GibbsSampling_tpl.h

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/**
 *
 *   Copyright (c) 2005-2021 by Pierre-Henri WUILLEMIN(@LIP6) & Christophe GONZALES(@AMU)
 *   info_at_agrum_dot_org
 *
 *  This library is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Lesser General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This library 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 Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public License
 *  along with this library.  If not, see <http://www.gnu.org/licenses/>.
 *
 */


/**
 * @file
 * @brief Implementation of Gibbs Sampling for inference in Bayesian networks.
 *
 * @author Paul ALAM & Pierre-Henri WUILLEMIN(@LIP6)
 */

#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 {

  ///  default constructor
  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);
  }

  /// destructor
  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_() {
    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;
  }

  /// draws next sample for gibbs sampling

  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