test_tie.py

General Hidden Markov Model Library 一个通用的隐马尔科夫模型的C代码库

################################################################################
#
#       This file is part of the General Hidden Markov Model Library,
#       GHMM version 0.8_beta1, see http://ghmm.org
#
#       file:    test_tie.py
#       authors: 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: 1815 $
#                       from $Date: 2007-01-25 15:24:21 +0100 (Thu, 25 Jan 2007) $
#             last change by $Author: cic99 $.
#
################################################################################
from ghmm import *

sigma = IntegerRange(0,4)

m = HMMFromMatrices(sigma,DiscreteDistribution(sigma),
                       [[0.5,0.5,0.0,0.0],[0.0,0.0,1.0,0.0],[0.6,0.0,0.0,0.4],[1.0,0.0,0.0,0.0]],
                       [[1.0,0.0,0.0,0.0],[0.0,1.0,0.0,0.0],[0.0,0.0,1.0,0.0],[0.0,0.0,0.0,1.0]],
                       [1.0,0.0,0.0,0.0] )
#t2 = (0,-1,0,-1)
#m.setTieGroups(t2)


m2 = HMMFromMatrices(sigma,DiscreteDistribution(sigma),
                       [[0.3,0.3,0.4],[0.6,0.1,0.3],[0.3,0.3,0.4]],
                       [[0.3,0.2,0.3,0.2],[0.1,0.2,0.4,0.3],[0.0,0.0,0.0,0.0] ], # [0.25,0.25,0.25,0.25]
                       [1.0,0,0])                       
                       
t = (0,-1,0)
m2.setTieGroups(t)

#m2.updateTieGroups()

#print m2

#train = m2.sample(50,1000)
#print m2
#m2.baumWelch(train,3,10)
print m2
m2.updateTieGroups()
print "after update"
print m2
#m2.baumWelch(train,3,10)