logistic_regression.hpp

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#ifndef MLPACK_METHODS_LOGISTIC_REGRESSION_LOGISTIC_REGRESSION_HPP
#define MLPACK_METHODS_LOGISTIC_REGRESSION_LOGISTIC_REGRESSION_HPP

#include <mlpack/prereqs.hpp>
#include <ensmallen.hpp>

#include "logistic_regression_function.hpp"

namespace mlpack {
namespace regression {

template<typename MatType = arma::mat>
class LogisticRegression
{
 public:
  LogisticRegression(const MatType& predictors,
                     const arma::Row<size_t>& responses,
                     const double lambda = 0);

  LogisticRegression(const MatType& predictors,
                     const arma::Row<size_t>& responses,
                     const arma::rowvec& initialPoint,
                     const double lambda = 0);

  LogisticRegression(const size_t dimensionality = 0,
                     const double lambda = 0);

  template<typename OptimizerType>
  LogisticRegression(const MatType& predictors,
                     const arma::Row<size_t>& responses,
                     OptimizerType& optimizer,
                     const double lambda);

  template<typename OptimizerType = ens::L_BFGS, typename... CallbackTypes>
  double Train(const MatType& predictors,
               const arma::Row<size_t>& responses,
               CallbackTypes&&... callbacks);

  template<typename OptimizerType, typename... CallbackTypes>
  double Train(const MatType& predictors,
               const arma::Row<size_t>& responses,
               OptimizerType& optimizer,
               CallbackTypes&&... callbacks);

  const arma::rowvec& Parameters() const { return parameters; }
  arma::rowvec& Parameters() { return parameters; }

  const double& Lambda() const { return lambda; }
  double& Lambda() { return lambda; }

  template<typename VecType>
  size_t Classify(const VecType& point,
                  const double decisionBoundary = 0.5) const;

  void Classify(const MatType& dataset,
                arma::Row<size_t>& labels,
                const double decisionBoundary = 0.5) const;

  void Classify(const MatType& dataset,
                arma::mat& probabilities) const;

  double ComputeAccuracy(const MatType& predictors,
                         const arma::Row<size_t>& responses,
                         const double decisionBoundary = 0.5) const;

  double ComputeError(const MatType& predictors,
                      const arma::Row<size_t>& responses) const;

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */);

 private:
  arma::rowvec parameters;
  double lambda;
};

} // namespace regression
} // namespace mlpack

// Include implementation.
#include "logistic_regression_impl.hpp"

#endif // MLPACK_METHODS_LOGISTIC_REGRESSION_LOGISTIC_REGRESSION_HPP