leaspy.algo.fit.mcmc_saem
=========================

.. py:module:: leaspy.algo.fit.mcmc_saem

.. autoapi-nested-parse::

   This module defines the `TensorMCMCSAEM` class.

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Classes
-------

.. autoapisummary::

   leaspy.algo.fit.mcmc_saem.TensorMcmcSaemAlgorithm


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

.. py:class:: TensorMcmcSaemAlgorithm(settings)

   Bases: :py:obj:`leaspy.algo.algo_with_device.AlgorithmWithDeviceMixin`, :py:obj:`leaspy.algo.algo_with_annealing.AlgorithmWithAnnealingMixin`, :py:obj:`leaspy.algo.algo_with_samplers.AlgorithmWithSamplersMixin`, :py:obj:`leaspy.algo.fit.base.FitAlgorithm`\ [\ :py:obj:`leaspy.models.McmcSaemCompatibleModel`\ , :py:obj:`leaspy.variables.state.State`\ ]


   
   Main algorithm for MCMC-SAEM.


   :Parameters:

       **settings** : :class:`~leaspy.algo.AlgorithmSettings`
           MCMC fit algorithm settings

   :Attributes:

       **samplers** : :obj:`dict` [:obj:`str`, :class:`~leaspy.samplers.AbstractSampler` ]
           Dictionary of samplers per each variable

       **random_order_variables** : :obj:`bool` (default True)
           This attribute controls whether we randomize the order of variables at each iteration.
           `Article <https://proceedings.neurips.cc/paper/2016/hash/e4da3b7fbbce2345d7772b0674a318d5-Abstract.html>`_
           gives a reason on why we should activate this flag.

       **temperature** : :obj:`float`
           ..

       **temperature_inv** : :obj:`float`
           Temperature and its inverse are modified during algorithm if annealing is used









   .. seealso::

       
       :mod:`leaspy.samplers`
           ..
       



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   .. py:attribute:: name
      :type:  leaspy.algo.base.AlgorithmName


   .. py:method:: log_current_iteration(state)


   .. py:method:: is_current_iteration_in_last_n()

      
      Return True if current iteration is within the last n realizations defined in logging settings.
















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   .. py:method:: should_current_iteration_be_saved()

      
      Return True if current iteration should be saved based on log saving periodicity.
















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