def merge(self,
peaks: Peaks,
_time: float,
corpus: Corpus = None,
**_kwargs) -> Peaks:
if peaks.size() <= 1:
return peaks
self.logger.debug(
f"[merge] Merging activity with {peaks.size()} peaks.")
duration: float = corpus.duration()
inv_duration: float = 1 / duration
num_rows: int = int(duration / self._t_width.value)
peaks_list: List[Peaks] = []
for transform_hash in np.unique(peaks.transform_ids):
indices: np.ndarray = np.argwhere(
peaks.transform_ids == transform_hash)
indices = indices.reshape((indices.size, ))
scores: np.ndarray = peaks.scores[indices]
times: np.ndarray = peaks.times[indices]
num_cols: int = scores.size
row_indices: np.ndarray = np.floor(times * inv_duration *
num_rows).astype(np.int32)
interp_matrix: sparse.coo_matrix = sparse.coo_matrix(
(np.ones(num_cols), (row_indices, np.arange(num_cols))),
shape=(num_rows + 1, num_cols))
interp_matrix: sparse.csc_matrix = interp_matrix.tocsc()
interpolated_scores: np.ndarray = interp_matrix.dot(scores)
interpolated_times: np.ndarray = interp_matrix.dot(times)
num_peaks_per_index: np.ndarray = np.array(
interp_matrix.sum(axis=1)).reshape(interp_matrix.shape[0])
peak_indices: np.ndarray = interpolated_scores.nonzero()[0]
scores: np.ndarray = interpolated_scores[peak_indices]
times: np.ndarray = np.divide(interpolated_times[peak_indices],
num_peaks_per_index[peak_indices])
transforms: np.ndarray = np.ones(peak_indices.size,
dtype=np.int32) * transform_hash
# print("After merge:", scores.shape, times.shape, transforms.shape)
peaks_list.append(Peaks(scores, times, transforms))
merged_peaks: Peaks = Peaks.concatenate(peaks_list)
self.logger.debug(
f"[merge] Merge successful. Number of peaks after merge: {merged_peaks.size()}."
)
return merged_peaks
def _merged_peaks(self, time: float, corpus: Corpus, **kwargs) -> Peaks:
weight_sum: float = 0.0
for atom in self.atoms.values():
weight_sum += atom.weight if atom.is_enabled_and_eligible(
) else 0.0
if weight_sum < 1e-6:
self.logger.warning(f"Weights are invalid. Setting weight to 1.0.")
weight_sum = 1.0
peaks_list: List[Peaks] = []
for atom in self.atoms.values():
if atom.is_enabled_and_eligible():
peaks: Peaks = atom.pop_peaks()
peaks.scale(atom.weight / weight_sum)
peaks_list.append(peaks)
all_peaks: Peaks = Peaks.concatenate(peaks_list)
return self.merge_action.merge(all_peaks, time, corpus, **kwargs)
