AdaptiveImportanceSampler.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

#pragma once

#include "Sampler.h"
#include "TransientInterface.h"

class AdaptiveImportanceSampler : public Sampler, public TransientInterface
{
public:
  static InputParameters validParams();

  AdaptiveImportanceSampler(const InputParameters & parameters);

  // Access the initial values vector
  const std::vector<Real> & getInitialValues() const { return _initial_values; }

  // Access the number of training samples
  const int & getNumSamplesTrain() const { return _num_samples_train; }

  // Access use absolute value bool
  const bool & getUseAbsoluteValue() const { return _use_absolute_value; }

  // Access the output limit
  const Real & getOutputLimit() const { return _output_limit; }

  // Access the mean vector of the importance distribution
  const std::vector<Real> & getImportanceVectorMean() const { return _mean_sto; }

  // Access the std vector of the importance distribution
  const std::vector<Real> & getImportanceVectorStd() const { return _std_sto; }

  // Access the std vector of the importance distribution
  const std::vector<const Distribution *> & getDistributionNames() const { return _distributions; }

  // Access the output limit
  const Real & getStdFactor() const { return _std_factor; }

  virtual bool isAdaptiveSamplingCompleted() const override { return _is_sampling_completed; }

protected:
  virtual Real computeSample(dof_id_type row_index, dof_id_type col_index) override;

  std::vector<const Distribution *> _distributions;

  const std::vector<Real> & _proposal_std;

  const std::vector<Real> & _initial_values;

  const Real & _output_limit;

  const int & _num_samples_train;

  const int & _num_importance_sampling_steps;

  const Real & _std_factor;

  const bool & _use_absolute_value;

  const unsigned int & _num_random_seeds;

  bool _is_sampling_completed;

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

  int _check_step;

  std::vector<Real> _prev_value;

  std::vector<Real> _mean_sto;

  std::vector<Real> _std_sto;

  std::vector<std::vector<Real>> _inputs_sto;

  int _retraining_steps;

  const std::vector<bool> * const _gp_flag;
};