/*******************************************************************************
*
* This file is part of the General Hidden Markov Model Library,
* GHMM version __VERSION__, see http://ghmm.org
*
* Filename: ghmm/ghmm/sfoba.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: 2262 $
* from $Date: 2009-04-22 09:44:25 -0400 (Wed, 22 Apr 2009) $
* last change by $Author: grunau $.
*
*******************************************************************************/
#ifndef GHMM_SFOBA_H
#define GHMM_SFOBA_H
#include "smodel.h"
#ifdef __cplusplus
extern "C" {
#endif
/**@name SHMM Forward-Backward-Algorithm */
/*@{ (Doc++-Group: sfoba) */
/** Forward-Backward-Algorithm for multiple double
sequences with scaling.
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
*/
/** Forward-Algorithm.
Calculates alpha[t][i], scaling factors scale[t] and log( P(O|lambda) ) for
a given double sequence and a given model.
@param smo model
@param O sequence
@param T length of sequence (O is actually T*smo->dim long)
@param b optionally precomputed emission probabilities
@param alpha alpha[t][i]
@param scale scale factors
@param log_p log likelihood log( P(O|lambda) )
@return 0 for success, -1 for error
*/
int ghmm_cmodel_forward (ghmm_cmodel * smo, double *O, int T, double ***b,
double **alpha, double *scale, double *log_p);
/**
Backward-Algorithm.
Calculates beta[t][i] given a double sequence and a model. Scale factors
given as parameter (come from ghmm_cmodel_forward()).
@param smo model
@param O sequence
@param T length of sequence (O is actually T*smo->dim long)
@param b matrix with precalculated output probabilities. May be NULL
@param beta beta[t][i]
@param scale scale factors
@return 0 for success, -1 for error
*/
int ghmm_cmodel_backward (ghmm_cmodel * smo, double *O, int T, double ***b,
double **beta, const double *scale);
/**
Calculation of log( P(O|lambda) ).
Done by calling ghmm_cmodel_forward(). Use this function if only the
log likelihood and not alpha[t][i] is needed, alpha matrix is allocated with
ighmm_cmatrix_stat_alloc
@param smo model
@param O sequence
@param T length of sequence (O is actually T*smo->dim long)
@param log_p log likelihood log( P(O|lambda) )
@return 0 for success, -1 for error
*/
int ghmm_cmodel_logp (ghmm_cmodel * smo, double *O, int T, double *log_p);
/**
Calculation of log( P(O,S | lambda) ).
Computes joint probability of sequence and state sequence
@param mo model
@param O sequence
@param len length of sequence
@param S state sequence
@param slen length of state sequence (differs from len only for HMMs
with silent states)
@param log_p log likelihood log( P(O|lambda) )
@return 0 for success, -1 for error
*/
int ghmm_cmodel_logp_joint(ghmm_cmodel *mo, const double *O, int len,
const int *S, int slen, double *log_p);
#ifdef __cplusplus
}
#endif
/*@} (Doc++-Group: sfoba) */
#endif