standard_scaler.hpp

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#ifndef MLPACK_CORE_DATA_STANDARD_SCALE_HPP
#define MLPACK_CORE_DATA_STANDARD_SCALE_HPP
 
#include <mlpack/prereqs.hpp>
 
namespace mlpack {
namespace data {
 
class StandardScaler
{
 public:
  template<typename MatType>
  void Fit(const MatType& input)
  {
    itemMean = arma::mean(input, 1);
    itemStdDev = arma::stddev(input, 1, 1);
    // Handle zeros in scale vector.
    itemStdDev.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() || itemStdDev.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() / itemStdDev;
  }
 
  template<typename MatType>
  void InverseTransform(const MatType& input, MatType& output)
  {
    output.copy_size(input);
    output = (input.each_col() % itemStdDev).each_col() + itemMean;
  }
 
  const arma::vec& ItemMean() const { return itemMean; }
  const arma::vec& ItemStdDev() const { return itemStdDev; }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */)
  {
    ar & BOOST_SERIALIZATION_NVP(itemMean);
    ar & BOOST_SERIALIZATION_NVP(itemStdDev);
  }
 
 private:
  // Vector which holds mean of each feature.
  arma::vec itemMean;
  // Vector which holds standard devation of each feature.
  arma::vec itemStdDev;
}; // class StandardScaler
 
} // namespace data
} // namespace mlpack
 
#endif