<gh_stars>1-10
import torch


class LinearTransformation(object):
    r"""Transforms node positions with a square transformation matrix computed
    offline.

    Args:
        matrix (Tensor): tensor with shape :math:`[D, D]` where :math:`D`
            corresponds to the dimensionality of node positions.

    .. testsetup::

        import torch
        from scripts.study_case.ID_13.torch_geometric.data import Data

    .. testcode::

        from scripts.study_case.ID_13.torch_geometric.transforms import LinearTransformation

        pos = torch.tensor([[-1, 1], [-3, 0], [2, -1]], dtype=torch.float)
        data = Data(pos=pos)

        matrix = torch.tensor([[2, 0], [0, 2]], dtype=torch.float)
        data = LinearTransformation(matrix)(data)

        print(data.pos)

    .. testoutput::

        tensor([[-2.,  2.],
                [-6.,  0.],
                [ 4., -2.]])
    """

    def __init__(self, matrix):
        assert matrix.dim() == 2, (
            'Transformation matrix should be two-dimensional.')
        assert matrix.size(0) == matrix.size(1), (
            'Transformation matrix should be square. Got [{} x {}] rectangular'
            'matrix.'.format(*matrix.size()))

        self.matrix = matrix

    def __call__(self, data):
        pos = data.pos.view(-1, 1) if data.pos.dim() == 1 else data.pos

        assert pos.size(1) == self.matrix.size(0), (
            'Node position matrix and transformation matrix have incompatible '
            'shape.')

        data.pos = torch.mm(pos, self.matrix.to(pos.dtype).to(pos.device))

        return data

    def __repr__(self):
        return '{}({})'.format(self.__class__.__name__, self.matrix.tolist())
