reestimate.h

一个通用的隐性马尔可夫C代码库 开发环境:C语言 简要说明:这是一个通用的隐性马尔可夫C代码库

/*******************************************************************************
  author       : Bernhard Knab
  filename     : ghmm/ghmm/reestimate.h
  created      : TIME: 12:39:14     DATE: Wed 18. February 1998
  $Id: reestimate.h,v 1.8 2004/04/07 09:43:30 cic99 Exp $

Copyright (C) 1998-2001, ZAIK/ZPR, Universit鋞 zu K鰈n

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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA


*******************************************************************************/


#ifndef REESTIMATE_H
#define REESTIMATE_H
#include <ghmm/sequence.h>
#include <ghmm/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
	stat_matrix_d_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 reestimate_baum_welch(model *mo, sequence_t *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 reestimate_baum_welch_nstep(model *mo, sequence_t *sq, int max_step, double likelihood_delta);


/** Update the emissions according to the tie groups by computing the mean
    values within all groups.
    */
void reestimate_update_tie_groups(model *mo);

#ifdef __cplusplus
}
#endif


#endif /* REESTIMATE_H */

/*@} (Doc++-Group: reestimate) */