Line data    Source code

       1             : //* This file is part of the MOOSE framework
       2             : //* https://www.mooseframework.org
       3             : //*
       4             : //* All rights reserved, see COPYRIGHT for full restrictions
       5             : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
       6             : //*
       7             : //* Licensed under LGPL 2.1, please see LICENSE for details
       8             : //* https://www.gnu.org/licenses/lgpl-2.1.html
       9             : 
      10             : #include "UpperConfidenceBound.h"
      11             : #include <cmath>
      12             : 
      13             : registerMooseObject("StochasticToolsApp", UpperConfidenceBound);
      14             : 
      15             : InputParameters
      16           8 : UpperConfidenceBound::validParams()
      17             : {
      18           8 :   InputParameters params = ParallelAcquisitionFunctionBase::validParams();
      19           8 :   params.addClassDescription("Upper Confidence Bound acquisition function.");
      20          24 :   params.addRangeCheckedParam<Real>(
      21          16 :       "tuning", 1.0, "tuning > 0", "Tuning parameter to control exploration vs exploitation.");
      22           8 :   return params;
      23           0 : }
      24             : 
      25           4 : UpperConfidenceBound::UpperConfidenceBound(const InputParameters & parameters)
      26           8 :   : ParallelAcquisitionFunctionBase(parameters), _tuning(getParam<Real>("tuning"))
      27             : {
      28           4 : }
      29             : 
      30             : void
      31          16 : UpperConfidenceBound::computeAcquisitionInternal(
      32             :     std::vector<Real> & acq,
      33             :     const std::vector<Real> & gp_mean,
      34             :     const std::vector<Real> & gp_std,
      35             :     const std::vector<std::vector<Real>> & /*test_inputs*/,
      36             :     const std::vector<std::vector<Real>> & /*train_inputs*/,
      37             :     const std::vector<Real> & /*generic*/) const
      38             : {
      39       16016 :   for (unsigned int i = 0; i < gp_mean.size(); ++i)
      40       16000 :     acq[i] = gp_mean[i] + _tuning * gp_std[i];
      41          16 : }