def _compute_trial_pred_labels_from_cnt_y(self, dataset, all_preds, ):
# Todo: please test this
# we only want the preds that are for the same labels as the last label in y
# (there might be parts of other class-data at start, for trialwise misclass we assume
# they are contained in other trials at the end...)
preds_per_trial = compute_preds_per_trial_for_set(
all_preds, self.input_time_length, dataset)
trial_labels = []
trial_pred_labels = []
for trial_pred, trial_y in zip(preds_per_trial, dataset.y):
# first cut to the part actually having predictions
trial_y = trial_y[-trial_pred.shape[1]:]
wanted_class = trial_y[-1]
trial_labels.append(wanted_class)
# extract the first marker different from the wanted class
# by starting from the back of the trial
i_last_sample = np.flatnonzero(trial_y[::-1] != wanted_class)
if len(i_last_sample) > 0:
i_last_sample = i_last_sample[0]
# remember last sample is now from back
trial_pred = trial_pred[:, -i_last_sample:]
trial_pred_label = np.argmax(np.mean(trial_pred, axis=1))
trial_pred_labels.append(trial_pred_label)
trial_labels = np.array(trial_labels)
trial_pred_labels = np.array(trial_pred_labels)
return trial_labels, trial_pred_labels
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