gibbsOperator_tpl.h

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
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#pragma once
 

 
 
#include <agrum/BN/inference/tools/gibbsOperator.h>
 
#include <agrum/base/core/utils_random.h>
 
namespace gum {
 
  template < GUM_Numeric GUM_SCALAR >
  GibbsOperator< GUM_SCALAR >::GibbsOperator(const IBayesNet< GUM_SCALAR >& BN,
                                             const NodeProperty< Idx >*     hardEv,
                                             Size                           nbr,
                                             bool                           atRandom) :
      counting_(0), samplingBn_(BN), hardEv_(hardEv), nbr_(nbr), atRandom_(atRandom) {
    GUM_CONSTRUCTOR(GibbsOperator);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  GibbsOperator< GUM_SCALAR >::~GibbsOperator() {
    GUM_DESTRUCTOR(GibbsOperator);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void GibbsOperator< GUM_SCALAR >::updateSamplingNodes_() {
    samplingNodes_.clear();
    for (const auto node: samplingBn_.nodes())
      if (hardEv_ == nullptr || !hardEv_->exists(node)) { samplingNodes_.insert(node); }
    if (samplingNodes_.size() == 0) {
      GUM_ERROR(InvalidArgument, "No node to sample (too many nodes or too much evidence)!")
    }
    if (nbr_ > samplingNodes_.size()) nbr_ = samplingNodes_.size();
  }

  template < GUM_Numeric GUM_SCALAR >
  Instantiation GibbsOperator< GUM_SCALAR >::monteCarloSample() {
    gum::Instantiation I;
 
    for (const auto nod: samplingBn_.topologicalOrder()) {
      I.add(samplingBn_.variable(nod));
      if (hardEv_ != nullptr && hardEv_->exists(nod)) {
        I.chgVal(samplingBn_.variable(nod), (*hardEv_)[nod]);
      } else {
        _drawVarMonteCarlo_(nod, &I);
      }
    }
    return I;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void GibbsOperator< GUM_SCALAR >::_drawVarMonteCarlo_(NodeId nod, Instantiation* I) {
    gum::Instantiation Itop(*I);
    Itop.erase(samplingBn_.variable(nod));
    I->chgVal(samplingBn_.variable(nod), samplingBn_.cpt(nod).extract(Itop).draw());
  }
 
  template < GUM_Numeric GUM_SCALAR >
  Instantiation GibbsOperator< GUM_SCALAR >::nextSample(Instantiation prev) {
    for (Idx i = 0; i < nbr_; i++) {
      auto pos
          = atRandom_ ? randomValue(samplingNodes_.size()) : (counting_ % samplingNodes_.size());
      this->_GibbsSample_(samplingNodes_[pos], &prev);
      counting_++;
    }
    return prev;
  }

 
  template < GUM_Numeric GUM_SCALAR >
  void GibbsOperator< GUM_SCALAR >::_GibbsSample_(NodeId id, Instantiation* I) {
    gum::Instantiation Itop(*I);
    Itop.erase(samplingBn_.variable(id));
    gum::Tensor< GUM_SCALAR > p = samplingBn_.cpt(id).extract(Itop);
    for (const auto nod: samplingBn_.children(id))
      p *= samplingBn_.cpt(nod).extract(Itop);
    GUM_ASSERT(p.nbrDim() == 1);
    if (p.sum() != 0) {
      p.normalize();
      I->chgVal(samplingBn_.variable(id), p.draw());
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  Size GibbsOperator< GUM_SCALAR >::nbrDrawnVar() const {
    return nbr_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void GibbsOperator< GUM_SCALAR >::setNbrDrawnVar(Size nbr) {
    nbr_ = nbr;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  bool GibbsOperator< GUM_SCALAR >::isDrawnAtRandom() const {
    return atRandom_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void GibbsOperator< GUM_SCALAR >::setDrawnAtRandom(bool atRandom) {
    atRandom_ = atRandom;
  }
 
}   // namespace gum