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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 .base_population_optimizer import BasePopulationOptimizer |
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from ...search import Search |
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from ._individual import Individual |
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class EvolutionStrategyOptimizer(BasePopulationOptimizer, Search): |
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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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mutation_rate=0.7, |
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crossover_rate=0.3, |
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rand_rest_p=0.03, |
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): |
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super().__init__(search_space, initialize) |
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self.mutation_rate = mutation_rate |
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self.crossover_rate = crossover_rate |
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self.rand_rest_p = rand_rest_p |
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self.individuals = self.optimizers |
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def _random_cross(self, array_list): |
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n_arrays = len(array_list) |
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size = array_list[0].size |
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shape = array_list[0].shape |
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choice = np.random.randint(n_arrays, size=size).reshape(shape).astype(bool) |
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return np.choose(choice, array_list) |
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def _sort_best(self): |
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scores_list = [] |
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for ind in self.individuals: |
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scores_list.append(ind.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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return [self.individuals[idx] for idx in idx_sorted_ind] |
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def _cross(self): |
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ind_sorted = self._sort_best() |
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p_best = ind_sorted[0] |
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rnd_int = random.randint(1, len(ind_sorted) - 1) |
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p_sec_best = ind_sorted[rnd_int] |
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two_best_pos = [p_best.pos_current, p_sec_best.pos_current] |
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pos_new = self._random_cross(two_best_pos) |
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self.p_current = p_sec_best |
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p_sec_best.pos_new = pos_new |
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return pos_new |
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def _evo_iterate(self): |
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total_rate = self.mutation_rate + self.crossover_rate |
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rand = np.random.uniform(low=0, high=total_rate) |
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if len(self.individuals) == 1 or rand <= self.mutation_rate: |
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return self.p_current.iterate() |
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else: |
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return self._cross() |
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def init_pos(self, pos): |
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individual = Individual(self.conv.search_space, rand_rest_p=self.rand_rest_p) |
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self.individuals.append(individual) |
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individual.init_pos(pos) |
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self.p_current = individual |
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def iterate(self): |
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nth_iter = self._iterations(self.individuals) |
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self.p_current = self.individuals[nth_iter % len(self.individuals)] |
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return self._evo_iterate() |
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def evaluate(self, score_new): |
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self.p_current.score_new = score_new |
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self.p_current._evaluate_new2current(score_new) |
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self.p_current._evaluate_current2best() |
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SimonBlanke /
Gradient-Free-Optimizers
