randomized_svd_method.hpp

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#ifndef MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_RANDOMIZED_SVD_METHOD_HPP
#define MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_RANDOMIZED_SVD_METHOD_HPP

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

namespace mlpack {
namespace pca {

class RandomizedSVDPolicy
{
 public:
  RandomizedSVDPolicy(const size_t iteratedPower = 0,
                      const size_t maxIterations = 2) :
      iteratedPower(iteratedPower),
      maxIterations(maxIterations)
  {
    /* 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;

    // Do singular value decomposition using the randomized SVD algorithm.
    svd::RandomizedSVD rsvd(iteratedPower, maxIterations);
    rsvd.Apply(data, eigvec, eigVal, v, rank);

    // 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 %= eigVal / (data.n_cols - 1);

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

  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 pca
} // namespace mlpack

#endif