"""
Ready-to-run Random Search wrappers.
"""
from __future__ import annotations
from typing import Optional
import numpy as np
from metaheuristic_designer.encoding import Encoding
from metaheuristic_designer.objective_function import ObjectiveFunc
from ..initializers import UniformInitializer, PermInitializer
from ..encodings import TypeCastEncoding
from ..strategies import RandomSearch
from ..algorithms import Algorithm
from ..utils import RNGLike, check_random_state
[docs]
def random_search_binary(objfunc: ObjectiveFunc, encoding: Optional[Encoding] = None, random_state: Optional[RNGLike] = None, **kwargs) -> Algorithm:
"""Random Search for binary-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimise.
encoding : Encoding, optional
Encoding; defaults to :class:`TypeCastEncoding` (int → bool).
random_state : RNGLike, optional
Random seed or generator.
**kwargs
Forwarded to :class:`Algorithm`.
"""
random_state = check_random_state(random_state)
encoding = TypeCastEncoding(int, bool) if encoding is None else encoding
pop_initializer = UniformInitializer(objfunc.dimension, 0, 1, population_size=1, dtype=np.uint8, encoding=encoding, random_state=random_state)
search_strat = RandomSearch(pop_initializer, random_state=random_state)
return Algorithm(objfunc, search_strat, **kwargs)
[docs]
def random_search_permutation(
objfunc: ObjectiveFunc, encoding: Optional[Encoding] = None, random_state: Optional[RNGLike] = None, **kwargs
) -> Algorithm:
"""Random Search for permutation-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimise.
encoding : Encoding, optional
Encoding applied to the genotype.
random_state : RNGLike, optional
Random seed or generator.
**kwargs
Forwarded to :class:`Algorithm`.
"""
random_state = check_random_state(random_state)
pop_initializer = PermInitializer(objfunc.dimension, population_size=1, encoding=encoding, random_state=random_state)
search_strat = RandomSearch(pop_initializer, random_state=random_state)
return Algorithm(objfunc, search_strat, **kwargs)
[docs]
def random_search_discrete(
objfunc: ObjectiveFunc, encoding: Optional[Encoding] = None, random_state: Optional[RNGLike] = None, **kwargs
) -> Algorithm:
"""Random Search for integer-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimise.
encoding : Encoding, optional
Encoding applied to the genotype.
random_state : RNGLike, optional
Random seed or generator.
**kwargs
Forwarded to :class:`Algorithm`.
"""
random_state = check_random_state(random_state)
pop_initializer = UniformInitializer(
objfunc.dimension, objfunc.lower_bound, objfunc.upper_bound, population_size=1, dtype=int, encoding=encoding, random_state=random_state
)
search_strat = RandomSearch(pop_initializer, random_state=random_state)
return Algorithm(objfunc, search_strat, **kwargs)
[docs]
def random_search_real(objfunc: ObjectiveFunc, encoding: Optional[Encoding] = None, random_state: Optional[RNGLike] = None, **kwargs) -> Algorithm:
"""Random Search for real-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimise.
encoding : Encoding, optional
Encoding applied to the genotype.
random_state : RNGLike, optional
Random seed or generator.
**kwargs
Forwarded to :class:`Algorithm`.
"""
random_state = check_random_state(random_state)
pop_initializer = UniformInitializer(
objfunc.dimension, objfunc.lower_bound, objfunc.upper_bound, population_size=1, dtype=float, encoding=encoding, random_state=random_state
)
search_strat = RandomSearch(pop_initializer, random_state=random_state)
return Algorithm(objfunc, search_strat, **kwargs)