, "highly positive")
    self.guesser.train(POSITIVE, "my hero")
    self.guesser.train(POSITIVE, "yummy")
    self.guesser.train(POSITIVE, "awesome")
    self.guesser.train(POSITIVE, "congrats")
    self.guesser.train(POSITIVE, "would recommend")
    self.guesser.train(POSITIVE, "intellectual vigor")
    self.guesser.train(POSITIVE, "really neat")
    self.guesser.train(POSITIVE, "yay")
    self.guesser.train(POSITIVE, "ftw")
    self.guesser.train(POSITIVE, "I want")
    self.guesser.train(POSITIVE, "best looking")
    self.guesser.train(POSITIVE, "imrpessive")
    self.guesser.train(POSITIVE, "positive")
    self.guesser.train(POSITIVE, "thx")
    self.guesser.train(POSITIVE, "thanks")
    self.guesser.train(POSITIVE, "thank you")
    self.guesser.train(POSITIVE, "endorse")
    self.guesser.train(POSITIVE, "clearly superior")
    self.guesser.train(POSITIVE, "superior")
    self.guesser.train(POSITIVE, "really love")
    self.guesser.train(POSITIVE, "woot")
    self.guesser.train(POSITIVE, "w00t")
    self.guesser.train(POSITIVE, "super")
    self.guesser.train(POSITIVE, "wonderful")
    self.guesser.train(POSITIVE, "leaning towards")
    self.guesser.train(POSITIVE, "rally")
    self.guesser.train(POSITIVE, "incredible")
    self.guesser.train(POSITIVE, "the best")
    self.guesser.train(POSITIVE, "is the best")
    self.guesser.train(POSITIVE, "strong")
    self.guesser.train(POSITIVE, "would love")
    self.guesser.train(POSITIVE, "rally")
    self.guesser.train(POSITIVE, "very quickly")
    self.guesser.train(POSITIVE, "very cool")
    self.guesser.train(POSITIVE, "absolutely love")
    self.guesser.train(POSITIVE, "very exceptional")
    self.guesser.train(POSITIVE, "so proud")
    self.guesser.train(POSITIVE, "funny")
    self.guesser.train(POSITIVE, "recommend")
    self.guesser.train(POSITIVE, "so proud")
    self.guesser.train(POSITIVE, "so great")
    self.guesser.train(POSITIVE, "so cool")
    self.guesser.train(POSITIVE, "cool")
    self.guesser.train(POSITIVE, "wowsers")
    self.guesser.train(POSITIVE, "plus")
    self.guesser.train(POSITIVE, "liked it")
#.........這裏部分代碼省略.........

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
    for line in reader:
        body = line[1]
        if line[2] == "visible":
            status = "visible"
        else:
            status = "moderated"
        clean_body = re.sub("<[^>]*>","",body)
        guesser.train(status, clean_body)


try:
    guesser.load('dataset.dat')
except IOError as e:
    load_csv_to_bayes('good.csv')
    load_csv_to_bayes('bad.csv')
    guesser.save('dataset.dat')


from flask import Flask, request
from flask import render_template
app = Flask(__name__)

@app.route("/moderate")
def moderate():
    if request.args.has_key('callback'):
        wrapper = request.args.get('callback')+"(%s)"
    else:
        wrapper = "%s"
    results = guesser.guess(request.args.get('body'))
    return wrapper % (json.dumps(results))

# 需要導入模塊: from reverend.thomas import Bayes [as 別名]
# 或者: from reverend.thomas.Bayes import save [as 別名]
 def save(self):
   """
   Save the brain to disk
   """
   Bayes.save(self,self.brain)