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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 random |
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import numpy as np |
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from . import HillClimbingOptimizer |
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from ...search import Search |
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class StochasticHillClimbingOptimizer(HillClimbingOptimizer, Search): |
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def __init__(self, search_space, p_down=0.1, norm_factor=1): |
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super().__init__(search_space) |
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self.p_down = p_down |
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self.norm_factor = norm_factor |
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if self.norm_factor == "adaptive": |
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self._accept = self._accept_adapt |
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self.diff_max = 0 |
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else: |
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self._accept = self._accept_default |
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def _consider(self, p_accept): |
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rand = random.uniform(0, self.p_down) |
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# print("\np_accept", p_accept) |
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# print("rand", rand) |
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if p_accept > 1 - rand: |
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self._new2current() |
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def _score_norm_default(self): |
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denom = self.score_current + self.score_new |
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if denom == 0: |
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return 1 |
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elif abs(denom) == np.inf: |
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return 0 |
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else: |
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return self.norm_factor * (self.score_current - self.score_new) / denom |
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def _score_norm_adapt(self): |
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diff = abs(self.score_current - self.score_new) |
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if self.diff_max < diff: |
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self.diff_max = diff |
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denom = self.diff_max + diff |
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if denom == 0: |
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return 1 |
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elif abs(denom) == np.inf: |
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return 0 |
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else: |
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return abs(self.diff_max - diff) / denom |
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def _accept_default(self): |
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return np.exp(-self._score_norm_default()) |
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def _accept_adapt(self): |
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return self._score_norm_adapt() |
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def _transition(self, score_new): |
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if score_new <= self.score_current: |
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p_accept = self._accept() |
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self._consider(p_accept) |
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def evaluate(self, score_new): |
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self.score_new = score_new |
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self._evaluate_new2current(score_new) |
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self._transition(score_new) |
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self._evaluate_current2best() |
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SimonBlanke /
Gradient-Free-Optimizers
