lmnn_impl.hpp

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#ifndef MLPACK_METHODS_LMNN_LMNN_IMPL_HPP
#define MLPACK_METHODS_LMNN_LMNN_IMPL_HPP

// In case it was not already included.
#include "lmnn.hpp"

namespace mlpack {
namespace lmnn {

template<typename MetricType, typename OptimizerType>
LMNN<MetricType, OptimizerType>::LMNN(const arma::mat& dataset,
                       const arma::Row<size_t>& labels,
                       const size_t k,
                       const MetricType metric) :
    dataset(dataset),
    labels(labels),
    k(k),
    regularization(0.5),
    range(1),
    metric(metric)
{ /* nothing to do */ }

template<typename MetricType, typename OptimizerType>
template<typename... CallbackTypes>
void LMNN<MetricType, OptimizerType>::LearnDistance(arma::mat& outputMatrix,
    CallbackTypes&&... callbacks)
{
  // LMNN objective function.
  LMNNFunction<MetricType> objFunction(dataset, labels, k,
      regularization, range);

  // See if we were passed an initialized matrix. outputMatrix (L) must be
  // having r x d dimensionality.
  if ((outputMatrix.n_cols != dataset.n_rows) ||
      (outputMatrix.n_rows > dataset.n_rows) ||
      !(arma::is_finite(outputMatrix)))
  {
    Log::Info << "Initial learning point have invalid dimensionality.  "
        "Identity matrix will be used as initial learning point for "
         "optimization." << std::endl;
    outputMatrix.eye(dataset.n_rows, dataset.n_rows);
  }

  Timer::Start("lmnn_optimization");

  optimizer.Optimize(objFunction, outputMatrix, callbacks...);

  Timer::Stop("lmnn_optimization");
}


} // namespace lmnn
} // namespace mlpack

#endif