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backward.c
baum.c
esthmm.c
forward.c
genseq.c
hmm.h
hmmrand.c
hmmtut.ps
hmmutils.c
nrutil.c
nrutil.h
sequence.c
t2.100.seq
t2.1500.seq
t2.50.seq
t2.hmm
t3.hmm
test.hmm
test.seq
testfor.c
testvit.c
viterbi.c

README

 $Id: README,v 1.5 1998/03/16 08:21:26 kanungo Exp kanungo $

Package: UMDHMM version 1.02
Author: Tapas Kanungo (kanungo@cfar.umd.edu)
Organization: University of Maryland, Collge Park, MD
Web: 	http://www.cfar.umd.edu/~kanungo
Date:	19 February, 1998 

Updated on 5 May, 1999: see CHANGES file.

Updated on 6 May, 1999: see CHANGES file.

This software contains code for understanding the basics
of hidden Markov models (HMM). The notation used is
very similar to that used by Rabiner and Juang in:

- Rabiner, L. R. and B. H. Juang, "Fundamentals of Speech Recognition,"
  Prentice Hall, 1993.
- Rabiner, L. R., "A Tutorial on Hidden Markov Models and Selected 
  Applications in Speech Recognition, Prov. of IEEE, vol. 77, no. 2, 
  pp. 257-286, 1989.
- Rabiner, L. R., and B. H. Juang, "An Introduction to Hidden Markov Models,"
  IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, Jan. 1986. 

---------------------------------------------
Installation:
---------------------------------------------
  --------------------
  UNIX: Dec, Sun Solaris, Linux (redhat):
  --------------------

  Type "make all" at the unix prompt. It should
  compile the package.

  --------------------
  Microsoft NT/95/98:
  --------------------

  1. Get the GNU package from:
     ftp://go.cygnus.com/pub/sourceware.cygnus.com/cygwin/latest/full.exe
    
     This package includes gcc and various commands and 
     shells (sh, bash, etc.) that make the PC have a unix
     like environment.

  2. Change to the UMDHMM directory and type "make all".
  

---------------------------------------------
Executables:
---------------------------------------------
genseq: Generates a symbol sequence using the specified model

testvit: Generates the most like state sequence for a given symbol sequence,
	given the HMM, using Viterbi.

esthmm: Estimates the HMM from a given symbol sequence using BaumWelch.

testfor: Computes log Prob(observation|model) using the Forward algorithm.

Note 1: The model test.hmm and sequence test.seq solve exercise 6.3 in 
the book by Rabiner and Juang (page 341). Just execute the command:
   prompt% testvit test.hmm test.seq
and compare the output with the solution given in the book.

---------------------------------------------
HMM file format:
---------------------------------------------
M= <number of symbols>
N= <number of states>
A:
a11 a12 ... a1N
a21 a22 ... a2N
 .   .   .   .
 .   .   .   .
 .   .   .   .
aN1 aN2 ... aNN
B:
b11 b12 ... b1M
b21 b22 ... b2M
 .   .   .   .
 .   .   .   .
 .   .   .   .
bN1 bN2 ... bNM
pi:
pi1 pi2 ... piN

---------------------------------------------
Sample HMM file:
---------------------------------------------
M= 2
N= 3
A:
0.333 0.333 0.333
0.333 0.333 0.333
0.333 0.333 0.333
B:
0.5   0.5  
0.75  0.25
0.25  0.75
pi:
0.333 0.333 0.333
---------------------------------------------
Sequence file format:
---------------------------------------------
T=<seqence lenght>
o1 o2 o3 . . . oT
---------------------------------------------
Sample sequence file:
---------------------------------------------
T= 10
1 1 1 1 2 1 2 2 2 2