regularized_svd_function.hpp

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#ifndef MLPACK_METHODS_REGULARIZED_SVD_REGULARIZED_FUNCTION_SVD_HPP
#define MLPACK_METHODS_REGULARIZED_SVD_REGULARIZED_FUNCTION_SVD_HPP

#include <mlpack/prereqs.hpp>
#include <ensmallen.hpp>

namespace mlpack {
namespace svd {

template <typename MatType = arma::mat>
class RegularizedSVDFunction
{
 public:
  RegularizedSVDFunction(const MatType& data,
                         const size_t rank,
                         const double lambda);

  void Shuffle();

  double Evaluate(const arma::mat& parameters) const;

  double Evaluate(const arma::mat& parameters,
                  const size_t start,
                  const size_t batchSize = 1) const;

  void Gradient(const arma::mat& parameters,
                arma::mat& gradient) const;

  template <typename GradType>
  void Gradient(const arma::mat& parameters,
                const size_t start,
                GradType& gradient,
                const size_t batchSize = 1) const;

  const arma::mat& GetInitialPoint() const { return initialPoint; }

  const arma::mat& Dataset() const { return data; }

  size_t NumFunctions() const { return data.n_cols; }

  size_t NumUsers() const { return numUsers; }

  size_t NumItems() const { return numItems; }

  double Lambda() const { return lambda; }

  size_t Rank() const { return rank; }

 private:
  MatType data;
  arma::mat initialPoint;
  size_t rank;
  double lambda;
  size_t numUsers;
  size_t numItems;
};

} // namespace svd
} // namespace mlpack

namespace ens {

  template <>
  template <>
  inline double StandardSGD::Optimize(
      mlpack::svd::RegularizedSVDFunction<arma::mat>& function,
      arma::mat& parameters);

  template <>
  template <>
  inline double ParallelSGD<ExponentialBackoff>::Optimize(
      mlpack::svd::RegularizedSVDFunction<arma::mat>& function,
      arma::mat& parameters);

} // namespace ens

#include "regularized_svd_function_impl.hpp"

#endif