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RandomRestartHillClimbingOptimizer.iterate()   A

↳ Parent: gradient_free_optimizers.optimizers.global_opt.random_restart_hill_climbing

Complexity

Conditions 3

Size

Total Lines 12
Code Lines 9

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 3
eloc 9
nop 1
dl 0
loc 12
rs 9.95
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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from ..local_opt import HillClimbingOptimizer

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from ...search import Search

            


            
                                                                                                            
                            
            
                                    
            
            

                
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class RandomRestartHillClimbingOptimizer(HillClimbingOptimizer, Search):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    name = "Random Restart Hill Climbing"

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def __init__(

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self,

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        *args,

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        epsilon=0.03,

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        distribution="normal",

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        n_neighbours=3,

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        n_iter_restart=10,

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        **kwargs,

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    ):

            


            
                                                                                                            
                            
            
                                    
            
            

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

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.epsilon = epsilon

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.distribution = distribution

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.n_neighbours = n_neighbours

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.n_iter_restart = n_iter_restart

            


            
                                                                                                            
                                                                
            
                                    
            
            

                
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    @HillClimbingOptimizer.track_nth_iter

            


            
                                                        
            
                                    
            
            

                
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    @HillClimbingOptimizer.random_restart

            


            
                                                        
            
                                    
            
            

                
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    def iterate(self):

            


            
                                                        
            
                                    
            
            

                
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        notZero = self.nth_iter != 0

            


            
                                                        
            
                                    
            
            

                
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        modZero = self.nth_iter % self.n_iter_restart == 0

            


            
                                                        
            
                                    
            
            

                
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        if notZero and modZero:

            


            
                                                        
            
                                    
            
            

                
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            pos = self.move_random()

            


            
                                                        
            
                                    
            
            

                
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        else:

            


            
                                                        
            
                                    
            
            

                
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            pos = self._move_climb(self.pos_current)

            


            
                                                        
            
                                    
            
            

                
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        return pos

            


            
                                                        
            
                                    
            
            

                
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