Hi, I want to calibrate a Kriging surrogate model in OpenTurns and I face difficulties. Indeed, I want to use the Matern covariance model. But, when I run the KrigingAlgorithm optimization, the scale parameters (theta) are not optimized. I have studied the problem and found that the LogLikelihood function is constant, no matter how points are in the DoE, what the performance function is and what the dimension is.
I think that the problem is on the definition of the covariance model since if I use SquaredExponential there is not any problems. But, I don't find how to define it correctly. May you provide me some helpful advice ? Thank you in advance. A little example: import numpy as np import openturns as ot def G(X): out = 15 - (X[:,0]**2 + X[:,1]**2 - 5*np.cos(2*np.pi*X[:,0]) - 5*np.cos(2*np.pi*X[:,1])) return out dim = 2 Loi = np.ones(dim) Moy = np.ones(dim) Stdev = np.ones(dim) nini = 100 nva = np.size(Loi) DOE_u = np.random.normal(0,1,(nini,nva)) DOE_y = G(DOE_u) DOE_y = DOE_y.reshape((nini,1)) inputSample = ot.Sample(DOE_u) outputSample = ot.Sample(DOE_y) basis = ot.ConstantBasisFactory(nva).build() covarianceModel = ot.MaternModel(nva) covarianceModel.setNu(5/2) algo = ot.KrigingAlgorithm(inputSample, outputSample, covarianceModel, basis) algo.run() result = algo.getResult() print(result.getCovarianceModel()) LogLikelihood = algo.getReducedLogLikelihoodFunction() Nicolas Lelièvre Doctorant Institut Pascal Clermont-Ferrand
_______________________________________________ OpenTURNS users mailing list [email protected] http://openturns.org/mailman/listinfo/users
