porcupy.base
============

.. py:module:: porcupy.base


Classes
-------

.. autoapisummary::

   porcupy.base.Optimizer


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

.. py:class:: Optimizer(dimensions: int, bounds: Tuple[numpy.ndarray, numpy.ndarray], pop_size: int = 30, max_iter: int = 100, options: Optional[Dict[str, Any]] = None, ftol: float = -np.inf, ftol_iter: int = 1)

   Bases: :py:obj:`abc.ABC`


   Base class for optimization algorithms in Porcupy.

   This abstract base class provides the foundation for all optimization
   algorithms in the Porcupy library. It defines the common interface and
   functionality that all optimizers should implement.

   :param dimensions: Number of dimensions in the search space.
   :type dimensions: int
   :param bounds: A tuple of size 2 where the first entry is the minimum bound while
                  the second entry is the maximum bound. Each array must be of shape
                  (dimensions,).
   :type bounds: tuple of numpy.ndarray
   :param pop_size: Number of search agents in the population (default: 30).
   :type pop_size: int, optional
   :param max_iter: Maximum number of iterations (default: 100).
   :type max_iter: int, optional
   :param options: A dictionary containing algorithm-specific parameters.
   :type options: dict, optional
   :param ftol: Relative error in objective_func(best_pos) acceptable for
                convergence (default: -np.inf).
   :type ftol: float, optional
   :param ftol_iter: Number of iterations over which the relative error in
                     objective_func(best_pos) is acceptable for convergence (default: 1).
   :type ftol_iter: int, optional


   .. py:attribute:: dimensions


   .. py:attribute:: bounds


   .. py:attribute:: pop_size
      :value: 30



   .. py:attribute:: max_iter
      :value: 100



   .. py:attribute:: options
      :value: None



   .. py:attribute:: ftol


   .. py:attribute:: ftol_iter
      :value: 1



   .. py:class:: ToHistory

      Bases: :py:obj:`tuple`


      .. py:attribute:: best_cost


      .. py:attribute:: mean_cost


      .. py:attribute:: position


      .. py:attribute:: population_size



   .. py:method:: reset()

      Reset the attributes of the optimizer

      This method reinitializes all history tracking attributes and
      prepares the optimizer for a new optimization run.



   .. py:method:: optimize(objective_func: Callable, n_processes: Optional[int] = None, verbose: bool = False, **kwargs) -> Tuple[numpy.ndarray, float, numpy.ndarray]
      :abstractmethod:


      Optimize the objective function

      :param objective_func: The objective function to be minimized
      :type objective_func: callable
      :param n_processes: Number of processes to use for parallel evaluation
      :type n_processes: int, optional
      :param verbose: Whether to display progress information
      :type verbose: bool, optional
      :param \*\*kwargs: Additional arguments to pass to the objective function

      :returns: A tuple containing (best_position, best_cost, cost_history)
      :rtype: tuple