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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, _opt_args_): |
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super().__init__(_opt_args_) |
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self.n_positioners = 1 |
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def _hill_climb_iter(self, i, _cand_): |
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score_new = -np.inf |
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pos_new = None |
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self.p_list[0].move_climb(_cand_, self.p_list[0].pos_current) |
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self._optimizer_eval(_cand_, self.p_list[0]) |
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if self.p_list[0].score_new > score_new: |
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score_new = self.p_list[0].score_new |
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pos_new = self.p_list[0].pos_new |
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if i % self._opt_args_.n_neighbours == 0: |
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self.p_list[0].pos_new = pos_new |
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self.p_list[0].score_new = score_new |
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self._update_pos(_cand_, self.p_list[0]) |
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def _iterate(self, i, _cand_): |
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self._hill_climb_iter(i, _cand_) |
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def _init_iteration(self, _cand_): |
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p = super()._init_base_positioner(_cand_, positioner=HillClimbingPositioner) |
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self._optimizer_eval(_cand_, p) |
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self._update_pos(_cand_, p) |
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return p |
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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
