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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 HillClimbingOptimizer(BaseOptimizer): |
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def __init__(self, _main_args_, _opt_args_): |
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super().__init__(_main_args_, _opt_args_) |
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def _hill_climb_iter(self, _cand_, _p_): |
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score_new = -np.inf |
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pos_new = None |
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for _ in range(self._opt_args_.n_neighbours): |
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_p_.pos_new = _p_.move_climb(_cand_, _p_.pos_current) |
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_p_.score_new = _cand_.eval_pos(_p_.pos_new) |
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if _p_.score_new > score_new: |
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score_new = _p_.score_new |
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pos_new = _p_.pos_new |
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if score_new > _cand_.score_best: |
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_p_.pos_new = pos_new |
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_p_.score_new = score_new |
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_cand_, _p_ = self._update_pos(_cand_, _p_) |
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return _cand_, _p_ |
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def _iterate(self, i, _cand_, _p_): |
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_cand_, _p_ = self._hill_climb_iter(_cand_, _p_) |
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return _cand_ |
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def _init_opt_positioner(self, _cand_): |
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return super()._init_base_positioner(_cand_, positioner=HillClimbingPositioner) |
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class HillClimbingPositioner(BasePositioner): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.epsilon = kwargs["epsilon"] |
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self.climb_dist = kwargs["climb_dist"] |
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SimonBlanke /
Hyperactive
