pyepo.func.utils
================

.. py:module:: pyepo.func.utils

.. autoapi-nested-parse::

   Utility function



Attributes
----------

.. autoapisummary::

   pyepo.func.utils._worker_model
   pyepo.func.utils._worker_model_key


Classes
-------

.. autoapisummary::

   pyepo.func.utils.sumGammaDistribution


Functions
---------

.. autoapisummary::

   pyepo.func.utils._solve_or_cache
   pyepo.func.utils._solve_in_pass
   pyepo.func.utils._solve_batch
   pyepo.func.utils._update_solution_pool
   pyepo.func.utils._cache_in_pass
   pyepo.func.utils._solveWithObj4Par
   pyepo.func.utils._check_sol


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

.. py:function:: _solve_or_cache(cp, module)

   A function to get optimization solution in the forward/backward pass


.. py:function:: _solve_in_pass(cp, optmodel, processes, pool, solpool=None, solset=None)

   A function to solve optimization and update solution pool


.. py:function:: _solve_batch(cp, optmodel, processes, pool)

   A function to solve optimization in the forward/backward pass


.. py:function:: _update_solution_pool(sol, solpool, solset)

   Add new solutions to solution pool

   :param sol: new solutions
   :type sol: torch.tensor
   :param solpool: existing solution pool
   :type solpool: torch.tensor
   :param solset: hash set for deduplication
   :type solset: set

   :returns: updated solution pool
   :rtype: torch.tensor


.. py:function:: _cache_in_pass(cp, optmodel, solpool)

   A function to use solution pool in the forward/backward pass


.. py:data:: _worker_model
   :value: None


.. py:data:: _worker_model_key
   :value: None


.. py:function:: _solveWithObj4Par(cost, args, model_type)

   A function to solve function in parallel processors

   :param cost: cost of objective function
   :type cost: np.ndarray
   :param args: optModel args
   :type args: dict
   :param model_type: optModel class type
   :type model_type: ABCMeta

   :returns: optimal solution (list) and objective value (float)
   :rtype: tuple


.. py:function:: _check_sol(c, w, z)

   A function to check solution is correct


.. py:class:: sumGammaDistribution(kappa, n_iterations=10, seed=135)

   creates a generator of samples for the Sum-of-Gamma distribution


   .. py:attribute:: κ


   .. py:attribute:: n_iterations
      :value: 10



   .. py:attribute:: rnd


   .. py:method:: sample(size)