svd_batch_learning.hpp

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#ifndef MLPACK_METHODS_AMF_UPDATE_RULES_SVD_BATCH_LEARNING_HPP
#define MLPACK_METHODS_AMF_UPDATE_RULES_SVD_BATCH_LEARNING_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace amf {

class SVDBatchLearning
{
 public:
  SVDBatchLearning(double u = 0.0002,
                   double kw = 0,
                   double kh = 0,
                   double momentum = 0.9)
        : u(u), kw(kw), kh(kh), momentum(momentum)
  {
    // empty constructor
  }

  template<typename MatType>
  void Initialize(const MatType& dataset, const size_t rank)
  {
    const size_t n = dataset.n_rows;
    const size_t m = dataset.n_cols;

    mW.zeros(n, rank);
    mH.zeros(rank, m);
  }

  template<typename MatType>
  inline void WUpdate(const MatType& V,
                      arma::mat& W,
                      const arma::mat& H)
  {
    size_t n = V.n_rows;
    size_t m = V.n_cols;

    size_t r = W.n_cols;

    // initialize the momentum of this iteration.
    mW = momentum * mW;

    // Compute the step.
    arma::mat deltaW;
    deltaW.zeros(n, r);
    for (size_t i = 0; i < n; i++)
    {
      for (size_t j = 0; j < m; j++)
      {
        const double val = V(i, j);
        if (val != 0)
          deltaW.row(i) += (val - arma::dot(W.row(i), H.col(j))) *
                                            arma::trans(H.col(j));
      }
      // Add regularization.
      if (kw != 0)
        deltaW.row(i) -= kw * W.row(i);
    }

    // Add the step to the momentum.
    mW += u * deltaW;
    // Add the momentum to the W matrix.
    W += mW;
  }

  template<typename MatType>
  inline void HUpdate(const MatType& V,
                      const arma::mat& W,
                      arma::mat& H)
  {
    size_t n = V.n_rows;
    size_t m = V.n_cols;

    size_t r = W.n_cols;

    // Initialize the momentum of this iteration.
    mH = momentum * mH;

    // Compute the step.
    arma::mat deltaH;
    deltaH.zeros(r, m);
    for (size_t j = 0; j < m; j++)
    {
      for (size_t i = 0; i < n; i++)
      {
        const double val = V(i, j);
        if (val != 0)
          deltaH.col(j) += (val - arma::dot(W.row(i), H.col(j))) * W.row(i).t();
      }
      // Add regularization.
      if (kh != 0)
        deltaH.col(j) -= kh * H.col(j);
    }

    // Add this step to the momentum.
    mH += u * deltaH;
    // Add the momentum to H.
    H += mH;
  }

  template<typename Archive>
  void Serialize(Archive& ar, const unsigned int /* version */)
  {
    using data::CreateNVP;
    ar & CreateNVP(u, "u");
    ar & CreateNVP(kw, "kw");
    ar & CreateNVP(kh, "kh");
    ar & CreateNVP(momentum, "momentum");
    ar & CreateNVP(mW, "mW");
    ar & CreateNVP(mH, "mH");
  }

 private:
  double u;
  double kw;
  double kh;
  double momentum;

  arma::mat mW;
  arma::mat mH;
}; // class SVDBatchLearning


template<>
inline void SVDBatchLearning::WUpdate<arma::sp_mat>(const arma::sp_mat& V,
                                                    arma::mat& W,
                                                    const arma::mat& H)
{
  const size_t n = V.n_rows;
  const size_t r = W.n_cols;

  mW = momentum * mW;

  arma::mat deltaW;
  deltaW.zeros(n, r);

  for (arma::sp_mat::const_iterator it = V.begin(); it != V.end(); ++it)
  {
    const size_t row = it.row();
    const size_t col = it.col();
    deltaW.row(it.row()) += (*it - arma::dot(W.row(row), H.col(col))) *
                                             arma::trans(H.col(col));
  }

  if (kw != 0)
    deltaW -= kw * W;

  mW += u * deltaW;
  W += mW;
}

template<>
inline void SVDBatchLearning::HUpdate<arma::sp_mat>(const arma::sp_mat& V,
                                                    const arma::mat& W,
                                                    arma::mat& H)
{
  const size_t m = V.n_cols;
  const size_t r = W.n_cols;

  mH = momentum * mH;

  arma::mat deltaH;
  deltaH.zeros(r, m);

  for (arma::sp_mat::const_iterator it = V.begin(); it != V.end(); ++it)
  {
    const size_t row = it.row();
    const size_t col = it.col();
    deltaH.col(col) += (*it - arma::dot(W.row(row), H.col(col))) *
        W.row(row).t();
  }

  if (kh != 0)
    deltaH -= kh * H;

  mH += u * deltaH;
  H += mH;
}

} // namespace amf
} // namespace mlpack

#endif // MLPACK_METHODS_AMF_UPDATE_RULES_SVD_BATCH_LEARNING_HPP