/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *                                                                          *
 *   The aGrUM/pyAgrum library is free software; you can redistribute it    *
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 *    - the MIT license (MIT),                                              *
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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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 *                                                                          *
 ****************************************************************************/


/**
 * @file
 * @brief the smooth a priori: adds a weight w to all the counting
 *
 * @author Pierre-Henri WUILLEMIN(_at_LIP6) & Christophe GONZALES(_at_AMU)
 */

#include <agrum/BN/learning/priors/smoothingPrior.h>

#ifndef DOXYGEN_SHOULD_SKIP_THIS

/// include the inlined functions if necessary
#  ifdef GUM_NO_INLINE
#    include <agrum/BN/learning/priors/smoothingPrior_inl.h>
#  endif /* GUM_NO_INLINE */

namespace gum::learning {

  /// returns the prior vector over only the conditioning set of an idset
  void SmoothingPrior::addConditioningPseudoCount(const IdCondSet&       idset,
                                                  std::vector< double >& counts) {
    // if the conditioning set is empty or the weight is equal to zero,
    // the prior is also empty
    if ((idset.size() == idset.nbLHSIds()) || (this->weight_ == 0.0)
        || (idset.nbLHSIds() == std::size_t(0)))
      return;

    // compute the weight of the conditioning set
    double weight = this->weight_;
    if (this->nodeId2columns_.empty()) {
      for (auto i = std::size_t(0); i < idset.nbLHSIds(); ++i) {
        weight *= double(this->database_->domainSize(idset[i]));
      }
    } else {
      for (auto i = std::size_t(0); i < idset.nbLHSIds(); ++i) {
        weight *= double(this->database_->domainSize(this->nodeId2columns_.second(idset[i])));
      }
    }

    // add the weight to the counting vector
    for (auto& count: counts)
      count += weight;
  }

}   // namespace gum::learning

#endif /* DOXYGEN_SHOULD_SKIP_THIS */
