/**
 * @file methods/cf/svd_wrapper_impl.hpp
 * @author Sumedh Ghaisas
 *
 * Implementation of the SVD wrapper 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_SVDWRAPPER_IMPL_HPP
#define MLPACK_METHODS_SVDWRAPPER_IMPL_HPP

// In case it hasn't yet been included.
#include "svd_wrapper.hpp"

namespace mlpack {

template<class Factorizer>
double SVDWrapper<Factorizer>::Apply(const arma::mat& V,
                                     arma::mat& W,
                                     arma::mat& sigma,
                                     arma::mat& H) const
{
  // get svd factorization
  arma::vec E;
  factorizer.Apply(W, E, H, V);

  // construct sigma matrix
  sigma.zeros(V.n_rows, V.n_cols);

  for (size_t i = 0; i < sigma.n_rows && i < sigma.n_cols; ++i)
    sigma(i, i) = E(i, 0);

  arma::mat V_rec = W * sigma * trans(H);

  // return normalized frobenius error
  return norm(V - V_rec, "fro") / norm(V, "fro");
}

template<>
double SVDWrapper<DummyClass>::Apply(const arma::mat& V,
                                     arma::mat& W,
                                     arma::mat& sigma,
                                     arma::mat& H) const
{
  // get svd factorization
  arma::vec E;
  arma::svd(W, E, H, V);

  // construct sigma matrix
  sigma.zeros(V.n_rows, V.n_cols);

  for (size_t i = 0; i < sigma.n_rows && i < sigma.n_cols; ++i)
    sigma(i, i) = E(i, 0);

  arma::mat V_rec = W * sigma * trans(H);

  // return normalized frobenius error
  return norm(V - V_rec, "fro") / norm(V, "fro");
}

template<class Factorizer>
double SVDWrapper<Factorizer>::Apply(const arma::mat& V,
                         size_t r,
                         arma::mat& W,
                         arma::mat& H) const
{
  // check if the given rank is valid
  if (r > V.n_rows || r > V.n_cols)
  {
    Log::Info << "Rank " << r << ", given for decomposition is invalid."
        << std::endl;

    r = (V.n_rows > V.n_cols) ? V.n_cols : V.n_rows;
    Log::Info << "Setting decomposition rank to " << r << std::endl;
  }

  // get svd factorization
  arma::vec sigma;
  factorizer.Apply(W, sigma, H, V);

  // remove the part of W and H depending upon the value of rank
  W = W.submat(0, 0, W.n_rows - 1, r - 1);
  H = H.submat(0, 0, H.n_cols - 1, r - 1);

  // take only required eigenvalues
  sigma = sigma.subvec(0, r - 1);

  // eigenvalue matrix is multiplied to W
  // it can either be multiplied to H matrix
  W = W * arma::diagmat(sigma);

  // take transpose of the matrix H as required by CF module
  H = trans(H);

  // reconstruct the matrix
  arma::mat V_rec = W * H;

  // return the normalized frobenius norm
  return norm(V - V_rec, "fro") / norm(V, "fro");
}

template<>
double SVDWrapper<DummyClass>::Apply(const arma::mat& V,
                                     size_t r,
                                     arma::mat& W,
                                     arma::mat& H) const
{
  // check if the given rank is valid
  if (r > V.n_rows || r > V.n_cols)
  {
    Log::Info << "Rank " << r << ", given for decomposition is invalid."
        << std::endl;

    r = (V.n_rows > V.n_cols) ? V.n_cols : V.n_rows;
    Log::Info << "Setting decomposition rank to " << r << std::endl;
  }

  // get svd factorization
  arma::vec sigma;
  arma::svd(W, sigma, H, V);

  // remove the part of W and H depending upon the value of rank
  W = W.submat(0, 0, W.n_rows - 1, r - 1);
  H = H.submat(0, 0, H.n_cols - 1, r - 1);

  // take only required eigenvalues
  sigma = sigma.subvec(0, r - 1);

  // eigenvalue matrix is multiplied to W
  // it can either be multiplied to H matrix
  W = W * arma::diagmat(sigma);

  // take transpose of the matrix H as required by CF module
  H = trans(H);

  // reconstruct the matrix
  arma::mat V_rec = W * H;

  // return the normalized frobenius norm
  return norm(V - V_rec, "fro") / norm(V, "fro");
}

} // namespace mlpack

#endif
