Source code for NiaPy.util.repair

import numpy as np

__all__ = ['limit', 'limit_inverse', 'wang', 'random', 'reflect']


def limit(x, lower, upper, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            lower (numpy.ndarray): Lower bounds of search space.
            upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Solution in search space.

    """
    return np.clip(x, lower, upper, out=x)


def limit_inverse(x, lower, upper, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            lower (numpy.ndarray): Lower bounds of search space.
            upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Solution in search space.

    """
    ir = np.where(x < lower)
    x[ir] = upper[ir]
    ir = np.where(x > upper)
    x[ir] = lower[ir]
    return x


def wang(x, lower, upper, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            lower (numpy.ndarray): Lower bounds of search space.
            upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Solution in search space.

    """
    ir = np.where(x < lower)
    x[ir] = np.amin([upper[ir], 2 * lower[ir] - x[ir]], axis=0)
    ir = np.where(x > upper)
    x[ir] = np.amax([lower[ir], 2 * upper[ir] - x[ir]], axis=0)
    return x


def random(x, lower, upper, rnd=np.random, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            lower (numpy.ndarray): Lower bounds of search space.
            upper (numpy.ndarray): Upper bounds of search space.
            rnd (mtrand.RandomState): Random generator.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Fixed solution.

    """
    ir = np.where(x < lower)
    x[ir] = rnd.uniform(lower[ir], upper[ir])
    ir = np.where(x > upper)
    x[ir] = rnd.uniform(lower[ir], upper[ir])
    return x


def reflect(x, lower, upper, **kwargs):
    r"""Repair solution and put the solution in search space with reflection of how much the solution violates a bound.

    Args:
            x (numpy.ndarray): Solution to be fixed.
            lower (numpy.ndarray): Lower bounds of search space.
            upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Fix solution.

    """
    ir = np.where(x > upper)
    x[ir] = lower[ir] + x[ir] % (upper[ir] - lower[ir])
    ir = np.where(x < lower)
    x[ir] = lower[ir] + x[ir] % (upper[ir] - lower[ir])
    return x