文件列表:

hmmbox_3_2\arembed.m (733, 1999-01-12)
hmmbox_3_2\arwls.m (912, 1999-01-12)
hmmbox_3_2\ar_spec.m (636, 2001-08-29)
hmmbox_3_2\Contents.m (1480, 1999-02-02)
hmmbox_3_2\dar.m (3689, 1999-02-02)
hmmbox_3_2\DATA-STRUCTURE (1346, 2002-02-25)
hmmbox_3_2\demar.m (2510, 1998-10-16)
hmmbox_3_2\demar.mat (791688, 1998-10-16)
hmmbox_3_2\demar2.m (1183, 1999-02-02)
hmmbox_3_2\demgauss.mat (10896, 1998-08-19)
hmmbox_3_2\demgausshmm.m (1630, 2002-02-25)
hmmbox_3_2\demlike.m (1056, 1998-10-16)
hmmbox_3_2\demlike.mat (12984, 1998-08-19)
hmmbox_3_2\embed.m (691, 1999-01-12)
hmmbox_3_2\hmmdecode.m (2898, 2001-08-30)
hmmbox_3_2\hmminit.m (1531, 2002-02-25)
hmmbox_3_2\hmmposterior.m (2663, 2001-08-29)
hmmbox_3_2\hmmsim.m (2915, 2001-08-29)
hmmbox_3_2\hmmtrain.m (3857, 2001-12-20)
hmmbox_3_2\init_ar.m (1756, 1999-02-02)
hmmbox_3_2\init_trans.m (610, 1998-10-15)
hmmbox_3_2\INSTALLATION (163, 1999-02-03)
hmmbox_3_2\obsinit.m (1133, 1998-10-15)
hmmbox_3_2\obslike.m (1556, 1998-10-15)
hmmbox_3_2\obsupdate.m (1826, 2002-02-25)
hmmbox_3_2\plotseg.m (1482, 1998-10-16)
hmmbox_3_2\rdiv.m (273, 1998-05-28)
hmmbox_3_2\rprod.m (184, 1998-05-28)
hmmbox_3_2\rsum.m (117, 1998-05-28)
hmmbox_3_2\sampgauss.m (583, 2001-08-29)
hmmbox_3_2\shufflestates.m (1022, 2001-08-30)
hmmbox_3_2\testlag.m (2543, 2001-11-16)
hmmbox_3_2\VERSION (900, 2002-02-25)
hmmbox_3_2\vittest.m (1219, 2001-08-29)
hmmbox_3_2\wgmmem.m (3379, 1998-10-15)
hmmbox_3_2 (0, 2016-08-21)

HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999 Matlab toolbox for Hidden Markov Models (Adapted from Machine Learning Toolbox Version 1.0 01-Apr-96 Copyright (c) by Zoubin Ghahramani, University of Toronto) The software uses some NETLAB routines (see http://neural-server.aston.ac.uk/netlab/index.html) so you'll need to have NETLAB on your search path See the file VERSION for what's new in this version. The following observation models have so far been implemented: Gaussian, Gaussian with common covariances, Likelihood, AR. DEMONSTRATIONS: demgausshmm.m uses Gaussian observation model on AR features demlike.m the time series values are themselves likelihoods demar.m uses AR observation model on original time series ROUTINES: hmminit initialise Gaussian observation HMM hmmtrain train HMM hmmdecode make classifications using HMM obsinit initialise Gaussian observation model obslike calculate likelihood of data given observation model obsupdate update parameters of observation model rsum row sum of matrix rprod row product of matrix and vector rdiv row division of matrix by vector To extend the HMM to different observation models add the required code into obsinit, obslike and obsupdate. DATA: Read the file DATA-STRUCTURE.

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