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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 numpy as np |
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from ..base_optimizer import BaseOptimizer |
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def max_list_idx(list_): |
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max_item = max(list_) |
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max_item_idx = [i for i, j in enumerate(list_) if j == max_item] |
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return max_item_idx[-1:][0] |
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class HillClimbingOptimizer(BaseOptimizer): |
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name = "Hill Climbing" |
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_name_ = "hill_climbing" |
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__name__ = "HillClimbingOptimizer" |
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optimizer_type = "local" |
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computationally_expensive = False |
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def __init__( |
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self, |
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search_space, |
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initialize={"grid": 4, "random": 2, "vertices": 4}, |
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constraints=[], |
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random_state=None, |
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rand_rest_p=0, |
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nth_process=None, |
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epsilon=0.03, |
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distribution="normal", |
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n_neighbours=3, |
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): |
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super().__init__( |
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search_space=search_space, |
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initialize=initialize, |
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constraints=constraints, |
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random_state=random_state, |
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rand_rest_p=rand_rest_p, |
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nth_process=nth_process, |
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) |
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self.epsilon = epsilon |
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self.distribution = distribution |
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self.n_neighbours = n_neighbours |
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@BaseOptimizer.track_new_pos |
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@BaseOptimizer.random_iteration |
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def iterate(self): |
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return self.move_climb( |
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self.pos_current, |
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epsilon=self.epsilon, |
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distribution=self.distribution, |
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) |
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@BaseOptimizer.track_new_score |
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def evaluate(self, score_new): |
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BaseOptimizer.evaluate(self, score_new) |
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if len(self.scores_valid) == 0: |
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return |
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modZero = self.nth_trial % self.n_neighbours == 0 |
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if modZero: |
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score_new_list_temp = self.scores_valid[-self.n_neighbours :] |
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pos_new_list_temp = self.positions_valid[-self.n_neighbours :] |
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idx = max_list_idx(score_new_list_temp) |
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score = score_new_list_temp[idx] |
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pos = pos_new_list_temp[idx] |
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self._eval2current(pos, score) |
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self._eval2best(pos, score) |
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SimonBlanke /
Gradient-Free-Optimizers
