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test_epsilon_0()   A
last analyzed

↳ Parent: tests.test_parameters.test_hill_climbing

Complexity

Conditions 1

Size

Total Lines 12
Code Lines 8

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 1
eloc 8
nop 0
dl 0
loc 12
rs 10
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 pytest

            


            
                                                                                                            
                            
            
                                    
            
            

                
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import numpy as np

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from gradient_free_optimizers import HillClimbingOptimizer

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def parabola_function(para):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    loss = para["x"] * para["x"] + para["y"] * para["y"]

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    return -loss

            


            
                                                                                                            
                            
            
                                    
            
            

                
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search_space = {

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    "x": np.arange(-10, 11, 1),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    "y": np.arange(-10, 11, 1),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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}

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_epsilon_0():

            


            
                                                        
            
                                    
            
            

                
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    epsilon = 1 / np.inf

            


            
                                                        
            
                                    
            
            

                
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    opt = HillClimbingOptimizer(

            


            
                                                        
            
                                    
            
            

                
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        search_space, initialize={"vertices": 1}, epsilon=epsilon

            


            
                                                        
            
                                    
            
            

                
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    )

            


            
                                                        
            
                                    
            
            

                
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    opt.search(parabola_function, n_iter=100)

            


            
                                                        
            
                                    
            
            

                
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    search_data = opt.search_data

            


            
                                                        
            
                                    
            
            

                
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    scores = search_data["score"].values

            


            
                                                        
            
                                    
            
            

                
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    assert np.all(scores == -200)

            


            
                                                        
            
                                    
            
            

                
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