def _loss_lr(self, data, labels, w, theta, bias):
        """Compute the Loss MLR (with the regularization)

        Parameters
        ----------

        data : list of 2D arrays, element i has shape=[voxels_i, samples_i]
            Each element in the list contains the fMRI data of one subject for
            the classification task.

        labels : list of arrays of int, element i has shape=[samples_i]
            Each element in the list contains the labels for the samples in
            data.

        w : list of array, element i has shape=[voxels_i, features]
            The orthogonal transforms (mappings) :math:`W_i` for each subject.

        theta : array, shape=[classes, features]
            The MLR class plane parameters.

        bias : array, shape=[classes]
            The MLR class biases.


        Returns
        -------

        loss : float
            The loss MLR for the SS-SRM model
        """
        subjects = len(data)
        loss = 0.0
        for subject in range(subjects):
            if labels[subject] is not None:
                loss += self._loss_lr_subject(data[subject], labels[subject],
                                              w[subject], theta, bias)

        return loss + 0.5 * np.linalg.norm(theta, 'fro')**2
Compute the Loss MLR (with the regularization)

        Parameters
        ----------

        data : list of 2D arrays, element i has shape=[voxels_i, samples_i]
            Each element in the list contains the fMRI data of one subject for
            the classification task.

        labels : list of arrays of int, element i has shape=[samples_i]
            Each element in the list contains the labels for the samples in
            data.

        w : list of array, element i has shape=[voxels_i, features]
            The orthogonal transforms (mappings) :math:`W_i` for each subject.

        theta : array, shape=[classes, features]
            The MLR class plane parameters.

        bias : array, shape=[classes]
            The MLR class biases.


        Returns
        -------

        loss : float
            The loss MLR for the SS-SRM model