def __init__(self, weight: float = 1.0, mu: float = DEFAULT_ENERGY, sigma: float = DEFAULT_SIGMA,
listening_mode: ListeningMode = ListeningMode.MANUAL, moving_average_len: int = DEFAULT_MA_LEN):
super().__init__()
self.listen_to: Parameter = ParamWithSetter(listening_mode, 0, 2, "int",
"listen to (manual=0, external=1, feedback=2)",
self._set_listening_mode)
self.moving_average_len: Parameter = Parameter(moving_average_len, 1, None, "int",
"num previous events to take into account if listening to input")
self._weight: Parameter = Parameter(weight, None, None, 'float', "Relative weight of filter")
self._mu: Parameter = ParamWithSetter(mu, None, None, 'float', "Mean value of gaussian.", self._set_mu)
self._sigma: Parameter = Parameter(sigma, None, None, 'float', "Standard deviation of gaussian")
self.history: deque[float] = deque([], self.moving_average_len.value)
def __init__(self,
name: str,
weight: float,
classifier: AbstractClassifier,
activity_pattern: AbstractActivityPattern,
memory_space: AbstractMemorySpace,
corpus: Corpus,
self_influenced: bool,
enabled: bool = True):
super().__init__()
self.logger = logging.getLogger(__name__)
self.logger.debug(f"[__init__ Creating atom '{name}'.")
self.name = name
self._weight: Parameter = Parameter(weight, 0.0, None, 'float',
"Relative scaling of atom peaks.")
self._enabled: Parameter = ParamWithSetter(enabled, False, True,
"bool",
"Enables this Atom.",
self._set_enabled)
self._classifier: AbstractClassifier = classifier
self._memory_space: AbstractMemorySpace = memory_space
self._activity_pattern: AbstractActivityPattern = activity_pattern
self._self_influenced: Parameter = Parameter(
self_influenced, 0, 1, 'bool',
"Whether new events creates by player should influence this atom or not."
)
self._corpus: Optional[Corpus] = None
if corpus:
self.read_corpus(corpus)
self._parse_parameters()
def __init__(self, taboo_length: int = DEFAULT_TABOO_LENGTH):
super().__init__()
self.logger = logging.getLogger(__name__)
self._taboo_length: Parameter = ParamWithSetter(taboo_length, 1, None, 'int',
"Number of events to pass before event can be played again",
self._set_taboo_length)
self._taboo_indices: deque[int] = deque([], self.taboo_length)
def __init__(self, history_len: int = 3, **_kwargs):
super(NGramMemorySpace, self).__init__(**_kwargs)
self.logger.debug(
f"[__init__] Initializing {self.__class__.__name__} with history length {history_len}."
)
self._structured_data: Dict[Tuple[int, ...], List[CorpusEvent]] = {}
self._ngram_size: Parameter = ParamWithSetter(
history_len, 1, None, 'int',
"Number of events to hard-match. (TODO)",
self.set_ngram_size) # TODO
# history per transform stored with transform id as key
self._influence_history: Dict[int, deque[int]] = {}
self._parse_parameters()
def __init__(self, corpus: Corpus = None):
super().__init__(corpus)
self.logger.debug("[__init__]: ManualActivityPattern initialized.")
self.extinction_threshold: Parameter = Parameter(
0.1, 0.0, None, 'float', "Score below which peaks are removed")
self.tau_mem_decay: Parameter = ParamWithSetter(
self._calc_tau(self.DEFAULT_N), 1, None, "int",
"Number of updates until peak is decayed below threshold.",
self._set_tau)
self.default_score: Parameter = Parameter(
1.0, None, None, 'float', "Value of a new peaks upon creation.")
self._peaks: Peaks = Peaks.create_empty()
self.last_update_time: float = 0.0
self._event_indices: np.ndarray = np.zeros(0, dtype=np.int32)
self._parse_parameters()
def __init__(self,
corpus: Corpus = None,
tau_mem_decay: float = DEFAULT_T):
super().__init__(corpus)
self.logger.debug("[__init__]: ClassicActivityPattern initialized.")
self.extinction_threshold: Parameter = Parameter(
0.1, 0.0, None, 'float', "Score below which peaks are removed")
# TODO: tau shouldn't be the parameter: t should
self.tau_mem_decay: Parameter = ParamWithSetter(
self._calc_tau(tau_mem_decay), 0, None, "float",
"Number of updates until peak is decayed below threshold.",
self._set_tau)
self.default_score: Parameter = Parameter(
1.0, None, None, 'float', "Value of a new peaks upon creation.")
self._peaks: Peaks = Peaks.create_empty()
self.last_update_time: float = 0.0
self._parse_parameters()
def __init__(self):
super().__init__()
self._band_distribution: Parameter = ParamWithSetter(OctaveBandsScaleAction.DEFAULT_BAND_DISTRIBUTION, None,
None, "list[11]", "TODO", self._set_band_distribution)