svd_complete_method.hpp

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#ifndef MLPACK_METHODS_CF_DECOMPOSITION_POLICIES_SVD_COMPLETE_METHOD_HPP
#define MLPACK_METHODS_CF_DECOMPOSITION_POLICIES_SVD_COMPLETE_METHOD_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/methods/amf/amf.hpp>
#include <mlpack/methods/amf/update_rules/nmf_als.hpp>
#include <mlpack/methods/amf/termination_policies/max_iteration_termination.hpp>
#include <mlpack/methods/amf/termination_policies/simple_residue_termination.hpp>

namespace mlpack {
namespace cf {

class SVDCompletePolicy
{
 public:
  template<typename MatType>
  void Apply(const MatType& /* data */,
             const arma::sp_mat& cleanedData,
             const size_t rank,
             arma::mat& w,
             arma::mat& h,
             const size_t maxIterations,
             const double minResidue,
             const bool mit)
  {
    if (mit)
    {
      amf::MaxIterationTermination iter(maxIterations);

      // Do singular value decomposition using complete incremental method
      // using cleaned data in form of sparse matrix.
      amf::AMF<amf::MaxIterationTermination, amf::RandomInitialization,
          amf::SVDCompleteIncrementalLearning<arma::sp_mat>> svdci(iter);

      svdci.Apply(cleanedData, rank, w, h);
    }
    else
    {
      amf::SimpleResidueTermination srt(minResidue, maxIterations);

      // Do singular value decomposition using complete incremental method
      // using cleaned data in form of sparse matrix.
      amf::SVDCompleteIncrementalFactorizer<arma::sp_mat> svdci(srt);

      svdci.Apply(cleanedData, rank, w, h);
    }
  }
};

} // namespace cf
} // namespace mlpack

#endif