def observe(self, observation):
self.episode_done = observation['episode_done']
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
obs['text2vec'] = maintain_dialog_history(
self.history, obs,
historyLength=self.opt['history_length'],
useReplies=self.opt['history_replies'],
dict=self.dict, useStartEndIndices=False)
self.observation = obs
return obs
def observe(self, observation):
self.episode_done = observation['episode_done']
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
obs['text2vec'] = maintain_dialog_history(
self.history, obs,
historyLength=self.opt['history_length'],
useReplies=self.opt['history_replies'],
dict=self.dict, useStartEndIndices=False)
self.observation = obs
return obs
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
"""
self.episode_done = observation['episode_done']
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
batch_idx = self.opt.get('batchindex', 0)
obs['text2vec'] = maintain_dialog_history(
self.history,
obs,
reply=self.answers[batch_idx],
historyLength=self.opt['history_length'],
useReplies=self.opt['history_replies'],
dict=self.dict)
self.observation = obs
self.answers[batch_idx] = None
return obs
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
"""
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
if not obs.get('preprocessed', False):
obs['text2vec'] = maintain_dialog_history(
self.history, obs,
reply=self.answers[self.batch_idx],
historyLength=self.truncate,
useReplies=self.opt.get('history_replies'),
dict=self.dict,
useStartEndIndices=False)
else:
obs['text2vec'] = deque(obs['text2vec'], maxlen=self.truncate)
self.observation = obs
self.answers[self.batch_idx] = None
return obs
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
"""
self.episode_done = observation['episode_done']
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
batch_idx = self.opt.get('batchindex', 0)
obs['text'] = '\n'.join(maintain_dialog_history(
self.history, obs,
reply=self.answers[batch_idx] if self.answers[batch_idx] is not None else '',
historyLength=self.opt['history_length'],
useReplies=self.opt['history_replies'],
dict=None, useStartEndIndices=False))
self.observation = obs
self.answers[batch_idx] = None
return obs
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
"""
self.episode_done = observation['episode_done']
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
batch_idx = self.opt.get('batchindex', 0)
obs['text'] = (maintain_dialog_history(
self.history, obs,
reply=self.answers[batch_idx] if self.answers[batch_idx] is not None else '',
historyLength=self.opt['mem_size'] + 1,
useReplies=self.opt['history_replies'],
dict=self.dict, useStartEndIndices=False, splitSentences=True))
self.observation = obs
self.answers[batch_idx] = None
return obs
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
"""
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
batch_idx = self.opt.get('batchindex', 0)
if not obs.get('preprocessed', False):
obs['text2vec'] = maintain_dialog_history(
self.history, obs,
reply=self.answers[batch_idx],
historyLength=self.opt['history_length'],
useReplies=self.opt['history_replies'],
dict=self.dict,
useStartEndIndices=False)
else:
obs['text2vec'] = deque(obs['text2vec'], self.opt['history_length'])
self.observation = obs
self.answers[batch_idx] = None
return obs
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
"""
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
obs['persona'], obs['text'] = self.extract_persona(obs.get('text', ''))
# obs['persona'], _ = self.extract_persona(obs.get('text', ''))
batch_idx = self.opt.get('batchindex', 0)
if not obs.get('preprocessed', False):
obs['text2vec'] = maintain_dialog_history(
self.history,
obs,
reply=self.answers[batch_idx],
historyLength=self.truncate,
useReplies=self.opt['include_labels'],
dict=self.dict,
useStartEndIndices=True)
else:
obs['text2vec'] = deque(obs['text2vec'], maxlen=self.truncate)
self.observation = obs
self.answers[batch_idx] = None
return obs
