becc945240a424bc08f74476c46f6a50b69928a5,src/skmultiflow/meta/additive_expert_ensemble.py,AdditiveExpertEnsemble,fit_single_sample,#AdditiveExpertEnsemble#Any#Any#Any#Any#,60

Before Change

        predictions_probs = self.get_expert_predictions_probs([x])

        //// 2. Output prediction:
        output_pred = np.argmax(np.sum(
            [pred_probs * w for pred_probs, w in zip(predictions_probs, (exp.weight for exp in self.experts))],
            axis=0
        ))

        //// 3. Update expert weights:
        self.update_expert_weights(map(np.argmax, predictions_probs), y)

After Change

        (As was originally described by [1])
        
        //// 1. Get expert predictions:
        predictions = self.get_expert_predictions(X)

        //// 2. Get aggregate prediction:
        output_pred = np.argmax(self._aggregate_expert_predictions(predictions))

        //// 3. Update expert weights:
        self.update_expert_weights(predictions, y)

Instances