leaspy.models.utils.attributes.linear_attributes
================================================

.. py:module:: leaspy.models.utils.attributes.linear_attributes


Classes
-------

.. autoapisummary::

   leaspy.models.utils.attributes.linear_attributes.LinearAttributes


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

.. py:class:: LinearAttributes(name, dimension, source_dimension)

   Bases: :py:obj:`leaspy.models.utils.attributes.abstract_manifold_model_attributes.AbstractManifoldModelAttributes`


   
   Attributes of leaspy linear models.

   Contains the common attributes & methods to update the linear model's attributes.

   :Parameters:

       **name** : str
           ..

       **dimension** : int
           ..

       **source_dimension** : int
           ..

   :Attributes:

       **name** : str (default 'linear')
           Name of the associated leaspy model.

       **dimension** : int
           ..

       **source_dimension** : int
           ..

       **univariate** : bool
           Whether model is univariate or not (i.e. dimension == 1)

       **has_sources** : bool
           Whether model has sources or not (not univariate and source_dimension >= 1)

       **update_possibilities** : set[str] (default {'all', 'g', 'v0', 'betas'} )
           Contains the available parameters to update. Different models have different parameters.

       **positions** : :class:`torch.Tensor` [dimension] (default None)
           positions = realizations['g'] such that "p0" = positions

       **velocities** : :class:`torch.Tensor` [dimension] (default None)
           Always positive: exp(realizations['v0'])

       **orthonormal_basis** : :class:`torch.Tensor` [dimension, dimension - 1] (default None)
           ..

       **betas** : :class:`torch.Tensor` [dimension - 1, source_dimension] (default None)
           ..

       **mixing_matrix** : :class:`torch.Tensor` [dimension, source_dimension] (default None)
           Matrix A such that w_i = A * s_i.









   .. seealso::

       
       :class:`~leaspy.models.univariate_model.UnivariateModel`
           ..
       :class:`~leaspy.models.multivariate_model.MultivariateModel`
           ..
       



   ..
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   .. py:method:: update(names_of_changed_values, values)

      
      Update model group average parameter(s).


      :Parameters:

          **names_of_changed_values** : set[str]
              Elements of set must be either:
                  * ``all`` (update everything)
                  * ``g`` correspond to the attribute :attr:`positions`.
                  * ``v0`` (only for multivariate models) correspond to the attribute :attr:`velocities`.
                    When we are sure that the v0 change is only a scalar multiplication
                    (in particular, when we reparametrize log(v0) <- log(v0) + mean(xi)),
                    we may update velocities using ``v0_collinear``, otherwise
                    we always assume v0 is NOT collinear to previous value
                    (no need to perform the verification it is - would not be really efficient)
                  * ``betas`` correspond to the linear combination of columns from the orthonormal basis so
                    to derive the :attr:`mixing_matrix`.

          **values** : dict [str, `torch.Tensor`]
              New values used to update the model's group average parameters







      :Raises:

          :exc:`.LeaspyModelInputError`
              If `names_of_changed_values` contains unknown parameters.







      ..
          !! processed by numpydoc !!