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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import numpy as np |
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from ...base_optimizer import BaseOptimizer |
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from ...base_positioner import BasePositioner |
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class SBOM(BaseOptimizer): |
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def __init__(self, _opt_args_): |
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super().__init__(_opt_args_) |
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self.n_positioners = 1 |
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def get_random_sample(self): |
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sample_size = self._sample_size() |
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if sample_size > self.all_pos_comb.shape[0]: |
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sample_size = self.all_pos_comb.shape[0] |
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row_sample = np.random.choice( |
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self.all_pos_comb.shape[0], size=(sample_size,), replace=False |
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) |
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return self.all_pos_comb[row_sample] |
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def _sample_size(self): |
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n = self._opt_args_.max_sample_size |
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return int(n * np.tanh(self.all_pos_comb.size / n)) |
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def _all_possible_pos(self, cand): |
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pos_space = [] |
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for dim_ in cand._space_.dim: |
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pos_space.append(np.arange(dim_ + 1)) |
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self.n_dim = len(pos_space) |
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self.all_pos_comb = np.array(np.meshgrid(*pos_space)).T.reshape(-1, self.n_dim) |
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def _init_iteration(self, _cand_): |
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p = SbomPositioner() |
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p.move_random(_cand_) |
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self._optimizer_eval(_cand_, p) |
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self._update_pos(_cand_, p) |
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self._all_possible_pos(_cand_) |
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if self._opt_args_.warm_start_smbo: |
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self.X_sample = _cand_.mem._get_para() |
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self.Y_sample = _cand_.mem._get_score() |
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else: |
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self.X_sample = _cand_.pos_best.reshape(1, -1) |
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self.Y_sample = np.array(_cand_.score_best).reshape(1, -1) |
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return p |
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class SbomPositioner(BasePositioner): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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SimonBlanke /
Hyperactive
