exact_svd_method.hpp

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#ifndef MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_EXACT_SVD_METHOD_HPP
#define MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_EXACT_SVD_METHOD_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace pca {

class ExactSVDPolicy
{
 public:
  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.  Use the economical singular value
    // decomposition if the columns are much larger than the rows.
    if (data.n_rows < data.n_cols)
    {
      // Do economical singular value decomposition and compute only the left
      // singular vectors.
      arma::svd_econ(eigvec, eigVal, v, centeredData, 'l');
    }
    else
    {
      arma::svd(eigvec, eigVal, v, centeredData);
    }

    // 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;
  }
};

} // namespace pca
} // namespace mlpack

#endif