pyuncertainnumber.opt.get_range
===============================

.. py:module:: pyuncertainnumber.opt.get_range


Functions
---------

.. autoapisummary::

   pyuncertainnumber.opt.get_range.get_range_BO
   pyuncertainnumber.opt.get_range.get_range_BO_raw
   pyuncertainnumber.opt.get_range.get_range_GA


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

.. py:function:: get_range_BO(f: callable, design_bounds: list | numpy.ndarray, acquisition_function: str = 'UCB', verbose: bool = False, **kwargs) -> tuple[pyuncertainnumber.pba.intervals.number.Interval, dict]

   Compute the range of a black-box function using BayesOpt with vectorised or iterable function signature

   :param f: function as the objective to be optimized, with vectorised or iterable function signature
   :type f: callable
   :param design_bounds: nested list or 2D array, containing the lower and upper bounds for each dimension; e.g. [[0, 1], [0, 1]] for 2D input space.
   :type design_bounds: list | np.ndarray
   :param acquisition_function: the acquisition function to be used, e.g. 'UCB', 'EI', 'PI'. If None, defaults to 'UCB'.
   :type acquisition_function: str or callable, optional
   :param verbose: if True, prints the optimization progress
   :type verbose: Boolean
   :param \*\*kwargs: additional keyword arguments for the BayesOpt class. For example, one can pass 'num_explorations', 'num_iterations', etc.

   .. tip:: for a less verbose output, use (convergence_curve=False, progress_bar=False)

   :returns:

             A tuple containing:
                 - response_itvl: The interval of the minimum and maximum from the optimization of the black-box function.
                 - opt_hint: A dictionary with optimal input points for the minimum and maximum values.
   :rtype: Tuple[Interval, dict]


.. py:function:: get_range_BO_raw(f: callable, design_bounds: list, acquisition_function='UCB', verbose=False, **kwargs)

   Compute the range of a black-box function using BayesOpt with arguments-signature function;

   :param f: callable function to be optimized
   :param dimension: the number of dimensions of the input space
   :type dimension: int
   :param design_bounds: each tuple contains the lower and upper bounds for each dimension
   :type design_bounds: list
   :param acquisition_function: the acquisition function to be used, e.g. 'UCB', 'EI', 'PI'. If None, defaults to 'UCB'.
   :type acquisition_function: str or callable, optional
   :param verbose: if True, prints the optimization progress
   :type verbose: Boolean
   :param \*\*kwargs: additional keyword arguments for the BayesOpt class

   .. tip:: for a less verbose output, use (convergence_curve=False, progress_bar=False)

   :returns:

             A tuple containing:
                 - response_itvl: The interval of the minimum and maximum from the optimization of the black-box function.
                 - opt_hint: A dictionary with optimal input points for the minimum and maximum values.
   :rtype: Tuple[Interval, dict]


.. py:function:: get_range_GA(f: callable, dimension: int, varbound, algorithm_param=None, verbose=False, **kwargs)

   compute the range of the black-box function using GA

   :param varbound: The variable bounds for the optimization.
   :type varbound: np.ndarray

   :returns:

             A tuple containing:
                 - response_itvl: The interval of the minimum and maximum from the optimization of the black-box function.
                 - opt_hint: A dictionary with optimal input points for the minimum and maximum values.
   :rtype: Tuple[Interval, dict]

   .. note:: It's suggested to use `EpistemicDomain` which facilitates the specification of varbound.