Hi Sofiane, Good to know for the enhancement plans.
I am talking about the post processing. I need to be able to interrupt the python script between the generation of the experiment and the post processing. In some case, using the same experiment I get the correct indices, and sometimes indices are off. Thanks. Sincerely, Pamphile ROY > Le 7 déc. 2017 à 20:30, HADDAD Sofiane <[email protected]> a écrit : > > Hi Roy, > > We will probably enhance the experiment class. It may reduce 2N evaluations, > which is substantial (only in 2d case) > > What do you mean by "not computed correctly" in your mail? Are you talking > about generated experiment or post processing (SaltelliSensitivityAlgorithm?) > > Sofiane > > Le jeudi 7 décembre 2017 à 10:42:29 UTC+1, roy <[email protected]> a écrit : > > > Hi Sofiane, > > Thanks for the update. > > Do you plan to have it implemented on OT? Or should I handle this in my > package? > > Also, I have a last interrogation. When I create the sample with > SobolIndicesExperiment, > I found that Sobol' indices are not always computed correctly. I am not able > to provide a MCVE here as > from the sample generation to the computation of the indices, a lot happens. > Sorry. > But from my trying, if I set OT’s seed, I get correct results. > > > So is there a way to ensure that the sample generated by > SobolIndicesExperiment will correspond to > what is expected by the indices classes? Seems that there is a randomization > effect here. > > > > Thanks again for the support. > > Sincerely, > > Pamphile ROY > > >> Le 7 déc. 2017 à 00:39, HADDAD Sofiane <[email protected] >> <mailto:[email protected]>> a écrit : >> >> Hi >> >> You are right only the case dim=2 has duplicates if compute second order is >> set tot True. >> I miss it, sorry! >> >> An enhancement is to generate samples of size N * (dim + 1) in case dim=2 >> whatever second order is True or False >> >> Thanks for the report. >> >> Regards, >> Sofiane >> >> >> Le samedi 2 décembre 2017 à 13:25:25 UTC+1, Pamphile ROY <[email protected] >> <mailto:[email protected]>> a écrit : >> >> >> 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" <[email protected] >> <mailto:[email protected]>> >> À: "users" <[email protected] <mailto:[email protected]>>, "roy" >> <[email protected] <mailto:[email protected]>> >> 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 SobolIndicesAlgorithm — OpenTURNS documentation >> <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> >> >> >> 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 <[email protected] >> <mailto:[email protected]>> 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 >> [email protected] <mailto:[email protected]> >> http://openturns.org/mailman/listinfo/users >> <http://openturns.org/mailman/listinfo/users> >> >
_______________________________________________ OpenTURNS users mailing list [email protected] http://openturns.org/mailman/listinfo/users
