SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 4 |
| Total Lines | 37 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | # Author: Simon Blanke |
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| 2 | # Email: [email protected] |
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| 3 | # License: MIT License |
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| 4 | |||
| 5 | |||
| 6 | import numpy as np |
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| 7 | |||
| 8 | from ..local_opt import StochasticHillClimbingOptimizer |
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| 9 | from ...search import Search |
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| 10 | |||
| 11 | |||
| 12 | class SimulatedAnnealingOptimizer(StochasticHillClimbingOptimizer, Search): |
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| 13 | name = "Simulated Annealing" |
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| 14 | |||
| 15 | def __init__( |
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| 16 | self, |
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| 17 | *args, |
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| 18 | annealing_rate=0.97, |
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| 19 | start_temp=1, |
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| 20 | **kwargs, |
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| 21 | ): |
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| 22 | super().__init__(*args, **kwargs) |
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| 23 | self.annealing_rate = annealing_rate |
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| 24 | self.start_temp = start_temp |
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| 25 | self.temp = start_temp |
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| 26 | |||
| 27 | def _accept_default(self): |
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| 28 | return np.exp(-self._score_norm_default() / self.temp) |
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| 29 | |||
| 30 | def _accept_adapt(self): |
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| 31 | return self._score_norm_adapt() / self.temp |
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| 32 | |||
| 33 | def evaluate(self, score_new): |
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| 34 | StochasticHillClimbingOptimizer.evaluate(self, score_new) |
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| 35 | |||
| 36 | self.temp = self.temp * self.annealing_rate |
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| 37 |
