GaussianProcessTrainer.h

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//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* All rights reserved, see COPYRIGHT for full restrictions
//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
//*
//* Licensed under LGPL 2.1, please see LICENSE for details
//* https://www.gnu.org/licenses/lgpl-2.1.html
#ifdef MOOSE_LIBTORCH_ENABLED
 
#pragma once
 
#include "SurrogateTrainer.h"
#include "Standardizer.h"
 
#include "Distribution.h"
 
#include "CovarianceFunctionBase.h"
#include "CovarianceInterface.h"
 
#include "GaussianProcess.h"
 
#include "LibtorchUtils.h"
 
class GaussianProcessTrainer : public SurrogateTrainer, public CovarianceInterface
{
public:
  static InputParameters validParams();
  GaussianProcessTrainer(const InputParameters & parameters);
  virtual void preTrain() override;
  virtual void train() override;
  virtual void postTrain() override;
 
  StochasticTools::GaussianProcess & gp() { return _gp; }
  const StochasticTools::GaussianProcess & gp() const { return _gp; }
 
private:
  const std::vector<Real> & _predictor_row;
 
  StochasticTools::GaussianProcess & _gp;
 
  std::vector<std::vector<Real>> _params_buffer;
 
  std::vector<std::vector<Real>> _data_buffer;
 
  torch::Tensor & _training_params;
 
  torch::Tensor _training_data;
 
  bool _standardize_params;
 
  bool _standardize_data;
 
  bool _do_tuning;
 
  const StochasticTools::GaussianProcess::GPOptimizerOptions _optimization_opts;
 
  const std::vector<Real> & _sampler_row;
};
 
#endif