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Pull Request — master (#202)
by Grega
01:02
created

run_esML.logging_example()   A

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Complexity

Conditions 1

Size

Total Lines 5
Code Lines 5

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 1
eloc 5
nop 8
dl 0
loc 5
rs 10
c 0
b 0
f 0

How to fix   Many Parameters   

Many Parameters

Methods with many parameters are not only hard to understand, but their parameters also often become inconsistent when you need more, or different data.

There are several approaches to avoid long parameter lists:

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# encoding=utf8
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# This is temporary fix to import module from parent folder
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# It will be removed when package is published on PyPI
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import sys
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sys.path.append('../')
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# End of fix
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import random

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from NiaPy.algorithms.basic import EvolutionStrategyML

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from NiaPy.util import StoppingTask, OptimizationType

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from NiaPy.benchmarks import Sphere

            


            
                                                                                                            
                            
            
                                    
            
            

                
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#we will run Differential Evolution for 5 independent runs

            


            
                                                                                                            
                            
            
                                    
            
            

                
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for i in range(5):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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	task = StoppingTask(D=10, nFES=1000, optType=OptimizationType.MINIMIZATION, benchmark=Sphere())

            


            
                                                                                                            
                            
            
                                    
            
            

                
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	algo = EvolutionStrategyML()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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	best = algo.run(task=task)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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	print('%s -> %f' % (best[0].x, best[1]))

            


            
                                                                                                            
                            
            
                                    
            
            

                
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3

            


            
                                                                                                            
                            
            
                                    
            
            

                
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