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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 .search_tracker import SearchTracker |
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class BaseOptimizer(SearchTracker): |
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def __init__(self, search_space, rand_rest_p=0): |
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super().__init__() |
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self.search_space = search_space |
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self.space_dim_size = np.array( |
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[len(array) for array in search_space.values()] |
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) |
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self.rand_rest_p = rand_rest_p |
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self.optimizers = [self] |
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def move_random(self): |
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self.pos_new = np.random.randint( |
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self.space_dim_size, size=self.space_dim_size.shape |
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) |
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return self.pos_new |
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def track_nth_iter(func): |
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def wrapper(self, *args, **kwargs): |
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self.nth_iter = len(self.score_new_list) |
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return func(self, *args, **kwargs) |
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return wrapper |
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def random_restart(func): |
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def wrapper(self, *args, **kwargs): |
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if self.rand_rest_p > random.uniform(0, 1): |
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return self.move_random() |
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else: |
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return func(self, *args, **kwargs) |
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return wrapper |
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@track_nth_iter |
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def init_pos(self, pos): |
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self.pos_new = pos |
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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._evaluate_current2best() |
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SimonBlanke /
Gradient-Free-Optimizers
