pca_whitening.hpp

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#ifndef MLPACK_CORE_DATA_PCA_WHITENING_SCALE_HPP
#define MLPACK_CORE_DATA_PCA_WHITENING_SCALE_HPP
 
#include <mlpack/prereqs.hpp>
#include <mlpack/core/math/lin_alg.hpp>
#include <mlpack/core/math/ccov.hpp>
 
namespace mlpack {
namespace data {
 
class PCAWhitening
{
 public:
  PCAWhitening(double eps = 0.00005)
  {
    epsilon = eps;
    // Ensure scaleMin is smaller than scaleMax.
    if (epsilon < 0)
    {
      throw std::runtime_error("Regularization parameter is not correct");
    }
  }
 
  template<typename MatType>
  void Fit(const MatType& input)
  {
    itemMean = arma::mean(input, 1);
    // Get eigenvectors and eigenvalues of covariance of input matrix.
    eig_sym(eigenValues, eigenVectors, mlpack::math::ColumnCovariance(
        input.each_col() - itemMean));
    eigenValues += epsilon;
  }
 
  template<typename MatType>
  void Transform(const MatType& input, MatType& output)
  {
    if (eigenValues.is_empty() || eigenVectors.is_empty())
    {
      throw std::runtime_error("Call Fit() before Transform(), please"
          " refer to the documentation.");
    }
    output.copy_size(input);
    output = (input.each_col() - itemMean);
    output = arma::diagmat(1.0 / (arma::sqrt(eigenValues))) * eigenVectors.t()
        * output;
  }
 
  template<typename MatType>
  void InverseTransform(const MatType& input, MatType& output)
  {
    output = arma::diagmat(arma::sqrt(eigenValues)) * inv(eigenVectors.t())
        * input;
    output = (output.each_col() + itemMean);
  }
 
  const arma::vec& ItemMean() const { return itemMean; }
  const arma::vec& EigenValues() const { return eigenValues; }
  const arma::mat& EigenVectors() const { return eigenVectors; }
  const double& Epsilon() const { return epsilon; }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */)
  {
    ar & BOOST_SERIALIZATION_NVP(eigenValues);
    ar & BOOST_SERIALIZATION_NVP(eigenVectors);
    ar & BOOST_SERIALIZATION_NVP(itemMean);
    ar & BOOST_SERIALIZATION_NVP(epsilon);
  }
 
 private:
  // Vector which holds mean of each feature.
  arma::vec itemMean;
  // Mat which hold the eigenvectors.
  arma::mat eigenVectors;
  // Regularization Paramter.
  double epsilon;
  // Vector which hold the eigenvalues.
  arma::vec eigenValues;
}; // class PCAWhitening
 
} // namespace data
} // namespace mlpack
 
#endif