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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 math |
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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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def sigmoid(x): |
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return 1 / (1 + math.exp(-x)) |
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class StochasticHillClimbingOptimizer(HillClimbingOptimizer): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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def _consider(self, _p_, p_accept): |
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rand = random.uniform(0, self._arg_.p_down) |
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if p_accept > rand: |
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_p_.score_current = _p_.score_new |
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_p_.pos_current = _p_.pos_new |
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def _score_norm(self, _p_): |
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return ( |
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100 |
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* (_p_.score_current - _p_.score_new) |
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/ (_p_.score_current + _p_.score_new) |
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) |
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def _accept(self, _p_): |
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return np.exp(-self._score_norm(_p_)) |
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def _iterate(self, i, _cand_, _p_, X, y): |
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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, X, y) |
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if _p_.score_new > _cand_.score_best: |
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_cand_, _p_ = self._update_pos(_cand_, _p_) |
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else: |
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p_accept = self._accept(_p_) |
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self._consider(_p_, p_accept) |
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return _cand_ |
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SimonBlanke /
Hyperactive
