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/*!
* \file
* \brief Definition of a Gaussian Mixture Model Class
* \author Thomas Eriksson
*
* -------------------------------------------------------------------------
*
* Copyright (C) 1995-2010 (see AUTHORS file for a list of contributors)
*
* This file is part of IT++ - a C++ library of mathematical, signal
* processing, speech processing, and communications classes and functions.
*
* IT++ 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 3 of the License, or (at your option) any
* later version.
*
* IT++ 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 IT++. If not, see <http://www.gnu.org/licenses/>.
*
* -------------------------------------------------------------------------
*/
#ifndef GMM_H
#define GMM_H
#include <itpp/base/mat.h>
#include <itpp/itexports.h>
namespace itpp
{
/*!
\ingroup sourcecoding
\brief Gaussian Mixture Model Class
\author Thomas Eriksson
*/
class ITPP_EXPORT 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_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);}
ITPP_EXPORT GMM gmmtrain(Array<vec> &TrainingData, int M, int NOITER = 30, bool VERBOSE = true);
//! \endcond
} // namespace itpp
#endif // #ifndef GMM_H
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