softmax_regression.hpp

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#ifndef MLPACK_METHODS_SOFTMAX_REGRESSION_SOFTMAX_REGRESSION_HPP
#define MLPACK_METHODS_SOFTMAX_REGRESSION_SOFTMAX_REGRESSION_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/core/optimizers/lbfgs/lbfgs.hpp>

#include "softmax_regression_function.hpp"

namespace mlpack {
namespace regression {

template<
  template<typename> class OptimizerType = mlpack::optimization::L_BFGS
>
class SoftmaxRegression
{
 public:
  SoftmaxRegression(const size_t inputSize = 0,
                    const size_t numClasses = 0,
                    const bool fitIntercept = false);

  SoftmaxRegression(const arma::mat& data,
                    const arma::Row<size_t>& labels,
                    const size_t numClasses,
                    const double lambda = 0.0001,
                    const bool fitIntercept = false);

  SoftmaxRegression(OptimizerType<SoftmaxRegressionFunction>& optimizer);

  mlpack_deprecated void Predict(const arma::mat& testData,
                                 arma::Row<size_t>& predictions) const;

  void Classify(const arma::mat& dataset, arma::Row<size_t>& labels) const;

  double ComputeAccuracy(const arma::mat& testData,
                         const arma::Row<size_t>& labels) const;

  double Train(OptimizerType<SoftmaxRegressionFunction>& optimizer);

  double Train(const arma::mat &data, const arma::Row<size_t>& labels,
               const size_t numClasses);

  size_t& NumClasses() { return numClasses; }
  size_t NumClasses() const { return numClasses; }

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

  bool FitIntercept() const { return fitIntercept; }

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

  size_t FeatureSize() const
  { return fitIntercept ? parameters.n_cols - 1 :
                          parameters.n_cols; }

  template<typename Archive>
  void Serialize(Archive& ar, const unsigned int /* version */)
  {
    using mlpack::data::CreateNVP;

    ar & CreateNVP(parameters, "parameters");
    ar & CreateNVP(numClasses, "numClasses");
    ar & CreateNVP(lambda, "lambda");
    ar & CreateNVP(fitIntercept, "fitIntercept");
  }

 private:
  arma::mat parameters;
  size_t numClasses;
  double lambda;
  bool fitIntercept;
};

} // namespace regression
} // namespace mlpack

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

#endif