adaboost.hpp

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#ifndef MLPACK_METHODS_ADABOOST_ADABOOST_HPP
#define MLPACK_METHODS_ADABOOST_ADABOOST_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/methods/perceptron/perceptron.hpp>
#include <mlpack/methods/decision_tree/decision_tree.hpp>

namespace mlpack {
namespace adaboost {

template<typename WeakLearnerType = mlpack::perceptron::Perceptron<>,
         typename MatType = arma::mat>
class AdaBoost
{
 public:
  AdaBoost(const MatType& data,
           const arma::Row<size_t>& labels,
           const size_t numClasses,
           const WeakLearnerType& other,
           const size_t iterations = 100,
           const double tolerance = 1e-6);

  AdaBoost(const double tolerance = 1e-6);

  double Tolerance() const { return tolerance; }
  double& Tolerance() { return tolerance; }

  size_t NumClasses() const { return numClasses; }

  size_t WeakLearners() const { return alpha.size(); }

  double Alpha(const size_t i) const { return alpha[i]; }
  double& Alpha(const size_t i) { return alpha[i]; }

  const WeakLearnerType& WeakLearner(const size_t i) const { return wl[i]; }
  WeakLearnerType& WeakLearner(const size_t i) { return wl[i]; }

  double Train(const MatType& data,
               const arma::Row<size_t>& labels,
               const size_t numClasses,
               const WeakLearnerType& learner,
               const size_t iterations = 100,
               const double tolerance = 1e-6);

  void Classify(const MatType& test,
                arma::Row<size_t>& predictedLabels,
                arma::mat& probabilities);

  void Classify(const MatType& test,
                arma::Row<size_t>& predictedLabels);

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

 private:
  size_t numClasses;
  // The tolerance for change in rt and when to stop.
  double tolerance;

  std::vector<WeakLearnerType> wl;
  std::vector<double> alpha;
}; // class AdaBoost

} // namespace adaboost
} // namespace mlpack

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

#endif