73b13f228b398e334e4c65ff286f5ad02932ce7f,src/skmultiflow/meta/adaptive_random_forests.py,AdaptiveRandomForest,get_votes_for_instance,#AdaptiveRandomForest#Any#,316

Before Change

        combined_votes = {}

        for i in range(self.n_estimators):
            vote = self.ensemble[i].get_votes_for_instance(X)
            if vote != {} and sum(vote.values()) > 0:
                normalize_values_in_dict(vote)
                if not self.disable_weighted_vote:
                    performance = self.ensemble[i].evaluator.get_accuracy()\

After Change

        combined_votes = {}

        for i in range(self.n_estimators):
            vote = deepcopy(self.ensemble[i].get_votes_for_instance(X))
            if vote != {} and sum(vote.values()) > 0:
                vote = normalize_values_in_dict(vote, inplace=False)
                if not self.disable_weighted_vote:
                    performance = self.ensemble[i].evaluator.get_accuracy()\
                        if self.performance_metric == "acc"\
                        else self.ensemble[i].evaluator.get_kappa()

Instances