def _get_aggregate_classification(self, minibatch, trials=10, skip=0):
        """
        See classify method.

        Returns the prediction vector aggregated over all time steps where
        axis 0 is the minibatch item and axis 1 is the output for the label.
        """
        clamped = np.ones(minibatch.shape, dtype=np.float32)

        data = self.get_samples([(self.input_idx, minibatch)],
                                walkback=self.calc_walkback(trials + skip),
                                indices=[self.label_idx],
                                clamped=[clamped],
                                symbolic=False)

        # 3d tensor: axis 0 is time step, axis 1 is minibatch item,
        # axis 2 is softmax output for label (after slicing)
        data = np.asarray(data[skip:skip+trials])[:, 0, :, :]

        return data.mean(axis=0)