// If all weights is equals to zero, it means that no classifier was selected. Hence, use all of them with equal
// weights.
if np.sum(weights) == 0:
weights = np.ones(self.n_classifiers, dtype=int)
votes = np.array([], dtype=int)
for clf_idx, clf in enumerate(self.pool_classifiers):
votes = np.hstack(
After Change
indices = (competence > 0)
// For the rows that are all False (i.e., no base classifier was selected, select all classifiers (set all True)
indices[~np.any(indices, axis=1), :] = True
//
// output_profile_query = self._output_profile_transform(query)
// weights = self.estimate_competence(output_profile_query.reshape(1, -1))
