Source code for torch_openreml.covariance.scalar_matrix

"""
Scalar covariance matrix.

This module provides a scaled identity covariance matrix with a single
shared variance parameter, for use in linear mixed-effects models.

Classes:
    ScalarMatrix:
        A scaled identity covariance matrix :math:`V = \\sigma^2 I_n`.
"""

from torch_openreml.covariance.matrix import Matrix
from torch_openreml.covariance.transform import TransformExpPow2
import torch



class ScalarMatrix(Matrix):
    r"""
    Scaled identity covariance matrix with a single shared variance parameter.

    .. math::
        \symbf{V} = \sigma^2 \symbf{I}_n

    A single unconstrained scalar parameter is transformed to a positive
    variance via :class:`~torch_openreml.covariance.transform.TransformExpPow2`
    by default and then broadcast across all diagonal entries. This
    structure assumes equal, independent variances across all observations.
    """
  
    def __init__(self, n, param_specs=None):
        """
        Initialize a scaled identity covariance matrix of size ``n x n``.

        Args:
            n (int): Matrix dimension.
            param_specs (dict): Parameter specifications. Keys should be strings
                representing parameter names. Values should be dictionaries
                containing the specification for each parameter. Each specification
                dictionary should contain the keys ``"fixed"``, ``"default"``, and ``"trans"``,
                representing whether the parameter is fixed or free (bool), the
                default value (1D torch.Tensor), and the transform (:class:`~torch_openreml.covariance.transform.Transform`),
                respectively.

        Example:

        .. jupyter-execute::

            import torch
            from torch_openreml.covariance import ScalarMatrix

            mat = ScalarMatrix(3)
            mat

        .. jupyter-execute::

            free_params = torch.tensor([0.5])
            mat(free_params)

        .. jupyter-execute::

            mat.grad(free_params)
        """

        param_specs = param_specs or {
            "sigma^2": {
                "fixed": False,
                "default": torch.tensor([0.0]),
                "trans": TransformExpPow2()
            }
        }
        super().__init__((n, n), param_specs)
        


    def __call__(self, free_params=None):
        if free_params is None:
            free_params = self.free_param_defaults
        sigma2 = self.build_params(free_params)
        device = sigma2.device
        dtype = sigma2.dtype

        i_n = torch.eye(self.shape[0], device=device, dtype=dtype)
        v = sigma2 * i_n
        
        return v




    def manual_grad(self, free_params=None):
        """
        Compute the Jacobian of :meth:`__call__` with respect to trainable
        parameters using a closed-form analytic expression.

        Args:
            free_params (torch.Tensor or dict): Flat 1D parameter tensor or
                parameter dictionary. If omitted, default values are used.
                Default: ``None``.

        Returns:
            tuple: ``(grad, grad_names)``, where ``grad`` is a 3D tensor of
            shape ``(num_free_params, *shape)`` and
            ``grad_names`` is a list of the corresponding parameter names.
            Returns ``(None, [])`` if all parameters are fixed.
        """
        if free_params is None:
            free_params = self.free_param_defaults
        if len(free_params) == 0:
            return None, []

        free_params = self.build_params(free_params, include_fixed=False, trans=False, out_format="tensor")
        device = free_params.device
        dtype = free_params.dtype

        i_n = torch.eye(self.shape[0], device=device, dtype=dtype)
        grad = (self.trans_grad(free_params) * i_n).unsqueeze(0)

        return grad, self.free_param_names