bias_svd_function.hpp

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#ifndef MLPACK_METHODS_BIAS_SVD_BIAS_SVD_FUNCTION_HPP
#define MLPACK_METHODS_BIAS_SVD_BIAS_SVD_FUNCTION_HPP
 
#include <mlpack/prereqs.hpp>
#include <ensmallen.hpp>
 
namespace mlpack {
namespace svd {
 
template <typename MatType = arma::mat>
class BiasSVDFunction
{
 public:
  BiasSVDFunction(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::BiasSVDFunction<arma::mat>& function,
      arma::mat& parameters);
 
  template <>
  template <>
  inline double ParallelSGD<ExponentialBackoff>::Optimize(
      mlpack::svd::BiasSVDFunction<arma::mat>& function,
      arma::mat& parameters);
 
} // namespace ens
 
#include "bias_svd_function_impl.hpp"
 
#endif