def __init__(
self,
name: str,
peak_selector: AbstractPeakSelector = AbstractPeakSelector.default(),
merge_action: AbstractMergeAction = AbstractMergeAction.default(),
corpus: Optional[Corpus] = None,
scale_actions: List[AbstractScaleAction] = AbstractScaleAction.
default_set()):
super().__init__()
self.logger = logging.getLogger(__name__)
self.name: str = name
self._transform_handler: TransformHandler = TransformHandler()
self.peak_selector: AbstractPeakSelector = peak_selector
self.corpus: Optional[Corpus] = corpus
self.scale_actions: Dict[Type[AbstractScaleAction],
AbstractScaleAction] = {}
self.merge_action: AbstractMergeAction = merge_action
self.atoms: Dict[str, Atom] = {}
for scale_action in scale_actions:
self.add_scale_action(scale_action)
self.previous_peaks: Peaks = Peaks.create_empty()
self._transform_handler: TransformHandler = TransformHandler()
self._force_jump_index: Optional[int] = None
self.enabled: Parameter = Parameter(default_value=True,
min=False,
max=True,
type_str="bool",
description="Enables this Player.")
self._parse_parameters()
def __init__(self, corpus: Optional[Corpus] = None):
super().__init__(corpus=corpus)
self.logger.debug("[__init__]: ManualActivityPattern initialized.")
self.default_score: Parameter = Parameter(
1.0, None, None, 'float', "Value of a new peaks upon creation.")
self._peaks: Peaks = Peaks.create_empty()
self._parse_parameters()
def clear(self):
""" Reset runtime state without modifying any parameters or settings """
self.previous_peaks = Peaks.create_empty()
self.peak_selector.clear()
for scale_action in self.scale_actions.values():
scale_action.clear()
for atom in self.atoms.values():
atom.clear()
self._transform_handler.clear()
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 clear(self) -> None:
self._peaks = Peaks.create_empty()
self.last_update_time = 0.0
def pop_peaks(self) -> Peaks:
return_peaks: Peaks = self._peaks
self._peaks = Peaks.create_empty()
return return_peaks
def clear(self) -> None:
self._peaks = Peaks.create_empty()
def clear(self) -> None:
self._peaks = Peaks.create_empty()
self._event_indices = np.zeros(0, dtype=np.int32)
self.last_update_time = 0.0
def __init__(self, corpus: Optional[Corpus] = None):
super(AbstractActivityPattern, self).__init__()
self.logger = logging.getLogger(__name__)
self._peaks: Peaks = Peaks.create_empty()
self.corpus: Corpus = corpus
