def __init__( self, num_features: int, num_hidden: int, weights: List[float] = None ) -> None: Player.__init__(self) self.num_features = num_features self.num_hidden = num_hidden self._process_weights(weights, num_features, num_hidden)
def __init__(self, transitions: Tuple[Transition, ...] = ((1, C, 1, C), (1, D, 1, D)), initial_state: int = 1, initial_action: Action = C) -> None: Player.__init__(self) self.initial_state = initial_state self.initial_action = initial_action self.fsm = SimpleFSM(transitions, initial_state)
def __init__(self, cycle: str = "CCD") -> None: """This strategy will repeat the parameter `cycle` endlessly, e.g. C C D C C D C C D ... Special Cases ------------- Cooperator is equivalent to Cycler("C") Defector is equivalent to Cycler("D") Alternator is equivalent to Cycler("CD") """ Player.__init__(self) self.cycle = cycle self.set_cycle(cycle=cycle)
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) -> None: Player.__init__(self) self.parameters = parameters self.pattern = pattern self._lookup = self._get_lookup_table(lookup_dict, pattern, parameters) self._set_memory_depth() self.initial_actions = self._get_initial_actions(initial_actions) self._initial_actions_pool = list(self.initial_actions)
def __init__(self, transitions_C=None, transitions_D=None, emission_probabilities=None, initial_state=0, initial_action=C) -> None: Player.__init__(self) if not transitions_C: transitions_C = [[1]] transitions_D = [[1]] emission_probabilities = [0.5] # Not stochastic initial_state = 0 self.initial_state = initial_state self.initial_action = initial_action self.hmm = SimpleHMM(copy_lists(transitions_C), copy_lists(transitions_D), list(emission_probabilities), initial_state) assert self.hmm.is_well_formed() self.state = self.hmm.state self.classifier["stochastic"] = self.is_stochastic()