ActiveLearningGPDecision.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 "ActiveLearningReporterBase.h"
#include "ActiveLearningGaussianProcess.h"
#include "GaussianProcessSurrogate.h"
#include "SurrogateModelInterface.h"

class ActiveLearningGPDecision : public ActiveLearningReporterTempl<Real>,
                                 public SurrogateModelInterface
{
public:
  static InputParameters validParams();
  ActiveLearningGPDecision(const InputParameters & parameters);

  const int & getTrainingSamples() const { return _n_train; }

protected:
  virtual void preNeedSample() override;

  virtual bool needSample(const std::vector<Real> & row,
                          dof_id_type local_ind,
                          dof_id_type global_ind,
                          Real & val) override;

  virtual bool facilitateDecision();

  virtual void setupData(const std::vector<std::vector<Real>> & inputs,
                         const std::vector<Real> & outputs);

  bool learningFunction(const Real & gp_mean, const Real & gp_std) const;

  const MooseEnum & _learning_function;
  const Real & _learning_function_threshold;
  const Real & _learning_function_parameter;

  std::vector<std::vector<Real>> _inputs_batch;
  std::vector<Real> _outputs_batch;

  const ActiveLearningGaussianProcess & _al_gp;
  const SurrogateModel & _gp_eval;

  std::vector<bool> & _flag_sample;

  const int _n_train;

  std::vector<std::vector<Real>> & _inputs;

  std::vector<Real> & _gp_mean;
  std::vector<Real> & _gp_std;

  bool _decision;

  const std::vector<std::vector<Real>> & _inputs_global;
  const std::vector<Real> & _outputs_global;
};

#endif