/*******************************************************************************
*
* This file is part of the General Hidden Markov Model Library,
* GHMM version __VERSION__, see http://ghmm.org
*
* Filename: ghmm/ghmm/reestimate.h
* Authors: Bernhard Knab, Benjamin Georgi
*
* Copyright (C) 1998-2004 Alexander Schliep
* Copyright (C) 1998-2001 ZAIK/ZPR, Universitaet zu Koeln
* Copyright (C) 2002-2004 Max-Planck-Institut fuer Molekulare Genetik,
* Berlin
*
* Contact: schliep@ghmm.org
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Library General Public
* License as published by the Free Software Foundation; either
* version 2 of the License, or (at your option) any later version.
*
* This library 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library General Public
* License along with this library; if not, write to the Free
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
*
* This file is version $Revision: 1713 $
* from $Date: 2006-10-16 10:06:28 -0400 (Mon, 16 Oct 2006) $
* last change by $Author: grunau $.
*
*******************************************************************************/
#ifndef GHMM_REESTIMATE_H
#define GHMM_REESTIMATE_H
#include "sequence.h"
#include "model.h"
#ifdef __cplusplus
extern "C" {
#endif
/**@name Baum-Welch-Algorithmus */
/*@{ (Doc++-Group: reestimate) */
/** Baum-Welch-Algorithm for parameter reestimation (training) in
a discrete (discrete output functions) HMM. Scaled version
for multiple sequences, alpha and beta matrices are allocated with
ighmm_cmatrix_stat_alloc
New parameters set directly in hmm (no storage of previous values!).
For reference see:
Rabiner, L.R.: "`A Tutorial on Hidden {Markov} Models and Selected
Applications in Speech Recognition"', Proceedings of the IEEE,
77, no 2, 1989, pp 257--285
@return 0/-1 success/error
@param mo initial model
@param sq training sequences
*/
int ghmm_dmodel_baum_welch (ghmm_dmodel * mo, ghmm_dseq * sq);
/** Just like reestimate_baum_welch, but you can limit
the maximum number of steps
@return 0/-1 success/error
@param mo initial model
@param sq training sequences
@param max_step maximal number of Baum-Welch steps
@param likelihood_delta minimal improvement in likelihood required for carrying on. Relative value
to log likelihood
*/
int ghmm_dmodel_baum_welch_nstep (ghmm_dmodel * mo, ghmm_dseq * sq, int max_step,
double likelihood_delta);
/** Baum-Welch-Algorithm for parameter reestimation (training) in
a StateLabelHMM. Scaled version for multiple sequences, alpha and
beta matrices are allocated with ighmm_cmatrix_stat_alloc
New parameters set directly in hmm (no storage of previous values!).
For reference see:
Rabiner, L.R.: "`A Tutorial on Hidden {Markov} Models and Selected
Applications in Speech Recognition"', Proceedings of the IEEE,
77, no 2, 1989, pp 257--285
@return 0/-1 success/error
@param mo initial model
@param sq training sequences
*/
int ghmm_dmodel_label_baum_welch (ghmm_dmodel * mo, ghmm_dseq * sq);
/** Just like reestimate_baum_welch_label, but you can limit
the maximum number of steps
@return 0/-1 success/error
@param mo initial model
@param sq training sequences
@param max_step maximal number of Baum-Welch steps
@param likelihood_delta minimal improvement in likelihood required for
carrying on. Relative value to log likelihood
*/
int ghmm_dmodel_label_baum_welch_nstep (ghmm_dmodel * mo, ghmm_dseq * sq,
int max_step,
double likelihood_delta);
#ifdef __cplusplus
}
#endif
#endif /* GHMM_REESTIMATE_H */
/*@} (Doc++-Group: reestimate) */