def state_vectorize(self, state):
"""vectorize a state
Args:
state (tuple):
Dialog state
action (tuple):
Dialog act
Returns:
state_vec (np.array):
Dialog state vector
"""
self.state = state['belief_state']
action = state['user_action'] if self.character == 'sys' else state[
'system_action']
opp_action = delexicalize_da(action, self.requestable)
opp_action = flat_da(opp_action)
opp_act_vec = np.zeros(self.da_opp_dim)
for da in opp_action:
if da in self.opp2vec:
opp_act_vec[self.opp2vec[da]] = 1.
action = state['system_action'] if self.character == 'sys' else state[
'user_action']
action = delexicalize_da(action, self.requestable)
action = flat_da(action)
last_act_vec = np.zeros(self.da_dim)
for da in action:
if da in self.act2vec:
last_act_vec[self.act2vec[da]] = 1.
inform = np.zeros(self.inform_dim)
for domain in self.belief_domains:
for slot, value in state['belief_state'][
domain.lower()]['semi'].items():
dom_slot, p = domain + '-' + REF_USR_DA[domain][slot] + '-', 1
key = dom_slot + str(p)
while inform[self.inform2vec[key]]:
p += 1
key = dom_slot + str(p)
if key not in self.inform2vec:
break
else:
inform[self.inform2vec[key]] = 1.
book = np.zeros(len(self.db_domains))
for i, domain in enumerate(self.db_domains):
if state['belief_state'][domain.lower()]['book']['booked']:
book[i] = 1.
degree = self.pointer(state['belief_state'])
final = 1. if state['terminal'] else 0.
state_vec = np.r_[opp_act_vec, last_act_vec, inform, book, degree,
final]
return state_vec
def state_vectorize(self, state):
"""vectorize a state
Args:
state (dict):
Dialog state
action (tuple):
Dialog act
Returns:
state_vec (np.array):
Dialog state vector
"""
self.state = state['belief_state']
action = state['user_action'] if self.character == 'sys' else state[
'system_action']
opp_action = delexicalize_da(action, self.requestable)
opp_action = flat_da(opp_action)
opp_act_vec = np.zeros(self.da_opp_dim)
for da in opp_action:
if da in self.opp2vec:
opp_act_vec[self.opp2vec[da]] = 1.
action = state['system_action'] if self.character == 'sys' else state[
'user_action']
action = delexicalize_da(action, self.requestable)
action = flat_da(action)
last_act_vec = np.zeros(self.da_dim)
for da in action:
if da in self.act2vec:
last_act_vec[self.act2vec[da]] = 1.
belief_state = np.zeros(self.belief_state_dim)
i = 0
for domain in self.belief_domains:
for slot, value in state['belief_state'][
domain.lower()]['semi'].items():
if value:
belief_state[i] = 1.
i += 1
book = np.zeros(len(self.db_domains))
for i, domain in enumerate(self.db_domains):
if state['belief_state'][domain.lower()]['book']['booked']:
book[i] = 1.
degree = self.pointer(state['belief_state'])
final = 1. if state['terminated'] else 0.
state_vec = np.r_[opp_act_vec, last_act_vec, belief_state, book,
degree, final]
assert len(state_vec) == self.state_dim
return state_vec
def action_vectorize(self, action):
action = delexicalize_da(action, self.requestable)
action = flat_da(action)
act_vec = np.zeros(self.da_dim)
for da in action:
if da in self.act2vec:
act_vec[self.act2vec[da]] = 1.
return act_vec