quic_svd_method.hpp

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#ifndef MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_QUIC_SVD_METHOD_HPP
#define MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_QUIC_SVD_METHOD_HPP

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

namespace mlpack {
namespace pca {

class QUICSVDPolicy
{
 public:
  QUICSVDPolicy(const double epsilon = 0.03, const double delta = 0.1) :
       epsilon(epsilon),
       delta(delta)
  {
    /* Nothing to do here */
  }

  void Apply(const arma::mat& data,
             const arma::mat& centeredData,
             arma::mat& transformedData,
             arma::vec& eigVal,
             arma::mat& eigvec,
             const size_t /* rank */)
  {
    // This matrix will store the right singular values; we do not need them.
    arma::mat v, sigma;

    // Do singular value decomposition using the QUIC-SVD algorithm.
    svd::QUIC_SVD quicsvd(centeredData, eigvec, v, sigma, epsilon, delta);

    // Now we must square the singular values to get the eigenvalues.
    // In addition we must divide by the number of points, because the
    // covariance matrix is X * X' / (N - 1).
    eigVal = arma::pow(arma::diagvec(sigma), 2) / (data.n_cols - 1);

    // Project the samples to the principals.
    transformedData = arma::trans(eigvec) * centeredData;
  }

  double Epsilon() const { return epsilon; }
  double& Epsilon() { return epsilon; }

  double Delta() const { return delta; }
  double& Delta() { return delta; }

 private:
  double epsilon;

  double delta;
};

} // namespace pca
} // namespace mlpack

#endif