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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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from ..converter import Converter |
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from ..results_manager import ResultsManager |
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class BaseOptimizer(SearchTracker): |
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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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): |
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super().__init__() |
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self.conv = Converter(search_space) |
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self.results_mang = ResultsManager(self.conv) |
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self.initialize = initialize |
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self.optimizers = [self] |
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def move_random(self): |
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position = [] |
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for search_space_pos in self.conv.search_space_positions: |
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pos_ = random.choice(search_space_pos) |
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position.append(pos_) |
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return np.array(position) |
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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.pos_new_list) |
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pos = func(self, *args, **kwargs) |
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self.pos_new = pos |
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return pos |
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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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def conv2pos(self, pos): |
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# position to int |
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r_pos = np.rint(pos) |
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n_zeros = [0] * len(self.conv.max_positions) |
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# clip into search space boundaries |
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return np.clip(r_pos, n_zeros, self.conv.max_positions).astype(int) |
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def init_pos(self, pos): |
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self.pos_new = pos |
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self.nth_iter = len(self.pos_new_list) |
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def finish_initialization(self): |
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pass |
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def evaluate(self, score_new): |
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self.score_new = score_new |
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if self.pos_best is None: |
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self.pos_best = self.pos_new |
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self.pos_current = self.pos_new |
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self.score_best = score_new |
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self.score_current = 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
