importanceSampling_tpl.h

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/***************************************************************************
 *   Copyright (C) 2005 by Christophe GONZALES et Pierre-Henri WUILLEMIN   *
 *   {prenom.nom}_at_lip6.fr                                               *
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 *   it under the terms of the GNU General Public License as published by  *
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 *   This program is distributed in the hope that it will be useful,       *
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of        *
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         *
 *   GNU General Public License for more details.                          *
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 *   Free Software Foundation, Inc.,                                       *
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 ***************************************************************************/
#include <agrum/BN/inference/importanceSampling.h>


namespace gum {

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


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

  template < typename GUM_SCALAR >
  Instantiation ImportanceSampling< GUM_SCALAR >::_burnIn() {
    Instantiation I;
    return I;
  }

  template < typename GUM_SCALAR >
  Instantiation ImportanceSampling< GUM_SCALAR >::_draw(GUM_SCALAR*   w,
                                                        Instantiation prev) {
    GUM_SCALAR pSurQ;

    do {
      prev.clear();
      pSurQ = 1.;
      for (const auto ev : this->hardEvidenceNodes()) {
        prev.add(this->BN().variable(ev));
        prev.chgVal(this->BN().variable(ev), this->hardEvidence()[ev]);
      }

      for (const auto nod : this->BN().topologicalOrder()) {
        if (!this->hasHardEvidence(nod)) { this->_addVarSample(nod, &prev); }
        auto probaP = this->BN().cpt(nod).get(prev);
        auto probaQ = this->samplingBN().cpt(nod).get(prev);
        if ((probaP == 0) || (probaQ == 0)) {
          pSurQ = 0;
        } else {
          pSurQ = probaP / probaQ;
        }
      }
      if (pSurQ > 0.0) {
        for (const auto ev : this->hardEvidenceNodes()) {
          pSurQ *= this->samplingBN().cpt(ev).get(prev);
        }
      }
    } while (pSurQ == 0);

    *w = pSurQ;
    return prev;
  }


  template < typename GUM_SCALAR >
  void ImportanceSampling< GUM_SCALAR >::_unsharpenBN(
     BayesNetFragment< GUM_SCALAR >* bn, float epsilon) {
    for (const auto nod : bn->nodes().asNodeSet()) {
      auto p = new Potential< GUM_SCALAR >();
      *p = bn->cpt(nod).isNonZeroMap().scale(epsilon) + bn->cpt(nod);
      p->normalizeAsCPT();
      bn->installCPT(nod, p);
    }
  }

  template < typename GUM_SCALAR >
  void ImportanceSampling< GUM_SCALAR >::_onContextualize(
     BayesNetFragment< GUM_SCALAR >* bn) {
    for (const auto ev : this->hardEvidenceNodes()) {
      bn->uninstallCPT(ev);
      bn->installCPT(ev, new Potential< GUM_SCALAR >(*this->evidence()[ev]));
      // we keep the variables with hard evidence but alone
      // bn->uninstallNode( sid[i] );
    }
    GUM_SCALAR minParam = bn->minNonZeroParam();
    GUM_SCALAR minAccepted = GUM_SCALAR(this->epsilon() / bn->maxVarDomainSize());
    if (minParam < minAccepted) this->_unsharpenBN(bn, float(minAccepted));
  }
}   // namespace gum