# Source code for niapy.util.repair

```import numpy as np
from numpy.random import default_rng

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

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

Args:
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.

Returns:
numpy.ndarray: Solution in search space.

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

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

Args:
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.

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

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

Args:
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.

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

[docs]def rand(x, lower, upper, rng=None, **_kwargs):
r"""Repair solution and put the solution in the random position inside of the bounds of problem.

Args:
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.
rng (numpy.random.Generator): Random generator.

Returns:
numpy.ndarray: Fixed solution.

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

[docs]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.

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
```