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by Simon
01:28
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test_p_down()   A

↳ Parent: tests.optimizer_parameter.StochasticHillClimbing

Complexity

Conditions 2

Size

Total Lines 7
Code Lines 7

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 2
eloc 7
nop 0
dl 0
loc 7
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 numpy as np

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from hyperactive import Hyperactive

            


            
                                                                                                            
                            
            
                                    
            
            

                
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X, y = np.array([0]), np.array([0])

            


            
                                                                                                            
                            
            
                                    
            
            

                
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memory = False

            


            
                                                                                                            
                            
            
                                    
            
            

                
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n_iter = 25

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def sphere_function(para, X_train, y_train):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    loss = []

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    for key in para.keys():

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        if key == "iteration":

            


            
                                                                                                            
                            
            
                                    
            
            

                
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            continue

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        loss.append(para[key] * para[key])

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    return -np.array(loss).sum()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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search_config = {

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    sphere_function: {"x1": np.arange(-3, 3, 0.1), "x2": np.arange(-3, 3, 0.1)}

            


            
                                                                                                            
                            
            
                                    
            
            

                
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}

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_p_down():

            


            
                                                        
            
                                    
            
            

                
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    for p_down in [0.0001, 100]:

            


            
                                                        
            
                                    
            
            

                
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        opt = Hyperactive(X, y, memory=memory)

            


            
                                                        
            
                                    
            
            

                
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        opt.search(

            


            
                                                        
            
                                    
            
            

                
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            search_config,

            


            
                                                        
            
                                    
            
            

                
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            n_iter=n_iter,

            


            
                                                        
            
                                    
            
            

                
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            optimizer={"StochasticHillClimbing": {"p_down": p_down}},

            


            
                                                        
            
                                    
            
            

                
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        )

            


            
                                                        
            
                                    
            
            

                
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