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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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from ...converter import Converter |
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from ...results_manager import ResultsManager |
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class BasePopulationOptimizer: |
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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.eval_times = [] |
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self.iter_times = [] |
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def _iterations(self, positioners): |
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nth_iter = 0 |
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for p in positioners: |
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nth_iter = nth_iter + len(p.pos_new_list) |
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return nth_iter |
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def _create_population(self, Optimizer): |
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if isinstance(self.population, int): |
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population = [] |
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for pop_ in range(self.population): |
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population.append( |
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Optimizer(self.conv.search_space, rand_rest_p=self.rand_rest_p) |
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) |
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else: |
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population = self.population |
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n_inits = 0 |
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for key_ in self.initialize.keys(): |
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init_value = self.initialize[key_] |
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if isinstance(init_value, int): |
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n_inits += init_value |
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else: |
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n_inits += len(init_value) |
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if n_inits < len(population): |
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print("\n Warning: Not enough initial positions for population size") |
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print(" Population size is reduced to", n_inits) |
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population = population[:n_inits] |
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return population |
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def finish_initialization(self): |
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pass |
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SimonBlanke /
Gradient-Free-Optimizers
