def predict_proba(self, X):
        """Probability estimates for all classes (ordered by class label)"""

        n_models = float(sum(self._ensemble.values()))

        probs = np.zeros((len(X), self._n_classes))

        for model_idx, weight in self._ensemble.items():
            probs += self._model_predict_proba(X, model_idx) * n_models/weight

        return probs
dataset/ETHPy150Open dclambert/pyensemble/ensemble.py/EnsembleSelectionClassifier.predict_proba/swapped operands of `/`