/****************************************************************************
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 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *   Contact  : info_at_agrum_dot_org                                       *
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 ****************************************************************************/

#pragma once


#include <agrum/BN/learning/priors/bdeuPrior.h>
/** @file
 * @brief the internal prior for the BDeu score (N' / (r_i * q_i)
 *
 * @author Christophe GONZALES(_at_AMU) and Pierre-Henri WUILLEMIN(_at_LIP6)
 */
#ifndef DOXYGEN_SHOULD_SKIP_THIS

namespace gum::learning {

  /// default constructor
  INLINE BDeuPrior::BDeuPrior(const DatabaseTable&                    database,
                              const Bijection< NodeId, std::size_t >& nodeId2columns) :
      Prior(database, nodeId2columns) {
    GUM_CONSTRUCTOR(BDeuPrior)
  }

  /// copy constructor
  INLINE BDeuPrior::BDeuPrior(const BDeuPrior& from) : Prior(from) { GUM_CONS_CPY(BDeuPrior) }

  /// move constructor
  INLINE BDeuPrior::BDeuPrior(BDeuPrior&& from) noexcept : Prior(std::move(from)) {
    GUM_CONS_MOV(BDeuPrior)
  }

  /// virtual copy constructor
  INLINE BDeuPrior* BDeuPrior::clone() const { return new BDeuPrior(*this); }

  /// destructor
  INLINE BDeuPrior::~BDeuPrior() { GUM_DESTRUCTOR(BDeuPrior) }

  /// copy operator
  INLINE BDeuPrior& BDeuPrior::operator=(const BDeuPrior& from) = default;

  /// move operator
  INLINE BDeuPrior& BDeuPrior::operator=(BDeuPrior&& from) noexcept {
    Prior::operator=(std::move(from));
    return *this;
  }

  /// sets the effective sample size N' (alias of setEffectiveSampleSize ())
  INLINE void BDeuPrior::setWeight(const double weight) {
    if (weight < 0.0) {
      GUM_ERROR(OutOfBounds, "A negative weight (" << weight << ") is forbidden for the BDeu prior")
    }
    this->weight_ = weight;
  }

  /// sets the effective sample size N'
  INLINE void BDeuPrior::setEffectiveSampleSize(const double weight) { setWeight(weight); }

  /// returns the type of the prior
  INLINE PriorType BDeuPrior::getType() const { return PriorType::BDeuPriorType; }

  /// indicates whether the prior is potentially informative
  INLINE bool BDeuPrior::isInformative() const { return this->weight_ != 0.0; }

  /// returns the prior vector all the variables in the idset
  INLINE void BDeuPrior::addJointPseudoCount(const IdCondSet&       idset,
                                             std::vector< double >& counts) {
    // if the idset is empty or the weight is zero, the prior is also empty
    if (idset.empty() || (this->weight_ == 0.0)) return;

    // otherwise, add the weight to all the cells in the counting vector
    const double weight = this->weight_ / double(counts.size());
    for (auto& count: counts)
      count += weight;
  }


}   // namespace gum::learning

#endif /* DOXYGEN_SHOULD_SKIP_THIS */
