lars_impl.hpp

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#ifndef MLPACK_METHODS_LARS_LARS_IMPL_HPP
#define MLPACK_METHODS_LARS_LARS_IMPL_HPP

#include "lars.hpp"

namespace mlpack {
namespace regression {

template<typename Archive>
void LARS::serialize(Archive& ar, const uint32_t /* version */)
{
  // If we're loading, we have to use the internal storage.
  if (cereal::is_loading<Archive>())
  {
    matGram = &matGramInternal;
    ar(CEREAL_NVP(matGramInternal));
  }
  else
  {
    ar(cereal::make_nvp("matGramInternal",
        (const_cast<arma::mat&>(*matGram))));
  }

  ar(CEREAL_NVP(matUtriCholFactor));
  ar(CEREAL_NVP(useCholesky));
  ar(CEREAL_NVP(lasso));
  ar(CEREAL_NVP(lambda1));
  ar(CEREAL_NVP(elasticNet));
  ar(CEREAL_NVP(lambda2));
  ar(CEREAL_NVP(tolerance));
  ar(CEREAL_NVP(betaPath));
  ar(CEREAL_NVP(lambdaPath));
  ar(CEREAL_NVP(activeSet));
  ar(CEREAL_NVP(isActive));
  ar(CEREAL_NVP(ignoreSet));
  ar(CEREAL_NVP(isIgnored));
}

} // namespace regression
} // namespace mlpack

#endif