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/*!
* \file
* \brief Expectation Maximisation (EM) based optimisers for MOG - header file
* \author Conrad Sanderson
*
* -------------------------------------------------------------------------
*
* 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 MOG_DIAG_EM_H
#define MOG_DIAG_EM_H
#include <itpp/stat/mog_diag.h>
namespace itpp {
/*!
\brief support class for MOG_diag_ML() and MOG_diag_MAP()
\author Conrad Sanderson
*/
class MOG_diag_EM_sup : public MOG_diag {
public:
//! Default constructor
MOG_diag_EM_sup() { verbose=false; }
//! Default destructor
~MOG_diag_EM_sup() { }
//! ADD DOCUMENTATION HERE
void ml(MOG_diag &model_in, Array<vec> &X_in, int max_iter_in=10, double var_floor_in=0.0, double weight_floor_in=0.0, bool verbose_in=false);
//! ADD DOCUMENTATION HERE
void map(MOG_diag &model_in, MOG_diag &prior_model, Array<vec> &X_in, int max_iter_in=10, double alpha_in=0.5, double var_floor_in=0.0, double weight_floor_in=0.0, bool verbose_in=false);
protected:
//! Whether we print the progress
bool verbose;
//! number of training vectors
int N;
//! Maximum number of iterations
int max_iter;
//! 'C' pointers to training vectors
double ** c_X;
//! ADD DOCUMENTATION HERE
double var_floor;
//! ADD DOCUMENTATION HERE
double weight_floor;
//! ADD DOCUMENTATION HERE
void inline update_internals();
//! ADD DOCUMENTATION HERE
void inline sanitise_params();
//! ADD DOCUMENTATION HERE
double ml_update_params();
//! ADD DOCUMENTATION HERE
void ml_iterate();
private:
vec tmpvecK;
vec tmpvecD;
vec acc_loglhood_K;
Array<vec> acc_means;
Array<vec> acc_covs;
double * c_tmpvecK;
double * c_tmpvecD;
double * c_acc_loglhood_K;
double ** c_acc_means;
double ** c_acc_covs;
};
//
// convenience functions
/*!
\ingroup MOG
\author Conrad Sanderson
Maximum Likelihood Expectation Maximisation based optimisation of the
parameters of an instance of the MOG_diag class. The seed values
(starting points) are typically first obtained via MOG_diag_kmeans().
See [CSB06] and the references therein for detailed mathematical descriptions.
- [CSB06]
<a href="http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=1561601">
F. Cardinaux, C. Sanderson and S. Bengio,
"User authentication via adapted statistical models of face images",
IEEE Transactions on Signal Processing, Vol 54, No. 1, 2006, pp. 361-373.
</a>
\param model_in The model to optimise (MOG_diag)
\param X_in The training data (array of vectors)
\param max_iter_in Maximum number of iterations. Default is 10.
\param var_floor_in Variance floor (lowest allowable variance). Default is 0.0 (but see the note below)
\param weight_floor_in Weight floor (lowest allowable weight). Default is 0.0 (but see the note below)
\param verbose_in Whether progress in printed. Default is false.
\note The variance and weight floors are set to std::numeric_limits<double>::min()
if they are below that value. As such, they are machine dependant.
The largest allowable weight floor is 1/K, where K is the number of Gaussians.
*/
void MOG_diag_ML(MOG_diag &model_in, Array<vec> &X_in, int max_iter_in=10, double var_floor_in=0.0, double weight_floor_in=0.0, bool verbose_in=false);
/*!
NOT YET IMPLEMENTED.
Maximum a Posteriori (MAP) Expectation Maximisation optimiser for Mixtures of Gaussians.
\param model_in The model to optimise (MOG_diag)
\param prior_model_in The model representing the prior
\param X_in The training data (array of vectors)
\param max_iter_in Maximum number of iterations
\param alpha_in Coefficient for combining the parameters with the prior. 0 <= _alpha <= 1.
\param var_floor_in Variance floor (lowest allowable variance). Set to 0.0 to use the default.
\param weight_floor_in Weight floor (lowest allowable weight). Set to 0.0 to use the default.
\param verbose_in ADD DOCUMENTATION HERE
\note NOT YET IMPLEMENTED.
\note The variance and weight floors are set to std::numeric_limits<double>::min()
if they are below that value.
The largest allowable weight floor is 1/K, where K is the number of Gaussians.
*/
void MOG_diag_MAP(MOG_diag &model_in, MOG_diag &prior_model_in, Array<vec> &X_in, int max_iter_in=10, double alpha_in=0.5, double var_floor_in=0.0, double weight_floor_in=0.0, bool verbose_in=false);
}
#endif // #ifndef MOG_DIAG_EM_H
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