def predict(self, X):
        if self.voting == 'soft':

            maj = np.argmax(self.predict_proba(X), axis=1)

        else:  # 'hard' voting
            predictions = self._predict(X)

            maj = np.apply_along_axis(lambda x:
                                      np.argmax(np.bincount(x,
                                                weights=self.weights)),
                                      axis=1,
                                      arr=predictions)

        maj = self.le_.inverse_transform(maj)
        return maj
Configure Velruse's session factory and backend storage.
dataset/ETHPy150Open rasbt/mlxtend/mlxtend/classifier/ensemble_vote.py/EnsembleVoteClassifier.predict--crossover with--dataset/ETHPy150Open bbangert/velruse/velruse/app/__init__.py/default_setup