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gradient_free_optimizers.optimizers.pop_opt._evolutionary_algorithm   A

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Complexity

Total Complexity 3

Size/Duplication

Total Lines 25
Duplicated Lines 0 %

Importance

Changes 0
Metric Value
wmc 3
eloc 15
dl 0
loc 25
rs 10
c 0
b 0
f 0

2 Methods

Rating   Name   Duplication   Size   Complexity  
A EvolutionaryAlgorithmOptimizer.__init__() 0 2 1
A EvolutionaryAlgorithmOptimizer.discrete_recombination() 0 10 2
1 
# 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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class EvolutionaryAlgorithmOptimizer(BasePopulationOptimizer):

            


            
                                                        
            
                                    
            
            

                
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    def __init__(self, *args, **kwargs) -> None:

            


            
                                                        
            
                                    
            
            

                
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        super().__init__(*args, **kwargs)

            


            
                                                        
            
                                    
            
            

                
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    def discrete_recombination(self, parent_pos_l, crossover_rates=None):

            


            
                                                        
            
                                    
            
            

                
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        n_parents = len(parent_pos_l)

            


            
                                                        
            
                                    
            
            

                
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        size = parent_pos_l[0].size

            


            
                                                        
            
                                    
            
            

                
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        choice = []

            


            
                                                        
            
                                    
            
            

                
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        for _ in range(size):

            


            
                                                        
            
                                    
            
            

                
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            choices = list(range(n_parents))

            


            
                                                        
            
                                    
            
            

                
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            choice.append(np.random.choice(choices, p=crossover_rates))

            


            
                                                        
            
                                    
            
            

                
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        return np.choose(choice, parent_pos_l)

            


            
                                                        
            
                                    
            
            

                
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