GibbsSampling_tpl.h

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#pragma once
 

 
#include <agrum/BN/inference/GibbsSampling.h>
 
#define GIBBS_SAMPLING_DEFAULT_EPSILON          std::exp(-1.6)
#define GIBBS_SAMPLING_DEFAULT_MIN_EPSILON_RATE std::exp(-5)
#define GIBBS_SAMPLING_DEFAULT_BURNIN           300
 
#define GIBBS_SAMPLING_POURCENT_DRAWN_SAMPLE 50   // percent drawn
#define GIBBS_SAMPLING_DRAWN_AT_RANDOM       true
 
namespace gum {

  template < typename GUM_SCALAR >
  GibbsSampling< GUM_SCALAR >::GibbsSampling(const IBayesNet< GUM_SCALAR >* bn) :
      SamplingInference< GUM_SCALAR >(bn),
      GibbsOperator< GUM_SCALAR >(*bn,
                                  &this->hardEvidence(),
                                  1 + (bn->size() * GIBBS_SAMPLING_POURCENT_DRAWN_SAMPLE / 100),
                                  GIBBS_SAMPLING_DRAWN_AT_RANDOM) {
    GUM_CONSTRUCTOR(GibbsSampling);
 
    this->setEpsilon(GIBBS_SAMPLING_DEFAULT_EPSILON);
    this->setMinEpsilonRate(GIBBS_SAMPLING_DEFAULT_MIN_EPSILON_RATE);
    this->setBurnIn(GIBBS_SAMPLING_DEFAULT_BURNIN);
  }

  template < typename GUM_SCALAR >
  GibbsSampling< GUM_SCALAR >::~GibbsSampling() {
    GUM_DESTRUCTOR(GibbsSampling);
  }
 
  template < typename GUM_SCALAR >
  Instantiation GibbsSampling< GUM_SCALAR >::monteCarloSample_() {
    return GibbsOperator< GUM_SCALAR >::monteCarloSample();
  }
 
  template < typename GUM_SCALAR >
  Instantiation GibbsSampling< GUM_SCALAR >::burnIn_() {
    // we initialize the nodes with hard evidence
    // hypothesis : burnIn_ is called at the beginning of makeInference
    this->updateSamplingNodes_();
 
    gum::Instantiation Ip;
    if (this->burnIn() == 0) return Ip;
 
    GUM_SCALAR w = 1.0f;
    Ip           = monteCarloSample_();
    for (Size i = 1; i < this->burnIn(); i++)
      Ip = this->draw_(&w, Ip);
 
    return Ip;
  }

 
  template < typename GUM_SCALAR >
  Instantiation GibbsSampling< GUM_SCALAR >::draw_(GUM_SCALAR* w, Instantiation prev) {
    *w = 1.0;
    return GibbsOperator< GUM_SCALAR >::nextSample(prev);
  }
}   // namespace gum