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 "AffineInvariantStretchDecision.h"
      11             : 
      12             : registerMooseObject("StochasticToolsApp", AffineInvariantStretchDecision);
      13             : 
      14             : InputParameters
      15          88 : AffineInvariantStretchDecision::validParams()
      16             : {
      17          88 :   InputParameters params = PMCMCDecision::validParams();
      18          88 :   params.addClassDescription("Perform decision making for Affine Invariant stretch MCMC.");
      19          88 :   return params;
      20           0 : }
      21             : 
      22          44 : AffineInvariantStretchDecision::AffineInvariantStretchDecision(const InputParameters & parameters)
      23             :   : PMCMCDecision(parameters),
      24          44 :     _aiss(dynamic_cast<const AffineInvariantStretchSampler *>(&_sampler)),
      25          88 :     _step_size(_aiss->getAffineStepSize())
      26             : {
      27             :   // Check whether the selected sampler is a stretch sampler or not
      28          44 :   if (!_aiss)
      29           0 :     paramError("sampler", "The selected sampler is not of type AffineInvariantStretchSampler.");
      30          44 : }
      31             : 
      32             : void
      33         132 : AffineInvariantStretchDecision::computeTransitionVector(std::vector<Real> & tv,
      34             :                                                         const std::vector<Real> & evidence)
      35             : {
      36         792 :   for (unsigned int i = 0; i < tv.size(); ++i)
      37         924 :     tv[i] = std::exp(std::min((_priors.size() - 1) * std::log(_step_size[i]) + evidence[i], 0.0));
      38         132 : }