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by Grega
01:21
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run_cmaes.plot_example()   A

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Complexity

Conditions 1

Size

Total Lines 6
Code Lines 6

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 1
eloc 6
nop 8
dl 0
loc 6
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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from NiaPy.algorithms.basic import CovarianceMatrixAdaptionEvolutionStrategy
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from NiaPy.task import StoppingTask, OptimizationType
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from NiaPy.benchmarks import Sphere
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import sys

            


            
                                                                                                            
                            
            
                                    
            
            

                
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sys.path.append('../')

            


            
                                                                                                            
                            
            
                                    
            
            

                
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# End of fix

            


            
                                                                                                            
                            
            
                                    
            
            

                
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# we will run CMA-ES 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, logger=True, benchmark=Sphere())

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    algo = CovarianceMatrixAdaptionEvolutionStrategy(NP=20)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    best = algo.run(task=task)

            


            
                                                                                                            
                            
            
                                    
            
            

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

            


            
                                                                                                            
                            
            
                                    
            
            

                
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