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test_p_accept()   A

↳ Parent: tests.test_parameters.test_stochastic_hill_climbing

Complexity

Conditions 1

Size

Total Lines 32
Code Lines 24

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 1
eloc 24
nop 0
dl 0
loc 32
rs 9.304
c 0
b 0
f 0
1 
# Author: Simon Blanke
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# Email: [email protected]
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# License: MIT License
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import pytest

            


            
                                                                                                            
                            
            
                                    
            
            

                
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import numpy as np

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from surfaces.test_functions.mathematical import SphereFunction

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from gradient_free_optimizers import StochasticHillClimbingOptimizer

            


            
                                                                                                            
                            
            
                                    
            
            

                
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sphere_function = SphereFunction(n_dim=2)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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objective_function = sphere_function.objective_function

            


            
                                                                                                            
                            
            
                                    
            
            

                
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search_space = sphere_function.search_space()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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n_iter = 1000

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_p_accept():

            


            
                                                        
            
                                    
            
            

                
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    p_accept_low = 0.5

            


            
                                                        
            
                                    
            
            

                
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    p_accept_high = 1

            


            
                                                        
            
                                    
            
            

                
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    epsilon = 1 / np.inf

            


            
                                                        
            
                                    
            
            

                
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    opt = StochasticHillClimbingOptimizer(

            


            
                                                        
            
                                    
            
            

                
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        search_space,

            


            
                                                        
            
                                    
            
            

                
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        p_accept=p_accept_low,

            


            
                                                        
            
                                    
            
            

                
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        epsilon=epsilon,

            


            
                                                        
            
                                    
            
            

                
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        initialize={"random": 1},

            


            
                                                        
            
                                    
            
            

                
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    )

            


            
                                                        
            
                                    
            
            

                
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    opt.search(objective_function, n_iter=n_iter)

            


            
                                                        
            
                                    
            
            

                
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    n_transitions_low = opt.n_transitions

            


            
                                                        
            
                                    
            
            

                
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    opt = StochasticHillClimbingOptimizer(

            


            
                                                        
            
                                    
            
            

                
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        search_space,

            


            
                                                        
            
                                    
            
            

                
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        p_accept=p_accept_high,

            


            
                                                        
            
                                    
            
            

                
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        epsilon=epsilon,

            


            
                                                        
            
                                    
            
            

                
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        initialize={"random": 1},

            


            
                                                        
            
                                    
            
            

                
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    )

            


            
                                                        
            
                                    
            
            

                
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    opt.search(objective_function, n_iter=n_iter)

            


            
                                                        
            
                                    
            
            

                
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    n_transitions_high = opt.n_transitions

            


            
                                                        
            
                                    
            
            

                
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    print("\n n_transitions_low", n_transitions_low)

            


            
                                                        
            
                                    
            
            

                
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    print("\n n_transitions_high", n_transitions_high)

            


            
                                                        
            
                                    
            
            

                
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    lower_bound = int(n_iter * p_accept_low)

            


            
                                                        
            
                                    
            
            

                
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    lower_bound -= lower_bound * 0.1

            


            
                                                        
            
                                    
            
            

                
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    higher_bound = n_iter

            


            
                                                        
            
                                    
            
            

                
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    assert lower_bound < n_transitions_low < n_transitions_high < higher_bound

            


            
                                                        
            
                                    
            
            

                
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