Hi François,
I begin with your 2nd question. Yes it is possible to train the metamodel based only on the input and the output data sets. Indeed, a possible usage of ``FunctionalChaosAlgorithm`` is as follows: FunctionalChaosAlgorithm(inputSample, outputSample, distribution, adaptiveStrategy) Note that this usage requires the knowledge of the probability distribution (argument ``distribution``) of your inputs. You may have a look at the doc<http://doc.openturns.org/openturns-1.5/sphinx/user_manual/_generated/openturns.FunctionalChaosAlgorithm.html> and adapt the example located at the bottom of the page. This way it is easy to construct a polynomial chaos from a training set and to validate it based on a separated validation set. Here are a few commands (partially based on the ``numpy`` module) which you may use to this purpose: >>> import numpy as np >>> import openturns as ot >>> from openturns.viewer import View >>> >>> # Import input and output data sets as OT Numerical Samples >>> inputs = ot.NumericalSample.ImportFromCSVFile("my_inputs.txt") >>> outputs = ot.NumericalSample.ImportFromCSVFile("my_outputs.txt") >>> >>> # Convert them to numpy arrays to allow easy manipulations >>> inputs_arr, outputs_arr = np.array(inputs), np.array(outputs) >>> >>> # Randomly split the data into training and validation sets >>> np.random.seed(10) # set the random generator seed >>> n = inputs.getDimension() >>> fraction_train = 0.7 # proportion of points used for training >>> n_train = int(n*fraction_train) # number of training points >>> inds = np.random.permutation(n) >>> xs, ys = x[inds,:], y[inds,:] >>> x_train, x_valid = xs[:n_train, :], xs[n_train:, :] >>> y_train, y_valid = ys[:n_train, :], ys[n_train:, :] >>> (... polynomial chaos command lines ...) >>> algo = ot.FunctionalChaosAlgorithm(x_train, y_train, distribution, >>> fixedStrategy, self.projectionStrategy) >>> algo.run() >>> result = algo.getResult() >>> >>> # Validate the metamodel >>> valid = ot.MetaModelValidation(x_train, y_train, result.getMetaModel()) >>> self.relative_accuracy = valid.computePredictivityFactor() >>> graph = valid.drawValidation() >>> View(graph) Regards, Géraud ________________________________ De : [email protected] <[email protected]> Envoyé : mercredi 25 janvier 2017 11:08 À : [email protected] Objet : [ot-users] gPC generation: seperate training set calculations and generation of the polynomial Hello everyone, I am new to OT and I would like to make a polynomial approximation of an expensive function. For that I would like first to evaluate the function at the colocation points and then train my polynomial. In the example in OT, those two steps are not separated. However is there a way to run them separately ? Alternatively, assuming I already have the training set (colocation points and the corresponding function value) is it possible to train the polynomial with OT winthout having OT to evaluate the expensive function (with the function FunctionalChaosAlgorithm.run()) ? Thanks a lot Francois Sanson Ce message et toutes les pièces jointes (ci-après le 'Message') sont établis à l'intention exclusive des destinataires et les informations qui y figurent sont strictement confidentielles. Toute utilisation de ce Message non conforme à sa destination, toute diffusion ou toute publication totale ou partielle, est interdite sauf autorisation expresse. Si vous n'êtes pas le destinataire de ce Message, il vous est interdit de le copier, de le faire suivre, de le divulguer ou d'en utiliser tout ou partie. Si vous avez reçu ce Message par erreur, merci de le supprimer de votre système, ainsi que toutes ses copies, et de n'en garder aucune trace sur quelque support que ce soit. Nous vous remercions également d'en avertir immédiatement l'expéditeur par retour du message. Il est impossible de garantir que les communications par messagerie électronique arrivent en temps utile, sont sécurisées ou dénuées de toute erreur ou virus. ____________________________________________________ This message and any attachments (the 'Message') are intended solely for the addressees. The information contained in this Message is confidential. Any use of information contained in this Message not in accord with its purpose, any dissemination or disclosure, either whole or partial, is prohibited except formal approval. If you are not the addressee, you may not copy, forward, disclose or use any part of it. If you have received this message in error, please delete it and all copies from your system and notify the sender immediately by return message. E-mail communication cannot be guaranteed to be timely secure, error or virus-free.
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