/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *   See LICENCES for more details.                                         *
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 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
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 *   Contact  : info_at_agrum_dot_org                                       *
 *   homepage : http://agrum.gitlab.io                                      *
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 *                                                                          *
 ****************************************************************************/

#pragma once


/**
 * @file
 * @brief Implementation of Weighted Sampling for inference in Bayesian networks.
 *
 * @author Paul ALAM & Pierre-Henri WUILLEMIN(_at_LIP6)
 */


#include <agrum/BN/inference/weightedSampling.h>

namespace gum {


  /// Default constructor
  template < GUM_Numeric GUM_SCALAR >
  WeightedSampling< GUM_SCALAR >::WeightedSampling(const IBayesNet< GUM_SCALAR >* bn) :
      SamplingInference< GUM_SCALAR >(bn) {
    GUM_CONSTRUCTOR(WeightedSampling)
  }

  /// Destructor
  template < GUM_Numeric GUM_SCALAR >
  WeightedSampling< GUM_SCALAR >::~WeightedSampling() {
    GUM_DESTRUCTOR(WeightedSampling)
  }

  /// No burn in needed for Weighted sampling
  template < GUM_Numeric GUM_SCALAR >
  Instantiation WeightedSampling< GUM_SCALAR >::burnIn_() {
    gum::Instantiation I;
    return I;
  }

  template < GUM_Numeric 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
