lmnn.hpp

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#ifndef MLPACK_METHODS_LMNN_LMNN_HPP
#define MLPACK_METHODS_LMNN_LMNN_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/core/metrics/lmetric.hpp>
#include <ensmallen.hpp>

#include "lmnn_function.hpp"

namespace mlpack {
namespace lmnn  {

template<typename MetricType = metric::SquaredEuclideanDistance,
         typename OptimizerType = ens::AMSGrad>
class LMNN
{
 public:
  LMNN(const arma::mat& dataset,
       const arma::Row<size_t>& labels,
       const size_t k,
       const MetricType metric = MetricType());


  void LearnDistance(arma::mat& outputMatrix);


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

  const arma::Row<size_t>& Labels() const { return labels; }

  const double& Regularization() const { return regularization; }
  double& Regularization() { return regularization; }

  const size_t& Range() const { return range; }
  size_t& Range() { return range; }

  const size_t& K() const { return k; }
  size_t K() { return k; }

  const OptimizerType& Optimizer() const { return optimizer; }
  OptimizerType& Optimizer() { return optimizer; }

 private:
  const arma::mat& dataset;

  const arma::Row<size_t>& labels;

  size_t k;

  double regularization;

  size_t range;

  MetricType metric;

  OptimizerType optimizer;
}; // class LMNN

} // namespace lmnn
} // namespace mlpack

// Include the implementation.
#include "lmnn_impl.hpp"

#endif