gradescent.h

来自「General Hidden Markov Model Library 一个通用」· C头文件 代码 · 共 61 行

/*******************************************************************************
*
*       This file is part of the General Hidden Markov Model Library,
*       GHMM version 0.8_beta1, see http://ghmm.org
*
*       Filename: ghmm/ghmm/gradescent.h
*       Authors:  Janne Grunau, Alexander Riemer, 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: [e-mail]             
*
*       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 16:06:28 +0200 (Mon, 16 Oct 2006) $
*             last change by $Author: grunau $.
*
*******************************************************************************/
#ifndef GHMM_GRADESCENT_H
#define GHMM_GRADESCENT_H

#ifdef __cplusplus
extern "C" {
#endif

/*----------------------------------------------------------------------------*/
/**
   Trains the model with a set of annotated sequences till convergence using
   gradient descent.
   Model must not have silent states. (checked in Python wrapper)
   @return            trained model/NULL pointer success/error
   @param mo:         pointer to a ghmm_dmodel
   @param sq:         struct of annotated sequences
   @param eta:        intial parameter eta (learning rate)
   @param no_steps    number of training steps
 */
ghmm_dmodel* ghmm_dmodel_label_gradient_descent (ghmm_dmodel* mo, ghmm_dseq * sq, double eta, int no_steps);


#ifdef __cplusplus
}
#endif
#endif