e25ce99878d2b5c151ed8fde92276fbb2cb338b2,deslib/des/knora_u.py,KNORAU,select,#KNORAU#Any#,99

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

        // weights = self.estimate_competence(query)
        // if np.sum(weights) == 0:
        //     weights = np.ones(self.n_classifiers, dtype=int)

Instances