leaspy.models.linear
====================

.. py:module:: leaspy.models.linear


Classes
-------

.. autoapisummary::

   leaspy.models.linear.LinearInitializationMixin
   leaspy.models.linear.LinearModel


Module Contents
---------------

.. py:class:: LinearInitializationMixin

   
   Compute initial values for model parameters.
















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.. py:class:: LinearModel(name, **kwargs)

   Bases: :py:obj:`LinearInitializationMixin`, :py:obj:`leaspy.models.riemanian_manifold.RiemanianManifoldModel`


   
   Manifold model for multiple variables of interest (linear formulation).
















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   .. py:method:: get_variables_specs()

      
      Return the specifications of the variables (latent variables, derived variables,
      model 'parameters') that are part of the model.





      :Returns:

          :class:`~leaspy.variables.specs.NamedVariables
              A dictionary-like object mapping variable names to their specifications.











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   .. py:method:: metric(*, g)
      :staticmethod:


      
      Compute the metric tensor for the model.


      :Parameters:

          **g** :  :class:`torch.Tensor`
              Input tensor with values of the population parameter `g` for each feature.



      :Returns:

          :class:`torch.Tensor`
              A tensor of ones with the same shape as `g`.











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   .. py:method:: model_with_sources(*, rt, space_shifts, metric, v0, g)
      :classmethod:


      
      Return the model output when sources(spatial components) are present.


      :Parameters:

          **rt** :  :class:`torch.Tensor`
              The reparametrized time.

          **space_shifts** :  :class:`torch.Tensor`
              The values of the space-shifts

          **metric** : Any
              The metric tensor used for computing the spatial/temporal influence.

          **v0** : Any
              The values of the population parameter `v0` for each feature.

          **g** : Any
              The values of the population parameter `g` for each feature.



      :Returns:

          :class:`torch.Tensor`
              The model output with contribution from sources.











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