e25ce99878d2b5c151ed8fde92276fbb2cb338b2,deslib/des/knop.py,KNOP,select,#KNOP#Any#,137

Before Change

            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(
                (votes, np.ones(weights[clf_idx], dtype=int) * clf.predict(query)[0]))

        return votes

    def classify_instance(self, query, predictions):
        Predicts the label of the corresponding query sample.

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))

Instances