pyuncertainnumber.propagation.helper
====================================

.. py:module:: pyuncertainnumber.propagation.helper


Classes
-------

.. autoapisummary::

   pyuncertainnumber.propagation.helper.EpistemicDomain


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

.. py:class:: EpistemicDomain(*vars: pyuncertainnumber.pba.intervals.Interval)

   Representation of the epistemic space which are indeed bounds of each dimension

   This class provides a set of handy functions to work with epistemic uncertainty in the form of bounds.
   It will be useful for tasks such as propagation or optimization where epistemic uncertainty is involved.

   :param vars: a set of Interval variables

   .. tip:: Recommended to use for optimisation tasks where the design bounds can be quickly specified with the ``toOptBounds()`` method.

   .. seealso::

       :class:`pyuncertainnumber.src.pyuncertainnumber.opt.bo` : Bayesian optimisation class.

       :class:`pyuncertainnumber.src.pyuncertainnumber.opt.ga` : Genetic algorithm class.

   .. rubric:: Example

   >>> from pyuncertainnumber import pba
   >>> e = EpistemicDomain(pba.I(-1, 3), pba.I(5, 9))

   >>> # convert the epistemic space to bounds for the optimizer
   >>> e.toOptBounds(method='GA')  # `varbound` for genetic algorithm
   >>> e.toOptBounds(method='BO')  # `xc_bounds` for Bayesian optimisation

   >>> # perform lhs sampling on the epistemic space
   >>> sample = e.lhs_sampling(1000)


   .. py:method:: lhs_sampling(n_samples: int)

      perform lhs sampling on the epistemic space



   .. py:method:: lhs_plus_endpoints(n_samples: int)

      perform lhs sampling on the epistemic space and add endpoints



   .. py:method:: bound_rep()

      return the bounds (vec or matrix) of the epistemic space



   .. py:method:: toOptBounds(method: str)

      convert the epistemic space to bounds for the optimizer

      :param method: the optimization method to use, e.g. 'BayesOpt', 'GA'
      :type method: str

      :returns: the bounds of the design varibale used for the optimisation method



   .. py:method:: to_GA_varBounds() -> numpy.ndarray

      convert the epistemic space to bounds for the genetic algorithm optimizer



   .. py:method:: to_BayesOptBounds(func_signature='vectorisation') -> dict

      convert the epistemic space to bounds for the Bayesian optimisation optimizer