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