1002cfbcc9f8182404fb058f959d625de2eabbfc,deslib/des/knora_u.py,KNORAU,estimate_competence,#KNORAU#Any#Any#,68

Before Change

                     for each base classifier
        
        dists, idx_neighbors = self._get_region_competence(query)
        competences = np.zeros(self.n_classifiers)

        for clf_index in range(self.n_classifiers):
            // Check if the dynamic frienemy pruning (DFP) should be used used
            if self.DFP_mask[clf_index]:
                competences[clf_index] = np.sum(self.processed_dsel[idx_neighbors, clf_index])

        return competences.astype(dtype=int)

    def select(self, query):
        Select the base classifiers for the classification of the query sample.

After Change

                     for each base classifier
        
        _, idx_neighbors = self._get_region_competence(query)
        idx_neighbors = np.atleast_2d(idx_neighbors)
        competences = np.sum(self.processed_dsel[idx_neighbors, :], axis=1)

        // competences = np.zeros(self.n_classifiers)
        //
        // for clf_index in range(self.n_classifiers):

Instances