def __init__(self,
transitions_C=None,
transitions_D=None,
emission_probabilities=None,
initial_state=0,
initial_action=C,
num_states=None,
mutation_probability=None) -> None:
transitions_C, transitions_D, emission_probabilities, initial_state, initial_action, num_states, mutation_probability = self._normalize_parameters(
transitions_C, transitions_D, emission_probabilities,
initial_state, initial_action, num_states, mutation_probability)
self.mutation_probability = mutation_probability
HMMPlayer.__init__(self,
transitions_C=transitions_C,
transitions_D=transitions_D,
emission_probabilities=emission_probabilities,
initial_state=initial_state,
initial_action=initial_action)
EvolvablePlayer.__init__(self)
self.overwrite_init_kwargs(
transitions_C=transitions_C,
transitions_D=transitions_D,
emission_probabilities=emission_probabilities,
initial_state=initial_state,
initial_action=initial_action,
num_states=num_states,
mutation_probability=mutation_probability)
def __init__(
self,
lookup_dict: dict = None,
initial_actions: tuple = None,
pattern: Any = None, # pattern is str or tuple of Action's.
parameters: Plays = None,
mutation_probability: float = None
) -> None:
lookup_dict, initial_actions, pattern, parameters, mutation_probability = self._normalize_parameters(
lookup_dict, initial_actions, pattern, parameters, mutation_probability
)
LookerUp.__init__(
self,
lookup_dict=lookup_dict,
initial_actions=initial_actions,
pattern=pattern,
parameters=parameters,
)
EvolvablePlayer.__init__(self)
self.mutation_probability = mutation_probability
self.overwrite_init_kwargs(
lookup_dict=lookup_dict,
initial_actions=initial_actions,
pattern=pattern,
parameters=parameters,
mutation_probability=mutation_probability,
)
def __init__(self,
cycle: str = None,
cycle_length: int = None,
mutation_probability: float = 0.2,
mutation_potency: int = 1,
seed: int = None) -> None:
EvolvablePlayer.__init__(self, seed=seed)
cycle, cycle_length = self._normalize_parameters(cycle, cycle_length)
Cycler.__init__(self, cycle=cycle)
# Overwrite init_kwargs in the case that we generated a new cycle from cycle_length
self.overwrite_init_kwargs(cycle=cycle, cycle_length=cycle_length)
self.mutation_probability = mutation_probability
self.mutation_potency = mutation_potency
def __init__(
self, num_features: int, num_hidden: int,
weights: List[float] = None,
mutation_probability: float = None,
mutation_distance: int = 5,
) -> None:
num_features, num_hidden, weights, mutation_probability = self._normalize_parameters(
num_features, num_hidden, weights, mutation_probability)
ANN.__init__(self,
num_features=num_features,
num_hidden=num_hidden,
weights=weights)
EvolvablePlayer.__init__(self)
self.mutation_probability = mutation_probability
self.mutation_distance = mutation_distance
self.overwrite_init_kwargs(
num_features=num_features,
num_hidden=num_hidden,
weights=weights,
mutation_probability=mutation_probability)
def __init__(self,
transitions: tuple = None,
initial_state: int = None,
initial_action: Action = None,
num_states: int = None,
mutation_probability: float = 0.1,
seed: int = None) -> None:
"""If transitions, initial_state, and initial_action are None
then generate random parameters using num_states."""
EvolvablePlayer.__init__(self, seed=seed)
transitions, initial_state, initial_action, num_states = self._normalize_parameters(
transitions, initial_state, initial_action, num_states)
FSMPlayer.__init__(self,
transitions=transitions,
initial_state=initial_state,
initial_action=initial_action)
self.mutation_probability = mutation_probability
self.overwrite_init_kwargs(transitions=transitions,
initial_state=initial_state,
initial_action=initial_action,
num_states=self.num_states)