randomized_svd_method.hpp

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#ifndef MLPACK_METHODS_CF_DECOMPOSITION_POLICIES_RANDOMIZED_SVD_METHOD_HPP
#define MLPACK_METHODS_CF_DECOMPOSITION_POLICIES_RANDOMIZED_SVD_METHOD_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/methods/randomized_svd/randomized_svd.hpp>

namespace mlpack {
namespace cf {

class RandomizedSVDPolicy
{
 public:
  RandomizedSVDPolicy(const size_t iteratedPower = 0,
                      const size_t maxIterations = 2) :
      iteratedPower(iteratedPower),
      maxIterations(maxIterations)
  {
    /* Nothing to do here */
  }

  template<typename MatType>
  void Apply(const MatType& /* data */,
             const arma::sp_mat& cleanedData,
             const size_t rank,
             arma::mat& w,
             arma::mat& h,
             const size_t maxIterations,
             const double /* minResidue */,
             const bool /* mit */)
  {
    arma::vec sigma;

    // Do singular value decomposition using the randomized SVD algorithm.
    svd::RandomizedSVD rsvd(iteratedPower, maxIterations);
    rsvd.Apply(cleanedData, w, sigma, h, rank);

    // Sigma matrix is multiplied to w.
    w = w * arma::diagmat(sigma);

    // Take transpose of the matrix h as required by CF class.
    h = arma::trans(h);
  }

  size_t IteratedPower() const { return iteratedPower; }
  size_t& IteratedPower() { return iteratedPower; }

  size_t MaxIterations() const { return maxIterations; }
  size_t& MaxIterations() { return maxIterations; }

 private:
  size_t iteratedPower;

  size_t maxIterations;
};

} // namespace cf
} // namespace mlpack

#endif