Hi, I am facing consistency concerns in the API regarding distributions and sampling.
The initial goal was to get the sampling for Polynomial Chaos as I must not use the model variable. So for least square strategy I do something like this: proj_strategy = ot.LeastSquaresStrategy(montecarlo_design) sample = np.array(proj_strategy.getExperiment().generate()) sample is correct as the bounds of each feature lie in the corresponding ranges. But now if I want to use IntegrationStrategy: ot.IntegrationStrategy(ot.GaussProductExperiment(dists, list)) sample = np.array(proj_strategy.getExperiment().generate()) sample’s outputs lie between [-1, 1] which does not corresponds to the distribution I have initially. So I used the conversion class but it does not work well with GaussProductExperiment as it requires [0, 1] instead of [-1, 1]. Thus I use this hack: # Convert from [-1, 1] -> input distributions marg_inv_transf = ot.MarginalTransformationEvaluation(distributions, 1) sample = (proj_strategy.getExperiment().generate() + 1) / 2. Is it normal that the distribution classes are not returning in the same intervals? Thanks for your support! Pamphile ROY Chercheur doctorant en Quantification d’Incertitudes CERFACS - Toulouse (31) - France +33 (0) 5 61 19 31 57 +33 (0) 7 86 43 24 22
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