bdeuPrior_inl.h

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#pragma once
 
 
#include <agrum/BN/learning/priors/bdeuPrior.h>
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
namespace gum::learning {

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

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

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

  INLINE BDeuPrior* BDeuPrior::clone() const { return new BDeuPrior(*this); }

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

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

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

  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;
  }

  INLINE void BDeuPrior::setEffectiveSampleSize(const double weight) { setWeight(weight); }

  INLINE PriorType BDeuPrior::getType() const { return PriorType::BDeuPriorType; }

  INLINE bool BDeuPrior::isInformative() const { return this->weight_ != 0.0; }

  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 */