File: gmm.h

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/*!
 * \file
 * \brief Definition of a Gaussian Mixture Model Class
 * \author Thomas Eriksson
 *
 * -------------------------------------------------------------------------
 *
 * IT++ - C++ library of mathematical, signal processing, speech processing,
 *        and communications classes and functions
 *
 * Copyright (C) 1995-2008  (see AUTHORS file for a list of contributors)
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
 *
 * -------------------------------------------------------------------------
 */

#ifndef GMM_H
#define GMM_H

#include <itpp/base/mat.h>


namespace itpp {

  //! \cond

  /*!
    \ingroup sourcecoding
    \brief Gaussian Mixture Model Class
    \author Thomas Eriksson
   */
  class GMM {
  public:
    GMM();
    GMM(int nomix, int dim);
    GMM(std::string filename);
    void init_from_vq(const vec &codebook, int dim);
    //	void init(const vec &w_in, const vec &m_in, const vec &sigma_in);
    void init(const vec &w_in, const mat &m_in, const mat &sigma_in);
    void load(std::string filename);
    void save(std::string filename);
    void set_weight(const vec &weights, bool compflag=true);
    void set_weight(int i, double weight, bool compflag=true);
    void set_mean(const mat &m_in);
    void set_mean(const vec &means, bool compflag=true);
    void set_mean(int i, const vec &means, bool compflag=true);
    void set_covariance(const mat &sigma_in);
    void set_covariance(const vec &covariances, bool compflag=true);
    void set_covariance(int i, const vec &covariances, bool compflag=true);
    int get_no_mixtures();
    int get_no_gaussians() const { return M; }
    int get_dimension();
    vec get_weight();
    double get_weight(int i);
    vec get_mean();
    vec get_mean(int i);
    vec get_covariance();
    vec get_covariance(int i);
    void marginalize(int d_new);
    void join(const GMM &newgmm);
    void clear();
    double likelihood(const vec &x);
    double likelihood_aposteriori(const vec &x, int mixture);
    vec likelihood_aposteriori(const vec &x);
    vec draw_sample();
  protected:
    vec			m,sigma,w;
    int			M,d;
  private:
    void		compute_internals();
    vec			normweight,normexp;
  };

  inline void GMM::set_weight(const vec &weights, bool compflag) {w=weights; if (compflag) compute_internals(); }
  inline void GMM::set_weight(int i, double weight, bool compflag) {w(i)=weight; if (compflag) compute_internals(); }
  inline void GMM::set_mean(const vec &means, bool compflag) {m=means; if (compflag) compute_internals(); }
  inline void GMM::set_covariance(const vec &covariances, bool compflag) {sigma=covariances; if (compflag) compute_internals(); }
  inline int GMM::get_no_mixtures()
  {
    it_warning("GMM::get_no_mixtures(): This function is depreceted and might be removed from feature releases. Please use get_no_gaussians() instead.");
    return M;
  }
  inline int GMM::get_dimension() {return d;}
  inline vec GMM::get_weight() {return w;}
  inline double GMM::get_weight(int i) {return w(i);}
  inline vec GMM::get_mean() {return m;}
  inline vec GMM::get_mean(int i) {return m.mid(i*d,d);}
  inline vec GMM::get_covariance() {return sigma;}
  inline vec GMM::get_covariance(int i) {return sigma.mid(i*d,d);}

  GMM gmmtrain(Array<vec> &TrainingData, int M, int NOITER=30, bool VERBOSE=true);

  //! \endcond

} // namespace itpp

#endif // #ifndef GMM_H