sharded
=================

.. py:module:: jaxns.nested_samplers.sharded

.. rubric:: :code:`jaxns.nested_samplers.sharded`


.. rubric:: Submodules

.. toctree::
   :titlesonly:
   :maxdepth: 1

   sharded_static/index.rst


.. rubric:: Package Contents

.. py:class:: ShardedStaticNestedSampler

   Bases: :py:obj:`jaxns.nested_samplers.abc.AbstractNestedSampler`


   A static nested sampler that uses a fixed number of live points. This uses a uniform sampler to generate the
   initial set of samples down to an efficiency threshold, then uses a provided sampler to generate the rest of the
   samples until the termination condition is met.

   :param init_efficiency_threshold: the efficiency threshold to use for the initial uniform sampling. If 0 then
                                     turns it off.
   :param sampler: the sampler to use after the initial uniform sampling.
   :param num_live_points: the number of live points to use.
   :param model: the model to use.
   :param max_samples: the maximum number of samples to take.
   :param devices: the devices to use, default is 1.
   :param verbose: whether to log as we go.


   .. py:attribute:: model
      :type:  jaxns.framework.bases.BaseAbstractModel


   .. py:attribute:: max_samples
      :type:  int


   .. py:attribute:: init_efficiency_threshold
      :type:  float


   .. py:attribute:: sampler
      :type:  jaxns.samplers.abc.AbstractSampler


   .. py:attribute:: num_live_points
      :type:  int


   .. py:attribute:: shell_fraction
      :type:  Optional[float]
      :value: None



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



   .. py:attribute:: refine_threshold
      :type:  float
      :value: 0.01



   .. py:attribute:: devices
      :type:  Optional[List[jaxlib.xla_client.Device]]
      :value: None



   .. py:attribute:: verbose
      :type:  bool
      :value: False



   .. py:method:: __post_init__()