estimator.h

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/***************************************************************************
 *   Copyright (C) 2005 by Pierre-Henri WUILLEMIN et Christophe GONZALES   *
 *   {prenom.nom}_at_lip6.fr                                               *
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 *   This program is free software; you can redistribute it and/or modify  *
 *   it under the terms of the GNU General Public License as published by  *
 *   the Free Software Foundation; either version 2 of the License, or     *
 *   (at your option) any later version.                                   *
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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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 *   You should have received a copy of the GNU General Public License     *
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 *   Free Software Foundation, Inc.,                                       *
 *   59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.             *
 ***************************************************************************/
#ifndef GUM_ESTIMATOR_H
#define GUM_ESTIMATOR_H

#include <agrum/BN/IBayesNet.h>
#include <agrum/BN/inference/loopyBeliefPropagation.h>
#include <agrum/core/hashTable.h>
#include <vector>

namespace gum {

  template < typename GUM_SCALAR >
  class Estimator {
    public:
    Estimator();

    explicit Estimator(const IBayesNet< GUM_SCALAR >* bn);

    /* Destructor */
    ~Estimator();

    void setFromBN(const IBayesNet< GUM_SCALAR >* bn, const NodeSet& hardEvidence);

    void setFromLBP(LoopyBeliefPropagation< GUM_SCALAR >* lbp,
                    const NodeSet&                        hardEvidence,
                    GUM_SCALAR                            virtualLBPSize);

    GUM_SCALAR confidence();


    void update(Instantiation I, GUM_SCALAR w);


    const Potential< GUM_SCALAR >& posterior(const DiscreteVariable& var);


    void clear();

    private:
    HashTable< std::string, std::vector< GUM_SCALAR > > _estimator;

    GUM_SCALAR _wtotal;

    Size _ntotal;

    const IBayesNet< GUM_SCALAR >* _bn;


    GUM_SCALAR EV(std::string name, Idx val);


    GUM_SCALAR variance(std::string name, Idx val);   // variance corrigée

    private:

    HashTable< std::string, Potential< GUM_SCALAR >* > __target_posteriors;
  };

#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
  extern template class Estimator< double >;
#endif

}   // namespace gum

#include <agrum/BN/inference/tools/estimator_tpl.h>
#endif