def _decide_default(
self, peaks: Peaks, corpus: Corpus,
transform_handler: TransformHandler,
**kwargs) -> Optional[Tuple[CorpusEvent, AbstractTransform]]:
if peaks.is_empty():
return None
max_peak_value: float = np.max(peaks.scores)
if max_peak_value < self.threshold:
return None
else:
return super()._decide_default(peaks, corpus, transform_handler)
def _decide_default(
self, peaks: Peaks, corpus: Corpus,
transform_handler: TransformHandler,
**kwargs) -> Optional[Tuple[CorpusEvent, AbstractTransform]]:
if peaks.is_empty():
return None
score_cumsum: np.ndarray = np.cumsum(peaks.scores)
max_value: float = score_cumsum[
-1] - 1e-5 # slight offset to avoid an extremely rare case of a fp error
output_target_score: float = float(np.random.random(1) * max_value)
peak_idx: int = np.argwhere(score_cumsum > output_target_score)[0]
transform_hash: int = int(peaks.transform_ids[peak_idx])
return corpus.event_around(
peaks.times[peak_idx]), transform_handler.get_transform(
transform_hash)
def _scale_peaks(self, peaks: Peaks, scheduler_time: float, corpus: Corpus,
**kwargs):
if peaks.is_empty():
return peaks
corresponding_events: List[CorpusEvent] = corpus.events_around(
peaks.times)
corresponding_transforms: List[AbstractTransform] = [
self._transform_handler.get_transform(t)
for t in np.unique(peaks.transform_ids)
]
for scale_action in self.scale_actions.values():
if scale_action.is_enabled_and_eligible():
peaks = scale_action.scale(peaks, scheduler_time,
corresponding_events,
corresponding_transforms, corpus,
**kwargs)
return peaks
def _decide_default(
self, peaks: Peaks, corpus: Corpus,
transform_handler: TransformHandler,
**kwargs) -> Optional[Tuple[CorpusEvent, AbstractTransform]]:
self.logger.debug("[decide] _decide_default called.")
if peaks.is_empty():
return None
max_peak_value: float = np.max(peaks.scores)
self.logger.debug(
f"[decide_default] Max peak value is {max_peak_value}.")
max_peaks_idx: List[int] = np.argwhere(
np.abs(peaks.scores - max_peak_value) < 0.001)
peak_idx: int = random.choice(max_peaks_idx)
transform_hash: int = int(peaks.transform_ids[peak_idx])
return corpus.event_around(
peaks.times[peak_idx]), transform_handler.get_transform(
transform_hash)
