gmm.hpp

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#ifndef MLPACK_METHODS_MOG_MOG_EM_HPP
#define MLPACK_METHODS_MOG_MOG_EM_HPP

#include <mlpack/prereqs.hpp>

// This is the default fitting method class.
#include "em_fit.hpp"

namespace mlpack {
namespace gmm  {

class GMM
{
 private:
  size_t gaussians;
  size_t dimensionality;

  std::vector<distribution::GaussianDistribution> dists;

  arma::vec weights;

 public:
  GMM() :
      gaussians(0),
      dimensionality(0)
  {
    // Warn the user.  They probably don't want to do this.  If this constructor
    // is being used (because it is required by some template classes), the user
    // should know that it is potentially dangerous.
    Log::Debug << "GMM::GMM(): no parameters given; Estimate() may fail "
        << "unless parameters are set." << std::endl;
  }

  GMM(const size_t gaussians, const size_t dimensionality);

  GMM(const std::vector<distribution::GaussianDistribution> & dists,
      const arma::vec& weights) :
      gaussians(dists.size()),
      dimensionality((!dists.empty()) ? dists[0].Mean().n_elem : 0),
      dists(dists),
      weights(weights) { /* Nothing to do. */ }

  GMM(const GMM& other);

  GMM& operator=(const GMM& other);

  size_t Gaussians() const { return gaussians; }
  size_t Dimensionality() const { return dimensionality; }

  const distribution::GaussianDistribution& Component(size_t i) const {
      return dists[i]; }
  distribution::GaussianDistribution& Component(size_t i) { return dists[i]; }

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

  double Probability(const arma::vec& observation) const;

  double LogProbability(const arma::vec& observation) const;

  double Probability(const arma::vec& observation,
                     const size_t component) const;

  double LogProbability(const arma::vec& observation,
                        const size_t component) const;
  arma::vec Random() const;

  template<typename FittingType = EMFit<>>
  double Train(const arma::mat& observations,
               const size_t trials = 1,
               const bool useExistingModel = false,
               FittingType fitter = FittingType());

  template<typename FittingType = EMFit<>>
  double Train(const arma::mat& observations,
               const arma::vec& probabilities,
               const size_t trials = 1,
               const bool useExistingModel = false,
               FittingType fitter = FittingType());

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

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

 private:
  double LogLikelihood(
      const arma::mat& dataPoints,
      const std::vector<distribution::GaussianDistribution>& distsL,
      const arma::vec& weights) const;
};

} // namespace gmm
} // namespace mlpack

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

#endif