def main():
markov = Markov()
sep = r'[。？?！! ]+'
filename = sys.argv[1]
dicfile = '{}.dat'.format(filename)
if os.path.exists(dicfile):
markov.load(dicfile)
else:
with open(filename, encoding='utf-8') as f:
sentences = []
for line in f:
sentences.extend(re.split(sep, line.strip()))
for sentence in tqdm.tqdm(sentences):
if sentence:
markov.add_sentence(morph.analyze(sentence))
print('.', end='')
sys.stdout.flush()
markov.save(dicfile)
print('\n')
while True:
line = input('> ')
if not line:
break
parts = morph.analyze(line)
keyword = next(
(word for word, part in parts if morph.is_keyword(part)), '')
print(markov.generate(keyword))
def dialogue(self, text):
"""ユーザーからの入力を受け取り、Responderに処理させた結果を返す。
呼び出されるたびにランダムでResponderを切り替える。
入力をDictionaryに学習させる。"""
chance = randrange(0, 100)
if chance in range(0, 29):
self._responder = self._responders['pattern']
elif chance in range(30, 49):
self._responder = self._responders['template']
elif chance in range(50, 69):
self._responder = self._responders['random']
elif chance in range(70, 89):
self._responder = self._responders['markov']
else:
self._responder = self._responders['what']
# print(type(self._responder)) 封印 クラスを見るときに必要だったよ。
parts = analyze(text) # 追加
if isinstance(self._responder, TemplateResponder):
response = self._responder.response(parts)
elif isinstance(self._responder, MarkovResponder):
response = self._responder.response(parts)
else:
response = self._responder.response(text)
# response = self._responder.response(text, parts) #これじゃ見れなかったよ？
self._dictionary.study(text, parts) # 変更
return response
def create_encoder_input(self, text):
tokens = morph.analyze(text, 0)
words = []
for word, part in tokens:
words.append(word)
input_data = np.zeros((1, len(words)), dtype='float32')
for t, word in enumerate(words):
input_data[0][t] = self.index[word]
return input_data
def text2onehot(self, text):
input_data = copy.deepcopy(self.encoder_input_data)
text = text.strip()
i_want_sleep = morph.analyze(text, 1)
for t, w in enumerate(i_want_sleep):
print(w)
# t=time(文字の出た時の要素数)
# one-hot表現
input_data[0, t, self.index[w[0]]] = 1.
return input_data
def markov_twitter(filename):
markov = Markov()
sep = r'[.?？!！ ]+'
dicfile = '{}.dat'.format(filename)
if os.path.exists(dicfile):
markov.load(dicfile)
else:
with open(filename, encoding='utf-8') as f:
sentences = []
for line in f:
sentences.extend(re.split(sep, line.strip()))
for sentence in tqdm.tqdm(sentences):
if sentence:
markov.add_sentence(morph.analyze(sentence))
markov.save(dicfile)
print('\n')
line = '*'
parts = morph.analyze(line)
keyword = next((word for word, part in parts if morph.is_keyword(part)),
'')
return markov.generate(keyword)
def dialogue(self, text):
chance = randrange(0, 100)
if chance in range(0, 39):
self._responder = self._responders['pattern']
elif chance in range(40, 69):
self._responder = self._responders['template']
elif chance in range(70, 89):
self._responder = self._responders['random']
else:
self._responders = self._responders['what']
parts = morph.analyze(text)
response = self._responder.response(text, parts)
self._dictionary.study(text, parts)
return response
def dialogue(self, text):
"""ユーザーからの入力を受け取り、Responderに処理させた結果を返す。
呼び出されるたびにランダムでResponderを切り替える。
入力をDictionaryに学習させる。"""
chance = randrange(0, 100)
if chance in range(0, 39):
self._responder = self._responders['pattern']
elif chance in range(40, 69):
self._responder = self._responders['template']
elif chance in range(70, 89):
self._responder = self._responders['random']
else:
self._responder = self._responders['what']
parts = morph.analyze(text)
response = self._responder.response(text, parts)
self._dictionary.study(text, parts)
return response
def dialogue(self, text):
"""ユーザーからの入力を受け取り，Responderに処理させた結果を返す．
呼び出されるたびにランダムでResponderを切り替える．"""
chance = randrange(0, 100)
if chance in range(0, 29):
self._responder = self._responders['pattern']
elif chance in range(30, 49):
self._responder = self._responders['template']
elif chance in range(50, 69):
self._rensponder = self._responders['random']
elif chance in range(70, 89):
self._responder = self._responders['markov']
else:
self._responder = self._responders['what']
parts = morph.analyze(text)
response = self._responder.response(text, parts)
self._dictionary.study(text, parts)
return response
def dialogue(self, text, markov_training):
"""ユーザーからの入力を受け取り、Responderに処理させた結果を返す。
呼び出されるたびにランダムでResponderを切り替える。
入力をDictionaryに学習させる。"""
chance = randrange(0, 100)
chance = 80
if chance in range(0, 29):
self._responder = self._responders['pattern']
elif chance in range(30, 49):
self._responder = self._responders['template']
elif chance in range(50, 69):
self._responder = self._responders['random']
elif (chance in range(70, 89)) or (markov_training==1):
self._responder = self._responders['markov']
else:
self._responder = self._responders['what']
parts = analyze(text)
response = self._responder.response(text, parts)
self._dictionary.study(text, parts)
return response
def study(self, text, parts):
"""ランダム辞書、パターン辞書をメモリに保存する。"""
self.study_random(text)
self.study_pattern(text, morph.analyze(text))
self.study_template(parts)
