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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import random
from . import HillClimbingOptimizer
class StochasticHillClimbingOptimizer(HillClimbingOptimizer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _consider(self, _p_, p_accept):
rand = random.uniform(0, self._arg_.r)
if p_accept > rand:
_p_.score_current = _p_.score_new
_p_.pos_current = _p_.pos_new
def _accept(self, _p_):
score_diff_norm = (_p_.score_new - _p_.score_current) / (
_p_.score_new + _p_.score_current
)
if score_diff_norm == 0:
return 100
else:
return 0.001 / abs(score_diff_norm)
def _iterate(self, i, _cand_, _p_, X, y):
_p_.pos_new = _p_.move_climb(_cand_, _p_.pos_current)
_p_.score_new = _cand_.eval_pos(_p_.pos_new, X, y)
if _p_.score_new > _cand_.score_best:
_cand_, _p_ = self._update_pos(_cand_, _p_)
p_accept = self._accept(_p_)
self._consider(_p_, p_accept)
return _cand_
SimonBlanke /
Hyperactive
