paramEstimator_inl.h

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
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#pragma once
 

 
#include <agrum/BN/learning/paramUtils/paramEstimator.h>   // to ease IDE parser
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
#  include <agrum/BN/learning/paramUtils/paramEstimator.h>
 
namespace gum {
 
  namespace learning {

    INLINE ParamEstimator::ParamEstimator(const ParamEstimator& from) :
        external_prior_(from.external_prior_->clone()),
        score_internal_prior_(from.score_internal_prior_->clone()), counter_(from.counter_) {
      GUM_CONS_CPY(ParamEstimator);
    }

    INLINE ParamEstimator::ParamEstimator(ParamEstimator&& from) noexcept :
        external_prior_(from.external_prior_), score_internal_prior_(from.score_internal_prior_),
        counter_(std::move(from.counter_)) {
      from.external_prior_       = nullptr;
      from.score_internal_prior_ = nullptr;
      GUM_CONS_MOV(ParamEstimator);
    }

    INLINE void ParamEstimator::clear() { counter_.clear(); }

    INLINE void ParamEstimator::setNumberOfThreads(Size nb) { counter_.setNumberOfThreads(nb); }

    INLINE Size ParamEstimator::getNumberOfThreads() const { return counter_.getNumberOfThreads(); }

    INLINE bool ParamEstimator::isGumNumberOfThreadsOverriden() const {
      return counter_.isGumNumberOfThreadsOverriden();
    }

    INLINE void ParamEstimator::setMinNbRowsPerThread(const std::size_t nb) const {
      counter_.setMinNbRowsPerThread(nb);
    }

    INLINE std::size_t ParamEstimator::minNbRowsPerThread() const {
      return counter_.minNbRowsPerThread();
    }

    INLINE const std::vector< std::pair< std::size_t, std::size_t > >&
                 ParamEstimator::ranges() const {
      return counter_.ranges();
    }

    INLINE std::vector< double > ParamEstimator::parameters(const NodeId target_node) {
      return parameters(target_node, empty_nodevect_);
    }

    INLINE std::pair< std::vector< double >, double >
           ParamEstimator::parametersAndLogLikelihood(const NodeId target_node) {
      return parametersAndLogLikelihood(target_node, empty_nodevect_);
    }

    INLINE const Bijection< NodeId, std::size_t >& ParamEstimator::nodeId2Columns() const {
      return counter_.nodeId2Columns();
    }

    INLINE const DatabaseTable& ParamEstimator::database() const { return counter_.database(); }
 
 
  } /* namespace learning */
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */