mean_normalization.hpp

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#ifndef MLPACK_CORE_DATA_MEAN_NORMALIZATION_HPP
#define MLPACK_CORE_DATA_MEAN_NORMALIZATION_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace data {

class MeanNormalization
{
 public:
  template<typename MatType>
  void Fit(const MatType& input)
  {
    itemMean = arma::mean(input, 1);
    itemMin = arma::min(input, 1);
    itemMax = arma::max(input, 1);
    scale = itemMax - itemMin;
    // Handling zeros in scale vector.
    scale.for_each([](arma::vec::elem_type& val) { val =
        (val == 0) ? 1 : val; });
  }

  template<typename MatType>
  void Transform(const MatType& input, MatType& output)
  {
    if (itemMean.is_empty() || scale.is_empty())
    {
      throw std::runtime_error("Call Fit() before Transform(), please"
        " refer to the documentation.");
    }
    output.copy_size(input);
    output = (input.each_col() - itemMean).each_col() / scale;
  }

  template<typename MatType>
  void InverseTransform(const MatType& input, MatType& output)
  {
    output.copy_size(input);
    output = (input.each_col() % scale).each_col() + itemMean;
  }

  const arma::vec& ItemMean() const { return itemMean; }
  const arma::vec& ItemMin() const { return itemMin; }
  const arma::vec& ItemMax() const { return itemMax; }
  const arma::vec& Scale() const { return scale; }

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */)
  {
    ar(CEREAL_NVP(itemMin));
    ar(CEREAL_NVP(itemMax));
    ar(CEREAL_NVP(scale));
    ar(CEREAL_NVP(itemMean));
  }

 private:
  // Vector which holds mean of each feature.
  arma::vec itemMean;
  // Vector which holds minimum of each feature.
  arma::vec itemMin;
  // Vector which holds maximum of each feature.
  arma::vec itemMax;
  // Vector which is used to scale up each feature.
  arma::vec scale;
}; // class MeanNormalization

} // namespace data
} // namespace mlpack

#endif