SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 5 |
| Total Lines | 40 |
| 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 | import random |
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| 6 | import numpy as np |
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| 7 | |||
| 8 | |||
| 9 | from . import HillClimbingOptimizer |
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| 10 | from ...search import Search |
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| 11 | from scipy.spatial.distance import euclidean |
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| 12 | |||
| 13 | |||
| 14 | def gaussian(distance, sig, sigma_factor=1): |
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| 15 | return ( |
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| 16 | sigma_factor |
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| 17 | * sig |
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| 18 | * np.exp(-np.power(distance, 2.0) / (sigma_factor * np.power(sig, 2.0))) |
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| 19 | ) |
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| 20 | |||
| 21 | |||
| 22 | class TabuOptimizer(HillClimbingOptimizer, Search): |
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| 23 | def __init__(self, search_space, tabu_factor=3): |
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| 24 | super().__init__(search_space) |
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| 25 | |||
| 26 | self.tabus = [] |
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| 27 | self.tabu_factor = tabu_factor |
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| 28 | self.epsilon_mod = 1 |
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| 29 | |||
| 30 | def iterate(self): |
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| 31 | return self._move_climb(self.pos_current) |
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| 32 | |||
| 33 | def evaluate(self, score_new): |
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| 34 | super().evaluate(score_new) |
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| 35 | |||
| 36 | if score_new <= self.score_current: |
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| 37 | self.epsilon_mod = self.tabu_factor |
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| 38 | else: |
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| 39 | self.epsilon_mod = 1 |
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| 40 |
