perceptron.hpp

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#ifndef MLPACK_METHODS_PERCEPTRON_PERCEPTRON_HPP
#define MLPACK_METHODS_PERCEPTRON_PERCEPTRON_HPP

#include <mlpack/prereqs.hpp>

#include "initialization_methods/zero_init.hpp"
#include "initialization_methods/random_init.hpp"
#include "learning_policies/simple_weight_update.hpp"

namespace mlpack {
namespace perceptron {

template<typename LearnPolicy = SimpleWeightUpdate,
         typename WeightInitializationPolicy = ZeroInitialization,
         typename MatType = arma::mat>
class Perceptron
{
 public:
  Perceptron(const size_t numClasses = 0,
             const size_t dimensionality = 0,
             const size_t maxIterations = 1000);

  Perceptron(const MatType& data,
             const arma::Row<size_t>& labels,
             const size_t numClasses,
             const size_t maxIterations = 1000);

  Perceptron(const Perceptron& other,
             const MatType& data,
             const arma::Row<size_t>& labels,
             const size_t numClasses,
             const arma::rowvec& instanceWeights);

  void Train(const MatType& data,
             const arma::Row<size_t>& labels,
             const size_t numClasses,
             const arma::rowvec& instanceWeights = arma::rowvec());

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

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

  size_t MaxIterations() const { return maxIterations; }
  size_t& MaxIterations() { return maxIterations; }

  size_t NumClasses() const { return weights.n_cols; }

  const arma::mat& Weights() const { return weights; }
  arma::mat& Weights() { return weights; }

  const arma::vec& Biases() const { return biases; }
  arma::vec& Biases() { return biases; }

 private:
  size_t maxIterations;

  arma::mat weights;

  arma::vec biases;
};

} // namespace perceptron
} // namespace mlpack

#include "perceptron_impl.hpp"

#endif