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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 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 ._spiral import Spiral |
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View Code Duplication |
def centeroid(array_list): |
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centeroid = [] |
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for idx in range(array_list[0].shape[0]): |
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center_dim_pos = [] |
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for array in array_list: |
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center_dim_pos.append(array[idx]) |
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center_dim_mean = np.array(center_dim_pos).mean() |
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centeroid.append(center_dim_mean) |
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return centeroid |
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class SpiralOptimization(BasePopulationOptimizer, Search): |
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name = "Spiral Optimization" |
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_name_ = "spiral_optimization" |
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def __init__(self, *args, population=10, decay_rate=0.99, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.population = population |
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self.decay_rate = decay_rate |
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self.particles = self._create_population(Spiral) |
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self.optimizers = self.particles |
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def init_pos(self, pos): |
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nth_pop = self.nth_iter % len(self.particles) |
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self.p_current = self.particles[nth_pop] |
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self.p_current.init_pos(pos) |
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self.p_current.decay_rate = self.decay_rate |
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def finish_initialization(self): |
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self.sort_pop_best_score() |
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self.center_pos = self.pop_sorted[0].pos_current |
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self.center_score = self.pop_sorted[0].score_current |
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self.init_done = True |
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def iterate(self): |
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n_iter = self._iterations(self.particles) |
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self.p_current = self.particles[n_iter % len(self.particles)] |
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self.sort_pop_best_score() |
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self.p_current.global_pos_best = self.pop_sorted[0].pos_current |
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return self.p_current.move_spiral(self.center_pos) |
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def evaluate(self, score_new): |
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if self.init_done: |
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if self.pop_sorted[0].score_current > self.center_score: |
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self.center_pos = self.pop_sorted[0].pos_current |
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self.center_score = self.pop_sorted[0].score_current |
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self.p_current.evaluate(score_new) |
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SimonBlanke /
Gradient-Free-Optimizers
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