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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from ...base_optimizer import BaseOptimizer
from ...base_positioner import BasePositioner
class SBOM(BaseOptimizer):
def __init__(self, _opt_args_):
super().__init__(_opt_args_)
self.n_positioners = 1
def _all_possible_pos(self, cand):
pos_space = []
for dim_ in cand._space_.dim:
pos_space.append(np.arange(dim_ + 1))
self.n_dim = len(pos_space)
self.all_pos_comb = np.array(np.meshgrid(*pos_space)).T.reshape(-1, self.n_dim)
def _init_iteration(self, _cand_):
p = SbomPositioner()
p.move_random(_cand_)
self._optimizer_eval(_cand_, p)
self._update_pos(_cand_, p)
self._all_possible_pos(_cand_)
if self._opt_args_.warm_start_smbo:
self.X_sample = _cand_.mem._get_para()
self.Y_sample = _cand_.mem._get_score()
else:
self.X_sample = _cand_.pos_best.reshape(1, -1)
self.Y_sample = np.array(_cand_.score_best).reshape(1, -1)
return p
class SbomPositioner(BasePositioner):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
SimonBlanke /
Hyperactive
