for clf_index, clf in enumerate(self.pool_classifiers):
// Check if the dynamic frienemy pruning (DFP) should be used used
if self.DFP_mask[clf_index]:
result = []
predicted_label = predictions[clf_index]
for index in idx_neighbors:
// Get only neighbors from the same class as predicted by the
// classifier (clf) to form the region of competence
After Change
// Expanding the dimensions of the predictions and target arrays in order to compare both.
predictions_3d = np.expand_dims(predictions, axis=1)
target_3d = np.expand_dims(self.DSEL_target[idx_neighbors], axis=2)
// Create a mask to remove the neighbors belonging to a different class than the predicted by the base classifier
mask = (predictions_3d != target_3d)
masked_preprocessed = np.ma.MaskedArray(self.processed_dsel[idx_neighbors, :], mask=mask)
competences_masked = np.mean(masked_preprocessed, axis=1)
// Fill 0 to the masked values in the resulting array (when no neighbors belongs to the class predicted by
// the corresponding base classifier)
competences = np.ma.filled(competences_masked, 0)
