leaspy.models.time_reparametrized
=================================

.. py:module:: leaspy.models.time_reparametrized


Classes
-------

.. autoapisummary::

   leaspy.models.time_reparametrized.TimeReparametrizedModel


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

.. py:class:: TimeReparametrizedModel(name, source_dimension = None, **kwargs)

   Bases: :py:obj:`leaspy.models.mcmc_saem_compatible.McmcSaemCompatibleModel`


   
   Contains the common attributes & methods of the multivariate time-reparametrized models.


   :Parameters:

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

       **source_dimension** : Optional[:obj:`int`]
           Number of sources. Dimension of spatial components (default is None).

       **\*\*kwargs**
           Hyperparameters for the model (including `obs_models`).







   :Raises:

       :exc:`.LeaspyModelInputError`
           If inconsistent hyperparameters.







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   .. py:property:: xi_std
      :type: torch.Tensor


      
      Return the standard deviation of xi as a tensor.
















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   .. py:property:: tau_std
      :type: torch.Tensor


      
      Return the standard deviation of tau as a tensor.
















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   .. py:property:: noise_std
      :type: torch.Tensor


      
      Return the standard deviation of the model as a tensor.
















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   .. py:property:: sources_std
      :type: float


      
      Return the standard deviation of sources as a float.
















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   .. py:property:: source_dimension
      :type: Optional[int]


      
      Return the number of the sources
















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   .. py:property:: has_sources
      :type: bool


      
      Indicates whether the model includes sources.





      :Returns:

          :obj:`bool`
              True if `source_dimension` is a positive integer.
              False otherwise.











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   .. py:method:: time_reparametrization(*, t, alpha, tau)
      :staticmethod:


      
      Tensorized time reparametrization formula.

      .. warning::
          Shapes of tensors must be compatible between them.

      :Parameters:

          **t** : :class:`torch.Tensor`
              Timepoints to reparametrize

          **alpha** : :class:`torch.Tensor`
              Acceleration factors of individual(s)

          **tau** : :class:`torch.Tensor`
              Time-shift(s) of individual(s)



      :Returns:

          :class:`torch.Tensor`
              Reparametrized time of same shape as `timepoints`











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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 containing specifications for the variables











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   .. py:method:: put_individual_parameters(state, dataset)

      
      Initialize individual latent parameters in the given state if not already set.


      :Parameters:

          **state** : State
              The current state object that holds all the variables

          **dataset** : Dataset
              Dataset used to initialize latent variables accordingly.














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   .. py:method:: to_dict(*, with_mixing_matrix = True)

      
      Export model object as dictionary ready for :term:`JSON` saving.


      :Parameters:

          **with_mixing_matrix** : :obj:`bool` (default ``True``)
              Save the :term:`mixing matrix` in the exported file in its 'parameters' section.
              
              .. warning::
                  It is not a real parameter and its value will be overwritten at model loading
                  (orthonormal basis is recomputed from other "true" parameters and mixing matrix
                  is then deduced from this orthonormal basis and the betas)!
                  It was integrated historically because it is used for convenience in
                  browser webtool and only there...



      :Returns:

          KwargsType
              The object as a dictionary.











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   .. py:method:: compute_individual_ages_from_biomarker_values(value, individual_parameters, feature = None)
      :abstractmethod:


      
      For one individual, compute age(s) at which the given features values
      are reached (given the subject's individual parameters).

      Consistency checks are done in the main :term:`API` layer.

      :Parameters:

          **value** : scalar or array_like[scalar] (:obj:`list`, :obj:`tuple`, :class:`numpy.ndarray`)
              Contains the :term:`biomarker` value(s) of the subject.

          **individual_parameters** : :obj:`dict`
              Contains the individual parameters.
              Each individual parameter should be a scalar or array_like.

          **feature** : :obj:`str` (or None)
              Name of the considered :term:`biomarker`.
              
              .. note::
                  Optional for :class:`.UnivariateModel`, compulsory
                  for :class:`.MultivariateModel`.



      :Returns:

          :class:`torch.Tensor`
              Contains the subject's ages computed at the given values(s).
              Shape of tensor is ``(1, n_values)``.




      :Raises:

          :exc:`.LeaspyModelInputError`
              If computation is tried on more than 1 individual.







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   .. py:method:: compute_individual_ages_from_biomarker_values_tensorized(value, individual_parameters, feature)
      :abstractmethod:


      
      For one individual, compute age(s) at which the given features values are
      reached (given the subject's individual parameters), with tensorized inputs.


      :Parameters:

          **value** : :class:`torch.Tensor` of shape ``(1, n_values)``
              Contains the :term:`biomarker` value(s) of the subject.

          **individual_parameters** : DictParamsTorch
              Contains the individual parameters.
              Each individual parameter should be a :class:`torch.Tensor`.

          **feature** : :obj:`str` (or None)
              Name of the considered :term:`biomarker`.
              
              .. note::
                  Optional for :class:`.UnivariateModel`, compulsory
                  for :class:`.MultivariateModel`.



      :Returns:

          :class:`torch.Tensor`
              Contains the subject's ages computed at the given values(s).
              Shape of tensor is ``(n_values, 1)``.











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