leaspy.models.riemanian_manifold
================================

.. py:module:: leaspy.models.riemanian_manifold


Classes
-------

.. autoapisummary::

   leaspy.models.riemanian_manifold.RiemanianManifoldModel


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

.. py:class:: RiemanianManifoldModel(name, variables_to_track = None, **kwargs)

   Bases: :py:obj:`leaspy.models.time_reparametrized.TimeReparametrizedModel`


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


   :Parameters:

       **name** : :obj:`str`
           The name of the model.

       **\*\*kwargs**
           Hyperparameters of the model (including `noise_model`)







   :Raises:

       :exc:`.LeaspyModelInputError`
           * If hyperparameters are inconsistent







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   .. py:method:: compute_sufficient_statistics(state)
      :classmethod:


      
      Compute the model's :term:`sufficient statistics`.


      :Parameters:

          **state** : :class:`.State`
              The state to pick values from.



      :Returns:

          SuffStatsRW
              The computed sufficient statistics.











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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:

          NamedVariables
              A dictionary-like object mapping variable names to their specifications.
              These include `ModelParameter`, `Hyperparameter`, `PopulationLatentVariable`,
              and `LinkedVariable` instances.











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

      :abstractmethod:



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


      
      Return the model output when sources(spatial components) are not present.


      :Parameters:

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

          **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 without contribution from source shifts.








      .. rubric:: Notes

      This implementation delegates to `model_with_sources` with `space_shifts`
      set to a zero tensor of shape (1, 1), effectively removing source effects.



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

      :abstractmethod: