def predict(self, float_state_features, int_state_features, actions):
""" Returns values for each state/action pair.
:param float_state_features states as list of feature -> float value dict
:param int_state_features states as list of feature -> int value dict
:param actions actions as list of feature -> value dict
"""
float_examples = []
for i in range(len(float_state_features)):
float_examples.append({**float_state_features[i], **actions[i]})
if int_state_features is None:
return RLPredictor.predict(self, float_examples)
return RLPredictor.predict(self, float_examples, int_state_features)
def predict(self, states, actions):
""" Returns values for each state/action pair
:param states states as list of feature -> value dict
:param actions actions as list of feature -> value dict
"""
examples = []
for i in range(len(states)):
examples.append({**states[i], **actions[i]})
return RLPredictor.predict(self, examples)