weightedSampling_tpl.h

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 *       - Christophe GONZALES(_at_AMU)                                     *
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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#pragma once
 

 
 
#include <agrum/BN/inference/weightedSampling.h>
 
namespace gum {
 

  template < typename GUM_SCALAR >
  WeightedSampling< GUM_SCALAR >::WeightedSampling(const IBayesNet< GUM_SCALAR >* bn) :
      SamplingInference< GUM_SCALAR >(bn) {
    GUM_CONSTRUCTOR(WeightedSampling)
  }

  template < typename GUM_SCALAR >
  WeightedSampling< GUM_SCALAR >::~WeightedSampling() {
    GUM_DESTRUCTOR(WeightedSampling)
  }

  template < typename GUM_SCALAR >
  Instantiation WeightedSampling< GUM_SCALAR >::burnIn_() {
    gum::Instantiation I;
    return I;
  }
 
  template < typename GUM_SCALAR >
  Instantiation WeightedSampling< GUM_SCALAR >::draw_(GUM_SCALAR* w, Instantiation prev) {
    *w              = 1.0f;
    bool wrongValue = false;
    do {
      prev.clear();
      wrongValue = false;
      *w         = 1.0f;
 
      for (const auto nod: this->BN().topologicalOrder()) {
        if (this->hardEvidenceNodes().contains(nod)) {
          prev.add(this->BN().variable(nod));
          prev.chgVal(this->BN().variable(nod), this->hardEvidence()[nod]);
          auto localp = this->BN().cpt(nod).get(prev);
 
          if (localp == 0) {
            wrongValue = true;
            break;
          }
 
          *w *= localp;
        } else {
          this->addVarSample_(nod, &prev);
        }
      }
    } while (wrongValue);
    return prev;
  }
}   // namespace gum