smoothingPrior.cpp

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

#  ifdef GUM_NO_INLINE
#    include <agrum/BN/learning/priors/smoothingPrior_inl.h>
#  endif /* GUM_NO_INLINE */
 
namespace gum::learning {

  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 */