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 "CoefficientOfVariation.h"
      11             : #include <cmath>
      12             : 
      13             : registerMooseObject("StochasticToolsApp", CoefficientOfVariation);
      14             : 
      15             : InputParameters
      16           8 : CoefficientOfVariation::validParams()
      17             : {
      18           8 :   InputParameters params = ParallelAcquisitionFunctionBase::validParams();
      19           8 :   params.addClassDescription("Coefficient of variation acquisition function.");
      20           8 :   return params;
      21           0 : }
      22             : 
      23           4 : CoefficientOfVariation::CoefficientOfVariation(const InputParameters & parameters)
      24           4 :   : ParallelAcquisitionFunctionBase(parameters)
      25             : {
      26           4 : }
      27             : 
      28             : void
      29          16 : CoefficientOfVariation::computeAcquisitionInternal(
      30             :     std::vector<Real> & acq,
      31             :     const std::vector<Real> & gp_mean,
      32             :     const std::vector<Real> & gp_std,
      33             :     const std::vector<std::vector<Real>> & /*test_inputs*/,
      34             :     const std::vector<std::vector<Real>> & /*train_inputs*/,
      35             :     const std::vector<Real> & /*generic*/) const
      36             : {
      37       16016 :   for (unsigned int i = 0; i < gp_mean.size(); ++i)
      38       16000 :     acq[i] = gp_std[i] / gp_mean[i];
      39          16 : }