test_switch.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_switch.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: 1791 $
#                       from $Date: 2006-11-15 15:09:43 +0100 (Wed, 15 Nov 2006) $
#             last change by $Author: grunau $.
#
################################################################################
import ghmm
import ghmmwrapper
import ghmmhelper
import class_change


def do1():

     # Interpretation of B matrix for the mixture case (Example with three states and two components each):
                    #  B = [ 
                    #      [ ["mu11","mu12"],["sig11","sig12"],["w11","w12"]   ],
                    #      [  ["mu21","mu22"],["sig21","sig22"],["w21","w22"]  ],
                    #      [  ["mu31","mu32"],["sig31","sig32"],["w31","w32"]  ],
                    #      ]



    F = ghmm.Float()            		   
    m2 = ghmm.HMMFromMatrices(F,ghmm.GaussianMixtureDistribution(F),
                             [[0.0,1.0,0],[0.5,0.0,0.5],[0.3,0.3,0.4]],
                          
                             [
                             [ [0.0,1.0],[1.0,0.5], [0.6,0.4] ],
                             [ [2.0,1.0],[1.0,0.5], [0.8,0.2] ],
                             [ [15.0,1.0],[1.0,0.5], [0.8,0.2] ]
                             ],
                         
                             [1.0,0,0])





    A = [[0.9,0.1],[0.2,0.8]]
    B = [ [[0.0,0.0],[0.1,0.1],[0.5,0.5]],
        [[10.0,10.0],[0.1,0.1],[0.5,0.5]] ]
    pi = [0.0,1.0]
    model = ghmm.HMMFromMatrices(ghmm.Float(),ghmm.GaussianMixtureDistribution(ghmm.Float), A, B, pi)

    #short = ghmm.EmissionSequence(ghmm.Float(),[0.0,10.0,10.0,0.0,10.0,0.0,10.0,10.0,0.0])
    #short2 = ghmm.EmissionSequence(ghmm.Float(),[10.0,0.0]*6)


    #print model
    #model.baumWelch(short2,1,0.2)
    #print model

    #def getClass(seq, k,t):
    #    classes = [0] * 10 + [1] * 10
    #    return classes[t]


    swA = [
        [[0.9,0.1],[0.2,0.8]],
        [[0.3,0.7],[0.7,0.3]]
        ]

    swB = [ [[0.0,0.0],[0.1,0.1],[0.5,0.5]],
        [[10.0,10.0],[0.1,0.1],[0.5,0.5]] ]
    swpi = [0.5,0.5]
    swmodel = ghmm.HMMFromMatrices(ghmm.Float(),ghmm.GaussianMixtureDistribution(ghmm.Float), swA, swB, swpi)
    ghmmwrapper.ghmm_c_class_change_alloc(swmodel.cmodel)


    swshort = ghmm.EmissionSequence(ghmm.Float(),[10.0]+[0.0]*5+[0.0]+[10.0]*5)

    classes = [0] * 12
    unique = class_change.testChange()
    unique.setData(classes)

    unique([],1,1)

    ghmmwrapper.setPythonSwitching(swmodel.cmodel,"XXXX","unique")   # "class_change","getClass"

    
    #print swshort

    swmodel.baumWelch(swshort,5,0.2)


def do2():
    

    swA = [
        [[0.9,0.1],[0.2,0.8]],
        [[0.3,0.7],[0.7,0.3]]
        ]

    swB = [ [[0.0,0.0],[0.1,0.1],[0.5,0.5]],
        [[10.0,10.0],[0.1,0.1],[0.5,0.5]] ]
    swpi = [0.5,0.5]
    swmodel = ghmm.HMMFromMatrices(ghmm.Float(),ghmm.GaussianMixtureDistribution(ghmm.Float), swA, swB, swpi)
    ghmmwrapper.ghmm_c_class_change_alloc(swmodel.cmodel)



    swshort = ghmm.EmissionSequence(ghmm.Float(),[10.0]+[0.0]*5+[0.0]+[10.0]*5)

    classes = [0] * 12
    unique = class_change.testChange()
    unique.setData("I am unique !")

    unique2 = class_change.testChange()
    unique2.setData("Me too !")
   
    # switching function is a Python object with __call__ function
    ghmmwrapper.setPythonCallback(swmodel.cmodel, unique)
    
    
    #ghmmwrapper.setPythonSwitching(swmodel.cmodel,"XXXX","unique")   # "class_change","getClass"
    swmodel.baumWelch(swshort,5,0.2)

    # switching function is a Python object with __call__ function
    ghmmwrapper.setPythonCallback(swmodel.cmodel, unique2)
    swmodel.baumWelch(swshort,5,0.2)

    # switching function if a Python function
    ghmmwrapper.setPythonCallback(swmodel.cmodel, class_change.getClass)
    swmodel.baumWelch(swshort,5,0.2)



do2()