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init_grid_search()   A

↳ Parent: gradient_free_optimizers.init_positions

Complexity

Conditions 3

Size

Total Lines 22
Code Lines 15

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 3
eloc 15
nop 2
dl 0
loc 22
rs 9.65
c 0
b 0
f 0
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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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def init_grid_search(space_dim, n_pos):

            


            
                                                                        
                            
            
                                    
            
            

                
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    positions = []

            


            
                                                                        
                            
            
                                    
            
            

                
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    n_dim = len(space_dim)

            


            
                                                                        
                            
            
                                    
            
            

                
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    p_per_dim = int(np.power(n_pos, 1 / n_dim))

            


            
                                                                        
                            
            
                                    
            
            

                
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    for dim in space_dim:

            


            
                                                                        
                            
            
                                    
            
            

                
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        dim_dist = int(dim / (p_per_dim + 1))

            


            
                                                                        
                            
            
                                    
            
            

                
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        n_points = [n * dim_dist for n in range(1, p_per_dim + 1)]

            


            
                                                                        
                            
            
                                    
            
            

                
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        positions.append(n_points)

            


            
                                                                        
                            
            
                                    
            
            

                
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    pos_mesh = np.array(np.meshgrid(*positions))

            


            
                                                                        
                            
            
                                    
            
            

                
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    positions = list(pos_mesh.T.reshape(-1, n_dim))

            


            
                                                                        
                            
            
                                    
            
            

                
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    diff_pos = n_pos - len(positions)

            


            
                                                                        
                            
            
                                    
            
            

                
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    if diff_pos > 0:

            


            
                                                                        
                            
            
                                    
            
            

                
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        pos_rnd = init_random_search(space_dim, n_pos=diff_pos)

            


            
                                                                        
                            
            
                                    
            
            

                
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        return positions + pos_rnd

            


            
                                                                        
                            
            
                                    
            
            

                
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    return positions

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def init_random_search(space_dim, n_pos):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    positions = []

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    for nth_pos in range(n_pos):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        pos = np.random.randint(space_dim, size=space_dim.shape)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        positions.append(pos)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    return positions

            


            
                                                        
            
                                    
            
            

                
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