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_had...@yahoo.fr> À: "users" <users@openturns.org>, "roy" <r...@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 <r...@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 _______________________________________________ OpenTURNS users mailing list [ mailto:users@openturns.org | users@openturns.org ] [ http://openturns.org/mailman/listinfo/users | http://openturns.org/mailman/listinfo/users ]
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