[ot-users] duplicate with SobolIndicesExperiment

Pamphile ROY roy at cerfacs.fr
Sat Dec 2 13:25:24 CET 2017


Hi Sofiane, 

I got it now. 

But if in 2dim this behavior is to be expected, why not doing this internally? 
The root of this was that I have an expensive numerical model. So having to compute twice a sample is not tractable. 

Thanks for your support. 

Sincerely, 

Pamphile ROY 

De: "HADDAD Sofiane" <sofiane_haddad at yahoo.fr> 
À: "users" <users at openturns.org>, "roy" <roy at cerfacs.fr> 
Envoyé: Vendredi 1 Décembre 2017 13:31:14 
Objet: Re: [ot-users] duplicate with SobolIndicesExperiment 

Hi 

There is no problem here 

You can find here how the experiment is defined. As you set second order to True and your sub samples are of size 5, you have (2 * 2 + 2) blocks of size 5 Have a look at [ http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolIndicesAlgorithm.html?highlight=sobolindices#openturns.SobolIndicesAlgorithm | SobolIndicesAlgorithm — OpenTURNS  documentation ] 

[ http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolIndicesAlgorithm.html?highlight=sobolindices#openturns.SobolIndicesAlgorithm | SobolIndicesAlgorithm — OpenTURNS  documentation ] 



Using this : 
import openturns as ot 
ot.RandomGenerator.SetSeed(0) 
distribution = ot.ComposedDistribution([ot.Uniform(15, 60), ot.Normal(3000, 400)]) 
sample = np.array(ot.SobolIndicesAlgorithmImplementation.Generate(distribution, 5, True)) 
print(sample.reshape(-1,5,2)) 

you will see all matrices defines in link. 
Hope this helps 

Bien cordialement, 
Sofiane HADDAD 


Le mercredi 29 novembre 2017 à 19:30:39 UTC+1, roy <roy at cerfacs.fr> a écrit : 


Hi, 

I am using ot.SobolIndicesExperiment and if I set my input dimension to 2, 
I get some repeated points. From my understanding of the method, this should not be the case. 
Also going with a higher dimension does not do that. 

Am I wrong? 

Here is an example (same behaviour with the old method available in OT 1.9) where I highlighted some duplicate: 

import openturns as ot 
distribution = ot.ComposedDistribution([ot.Uniform(15, 60), ot.Normal(3000, 400)]) 
sample = np.array(ot.SobolIndicesAlgorithmImplementation.Generate(distribution, 5, True)) 

array([[ 29.65970938, 2535.47991432], 
[ 33.01991727, 2559.28624639], 
[ 33.25474751, 2682.95080229], 
[ 32.95380182, 2419.44678937], 
[ 55.23575378, 3039.33121131], 
[ 26.05095231, 3271.18330661], 
[ 41.00594229, 3683.75154513], 
[ 54.81729255, 3428.24812578], 
[ 28.2423326 , 2797.23010815], 
[ 52.36310769, 2335.65441869], 
[ 26.05095231, 2535.47991432], 
[ 41.00594229, 2559.28624639], 
[ 54.81729255, 2682.95080229], 
[ 28.2423326 , 2419.44678937], 
[ 52.36310769, 3039.33121131], 
[ 29.65970938, 3271.18330661], 
[ 33.01991727, 3683.75154513], 
[ 33.25474751, 3428.24812578], 
[ 32.95380182, 2797.23010815], 
[ 55.23575378, 2335.65441869], 
[ 29.65970938, 3271.18330661], 
[ 33.01991727, 3683.75154513], 
[ 33.25474751, 3428.24812578], 
[ 32.95380182, 2797.23010815], 
[ 55.23575378, 2335.65441869], 
[ 26.05095231, 2535.47991432], 
[ 41.00594229, 2559.28624639], 
[ 54.81729255, 2682.95080229], 
[ 28.2423326 , 2419.44678937], 
[ 52.36310769, 3039.33121131]]) 

Thanks for your support. 

Sincerely, 


Pamphile ROY 
PhD candidate in Uncertainty Quantification 
CERFACS - Toulouse (31) - France 
+33 (0) 5 61 19 31 57 
+33 (0) 7 86 43 24 22 

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