GibbsSampling.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 This file contains Gibbs sampling class definition.
 *
 * @author Paul ALAM & Pierre-Henri WUILLEMIN(@LIP6)
 */


#ifndef GUM_GIBBS_SAMPLING_H
#define GUM_GIBBS_SAMPLING_H

#include <agrum/BN/inference/tools/gibbsOperator.h>
#include <agrum/BN/inference/tools/samplingInference.h>


namespace gum {

  /**
   * @class GibbsSampling gibbsSampling.h
   *<agrum/BN/inference/gibbsSampling.h>
   * @brief class for making Gibbs sampling inference in Bayesian networks.
   * @ingroup bn_approximation
   *
   * This class overrides pure function declared in the inherited class
   *ApproximateInference.
   * It defines the way Gibbs sampling draws a sample. It also inherits
   *GibbsOperator
   * which contains Gibbs sampling methods.
   *
   */

  template < typename GUM_SCALAR >
  class GibbsSampling: public SamplingInference< GUM_SCALAR >, public GibbsOperator< GUM_SCALAR > {
    public:
    /**
     * Default constructor
     */
    explicit GibbsSampling(const IBayesNet< GUM_SCALAR >* bn);

    /**
     * Destructor
     */
    ~GibbsSampling() override;

    /**
     * @brief Number of burn in for one iteration.
     * @param b The number of burn in.
     * @throw OutOfLowerBound Raised if b < 1.
     */
    void setBurnIn(Size b) { this->burn_in_ = b; };

    /**
     * @brief Returns the number of burn in.
     * @return Returns the number of burn in.
     */
    Size burnIn() const { return this->burn_in_; };

    protected:
    /// draws a defined number of samples without updating the estimators
    Instantiation burnIn_() override;

    /// draws a sample given previous one according to Gibbs sampling
    /**
     * @param w the weight of sample being generated
     * @param prev the previous sample generated
     * @param bn the Bayesian network containing the evidence
     * @param hardEvNodes hard evidence nodes
     * @param hardEv hard evidences values
     *
     * Uses the Gibbs sampling method to generate a new sample given the previous
     * one. The method is implemented in the inherited class GibbsOperator. This
     * function only makes the call to it. It consists of choosing one node x to
     * sample, given the instantiation of all other nodes. It requires computing of
     * P( x \given instantiation_markovblanket(x)).
     */
    Instantiation draw_(GUM_SCALAR* w, Instantiation prev) override;

    /// draws a Monte Carlo sample
    /**
     * @param bn the reference Bayesian network
     *
     * This Monte Carlo sample generates a good starting point for Gibbs sampling,
     * because it returns
     * a sample consistent with the evidence, but it differs from the one
     * implemented in the inherited
     * class Approximate Inference because it also initializes attributes needed
     * for Gibbs sampling.
     */
    Instantiation monteCarloSample_();
  };


#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
  extern template class GibbsSampling< double >;
#endif
}   // namespace gum

#include <agrum/BN/inference/GibbsSampling_tpl.h>
#endif