leaspy.algo.algo_with_samplers
==============================

.. py:module:: leaspy.algo.algo_with_samplers


Classes
-------

.. autoapisummary::

   leaspy.algo.algo_with_samplers.AlgorithmWithSamplersMixin


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

.. py:class:: AlgorithmWithSamplersMixin(settings)

   
   Mixin class to use in algorithms that require `samplers`.

   Note that this mixin should be used with a class inheriting from `AbstractAlgo`, which must have `algo_parameters`
   attribute.

   :Parameters:

       **settings** : :class:`.AlgorithmSettings`
           The specifications of the algorithm as a :class:`.AlgorithmSettings` instance.
           
           Please note that you can customize the number of memory-less (burn-in) iterations by setting either:
               * `n_burn_in_iter_frac`, such that duration of burn-in phase is a ratio of algorithm `n_iter` (default of 90%)

   :Attributes:

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

       **current_iteration** : :obj:`int`, default 0
           Current iteration of the algorithm.
           The first iteration will be 1 and the last one `n_iter`.

       **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 rationale on why we should activate this flag.













   ..
       !! processed by numpydoc !!

   .. py:attribute:: samplers
      :type:  dict[str, leaspy.samplers.AbstractSampler]
      :value: None



   .. py:attribute:: random_order_variables
      :type:  bool


   .. py:attribute:: current_iteration
      :type:  int
      :value: 0