Source code for star_pso.benchmarks.test_function

import numpy as np
from scipy.stats import qmc
from numpy.typing import NDArray
from numpy.random import default_rng, Generator

from star_pso.population.particle import Particle

# Public interface.
__all__ = ["TestFunction"]




class TestFunction:
    """
    Description:
        All benchmark test functions should inherit from this class.
        Here is provided the interface for all the test problems.
    """

    # Make a random number generator.
    rng: Generator = default_rng()
    """
    Random number generator for the whole class.
    """

    # Object variables.
    __slots__ = ("_name", "_n_dim", "_x_min", "_x_max", "_lhc")

    def __init__(self, name: str, n_dim: int,
                 x_min: float | NDArray,
                 x_max: float | NDArray) -> None:
        """
        Default initializer of the "TestFunction" class.

        :param name: (str) the name of the function.

        :param n_dim: (int) the number of dimension of the input space.

        :param x_min: (float) the lower bound values of the search space.

        :param x_max: (float) the upper bound values of the search space.
        """
        # Sanity check.
        if np.any(x_min >= x_max):
            raise ValueError(f"{self.__class__.__name__}: "
                             f"x_min must be smaller than x_max.")
        # _end_if_

        # Assign the function name.
        self._name = name

        # Assign the dimensions.
        self._n_dim = n_dim

        # Assign the minimum value(s).
        self._x_min = x_min

        # Assign the maximum value(s).
        self._x_max = x_max

        # Construct a Latin Hyper Cube sampler.
        self._lhc = qmc.LatinHypercube(d=self._n_dim, rng=TestFunction.rng,
                                       optimization="random-cd")
    # _end_def_

    @property
    def n_dim(self) -> int:
        """
        Accessor (getter) of the test function dimensions.

        :return: (int) number of dimensions.
        """
        return self._n_dim
    # _end_def_

    @property
    def name(self) -> str:
        """
        Accessor (getter) of the test function name.

        :return: string name of the test function.
        """
        return self._name
    # _end_def_

    @property
    def x_min(self) -> float | NDArray:
        """
        Accessor (getter) of the lower bounds of the test function.

        :return: numpy array with minimum values.
        """
        return self._x_min
    # _end_def_

    @property
    def x_max(self) -> float | NDArray:
        """
        Accessor (getter) of the upper bounds of the test function.

        :return: numpy array with maximum values.
        """
        return self._x_max
    # _end_def_



    @classmethod
    def set_seed(cls, new_seed=None) -> None:
        """
        Sets a new seed for the random number generator.

        :param new_seed: New seed value (default=None).

        :return: None.
        """
        # Re-initialize the class variable.
        cls.rng = default_rng(seed=new_seed)

    # _end_def_



    def func(self, x_pos: NDArray) -> NDArray:
        """
        This method will implement the objective function to be optimized.

        :return: None.
        """
        raise NotImplementedError(f"{self.__class__.__name__}: "
                                  f"You should implement this method!")

    # _end_def_



    def sample_random_positions(self, n_pos: int = 100, method: str = "random") -> NDArray:
        """
        Generate an initial set of uniformly random sampled positions within
        the lower / upper bounds of the test problem.

        :param n_pos: (int) number of random positions to create.

        :param method: (str) method to use for sampling ("random", "latin-hc").

        :return: a uniformly sampled set of random positions.
        """
        # Sanity check.
        if method.lower() == "random":
            # Draw uniform random samples.
            return self.rng.uniform(self.x_min, self.x_max,
                                    size=(n_pos, self.n_dim))

        # Sanity check.
        if method.lower() == "latin-hc":
            # Draw uniform random samples from LHC.
            sample = self._lhc.random(n_pos)

            # Scale the samples to the limits.
            return qmc.scale(sample, self.x_min, self.x_max)

        # If we get here, raise an error!
        raise ValueError(f"{self.__class__.__name__}: "
                         f"Unknown sampling method: {method}. Use 'random' or 'latin-hc'.")

    # _end_def_



    def search_for_optima(self, population: list[Particle],
                          epsilon: float = 1.0e-4) -> tuple[int, int]:
        """
        Searches the input population for the global optimum values
        of the specific test function, using default (problem specific)
        parameters.

        :param population: a list of Particles to search the global optimum.

        :param epsilon: (float) accuracy level of the global optimal solution.

        :return: a tuple with the number of global optima found and the
                 total number that exist.
        """
        raise NotImplementedError(f"{self.__class__.__name__}: "
                                  f"You should implement this method!")

    # _end_def_

    def __str__(self) -> str:
        """
        Returns a string representation of the TestFunction.
        """
        return f"{self._name}(x_min={self._x_min}, x_max={self._x_max})"

    # _end_def_

# _end_class_