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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 math |
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import numpy as np |
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from ..core_optimizer import CoreOptimizer |
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def split(positions_l, population): |
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div_int = math.ceil(len(positions_l) / population) |
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dist_init_positions = [] |
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for nth_indiv in range(population): |
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indiv_pos = [] |
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for nth_indiv_pos in range(div_int): |
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idx = nth_indiv + nth_indiv_pos * population |
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if idx < len(positions_l): |
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indiv_pos.append(positions_l[idx]) |
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dist_init_positions.append(indiv_pos) |
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return dist_init_positions |
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class BasePopulationOptimizer(CoreOptimizer): |
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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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): |
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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.eval_times = [] |
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self.iter_times = [] |
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self.init_done = False |
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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 sort_pop_best_score(self): |
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scores_list = [] |
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for _p_ in self.optimizers: |
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scores_list.append(_p_.score_current) |
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scores_np = np.array(scores_list) |
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idx_sorted_ind = list(scores_np.argsort()[::-1]) |
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self.pop_sorted = [self.optimizers[i] for i in idx_sorted_ind] |
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def _create_population(self, Optimizer): |
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if isinstance(self.population, int): |
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pop_size = self.population |
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else: |
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pop_size = len(self.population) |
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diff_init = pop_size - self.init.n_inits |
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if diff_init > 0: |
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self.init.add_n_random_init_pos(diff_init) |
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if isinstance(self.population, int): |
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distributed_init_positions = split( |
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self.init.init_positions_l, self.population |
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) |
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population = [] |
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for init_positions in distributed_init_positions: |
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init_values = self.conv.positions2values(init_positions) |
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init_paras = self.conv.values2paras(init_values) |
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population.append( |
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Optimizer( |
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self.conv.search_space, |
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rand_rest_p=self.rand_rest_p, |
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initialize={"warm_start": init_paras}, |
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constraints=self.constraints, |
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) |
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) |
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else: |
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population = self.population |
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return population |
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@CoreOptimizer.track_new_score |
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def evaluate_init(self, score_new): |
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self.p_current.evaluate_init(score_new) |
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def finish_initialization(self): |
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self.search_state = "iter" |
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SimonBlanke /
Gradient-Free-Optimizers
