Line data    Source code

       1             : //* This file is part of the MOOSE framework
       2             : //* https://mooseframework.inl.gov
       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 "NearestPointSurrogate.h"
      11             : 
      12             : registerMooseObject("StochasticToolsApp", NearestPointSurrogate);
      13             : 
      14             : InputParameters
      15         170 : NearestPointSurrogate::validParams()
      16             : {
      17         170 :   InputParameters params = SurrogateModel::validParams();
      18         170 :   params.addClassDescription("Surrogate that evaluates the value from the nearest point from data "
      19             :                              "in [NearestPointTrainer.md]");
      20         170 :   return params;
      21           0 : }
      22             : 
      23          85 : NearestPointSurrogate::NearestPointSurrogate(const InputParameters & parameters)
      24             :   : SurrogateModel(parameters),
      25          85 :     _sample_points(getModelData<std::vector<std::vector<Real>>>("_sample_points")),
      26         255 :     _sample_results(getModelData<std::vector<std::vector<Real>>>("_sample_results"))
      27             : {
      28          85 : }
      29             : 
      30             : Real
      31       10700 : NearestPointSurrogate::evaluate(const std::vector<Real> & x) const
      32             : {
      33             :   // Check whether input point has same dimensionality as training data
      34             :   mooseAssert(_sample_points.size() == x.size(),
      35             :               "Input point does not match dimensionality of training data.");
      36             : 
      37       10700 :   return _sample_results[0][findNearestPoint(x)];
      38             : }
      39             : 
      40             : void
      41          50 : NearestPointSurrogate::evaluate(const std::vector<Real> & x, std::vector<Real> & y) const
      42             : {
      43             :   mooseAssert(_sample_points.size() == x.size(),
      44             :               "Input point does not match dimensionality of training data.");
      45             : 
      46          50 :   y.assign(_sample_results.size(), 0.0);
      47             : 
      48          50 :   unsigned int idx = findNearestPoint(x);
      49             : 
      50         550 :   for (const auto & r : index_range(y))
      51         500 :     y[r] = _sample_results[r][idx];
      52          50 : }
      53             : 
      54             : unsigned int
      55       10750 : NearestPointSurrogate::findNearestPoint(const std::vector<Real> & x) const
      56             : {
      57             :   unsigned int idx = 0;
      58             : 
      59             :   // Container of current minimum distance during training sample loop
      60             :   Real dist_min = std::numeric_limits<Real>::max();
      61             : 
      62    10030750 :   for (dof_id_type p = 0; p < _sample_points[0].size(); ++p)
      63             :   {
      64             :     // Sum over the distance of each point dimension
      65             :     Real dist = 0;
      66    40095200 :     for (unsigned int i = 0; i < x.size(); ++i)
      67             :     {
      68    30075200 :       Real diff = (x[i] - _sample_points[i][p]);
      69    30075200 :       dist += diff * diff;
      70             :     }
      71             : 
      72             :     // Check if this training point distance is smaller than the current minimum
      73    10020000 :     if (dist < dist_min)
      74             :     {
      75      336750 :       idx = p;
      76             :       dist_min = dist;
      77             :     }
      78             :   }
      79       10750 :   return idx;
      80             : }