/**
 * @file methods/lmnn/lmnn_impl.hpp
 * @author Manish Kumar
 *
 * Implementation of Large Margin Nearest Neighbor class.
 *
 * mlpack is free software; you may redistribute it and/or modify it under the
 * terms of the 3-clause BSD license.  You should have received a copy of the
 * 3-clause BSD license along with mlpack.  If not, see
 * http://www.opensource.org/licenses/BSD-3-Clause for more information.
 */
#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 {

/**
 * Takes in a reference to the dataset. Copies the data, initializes
 * all of the member variables and constraint object and generate constraints.
 */
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